1 

### Chappell, Regina
From: Sasser, Erika Sent: Thursday, September 17, 2015 8:26 AM To: Chappell, Regina Subject: FW: Part of E.O. 12866 Review: Ozone N AAQS RIA Chapters & Appendices RIN2060-AP38



### From: Sasser, Erika Sent: Wednesday, September 16, 2015 6:04 PM To: 'Frey, Nathan J.' Subject: RE: Part of E.O. 12866 Review: Ozone NAAQS RIA Chapters & Appendices RIN2060

‐ AP38
You’re welcome. Also ‐‐ I just sent a scheduler for a brief discussion tomorrow at 11:00. Looks like that time will work fine. Let me know if you did not receive it. Thanks

From: Frey, Nathan J. [ mailto:Nathan_J._Frey@omb.eop.gov ] Sent: Wednesday, September 16, 2015 5:50 PM To: Sasser, Erika Cc: Szabo, Aaron; Cooperstein, Sharon; Weatherhead, Darryl; Chappell, Regina; Langdon, Robin; Rush, Alan; Henigin, Mary; Hetes, Bob; Pekar, Zachary Subject: RE: Part of E.O. 12866 Review: Ozone NAAQS RIA Chapters & Appendices RIN2060

‐ AP38 Many thanks Erika.

From: Sasser, Erika [ mailto:Sasser.Erika@epa.gov ] Sent: Wednesday, September 16, 2015 5:46 PM To: Frey, Nathan J. < Nathan_J._Frey@omb.eop.gov > Cc: Szabo, Aaron < Aaron_L_Szabo@omb.eop.gov >; Cooperstein, Sharon < Cooperstein.Sharon@epa.gov >; Weatherhead, Darryl < Weatherhead.Darryl@epa.gov >; Chappell, Regina < Chappell.Regina@epa.gov >; Langdon, Robin < Langdon.Robin@epa.gov >; Rush, Alan < Rush.Alan@epa.gov >; Henigin, Mary < Henigin.Mary@epa.gov >; Hetes, Bob < Hetes.Bob@epa.gov >; Pekar, Zachary < Pekar.Zachary@epa.gov > Subject: Part of E.O. 12866 Review: Ozone NAAQS RIA Chapters & Appendices RIN2060

‐ AP38 Nathan,
As requested, attached please find Microsoft word versions of the Benefits chapter and associated appendices from the draft Final Ozone NAAQS RIA. Please let me know if you have any questions. Thanks, Erika

### 6 A

- 1 APPENDIX 6 A: COMPREHENSIVE CHARACTERIZATION OF UNCERTAINTY IN OZONE BENEFITS ANALYSIS
Overview As noted in Chapter 6 , the benefits analysis relies on an array of data inputs

— including air quality modeling, health impact functions and valuation estimates among others

— which are themselves subject to uncertainty and may contribute to the overall uncertainty in this analysis. The RIA employs a variety of analytic approaches designed to redu ce the extent of the uncertainty and/or characterize the impact that uncertainty has on the final estimate s . We strive to incorporate as many quantitative assessments of uncertainty as possible (e.g., Monte Carlo assessments, sensitivity analyses); however , there are some aspects we are only able to characterize qualitatively. To more comprehensively and systematically address these uncertainties, including those we cannot quantify, we adapt the World Health Organization ( WHO ) uncertainty framework (WHO, 2 008), which provides a means for systematically linking the characterization of uncertainty to the sophistication of the underlying health impact assessment. EPA has applied similar approaches in peer

reviewed analyses of PM 2.5 

### - related impacts (U.S. EPA, 2 010a , 2011, 2012) and ozone - related impacts (U.S. EPA, 2014). EPA’s Science Advisory Board (SAB) has supported using a tabular format to qualitatively assess the uncertainties inherent in the quantification and monetization of health benefits , including id entifying potential bias, potential magnitude, confidence in our approach, and the level of quantitative assessment of each uncertainty (U.S. EPA

- SAB, 1999, 2001, 2004 a , 2004b, 2011a, 2011b). The assessments presented here are largely consistent with those previous peer

- reviewed assessments. This appendix focuses on uncertainties inherent in the ozone benefits estimates. For more information regarding the uncertainties inherent in the PM 2.5 

### benefits estimates, please see the 2012 PM 2.5 

### NAAQS RIA (U.S. EPA , 2012).
6 A.1 Description of Classifications Applied in the Uncertainty Characterization Table 6 A

- 1 catalogs the most significant sources of uncertainty in the ozone benefits analysis and then characterizes four dimensions of that uncertainty briefly descr ibed below. The first two dimensions focus on the nature of the uncertainty. The third and fourth dimensions focus on the extent to which the analytic approach chosen in the benefits analysis either minimizes the impact of the uncertainty or quantitatively characterizes its impact.

### 6 A

- 2 1) The direction of the bias that a given uncertainty may introduce into the benefits assessment if not taken into account in the analysis approach; 2) The magnitude of the impact that uncertainty is likely to have on the benefits est imate if not taken into account in the analysis approach; 3) The extent to which the analytic approach chosen is likely to minimize the impact of that uncertainty on the benefits estimate; and 4) The extent to which EPA has been able to quantify the residual un certainty after the preferred analytic approach has been incorporated into the benefits model. 6

A.1.1 Direction of Bias The “dire ction of bias” column in Table 6 A

- 1 is an assessment of whether, if left unaddressed, an uncertainty would likely lead to an u nderestimate or overestimate of the total monetized benefits. In some cases we indicate that there are reasons why the bias might go either direction, depending upon the true nature of the underlying relationship. Where available, we base the classificatio n of the “direction of bias” on the analysis in the Integrated Science Assessment for Ozone and Related Photochemical Oxidants ( Ozone ISA) (U.S. EPA, 2013). Additional sources of information include advice from SAB and the National Academies of Science (NA S), as well as studies from the peer

reviewed literature. In some cases we indicate that there is not sufficient information to estimate whether the uncertainty would likely lead to under or overestimation of benefits; these cases are identified as “unable to determine.”
6

A.1.2 Magnitude of Impact The “magnitude of impact” column in Table 6 A

- 1 is an assessment of how much plausible alternative assumptions about the underlying relationship about which we are uncertain could influence the overall monetary be nefits. EPA has applied similar classifications in previous risk and b enefit analyses (U.S. EPA, 2010a , 2011 , 2014), but we have slightly revised the category names and the cut

offs here. 1 

### The definitions used here are provided below.

1 In The Benefits and Costs of the Clean Air Act from 1990 to 2020 (U.S. EPA, 2011 ), EPA applied a classification of

“potentially major” if a plausible alternative assumption or approach could influence the overall monetary benefit estimate by five percent or more and “probably minor” if an alternative assumption or approach is likely to change the total benefit estimate by less than five percent. In the Quantitative Health Risk Assessment for Particulate Matter (U.S. EPA, 2010b), EPA applied classifications of “low ” if the impact would not be expected to impact the interpretation of risk estimates in the context of the PM NAAQS review, “medium” if the impact had the potential to change the interpretation; and “high” if it was likely to influence the interpretation o f risk in the context of the PM NAAQS review.

### 6 A

- 3 • High

— if the uncertainty associated with an assumption could influence the total monetized benefits by more than 25%.

• Medium

— if the uncertainty associated with an assumption could influence the total monetized benefits by 5% to 25%.

• Low

— if the uncertainty associated wi th an assumption could influence the total monetized benefits by less than 5%. For each uncertainty, we provide as much quantitative information as is available in the table to support the classification. Although many of the sources of uncertainty could affect both morbidity and mortality endpoints, because mortality benefits comprise over 94% of the monetized benefits that we are able to quantify in this analysis, uncertainties that affect the mortality estimate have the potential to have larger impacts on the total monetized benefits than uncertainties affecting only morbidity endpoints. One morbidity

- related uncertainty that could have a significant impact on the benefits estimate is the extent to which omitted morbidity endpoints are included in the be nefits analysis. Including additional morbidity endpoints that are currently not monetized would reduce the fraction of total benefits from mortality. Ultimately, the magnitude classification is determined by professional judgment of EPA staff based on the results of available information, including other U.S. EPA assessments of uncertainty (U.S. EPA, 2010a , 2011)

. Based on this assessment, the uncertainties that we classified as high or medium

- high impact are: the causal relationship between long

- term and short

- term ozone exposure and mortality, the shape of the concentration

- response (C

- R) function for both categories of ozone

- related mortality, and the mortality valuation, specifically for long

- term exposure

- related mortality.
6

A .1.3 Confidence in Analy tic Approach The “confidence in anal ytic approach” column of Table 6 A

- 1 is an assessment of the scientific support for the analytic approach chosen (or the inherent assumption made) to account for the relationship about which we are uncertain. In other wor ds, based on the available evidence, how certain are we that EPA’s selected approach is the most plausible of the potential alternatives. Similar

### 6 A

- 4 classifications have been included in previous risk and be nefits analyses (U.S. EPA, 2010a , 2011). 2 

### The three categories used to characterize the degree of confidence are:

• High

— the current evidence is plentiful and strongly supports the selected approach;

• Medium

— some evidence exists to support the selected approach, but data gaps are present; and

• Low

— limited data exists to support the selected approach. Ultimately, the degree of confidence in the analytic approach is EPA staff’s professional judgment based on the volume and consistency of supporting evidence, much of which has been evaluated in the O 3 

### ISA (U.S. EP A, 2013 ) and by SAB. The O 3 

### ISA evaluated the entire body of scientific literature on ozone science and was twice peer

- reviewed by EPA’s Clean Air Scientific Advisory Committee (CASAC). In general, we regard a conclusion in the O 3 

### ISA or specific advice fr om SAB as supporting a high degree of confidence in the selected approach.
Based on this assessment, we have low or low

- medium confidence in the evidence available to assess exposure error in epidemiology studies, morbidity valuation, baseline incidence p rojections for morbidity, and omitted morbidity endpoints. However, because these uncertainties have been classified as having a low or low

- medium impact on the magnitude of the benefits, further investment in improving the available evidence would not hav e a substantial impact on the total monetized benefits.
6

A .1.4 Uncertainty Quantification The column of Table 6 A

- 1 labeled “uncertainty quantification” is an assessment of the extent to which we were able to use quantitative methods to characterize the re sidual uncertainty in the benefits analysis, after addressing it to the extent feasible in the analytic approach for this RIA. We categorize the level of quantification using the four tiers used in the WHO uncertainty framework. The WHO uncertainty framewo rk is a well

- established approach to assess uncertainty in risk estimates that systematically links the characterization of uncertainty to the sophistication of the health impact assessment. The advantage of using this framework is that it clearly highligh ts the level of uncertainty quantification applied in this assessment and the potential sources of uncertainty that require methods development in order to assess quantitatively. Specifically, EPA applied this framework in multiple

2 We have applied the same classification as The Benefits and Costs of the Clean Air Act from 1990 to 2020 (U.S. EPA,

2011) in this analysis. In the Quantitative Health Risk Assessment for Particulate Matter (U .S. EPA, 2010b), EPA assessed the degree of uncertainty (low, medium, or high) associated with the knowledge

- base (i.e., assessed how well we understand each source of uncertainty), but did not provide specific criteria for the classification.

### 6 A

- 5 risk and expos ure assess ments (U.S. EPA, 2010a , 2014), and it has been recommended in EPA guidance documents assessing air toxics

- related risk and Superfund s ite risks (U.S. EPA, 2004 and 2001, respectively). Ultimately, the tier decision is the professional judgment of EPA staff based on the availability of information for this assessment. The tiers used in this assessment are defined below.

• Tier 0

— screening level, generic qualitative characterization.

• Tier 1

— Scenario

- specific qualitative characterization.

• Tier 2

— Scenario

- specifi c sensitivity analysis.

• Tier 3

— Scenario

- specific probabilistic assessment of individual and combined uncertainty. Within the limits of the data, we strive to use more sophisticated approaches (e.g., Tier 2 or 3) for characterizing uncertainties that have t he largest magnitudes and could not be completely addressed through the analytic approach. The uncertainties for which we have conducted probabilistic (Tier 3) assessments in this analysis are mortality causality, the shape of the concentration

- response fu nction, and mortality and morbidity valuation. For lower magnitude uncertainties, we include qualitative discussions of the potential impact of uncertainty on risk results (WHO Tier 0/1) and/or completed sensitivity analyses assessing the potential impact of sources of uncertainty on risk results (WHO Tier 2).
6 A.2 Organization of the Qualitative Uncertainty Table Table 6 A

- 1 is organized as follows : t he uncertainties are grouped by category (i.e., concentration

- response function, valuation, population and baseline incidence, omitted benefits categories, and exposure changes).

### 6 A

- 6 Table 6 A
- 1 . Summary of Qualitative Uncertainty for Key Modeling Elements in Ozone Benefits

Potential Source of Uncertainty Direction of Potential Bias Magnitude of Impact on Monetized Benefits Confidence in Analytical Approach Uncertainty Quantification Uncertainties Associated with Concentration
- Response Functions

Causal relationship between short

- term ozone exposure and premature mortality Overestimate, if short

- term ozone exposure does not have a causal relationship with premature mortality. High High Tier 1 (qualitative)

Mortality generally dominates monetized benefits, so small uncertainties could have large impacts on the total monetized benefits. Our approach is consistent with the O 3 ISA, which determined that premature mortality has a likely causal relationship with short

term ozone exposure based on the collective body of evidence (p. 6

- 264). In addition, the NAS recommended that EPA “should give little or no weight to the assumptio n that there is no causal association between estimated reductions in premature mortality and reduced ozone exposure” (NRC, 2008). In 2010, the Health Effects Subcommittee of the Advisory Council on Clean Air Compliance Analysis, while reviewing EPA’s The Benefits and Costs of the Clean Air Act 1990 to 2020 (U.S. EPA, 2011), also confirmed the NAS recommendation to include ozone mortality benefits (U.S. EPA

SAB, 2010). Either Medium

- High Medium Tier 2 (sensitivity analysis)

### 6 A

- 7 Potential Source of Uncertainty Direction of Potential Bias Magnitude of Impact on Monetized Benefits Confidence in Analytical Approach Uncertainty Quantification

Shape of the C

- R functions, particularly at low concentrations for short

- term ozone exposure - related mortality The direction of bias that assuming a linear

- no threshold model or alternative model introduces depends upon the “true” functional from of the relationship and the specific assumptions and data in a particular analysis. For example, if the true function identifies a threshold below which health effects do not occur, benefits may be overestimated if a substantial portion of those benefits were estimated to occur below that threshold. Alternately, if a substantial portion of the benefits occurred above that threshold, the benefits may be underestimated because an assumed linear no

threshold function may not reflect the steeper slope above that threshold to account for a ll health effects occurring above that threshold. The magnitude of this impact depends on the fraction of benefits occurring in areas with lower concentrations. Mortality generally dominates monetized benefits, so small uncertainties could have large impac ts on total monetized benefits. The O 3 ISA did not find any evidence that supports a threshold in the relationship between short

- term exposure to ozone and mortality within the range of ozone concentrations observed in the U

. S

. , and recent evidence suggest s that the shape of the ozone - mortality C

- R curve remains linear across the full range of ozone concentrations (p. 6

- 257). Consistent with the O 3 ISA, we assume a log

- linear no

threshold model for the C

- R functions for short

- term ozone mortality. However, the ISA notes that there is less certainty in the shape of the C

- R function below 20 ppb due to the low density of data in this range (p. 6

- 254

- 255). The comparison of short

- term mortality against the associated distribution of (ozone season

averaged) 8

- h ou r max imum ozone concentrations (see Appendix 6 B , section 6 B

. 7 ) suggests that the vast majority of predicted reductions in mortality are associated with days having 8

- h ou r max imum concentrations that fall within the higher confidence range.

Causal relationship between long

- term ozone exposure and premature respiratory mortality Overestimate, if long

- term ozone exposure does not have a causal relationship with premature mortality. Potentially High , if included in monetized benefits High Tier 1 (qualitative) Mortality generally dominates monetized benefits , so small uncertainties could have large impacts on the total monetized benefits. However, we have not included long

- term ozone mortality in the monetized benefits for this analysis due to uncertainties in the cessation lag. While the O 3 ISA concludes that evidence is suggestive of a causal association between total mortality and long

- term ozone exposure (section 7.7.1), specifically with regard to respiratory health effects (including mortality), the ISA concludes that there is likely to be a causal association (section 7.2.8). Shape of the C

- R functions, particularly at low concentrations for long

- term ozone exposure - related respiratory mortality Either Potentially High , if included in monetized benefits Medium Tier 2 (sensitivity analysis)

### 6 A

- 8 Potential Source of Uncertainty Direction of Potential Bias Magnitude of Impact on Monetized Benefits Confidence in Analytical Approach Uncertainty Quantification
The direction of bias that assuming a linear

- no threshold model or alternative model introduces depends upon the “true” functional fo r m of the relationship and the specific assumptions and data in a particular analysis. For example, if the true function id entifies a threshold below which health effects do not occur, benefits may be overestimated if a substantial portion of those benefits were estimated to occur below that threshold. Alternately, if a substantial portion of the benefits occurred above that t hreshold, the benefits may be underestimated because an assumed linear no

threshold function may not reflect the steeper slope above that threshold to account for all health effects occurring above that threshold. The magnitude of this impact depends on th e fraction of benefits occurring in areas with lower concentrations. However, we have not included long

- term ozone mortality in the monetized benefits for this analysis due to uncertainties in the cessation lag. In their memo (see Sasser 2014) clarifying the results of their study (Jerrett et al., 2009) regarding long

- term ozone exposure

- related respiratory mortality, the study authors note that in terms of goodness of fit, long

- term health risk models including ozone clearly performed better than models w ithout ozone , indicating the improved predictions of respiratory mortality when ozone is included. In the article proper, the authors state that, “There was limited evidence that a threshold model specification improved model fit as compared with a non

- thr eshold linear mode l ...”. Furthermore, in the memo referenced above, the authors conclude that considerable caution should be exercised in using any specific threshold, particularly when the more stringent statistical test indicates there is no significantly improved prediction. The CASAC was supportive of the approach EPA used in the HR E A of using a non

- threshold C

- R function based on this study to generate core estimates and consider the impact of potential thresholds (ranging from 40

- 60 ppb) as a sensitivit y analysis. We examine d potential thresholds (from 40 to 60 ppb) in the C

- R function for long

term exposure

- related morality. That analysis suggested that thresholds between 55 and 60 ppb would have a substantial impact on overall modeled benefits, whil e thresholds below 50 ppb would have a minor impact on predicted benefits (see Appendix 6 B, Table 6 B

- 3 ).

Exposure error in epidemiology studies Underestimate (generally) Medium Low

- Medium Tier 1 (qualitative) The O 3 ISA states that exposure measurement error can also be an important contributor to uncertainty in effect estimates associated with both short

- term and long

- term studies (p. 1xii). Together with other factors (e.g., low data density), exposure error can sm ooth the C

- R functions and obscure potential thresholds (p. lxix). In addition, the O 3 ISA states that exposure error can bias effect estimates toward or away from the null and widen confidence intervals (p. lxii). Recent analyses reported in Krewski et a l. (2009) demonstrate the potentially significant effect that this source of uncertainty can have on effect estimates. These analyses also illustrate the complexity and site

- specific nature of this source of uncertainty. Although this underestimation is we ll documented, including in the O 3 ISA, the SAB has not suggested an approach to adjust for this bias. (No quantitative method available)

Unknown Medium Medium Tier 1 (qualitative)

### 6 A

- 9 Potential Source of Uncertainty Direction of Potential Bias Magnitude of Impact on Monetized Benefits Confidence in Analytical Approach Uncertainty Quantification

Adjustment of risk coefficients to 8

- hour maximum from 24

- hour average or 1

- hour maximum in the epidemiology studies W e converted these metric s to maximum 8

- hour average ozone concentration using standard conversion functions based on observed relationships in the underlying studies. If the relationships between air metrics reported in the studies differ systematically from the relationships seen across the modeling domain, then bias could be introduced. This conversion does not affect the relative magnitude of the health impact func tion. However, the pattern of 8

- h ou r max imum concentrations for a particular location over an ozone season could differ from the pattern of 1

- h ou r max or 24

- hour average metrics for that same location. Consequently, monetized benefits could differ for a pa rticular location depending on the metric used in modeling benefits

. This practice is consistent both with the available exposure modeling and with the form of the current ozone standard. However, in some cases, these conversions were not specific to the ozone “warm” season, which introduces additional uncertainty due to the use of effect estimates based on a mixture of warm season and all year data in the epidemiological studies. (No quantitative method available)

Confounding by individual risk factors, other than socioeconomic status

— e.g., smoking, or ecologic factors, which represent the neighborhood, such as unemployment Either, depending on the factor and study Medium Medium Tier 2 (sensitivity analysis) Individual, social, economic, and demographic covariates can bias the relationship between particulate air pollution and mortality, particularly in cohort studies that rely on regional air pollution levels. Because mortality dominates monetized benefits, e ven a small amount of confounding could have medium impacts on total monetized benefits. To minimize confounding effects, we use risk coefficients that control for individual risk factors to the extent practical. (Quantitative methods available but not ass essed in this analysis.)

Confounding and effect modification by co

pollutants Either, depending upon the pollutant. Medium Medium Tier 1 (qualitative) Disentangling the health responses of combustion

- related pollutants (i.e., PM, SOx, NOx, ozone, and CO ) is a challenge. The PM ISA states that co

- pollutants may mediate the effects of PM or PM may influence the toxicity of co

pollutants ( U.S. EPA, 2009, p . 1

– 16). Alternately, effects attributed to one pollutants may be due to another. Because this uncertai nty could affect mortality and because mortality generally dominates monetized benefits, even small uncertainties could have medium impacts on total monetized benefits. The O 3 ISA states that there is high confidence that unmeasured confounders are not pr oducing the findings when multiple studies are conducted in various settings using different subjects or exposures, such as multi

- city studies (p. lxi). When modeling effects of pollutants jointly (e.g., PM and O 3 ), we apply multi

- pollutant effect estimate s when those estimates are available to avoid double

- counting and satisfy other selection criteria. In addition, we apply multi

- city effect estimates when available. (No quantitative method available)

Application of C

- R relationships only to the original study population Underestimate Low High Tier 2 (sensitivity analysis) Estimating health effects for only the original study population may underestimate the whole population benefits of reductions in pollutant exposures. Mortality generally dominates monetized benefits, so further age range expansions for morbidity endpoints would have a small impact on total monetized benefits. Following advice from the SAB (U.S. EPA

SAB, 2004a, pg. 7) and NAS (NRC, 2002, pg. 114), we expanded the age range for childh ood asthma exacerbations beyond the original study population to ages 6

- 18. (Quantitative methods available but not assessed in this analysis.)
Uncertainties Associated with Economic Valuation Unknown High Medium Tier 3 (probabilistic)

### 6 A

- 10 Potential Source of Uncertainty Direction of Potential Bias Magnitude of Impact on Monetized Benefits Confidence in Analytical Approach Uncertainty Quantification

Mortality Risk Valuation/Value

- of

- a Statistical

- Life (VSL) Some studies suggest that EPA’s mortality valuation is too high, while other studies suggest that it is too low. Differences in age, income, risk aversion, altruism, nature of risk (e.g., cancer), and study desi gn could lead to higher or lower estimates of mortality valuation. Mortality generally dominates monetized benefits, so moderate uncertainties could have a large effect on total monetized benefits. The VSL used by EPA is based on 26 labor market and stated preference studies published between 1974 and 1991. EPA is in the process of reviewing this estimate and will issue revised guidance based on the most up

- to

- date literature and recommendations from the SAB

- EEAC in the near future (U.S. EPA, 2010 b , U.S. EP A

- SAB, 2011c). Assessed uncertainty in mortality valuation using a Weibull distribution.

Cessation lag structure for long

- term ozone mortality Unkonwn Medium , if included in the monetized benefits Low Tier 2 (sensitivity analysis)

We included both a zero (no) lag and 20

- year segmented lag model in completing the sensitivity analysis involving dollar benefits for long

- term respiratory mortality. Given that available information does not lead to the selection of a particular lag model , we are not in a position to classify the direction of potential bias associated with this source of uncertainty. Although the cessation lag does not affect the number of premature deaths attributable to long

- term ozone exposure, it affects the timing o f those deaths and thus the discounted monetized benefits. Mortality generally dominates monetized benefits, so moderate uncertainties could have a large effect on total monetized benefits. However, we have not included long

- term ozone mortality in the mon etized benefits for this analysis due to uncertainties in the cessation lag. As discussed in section 6

. 7

. 3 .1, in presenting dollar benefit estimates as part of the sensitivity analysis (presenting dollar benefits for long

- term ozone

- related morality), we i nclude both an assumption of zero lag and a lag structure matching that used for the core PM 2.5 estimate (the SAB 20

- year segmented lag). Inclusion of the zero lag reflects consideration for the possibility that the long

- term respiratory mortality estimate captures primarily an accumulation of short

- term mortality effects across the ozone season. The use of the 20

- year segmented lag reflects consideration for advice provided by the SAB

- HES (USEPA

SAB , 2010 , 2004c ). Using the 20

- year segmented lag developed for PM 2.5
– related mortality results in a 10

- 20% reduction in the total dollar benefit (using a 3% and 7% discount rate, respectively) relative to the alternative approach of applying no lag (i.e., assuming all of the mortality reductions occur in the same year).

Income growth adjustments Either Medium Medium Tier 2 (sensitivity analysis) Income growth increases willingness

- to

- pay (WTP) valuation estimates, including mortality, over time. From 1997 to 2010, personal income and GDP growth have begun to diverge. If this trend continues, the assumption that per capita GDP growth is a reasonable proxy for income growth may lead to an overstatement of benefits. (IEc, 2012). Income growth from 1990 to 2020 increases mortality valuation by 20%. Alternate estim ates for this adjustment vary by 20% (IEc, 2012). Because we do not adjust for income growth over the 20

- year cessation lag, this approach could also underestimate the benefits for the later years of the lag. Consistent with SAB recommendations (U.S. EPA,

- SAB, 2000, pg. 16), we adjust WTP for income growth. It is d ifficult to forecast future income growth ; h owever, in the absence of readily available income data projections, per capita GDP is the best available option. As shown in Appendix 6 B (section 6B.5 ) , the use of alternate income growth adjustments would result in an increase of from 8 to 75% in the dollar benefits for short

term ozone

- related mortality

.

Morbidity valuation Underestimate Low Low Tier 3 (probabilistic), where available

### 6 A

- 11 Potential Source of Uncertainty Direction of Potential Bias Magnitude of Impact on Monetized Benefits Confidence in Analytical Approach Uncertainty Quantification Morbidity benefits such as hospital admissions are calculated using cost

- of

- illness (COI) estimates, which are generally half the WTP to avoid the illness (Alberini and Krupnick, 2000). In addition, the morbidity costs do not reflect physiological response s or sequelae events, such as increased susceptibility for future morbidity. Even if we doubled the monetized valuation of morbidity endpoints using COI valuation s that are currently included in the RIA, the change would still be less than 5% of the moneti zed benefits. It is unknown how much including sequelae events could increase morbidity valuation. Although the COI estimates for hospitalizations reflect recent data, we have not yet updated other COI estimates such as for school loss days. The SAB conclu ded that COI estimates could be used as placeholders where WTP estimates are unavailable, but it is reasonable to presume that this strategy typically understates WTP values (U.S. EPA

- SAB, 2004b, pg. 3). Assessed uncertainty in morbidity valuation using di stributions specified in the underlying literatu re, where available (see Table 6

- 10).

Uncertainties Associated with Baseline Incidence and Population Projections

Population estimates and projections Either Low

– Medium Medium Tier 1 (qualitative)

The monetized benefits would change in the same direction as the over - or underestimate in population projections in areas where exposure changes. Monetized benefits are substantially affected by population density. Comparisons using historical C ensus data sho w that population projections are ±5% nationally, but projection accuracy can vary by locality. Historical error for Woods & Poole’s population projections has been ±8.1% for county

- level projections and ±4.1% for states (Woods and Poole, 2012). The magnit ude of impact on total monetized benefits depends on the specific location where ozone is reduced. We use population projections for 5

- year increments for 304 race/ethnicity/gender/age groups (Woods and Poole, 2012) at Census blocks. Population forecasting is well

established but projections of future migration due to possible catastrophic events are not considered. In addition, projections at the small spatial scales used in this analysis are inherently more uncertain than projections at the county

- or sta te

- level. (No quantitative method available)

Uncertainty in projecting baseline incidence rates for mortality Unknown Low

- Medium Medium Tier 1 (qualitative)
Because the mortality rate projections for future years reflect changes in mortality patterns as well as population growth, the projections are unlikely to be biased. Because mortality generally dominates monetized benefits, small uncertainties could have medium impacts on total monetized benefits. The county

- level baseline mortality rates reflect re cent databases (i.e., 2004

– 2006 data) and are projected for 5

- year increments for multiple age groups. This database is generally considered to have relatively low uncertainty (CDC Wonder, 2008). The projections account for both spatial and temporal change s in the population. (No quantitative method available)

Uncertainty in projecting baseline Either, depending on the health endpoint Low Low

- Medium Tier 1 (qualitative)

### 6 A

- 12 Potential Source of Uncertainty Direction of Potential Bias Magnitude of Impact on Monetized Benefits Confidence in Analytical Approach Uncertainty Quantification incidence rates and prevalence rates for morbidity Morbidity baseline incidence is available for current year only (i.e., no projections available). Assuming current year levels can bias the benefits for a specific endpoint if the data has clear trends over time. Specifically, asthma prevalence rates have increased substantially over the past few years while hospital admissions have decreased substantially. The magnitude varies with the health endpoint, but the overall impact on the total benefits estimate from these morbidity endpoints is likely to be low. We do not have a method to proj ect future baseline morbidity rates, thus we assume current year levels will continue. While we try to update the baseline incidence and prevalence rates as frequently as practicable, this does not continue trends into the future. Some endpoints , such as h ospitalizations and ER visits , have more recent data (i.e., 2007) stratified by age and geographic location. Other endpoints, such as respiratory symptoms , reflect a national average. Asthma prevalence rates reflect recent increases in baseline asthma rate s (i.e., 2008). (No quantitative method available)

Uncertainties Associated with Omitted Benefits Categories

Unquantified ozone health benefit categories, such as worker productivity and long

- term mortality Underestimate High Low Tier 2 (sensitivity analysis)

EPA has not included monetized estimates of these benefits categories in the core benefits estimate. Although the potential magnitude is unknown, including all of the additional endpoints associated with ozone exposure that are currently not mo netized could increase the total benefits by a large amount. Current data and methods are insufficient to value national quantitative estimates of these health effects. The O 3 ISA determined that respiratory effects (including mortality) are causally assoc iated with long

- term ozone exposure (p. 2

– 17). The O 3 ISA also determined that outdoor workers have an increased risk of ozone - related health effects (p. 1

- 15), and that studies on outdoor workers show consistent evidence that short

- term increases in ambient ozone exposure can decrease lung function in healthy adults (p.6

- 38). Additional studies link short

- term ozone exposure to reduced productivity in outdoor workers (Graf Zivin and Neidell, 2013; Crocker and Horst, 1981). We include sensitivity analyses reflecting long

- term mortality, which shows that this endpoint could add substantially to the monetized benefits (see Appendix 6B, section 6B.2). We have also included a supplemental analysis reflecting application of an updated worker productivity analysis (see section 6.5.3)

.

Uncertainties Associated with Estimated Exposure Changes Unknown Unknown Low Tier 1 (qualitative)

### 6 A

- 13 Potential Source of Uncertainty Direction of Potential Bias Magnitude of Impact on Monetized Benefits Confidence in Analytical Approach Uncertainty Quantification

Spatial matching of air quality estimates from epidemiology studies to air quality estimates from air quality modeling Epidemiology studies often assume one air quality concentration is representative of an entire urban area when calculating hazard ratios, while benefits are calculated using air quality modeling conducted at 12 km spatial resolution. This spatial mismatch could introduce uncertainty. We have not controlled for this potential bias, and the SAB has not suggested an approach to adjust for this bias. (No quantitative method available)

### 6 A

- 14 6 A .3 References Alberini, Anna and Alan Krupnick. 2000. “Cost

- of

- Illness and Willingness

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- Pay Estimates of the Benefits of Improved Air Quality: Evidence from Taiwan.” Land Economics Vol. 76, No. 1 (Feb), pp. 37

- 53. Centers for Disease Control and Prevention (CDC). 2008. National Center for Health Statistics. National Health Interview Survey, 1999

- 2008. Crocker, T. D., & Horst, R. L. (1981). Hours of work, labo r productivity, and environmental conditions: A case study. The Review of Economics and Statistics, 63(3), 361

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– 151. Graff Zivin, J., Neidell, M. (2012). “The impact of pollution on worker productivity.” American Econo mic Review , 102, 3652

- 3673. Industrial Economics, Incorporated (IEc). 2012. Updating BenMAP Income Elasticity Estimates

— Literature Review. Memo to Neal Fann. March. Available on the Internet at <http://www.epa.gov/ttn/ecas/regdata/Benefits/Income%20Elastic ity%20Update_Recommendations_noAppD.p df> Jerrett M, Burnett RT, Pope CA, III, et al. 2009. “Long

- Term Ozone Exposure and Mortality.” New England Journal of Medicine 360:1085

- 95. Krewski D, Jerrett M, Burnett RT, Ma R, Hughes E, Shi, Y, et al. 2009. “ Extend ed follow

- up and spatial analysis of the American Cancer Society study linking particulate air pollution and mortality. ” HEI Research Report, 140, Health Effects Institute, Boston, MA. Mansfield, Carol; Paramita Sinha; Max Henrion. 2009. Influence Analysis in Support of Characterizing Uncertainty in Human Health Benefits Analysis: Final Report. Prepared for U.S. EPA, Office of Air Quality Planning and Standards. November. Available on the internet at <http://www.epa.gov/ttn/ecas/regdata/Benefits/influence_a nalysis_final_report_psg.pdf>. National Research Council (NRC). 2002. Estimating the Public Health Benefits of Proposed Air Pollution Regulations. Washington, DC: The National Academies Press. Washington, DC. National Research Council (NRC). 2008. Estimating Mortality Risk Reduction and Economic Benefits from Controlling Ozone Air Pollution . National Academies Press. Washington, DC. Sasser, E. 2014. Response to Comments Regarding the Potential Use of a Threshold Model in Estimating the Mortality Ris ks from Long

- term Exposure to Ozone in the Health Risk and Exposure Assessment for Ozone, Second External Review Draft. Memorandum to Holly Stallworth, Designated Federal Officer, Clean Air Scientific Advisory Committee from EPA/OAQPS Health and Environmen tal Impacts Division. U.S. Environmental Protection Agency (U.S. EPA). 200 1

. Risk assessment guidance for Superfund. Vol. III, Part A. Process for conducting probabilistic risk assessment (RAGS 3A). Washington, DC, United States Environmental Protection Agency (EPA 540

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- 45; PB2002 963302. Available on the Inte rnet at <http://www.epa.gov/oswer/riskassessment/rags3adt/index.htm>. U.S. Environmental Protection Agency (U.S. EPA). 200 4

. EPA’s risk assessment process for air toxics: History and overview. In: Air toxics risk assessment reference library. Vol. 1. Techn ical resource manual. Washington, DC,

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— RTP Division. December. Available at <http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=216546>.
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— Final Report. EPA

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U.S. Environmental Pro tection Agency (U.S. EPA). 2011 . The Benefits and Costs of the Clean Air Act 1990 to 2020: EPA Report to Congress. Office of Air and Radiation, Office of Policy, Washington, DC. March. Available on the Internet at <http://www.epa.gov/oar/sect812/feb11/fullreport.pdf>. U.S. Environmental Protection Agency (U.S. EPA). 2012. Regulatory Impact Analysis for the Final Revisions to the National Ambient Air Quality Standards for Particulate Matter. EPA

- 452/R

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- 003. Office of Air Quality Planning and Standards, Health and Environmental Impacts Division. December. Available at: <http://www.epa.gov/pm/2012/finalria.pdf>. U.S. Environmental Protection Agency (U.S. EPA ). 2013. Integrated Science Assessment of Ozone and Related Photochemical Oxidants (Final Report). U.S. Environmental Protection Agency, Washington, DC, EPA/600/R

10/076F. Available at: <http://cfpub.epa.gov/ncea/isa/recordisplay.cfm?deid=247492#Download>. U.S. Environmental Protection Agency (U.S. EPA). 2014. Health Risk and Exposure Assessment for Ozone Final Report. EPA

- 452/R

- 14

- 004a. Office of Air Quality Planning and Standards, Health and Environmental Impacts Division. August. Available at: < http:// www.epa.gov/ttn/naaqs/standards/ozone/data/20140829healthrea.pdf>. U.S. Environmental Protection Agency

— Science Advisory Board (U.S. EPA

- SAB). 1999. An SAB Advisory: The Clean Air Act Section 812 Prospective Study Health and Ecological Initial Studies. EP A

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ADV

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- 005, Available on the Internet at <http://yosemite.epa.gov/sab/sabproduct.nsf/1AFDEBB45C57F6E5852571930058C1A5/$File/coua9905.pdf>. U.S. Environmental Protection Agency

— Science Advisory Board (U.S. EPA

- SAB). 2000

. The Clean Air Act Ame ndments (CAAA) Section 812 Prospective Study of Costs and Benefits (1999): Advisory by the Advisory Council on Clean Air Compliance Analysis: Costs and Benefits of the CAAA. EPA

- SAB

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- 002. October. Available on the Internet at <http://yosemite .epa.gov/sab/sabproduct.nsf/E992F918DF32131A852571B9004571F7/$File/coua0002.pdf>. U.S. Environmental Protection Agency

— Science Advisory Board (U.S. EPA

- SAB). 2001. REVIEW OF THE DRAFT ANALYTICAL PLAN FOR EPA’S SECOND PROSPECTIVE ANALYSIS

- BENEFITS AND COS TS OF THE CLEAN AIR ACT 1990

- 2020. EPA

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- ADV

- 01

- 004, Available on the Internet at <http://yosemite.epa.gov/sab/sabproduct.nsf/572DC1989A3986CC8525718D006BAB8B/$File/councila01004. pdf>. U.S. Environmental Protection Agency

— Science Advisory Board (U.S. EPA

- SAB). 2004 a . Advisory on Plans for Health Effects Analysis in the Analytical Plan for EPA ’ s Second Prospective Analysis

— Benefits and Costs of the Clean Air Act, 1990

– 2020 . EPA

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- 002

. March. Available on the Internet at

### 6 A

- 16 < http://yosemite.epa.gov/sab%5CSABPRODUCT.NSF/08E1155AD24F871C85256E5400433D5D/$File/counc il_adv_04002.pdf>. U.S. Environmental Protection Agency

— Science Advisory Board (U.S. EPA

- SAB). 2004 b

. Review of the Draft Analytical Plan for EPA’s Second Prospective Analysis

— Benefits and Costs of the Clean Air Act, 1990

- 2020: An Advisory by the Advisory Council for Clean Air Compliance Analysis . EPA

- SAB

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- ADV

- 04

- 00 4. May

. Available on the Internet at <http://yosemite.epa.gov/sab/sabproduct.nsf/7CCBBFE15CD4C8B185 256F17005E3079/$File/council_adv_04 004.pdf>. U.S. Environmental Protection Agency

— Science Advisory Board (U.S. EPA

- SAB). 2004 c
. Advisory Council on Clean Air Compliance Analysis Response to Agency Request on Cessation Lag. EPA

- COUNCIL

- LTR

- 05

- 001. December. Available on the Internet at <http://yosemite.epa.gov/sab/sabproduct.nsf/0/39F44B098DB49F3C85257170005293E0/$File/council_ltr_05_ 001.pdf>. U.S. Environmental Protection Agency

— Science Advisory Board (U.S. EPA

- SAB). 2010. Review of EPA’s DRAFT Health Benef its of the Second Section 812 Prospective Study of the Clean Air Act. EPA

- COUNCIL

10

- 001. June. Available on the Internet at <http://yosemite.epa.gov/sab/sabproduct.nsf/9288428b8eeea4c885257242006935a3/72D4EFA39E48CDB2852 5774500738776/$File/EPA

- COUNCIL

- 10

- 001

- unsigned.pdf>. U.S. Environmental Protection Agency

— Science Advisory Board (U.S. EPA

- SAB). 2011a. CASAC Review of Quantitative Health Risk Assessment for Particulate Matter

— Second External Review Draft (February 2010). EPA

- CASAC

- 10

- 008. Available on th e Internet at <http://yosemite.epa.gov/sab/sabproduct.nsf/BC4F6E77B6385155852577070002F09F/$File/EPA

- CASAC

- 10

- 008

- unsigned.pdf>. U.S. Environmental Protection Agency

— Science Advisory Board (U.S. EPA

- SAB). 2011b. Review of the Final Integrated Report for th e Second Section 812 Prospective Study of the Benefits and Costs of the Clean Air Act (August 2010). EPA

- COUNCIL

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- 001. Available on the Internet at <http://yosemite.epa.gov/sab/sabproduct.nsf/1E6218DE3BFF682E852577FB005D46F1/$File/EPA

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11

- 001

- unsi gned.pdf>. U.S. Environmental Protection Agency

— Science Advisory Board (U.S. EPA

- SAB). 2011c. Review of Valuing Mortality Risk Reductions for Environmental Policy: A White Paper (December 10, 2010). EPA

- SAB

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- 011. July. Available on the Internet at < http://yosemite.epa.gov/sab/sabproduct.nsf/298E1F50F844BC23852578DC0059A616/$File/EPA

- SAB

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- 011

- unsigned.pdf>. Woods & Poole Economics, Inc. 2012. Complete Economic and Demographic Data Source (CEDDS). CD

- ROM. Woods & Poole Economics, Inc. Washington, D.C

. World Health Organization (WHO). 2008. Part 1: Guidance Document on Characterizing and Communicating Uncertainty in Exposure Assessment, Harmonization Project Document No. 6. Published under joint sponsorship of the World Health Organization, the Interna tional Labour Organization and the United Nations Environment Programme. WHO Press: Geneva, Switzerland. Available on the Internet at <www.who.int/ipcs/methods/harmonization/areas/uncertainty%20.pdf>.

### 6B

- 1 A PPENDIX 6 B: SENSITIVITY ANALYSES RELATED TO THE BENEFITS ANALYSIS

Overview The benefits analysis presented in Chapter 6 of this RIA is based on our current interpretation of the scientific and economic literature. That interpretation requires judgments regarding the best available data, models, and analytical methodologies and the assumptions that are most appropriate to a dopt in the face of important uncertainties. The majority of the analytical assumptions used to develop the main estimates of benefits have been reviewed and supported by EPA’s independent Science Advisory Board (SAB). Both EPA and the SAB recognize that d ata and modeling limitations , as well as simplifying assumptions can introduce uncertainty into the estimates of benefits and that alternative choices exist for some inputs to the analysis, such as the concentration

- response (C

- R) functions for mortality. This appendix assesses the sensitivity of the benefits results to: (a) alternative studies for short

- term ozone exposure

- related mortality (section 6B.1), (b) monetized benefits associated with mortality resulting from long

- term exposure to ozone (section 6B.2), ( c ) the potential impact of thresholds in long

- term ozone exposure

- related mortality in incidence a nd benefits estimates (section 6 B. 3 ), ( d ) alternative response functions developed through expert elicitation for long

- term PM 2.5 

### expos ure - related mor tality (section 6 B. 4 ), and ( e ) alternative assumptions regarding income elasticity on benefits derived using willingness

- t o - pay (WTP) functions (section 6 B. 5 ). For the core analyses , we estimated benefits for two scenarios: 2025 and post

2025. However, in conducting these sensitivity analyses, we used the 2025 scenario as the basis for making our calculations, since sensitivity analysis findings for this scenario would generally hold for the post

- 2025 scenario. In addition to the sensitivity analyses descri bed above, this appendix presents several analyses intended to supplement the core estimates generated for the RIA. These supplemental analyses give greater insight to the manner in which these impacts are distributed amon g populations of different ages an d the ambient levels of ozone at which the avoided deaths are estimated to occur and include: (a) age group - differentiated aspects of short

- term ozone exposure - related mortality (including total avoided incidence, life years gained and percent

### 6B

- 2 reduction in b aseline mortality

- Section 6B.6 ), (b) evaluation of mortality impacts relative to the baseline pollutant concentrations (used in generating those mortality estimates) for both short

term ozone exposure

- related mortality and long

- term PM 2.5 

### exposure - rela ted mortality (Section 6B .7 ) and (c) a detailed discussion of the methodology used in modeling ozone

- related impacts on outdoor worker productivity, including presentation of results (Section 6B.8)

. 6B.1 Sensitivity Analysis for Alternative C
- R Functions f or Short
- term Exposure to Ozone Table 6B

- 1 presents the results of applying alternative effect estimates identified for modeling short

- term exposure

- related mortality for ozone, along with the set of core risk estimates (these sensitivity analysis results are also reflected in Figure 6

- 4 presented in the body of the document). The suite of effect estimates included in the sensitivity analysis reflects consideration for both meta

- analyses and multi

- city epidemiology studies. The rationale for the specific mi x of effect estimates included in the sensitivity analysis is presented in section 6.6.3.2.
Table 6B
- 1. Sensitivity Analysis for Alternative C
- R Functions for Short
- term Exposure to Ozone a

Health Effect Revised and Alter n ative Standards (95 th percentile confidence intervals)

70 ppb 65 ppb Avoided Short
- Term Mortality
- Core Analysis

multi

- city studies Smith et al. (2009) (all ages) 96 490 (47 to 140) (240 to 740) Zanobetti and Schwartz (2008) (all ages) 160 820 (86 to 240) (440 to 1,200) Avoided Short
- Term Mortality
- Sensitivity Analysis

multi

- city studies Smith et al. (2009) (all ages) co - pollutant model with PM 10 77 390 (

- 21 to 170) (

- 110 to 890) Schwartz (2005) (all ages) 120 610 (37 to 200) (190 to 1,000) Huang et al. (2005) (cardiopulmonary) 110 580 (42 to 180) (220 to 940) Bell et al. (2004) (all ages) 78 400 (26 to 130) (130 to 660)

meta

- analyses Bell et al. (2005) (all ages) 250 1,300 (120 to 380) (610 to 2,000) Ito et al. (2005) (all ages) 350 1,800 (210 to 490) (1,100 to 2,500) Levy et al. (2005) (all ages) 350 1,800 (240 to 470) (1,200 to 2,400)

a All estimates are rounded to two significant digits.

### 6B

- 3 This sensitivity analysis showed that the two core incidence estimates fall within (and towards the lower end of) the broader range resulting from application of the seven alternative effect estimates (note that these observations based on incidence would also hold for the matching set of dollar benefit estimates generated for this endpo int category).
6B.2 Sensitivity Analysis for Monetized Benefits for Premature Mortality from Long
term Exposure to Ozone As discussed in section 6.3, due to uncertainty in specifying the temporal lag structure associated with reductions in long

- term ozone

- related respiratory mortality, we have included these estimates as a sensitivity analysis and not as part of the core dollar benefit estimate. Table 6B

- 2 presents the dollar benefits (2011$) associated with modeled reductions in long

- term
ozone - related re spiratory mortality. These benefit estimates are generated using non

- threshold models obtained from Jerrett et al. ( 2009 ) because we present the potential threshold analysis in the section 6B.3 . Additional detail on the effect estimates used in the core an alysis and this sensitivity analysis is presented in section 6.6.3.2. Section 6.6.4 provides additional detail on the approach used in valuing these mortality estimates including how uncertainty re lated to cessation lag was addressed

.

### 6B

- 4 Table 6 B
- 2 . Sensitivity Analysis for Monetized Benefits for Mortality from Long
- term Exposure to Ozone (millions of 2011$) ( 2025 and post
- 2025 scenarios) a , d

a See section 6B.3 for the sensitivity analysis for potential thresholds in the C

- R function for long

- term e xposure related mortality Observations from that analysis can be applied to these results. b T he zero

- lag model is not affected by discounting. The values in parentheses reflect 95 th percentile confidence interval s
. c The range (outside of the parentheses) results from application of 7% and 3% discount rates after applying the 20

- year segmented lag ( i.e., ranging from the 7% discount rate up to the 3% discount rate ). The range in parentheses reflects the 95 th percentile confidence interval in combination with the discount rate ( i.e., ranging from the 2.5 th percentile and the 7% discount rate up to the 97.5 th percentile and the 3% discount rate ). d All estimates rounded to two significant digits.



### This sensitivity analysis suggests that if included in the core benefit estimate, long

- term ozone exposure - related mortality could add substantially to the overall benefits. Additionally, use of a 20

- year segment lag can reduce benefits by 10

- 20% (relative to a zero lag) depending on the discount rate applied.
6 B. 3 Threshold Sensitivity Analysis for Premature Mortality Incidence and Benefits from Long
- term Exposure to Ozone
In estimating long

- term ozone mortality (including the benefit estimates presented in the last section) , we employ ed a continuous non

- threshold C

- R function relating ozone exposure to premature death . However, as discussed in S ection 6 .6.3.2 , there is uncertainty regarding the 70 ppb 65 ppb Avoided Long-term Respiratory Mortality 2025 scenario $3,400 $17,000 ($280 to $10,000) ($1,400 to $52,000) $2,800 to $3,100 $14,000 to $16,000 ($250 to $8,400) ($1,200 to $47,000) post-2025 scenario $3,000 $6,000 ($240 to $8,400) ($490 to $18,000) $2,400 to $2,700 $4,900 to $5,400 ($200 to $8,000) ($400 to $18,000) Revised and Alterative Standard Levels

(95th percentile confidence intervals)

multi-city study Jerrett et al. (2009) (age 30-99) copollutants model (PM 2.5 ) no lag b

multi-city study Jerrett et al. (2009) (age 30-99) copollutants model (PM 2.5 ) no lag b Jerrett et al. (2009) (age 30-99) copollutants model (PM 2.5 ) 20 yr segmented lag c Jerrett et al. (2009) (age 30-99) copollutants model (PM 2.5 ) 20 yr segmented lag c Health Effectb

### 6B

- 5 potential existence and location of a threshold in the C

- R function relating mortality and lon g term ozone concentrations. Thus, we have included a sensitivity analysis exploring the impact of potential thresholds in the C

- R relationship on estimates of long

- term exposure

- related mortality that were evaluated in Jerrett et al. (2009), consistent wi th advice from the Clean Air Scientific Advisory Committee ( CASAC ) ( U.S. EPA

- SAB , 2014 ). In their memo clarifying the results of their study (Sasser, 2014), the authors note that in terms of goodness of fit, long

- term health risk models including ozone cl early performed better than models without ozone , indicating the improved predictions of respiratory mortality when

ozone is included. In exploring different functional forms, the authors report that the model including a threshold at 56 ppb had the lowest log

- likelihood value of all models evaluated (i.e., linear models and models including thresholds ranging from 40

- 60 ppb), and thus provided the best overall statistical fit to the data. However, they also note that it is not clear whether the 56 ppb thre shold model is a better predictor of respiratory mortality than when using a linear (no

threshold) model for this dataset. Using one statistical test, the model with a threshold at 56 ppb was determined to be statistically superior to the linear model. Usi ng another, more stringent test, none of the threshold models considered were statistically superior to the linear model. Under the less stringent test, although the threshold model produces a statistically superior prediction than the linear model, there is uncertainty about the specific location of the threshold, if one exists. This is because the confidence intervals on the model predictions indicate that a threshold could exist anywhere from 0 to 60 ppb. The authors conclude that considerable caution sh ould be exercised in using any specific threshold, particularly when the more stringent statistical test indicates there is no significantly improved prediction. Based on this additional information from the authors, we have chosen to reflect the uncertain ty about the existence and location of a potential threshold by estimating mortality attributable to long

- term ozone exposures using a range of threshold

- based effect coefficient as sensitivity analyses. Specifically, we estimate long

term ozone mortality benefits using unique risk coefficients that include a range of thresholds from 40 ppb to 60 ppb in 5 ppb increments, while also including a model with a threshold equal to 56 ppb, which had the lowest log

- likelihood value for all models examined. 1 

### Table 6 B

- 3

1 There is a separate effect coefficient (and associated standard error) for each of the fitted threshold models

estimated in Jerrett et al. (2009). As a result, the sensitivity of estimated mortality attributable to long

- term ozone

### 6B

- 6 provides the results of these sensitivity analyses (based on modeling incidence) for 65 ppb and 70 ppb

. T he same pattern in terms of relative reductions across threshold s would also hold for the monetized benefit estimates.

Table 6 B
- 3 . Long
- term Ozone Mortality Incidence at Various Assumed Thresholds a Threshold Concentration 70 ppb 65 ppb No threshold 340 1,7 00 4 0 ppb 260 1,300 45 ppb 200 910 5 0 ppb 59 210 5 5 ppb 6 66 56 ppb 5 60 6 0 ppb 3 19

a All estimates rounded to two significant digits. 

### The results of the sensitivity analysis based on the suite of threshold

- based risk coefficients suggest that threshold models can substantially lower estimates of ozone - attributable long

- term mortality. For example, estimated benefits for long

- term mortality using a model that includes a 55 ppb threshold are approximately 70% less than long

- term mortality benefits estimated using the co

- pollutant , non - threshold model. Generally, estimated long

- term mortality benefits are progressively reduced when us ing models with increasing thresholds, with the highest threshold considered (60 ppb) removing virtually all of the estimated incidence reduction and associated benefits.
6 B. 4 Sensitivity Analysis for Alternative C
- R Functions for PM 2.5 

### – Related Mortality
In estimating PM 2.5 

### co - benefits, monetized benefits are driven largely by reductions in mortality. Therefore, it is particularly important to attempt to characterize the uncertainties associated with reductions in premature mortality. In addition to the AC S and Six Cities cohort

s tudies , several recent cohort studies conducted in North America provide evidence for the relationship between long

- term exposure to PM 2.5 

### and the risk of premature death. Many of these additional cohort studies are described in th e PM ISA (U.S. EPA, 2009) and the Provisional Assessment (U.S. EPA, 2012a ). 2 , 

### Table 6 B

- 4 provides the effect estimates from each of these

concentrations is affected by both the assumed threshold level (below which there is no effect of ozone ) and the effect coefficient applied to ozone concentrations above the threshold. 2 It is important to note that the newer studies in the Provisional Assessment are published in peer

- reviewed journals

and meet our st udy selection criteria, but they have not been assessed in the context of an Integrated Science

### 6B

- 7 cohort studies for all

- cause, cardiovascular, cardiopulmonary, and ischemic heart disease (IHD) mortality , as well as the lowest measured air quality level (LML) and mean concentration in the study. We also draw upon the results of the 2006 expert elicitation 3 

### sponsored by the EPA (Roman et al., 2008; IEc, 2006) to demonstrate the sensitivity of the benefits estimates to 12 expert

- defined C

- R functions. The PM 2.5 

### expert elicitation and the derivation of C

- R functions from the expert elicitation results are desc ribed in detail in the 2006 PM 2.5 

### NAAQS RIA (U.S. EPA, 2006 ), the elicitation summary report (IEc, 2006) and Roman et al. (2008), and we summarize the key attributes of this study relative to the interpretation of the estimates of PM

related mortality repo rted here. We describe also how the epidemiological literature has evolved since the expert elicitation was conducted in 2005 and 2006.

Assessment nor gone through review by the SAB. In addition, only the ACS and Harvard Six Cities’ cohort studies have been recommended by the SAB as appropriate for benefits analysis of national rulemakings. 3 Expert elicitation is a formal, highly

- structured and well

- documented process whereby expert judgments, usually

of multiple experts, are obtained (Ayyub, 2002).

### 6B

- 8 Table 6 B
- 4 . Summary of Effect E stimates from Recent Cohort Studies in North America Associated with Change in Long
- Term Exposure to PM 2.5

Study Cohort (age) LML (μg/ m 3 ) Mean (μg/ m 3 ) Hazard Ratios per 10 μg/m 3 Change in PM 2.5 (95 th percentile confidence intervals) All Causes Cardiovascular Cardiopulmonary IHD Pope et al. (2002) ACS (age >30) 7.5 18.2 1.06 (1.02

– 1.11) 1.12 (1.08

– 1.15) 1.09 (1.03

– 1.16) N/A
Laden et al. (2006) Six Cities (age > 25) 10 16.4 1.16 (1.07

– 1.26) 1.28 (1.13

– 1.44) N/A N/A Lipfert et al. (2006) a Veterans (age 39

– 63) <14.1 14.3 1.15 (1.05

– 1.25) N/A N/A N/A
Miller et al. (2007) b WHI (age 50

– 79) 3.4 13.5 N/A 1.76 (1.25

– 2.47) N/A 2.21 (1.17

– 4.16) Eftim et al. (2008) Medicare (age > 65) 6 13.6 1.21 (1.15

– 1.27) N/A N/A N/A Zeger et al. (2008) c Medicare (age > 65) <9.8 13.2 1.068 (1.049

– 1.087) N/A N/A N/A
Krewski et al. (2009) d ACS (age >30) 5.8 14 1.06 (1.04

– 1.08) N/A 1.13 (1.10

– 1.16) 1.24 (1.19

– 1.29) Puett et al. (2009) b NHS (age 30

– 55) 5.8 13.9 1.26 (1.02

– 1.54) N/A N/A 2.02 (1.07

– 3.78) Crouse et al. (2012 ) d,e Canadian census 1.9 8.7 1.06 (1.01

– 1.10) N/A N/A N/A
Puett et al. (2011) f Health Professionals (age 40

– 75) <14.4 17.8 0.86 (0.70

– 1.00) 1.02 (0.84

– 1.23) N/A N/A
Lepeule et al. (2012) d Six Cities (age > 25) 8 15.9 1.14 (1.07

– 1.22) 1.26 (1.14

– 1.40) N/A N/A

a Low socio

- economic status (SES) men only. Used traffic proximity as a surrogate of exposure. b Women only.
c Reflects risks in the Eastern U.S. Risks in the Central U.S. were higher, but the authors found no association in the Western U.S. d Random effects Cox model with individual and ecologic covariates.
e Canadian population. f Men with high socioeconomic status only. 

### The primary goal of the 2006 study was to elicit from a sample of health experts probabilistic distributions describing un certainty in estimates of the reduction in mortality among the adult U.S. population resulting from reductions in ambient annual average PM 2.5 

### levels. These distributions were obtained through a formal interview protocol using methods designed to elicit su bjective expert judgments. These experts were selected through a peer

- nomination process and included experts in epidemiology, toxicology, and medicine. The elicitation interview consisted of a protocol of carefully structured questions, both qualitative a nd quantitative, about the nature of the PM 2.5 

### - mortality relationship designed to build twelve individual distributions for the coefficient (or slope) of the C

- R function relating changes in annual average PM 2.5 

### exposures to annual, adult all

- cause mortali ty. The elicitation also provided useful information regarding uncertainty characterization in the PM 2.5 

### - mortality

### 6B

- 9 relationship. Specifically, during their interviews, the experts highlighted several uncertainties inherent within the epidemiology literatur e, such as causality, concentration thresholds, effect modification, role of short

- and long

- term exposures, potential confounding, and exposure misclassification. For several of these uncertainties, such as causality, we are able to use the expert

- derived functions to quantify the impacts of applying different assumptions. The elicitation received favorable peer review in 2006 (Mansfield and Patil, 2006). Prior to providing a quantitative estimate of the risk of premature death associated with long

- term PM 2.5 

### exposure, the experts answered a series of “conditioning questions.” One such question asked the experts to identify which epidemiological studies they found most informative. The “ideal study attributes” 4 

### according to the experts included:

1. Geographic representation of the entire U.S. (e.g., monitoring sites across the country) 2. Collection of information on individual risk factors and residential information both at the beginning and throughout the follow

- up period 3. Large sample size that is representativ e of the general U.S. population 4. Collection of genetic information from cohort members to identify and assess potential effect modifiers 5. Monitoring of individual exposures (e.g., with a personal monitor) 6. Collection of data on levels of several co

- pollutant s (not only those that are monitored for compliance purposes ) 7. Accurate characterization of outcome (i.e., cause of death) 8. Follow

- up for a long period of time, up to a lifetime 9. Prospective study design Although no single epidemiological study completely sat isfies each of these criteria, the experts determined that the ACS and Six Cities ’ cohort studies best satisfy a majority of these ideal attributes. To varying degrees the studies examining these two cohorts are geographically representative; have collecte d information on individual risk factors; include a large sample size; have collected data on co

- pollutants in the case of the ACS study; have accurately characterized the health outcome; include a long (and growing) follow

- up period; and, are prospective in nature. The experts also noted a series of limitations in these two cohort studies. In the case of the Six Cities study (Laden et al., 2006), the experts identified the “small sample size, limited

4 These criteria are substantively similar to EPA’s study selection criteria identified in Table 6

- 5 of Chapter 6.

### 6B

- 10 number of cities, and concerns about representativeness of the six cities for the U.S. as a whole” as weaknesses. When considering the ACS study (Pope et al., 2002), the experts indicated that the “method of recruitment for the study, which resulted in a group with higher income, more education, and a greater p roportion of whites than is representative of the general U.S. population” represented a shortcoming. Several experts also argued that because the ACS study relied upon “...whatever monitors were available to the study...a single monitor represent[ed] exposure for an entire metropolitan area...whereas [the Six Cities study] often had exposures assigned at the county level.” Despite these limitations, the experts considered the Pope et al. (2002) extended analysis of the ACS cohort and the Laden et al. (2006) exte nded analysis of the Six Cities cohort to be particularly influential in their opinions (see Exhibit 3

- 3 of the elicitation summary report [IEc, 2006]). It is important to note that the benefits estimates results presented are not the direct results from the studies or expert elicitation; rather, the estimates are based in part on the effect coefficients provided in those studies or by experts . In addition, the experts provided distributions around their mean PM 2.5 

### effect estimates, which provides more inf ormation regarding the overall range of uncertainty, and this overall range is larger than the range of the mean effect estimates from each of the experts. Since the completion of the EPA’s expert elicitation in 2006, additional epidemiology literature has become available, including 9 new multi

- state cohort studies shown in Table 6 B

- 4

. This newer literature addresses some of the weaknesses identified in the prior literature. For example, in an attempt to improve its characterization of population exposure the most recent extended analysis of the ACS cohort Krewski et al. (2009) incorpor ates two case studies that employ more spatially resolved estimates of population exposure. In light of the availability of this newer literature, we have updated the presentation of results in the RIA. Specifically, we focus the core analysis on results derived from the two most recent studies of the ACS and Six Cities cohorts (Krewski et al., 2009; Lepeule et al., 2012). Because the other multi

- state cohorts generally have limited geography and age/gender representativeness, these limitations preclude us from using these studies in our core benefits results, and we instead present the risk coefficients from these othe r multi

- state cohorts in Table

### 6B

- 11 6 B

- 4

. However, we have completed a sensitivity analysis based on application of the full set of expert

- derive d effect estimates

. To preserve the breadth and diversity of opinion on the expert panel, w e do not combine the expert results (Roman et. al., 2008). This presentation of the expert

- derived results is generally consistent with SAB advice (U.S. EPA

- SAB, 200 8), which suggest ed that the EPA emphasize that “scientific differences existed only with respect to the magnitude of the effect of PM 2.5 

### on mortality, not whether such an effect existed” and that the expert elicitation “supports the conclusion that the be nefits of PM 2.5 

### control are very likely to be substantial

. ” Although it is possible that the newer literature could revise the experts’ quantitative responses if elicited again, we believe that these general conclusions are unlikely to change.
Table 6 B

- 5 p resents the results of this sensitivity analysis using the expert elicitation results for the 2025 scenario for 70 ppb. O verall conclusions from this analysis are also applicable to the post

- 2025 scenario.

Table 6 B
- 5 . PM 2.5 

### Co
- benefit Estimates using Two Epidemiology Studies and Functions Supplied from the Expert Elicitation C
- R Function 70 ppb (2025 Scenario) Krewski et al. (2009) 220 Lepeule et al. (2012) 500 Expert K 50 Expert G 260 Expert L 310 Expert D 320 Expert H 330 Expert J 360 Expert F 430 Expert C 450 Expert I 450 Expert B 460 Expert A 570 Expert E 740

a All estimates rounded to two significant digits. 

### The values presented in Table 6B

- 5 indicate that the two core incidence estimate fall within the range of alternative C

- R function

- based estimates obtained through expert elicitation. Figure 6

- 5 in the body of the document reproduces these benefit estimate s, but presents them in terms of the associated dollar benefits (7% discount rate in 2011$) rather than incidence estimates (observations presented here for the sensitivity analysis results reflected modeled

### 6B

- 12 incidence estimates

- i.e., spread in results an d relative position of the two core estimates

- also hold for dollar benefit estimates presented in Figure 4

- 6.
6 B . 5 Sensitivity Analysis for Income Elasticity of Willingness
- to - Pay As discussed in Chapter 6 , our estimates of monetized benefits account for growth in real GDP per capita by adjusting the WTP for individual endpoints based on the central estimate of the adjustment factor for each of the categories (minor health effects, severe and chronic health effects, premature mortality, and visibility). We examined how sensitive the estimate of total benefits is to alternative estimates of the income elasticities. Income growth projections are only currently available in BenMAP through 2024, so both the 2025 and post

- 2025 scenario estimates use income growth through 2024 only and are therefore likely underestimates.
Table 6 B

- 6 lists the ranges of elasticity values used to calculate the income adjustment factors, while Table 6 B

- 7 lists the ranges of correspon ding adjustment factors. The results of this sensitivity analysis for the two benefit cate gories are presented in Table 6 B

- 8

. Table 6 B
- 6 . Ranges of Elasticity Values Used to Account for Projected Real Income Growth a Benefit Category Lower Sensitivity Bound Upper Sensitivity Bound Minor Health Effect b 0.04 0.30 Premature Mortality 0.08 1.00

a Derivation of these ranges can be found in Kleckner and Neumann (1999). COI estimates are assigned an adjustment factor of 1.0. b Minor health effects included in this RIA and valued using WTP

- based functions include: upper and lower respiratory symptoms, asthma exacerbations, minor restricted activity days, and acute bronchitis. 

### Table 6 B
- 7 . Ranges of Adjustment Factors Used to Account for Projected Real Income Growth to 2024 a Benefit Category Lower Sensitivity Bound Upper Sensitivity Bound Minor Health Effect b 1.021 1.170 Premature Mortality 1.043 1.705

a Based on elasticity values reported in Table C

- 4, U.S. Census population projections, and projections of real GDP per capita. b Minor health effects included in this RIA and valued using WTP

- based functions include: upper and lower respiratory symptoms, a sthma exacerbations, minor restricted activity days, and acute bronchitis.

### 6B

- 13 Table 6 B
- 8 . Sensitivity of Monetized Ozone Benefits to Alternative Income Elasticities in 2025 (Millions of 2011$) a Benefit Category No adjustment Lower Sensitivity Bound Upper Sensitivity Bound 70 ppb 65 ppb 70 ppb 65 ppb 70 ppb 65 ppb Minor Health Effect b $ 31 $ 150 $ 33 $ 162 $ 38 $ 186 Premature Mortality c $ 950 $ 4,1 00 $ 1,000 $ 5,200 $ 1,700 $ 8,500

a All estimates rounded to two significant digits. Only reflects income growth to 2024. b For illustrative purposes, we evaluate minor restricted activity days (MRADS) resulting from short

- term ozone exposure is the minor health effect here.
c S hort

- term mo rtality using Smith et al. (2009) and 3% discount rate. Results using other short

- term mortality studies and a 7% discount rate would show the same proportional range. 

### Consistent with the impact of mortality on total benefits, the adjustment factor for

mo rtality has the largest impact on total benefits. The value of mortality in 2025 ranges from 8 % to 76 % greater than the core estimate for mortality based on the lower and upper sensitivity bounds on the mortality income adjustment factor. The effect on the value of minor health effects is much less pronounced, ranging from 5 % to 21 % greater than the core estimate for minor effects. These observations (in terms of relative impact from alternative elasticities) hold for the revised and alternative standard levels analyzed under both the 2025 and post

- 205 scenarios.
6 B.6 Supplemental Analysis: Age Group
- Differentiated Aspects of Short
- Term Ozone Exposure
- Related Mortality In their 2008 review of the EPA’s approach to estimating ozone - rel ated mortality benefits, the National Research Council ( NRC ) indicated, “EPA should consider placing greater emphasis on reporting decreases in age

- specific death rates in the relevant population and develop models for consistent calculation of changes in life expectancy and changes in number of deaths at all ages” (NRC, 2008). In addition, NRC noted in an earlier report that “[f]rom a public

- health perspective, life

- years lost might be more relevant than annual number of mortality cases” (NRC, 2002). This advice is consistent with that of the Health Effects Subcommittee of the Advisory Council on Clean Air Compliance Analysis ( SAB

- HES ) , which agreed that “...the interpretation of mortality risk results is enhanced if estimates of lost life

- years can be made” ( U.S. EPA

- SAB , 2004 ). To address these recommendations, we use simplifying assumptions to estimate the number of life years that might be gained. We also estimate the reduction in the percentage of deaths attributed to ozone resulting from the illustrative emission s reduction strategies to reach the revised and alternative primary standards. The EPA included similar estimates of life years gained in a previous assessment of ozone and/or PM 2.5 

### benefits (U.S.

### 6B

- 14 EPA, 2006, 2010, 2011a), the latter of which was p eer reviewed by the SAB

- HES (U.S. EPA

SAB, 2010a ). Changes in life years and changes in life expectancy at birth are frequently conflated, thus it is important to distinguish these two very different metrics. Life expectancy varies by age. The Centers for Disease Control and Prevention ( CDC ) defines life expectancy as the “average number of years of life remaining for persons who have attained a given age” (CDC, 2011). In other words, changes in life expectancy refer to an average change for the entire popu lation, and refer to the future. Over the past 50 years, average life expectancy at birth in the U.S. has increased by 8.4 years (CDC, 201 1). For example, life expectancy at birth was estimated in 2007 to be 77.9 years for an average person born in the U.S ., but for people surviving to age 60, estimated life expectancy is 82.5 years (i.e., 4.6 years more than life expectancy at birth) (CDC, 2011). Life years, on the other hand, measure the amount of time that an individual loses if they die before the age o f their life expectancy. Life years refer to individuals, and refer to the past, e.g., when the individual has already died. If a 60

- year old individual dies, we estimate that this individual would lose about 22.5 years of life (i.e., the average populatio n life expectancy for an individual of this age minus this person’s age at death).
Due to the use of benefit

- per - ton estimates for the PM 2.5 

### co - benefits, we are unable to estimate the life years gained by reducing exposure to PM 2.5 

### in this analysis. Instea d, we refer the reader to the 2012 PM 2.5 

### NAAQs RIA for more information about the avoided life years lost from PM 2.5 

### exposure (U.S. EPA, 2012b). Th e analysis performed for the PM NAAQS RIA found that about half of the avoided PM

- related deaths occur in pop ulations age 75 to 99, but half of the avoided life years lost would occur in populations younger than 65 because the younger populations have the potential to lose more life years per death than older populations. In addition, that analysis found that the average individual who would otherwise have died prematurely from PM exposure would gain 16 additional years of life.
Estimated Life Years Gained
To estimat e the potential life years gained by reducing exposure to ozone in the U.S. adult population, we use the same general approach as Hubbe ll (2006) and Fann et al. (2012 ). We have not estimated the change in average life expectancy at birth in this RIA. Because life

### 6B

- 15 expectancy is an average of the entire population (including both t hose whose deaths would likely be attributed to air pollution exposure as well as those whose deaths would not), we would expect average life expectancy changes associated with air pollution exposure to always be significantly smaller than the average numb er of life years lost by an individual projected to die prematurely from air pollution exposure. To estimate the potential distribution of life years gained for population subgroups defined by the age range at which their reduction in air pollution exposur e is modeled to occur , we use standard life tables available from the CDC (2014) and the following formula:
TTTTTTTTTT LLLLLLLL YY YYYY = ∑ LLLL ii 

### × MM ii nn ii = 1 

### ( 6 .2)
where LE i 

### is the average remaining life expectancy for age interval i, M i 

### is the estimate d change in number of deaths in age interval i, and n is the number of age intervals. To get M i 

### (the estimate d number of avoided premature deaths attributed to changes in ozone exposure for the 2025 scenario), we use a health impact function that incorpor ates risk coefficients estimated for the adult population in the U.S. and age

- specific mortality rates. That is, we use risk coefficients that do not vary by age, but use baseline mortality rates that do. Because mortality rates for younger populations are much lower than mortality rates for older populations, most but not all, of the avoided deaths tend to be in older populations. Table 6 B

- 9 summarizes the number of avoided deaths (by age range) attributable to ozone for the revised and alternative standar d s for the 2025 scenario. Table 6 B

- 10 summarizes the modeled number of life years gained (for each age range) by reducing ozone for the revised and alternative standard s analyzed for the 2025 scenario. We then calculated the average number of life years ga ined per avoided premature mortality.

### 6B

- 16 Table 6 B
- 9 . Potential Reduction in Premature Mortality by Age Range from Attaining the Revised and Alternat iv e Ozone Standards (2025 scenario) a, b Age Range b Revised and Alternative Standards 70 ppb 65 ppb 0

- 4 0.3 1.5 5

- 9 0.068 0.33 10

- 14 0.072 0.35 15

- 19 0.1 0.5 20

- 24 0.14 0.69 25

– 29 0.29 1.4 30

– 34 0.3 1.5 35

– 44 1.6 7.9 45

– 54 4.1 20 55

– 64 10 52 65

– 74 22 110 75

– 84 29 150 85

– 99 28 150 Total ozone

- attributable mortality 96 490

a Estimates rounded to two significant digits

. b Effects calculated using Smith et al. (2009)

.



### Table 6 B
- 10 . Potential Years of Life Gained by Age Range from Attaining the Revised and Alternat iv e Ozone Standards (2025 Scenario) a,b Age Range b Revised and Alternative Standard 70 ppb 65 ppb 0

- 4 24 110 5

- 9 5 23 10

- 14 5 23 15

- 19 6 30 20

- 24 8 38 25

– 29 14 70 30

– 34 13 66 35

– 44 57 280 45

– 54 110 550 55

– 64 200 990 65

– 74 260 1,300 75

– 84 190 970 85

– 99 65 340 Total life years gained 960 4,800 Average life years gained per individual 10 10

a Estimates rounded to two significant digits

. b Effects calculated using Smith et al. (200 9). 

### By comparing the projected age distribution of the avoided premature deaths with the age distribution of life years gained, we observed that about half of the deaths occur in populations age 75

– 99 (see Table 6 B

- 9 ), but half of the life years would occur in populations younger than

### 6B

- 17 65 (see Table 6 B

- 10 ). This is because the younger populations have the potential to lose more life years per death than older populations based on changes in ozone exposure for the 2025 scenario. We estimate that the average indiv idual who would otherwise have died prematurely from ozone exposure would gain 10 additional years of life. However, this approach does not account for whether or not people who are older are more likely to be susceptible to the health effects of air pollu tion or whether that susceptibility was caused by air pollution exposure (for a more complete discussion of this issue, see Kunzli et al., 2001).
Percent of Ozone

- related Mortality Reduced To estimate the percentage reduction in all

- cause mortality attributed to reduced ozone exposure for the 2025 scenario as a result of the illustrative emission s reduction strategies, we use M i 

### from the equation above, dividing the number of excess deaths e stimated for the revised and alternative standard s by the total number o f deaths in each county. Table 6 B

- 11 shows the reduction in all

- cause mortality attributed to reducing ozone exposure to the revised and alternative primary standards for the 2025 scen ario.
Table 6 B
- 11 . Estimated Percent Reduction in All
- Cause Mortality Attributed to the Proposed Primary Ozone Standards (2025 Scenario) a Age Range b Revised and Alternative Standard s 70 ppb 65 ppb 0

- 4 0.0085% 0.0409% 5

- 9 0.0084% 0.0411% 10

- 14 0.0084% 0.0415% 15

- 19 0.0086% 0.0419% 20

- 24 0.0085% 0.0412% 25

– 29 0.0084% 0.0409% 30

– 34 0.0083% 0.0408% 35

– 44 0.0081% 0.0404% 45

– 54 0.0082% 0.0410% 55

– 64 0.0085% 0.0429% 65

– 74 0.0086% 0.0436% 75

– 84 0.0085% 0.0433% 85

– 99 0.0079% 0.0415%

a T o illustrate the slight variations in percent reductions across age ranges , we present results rounded to three significant digits (rather than two as is typically done for other estimates in this RIA).



### Results presented in Table 6 B

- 11 highlight that when reductions in ozone

- attributable mortality (in going from baseline to the revised or alternative standard level) are considered as a

### 6B

- 18 percentage of total all

- cause baseline mortality, the estimates are relatively small and are fairly constant across age ran ges. However, it is important to point out that estimates of total ozone attributable mortality represent a substantially larger fraction of all

- cause baseline mortality.
6 B.7 Supplemental Analysis: Evaluation of Mortality Impacts R elative to the Baseline Pollutant C oncentrations for both Short
- Term O zone Exposure
- Related M ortality and L ong - T erm PM 2.5 

### E xposure - Related M ortality Analysis of baseline ozone levels used in modeling short

- term ozone exposure

- related mortality Our review of the current body of scientific literature indicates that a log

- linear no

threshold model provides the best estimate of ozone - related short

- term mortality (see section 2.5.4.4, in the O 3 

### ISA, U.S. EPA, 2013 ). Consistent with this finding, we estimate benefits associated with the full range of ozone exposure. Our confidence in the estimated number of premature deaths avoided (but not in the existence of a causal relationship between ozone and premature mortalit y) diminishes as we estimate these impacts at successively lower concentrations. However, there are uncertainties inherent in identifying any particular point at which our confidence in reported associations becomes appreciably less, and the scientific evi dence provides no clear dividing line. The O 3 

### ISA noted that the studies indicate reduced certainty in specifying the shape of the C

- R function specifically for short

- term ozone attributable respiratory morbidity and mortality, in the range generally below 20 ppb (for these reasons, the ≤ 20 ppb range discussed in the O 3 

### ISA should be viewed as a more generalized range to be considered qualitatively or semi

- quantitatively, along with many other factors, when interpreting the risk estimates rather than as a fixed, bright

- line). 5 

### Figures 6 B

- 1 and 6 B

- 2 compare the distribution of short

- term ozone exposure

- related mortality to the underlying distribution of the summer season 8

- h ou r max imum ozone concentrations. Both figures ( probability and cumulative probabilit y) are based on Smith et al.

5 While clinical studies have suggested the presence of a threshold for respiratory effects, these should not be used to

support specification of population

- level thresholds for use in th e epidemiological

- based risk assessment focusing on short

- term exposure

- related endpoints. The clinical studies focus on relatively small and clearly defined populations of healthy adults , which are not representative of the broader residential populations typically associated with epidemiological studies, including older individuals and individuals with existing health conditions that place them at greater risk for ozone - related effects. Therefore, the clinical studies are unlikely to have the power to cap ture population thresholds in a broader and more diverse urban residential population, should those thresholds exist.

### 6B

- 19 ( 2009 ) using mortality results at each 12 km grid cell and the associated summer season 8

- h ou r
max imum concentration in the baseline

. In addition, each figure includes separate plots for the revised and alternative standard lev els

. Figure 6 B

- 1 shows that approximately 45% of the premature mortalit ies estimated for the 65 ppb alternative standard is associated with baseline

ozone concentrations between 40 and 45 ppb

. Because this baseline range is represents the mean across the ozone season of 8

- h ou r max values within a given grid cell, the actual distribution of 8

- h ou r max values on a daily basis is likely wider than the 40

- 45 ppb range.

Figure 6 B
- 1. Premature Ozone
- related Deaths Avoided for the Revised and A lternative Standards (2025 scenario) According to the Baseline Ozone Concentrations 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50%0 to 1515 to 2020 to 2525 to 3030 to 3535 to 4040 to 4545 to 5050 to 5555 to 6060 to 6565 to 7070 to 7575 to 80Percentage ofreductionin short-term ozonerelated mortality



### Ozone season mean daily 8hr max baseline (ppb) 75-70 75-65

### 6B

- 20 Figure 6 B
- 2. Cumulative Probability Plot of Premature Ozone
- related Deaths Avoided for the Revised and Alternative Standards (2025 scenario) According to the Baseline Ozo ne Concentrations When interpreting these results, it is important to understand that the avoided ozone related deaths are estimated to occur from ozone reductions in the baseline air quality simulation, which assumes that 75 ppb is already met. When simul ating attainment with the revised and alternative standards, we adjust the design value at each monitor exceeding the standard to equal that standard and use an air quality interpolation technique to simulate the change in ozone concentrations surrounding that monitor. This technique tends to simulate the greatest air quality changes nearest the monitor. We estimate benefits using modeled air quality data with 12 km grid cells, which is important because the grid cells are often substantially smaller than c ounties and ozone concentrations vary spatially within a county. Therefore, there may be a small number of grid cells with concentrations slightly greater than 75 ppb in the gridded baseline even though all monitors could meet an annual standard of 75 ppb. In addition, some grid cells in a county can be below the level of a standard even though the highest monitor value is above that standard. Thus, emission s reductions can lead to benefits in grid cells that are below a standard even within a county with a monitor that exceeds that standard. Furthermore, our approach to 

### 0 0.2 0.4 0.6 0.8 1 1.20 to 1515 to 2020 to 2525 to 3030 to 3535 to 4040 to 4545 to 5050 to 5555 to 6060 to 6565 to 7070 to 7575 to 80Cumulative percentage ofreductioninshort-term ozone related mortality



## Ozone season mean daily 8hr max baseline (ppb) 75-70 75-65

### 6B

- 21 simulating attainment can lead to benefits in counties that are below the revised or alternative standard being evaluated. Emission s reduction strategies designed to reduce ozone concentrati ons at a given monitor will frequently improve air quality in neighboring counties. In order to make a direct comparison between the benefits and costs of these emission s reduction strategies, it is appropriate to include all the benefits occurring as a result of the emission s reduction strategies applied , regardless of where they occur. Therefore, it is not appropriate to estimate the fraction of benefits that occur only in counties that exceed the revised or alternative standards because it would omit benefits attributable to emission s reductions in exceeding counties. One final caveat in interpreting the information presented in these figures is that in modeling this mortality endpoint, rather than using a true distribution of daily 8hr max ozone levels for each grid cell, due to resource limitations, we used a single mean value for the ozone season within each grid cell. While this will generate the same total ozone b enefit estimate for each grid cell compared with application of a full distribution of daily 8hr max values, use of a mean daily value means that an assessment such as this one , that considers both the spatial and temporal association between mortality ben efit estimates and ozone levels, will be limited somewhat in its treatment of the temporal dimension.
As shown in Figures 6B

- 1 and 6 B

- 2 , t he vast majority of reductions in short

- term exposure - related mortality for ozone occur in grid cells with mean 8

- h ou r max baseline levels (across the ozone season) between 35 and 55 ppb. Importantly, virtually all of the mortality reductions are associat ed with ozone levels above the greater than 20 ppb range identified within the O 3 

### ISA as being associated with less con fidence in specifying the nature of the C

- R function for ozone mortality (O 3 

### ISA, section 2.5.4.4). 6 

### C ompar ing patterns across the revised and

6 As noted earlier, care needs to be taken in interpreting these mortality vs. ozone air level distributions in the context of the range o f reduced confidence (<20 ppb) identified in the O 3 ISA. The region of reduced confidence identified by the ISA reflects the composite monitor daily time series values (including 8

- h ou r max values) used in short

- term mortality studies, while the ozone leve ls summarized in Figures 6B

- 1 and 6B

- 2 are the mean (across the ozone season) of daily 8

- h ou r max values within each grid cell. The use of these mean values, while not impacting the total mortality reductions estimated, could significantly reduce variabili ty in the spread of values presented in these two figures.

### 6B

- 22 alternative standard levels, the upper end of the distribution s hift s downwards as increasingly lower standard le vel s are analyzed (see Figure 6 B

- 2). Concentration Benchmark Analysis for PM 2.5 

### Benefit

- per - ton Estimates In general, we are more confident in the magnitude of the risks we estimate from simulated PM 2.5 

### concentrations that coincide with the bulk of the o bserved PM concentrations in the epidemiological studies that are used to estimate the benefits. Likewise, we are less confident in the risk we estimate from simulated PM 2.5 

### concentrations that fall below the bulk of the observed data in these studies. Con centration benchmark analyses (e.g., lowest measured level [LML], one standard deviation below the mean of the air quality data in the study, etc.) allow readers to determine the portion of population exposed to annual mean PM 2.5 

### levels at or above different concentrations, which provides some insight into the level of uncertainty in the estimated PM 2.5 

### mortality benefits. In this analysis, we apply two concentration benchmark approaches (LML and one standard deviation below the mean) that have been incorporated into recent RIAs and EPA’s Policy Assessment for Par ticulate Matter (U.S. EPA, 2011c ). There are uncertainties inherent in identifying any particular point at which our confidence in reported associations becomes appreciably less, and the scie ntific evidence provides no clear dividing line. However, the EPA does not view these concentration benchmarks as a concentration threshold below which we would not quantify health co

- benefits of air quality improvements. 7 

### Rather, the co

benefits estimates reported in this RIA are the best estimates because they reflect the full range of air quality concentrations associated with the emission s reduction strategies. The PM ISA concluded that the scientific evidence collectively is sufficient to conclude that the relationship between long

- term PM 2.5 

### exposures and mortality is causal and that overall the studies support the use of a no

- threshold log

- linear model to estimate PM

- related long

- term mortality (U.S. EPA, 2009 ). For this analysis, policy

- specific air quality data is not available, and the compliance strategies are illustrative of what states may choose to do. For this RIA, we are unable to estimate the percentage of premature mortality associated with the emission s reductions at each

7 For a summary of the scientific review statements regarding the lack of a threshold in the PM 2.5 - mortality

relationship, see the TSD entitled Summary of Expert Opinions on the Existence of a Threshold in the Concentration

- Response Function for PM 2.5 - related Mortality (U.S. EPA, 2010b).

### 6B

- 23 PM 2.5 

### concentrati on, as we have done for previous rules with air quality modeling (e.g., U.S. EPA, 2011b, 2012b ). However, we believe that it is still important to characterize the distribution of exposure to baseline concentrations. As a surrogate measure of mortality imp acts, we provide the percentage of the population exposed at each PM 2.5 

### concentration in the baseline of the source apportionment modeling used to calculate the benefit

- per - ton estimates for this sector using 12 km grid cells across the contiguous U.S. 8 

### It is important to note that baseline exposure is only one parameter in the health impact function, along with baseline incidence rates , population and change in air quality. In other words, the percentage of the population exposed to air pollution below the LML is not the same as the percentage of the population experiencing health impacts as a result of a specific emission s reduction policy. The most important aspect, which we are unable to quantify without rule

- specific air quality modeling, is the shift i n exposure anticipated by the revised or alternative standards . Therefore, caution is warranted when interpreting the LML assessment in this RIA because these results are not consistent with results from RIAs that had air quality modeling.
Table 6 B

- 12 pro vides the percentage of the population exposed above and below two concentration benchmarks in the modeled baseline f or the sector modeling. Figure 6B

- 3 shows a bar chart of the percentage of the population exposed to various air quality levels in the base li ne, and Figure 6 B

- 4 shows a cumulative distribution function of the same data. Both figures identify the LML for each of the major cohort studies.

8 As noted above, the modeling used to generate the benefit

- per - ton estimates does not reflect emission s reductions

anticipated from MATS rule. Therefore, the baseline PM 2.5 concentrations in the LML assessment are higher than would be expected if MATS was reflected.

### 6B

- 24 Table 6 B
- 12 . Population Exposure in the Baseline Sector Modeling (used to generate the benefit
- per - ton est imates) Above and Below Various Concentration Benchmarks in the Underlying Epidemiology Studies a

Epidemiology Study Below 1 Standard Deviation. Below AQ Mean At or Above 1 Standard Deviation

Below AQ Mean Below LML At or Above LML Krewski et al. (2009) 89% 11% 7% 93% Lepeule et al. (2012) N/A N/A 23% 67%

a One standard deviation below the mean is equivalent to the middle of the range between the 10 th and 25 th

percentile. For Krewski et al. , the LML is 5.8 μg/m 3 and one standard deviation below the mean is 11.0 μg/m 3

. For Lepeule et al., the LML is 8 μg/m 3 and we do not have the data for one standard deviation below the mean. It is important to emphasize that although we have lower levels of confidence in levels below the LML for each study, the scientific evidence does not support the existence of a level below which health effects from exposure to PM 2.5 do not occur.



### Figure 6 B
- 3. Percentage of Adult Population (age 30+) by Annual Mean PM 2.5 

### Exposure in the Baseline Sector Modeling (used to generate the benefit
- per - ton estimates)* * This graph shows the population exposure in the modeling baseline used to generate the benefit

- per - ton estimates. Similar graphs for analyses with air quality modeling show premature mortality impacts at each PM 2.5 concentration. Therefore, caution is warranted when interpreting this graph because it is not consistent with similar graphs from RIAs that had air quality modeling (e.g., MATS).

Among the populations exposed to PM 2.5 in the baseline: 93% are exposed to PM 2.5 levels at or above the LML of the Krewski et al. (2009) study 67% are exposed to PM 2.5 levels at or above the LML of the Lepeule et al. (2012) study 0% 5% 10% 15% 20% 25%

<1 1

- 2 2

- 3 3

- 4 4

- 5 5

- 6 6

- 7 7

- 8 8

- 9 9

- 10 10

- 11 11

- 12 12

- 13 13

- 14 14

- 15 15

- 16 16

- 17 17

- 18 18

- 19 19

- 20Percentage of Population Exposed

Baseline Annual Mean PM 2.5 Level (μg/m 3 ) LML of Lepeule et al. (2012) study LML of Krewski et al. (2009) study

### 6B

- 25 Figure 6 B
- 4. Cumulative Distribution of Adult Population (age 30+) by Annual Mean PM 2 .5 

### Exposure in the Baseline Sector Modeling (used to generate the benefit
per - ton estimates)* * This graph shows the population exposure in the modeling baseline used to generate the benefit

- per - ton estimates. Similar graphs for analyses with air quality m odeling show premature mortality impacts at each PM 2.5 concentration. Therefore, caution is warranted when interpreting this graph because it is not consistent with similar graphs from RIAs that had air quality modeling (e.g., MATS). 

### 6B.8 Sensitivity Analy sis of Ozone
- related Impacts on Outdoor Worker Productivity The EPA last quantified the value of ozone

- related worker productivity in the final Regulatory Impact Analysis supporting the Transport Rule (U.S. EPA, 2011d). That analysis applied information reported in Crocker and Horst (1981) to relate changes in grou nd

- level ozone to changes in the productivity of outdoor citrus workers. That study found that a 10 percent reduction in ozone translated to a 1.4 increase in income among outdoor citrus workers. Concerned that this study might not adequately characterize the relationship between ground

Among the populations exposed to PM 2.5 in the baseline: 93% are exposed to PM 2.5 levels at or above the LML of the Krewski et al. (2009) study 67% are exposed to PM 2.5 levels at or above the LML of the Lepeule et al. (2012) study 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20Cumulative percentage of population exposed

Baseline Annual Mean PM 2.5 Level (μg/m 3 ) LML of Krewski et al. (2009) study LML of Lepeule et al. (2012) study

### 6B

- 26 level ozone and the productivity of agricultural workers because of the vintage of the underlying data, the Agency subsequently omitted this endpoint.

In a recent study , Graff Zivin and Neidell (2012) combined data on indivi dual

- level daily harvest rates for Outdoor Agricultural Workers (OWAs) with ground

- level ozone concentrations to characterize changes in worker productivity as a result of ozone exposure . The authors used data on harvest rates from a 500

- acre farm in the C entral Valley of California. That farm produced three crops (blueberries and two types of grapes) and the harvesting laborers were paid through piece rate contracts. The analyses in the paper were based on 2009 and 2010 California growing seasons. The anal yses were not affected by: (i) endogenous ozone exposure (because there were limited local sources of ozone precursors); (ii) avoidance behavior (because the work has to be performed outdoors); and (iii) shirking (due to the nature of the piece rate contra ct). Table 3 in Graff Zivin and Neidell (2012) reports the main result: A 10 ppb increase in work

- day ozone concentration (represented by hourly measurements averaged between 6am and 3pm) will result in a decline of 0.143 (with a standard error of 0.068) i n standardized hourly pieces collected on a given work day. The standardized hourly pieces were “the average hourly productivity minus the minimum number of pieces per hour required to reach the piece rate regime, divided by the standard deviation of produ ctivity for each crop” (Graff Zivin and Neidell, 2012; p. 3665). The range of ozone concentrations in the sample was between 10.50 ppb and 86.0 ppb (Table 1 in Graff Zivin and Neidell, 2012). This result is significant and robust under different model spec ifications designed to test modeling assumptions. Based on the effect estimate and individual

- level information in their dataset, the authors estimated the effect of an increase in ozone concentration on worker productivity, as measured by the average numb er of pieces collected per hour during a given work day (rather than by standardized hourly piece rate that was used in regression modeling). They found a decline of 5.5% in worker productivity due to a 10 ppb increase in average work

- day ozone concentrati on. While Graff Zivin and Neidell (2012) report the information needed to quantify ozone

related worker productivity, we are still evaluating whether and how to most appropriately apply the limited evidence from this study in a national benefits assessment . An important issue is the

### 6B

- 27 generalizability of the results to the appropriate population. Because we received no comments on this proposed approach, we now include the results in a sensitivity analysis.
Below we provide the function, input data and re sults for the sensitivity analysis.

YY = ββ ∗ DD DDDD ∗ DD DDDDDD DD DD DD ∗ OO ∗ EEEE EE EE Table 6B
- 13 . Definitions of Variables Used to Calculate Changes in Worker Productivity Variable Definition ββ Percent change in daily outdoor worker productivity per 1ppb change in ozone (Graff Zivin and Neidell, 2012) DD DDDD Summer season average of daily 9 hour average (6am to 3pm) DD DDDDDD DD DD DD Summer season daily wage for agricultural non

- livestock workers in 17 USDA

- defined regions (USDA, 2012) OO Summer season number of workers employed in outdoor non

- livestock agricultural per county (USDA, 2010; US Census, 2010) EEEE EE EE Growth in agricultural non

- livestock workers to 2025 (Woods and Poole, 2012)



### Table 6B
- 1 4 . Population Estimated E conomic V alue of I ncreased P roductivity among O utdoor A gricultural W orkers from A ttaining the R evised and A lternative O zone S tandards in 2025 (millions of 2011$) Standard Economic Value (95 th percentile confidence interval) 70 ppb $1.7 ($0.1 to $3.3) 65 ppb $8.9 ($0.6 to $17) 60 ppb $20 ($1.4 to $39)


### 6 B . 9 References
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- 657. Mansfield and Patil, 2006. Peer Review of Expert Elicitation . Memo from RTI International to Ron Evans, EPA. September 26. Available at < http://www.epa.gov/ttn/ecas/regdata/Uncertainty/peer_review_summary_rti_29sept06.pdf >. Miller KA, Siscovick DS, Sheppard L, Shepherd K, Sullivan JH, Anderson GL, Kaufman JD. (2007). “ Long

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. Washington, DC: The National Academies Press. Washington, DC. National Research Council (NRC). 2008. Estimating Mortality Risk Reduction and Economic Benefits from Controlling Ozone Air Pollution . National Academies Press. Washington, DC.

### 6B

- 29 Pope, C.A. III ; Burnett, R .T. ; Thun, M .J.; Calle, E.E.; Krewski, D.; Ito, K.; T hurston G.D. 2002. “ Lung cancer, cardiopulmonary mortality, and long

- term exposure to fine particulate air pollution. ” JAMA 287(9):1132

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- term Exposure to Ozone in the Health Risk and Exposure Assessment for Ozone, Second External Review Draft . Memorandum to Holly Stallworth, Designated Federal Offic er, Clean Air Scientific Advisory Committee from EPA/OAQPS Health and Environmental Impacts Division. Smith, R.L.; B. Xu and P. Switzer. 2009. Reassessing the relationship between ozone and short

- term mortality in U.S. urban communities. Inhalation Toxico logy 21:37

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. Office of Air Quality Planning and Standards, Research Triangle Park, NC. Available a t < http://www.epa.gov/ttn/ecas/regdata/RIAs/Chapter%205

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. Integrated Science Assessment for Particulate Matter: Final . U.S. Environmental Protection Agency, Research Triangle Park, NC, EPA/600/R

- 08/139F. Available at < http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=216546 >
. U.S. Environmental Prot ection Agency (U.S. EPA). 2010

. Final Regulatory Impact Analysis (RIA) for the S O2 National Ambient Air Quality Standards (NAAQS)

. Office of Air Quality Planning and Standards, Research Triangle Park, NC. June

. Available at < http://www.epa.gov/ttn/ecas/regdata/RIAs/fso2ria100602full.pdf >. U.S. Environmental Pro tection Agency (U.S. EPA). 2011a

. The Benefits and Costs of the Clean Air Act 1990 to 2020: EPA Report to Congress . Office of Air and Radiation, Office of Policy, Washington, DC. March. Available at <http://www.epa.gov/oar/sect812/feb11/fullreport.pdf>. U.S. Environmental Protection Agency (U.S. EPA). 2011b. Regulatory Impact Analysis f or the Final Mercury and Air Toxics Standards . EPA

- 452/R

- 11

- 011. December. Available at: < http://www.epa.gov/ttn/ecas/regdata/RIAs/matsriafinal.pdf >. U.S. Environmental Pro tection Ag ency (U.S. EPA). 2011c

. Policy Assessment for the Review of the Particulate Matter National Ambient Air Quality Standards. EPA

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. Regulatory Impact Analysis for the Federal Implementation Plans to Reduce Interstate Transport of Fine Particulate Matter and ozone in 27 States; Correction of SIP Approvals for 22 States . June. Available at <http://w ww.epa.gov/airtransport/pdfs/FinalRIA.pdf>.
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. Provisional Assessment of Recent Studies on Health Effects of Particulate Matter Exposure

. Office of Research and Development, Research Triangle Park, NC.

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. Regulatory Impact Analysis for the Final Revisions to the National Ambient Air Quality Standards for Particulate Matter . EPA

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- SAB) . 2004

. Advisory on Plans for Health Effects Analysis in the Analytical Plan for EPA’s Second Prospective Analysis

— Benefits and Costs of the Clean Air Act, 1990

- 2020: Advisory by the Health Effects Subcommittee of the Advisory Council on Clean Air Compliance Analysis. EPA

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- COUNCIL

- ADV

- 04

- 002. March. Available on the Internet at <http://yosemite.epa.gov/sab%5CSABPRODUCT.NSF/08E1155AD24F871C85256E5400433D5D/$File/counc il_adv_04002.pdf>. U.S. Environmental Protection Agency Science Advisor y Board (U.S. EPA

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COUNCIL

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- 1619.

### 6

- 1 CHAPTER 6 : HUMAN HEALTH BENEFITS ANALYSIS APPROACH AND RESULTS
6 .1 S ummary This chapter of the Regulatory Impact Analysis (RIA) presents the estimated human health benefits for the revised National Ambient Air Quality Standards (NAAQS) for ozone. In this chapter, we quantify the health

- related benefits of the ozone air quality improvements resulting from the illustrative emission s control strategies that reduce emissions of the ozone precursor pollutants (i.e., nitrogen oxides (NO x 

### ) and volatile organic compounds (VOCs) ) to reach the revised and alternative ozone NAAQS standard levels. We also estimate the health co

benefits of the fine particulate matter (PM 2.5 

### )

- related air quality i mprovements that would occur as a result of reducing NO x 

### emissions. 1 

### We selected 2025 as the primary year of analysis because the Clean Air Act requires most areas of the U.S. to meet a revised ozone standard by 2025. The benefits of each standard altern ative are estimated as being incremental to attaining the existing standard of 75 ppb. 2 

### The se estimated benefits are incremental to the benefits estimated for several recent rules (e.g., U.S. EPA, 2011c and U.S. EPA, 2014a). We estimated the benefits of California attaining a revised standard in 2038 to account for the fact that many locations in this state must attain a revised standard later than the rest of the U.S. In this chapter , we refer to estimates of nationwide benef its of attaining an alternative standard everywhere in the U.S. except California as the 2025 scenario . The post

- 2025 scenario refers to estimates of nationwide benefits of attaining an alternative standard just in California

. Because we estimate incremen tal costs and benefits for these two distinct scenarios reflecting attainment in different years it is not appropriate to either sum, or directly compare, the estimates.

T able 6

- 1 summarizes the estimated monetized benefits (total and ozone only) of att aining the revised and alternative ozone standard s of 70 ppb and 65 ppb , respectively , in 2025

. Table 6

- 2 presents the same types of benefit estimates for the scenario. These estimates reflect the sum of the economic value of estimated morbidity and morta lity effects related to changes in

1 VOC reductions associated with simulated attainment of the revised and alternative ozone standards also have the

potential to impact PM 2.5 concentrations, but we were not able to estimate those effects. 2 The current standard is the 4 th highest daily maxi mum 8

- hour ozone concentration of 75 ppb

.

### 6

- 2 exposure to ozone and PM 2.5 

### . Although these tables present ozone and PM 2.5 

### - related benefits separately , it is not appropriate to compare the ozone

- only benefits to total costs. Compared with benefit estimates generated in the proposal RIA, the total benefit estimates generated for the 2025 scenario are ~55% lower for the revised standard (70 ppb) and ~2 2 % lower for the alternative standard (65 ppb). Benefit estimates for the post

- 2025 scenarios are slightly higher than t hose generated at proposal (~ 6 % for the revised standard and ~ 2 % for the alternative standard). The proposal and final RIA estimates differ principally because we projected smaller reduction in ozone concentrations needed to simulate attainment. We have al so slightly modified our approach to estimating morbidity benefits, which had a negligible (~1%) influence on the total monetized benefits in this RIA (see sections 6.3 and 6.6.3).

Table 6
- 1 . Estimated Monetized Benefits of Attainment of the Revised and Al ternative O zone Standards for 2025 (nationwide benefits of attaining the standards everywhere in the U.S. except California) (billions of 2011$) a Discount

Rate 70 ppb 65 ppb Ozone
- only Benefits c b $1.0 to $1.7 $5. 3 to $8. 7 PM 2.5 Co
- benefits d 3% $2.1 to $4.7 $10 to $23 7% $1.9 to $4.2 $9.3 to $21
Total Benefits 3% $ 3.1 to $ 6. 4 +B $ 1 6 to $ 32 +B 7% $ 2.9 to $ 5.9 +B $ 1 5 to $ 30 +B

a Rounded to two significant figures. It was not possible to quantify all benefits in this analysis due to data limitations. “B” is the sum of all unquantified health and welfare benefits. These estimates reflect the e conomic value of avoided morbidities an d premature deaths using risk coefficients from the studies noted. b Ozone

- only benefits reflect short

- term exposure impacts and as such are assumed to occur in the same year as ambient ozone reductions. Consequently, social discounting is not applied to t he benefits for this category.
c Range reflects application of effect estimates from Smith et al. ( 2009 ) and Zanobetti and Schwartz ( 2008 )

. d Range reflects application of effect estimates from Krewski et al. ( 2009 ) and Lepeule et al. ( 2012 )

.

### 6

- 3 Table 6
- 2 . Estimated Monetized Benefits of Attainment of the Revised and Alternative O zone Standards for p ost - 2025 (nationwide benefits of attaining the standards just in California) (billions of 2011$) a Discount

Rate 70 ppb 65 ppb Ozone
- only Benefits c b $0.7 9 to $1. 3 $1. 6 to $2. 6 PM 2.5 Co
- benefits d 3% $0.40 to $0.91 $0.79 to $1.8 7% $0.37 to $0.82 $0.71 to $1.6
Total Benefits 3% $ 1.2 to $ 2.2 +B $ 2. 4 to $ 4. 4 +B 7% $ 1. 2 to $ 2.1 +B $ 2.3 to $ 4. 2 +B

a Rounded to two significant figures. It was not possible to quantify all benefits in this analysis due to data limitations. “B” is the sum of all unquantified health and welfare benefits. These estimates reflect the e conomic value of avoided morbidities an d premature deaths using risk coefficients from the studies noted. b Ozone

- only benefits reflect short

- term exposure impacts and as such are assumed to occur in the same year as ambient ozone reductions. Consequently, social discounting is not applied to t he benefits for this category.
c Range reflects application of effect estimates from Smith et al. ( 2009 ) and Zanobetti and Schwartz ( 2008 )

. d Range reflects application of effect estimates from Krewski et al. ( 2009 ) and Lepeule et al. ( 2012 )

.



### The control measures (identified and unidentified) applied to reach the revised and alternative ozone standards would reduce other ambient pollutants, including VOCs and NO 2 

### . However, because the method used in this analysis to simulate attainment does not acc ount for changes in ambient concentrations of other pollutants, we were un able to quantify the co

- benefits of reduced exposure to these pollutants. D ue to limited data and methods , we were unable to estimate some anticipated health benefits associated with exposure to ozone and PM 2.5 

### . 6 .2 Overview This chapter presents estimated health benefits for the revised and alternative ozone standards (70 ppb and 65 ppb , respectively ) that the EPA could quantify, given the available resources, data and methods. This chapter characterizes the benefits of the application of the identified and unidentified control strategies for the revised and alternative ozone standard s by answering three key questions:

1. What health effects are avoided by reducing ambient ozone levels to attain the revised and alternative ozone standard s ? 2. What is the economic value of these effects? 3. What are the co

- benefits of reductions in ambient PM 2.5 

### associated with reductions in emiss ions of ozone precursors (specifically NOx)?

### 6

- 4 In this analysis, we quantify an array of adverse health impacts attributable to ozone and PM 2.5 

### that would be avoided by attaining a revised ozone standard . The Integrated Science Assessment for Ozone and Rela ted Photochemical Oxidants (“ozone ISA”) (U.S. EPA, 2013a) identifies the human health effects associated with ozone exposure, which include premature death and a variety of illnesses associated with acute (days

- long) and chronic (months to years

long) exp osures. Similarly, the Integrated Science Assessment for Particulate Matter (“PM ISA”) (U.S. EPA, 2009b) identifies the human health effects associated with ambient particles, which include premature death and a variety of illnesses associated with acute a nd chronic exposures. Air pollution can affect human health in a variety of ways . In Table 6

- 3 we summarize the “categories” of effects and describe those that we quantified for this analysis and those we were unable to quantify due to lack of resources, data, or methods

. This list of benefit categories is not exhaustive , and we are not always able to quantify each effect completely. In this RIA, we only quantify e ndpoints that are classified in the ozone and PM ISAs as being causal ly related, or likely t o be causal ly related, to each pollutant . We excluded e ndpoints not identified as having at least a causal or likely causal relationship with the affected pollutants. Selecting endpoints in this way should not imply that these pollutants are unrelated to o ther human health and environmental effects. Following this criterion, we excluded some effects that were identified in previous lists of unquantified benefits in other RIAs (e.g., UVb exposure), but are not identified in the most recent ISA as having a ca usal or likely causal relationship with ozone. This benefits analysis relies on an array of data inputs

— including emissions estimates, modeled ozone concentrations , health impact functions and valuation estimates among others

— which are themselves subject to uncertainty and may in turn contribute to the overall uncertainty in this analysis. We employ several techniques to characterize this uncertainty, which are described in detail in sections 6 .5 and 6 .7.3.

### 6

- 5 Table 6
- 3 . Human Health Effects of Pollutants Potentially Affected by Strategies to Attain the Primary O zone S tandards (endpoints included in the core analysis are identified with a red checkmark) Benefits Category Specific Effect Effect Has Been Quantified Effect Ha s Been Monetized Source of More Information Improved Human Health Reduced incidence of premature mortality from exposure to ozone Premature mortality based on short

- term exposure (all ages)  

Section 6 .6 Premature respiratory mortality based on long

- term exposure (age 30

– 99) b b
Reduced incidence of morbidity from exposure to ozone Hospital admissions

— respiratory (age > 65)   Emergency department visits for asthma (all ages)   Asthma exacerbation (age 6

- 18)   Minor restricted

- activity days (age 18

– 65)   School absence days (age 5

– 17)   Decreased outdoor worker productivity (age 18

– 65) b b Other respiratory effects (e.g., medi c ation use, pulmonary inflammation, decrements in lung functioning) — —

ozone ISA d Cardiovascular (e.g., hospital admissions, emergency department visits) — — Reproductive and developmental effects (e.g., reduced birthweight, restricted fetal growth) — —

Reduced incidence of premature mortality from exposure to PM 2.5 Adult premature mortality based on cohort study estimates and expert elicitation estimates (age >25 or age >30)  

S ection 6 .6 Infant mortality (age <1)   Reduced incidence of morbidity from exposure to PM 2.5 Non

- fatal heart attacks (age > 18)   Hospital admissions

— respiratory (all ages)   Hospital admissions

— cardiovascular (age >20)   Emergency department visits for asthma (all ages)   Acute bronchitis (age 8

– 12)   Lower respiratory symptoms (age 7

– 14)   Upper respiratory symptoms (asthmatics age 9

– 11)   Asthma exacerbation (asthmatics age 6

– 18)   Lost work days (age 18

– 65)   Minor restricted

- activity days (age 18

– 65)   Chronic Bronchitis (age >26)

— — Emergency department visits for cardiovascular effects (all ages) — — Strokes and cerebrovascular disease (age 50

– 79) — —

### 6

- 6 Benefits Category Specific Effect Effect Has Been Quantified Effect Ha s Been Monetized Source of More Information Other cardiovascular effects (e.g., other ages) — —
PM ISA c Other respiratory effects (e.g., pulmonary function, non

- asthma ER visits, non

bronchitis chronic diseases, other ages and populations) — —

Reproductive and developmental effects (e.g., low birth weight, pre

- term births, etc.) — — PM ISA c,d Cancer, mutagenicity, and genotoxicity effects — — Reduced incidence of morbidity from exposure to NO 2 Asthma hospital admissions (all ages)

— —

NO 2 ISA e Chronic lung disease hospital admissions (age > 65) — — Respiratory emergency department visits (all ages) — — Asthma exacerbation (asthmatics age 4

– 18) — — Acute respiratory symptoms (age 7

– 14)

— — Premature mortality

— — NO 2 ISA c,d Other respiratory effects (e.g., airway hyperresponsiveness and inflammation, lung function, other ages and populations) — —

b We quantified these benefits, but they are not part of the core monetized benefits.
c We assess these benefits qualitatively because we do not have sufficient confidence in available data or methods. d We assess these benefits qualitatively because current evidence is only suggestive of causality or there are other significant concerns ove r the strength of the association.
e We assess these benefits qualitatively due to time and resource limitations for this analysis. 

### The remainder of this chapter is organized as follows: Section 6.3 includes a discussion of the methodological updates rep resented in this analysis; Section 6.4 includes a discussion of the methodologies used in the human health benefits analyses; Section 6.5 includes a characterization of uncertainty; Section 6.6 details the data inputs used in the analysis, including demogr aphic data, baseline incidence and prevalence estimates, effect coefficients, and economic valuation estimates; Section 6.7 presents the results; and Section 6.8 includes a brief discussion of the results. In addition, the chapter has several appendices t hat provide more details on the following: Appendix 6A includes a detailed characterization of uncertainty in the analysis; Appendix 6B includes additional sensitivity analyses and supplemental analysis.
6 .3 Updated Methodology Presented in the Proposal a nd Final RIA s Th is RIA incorporates an array of policy and technical updates to the benefits analysis methods that since the previous review the ozone standards in 2008 and the proposed

### 6

- 7 reconsideration in 2010. These updates to the method s are summarized h ere in chronological order. Following the publication of the 2008 ozone NAAQS RIA (U.S. EPA, 2008a) , EPA change d its methods for estimating the benefits of improved air quality, which were reflected in the 2010 ozone reconsideration RIA (U.S. EPA, 2010d). Two significant changes included:
1. When estimating PM 2.5 

### - related health effects, EPA no longer assumed a minimum concentration at which no effects occurred, while still reporting a range of sensitivity estimates based on the EPA’s PM 2.5 

### mortality expert elicitation (see the Portland Cement NESHAP proposal RIA (U.S. EPA, 2009a) for further discussion). 2. To be consistent with Agency guidance , EPA revised the Value of Statistical Life (VSL) it used to quantify the value of reduced mortality risk (see the NO 2 

### NAAQS final RIA (U.S. EPA, 2010a) for further discussion). Following completion of the 2010 reconsideration RIA, EPA made a number of additional updates to the method s, which are reflected in the 2014 RIA for the propos ed ozone NAAQS (U.S. EPA, 2014c) , including:

1. The population demographic data in BenMAP

- CE (U.S. EPA, 2015a) reflects the 2010 Census and future projections based on economic forecasting models developed by Woods and Poole, Inc. (Woods and Poole, 2012). The se data replace older demographic projection data from Woods and Poole (2007). This update was introduced in the final PM NAAQS RIA (U.S. EPA, 2012a). 2. The baseline incidence rates used to quantify air pollution

- related hospital admissions and emergency dep artment visits and the asthma prevalence rates were updated to replace the earlier rates. This update was introduced in the final CSAPR (U.S. EPA, 2011c). 3. The cost

- of

- illness estimates for hospital admissions, including median wages, have been updated to reflect 2007 data. This update was introduced in the proposal PM NAAQS RIA (U.S. EPA, 2012a). 4. Updates affecting estimated ozone

- related effects:

a. New studies used to quantify ozone

- related premature death . The ozone ISA (U.S. EPA, 2013a) identifies several new epidemiological studies examining the association between ozone exposure and premature mortality. We include two new multi

- city studies to estimate deaths attributable to short

- term exposure (Smith et al., 2009 and Zanobetti and Schwartz 2008). We also estimate long

- term respiratory deaths using

### 6

- 8 Jerrett et al. (2009). 3 

### We introduced this update in the proposal RIA (U.S. EPA, 2014c). b. New studies used to quantify ozone

- related morbidity effects . The ozone ISA (U.S. EPA, 2013a) identifies several new epide miological studies examining the association between short

- term ozone exposure and respiratory hospitalizations, respiratory emergency department visits, and exacerbated asthma. We introduced this update in the proposal RIA (U.S. EPA, 2014c).

c. Expanded unce rtainty assessment . We added a comprehensive, qualitative assessment of the various uncertain parameters and assumptions within the benefits analysis and expanded the evaluation of air quality benchmarks for ozone

- related mortality. We introduce d this expa nded assessment in the proposal RIA (U.S. EPA, 2014c). 5. Updates affecting estimated PM 2.5 

### - related effects:

a. New studies used to quantify PM 2.5 

### - related premature death . We updated the American Cancer Society cohort study to Krewski et al. (2009) and updated t he Harvard Six Cities cohort study to Lepeule et al. (2012). The update for the American Cancer Society cohort was introduced in the proposal RIA for the PM NAAQS review (U.S. EPA, 2012a) and the update for the Harvard Six Cities cohort was introduced in t he final RIA for the PM NAAQS review (U.S. EPA, 2012c). b. New studies used to quantify PM 2.5 

### morbidity . Based on the PM ISA and PM Provisional Assessment, we added several new studies and morbidity endpoints to our health impact assessment, including hospita l admissions and emergency department visits. These updates were introduced in the proposal (U.S. EPA, 2012a) and final RIAs for the PM NAAQS review (U.S. EPA, 2012c).

c. More recent survival rates for non

- fatal acute myocardial infarctions . Based on recent data from Agency for Healthcare Research and Quality’s Healthcare Utilization Project National Inpatient Sample database (AHRQ, 2009), we identified death rates for adults hospitalized with acute myocardial infarction stratified by age. These rates replace d the survival rates from Rosamond et al. (1999). This update was introduced in the final RIA for the PM NAAQS review (U.S. EPA, 2012c). d. Expanded uncertainty assessment . We expanded the comprehensive assessment of the various uncertain parameters and assumptions within the benefits analysis including the evaluation of air quality benchmarks. This update was introduced in the proposed CSAPR RIA (U.S. EPA, 2010f) and refined in the final PM NAAQS RIA (U.S. EPA, 2012c).

3 Because we do not have information on the cessation lag for premature mortality from long

- term ozone exposure, we do not include the monetized benefits in the core analysis . Instead, monetized benefits associated with long

term ozone

- related respiratory mortality are included as a sensitivity analysis (see Appendix 5B, section 5B.2).

### 6

- 9 Following completion of the proposa l RIA (U.S. EPA, 2014c) , we slight ly modified the methods applied in this RIA . As described in sections 6.6.3, we modified the set of epidemiology studies and associated effect coefficients used in estimating changes in asthma exacerbation and respiratory hospital admissions associated with ozone exposure. Because these changes do not involve our approach for estimating air pollution

- related premature deaths , which largely drives overall monetized benefits, they have a negligible (~1%) impact on total monet ized benefits (see section 6.6.3 for further discussion).
6 .4 Human Health Benefits Analysis Methods We follow a “damage

- function” approach in calculating total benefits of the modeled changes in environmental quality. This approach estimates changes in in dividual health endpoints ( i.e., specific effects that can be associated with changes in air quality) and assigns values to those changes assuming independence of the values for those individual endpoints. Total benefits are calculated simply as the sum of the values for all non

- overlapping health endpoints. The “damage

- function” approach is the standard method for assessing costs and benefits of environmental quality programs and has been used in several recent published analyses (Levy et al., 2009; Fann e t al., 2012a; Tagaris et al., 2009). To assess economic values in a damage

- function framework, the changes in environmental quality must be translated into effects on people or on the things that people value. In some cases, the changes in environmental qu ality can be valued directly, as is the case for changes in visibility. In other cases, such as for changes in health outcomes associated with reductions in ozone and PM concentrations, an impact analysis must first be conducted to convert air quality chan ges into effects that can be assigned dollar values. For the purposes of this RIA, the health impacts analysis (HIA) is limited to those health effects that are directly linked to ambient levels of air pollution and specifically to those linked to ozone an d PM 2.5 

### . We note at the outset that the EPA rarely has the time or resources to perform extensive new research to measure directly either the health outcomes or their values for regulatory analyses. Thus, similar to Kunzli et al. (2000) and other, more rec ent health impact analyses, our estimates are based on the best available methods of benefits transfer. Benefits transfer is the science and art of adapting primary research from similar contexts to obtain the most accurate

### 6

- 10 measure of benefits for the envi ronmental quality change being analyzed

. Adjustments are made for the level of environmental quality change, the socio

- demographic and economic characteristics of the affected population, and other factors to improve the accuracy and robustness of benefits estimates.
Benefits estimates for ozone were generated using the damage function approach outlined above wherein potential changes in ambient ozone levels (associated with future attainment of the revised and alternative standard levels) were explicitly modeled and then translated into reductions in the incidence of specific health endpoints (see Chapter 2 for more information)

. In contrast , in estimating PM 2.5 

### co - benefits we utilized a reduced form approach

. The details of both approaches are described in additional detail below.
6 .4.1 Health Impact Assessment The health impact assessment (HIA) quantifies the changes in the incidence of adverse health impacts resulting from changes in human exposure to PM 2.5 

### and ozone air quality. HIAs are a well

- established approach for estimating the retrospective or prospective change in adverse health impacts expected to result from population

- level changes in exposure to pollutants (Levy et al., 2009). PC

- based tools suc h as the environmental Benefits Mapping and Analysis Program

– Community Edition (BenMAP

- CE) can systematize health impact analyses by applying a database of key input parameters, including health impact functions and population projections

— provided that k ey input data are available, including air quality estimates and ri sk coefficients (U.S. EPA, 2015a ). Analysts have applied the HIA approach to estimate human health impacts resulting from hypothetical changes in pollutant levels (Hubbell et al., 2005; Tag aris et al., 2009; Fann et al., 2012a). The EPA and others have relied upon this method to predict future changes in health impacts expected to result from the implementation of regulations affecting ai r quality (e.g., U.S. EPA, 2014a ). For this assessment , the HIA is limited to those health effects that are directly linked to ambient ozone and PM 2.5 

### concentrations. There may be other indirect health impacts associated with applying emissions controls, such as occupational health exposures.

### 6

- 11 The HIA approach used in this analysis involves three basic steps: (1) u s ing projections of ozone air quality 4 

### and estimating the change in the spatial distribution of the ambient air quality; (2) determining the subsequent change in population

- level exposure; and (3) cal culating health impacts by applying concentration

- response (C

- R) relationships drawn from the epidemiological literature to this change in population exposure (Hubbell et al., 2009). A typical health impact function might look as follows:

∆ yy = 1

− � ee ββ ∙ ∆ xx 

### � yy oo 

### ∙ PPPPPP ( 6 .1) where y 0 

### is the baseline incidence rate for the health endpoint being quantified (for example, a health impact function quantifying changes in mortality would use the baseline, or background, mortality rate for the given population of interest); Pop is the populat ion affected by the change in air quality; ∆ x is the change in air quality; and β is the effect coefficient drawn from the epidemiological study. Figure 6

- 1 provides a simplified overview of this approach.

### 6

- 12 Figure 6
- 1 . Illustration of BenMAP
- CE Approach
6 .4.2 Economic Valuation of Health Impacts After quantifying the change in adverse health impacts, the final step is to estimate the economic value of these avoided impacts. The appropriate economic value for a change in a health effect depends on whether the health effect is viewed ex ante (before the effect has occurred) or ex post (after the effect has occurred). Reductions in ambient concentrations of air pollution generally lower the risk of future adverse health effects by a small amount for a large population. The appropriate economic measure is therefore ex ante willingness to pay (WTP) for changes in risk. Epidemiological studies generally provide estimates of the changes in risk for a particular health effect for a given increment of air pollution (often per 10 ppb ozone). These relative ri sks can be used to develop risk coefficients that relate a unit reduction in ozone or PM 2.5 

### to changes in the incidence of a health effect. In order to value these changes in incidence, WTP for changes in risk need to be converted into WTP per statistical incidence. This measure is calculated by dividing individual WTP for a risk reduction by th e related observed change in Baseline Air Quality Post

- Policy Scenario Air Quality

Incremental Air Quality Improvement

Ozone Reduction

Population Ages 30

- 99 Background Incidence Rate Effect Estimate Mortality Reduction

### 6

- 13 risk. For some health effects, such as hospital admissions, WTP estimates are generally not available. In these cases, we use the costs of treating or mitigating the effect, which generally understate the true value of reductio ns in risk of a health effect because they exclude the value of avoided pain and suffering from the health effect.
We use the BenMAP

- CE version 1 .1 (U.S. EPA, 2015a, 2015b ) to estimate the health impacts and monetized health benefits for the standards eval uated here . Figure 6

- 2 shows the data inputs and outputs for the BenMAP

- CE program . The environmental Benefits Mapping and Analysis Program

— Community Edition (BenMAP

- CE) is an open

- source PC

- based tool that quantifies the number and economic value of air p ollution

- related deaths and illnesses

. 5



### Figure 6
- 2 . Data Inputs and Outputs for the BenMAP
- CE Program

5 As compared to the version that it replaces ( BenMAP v4 ) , BenMAP

- CE uses the same computational algorithms

and input data to calcu late benefits for a given air quality change, both versions report the same estimates, within rounding. BenMAP

- CE differs from the legacy version of BenMAP in two important ways: (1) it is open

- source and the uncompiled code is available to the public; and (2) it is written in C#, which is both more broadly used and modern than the code it replaces (Delphi). BenMAP

- CE was last used to support the Health Risk and Exposure Assessment for Ozone (ozone HREA) (U.S. EPA, 2014b)

. Modeled Baseline and Post
- Control Ambient Ozone

Ozone Health

Functions

Economic Valuation Functions 2025 Population Projections

Ozone Incremental Air Quality Change

Ozone
- Related Health Impacts Woods & Poole Population Projections

Background Incidence and Prevalence Rates

Monetized Ozone
related Benefits Blue identifies a user

- selected input within the BenMAP

- CE program Green identifies a data input generated outside of the BenMAP

- CE program Ce nsus Population Data

### 6

- 14 6 .4.3 Estimating Benefits fo r 2025 and Post

- 2025 Analysis Years
P ortions of the country with more significant air quality problems ( particularly several areas in California) may not be required to meet the revised standard until as late as December 31, 203 8 . Consequently, for the revised and alternative standard s we evaluate a 2025 scenario reflecting the nationwide benefits of attaining the standa rd s everywhere in the U.S. except California. We then evaluate a post

- 2025 scenario, which represents nationwide benefits from attaining the standard s in California. W e report the 2025 and 2038 estimates separately because deriving a summed estimate would require us to calculate the Present Value (PV) of the stream of benefits occurring between those two years, which is not possible with the available data

. Our approach for estimating the benefits of attaining the revised and alternat iv e ozone standards pos t

- 2025 is illustrated in Figure 6

- 3 . In this figure , Simulation A represents our approach for estimating the benefits of attaining the revised and alternat iv e ozone standards in every state except California in 2025. To estimate benefits for the post

- 2025 scenario, we fi rst estimated the benefits occurring in 203 8 from all areas (including California) attaining the revised and alternative standard s (Simulation C). Next, we simulated the nationwide benefits of attaining the revised and alternat iv e ozone standard s in 2038 for every state except California (Simulation B). We then s ubtract ed Simulation B from Simulation C to calculate the benefits of attaining the revised and alternat iv e ozone standard s after 2025

— that is, to calculate the nationwide benef its from California alone attaining the standard s in 2038. I mportant caveats are associated with this approach mentioned in Section 6 .1 and discussed further in section 6 .7.3.

### 6

- 15 Figure 6
- 3 . Procedure for Generating Benefits Estimates for the 2025 and Pos t - 2025 Scenarios

6 .4.4 Benefit

- per - ton E stimates for PM 2.5 

### W e used a reduced form approach to estimate the PM 2.5 

### co - benefits in this RIA due to data and resource constraints

. Specifically, w e used “benefit

- per - ton” estimates. EPA has applied this approach in several previous RIAs (e.g., U.S. EPA, 2014a ). These benefit

- per - ton estimates provide the total monetized human health co

- benefits (the sum of premature mortality) of reducing one ton of NOx (as a PM 2.5 

### precursor) from a specif ied source. In general, these estimates apply the same benefits methods (e.g., health impact assessment then economic valuation) for all PM 2.5 

### impacts attributable to a sector, and these benefits are then divided by the tons of a PM 2.5 

### precursor (e.g., NOx ) from that sector.
We used national benefit

- per - ton estimates described in the TSD: Estimating the Benefit per Ton of Reducing PM 2.5 

### Precursors from 17 Sectors (U.S. EPA, 2013b). The national estimates used in this RIA were derived using the approach pub lished in Fann et al. (2012b ), but have since been updated to reflect the epidemiology studies and Census population data first applied in the final PM 2.5 

### NAAQS RIA (U.S. EPA, 2012 c ). Th e approach in Fann et al. (2012b ) 75 ppb baseline ( CA does not attain 75)

70 ppb full attainment scenario everywhere but CA

70 ppb full attainment scenario everywhere including CA 75 ppb baseline reductions in ( all locations attain 75) A

C 2025 scenario : Nationwide benefits in 2025 resulting from all areas in the U.S. attaining the alternative standard under consideration excluding California (here 70ppb). This is Simulation A

70 ppb full attainment scenario everywhere but CA 75 ppb baseline ( CA does not attain 75) B BenMAP B enefits Simulations

Benefits year = 2025 Cost year = 2025

Benefits year = 2038 Cost year = 2037/2038

Benefits year = 2038 Cost year = 2037/2038 Post
- 2025 scenario : Nationwide benefits in 2038 having California attain the alternative standard (here 70ppb). This is the difference between Simulation C and Simulation B

### 6

- 16 is similar to the work previously pu blished by Fann et al. (2009), but the newer study includes improvements that provide more refined estimates of PM 2.5 

### - related health benefits for emissions reductions in the various sectors. Specifically, the air quality modeling data reflect industrial

se ctors that are more narrowly defined. In addition, the updated air quality modeling data reflects more recent emissions data

-- a 2005 baseline projected to 2016 rather than 2001 baseline projected to 2015

-- and has higher spatial resolution (12 km rather than 36 km grid cells).
In Section 6 .6, we describe all of the data inputs used in deriving the benefit

- per - ton values for each sector , including the demographic data, baseline incidence, and valuation functions. The benefit

- per - ton estimates (by sector) that resulted from this modeling are presented in Section 6. 6.5

. We then multiply these benefit

- per - ton estimates for each sector with the NOx emission reductions from that sector to estimate the PM 2.5 

### co - benefits. 6 

### Additional information on the

source apportionment modeling for each of the sectors can be found in Fann et al. (2012b ) and the TSD (U.S. EPA , 2013b). Specifically for this analysis, we applied the benefit

- per - ton estimates for 2025 and 2030 in generating PM 2.5 

### co - benefit estimates for the 20 25 and post

- 2025 scenario s, respectively. 7 

### Applying benefit

- per - ton estimates introduces uncertainty, which we discuss in section 6.7.3 and Appendix 6A . The benefit

- per - ton estimates used here reflect specific geographic patterns of emissions reductions an d specific air quality and benefits modeling assumptions associated with the derivation of those estimates. Consequently, these estimates may not reflect local variability in factors associated with PM 2.5 

### - rel a ted health impacts (e.g., population density, b aseline health incidence rates ) . Therefore, us ing benefit

- per - ton values to estimate co

- benefits may lead to higher or lower benefit estimates than if co

- benefits were calculated based on direct air quality modeling. In addition, the use of national benefit

- per - ton estimates results in a known underestimation bias in California in the post

- 2025 scenario due to population density.

6 For unidentified controls, we us e a weighted average of the benefit

- per - ton estimates from all of the sectors. 7 We do not have benefit

- per - ton estimates for 2038. The last year available is 2030, which is an underestimate of

the 2038 benefits because the population grows and ages over time.

### 6

- 17 6 .5 Characterizing Uncertainty In any complex analysis using estimated parameters and i nputs from numerous models, there are likely to be many sources of uncertainty , and t his analysis is no exception. T his analysis includes many data sources as inputs, including emission s inventories, air quality data from models (with their associated para meters and inputs), population data, population estimates, health effect estimates from epidemiology studies, economic data for monetizing benefits, and assumptions regarding the future state of the world ( e.g. , regulations, technology, and human behavior) . Each of these inputs may be uncertain and would affect the benefits estimate s . When the uncertainties from each stage of the analysis are compounded, even small uncertainties can have large effects on the total quantified benefits. After reviewing the EP A’s approach, the National Research Council (NRC) (2002, 2008), which is part of the National Academies of Science, concluded that the EPA’s general methodology for calculating the benefits of reducing air pollution is reasonable and informative in spite o f inherent uncertainties. The NRC also highlighted the need to conduct rigorous quantitative analyses of uncertainty and to present benefits estimates to decision makers in ways that foster an appropriate appreciation of their inherent uncertainty. Since t he publication of these reports, the EPA continues to improve how it characterizes uncertainty in health incidence and benefits estimates. In response to these recommendations, we incorporate d additional quantitative and qualitative characterizations of un certainty. Although we are not yet able to perform the probabilistic uncertainty assessment the NAS envisioned, we added several quantitative and qualitative analyses . These additional analyses characterize uncertainty related to estimated premature mortal ity , since this endpoint is assigned the largest dollar value

. For other inputs into the benefits analysis, such as the air quality data, it is difficult to address uncertainty probabilistically due to the complexity of the underlying air quality models an d emission s inputs. Therefore, we decline to construct alternative assumptions simply for the purpose of probabilistic uncertainty characterization when there is no scientific literature to support those alternate assumptions. To characterize uncertainty a nd variability, we follow an approach that combines elements from two recent analyses b y the EPA (U.S. EPA, 2010b; 2014 b), and use a tiered approach developed by the World Health Organization (WHO) (WHO, 2008). We present this

### 6

- 18 assessment in Appendix 6 A (re sults of these assessments are summarized in section 6 .7.3) . Data limitations prevent us from treating each source of uncertainty quantitatively and from reaching a full

- probabilistic simulation of our results, but we were able to consider the influence of uncertainty in the risk coefficients and economic valuation functions by incorporating several quantitative analyses: Monte Carlo assessments, sensitivity analyses, and supplemental analyses.
6 .5.1 Monte Carlo Assessment Similar to other recent RIAs, we used Monte Carlo methods for characterizing random sampling error associated with the C

- R functions from epidemiological studies and random effects modeling to characterize both sampling error and variability across the economic valuation functions. The Mo nte Carlo simulation in the BenMAP

- CE software randomly samples from a distribution of incidence and valuation estimates to characterize the effects of uncertainty on output variables. Specifically, we used Monte Carlo methods to generate confidence interv als around the estimated health impact and monetized benefits. The reported standard errors in the epidemiological studies determined the distributions for individual effect estimates for endpoints estimated using a single study. For endpoints estimated us ing a pooled estimate of multiple studies, the confidence intervals reflect both the standard errors and the variance across studies. The confidence intervals around the monetized benefits incorporate the epidemiology standard

errors , as well as the distri bution of the valuation function. These confidence intervals do not reflect other sources of uncertainty inherent within the estimates, such as baseline incidence rates, populations exposed and transferability of the effect estimate to diverse locations. A s a result, the reported confidence intervals and range of estimates give an incomplete picture about the overall uncertainty in the benefits estimates . W e report confidence intervals for ozone

- related benefits, but we re unable to provide confidence interv als for PM 2.5 

### - related co

- benefits because we used the benefit

- per - ton estimates.
6 .5.2 Sensitivity Analys e s Because over 90% of the monetized benefits are from avoided premature mortality , it is particularly important to characterize the uncertainties associated with reductions in premature mortality. Each of the sensitivity analyses completed for this RIA are briefly described below

### 6

- 19 and section 6 .7.3.1 provides a discussion of the results and observations stemming from these sensitivity analyses.

• Alte rnative C
- R functions for short
- term ozone exposure - related mortality: Alternative C

- R functions are useful for assessing uncertainty beyond random statistical error, including uncertainty in the functional form of the model or alternative study designs. W e used two multi

- city studies (Smith et al., 2009 ; Zanobetti and Schwartz 2008) to estimate short

- term ozone

- related mortality in our core estimate. W e performed a sensitivity analysis using effect coefficients from additional multi

- city studies and meta

- a nalys e s utilized in prior RIAs ( Bell et al., 2004 ; 2005, Huang, 2005 ; Ito et al., 2005 ; Levy et al., 2005), as well as alternative model specifications from the Smith et al

. (2009) study (see Figure 6

- 4 and Appendix 6B, section 6B.1)

. When selecting studie s for the core and sensitivity analyses we consider ed NAS (p. 80, NRC, 2008) and CASAC recommendations ( U.S. EPA

- SAB , 2012, 2014 ).

• P otential thresholds in the long
- term ozone exposure - related respiratory mortality C
- R function : Consistent with the ozone HREA, we estimate premature respiratory deaths from long

- term exposure to ozone. T he Jerrett et al. ( 2009 ) study explor ed p otential thresholds in the C

- R function. W e use the results of the threshold analys e s conducted by Jerrett et al

. ( 2009 ) to conduct a sensitivity analysis evaluating models with a range of potential thresholds in addition to a non

- threshold (see Appendix 6B, section 6B.3)

.

• A lternative C
- R functions in estimating long
- term PM 2.5 

### exposure - related mortality: In estimating PM 2.5 

### co - benef its, we use two studies (Krewski et al., 2009 ; Lepeule et al., 2012). To better understand the C

- R relationship between PM 2.5 

### exposure and premature mortality, the EPA conducted an expert elicitation in 2006 (Roman et al., 2008; IEc, 2006). 8 

### We apply the functions from the experts as a characterization of uncertainty (see Figure 6

- 5 and Appendix 6B, section 6B.2)

.

• C essation lag for long
- term O 3 

### exposure - related respiratory mortality: We do not know how long

- term O 3 

### exposure - related respiratory deaths are distributed over time and so w e use two lag structures originally developed for PM 2.5 

### (the 20

- year segmented lag used for PM 2.5 

### and an assumption of zero lag) in this sensitivity analysis (see Appendix 6B, section 6B.2)

.

• Income elasticity in the specifi cation of willingness
- to - pay (WTP) functions used for mortality and morbidity endpoints: The degree to which the WTP function used in valuing mortality and some morbidity endpoints changes in proportion to future changes in income is uncertain . We evaluate d the potential impact of this factor on the monetized benefits in a sensitivity analysis (see Appendix 6B, section 6B .5)

.

8 Expert elicitation is a formal, highly structured and well documented process whereby expert judgments, usually of

multiple experts, are obtained (Ayyub, 2002).

### 6

- 20 Even these multiple estimates (including confidence intervals , where available ) cannot account for the role of other input variables in contributing to overall uncertainty, including emissions and air quality modeling, baseline incidence rates, and population exposure estimates. Furthermore, the approach presented here does not yet include methods for addressing correlation between inp ut parameters and identif ying a reasonable upper and lower bounds for input distributions. As a result, the reported confidence intervals and range of estimates give an incomplete picture of the overall uncertainty in the estimates. Thus, confidence interv als reported for individual endpoints and for total benefits should be interpreted within the context of the larger uncertainty surrounding the entire analysis.
6 .5.3 Supplemental Analyses A number of supplemental analyses provide additional perspectives o n the benefits results (the results of which are described in section 6 .7.3.2) , includ ing :

• Age group
- differentiated aspects of short
- term ozone exposure - related mortality: We examined several risk metrics intended to characterize how mortality risk reducti ons are distributed across different age ranges. These include (a) estimated reduction in life years lost, (b) distribution of mortality incidence reductions across age ranges , and (c) estimated reductions in baseline mortality incidence rates by age group

.

• Analysis of baseline ozone concentrations used in estimating ozone mortality: We assess the relationship between short

- term exposure

- related mortality for ozone and the distribution of baseline ozone concentrations

.

• Analysis of baseline PM 2.5 

### concentrations used in modeling short
- term ozone exposure - related mortality: We also include a similar plot of the baseline annual PM 2.5 

### levels used in estimating PM 2.5 

### mortality from the earlier analysis that generate d the benefit

- per - ton values.

• Outdoor worker productivity : In this supplemental analysis, we quantify the economic value of improved productivity among outdoor agricultural workers using Graff Zivin and Neidell (2012). 6 .5.4 Qualitative Assessment of Uncertainty and Other Analysis Limitations To more fully address uncertainties , including those we cannot quantify, we apply a four

tiered approach using the WHO uncertainty framework (WHO, 2008), which provides a means for systematically linking the characterization of uncertainty to t he sophistication of the underlying risk assessment. The EPA has applied similar approaches in previous analyses (U.S.

### 6

- 21 EPA, 2010b, 2011a , 2012a , 2012b ). Using this framework, we summarize the key uncertainties in the health benefits analysis, including our assessment of the direction of potential bias, magnitude of impact on the monetized benefits, degree of confidence in our analytical approach, and our ability to assess the source of uncertainty. More information on this approach and the qualitative uncer tainty characterization are available in Appendix 6 A.
6 .6 Benefits Analysis Data Inputs In Figure 6

- 2 above, we summarized the key data inputs to the health impact and economic valuation estimate. Below we summarize the data sources for each of these inpu ts, including demographic projections, incidence and prevalence rates, effect coefficients, and economic valuation. We indicate where we have updated key data inputs since the benefits analysis conducted for the 2008 ozone NAAQS RIA (U.S. EPA, 2008a) and t he 2010 ozone NAAQS Reconsideration RIA (U.S. EPA, 2010d). As noted above , only slight modifications have been made to the epidemiology studies used to estimate two morbidity endpoints since proposal , and those modifications are described in section 6.6.3.
6 .6.1 Demographic Data Quantifying the incidence and dollar value of pollution impacts requires information regarding the demographic characteristics of the exposed population, including age, location, and income. We use population projections based on e conomic forecasting models developed by Woods and Poole, Inc. (Woods and Poole, 2012). The Woods and Poole (WP) database contains county

- level project ed population by age, sex, and race to 2040, relative to a baseline using the 2010 Census data; the 2008 o zone NAAQS RIA incorporated WP projections relative to a baseline using 2000 Census data. Projections in each county are determined simultaneously with every other county in the United States to take into account patterns of economic growth and migration. The sum of growth in county

- level populations is constrained to equal a previously determined national population growth, based on Bureau of Census estimates (Hollman et al., 2000). According to WP, linking county

- level growth projections together and cons training to a national

- level total growth avoids potential errors introduced by forecasting each county independently. County projections are developed in a four

- stage process:

### 6

- 22 • First, national

- level variables such as income, employment, and populations are forecasted.

• Second, employment projections are made for 179 economic areas defined by the Bureau of Economic Analysis (U.S. BEA, 2004), using an “export

- base d ” approach, which relies on linking industrial

- sector production of non

- locally consumed producti on items, such as outputs from mining, agriculture, and manufacturing with the national economy. The export

- based approach requires estimation of demand equations or calculation of historical growth rates for output and employment by sector.

• Third, populat ion is projected for each economic area based on net migration rates derived from employment opportunities and following a cohort

- component method based on fertility and mortality in each area.

• Fourth, employment and population projections are repeated for counties, using the economic region totals as bounds. The age, sex, and race distributions for each region or county are determined by aging the population by single year of age , by sex , and by race for each year through 2040 based on historical rates of mortality, fertility, and migration. 6 .6.2 Baseline Incidence and Prevalence Estimates Epidemiological studies of the association between pollution levels and adverse health effects generally provide a n estimate of the relationship of air quality changes t o the relative risk of a health effect, rather than estimating the absolute number of avoided cases. For example, a 5 ppb decrease in 8

- h ou r max imum daily ozone concentration might be associated with a decrease in hospital admissions of three percent. The baseline incidence of the health effect is necessary to convert this relative change into a number of cases. A baseline incidence rate is the estimate of the number of cases of the health effect per year in the assessment location, as it corresponds to bas eline pollutant levels in that location. To derive the total baseline incidence per year, this rate must be multiplied by the corresponding population number. For example, if the baseline incidence rate is the number of cases per year per million people, t hat number must be multiplied by the millions of people in the total population in that location

. Table 6

- 4 summarizes the sources of baseline incidence rates and provides national average (where used) incidence rates for the endpoints included in the analysis. For both baseline incidence and prevalence data, we used age

- specific rates where available. We applied C

- R functions to individual age groups and then summed over the relevant age range to estima te total population benefits. In many cases we used a single national incidence rate, due to a lack of more spatially disaggregated data ; in these cases, w henever possible we used national average rates ,

### 6

- 23 because these data are most applicable to a national assessment of benefits. For some studies, however, the only available incidence information comes from the studies themselves; in these cases, incidence in the study population is assumed to represent typical incidence at the national level. County, state and regional incidence rates are available for hospital admissions, and county

- level data are available for premature mortality. We projected mortality rates such that future mortality rates are consistent with our projections of population growth ( U.S. EPA, 2015 b ). To perform this calculation, we began first with an average of 2004

– 2006 cause

- specific mortality rates. Using Census Bureau projected national

- level annual mortality rates stratified by age range, we projected these mortality rates to 2050 in 5

- year increments ( U.S. EPA, 2015 b ; U.S. Bureau of the Census , 2002). The baseline incidence rates for hospital admissions and emergency department visits reflect the updated rates first applied in the CSAPR RIA (U.S. EPA, 2011c ). In addition, we previous ly updated the baseline incidence rates for acute myocardial infarction. These updated rates (AHRQ, 2007) provide a better representation of the rates at which populations of different ages, and in different locations, visit the hospital and emergency depa rtment for air pollution

related illnesses. Also, the updated baseline incidence rates are more spatially refined. For many locations within the U.S., these data are resolved at the county

- or state

- level, providing a better characterization of the geograp hic distribution of hospital and emergency department visits than the previous national rates. Lastly, these rates reflect unscheduled hospital admissions only, which represents a conservative assumption that most air pollution

- related visits are likely to be unscheduled. If a portion of scheduled hospital admissions are air pollution

- related, this assumption would underestimate these benefits

. For the set of endpoints affecting the asthmatic population, in addition to baseline incidence rates, prevalence r ates of asthma in the population are needed to define th e applicable population. Table 6

- 5 lists the prevalence rates used to determine the applicable population for asthma symptoms. Note that these reflect recent asthma prevalence and assume no change in prevalence rates in future years. We last updated these rates in the CSAPR RIA (U.S. EPA, 2011c ).

### 6

- 24 Tab le 6
- 4 . Baseline Incidence Rates and Population Prevalence Rates for Use in Impact Functions, General Population
Endpoint Parameter Rates Value Source Mortality Daily or annual mortality rate projected to 2025 a Age

- , cause - , and county

- specific rate CDC WONDER (2004

– 2006) U.S. Census bureau, 2000 Hospitalizations Daily hospitalization rate Age

- , region

- , state

- , county

- and cause

specific rate 2007 HCUP data files b

ER Visits Daily ER visit rate for asthma and cardiovascular events Age

- , region

- , state

- , county

- and cause

specific rate 2007 HCUP data files b
Nonfatal Myocardial Infarction (heart attacks) Daily nonfatal myocardial infarction incidence rate per person, 18+ Age

- , region

- , state

- , and county

- specific rate 2007 HCUP data files b adjusted by 0.93 for probability of surviving after 28 days (Rosamond et al., 1999) Asthma Exacerbations Incidence among asthmatic African

- American child ren

daily wheeze daily cough daily shortness of breath 0.173 0.145 0.074 Ostro et al. (2001)

Acute Bronchitis Annual bronchitis incidence rate, children 0.043 American Lung Association (2002, Table 11) Lower Respiratory Symptoms Daily lower respiratory symptom incidence among children c 0.0012 Schwartz et al. (1994, Table 2)
Upper Respiratory Symptoms Daily upper respiratory symptom incidence among asthmatic children 0.3419 Pope et al. (1991, Table 2)
Work Loss Days Daily WLD in cidence rate per person (18

– 65) Aged 18

– 24 Aged 25

– 44 Aged 45

– 64 0.00540 0.00678 0.00492 1996 HIS (Adams, Hendershot, and Marano, 1999, Table 41); U.S. Census Bureau (2000)
School Loss Days Rate per person per year, assuming 180 school days per year 9.9 National Center for Education Statistics (1996) and 1996 HIS (Adams et al., 1999, Table 47); Minor Restricted

Activity Days Daily MRAD incidence rate per person 0.02137 Ostro and Rothschild (1989, p. 243)

a Mortality rates are only available at 5

- year increments. b Healthcare Cost and Utilization Program (HCUP) database contains individual level, state and regional

- level hospital and emergency department discharges for a variety of International Classification of Diseases ( ICD ) codes (AHRQ, 2007).
c Low er respiratory symptoms are defined as two or more of the following: cough, chest pain, phlegm, and wheeze.

### 6

- 25 Table 6
- 5. Asthma Prevalence Rates
Population Group Asthma Prevalence Rates Value Source All Ages 0.0780 American Lung Association (2010, Table 7) < 18 0.0941 5

– 17 0.1070 18

– 44 0.0719 45

– 64 0.0745 65+ 0.0716 African American, 5

– 17 0.1776 American Lung Association (2010, Table 9) African American, <18 0.1553 American Lung Association a

a Calculated by ALA for U.S. EPA, based on NHIS data (CDC, 2008). 

### 6 .6.3 Effect Coefficients In this section, we describe our general process for selecting effect coefficients from epidemiology studies. The first step in selecting effect coefficients is to identify the health endpoints to be quantified. We base d our selection of health endpoints on consistency with the EPA’s ISAs, with input and advice from the SAB

- HES

. 9 

### In addition, we included more recent epidemiology studies from the ozone ISA (U.S. EPA, 2013a), PM ISA (U.S. EPA, 2009b), and the PM Provisi onal Assessment (U.S. EPA, 2012d ). 10 

### In selecting health endpoints for ozone, we also considered the suite of endpoints included in core modeling for the ozone HREA, which was supported by CASAC ( U.S. EPA

- SAB, 2012, 2014 ) . In general, we follow a weight

- of

- evidence approach, based on the biological plausibility of effects, availability of C

- R functions from well conducted peer

- reviewed epidemiological studies, cohesiveness of results across studies, and a focus on endpoints reflecting public health impacts (like hospital admissions) rather than physiological responses (such as changes in clini cal measures like Forced Expiratory Volume [FEV1]). There are several types of data that can support the determination of types and magnitude of health effects associated with air pollution exposures. These sources of data include toxicological studies (in cluding animal and cellular studies), human clinical trials, and observational epidemiology studies. All of these data sources provide important contributions to the weight of evidence surrounding a particular health impact. However, only epidemiology

9 The SAB

- HES is a scientific review panel specifically established to provide advice o n the use of the scientific

literature in developing benefits analyses for the EPA’s Report to Congress on The Benefits and Costs of the Clean Air Act 1990 to 2020 (U.S. EPA, 2011a ). 10 The peer

- reviewed studies in the Provisional Assessment have not yet un dergone external review by the SAB.

### 6

- 26 stud ies provide direct C

- R relationships that can be used to evaluate population

- level impacts of reductions in ambient pollution levels in a health impact assessment.
For the data

- derived estimates, we relied on the published scientific literature to ascertai n the relationship between ozone and PM 2.5 

### and adverse human health effects. We evaluated epidemiological studies using the selection criteria summarized in Table 6

- 6. These criteria include consideration of whether the study was peer

- reviewed, the match b etween the pollutant studied and the pollutant of interest, the study design and location, and characteristics of the study population, among other considerations. In general, us ing C

- R functions from more than a single study can provide a more representat ive distribution of the effect estimate. However, there are often differences between studies examining the same endpoint, making it difficult to pool the results in a consistent manner. For example, studies may examine different pollutants or different ag e groups. For this reason, we consider very carefully the set of studies available examining each endpoint and select a consistent subset that provides a good balance of population coverage and match with the pollutant of interest. In many cases, either be cause of a lack of multiple studies, consistency problems, or clear superiority in the quality or comprehensiveness of one study over others, a single published study is selected as the basis of the effect estimate. When several effect estimates for a pol lutant and a given health endpoint (with the exception of mortality) 11 

### have been selected, they are quantitatively combined or pooled to derive a more robust estimate of the relationship. The BenMAP Manual Technical Appendices for an earlier version of the program provides details of the procedures used to combine multiple impact functions ( U.S. EPA, 2015 b ). In general, we used fixed or random effects models to pool estimates from different single city studies of the same endpoint. Fixed effect pooling simpl y weights each study’s estimate by the inverse variance, giving more weight to studies with greater statistical power (lower variance). Random effects pooling accounts for both within

- study variance and between

- study variability, due, for example, to diffe rences in population susceptibility. We used the fixed effect model as our null hypothesis and then determined whether the data suggest that we should reject this null hypothesis, in which case we would use

11 In the case of mortality, we do not pool results. Instead, we provide the results from each study separately.

### 6

- 27 the random effects model. 12 

### Pooled impact function s are used to estimate hospital admissions and asthma exacerbations. When combining evidence across multi

- city studies (e.g., cardiovascular hospital admission studies), we use equal weights pooling. The effect estimates drawn from each multi

- city study ar e themselves pooled across a large number of urban areas. For this reason, we elected to give each study an equal weight rather than weighting by the inverse of the variance reported in each study. Effect estimates selected for a given health endpoint wer e applied consistently across all locations nationwide. This applies to both impact functions defined by a single effect estimate and those defined by a pooling of multiple effect estimates. Although the effect estimate may vary from one location to anothe r (e.g., because of differences in population susceptibilities or differences in the composition of PM), location

- specific effect estimates are generally not available.
Table 6
- 6. Criteria Used When Selecting C
- R Functions Consideration Comments Peer

- Reviewed Research Peer

- reviewed research is exclusively used to select C

- R functions

. Study Type Prospective cohort vs. ecological : Among studies that consider chronic exposure (e.g., over a year or longer), prospective cohort studies are preferred over ecological studies because they control for important individual

- level confounding variables that cannot be controlled for in ecolog ical studies. Multi

- city vs. pooled/meta

- analysis: M ulti

- city time series studies have advantages to meta

- analyses. Multi

- city studies u s e a consistent model structure and can include factors that explain differences between effect estimates among the cit ies. By contrast, meta

analyses can become imprecise and the results difficult to interpret due to the aggregation of large sets of studies . In addition, meta

- analyses can suffer from publication bias , which can result in high

- biased effect estimates. Alth ough we generally prefer multi

- city studies, we may consider meta

- analyses if multi

- city studies are not available. Study Period Studies examining a relatively longer period of time (and therefore having more data) are preferred, because they have greater statistical power to detect effects. Studies that are more recent are also preferred because of possible changes in pollution mix es, medical care, and lifestyle over time. However, when there are only a few studies available, studies from all years would be included. Seasonality While the measurement of PM is typically collected across the full year, ozone monitoring seasons can vary substantially across different regions of the country. Consequently, studies matching the ozone seasons in the air quality modeling are preferred

.

12 EPA recently changed the algorithm BenMAP use d to calculate study variance, which is used in the pooling

process. Prior versions of the model calculated population variance, while the version used here calculate s sample variance.

### 6

- 28 Consideration Comments Population Attributes The most technically appropriate measures of benefits would be based on impact functions that cover the entire sensitive population but allow for heterogeneity across age or other relevant demographic factors. In the absence of effect estimates specific to age, sex, preexisting condition status, or other relevant factors, it may be a ppropriate to select effect estimates that cover the broadest population to match with the desired outcome of the analysis, which is total national

- level health impacts. Study Size Studies examining a relatively large sample are preferred because they ge nerally have more power to detect small magnitude effects. A large sample can be obtained in several ways, including through selection of a large population or through repeated observations on a smaller population (e.g., through a symptom diary recorded fo r a panel of asthmatic children). Study Location U.S. studies are more desirable than non

- U.S. studies because of potential differences in pollution characteristics, exposure patterns, medical care system, population behavior, and lifestyle

. Depending on the endpoint and the study, we may consider using Canadian studies. National estimates are most appropriate when benefits are nationally distributed; the impact of regional differences may be important when benefits only accrue to a single area. Polluta nts Included in Model An important factor affecting the specification of co

- pollutant models for ozone and PM is sampling frequency. While ozone is typically measured every hour of each day during the ozone season for a specific location, PM is typically measured every 3 rd or 6 th day. For this reason, co

- pollutant model s are preferred for estimating PM effects because this approach controls for the potential ozone effect while not diminishing the effective sample size available for specifying the PM effect . However, when estimating the ozone effect, the use of co

- pollutant model s (with PM) can substantially reduce sample size since only days with both ozone and PM can be used. While these co

- pollutant models may control for potential PM effects, they also r esult in a substantially less robust characterization of the ozone effect due to the reduced number of ozone measurements. For this reason, while we generally favor co

- pollutant models in modeling PM benefits, for ozone we generally favor single pollutant models

. Measure of PM In general , impact functions based on PM 2.5 are preferred to PM 10 because of the focus on reducing emissions of PM 2.5 precursors and because air quality modeling was conducted for this size fraction of PM. Where PM 2.5 functions are not available, PM 10 functions are used as surrogates, recognizing that there will be potential downward (upward) biases if the fine fraction of PM 10 is more (less) toxic than the coarse fraction. Economically Valuable Health Effects Some health effects, such as forced expiratory volume and other technical measurements of lung function, are difficult to value in monetary terms. Therefore, we generally do not include these effects in benefits analyses. Non

- overlapping Endpoints Although the benefits associated with each individual health endpoint may be analyzed separately, care must be exercised in selecting health endpoints to include in the overall benefits analysis because of the possibility of double

- counting of benefits.



### The specifi c studies from which effect estimates are drawn are shown in Table s 6

- 7 and 6

- 8. We highlight in red those studies that have been added since the benefits analysis conducted for the ozone reconsideration (U.S. EPA, 2010d) or the 2008 ozone NAAQS RIA (U.S. EPA, 2008a). In all cases where effect estimates are drawn directly from epidemiological studies,

### 6

- 29 standard errors are used as a partial representation of the uncertainty in the size of the effect estimate. Table 6

- 9 summarizes those health endpoints and st udies we have included in sensitivity analyses.

Table 6
- 7. Health Endpoints and Epidemiological Studies Used to Qu antify O zone - R elated Health Impacts a Endpoint Study Study Population Relative Risk or Effect Estimate ( β) (with 95 th Percentile Confidence Interval or SE) Premature Mortality Premature mortality

— short

- term Smith et al. (2009) All ages β = 0.00032 (0.00008) Zanobetti and Schwartz (2008) β = 0.00051 (0.00012) Premature respiratory mortality

- long

- term Jerrett et al. (2009) >29 years β = 0.003971 (0.00133) Hospital Admissions Respiratory Pooled estimate: Katsouyanni et al. (2009) > 65 years β = 0.00064 (0.00040) penalized splines

Asthma

- related emergency department visits Pooled estimate: Glad et al. (2012)

0

- 99 years β = 0.00306 (0.00117) Ito et al. (2007) β = 0.00521 (0.00091) Mar and Koenig (2010) β = 0.01044 (0.00436) (0

- 17 yr olds) β = 0.00770 (0.00284) (18

- 99 yr olds) Peel et al. (2005) β = 0.00087 (0.00053) Sarnat et al. (2013) β = 0.00111 (0.00028) Wilson et al. (2005) RR = 1.022 (0.996

– 1.049) per 25 Other Health Endpoints

Asthma exacerbation Pooled estimate: b Mortimer et al. (2002) Schildcrout et al. (2006) 6

– 18 years β = 0.00929 (0.00387) β = 0.00222 (0.00282) School loss days Pooled estimate: Chen et al. (2000) 5

- 17 years β = 0.015763 (0.004985) Gilliland et al. (2001) β = 0.007824 (0.004445) Acute respiratory symptoms (MRAD) Ostro and Rothschild (1989) 18

– 65 years β = 0.002596 (0.000776)

a Studies highlighted in red represent updates incorporated since the 2008 ozone NAAQS RIA (U.S. EPA, 2008a). b The original study populations were 5 to 12 y ea rs for the O’Conner et al. (200 8) and Schildcrout et al. (2006) and 5

- 9 y ea rs for the Mortimer et al. (2002) study. Based on advice from the SAB

- HES, we extended the applied population to 6

- 18 y ea rs for all three studies, reflecting the common biological basis for the effect in children in the broader age group. See: U.S. EPA

- SAB (2004a) and NRC (2002)

### 6

- 30 Table 6
- 8. Health Endpoints and Epidemiological Studies Used to Quantify PM 2.5 

### - R elated Health Impacts a

Endpoint Study Study Population Relative Risk or Effect Estimate ( β) (with 95 th Percentile Confidence Interval or SE) Premature Mortality Premature mortality

— cohort study, all

- cause Krewski et al. (2009) > 29 years > 24 years RR = 1.06 (1.04

– 1.06) per 10 μg/m 3 Lepeule et al. (2012) RR = 1.14 (1.07

– 1.22) per 10 μg/m 3 Premature mortality

— all

- cause Woodruff et al. (1997) Infant (< 1 year) OR = 1.04 (1.02

– 1.07) per 10 μg/m 3
Chronic Illness Nonfatal heart attacks Peters et al. (2001) Adults (> 18 years) OR = 1.62 (1.13

– 2.34) per 20 μg/m 3 Pooled estimate: Pope et al. (2006) β = 0.00481 (0.00199) Sullivan et al. (2005) β = 0.00198 (0.00224) Zanobetti et al. (2009) β = 0.00225 (0.000591) Zanobetti and Schwartz (2006) β = 0.0053 (0.00221) Hospital Admissions Respiratory Zanobetti et al. (2009)

— ICD 460

- 519 (All respiratory) > 64 years β=0.00207 (0.00446) Kloog et al. (2012)

— ICD 460

- 519 (All Respiratory β=0.0007 (0.000961) Moolgavkar (2000)

— ICD 490

– 496 (Chronic lung disease) 18

– 64 years 1.02 (1.01

– 1.03) per 36 μg/m 3 Babin et al. (2007)

— ICD 493 (asthma) < 19 years β=0.002 (0.004337) Sheppard (2003)

— ICD 493 (asthma) < 18 RR = 1.04 (1.01

– 1.06) per 11.8 μg/m 3 Cardiovascular Pooled estimate: Zanobetti et al. (2009)

— ICD 390

- 459 (all cardiovascular) > 64 years β=0.00189 (0.000283) Peng et al. (2009)

— ICD 426

- 427; 428; 430

- 438; 410

- 414; 429; 440

- 449 (Cardio

- , cerebro

and peripheral vascular disease) β=0.00068 (0.000214)
Peng et al. (2008)

— ICD 426

- 427; 428; 430

- 438; 410

- 414; 429; 440

- 449 (Cardio

- , cerebro

and peripheral vascular disease) β=0.00071 (0.00013)

Bel l et al. (2008 )

— ICD 426

- 427; 428; 430

- 438; 410

- 414; 429; 440

- 449 (Cardio

- , cerebro

and peripheral vascular disease) β=0.0008 (0.000107)
Moolgavkar (2000)

— ICD 390

– 429 (all cardiovascular) 20

– 64 years RR=1.04 (t statistic: 4.1) per 10 μg/m 3
Asthma

- related emergency department visits Pooled estimate: Mar et al. (2010) All ages RR = 1.04 (1.01

– 1.07) per 7 μg/m 3 Slaughter et al. (2005) RR = 1.03 (0.98

– 1.09) per 10 μg/m 3 Glad et al. (2012) β=0.00392 (0.002843) Other Health Endpoints Acute bronchitis Dockery et al. (1996) 8

– 12 years OR = 1.50 (0.91

– 2.47) per 14.9 μg/m 3

### 6

- 31 Asthma exacerbations Pooled estimate: Ostro et al. (2001) (cough, wheeze, shortness of breath) b 6

– 18 years b OR = 1.03 (0.98

– 1.07) OR = 1.06 (1.01

– 1.11) OR = 1.08 (1 .00

– 1.17) per 30 μg/m 3 Mar et al. (2004) (cough, shortness of breath) RR = 1.21 (1

– 1.47) per RR = 1.13 (0.86

– 1.48) per 10 μg/m 3 Work loss days Ostro (1987) 18

– 65 years β=0.0046 (0.00036) Acute respiratory symptoms (MRAD) Ostro and Rothschild (1989) (Minor restricted activity days) 18

– 65 years β=0.00220 (0.000658) Upper respiratory symptoms Pope et al. (1991) Asthmatics, 9

– 11 years 1.003 (1

– 1.006) per 10 μg/m 3 Lower respiratory symptoms Schwartz and Neas (2000) 7

– 14 years OR = 1.33 (1.11

– 1.58) per 15 μg/m 3

a Studies highlighted in red represent updates incorporated since the ozone NAAQS RIA (U.S. EPA, 2008a). These updates were introduced in the PM NAAQS RIA (U.S. EPA, 2012c ). b The original study populations were 8 to 13 years for the Ostro et al. (2001) study and 7 to 12 years for the Mar et al. (2004) study. Based on advice from the SAB

- HES, we extended the applied population to 6

- 18 years , reflecting the common biological basis for the effect in children in the broader age group. See: U.S. EPA

- SAB (2004a,b) and NRC (2002). 

### Table 6
- 9. Health Endpoints and Epidemiolo gical Studies Used to Quantify Ozone
- R elated Health Impacts in Sensitivity Analys e s a

Endpoint Study Study Population Effect Estimate ( β) (with 95 th Percentile Confidence Interva l) Premature Mortality Premature respiratory mortality

- long

- term Jerrett et al. (2009)

- based models:

> 29 years - non - threshold ozone only (86 cities) β= 0.00266 (0.000969)

- non - threshold ozone only (96 cities) β= 0.00286 (0.000942)

- threshold 40 ppb b β=0.00312 (0.00096)

- threshold 45 ppb β=0.00336 (0.001)

- threshold 50 ppb β=0.00356 0.00106)

- threshold 55 ppb β=0.00417 (0.00118)

- threshold 56 ppb β=0.00432 (0.00121)

- threshold 60 ppb β=0.00402 (0.00137) Premature mortality

- short

- term Smith et al. (2009) (co

- pollutant model with PM 10 )
All ages β=0.00026 (0.00017) Bell et al. (2005) β=0.00080 (0.00021) Levy et al. (2005) β=0.00112 (0.00018) Bell et al. (2004) β=0.00026 (0.00009) Ito et al. (2005) β=0.00117 (0.00024) Schwartz et al. (2005) β=0.00043 (0.00015) Huang et al. (2005) β=0.00026 (0.00009)

a Studies highlighted in red represent updates incorporated since the 2008 ozone NAAQS RIA (U.S. EPA, 2008a ). b All threshold models are ozone

- only and based on the full 96 city dataset.

### 6

- 32 6 .6.3.1 Ozone Exposure Metric Both the NMMAPS analysis and the individual time series studies upon which the meta

analyses were based use the 24

- hour average or 1

- hour maximum ozone concentrations as exposure metrics. The 24

- hour average is not the most relevant ozone exposure metric to characterize population

- level exposure. Given that the majority of the people tend to be outdoors during the daylight hours and concentrations are highest during the daylight hours, the 24

- hour average metric is not appropriate. Moreover, the 1

- hour maximum metric uses an exposure window different than that used for the current ozone NAAQS. Together, this means that the most biologically relevant metric, and the one used in the ozone NAAQS since 1997 , is the maximum daily 8

- hour average ozone. Thus, we converted ozone mortality health impact functions that use a 24

- hour average or 1

- hour maximum ozone metric to maximum 8

- hour average ozone concentration using standard conversion functions. This practice is consistent with the form of the current ozone standard. This conversion also does not affect the relative magnitude of the health impact function from a mathematical standpoint. An equival ent change in the 24

- hour average, 1

- hour maximum and 8

- hour maximum will provide the same overall change in incidence of a health effect. 13 

### The conversion ratios are based on observed relationships between the 24

- hour average and 8

- hour maximum ozone value s. For example, in the Bell et al., 2004 analysis of ozone

- related premature mortality, the authors found that the relationship between the 24

- hour average, the 8

- hour maximum, and the 1

- hour maximum was 2:1.5:1, so that the derived health impact effect es timate based on the 1

- hour maximum should be half that of the effect estimate based on the 24

- hour values (and the 8

- hour maximum three

- quarters of the 24

- hour effect estimate). In the sensitivity analyses for this benefits analysis, we apply national effe ct estimates based on the pooled multi

- city results reported in Bell et al

. (2004) and the three meta

- analysis studies. Bell et al

. (2004), Bell et al

. (2005), Levy et al

. (2005), and Ito et al

. (2005) all provide

13 However, different ozone metrics may not be well correlated (from either a spatial or temporal standpoint) within

a given geographic area , which means that application of ratio

- converted effect estimates for the same endpoint can result in different incidence estimates for the same location under certain conditions. This introduces uncertainty into the use of these ratio

- adju sted effect estimates (see Appendix 6 A).

### 6

- 33 national conversion ratios between daily a verage and 8

- hour and 1

- hour maxima, based on national data.
6 .6.3.2 Ozone Premature Mortality Effect Coefficients Mortality Effect Coefficients for Short
- term O zone Exposure. The overall body of evidence indicates that there is likely to be a causal rela tionship between short

- term ozone exposure and premature death. The 2013 ozone ISA concludes that the evidence suggests that ozone effects are independent of the relationship between PM and mortality. (U

. S

. EPA , 2013a). However, the ISA note s that the inte rpretation of the potential confounding effects of PM on

ozone - mortality risk estimates requires caution due to the PM sampling schedule (in most cities) which limits the overall sample size available for evaluating potential confounding of the ozone effec t by PM (U

. S

. EPA 2013a). 14 

### In 2006, the EPA requested an NAS study to answer the following four key questions regarding ozone

- related mortality: (1) H ow did the epidemiological literature to that point improve our understanding of the size of the ozone

- r elated mortality effect? ; (2) How best can EPA quantify the level of ozone

- related mortality impacts from short

- term exposure? ; (3) How might EPA estimate the change in life expectancy? ; and (4) What methods should EPA use to estimate the monetary value of changes in ozone

- related mortality risk and life expectancy? In 2008, the NAS (NRC, 2008) issued a series of recommendations to the EPA regarding the quantification and valuation of ozone

- related short

- term mortality. Chief among these was that “...short

- te rm exposure to ambient ozone is likely to contribute to premature deaths” and the committee recommended that “ozone

- related mortality be included in future estimates of the health benefits of reducing ozone exposures...” The NAS also recommended that “...the g reatest emphasis be placed on the multi

- city and NMMAPS studies without exclusion of the meta

analyses” (NRC, 2008). In addition, NAS recommended that EPA “should give little or no weight to the assumption that there is no causal association between estima ted reductions in premature mortality and reduced ozone exposure” (NRC, 2008). In 2010, the Health Effects

14 Consequently, as noted later in this section, while we have used a single

- pollutant model in generating short

- term ozone - related mortality benefits for this RIA, we have included a co

- pollutants model as a sensiti vity analysis to address potential uncertainty associated with this assumption of an independent ozone mortality effect.

### 6

- 34 Subcommittee of the Advisory Council on Clean Air Compliance Analysis, while reviewing EPA’s The Benefits and Costs of the Clean Air Act 1990 to 2020 (U.S. EPA, 2011a ), also confirmed the NAS recommendation to include ozone mortality benefits (U.S. EPA

- SAB, 2010a). In view of the findings of the ozone ISA, the NAS panel, the SAB

- HES panel, and the CASAC panel, we estimate ozone - related premature mortal ity for short

- term exposure in the core health effects analysis using effect coefficients from the Smith et al. (2009) NMMAPS analysis and the Zanobetti and Schwartz (2008) multi

- city study with several additional studies as sensitivity analyses. This emph asis on newer multi

- city studies is consistent with recommendations provided by the NAS in their ozone mortality report (NRC, 2008 )

. CASAC support ed using the Smith et al. (2009) and Zanobetti and Schwartz (2008) studies for the ozone HREA ( U.S. EPA

- SAB, 2 012 , 2014 ) , and these are multi

- city studies published more recently (as compared with other multi

- city studies or meta

- analyses included in the sensitivity analyses

– see discussion below). Smith et al. (2009) reanaly ze d the NMMAPS dataset , evaluating the relationship between short

- term ozone exposure and mortality. While this study reproduces the core national

- scale estimates presented in Bell et al. (2004), it also explore d the sensitivity of the mortality effect to different model specifications includi ng (a) regional versus national Bayes

- based adjustment, 15 

### (b) co

- pollutant models considering PM 10 

### , (c) all

- year versus ozone - season based estimates, and (d) consideration of a range of ozone metrics, including the daily 8

- h ou r max. In addition, th e Smith et al. (2009) study did not use the trimmed mean approach employed in the Bell et al. (2004) study in preparing ozone monitor data. 16 

### In selecting effect estimates from Smith et al. (2009), we focused on an ozone

- only estimate for non

- accidental mortality u sing the 8

- h ou r max

15 In Bayesian modeling, effect estimates are “updated” from an assumed prior value using observational data. In the

Smith et al

. (2009) approach, the prior values are either a regional or national mean of the individual effect estimates obtained for each individual city. The Bayesian adjusted city

- specific effect estimates are then calculated by updating the selected prior value based on the relative precision of each city

- specific estimate and the variation observed across all city

- specific individual effect estimates. City

- specific estimates are pulled towards the prior value if they have low precision and/or if there is low overall vari ation across estimates. City

specific estimates are given less adjustment if they are precisely estimated and/or there is greater overall variation across estimates. 16 There are a number of concerns regarding the trimmed mean approach including (1) the pot ential loss of temporal

variation in the data when the approach is used (this could impact the size of the effect estimate) , and (2) a lack of complete documentation for the approach , which prevents a full reviewing or replication of the technique.

### 6

- 35 metric for the warmer ozone season. F or the sensitivity analysis, we included a co

- pollutant model (ozone and PM 10 

### ) from Smith et al. (2009) for all

- cause mortality , using the 8

- h ou r max ozone metric for the ozone season. Using a singl e pollutant model for the core analysis and the

co - pollutant model in the sensitivity analysis reflects our concern that the reduced sampling frequency for days with co

- pollutant measurements (1/3 and 1/6) could affect the ability of the study to character ize the ozone effect. T his choice is consistent with the ozone ISA , which concludes that ozone effects are likely to be independent of the relationship between PM and mortality (U.S. EPA, 2013 a )

. The Zanobetti and Smith (2008) study evaluated the relations hip between ozone exposure (using an 8

- h ou r mean metric for the warm season June

- August) and all

- cause mortality in 48 U.S. cities using data collected between 1989 and 2000. The study presented single pollutant C

R functions based on shorter (0

- 3 day) and longer (0

- 20 day ) lag structures, with the comparison of effects based on these different lag structures being a central focus of the study. We used the shorter day lag based C

- R function since this had the strongest effect and tighter confidence interval. We co n vert ed the effect estimate from an 8

- h ou r mean metric to an equivalent effect estimate based on an 8

- h ou r max. To do this, we used the ozone metric approach wherein the

or iginal effect estimate (and standard error) is multiplied by the appropriate ozone metric adjustment ratio. 17 

### Mortality Effect Coefficient for Long
- term O zone Exposure. We also estimated long

term ozone exposure

- related respiratory mortality incidence . Su pport for modeling long

- term exposure - related mortality incidence comes from the ozone ISA as well as recommendations provided by CASAC in their review of the ozone HREA ( U.S. EPA

- SAB , 2014, p. 3 and 9). While the ozone ISA concludes that evidence is sugge stive of a causal association between total mortality and long

- term ozone exposure, specifically with regard to respiratory health effects

17 These adjustment ratios are created by (a) obtaining summary air quality (composite monitor values) for each

urban study area/ozone season combination reflected in the original epidemiology study, (b) calculating the ratio of the 8

- h ou r max to the study

- specifi c air metric (for each of the urban study areas), and (c) taking the average of these urban

- study area ratios. Ratio adjustment of the effect estimate does introduce uncertainty into the benefits estimates generated using these adjusted effect estimates ; h owever, adjustments of relatively similar metrics (e.g., 8

- h ou r max and 8

- h ou r mean) , as is the case with the Zanobetti and Schwartz (2008) study , are likely to introduce less uncertainty tha n adjustments for more disparate ratios (e.g., 24

- h ou r or 1

- h ou r max ratios to 8

- h ou r max equivalents).

### 6

- 36 (including mortality), the ISA concludes that there is likely to be a causal association (U.S. EPA, 2013 a )

. Consisten t with the ozone HREA, we use Jerrett et al. (2009) to estimate premature respiratory mortality from long

- term ozone exposure. In review of the ozone HREA, CASAC concluded that “ Jerrett et al. (2009) is an appropriate study to use at this time as the basis for the long

- term mortality risk estimates given its adequacy and the lack of alternative data.” ( U.S. EPA

- SAB , 2014). This advice supersedes previous advice provided by the SAB

- HES to include long

- term ozone exposure

- related mortality only as a sensitiv ity analysis. The Jerrett et al. (2009) study was the first to explore the relationship between long

- term ozone exposure and respiratory mortality (rather than other causes of mortality). Jerrett et al. (2009) exhibits a number of stre ngths including (a) t he study was based on the 1.2 million participant American Cancer Society cohort drawn from all 50 states, DC, and Puerto Rico (included ozone data from 1977 , 5 years before enrollment in the cohort began , to 2000); (b) it included co - pollutant models that controlled for PM 2.5 

### ; and (c) it explored the potential for a threshold concentration associated with the long

- term mortality endpoint. However , attributes of this study affect how we interpret the long

- term exposure

- related respiratory mortality estimates. First, while CASAC not es that Jerrett et al. (2009) was well designed, it is a single study and provides the only quantitative basis for estimating this end point. By comparison, we estimate short

- term exposure

- related mortality risk using several studies. There is also the potential existence and location of a threshold in the C

- R function relating mortality and long

- term ozone c oncentrations , which can grea tly affect the results

. CASAC concluded, “ it is not clear whether the 56 ppb threshold model is a better predictor of respiratory mortality than when using a linear model for the Jerrett et al. data ” ( U.S. EPA

- SAB , 2014, p. 13

- 14) Reflecting this CASAC a dvice , we estimate long

- term exposure

- related respiratory mortality us ing a non - threshold co

- pollutant model (with PM 2.5 

### ) from Jerrett et al. (2009). Because the Jerrett et al. (2009) study uses seasonal average metrics (rather than shorter single day or m ulti

- day lagged models as with time series studies studies), co

- pollutants models obtained from Jerrett et al., (2009) are not affected by the lower PM 2.5 

### sampling rates. Using a
co - pollutant model is consistent with this study appl ying seasonal average me trics that are

### 6

- 37 insensitive to co

- pollutant monitoring for PM 2.5 

### . The effect estimates used to model long

- term
ozone - attributable mortality are calculated using a seasonal average of peak (1

- h ou r maximum) measurements. These long

- term exposure metrics can b e viewed as long

- term exposures to daily peak ozone over the warmer months, as compared with annual average levels such as are used in PM benefits

. Therefore, combining long

- term and s hort

- term ozone

- attributable mortality estimates could lead to double counting. E stimates of short

- term ozone mortality are for all

causes, while estimates of long

- term ozone mortality are for respiratory

- related mortality only

. Sensitivity Analysis: Alternate Mortality Effect Coefficients for Short
- term O zone Expos ure
. Although we believe the evidence supports an ozone

- only effect on short

- term exposure - related mortality (as supported by the o zone ISA ), we recognize limitations in the ability of studies to explore copollutants effects due to lower sampling frequenc y for PM relative to ozone. For that reason, we conduct a sensitivity analysis using the co - pollutants model (with PM 10 

### ) from Smith et al. ( 2009 )

. Sensitivity Analysis: Threshold
- Based Eff ect Coefficients for Long
- term O zone Exposure. C onsistent with the ozone HREA (U.S. EPA, 2014b), we explor e the sensitivity of estimated ozone

- related deaths to a concentration threshold using the Jerrett et al. ( 2009 ) study. 18 

### In their memo clarifying the results of their study (see Sasser, 2014), the authors note that in terms of goodness of fit, long

- term health risk models including ozone clearly performed better than models without ozone, indicating the improved predictions of respiratory mortality when ozone is included. In exploring different functional forms, they report that the model including a threshold at 56 ppb had the lowest log

- likelihood value of all models evaluated (i.e., linear models and models including thre sholds ranging from 40

- 60 ppb), and thus provided the best overall statistical fit to the data. However, they also note that it is not clear whether the 56 ppb threshold model is a better predictor of respiratory mortality than when using a linear (no

thre shold) model for this dataset. Using one statistical test, the model with a threshold at 56 ppb was determined to be statistically superior to the linear model. Using another, more stringent test, none of the threshold models considered were statistically superior to the linear model. Under the

18 The approach in the ozone HREA to explore the potential for thresholds related to long

- term exposure

- related

mortality is described in Sasser ( 2014). That memorandum also describes additional data obtained from the authors of Jerrett et al. (2009) to support modeling potential thresholds.

### 6

- 38 less stringent test, although the threshold model produces a statistically superior prediction than the linear model, there is uncertainty about the specific location of the threshold, if one exists. This is because the confidence intervals on the model predictions indicate that a threshold could exist anywhere from 0 to 60 ppb. The authors conclude that considerable caution should be exercised in using any specific threshold, particularly when the more stringent stat istical test indicates there is no significantly improved prediction. Based on this additional information from the authors (Sasser, 2014), we have chosen to reflect the uncertainty about the existence and location of a potential threshold by estimating mortality attributable to long

- term ozone exposures using a range of threshold

- based effect estimates as sensitivity analyses. Specifically, we generate additional long

- term risk results using unique risk models that include a range of thresholds from 40 p pb to 60 ppb in 5 ppb increments, while also including a model with a threshold equal to 56 ppb, which had the lowest log likelihood value for all models examined. 19 

### In addition to exploring the impact of potential thresholds, as part of the sensitivity an alysis we explore the impact of using ozone

- only (non

threshold) models in estimating long

- term exposure

- related respiratory mortality. 20


### 6 .6.3. 3 PM 2.5 

### Premature Mortality Coefficients PM 2.5 

### Mortality Effect Coefficients for Adults. A substantial body of published scientific literature documents the association between elevated PM 2.5 

### concentrations and increased premature mortality (U.S. EPA, 2009b). This body of literature reflects thousands of epidemiology, toxicology, and clinical studies. The PM ISA co mpleted as part of the most recent review of the PM NAAQS , which was twice reviewed by the SAB

- CASAC (U.S. EPA

- SAB, 2009 a , 2009b ), concluded that there is a causal relationship between mortality and both long

term and short

- term exposure to PM 2.5 

### based on the entire body of scientific evidence (U.S. EPA, 2009b). The size of the mortality effect estimates from epidemiological studies, the serious

19 There is a separate effect estimate (and associated standard error) for each of the fitted threshold models estimated

in Jerrett et al. (2009). As a result, the sensitivity of es timated mortality attributable to long

- term ozone concentrations is affected by both the assumed threshold level (below which there is no effect of ozone) and the effect estimate applied to ozone concentrations above the threshold. 20 The set of ozone

- only non - threshold effect estimates include (a) a value based on the 86 cities for which there are

co - pollutant monitoring data for both ozone and PM 2.5 (this is best compared with the core estimate based on the co - pollutant non

- threshold model), and (b) a valu e based on the 96 cities for which there is PM 2.5 data (these 96 cities were used in developing the threshold

- based effect estimates used in the analysis).

### 6

- 39 nature of the effect itself, and the high monetary value ascribed to prolonging life make mortality risk reductio n the most significant health endpoint quantified in this analysis. Researchers have found statistically significant associations between PM 2.5 

### and premature mortality using different types of study designs. Time

- series methods have been used to relate sh ort

- term (often day

- to

- day) changes in PM 2.5 

### concentrations and changes in daily mortality rates up to several days after a period of exposure to elevated PM 2.5 

### concentrations. Cohort methods have been used to examine the potential relationship between community

- level PM 2.5 

### exposures over multiple years (i.e., long

- term exposures) and community

- level annual mortality rates that have been adjusted for individual l evel risk factors. When choosing between using short

- term studies or cohort studies for estimating mortality benefits, cohort analyses are thought to capture more of the public health impact of exposure to air pollution over time because they account for t he effects of long

- term exposures , as well as some fraction of short

term exposures (Kunzli et al., 2001; NRC, 2002). The N ational R esearch C ouncil (NRC) stated that “it is essential to use the cohort studies in benefits analysis to capture all important e ffects from air pollution exposure” (NRC, 2002, p. 108). The NRC further note d that “ the overall effect estimates may be a combination of effects from long

- term exposure plus some fraction from short

- term exposure. The amount of overlap is unknown” (NRC, 2 002, p. 108

- 9). To avoid double counting, we focus on applying the risk coefficients from the long

- term cohort studies in estimating the mortality impacts of reductions in PM 2.5 

### . Over the last two decades, several studies using “prospective cohort” designs have been published that are consistent with the earlier body of literature. Two prospective cohort studies, often referred to as the Harvard “Six Cities Study” (Dockery et al., 1993; Laden et al., 2006; Lepeule et al., 2012) and the “American Cancer Soci ety” or “ACS study” (Pope et al., 1995; Pope et al., 2002; Pope et al., 2004; Krewski et al., 2009), provide the most extensive analyses of ambient PM 2.5 

### concentrations and mortality. These studies have found consistent relationships between fine particle indicators and premature mortality across multiple locations in the United States. The credibility of these two studies is further enhanced by the fact that the initial published studies (Pope et al., 1995; Dockery et al., 1993) were subject to extensive

r eexamination and reanalysis by an independent team of scientific experts commissioned by the Health Effect s Institute (HEI) and by a Special Panel of the HEI Health Review Committee

### 6

- 40 (Krewski et al., 2000). Publication of studies confirming and extending th e findings of the 1993 Six Cities Study and the 1995 ACS study using more recent air quality data and a longer follow

up period for the ACS cohort provides additional validation of the findings of these original studies (Pope et al., 2002, 2004; Laden et a l., 2006; Krewski et al., 2009; Lepeule et al., 2012). The SAB

- HES also supported using these two cohorts for analyses of the benefits of PM reductions, and concluded, “the selection of these cohort studies as the underlying basis for PM mortality benefit estimates [is] a good choice. These are widely cited, well studied and extensively reviewed data sets” (U.S. EPA

- SAB, 2010a). As both the ACS and Six Cities studies have inherent strengths and weaknesses, we present benefits estimates using relative risk

e stimates from the most recent extended reanalysis of these cohorts (Krewski et al., 2009; Lepeule et al., 2012). Presenting results using both ACS and Six Cities is consistent with other recent RIAs (e.g., U.S. EPA, 2010c, 2011a , 2011c ). The PM ISA conclud es that the ACS and Six Cities cohorts provide the strongest evidence of the association between long

- term PM 2.5 

### exposure and premature mortality with support from a number of additional cohort studies (described below). The extended analyses of the ACS co hort data (Krewski et al., 2009) refined the earlier ACS studies by (a) extending the follow

- up period by 2 years to the year 2000, for a total of 18 years; (b) incorporating almost double the number of urban areas ; (c) addressing confounding by spatial au tocorrelation by incorporating ecological, or community

- level, co

- variates; and (d) performing an extensive spatial analysis using land use regression modeling in two large urban areas. These enhancements make this analysis well

- suited for the assessment o f mortality risk from long

- term PM 2.5 

### exposures for the EPA’s benefits analyses.
In 2009, the SAB

- HES again reviewed the choice of mortality risk coefficients for benefits analysis, concluding that “[t]he Krewski et al. (2009) findings, while informative, have not yet undergone the same degree of peer review as have the aforementioned studies. Thus, the SAB

- HES recommends that EPA not use the Krewski et al. (2009) findings for generating the Primary Estimate” (U.S. EPA

- SAB, 2010a). Since this time, the Kre wski et al. (2009) has undergone additional peer review, which we believe strengthens the support for including this study in this RIA. For example, the PM ISA (U.S. EPA, 2009b) included this study among the key mortality studies. In addition, the risk ass essment supporti ng the PM NAAQS (U.S. EPA,

### 6

- 41 2010 b ) u s ed risk coefficients drawn from the Krewski et al. (2009) study, the most recent reanalysis of the ACS cohort data. The PM risk assessment cited a number of advantages that informed the selection of the K rewski et al. (2009) study as the source of the core effect estimates, including the extended period of observation, the rigorous examination of model forms and effect estimates, the coverage for ecological variables, and the large dataset with over 1.2 mi llion individua ls and 156 MSAs (U.S. EPA, 2010 b ). The CASAC also provided extensive peer review of the PM risk assessment and supported the use of effect estimates from this study (U.S. EPA

- SAB, 2009 a, b , 2010b).
Consistent with the PM risk assessment (U. S. EPA, 2 010b ) which was reviewed by the CASAC (U.S. EPA

- SAB, 2009 a, b ), we use the all

- cause mortality risk estimate based on the random

- effects Cox proportional hazard model that incorporates 44 individual and 7 ecological covariates (RR=1.06, 95% confid ence intervals 1.04

– 1.08 per 10 μg/m 3 

### increase in PM 2.5 

### ). The relative risk estimate (1.06 per 10 μg/m 3 

### increase in PM 2.5 

### ) is identical to the risk estimate drawn from the earlier Pope et al. (2002) study, though the confidence interval around the Krewski et al. (2009) risk estimate is tighter. In the most recent Six Cities study, which was published after the last SAB

- HES review, Lepeule et al. (2012) evaluated the sensitivity of previous Six Cities results to model specifications, lower exposures, and ave raging time using eleven additional years of cohort follow

- up that incorporated recent lower exposures. The authors found significant associations between PM 2.5 

### exposure and increased risk of all

- cause, cardiovascular and lung cancer mortality. The authors also concluded that the C

- R relationship was linear down to PM 2.5 

### concentrations of 8 μg/ m 3 

### and that mortality rate ratios for PM 2.5 

### fluctuated over time, but without clear trends, despite a substantial drop in the sulfate fraction. We use the all

- cause
m ortality risk estimate based on a Cox proportional hazard model that incorporates 3 individual covariates. (RR=1.14, 95% confidence intervals 1.07

– 1.22 per 10 μg/m 3 

### increase in PM 2.5 

### ). The relative risk estimate is slightly smaller than the risk estimate d rawn from Laden et al. ( 2006), with relatively smaller confidence intervals.
Given that monetized benefits associated with PM 2.5 

### are driven largely by reductions in premature mortality, it is important to characterize the uncertainty in this endpoint. In o rder to do

### 6

- 42 so, we utilize the results of an expert elicitation sponsored by the EPA and completed in 2006 (Roman et al., 2008; IEc, 2006) . The results of that expert elicitation can be used as a characterization of uncertainty in the C

- R functions. The co - benefits results derived from expert elicitation is discussed in Appendix 6B (section 6B.4)

. PM 2.5 

### Mortality Effect Coefficients for Infants. In addition to the adult mortality studies described above, several studies show an association between PM exposure and premature mortality in children under 5 years of age. 21 

### The PM ISA states that less evidence is available regarding the potential impact of PM 2.5 

### exposure on infant mortality than on adult mortality and the results of studies in several countri es include a range of findings with some finding significant associations. Specifically, the PM ISA concluded that evidence exists for a stronger effect at the post

- neonatal period and for respiratory

- related mortality, although this trend is not consisten t across all studies. In addition, compared to avoided premature deaths estimated for adult mortality, avoided premature deaths for infants are significantly smaller because the number of infants in the population is much smaller than the number of adults and the epidemiology studies on infant mortality provide smaller risk coefficients associated with exposure to PM 2.5 

### . In 2004, the SAB

- HES noted the release of the WHO Global Burden of Disease Study focusing on ambient air, which cites several recently pub lished time

- series studies relating daily PM exposure to mortality in children (U.S. EPA

- SAB, 2004 ). With regard to the cohort study conducted by Woodruff et al. (1997), the SAB

- HES note d several strengths of the study, including the use of a larger cohort drawn from a large number of metropolitan areas and efforts to control for a variety of individual risk factors in infants (e.g., maternal educational level, maternal ethnicity, parental marital status, and maternal smoking status). Based on these finding s, the SAB

- HES recommended that the EPA incorporate infant mortality into the primary benefits estimate and that infant mortality be evaluated using an impact function developed from the Woodruff et al. (1997) study (U.S. EPA

- SAB, 2004 ).
In 2010, the SAB

- H ES again noted the increasing body of literature relating infant mortality and PM exposure and supported the inclusion of infant mortality in the monetized

21 For the purposes of this analysis, we only calculate benefits for infants age 0

– 1, not all childr en under 5 years old.

### 6

- 43 benefits (U.S. EPA

- SAB, 2010a). The SAB

- HES generally supported the approach of estimating infant mo rtality based on Woodruff et al. (1997) but also noted that a more recent study by Woodruff et al. (2006) continued to find associations between PM 2.5 

### and infant mortality in California. The SAB

- HES also noted, “when PM 10 

### results are scaled to estimate PM 2 .5 

### impacts, the results yield similar risk estimates.” Consistent with The Benefits and Costs of the Clean Air Act 1990 to 2020 (U.S. EPA, 2011a ), we continue to rely on the earlier 1997 study in part due to the national

– scale of the earlier study.
6 .6.3. 4 Hospital Admissions and Emergency Department Visits
W e pool together the incidence estimates using several different studies for many of the hospital admission endpoints. S ome studies have examined the relationship between air pollution and emergency depa rtment (ED) visits. Since most emergency department visits do not result in an admission to the hospital (i.e., most people going to the emergency department are treated and return home), we treat hospital admissions and emergency department visits separat ely, taking account of the fraction of emergency department visits that are admitted to the hospital. Specifically, within the baseline incidence rates, we parse out the scheduled hospital visits from unscheduled ones as well as the hospital visits that or iginated in the emergency department.
The ozone ISA states that studies consistently found positive associations between short

term ozone exposures and asthma and COPD hospital admissions and ED visits, with more limited evidence for pneumonia ( U

. S

. EPA, 2 013a). T he ISA found no evidence of a threshold between short term ozone exposure and respiratory hospital admissions and ED visits

. T he ISA also observes that effect estimates remained robust to copollutants (U

. S

. EPA 2013a). Considering these observation from the ISA and a thorough ly review ing available epidemiological studies, we estimate respiratory hospital admissions (for 65

- 99 y ea r olds) using an effect estimate obtained from Katsouyanni et al. ( 2009 ) and asthma

- related emergency ro om visits (for all ages) using several single

- city studies. Although Katsouyanni et al. ( 2009 ) provides effect estimates specific to the summer season, we adjusted the 1

- h ou r max metric to the

### 6

- 44 equivalent 8

- h ou r max effect estimates. 22 

### The study provides sum mer season single pollutant effect estimates based both on natural and penalized splines. We have re

- evaluated the choice of these models in this final RIA. In contrast to the proposal RIA, where we average d both the penalized and natural spline estimates , we decided to focus on the penalized spline model because it displays a higher degree of precision , thus less potential for random error

. 23 

### W hile Katsouyanni et al. ( 2009 ) include d a set of effect estimates based on co

- pollutant modeling (with PM 10 

### ), but we could not use them because they were based on the full year rather than the

summer season. A number of studies are available to model respiratory ED visits. Because we do not yet have the information needed to value this endpoint , we focused on the na rrower category of asthma

- related ED visits We used a set of single city studies together with random

- effects pooling to generate a single pooled estimate. The set of single city studies used in this calculation include: Peel et al. (2005) and Sarnat et a l. (2013) both for Atlanta, Wilson et al. (2005) and Mar and Koenig (2009) for Seattle, Wilson et al. (2005) for Portland , Maine, , Ito et al. (2007) for New York City, and Glad et al. (2012) for Pittsburgh. We note that of these single city studies, only t he Ito et al. (2007) study included a co

- pollutant model (for PM 2.5 

### ). 24 

### In addition, two of the studies required adjustments to reflect the 8

- h ou r max air metric. Specifically, Glad et al. (2012) u s es the 1

- h ou r max air metric, while Sarnat et al. ( 2013 ) u s ed the 24

- h ou r average metric. Each required the use of air metric ratios to adjust their betas. In generating a single pooled benefit estimate for this endpoint, we used random/fixed effects pooling to combine estimates across these single city studies. The two main groups of hospital admissions estimated in this analysis for PM 2.5 

### are: respiratory admissions and cardiovascular admissions. There is not sufficient evidence linking

22 Given that Katsouyanni et al. (2009) included a larger number of cities (14), rather than constructing an air metric

adjustment ratio based on this set of urban study areas, we used a national ratio to adjust effect estimates to rep resent the 8

- hour metric. 23 We evaluated the impact from this change (i.e., use of the penalized spline model alone as contrasted with an average of estimates generated using both the natural and penalized splines), and this change made a negligible (<<1% ) difference in monetized ozone benefits due to the similarity in effect coefficients between the two models. 24 While we have included co

- pollutant models as sensitivity analyses for mortality, we did not include separate co

pollutant models for any of th e morbidity endpoints as sensitivity analyses because morbidity endpoint represent a small fraction of the total monetized benefits. .

### 6

- 45 PM 2.5 

### with other types of hospital admissions. Both asthma

- and cardiovascul ar - related visits have been linked to exposure to PM 2.5 

### in the United States, though as we note below, we are able to assign an economic value to asthma

- related events only. To estimate the effects of PM 2.5 

### air pollution reductions on asthma

- related ER vis its, we use the effect estimate s from stud ies of children 18 and under by Mar et al. (2010), Slaughter et al. (2005), and Glad et al. (2012). The first two studies examined populations 0 to 99 in Washington, while Glad et al. examined populations 0

- 99 in P ittsburgh, Pennsylvania. Mar and colleagues perform their study in Tacoma, while Slaughter and colleagues base their study in Spokane. We apply random/fixed effects pooling to combine evidence across these two studies.
To estimate avoided incidences of car d iovascular hospital admissions associated with exposure to PM 2.5 

### , we used studies by Moolgavkar (2000), Zanobetti et al. (2009), Peng et al. (2008, 2009) and Bell et al. (2008). Only Moolgavkar (2000) provided a separate effect estimate for adults 20 to 6 4, while the remainder estimate risk among adults over 64. Total cardiovascular hospital admissions are the sum of the pooled estimate for adults over 64 and the single study estimate for adults 20 to 64. To avoid double

- counting benefits from reductions in myocardial infarctions when applying the impact function for cardiovascular hospital admissions, we first adjusted the baseline cardiovascular hospital admissions to remove admissions for myocardial infarctions. We applied equal weights pooling to the m ulti

- city studies assessing risk among adults over 64 because these studies already incorporated pooling across the city

- level estimates. One potential limitation of our approach is that while the Zanobetti et al. (2009) study assesses all cardiovascular r isk, the Bell et al. (2008), and Peng et al. (2008, 2009) studies estimate a subset of cardiovascular hospitalizations as well as certain cerebro

- and peripheral

- vascular diseases. To address the potential for the pooling of these four studies to produce a biased estimate, we match the pooled risk estimate with a baseline incidence rate that excludes cerebro

and peripheral

- vascular disease. An alternative approach would be to use the Zanobetti et al. (2009) study alone, though this would prevent us from dr awing upon the strengths of the three multi

- city studies. To estimate avoided incidences of respiratory hospital admissions associated with PM 2.5 

### , we used a number of studies examining total respiratory hospital admissions as well as asthma and chronic lun g disease. We estimated impacts among three age groups: adults 65 and over,

### 6

- 46 adults 18 to 64 and children 0 to 17. For adults over 65, the multi

- city studies by Zanobetti et al. (2009) and Kloog et al. (2012) provide effect coefficients for total respirator y hospital admissions. We pool these two studies using equal weights. Moolgavkar et al. (2003) examines PM 2.5 

### and chronic lung disease hospital admissions (not including asthma) in Los Angeles, California among adults 18 to 64. For children 0 to 18, we poo l two studies using random/fixed effects. The first is Babin et al. (2007), which assessed PM 2.5 

### and asthma hospital admissions in Washington, DC among children 1 to 18; we adjusted the age range for this study to apply to children 0 to 18. The second is S heppard et al. (2003), which assessed PM 2.5 

### and asthma hospitalizations in Seattle, Washington, among children 0 to 18.
6 .6.3. 5 Acute Health Events A number of acute health effects not requiring hospitalization are also associated with exposure to ozone and PM 2.5 

### . The sources for the effect estimates used to quantify these endpoints are described below.
Asthma exacerbations. For this RIA, we followed the SAB

- HES recommendations regarding asthma exacerbations in developing the core estimate (U.S. EPA

- SAB, 2004a). Although certain studies of acute respiratory events characterize these impacts among only asthmatic populations, others consider the full population, including both asthmatics and non

asthmatics. For this reason and to avoid double counting impac ts , incidence estimates derived from studies focused only on asthmatics cannot be added to estimates from studies that consider the full population. To prevent such double

- counting, we estimated the exacerbation of asthma among children and excluded adults from the calculation. Asthma exacerbations occurring in adults are assumed to be captured in the general population endpoints , such as work loss days and minor restricted activity days (MRADs). Finally, we note the important distinction between the exacer bation of asthma among asthmatic populations and the onset of asthma among populations not previously suffering from asthma

. I n this RIA, we quantify the exacerbation of asthma among asthmatic populations and not the onset of new cases of asthma.
Based on advice from the SAB

- HES ( U.S. EPA

- SAB 2004a), regardless of the age ranges included in the source epidemiology studies, we extend the applied population to ages 6

### 6

- 47 to 18, reflecting the common biological basis for the effect in children in the broader age g roup. This age range expansion is also supported by NRC (2002, pp. 8, 116). To characterize asthma exacerbations in children from exposure to ozone, for the proposed RIA, we selected three multi

- city studies (Mortimer et al., 2002 ; O’Connor et al., 2008 ; Schildcrout et al., 2006). All three of these studies required the application of air metric ratios to adjust effect estimates to represent the 8

- hour max metric. 25 

### In this final RIA, following a final review of our technical approach, we removed O’Connor (2008) due to concerns about potential exposure measurement error and residual confounding from meteorological variables in its 19

- day lag structure. Current evidence in the ozone ISA suggests a more immediate effect with exposure to ozone for respiratory

- related effects, such as hospital admissions and emergency department visits, with additional supporting evidence from studies of respiratory symptoms (U.S. EPA, 2013a). Consequently, the 19

- day lag structure reflected in the O’Connor (2008) study is not a s strongly supported by the evidence as the shorter lag structures associated with the other two asthma exacerbation studies. 26 

### In generating the asthma exacerbation estimate, we pool estimates from Mortimer et al. (2012) and Schildcrout et al. (2006) using a random/fixed effects approach applied within BenMAP

- CE (U.S. EPA, 2015a). 27 

### To characterize asthma exacerbations in children from exposure to PM 2.5 

### , we selected two studies (Ostro et al., 2001; Mar et al., 2004) that followed panels of asthmatic children

. Ostro et al. (2001) followed a group of 138 African

- American children in Los Angeles for 13 weeks, recording daily occurrences of respiratory symptoms associated with asthma exacerbations (e.g., shortness of breath, wheeze, and cough). This study found a statistically significant association between exposure to PM 2.5 

### , measured as a 12

- hour average, and the daily prevalence of shortness of breath and wheeze endpoints. Although the association was not statistically significant for cough, the results were st ill positive and close to significance;

25 Mortimer et al. (2002) had effect estimates based on an 8

- hour mean metric, O’Connor et al. (2008) used a 24

-

hour metr ic, and Schildcrout et al. (2006) was based on a 1

- hour max metric. 26 We evaluated the impact from excluding O’Connor (2008) in estimating asthma exacerbations, and the change made a negligible ( < 1%) difference in monetized ozone benefits. Furthermore, the combined changes in estimating asthma exacerbations and respiratory hospital admissions resulted in only a 1% change in monetized ozone benefits compared to the approaches used in the proposal RIA (U.S. EPA, 2014c). 27 BenMAP

- CE applies a chi

- squared test to determine whether a fixed or random effect pooling approach should be used (for additional detail see U.S. EPA (2015b), p. 206).

### 6

- 48 consequently, we decided to include this endpoint, along with shortness of breath and wheeze, in generating asthma exacerbation benefits

. 28 

### Mar et al. (2004) studied the effects of various size fractions of particulat e matter on respiratory symptoms of adults and children with asthma, monitored over many months. The study was conducted in Spokane, Washington, a semi

- arid city with diverse sources of particulate matter. Data on respiratory symptoms and medication use we re recorded daily by the study’s subjects, while air pollution data was collected by the local air agency and Washington State University. Subjects in the study consisted of 16 adults

— the majority of whom participated for over a year

— and nine children, all of whom were studied for over eight months. Among the children, the authors found a strong association between cough symptoms and several metrics of particulate matter, including PM 2.5 

### . However, the authors found no association between respiratory symptom s and exposure to PM for any metric in adults. Mar et al. therefore concluded that the discrepancy in results between children and adults was due either to the way in which air quality was monitored, or a greater sensitivity of children than adults to incr eased levels of PM air pollution. We employed the following pooling approach in combining estimates generated using effect estimates from the two studies to produce a single estimate for PM

- related asthma exacerbation incidence. First, we used random/fixed effects pooling to combine the Ostro and Mar estimates for shortness of breath and cough. Next, we pooled the Ostro estimate of wheeze with the pooled cough and shortness of breath estimates to derive an overall estimate of asthma exacerbation in children

. Acute Respiratory Symptoms. We estimate one type of acute respiratory symptom related to ozone exposure. Minor Restricted Activity Days ( MRADs ) result when individuals reduce most usual daily activities and replace them with less strenuous activities or rest, yet not to the point of missing work or school. For example, a mechanic who would usually be doing

28 The random effects pooling used in BenMAP (U.S. EPA, 2015b) to generate a single estimate for asthma exacerbations weights estimates based on variance, and consequently those estimates with less statistical significance and wider confidence intervals will be down

- weighted in generating the total aggregated estimate.

### 6

- 49 physical work most of the day will instea d spend the day at a desk doing paper work and phone work because of difficulty breathing or chest pain. For ozone, we modeled MRADs using Ostro and Rothschild (1989). This study provides

a co - pollutant model (with PM 2.5 

### ) based on a national sample of 18

- 6 4 y ea r olds. The original study used a 24

- h ou r average metric and included control for PM 2.5 

### , which necessitated the use of an air metric ratio to convert the effect estimate to an 8

- h ou r max equivalent. We estimate three types of acute respiratory sympto ms related to PM 2.5 

### exposure: lower respiratory symptoms, upper respiratory symptoms, and MRAD. Incidences of lower respiratory symptoms (e.g., wheezing, deep cough) in children aged 7 to 14 were estimated for PM 2.5 

### using an effect estimate from Schwartz a nd Neas (2000). Incidences of upper respiratory symptoms in asthmatic children aged 9 to 11 are estimated for PM 2.5 

### using an effect estimate developed from Pope et al. (1991). Because asthmatics have greater sensitivity to stimuli (including air pollution) , children with asthma can be more susceptible to a variety of upper respiratory symptoms (e.g., runny or stuffy nose; wet cough; and burning, aching, or red eyes). Research on the effects of air pollution on upper respiratory symptoms has focused on effec ts in asthmatics.
The effect of PM 2.5 

### on MRAD was estimated using an effect estimate derived from Ostro and Rothschild (1989). More recently published literature examining the relationship between short

- term PM 2.5 

### exposure and acute respiratory symptoms w as available in the PM ISA (U.S. EPA, 2009 b ), but proved to be unsuitable for use in this benefits analysis. In particular, the best available study (Patel et al., 2010) specified a population aged 13

– 20, which overlaps with the population in which we asse s s asthma exacerbation. As we describe in detail below, to avoid the chance of double

- counting impacts, we do not estimate changes in acute respiratory symptoms and asthma exacerbation among populations of the same age. School loss days (absences). Childr en may be absent from school due to respiratory or other acute diseases caused, or aggravated by, exposure to air pollution. Several studies have found a significant association between ozone levels and school absence rates. We use two studies (Gilliland e t al., 2001; Chen et al., 2000) to estimate changes in school absences resulting from changes in ozone levels. The Gilliland et al. study estimated the incidence of new periods of absence, while the Chen et al. study examined daily absence rates. We conver ted the Gilliland

### 6

- 50 et al. estimate to days of absence by multiplying the absence periods by the average duration of an absence. We estimated 1.6 days as the average duration of a school absence, the result of dividing the average daily school absence rate f rom Chen et al. (2000) and Ransom and Pope (1992) by the episodic absence duration from Gilliland et al. (2001). Thus, each Gilliland et al. period of absence is converted into 1.6 absence days. Following advice from the National Research Council (NRC, 200 2), we calculated reductions in school absences for the full population of school age children, ages five to 17. This is consistent with recent peer

- reviewed literature on estimating the impact of ozone exposure on school absences (Hall et al., 2003). We e stimated the change in school absences using both Chen et al. (2000) and Gilliland et al. (2001) and then pooled the results using the random effects pooling procedure. Acute Bronchitis. Approximately 4% of U.S. children between the ages of 5 and 17 experience episodes of acute bronchitis annually (ALA, 2002). Acute bronchitis is characterized by coughing, chest discomfort, slight fever, and extreme tiredness, lasting for a number of days. According to the MedlinePlus medical encyclopedia, 29 

### with the e xception of cough, most acute bronchitis symptoms abate within 7 to 10 days. Incidence of episodes of acute bronchitis in children between the ages of 5 and 17 were estimated using an effect estimate developed from Dockery et al. (1996). Work Loss Days. Health effects from air pollution can also result in missed days of work (either from personal symptoms or from caring for a sick family member). Days of work lost resulting from exposure to PM 2.5 

### were estimated using an effect estimate developed from Ostr o (1987). Ostro (1987) estimated the impact of PM 2.5 

### on the incidence of work loss days in a national sample of the adult working population, ages 18 to 65 living in metropolitan areas. Ostro reported that two

- week average PM 2.5 

### levels were significantly l inked to work loss days, but there was some year

- to

- year variability in the results.
6.6.3

. 6 Nonfatal Acute Myocardial Infarctions (AMI) (Heart Attacks) Nonfatal heart attacks have been linked with short

- term exposures to PM 2.5 

### in the United States (Must afić et al., 2012; Peters et al., 2001; Sullivan et al., 2005; Pope et al., 2006;

29 See http://www.nlm.nih.gov/me dlineplus/ency/article/001087.htm, accessed April 2012.

### 6

- 51 Zanobetti and Schwartz, 2006; Zanobetti et al., 2009) and other countries (Poloniecki et al., 1997; Barnett et al., 2006; Peters et al., 2005). In previous health impact asse ssments, we have relied upon a study by Peters et al. (2001) as the basis for the impact function estimating the relationship between PM 2.5 

### and nonfatal heart attacks. The Peters et al. (2001) study exhibits a number of strengths. In particular, it include s a robust characterization of populations experiencing AMIs. The researchers interviewed patients within 4 days of their AMI events and, for inclusion in the study, patients were required to meet a series of criteria including minimum kinase levels, an id entifiable onset of pain or other symptoms , and the ability to indicate the time, place and other characteristics of their AMI pain in an interview. Since the publication of Peters et al. (2001), a number of other single and multi

- city studies have appeare d in the literature , including: Sullivan et al. (2005), which considered the risk of PM 2.5 

### - related hospitalization for AMIs in King County, W ashington ; Pope et al. (2 006), based in Wasatch Range, Utah ; Zanobetti and Schwartz (2006), based in Boston, M assac husetts ; and, Zanobetti et al. (2009), a multi

- city study of 26 U.S. communities. Each of these single and multi

- city studies, with the exception of Pope et al. (2006), measure AMIs using hospital discharge rates. Conversely, the Pope et al. (2006) study i s based on a large registry with angiographically characterized patients

— arguably a more precise indicator of AMI. Because the Pope et al. (2006) study reflected both myocardial infarctions and unstable angina, this produces a more comprehensive estimate o f acute ischemic heart disease events than the other studies. However, unlike the Peters study (Peters et al., 2006), Pope and colleagues did not measure the time of symptom onset, and PM 2.5 

### data were not measured on an hourly basis. As a means of recogniz ing the strengths of the Peters study while also incorporating the newer evidence found in the four single and multi

- city studies, we present a range of AMI estimates. The upper end of the range is calculated using the Peters study , while the lower end of the range is the result of an equal

- weights pooling of these four newer studies. It is important to note that when calculating the incidence of nonfatal AMI, the fraction of fatal heart attacks is subtracted to ensure that there is no double

- counting with premature mortality estimates. Specifically, we apply an adjustment factor in the C

- R function to reflect the probability of surviving a heart attack. Based on recent data from the Agency for Healthcare Research and Quality’s Healthcare Utilization Project National Inpatient Sample database (AHRQ, 2009), we

### 6

- 52 identified death rates for adults hospitalized with acute myocardial infarction stratified by age (e.g., 1.852% for ages 18

– 44, 2.8188% for ages 45

– 64, and 7.4339% for ages 65+). These rates show a clear downward trend over time between 1994 and 2009 for the average adult and replace the 7% survival rate previously applied across all age groups from Rosamond et al. (1999).
6.6.3

. 7 Worker Productivity The EPA last quantified the effect of ozone on outdoor agricultural worker productivity in the final Regulatory Impact Analysis accompanying the Transport Rule (U.S. EPA, 2011c ) ; that analysis reported the value of worker productivity in the core benefits analysis . That RIA applied information reported in Crocker and Horst (1981), which observed that reducing ozone by 10 percent translated to a 1.4 increase in income among outdoor citrus workers. The RIA accompanying the proposed Ozone NAAQS (US EPA, 2014 c ) noted that, due in part to the vintage of the data used in this study, the Agency omitted this analysis in subsequent RIA’s. A recent study by Graff Zivin, and Neidell (2012) provides new evidence of the effect of ozone exposure on productivity among outdoor agricultural workers which we use in a supplem ental analysis. Specifically, the study combined individual

- level daily harvest rates for outdoor agricultural workers on a 500

- acre farm in the Central Valley of California, with ground

level ozone data. The authors observed that a 10 ppb increase in work

- day ozone concentrations (from 6 :00 am to 3 :00 pm) was associated with a 5.5% decrease in productivity among outdoor agricultural workers on a given day. Additional detail on the worker productivity supplemental analysis (including results ) is presented i n Appendix 6B (section 6B. 8 ).
6 .6.3. 8 Unquantified Human Health Effects A ttaining a revised ozone NAAQS would reduce emissions of NOx and VOC. Although we have quantified many of the health benefits associated with reducing exposure to ozone and PM 2.5 

### , as shown in Table 6

- 3, we are unable to quantify the health benefits associated with reducing direct exposure to NO 2 

### or VOC because of the absence of air quality modeling data for these pollutants. In addition, we are unable to quantify the effects of VOC emission reductions on ambient PM 2.5 

### and associated health effects. Below we provide a qualitative description of these health benefits. In general, previous analyses have shown that the monetized value of these

### 6

- 53 additional health benefits is much smaller t han ozone and PM 2.5 

### - related benefits (U.S. EPA, 2010a, 2010c, 2010d). Epidemiological researchers have associated NO 2 

### exposure with adverse health effects in numerous toxicological, clinical and epidemiological studies, as described in the Integrated Scie nce Assessment for Oxides of Nitrogen

— Health Criteria (NO 2 

### ISA) (U.S. EPA, 2008b). The NO 2 

### ISA provides a comprehensive review of the current evidence of health and environmental effects of NO 2 

### . The NO 2 

### ISA concluded that the evidence “is sufficient to infer a likely causal relationship between short

- term NO 2 

### exposure and adverse effects on the respiratory system.” These epidemiologic and experimental studies encompass a number of endpoints including

eme rgency department visits and hospitalizations, respiratory symptoms, airway hyperresponsiveness, airway inflammation, and lung function. Effect estimates from epidemiologic studies conducted in the United States and Canada generally indicate a 2

– 20 percent increase in risks for ED visits and hospital admissions and higher risks for respiratory symptoms. The NO 2 

### ISA concluded that the relationship between short

- term NO 2 

### exposure and premature mortality was “suggestive but not sufficient to infer a causal rel ationship” because it is difficult to attribute the mortality risk effects to NO 2 

### alone. Although the NO 2 

### ISA stated that studies consistently reported a relationship between NO 2 

### exposure and mortality, the effect was generally smaller than that for other pollutants such as PM. We did not quantify these benefits due to data constraints.
6 .6.4 Economic Valuation Estimates Reductions in ambient concentrations of air pollution generally lower the risk of future adverse health effects for a large population. Th erefore, the appropriate economic measure is willingness

- to

- pay ( WTP ) for changes in risk of a health effect rather than WTP for a health effect that would occur with certainty (Freeman, 1993). Epidemiological studies generally provide estimates of the rel ative risks of a particular health effect that is avoided because of a reduction in air pollution. We converted those changes in risk to units of avoided statistical incidence for ease of presentation. We calculated the value of avoided statistical inciden ces by dividing individual WTP for a risk reduction by the related observed change in risk. For example, suppose a measure is able to reduce the risk of premature mortality from 2 in 10,000 to 1 in 10,000 (a reduction of 1 in 10,000). If individual WTP for this risk reduction is $100, then

### 6

- 54 the WTP for an avoided statistical premature mortality amounts to $1 million ($100/0.0001 change in risk). Using this approach, the size of the affected population is automatically taken into account by the number of inci dences predicted by epidemiological studies applied to the relevant population. The same type of calculation can produce values for statistical incidences of other health endpoints. WTP estimates generally are not available for some health effects, such as hospital admissions. In these cases, we instead used the cost of treating or mitigating the effect to estimate the economic value. Cost

- of

- illness ( COI ) estimates generally (although not necessarily in all cases) understate the true value of reducing the risk of a health effect, because they reflect the direct expenditures related to treatment, but not the value of avoided pain and suffering (Harrington and Portney, 1987; Berger, 1987). We provide unit values for health endpoints (along with information o n the distribution of the unit value) in Table 6

- 1 0 . All values are in constant year 2011$, adjusted for growth in real income for WTP estimates out to 2024 using projections provided by Standard and Poor’s, which is discussed in further detail below. 30 

### Eco nomic theory argues that WTP for most goods (such as environmental protection) will increase if real income increases. Several of the valuation studies used in this analysis were conducted in the late 1980s and early 1990s, and we are in the process of rev iewing the literature to update these unit values. The discussion below provides additional details on valuing specific PM 2.5 

### - related related endpoints.
6 .6.4.1 Mortality Valuation Following the advice of the SAB’s Environmental Economics Advisory Committe e (SAB

- EEAC), the EPA currently uses the value of statistical life (VSL) approach in calculating the core estimate of mortality benefits, because we believe this calculation provides the most reasonable single estimate of an individual’s willingness to tra de off money for reductions in mortality risk (U.S. EPA

- SAB, 2000). The VSL approach is a summary measure for the value of

30 Income growth projections are only currently available in BenMAP through 2024, so both the 2025 and 2038

estimates use income growth through 2024 and are therefore likely underestimates. Currently, BenMAP does not have an inflation adjustment to 2011$. We ran BenMAP for a currency year of 2010$ and calculated the benefit

per - ton estimates in 2010$. We then adjusted the resulting benefit

- per - ton estimates to 2011$ using the Consumer Price Index (CPI

- U, all items)

. This approach slightly underestimates the inflation for medical index and wage index between 2010 and 2011 , which affects COI estimates and wage

- based estimates

.

### 6

- 55 small changes in mortality risk experienced by a large number of people. For a period of time (2004

– 2008), the Office of Air and Radi ation (OAR) valued mortality risk reductions using a VSL estimate derived from a limited analysis of some of the available studies. OAR arrived at a VSL using a range of $1 million to $10 million (2000$) consistent with two meta

- analyses of the wage - risk l iterature. The $1 million value represented the lower end of the interquartile range from the Mrozek and Taylor (2002) meta

- analysis of 33 studies. The $10 million value represented the upper end of the interquartile range from the Viscusi and Aldy (2003) meta

analysis of 43 studies. The mean estimate of $5.5 million (2000$) was also consistent with the mean VSL of $5.4 million estimated in the Kochi et al. (2006) meta

- analysis. However, t he EPA neither changed its official guidance on the use of VSL in rul e - makings nor subjected the interim estimate to a scientific peer

- review process through SAB or other peer

- review group.6-56 

### Table 6
- 10 . Unit Values for Economic Valuation of Health Endpoints (2011$) a

Health Endpoint Central Estimate of Value Per Statistical Incidence Derivation of Distributions of Estimates 1990 Income Level 2024 Income Level Premature Mortality (Value of a Statistical Life) $8,300,000 $10,000,000 The EPA currently recommends a central VSL of $4.8 million (1990$, 1990 income) based on a Weibull distribution fitted to 26 published VSL estimates (5 contingent valuation and 21 labor market studies). The underlying studies, the distribution parameters, and other useful information are available in Appendix B o f the EPA’s Guidelines for Preparing Economic Analyses (U.S. EPA, 2010e). Nonfatal Myocardial Infarction (heart attack) 3% discount rate Age 0

– 24 Age 25

– 44 Age 45

– 54 Age 55

– 64 Age 65 and over
7% discount rate Age 0

– 24 Age 25

– 44 Age 45

– 54 Age 55

– 64 Age 65 and over $100,000 $110,000 $120,000 $210,000 $100,000

$100,000 $110,000 $120,000 $190,000 $100,000 $100,000 $110,000 $120,000 $210,000 $100,000

$100,000 $110,000 $120,000 $190,000 $100,000 No distributional information available. Age

- specific cost

- of

- illness values reflect lost earnings and direct medical costs over a 5

- year period following a nonfatal MI. Lost earnings estimates are based on Cropper and Krupnick (1990). Direct medical costs are based on simple average of estimates from Russell et al. (1998) and Wittels et al. (1990). Lost earnings: Cropper and Krupnick (1990). Present discounted value of 5 years of lost earnings in 2000$: age of onset: at 3% at 7% 25

– 44 $9,000 $8,000 45

– 54 $13,000 $12,000 55

– 65 $77,000 $69,000 D irect medical expenses (2000$): An average of: 1. Wittels et al. (1990) ($100,000

— no discounting) 2. Russell et al. (1998), 5

- year period ($22,000 at 3% discount rate; $21,000 at 7% discount rate) 

### (continued)6-57 

### Table 6
- 10 . Unit Values for Economic Valuation of Health Endpoints (2011$) a 

### (continued)

Health Endpoint Central Estimate of Value Per Statistical Incidence Derivation of Distributions of Estimates 2000 Income Level 2024 Income Level Hospital Admissions Chronic Lung Disease (18

– 64) $22,000 $22,000 No distributional information available. The COI estimates (lost earnings plus direct medical costs) are based on ICD

- 9 code

- level information (e.g., average hospital care costs, average length of hospital stay, and wei ghted share of total chronic lung illnesses) reported in Agency for Healthcare Research and Quality (2007) (www.ahrq.gov). Asthma Admissions (0

– 64) $16,000 $16,000 No distributional information available. The COI estimates (lost earnings plus direct medical costs) are based on ICD

- 9 code

- level information (e.g., average hospital care costs, average length of hospital stay, and weighted share of total asthma category illnesses) reported in Agency for Healthcare Research and Quality (2007) (www.ahrq.gov ). All Cardiovascular Age 18

– 64 Age 65

– 99 $44,000 $42,000 $44,000 $42,000 No distributional information available. The COI estimates (lost earnings plus direct medical costs) are based on ICD

- 9 code

- level information (e.g., average hospital care costs, average length of hospital stay, and weighted share of total cardiovascular category illnesses) reported in Agency for Healthcare Research and Quality (2007) (www.ahrq.gov). All respiratory (ages 65+) $37,000 $37,000 No distributions available. The COI point estimates (lost earnings plus direct medical costs) are based on ICD

- 9 code level information (e.g., average hospital care costs, average length of hospital stay, and weighted share of total respiratory category illnesses) reported in Agency for Heal thcare Research and Quality, 2007 (www.ahrq.gov). Emergency Department Visits for Asthma $440 $440 No distributional information available. Simple average of two unit COI values (2000$): (1) $310, from Smith et al. (1997) and (2) $260, from Stanford et al . (1999). 

### (continued)6-58 

### Table 6
- 10 . Unit Values for Economic Valuation of Health Endpoints (2011$) a 

### (continued)

Health Endpoint Central Estimate of Value Per Statistical Incidence Derivation of Distributions of Estimates 2000 Income Level 2024 Income Level Respiratory Ailments Not Requiring Hospitalization Upper Respiratory Symptoms (URS) $35 $32 Combinations of the three symptoms for which WTP estimates are available that closely match those listed by Pope et al. result in seven different “symptom clusters,” each describing a “type” of URS. A dollar value was derived for each type of URS, using mid

- range estimates of WTP (IEc, 1994) to avoid each symptom in the cluster and assuming additivity of WTPs. In the absence of information surrounding the fr equency with which each of the seven types of URS occurs within the URS symptom complex, we assumed a uniform distribution between $9.2 and $43 (2000$). Lower Respiratory Symptoms (LRS) $22 $21 Combinations of the four symptoms for which WTP estimates are available that closely match those listed by Schwartz et al. result in 11 different “symptom clusters,” each describing a “type” of LRS. A dollar value was derived for each type of LRS, using mid

- range estimates of WTP (IEc, 1994) to avoid each symptom in the cluster and assuming additivity of WTPs. The dollar value for LRS is the average of the dollar values for the 11 different types of LRS. In the absence of information surrounding the frequency with which each of the 11 types of LRS occurs within the L RS symptom complex, we assumed a uniform distribution between $6.9 and $25 (2000$). Asthma Exacerbations $56 $60 Asthma exacerbations are valued at $45 per incidence, based on the mean of average WTP estimates for the four severity definitions of a “bad asthma day,” described in Rowe and Chestnut (1986). This study surveyed asthmatics to estimate WTP for avoidance of a “bad asthma day,” as defined by the subjects. For purposes of valuation, an asthma exacerbation is assumed to be equivalent to a day in which asthma is moderate or worse as reported in the Rowe and Chestnut (1986) study. The value is assumed to have a u niform distribution between $16 and $71 (2000$). 

### (continued)6-59 

### Table 6
- 10 . Unit Values for Economic Valuation of Health Endpoints (2011$) a 

### (continued)
Health Endpoint Central Estimate of Value Per Statistical Incidence Derivation of Distributions of Est imates 2000 Income Level 2024 Income Level Respiratory Ailments Not Requiring Hospitalization (continued) Acute Bronchitis $460 $500 Assumes a 6

- day episode, with the distribution of the daily value specified as uniform with the low and high values based on those recommended for related respiratory symptoms in Neumann et al. (1994). The low daily estimate of $10 is the sum of the mid

- ra nge values recommended by IEc (1994) for two symptoms believed to be associated with acute bronchitis: coughing and chest tightness. The high daily estimate was taken to be twice the value of a minor respiratory restricted

- activity day, or $110 (2000$). Work Loss Days (WLDs) Variable (U.S. median = $150) Variable (U.S. median = $150) No distribution available. Point estimate is based on county

- specific median annual wages divided by 52 and then by 5

— to get median daily wage. U.S. Year 2000 Census, compi led by Geolytics, Inc. (Geolytics, 2002) School Loss Days $98 $98 No distribution available. Based on (1) the probability that, if a school child stays home from school, a parent will have to stay home from work to care for the child, and (2) the value of the parent’s lost productivity. Minor Restricted Activity Days (MRADs) $64 $68 Median WTP estimate to avoid one MRAD from Tolley et al. (1986). Distribution is assumed to be triangular with a minimum of $22 and a maximum of $83, with a most likely value of $52 (2000$). Range is based on assumption that value should exceed WTP for a single mild symptom (the highest estimate for a single symptom

— for eye irritation

— is $16) and be less than that for a WLD. The triangular distribution acknowledges that the act ual value is likely to be closer to the point estimate than either extreme.

a All estimates are rounded to two significant digits. Unrounded estimates in 2000$ are available in the Appendix J of the BenM AP user manual ( U.S. EPA, 2015 ). Income growth projections are only currently available in BenMAP through 2024, so both the 2025 and 2038 estimates use income growth through 2024 and are therefore likely underestimates. Currently, BenMAP does not have an inflation adjustment t o 2011$. We ran BenMAP for a currency year of 2010$ and calculated the benefit

- per - ton estimates in 2010$. We then adjusted the resulting benefit

- per - ton estimates to 2011$ using the Consumer Price Index (CPI

- U, all items) . This approach slightly underesti mates the inflation for medical index and wage index between 2010 and 2011 , which affects COI estimates and wage

based estimates

.

### 6

- 60 During this time, the Agency continued work to update its guidance on valuing mortality risk reductions, including commissio ning a report from meta

- analytic experts to evaluate methodological questions raised by the EPA and the SAB on combining estimates from the various data sources. In addition, the Agency consulted several times with the SAB

- EEAC on the issue. With input fro m the meta

- analytic experts, the SAB

- EEAC advised the Agency to update its guidance using specific, appropriate meta

- analytic techniques to combine estimates from unique data sources and different studies, including those using different methodologies (i.e ., wage - risk and stated preference) (U.S. EPA

- SAB, 2007). Until updated guidance is available, the Agency determined that a single, peer

- reviewed estimate applied consistently best reflects the SAB

- EEAC advice it has received. Therefore, the Agency has dec ided to apply the VSL that was vetted and endorsed by the SAB in the Guidelines for Preparing Economic Analyses (U.S. EPA, 2000) 31 

### while the Agency continues its efforts to update its guidance on this issue. This approach calculates a mean value across VSL estimates derived from 26 labor market and contingent valuation studies published between 1974 and 1991. The mean VSL across these studies is $4.8 million (1990$) or $6.3 million (2000$). 32 

### The Agency is committed to using scientifically sound, appropriatel y reviewed evidence in valuing mortality risk reductions and has made significant progress in responding to the SAB

- EEAC’s specific recommendations. In the process, the Agency has identified a number of important issues to be considered in updating its mor tality risk valuation estimates. These are detailed in a white paper on “Valuing Mortality Risk Reductions in Environmental Policy,” which underwent review by the SAB

- EEAC. A meeting with the SAB on this paper was held on March 14, 2011 and formal recommen dations were transmitted on July 29, 2011 (U.S. EPA

- SAB, 2011). EPA is taking SAB’s recommendations under advisement. The economics literature concerning the appropriate method for valuing reductions in premature mortality risk is still developing. The ado ption of a value for the projected reduction in the risk of premature mortality is the subject of continuing discussion within the economics and

31 In the updated Guidelines for Preparing Economic Analyses (U.S. EPA, 2010e), EPA retained the VSL endorsed

by the SAB with the understanding that further updates to the mortality risk valuation guidance would be forthcoming in the near future. 32 In this analysis, we adjust the VSL to account for a different currency year (2011$) and to account for income

growth to 2024. After applying these adjustments to the $6.3 million value, the VSL is $10 million.

### 6

- 61 public policy analysis communit ies . The EPA strives to use the best economic science in its analyses. Given the mixed theoretical finding and empirical evidence regarding adjustments to VSL for risk and population characteristics (e.g., Smith et al., 2004; Alberini et al., 2004; Aldy and Viscusi, 2008), we use a single VSL for all reductions in mortality risk.
Alth ough there are several differences between the studies the EPA uses to derive a VSL estimate and the ozone and PM 2.5 

### air pollution context addressed here, those differences in the affected populations and the nature of the risks imply both upward a nd downw ard adjustments. Table 6

- 11 lists some of these differences and the expected effect on the VSL estimate for air pollution

- related mortality. In the absence of a comprehensive and balanced set of adjustment factors, the EPA believes it is reasonable to cont inue to use the $4.8 million (1990$) value adjusted for inflation and income growth over time while acknowledging the significant limitations and uncertainties in the available literature.
Table 6
- 11 . Influence of Applied VSL Attributes on the Size of the Economic Benefits of Reductions in the Risk of Premature Death (U.S. EPA, 2006a) Attribute Expected Direction of Bias Age Uncertain, perhaps overestimate Life Expectancy/Health Status Uncertain, perhaps overestimate Attitudes Toward Risk Underestimate Income Uncertain Voluntary vs. Involuntary Uncertain, perhaps underestimate Catastrophic vs. Protracted Death Uncertain, perhaps underestimate



### The SAB

- EEAC has reviewed many potential VSL adjustments and the state of the economics literature. The SAB

- E EAC advised the EPA to “continue to use a wage

- risk

- based VSL as its primary estimate, including appropriate sensitivity analyses to reflect the uncertainty of these estimates,” and that “the only risk characteristic for which adjustments to the VSL can be made is the timing of the risk” (U.S. EPA

- SAB, 2000). In developing our core estimate of the benefits of premature mortality reductions, we followed this advice.

For PM 2.5 

### - related premature mortality, we assume that there is a “cessation” lag between exp osures and the total realization of changes in health effects. For PM 2.5 

### , we assume d that some of the incidences of premature mortality related to PM 2.5 

### exposures occur in a distributed fashion over the 20 years following exposure and discounted over the period between exposure and

### 6

- 62 premature mortality. Although the structure of the lag is uncertain, the EPA follows the advice of the SAB

- HES to assume a se gmented lag structure characterized by 30 percent of mortality reductions in the first year, 50 percent over years 2 to 5, and 20 percent over the years 6 to 20 after the reduction in PM 2.5 

### (U.S. EPA

- SAB, 2004c). To take this into account in the valuation of reductions in premature mortality, we discount the value of premature mortality occurring in future years using rates of 3 percent and 7 percent. 33 

### Changes in the cessation lag assumptions do not change the total number of estimated deaths but rather the timing of those deaths. As such, the monetized PM 2.5 

### co - benefits using a 7 percent discount rate are only approximately 10 percent less than the monetized benefits using a 3 percent discount rate. Further discussion of this topic appears in the EPA’s Guid elines for Preparing Economic Analyses (U.S. EPA, 2010e). For ozone, we acknowledge substantial uncertainty associated with specifying the lag for long

- term respiratory mortality. As stated earlier, it is this uncertainty related to specifying a lag structure that prevent ed us monetizing these benefits . In the se nsitivity analysis, we include both an assumption of zero lag and the PM lag structure ( i.e., the SAB 20

- year segmented lag). Inclusion of the zero lag reflects consideration of the possibility that the long

- term respiratory mortality estimate primarily ca ptures an accumulation of short

- term mortality effects across the

ozone season. 34 

### The use of the 20

- year segmented lag reflects consideration of advice provided by the SAB

- HES (U

. S

. EPA

- SAB , 2010a), where they state that, “[i]f Alternative e stimates are der ived using cohort mortality evidence, there is no evidence in the literature to support a different cessation lag between ozone and particulate matter. The HES therefore recommends using the same cessation lag structure and assumptions as for particulate m atter when utilizing

33 The choice of a discount rate, and its associated conceptual basis, is a topic of ongoing discussion within the

federal government. To comply with OMB Circular A

- 4, EPA provides monetized benefits using discount rates of 3% and 7% (OMB, 2003). A 3% discou nt reflects reliance on a “social rate of time preference” discounting concept. A 7% rate is consistent with an “opportunity cost of capital” concept to reflect the time value of resources directed to meet regulatory requirements.
34 The ozone HREA noted,: “The effect estimates used in modeling long

- term O 3 - attributable mortality, utilize a

seasonal average of peak (1

- hr maximum) measurements. These long

- term exposure metrics can be viewed as long

- term exposures to daily peak O 3 over the warmer months, as compared with annual average levels such as are used in long

- term PM exposure calculations. This increases the need for care in interpreting these long

- term O 3 - attributable mortality estimates together with the short

- term O 3 - att ributable mortality estimates, in order to avoid double counting.” (U.S. EPA, 2014b).

### 6

- 63 cohort mortality evidence for ozone.” Monetized benefit estimates generated using both lag assumptions are presented as sen sitivity analyses (see section 6 .7.3.1). Uncertainties Specific to Premature Mortality Valuation. The economic benefits associated with reductions in the risk of premature mortality are the largest category of monetized benefits in this RIA. In addition, in prior analyses, the EPA identified valuation of mortality

- related benefits as the largest contributor to the range of uncertainty in monetized benefits (Mansfield et al., 2009). 35 

### Because of the uncertainty in estimates of the value of reducing premature mortality risk, it is important to adequately characterize and understand the various types of economic approac hes available for valuing reductions in mortality risk. Such an assessment also requires an understanding of how alternative valuation approaches reflect that some individuals may be more susceptible to air pollution

- induced mortality or reflect difference s in the nature of the risk presented by air pollution relative to the risks studied in the relevant economics literature. The health science literature on air pollution indicates that several human characteristics affect the degree to which mortality risk affects an individual. For example, some age groups appear to be more susceptible to air pollution than others (e.g., the elderly and children). Health status prior to exposure also affects susceptibility. An ideal benefits estimate of mortality risk redu ction would reflect these human characteristics, in addition to an individual’s WTP to improve one’s own chances of survival along with WTP to improve other individuals’ survival rates. The ideal measure would also take into account the specific nature of the risk reduction commodity that is provided to individuals, as well as the context in which risk is reduced. To measure this value, i t is important to assess how reductions in air pollution reduce the risk of dying from the time that reductions take effect onward and how individuals value these changes. Each individual’s survival curve, or the probability of surviving beyond a given age , should shift as a result of an environmental quality improvement. For example, changing the current probability of survival for an individual also shifts future probabilities of that individual’s survival. This probability shift will differ across indivi duals because survival curves depend on

35 This conclusion was based on an assessment of uncertainty based on statistical error in epidemiological effect

estimates and economic valuation estimates. Additional s ources of model error such as those examined in the PM 2.5 mortality expert elicitation (Roman et al., 2008) may result in different conclusions about the relative contribution of sources of uncertainty.

### 6

- 64 such characteristics as age, health state, and the current age to which the individual is likely to survive. Although a survival curve approach provides a theoretically preferred method for valuing the benefits o f reduced risk of premature mortality associated with reducing air pollution, the approach requires a great deal of data to implement. The economic valuation literature does not yet include good estimates of the value of this risk reduction commodity. As a result, in this analysis we value reductions in premature mortality risk using the VSL approach. Other uncertainties specific to premature mortality valuation include the following:

• Across
- study variation: There is considerable uncertainty as to whether t he available literature on VSL provides adequate estimates of the VSL for risk reductions from air pollution reduction. Although there is considerable variation in the analytical designs and data used in the existing literature, the majority of the studies involve the value of risks to a middle

- aged working population. Most of the studies examine differences in wages of risky occupations, using a hedonic wage approach. Certain characteristics of both the population affected and the mortality risk facing tha t population are believed to affect the average WTP to reduce the risk. The appropriateness of a distribution of WTP based on the current VSL literature for valuing the mortality

- related benefits of reductions in air pollution concentrations therefore depe nds not only on the quality of the studies (i.e., how well they measure what they are trying to measure), but also on the extent to which the risks being valued are similar and the extent to which the subjects in the studies are similar to the population a ffected by changes in pollution concentrations.

• Level of risk reduction: The transferability of estimates of the VSL from the wage

- risk studies to the context of this analysis rests on the assumption that, within a reasonable range, WTP for reductions in m ortality risk is linear in risk reduction. For example, suppose a study provides a result that the average WTP for a reduction in mortality risk of 1/100,000 is $50, but that the actual mortality risk reduction resulting from a given pollutant reduction is 1/10,000. If WTP for reductions in mortality risk is linear in risk reduction, then a WTP of $50 for a reduction of 1/100,000 implies a WTP of $500 for a risk reduction of 1/10,000 (which is 10 times the risk reduction valued in the study). Under the assu mption of linearity, the estimate of the VSL does not depend on the particular amount of risk reduction being valued. This assumption has been shown to be reasonable provided the change in the risk being valued is within the range of risks evaluated in the underlying studies (Rowlatt et al., 1998).

• Voluntariness of risks evaluated: Although job

- related mortality risks may differ in several ways from air pollution

- related mortality risks, the most important difference may be that job

- related risks are incurr ed voluntarily, or generally assumed to be, whereas air pollution

- related risks are incurred involuntarily. Some evidence suggests that people will pay more to reduce involuntarily incurred risks than risks incurred voluntarily (e.g.,

### 6

- 65 Lichtenstein and Slov ic, 2006). If this is the case, WTP estimates based on wage

- risk studies may understate WTP to reduce involuntarily incurred air pollution

- related mortality risks.

• Sudden versus protracted death: A final important difference related to the nature of the ri sk may be that some workplace mortality risks tend to involve sudden, traumatic events, whereas air pollution

- related risks tend to involve longer periods of disease and suffering prior to death. Some evidence suggests that WTP to avoid a risk of a protrac ted death involving prolonged suffering and loss of dignity and personal control is greater than the WTP to avoid a risk (of identical magnitude) of sudden death (e.g., Tsuge et al., 2005; Alberini and Scasny, 2011). To the extent that the mortality risks addressed in this assessment are associated with longer periods of illness or greater pain and suffering than are the risks addressed in the valuation literature, the WTP measurements employed in the present analysis would reflect a downward bias.

• Self
- sel ection and skill in avoiding risk : Recent research (Shogren and Stamland, 2002) suggests that VSL estimates based on hedonic wage studies may overstate the average value of a risk reduction. This is because the risk

- wage trade

- off revealed in hedonic studi es reflects the preferences of the marginal worker (i.e., that worker who demands the highest compensation for his risk reduction for a given job). This worker must have either a higher workplace risk than the average worker in a given occupation, a lower risk tolerance than the average worker in that occupation, or both. Conversely, the marginal worker should have a higher risk tolerance than workers employed in less

- risky sectors. However, the risk estimate used in hedonic studies is generally based on av erage risk, so the VSL may be biased, in an ambiguous direction, because the wage differential and risk measures do not match.

• Baseline risk and age: Recent research (Smith, Pattanayak, and Van Houtven, 2006) finds that because individuals reevaluate their baseline risk of death as they age, the marginal value of risk reductions does not decline with age as predicted by some lifetime consumption models. This research supports findings in recent stated preference studies that suggest only small reductions in the value of mortality risk reductions with increasing age (e.g., Alberini et al., 2004).

6 .6.4.2 Hospital Admissions and Emergency Department Valuation In the absence of estimates of societal WTP to avoid hospital visits/admissions for specific illnesse s, we derive COI estimates for use in the benefits analysis. The International Classification of Diseases (ICD) (WHO, 1977) code

- specific COI estimates used in this analysis consist of estimated hospital charges and the estimated opportunity cost of time s pent in the hospital (based on the average length of a hospital stay for the illness). We based all estimates of hospital charges and length of stays on statistics provided by the Agency for Healthcare Research and Quality’s Healthcare Utilization Project National Inpatient Sample (NIS) database ( AHRQ ,

### 6

- 66 2007). We estimated the opportunity cost of a day spent in the hospital as the value of the lost daily wage, regardless of whether the hospitalized individual is in the workforce. To estimate the lost daily w age, we divided the median weekly wage reported by the 2007 American Community Survey (ACS) by five and deflated the result to the correct currency year using the CPI

- U “all items” ( U.S. EPA, 2015 ). The resulting national average lost daily wage is $150 (2 011$). The total cost

- of

- illness estimate for an ICD code

- specific hospital stay lasting n days, then, was the mean hospital charge plus daily lost wage multiplied by n . In general, the mean length of stay has decreased since the 2000 database used in the previous version of BenMAP , while the mean hospital charge has increased. We provide the rounded unit values in 2011$ for the COI functions used in this analysis in Table 6

- 12

. Table 6
- 12 . Unit Values for Hospital Admissions a
End Point ICD Codes Age Range Mean Hospital Charge (2011$) Mean Length of Stay (days) Total Cost of Illness (unit value in 2011$) min. max. HA, Chronic Lung Disease 490

– 496 18 64 $20,000 3.9 $22,000 HA, Asthma 493 0 64 $15,000 3.0 $16,000 HA, All Cardiovascular 390

– 429 18 64 $41,000 4.1 $44,000 HA, All Cardiovascular 390

– 429 65 99 $38,000 4.9 $42,000 HA, All Respiratory 460

– 519 65 99 $32,000 6.1 $37,000

a All estimates rounded to two significant digits. Unrounded estimates in 2000$ are available in Appendix J of the BenMAP user manual ( U.S. EPA, 2015 ). 

### To value asthma emergency department visits, we used a simple average of two estimates from the health e conomics literature. The first estimate comes from Smith et al. (1997), who reported approximately 1.2 million asthma

- related emergency department visits in 1987, at a total cost of $186 million (1987$). The average cost per visit that year was $155; in 20 11$, that cost i s $480 (using the CPI

- U for medical care to adjust to 2011$). The second estimate comes from Stanford et al. (1999), who reported the cost of an average asthma

- related emergency department visit based on 1996

– 1997 data at $400 (using the CP I

- U for medical care to adjust to 2011$). A simple average of the two estimates yields a unit value of $440 (2011$).

6 .6.4.3 Nonfatal Myocardial Infarctions Valuation We were not able to identify a suitable WTP value for reductions in the risk of nonfatal heart attacks. Instead, we use a COI unit value with two components: the direct medical costs and the opportunity cost (lost earnings) associated with the illness event. Because the costs

### 6

- 67 associated with a myocardial infarction extend beyond the initial ev ent itself, we consider costs incurred over several years. Using age

- specific annual lost earnings estimated by Cropper and Krupnick (1990) and a 3% discount rate, we estimated a rounded present discounted value in lost earnings (in 2000$) over 5 years due to a myocardial infarction of $8,800 for someone between the ages of 25 and 44, $13,000 for someone between the ages of 45 and 54, and $75,000 for someone between the ages of 55 and 65. The rounded corresponding age

- specific estimates of lost earnings (in 2000$) using a 7% discount rate are $7,900, $12,000, and $67,000, respectively. Cropper and Krupnick (1990) do not provide lost earnings estimates for populations under 25 or over 65. As such, we do not include lost earnings in the cost estimates for thes e age groups. We found three possible sources in the literature of estimates of the direct medical costs of myocardial infarction, which provide significantly different values (see Table 6

- 13 ):

• Wittels et al. (1990) estimated expected total medical costs of myocardial infarction over 5 years to be $51,000 (rounded in 1986$) for people who were admitted to the hospital and survived hospitalization. (There does not appear to be any discounting used.) This estimated cost is based on a medical cost model, whic h incorporated therapeutic options, projected outcomes, and prices (using “knowledgeable cardiologists” as consultants). The model used medical data and medical decision algorithms to estimate the probabilities of certain events and/or medical procedures b eing used. The authors note that the average length of hospitalization for acute myocardial infarction has decreased over time (from an average of 12.9 days in 1980 to an average of 11 days in 1983). Wittels et al. used 10 days as the average in their stud y. It is unclear how much further the length of stay for myocardial infarction may have decreased from 1983 to the present. The average length of stay for ICD code 410 (myocardial infarction) in the year

- 2000 Agency for Healthcare Research and Quality (AHR Q) HCUP database is 5.5 days ( AHRQ , 2000). However, this may include patients who died in the hospital (not included among our nonfatal myocardial infarction cases), and as a result whose length of stay was substantially shorter.

• Eisenstein et al. (2001) e stimated 10

- year costs of $45,000 in rounded 1997$ (using a 3% discount rate) for myocardial infarction patients, using statistical prediction (regression) models to estimate inpatient costs. Only inpatient costs (physician fees and hospital costs) were in cluded.

### 6

- 68 Table 6
- 13 . Alternative Direct Medical Cost of Illness Estimates for Nonfatal Heart Attacks a Study Direct Medical Costs (2011$) Over an x
- Year Period, for x = Wittels et al. (1990) $170,000 b 5 Russell et al. (1998) $34,000 c 5 Average (5

- year) costs $100,000 5 Eisenstein et al. (2001) $76,000 c 10

a All estimates rounded to two significant digits. Unrounded estimates in 2000$ are available in appendix J of the BenMAP user manual ( U.S. EPA, 2015 ). b Wittels et al. (1990) did not appear to discount costs incurred in future years.
c Using a 3% discount rate. Discounted values as reported in the study. 

### As noted above, the estimates from these three studies are substantially different, and we have not a dequately resolved the sources of differences in the estimates. Because the wage

related opportunity cost estimates from Cropper and Krupnick (1990) cover a 5

- year period, we used estimates for medical costs that similarly cover a 5

- year period (i.e., esti mates from Wittels et al. (1990) and Russell et al. (1998). We used a simple average of the two 5

- year estimates, or rounded to $85,000, and added it to the 5

- year opportunity cost estimate. The resulting estimates

are given in Table 6

- 14

. Table 6
- 14 . Esti mated Costs Over a 5
- Year Period of a Nonfatal Myocardial Infarction (in 2011$) a Age Group Opportunity Cost Medical Cost b Total Cost 0

– 24 $0 $100,000 $100,000 25

– 44 $12,000 c $100,000 $110,000 45

– 54 $18,000 c $100,000 $120,000 55

– 65 $100,000 c $100,000 $210,000 > 65 $0 $100,000 $10 0 ,000

a All estimates rounded to two significant digits, so estimates may not sum across columns. Unrounded estimates in 2000$ are available in appendix J of the BenMAP user manual ( U.S. EPA, 2015 ). b An average of the 5

- year costs estimated by Wittels et al. (1990) and Russell et al. (1998).
c From Cropper and Krupnick (1990), using a 3% discount rate for illustration. 

### 6 .6.4.4 Valuation of Acute Health Events Asthma E xacerbation Valuation . Several r espiratory symptoms in asthmatics or characterizations of an asthma episode have been associated with exposure to air pollutants. All of these can generally be taken as indications of an asthma exacerbation when they occur in an asthmatic. Therefore, we ap ply the same set of unit values for all of the variations of “asthma exacerbation”. Specifically, we use a unit value based on the mean WTP estimates for a “bad asthma day,” described in Rowe and Chestnut (1986). This study surveyed asthmatics to estimate WTP for avoidance of a “bad asthma day,” as defined by the subjects.

### 6

- 69 Minor Restricted Activity Days Valuation. No studies are reported to have estimated WTP to avoid a minor restricted activity day. However, Neumann et al. (1994) derived an estimate of w illingness to pay to avoid a minor respiratory restricted activity day, using estimates from Tolley et al. (1986) of WTP for avoiding a combination of coughing, throat congestion and sinusitis. This estimate of WTP to avoid a minor respiratory restricted a ctivity day is $38 (1990$), or about $71 (2011$). Although Ostro and Rothschild (1989) statistically linked ozone and minor restricted activity days, it is likely that most MRADs associated with ozone and PM 2.5 

### exposure are, in fact, minor respiratory rest ricted activity days. For the purpose of valuing this health endpoint, we used the estimate of mean WTP to avoid a minor respiratory restricted activity day. School Loss Days Valuation. To value a school absence, we: (1) estimated the probability that if a school child stays home from school, a parent will have to stay home from work to care for the child; and (2) valued the lost productivity at the parent’s wage. To do this, we estimated the number of families with school

- age children in which both parent s work, and we valued a school

- loss day as the probability that such a day also would result in a work

- loss day. We calculated this value by multiplying the proportion of households with school

- age children by a measure of lost wages.
We used this method i n the absence of a preferable WTP method. However, this approach suffers from several uncertainties. First, it omits willingness to pay to avoid the symptoms/illness that resulted in the school absence; second, it effectively gives zero value to school abs ences that do not result in work

- loss days; and third, it uses conservative assumptions about the wages of the parent staying home with the child. Finally, this method assumes that parents are unable to work from home. If this is not a valid assumption, th en there would be no lost wages. For this valuation approach, we assumed that in a household with two working parents, the female parent will stay home with a sick child. From the Statistical Abstract of the United States (U.S. Census Bureau, 2001), we obt ained: (1) the numbers of single, married and “other” (widowed, divorced or separated) working women with children; and (2) the rates of participation in the workforce of single, married and “other” women with children. From these two sets of statistics, w e calculated a weighted average participation rate of 72.85 percent. Our

### 6

- 70 estimate of daily lost wage (wages lost if a mother must stay at home with a sick child) is based on the year 2000 median weekly wage among women ages 25 and older (U.S. Census Bureau , 2001). This median weekly wage is $551 (2000$). Dividing by five gives an estimated median daily wage of $103 (2000$). To estimate the expected lost wages on a day when a mother has to stay home with a school

- age child, we first estimated the probability that the mother is in the workforce then multiplied that estimate by the daily wage she would lose by missing a workday: 72.85 percent times $103, for a total loss of $75 (2000$). This valuation approach is similar to that used by Hall et al. (2003).

Wor k Loss Days Valuation. Work loss days are valued at a day’s wage. BenMAP

- CE calculates county

- specific median daily wages from county

- specific annual wages (by dividing the annual wage by 52 weeks multiplied by 5 work days per week), on the theory that a worker’s vacation days are valued at the same daily rate as work days. Upper and Lower R espiratory S ymptoms Valuation . Lower and upper respiratory symptoms are each considered a complex of symptoms. A dollar value was derived for clusters of these symptom s that most closely match the studies used to calculate incidence (Schwartz and Neas, 2000; Pope et al

. , 1991) based on mid

- range estimates from each cluster (IEc, 1994).
6 .6.4.5 Growth in WTP Reflecting National Income Growth over Time Our analysis accounts for expected growth in real income over time. This is a distinct concept from inflation and currency year. Economic theory argues that WTP for most goods (such as environmental protection) will increase if real incomes increase. There is substanti al empirical evidence that the income elasticity 36 

### of WTP for health risk reductions is positive, although there is uncertainty about its exact value. Thus, as real income increases, the WTP for environmental improvements also increases. Although many analy ses assume that the income elasticity of WTP is unit elastic (i.e., a 10% higher real income level implies a 10% higher WTP to reduce risk changes), empirical evidence suggests that income elasticity is substantially less

36 Income elasticity is a common economic measure equa l to the percentage change in WTP for a 1 percent change

in income.

### 6

- 71 than one and thus relatively inela stic. As real income rises, the WTP value also rises but at a slower rate than real income. The effects of real income changes on WTP estimates can influence benefits estimates in two different ways: through real (national average) income growth between th e year a WTP study was conducted and the year for which benefits are estimated, and through differences in income between study populations and the affected populations at a particular time. The SAB

EEAC advised the EPA to adjust WTP for increases in real income over time but not to adjust WTP to account for cross

- sectional income differences “because of the sensitivity of making such distinctions, and because of insufficient evidence available at present” (U.S. EPA

- SAB, 2000). An advisory by another commit tee associated with the SAB, the Advisory Council on Clean Air Compliance Analysis (SAB

- Council), has provided conflicting advice. While agreeing with “the general principle that the willingness to pay to reduce mortality risks is likely to increase with g rowth in real income” and that “[t]he same increase should be assumed for the WTP for serious nonfatal health effects,” they note that “given the limitations and uncertainties in the available empirical evidence, the Council does not support the use of the proposed adjustments for aggregate income growth as part of the primary analysis” (U.S. EPA

- SAB, 2004b). More recently, in response to questions related to the adjustment for income growth over time, the SAB

- EEAC noted that “EPA should adjust for changes in income in evaluating benefits of risk reduction” (U

. S

. EPA

- SAB , 2011). Until these conflicting advisories can be reconciled, the EPA will continue to adjust valuation estimates to reflect income growth using the methods described below, while providing sensitivity analyses for alternative income growth adjustment factors. Based on a review of the available income elasticity literature, we adjusted the valuation of human health benefits upward to account for projected growth in real U.S. income. Faced wit h a dearth of estimates of income elasticities derived from time

- series studies, we applied estimates derived from cross

- sectional studies in our analysis. Details of the procedure can be found in Kleckner and Neumann (1999). We note that the literature ha s evolved since the publication of this memo and that an array of newer studies identifying potentially suitable income elasticity estimates are available (IEc, 2012). The EPA anticipates seeking a peer review in the near future of these studies and its ap proach to adjusting WTP estimates to account for changes in personal

### 6

- 72 income. As such, these newer studies have not yet been incorporated into the benefits analysis. An abbreviated description of the procedure we used to account for WTP for real income grow th between 1990 and 2024 is presented below. Reported income elasticities suggest that the severity of a health effect is a primary determinant of the strength of the relationship between changes in real income and WTP. As such, we use different elasticit y estimates to adjust the WTP for minor health effects, severe and chronic health effects, and premature mortality. Note that because of the variety of empirical sources used in deriving the income elasticities, there may appear to be inconsistencies in th e magnitudes of the income elasticities relative to the severity of the effects (a priori one might expect that more severe outcomes would show less income elasticity of WTP). We have not imposed any additional restrictions on the empirical estimates of in come elasticity. One explanation for the seeming inconsistency is the difference in timing of conditions. WTP for minor illnesses is often expressed as a short

- term payment to avoid a single episode. WTP for major illnesses and mortality risk reductions ar e based on longer

- term measures of payment (such as wages or annual income). Economic theory suggests that relationships become more elastic as the length of time grows, reflecting the ability to adjust spending over a longer time period (U.S. EPA, 2010e, p. A

- 9). Based on this theory, it would be expected that WTP for reducing long

- term risks would be more elastic than WTP for reducing short

- term risks. The relative magnitude of the income elasticity of WTP for visibility compared with those for health eff ects suggests that visibility is not as much of a necessity as health, thus, WTP is more elastic with respect to income. The elasticity values used to adjust estimates of benefits in 2024 are presented in Table 6

- 15

. Table 6
- 15. Elasticity Values Used to A ccount for Projected Real Income Growth a Benefit Category Central Elasticity Estimate Minor Health Effect 0.14 Severe and Chronic Health Effects 0.45 Premature Mortality 0.40

a Derivation of estimates can be found in Kleckner and Neumann (1999). COI estimates are not adjusted for income growth. 

### In addition to elasticity estimates, projections of real gross domestic product (GDP) and populations from 1990 to 2024 are needed to a djust benefits to reflect real per capita income growth. For consistency with the emissions and benefits modeling, we used national population

### 6

- 73 estimates for the years 1990 to 1999 based on U.S. Census Bureau estimates (Hollman, Mulder, and Kallan, 2000). T hese population estimates are based on application of a cohort

- component model applied to 1990 U.S. Census data projections (U.S. Bureau of Census, 2000). For the years between 2000 and 2024, we applied growth rates based on the U.S. Census Bureau projecti ons to the U.S. Census estimate of national population in 2000. We used projections of real GDP provided in Kleckner and Neumann (1999) for the years 1990 to 2010. 37 

### We used projections of real GDP (in chained 1996 dollars) provided by Standard and Poor’s ( 2000) for the years 2010 to 2024. 38 

### Using the method outlined in Kleckner and Neumann (1999) and the population and income data described above, we calculated WTP adjustment factors for each of the elasti city estimates listed in Table 6

- 16 . Benefits for eac h of the categories (minor health effects, severe and chronic health effects, premature mortality, and visibility) are adjusted by multiplying the unadjusted benefits by the appropriate adjustment factor. For premature mortality, we applied the income adju stment factor specific to the analysis year, but we do not adjust for income growth over the 20

- year cessation lag. Our approach could underestimate the benefits for the later years of the lag. There is some uncertainty regarding the total costs of illnes s in the future. Specifically, the nature of medical treatment is changing, including a shift towards more outpatient treatment. Although we adjust the COI estimates for inflation, we do not have data to project COI estimates for the cost of treatment in t he future or income growth over time, which leads to an inherent though unavoidable inconsistency between COI

- and WTP

- based estimates

. This approach may under predict benefits in future years because it is likely that increases in real U.S. income would

a lso result in increased COI (due, for example, to increases in wages paid to medical workers) and increased cost of work loss days and lost worker productivity (reflecting that if worker

37 U.S. Bureau of Economic Analysis, Table 2A

— Real Gross Domestic Product (1997) and U.S. Bureau of Economic Analysis, The Economic and Budget Outlook: An Update , Table 4

—Economic Projectio ns for Calendar Years 1997 Through 2007 (1997) . Note that projections for 2007 to 2010 are based on average GDP growth rates between 1999 and 2007. 38 In previous analyses, we used the Standard and Poor’s projections of GDP directly. This led to an apparent

discontinuity in the adjustment factors between 2010 and 2011. We refined the method by applying the relative growth rates for GDP derived from the Standard and Poor’s projections to the 2010 projected GDP based on the Bureau of Economic Analysis projecti ons.

### 6

- 74 incomes are higher, the losses resulting from reduced worker producti on would also be higher). In addition, cost

- of

- illness estimates do not include sequelae costs or pain and suffering, the value of which would likely increase in the future. To the extent that costs would be expected to increase over time, this increase ma y be partially offset by advancement in medical technology that improves the effectiveness of treatment at lower costs. For these reasons, we believe that the cost

- of

- illness estimates in this RIA may underestimate (on net) the total economic value of avoi ded health impacts.

Table 6
- 16 . Adjustment Factors Used to Account for Projected Real Income Growth a

Benefit Category 2024 Minor Health Effect 1.07 Severe and Chronic Health Effects 1.22 Premature Mortality 1.20

a Based on elast icity values reported in Table 6

- 15 , U.S. Census population projections, and projections of real GDP per capita.



### 6. 6.5 Benefit per Ton Estimates Used in Modeling PM 2.5 

### - Related Co

- benefits This section presents the benefit

- per - ton estimates (dimensioned by mortality study and simulation year) used as inputs in generating PM 2.5 

### co - benefits estimates including (Table 6

- 17 ). Benefit

- per - ton estimates were generated for each of the long

- term exposure

- related mortality studies used in generating core benefits estimates for this RIA including Krewski et al. (2009) and Lepeule et al. (2012) (see section 6.6.3.3). Estimates were available for 2025 and 2030, with those being used to model co

- benefits for the 2025 scenario and post

- 2025 scenario, respectively. For additional detail on th e approach used to generate PM 2.5 

### co - benefits estimates and the role played by these two types of inputs, see Se ction 6 .4.4. 39

39 Sector

- level NOx reductions (for each alternative standard level) were generated using methods described in

Chapter 4, section 4.2 and 4.3. As noted in section 5.4.4, NOx emissions reductions associated with alternative standard levels considered fo r this NAAQS review involved seven of the 17 sectors for which we had benefit

- per ton values and consequently, the co

- benefits PM 2.5 estimates are based on simulated benefits for those seven sectors

.

### 6

- 75 Table 6
- 17 . Summary of PM 2.5 

### Benefit
- per - ton Estimates a

a Benefit

- per - ton estimates reflect application of the 20

- year segmented lag at either a 3% or 7% discount rate. In addition, separate sets of benefit

- per - ton estimates were generate for 2025 and 2030, reflecting application of appropriate projected demogra phic and baseline incidence data. b Benefit

- per - ton estimates for the non

- specified source category were generated as a weighted average of values for the 17 source categories, with weighting based on sector

- specific NOx emissions for 2005 obtained from Fann et al. ( 2012 b )

. air, locamotive and marine cement kilns coke ovens EGU point electric arc furnaces ferro alloys integrated iron and

steel iron and steel non-EGU point other nonpoint other nonroad onroad pulp and paper refineries residenti al wood taconite mining ocean going vessels Nonspecified
source b 2025 at 7% social discount Krewski et al., 2009 $7,200 $5,700 $10,000 $5,200 $9,600 $4,400 $13,000 $17,000 $6,300 $7,900 $7,000 $7,600 $3,700 $6,900 $14,000 $6,000 $2,000 $6,400 Lepeule et al., 2012 $16,000 $13,000 $24,000 $12,000 $22,000 $9,800 $30,000 $38,000 $14,000 $18,000 $16,000 $17,000 $8,500 $16,000 $31,000 $14,000 $4,600 $15,000 2025 at 3% social discount Krewski et al., 2009 $8,000 $6,300 $12,000 $5,800 $11,000 $4,800 $15,000 $19,000 $7,000 $8,700 $7,700 $8,400 $4,200 $7,700 $15,000 $6,600 $2,300 $7,100 Lepeule et al., 2012 $18,000 $14,000 $26,000 $13,000 $24,000 $11,000 $34,000 $42,000 $16,000 $20,000 $17,000 $19,000 $9,400 $17,000 $34,000 $15,000 $5,100 $16,000 2030 at 7% social discount Krewski et al., 2009 $7,800 $6,100 $11,000 $5,600 $10,000 $4,600 $14,000 $18,000 $6,800 $8,500 $7,600 $8,200 $4,000 $7,500 $15,000 $6,400 $2,300 $6,900 Lepeule et al., 2012 $18,000 $14,000 $25,000 $13,000 $23,000 $10,000 $32,000 $41,000 $15,000 $19,000 $17,000 $19,000 $9,100 $17,000 $33,000 $14,000 $5,100 $16,000 2030 at 3% social discount Krewski et al., 2009 $8,700 $6,800 $12,000 $6,200 $11,000 $5,100 $16,000 $20,000 $7,600 $9,400 $8,400 $9,100 $4,500 $8,300 $16,000 $7,100 $2,500 $7,700 Lepeule et al., 2012 $20,000 $15,000 $28,000 $14,000 $26,000 $12,000 $36,000 $46,000 $17,000 $21,000 $19,000 $21,000 $10,000 $19,000 $37,000 $16,000 $5,600 $17,000 Long-term mortality study Emissions sector

### 6

- 76 6 .7 Benefits Results W e estimated the benefits of attaining the revised and alternative ozone standard level s across the U.S. in 2025 except California. We estimated the b enefits of attaining these standard levels in California in 2038. We report the 2025 and 2038 estimates separately because deriving a summed estimate would require us to calculate the Present Value (PV) of the stream of benefits occurring between those t wo years, which is not possible with the available data. A dditional analyses (section 6 .7.3) inform the interpretation of these core analyses

. Applying the impact and valuation functions described above to the estimated changes in

ozone concentrations yi elds estimates of the changes in physical damages (e.g., premature deaths , cases of hospital admissions) and the associated monetary values for those changes. Not all known ozone and PM health effects could be quantified or monetized , and t he monetized

va lue of these unquantified effects is represented by adding an unknown “B” to the aggregate total. Values are rounded to two significant figures and so t otals may not sum across columns or

rows.
6 .7.1 Benefits of A ttaining a R evised O zone S tandard in 2025 This section presents the avoided health impacts and monetized benefits of attaining a more stringent ozone standard in 2025. Table 6

- 18 shows the population

- weighted air quality change for the revised and alternative standard level s averaged across the c ontinental U.S. Table 6

- 19 summarizes the tons of NOx emissions required to simulate attainment of the revised and alternative standard level s (further differentiated by geographic region including east, west and California). Tables 6

- 20 through 6

- 25 prese nt the benefits results for the standard level s analyzed . Table 6

- 25 summarizes total benefits by geographic region (including east, west minus California, and California).
In addition , Figure 6

- 4 presents a sensitivity analysis for short

- term ozone

- related benefits using additional C

- R functions for premature mortality, and Figure 6

- 5 presents a sensitivity analysis for PM 2.5 

### - related co - benefits using additional C

- R functions for premature mortalit y. S ee section s 6.6.3.2 and 6.6.3.3, respectively for additional discussion of the alternative effect estimates used in each sensitivity analysis.

### 6

- 77 Table 6
- 18 . Population
- Weighted Air Quality Change for the Revised and Alternative Annual Primary O zone St andards Relative to the Analytical Baseline in 2025 a
Standard Pop ulation
- Weighted Summer Season O zone Concentration Change (8
- h ou r max) b 70 ppb 0.2574 6 5 ppb 1.278

a Because we used benefit

- per - ton estimates for the PM 2.5 co - benefits, population

- weighted PM 2.5 changes are not available. b Population weighting based on all ages (demographic used in modeling short

- term exposure

- related mortality for ozone) for 2025.



### Table 6
- 19 . Sector
- Specific NO x 

### Emissions Reductions for the Revised and Alternative Standard Lev el s a

a All values are tons of NOx reductions (75 ppb vs revised and alternative standard levels ). Results are presented both for “CA NOx” (emissions in CA only

– used in post

- 2025 scenario PM 2.5 cobenefits modeling) and “nonCA NOx” (emissions reductions outside of CA

– used in 2025 scenario PM 2.5 cobenefits modeling). 

### CA NOX Emis Rdxn nonCA NOX Emis Rdxn CA NOX Emis Rdxn nonCA NOX Emis Rdxn Aircraft, locomotives and marine vessels

- - - - Area sources

- 32,224

- 69,576 Cement kilns

- 19,285

- 31,963 Electricity Generating Units

- 47,507

- 113,678 Industrial point sources

- 134,763

- 319,484 Non-road mobile sources

- 2,832

- 8,791 On-road mobile sources

- Pulp and paper facilities

- 42

- 265 Refineries 3,134

- 7,735 Residential wood combustion Unknown sector 51,000 46,542 99,500 862,803 TOTAL 51,000 286,330 99,500 1,414,296 Emissions Sector 70ppb 65ppb Revised and Alternative Standard Levels

### 6

- 78 Table 6
- 20 . Estimated Number of Avoided Ozone
- Related Health Impacts for the Revised and Alternative Standard Levels (Incremental to the Baseline) for the 2025 S cenario (nationwide benefits of attaining the standard s in the U.S. except California) a, b

a All incidence estimates are rounded to whole numbers with a maximum of two significant digits. b All incidence estimates are based on ozone

- only models unless otherwise noted. d The negative estimates at the 5 th percentile confidence estimates for these morbidity endpoints reflect the statistical power of the studies used to calculate these health impacts. These re sults do not suggest that reducing air pollution results will adversely affect health, but rather, that we are less confident in the magnitude of the expected benefits for this endpoint. 70 ppb 65 ppb

Avoided Short-Term Mortality 96 490 (47 to 140) (240 to 740) 160 820 (86 to 240) (440 to 1,200) Avoided Long-term Respiratory Mortality 340 1,700 (110 to 560) (580 to 2,800) Avoided Morbidity 180 920 (-42 to 400) (-220 to 2,000) 510 2,700 (47 to 1,600) (250 to 8,300)

220,000 1,100,000 (-67,000 to 440,000) (-330,000 to 2,100,000) 450,000 2,200,000 (190,000 to 720,000) (920,000 to 3,500,000) 160,000 790,000 (57,000 to 360,000) (280,000 to 1,700,000) Health Effectb Revised and Alterative Standard Levels (95th percentile confidence intervals)

multi-city studies Smith et al. (2009) (all ages) Zanobetti and Schwartz (2008) (all ages)

multi-city study Jerrett et al. (2009) (30-99yrs) copollutants model (PM 2.5 ) Hospital admissions - respiratory (age 65+) d Emergency department visits for asthma (all ages) Asthma exacerbation (age 6-18) d Minor restricted-activity days (age 18-65) School Loss Days (age 5-17)

### 6

- 79 Table 6
- 21 . Total Monetized O zone - Related Benefits for the Revised and Alternative Annual O zone Standards (Incremental to the Baseline) for the 2025 S cenario (nationwide benefits of attaining the standard s everywhere in the U.S. except California) ( millions of 2011) a

a All benefits estimates are rounded to whole number s with a maximum of two significant digits. The monetized value of the ozone

- related morbidity benefits are included in the estimates shown in this table for each mortality study.



### Figure 6
- 4. Sensitivity Analysis for Short
- Term Ozone
- Related Mortality Benefits 70 ppb 65 ppb Avoided Short-Term Mortality - Core Analysis $1,000 $5,300 ($99 to $2,900) ($500 to $15,000) 1,700 8,700 ($160 to $4,800) ($800 to $24,000) Health Effectb (95th percentile confidence intervals)

multi-city studies Smith et al. (2009) (all ages) Zanobetti and Schwartz (2008) (all ages) Revised and Alterative Standard Levels

$0 $500 $1,000 $1,500 $2,000 $2,500 $3,000 $3,500 $4,000Millions (2011$)

### 6

- 80 Ta ble 6
- 22 . Estimated Number of Avoided PM 2.5 

### - Related Health Impacts for the Revised and Alternative Annual Ozone Standards (Incremental to the Baseline) for the 2025 S cenario ( N ationwide B enefits of A ttaining the S tandard s in the U.S. e xcept Ca lifornia) a

a All incidence estimates are rounded to whole numbers with a maximum of two significant digits. Because these estimates were generated using benefit

- per - ton estimates, confidence intervals are not available. In general, the 95 th percentile confidence interval for the health impact function alone ranges from approximately ±30 percent for mortality incidence based on Krewski et al. (2009) and ±46 percent based on Lepeule et al. (2012). 

### 70ppb 65ppb Avoided PM2.5-related Mortality Krewski et al. (2009) (adult mortality age 30+) 220 1,100 Lepeule et al. (2012) (adult mortality age 25+) 500 2,500 Woodruff et al. (1997) (infant mortality) <1 2 Avoided PM2.5-related Morbidity Non-fatal heart attacks Peters et al. (2001) (age >18) 260 1,300 Pooled estimate of 4 studies (age >18) 28 140 Hospital admissions—respiratory (all ages) 66 330 Hospital admissions—cardiovascular (age > 18) 80 400 Emergency department visits for asthma (all ages) 120 600 Acute bronchitis (ages 8–12) 340 1,700 Lower respiratory symptoms (ages 7–14) 4,400 22,000 Upper respiratory symptoms (asthmatics ages 9–11) 6,300 31,000 Asthma exacerbation (asthmatics ages 6–18) 7,000 42,000 Lost work days (ages 18–65) 28,000 140,000 Minor restricted-activity days (ages 18–65) 170,000 830,000 Revised and Alterative Standard Levels Health Effectb

### 6

- 81 Table 6
- 23 . Monetized PM 2.5 

### - Related Health C o - Benefits for the Revised and Alternative Annual O zone Standards (Incremental to Baseline) for the 2025 S cenario ( N ationwide B enefits of A ttaining the S tandard s in the U.S. e xcept California) ( Millions of 2011) a,b,c

Monetized Benefits Revised and Alternative Standard Levels 70 ppb 65 ppb 3% Discount Rate Krewski et al. (2009) (adult mortality age 30+) $2,1 00 $10 ,000 Lepeule et al. (2012) (adult mortality age 25+) $4,7 00 $23 ,000 7% Discount Rate Krewski et al. (2009) (adult mortality age 30+) $1,9 00 $9,3 00 Lepeule et al. (2012) (adult mortality age 25+) $4,2 00 $21 ,000

a All estimates are rounded to two significant digits. Because these estimates were generated using benefit

- per - ton

estimates, confidence intervals are not available. In general, the 95th percentile confidence interval for monetized PM 2.5 benefits ranges from approximately

- 90 percent to +180 percent of the central estimates based on Krewski et al. (2009) and Lepeule et al. (2012). b The reduction in premature fatalities each year accounts for over 98% of total monetized benefits in this analysis.

Mortality risk valuation assumes discounting over the SAB

- recommended 20

- year segmented lag structure.



### Figure 6
- 5. Sensitivity Analysis Long
- Term PM 2.5 

### - Related Mortality Co
- Benefits $0 $1,000 $2,000 $3,000 $4,000 $5,000 $6,000 $7,000 $8,000Millions (2011$)



### Dollar benefit (7%) Dollar benefit (3%)

### 6

- 82 Table 6
- 24 . Estimate d Monetized O zone and PM 2.5 

### Benefits for Revised and Alternative Annual O zone Standards Incremental to the Baseline for the 2025 S cenario ( N ationwide B enefits of A ttaining the S tandard s in the U.S. E xcept California)

– Identified + Unidentified Control Strategies (combined) and Identified Control Strategies Only (billions of 2011$) a Discount

Rate Revised and Alternative Standard Levels 70 ppb 65 ppb Identified + Unidentified Control Strategies Ozone
- only Benefits (range reflects Smith et al. ( 2009 ) to Zanobetti and Schwartz ( 2008) ) b $1.0 to $1.7 $5. 3 to $8. 7 PM 2.5 Co
- benefits ( range reflects Krewski et al. ( 2009 ) to Lepeule et al. ( 20 12) 3% $2.1 to $4.7 $10 to $23 7% $1.9 to $4.2 $9.3 to $21 Total Benefits 3% $ 3.1 to $ 6. 4 +B $ 1 6 to $ 32 +B 7% $ 2.9 to $ 5.9 +B $ 1 5 to $ 30 +B Identified Control Strategies Only Ozone
- only Benefits (range reflects Smith et al. ( 2009 ) to Zanobetti and Schwartz ( 2008) ) b $0.8 6 to $1.4 $2. 2 to $3. 5 PM 2.5 Co
- benefits ( range reflects Krewski et al. ( 2009 ) to Lepeule et al. ( 20 12) 3% $1.7 to $3.9 $ 4 .0 to $9.0 7% $1.6 to $3.5 $3.6 to $8.1 Total Benefits 3% $ 2.6 to $ 5.3 +B $ 6.1 to $ 12 +B 7% $ 2.4 to $ 4.9 +B $ 5.7 to $ 1 2 +B

a Rounded to two significant figures. The reduction in premature fatalities each year accounts for over 98% of total monetized benefits in this analysis. Mortality risk valuation for PM 2.5 assumes discounting over the SAB

recommended 20

- year segmented lag structure. Not all possible benefits are quantified and monetized in this analysis. B is the sum of all unquantified health and welfare co

- benefits. Data limitations prevented us from quan tifying these endpoints, and as such, these benefits are inherently more uncertain than those benefits that we were able to quantify. These estimates reflect the e conomic value of avoided morbidities and premature deaths using risk coefficients from the st udies noted. b Ozone

- only benefits reflect short

- term exposure impacts and as such are assumed to occur in the same year as ambient ozone reductions. Consequently, social discounting is not applied to the benefits for this category.



### Table 6
- 25 . Regiona l Breakdown of Monetized O zone - Specific Benefits Results for the 2025 S cenario ( N ationwide B enefits of Attaining the Standards in the U.S. e xcept California)

– Identified + Unidentified Control Strategies a , b Region Revised and Alterative Standard Level s 70 ppb 65 ppb East c 98 % 96% California ~ 0% ~ 0% Rest of West 2 % 4%

a Because we use benefit

- per - ton estimates to calculate the PM 2.5 co - benefits, a regional breakdown for the co

benefits is not available. Therefore, this table only reflects the ozone benefits. b These regional breakdown results reflect application of identified and unidentified control strategies. Regional breakdown results are the same for benefits based on application of identified control strategies only.
c Includes Texas and those states to the north and east.

### 6

- 83 6 .7.2 Benefits of the P ost

- 2025 S cenario This section presents the estimated number and economic value of avoided ozone

- and PM 2.5 

### - related effects associated with attaining a revised ozone standard after 2025 (note, sector

specific NOx emissions reductions levels used in modeling benefits for the post

- 2025 scenario are presented earlier in Table 6

- 19

- see entries under “CA NOx”). In addition, general trends and observations drawn from the sensitivity anal yses presented in Figures 6

- 4 and 6

- 5 hold for the post

- 2025 scenario and for that reason separate plots for these sensitivity analyses (for the post

- 2025 scenario) are not presented (see section 6.7.3 for further discussion ). While simulated attainment of standard levels for the 2025 scenario involved application of both identified and unidentified controls outside of California , simulated attainment of standard levels for the post

2025 scenario involved application exclusively of unidentified controls wit hin California (see Table 6

- 18 and section 2.2.2 ). For that reason, results tables in this section represent exclusively, benefits based on application of unidentifi ed controls.
Table 6
- 26 . Population
- Weighted Air Quality Change for the Revised and Altern ative Annual Primary O zone Standards Relative to Baseline for P ost - 2025 a
Standard Po pulation
- Weighted Ozone Season O zone Concentration Change (8
- h ou r max) b 70 ppb 0.1708 65 ppb 0.3464

a Because we used benefit

- per - ton estimates for the PM 2.5 co - benefits, population

- weighted PM 2.5 changes are not available. b Population weighting based on all ages (demographic used in modeling short

- term exposure

- related mortality for ozone) for 2025.

### 6

- 84 Table 6
- 27 . Estimated Number of Avoided O zone - Related Health Impacts for the Revised and Alternative Annual Ozone Standards (Incremental to the Baseline) for the Post
- 2025 S cenario ( N ationwide B enefits of Attaining the Standards j ust in California) a , b

a All incidence estimates are rounded to whole numbers with a maximum of two significant digits. b All incidence estimates are based on ozone

- only models unless otherwise noted. d The negative estimates at the 5 th percentile confidence estimates for these morbidity endpoints reflect the statistical power of the studies used to calculate these health impacts. These results do not suggest that reducing air pollution results in additional health impacts. 70 ppb 65 ppb

Avoided Short-Term Mortality 72 150 (35 to 110) (71 to 220) 120 240 (64 to 180) (130 to 350) Avoided Long-term Respiratory Mortality 290 590 (98 to 480) (200 to 970) Avoided Morbidity 140 270 (-32 to 300) (-65 to 610) 360 720 (33 to 1,100) (67 to 2,200) 160,000 330,000 (-49,000 to 320,000) (-100,000 to 650,000) 320,000 660,000 (130,000 to 510,000) (270,000 to 1,000,000) 120,000 240,000 (42,000 to 260,000) (85,000 to 530,000) Asthma exacerbation (age 6-18) d Minor restricted-activity days (age 18-65) School Loss Days (age 5-17) Hospital admissions - respiratory (age 65+) d Emergency department visits for asthma (all ages) multi-city study Health Effectb Revised and Alterative Standard Levels (95th percentile confidence intervals)

multi-city studies Smith et al. (2009) (all ages) Zanobetti and Schwartz (2008) (all ages) Jerrett et al. (2009) (30-99yrs) copollutants model (PM 2.5 )

### 6

- 85 Table 6
- 28 . Total Monetized O zone - Only Benefits for the Revised and Alternative Annual O zone Standards (Incremental to the Baseline) for the P ost - 2025 S cenario ( N ationwide B enefits of Attaining the Standards just in California) ( millions of 2011) a

a All benefits estimates are rounded to who le numbers with a maximum of two significant digits. The monetized value of the ozone

- related morbidity benefits are included in the estimates shown in this table for each mortality study 

### 70 ppb 65 ppb

Avoided Short-Term Mortality - Core Analysis $790 $1,600 ($74 to $2,200) ($150 to $4,500) 1,300 2,600 ($120 to $3,600) ($240 to $7,200) multi-city studies Smith et al. (2009) (all ages) Zanobetti and Schwartz (2008) (all ages) Health Effectb (95th percentile confidence intervals) Revised and Alterative Standard Levels

### 6

- 86 Table 6
- 29 . Estimated Number of Avoided PM 2.5 

### - Related Health Impac ts for the Revised and Alternative Annual O zone Standards (Incremental to the Baseline) for the P ost - 2025 S cenario ( N ationwide B enefits of Attaining the Standards j ust in California) a

a All incidence estimates are rounded to whole numbers with a maximum of two significant digits. Because these estimates were generated using benefit

- per - ton estimates, confidence intervals are not available. In general, the 95 th percentile confidence inter val for the health impact function alone ranges from approximately ±30 percent for mortality incidence based on Krewski et al. (2009) and ±46 percent based on Lepeule et al. (2012). 70 ppb 65 ppb Avoided PM2.5-related Mortality Krewski et al. (2009) (adult mortality age 30+) 43 84 Lepeule et al. (2012) (adult mortality age 25+) 98 190 Woodruff et al. (1997) (infant mortality) <1 <1 Avoided PM2.5-related Morbidity Non-fatal heart attacks Peters et al. (2001) (age >18) 51 100 Pooled estimate of 4 studies (age >18) 6 11 Hospital admissions—respiratory (all ages) 13 26 Hospital admissions—cardiovascular (age > 18) 16 31 Emergency department visits for asthma (all ages) 23 44 Acute bronchitis (ages 8–12) 64 130 Lower respiratory symptoms (ages 7–14) 820 1,600 Upper respiratory symptoms (asthmatics ages 9–11) 1,200 2,300 Asthma exacerbation (asthmatics ages 6–18) 1,900 3,600 Lost work days (ages 18–65) 5,300 10,000 Minor restricted-activity days (ages 18–65) 31,000 61,000 Revised and Alterative Standard Levels Health Effectb

### 6

- 87 Table 6
- 30 . Monetized PM 2.5 

### - Related Health Co
- Benefits for the Revised and Alternative Annual O zone Standards (Incremental to Baseline) for the P ost - 2025 S cenario ( N ationwide B enefits of Attaining the Standards j ust in California) ( Millions of 2011 $ ) a,b Revised and Alterative Standard Level s Monetized Benefits 70 ppb 65 ppb 3% Discount Rate Krewski et al. (2009) (adult mortality age 30+) $4 00 $ 79 0 Lepeule et al. (2012) (adult mortality age 25+) $91 0 $1, 800 7% Discount Rate Krewski et al. (2009) (adult mortality age 30+) $37 0 $71 0 Lepeule et al. (2012) (adult mortality age 25+) $82 0 $1,6 00

a All estimates are rounded to two significant digits. Because these estimates were generated using benefit

- per - ton

estimates, confidence intervals are not available. In general, the 95 th percentile confidence interval for monetized 

### PM 2.5 benefits ranges from approximately

- 90 percent to +180 percent of the central estimates based on Krewski et al. (2009) and Lepeule et al. (2012). b The reduction in premature fatalities each year account s for over 98% of total monetized benefits in this analysis.

Mortality risk valuation assumes discounting over the SAB

- recommended 20

- year segmented lag structure.



### Table 6
- 31 . Estimate of Monetized O zone and PM 2.5 

### Benefits for Revised and Alternative Annual O zone Standards Incremental to the Baseline for the P ost - 2025 S cenario ( N ationwide B enefits of Attaining the Standards j ust in California)

– Identified + Unidentified Control Strategies (billions of 2011$) a Discount

Rate Revised and Alternative Standard Levels 70 ppb 65 ppb Identified + Unidentified Control Strategies Ozone
- only Benefits (range reflects Smith et al. ( 2009 ) to Zanobetti and Schwartz ( 2008) ) b $0.7 9 to $1. 3 $1. 6 to $2. 6
PM 2.5 Co
- benefits ( range reflects Krewski et al. ( 2009 ) to Lepeule et al. ( 20 12) 3% $0.40 to $0.91 $0.79 to $1.8 7% $0.37 to $0.82 $0.71 to $1.6 Total Benefits 3% $ 1.2 to $ 2. 2 +B $ 2. 4 to $ 4. 4 +B 7% $ 1. 2 to $ 2.1 +B $ 2.3 to $ 4. 2 +B

a Rounded to two significant figures. The reduction in premature fatalities each year accounts for over 98% of total monetized benefits in this analysis. Mortality risk valuation for PM 2.5 assumes discounting over the SAB

recommended 20

- year segmented lag s tructure. Not all possible benefits are quantified and monetized in this analysis. B is the sum of all unquantified health and welfare co

- benefits. Data limitations prevented us from quantifying these endpoints, and as such, these benefits are inherently m ore uncertain than those benefits that we were able to quantify. These estimates reflect the e conomic value of avoided morbidities and premature deaths using risk coefficients from the studies noted. b Ozone

- only benefits reflect short

- term exposure impacts and as such are assumed to occur in the same year as ambient ozone reductions. Consequently, social discounting is not applied to the benefits for this category.

### 6

- 88 Table 6
- 32 . Regional Breakdown of Monetized O zone - Specific Benefits Results for the Post
- 2025 S cenario ( N ationwide B enefits of Attaining the Standards j ust in California)

– Identified + Unidentified Control Strategies a Region Revised and Alterative Standard Level s 70 ppb 65 ppb East b 3 % 2 % California 90 % 91 % Rest of West 7 % 7 %

a Because we use benefit

- per - ton estimates to calculate the PM 2.5 co - benefits, a regional breakdown for the co

benefits is not available. Therefore, this table only reflects the ozone benefits. b Includes Texas and those states to the north and east.



### 6 .7.3 Uncertainty in Benefits Results (including Results of S ensitivity Analyses and S upplemental A nalyses) A voided ozone and PM 2.5 

### related premat ure deaths account for 94% to 96 % of the total monet ized benefits. This is true in part because we are unable to quantify many categories of benefits. The next largest benefit is for reducing the incidence of nonfatal heart attacks. The remaining categories each account for a small percentage of total monet ized benefit s ; however, the se outcomes occur among a significantly larger population . Comparing an incidence table to the monet ized benefits table reveals that the number of incidences avoided and the unit value for that endpoint do not always closely correspond. For example, for ozone we estimate almost 1,000 times more cases of exacerbated ozone would be avoided than premature deaths , yet cases of exacerbated asthma account for only a very small fraction (<1%) of total monetized benefits (see Table 6

- 2 0 ). This is because many of the less severe health effects, while more common, are valued at a lower level than the more severe health effects. Also, some effects, such as hospital admissions, are valued using a proxy measure of WTP. As such, the true val ue of these effects may be higher than that reported in the tables above. Sources of uncertainty associated with both the modeling of ozone

- related benefits and PM 2.5 

### - related co

- benefits are discussed qualitatively in Appendix 6 A.

PM 2.5 

### mortality co

- benef its represent a substantial proportion of total monetized benefits (over 98% of the co

- benefits), and these estimates have the following key assumptions and uncertainties.

• We assume that all fine particles, regardless of their chemical composition, are equ ally potent in causing premature mortality. This is an important assumption, because PM 2.5

### 6

- 89 produced varies considerably in composition across sources, but the scientific evidence is not yet sufficient to allow differential effects estimates by particle typ e. The PM ISA, which was twice reviewed by SAB

- CASAC, concluded that “many constituents of PM 2.5 

### can be linked with multiple health effects, and the evidence is not yet sufficient to allow differentiation of those constituents or sources that are more clos ely related to specific outcomes” (U.S. EPA, 2009b).

• We assume that the health impact function for fine particles is log

- linear without a threshold in this analysis. Thus, the estimates include health benefits from reducing fine particles in areas with varied concentrations of PM 2.5 

### , including both areas that do not meet the fine particle standard and those areas that are in attainment, down to the lowest modeled concentrations.

• We assume that there is a “cessation” lag between the change in PM exposures and the total realization of changes in mortality effects. Specifically, we assume that some of the incidences of premature mortality related to PM 2.5 

### exposures occur in a distributed fashion over the 20 years following exposure based on the advice of the SAB

- HES (U.S. EPA

- SAB, 2004c), which affects the valuation of mortality benefits at different discount rates.

• We recognize uncertainty associated with application of the benefit

- per - ton approach used in modeling PM 2.5 

### co - benefits. The benefit

- per - ton est imates used here reflect specific geographic patterns of emissions reductions and specific air quality and benefits modeling assumptions associated with the derivation of those estimates (see the TSD describing the calculation of the national benefit

- per - t on estimates (U.S. EP A, 2013b) and Fann et al. (2012b) . Consequently, these estimates may not reflect local variability in population density, meteorology, exposure, baseline health incidence rates, or other local factors associated with the current ozone NAAQS review. Therefore, use of these benefit

per - ton values to estimate co

- benefits may lead to higher or lower benefit estimates than if co

- benefits were calculated based on direct air quality modeling. 6 .7.3.1 Sensitivity Analyses This RIA includes a n umber of sensitivity analyses. Given that the size of the monetized benefits is sensitive to the number of estimated ozone and PM 2.5 

### related premature deaths , the sensitivity analyses focus largely on the mortality endpoint. Each sensitivity analysis is br iefly described below , including an overview of the approach used in conducting the analysis and key observations. See Appendix 6B for more detailed results.

• Short
- term ozone - exposure related mortality (alternative epidemiological studies and C
- R function s): W e estimated the number of premature deaths using seven additional effect estimates including four multi

- city studies and three meta

- analysis studies. This sensitivity analysis showed that the two core incidence and benefits

### 6

- 90 estimates fall within (and towards the lower end of) the broader range resulting from application of the seven alternative ef fect estimates (see Figure 6

- 4 ).

• Economic value of avoided premature deaths from long
- term exposure to ozone : We estimated the economic valu e of long

- term ozone mortality u sing two cessation lag structu res: 20

- year segment lag (as used for PM 2.5 

### ) and a zero lag. The sensitivity analysis suggests that if included in the core benefit estimate, long

- term ozone exposure

related mortality could add substantially to t he overall benefits . Additionally, use of a 20

- year segment lag can reduce benefits by 10

- 20% (relative to a zero lag) depending on the discount rate applied.

• Long
- term ozone - exposure related respiratory mortality and potential threshold s: W e evaluated the impact of several assumed thresholds ranging from 40

- 60 ppb

. This sensitivity analysis suggested that a threshold of 50 ppb or greater could have a substantial impact on estimated benefits, while thresholds below this range have a relat ively minor impact.

• Long
- term PM 2.5 

### exposure - related mortality and alternative C
- R functions (based on the E xpert E licitation ): W e applied the set of expert elicitation

- based functions to generate an alternative set of PM 2.5 

### b enefit estimates (see Figure 6

- 5) . The estimates based on Krewski et al. (2009) and Lepeule et al. (2012) fall within the range of estimates based on the functions from the 2006 expert elicitation

.

• Income elasticity for mortality and certain morbidity endpoints: W e examined the impact of alternative assumptions regarding income elasticity (i.e., the degree to which WTP changes as income changes) and the degree of impact on WTP functions used for mortality and for morbidity endpoints. That sensitivity analysis suggests that alternative assumptions regarding income elasticity could result in a moderate impact on mortality benefits (values ranging from ~90% to ~130% of the core estimate depending on the assumption regarding elasticity). Income elasticity was found to have a far more modes t impact on morbidity endpoints modeled using WTP functions. 6 .7.3.2 Supplemental Analyses In addition to the sensitivity analyses, we also included a number of supplemental analyses intended to examine other attributes of the core risk estimates

. T hese are briefly described below with addit ional detail found in Appendix 6 B

.

• Ozone
- related premature deaths by age : These analyses report: (a) the number of avoided ozone

- related deaths by age range ; (b) the estimate d number of life years gained by age range ; and (c) the percent reduction in baseline mortality by age range. We observe that about half of the ozone

- attributable deaths occur in popul ations age 75

– 99 , but half of the life years would occur in populations younger than 65. This is because the younger populations have the potential to lose more life years per death than older populations

. The percent reduction in total all

- cause mortality attributable to simulated attainment of both the revised and alternative standards are relatively small and fairly

### 6

- 91 consistent across age ranges (see Appendix B, Table 6B

- 11). However, estimates of total ozone - attributable mortality associated with attainment of both standards is not evenly distributed across age ranges with the vast majority of projected avoided deaths occurring in older age groups (see Appendix B, Table 6B

- 9).

• Avoided deaths according to baseline pollutant concentrations : We describe the summer season average 8

- h ou r max ozone levels at which the avoided deaths occurred. We found that: (a) the vast maj ority of avoided short

- term ozone - related deaths occur in grid cells with mean 8

- h ou r max baseline levels between 35 and 55 ppb ; and (b) virtually all of the avoided deaths are associate d with ozone levels above the 20

- 40 ppb range , where we are less confident in the shape of the C

- R function for ozone mortality (ozone ISA, section 2.5.4.4). For PM 2.5 

### mortality , we evaluated the lowest measured level (LML) associated with the epidemiological studies providing the effect estimates. W e are less confident in specifying the shape of the C

- R function at exposure levels below the range observed in the epidemiological study (i.e., below the LML). In the supplemental analysis, we found that, depending on the mortality study, between 67% and 93% of the mortality estimate is based on modeling involving PM 2.5 

### levels above the LML.

• Value of increased productivity among outdoor agricultural worker s due to reduced exposure to ozone : Using information from the Graff Zivin and Neidell (2012) study, we estimate the econo mic value of improved productivity among outdoor non

- livestock workers for the ozone standard s in 2025. We estimate the monetized worker productivity benefits of attaining a 70 ppb standard would be about $1.7 million and a 65 ppb standard would yield mone tized benefits of about $8.9 million

. 6 .8 Discussion Th is analysis demonstrates the potential for significant health benefits of the illustrative emission s controls applied to simulate attainment with the revised and alternative primary ozone standard level s. We estimate that by 2025, the emissions reductions to reach the revised and alternative standard level s everywhere except California, would have reduced the number of

ozone - and PM 2.5 

### - related premature mortalities and produce substantial non

- morta lity benefits. Furthermore, emissions reductions required to meet alternative standards in California post

- 2025 are also likely to produce substantial reductions in these same endpoints. This rule would also yield significant welfare impacts as well (see C hapter 7 ). Even considering the quantified and unquantified uncertainties identified in this chapter, we believe that the revised and alternative standard s would have substantial public health benefits that are likely to outweigh the costs of the control s trategies for the revised and alternative standard level s analyzed (see Chapter 4 ). Inherent in any complex RIA such as this one are multiple sources of uncertainty. Some of these we characterized through our quantification of statistical error in the C

- R relationships

### 6

- 92 and our use of alternate mortality functions. Others, including the projection of atmospheric conditions and source

- level emissions, the projection of baseline morbidity rates, incomes and technological development are unquantified. When eva luated within the context of these uncertainties, the health impact and monetized benefits estimates in this RIA can provide useful information regarding the public health benefits associated with the revised and alternative standard level s.
As discussed i n Chapter 1, t here are important differences worth noting in the design and analytical objectives of NAAQS RIAs compared to RIAs for implementation rules, suc h as the Tier 3 (U.S. EPA, 2014a ). S etting a NAAQS does not directly result in costs or benefits

. The NAAQS RIAs illustrate the potential costs and benefits of the revised and alternative air quality standard s nationwide based on an array of emission s reduction strategies for different sources, incremental to implementation of existing regulations and controls needed to attain the current standards. In short, NAAQS RIAs hypothesize, but do not predict, the emission s reduction strategies that States may choose to enact when implementing a revised NAAQS, and as such, b y contrast, the emission s reductions from implementation rules are generally for specific, well

characterized sources, such as the recent MATS rule (U.S. EPA, 2011c ). In general, the EPA is more confident in the magnitude and location of the emission s reductions for implementation rules. As such, emission s reductions achieved under promulgated implementation rules , such as MATS, have been reflected in the baseline of this NAAQS analysis. 40 

### For this reason, the benefits estimated provided in this RIA and all other NAAQS RIAs should not be added to the benefits estimated for implementation rules. Subsequent implementation rules will be reflected in the baseline for the next ozone NAAQS review. In setting the NAAQS, the EPA considers that ozone concentrati ons vary over space and time. While the standard is designed to limit concentrations at the highest monitor in an area, it is understood that emission s controls put in place to reduce concentrations at the highest monitor will simultaneously result in lowe r ozone concentrations throughout the entire area. In fact, the

ozone H R E A (U.S. EPA, 2014b) shows how different standard levels would affect the entire distribution of ozone concentrations, and thus people’s exposures and risk, across a selected set

40 The full set of rules reflected in the baseline are presented in Chapter 2, Section 2.1.3.

### 6

- 93 of ur ban areas. For this reason, it is inappropriate to use the NAAQS level as a bright line for health effects. The NAAQS are not set at levels that eliminate the risk of air pollution completely. Instead, the Administrator sets the NAAQS at a level requisite to protect public health with an adequate margin of safety, taking into consideration effects on susceptible populations based on the scientific literature. The risk analysis prepared in support of this ozone NAAQS reported risks below these levels, while acknowledging that the confidence in those effect estimates is higher at levels closer to the standard (U.S. EPA, 2014b). While benefits occurring below the standard may be somewhat more uncertain than those occurring above the standard, the EPA considers these to be legitimate components of the total benefits estimate. Though there are greater uncertainties at lower ozone and PM 2.5 

### concentrations, there is no evidence of a threshold in short

- term ozone or PM 2.5 

### - related health effects in the epidemiology li terature. Given that the epidemiological literature in most cases has not provided estimates based on threshold models, there would be additional uncertainties imposed by assuming thresholds or other non

- linear C

- R functions for the purposes of benefits an alysis.
The estimated benefits shown here are in addition to the substantial benefits estimated for several recent air quality rule s (U.S. EPA, 2009a, 2011c , 2014a ). Emissions reductions from

r ules such as Tier 3 will have substantially reduced ambient ozo ne concentrations by 2025 in the East, such that few additional controls would be needed to reach 70 ppb. These rules that have already been promulgated have tremendous combined benefits that explain why the number of avoided premature deaths associated wi th this NAAQS revision are smaller than were estimated in the previous ozone NAAQS RIA (U.S. EPA, 2006) for the year 202 0 and even smaller than the mortality risks estimated for the current year in the ozone HREA (U.S. EPA, 2014b).

### 6

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### Chappell, Regina
From: Sasser, Erika Sent: Wednesday, September 16, 2015 5:46 PM To: Frey, Nathan J. Cc: Aaron_L_Szabo@omb.eop.gov; Cooperstein, S haron; Weatherhead, Darryl; Chappell, Regina; Langdon, Robin; Rush, Alan; Henigin, Mary; Hetes, Bob; Pekar, Zachary Subject: Part of E.O. 12866 Review: Ozone NAAQ S RIA Chapters & Appendices RIN2060-AP38 Attachments: chapter6.docx; appendix-6A .docx; appendix-6B.docx


### Nathan,
As requested, attached please find Microsoft word versions of the Benefits chapter and associated appendices from the draft Final Ozone NAAQS RIA. Please let me know if you have any questions. Thanks, Erika