### 5

- 1

CHAPTER 5: HUMAN HEALTH BENEFITS ANALYSIS APPROACH AND RESULTS
5.1 Synopsis This chapter presents the estimated human health benefits for the proposed range of 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 control scenarios that reduce emissions of the ozone precursor pollutants nitrogen oxides (NO x 

### ) and volatile organic compounds (VOCs) to reach the set of alternative ozone NAAQS levels being considered. This chapter also estimates the health co

- benefits of the fine particulate matter (PM 2.5 

### )

- related air quality improvements that would oc cur as a result of reducing NOx emissions. 1 

### The EPA Administrator is proposing to revise the level of the primary ozone standard to within a range of 65 to 70 ppb and is soliciting comment on alternative standard levels below 65 ppb, and as low as 60 ppb. In the Regulatory Impact Analysis (RIA) we analyze the following alternative standard levels: 70, 65 and 60 ppb. 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. Benefits are estimated incremental to attainment of the existing standard of 75 ppb. In estimating the incremental costs and benefits of potential alternative standards, we recognize that there are several areas that the Act does not require to me et the existing ozone standard of 75 ppb by 2025. The Clean Air Act provides areas with more significant air quality problems with additional time to reach the existing standard. Several areas in California are not expected to meet the existing standard by 2025 and may not be required to meet a revised standard until 2038. 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 at a later date th an the rest of the U.S. We assume that projected nonattainment areas everywhere in the U.S. excluding California will be designated such that they attain a revised standard by 2025, and we develop our projected baseline emissions, air quality, and populati on estimates for 2025. We also assume

1 VOC reductions associated with simulated attainment of alternative ozone standards also have the potential to impact PM 2.5 concentrations, but we are not able to model those effec ts at this time.

### 5

- 2

that the projected nonattainment areas in California will be designated such that they reach attainment by approximately 2038. We were not able to project baseline emissions and air quality levels beyond 2025 for Ca lifornia for sectors other than mobile sources. We account for changes in mobile source precursor emissions expected to occur between 2025 and 2030. W hile there is uncertainty about the precise timing of e missions reductions and related costs for Californ ia , we assume costs
occur through 2038. We also model benefits for California accounting for population growth to 2038 (see section 5.6.1). Because we were unable to account for the change in emissions from other sectors our projected baseline may under

- or over - estimate the post

- 2025 ozone levels in California. In this analysis, we refer to estimates of nationwide benefits 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 incremental costs and benefits for these two distinct scenarios reflecting attainment in different years, it is not appropriate to either sum, o r directly compare, the estimates. Consequently, in presenting both incidence and dollar benefit estimates in this chapter, we present and discuss the 2025 scenario and post

- 2025 scenario in separate sections (see sections 5.7.1 and 5.7.2, respectively). Benefits estimated for the 2025 and post

- 2025 scenarios are relative to an analytical baseline in which the nation attains the current primary ozone standard (i.e., 4 th 

### highest daily maximum 8

- hour ozone concentration of 75 ppb) and incorporates promulga ted national regulations and illustrative emission controls to simulate attainment with 75 ppb. Table 5

- 1 summarizes the total estimated monetized benefits (total and ozone only) of attaining alternative ozone standards of 70 ppb, 65 ppb, and 60 ppb for t he 2025 scenario (i.e., nationwide benefits of attaining everywhere in the U.S. but California). Table 5

- 2 presents the same types of benefit estimates for the post

- 2025 scenario (i.e., nationwide benefits of attaining just in California). These estimate s reflect the sum of the economic value of estimated morbidity and mortality effects related to changes in exposure to ozone and fine particulate matter (PM 2.5 

### ). However, it is important to emphasize that it is not appropriate to compare the ozone

- only ben efits to total

### 5

- 3

costs. There are additional unquantified benefits which are described in Section 5.2. The estimated benefits for attaining the proposed standards are incremental to the substantial benefits estimated for several recent implementation rules (e.g., U.S. EPA, 2011e, 2014a).
Table 5
- 1 . Estimated Monetized Benefits of Attainment of the Alternative O zone Standards

for the 2025 Scenario (nationwide benefits of attaining each alternative standard everywhere in the U.S. except California)

– Full Attainment (billions of 20 11$) a

Discount Rate 70 ppb 65 ppb 60 ppb Ozone
- only Benefits (range reflects Smith et al., 2009 and Zanobetti and Schwartz, 2008) b $2.0 to $3.4 +B $6.4 to $11 +B $12 to $20 +B
PM 2.5 Co
- benefits ( range reflects Krewski et al., 2009 and Lepeule et al. , 20 12) 3% $4.8 to $11 $14 to $31 $25 to $56 7% $4.3 to $9.7 $12 to $28 $22 to $50 Total Benefits 3% $ 6.9 to $ 14 +B $ 20 to $ 41 +B $ 37 to $ 75 +B 7% $ 6.4 to $ 13 +B $ 19 to $ 38 +B $ 34 to $ 70 +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 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 n ot applied to the benefits for this category.



### Table 5
- 2 . Estimated Monetized Benefits of Attainment of the Alternative O zone Standards

for the Post

- 2025 Scenario (nationwide benefits of attaining each alternative standard just in California)

– Full Attainment (billions of 2011$) a Discount Rate 70 ppb 65 ppb 60 ppb Ozone
- only Benefits (range reflects Smith et al., 2009 and Zanobetti and Schwartz, 2008) b $0.66 to $1.1 $1.4 to $2.4 $2.1 to $3.6
PM 2.5 Co
- benefits ( range reflects Krewski et al., 2009 and Lepeule et al. , 20 12) 3% $0.42 to $0.95 $0.83 to $1.9 $1.1 to $2.6 7% $0.38 to $0.86 $0.75 to $1.7 $1.0 to $2.3 Total Benefits 3% $ 1.1 to $ 2. 0 +B $ 2.3 to $ 4.2 +B $ 3.4 to $ 6.2 +B 7% $ 1.1 to $ 2. 0 +B $ 2.2 to $ 4.1 +B $ 3.2 to $ 5.9 +B

### 5

- 4 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 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 discounti ng is not applied to the benefits for this category.



### In addition to ozone and PM 2.5 

### benefits, implementing emissions controls to reach some of the alternative ozone standards would reduce other ambient pollutants, such as VOCs and NO 2 

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

benefits of reduced exposure to these pollutants. In addition, due to data and methodolo gy limitations, we were unable to estimate some anticipated health benefits associated with exposure to ozone and PM 2.5 

### . 5.2 Overview This chapter presents estimated health benefits for three alternative ozone standards (70, 65 and 60ppb) that the EPA coul d quantify, given the available resources, data and methods. Separate set of benefits are presented for the 2025 scenario, representing nationwide benefits of attaining an alternative standard everywhere in the U.S. but California in 2025 and the post

- 2025 scenario, representing nationwide benefits of attaining an alternative standard just in California in 2038. This chapter characterizes the benefits of implementing new ozone standards by answering three key questions:
1. What health effects are avoided by reducing ambient ozone levels to attain a revised ozone standard? 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 emissions of ozone precursors (specifically NOx)?

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

### . The Integrated Science Assessment for Ozone and Related Photochemical Oxidants (“ozone ISA”) (U.S. EPA, 2013a) identifies the human health effects associated with ozo ne exposure, which include premature death and a variety of illnesses associated with acute (days

long) and chronic (months to years

- long) exposures. Similarly, the Integrated Science

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Assessment for Particulate Matter (“PM ISA”) (U.S. EPA, 2009b) identifie s the human health effects associated with ambient particles, which include premature death and a variety of illnesses associated with acute and chronic exposures. Air pollution can affect human health in a variety of ways, and in Table 5

- 3 we summarize th e “categories” of effects and describe those that we could quantify in our “core” benefits estimates and those we were unable to quantify because we lacked the data, time or techniques. This list of unquantified benefit categories is not exhaustive and we are not always able to quantify each effect completely. Endpoints that the ozone and PM ISAs classified as causal or likely causal we quantified with confidence. We excluded from quantification effects not identified as having at least a causal or , likely causal relationship , or suggestive relationship with the affected pollutants. Selecting endpoints in this way should not imply that these pollutants are unrelated to other human health and environmental effects. Following this criterion, we exclude d 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 causal or likely causal relationship with ozone. In designing this benefits analysi s, including the identification of endpoints to include in the core estimate, we also considered the design of the Health Risk and Exposure Assessment (HREA) completed as part of this ozone NAAQS review (USEPA, 2014b). The design and implementation of the HREA was subjected to rigorous peer review by the Science Advisory Board’s (SAB’s) Clean Air Scien tific Advisory Committee (CASAC) with the results of that review being presented in letter form ( Frey, and Samet 2012 for the first draft and Frey, 2014 for t he second draft of the HREA) . The overall design of the HREA, including the health endpoints selected for modeling was supported by the CASAC. 2 

### This benefits analysis relies on an array of data inputs

— including emissions estimates, modeled ozone air qual ity, 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 d escribed in detail in sections 5.5 and 5.7.3.

2 The CASAC expressed their support for the overall design of the HREA, including endpoints selected and epidemiological studies used in supplying the effect estimates used to model those endpoints (Samet and Frey, 2012, p. 15 and Frey, 20 14, p. 9).

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- 6

Table 5
- 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 Cat egory Specific Effect Effect Has Been Quantified Effect Has

Been Monetized More

Information Improved Human Health Reduced incidence of premature mortality from exposure to ozone Premature mortality based on short

- term

exposure (all ages)  

Section 5.6 Premature respiratory mortality based on long

- term exposure (age 30

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

— respiratory causes

(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)  

See section 5.6 and Appendix 5D 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) — —

### 5

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Benefits Cat egory Specific Effect Effect Has Been Quantified Effect Has

Been Monetized 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) — —

a Due to concerns over translating incidence estimates into dollar benefits, for long

- term ozone exposure

- related respiratory mortality, we included estimates of reduced incidence as part of the core analysis, but included associated dollar benefits as a sensitivity analysis (se e section 5.3). b We are in the process of considering an update to the worker productivity analysis for ozone based on more recent literature (see section 5.6.3.4)

. As noted in section 5.6.3.4 we are requesting public comment on the approach we present for modeling this endpoint and will consider that input in dete rmining whether to proceed with an updated simulation of this endpoint

. c We assess these benefits quali tatively 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 over the strength of the associati on. e We assess these benefits qualitatively due to time and resource limitations for this analysis. 

### As described in Chapter 1 of this RIA, there are important differences worth noting in the design and analytical objectives of NAAQS RIAs compared to RIAs for rules that implement technology standards, such as Tier 3 (U.S. EPA, 2014a). The NAAQS RIAs illustrate the potential costs and benefits of attaining a revised air quality standard nationwide. These analyses simulate an array of strategies to reduce em issions at different sources and may model well

established emission control technologies for sectors and emission controls for which the control technology has not yet been developed (i.e., “unknown” controls). This type of RIA accounts for existing regul ations and controls needed to attain the current standards and so estimated benefits and costs are incremental to attaining the current standard. In short, NAAQS RIAs hypothesize, but do not predict, the emission reduction strategies that States may enact when implementing a

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revised NAAQS. Setting a NAAQS does not result directly in costs or benefits. By contrast, the emission reductions from implementation rules are generally for specific, well

- characterized sources, such as the recent MATS rule addressing emissions from coal and oil

- fired electricity generating units (U.S. EPA, 2011e). In general, the EPA is more confident in the magnitude and location of the emission reductions for implementation rules. As such, emission reductions achieved under promulga ted implementation rules such as MATS have been reflected in the baseline of this NAAQS analysis (the full set of rules reflected in baseline are presented in section 3.1.3). Subsequent implementation rules will be reflected in the baseline for the next

oz one NAAQS review. 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.
5.3 Updated Methodology Presented in this RIA The benefits analysis presented in this chapter incorporates an array of policy and technical changes that the Agency has adopted since the previous review of the ozone standards in 2008 and the proposed reconsideration in 2010. Below we note the aspects of this analysis that differ from t he reconsideration RIA (U.S. EPA, 2010d):
1. The population demographic data in BenMAP

- CE (U.S. EPA, 2014d) reflects the 2010 Census and future projections based on economic forecasting models developed by Woods and Poole, Inc. (Woods and Poole, 2012). These data replace the earlier demographic projection data from Woods and Poole (2007). This update was introduced in the final PM NAAQS RIA (U.S. EPA, 2012b). 2. The baseline incidence rates used to quantify air pollution

- related hospital admissions and emergency department visits and the asthma prevalence rates were updated to replace the earlier rates. This update was introduced in the final CSAPR (U.S. EPA, 2011d). 3. We updated the median wage data in the cost

- of

- illness studies. This update was introduced in the final PM NAAQS RIA (U.S. EPA, 2012b). 4. Updates for ozone

- related effects:

a. Incorporated new mortality studies. We include two new multi

- city studies to estimate deaths attributable to short

- term exposure for the core analysis (Smith et al., 2009 and Zanobet ti and Schwartz 2008). We also estimate long

- term respiratory deaths using Jerrett et al. (2009). While we believe the evidence supports including long

- term respiratory deaths in the core analysis, limitations in our ability to specify a lag between exposu re and the onset of death (i.e. the cessation lag that is required for valuing these deaths) prevents us estimating dollar benefits in the core analysis. Both

### 5

- 9 the new short

- term and long

- term mortality studies were included in the HREA completed in support of this NAAQS review with the overall design of that HREA (including inclusion of these new studies) being subjected to rigorous review by CASAC ( Frey, and Samet 2012; Frey, 2014). b. Incorporated new morbidity studies. The ozone ISA (U.S. EPA, 2013a) identifies several new epidemiological studies examining the association between short

- term ozone exposure and respiratory hospitalizations, respiratory emergency department visits, and exacerbated asthma. Upon carefully evaluating this new literature, we added several new studies to our health impact assessment. Several of these studies were also included in the HREA, which as noted earlier, underwent rigorous review by CASAC.

c. Expanded uncertainty assessment. We added a comprehensive, qualitative assessmen t 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 this expanded assessment in this RIA (see sections 5.5 and 5.7.3). 5. Updates fo r PM 2.5 

### - related effects

a. Incorporated new mortality studies. We updated the American Cancer Society cohort study to Krewski et al. (2009) and updated the Harvard Six Cities cohort study to Lepeule et al. (2012). The effect coefficient for Krewski et al. (20 09) is identical to the previous coefficient, and the Lepeule et al. (2012) is roughly similar to the previous coefficient. Both studies show narrower confidence intervals. The update for the American Cancer Society cohort was introduced in the proposal RI A for the PM NAAQS review (U.S. EPA, 2012b) and the update for the Harvard Six Cities cohort was introduced in the final RIA for the PM NAAQS review (U.S. EPA, 2012b). b. Incorporated new morbidity studies. The epidemiological literature has produced several recent studies examining the association between short

- term PM 2.5 

### exposure and respiratory and cardiovascular hospitalizations, respiratory and cardiovascular emergency department visits, and stroke. Upon careful evaluation of new literature in the PM ISA and Provisional Assessment, we added several new studies and health endpoints to our health impact assessment. These updates were introduced in the proposal (U.S. EPA, 2012) and final RIAs for the PM NAAQS review (U.S. EPA, 2012b).

c. Updated the survival rat es 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 acu te myocardial infarction stratified by age. These rates replaced the survival rates from Rosamond et al. (1999). This update was introduced in the final RIA for the PM NAAQS review (U.S. EPA, 2012b). d. Expanded uncertainty assessment. We clarified the compre hensive assessment of the various uncertain parameters and assumptions within the benefits analysis and

### 5

- 10 expanded the evaluation of air quality benchmarks. This update was introduced in the proposed CSAPR RIA (U.S. EPA, 2010g) and refined in the final PM NA AQS RIA (U.S. EPA, 2012b). Although the list above identifies the major changes implemented since the 2010 ozone reconsideration RIA, the EPA has also updated several additional components of the benefits analysis since the 2008 ozone NAAQS RIA (U.S. EPA, 2008a), which were reflected in the reconsideration RIA. In the Portland Cement NESHAP proposal RIA (U.S. EPA, 2009a), the Agency no longer assumed a concentration threshold in the concentration

- response function for PM 2.5 

### - related health effects and began estimating the benefits derived from the two major cohort studies of PM 2.5 

### and mortality as the core benefits estimates, while still including a range of sensitivity estimates based on the EPA’s PM 2.5 

### mortality expert elicitation. In the NO 2 

### NAAQS proposal RIA (U.S. EPA, 2009a), we revised the estimate used for the value

- of

- a - statistical life to be consistent with Agency guidance.
5.4 Human Health Benefits Analysis Methods We follow a “damage

- function” approach in calculating total benefits of the modeled changes in environmental quality. 3 

### This approach estimates changes in individual health endpoints (specific effects that can be associated with changes in air quality) and assigns values to those changes assuming independence of the values for those indivi dual 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 us ed in several recent published analyses (Levy et al., 2009; Fann et 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 quality can be directly valued, as is the case for changes in visibility. In other cases, such as for changes in ozone and PM, an impact analysis must first be conducted to convert air quality changes into effects that can be assigned dollar

3 The damage function approach is a more comprehensive method of estimating total benefits than the hedonic price approach applied to housing prices, which requires homebuyers to be knowledgeable of the full magnitude of health risks associated with their home purchase.

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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

ozo ne and 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 recent healt h 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 measure of benefits for the environmental quality change under analysis. 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 estimate s.
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 alternative standard levels) were explicitly modeled and then translat ed into reductions in the incidence of specific health endpoints. In generating ozone benefits estimates for the two scenarios considered in the RIA (2025 and post

- 2025), we actually utilized three distinct benefits simulations including one completed for 2025 and two completed for 2038. The way in which these three benefits simulations were used to generate estimates for the two time periods reported in the RIA (2025 and post

- 2035) is described in section 5.4.3.
In contrast to ozone, we used a benefit

- per - ton (reduced form) approach in modeling PM 2.5


### co - benefits (see section 5.4.4 for additional detail) . With this approach, we utilize the results of previous benefits analysis simulations focusing on PM 2.5 

### to derive benefits

- per - ton estimates for NOx. 4 

### We t hen combine these dollar

- per - ton estimates with projected reductions in NOx associated with meeting a given alternative standard to project co

- benefits associated with PM 2.5 

### . We acknowledge increased uncertainty associated with the dollar

- per - ton approach for PM 2.5, 

### relative to explicitly modeling benefits using gridded PM 2.5 

### surfaces specific to the baseline and

4 In addition to dollar

- per - ton estimates for NOx, we also utilized incidence

- per - ton values (also for NOx) for specific health endpoints in order to generate incidence reduction estimates associated with the dollar benefits.

### 5

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alternative scenarios being considered in this review (see sections 5.4.4 and 5.7.3, and Appendix 5A, Table 5A

- 1 for additional discussion).
Sect ions 5.4.1 and 5.4.2 describe respectively, the underlying basis for the health and economic valuation estimates. Section 5.4.3 describes the procedure used to combine the three benefits simulations referenced above in order to generate benefits for the tw o time periods considered in the RIA (2025 and post

- 2025). Finally, section 5.4.4 provides an overview of the benefit

- per - ton estimates used to estimate the PM 2.5 

### co - benefits from NOx emission reductions in this RIA.
5.4.1 Health Impact Assessment The heal th 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 chang e in adverse health impacts expected to result from population

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

- based tools such as the environmental Benefits Mapping and Analysis Program

– Community Edition (BenMAP

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

— provided that key input data are available, including air quality estimates and risk coefficients (U.S. EPA, 2014d). Analysts hav e applied the HIA approach to estimate human health impacts resulting from hypothetical changes in pollutant levels (Hubbell et al., 2005; Tagaris 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 air quality (e.g., U.S. EPA, 2014d). For this assessment, the HIA is limited to those health effects that are directly linked to ambient ozone and PM 2.5 

### concentrations. T here may be other indirect health impacts associated with implementing emissions controls, such as occupational health exposures. The HIA approach used in this analysis involves three basic steps: (1) utilizing projections of ozone air quality 5 

### and estimat ing the change in the spatial distribution of the

5 Projections of ambient ozone concentrations for this analysis were generated by applying emissions reductions described in chapters 3 and 4 to gridded surfaces of recent

- year ozone concentrations. The full methodology which incorporates information both f rom ambient measurements and from photochemical modeling simulations is describ ed in section 3.4.1 of this RIA.

### 5

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ambient air quality; (2) determining the subsequent change in population

- level exposure; (3) calculating health impacts by applying concentration

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

∆ y = 1

− ( e β ∙ ∆ x 

### ) y o 

### ∙ Pop (5.1) 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 5

- 1 provides a simplified overview of this approach.

Figure 5
- 1 . Illustration of BenMAP
- CE Approach 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

### 5

- 14

5.4.2 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 t herefore ex ante willingness to pay (WTP) for changes in risk. Epidemiological studies generally provide estimates of the relative risks of a particular health effect for a given increment of air pollution (often per 10 ppb ozone). These relative risks 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 inciden ce. This measure is calculated by dividing individual WTP for a risk reduction by the related observed change in risk. For some health effects, such as hospital admissions, WTP estimates are generally not available. In these cases, we use the costs of trea ting or mitigating the effect, which generally understate the true value of reductions 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 1.0.8 (U.S. EPA, 2014d) to e stimate the health impacts and monetized health benefits for the proposed standard range. Figure 5

- 2 shows the data inputs and outputs for the BenMAP

- CE program. 6

6 The environmental Benefits Mapping and Analysis Program

— Community Edition (BenMAP

- CE) is an open

source PC

- based tool that quantifies the numb er and economic value of air pollution

- related deaths and illnesses. As compared to the version that it replaces, BenMAP v4, the BenMAP

- CE tool uses the same computational algorithms and input data to calculate incidence counts and dollar values

— for a give n 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; (2) it is written in C#, wh ich is both more broadly used and modern than the code it replaces (Delphi). BenMAP

- CE was last used to support the Ozone Health Risk and Exposure Assessment completed in support of the current review

.

### 5

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Figure 5
- 2 . Data Inputs and Outputs for the BenMAP
- CE Program 5.4.3 Estimating Benefits for th e 2025 and Post

- 2025 Scenarios As described in section 5.1, we estimated benefit for two scenarios: 2025 and post

- 2025. The need for these two distinct time periods reflects the fact that, while most of the U.S. will have attained both the current and any alternative standard by 2025, there are portions of the country with more significant air quality problems (including several areas in California) that may not be required to meet a n alternative standard until as late as 2038. Consequently, for each altern ative standard we model a 2025 scenario reflecting the nationwide benefits of attaining that standard everywhere in the U.S. except California. We then model a post

- 2025 scenario, which represents nationwide benefits from attaining that same standard in Ca lifornia. Due to the temporal disconnect between these two scenarios, we do not attempt to sum these two estimates, but instead present each estimate in separate sections of this document (sections 5.7.1 and 5.7.2, respectively). Our approach for estimatin g the benefits of attaining alternate ozone standards post

- 2025 is illustrated in Figure 5

- 3; in this figure, Simulation A represents our approach for estimating the

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 Census Population Data

### 5

- 16

benefits of attaining alternate ozone standards in every state except California in 2025. We first estimated the benefits occurring in 2038 from all areas (including California) attaining each alternative standard (Simulation C). Next, we simulated the nationwide benefits of attaining each alternate ozone standard in 2038 for every state except California (Simulation B). Subtracting Simulation B from Simulation C calculates the benefits of attaining each alternate ozone standard after 2025

— that is, the nationwide benefits from California alone attaining the standard in 2038. There are important caveats associated with this approach mentioned in Section 5.1 and discussed further in section 5.7.3.

Figure 5
- 3 . Procedure for Generating Benefits Estimates for the 2025 and Post
- 2025

Scenarios

5.4.4 Benefit

-

per - ton E stimates for PM

2.5 

### We used a “benefit

- per - ton” approach to estimate the PM 2.5 

### co - benefits in this RIA. EPA has applied this approach in several previou s RIAs (e.g., U.S. EPA, 2014a ). These benefit

- per ton estimates provide the total monetized human health co

- benefits (the sum of premature

### 5

- 17

mortality), of reducing one ton of NOx (as a PM 2.5 

### precursor) from a specified source. 7 

### In ge neral, these estimates apply the same benefits methods (e.g., health impact assessment then economic valuation), which are described further below, for all PM 2.5 

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

### p recursor (e.g., NOx) from that sector. As discussed below, we W e acknowledge that this approach has greater uncertainty relative to explicitly modeling benefits for PM 2.5 

### based on application of gridded surfaces specifically generated for the baseline and a lternative standard levels being considered in this ozone NAAQS review (see Appendix 5A, Table 5A

- 1) . However, resource and time constraints prevented us from completing detailed PM 2.5 

### modeling as part of this review. We used a method to calculate the regi onal benefit

- per - ton estimates that is a slightly modified version of the 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 NAAQS review were derived using the approach published in Fann et al. (2012c), but they have since been updated to reflect the epidemiology studies and Census population data first applied in the final PM NAAQS RIA (U.S. EPA, 2012b). The approach in Fann et al. (2012c) is similar to the work previously published 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. Specifica lly, the air quality modeling data reflect industrial sectors 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). 8

7 In generating these estimates, we first use inciden ce - per - ton values to generate estimates of reductions in morbidity and mortality incidence for core endpoints (see Table 5

- 3). Then in estimating dollar values associated with these reductions in incidence, we use dollar

- per - ton values for mortality only, noting that this is likely to provide coverage for upwards of 97% of the total dollar benefits (i.e., morbidity endpoints provide less than 3% of total benefits

– see RIA from last PM review, USEPA, 2012, Table 5

- 19). 8 Sector

- level estimates of PM 2.5 are modeled using CAMx version 5.30

. Specifically, the particulate source apportionment technology (PSAT) incorporated into CAMx generates estimates of the contribution from specific emission source groups to primary emitted and secondarily formed PM 2.5 , PSAT uses reactive tracers in generating these fractional estimates in order to capture nonlinear formation and removal processes related to PM 2.5 . Contributions from each sector are modeled at the 12km level, while boundary conditions are represented using a 3 6km grid resolution (additional detail on modeling can be found in Fann et al., 2012) Formatted: Subscript
Formatted: Subscript Formatted: Subscript

### 5

- 18

In Section 5.6 below, we describe all of the data inputs used in deriving the dollar

- per - ton values for each sector for the benefits analysis portion of the benefit

- per - ton c alculation , including the demographic data, baseline incidence, and valuation functions. The specification of effect estimates (including selection of epidemiology studies) used in the derivation of the benefit

- per - ton values for PM 2.5 

### is described in detail in Appendix 5D. The benefit

- per - ton estimates (by sector) that resulted from this modeling as well as the NOx reductions for each alternative standard level used in generating the PM 2.5 

### cobenefit estimates are presented in Appendix 5E. Additional information on the source apportionment modeling for each of the sectors can be found in Fann et al. (2012c) and the TSD (U.S. EPA, 2013b). Specifically for this analysis, we applied the benefit

- per - ton estimates for 2025 and 2030 in generat ing PM 2.5 

### co benefit estimates for the 2025 and post

- 2025 time periods, respectively (both sets of benefit

- per ton estimates are presented in Appendix 5E)

. 9 

### As discussed in greater detail in section 5.7.3 and Appendix 5A, Table 5A

- 1 , w e recognize uncertain ty associated with application of the benefit

- per - ton approach used in modeling PM 2.5 

### cobenefits. The benefit

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

### - realted health impacts (e.g., population density, baseline health incidence rates ) . Therefore, use of thes e 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.
5.5 Characterizing Uncertainty In any complex analysis using estimated parameters and inputs from numerous models, there are likely to be many sources of uncertainty. This analysis is no exception. As outlined both in this and preceding chapters, this analysis includes many data sources as inputs, including emission inventories, air quality data from models (with their associated parameters and inputs), population data, population estimates, health effect estimates from epidemiology studies,

9 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 ag es over time.

### 5

- 19

economic data for monetizing benefits, and ass umptions regarding the future state of the world (i.e., regulations, technology, and human behavior). Each of these inputs may be uncertain and would affect the benefits estimate. 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 EPA’s approach, the National Research Council (NRC) (2002, 2008), which is part of the National Academies of Science, concluded that the EPA’s general methodol ogy for calculating the benefits of reducing air pollution is reasonable and informative in spite of inherent uncertainties. The NRC also highlighted the need to conduct rigorous quantitative analyses of uncertainty and to present benefits estimates to dec ision makers in ways that foster an appropriate appreciation of their inherent uncertainty. Since the publication of these reports, the EPA has continued work to improve the characterization of uncertainty in both health incidence and benefits estimates. I n response to these recommendations, we have expanded our previous analyses to incorporate additional quantitative and qualitative characterizations of uncertainty. Although we have not yet been able to make as much progress towards a full, probabilistic u ncertainty assessment as envisioned by the NAS as we had hoped, we have added a number of additional quantitative and qualitative analyses to highlight the impact that uncertain assumptions may have on the benefits estimates . These additional analyses focu s primarily on uncertainty related to the mortality endpoint (for both ozone and PM 2.5 

### ) since mortality is the driver for dollar benefits . In addition, for some inputs into the benefits analysis, such as the air quality data, it is difficult to address unc ertainty probabilistically due to the complexity of the underlying air quality models and emission inputs. Therefore, we decline to construct alternative assumptions simply for the purpose of probabilistic uncertainty characterization when there is no scie ntific literature to support those alternate assumptions. To characterize uncertainty and variability, we follow an approach that combines elements from two recent analyses by the EPA (U.S. EPA, 2010b; 2011b), and uses a tiered approach developed by the Wo rld Health Organization (WHO) for characterizing uncertainty (WHO, 2008). We present this tiered assessment as well as an assessment of the potential impact and magnitude of each aspect of uncertainty in Appendix 5A (results of these assessments are

summar ized in section 5.7.3) . Data limitations prevent us from treating each source of uncertainty quantitatively and from reaching a full

- probabilistic simulation of our results, but we

### 5

- 20

were able to consider the influence of uncertainty in the risk coefficients and economic valuation functions by incorporating several quantitative analyses described in more detail below:
1. A Monte Carlo assessment that accounts for random sampling error and between study variability in the epidemiological and economic valuation st udies for ozone

- related health effects. See section 5.5.1 for additional detail on the Monte Carlo assessment. 2. A series of sensitivity analyses primarily focused on the mortality endpoint (for both ozone and PM 2.5 

### ). We focus on mortality in conducting sens itivity analyses reflecting the important role that this endpoint plays in driving both ozone

- related and PM 2.5 

### (co

benefit) related dollar benefits. These sensitivity analyses address factors related to (a) estimating incidence (e.g., multiple epidemiolog y studies providing alternative effect estimates, shape of the C

- R function including potential for thresholds) and (b) estimating associated dollar benefits (e.g., income elasticity related to willingness to pay functions and uncertainty in specifying lag structures for long

- term exposure

- related mortality). See section 5.5.2 for additional detail on the set of sensitivity analyses completed for this RIA. 3. Supplemental analyses which allow us to consider additional factors related to the benefits analysis. These include an assessment of the age

- related differentiation of short

term ozone exposure

- related mortality (including life year saved and how estimates of avoided mortality are distributed across age ranges). In addition, we also looked at the relations hip between estimates of mortality and the underlying baseline ambient air levels used in their derivation. These analyses allow us to consider which range of baseline levels drive the benefits estimates. Finally, we considered the fraction of benefit esti mates (for the 2025 scenario) which are associated with application of (higher

- confidence) known emissions controls. See section 5.5.3 for additional detail on the supplemental analyses completed for this RIA. 5.5.1 Monte Carlo Assessment Similar to other recent RIAs, we used Monte Carlo methods for characterizing random sampling error associated with the concentration response functions from epidemiological studies and random effects modeling to characterize both sampling error and variability across the e conomic valuation functions. The Monte 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 me thods to generate confidence intervals 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 singl e study. For endpoints estimated using a pooled estimate of multiple studies, the confidence intervals reflect both the standard errors and

### 5

- 21

the variance across studies. The confidence intervals around the monetized benefits incorporate the epidemiology sta ndard errors as well as the distribution 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 effec t estimate to diverse locations. As a result, the reported confidence intervals and range of estimates give an incomplete picture about the overall uncertainty in the benefits estimates. In this RIA, we provide confidence intervals for ozone

- related benefi ts, but we are unable to provide confidence intervals for PM 2.5 

### - related co

- benefits due to the use of benefit

- per - ton estimates.
5.5.2 Sensitivity Analysis Addressing Both Incidence and Dollar Benefit Valuation We assign the greatest economic value to the reduction in mortality risk. Therefore, it is particularly important to characterize to a reasonable extent the uncertainties associated with reductions in premature mortality, including both incidence estimatio n and the translation of reduced mortality into equivalent dollar benefits. Each of the sensitivity analyses completed for this RIA are briefly described below. The reader is referred to section 5.7.3.1 for discussion of the results and observations stemmi ng from these sensitivity analyses.

 Alternative C
- R functions for short
- term ozone exposure - related mortality: Alternative concentration

- response functions are useful for assessing uncertainty beyond random statistical error, including uncertainty in the functional form of the model or alternative study designs. For ozone we have included two multi

- city studies (Smith et al., 2009 and Zanobetti and Schwartz 2008) in our core estimate of the range for short

term exposure

- related mortality. For the sensitivi ty analysis addressing this endpoint, we have included additional multi

- city and meta

- analysis studies utilized in RIAs completed for previous ozone NAAQS reviews (Bell et al., 2004 and 2005, Huang, 2005, Ito et al., 2005 and Levy et al., 2005), as well as alternative model specifications from the Smith et al (2009) study. The selection of studies for the core and sensitivity analyses, reflects consideration for recommendations made both by the NAS in relation to modeling ozone benefits (p. 80, NRC, 2008) a nd the CASAC in their review of the HREA completed in support of this ozone NAAQS review. We also considered information and recommendations provided in the latest ozone ISA. Additional detail on the selection and use of studies in modeling short

- term exp osure - related mortality for the core analysis is presented in section 5.6.3.1.

 Impact of potential thresholds on the modeling of long
- term ozone exposure - related respiratory mortality: Consistent with the HREA, we estimate counts of respiratory

### 5

- 22

deaths from long

- term exposure to ozone in our core analysis. As discussed in detail in section 5.6.3.1, the Jerrett et al., 2009 study from which the mortality effect estimate was derived included an exploration of potential thresholds in the concentration

- response function. To provide a more comprehensive picture of potential benefits associated with long term ozone exposures, we use the results of the threshold analysis conducted by Jerrett et al, 2009 to conduct a sensitivity analysis evaluating models with a rang e of potential thresholds in addition to a non

- threshold model (see section 5.7.3.1 and Appendix 5B, section 5B.1)

.

 Considering alternative C
- R functions in estimating long
- term PM 2.5 

### exposure related mortality: In modeling co

- benefits related to reduct ions in long

- term exposure to PM 2.5 

### , our dollar

- per - ton approach relies on estimates based on two studies (Krewski et al., 2009 and Lepeule et al., 2012

– see Appendix 5D, section 5D.1). To better understand the concentration

- response relationship between PM 2.5 

### exposure and premature mortality, the EPA conducted an expert elicitation in 2006 (Roman et al., 2008; IEc, 2006). 10 

### In general, the results of the expert elicitation support the conclusion that the benefits of PM 2.5 

### control are very likely to be subs tantial. Using alternate relationships between PM 2.5 

### and premature mortality supplied by experts, higher and lower benefits estimates are plausible, but most of the expert

- based estimates of the mean PM 2.5 

### effect on mortality fall between the two epidemiol ogy - based estimates (Roman et al., 2008). Application of the expert elicitation

- based effect estimates (as part of characterizing uncertainty) is covered in section 5.7.3.1 and Appendix 5B, section 5B.2. In addition to these studies, we have included a dis cussion o f other recent multi

- state cohort studies conducted in North America, but we have not estimated benefits using the effect coefficients from these studies (see Appendix 5D, section 5D.1).

 Specifying the cessation lag for long
- term PM 2.5 

### exposure - re lated respiratory mortality: As discussed in section 5.1 and 5.6.4.1, uncertainty in projecting the cessation lag for long

- term PM 2.5 

### exposure - related respiratory mortality prevents us from estimating dollar benefits associated with projected reductions in mortality. In the absence of clear evidence pointing to a particular lag structure, we have decided to use two lag structures (the 20 year segmented lag used for PM 2.5 

### and an assumption of zero lag

– see section 5.6.4.1). The range of dollar benefits tha t result have been included in as sensitivity analyses and not in the core analysis.

 Income elasticity in the specification of willingness to pay (WTP) functions used for mortality and morbidity endpoints: There is uncertainty in specifying the degree to which the WTP function used in valuing mortality and some morbidity endpoints tracks projected increase in income over time (i.e., the income elasticity for WTP). We completed a sensitivity analysis to evaluate the potential impact of this factor on dollar estimates generated for mortality (and a subset of morbidity endpoints

– see section 5.6.4.1).

10 Expert elicitation is a formal, highly structured and well documented process whereby expert judgments, usually of multiple experts, are obtained (Ayyub, 2002).

### 5

- 23

Even these multiple estimates (including confidence intervals in the case of estimates generated for ozone) cannot account for the role of other input variable s 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 in put parameters and the identification of reasonable upper and lower bounds for input distributions characterizing uncertainty in additional model elements. As a result, the reported confidence intervals and range of estimates give an incomplete picture abo ut the overall uncertainty in the estimates. Thus, confidence intervals reported for individual endpoints and for total benefits should be interpreted within the context of the larger uncertainty surrounding the entire analysis.
5.5.3 Supplemental Analyses We have also conducted a number of supplemental analyses designed to provide additional perspectives on the core mortality estimates generated for this RIA. These analyses (the results of which are described in section 5.7.3.2) include:

 Age group
- differen tiated aspects of short
- term ozone exposure - related mortality: We examined several risk metrics intended to characterize how mortality risk reductions 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 levels used in modeling short
- term ozone exposure related mortality: We assess th e relationship between short

- term exposure

- related mortality for ozone and the distribution of baseline (i.e., reflecting attainment of the current standard) 8hr max daily values used in deriving those estimates (see section 5.7.3.2 and Appendix 5C, sectio n 5C.2). This analysis allows us to explore how estimates of ozone

- attributable mortality are distributed with regard to projected ambient ozone levels, including the fraction of overall mortality that falls within specific ozone ranges. We note that, whi le the latest ozone ISA did not provide support for a threshold in relation to short

- term exposure

- related mortality, it did note that there is reduced confidence in specifying the nature of the concentration

- response function at lower ozone levels (in the range of 20ppb and below) (ozone ISA, U.S. EPA, 2013a, section 2.5.4.4). We use the distribution of short

- term mortality across ozone levels to determine the

### 5

- 24

fraction of mortality reductions (i.e., benefits) that fall within this lower confidence range. 11



###  Analysis of baseline PM 2.5 

### levels 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 modeling long

- term PM 2.5 

### exposure - related mortality (see section 5.7.3.2 and Appendi x 5C, section 5C.2). However, we are using a reduced form dollar

- per - ton approach in modeling PM 2.5 

### cobenefits, we do not have spatially differentiated PM 2.5 

### values (and associated mortality estimates) with which to derive this type of distributional plot specifically for this RIA and consequently, we have reproduced a plot from the earlier analysis used to generate the benefit

- per - ton values.

 Fraction of core ozone and PM 2.5 

### (cobenefit) estimates associated with application of known emissions controls for the 2025 scenario : This analysis estimates the fraction of core incidence and associated dollar benefits that are associated with application of the set of known (higher confidence) emissions control measures. Note that this analysis is only completed for the 2025 scenario since application of controls in California (associated with the post

- 2025 scenario) exclusively involves application of unknown controls. 5.5.4 Qualitative Assessment of Uncertainty and Other Analysis Limitations Although we strive to incorporate as many quantitative assessments of uncertainty as possible, there are several aspects we are only able to address qualitatively. These aspects are important factors to consider when evaluating the relative benefits of the emission reductio n strategies for the proposed and alternative standards. The total monetized benefits presented in this chapter are based on our interpretation of the best available scientific literature and methods and supported by the EPA’s independent SAB (Health Effec ts Subcommittee of the Advisory Council on Clean Air Compliance Analysis) (SAB

- HES) (U.S. EPA

- SAB, 2010a) and the National Academies of Science (NAS) (NRC, 2002, 2008). The benefits estimates are subject to a number of assumptions and uncertainties.

To m ore fully address all these uncertainties including those we cannot quantify, we apply a four

- tiered approach using the WHO uncertainty framework (WHO, 2008), which

11 However, care must be taken in interpreting this range of reduced confidence since benefits estimates are based on the average daily 8hr max across the ozone season and not on a true daily time series of 8hr metrics within each grid cell. The use of a seasonal mean 8hr max (rather than the more temporally differentiated daily time series) has been shown to generate nearly identical benefit estimates at the national

- level due to underlying linearity in the benefits model being used. However, the use of the seasonal average 8hr metric, rather than a full daily time series, does decrea se overall temporal variability in a plot of mortality versus ozone level which introduces uncertainty into the interpretation of these plots.

### 5

- 25

provides a means for systematically linking the characterization of uncertainty to the soph istication of the underlying risk assessment. The EPA has applied similar approaches in previous analyses (U.S. EPA, 2010b, 2011b, U.S. EPA, 2012a

– the HREA). Using this framework, we summarize the key uncertainties in the health benefits analysis, includ ing 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 uncertaint y characterization are available in section 5.7.3 and Appendix 5A. As previously described, we strive to monetize as many of the benefits anticipated from the proposed and alternative standards as possible given data and resource limitations, but the

mone tized benefits estimated in this RIA inevitably only reflect a portion of the total health benefits. Data and methodological limitations prevented the EPA from quantifying or monetizing the benefits from several important health benefit categories from emi ssion reduction strategies to attain the alternative ozone standards analyzed in this RIA, including potential co

- benefits from reducing NO 2 

### exposure (see section 5.6.3.6 for more information) and reductions in VOC exposures.
5.6 Benefits Analysis Data In puts In Figure 5

- 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 inputs, including demographic projections, incidence and prevalence rates, effect coeffi cients, 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 the 2010 ozone NAAQS Reconsideration RIA (U.S. EPA, 2010d).
5.6.1 Demographic Data Q uantified and monetized human health impacts depend on the demographic characteristics of the population, including age, location, and income. We use population projections based on economic forecasting models developed by Woods and Poole, Inc. (Woods and Poole, 2012). The Woods and Poole (WP) database contains county

- level projections of population by age, sex, and race out to 2040, relative to a baseline using the 2010 Census data;

### 5

- 26

the 2008 proposal RIA incorporated WP projections relative to a baseline u sing 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 equ al 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 constraining to a national

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

- stage process:

 First, national

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

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

- base” approach, which relies on linking industrial

- sector production of non

- locally consumed production items, such as outputs from mining, agriculture, and manufacturin g 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, population 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 race for each year through 2040 based on historical rates of mortality, fertility, and migration. 5.6.2 Baseline Incidence and Prevale nce Estimates Epidemiological studies of the association between pollution levels and adverse health effects generally provide a direct estimate of the relationship of air quality changes to the relative risk of a health effect, rather than estimating the absolute number of avoided cases. For example, a typical result might be that a 5 ppb decrease in 8hr max daily ozone levels might be associated with a decrease in hospital admissions of three percent. The baseline incidence of the health effect is necessa ry 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 baseline pollutant levels in that location. To derive the total baseline incidence per year, this rate must be multiplied by the corresponding population number. For

### 5

- 27

example, if the baseline incidence rate is the number of cases per year per million people, that number must be multiplied by the millions of p eople in the total population.
Table 5

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

- specific rates where a vailable. We applied concentration

- response functions to individual age groups and then summed over the relevant age range to provide an estimate of total population benefits. In most cases, we used a single national incidence rate, due to a lack of more s patially disaggregated data. Whenever possible, the national rates used are national averages, because these data are most applicable to a national assessment of benefits. For some studies, however, the only available incidence information comes from the s tudies 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 (Abt Associates, 2012). To perform this calculation, we began first with an average of 2004

– 2006 cause

- specific mor tality 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 (Abt Associates, 2012; 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, 2011d). In addition, we have 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 department for air pollution

- related illnesses. Also, the new baseline incidence rates are more spatially refi ned. For many locations within the U.S., these data are resolved at the county

- or state

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

- related visits are likely to be

### 5

- 28

unscheduled. If air pollution

- related hospital admissions are scheduled, this assumption would underestima te these benefits. For the set of endpoints affecting the asthmatic population, in addition to baseline incidence rates, prevalence rates of asthma in the population are needed to define the applicable population. Table 5

- 5 lists the prevalence rates used to determine the applicable population for asthma symptoms. Note that these reflect current asthma prevalence and assume no change in prevalence rates in future years. We updated these rates in the CSAPR RIA (U.S. EPA, 2011d).
Table 5
- 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 children daily wheeze daily cough daily shortness of breath 0.173 0.145 0.074 Ostro et al. (2001)

Acut e 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 incidence 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 Restri cted

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

### 5

- 29 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 ICD codes (AHRQ, 2007).
c Lower respiratory symptoms are defined as two or more of th e following: cough, chest pain, phlegm, and wheeze. 

### Table 5
- 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). 

### 5.6.3 Effect Coefficients The modeling of incidence and benefits for each pollutant employ distinct and separate sets of effect estimates since these are obtained from epidemiological studies specific to a given endpoint/pollutant combination. In the case of PM 2.5 

### , the dollar

- per - ton a pproach being employed reflects application of effect estimates (for all endpoints) which have been used by the EPA in previous RIA’s (e.g., PM NAAQS, U.S. EPA, 2012b). Consequently, while we identify the studies and effect estimates reflected in the dolla r - per - ton values used for PM 2.5 

### (see Table 5

- 8), we do not present a detailed discussion of those effect estimates in this section and instead present that more detailed discussion in Appendix 5D. By contrast, with ozone, we conducted detailed benefits mod eling using an updated set of effect estimates that reflects a combination of values used in previous RIAs together with updated values. For that reason, we provide a detailed discussion of effect estimates for ozone (including the rationale for their sele ction) in this section. The first step in selecting effect coefficients is to identify the health endpoints to be quantified. We base our selection of health endpoints on consistency with the EPA’s ISAs (which replace previous “Criteria Documents”), with input and advice from the HES, a scientific review panel specifically established to provide advice on 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). In addition, we have included more recent

### 5

- 30

epidemiology studies from the ozone ISA (U.S. EPA, 2013a), PM ISA (U.S. EPA, 2009b), and the PM Provisional Assessment (U.S. EPA, 2012c). 12 

### In selecting health endpoints for ozone, we also considered the suite of endpoints included in core modeling for the HREA, which was supported by CASAC ( Frey, and Samet 2012; Frey, 2014) . In general, we follow a weight of evidence approach, based on the b iological plausibility of effects, availability of concentration

response 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 clinical 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 pol lution exposures. These sources of data include toxicological studies (including animal and cellular studies), human clinical trials, and observational epidemiology studies. All of these data sources provide important contributions to the weight of evidenc e surrounding a particular health impact. However, only epidemiology studies provide direct concentration

- response 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 ascertain the relationship between ozone and PM 2.5 

### and adverse human health effects. We evaluated epidemiological studies using the selection criteria summarized in Table 5

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

- reviewed, the match between the pollutant studied and the pollutant of interest, the study design and location, and characteristics of the study population, among other considerations. In general, the use of concentration

- response functions from more than a single study can provide a more representative distribution of the effect estimate. However, there are often differences between studies examining the same endpoint, making it difficu lt to pool the results in a consistent manner. For example, studies may examine different pollutants or different age groups. For this reason, we consider very carefully the set of studies available examining each endpoint and select a consistent subset th at provides a good balance of population coverage and match with the pollutant of interest. In many cases,

12 The peer

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

### 5

- 31

either because 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 pollutant and a given health endpoint (with the exception of mortality) 13 

### have been selected, they are quantitatively combined or pooled to deri ve 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 (Abt Associates, 2012). In general, we used fixed or ra ndom effects models to pool estimates from different single city studies of the same endpoint. Fixed effect pooling simply 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 differences 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 the random effects model. 14 

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

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

- city study are themselves pooled across a large number of urban areas. For this reason, we elected to give each study an equal weight rather than we ighting by the inverse of the variance reported in each study. For more details on methods used to pool incidence estimates, see the BenMAP Manual Appendices (Abt Associates, 2012). Effect estimates selected for a given health endpoint were applied consist ently 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, in fact, vary from one location to another (e.g. , because of differences in population susceptibilities

13 In the case of mortality, when we have multiple studies providing effect estimates for the core analysis, we include the range of the resulting incidence and dollar benefit estimates rather than pooling them in order to provide additional characterization of overall confidence associated with this key endpoint. However, for morbidity endpoints we do pool estimates as described here. 14 EPA recently changed the algorithm BenMAP uses to calculate study variance, which is used in the pooling process. Prior ver sions of the model calculated population variance, while the version used here calculate s sample variance.

### 5

- 32

or differences in the composition of PM), location

- specific effect estimates are generally not available.
Table 5
- 6. Criteria Used When Selecting C
- R Functions

Consideration Comments Peer

- Reviewed Research Peer

- reviewed research is preferred to research that has not undergone the peer

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

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

- city vs. pooled/meta

- analysis: In recommending approaches for modeling ozone

related mortality, the NAS notes a number of advantages multi

- city time series studies as compared to meta

- analyses. Multi

- city studies utilize a consistent model structure and can include factors that explain differences between effect estimates among the cities. By contra st, with meta

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

analyses can suffer from publication bias which can result in high

- biased effect estimates. Ultimately, the NAS recommends that the greatest emphasis be placed on estimates based on systematic new multi

- city analyses without excluding consideration of me t a analyses (NRC, 2008). Reflecting these observations by the NAS, in modeling ozo ne benefits, we have included several newer multi

- city studies in the core analysis and a combination of multi

- city and meta analyses in the accompanying sensitivity analysis. Placing emphasis on these two multi

- city studies was also supported by the CASAC in the context of the HREA ( Frey, and Samet 2012; Frey, 2014 )

. 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 a re more recent are also preferred because of possible changes in pollution mixes, medical care, and lifestyle over time. However, when there are only a few studies available, studies from all years will 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. Given modeling constraints, we were not able to consider variation in ozone seasons in modeling benefits and instead, had to select a standard ozone season for the entire country (May 1 st through September 31 st ). Consequently, in selecting effect estimates, we favored those values that reflected ozone seasons close to this fixed ozone season (i.e., we did not include effect estimates based on a full year of ozone monitoring data). 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 appropriate to select effect estimates that cover the broadest population to match with the des ired outcome of the analysis, which is total national

- level health impacts. When available, multi

- city studies are preferred to single city studies because they provide a more generalizable representation of the concentration

- response function. Study Size Studies examining a relatively large sample are preferred because they generally have more power to detect small magnitude effects. A large sample can be obtained in several ways, including through a large population or through repeated observations on a smaller population (e.g., through a symptom diary recorded for a panel of asthmatic children).

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Consideration Comments Study Location U.S. studies are more desirable than non

- U.S. studies because of potential differences in pollution characteristics, exposure patterns, medical c are system, population behavior, and lifestyle. 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. Pollutants 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, when modeling the PM effect, epidemiological models specifying co

pollutants are preferred because this approach controls for the potential ozone effect while not diminishing the effective sample size available for specifying the P M effect. However, when modeling the ozone effect, the use of copollutants modeling (with PM) can substantially reduce sample size (by 1/3 to 1/6) since only days with both ozone and PM can be used. While these copollutants models may control for potential PM effects, they also result in a substantially less robust characterization of the ozone effect due to the reduced number of ozone measurements. For this reason, while we favor copollutants models in modeling PM benefits, for ozone we favor single pollut ant models for the core estimate and reserve copollutants models for sensitivity analyses. Measure of PM For this analysis, 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) t oxic 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. These health effects are not quantified in this analysis. 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 specific studies from which effect estimates for the core analysis related to ozone exposure are drawn are included in Table 5

- 7. Table 5

- 8 identifies studies reflected in the dollar

per - ton analysis for PM 2.5 

### . We highlight in red those studies that h ave been added since the benefits analysis conducted for the ozone reconsideration (U.S. EPA, 2010d) or the ozone NAAQS RIA (U.S. EPA, 2008a). In all cases where effect estimates are drawn directly from epidemiological studies, standard errors are used as a partial representation of the uncertainty in the size of the effect estimate. Table 5

- 9 summarizes those health endpoints and studies we have included as sensitivity analyses for ozone.

### 5

- 34

Table 5
- 7. Health Endpoints and Epidemiological Studies Used to Qu antify O zone - R elated

Health Impacts in the Core Analysis a

Endpoint Study Study Population Relative Risk or Effect Estimate ( β)

(with 95 th Percentile Confidence Interval or SE, respectively) 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 ( incidence only ) Jerrett et al. (2009) >29 years β = 0.003971 (0.00133) Hospital Admissions
Respiratory Pooled estimate:

Katsouyanni et al. (2009) > 65 years β = 0.00061 (0.00041) natural splines 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 exacerbations Pooled estimate: b Mortimer et al. (2002) 6

– 18 years c β = 0.00929 (0.00387) O’Connor et al. (2008) β = 0.00097 (0.00299) Schildcrout et al. (2006) 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 As discussed in sections 5.3 and 5.6.3.1, while we believe that available evid ence supports inclusion of estimates of long

- term exposure

- related respiratory mortality incidence in the core analysis, uncertainty in specifying a lag structure for this endpoint (a key factor in valuation) prevents us from including dollar benefits in t he core analysis and instead, these are included as part of the sensitivity analysis exploring this endpoint.
c The original study populations were 5 to 12yrs for the O’Conner et al., (2008) and Schildcrout et al., (2006) and 5

- 9yrs for the Mortimer et al. , (2002) study. Based on advice from the SAB

- HES, we extended the applied population to 6

- 18yrs 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)

### 5

- 35

T able 5
- 8. Health Endpoints and Epidemiological Studies Used to Quantify PM 2.5 

### - R elated

Health Impacts in the Core Analysis a

Endpoint Study Study Population Relative Risk or Effect Estimate ( β)

(with 95 th Percentile Confidence Interval or SE, respectively) 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)

Bell et al. (2008a)

— 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

### 5

- 36

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, 2012b). b The original study populations were 8 to 13 for the Os tro et al. (2001) study and 7 to 12 for the Mar et al. (2004) study. Based on advice from the SAB

- HES, we extended the applied population to 6

- 18, 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 5
- 9. Health Endpoints and Epidemiolo gical Studies Used to Quantify Ozone
- R elated

Health Impacts in the Sensitivity Analysis a

Endpoint Study Study Population Relative Risk or Effect Estimate ( β)

(with 95 th Percentile Con fidence Interval or SE, respectively) Premature Mortality Premature respiratory mortality

— long

- term Jerrett et al. (2009)

- based models:

> 29 years

- non - threshold ozone only (86 cities) β= 0.002664 (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) (copollutant

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) (cardiopulmonary) β=0.00026 (0.00009)

a Studies highlighted in red represent updates incorporated since the 2008 ozone NAAQS RIA (U.S. EPA, 2008b). b All threshold models are ozone

- only and based on the full 96 city dataset.

### 5

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5.6.3.1 Ozone Premature Mortality Effect Coefficients Core Mortality Effect Coefficients for Short
- term ozone Exposure. The overall body of evidence indicates that there is likely to be a causal relationship between short

- term ozone exposure and premature death. The 2013 ozone ISA states that:
“The evaluation of new multi

- city studies that examined the association between short

- term ozone exposure and mortality found evidence which supports the conclusions of the 2006 ozone AQCD. These new studies reported consistent positive associations between short

- term ozone exposure and all

- cause (non

accidental) mortality, with asso ciations persisting or increasing in magnitude during the warm season, and provide additional support for associations between ozone exposure and cardiovascular and respiratory mortality” (ozone ISA section 6.6.3, USEPA 2013). The ISA concludes by stating that, “Although some uncertainties still remain, the collective body of evidence is sufficient to conclude there is likely to be a causal relationship between short

- term ozone exposure and total mortality.” (ozone ISA section 6.6.3, USEPA 2013a). Regardin g potential confounding of the ozone mortality effect by PM, the ISA states, “Overall, across studies, the potential impact of PM indices on ozone

- mortality risk estimates tended to be much smaller than the variation in ozone

- mortality risk estimates acros s cities suggesting that ozone effects are independent of the relationship between PM and mortality.” (ozone ISA section 6.3.3, USEPA 2013a). However, the ISA does note that the interpretation of the potential confounding effects of PM on ozone

- mortality r isk estimates requires caution. This caution reflects in part, the every - 3 rd



### - and every

- 6th

- day PM sampling schedule (in most cities) which limits the overall sample size available for evaluating potential confounding of the ozone effect by PM. (ozone ISA section 6.3.3, USEPA 2013a). In their review of the HREA, the SAB’s CASAC Ozone Review Panel expressed support for epidemiological studies and corresponding concentration

- response functions used in the HREA, which included effect estimates obtained from Smith et al., (2009) and Zanobetti and Schwartz (2008b) (Frey and Samet, 2012, p. 17

- 18 and Frey, 2014, p, 9). Furthermore, the CASAC specifically noted support for the use of multi

- city studies, where available in modeling

### 5

- 38

the health endpoints included in the analysis ( Frey and Samet, 2012, p. 15 ). In addition, they expressed support for modeling total risk, and de

- emphasized the importance of estimating risk associated with ozone concentrations above the lowest measure level (LML) from contributing epidemiological studies (Frey and Samet, 2012, p . 10 and 18). In 2006, the EPA requested an NAS study to answer four key questions regarding ozone

related mortality: (1) how did the epidemiological literature to that point improve our understanding of the size of the ozone

- related 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? (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

- term exposure to ambient ozone is lik ely 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 greatest emphasis be placed on the m ulti

- 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 estimated reductions in premature mortali ty 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, 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 HES panel, and the CASAC panel, we include ozone

- related premature mortality 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. As discussed below, we also include several additional studies as sensitivity analyses. This approach wit h an emphasis on newer multi

- city studies is consistent with recommendations provided by the NAS in their ozone mortality report

### 5

- 39

(NRC, 2008), “The committee recommends that the greatest emphasis be placed on estimates from new systematic multi

- city analyse s that use national databases of air pollution and mortality, such as in the NMMAPS, without excluding consideration of meta

- analyses of previously published studies.” In selecting the Smith et al. (2009) and Zanobetti and Schwartz (2008b) studies, we poin t both to CASAC support for the use of these two studies in the context of the HREA completed for this NAAQS review (Samet and Frey, 2012, p. 15 and Frey, 2014, p. 9) and the fact that both of these studies are multi

- city studies published more recently (a s compared with other multi

- city studies or meta

- analyses included in the sensitivity analyses

– see discussion below). The Smith et al., (2009) study is a reanalysis of the NMMAPS data set focused on evaluating the relationship between short

- term ozone ex posure and mortality. While this reproduces the core national

- scale estimates presented in Bell et al., (2004), it also explores the sensitivity of the mortality effect to different model specifications including (a) regional versus national Bayes

- based ad

justment, 15 

### (b) co

- pollutants models considering PM 10 

### , (c) all

- year versus
ozone - season based estimates, and (d) consideration of a range of ozone metrics, including the daily 8hr max , which is of particular interest in the context of the RIA given that is the metric that is used in the form of the ozone standard. In addition, the Smith et al. (2009) study does not use the trimmed mean approach employed in the Bell et al. (2004) study in preparing ozone monitor data, which is another advantage. 16 

### In selecting effect estimates from Smith et al. (2009) for use in the core analysis, we focused on an ozone

- only estimate for non

- accidental mortality based on the 8hr max metric for the warmer ozone season. In addition, for the sensitivity analysis, we included a co pollutants model (ozone and PM 10 

### ) from Smith et al. (2009) for all

cause mortality which also used the 8hr max ozone metric for the ozone season. As noted in

15 In Bayesian modeling, effect estimates are “updated” from an assumed prior value using observational data. In the Smith et al (2009) approach, th e 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 there is low overall variation across est imates. 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 potential loss of t emporal 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.

### 5

- 40

Table 5

- 6, the decision to use a single pollutant model for the core analysis and reserve the copo llutants model for the sensitivity analysis reflects our concern that the reduced sampling frequency for days with copollutants measurements (1/3 and 1/6) can impact characterization of the ozone effect which is the focus of this assessment. In addition, a s noted earlier in this section, the latest ozone ISA states that ozone effects are likely to be independent of the relationship between PM and mortality, which further supports the approach of favoring single pollutant models in the core analysis.
The Zan obetti and Smith (2008) study evaluated the relationship between ozone exposure (using an 8hr 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 poll utant concentration

- response 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. For the core analysis, we used the 0

- 3 day la g based concentration

- response function since this had the strongest effect and tighter confidence interval. Note, that because the RIA utilizes the 8hr max ozone metric, we had to covert the effect estimate from Zanobetti and Smith (2008) which is based o n an 8hr mean metric to an equivalent effect estimate based on an 8hr max. To do this, we used the ozone metric approach wherein the original effect estimate (and standard error) is multiplied by the appropriate ozone metric adjustment ratio. 17


### Core Mortal ity Effect Coefficient for Long
- term ozone Exposure. We also estimated long

- term ozone exposure

- related respiratory mortality incidence in the core analysis. The available evidence did not allow us to characterize how long

- term exposure to ozone related to the year of onset of mortality (i.e., a cessation lag), which is a necessary input to quantifying the discounted dollar benefits. For this reason, we report the dollar benefits of avoided long

- term exposure - related respiratory deaths as a sensitivity anal yses (see section 5.7.3.1). Support for modeling long

- term exposure

- related mortality incidence comes from the final ozone ISA as well

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 8hr max to the study

- specific air metric (for eac h 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, however, adjustments o f relatively similar metrics (e.g., 8hr max and 8hr 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., 24hr or 1hr max ratios to 8hr max equivalents ).

### 5

- 41

as recommendations provided by CASAC in their review of the HREA completed for the current

ozone NAAQS review (Frey, 2014 , p. 3 and 9). The final ozone ISA references long

- term respiratory mortality in section 7.2.1 (USEPA 2013a) where they state, “The positive results from various designs and locations support a relationship between long

- term exposure to ambient ozone conce ntrations and respiratory health effects and mortality.” Later in that chapter, the ISA states that: “The strongest evidence for an association between long

- term exposure to ambient ozone concentrations and mortality is derived from associations reported i n the Jerrett et al. (2009) study for respiratory mortality that remained robust after adjusting for PM 2.5 

### concentrations.” (Section 7.7.1, USEPA, 2013a). In that same section, the authors also state that: “Coherence and biological plausibility for this o bservation [the association between long

- term exposure and respiratory mortality] is provided by evidence from epidemiologic, controlled human exposure, and animal toxicological studies for the effects of short

- and long

- term exposure to ozone on respirato ry effects (see Sections 6.2 and 7.2). Respiratory mortality is a relatively small portion of total mortality [about 7.6% of all deaths in 2010 were due to respiratory causes (Murphy et al., 2012)], thus it is not surprising that the respiratory mortality signal may be difficult to detect in studies of cardiopulmonary or total mortality.”
While the ozone 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). In their review of the HREA completed for the ozone NAAQS review and specifically modeling of the long

- term expos ure - related respiratory mortality endpoint, the CASAC states that, “The basis for estimating long

- term mortality (respiratory) risks relies on a single study, Jerrett et al. (2009), and the HREA should acknowledge the uncertainty and confidence in modeling results from the use of a single study, albeit a good one.” And, while they comment on the size of the chronic obstructive pulmonary disease (COPD) mortality effect attributable to ozone (page 7

- 68) they go on state that, “the CASAC concurs that Jerrett e t al. (2009) is an

### 5

- 42

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.” (Frey, C., 2014). This advice supersedes previous advice provided by the HES to include long

- term ozone exposure

- related mortality only as a sensitivity analysis. The Jerrett et al. (2009) study was the first to explore the relationship between long

- term ozone exposure and respiratory mortality (rather than focusing on cardiopulmonary mortali ty). Jerrett et al. (2009) exhibits a number of strengths including (a) the study is 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 includes copollutants 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 there are attributes to this study that affect how we interpret the long

- term exposure

- related respiratory mortality estimates. First, while CASAC notes that Jerrett et al. (2009) is well designed, it is a single study

— and so provides the only quantitative basis for estimating this endpoint. 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. Th at uncertainty could greatly influence our quantitative risk estimates (we address the potential for a threshold in a sensitivity analysis below). The CASAC did address the use of the zero threshold versus threshold based models in the context of the HREA, stating that, “ The EPA examined the threshold analysis contained in Jerrett et al. (2009) and found that the mortality model including a threshold at 56 ppb had the lowest log likelihood value of all models examined. However, it is not clear whether the 56 ppb threshold mode l is a better predictor of respiratory mortality than when using a linear model for the Jerrett et al. data. Different, but valid statistical tests produced different conclusions about the threshold versus linear models. The less stringent test judged the 56 ppb threshold model to be superior to the linear model, but the confidence interval indicates the threshold could occur anywhere from 0 to 60 ppb. Using the more stringent statistical test, none of the threshold models produce better predictions than th e linear model. Given these results, the CASAC concurs with the EPA’s planned approach [as stated in the 2 nd 

### draft H REA] to conduct a sensitivity analysis evaluating potential thresholds in the C

- R functions that relate long

- term

### 5

- 43

ozone exposures with respi ratory mortality and to not make the threshold models the core analytical procedure in the PA.” (Frey, 2014, p. 13

- 14) Here, the CASAC clearly states their support for inclusion of the zero threshold (linear model) as the core approach, while treating the threshold models as sensitivities. Reflecting this advice provided by CASAC, we have generated the core benefit estimate for long

- term exposure

- related respiratory mortality us ing a non - threshold co

- pollutant model (with PM 2.5 

### ) obtained from Jerrett et a l. (2009) (see Tables 5

- 7 and 5

- 9). Using a co - pollutant model is consistent with the fact that this study applied seasonal average metrics that are insensitive to co

- pollutant monitoring for PM 2.5 

### ; we explore the influence of co

- pollutant models and thres holds in a sensitivity analysis below. 18 

### The effect estimates used to model long

- term
ozone - attributable mortality are calculated using a seasonal average of peak (1

- hr maximum) measurements. These long

- term exposure metrics can be viewed as long

- term expos ures to daily peak ozone 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 attempting to combine estimates of long

- term ozone

- attributable mortality an d s hort

- term ozone

- attributable mortality estimates, in order to avoid double counting. It is also important to keep in mind that our estimates of short

- term ozone

- attributable mortality are for all

- causes, while estimates of long

term ozone

- attributable mortality are focused on respiratory

- related mortality. This further limits the ability to compare estimates of long

- term and short

- term exposure related mortality.

18 See Table 5

- 6 for additional discussion of the issue involving reduced sampling frequency and modeling of short

term ozone exposure

- related mortality.

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- 44

Sensitivity Analysis: Alternate Mortality Effect Coefficients for Short
- term O zone Exposure . In addition to the ozone

- related studies we use for the core estimates, we also evaluate several alternative studies to characterize uncertainty in the core estimates. These alternative studies include a mix of meta

- analyses and multi

- city studie s which have been included in RIAs completed for previous ozone NAAQS reviews , including the review completed in 2008 as well as the Reconsideration completed in 2010 (USEPA, 2008a and USEPA 2010d, respectively). The decision to include a mixture of meta

- analyses and multi

- city studies in the sensitivity analysis reflects the recommendation from the NRC reference earlier, that in modeling short

- term exposure

- related mortality for ozone , emphasis be placed on more recent multi

- city studies without excluding consideration for meta

- analyses. In selecting effect estimates from each study, we followed the criteria presented in Table 5

- 6. Consequently, we favored effect estimates reflecting the warmer ozone monitoring period, if available. We also favored effect estimates based on the 8hr max air metric if available. And finally, while we considered multi

- pollutant models (providing some coverage for potential confounding by PM), we placed primary emphasis on single

- pollutant models since these would typically hav e a significantly larger dataset with which to specify the ozone mortality effect. The decision to emphasize single

- pollutant models and deemphasize copollutants models (specifically in modeling short

- term endpoints) reflected observations in the current o zone ISA. In relation to short

- term mortality, the authors note limitations of co

- pollutant models due to the reduced sampling frequency association with PM in most cities. However, they also note that, “Together, these co

- pollutant

- adjusted findings acros s respiratory endpoints provide support for the independent effects of short

- term exposures to ambient ozone.” (ozone ISA section 6.2.9, USEPA, 2013a). The set of multi

- city and meta

- analysis studies selected for inclusion in the sensitivity analysis (incl uding effect estimates and standard errors) is presented in Table 5

- 9. Studies and effect estimates selected for the core analysis are also included in the table for completeness. Detailed discussions of each of these studies can be found in the current oz one ISA as well as the RIA for the ozone NAAQS completed in 2008.

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Threshold
- Based Eff

ect Coefficients for Long
- term O zone Exposure. As discussed in the HREA completed as part of this NAAQS review (U.S. EPA, 2014b), the exploration of potential threshold s for long

- term exposure

- related respiratory mortality (as discussed in the Jerrett et al., 2009 study) deserves additional discussion. 19 

### In their memo clarifying the results of their study (see Sasser, 2014), the authors note that in terms of goodness of f it, 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 in cluding 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 threshold 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 sta tistically 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 less stringent test, although the threshold model produces a statistically super ior 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 a uthors 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.
Based on this additional information from the aut hors (Sasser, 2014), we have chosen to reflect the uncertainty about the existence and location of a potential threshold by estimating

19 The approach we developed to explore the potential for thresholds related to long

- term exposure

- r elated mortality was presented in a memorandum to CASAC which was also released to the public (Sasser, 2014). That memorandum describes additional data obtained from the authors of Jerrett et al. (2009) to support modeling of potential thresholds and also lays out our proposed approach for exploring the impact of potential thresholds on estimates of long

- term exposure

- related mortality (including presentation of the threshold

- based results as a sensitivity analysis and inclusion of the non

- threshold model

- b ased results as the core analysis). This plan, including these details related to presentation of the threshold and non

- threshold based estimates were supported by CASAC (Frey, 2014, p. 13

- 14).

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- 46

mortality attributable to long

- term ozone exposures using a range of threshold

- based effect estimates as sensitivity anal yses (see section 5.7.3.1 and Appendix 5B, section 5B.1). Specifically, we generate additional long

- term risk results using unique risk models 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. 20 

### In addition, to exploring the impact of potential thresholds, as part of the sensitivity analysis we also explore the impact of using ozone

- only (non

- threshold ) models in estimating long

- term exposure

- related respiratory mortality. 21 

### 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 hig hest 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 that used for the current ozone NAAQS. Together, this means that the most biologically relevant m etric, and the one used in the ozone NAAQS since 1997 is the maximum daily 8

- hour average ozone. Thus, we have converted ozone mortality health impact functions that use a 24

- hour average or 1

- hour maximum ozone metric to maximum 8

- hour average ozone conce ntration 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 equivalent c hange in the 24

- hour average, 1

- hour maximum and 8

- hour maximum will provide the same overall change in incidence of a health effect. 22 

### The conversion

20 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 estimated 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. 21 The set of ozone

- only non

- threshold effect estimates include (a) a value based on the 86 cities for which there are copollutants monitoring data for both ozone and PM 2.5 (this best compared with the core estimate based on the copollutants non

- threshold model) and (b) a value 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). 22 However, it is important to note that different ozone metrics may not be well

- correlated (from either a spatial or temporal standpoint) within a g iven geographic area which means that application of ratio

- converted effect

### 5

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ratios are based on observed relationships between the 24

- hour average and 8

- hour maximum ozone values. Fo r 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 estimat e 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 effect es timates 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 national conversion ratios between daily average and 8

- hour and 1

- hour maxima, based on national data.
5.6.3.2 Hospital Admissions and Emergency Department Visits Because of the availability of detailed hospital admission and discharge records, there is an extensive body of literature examining the relatio nship between hospital admissions and air pollution. For this reason, we pool together the incidence estimates using several different studies for many of the hospital admission endpoints. In addition, some studies have examined the relationship between ai r pollution and emergency department (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 emerg ency department visits separately, 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 originated in the emergency department. With regard to short

- term hospital admissions and ED visits, the current ozone ISA states that, “[c]ompared with studies reviewed in the 2006 ozone AQCD, a larger number of recent studies examined hospital admissions and ED visits for specific respiratory outcomes. Although limited in number, both single

- and multi

- city studies consistently found positive associations

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

- adjusted effect estimates (see Appendix 5A).

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between short

- term ozone exposures and asthma and COPD hospital admission s and ED visits, with more limited evidence for pneumonia. Consistent with the conclusions of the 2006 ozone AQCD, in studies that conducted seasonal analyses, risk estimates were elevated in the warm season compared to cold season or all

- season analyses, specifically for asthma and COPD.” (ozone ISA section 6.2.9, USEPA, 2013a). In this same section, the ISA also addresses potential thresholds in effect: “Although the C

- R relationship has not been extensively examined, preliminary examinations found no evi dence of a threshold between short term ozone exposure and asthma hospital admissions and pediatric asthma ED visits...” Regarding the potential for confounding by other pollutants including PM, the ISA observes that, “Several epidemiologic studies of respir atory morbidity and mortality evaluated the potential confounding effects of copollutants, in particular, PM 10 

### , PM 2.5 

### , or NO 2 

### . In most cases, effect estimates remained robust to the inclusion of copollutants.” (ozone ISA section 6.2.9, USEPA 2013a).
Based on consideration for these observation from the ISA, and a thorough review of available epidemiological studies, for the core analysis, we model respiratory hospital admissions (for 65

- 99yr olds) using effect estimates obtained from Katsouyanni et al., 200 9 and asthma

- related emergency room visits (for all ages) using several single

- city studies. The Katsouyanni et al., 2009 study is for all respiratory hospital admissions, and thus to avoid double counting, we do not provide separate estimates for specific subcategories of respiratory admissions such as asthma. The Katsouyanni et al., 2009 study provides effect estimates specific to the summer season, which is an advantage, however it also utilizes the 1hr max metric, which required adjustment using air met ric ratios to generate equivalent 8hr max effect estimates for use in the RIA. 23 

### The study provides summer season single pollutant effect estimates based both on natural and penalized splines. We used both of these effect estimates and pooled the results

us ing equal

- weight averaging. It is also important to note that, while the Katsouyanni et al., 2009 study did include a set of effect estimates based on copollutants modeling (with PM 10 

### ), these were based on the full year rather than the summer season. Given our focus on warmer ozone

season - based models, we only considered the single pollutant models in the RIA.

23 Given that the Katsouyanni et al., 2009 study 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 t o adjust effect estimates to represent the 8hr metric used in the RIA.

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A number of studies are available to model respiratory ED visits. However, at this time we do not have a valuation function for this endpoint. Since we do have a valuation function available for the narrower category of asthma

- related ED visits , for the core estimate, we have focused on asthma

- related ED visits (for which we can estimate the economic value), using 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 al., (2013) both for Atlanta, Wilson et al., (2005) and Mar and Koenig (2009) for Seattle, Wilson et al., (2005) for Portland ME, Ito et al., (2007) for New York City, and Glad et al., (2012) for Pittsburgh. We note that of these single city studies, only the Ito et al., (2007) study included a

co - pollutant model (for PM 2.5 

### ). 24 

### In addition, two of the studies required adjustments of their betas to reflect the 8hr max air metric. Specifically, Glad et al., (2012) utilizes the 1hr max air metric, while Sarnat et al., 2013 utilized the 24hr 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.
5.6.3.3 Acute Health Events and School/Work Loss Days In addition to morta lity, chronic illness, and hospital admissions, a number of acute health effects not requiring hospitalization are associated with exposure to ozone and PM 2.5 

### . The sources for the effect estimates used to quantify these effects are described below.
Asthma exacerbations. For this RIA, we have 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 asthm atic populations, others consider the full population, including both asthmatics and non

asthmatics. For this reason, incidence estimates derived from studies focused only on asthmatics cannot be added to estimates from studies that consider the full popul ation

— to do so would double

- count impacts. To prevent such double

- counting, we estimated the exacerbation of

24 While we have included copollutants models as sensitivity analyses for mortality, given the reduced role of morbidity endpoints in driving overall dollar benefits, we have not includ ed separate copollutants models for any of the morbidity endpoints as sensitivity analyses. In the case of the Ito et al., (2007) study, we do note, that while the copollutants model does result in a somewhat smaller effect estimate for asthma ED visits as compared with the single pollutant (ozone

- only) model, the SE for the copollutants model is also larger (as would be expected).

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- 50

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 exacerbation of asthma among asthmatic populations, and the onset of asthma among populations not previously suff ering from asthma; in 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 (EPA

- SAB 2004a), regardless of the age ranges included in the source epidemiology studies, we extend the applied population to ages 6 to 18, reflecting the common biological basis for the effect in children in the broader age group. This age range expansion is also supported by NRC (2002, pp. 8, 116).
To characterize asthma exacerbation s in children from exposure to ozone, we selected three multi

- city studies (Mortimer et al., 2002, O’Connor et al., 2008, and Schildcrout et al., 2006). Of these three, one of the studies (O’Connor et al., 2008) only included a multi

- pollutant model (for P M 2.5 

### and NO 2 

### ) and consequently, that effect estimate was used in the core analysis. All three of these studies required the application of air metric ratios to adjust effect estimates to represent the 8hr metric used in the RIA. 25 

### To combine these three est imates into a single pooled estimate, we used equal weights. Acute Respiratory Symptoms. We estimate one type of acute respiratory symptom related to ozone exposure - MRAD

. Minor restricted activity days 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 physical work most of the day will instead spend the day at a desk doing paper work and p hone work because of difficulty breathing or chest pain. For ozone, we modeled MRADs using Ostro and Rothschild (1989). This study provides a copollutants model (with PM 2.5 

### ) based on a national sample of 18

- 64yr olds. The original study

25 Mortimer et al., (2002) had effect estimates based on an 8hr mean metric, O’Connor et al., (2008) utilized a 24hr metric and Schildcrout et al., (2006) was based on a 1hr max metric. Consequently, all three studies required the application of air metric ratios to produce effect estimates reflecting an 8hr max metric.

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used a 24hr 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 8hr max equivalent. School loss days (absences). Children may be absent from school due to respiratory or other acute diseases c aused, 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 et al., 2001; Chen et al., 2000) to estimate changes in school absences re sulting 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 converted the Gilliland et al. estimate to days of absence by multiplying the a bsence 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 from 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, 2002), we calculated reductions in school absences for the full population o f 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 estimated 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.

5.6.3.4 Unquantified Human Health Effects The illustrative emission reduction strategies to reach the proposed and alternative standards described in Chapter 4 would reduce emissions of NOx and VOCs. Although we have quantified many of the health benefits associated with reducing exposure to ozone and PM 2.5 

### , as shown in Table 5

- 3, we are unable to quantify the health benefits associated with reducing the potenti al for NO 2 

### or VOC exposures due to the absence of air quality modeling data for these pollutants in this analysis. In addition, we are unable to quantify the effects of VOC reductions on ambient PM 2.5 

### and associated health effects. Although the method we a pplied simulated the impact of attaining the proposed and alternative standards on ambient levels of ozone, this method does not simulate how the illustrative emission reductions would affect ambient levels of

### 5

- 52

NO 2 

### or VOC. Below we provide a qualitative des cription of these health benefits. In general, previous analyses have shown that the monetized value of these additional health benefits is much smaller than ozone and PM 2.5 

### - related benefits (U.S. EPA, 2010a, 2010c, 2010d). Epidemiological researchers hav e associated NO 2 

### exposure with adverse health effects in numerous toxicological, clinical and epidemiological studies, as described in the Integrated Science Assessment for Oxides of Nitrogen

— Health Criteria (NO2 ISA) (U.S. EPA, 2008b). The NO 2 

### ISA provide s 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 emergency department visits and hospitalizations, respiratory symptoms, airway hyperresponsiveness, airway inflammation, and lung function. Effect estimates fr om 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 shor t

- term NO 2 

### exposure and premature mortality was “suggestive but not sufficient to infer a causal relationship” 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 re lationship 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. The EPA last quantified the value of ozone

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

- level ozone to changes in the productivity of outdoor citrus workers. Tha t 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

level ozone and the productivity of agricult ural workers because of the vintage of the underlying data, the Agency subsequently omitted this endpoint.

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In 2012, Graff Zivin and Neidell published “The impact of pollution on worker productivity” in the American Economic Review. That study combined dat a on individual

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

- level ozone pollution to characterize changes in worker productivity. The authors used data on harvest rates from a 500

- acre farm in the Central Valley of Califor nia. 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 analyses were not affected b y: (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 contract).
Table 3 in Graff Zi vin 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) in standardized hourly pi eces 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 productivity 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 specifications designed to t est 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 the worker productivity, as measured by the average number of pieces collect ed 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 concentration.
While Graff Zivi n and Neidell (2012) report the information needed to quantify ozone

related worker productivity, we are still evaluating the most appropriate way to apply the results of the study in a national benefits assessment. We are considering the feasibility of applying the results of this study to estimate the benefits of increased worker productivity as part of the final ozone RIA, and seek public comment on this approach as an important input to our consideration. While Graff Zivin and Neidell (2012) report the information needed to quantify

### 5

- 54

ozone - related worker productivity, we were unable to incorporate this endpoint into the RIA

. We lacked the time to modify the BenMAP

- CE tool so that it could quantify this impact , but look forward to estimating ozone

- related worker productivity in the final ozone RIA

.

5.6.4 Economic Valuation Estimates Reductions in ambient concentrations of air pollution generally lower the risk of future adverse health effects for a large po pulation. Therefore, 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 estimate s of the relative 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 statist ical incidences 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 indivi dual WTP for this risk reduction is $100, then 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 nu mber of incidences 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 effe cts, 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 r educing 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).

### 5

- 55

We provide unit values for health endpoints (along with i nformation on the distribution of the unit value) in Table 5

- 10. 26 

### 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 detai l below. 27 

### Economic 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 p rocess of reviewing the literature to update these unit values. The discussion below provides additional details on valuing specific PM 2.5 

### - related related endpoints.
5.6.4.1 Mortality Valuation Following the advice of the SAB’s Environmental Economics Advi sory Committee (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 willi ngness to trade off money for reductions in mortality risk (U.S. EPA

- SAB, 2000). The VSL approach is a summary measure for the value of small changes in mortality risk experienced by a large number of people. For a period of time (2004

– 2008), the Office of Air and Radiation (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 th e wage - risk literature. 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

26 We note that a number of the endpoints included in Table 5

- 10 and discussed in this section were only modeled for PM 2.5 and consequently could be moved to Appendix 5C (as was done with the discussion of effect estimates specific to PM 2.5 ). However, given that many of the valuation functions discussed are shared between the two pollutants and given the relatively shorter length of discussions associated with these valuation functions, we have included coverage of all valuation functions (for ozone and PM 2.5 - related endpoints) in this section

.
27 Income growth proj ections are only currently available in BenMAP through 2024, so both the 2025 and 2038 estimates use income growth only through 2024 and are therefore likely underestimates. Currently, BenMAP does not have an inflation adjustment to 2011$. We ran BenMAP fo r a currency year of 2010$ 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 m edical index and wage index between 2010 and 2011 , which affects COI estimates and wage

- based estimates

.

### 5

- 56

mean VSL of $5.4 million estimated in the Kochi et al. (2006) meta

- analysis. However, the Agency neither changed its official guidance on the u se of VSL in rule

- makings nor subjected the interim estimate to a scientific peer

- review process through SAB or other peer

- review group.5 - 57 

### Table 5
- 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 availa ble in Appendix B of 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 simp le 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 $69,000 Direct medical expenses (2000$): 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)5 - 58 

### Table 5
- 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)5 - 59 

### Table 5
- 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 wh ich asthma is moderate or worse as reported in the Rowe and Chestnut (1986) study. The value is assumed have a uniform distribution between $16 and $71 (2000$). 

### (continued)5 - 60 

### Table 5
- 10. Unit Values for Economic Valuation of Health Endpoints (2011$) a 

### (c ontinued)

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 (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 (Abt Associates, 2012). Income growth projections are only currently available in BenMAP through 2024, so both the 2 025 and 2038 estimates use income growth only 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 estim ates and wage

based estimates

.

### 5

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During this time, the Agency continued work to update its guidance on valuing mortality risk reductions, including commissioning 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 from 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 decided to apply the VSL that was vetted and endorsed by the SAB in the Guidelines for Preparing Econo mic Analyses (U.S. EPA, 2000) 28 

### 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 $6.3 million (2000$). 29 

### The Agency is committed to using scientifically sound, appropriately reviewed evidence in valuing mortality risk reductions and has made significant progress in respo nding 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 mortality risk valuation estimates. These are detailed in a white paper on “Valuing Mortality Risk Red uctions 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 recommendations 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 adoption of a value for the projected reduction in

28 In the updated Guidelines for Preparing Economic Analyses (U.S. EPA, 2010e), EPA retained the VSL endorsed by the SAB with the understanding that f urther updates to the mortality risk valuation guidance would be forthcoming in the near future. 29 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 adjustme nts to the $6.3 million value, the VSL is $10 million. Income growth projections are only currently available in BenMAP through 2024, so both the 2025 and 2038 estimates use income growth only through 2024 and are therefore likely underestimates.

### 5

- 62

the risk of premature mortality is the subject of c ontinuing discussion within the economics and public policy analysis community. 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 populatio n 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. Although there are several differences between the labor market studies the EPA uses to derive a VSL esti mate 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 and downward adjustments. Table 5

- 11 lists some of these differences and the expected effect on t he 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 continue 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 5
- 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)

Attribu te 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

- EEAC advised the EPA to “continue to use a wage

- risk

- based VSL as its primary e stimate, 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 have followed this advice.

For PM 2.5 

### - related premature mortality, we assume that there is a “cessation” lag between exposures 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

### 5

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over the 20 years following exposure and discounted over the period between exposure and premature mortality. Although the structure of the lag is uncertain, the EPA follows the advice of the SAB

- HES to assume a segmented 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 reduct ion 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. 30 

### Changes in the cessation la g 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 pe rcent discount rate. Further discussion of this topic appears in the EPA’s Guidelines 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 which prevents us from including this endpoint as a monetary benefit estimate within the core analysis. 31 

### In presenting dollar benefit estimates as part of the sensit ivity analysis, we include 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 respirator y mortality estimate captures primarily, an accumulation of short

- term mortality effects across the ozone season. 32 

### The use of the 20 year segmented lag

30 The cho ice 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% discount refle cts 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.
31 Recall however, that we consid er the estimate of reduced incidence of respiratory mortality associated with long

term ozone exposure to have sufficient support in the literature to be included as part of the core estimate. 32 In presenting risk estimates associated with modeling long

- term ozone

- related respiratory mortality in the HREA, we noted that: “The effect estimates used in modeling long

- term O 3 - attributable mortality, utilize a seasonal average of peak (1

- hr maximum) m easurements. 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.” (USEPA, 2014b). This statement was included in the 2nd draft of the HREA that CASAC commented extensively on (p. 7

- 21) (Frey 2014). While the CASAC made recommendations on specific aspects of our approach for modeling this endpoint (specifically the need to include consideration for potential thresholds as a sensitivity analysis), they did not criticize this observation regarding the potential that this metric could actually represent an acc umulation of short

- term daily peak exposures and that care needed to be taken to avoid

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reflects consideration for advice provided by the HES (USEPA

- SAB , 2010a), where they state that, “[i]f A lternative Estimates are derived 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 assu mptions as for particulate matter when utilizing cohort mortality evidence for ozone.” Dollar benefit estimates generated using both lag assumptions are presented as sensitivity analyses (see section 5.7.3.1). 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 la rgest contributor to the range of uncertainty in monetized benefits (Mansfield et al., 2009). 33 

### 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 approaches 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 mortal ity or reflect differences 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 degre e 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 estima te of mortality risk reduction would reflect these human characteristics, in addition to an individual’s WTP to improve one’s own chances of survival plus WTP to improve other individuals’ survival rates. The ideal measure would also take into account the specific nature of the risk reduction

double

- counting of mortality incidence. Our inclusion of a zero lag reflects the potential that the estimate of long

- term exposure

- related respiratory mortality could (t o a significant extent) capture an accumulation of short

term effects. Note, that we include the zero threshold model together with a 20 year segmented lag model in order to capture a potential range of lag effect and both are given equal coverage in gener ating the dollar benefit estimates included as a sensitivity analysis for this endpoint. 33 This conclusion was based on an assessment of uncertainty based on statistical error in epidemiological effect estimates and economic valuation estimates. Additional sources 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.

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- 65

commodity that is provided to individuals, as well as the context in which risk is reduced. To measure this value, it is important to assess how reductions in air pollution reduce the risk of dying from the time that r eductions 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 individuals because survival curves depend on 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 of 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 study we value reductions in premature mortality risk using the VSL approach.
Other u ncertainties specific to premature mortality valuation include the following:

 Across
- study variation: There is considerable uncertainty as to whether the available literature on VSL provides adequate estimates of the VSL for risk reductions from air pollut ion 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 that population are believed to affect the average WTP to reduce the risk. The appropriateness of a distribu tion of WTP based on the current VSL literature for valuing the mortality

- related benefits of reductions in air pollution concentrations therefore depends 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 affected 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 mortality risk is linear in risk reduction. For example, suppose a study provides a result that the averag e 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 r eduction of 1/100,000 implies a WTP of $500 for a

### 5

- 66

risk reduction of 1/10,000 (which is 10 times the risk reduction valued in the study). Under the assumption of linearity, the estimate of the VSL does not depend on the particular amount of risk reduction b eing 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 mortal ity risks may differ in several ways from air pollution

- related mortality risks, the most important difference may be that job

- related risks are incurred voluntarily, or generally assumed to be, whereas air pollution

- related risks are incurred involuntaril y. Some evidence suggests that people will pay more to reduce involuntarily incurred risks than risks incurred voluntarily (e.g., Lichtenstein and Slovic, 2006). If this is the case, WTP estimates based on wage

- risk studies may understate WTP to reduce inv oluntarily incurred air pollution

- related mortality risks.

 Sudden versus protracted death: A final important difference related to the nature of the risk may be that some workplace mortality risks tend to involve sudden, catastrophic events, whereas air po llution

- 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 protracted death involving prolonged suffering and loss of dignity and personal control is greater than the W TP 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 sufferi ng than are the risks addressed in the valuation literature, the WTP measurements employed in the present analysis would reflect a downward bias.

 Self
- selection and skill in avoiding risk : Recent research (Shogren and Stamland, 2002) suggests that VSL esti mates based on hedonic wage studies may overstate the average value of a risk reduction. This is based on the fact that the risk

- wage trade

- off revealed in hedonic studies reflects the preferences of the marginal worker (i.e., that worker who demands the h ighest 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 mar ginal 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 average risk, so the VSL may be biased, in an ambiguous direction, because the wage diffe rential 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 dec line 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., 200 4).

### 5

- 67

5.6.4.2 Hospital Admissions and Emergency Department Valuation In the absence of estimates of societal WTP to avoid hospital visits/admissions for specific illnesses, we derive COI estimates for use in the benefits analysis. The International Classifi cation 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 spent 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 , 2007). We estimated the opportunity cos t 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 wage, 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” (Abt Associates, 2012). The resulting national average lost daily wage is $150 (2011$). 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 prov ide the rounded unit values in 2011$ for the COI functions used in this analysis in Table 5

- 12.
Table 5
- 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 (Abt Associates, 2012). 

### To value asthma emergency department visits, we used a simple average of two estimates from the health economics literature. The first estimate comes from Smith et al. (1997), who reported approximately 1.2 million asthma

- related emergency department visit s in 1987, at a total cost of $186 million (1987$). The average cost per visit that year was $155; in 2011$, that cost was $480 (using the CPI

- U for medical care to adjust to 2011$). The second estimate comes

### 5

- 68

from Stanford et al. (1999), who reported the c ost of an average asthma

- related emergency department visit based on 1996

– 1997 data at $400 (using the CPI

- U for medical care to adjust to 2011$). A simple average of the two estimates yields a unit value of $440 (2011$).
5.6.4.3 Nonfatal Myocardial Infarc tions Valuation We were not able to identify a suitable WTP value for reductions in the risk of nonfatal heart attacks. 34 

### 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 associated with a myocardial infarction extend beyond the initial event itself, we consider costs incurred over several years. Using age

- specific annual lost earnings estimated by Cropper and Krupnick (1990) and a 3% d iscount 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

so meone 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 fo r populations under 25 or over 65. As such, we do not include lost earnings in the cost estimates for these 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 5

- 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, which incorporated therapeutic options, projected outcomes, and prices (using “knowledgeable cardiologists” as consultants). The model used medical da ta and medical decision algorithms to estimate the probabilities of certain events and/or medical procedures being 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 study. 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

34 We note that this endpoint was only modeled as pa rt of the cobenefits an a l ysis for PM 2.5 and is not included in the ozone - related benefits analysis. As such, we could have move d this discussion to Appendix 5D (as was done with the discussion of PM 2.5 - related effect estimates). However, since this was the only broader discussion related to valuation which is exclusively related to PM 2.5 we left it in this section.

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Research and Quality (AHRQ) 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 whose length of stay was therefore substantially shorter than it would be if they had not died.

 Eisenstein et al. (2001) estimated 10

- year costs of $45,000 in rounded 1997$ (using a 3% discount rate) for myocardial infarction patien ts, using statistical prediction (regression) models to estimate inpatient costs. Only inpatient costs (physician fees and hospital costs) were included. Table 5
- 13. Alternative Direct Medical Cost of Illness Estimates for Nonfatal Heart

Attacks a Study Di rect 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 (Abt Associates, 2012). b Wittels et al. (1990) did not appear to discount costs incurred in future years.
c Using a 3% dis count rate. Discounted values as reported in the study. 

### As noted above, the estimates from these three studies are substantially different, and we have not adequately resolved the sources of differences in the estimates. Because the wage

related opportuni ty 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., estimates 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 5

- 14.
Table 5
- 14. Estimated Costs Over a 5
- Year Period of a Nonfatal Myocardial Infarction

(in 2011$) a Age Group Opport unity 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 (Abt Associates, 2012). b An average of the 5

- year costs estimated by Wittels et a l. (1990) and Russell et al. (1998).
c From Cropper and Krupnick (1990), using a 3% discount rate for illustration.

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5.6.4.4 Valuation of Acute Health Events Asthma exacerbation. Several respiratory 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 apply 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. 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 willingness to pay to avoid a minor respiratory restricted activity da y, 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 activity 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 restricted 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 absenc e, 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 parents 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 in 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 absences that do not result in work

- loss days; and third, it uses conservative assumptions

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about the wages of the parent staying home with the child. Finally, this method assumes that parents are unable to work fro m home. If this is not a valid assumption, then 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 Uni ted States (U.S. Census Bureau, 2001), we obtained: (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 c hildren. From these two sets of statistics, we calculated a weighted average participation rate of 72.85 percent. Our 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 amon g 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 sim ilar to that used by Hall et al. (2003). Work 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 respiratory symptoms. Lower and upper respiratory symptoms are each considered a complex of symptoms. A dollar value was derived for clusters of these symptoms 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).
5.6.4.5 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 substantial

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empirical evidence that the income elasticity 35 

### of WTP for health risk reductions is positive, although there is uncertaint y about its exact value. Thus, as real income increases, the WTP for environmental improvements also increases. Although many analyses assume that the income elasticity of WTP is unit elastic (i.e., a 10% higher real income level implies a 10% higher WTP t o reduce risk changes), empirical evidence suggests that income elasticity is substantially less than one and thus relatively inelastic. As real income rises, the WTP value also rises but at a slower rate than real income. The effects of real income change s on WTP estimates can influence benefits estimates in two different ways: through real (national average) income growth between the year a WTP study was conducted and the year for which benefits are estimated, and through differences in income between stu dy 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 makin g such distinctions, and because of insufficient evidence available at present” (U.S. EPA

- SAB, 2000). An advisory by another committee associated with the SAB, the Advisory Council on Clean Air Compliance Analysis (SAB

- Council), has provided conflicting ad vice. While agreeing with “the general principle that the willingness to pay to reduce mortality risks is likely to increase with growth 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). Until these conflic ting 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 t he available income elasticity literature, we adjusted the valuation of human health benefits upward to account for projected growth in real U.S. income. Faced with a dearth of estimates of income elasticities derived from time

- series studies, we applied e stimates derived from cross

- sectional studies in our analysis. Details of the procedure can be found in

35 Income elasticity is a common economic measure equal to the percentage change in WTP for a 1% change in income.

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Kleckner and Neumann (1999). We note that the literature has evolved since the publication of this memo and that an array of newer studies identifying p otentially suitable income elasticity estimates are available (IEc, 2012). The EPA anticipates seeking an SAB review of these studies, and its approach to adjusting WTP estimates to account for changes in personal income, in the near future . 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 growth between 1990 and 2024 is presented below. Income growth projections are only currently a vailable in BenMAP through 2024, so both the 2025 and 2038 estimates use income growth only through 2024 and are therefore likely underestimates. Reported income elasticities suggest that the severity of a health effect is a primary determinant of the str ength of the relationship between changes in real income and WTP. As such, we use different elasticity estimates to adjust the WTP for minor health effects, severe and chronic health effects, and premature mortality. Note that because of the variety of emp irical sources used in deriving the income elasticities, there may appear to be inconsistencies in the 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 income elasticity. One explanation for the seeming inconsistency is the difference in timing of conditions. WTP for minor illnesses is often expressed as a s hort

- term payment to avoid a single episode. WTP for major illnesses and mortality risk reductions are 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 effects 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 adju st estimates of benefits in 2024 are presented in Table 5

- 15. 36

36 We expect that the WTP for i mproved visibility in Class 1 areas would also increase with growth in real income.

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Table 5
- 15. Elasticity Values Used to Account 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 1 990 to 2024 are needed to adjust benefits to reflect real per capita income growth. For consistency with the emissions and benefits modeling, we used national population estimates for the years 1990 to 1999 based on U.S. Census Bureau estimates (Hollman, M ulder, and Kallan, 2000). These 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 projections 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) provid ed 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 elasticity estimates listed in T able 5

- 16. Benefits for each 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 adjustment 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.

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 Projections 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 projections.

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There is some uncertainty regardin g the total costs of illness 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 the 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 also 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 incomes are higher, the losses resulting f rom reduced worker production 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 may 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 t otal economic value of avoided health impacts.
Table 5
- 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 elasticity values reported in Table 5

- 15 3 , U.S. Census population projections, and projections of real GDP per capita. 

### 5.7 Benefits Results As stated in section 5.1 and described in detail in section 5.4.3, we have estimated nationwide benefits for 2025 associated with attainment of alternative ozone standards across the U.S. with the exception of California. We have also estimated the nationwide benefits of attaining in California for 2038. Because of the temporal disconnect between these two scenarios, benefit estimates for each are not totaled and instead, are presented separately (section 5.7.1 for 2025 and section 5.7.2 for post

- 2025). In addition to these core incidence and benefits estimates, we also present a number of additional analys es which are intended to inform interpretation of these core benefit estimates (see section 5.7.3). In completing these additional analyses, in many cases, we did not have to

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generate separate assessments for both scenarios since observations from one sce nario could be readily applied to the other (in these cases, we tended to model the 2025 scenario and then discuss application of those observations to the post

- 2025 scenario).
5.7.1 Benefits of the Proposed and Alternative Annual Primary O zone S tandards for the 2025 S cenario This section presents incidence reductions and associated dollar benefit estimates associated with the 2025 scenario (i.e., every state apart from California

– see section 5.4.3). Applying the impact and valuation functions described previously in this chapter to the estimated changes in ozone yields estimates of the changes in physical damages (e.g., premature mortalities, cases of hospital admissions) and the associated monetary values for those changes. Similarly applying the incid ence per ton and dollar per ton values to the estimates of NOx reductions produces estimates of changes in PM

- related health effect incidence and associated dollar benefits. Not all known ozone and PM health effects could be quantified or monetized. The m onetized value of these unquantified effects is represented by adding an unknown “B” to the aggregate total. The estimate of total monetized health benefits is thus equal to the subset of monetized ozone and PM

- related health benefits plus B, the sum of th e non - monetized health benefits and welfare co

- benefits; this B represents both uncertainty and a bias in this analysis, as it reflects those benefits categories that we are unable to monetize in this analysis. We follow our standard rounding conventions i n presenting these benefits results. After reviewing the presentation of EPA’s benefits results, NRC (2002) concluded, “EPA should strive to present the results of the analyses in ways that avoid conveying an unwarranted degree of certainty. Such ways incl ude rounding to few significant digits, increasing the use of graphs, and placing less emphasis on single numbers and greater emphasis on ranges” (p. 161). Following this advice, we round all benefits estimates to two significant digits , and a ll rounding o ccurs after final summing of unrounded estimates. As such, totals may not sum across columns or rows
. In addition, all incidence estimates are rounded to whole numbers with a maximum of two significant digits.
Table 5

- 17 shows the population

- weighted air quality change for the alternative standards averaged across the continental U.S. Table 5

- 18 summarizes the tons of VOC and NOx

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emissions required to simulate attainment of each alternative standard (further differentiated by geographic region including ea st, west and California). Tables 5

- 19 through 5

- 23 present the benefits results for the p roposed and alternative ozone standards. Table 5

- 24 summarizes total benefits by geographic region (including east, west minus California, and California). Note that i n presenting estimates related to reductions in ozone (Tables 5

- 19 and 5

- 20), we include the full set of core estimates together with a subset of sensitivity analysis results (specifically, alternative estimates for both short

- term and long

- ter m mortality) . The benefit estimates presented are relative to a 2025 analytical baseline reflecting attainment nationwide (excluding California) of the current primary ozone standards (i.e., 75 ppb) that includes promulgated national regulations and illustrative emiss ion s controls to simulate attainment with 75 ppb.
Table 5
- 17. Population
- Weighted Air Quality Change for the Proposed and Alternative

Annual Primary O zone Standards Relative to Analytical Baseline for 2025 a

Standard Pop ulation
- Weighted Summer Season

O zone Concentration Change (8hr

max) b 70 ppb 0.5285 65 ppb 1.6317 60 ppb 3.0222

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 5
- 18. Emission Reductions in Illustrative Emission Reduction Strategies for the

Proposed and Alternative Annual Primary O zone S tandards, by Pollutant and Region Relative to Analytical Baseline

– Full Attainment (tons) a 70 ppb 65 ppb 60 ppb NOx East 600 1,700 2,800 West 0 110 500 CA 53 110 140 VOC East 55 99 150 West 0 7 7 CA 0 0 0

a See Chapter 4 for more information on the illustrative emission reduction strategies. The emissions in this table reflect both known and unknown controls. Several recent rules such as Tier 3 will have substantially reduced ozone concentrations by 2025 in the East, thus few additional controls would be needed to reach 70 ppb.

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Table 5
- 19. Estimated Number of Avoided O zone Ozone
- Only Health Impacts for the

Proposed and Alternative Annual Ozone Standards (Incremental to the Analytical Baseline) for the 2025 S cenario (nationwide benefits of attaining each alternative standard everywhere in the U.S. except California) a , b

a All incidence estimates are rounded to whole numbers with a maximum of two signif icant digits. b All incidence estimates are based on ozone

- only models unless otherwise noted.
c See Appendix 5B, section 5B.1 for additional detail on the threshold

- based sensitivity analysis.

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Table 5
- 20. Total Monetized O zone - Only Benefits for the Proposed and Alternative

Annual O zone Standards (Incremental to the Analytical Baseline) for the 2025 S cenario (nationwide benefits of attaining each alternative standard everywhere in the U.S. except California) ( millions of 2011) a, b

a All benefits estimates are rounded to whole 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 (and when combined accou nt for from 4

- 6% of the total benefits, depending on the total mortality estimate compared against. Note that asthma exacerbations accounts for <<1% of the total). b The sensitivity analysis for long

- term exposure

- related mortality included an assessment o f potential thresholds however this assessment was implemented using incidence estimates (see Table 5

- 19). Observations from that analysis (in terms of fractional impacts on incidence can be directly applied to these benefit results) (see Appendix 5B, sect ion 5B.1)
c A single central

- tendency value is provided in each cell, since the zero

- lag model used here did not require application of a 3% and 7% discount rates (see footnote d below) (note, however that as with all other entries in this study, we do in clude a 95 th percentile confidence interval range

– these are the values within parentheses)

. d The range (outside of the parentheses) within each cell results from application of a 7% and 3% discount rates in the context of applying the 20year segmented lag (with the 7% resulting in the lower estimate and the 3% the higher estimate). The range presented with in the parentheses reflects consideration for the 95 th % confidence interval generated for each of these estimates and ranges from a low value (2.5 th % CI for the 7% discount

- based dollar benefit) to an upper value (97.5 th % of the 3% discount

- based dollar be nefit).

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Table 5
- 21. Estimated Number of Avoided PM 2.5 

### - Related Health Impacts for the

Proposed and Alternative Annual O zone Standards (Incremental to the Analytical Baseline) for the 2025 S cenario (nationwide benefits of attaining each alternative standar d everywhere in the U.S. except 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).

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Table 5
- 22. Monetized PM 2.5 

### - Related Health Co
- Benefits for the Proposed and Alternative Annual O zone Standards (Incremental to Analytical Baseline) for the 2025 S cenario (nationwide benefits of attaining each alternative standard everywhere in the U.S. except California) ( Millions of 2011) a, b,c

Monetized Benefits Proposed and Alterative Standards 70 ppb 65 ppb 60 ppb 3% Discount Rate Krewski et al. (2009) (adult mortality age 30+) $4,800 $14,000 $25,000 Lepeule et al. (2012) (adult mortality age 25+) $11,000 $31,000 $56,000 7% Discount Rate Krewski et al. (2009) (adult mortality age 30+) $4,300 $12,000 $22,000 Lepeule et al. (2012) (adult mortality age 25+) $9,700 $28,000 $50,000

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). Estimates do not include unquantified health benefits noted in Table 5

- 2 or Section 5.6.3.6 or welfare co

- benefits noted in Chapter 6. b The reduction in premature fatalities each year accounts for over 98% of total mone tized benefits in this analysis. Mortality risk valuation assumes discounting over the SAB

- recommended 20

- year segmented lag structure.



### Table 5
- 23. Estimate of Monetized O zone and PM 2.5 

### Benefits for Proposed and

Alternative Annual O zone Standards Increme ntal to the Analytical Baseline for the 2025 S cenario (nationwide benefits of attaining each alternative standard everywhere in the U.S. except California)

– Full Attainment (billions of 2011$) a

Benefits Estimate 70 ppb 65 ppb 60 ppb
Economic value of avoided ozone

- related morbidities and premature deaths using ozone mortality modeled using risk coefficients from Smith et al., (2009) and Zanobetti and Schwartz (2008) plus PM 2.5 - related premature deaths using PM 2.5 mortality modeled using risk coefficie nts from Krewski et al. (2009)
Total benefits 3% discount rate $6.9 to $8.2 + B $20 to $24 + B $37 to $44 + B

7% discount rate $6.4 to $7.7 + B $19 to $23 + B $34 to $42 + B
Economic value of avoided ozone

- related morbidities and premature deaths using ozone mortality modeled using risk coefficients from Smith et al., (2009) and Zanobetti and Schwartz (2008) plus PM 2.5 - related premature deaths using PM 2.5 mortality modeled using risk coefficients from Lepeule et al. (2012) Total benefits 3% discount rate $13 to $14 + B $37 to $41 + B $68 to $75 + B

7% discount rate $12 to $13 + B $34 to $38 + B $62 to $70 + B

Discount Rate 70 ppb 65 ppb 60 ppb Ozone
- only Benefits (range reflects Smith et al., 2009 and Zanobetti and Schwartz, 2008) b $2.0 to $3.4 +B $6.4 to $11 +B $12 to $20 +B
PM 2.5 Co
- benefits ( range reflects Krewski et al., 2009 and Lepeule et al. , 20 12) 3% $4.8 to $11 $14 to $31 $25 to $56 7% $4.3 to $9.7 $12 to $28 $22 to $50 Total Benefits 3% $ 6.9 to $ 14 +B $ 20 to $ 41 +B $ 37 to $ 75 +B

### 5

- 82 7% $ 6.4 to $ 13 +B $ 19 to $ 38 +B $ 34 to $ 70 +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 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.



### Table 5
- 24. Regional Breakdown of Monetized O zone - Specific Benefits Results for the

2025 S cenario (nationwide benefits of attaining each alternative standard everywhere in the U.S. except California)

– Full Attainment a Region Proposed and Alterative Standards 70 ppb 65 ppb 60 ppb East b 99% 96% 92% California 0% 0% 0% Rest of West 1% 4% 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. Several recent rules such as Tier 3 will have substantially reduced ozone concentrations by 2025 in the East, thus few additional controls would be needed to reach 70 ppb

.



### 5.7.2 Benefits of the Proposed and Alternative Annual Primary O zone S tandards for the post

2025 S cenario This section presents incidence reductions and associated dollar benefit estimates associated with the post

- 2025 scenario (i.e., nationwide benefits estimates reflecting attainment of alternative standards in California

– see section 5.4.3). The same roun ding conventions described in section 5.7.1 (for the 2025 estimates) were applied in generating these estimates. As with estimates generated for the 2025 scenario, total monetized health benefits are equal to the subset of monetized ozone and PM

- related he alth benefits plus B, the sum of the non

- monetized health benefits and welfare co

- benefits. Organization and general content of tables presented here for the post

- 2025 scenario mirrors that of tables presented in section 5.7.1 for the 2025 scenario and the reader is referred there for further clarification.

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Table 5
- 25. Population
- Weighted Air Quality Change for the Proposed and Alternative

Annual Primary O zone Standards Relative to Analytical Baseline for post
- 2025 a
Standard Po pulation
- Weighted Ozone Season O zone Concentration Change (8hr max) b 70 ppb 0.1526 65 ppb 0.3311 60 ppb 0.5007

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 5
- 26. Estimated Number of Avoided O zone - Only Health Impacts for the Proposed

and Alternative Annual Ozone Standards (Incremental to the Analytical Formatted Table

### 5

- 84 Baseline) for the Post
- 2025 S cenario (nationwide benefits of attaining each alternative standard just 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.
c The sensitivity analysis for long

- term exposure

- related mortality included an ass essment of potential thresholds, which was completed for the 2025 scenario (see Table 5

- 19)

. C are should be taken in applying the results of that sensitivity analysis to the post

- 2025 scenario, although general patterns of impact across the thresholds may apply. 

### Table 5
- 27. Total Monetized O zone - Only Benefits for the Proposed and Alternative

Annual O zone Standards (Incremental to the Analytical Baseline) for the post ### 5

- 85 2025 S cenario (nationwide benefits of attaining each alternative standard just in California) ( millions of 2011) a, b

a All benefits estimates are rounded to whole numbers with a max imum 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 (and when combined account for from 4

- 6% of the total benefits, depending on the total mo rtality estimate compared against. Note that asthma exacerbations accounts for <<1% of the total). b The sensitivity analysis for long

- term exposure

- related mortality included an assessment of potential thresholds however this assessment was implemented us ing incidence estimates (see Table 5

- 19). Observations from that analysis (in terms of fractional impacts on incidence can be directly applied to these benefit results) (see Appendix 5B, section 5B.1)
c A single central

- tendency value is provided in each cell, since the zero

- lag model used here did not require application of a 3% and 7% discount rates (see footnote d below) (note, however that as with all other entries in this study, we do include a 95 th percentile confidence interval range

– these are the values within parentheses)

. d The range (outside of the parentheses) within each cell results from application of a 7% and 3% discount rates in the context of applying the 20year segmented lag (with the 7% resulting in the lower estimate and the 3% the higher estimate). The range presented within the parentheses reflects consideration for the 95 th % confidence interval generated for each of these estimates and ranges from a low value (2.5 th % CI for the 7% discount

- based dollar benefit) to an upper value (97.5 th % of the 3% discount

- based dollar benefit).



### Table 5
- 28. Estimated Number of Avoided PM 2.5 

### - Related Health Impacts for the

Proposed and Alternative Annual O zone Standards (Incremental to the

### 5

- 86 Analyti cal Baseline) for the post - 2025 S cenario (nationwide benefits of attaining each alternative standard just 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).

### 5

- 87

Table 5
- 29. Monetized PM 2.5 

### - Related Health Co
- Benefits for the Proposed and Alternative Annual O zone Standards (Incremental to Analytical Baseline) for the post 2025 S cenario (nationwide benefits of attaining each alternative standard just in California) ( Millions of 2011) a,b

Monetized Ben efits Proposed and Alterative Standards 70 ppb 65 ppb 60 ppb 3% Discount Rate Krewski et al. (2009) (adult mortality age 30+) $420 $830 $1,100 Lepeule et al. (2012) (adult mortality age 25+) $950 $1,900 $2,600 7% Discount Rate Krewski et al. (2009) (adult mortality age 30+) $380 $750 $1,000 Lepeule et al. (2012) (adult mortality age 25+) $860 $1,700 $2,300

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)

. Estimates do not include unquantified health benefits noted in Table 5

- 3 or Section 5.6.3.6 or welfare co

- benefits noted in Chapter 6. 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.



### Table 5
- 30 . Estimate of Monetized O zone and PM 2.5 

### Benefits for Proposed and Alternative

Annual O zone Standards Incremental to the Analytical Baseline for the post
2025 S cenario (nationwide benefits of attaining each alternative standard just in California)

– Full Attainment (billions of 2011$) a Discount Rate 70 ppb 65 ppb 60 ppb Ozone
- only Benefits (range reflects Smith et al., 2009 and Zanobetti and Schwartz, 2008) b $0.66 to $1.1 $1.4 to $2.4 $2.1 to $3.6
PM 2.5 Co
- benefits ( range reflects Krewski et al., 2009 and Lepeule et al. , 20 12) 3% $0.42 to $0.95 $0.83 to $1.9 $1.1 to $2.6 7% $0.38 to $0.86 $0.75 to $1.7 $1.0 to $2.3 Total Benefits 3% $ 1.1 to $ 2. 0 +B $ 2.3 to $ 4.2 +B $ 3.4 to $ 6.2 +B 7% $ 1.1 to $ 2. 0 +B $ 2.2 to $ 4.1 +B $ 3.2 to $ 5.9 +B

Benefits Estimate 70 ppb 65 ppb 60 ppb
Economic value of avoided ozone

- related morbidities and premature deaths using ozone mortality modeled using risk coefficients from Smith et al., (2009) and Zanobetti and Schwartz (2008) plus PM 2.5 - related premature deaths using PM 2.5 mortality modeled using risk coefficients from Krewski et al. (2009) Total benefits 3% discount rate $1.1 to $1.5 + B $2.3 to $3.2 + B $3.4 to $4.7 + B

7% discount rate $1.1 to $1.5 + B $2.2 to $3.1 + B $3.2 to $4.6 + B

Economic value of avoided ozone

- related morbidities and premature deaths using ozone mortality modeled using risk coefficients from Smith et al., (2009) and Zanobetti and Schwartz (2008) plus PM 2.5 - related premature deaths using PM 2.5 mortality modeled using risk coefficients from Lepeule et al. (2012) Total benefits 3% discount rate $1.6 to $2 + B $3.3 to $4.2 + B $4.8 to $6.2 + B

7% discount rate $1.5 to $2 + B $3.2 to $4.1 + B $4.5 to $5.9 + B

### 5

- 88 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 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.



### Table 5
- 31. Regional Breakdown of Monetized O zone - Specific Benefits Results for the

post - 2025 S cenario (nationwide benefits of attaining each alternative standard just in California)

– Full Attainment a Region Proposed and Alterative Standards 70 ppb 65 ppb 60 ppb East 0% 0% 0% California 93% 94% 94% Rest of West 6% 6% 6%

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.



### 5.7.3 Uncertainty in Benefits Results (including D iscussion of S ensitivity Analyses and S upplemental A nalyses) The dollar value of avoided ozone and PM 2.5 

### related premature deaths account for 94% to 98% of the total monetized 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 rema ining categories each account for a small percentage of total benefit; however, they represent a large number of avoided incidences affecting many individuals. Comparing an incidence table to the monetary benefits table reveals that the number of incidence s avoided and the dollar value for that endpoint do not always closely correspond. For example, for ozone we estimate almost 1,000 times more asthma exacerbations would be avoided than premature mortalities, yet asthma exacerbations account for only a very small fraction (<<1%) of total monetized benefits (see Tables 5

- 20). This reflects the fact that 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 hospit al admissions, are valued using a proxy measure of WTP. As such, the true value of these effects may be higher than that reported in the tables above.

### 5

- 89

Sources of uncertainty associated with both the modeling of ozone

- related benefits and PM 2.5 

### - related cobe nefits are discussed qualitatively in Appendix A. Key assumptions and uncertainties related to the modeling of ozone are presented below.

 We assume that short

- term exposure to ozone is associated with mortality and that this relationship holds across the f ull range of exposure. Furthermore, we assume that long

term exposure to ozone is associated with respiratory mortality and while we favor a no

threshold linear C

- R function, we consider the potential impact of thresholds ranging from 40 ppb to 60 ppb.

 In modeling long

- term exposure

- related respiratory mortality, we acknowledge uncertainty in specifying the nature of the cessation lag and have consequently included two alternative lag structures including a 20 year segmented lag and a zero lag (the zero la g reflects the potential that estimates of long

- term exposure

- related mortality could actually be capturing the accumulation of short

- term exposure

- related mortality events). In addition, we acknowledge the value in exploring the impact of potential thresh olds in effect on the core incidence and benefits estimates.
PM 2.5 

### mortality co

- benefits 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 equally potent in causing premature mortality. This is an important assumption, because PM 2.5 

### produced varies considerably in composition across sources, but the scientific e vidence is not yet sufficient to allow differential effects estimates by particle type. 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 su fficient to allow differentiation of those constituents or sources that are more closely 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 concentrat ions.

 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.

### 5

- 90

 We recognize uncertainty associated wit h application of the benefit

- per - ton approach used in modeling PM 2.5 

### cobenefits. The benefit

- per - ton estimates 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 - ton estimates (U.S. EPA, 2013b) and Fann et al. (2012c)). Consequently, these estimates may not reflect local variability in population density, meteorol ogy, 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. In order to evaluate the quantitative impact of specific sources of uncertainty on the core risk estimates, we completed a number of sensitivity analyses, some of which have already been described in p resenting the core risk estimates and some of which are presented in Appendix 5B. A brief overview of these sensitivity analyses, including key observations resulting from those observations are presented below in section 5.7.3.1. In addition to these sens itivity analyses, we have also included several supplemental analyses intended to provide additional perspectives on the core incidence and benefits analyses. These supplemental analyses are presented in detail in Appendix 5C and are also briefly summarize d below in section 5.7.3.2.
5.7.3.1 Sensitivity Analyses A number of sensitivity analyses have been completed as part of this RIA. Given the importance of mortality in driving benefits estimates (both for ozone and PM 2.5 

### cobenefits), the sensitivity analys es completed have been focused largely on the mortality endpoint. Each sensitivity analysis is briefly described below (including an overview of the approach used in conducting the analysis and key observations). As identified below, several of the sensiti vity analyses have been presented earlier parallel to presentation of the core estimates, while others are described in Appendix B. 39



###  Short
- term ozone - exposure related mortality (alternative epidemiological studies and C
- R functions): As described in sectio n 5.6.3.1, in addition to the two core effect estimates we estimated benefits 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 benefi ts estimates fall within (and towards the lower end of) the broader range resulting from application of the seven alternative effect estimates (see

39 Generally, we gave greater emphasis to those sensitivity analyses examining sources of potential uncertainty directly associated with estimates of ozone

- related mortality and have included summaries of those sensitivity analyses re sults parallel to presentation of core incidence and benefits estimates in sections 5.7.1 and 5.7.2.

### 5

- 91

Tables 5

- 19 and 5

- 20). This increased our overall confidence in the two core studies, particularly with rega rd to epidemiological study design and the characterization of the relationship between short

- term exposure and mortality.

 Long
- term ozone - exposure related respiratory mortality dollar benefits: As discussed in section 5.1, we could not specify a cessation lag structure specific to long

term ozone exposure

- related respiratory mortality. While we included incidence estimates as a part of the core analysis, we present the associated dollar benefits as a sensitivity analysis (see Tables 5

- 20 and 5

- 27). Given u ncertainty related to the lag structure, we also included two different lags in modeling these benefits as a sensitivity analysis

– a 20 year segment lag (as used for PM 2.5 

### ) and a zero lag (see section 5.6.4.1 for additional detail on the lag structures us ed). The sensitivity analysis suggests that if included in the core benefit estimate, long

- term ozone exposure

- related mortality could add substantially to the overall benefits (see Table 5

- 20 and 5

- 27). Additionally, use of a 20 year segment lag can reduc e benefits by 10

- 20% (relative to a zero lag) depending on the discount rate applied.

 Long
- term ozone - exposure related respiratory mortality and potential thresholds: As discussed in section 5.6.3.1, while we favor the no

- threshold model in estimating ben efits related to long

- term ozone exposure

- related respiratory mortality, as a sensitivity analysis, we evaluated the impact of thresholds ranging from 40

- 60ppb

. 40 

### These results are included alongside core incidence estimates in Table 5

- 19 ( see Appendix 5B, section 5B1 for additional details). This sensitivity analysis suggested that a threshold of 50ppb or greater could have a substantial impact on estimated benefits, while thresholds below this range have a relatively minor impact.

 Long
- term PM 2.5 

### exposure - related mortality and alternative C
- R functions (based on expert elicitation): As discussed in Appendix 5D (section 5D.1) in 2006 we conducted an expert elicitation to help better characterize uncertainty associated with long

- term PM 2.5 

### exposure - related m ortality and specifically the C

- R functions used in modeling that endpoint, including the shape of the functions and potential for thresholds in effect. As part of the sensitivity analysis for the current ozone RIA, we applied the set of expert elicitation

- based functions to generate an alternative set of PM 2.5 

### incidence and benefit estimates (see Appendix 5B, section 5B.2). That sensitivity analysis showed that the two core incidence estimate fall within the range of alternative C

- R function based estimate s obtained through expert elicitation (see Table 5B

- 2). This increases overall confidence in the core estimates with regard to the form of the functions and magnitude of the effect estimates.

 Income elasticity and the willingness
- to - pay (WTP) values used for mortality and certain morbidity endpoints: As described in Appendix 5B (section 5B.3) we 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 WT P functions used for mortality and for morbidity endpoints. That sensitivity analysis, suggests that

40 As noted in section 5.6.3.1, our decision to include benefit estimates based on the no

- threshold model in the core analysis reflects recommendations on the HREA provided by CASAC (Frey, 2014).

### 5

- 92

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 modest impact on morbidity endpoints modeled using WTP functions. 5.7.3.2 Supplemental Analyses In addition to the sensitivity analyses described in section 5.7.3.1, we also included a number of supplemental analyses intended to examine other attributes of the core risk estimates (these are briefly described below with additional detail found in Appendix 5C).

 Consideration for age group
- differentiated aspects of short
- term ozone exposure related mortality (including total avoided incidence, life years gained and percent reduction in baseline mortality): These analyses expand on the basic mortality incidence estimates presented in section 5.7 by conside ring (a) estimates of the reduction in mortality incidence differentiated by age range (i.e., how the avoided deaths map to different age ranges) and (b) estimates of life years gained by age range and (c) percent reduction in baseline mortality by age ran ge. 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 ozone

- attributable deaths occur in populations age 75

– 99 (see Appendix 5C, section 5C.1 Table 5C

- 1), but half of the life years would occur in populations younger than 65 (see Appendix 5C, section 5C.1, Table 5C

- 2). 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. Results presented in Table 5C

- 3 highlight that when reductions in ozone

- attributable mortality (in going from baseline to an alternative standard level) are considered as a percentage of total all

- cause baseline mor tality, the estimates are relatively small and are fairly constant across age ranges. However, it is important to point out that estimates of total ozone

attributable mortality represent a substantially larger fraction of all

- cause baseline mortality than the increment attributable to the simulated reduction in ozone associated with an alternative standard level.

 Evaluation of mortality impacts relative to the baseline pollutant concentrations (used in generating those mortality estimates) for both short
- te rm ozone exposure related mortality and long
- term PM 2.5 

### exposure - related mortality: In this supplemental analysis, we begin by comparing the distribution of short

- term ozone exposure - related mortality against the ozone season

- averaged 8hr max values used i n deriving those estimates (for the three alternative standards modeled for the 2025 scenario) (see section 5C.2). In making this comparison, we point out that, rather than using a full daily time series of 8hr max values for each grid cell, we used an ozo ne season - average 8hr max value for incidence modeling. While this simplification did not impact the overall core incidence estimate for this mortality endpoint, it does impact efforts to compare the distribution of mortality estimates against associated d aily values (by reducing temporal variation associated with each grid cell calculation). Key observations resulting from this supplemental analysis are that (a) the vast majority of

### 5

- 93

reductions in short

- term exposure

- related mortality for ozone occur in gr id cells with mean 8hr max baseline levels (across the ozone season) between 35 and 55ppb and (b) virtually all of the mortality reductions are associated with ozone levels above the <20ppb range identified within the Ozone ISA as being associated with les s confidence in specifying the nature of the C

- R function for ozone mortality (ozone ISA, section 2.5.4.4). The other portion of this supplemental analysis (which compares the distribution of PM 2.5 

### mortality estimates relative to associated PM 2.5 

### levels us ed in modeling) relies on the concept of the lowest measured level (LML) associated with the epidemiological studies providing the effect estimates used in morality modeling. Specifically, we observe that there is reduced confidence in specifying the natur e of the C

- R function at exposure levels below the range used 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 model ing involving PM 2.5 

### levels above the LML (i.e., levels at which we have increased confidence in specifying the PM 2.5 

### - mortality relationship) (see Appendix 5C.2).

 Core Incidence and Dollar Benefits Estimates Reflecting Application of Known Controls for the 2025 Scenario : In Appendix 5C, section 5C.3, we present a subset of core incidence and benefits for the 2025 scenario reflecting only application of known controls in modeling reductions in ozone to attain each alternative standard level. These known

- cont

rol based estimates include both ozone

- related and PM 2.5 

### co - benefit estimates as well as total benefits. As expected, the percent of benefits reflecting application of known controls decreases as you consider more stringent alternative standards. Estimates presented in Appendix 5C.3 suggest that 77%, 59% 34% of total benefits are associated with application of known controls (for the 70, 65 and 60 ppb alternative standards, respectively).

5.8 Discussion The analysis in this Chapter demonstrates the potent ial for significant health benefits of the illustrative emission s controls applied to simulate attainment with the alternative primary ozone standards. We estimate that by 2025, the emissions reductions to reach the alternative standards everywhere except California, would have reduced the number of ozone

- and PM 2.5 

### - related premature mortalities and produce substantial non

- mortality 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 proposed rule also promises to yield significant welfare impacts as well (see Chapter 6). Even considering the quantified and unquantified uncertainties identified in this chapter, we believe tha t the implementing the alternative standards would have substantial public health benefits that are likely to outweigh the costs for the three alternative standards analyzed (see Chapter 7).

### 5

- 94

Inherent in any complex RIA such as this one are multiple source s of uncertainty. Some of these we characterized through our quantification of statistical error in the concentration

response relationships 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 evaluated within the context of these uncertainties, the health impact and monetized benefits estimates in this RIA can provide usefu l information regarding the public health benefits associated with the proposed and alternative primary standards. There are important differences worth noting in the design and analytical objectives of NAAQS RIAs compared to RIAs for implementation rules, such as the Tier 3 (U.S. EPA, 2014c). The NAAQS RIAs illustrate the potential costs and benefits of a revised air quality standard nationwide based on an array of emission reduction strategies for different sources, incremental to implementation of existi ng regulations and controls needed to attain the current standards. In short, NAAQS RIAs hypothesize, but do not predict, the emission reduction strategies that States may choose to enact when implementing a revised NAAQS. The setting of a NAAQS does not d irectly result in costs or benefits, and as such, NAAQS RIAs are merely illustrative and are not intended to be added to the costs and benefits of other regulations that result in specific costs of control and emission reductions. By contrast, the emission reductions from implementation rules are generally for specific, well

- characterized sources, such as the recent MATS rule (U.S. EPA, 2011e). In general, the EPA is more confident in the magnitude and location of the emission reductions for implementation rules. As such, emission reductions achieved under promulgated implementation rules such as Tier 3 have been reflected in the baseline of this NAAQS analysis. Subsequent implementation rules will be reflected in the baseline for the next ozone NAAQS review . 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. In setting the NAAQS, the EPA considers that ozone concentrations vary over space and time. Whil e the standard is designed to limit concentrations at the highest monitor in an area, it is understood that emission controls put in place to meet the standard of the highest monitor will simultaneously result in lower ozone concentrations throughout the e ntire area. In fact, the

### 5

- 95

Health Risk and Exposure Assessment for 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 of urban 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 requisi te 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, whi le 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 conside rs 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 literature. 41 

### 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 concentration

- response functions for the purposes of benefits analysis. The estimated benefits for the proposed and alternative standards are in addition to the substantial benefits estimated for several recent implementation rules (U.S. EPA, 2009a, 2011d, 2014c). Rules such as Tier 3 and other e mission reductions will have substantially reduced ambient ozone concentrations by 2025 in the East, such that few additional controls would be needed to reach 70 ppb in the East beyond the analytical baseline. These rules that have already been promulgate d have tremendous combined benefits that explain why the number of avoided premature deaths associated with this NAAQS revision are smaller than were estimated in the

41 As discussed 5.7.3.1, our modeling of long

- term ozone exposure

- related respiratory mortality did include consideration of potential thresholds as a sensitivity analysis. However, key points need to be emphasized i n the context of interpreting this endpoint: (a) based on recommendations from CASAC the core incidence reduction estimates was based on a non

- threshold model (Frey, 2014) and (b) while the incidence estimates for this endpoint were included as core estima tes, associated dollar benefit estimates were only included as sensitivity analyses and consequently did not factor in to the core dollar benefit estimates generated.

### 5

- 96

previous ozone NAAQS RIA (U.S. EPA, 2006) for the year 202 0 and even smaller than the

mor tality risks estimated for the current year in the ozone HREA (U.S. EPA, 2014b).
5.9 References Abt Associates Inc. 1996. A Particulate Matter Risk Assessment for Philadelphia and Los Angeles. Prepared for Office of Air Quality Planning and Standards. Research Triangle Park, NC: EPA Office of Air and Radiation, OAQPS, July 1996. Available at: <http://www.epa.gov/ttnnaaqs/standards/pm/data/jly3amd.pdf>. Abt Associates, Inc. 2010. Environmental Benefits and Mapping Program (Version 4.0). Prepared for U.S

. Environmental Protection Agency, Bethesda, MD. Research Triangle Park, NC: EPA Office of Air Quality Planning and Standards. Available on the Internet at: <http://www.epa.gov/air/benmap>. Abt Associates, Inc. 2012. BenMAP User’s Manual Appendices

. Prepared for U.S. Environmental Protection Agency Office of Air Quality Planning and Standards. Research Triangle Park, NC. September. Available at <http://www.epa.gov/air/benmap/models/BenMAPAppendicesOct2012.pdf>. Adams PF, Hendershot GE, Marano MA. 1999

. “ Current Estimates from the National Health Interview Survey, 1996. ” Vital and Health Stat istics 10(200):1

- 212. Agency for Healthcare Research and Quality (AHRQ). 2000. HCUPnet, Healthcare Cost and Utilization Project

. Rockville, MD. Available at <http:/ /hcupnet.ahrq.gov>. Agency for Healthcare Research and Quality (AHRQ). 2007. HCUPnet, Healthcare Cost and Utilization Project. Rockville, MD. Available at <http://hcupnet.ahrq.gov>. Agency for Healthcare Research and Quality (AHRQ). 2009. HCUPnet, Healthca re Cost and Utilization Project. Rockville, MD. Available at <http://hcupnet.ahrq.gov>. Alberini A, Cropper M, Krupnick A, Simon NB. 2004. “Does the value of a statistical life vary with age and health status? Evidence from the US and Canada.” Journal of E nvironmental Economics and Management 48(1): 769

- 792. Alberini, Anna & Milan Ščasný. 2011. "Context and the VSL: Evidence from a Stated Preference Study in Italy and the Czech Republic." Environmental & Resource Economics 49(4), pages 511

- 538. Aldy JE, Vis cusi WK. 2008. “Adjusting the value of a statistical life for age and cohort effects.” The Review of Economics and Statistics 90(3):573

- 581. American Lung Association (ALA)

. 2002. Trends in Asthma Morbidity and Mortality

. American Lung Association, Best Pr actices and Program Services, Epidemiology and Statistics Unit. Available at <http://www.lungusa.org/data/asthma/ASTHMAdt.pdf>. American Lung Association (ALA). 2010b. Trends in Asthma Morbidity and Mortality. American Lung Association Epidemiology and Sta tistics Unit Research and Program Services Division. February. Table 7. Based on NHIS data (CDC, 2008). Available at http://www.lungusa.org/finding

- cures/our

- research/trend

- reports/asthma

- trend

report.pdf. Ayyub, B.M. 2002. Elicitation of Expert Opinions f or Uncertainty and Risk . CRC Press, Florida. Babin, S. M., H. S. Burkom, et al. 2007. “Pediatric Patient Asthma

- related Emergency Department Visits and Admissions in Washington, DC, from 2001

– 2004, and Associations with Air Quality, Socio

- economic Status a nd Age Group.” Environmental Health 6: 9.

### 5

- 97

Bell, ML; McDermott, A; Zeger, SL; Samet, JM; Dominici, F. (2004). Ozone and short

- term mortality in 95 US urban communities, 1987

- 2000. JAMA 292: 2372

- 2378. http://dx.doi.org/10.1001/jama.292.19.2372 Bell, ML; Do minici, F; Samet, JM. (2005). Ameta

- analysis of time

- series studies of ozone and mortality with comparison to the national morbidity, mortality, and air pollution study. Epidemiology 16: 436

- 445. http://dx.doi.org/10.1097/01.ede.0000165817.40152.85 Bell, M . L., K. Ebisu, et al. 2008. “Seasonal and Regional Short

- term Effects of Fine Particles on Hospital Admissions in 202 US Counties, 1999

– 2005.” American Journal of Epidemiology 168(11): 1301

- 1310. Bell, M.L. and F. Dominici. 2008. Effect modification by co mmunity characteristics on the short

- term effects of ozone exposure and mortality in 98 U.S. communities. American Journal of Epidemiology. 167:986

- 997. Bell, M.L.; A. McDermott; S.L. Zeger; J.M. Samet; F. Dominici. 2004. Ozone and short

- term mortality in 95 U.S. urban communities, 1987

- 2000. JAMA. 292:2372

- 2378. Berger, M.C., G.C. Blomquist, D. Kenkel, and G.S. Tolley. 1987. “ Valuing Changes in Health Risks: A Comparison of Alternative Measures. ” The Southern Economic Journal 53:977

- 984. Centers for Disease Control and Prevention (CDC). 2003. Health, United States . Table 30. Years of Potential Life Lost Before Age 75 for Selected Causes of Death, According to Sex, Race and Hispanic Origin: United States, Selected Years 1980

– 2000. Availabl e at <http://www.cdc.gov/nchs/data/hus/hus03.pdf>. Centers for Disease Control and Prevention (CDC). 2008. National Center for Health Statistics . National Health Interview Survey, 1999

- 2008. Centers for Disease Control and Prevention (CDC). 2010. Table C1 Adult Self

- Reported Current Asthma Prevalence Rate (Percent) and Prevalence (Number) by State or Territory. Centers for Disease Control and Prevention, Behavioral Risk Factor Surveillance System (BRFSS). Available at: <http://www.cdc.gov/asthma/brfss/2010 /current/tableC1.htm>. Centers for Disease Control and Prevention (CDC). 2011. United States Life Tables , 2007 National Vital Statistics Reports. Volume 59, Number 9. September. Available at <http://www.cdc.gov/nchs/data/nvsr/nvsr59/nvsr59_09.pdf>. Chen L, Jennison BL, Yang W, and Omaye ST. 2000. “ Elementary school absenteeism and air pollution. ” Inhal ation Toxicol ology 12(11):997

- 1016. Cropper, M. L. and A. J. Krupnick. 1990. The Social Costs of Chronic Heart and Lung Disease. Resources for the Future. Washington, DC. Discussion Paper QE 89

- 16

- REV. Dockery, D.W., J. Cunningham, A.I. Damokosh, L.M. Neas, J.D. Spengler, P. Koutrakis, J.H. Ware, M. Raizenne, and F.E. Speizer. 1996. “Health Effects of Acid Aerosols On North American Children

- Respiratory Symp toms.” Environmental Health Perspectives 104(5):500

- 505. Eisenstein, E.L., L.K. Shaw, K.J. Anstrom, C.L. Nelson, Z. Hakim, V. Hasselblad and D.B. Mark. 2001. “Assessing the Clinical and Economic Burden of Coronary Artery Disease: 1986

- 1998.” Medical Care 3 9(8):824

- 35. Fann N, Lamson A, Wesson K, Risley D, Anenberg SC, Hubbell BJ. 2012a. “Estimating the National Public Health Burden Associated with Exposure to Ambient PM 2.5 and ozone. Risk Analysis,” Risk Analysis 32(1): 81

- 95. Fann N, Roman HR, Fulcher CF, Gentile M, Wesson K, Hubbell BJ, Levy JI. 2012b. “Maximizing Health Benefits and Minimizing Inequality: Incorporating Local Scale Data in the Design and Evaluation of Air Quality Policies.” Risk Analysis 32(6): 908

— 922.

### 5

- 98

Fann, N.; K.R. Baker and C.M. Fulche r. 2012 c . “Characterizing the PM 2.5 - related health benefits of emission reductions for 17 industrial, area and mobile emission sectors across the U.S.” Environment International 49 41

– 151.
Freeman (III), AM. 1993. The Measurement of Environmental and Reso urce Values: Theory and Methods. Washington, DC: Resources for the Future. Frey, C. 2014. CASAC Review of the EPA’s Health Risk and Exposure Assessment for Ozone (Second External Review Draft

– February, 2014). U.S. Environmental Protection Agency Science Advisory Board. EPA

CASAC

- 14

- XXX.Gent, J.F.; E.W. Triche; T.R. Holford; K. Belanger; M.B. Bracken; W.S. Beckett, et al. 2003. Association of low

- level ozone and fine particles with respiratory symptoms in children with asthma. Journal of the American Medic al Association. 290(14):1859

- 1867. Frey, C. and J. Samet. 2012. CASAC Review of the EPA’s Health Risk and Exposure Assessment for Ozone (First External Review Draft

- Updated August 2012) and Welfare Risk and Exposure Assessment for Ozone (First External R eview Draft

- Updated August 2012). U.S. Environmental Protection Agency Science Advisory Board. EPA

- CASAC

- 13

- 002. GeoLytics Inc. 2002. GeoLytics CensusCD® 2000 Short Form Blocks. CD

- ROM Release 1.0. GeoLytics, Inc. East Brunswick, NJ. Available: http://ww w.geolytics.com/ [accessed 29 September 2004]. Gilliland FD, Berhane K, Rappaport EB, Thomas DC, Avol E, Gauderman WJ, et al. 2001. “ The effects of ambient air pollution on school absenteeism due to respiratory illnesses. ” Epidemiology 12(1):43

- 54. Glad, J.A., L.L. Brink, E.O. Talbott, P.C. Lee, X. Xu, M. Saul, and J. Rager. 2012. “The Relationship of Ambient ozone and PM 2.5 Levels and Asthma Emergency Department Visits: Possible Influence of Gender and Ethnicity.” Archives of Environmental & Occupational Health 62 (2): 103

- 108. Graff Zivin, J., Neidell, M. (2012). “The impact of pollution on worker productivity.” American Economic Review , 102, 3652

- 3673. Hall JV, Brajer V, Lurmann FW. 2003. “ Economic Valuation of ozone - related School Absences in the South Coast Air Basin of California. ” Contemporary Economic Policy 21(4):407

- 417. Harrington, W., and P.R. Portney. 1987. “ Valuing the Benefits of Health and Safety Regulation. ” Journal of Urban Economics 22:101

- 112. Hollman, F.W., T.J. Mulder, and J.E. Kallan. 2000. Methodology and Assumptions for the Population Projections of the United States: 1999 to 2100. Population Division Working Paper No. 38, Population Projections Branch, Population Division, U.S. Census Bureau, Department of Commerce. January . Availabl e at <http://www.census.gov/population/www/documentation/twps0038/twps0038.html>. Huang, Y., Dominici, F., & Bell, M. L. (2005). Bayesian Hierarchical Distributed Lag Models for Summer Ozone Exposure and Cardio

- Respiratory Mortality. Environmetrics, 16, 54 7

- 562. Hubbell BL. 2006. “Implementing QALYs in the analysis of air pollution regulations.” Environmental and Resource Economics 34:365

– 384. Hubbell, B., A. Hallberg, D.R. McCubbin, and E. Post. 2005. “Health

- Related Benefits of Attaining the 8

- Hr ozone St andard.” Environmental Health Perspectives 113:73

– 82. ICD, 1979 Industrial Economics, Incorporated (IEc). 2006. Expanded Expert Judgment Assessment of the Concentration

Response Relationship Between PM 2.5 Exposure and Mortality . Prepared for: Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC. September. Available at <http://www.epa.gov/ttn/ecas/regdata/Uncertainty/pm_ee_tsd_expert_interview_summaries.pdf>.

### 5

- 99

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

— Literature Review

. Prepared for: Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC. September. Available at <http://www.epa.gov/ttn/ecas/regdata /Benefits/IncomeElasticityUpdate_Recommendationswithappendices.pdf> Ito, K.; G.D. Thurston and R.A. Silverman. 2007. Characterization of PM2.5, gaseous pollutants, and meteorological interactions in the context of time

- series health effects models. Journa l of Exposure Science and Environmental Epidemiology. 17(S2):S45

- 60. Jerrett M, Burnett RT, Pope CA, III, et al. 2009. “Long

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

- 95. Katsouyanni, K.; J.M. Samet; H.R. Anderson; R. At kinson; A.L. Tertre; S. Medina, et al. 2009. Air Pollution and Health: A European and North American Approach (APHENA). Health Effects Institute. Kleckner, N., and J. Neumann. 1999. Recommended Approach to Adjusting WTP Estimates to Reflect Changes in Re al Income. Memorandum to Jim DeM ocker, U.S. EPA/OPAR

. June 3. Available at <http://www.epa.gov/ttn/ecas/regdata/Benefits/background/klecknerandneumann1999.pdf>. Kloog, I., B.A. Coull, A. Zanobetti, P. Koutrakis, J.D. Schwartz. 2012. “Acute and Chronic Effect s of Particles on Hospital Admissions in New England.” PLoS ONE . Vol 7 (4): 1

- 8. Kochi, I., B. Hubbell, and R. Kramer. 2006. “ An Empirical Bayes Approach to Combining Estimates of the Value of Statistical Life for Environmental Policy Analysis. ” Environmen tal and Resource Economics

. 34: 385

- 406. Krewski D, Jerrett M, Burnett RT, Ma R, Hughes E, Shi, Y, et al. 2009. Extended follow

- up and spatial analysis of the American Cancer Society study linking particulate air pollution and mortality

. HEI Research Repor t, 140, Health Effects Institute, Boston, MA. Krupnick, A.J., and M.L. Cropper. 1992. “ The Effect of Information on Health Risk Valuations.” Journal of Risk and Uncertainty 5(2):29

- 48. Kunzli, N., R. Kaiser, S. Medina, M. Studnicka, O. Chanel, P. Filliger, M. Herry, F. Horak Jr., V. Puybonnieux

Texier, P. Quenel, J. Schneider, R. Seethaler, J

- C Vergnaud, and H. Sommer. 2000. “Public

- Health Impact of Outdoor and Traffic

- Related Air Pollution: A European Assessment.” The Lancet 356:795

- 801. Lepeule J, Laden F , Dockery D, Schwartz J. 2012. “Chronic Exposure to Fine Particles and Mortality: An Extended Follow

- Up of the Harvard Six Cities Study from 1974 to 2009.” Environmental Health Perspectives 120 (7):965

- 70. Levy JI, Baxter LK, Schwartz J. 2009. “ Uncertainty and variability in health

- related damages from coal

- fired power plants in the United States .” Risk Analysis 29(7) 1000

- 1014. Lichtenstein, S. and P. Slovic, eds. 2006. The construction of preference . New York: Cambridge University Press. Mansfield, Carol and Sumeet Patil, 2006. Peer Review of Expert Elicitation. Memo to Ron Evans, EPA. September 29, 2006. RTI International. Available at <http://www.epa.gov/ttn/ecas/regdata/Uncertainty/pm_ee_peer_review_summary.pdf>. Mansfield, Carol; Paramita Sinha; Max H enrion. 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 at <http://www.epa.gov/ttn/ecas/regdata/Bene fits/influence_analysis_final_report_psg.pdf>.

### 5

- 100

Mar, TF; Koenig, JQ. (2009). Relationship between visits to emergency departments for asthma and ozone exposure in greater Seattle, Washington. Ann Allergy Asthma Immunol 103: 474

- 479.http://dx.doi.org/10.1016 /S1081

- 1206(10)60263

- 3 Mar, T. F., J. Q. Koenig and J. Primomo. 2010. “Associations between asthma emergency visits and particulate matter sources, including diesel emissions from stationary generators in Tacoma, Washington.” Inhalation Toxicology 22 (6): 445

- 8. Mar, T. F., T. V. Larson, et al. 2004. “An analysis of the association between respiratory symptoms in subjects with asthma and daily air pollution in Spokane, Washington.” Inhalation Toxicology 16(13): 809

- 15.
Medina

- Ramon, M.; A. Zanobetti and J. Schwartz. 2006. The effect of ozone and PM10 on hospital admissions for pneumonia and chronic obstructive pulmonary disease: a national multi

- city study. American Journal of Epidemiology. 163(6):579

- 588. Moolgavkar, S.H. 2000. “ Air Pollution and Hospital Admissions for Diseases of the Circulatory System in Three U.S. Metropolitan Areas. ” Journal of the Air and Waste Management Association 50:1199

- 1206. Mortimer, KM; Neas, LM; Dockery, DW; Redline, S; Tager, IB. (2002). The effect of air pollution on innerc ity children with asthma. Eur Respir J 19: 699

- 705. Mrozek, J.R., and L.O. Taylor. 2002. “ What Determines the Value of Life? A Meta

- Analysis. ” Journal of Policy Analysis and Management 21(2):253

- 270. National Center for Education Statistics (NCHS). 1996. The Condition of Education 1996, Indicator 42: Student Absenteeism and Tardiness . U.S. Department of Education. Washington, DC. 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. Neumann, J.E., M.T. Dickie, a nd R.E. Unsworth. 1994. Linkage between Health Effects Estimation and Morbidity Valuation in the Section 812 Analysis

— Draft Valuation Document . Industrial Economics Incorporated (IEc) Memorandum to Jim DeMocker, U.S. Environmental Protection Agency, Office of Air and Radiation, Office of Policy Analysis and Review. March 31. Available at <http://www.epa.gov/ttn/ecas/regdata/Benefits/background/neumannn1994.pdf>. NRC. 2009. Science and Decisions, Advancing Risk Assessment. Committee on Improving Risk Analysi s Approaches. Washington, DC: The National Academies Press, National Research Council. O'Connor, GT; Neas, L; Vaughn, B; Kattan, M; Mitchell, H; Crain, EF; III, ER; Gruchalla, R; Morgan, W; Stout, J; Adams, GK; Lippmann, M. (2008). Acute respiratory health effects of air pollution on children with asthma in US inner cities. J Allergy Clin Immunol 121: 1133

- 1139. Office of Management and Budget (OMB). 2003. Circular A

- 4: Regulatory Analysis . Washington, DC. Available at <http://www.whitehouse.gov/omb/circula rs/a004/a

- 4.html.> Ostro, B., M. Lipsett, J. Mann, H. Braxton

- Owens, and M. White. 2001. “Air Pollution and Exacerbation of Asthma in African

- American Children in Los Angeles.” Epidemiology 12(2):200

– 208. Ostro, B.D. 1987. “ Air Pollution and Morbidity Revi sited: A Specification Test. ” Journal of Environmental Economics Management 14:87

- 98.

### 5

- 101

Ostro, B.D. and S. Rothschild. 1989. “Air Pollution and Acute Respiratory Morbidity: An Observational Study of Multiple Pollutants.” Environmental Research 50:238

- 247. Ozkaynak, H., and G.D. Thurston. 1987. “ Associations between 1980 U.S. Mortality Rates and Alternative Measures of Airborne Particle Concentration. ” Risk Analysis 7(4):449

- 461. Peel, J.L., P.E. Tolbert, M. Klein et al. 2005. Ambient air pollution and respi ratory emergency department visits. Epidemiology. Vol. 16 (2): 164

- 74. Peng, R. D., H. H. Chang, et al. 2008. “Coarse particulate matter air pollution and hospital admissions for cardiovascular and respiratory diseases among Medicare patients.” Journal of the American Medical Association 299(18): 2172

- 9. Peng, R. D., M. L. Bell, et al. 2009. “Emergency admissions for cardiovascular and respiratory diseases and the chemical composition of fine particle air pollution.” Environmental Health Perspectives 117(6 ): 957

- 63. Peters A. 2005. “Particulate matter and heart disease: evidence from epidemiological studies.” Toxicology and Applied Pharmacology Sept 1;207(2 Suppl):477

- 82. Peters, A., D. W. Dockery, J. E. Muller and M. A. Mittleman. 2001. “Increased particul ate air pollution and the triggering of myocardial infarction.” Circulation 103 (23): 2810

- 5. Poloniecki, J.D., R.W. Atkinson., A.P. de Leon., and H.R. Anderson. 1997. “Daily Time Series for Cardiovascular Hospital Admissions and Previous Day’s Air Polluti on in London, UK.” Occupational and Environmental Medicine 54(8):535

- 540 Pope, C. A., III , J. B. Muhlestein, et al. 2006. “Ischemic heart disease events triggered by short

- term exposure to fine particulate air pollution.” Circulation 114(23): 2443

- 8. Pope, C.A., III, D.W. Dockery, J.D. Spengler, and M.E. Raizenne. 1991. “ Respiratory Health and PM10 Pollution: A Daily Time Series Analysis. ” American Review of Respiratory Diseases 144:668

- 674. Roman, Henry A., Katherine D. Walker, Tyra L. Walsh, Lisa Conner, Harvey M. Richmond, Bryan J. Hubbell, and Patrick L. Kinney. 2008. “ Expert Judgment Assessment of the Mortality Impact of Changes in Ambient Fine Particulate Matter in the U.S. ” Environ mental Sci ence & Technol ogy 42(7):2268

- 2274. Rosamond, W., G. Broda, E. Kawalec, S. Rywik, A. Pajak, L. Cooper, and L. Chambless. 1999. “Comparison of Medical Care and Survival of Hospitalized Patients with Acute Myocardial Infarction in Poland and the United States.” American Journal of Cardiology 8 3:1180

- 1185 Rowe, R.D., and L.G. Chestnut. 1986. “Oxidants and Asthmatics in Los Angeles: A Benefits Analysis

— Executive Summary.” Prepared by Energy and Resource Consultants, Inc. Report to the U.S. Environmental Protection Agency, Office of Policy Analysi s. EPA

- 230

- 09

- 86

- 018. Washington, DC. Rowlatt, P., Spackman, M., Jones, S., Jones

- Lee, M., Loomes, G. 1998. Valuation of deaths from air pollution . A Report for the Department of Environment, Transport and the Regions and the Department of Trade and Indus try. National Economic Research Associates (NERA), London. Russell, M.W., D.M. Huse, S. Drowns, E.C. Hamel, and S.C. Hartz. 1998. “Direct Medical Costs of Coronary Artery Disease in the United States.” American Journal of Cardiology 81(9):1110

- 1115.
Sarnat , J. A., Sarnat, S. E., Flanders, W. D., Chang, H. H., Mulholland, J., Baxter, L., Ozkaynak, H. (2013). Spatiotemporally resolved air exchange rate as a modifier of acute air pollution

- related morbidity in Atlanta. J Expo Sci Environ Epidemiol. doi: 10.103 8/jes.2013.32

### 5

- 102

Sasser, E. 2014. Response to Comments Regarding the Potential Use of a Threshold Model in Estimating the Mortality Risks from Long

- term Exposure to Ozone in the Health Risk and Exposure Assessment for Ozone, Second External Review Draft. Mem orandum to Holly Stallworth, Designated Federal Officer, Clean Air Scientific Advisory Committee from EPA/OAQPS Health and Environmental Impacts Division. Schildcrout, J. S., Sheppard, L., Lumley, T., Slaughter, J. C., Koenig, J. Q., & Shapiro, G. G. (2006 ). Ambient air pollution and asthma exacerbations in children: an eight

- city analysis. Am J Epidemiol, 164(6), 505

- 517. Schwartz J, Coull B, Laden F. 2008. “The Effect of Dose and Timing of Dose on the Association between Airborne Particles and Survival.” Environmental Health Perspectives 116: 64

- 69. Schwartz J, Dockery DW, Neas LM, Wypij D, Ware JH, Spengler JD, Koutrakis P, Speizer FE, Ferris BG Jr. 1994. “Acute effects of summer air pollution on respiratory symptom reporting in children.” American Journa l of Respiratory and Critical Care Medicine 150(5 Pt 1):1234

- 42. Schwartz, J. 1993. “Particulate Air Pollution and Chronic Respiratory Disease.” Environment Research 62:7

- 13. Schwartz, J., and L.M. Neas. 2000. “ Fine Particles are More Strongly Associated t han Coarse Particles with Acute Respiratory Health Effects in Schoolchildren. ” Epidemiology 11:6

- 10. Sheppard, L. 2003. “Ambient Air Pollution and Nonelderly Asthma Hospital Admissions in Seattle, Washington, 1987

- 1994.” In Revised Analyses of Time

- Series Studies of Air Pollution and Health . Special Report. Boston, MA: Health Effects Institute. Shogren, J., and T. Stamland. 2002. “Skill and the Value of Life.” Journal of Political Economy 110:1168

- 1197. Silverman, R.A.; and K. Ito. 2010. Age

- related associa tion of fine particles and ozone with severe acute asthma in New York City. Journal of Allergy Clinical Immunology. 125(2):367

- 373. Slaughter, James C et al. 2005. “Association between particulate matter and emergency room visits, hospital admissions and m ortality in Spokane, Washington.” Journal of Exposure Analysis and Environmental Epidemiology 15, 153

– 159. Smith et al. 2004. “Do the Near Elderly Value Mortality Risks Differently?” The Review of Economics and Statistics 86(1): 423

- 429. Smith, D.H., D.C. Malone, K.A. Lawson, L.J. Okamoto, C. Battista, and W.B. Saunders. 1997. “A National Estimate of the Economic Costs of Asthma.” American Journal of Respiratory and Critical Care Medicine 156(3 Pt 1):787

- 793. Smith, K., Pattanayak SK, Van Houtven G. 2006. “Structural benefit transfer: An example using VSL estimates.” Ecological Economics 60: 361

- 371. Smith, R.L.; B. Xu and P. Switzer. 2009. Reassessing the relationship between ozone and short

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

- 61. Standard and Poor’s. 2000. “The U.S. Economy: The 25 Year Focus.” Winter. Available at <http://:www.standardandpoors.com>. Stanford, R., T. McLaughlin and L. J. Okamoto. 1999. “ The cost of asthma in the emergency department and hospital. ” Am erican J

ournal of Respir

atory and Crit ical Care Medicine 160 (1): 211

- 5. Strickland, M.J.; L.A. Darrow; M. Klein; W.D. Flanders; J.A. Sarnat; L.A. Waller, et al. 2010. Short

- term associations between ambient air p ollutants and pediatric asthma emergency department visits. American Journal of Respiratory Critical Care Medicine. 182:307

- 316.

### 5

- 103

Sullivan, J., L. Sheppard, et al. 2005. “Relation between short

- term fine

- particulate matter exposure and onset of myocardial i nfarction.” Epidemiology 16(1): 41

- 8. Tagaris E, Liao KJ, Delucia AJ, et al. 2009. “ Potential impact of climate change on air

- pollution related human health effects. ” Environ mental Sci ence & Technolology 43: 4979

– 4 988. Tolbert, P. E., M. Klein, et al. 2007. “Multipollutant modeling issues in a study of ambient air quality and emergency department visits in Atlanta.” Journal of Exposure Science and Environmental Epidemiology 17 Suppl 2: S29

- 35. Tolbert, P.E.; M. Klein; J. L. Peel; S.E. Sarnat and J.A. Sarnat. 2007. Multipollutant modeling issues in a study of ambient air quality and emergency department visits in Atlanta. Journal of Exposure Science and Environmental Epidemiology. 17(S2):S29

- 35. Tolley, G.S. et al. 1986. Va luation of Reductions in Human Health Symptoms and Risks . University of Chicago. Final Report for the U.S. Environmental Protection Agency , Office of Policy Analysis

. January. Available at <http://yosemite.epa.gov/ee/epa/eerm.nsf/a7a2ee5c6158cedd8525639700 80ee30/eaa2842b3d2bc2798525644d0 053bf48!OpenDocument#_Section6>. Tsuge T, Kishimot A, Takeuchi K. 2005. “A choice experiment approach to the valuation of mortality.” Journal of Risk and Uncertainty 31(1):73

- 95. U.S. Bureau of Economic Analysis (U.S. BEA). 1997. Real Gross Domestic Product

— Table 2A . U.S. Department of Commerce. U.S. Bureau of Economic Analysis (U.S. BEA). 1997. The Economic and Budget Outlook: An Update, Table 4

— Economic Projections for Calendar Years 1997 through 2007 . September. U.S. Depar tment of Commerce. U.S. Bureau of Economic Analysis (U.S. BEA). 2004. BEA Economic Areas (EAs). U.S. Department of Commerce. Available at <http://www.bea.gov/regional/docs/econlist.cfm>.
U.S. Bureau of the Census. 2001. Statistical Abstract of the United S tates, 2001, Section 12: Labor Force, Employment, and Earnings, Table No. 521 . Washington, DC.
U.S. Bureau of the Census. 2002a. Modified Race Data Summary File 2000 Technical Documentation . Census of Population and Housing: Washington DC. Available at <ht tp://www.census.gov/popest/archives/files/MRSF

01

- US1.pdf>. U.S. Environmental Protection Agency (U.S. EPA). 1999a. The Benefits and Costs of the Clean Air Act, 1990

– 2010. Prepared for U.S. Congress by U.S. EPA, Office of Air and Radiation/Office of Policy Analysis and Review, Washington, DC, November; EPA report no. EPA

- 410

- R

- 99

- 001. Available at <http://www.epa.gov/air/sect812/1990

- 2010/fullrept.pdf>. U.S. Environmental Protection Agency (U.S. EPA). 2000. Guidelines for Preparing Economic Analyses. EPA 24 0

- R

- 00

- 003. National Center for Environmental Economics, Office of Policy Economics and Innovation. Washington, DC. September. Available at <http://yosemite.epa.gov/ee/epa/eed.nsf/webpages/Guidelines.html/$file/cover.pdf>. U.S. Environmental Protection Age ncy (U.S. EPA). 2001. Risk Assessment Guidance for Superfund. Vol. III, Part A. Process for Conducting Probabilistic Risk Assessment (RAGS 3A). Washington, DC: EPA. (EPA document number EPA 540

- R

- 02

- 002; OSWER 9285.7

- 45; PB2002 963302). Available at: <http ://www.epa.gov/oswer/riskassessment/rags3adt/index.htm)>. U.S. Environmental Protection Agency (U.S. EPA). 2004a. Air Quality Criteria for Particulate Matter. National Center for Environmental Assessment, Office of Research and Development, U.S. Environmen tal Protection

### 5

- 104

Agency, Research Triangle Park, NC EPA/600/P

- 99/002bF. Available at <http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=87903>. U.S. Environmental Protection Agency (U.S. EPA). 2004b. EPA’s Risk Assessment Process for Air Toxics: History an d Overview. In: Air Toxics Risk Assessment Reference Library, Technical Resource Manual, Vol. 1, pp. 3

- 1

– 3

- 30. (EPA document number EPA

- 453

- K

- 04

- 001A). Washington, DC: EPA. Available at: <http://www.epa.gov/ttn/fera/data/risk/vol_1/chapter_03.pdf)>.
U.S. Environmental Protection Agency, 2006. Final Regulatory Impact Analysis: PM2.5 NAAQS. Prepared by Office of Air and Radiation. Available: http://www.epa.gov/ttn/ecas/ria.html [accessed 10 March 2008]. U.S. Environmental Protection Agency (U.S. EPA). 2007. Ozone Health Risk Assessment for Selected Urban Areas. Research Triangle Park, NC: EPA Office of Air Quality Planning and Standards. (EPA document number EPA 452/R

- 07

- 009). Available at: <http://www.epa.gov/ttn/naaqs/standards/ozone/s_O3_cr.html>.
U.S. E nvironmental Protection Agency (U.S. EPA). 200 8a

. Regulatory Impact Analysis, 2008 National Ambient Air Quality Standards for Ground

- level ozone, Chapter 6 . Office of Air Quality Planning and Standards, Research Triangle Park, NC. March. Available at <http ://www.epa.gov/ttn/ecas/regdata/RIAs/6

- ozoneriachapter6.pdf>. U.S. Environmental Protection Agency (U.S. EPA). 2008b. Integrated Science Assessment for Oxides of Nitrogen

— Health Criteria (Final Report) . National Center for Environmental Assessment, Researc h Triangle Park, NC. July. Available at http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=194645 U.S. Environmental Protection Agency (U.S. EPA). 2009a. Regulatory Impact Analysis: National Emission Standards for Hazardous Air Pollutants from the Portlan d Cement Manufacturing Industry . Office of Air Quality Planning and Standards, Research Triangle Park, NC. April. Available at <http://www.epa.gov/ttn/ecas/regdata/RIAs/portlandcementria_4

- 20

- 09.pdf>. U.S. Environmental Protection Agency (U.S. EPA). 2009 b

. Integrated Science Assessment for Particulate Matter (Final Report)

. EPA

- 600

- R

- 08

- 139F. National Center for Environmental Assessment

— RTP Division. December. Available at <http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=216546>.
U.S. Environmental Prot ection Agency (U.S. EPA). 2010a . Final Regulatory Impact Analysis (RIA) for the NO2 National Ambient Air Quality Standards (NAAQS)

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

. Available at < http://www.epa.gov/ttn/ecas /regdata/RIAs/FinalNO2RIAfulldocument.pdf >. U.S. Environmental Protection Agency (U.S. EPA). 2010b. Quantitative Health Risk Assessment for Particulate Matter

— Final Report . EPA

- 452/R

- 10

- 005. Office of Air Quality Planning and Standards, Research Triangle P ark, NC. September. Available at <http://www.epa.gov/ttnnaaqs/standards/pm/data/PM_RA_FINAL_June_2010.pdf>.
U.S. Environmental Prot ection Agency (U.S. EPA). 2010c

. Final Regulatory Impact Analysis (RIA) for the S O2 National Ambient Air Quality Standards (N AAQS). 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 Prot ection Agency (U.S. EPA). 2010d

. Regulatory Impact Analysis (RIA) f or the Proposed Reconsideration of the ozone National Ambient Air Quality Standards (NAAQS). Office of Air Quality Planning and Standards, Research Triangle Park, NC. January

. Available at < http://www.epa.gov/ttn/ecas/regdata/RIAs/s1

- supplemental_analysis_ full.pdf >. U.S. Environmental Protection Agency (U.S. EPA). 2010e. Guidelines for Preparing Economic Analyses. EPA 240

- R

- 10

- 001. National Center for Environmental Economics, Office of Policy Economics and Innovation. Washington, DC. December. Available at <http://yosemite.epa.gov/ee/epa/eerm.nsf/vwAN/EE

- 0568

- 50.pdf/$file/EE

- 0568

- 50.pdf>.

### 5

- 105

U.S. Environmental Protection Agency (U.S. EPA). 2010f. Technical Support Document: Summary of Expert Opinions on the Existence of a Threshold in the Concentration

- Respons e Function for PM 2.5 - related Mortality. Research Triangle Park, NC. June. Available at <www.epa.gov/ttn/ecas/regdata/Benefits/thresholdstsd.pdf>. U.S. Environmental Protection Agency (U.S. EPA). 2010g. Proposed Regulatory Impact Analysis (RIA) for the Tran sport Rule. Office of Air Quality Planning and Standards, Research Triangle Park, NC. January. Available at <http://www.epa.gov/ttn/ecas/regdata/RIAs/proposaltrria_final.pdf>. U.S. Environmental Protection Agency (U.S. EPA). 2011a. Ozone National Ambient A ir Quality Standards: Scope and Methods Plan for Health Risk and Exposure Assessment. Research Triangle Park, NC: EPA. (EPA document number EPA

- 452/P

- 11

- 001). U.S. Environmental Protection Agency (U.S. EPA). 2011b. The Benefits and Costs of the Clean Air A ct 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). 2011c. Policy Assessment for the Review of the Particulate Matter National Ambient Air Quality Standards . EPA

- 452/D

- 11

- 003. April. Available at <http://www.epa.gov/ttnnaaqs/standards/pm/s_pm_2007_pa.html>. U.S. Environmental Protection Agency (U.S. EPA). 2011d. Regulatory Impact Anal ysis 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://www.epa.gov/airtransport/pdfs/FinalRIA.pdf>.
U.S. Environmenta l Protection Agency (U.S. EPA). 2011e. Regulatory Impact Analysis for 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 Protection Agency (U. S. EPA). 2011f. Regulatory Impact Results for the Reconsideration Proposal for National Emission Standards for Hazardous Air Pollutants for Industrial, Commercial, and Institutional Boilers and Process Heaters at Major Sources . December. Available at <http ://www.epa.gov/ttnecas1/regdata/RIAs/boilermactreconria.pdf>. U.S. Environmental Protection Agency (U.S. EPA). 2012. Regulatory Impact Analysis for the Proposed Revisions to the National Ambient Air Quality Standards for Particulate Matter. EPA

- 452/R

- 12

- 00 3. Office of Air Quality Planning and Standards, Health and Environmental Impacts Division. December. Available at: http://www.epa.gov/ttn/ecas/regdata/RIAs/PMRIACombinedFile_Bookmarked.pdf

. U.S. Environmental Protection Agency (U.S. EPA). 2012a. Health Ri sk and Exposure Assessment for Ozone. First External Review Draft. Research Triangle Park, NC: U.S. Environmental Protection Agency, Research Triangle Park, NC. (EPA document number EPA 452/P

- 12

- 001). U.S. Environmental Protection Agency (U.S. EPA). 2012b. Regulatory Impact Analysis for the Final Revisions to the National Ambient Air Quality Standards for Particulate Matter . EPA

- 452/R

- 12

- 003. Office of Air Quality Planning and Standards, Health and Environmental Impacts Division. December. Available at: <ht tp://www.epa.gov/pm/2012/finalria.pdf>. U.S. Environmental Protection Agency (U.S. EPA). 2012c. Provisional Assessment of Recent Studies on Health Effects of Particulate Matter Exposure . EPA/600/R

- 12/056A. National Center for Environmental Assessment

— RTP Division. December. U.S. Environmental Protection Agency (U.S. EPA). 2013a. Integrated Science Assessment for Ozone and Related Photochemical Oxidants: Final. Research Triangle Park, NC: U.S. Environmental Protection Agency. (EPA document number EPA/600/R

- 10/076F).

### 5

- 106

U.S. Environmental Protection Agency (U.S. EPA). 2013b. Technical Support Document: Estimating the Benefit per ton of Reducing PM 2.5 Precursors from 17 sectors . Office of Air Quality Planning and Standards, Research Triangle Park, NC. February. Available at: <http://www.epa.gov/airquality/benmap/models/Source_Apportionment_BPT_TSD_1_31_13.pdf>. U.S. Environmental Protection Agency (U.S. EPA). 2013c. Environmental Benefits Mapping Analysis Program (BenMAP v4.0). Posted January, 2013. < http://www. epa.gov/air/benmap/download.html / >. U.S. Environmental Protection Agency (U.S. EPA). 2014a. Control of Air Pollution from Motor Vehicles: Tier 3 Motor Vehicle Emission and Fuel Standards Final Rule: Regulatory Impact Analysis. E PA

- 420

- R

- 14

- 005. Office of Transportation and Air Quality, Assessment and Standards Division. March. Available at: <http://www.epa.gov/otaq/documents/tier3/420r14005.pdf>. U.S. Environmental Protection Agency (U.S. EPA). 2014a. Environmental Benefits Mapping and Analysis Program

- Community Edition (Version 1.0.8) , 2014. Research Triangle Park, NC. Available at <http://www.epa.gov/air/benmap/>. U.S. Environmental Protection Agency (U.S. EPA). 2014b. Health Risk and Exposure Assessment for Ozone. Final Report. Research Triangle Park, NC: U.S. Environmental Protection Agency, Research Triangle Park, NC. (EPA document number EPA

- 452/R

- 14

- 004a). U.S. Environmental Protection Agency (U.S. EPA). 2014c. Regulatory Impact Analysis for the Proposed Carbon Pollution Guidelines for Existing Power Plants and Emission Standards for Modified and Reconstructed Power Plants . Research Triangle Park, NC: Office of Air Quality Planning and Standards. (EPA document number EPA

- 542/R

- 14

- 002, June). Available at <http://www.epa.gov/ttnecas1/regd ata/RIAs/111dproposalRIAfinal0602.pdf>. U.S. Environmental Protection Agency Science Advisory Board (U.S. EPA

- SAB). 1999. An SAB Advisory on t he Clean Air Act Section 812 Prospective Study Health and Ecological Initial Studies . Prepared by the Health and E cological Effects Subcommittee (HEES) of the Advisory Council on the Clean Air Compliance Analysis, Science Advisory Board, U.S. Environmental Protection Agency. Washington DC. EPA

- SAB

- COUNCIL

ADV

- 99

- 005. Available at <http://yosemite.epa.gov/sab%5CSABPROD UCT.NSF/1AFDEBB45C57F6E5852571930058C1A5/$File/coua 9905.pdf>. U.S. Environmental Protection Agency Science Advisory Board (U.S. EPA

- SAB). 200 0. An SAB Report on EPA’s White Paper Valuing the Benefits of Fatal Cancer Risk Reduction . EPA

- SAB

- EEAC

- 00

- 013. Jul y. Available at <http://yosemite.epa.gov/sab%5CSABPRODUCT.NSF/41334524148BCCD6852571A700516498/$File/eeacf0 13.pdf>. U.S. Environmental Protection Agency Science Advisory Board (U.S. EPA

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

— Benefits and Costs of the Clean Air Act, 1990

– 2020 . EPA

- SAB

- COUNCIL

- ADV

- 04

- 002

. March. Available at <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 Clea n Air Compliance Analysis . EPA

- SAB

- COUNCIL

- ADV

- 04

- 00 4. May

. Available at <http://yosemite.epa.gov/sab/sabproduct.nsf/7CCBBFE15CD4C8B185256F17005E3079/$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.

### 5

- 107

December. Available 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). 200 7. SAB Advisory on EPA’s Issues in Valuing Mortality Risk Reduction . EPA

- SAB

- 08

- 001. October. Available at <http://yosemite.epa.gov/sab/sabproduct.nsf/4128007E7876B8F08525737 60058A978/$File/sab

- 08

- 001.pdf>. U.S. Environmental Protection Agency Science Advisory Board (U.S. EPA

- SAB). 2008 . Characterizing Uncertainty in Particulate Matter Benefits Using Expert Elicitation . EPA

- COUNCIL

- 08

- 002. July. Available at <http://yosemite. epa.gov/sab/sabproduct.nsf/0/43B91173651AED9E85257487004EA6CB/$File/EPA

COUNCIL

- 08

- 002

- unsigned.pdf>. U.S. Environmental Protection Agency Science Advisory Board (U.S. EPA

- SAB). 2009a. Review of EPA’s Integrated Science Assessment for Particulate Matter (F irst External Review Draft, December 2008) . EPA

COUNCIL

- 09

- 008. May. Available at <http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb85256ead006be86e/73ACCA834AB44A 10852575BD0064346B/$File/EPA

- CASAC

- 09

- 008

- unsigned.pdf>.
U.S. Environmental Protect ion Agency Science Advisory Board (U.S. EPA

- SAB). 2009b. Review of Integrated Science Assessment for Particulate Matter (Second External Review Draft, July 2009) . EPA

- CASAC

- 10

- 001. November. Available at < http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb85256ead006be86e/151B1F83B0231455 85257678006836B9/$File/EPA

- CASAC

- 10

- 001

- unsigned.pdf>. U.S. Environmental Protection Agency Science Advisory Board (U.S. EPA

- SAB). 2009c. Consultation on EPA’s Part iculate Matter National Ambient Air Quality Standards: Scope and Methods Plan for Health Risk and Exposure Assessment . EPA

- COUNCIL

- 09

- 009. May. Available at <http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb85256ead006be86e/723FE644C5D758D F852575 BD00763A32/$File/EPA

- CASAC

- 09

- 009

- unsigned.pdf>. U.S. Environmental Protection Agency Science Advisory Board (U.S. EPA

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

- COUNCIL

- 10

- 001. June. Available at <http://yosemite.epa.gov/sab/sabproduct.nsf/9288428b8eeea4c885257242006935a3/59e06b6c5ca66597852575 e7006c5d09!OpenDocument&TableRow=2.3#2.>. U.S. Environmental Protection Agency Science Advisory Board (U.S. EPA

- SAB). 2010b . Review of Ris k Assessment to Support the Review of the Particulate Matter (PM) Primary National Ambient Air Quality Standards

— External Review Draft (September 2009). EPA

- CASAC

- 10

- 003 . Available at <http://yosemite.epa.gov/sab/sabproduct.nsf/264cb1227d55e02c852574020074 46a4/BC1ECC5D539EF723852 57678006D5754/$File/EPA

- CASAC

- 10

- 003

- unsigned.pdf>. U.S. Environmental Protection Agency Science Advisory Board (U.S. EPA

- SAB). 2010c. CASAC Review of Quantitative Health Risk Assessment for Particulate Matter

— Second External Review Draft (February 2010)

. EPA

- CASAC

- 10

- 008. Available at <http://yosemite.epa.gov/sab/sabproduct.nsf/264cb1227d55e02c85257402007446a4/BC4F6E77B63851558525 77070002F09F/$File/EPA

- CASAC

- 10

- 008

- unsigned.pdf>. U.S. Environmental Protection Agency Science Advisory Board (U.S. EPA

- SAB). 2010d. CASAC Review of Policy Assessment for the Review of the PM NAAQS

— Second External Review Draft (June 2010) . EPA

- CASAC

- 10

- 015. Available at <http://yosemite.epa.gov/sab/sabproduct.nsf/264cb1227d55e02c85257402007446a4/CCF9F4C0500 C500F8525 779D0073C593/$File/EPA

- CASAC

- 10

- 015

- unsigned.pdf>.

### 5

- 108

U.S. Environmental Protection Agency Science Advisory Board (U.S. EPA

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

- SAB

- 1 1

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

- SAB

- 11

- 011

- unsigned.pdf>. Viscusi, V.K., and J.E. Aldy. 2003. “The Value of a Statistical Life: A Critical Review of Market Estimates throughout the World.” Journal of Risk and Uncertainty 27(1):5

- 76. Viscusi, W.K., W.A. Magat, and J. Huber. 1991. “ Pricing Environmental Health Risks: Survey Assessments of Risk

- Risk and Risk

- Dollar Trade

- Offs for Chronic Bronchitis. ” Journal of Environmental Econom ics and Management 21:32

- 51. 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 Internat ional Labour Organization and the United Nations Environment Programme. WHO Press, World Health Organization, 20 Avenue Appia, 1211 Geneva 27, Switzerland. Wilson, A.M., C.P. Wake, T. Kelly et al. 2005. Air pollution and weather, and respiratory emergency room visits in two northern New England cities: an ecological time

- series study. Environ Res. Vol. 97 (3): 312

- 21. Wittels, E.H., J.W. Hay, and A.M. Gotto, Jr. 1990. “ Medical Costs of Coronary Artery Disease in the United States. ” American Journal of Card iology 65(7):432

- 440. Woodruff, T.J., J. Grillo, and K.C. Schoendorf. 1997. “The Relationship Between Selected of postneonatal infant mortality and particulate air pollution in the United States.” Environmental Health Perspectives 105(6): 608

- 612.
Woods & Poole Economics Inc. 2007. Population by Single Year of Age . 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, Har monization Project Document No. 6. Published under joint sponsorship of the World Health Organization, the International Labour Organization and the United Nations Environment Programme. WHO Press: Geneva, Switzerland. Available at <www.who.int/ipcs/method s/harmonization/areas/uncertainty%20.pdf>. World Health Organization (WHO). 1977. International Classification of Diseases, 9th Revision (ICD

- 9) . Geneva: WHO. Zanobetti A. and Schwartz, J. 2006. “Air pollution and emergency admissions in Boston, MA.” Journal of Epidemiology and Community Health 60(10): 890

- 5. Zanobetti, A; J. Schwartz. 2008. Mortality displacement in the association of ozone with mortality: an analysis of 48 cities in the United States. American Journal of Respiratory and Critical Care Medicine. 177:184

- 189.