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CHAPTER 6: HUMAN HEALTH BENEFITS ANALYSIS APPROACH AND RESULTS 

6.1 Summary 

This chapter of the Regulatory Impact Analysis (RIA) presents the estimated human 

health benefits for the revised National Ambient Air Quality Standards (NAAQS) for ozone. In 

this chapter, we quantify the health-related benefits of the ozone air quality improvements 

resulting from the illustrative emissions control strategies that reduce emissions of the ozone 

precursor pollutants (i.e., nitrogen oxides (NOx) and volatile organic compounds (VOCs)) to 

reach the revised and alternative ozone NAAQS standard levels. We also estimate the health cobenefits of the fine particulate matter (PM2.5)-related air quality improvements that would occur 

as a result of reducing NOx emissions.1

 

We selected 2025 as the primary year of analysis because the Clean Air Act requires 

most areas of the U.S. to meet a revised ozone standard by 2025. The benefits of each standard 

alternative are estimated as being incremental to attaining the existing standard of 75 ppb.2

 

These estimated benefits are incremental to the benefits estimated for several recent rules (e.g., 

U.S. EPA, 2011c and U.S. EPA, 2014a). We estimated the benefits of California attaining a 

revised standard in 2038 to account for the fact that many locations in this state must attain a 

revised standard later than the rest of the U.S. In this chapter, we refer to estimates of nationwide 

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, or directly compare, the estimates. 

 Table 6-1 summarizes the estimated monetized benefits (total and ozone only) of 

attaining the revised and alternative ozone standards of 70 ppb and 65 ppb, respectively, in 2025. 

Table 6-2 presents the same types of benefit estimates for the scenario. These estimates reflect 

the sum of the economic value of estimated morbidity and mortality effects related to changes in 

 

1

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

potential to impact PM2.5 concentrations, but we were not able to estimate those effects. 2

 The current standard is the 4th highest daily maximum 8-hour ozone concentration of 75 ppb. 
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exposure to ozone and PM2.5. Although these tables present ozone and PM2.5-related benefits 

separately, it is not appropriate to compare the ozone-only benefits to total costs because xxxx. 

Compared with benefit estimates generated in the proposal RIA, the total benefit 

estimates generated for the 2025 scenario are ~55% lower for the revised standard (70 ppb) and 

~22% lower for the alternative standard (65 ppb). Benefit estimates for the post-2025 scenarios 

are slightly higher than those generated at proposal (~6% for the revised standard and ~2% for 

the alternative standard). The proposal and final RIA estimates differ principally because, as 

discussed in Chapter xxx, changes in xxx (state key changes) lead the model to we projected

smaller reduction in ozone concentrations needed to simulate attainment. We have also slightly 

modified our approach to estimating morbidity benefits, which had a negligible (~1%) influence 

on the total monetized benefits in this RIA (see sections 6.3 and 6.6.3). 

Table 6-1. Estimated Monetized Benefits of Attainment of the Revised and Alternative 

Ozone Standards for 2025 (nationwide benefits of attaining the standards 

everywhere in the U.S. except California) (billions of 2011$)a 

Discount 

Rate 70 ppb 65 ppb 

Ozone-only Benefits c b $1.0 to $1.7 $5.3 to $8.7 

PM2.5 Co-benefits of 

NOx Reductions d 

3% $2.1 to $4.7 $10 to $23 

7% $1.9 to $4.2 $9.3 to $21 

Total Benefits 3% $3.1 to $6.4 +B $16 to $32 +B 

7% $2.9 to $5.9 +B $15 to $30 +B 

a

 Rounded to two significant figures. It was not possible to quantify all benefits in this analysis due to data 

limitations. “B” is the sum of all unquantified health and welfare benefits. These estimates reflect the economic 

value of avoided morbidities and premature mortalitiesdeaths 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. 

c

 Range reflects application of effect estimates from Smith et al. (2009) and Zanobetti and Schwartz (2008). 

d

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

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Table 6-2. Estimated Monetized Benefits of Attainment of the Revised and Alternative 

Ozone Standards for post-2025 (nationwide benefits of attaining the standards 

just in California) (billions of 2011$)a 

Discount 

Rate 70 ppb 65 ppb 

Ozone-only Benefits c b $0.79 to $1.3 $1.6 to $2.6 

PM2.5 Co-benefits d 3% $0.40 to $0.91 $0.79 to $1.8 

7% $0.37 to $0.82 $0.71 to $1.6 

Total Benefits 3% $1.2 to $2.2 +B $2.4 to $4.4 +B 

7% $1.2 to $2.1 +B $2.3 to $4.2 +B 

a

 Rounded to two significant figures. It was not possible to quantify all benefits in this analysis due to data 

limitations. “B” is the sum of all unquantified health and welfare benefits. These estimates reflect the economic 

value of avoided morbidities and premature deathmortalitiess 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. 

c

 Range reflects application of effect estimates from Smith et al. (2009) and Zanobetti and Schwartz (2008). 

d

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

The control measures (identified and unidentified) applied to reach the revised and 

alternative ozone standards would reduce other ambient pollutants, including VOCs and NO2. 

However, because the method used in this analysis to simulate attainment does not account for 

changes in ambient concentrations of other pollutants, we were unable to quantify the co-benefits 

of reduced exposure to these pollutants. Due to limited data and methods, we were unable to 

estimate some anticipated health benefits associated with exposure to ozone and PM2.5. 

6.2 Overview 

This chapter presents estimated health benefits for the revised and alternative ozone 

standards (70 ppb and 65 ppb, respectively) that the EPA could quantify, given the available 

resources, data and methods. This chapter characterizes the benefits of the application of the 

identified and unidentified control strategies identified in Chapter xxx for the revised and 

alternative ozone standards by answering three key questions: 

1. What health effects are likely to be avoided by reducing ambient ozone levels to 

attain the revised and alternative ozone standards? 

2. What is the estimated economic value of these effects? 

3. What are the potential co-benefits of reductions in ambient PM2.5 associated with 

reductions in emissions of ozone precursors (specifically NOx)? 

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In this analysis, we quantify an array of adverse health impacts attributable associated 

with to ozone and PM2.5 that would potentially would be avoided by attaining a revised ozone 

standard. The Integrated Science Assessment for Ozone and Related Photochemical Oxidants

(“ozone ISA”) (U.S. EPA, 2013a) identifies the human health effects associated with ozone 

exposure, which include premature mortalitydeath and a variety of illnesses associated with 

acute (days-long) and chronic (months to years-long) exposures. Similarly, the Integrated 

Science Assessment for Particulate Matter (“PM ISA”) (U.S. EPA, 2009b) identifies the human 

health effects associated with ambient particles, which include premature mortalitydeath and a 

variety of illnesses associated with acute and chronic exposures. Air pollution can affect human 

health in a variety of ways. In Table 6-3 we summarize the “categories” of effects and describe 

those that we quantified for this analysis and those we were unable to quantify due to lack of 

resources, data, or methods. 

This list of benefit categories is not exhaustive, and we are not always able to quantify 

each effect completely. In this RIA, we only quantify endpoints that are classified in the ozone 

and PM ISAs as being causally related, or likely to be causally related, to each pollutant. We 

excluded endpoints not identified as having at least a causal or likely causal relationship with the 

affected pollutants. Selecting endpoints in this way should not imply that these pollutants are 

unrelated to other human health and environmental effects. Following this criterion, we excluded 

some effects that were identified in previous lists of unquantified benefits in other RIAs (e.g., 

UVb exposure), but are not identified in the most recent ISA as having a causal or likely causal 

relationship with ozone. This new way of selecting endpoints for quantification should not be 

interpreted as a change in the level of evidence regarding the association between these 

endpoints and ozone (or PM2.5) exposure.

This benefits analysis relies on an array of data inputs—including emissions estimates, 

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

which are themselves subject to uncertainty and may in turn contribute to the overall uncertainty 

in this analysis. We employ several techniques to characterize this uncertainty, which are 

described in detail in sections 6.5 and 6.7.3. 
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Table 6-3. Human Health Effects of Pollutants Potentially Affected by by Strategies to 

Attain Reducing the Primary Ozone Standards (endpoints included in the core 

analysis are identified with a red checkmark)

Benefits Category Specific Effect 

Effect Has 

Been 

Quantified 

Effect Has 

Been 

Monetized 

Source of More 

Information 

Improved Human Health 

Reduced incidence 

of premature 

mortality from 

exposure to ozone 

Premature mortality based on short-term 

exposure (all ages) 

 

Section 6.6 

Premature respiratory mortality based on 

long-term exposure (age 30–99) 

b b

Reduced incidence 

of morbidity from 

exposure to ozone 

Hospital admissions—respiratory 

(age > 65) 

 

Emergency department visits for asthma 

(all ages) 

 

Asthma exacerbation (age 6-18)  

Minor restricted-activity days (age 18–

65) 

 

School absence days (age 5–17)  

Decreased outdoor worker productivity 

(age 18–65) 

b b

Other respiratory effects (e.g., medication 

use, pulmonary inflammation, 

decrements in lung functioning) 

— — 

ozone ISAd 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 PM2.5

Adult premature mortality based on 

cohort study estimates and expert 

elicitation estimates (age >25 or age >30) 

 

Section 6.6 

Infant mortality (age <1)  

Reduced incidence 

of morbidity from 

exposure to PM2.5

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) 

— — 

Commented [A1]: Suggest dropping this column, as 

it seems to imply that there is not sourcing for the 

inclusion of most of the endpoints. 
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Benefits Category Specific Effect 

Effect Has 

Been 

Quantified 

Effect Has 

Been 

Monetized 

Source of More 

Information 

Other cardiovascular effects (e.g., other 

ages) 

— — 

Other respiratory effects (e.g., pulmonary PM ISAc

function, non-asthma ER visits, nonbronchitis 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 NO2

Asthma hospital admissions (all ages) — — 

NO2 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 — — 

NO2 ISA c,d

Other respiratory effects (e.g., airway 

hyperresponsiveness and inflammation, 

lung function, other ages and 

populations) 

— — 

b

 We quantified these benefits, but they are not part of the core monetized benefits. 

c

 We assess these benefits qualitatively because we do not have sufficient confidence in available data or methods. 

d

 We assess these benefits qualitatively because current evidence is only suggestive of causality or there are other 

significant concerns over the strength of the association. 

e

 We assess these benefits qualitatively due to time and resource limitations for this analysis. 

The remainder of this chapter is organized as follows: Section 6.3 includes a discussion 

of the methodological updates represented in this analysis; Section 6.4 includes a discussion of 

the methodologies used in the human health benefits analyses; Section 6.5 includes a 

characterization of uncertainty; Section 6.6 details the data inputs used in the analysis, including 

demographic data, baseline incidence and prevalence estimates, effect coefficients, and 

economic valuation estimates; Section 6.7 presents the results; and Section 6.8 includes a brief 

discussion of the results. In addition, the chapter has several appendices that provide more 

details on the following: Appendix 6A includes a detailed characterization of uncertainty in the 

analysis; Appendix 6B includes additional sensitivity analyses and supplemental analysis. 

Commented [A1]: Suggest dropping this column, as 

it seems to imply that there is not sourcing for the 

inclusion of most of the endpoints. 
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6.3 Updated Methodology since 2008 NAAQS Presented in the Proposal and Final RIAs

Both the proposed and finalThis RIAs this ozone standard incorporates an array of policy 

and technical updates to the benefits analysis methods that since the previous review the ozone 

standards in 2008 and the proposed reconsideration in 2010. These updates to the methods are 

summarized here in chronological order. 

Following the publication of the 2008 ozone NAAQS RIA (U.S. EPA, 2008a), EPA 

changed its methods for estimating the benefits of improved air quality, which were reflected in 

the 2010 ozone reconsideration RIA (U.S. EPA, 2010d). Two significant changes included: 

1. When estimating PM2.5-related health effects, EPA no longer assumed a minimum 

concentration at which no effects occurred, while still reporting a range of sensitivity 

estimates based on the EPA’s PM2.5 mortality expert elicitation (see the Portland Cement 

NESHAP proposal RIA (U.S. EPA, 2009a) for further discussion). 

2.1. To be consistent with Agency guidance, EPA revised the Value of Statistical Life (VSL) 

it used to quantify the value of reduced mortality risk (see the NO2 NAAQS final RIA 

(U.S. EPA, 2010a) for further discussion). 

Following completion of the 2010 reconsideration RIA, EPA made a number of 

additional updates to the methods, which are reflected in the 2014 RIA for the proposed ozone 

NAAQS (U.S. EPA, 2014c), including: 

1. The population demographic data in BenMAP-CE (U.S. EPA, 2015a) reflects the 2010 

Census and future projections based on economic forecasting models developed by 

Woods and Poole, Inc. (Woods and Poole, 2012). These data replace older demographic 

projection data from Woods and Poole (2007). This update was introduced in the final 

PM NAAQS RIA (U.S. EPA, 2012a). 

2. The baseline incidence rates used to quantify air pollution-related hospital admissions 

and emergency 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, 2011c). 

3. The cost-of-illness estimates for hospital admissions, including median wages, have been 

updated to reflect 2007 data. This update was introduced in the proposal PM NAAQS 

RIA (U.S. EPA, 2012a). 

4. Updates specific toaffecting estimatinged ozone-related effects: 

a. New studies used to quantify ozone-related premature mortality death. 

Commented [A2]: Removal of the “intro sentences” 

and incorporation of the items into the broader list is 

recommended given that the changes identified in 

these two sections had nothing to do with the ozone 

process, and you already provide the reference 

information at the end of each bullet. The PM bullet 

was moved to the PM section (not deleted) 
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i. The ozone ISA (U.S. EPA, 2013a) identifies several new epidemiological 

studies examining the association between ozone exposure and premature 

mortality. We include two new multi-city studies to estimate premature 

mortalitydeaths attributable to short-term exposure (Smith et al., 2009 and 

Zanobetti and Schwartz 2008). We also estimate long-term respiratory

premature mortality deaths using Jerrett et al. (2009).3 We introduced this 

update in the proposal RIA (U.S. EPA, 2014c).

ii. Following completion of the proposal RIA (U.S. EPA, 2014c), we slightly modified the

methods applied in this RIA. As described in sections 6.6.3, we modified the set of

epidemiology studies and associated effect coefficients used in estimating changes

in asthma exacerbation and respiratory hospital admissions associated with ozone

exposure. Because these changes do not involve our approach for estimating air

pollution‐related premature mortality, which largely drives overall monetized

benefits, they have a negligible (~1%) impact on total monetized benefits (see

section 6.6.3 for further discussion).

a.

b. New studies used to quantify ozone-related morbidity effects. 

b.c.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. 

We introduced this update in the proposal RIA (U.S. EPA, 2014c). 

d. Expanded uncertainty assessment. We added a comprehensive, qualitative assessment 

of the various uncertain parameters and assumptions within the benefits analysis and 

expanded the evaluation of air quality benchmarks for ozone-related mortality. We 

introduced this expanded assessment in the proposal RIA (U.S. EPA, 2014c).

c.

5. Updates specific to affecting estimatinged PM2.5-related effects: 

a. When estimating PM2.5-related health effects, EPA no longer assumed a minimum 

concentration at which no effects occurred, while still reporting a range of sensitivity 

estimates based on the EPA’s PM2.5 mortality expert elicitation (see the Portland 

Cement NESHAP proposal RIA (U.S. EPA, 2009a) for further discussion). 

a.b.New studies used to quantify PM2.5-related premature mortalitydeath. 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 update for the American 

Cancer Society cohort was introduced in the proposal RIA for the PM NAAQS 

 

3

 Because we do not have information on the cessation lag for premature mortality from long-term ozone exposure, 

we do not include the monetized benefits in the core analysis. Instead, monetized benefits associated with longterm ozone-related respiratory mortality are included as a sensitivity analysis (see Appendix 5B, section 5B.2). 

Formatted

Commented [A3]: Moved here from below. 

Formatted: Normal, Indent: Left: 1", No bullets

or numbering

Commented [A4]: Moved from above. 
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review (U.S. EPA, 2012a) and the update for the Harvard Six Cities cohort was 

introduced in the final RIA for the PM NAAQS review (U.S. EPA, 2012c). 

b.c.New studies used to quantify PM2.5 morbidity. Based on the PM ISA and PM 

Provisional Assessment, we added several new studies and morbidity endpoints to our 

health impact assessment, including hospital admissions and emergency department 

visits. These updates were introduced in the proposal (U.S. EPA, 2012a) and final 

RIAs for the PM NAAQS review (U.S. EPA, 2012c). 

c.d.More recent survival rates for non-fatal acute myocardial infarctions. Based on 

recent data from Agency for Healthcare Research and Quality’s Healthcare 

Utilization Project National Inpatient Sample database (AHRQ, 2009), we identified 

death premature mortality rates for adults hospitalized with acute 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, 2012c). 

d.e.Expanded uncertainty assessment. We expanded the comprehensive assessment of the 

various uncertain parameters and assumptions within the benefits analysis including 

the evaluation of air quality benchmarks. This update was introduced in the proposed 

CSAPR RIA (U.S. EPA, 2010f) and refined in the final PM NAAQS RIA (U.S. EPA, 

2012c). 

Following completion of the proposal RIA (U.S. EPA, 2014c), we slightly modified the 

methods applied in this RIA. As described in sections 6.6.3, we modified the set of epidemiology 

studies and associated effect coefficients used in estimating changes in asthma exacerbation and 

respiratory hospital admissions associated with ozone exposure. Because these changes do not 

involve our approach for estimating air pollution-related premature deaths, which largely drives 

overall monetized benefits, they have a negligible (~1%) impact on total monetized benefits (see 

section 6.6.3 for further discussion). 

6.4 Human Health Benefits Analysis Methods 

We follow a “damage-function” approach in calculating total benefits of the modeled 

changes in environmental quality. This approach estimates changes in individual health 

endpoints (i.e., specific effects that can be associated with changes in air quality) and assigns 

values to those changes assuming independence of the values for those individual endpoints. 

Total benefits are calculated simply as the sum of the values for all non-overlapping health 

endpoints. The “damage-function” approach is the standard method that EPA uses for assessing 

costs and benefits of environmental quality policiesrograms and has been used in several recent 
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published analyses published in the peer reviewed scientific literature as well (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 valued directly, as is the case 

for changes in visibility. In other cases, such as for changes in health outcomes associated with 

reductions in ozone and PM concentrations, an impact analysis must first be conducted to 

convert air quality changes into effects that can be assigned dollar values. For the purposes of 

this RIA, the health impacts analysis (HIA) is limited to those health effects that the ISA 

identified as causally or likely causallyare directly linked to ambient levels of air pollution and 

specifically to those linked to ozone and PM2.5. 

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 health impact analyses, our 

estimates are based on the best available methods of benefits transfer. Benefits transfer is the 

science and art of adapting primary research from similar contexts to obtain the most accurate 

measure of benefits for the environmental quality change being analyzed. Adjustments are made 

for the level of environmental quality change, the socio-demographic and economic 

characteristics of the affected population, and other factors to improve the accuracy and 

robustness of benefits estimates. 

Benefits estimates for ozone were generated using the damage function approach outlined 

above wherein potential changes in ambient ozone levels (associated with future attainment of 

the revised and alternative standard levels) were explicitly modeled and then translated into 

reductions in the incidence of specific health endpoints (see Chapter 2 for more information). In 

contrast, in estimating PM2.5 co-benefits we utilized a reduced form approach. The details of both 

approaches are described in additional detail below. 
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6.4.1 Health Impact Assessment 

The health impact assessment (HIA) quantifies the changes in the incidence of adverse 

health impacts resulting from changes in human exposure to PM2.5 and ozone air quality. HIAs 

are a well-established approach for estimating the retrospective or prospective change in adverse 

health impacts expected to result from population-level changes in exposure to pollutants (Levy 

et al., 2009). PC-based tools such as the environmental Benefits Mapping and Analysis Program 

– Community Edition (BenMAP-CE) can systematize health impact analyses by applying a 

database of key input parameters, including health impact functions and population projections—

provided that key input data are available, including air quality estimates and risk coefficients 

(U.S. EPA, 2015a). Analysts have applied the HIA approach to estimate human health impacts 

resulting from hypothetical changes in pollutant levels (Hubbell et al., 2005; 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, 2014a). For this assessment, the HIA is limited to those health effects that are 

directly linked to ambient ozone and PM2.5 concentrations. There may be other indirect health 

impacts associated with applying emissions controls, such as occupational health exposures. 

The HIA approach used in this analysis involves three basic steps: (1) using projections 

of ozone air quality4

 and estimating the change in the spatial distribution of the ambient air 

quality; (2) determining the subsequent change in population-level exposure; and (3) calculating 

health impacts by applying concentration-response (C-R) relationships drawn from the 

epidemiological literature to this change in population exposure (Hubbell et al., 2009). 

A typical health impact function might look as follows: 

∆ݕ ൌ 1 െ ൫݁ఉ∙∆௫൯ݕ ∙ ௢ܲ݌݋) 6.1 (

where y0 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 population affected by 

 

Formatted: Font: Italic
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6-12 

the change in air quality; x is the change in air quality; and β is the effect coefficient drawn 

from the epidemiological study. Figure 6-1 provides a simplified overview of this approach. 

Figure 6-1. Illustration of BenMAP-CE Approach 

6.4.2 Economic Valuation of Health Impacts 

After quantifying the change in adverse health impacts, the final step is to estimate the 

economic value of these avoided impacts. The appropriate economic value for a change in a 

health effect depends on whether the health effect is viewed ex ante (before the effect has 

occurred) or ex post (after the effect has occurred). Reductions in ambient concentrations of air 

pollution generally lower the risk of future adverse health effects by a small amount for a large 

population. The appropriate economic measure is therefore ex ante willingness to pay (WTP) for 

changes in risk. Epidemiological studies generally provide estimates of the changes in risk for a 

particular health effect for a given increment of air pollution (often per 10 ppb ozone). These 

relative risks can be used to develop risk coefficients that relate a unit reduction in ozone or 

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
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6-13 

PM2.5 to changes in the incidence of a health effect. In order to value these changes in incidence, 

WTP for changes in risk need to be converted into WTP per statistical incidence. This measure is 

calculated by dividing individual WTP for a risk reduction by 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 treating 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 1.1 (U.S. EPA, 2015a, 2015b) to estimate the health 

impacts and monetized health benefits for the standards evaluated here. Figure 6-2 shows the 

data inputs and outputs for the BenMAP-CE program. The environmental Benefits Mapping and 

Analysis Program—Community Edition (BenMAP-CE) is an open-source PC-based tool that 

quantifies the number and economic value of air pollution-related premature mortalitydeaths and 

illnesses.5 Figure 6-2 shows the data inputs and outputs for the BenMAP-CE program.

 

5

 As compared to the version that it replaces (BenMAP v4), BenMAP-CE uses the same computational algorithms 

and input data to calculate benefits for a given air quality change, both versions report the same estimates, within 

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

and the uncompiled code is available to the public; and (2) it is written in C#, which is both more broadly used 

and modern than the code it replaces (Delphi). BenMAP-CE was last used to support the Health Risk and 

Exposure Assessment for Ozone (ozone HREA) (U.S. EPA, 2014b). 
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Figure 6-2. Data Inputs and Outputs for the BenMAP-CE Program 

6.4.3 Estimating Benefits for 2025 and Post-2025 Analysis Years 

Portions of the country with more significant air quality problems (particularly several 

areas in California) may not be required to meet the revised standard until as late as December 

31, 2038. Consequently, for the revised and alternative standards we evaluate a 2025 scenario 

reflecting the nationwide benefits of attaining the standards everywhere in the U.S. except 

California. We then evaluate a post-2025 scenario, which represents nationwide benefits from 

attaining the standards in California. We report the 2025 and 2038 estimates separately because 

deriving a summed estimate would require us to calculate the Present Value (PV) of the stream 

of benefits occurring between those two years, which is not possible with the available data. 

Our approach for estimating the benefits of attaining the revised and alternative ozone 

standards post-2025 is illustrated in Figure 6-3. In this figure, Simulation A represents our 

approach for estimating the benefits of attaining the revised and alternative ozone standards in 

every state except California in 2025. To estimate benefits for the post-2025 scenario, we first 

estimated the benefits occurring in 2038 from all areas (including California) attaining 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
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revised and alternative standards (Simulation C). Next, we simulated the nationwide benefits of 

attaining the revised and alternative ozone standards in 2038 for every state except California 

(Simulation B). We then subtracted Simulation B from Simulation C to calculate the benefits of 

attaining the revised and alternative ozone standards after 2025— that is, to calculate the 

nationwide benefits from California alone attaining the standards in 2038. Important caveats are 

associated with this approach mentioned in Section 6.1 and discussed further in section 6.7.3. 

Figure 6-3. Procedure for Generating Benefits Estimates for the 2025 and Post-2025 

Scenarios 

6.4.4 Benefit-per-ton Estimates for PM2.5

We used a reduced form approach to estimate the PM2.5 co-benefits in this RIA due to data 

and resource constraints. Specifically, we used “benefit-per-ton” estimates. EPA has applied this 

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

provide the total monetized human health co-benefits (the sum of premature mortality) of 

reducing one ton of NOx (as a PM2.5 precursor) from a specified source. In general, these 

estimates apply the same benefits methods (e.g., health impact assessment then economic 

75 ppb baseline (CA does

not attain 75)

70 ppb full

attainment scenario

everywhere but CA

70 ppb full attainment

scenario everywhere

including CA

75 ppb baseline reductions in

(all locations attain 75)

A

C

2025 scenario: Nationwide benefits in 2025

resulting from all areas in the U.S. attaining

the alternative standard under consideration

excluding California (here 70ppb).  This is

Simulation A

70 ppb full

attainment scenario

everywhere but CA

75 ppb baseline (CA does

not attain 75)

B

BenMAP Benefits Simulations

Benefits year = 2025

Cost year = 2025

Benefits year = 2038

Cost year = 2037/2038

Benefits year = 2038

Cost year = 2037/2038

Post‐2025 scenario: Nationwide benefits in

2038 having California attain the

alternative standard (here 70ppb). This is

the difference between Simulation C and

Simulation B

Commented [A5]: Reviewer recommends 

clarifying. 
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valuation) for all PM2.5 impacts attributable to a sector, and these benefits are then divided by the 

tons of a PM2.5 precursor (e.g., NOx) from that sector. 

We used national benefit-per-ton estimates described in the TSD: Estimating the Benefit 

per Ton of Reducing PM2.5 Precursors from 17 Sectors (U.S. EPA, 2013b). The national 

estimates used in this RIA were derived using the approach published in Fann et al. (2012b), but 

have since been updated to reflect the epidemiology studies and Census population data first 

applied in the final PM2.5 NAAQS RIA (U.S. EPA, 2012c). The approach in Fann et al. (2012b) 

is similar to the work previously published by Fann et al. (2009), but the newer study includes 

improvements that provide more refined estimates of PM2.5-related health benefits for emissions 

reductions in the various sectors. Specifically, 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). 

In Section 6.6, we describe all of the data inputs used in deriving the benefit-per-ton values 

for each sector, including the demographic data, baseline incidence, and valuation functions. The 

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

6.6.5. We then multiply these benefit-per-ton estimates for each sector with the NOx emission 

reductions from that sector to estimate the PM2.5 co-benefits.6

 Additional information on the 

source apportionment modeling for each of the sectors can be found in Fann et al. (2012b) and 

the TSD (U.S. EPA, 2013b). Specifically for this analysis, we applied the benefit-per-ton 

estimates for 2025 and 2030 in generating PM2.5 co-benefit estimates for the 2025 and post-2025 

scenarios, respectively.7

Applying benefit-per-ton estimates introduces uncertainty, which we discuss in section 

6.7.3 and Appendix 6A. The benefit-per-ton estimates used here reflect specific geographic 

patterns of emissions reductions and specific air quality and benefits modeling assumptions 

associated with the derivation of those estimates. Consequently, these estimates may not reflect 

 

6

 For unidentified controls, we use a weighted average of the benefit-per-ton estimates from all of the sectors. 

7

 We do not have benefit-per-ton estimates for 2038. The last year available is 2030, which is an underestimate of 

the 2038 benefits because the population grows and ages over time. 
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local variability in factors associated with PM2.5-related health impacts (e.g., population density, 

baseline health incidence rates). Therefore, using benefit-per-ton values to estimate co-benefits 

may lead to higher or lower benefit estimates than if co-benefits were calculated based on direct 

air quality modeling. In addition, the use of national benefit-per-ton estimates results in a known 

underestimation bias in California in the post-2025 scenario due to population density. 

6.5 Characterizing Uncertainty 

In any complex analysis using estimated parameters and inputs from numerous models, 

there are likely to be many sources of uncertainty, and this analysis is no exception. This analysis 

includes many data sources as inputs, including emissions inventories, air quality data from 

models (with their associated parameters and inputs), population data, population estimates, 

health effect estimates from epidemiology studies, economic data for monetizing benefits, and 

assumptions regarding the future state of the world (e.g., regulations, technology, and human 

behavior). Each of these inputs may be uncertain and would affect the benefits estimates. 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 

methodology 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 decision makers in ways 

that foster an appropriate appreciation of their inherent uncertainty. Since the publication of these 

reports, the EPA continues to improve how it characterizes uncertainty in health incidence and 

benefits estimates. In response to these recommendations, we incorporated additional 

quantitative and qualitative characterizations of uncertainty. Although we are not yet able to 

perform the probabilistic uncertainty assessment the NAS envisioned, we added several 

quantitative and qualitative analyses. These additional analyses characterize uncertainty related 

to estimated premature mortality, since this endpoint is assigned the largest dollar value. For 

other inputs into the benefits analysis, such as the air quality data, it is too difficult to address 

uncertainty probabilistically for this analysis due to the complexity of the underlying air quality 

models and emissions inputs. Therefore, we decline to construct alternative assumptions simply 
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for the purpose of probabilistic uncertainty characterization when there is no scientific 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; 2014b), and use a tiered 

approach developed by the World Health Organization (WHO) (WHO, 2008). We present this 

assessment in Appendix 6A (results of these assessments are summarized in section 6.7.3). Data 

limitations prevent us from treating each source of uncertainty quantitatively and from reaching a 

full-probabilistic simulation of our results, but we were able to consider the influence of 

uncertainty in the risk coefficients and economic valuation functions by incorporating several 

quantitative analyses: Monte Carlo assessments, sensitivity analyses, and supplemental analyses. 

6.5.1 Monte Carlo Assessment 

Similar to other recent RIAs, we used Monte Carlo methods for characterizing random 

sampling error associated with the C-R functions from epidemiological studies and random 

effects modeling to characterize both sampling error and variability across the economic 

valuation functions. The 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 methods 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 single study. For endpoints estimated using a pooled estimate of multiple 

studies, the confidence intervals reflect both the standard errors and the variance across studies. 

The confidence intervals around the monetized benefits incorporate the epidemiology standard 

errors, as well as the 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 effect 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. We report confidence intervals for ozone-related 

benefits, but were unable to provide confidence intervals for PM2.5-related co-benefits because 

we used the benefit-per-ton estimates. 
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6.5.2 Sensitivity Analyses 

Because over 90% of the monetized benefits are from avoided premature mortality, it is 

particularly important to characterize the uncertainties associated with reductions in premature 

mortality. Each of the sensitivity analyses completed for this RIA are briefly described below 

and section 6.7.3.1 provides a discussion of the results and observations stemming from these 

sensitivity analyses. 

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

Alternative C-R functions are useful for assessing uncertainty beyond random statistical 

error, including uncertainty in the functional form of the model or alternative study 

designs. This analysis is particularly important considering the We used two multi-city 

studies (Smith et al., 2009; Zanobetti and Schwartz 2008) to estimate short-term ozonerelated mortality in our core estimate. We performed a sensitivity analysis using effect 

coefficients from additional multi-city studies and meta-analyses utilized in prior RIAs 

(Bell et al., 2004; 2005, Huang, 2005; Ito et al., 2005; Levy et al., 2005), as well as 

alternative model specifications from the Smith et al. (2009) study (see Figure 6-4 and 

Appendix 6B, section 6B.1). When selecting studies for the core and sensitivity analyses 

we considered NAS (p. 80, NRC, 2008) and CASAC recommendations (U.S. EPA-SAB, 

2012, 2014). 

 Potential thresholds in the long-term ozone exposure-related respiratory mortality 

C-R function: Consistent with the ozone HREA, we estimate premature respiratory 

deathsmortality from long-term exposure to ozone. The Jerrett et al. (2009) study 

explored potential thresholds in the C-R function. We use the results of the threshold 

analyses conducted by Jerrett et al. (2009) to conduct a sensitivity analysis evaluating 

models with a range of potential thresholds in addition to a non-threshold (see Appendix 

6B, section 6B.3). 

 Alternative C-R functions in estimating long-term PM2.5 exposure-related mortality:

In estimating PM2.5 co-benefits, we use two studies (Krewski et al., 2009; Lepeule et al., 

2012). To better understand uncertainty in the C-R relationship between PM2.5 exposure 

and premature mortality, the EPA conducted an expert elicitation in 2006 (Roman et al., 

2008; IEc, 2006).8

 We apply the functions from the experts as a characterization of 

uncertainty (see Figure 6-5 and Appendix 6B, section 6B.2). 

 Analysis of baseline ozone concentrations used in estimating ozone premature 

mortality associated with short-term ozone concentrations: We characterize assess 

the distribution of premature mortality attributed to short term ozone exposure with 

respect to relationship between short-term exposure-related mortality for ozone and the 

distribution of baseline ozone concentrations in the counties where the analysis predicts 

 

8

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

multiple experts, are obtained (Ayyub, 2002). 

Formatted: Underline

Commented [A6]: This and the next analysis are 

more appropriately characterized as uncertainty 

analysis, consistent with prior practice. 
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the premature mortalities will be avoided. This analysis is particularly important given 

the reduced certainty that the Administrator has expressed in the nature of this 

relationship at lower ozone concentrations, particularly those lower than 40 ppb. . 

 Analysis of baseline PM2.5 concentrations used in modeling short-term ozone 

exposure-related mortality: We also include a similar plot of the baseline annual PM2.5

levels used in estimating PM2.5 mortality from the earlier analysis that generated the 

benefit-per-ton values. This analysis is particularly important because, in general, we are 

more confident in the magnitude of the risks we estimate from simulated PM2.5 

concentrations that coincide with the bulk of the observed PM2.5 concentration in the 

epidemiological studies that are used to estimate the benefits.



 Cessation lag for long-term O3 exposure-related respiratory mortality: We do not 

know how long-term O3 exposure-related respiratory mortalitydeaths are distributed over 

time and so we use two lag structures originally developed for PM2.5 (the 20-year 

segmented lag used for PM2.5 and an assumption of zero lag) in this sensitivity analysis 

(see Appendix 6B, section 6B.2). 

 Income elasticity in the specification of willingness-to-pay (WTP) functions used for 

mortality and morbidity endpoints: The degree to which the WTP function used in 

valuing mortality and some morbidity endpoints changes in proportion to future changes 

in income is uncertain. We evaluated the potential impact of this factor on the monetized 

benefits in a sensitivity analysis (see Appendix 6B, section 6B.5). 

Even these multiple estimates (including confidence intervals, where available) cannot 

account for the role of other input variables in contributing to overall uncertainty, including 

emissions and air quality modeling, baseline incidence rates, and population exposure estimates. 

Furthermore, the approach presented here does not yet include methods for addressing 

correlation between input parameters and identifying a reasonable upper and lower bounds for 

input distributions. As a result, the reported confidence intervals and range of estimates give an 

incomplete picture of the overall uncertainty in the estimates. Thus, statistical confidence 

intervals reported for individual endpoints and for total benefits should be interpreted within the 

context of the larger uncertainty surrounding the entire analysis. 

6.5.3 Supplemental Analyses 

TwoA number of supplemental analyses provide additional perspectives on the benefits 

results (the results of which are described in section 6.7.3.2), including: 

Commented [A7]: This sentence is designed to be 

consistent with preamble discussion on page 

Commented [A8]: Sentence is verbatim copy of 1st

sentence of the results section in Appendix 6B (page 

22). 
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 Age group-differentiated 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 concentrations used in estimating ozone mortality: We 

assess the relationship between short-term exposure-related mortality for ozone and the 

distribution of baseline ozone concentrations. 

 Analysis of baseline PM2.5 concentrations used in modeling short-term ozone 

exposure-related mortality: We also include a similar plot of the baseline annual PM2.5

levels used in estimating PM2.5 mortality from the earlier analysis that generated the 

benefit-per-ton values.

 Outdoor worker productivity: In this supplemental analysis, we quantify the economic 

value of improved productivity among outdoor agricultural workers using Graff Zivin 

and Neidell (2012). 

6.5.4 Qualitative Assessment of Uncertainty and Other Analysis Limitations 

To more fully address uncertainties, including those we cannot quantify, we apply a fourtiered approach using the WHO uncertainty framework (WHO, 2008), which provides a means 

for systematically linking the characterization of uncertainty to the sophistication of the 

underlying risk assessment. The EPA has applied similar approaches in previous analyses (U.S. 

EPA, 2010b, 2011a, 2012a, 2012b). Using this framework, we summarize the key uncertainties 

in the health benefits analysis, including our assessment of the direction of potential bias, 

magnitude of impact on the monetized benefits, degree of confidence in our analytical approach, 

and our ability to assess the source of uncertainty. More information on this approach and the 

qualitative uncertainty characterization are available in Appendix 6A. 

6.6 Benefits Analysis Data Inputs 

In Figure 6-2 above, we summarized the key data inputs to the health impact and 

economic valuation estimate. Below we summarize the data sources for each of these inputs, 

including demographic projections, incidence and prevalence rates, effect coefficients, and 

economic valuation. We indicate where we have updated key data inputs since the benefits 

analysis conducted for the 2008 ozone NAAQS RIA (U.S. EPA, 2008a) and the 2010 ozone 

NAAQS Reconsideration RIA (U.S. EPA, 2010d). As noted above, only slight modifications 
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have been made to the epidemiology studies used to estimate two morbidity endpoints since 

proposal, and those modifications are described in section 6.6.3. 

6.6.1 Demographic Data 

Quantifying the incidence and dollar value of pollution impacts requires information 

regarding the demographic characteristics of the exposed population, including age, location, and 

income. We use population projections based on economic forecasting models developed by 

Woods and Poole, Inc. (Woods and Poole, 2012). The Woods and Poole (WP) database contains 

county-level projected population by age, sex, and race to 2040, relative to a baseline using the 

2010 Census data; the 2008 ozone NAAQS RIA incorporated WP projections relative to a 

baseline using 2000 Census data. Projections in each county are determined simultaneously with 

every other county in the United States to take into account patterns of economic growth and 

migration. The sum of growth in county-level populations is constrained to equal a previously 

determined national population growth, based on Bureau of Census estimates (Hollman et al., 

2000). According to WP, linking county-level growth projections together and constraining to a 

national-level total growth avoids potential errors introduced 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 economic areas defined by the Bureau 

of Economic Analysis (U.S. BEA, 2004), using an “export-based” approach, which relies 

on linking industrial-sector production of non-locally consumed production items, such 

as outputs from mining, agriculture, and manufacturing with the national economy. The 

export-based approach requires estimation of demand equations or calculation of 

historical growth rates for output and employment by sector. 

 Third, 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 by race 

for each year through 2040 based on historical rates of mortality, fertility, and migration. 
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6.6.2 Baseline Incidence and Prevalence Estimates 

Epidemiological studies of the association between pollution levels and adverse health 

effects generally provide an 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 5 

ppb decrease in 8-hour maximum daily ozone concentration might be associated with a decrease 

in hospital admissions of three percent. The baseline incidence of the health effect is necessary to 

convert this relative change into a number of cases. A baseline incidence rate is the estimate of 

the number of cases of the health effect per year in the assessment location, as it corresponds to 

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 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 people in the total population in that location. 

Table 6-4 summarizes the sources of baseline incidence rates and provides national 

average (where used) incidence rates for the endpoints included in the analysis. For both baseline 

incidence and prevalence data, we used age-specific rates where available. We applied C-R 

functions to individual age groups and then summed over the relevant age range to estimate total 

population benefits. In many cases we used a single national incidence rate, due to a lack of more 

spatially disaggregated data; in these cases, whenever possible we used national average rates, 

because these data are most applicable to a national assessment of benefits. For some studies, 

however, the only available incidence information comes from the studies themselves; in these 

cases, incidence in the study population is assumed to represent typical incidence at the national 

level. County, state and regional incidence rates are available for hospital admissions, and 

county-level data are available for premature mortality. 

We projected mortality rates such that future mortality rates are consistent with our 

projections of population growth (U.S. EPA, 2015b). To perform this calculation, we began first 

with an average of 2004–2006 cause-specific mortality rates. Using Census Bureau projected 

national-level annual mortality rates stratified by age range, we projected these mortality rates to 

2050 in 5-year increments (U.S. EPA, 2015b; U.S. Bureau of the Census, 2002). 
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The baseline incidence rates for hospital admissions and emergency department visits 

reflect the updated rates first applied in the CSAPR RIA (U.S. EPA, 2011c). In addition, we 

previously 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 symptomsair 

pollution-related and illnesses identified in the ISA as associated with short-term ozone 

exposures. Also, the updated baseline incidence rates are more spatially refined. For many 

locations within the U.S., these data are resolved at the county- or state-level, providing a better 

characterization of the geographic distribution of hospital and emergency department visits than 

the previous national rates. Lastly, these rates reflect unscheduled hospital admissions only, 

which represents a conservative the assumption that most air pollution-related hospital visits

associated with short-term ozone exposure are likely to be unscheduled. If a portion of scheduled 

hospital admissions are air pollution-related, this assumption would underestimate these benefits. 

For the set of endpoints affecting the asthmatic population, in addition to baseline 

incidence rates, prevalence rates of asthma in the population are needed to define the applicable 

population. Table 6-5 lists the prevalence rates used to determine the applicable population for 

asthma symptoms. Note that these reflect recent asthma prevalence and assume no change in 

prevalence rates in future years. We last updated these rates in the CSAPR RIA (U.S. EPA, 

2011c). 

Formatted: Font: Italic
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Table 6-4. Baseline Incidence Rates and Population Prevalence Rates for Use in Impact 

Functions, General Population 

Endpoint Parameter 

Rates 

Value Source 

Mortality Daily or annual mortality 

rate projected to 2025 a

Age-, cause-, and 

county-specific rate 

CDC WONDER (2004–2006) 

U.S. Census bureau, 2000 

Hospitalizations Daily unscheduled 

hospitalization rate 

Age-, region-, state-, 

county- and causespecific rate 

2007 HCUP data files b

ER Visits Daily ER visit rate for 

asthma and cardiovascular 

events 

Age-, region-, state-, 

county- and causespecific 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) 

Acute Bronchitis Annual bronchitis incidence 

rate, children 

0.043 American Lung Association (2002, 

Table 11) 

Lower Respiratory 

Symptoms 

Daily lower respiratory 

symptom incidence among 

children c

0.0012 Schwartz et al. (1994, Table 2) 

Upper Respiratory 

Symptoms 

Daily upper respiratory 

symptom incidence among 

asthmatic children 

0.3419 Pope et al. (1991, Table 2) 

Work Loss Days Daily WLD 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 RestrictedActivity Days Daily MRAD incidence rate 

per person 

0.02137 Ostro and Rothschild (1989, 

p. 243) a

 Mortality rates are only available at 5-year increments. 

b

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

hospital and emergency department discharges for a variety of International Classification of Diseases (ICD) codes 

(AHRQ, 2007). 

c

 Lower respiratory symptoms are defined as two or more of the following: cough, chest pain, phlegm, and wheeze. 

Commented [A9]: Please clarify whether this mean 

that EPA only estimates asthma exacerbations for 

African-American children or that the rate for A-A 

children is applied to the total population? 
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Table 6-5. Asthma Prevalence Rates 

Population Group 

Asthma Prevalence Rates 

Value Source 

All Ages 0.0780 American Lung Association (2010, Table 7) 

< 18 0.0941 

5–17 0.1070 

18–44 0.0719 

45–64 0.0745 

65+ 0.0716 

African American, 5–17 0.1776 American Lung Association (2010, Table 9) 

African American, <18 0.1553 American Lung Association a

a

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

6.6.3 Effect Coefficients 

In this section, we describe our general process for selecting effect coefficients from 

epidemiology studies. The first step in selecting effect coefficients is to identify the health 

endpoints to be quantified. We based our selection of health endpoints on consistency with the 

EPA’s ISAs, with input and advice from the SAB-HES.9

 In addition, we included more recent 

epidemiology studies from the ozone ISA (U.S. EPA, 2013a), PM ISA (U.S. EPA, 2009b), and 

the PM Provisional Assessment (U.S. EPA, 2012d).10 In selecting health endpoints for ozone, we 

also considered the suite of endpoints included in core modeling for the ozone HREA, which was 

supported by CASAC (U.S. EPA-SAB, 2012, 2014). In general, we follow a weight-of-evidence 

approach, based on the biological plausibility of effects, availability of C-R functions from well 

conducted peer-reviewed epidemiological studies, cohesiveness of results across studies, and a 

focus on endpoints reflecting public health impacts (like hospital admissions) rather than 

physiological responses (such as changes in 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 pollution 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 evidence surrounding a particular health impact. However, only epidemiology 

 

9

 The SAB-HES is 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). 10 The peer-reviewed studies in the Provisional Assessment have not yet undergone external review by the SAB. 
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studies provide direct C-R relationships that can be used to evaluate population-level impacts of 

reductions in ambient pollution levels in a health impact assessment. 

For the data-derived estimates, we relied on the published scientific literature to 

modelascertain the relationship between ozone and PM2.5 and adverse human health effects. We 

evaluated epidemiological studies using the selection criteria summarized in Table 6-6. These 

criteria include consideration of whether the study was peer-reviewed, the match 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, using C-R 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 

difficult 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 that provides a good balance of 

population coverage and match with the pollutant of interest. In many cases, 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)11 have been selected, they are quantitatively combined or pooled to derive 

a more robust estimate of the relationship. The BenMAP Manual Technical Appendices for an 

earlier version of the program provides details of the procedures used to combine multiple 

impact functions (U.S. EPA, 2015b). In general, we used fixed or random 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 

 

11 In the case of mortality, we do not pool results. Instead, we provide the results from each study separately. 
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the random effects model.12 Pooled impact functions are used to estimate hospital admissions and 

asthma exacerbations. When combining evidence across multi-city studies (e.g., cardiovascular 

hospital admission studies), we use equal weights pooling. The effect estimates drawn from each 

multi-city study are themselves pooled across a large number of urban areas. For this reason, we 

elected to give each study an equal weight rather than weighting by the inverse of the variance 

reported in each study. 

Effect estimates selected for a given health endpoint were applied consistently across all 

locations nationwide. This applies to both impact functions defined by a single effect estimate 

and those defined by a pooling of multiple effect estimates. Although the effect estimate may 

vary from one location to another (e.g., because of differences in population susceptibilities or 

differences in the composition of PM), location-specific effect estimates are generally not 

available. 

Table 6-6. Criteria Used When Selecting C-R Functions 

Consideration Comments 

Peer-Reviewed 

Research 

Peer-reviewed research is exclusively used to select C-R functions. 

Study Type Prospective cohort vs. ecological: Among studies that consider chronic exposure (e.g., 

over a year or longer), prospective cohort studies are preferred over ecological studies 

because they control for important individual-level confounding variables that cannot be 

controlled for in ecological studies. 

Multi-city vs. pooled/meta-analysis: Multi-city time series studies have advantages to 

meta-analyses. Multi-city studies use a consistent model structure and can include factors 

that explain differences between effect estimates among the cities. By contrast, metaanalyses can become imprecise and the results difficult to interpret due to the aggregation 

of large sets of studies. In addition, meta-analyses can suffer from publication bias, 

which can result in high-biased effect estimates. Although we generally prefer multi-city 

studies, we may consider meta-analyses if multi-city studies are not available. 

Study Period Studies examining a relatively longer period of time (and therefore having more data) are 

preferred, because they have greater statistical power to detect effects. Studies that are 

more recent are also preferred because of possible changes in pollution mixes, medical 

care, and lifestyle over time. However, when there are only a few studies available, 

studies from all years would be included. 

Seasonality While the measurement of PM is typically collected across the full year, ozone 

monitoring seasons can vary substantially across different regions of the country. 

Consequently, studies matching the ozone seasons in the air quality modeling are 

preferred. 

 

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

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

variance. 
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Consideration Comments 

Population Attributes The most technically appropriate measures of benefits would be based on impact 

functions that cover the entire sensitive population but allow for heterogeneity across age 

or other relevant demographic factors. In the absence of effect estimates specific to age, 

sex, preexisting condition status, or other relevant factors, it may be appropriate to select 

effect estimates that cover the broadest population to match with the desired outcome of 

the analysis, which is total national-level health impacts. 

Study Size Studies examining a relatively large sample are preferred because they generally have 

more power to detect small magnitude effects. A large sample can be obtained in several 

ways, including through selection of a large population or through repeated observations 

on a smaller population (e.g., through a symptom diary recorded for a panel of asthmatic 

children). 

Study Location U.S. studies are more desirable than non-U.S. studies because of potential differences in 

pollution characteristics, exposure patterns, medical care system, population behavior, 

and lifestyle. Depending on the endpoint and the study, we may consider using Canadian 

studies. National estimates are most appropriate when benefits are nationally distributed; 

the impact of regional differences may be important when benefits only accrue to a single 

area. 

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 3rd or 6th day. 

For this reason, co-pollutant models are preferred for estimating PM effects because this 

approach controls for the potential ozone effect while not diminishing the effective 

sample size available for specifying the PM effect. However, when estimating the ozone 

effect, the use of co-pollutant models (with PM) can substantially reduce sample size 

since only days with both ozone and PM can be used. While these co-pollutant models 

may control for potential PM effects, they also result in a substantially less robust 

characterization of the ozone effect due to the reduced number of ozone measurements. 

For this reason, while we generally favor co-pollutant models in modeling PM benefits, 

for ozone we generally favor single pollutant models. 

Measure of PM In general, impact functions based on PM2.5 are preferred to PM10 because of the focus on 

reducing emissions of PM2.5 precursors and because air quality modeling was conducted 

for this size fraction of PM. Where PM2.5 functions are not available, PM10 functions are 

used as surrogates, recognizing that there will be potential downward (upward) biases if 

the fine fraction of PM10 is more (less) toxic than the coarse fraction. 

 

Economically Valuable 

Health Effects 

Some health effects, such as forced expiratory volume and other technical measurements 

of lung function, are difficult to value in monetary terms. Therefore, we generally do not 

include these effects in benefits analyses. 

Non-overlapping 

Endpoints 

Although the benefits associated with each individual health endpoint may be analyzed 

separately, care must be exercised in selecting health endpoints to include in the overall 

benefits analysis because of the possibility of double-counting of benefits. 

The specific studies from which effect estimates are drawn are shown in Tables 6-7 and 

6-8. We highlight in red those studies that have been added since the benefits analysis conducted 

for the ozone reconsideration (U.S. EPA, 2010d) or the 2008 ozone NAAQS RIA (U.S. EPA, 

2008a). In all cases where effect estimates are drawn directly from epidemiological studies, 
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standard errors are used as a partial representation of the uncertainty in the size of the effect 

estimate. Table 6-9 summarizes those health endpoints and studies we have included in 

sensitivity analyses. 

Table 6-7. Health Endpoints and Epidemiological Studies Used to Quantify Ozone-Related 

Health Impacts a 

Endpoint Study Study 

Population

Relative Risk or Effect Estimate (β)

(with 95th Percentile Confidence 

Interval or SE) 

Premature Mortality 

Premature mortality—

short-term 

Smith et al. (2009) All ages β = 0.00032 (0.00008) 

Zanobetti and Schwartz 

(2008) β = 0.00051 (0.00012) 

Premature respiratory 

mortality-long-term Jerrett et al. (2009) >29 years β = 0.003971 (0.00133) 

Hospital Admissions 

Respiratory Pooled estimate: 

 Katsouyanni et al. (2009) > 65 years β = 0.00064 (0.00040) penalized splines 

Asthma-related 

emergency department 

visits 

Pooled estimate:

 Glad et al. (2012)

0-99 years 

β = 0.00306 (0.00117) 

 Ito et al. (2007) β = 0.00521 (0.00091) 

 Mar and Koenig (2010) 

 

β = 0.01044 (0.00436) (0-17 yr olds) 

β = 0.00770 (0.00284) (18-99 yr olds) 

 Peel et al. (2005) β = 0.00087 (0.00053) 

 Sarnat et al. (2013) β = 0.00111 (0.00028) 

 Wilson et al. (2005) RR = 1.022 (0.996 – 1.049) per 25 

Other Health Endpoints 

Asthma exacerbation 

Pooled estimate: b

Mortimer et al. (2002)

Schildcrout et al. (2006)

6–18 years β = 0.00929 (0.00387) 

β = 0.00222 (0.00282) 

School loss days Pooled estimate: 

 Chen et al. (2000) 5-17 years β = 0.015763 (0.004985) 

 Gilliland et al. (2001) β = 0.007824 (0.004445) 

Acute respiratory 

symptoms (MRAD) Ostro and Rothschild (1989) 18–65 years β = 0.002596 (0.000776) 

a

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

5-9 years for the Mortimer et al. (2002) study. Based on advice from the SAB-HES, we extended the applied 

population to 6-18 years 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) 

Commented [A10]: Suggest using a symbol rather 

than a color to signify to facilitate clarity for that 

people reading from hardcopy. 

Commented [A11]: This study is not listed above 
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Table 6-8. Health Endpoints and Epidemiological Studies Used to Quantify PM2.5-Related 

Health Impacts a 

Endpoint Study 

Study 

Population 

Relative Risk or Effect Estimate (β)

(with 95th Percentile Confidence 

Interval or SE) 

Premature Mortality 

Premature mortality—

cohort study, all-cause 

Krewski et al. (2009) > 29 years 

> 24 years 

RR = 1.06 (1.04–1.06) per 10 μg/m3

Lepeule et al. (2012) RR = 1.14 (1.07–1.22) per 10 μg/m3

Premature mortality—

all-cause 

Woodruff et al. (1997) Infant (< 1 

year) 

OR = 1.04 (1.02–1.07) per 10 μg/m3

Chronic Illness 

Nonfatal heart attacks Peters et al. (2001) Adults (> 18 

years) 

OR = 1.62 (1.13–2.34) per 20 μg/m3

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/m3

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/m3

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-, cerebroand peripheral vascular disease) 

β=0.00068 

(0.000214) 

Peng et al. (2008)—ICD 426-

427; 428; 430-438; 410-414; 

429; 440-449 (Cardio-, cerebroand peripheral vascular disease) 

β=0.00071 

(0.00013) 

Bell et al. (2008)—ICD 426-

427; 428; 430-438; 410-414; 

429; 440-449 (Cardio-, cerebroand 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/m3

Asthma-related 

emergency department 

visits 

Pooled estimate: 

Mar et al. (2010)

All ages RR = 1.04 (1.01–1.07) per 7 μg/m3

Slaughter et al. (2005) RR = 1.03 (0.98–1.09) per 10 μg/m3

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/m3
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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/m3

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/m3

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/m3

Lower respiratory 

symptoms Schwartz and Neas (2000) 7–14 years OR = 1.33 (1.11–1.58) per 15 μg/m3

a

 Studies highlighted in red represent updates incorporated since the ozone NAAQS RIA (U.S. EPA, 2008a). These 

updates were introduced in the PM NAAQS RIA (U.S. EPA, 2012c). 

b

 The original study populations were 8 to 13 years for the Ostro et al. (2001) study and 7 to 12 years for the Mar et 

al. (2004) study. Based on advice from the SAB-HES, we extended the applied population to 6-18 years, reflecting 

the common biological basis for the effect in children in the broader age group. See: U.S. EPA-SAB (2004a,b) and 

NRC (2002). 

Table 6-9. Health Endpoints and Epidemiological Studies Used to Quantify Ozone-Related 

Health Impacts in Sensitivity Analyses a 

Endpoint Study 

Study 

Population 

Effect Estimate (β)

(with 95th Percentile 

Confidence Interval) 

Premature Mortality 

Premature respiratory 

mortality - long-term Jerrett et al. (2009)-based models: 

> 29 years 

- non-threshold ozone only (86 cities) β=0.00266 (0.000969) 

- non-threshold ozone only (96 cities) β=0.00286 (0.000942) 

- threshold 40 ppbb β=0.00312 (0.00096) 

- threshold 45 ppb β=0.00336 (0.001) 

- threshold 50 ppb β=0.00356 0.00106) 

- threshold 55 ppb β=0.00417 (0.00118) 

- threshold 56 ppb β=0.00432 (0.00121) 

- threshold 60 ppb β=0.00402 (0.00137) 

Premature mortality - 

short-term 

Smith et al. (2009) (co-pollutant model with PM10)

All ages 

β=0.00026 (0.00017) 

Bell et al. (2005) β=0.00080 (0.00021) 

Levy et al. (2005) β=0.00112 (0.00018) 

Bell et al. (2004) β=0.00026 (0.00009) 

Ito et al. (2005) β=0.00117 (0.00024) 

Schwartz et al. (2005) β=0.00043 (0.00015) 

Huang et al. (2005) β=0.00026 (0.00009) 

a

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

 All threshold models are ozone-only and based on the full 96 city dataset. 
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6.6.3.1 Ozone Exposure Metric 

Both the NMMAPS analysis and the individual time series studies upon which the metaanalyses were based use the 24-hour average or 1-hour maximum ozone concentrations as 

exposure metrics. The 24-hour average is not the most relevant ozone exposure metric to 

characterize population-level exposure. Given that the majority of the people tend to be outdoors 

during the daylight hours and concentrations are highest during the daylight hours, the 24-hour 

average metric is not appropriate. Moreover, the 1-hour maximum metric uses an exposure 

window different than that used for the current ozone NAAQS. Together, this means that the A 

moremost biologically relevant metric, and the one used in the ozone NAAQS since 1997, is the 

maximum daily 8-hour average ozone. Thus, we converted ozone mortality health impact 

functions that use a 24-hour average or 1-hour maximum ozone metric to maximum 8-hour 

average ozone concentration using standard conversion functions. 

This practice is consistent with the form of the current ozone standard. This conversion 

also does not affect the relative magnitude of the health impact function from a mathematical 

standpoint. An equivalent change in the 24-hour average, 1-hour maximum and 8-hour 

maximum will provide the same overall change in incidence of a health effect.13 The conversion 

ratios are based on observed relationships between the 24-hour average and 8-hour maximum 

ozone values. For example, in the Bell et al., 2004 analysis of ozone-related premature mortality, 

the authors found that the relationship between the 24-hour average, the 8-hour maximum, and 

the 1-hour maximum was 2:1.5:1, so that the derived health impact effect estimate 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 estimates 

based on the pooled multi-city results reported in Bell et al. (2004) and the three meta-analysis 

studies. Bell et al. (2004), Bell et al. (2005), Levy et al. (2005), and Ito et al. (2005) all provide 

 

13 However, different ozone metrics may not be well correlated (from either a spatial or temporal standpoint) within 

a given geographic area, which means that application of ratio-converted effect estimates for the same endpoint 

can result in different incidence estimates for the same location under certain conditions. This introduces 

uncertainty into the use of these ratio-adjusted effect estimates (see Appendix 6A). 
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national conversion ratios between daily average and 8-hour and 1-hour maxima, based on 

national data. 

6.6.3.2 Ozone Premature Mortality Effect Coefficients 

Mortality Effect Coefficients for Short-term Ozone Exposure. As laid out in the 

preamble, tThe overall body of evidence indicates that there is likely to be a causal relationship 

between short-term ozone exposure and premature mortalitydeath, even as we are mindful of the 

uncertainty associated with shape of the concentration response curve at lower ozone 

concentrations. . The 2013 ozone ISA concludes that the evidence suggests that ozone effects are 

independent of the relationship between PM and mortality. (U.S. EPA, 2013a). However, the 

ISA notes that the interpretation of the potential confounding effects of PM on ozone-mortality 

risk estimates requires caution due to the PM sampling schedule (in most cities) which limits the 

overall sample size available for evaluating potential confounding of the ozone effect by PM 

(U.S. EPA 2013a).14 

These more observations are consistent with prior In 2006, the EPA requested an NAS 

study to answer the following four key questions regarding ozone-related mortality: (1) How did 

the epidemiological literature to that point improve our understanding of the size of the ozonerelated mortality effect?; (2) How best can EPA quantify the level of ozone-related mortality 

impacts from short-term exposure?; (3) How might EPA estimate the change in life expectancy?; 

and (4) What methods should EPA use to estimate the monetary value of changes in ozonerelated mortality risk and life expectancy? 

In 2008, the NAS (NRC, 2008) issued a series of recommendations to the EPA by the 

NAS regarding the quantification and valuation of ozone-related short-term mortality (NRC, 

2008). Chief among the NASse recommendations was that “...short-term exposure to ambient 

ozone is likely to contribute to premature deaths” and the committee recommended that “ozonerelated 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 multi-

 

14 Consequently, as noted later in this section, while we have used a single-pollutant model in generating short-term 

ozone-related mortality benefits for this RIA, we have included a co-pollutants model as a sensitivity analysis to 

address potential uncertainty associated with this assumption of an independent ozone mortality effect. 
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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 mortality and reduced ozone 

exposure” (NRC, 2008). In 2010, the Health Effects Subcommittee of the Advisory Council on 

Clean Air Compliance Analysis, while reviewing EPA’s The Benefits and Costs of the Clean Air 

Act 1990 to 2020 (U.S. EPA, 2011a), also confirmed the NAS recommendation to include ozone 

mortality benefits (U.S. EPA-SAB, 2010a). 

In view of the findings of the ozone ISA, the NAS panel, the SAB-HES panel, and the 

CASAC panel, we estimate ozone-related premature 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 with several additional studies 

as sensitivity analyses. This emphasis on newer multi-city studies is consistent with 

recommendations provided by the NAS in their ozone mortality report (NRC, 2008). CASAC 

supported using the Smith et al. (2009) and Zanobetti and Schwartz (2008) studies for the ozone 

HREA (U.S. EPA-SAB, 2012, 2014), and these are multi-city studies (published more recently 

(as compared with other multi-city studies or meta-analyses considered by the NAS in 2008, and 

included in the sensitivity analyses – see discussion below). 

Smith et al. (2009) reanalyzed the NMMAPS dataset, evaluating the relationship between 

short-term ozone exposure and mortality. TWhile this study first reproduces the core nationalscale estimates presented in Bell et al. (2004), and then it also exploresd the sensitivity of the 

mortality effect to different model specifications including (a) regional versus national Bayesbased adjustment,15 (b) co-pollutant models considering PM10, (c) all-year versus ozone-season 

 

15 In Bayesian modeling, effect estimates are “updated” from an assumed prior value using observational data. In the 

Smith et al. (2009) approach, the prior values are either a regional or national mean of the individual effect 

estimates obtained for each individual city. The Bayesian adjusted city-specific effect estimates are then 

calculated by updating the selected prior value based on the relative precision of each city-specific estimate and 

the variation observed across all city-specific individual effect estimates. City-specific estimates are pulled 

towards the prior value if they have low precision and/or if there is low overall variation across estimates. Cityspecific estimates are given less adjustment if they are precisely estimated and/or there is greater overall variation 

across estimates. 

Commented [A12]: Add a sentence to the beginning 

of this paragraph about use of Bell et al analysis of 

NMMAPS dataset in prior RIAs. 
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based estimates, and (d) consideration of a range of ozone metrics, including the daily 8-hour 

max. In addition, the Smith et al. (2009) study did not use the trimmed mean approach employed 

in the Bell et al. (2004) study in preparing ozone monitor data.16 In selecting among the effect 

estimates from Smith et al. (2009), we focused on an ozone-only estimate for non-accidental 

mortality using the 8-hour max metric for the warmer ozone season. This estimate is xxx than 

that produced by Bell et al 2004. For the sensitivity analysis, we included a co-pollutant model 

(ozone and PM10) from Smith et al. (2009) for all-cause mortality, using the 8-hour max ozone 

metric for the ozone season. Using a single pollutant model for the core analysis and the copollutant model in the sensitivity analysis reflects our concern that the reduced sampling 

frequency for days with co-pollutant measurements (1/3 and 1/6) could affect the ability of the 

study to characterize the ozone effect. This choice is consistent with the ozone ISA, which 

concludes that ozone effects are likely to be independent of the relationship between PM and 

mortality (U.S. EPA, 2013a). 

The Zanobetti and Smith (2008) study evaluated the relationship between ozone exposure 

(using an 8-hour mean metric for the warm season June-August) and all-cause mortality in 48 

U.S. cities using data collected between 1989 and 2000. The study presented single pollutant CR functions based on shorter (0-3 day) and longer (0-20 day) lag structures, with the comparison 

of effects based on these different lag structures being a central focus of the study. We used the 

shorter day lag based C-R function since this had the strongest effect and tighter confidence 

interval. We converted the effect estimate from an 8-hour mean metric to an equivalent effect 

estimate based on an 8-hour 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 

 

16 There are a number of concerns regarding the trimmed mean approach including (1) the potential loss of temporal 

variation in the data when the approach is used (this could impact the size of the effect estimate), and (2) a lack of 

complete documentation for the approach, which prevents a full reviewing or replication of the technique. 17 These adjustment ratios are created by (a) obtaining summary air quality (composite monitor values) for each 

urban study area/ozone season combination reflected in the original epidemiology study, (b) calculating the ratio 

of the 8-hour max to the study-specific air metric (for each of the urban study areas), and (c) taking the average of 

these urban-study area ratios. Ratio adjustment of the effect estimate does introduce uncertainty into the benefits 

estimates generated using these adjusted effect estimates; however, adjustments of relatively similar metrics (e.g., 

8-hour max and 8-hour mean), as is the case with the Zanobetti and Schwartz (2008) study, are likely to introduce 

Formatted: Highlight

Commented [A13]: Say how differs 

Formatted: Highlight
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Mortality Effect Coefficient for Long-term Ozone Exposure. Although previous 

advice provided by the SAB-HES was to include long-term ozone exposure-related mortality 

only as a sensitivity analysis, based on the more recent ISA, CASAC advice, and HREA, the 

current RIA movesWe also estimated long-term ozone exposure-related premature respiratory 

mortality incidenceinto the primary analysis. Support for modeling long-term exposure-related 

mortality incidence comes from the ozone ISA as well as recommendations provided by CASAC 

in their review of the ozone HREA (U.S. EPA-SAB, 2014, p. 3 and 9), despite the lower 

confidence in the results because that analysis is only based on one study, though that study is 

well designed, and because of the uncertainty in that study about the existence and identification 

of a potential threshold in the concentration-response function. Note that, whereasWhile the 

ozone ISA concludes that evidence is suggestive of a causal association between total mortality 

and long-term ozone exposure, specifically with regard to respiratory health effects (including 

mortality), the ISA concludes that there is likely to be a causal association (U.S. EPA, 2013a). 

Consistent with the ozone HREA, we use Jerrett et al. (2009) to estimate premature 

respiratory mortality from long-term ozone exposure. In review of the ozone HREA, CASAC 

concluded that “Jerrett et al. (2009) is an appropriate study to use at this time as the basis for the 

long-term mortality risk estimates given its adequacy and the lack of alternative data.” (U.S. 

EPA-SAB, 2014). This advice supersedes previous advice provided by the SAB-HES to include 

long-term ozone exposure-related mortality only as a 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 other causes of mortality). Jerrett et al. 

(2009) exhibits a number of strengths including (a) the study was based on the 1.2 million 

participant American Cancer Society cohort drawn from all 50 states, DC, and Puerto Rico 

(included ozone data from 1977, 5 years before enrollment in the cohort began, to 2000); (b) it 

included co-pollutant models that controlled for PM2.5; and (c) it explored the potential for a 

threshold concentration associated with the long-term mortality endpoint. However, attributes of 

this study affect how we interpret the long-term exposure-related respiratory mortality estimates. 

 

less uncertainty than adjustments for more disparate ratios (e.g., 24-hour or 1-hour max ratios to 8-hour max 

equivalents). 
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First, while CASAC notes that Jerrett et al. (2009) was well designed, it is a single study and 

provides the only quantitative basis for estimating this endpoint. By comparison, we estimate 

short-term exposure-related mortality risk using several studies. 

The sensitivity analyses take into consideration re is also the potential existence and 

location of a threshold in the C-R function relating mortality and long-term ozone 

concentrations, which can greatly affect the results. CASAC concluded, “it is not clear whether 

the 56 ppb threshold model is a better predictor of respiratory mortality than when using a linear 

model for the Jerrett et al. data” (U.S. EPA-SAB, 2014, p. 13-14) 

Reflecting this CASAC advice in the context of the HERA, we estimate long-term 

exposure-related respiratory mortality using a non-threshold co-pollutant model (with PM2.5) 

from Jerrett et al. (2009). Because the Jerrett et al. (2009) study uses seasonal average metrics 

(rather than shorter single day or multi-day lagged models as with time series studies studies), 

co-pollutants models obtained from Jerrett et al., (2009) are not affected by the lower PM2.5

sampling rates. Using a co-pollutant model is consistent with this study applying seasonal 

average metrics that are insensitive to co-pollutant monitoring for PM2.5. The effect estimates 

used to model long-term ozone-attributable mortality are calculated using a seasonal average of 

peak (1-hour maximum) measurements. These long-term exposure metrics can be viewed as 

long-term exposures to daily peak ozone over the warmer months, as compared with annual 

average levels such as are used in PM benefits. Therefore, combining long-term and short-term 

ozone-attributable mortality estimates could lead to double counting. Estimates of short-term 

ozone mortality are for all-causes, while estimates of long-term ozone mortality are for 

respiratory-related mortality only. 

Sensitivity Analysis: Alternate Mortality Effect Coefficients for Short-term Ozone 

Exposure. Although we believe the evidence supports an ozone-only effect on short-term 

exposure-related mortality (as supported by the ozone ISA), we recognize limitations in the 

ability of studies to explore copollutants effects due to lower sampling frequency for PM relative 

to ozone. For that reason, we conduct a sensitivity analysis using the co-pollutants model (with 

PM10) from Smith et al. (2009). 
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Sensitivity Analysis: Threshold-Based Effect Coefficients for Long-term Ozone 

Exposure. Consistent with the ozone HREA (U.S. EPA, 2014b), we explore the sensitivity of 

estimated ozone-related premature mortalitydeaths to a concentration threshold using the Jerrett 

et al. (2009) study.18 In their memo clarifying the results of their study (see Sasser, 2014), the 

authors note that in terms of goodness of fit, long-term health risk models including ozone 

clearly performed better than models without ozone, indicating the improved predictions of 

respiratory mortality when ozone is included. In exploring different functional forms, they report 

that the model including a threshold at 56 ppb had the lowest log-likelihood value of all models 

evaluated (i.e., linear models and models including 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 statistically superior to the linear model. Using another, 

more stringent test, none of the threshold models considered were statistically superior to the 

linear model. Under the less stringent test, although the threshold model produces a statistically 

superior prediction than the linear model, there is uncertainty about the specific location of the 

threshold, if one exists. This is because the confidence intervals on the model predictions 

indicate that a threshold could exist anywhere from 0 to 60 ppb. The authors conclude that 

considerable caution should be exercised in using any specific threshold, particularly when the 

more stringent statistical test indicates there is no significantly improved prediction. 

Based on this additional information from the authors (Sasser, 2014), we have chosen to 

reflect the uncertainty about the existence and location of a potential threshold by estimating 

mortality attributable to long-term ozone exposures using a range of threshold-based effect 

estimates as sensitivity analyses. Specifically, we generate additional long-term risk results using 

unique risk models that include a range of thresholds from 40 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 

 

18 The approach in the ozone HREA to explore the potential for thresholds related to long-term exposure-related 

mortality is described in Sasser (2014). That memorandum also describes additional data obtained from the 

authors of Jerrett et al. (2009) to support modeling potential thresholds. 

Commented [A14]: Confusing – check for mixed up 

or missing words? 
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likelihood value for all models examined.19 In addition to exploring the impact of potential 

thresholds, as part of the sensitivity analysis we explore the impact of using ozone-only (nonthreshold) models in estimating long-term exposure-related respiratory mortality.20 

6.6.3.3 PM2.5 Premature Mortality Coefficients

The co-benefits associated with NOx reductions made in the models to achieve ozone 

reductions are estimated in this RIA using methods consistent with those develop for the RIA for 

the final 2012 PM2.5 NAAQS. Below we provide additional background for readers who are not 

familiar with the PM2.5 literature that drives the approach developed for the 2012 PM2.5 

NAAQS and employed by EPA since then. 

PM2.5 Mortality Effect Coefficients for Adults. A substantial body of published 

scientific literature documents the association between elevated PM2.5 concentrations and 

increased premature mortality (U.S. EPA, 2009b). This body of literature reflects thousands of 

epidemiology, toxicology, and clinical studies. The PM ISA completed as part of the most recent 

review of the PM NAAQS, which was twice reviewed by the SAB-CASAC (U.S. EPA-SAB, 

2009a, 2009b), concluded that there is a causal relationship between mortality and both longterm and short-term exposure to PM2.5 based on the entire body of scientific evidence (U.S. EPA, 

2009b). The size of the mortality effect estimates from epidemiological studies, the serious 

nature of the effect itself, and the high monetary value ascribed to prolonging life make mortality 

risk reduction the most significant health endpoint quantified in this analysis. 

Researchers have found statistically significant associations between PM2.5 and 

premature mortality using different types of study designs. Time-series methods have been used 

to relate short-term (often day-to-day) changes in PM2.5 concentrations and changes in daily 

mortality rates up to several days after a period of exposure to elevated PM2.5 concentrations. 

Cohort methods have been used to examine the potential relationship between community-level 

 

19 There is a separate effect estimate (and associated standard error) for each of the fitted threshold models estimated 

in Jerrett et al. (2009). As a result, the sensitivity of 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. 20 The set of ozone-only non-threshold effect estimates include (a) a value based on the 86 cities for which there are 

co-pollutant monitoring data for both ozone and PM2.5 (this is best compared with the core estimate based on the 

co-pollutant non-threshold model), and (b) a value based on the 96 cities for which there is PM2.5 data (these 96 

cities were used in developing the threshold-based effect estimates used in the analysis). 

Commented [A15]: Suggest framing based on what 

has been done since 2012, rather than since 2008. Note 

that EPA has a lot more background on PM health 

effects than on ozone health effects. Is this because 

the preamble focuses on ozone? 
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PM2.5 exposures over multiple years (i.e., long-term exposures) and community-level annual 

mortality rates that have been adjusted for individual level risk factors. When choosing between 

using short-term studies or cohort studies for estimating mortality benefits, cohort analyses are 

thought to capture more of the public health impact of exposure to air pollution over time 

because they account for the effects of long-term exposures, as well as some fraction of shortterm exposures (Kunzli et al., 2001; NRC, 2002). The National Research Council (NRC) stated 

that “it is essential to use the cohort studies in benefits analysis to capture all important effects 

from air pollution exposure” (NRC, 2002, p. 108). The NRC further noted that “the overall effect 

estimates may be a combination of effects from long-term exposure plus some fraction from 

short-term exposure. The amount of overlap is unknown” (NRC, 2002, p. 108-9). To avoid 

double counting, we focus on applying the risk coefficients from the long-term cohort studies in 

estimating the mortality impacts of reductions in PM2.5. 

Over the last two decades, several studies using “prospective cohort” designs have been 

published that are consistent with the earlier body of literature. Two prospective cohort studies, 

often referred to as the Harvard “Six Cities Study” (Dockery et al., 1993; Laden et al., 2006; 

Lepeule et al., 2012) and the “American Cancer Society” or “ACS study” (Pope et al., 1995; 

Pope et al., 2002; Pope et al., 2004; Krewski et al., 2009), provide the most extensive analyses of 

ambient PM2.5 concentrations and mortality. These studies have found consistent relationships 

between fine particle indicators and premature mortality across multiple locations in the United 

States. The credibility of these two studies is further enhanced by the fact that the initial 

published studies (Pope et al., 1995; Dockery et al., 1993) were subject to extensive 

reexamination and reanalysis by an independent team of scientific experts commissioned by the 

Health Effects Institute (HEI) and by a Special Panel of the HEI Health Review Committee 

(Krewski et al., 2000). Publication of studies confirming and extending the findings of the 1993 

Six Cities Study and the 1995 ACS study using more recent air quality data and a longer followup period for the ACS cohort provides additional validation of the findings of these original 

studies (Pope et al., 2002, 2004; Laden et al., 2006; Krewski et al., 2009; Lepeule et al., 2012). 

The SAB-HES also supported using these two cohorts for analyses of the benefits of PM 

reductions, and concluded, “the selection of these cohort studies as the underlying basis for PM 

mortality benefit estimates [is] a good choice. These are widely cited, well studied and 
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extensively reviewed data sets” (U.S. EPA-SAB, 2010a). As both the ACS and Six Cities studies 

have inherent strengths and weaknesses, we present benefits estimates using relative risk 

estimates from the most recent extended reanalysis of these cohorts (Krewski et al., 2009; 

Lepeule et al., 2012). Presenting results using both ACS and Six Cities is consistent with other 

recent RIAs (e.g., U.S. EPA, 2010c, 2011a, 2011c). The PM ISA concludes that the ACS and Six 

Cities cohorts provide the strongest evidence of the association between long-term PM2.5

exposure and premature mortality with support from a number of additional cohort studies 

(described below). 

The extended analyses of the ACS cohort data (Krewski et al., 2009) refined the earlier 

ACS studies by (a) extending the follow-up period by 2 years to the year 2000, for a total of 18 

years; (b) incorporating almost double the number of urban areas; (c) addressing confounding by 

spatial autocorrelation by incorporating ecological, or community-level, co-variates; and (d) 

performing an extensive spatial analysis using land use regression modeling in two large urban 

areas. These enhancements make this analysis well-suited for the assessment of mortality risk 

from long-term PM2.5 exposures for the EPA’s benefits analyses. 

In 2009, the SAB-HES again reviewed the choice of mortality risk coefficients for 

benefits analysis, concluding that “[t]he Krewski et al. (2009) findings, while informative, have 

not yet undergone the same degree of peer review as have the aforementioned studies. Thus, the 

SAB-HES recommends that EPA not use the Krewski et al. (2009) findings for generating the 

Primary Estimate” (U.S. EPA-SAB, 2010a). Since this time, the Krewski et al. (2009) has 

undergone additional peer review, which we believe strengthens the support for including this 

study in this RIA. For example, the PM ISA (U.S. EPA, 2009b) included this study among the 

key mortality studies. In addition, the risk assessment supporting the PM NAAQS (U.S. EPA, 

2010b) used risk coefficients drawn from the Krewski et al. (2009) study, the most recent 

reanalysis of the ACS cohort data. The PM risk assessment cited a number of advantages that 

informed the selection of the Krewski et al. (2009) study as the source of the core effect 

estimates, including the extended period of observation, the rigorous examination of model 

forms and effect estimates, the coverage for ecological variables, and the large dataset with over 

1.2 million individuals and 156 MSAs (U.S. EPA, 2010b). The CASAC also provided extensive 
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peer review of the PM risk assessment and supported the use of effect estimates from this study 

(U.S. EPA-SAB, 2009a, b, 2010b). 

Consistent with the PM risk assessment (U.S. EPA, 2010b) which was reviewed by the 

CASAC (U.S. EPA-SAB, 2009a, b), we use the all-cause mortality risk estimate based on the 

random-effects Cox proportional hazard model that incorporates 44 individual and 7 ecological 

covariates (RR=1.06, 95% confidence intervals 1.04–1.08 per 10 μg/m3

 increase in PM2.5). The 

relative risk estimate (1.06 per 10 μg/m3

 increase in PM2.5) is identical to the risk estimate drawn 

from the earlier Pope et al. (2002) study, though the confidence interval around the Krewski et 

al. (2009) risk estimate is tighter. 

In the most recent Six Cities study, which was published after the last SAB-HES review, 

Lepeule et al. (2012) evaluated the sensitivity of previous Six Cities results to model 

specifications, lower exposures, and averaging time using eleven additional years of cohort 

follow-up that incorporated recent lower exposures. The authors found significant associations 

between PM2.5 exposure and increased risk of all-cause, cardiovascular and lung cancer 

mortality. The authors also concluded that the C-R relationship was linear down to PM2.5

concentrations of 8 μg/m3

 and that mortality rate ratios for PM2.5 fluctuated over time, but 

without clear trends, despite a substantial drop in the sulfate fraction. We use the all-cause 

mortality risk estimate based on a Cox proportional hazard model that incorporates 3 individual 

covariates. (RR=1.14, 95% confidence intervals 1.07–1.22 per 10 μg/m3

 increase in PM2.5). The 

relative risk estimate is slightly smaller than the risk estimate drawn from Laden et al. (2006), 

with relatively smaller confidence intervals. 

Given that monetized benefits associated with PM2.5 are driven largely by reductions in 

premature mortality, it is important to characterize the uncertainty in this endpoint. In order to do 

so, we utilize the results of an expert elicitation sponsored by the EPA and completed in 2006 

(Roman et al., 2008; IEc, 2006). The results of that expert elicitation can be used as a 

characterization of uncertainty in the C-R functions. The co-benefits results derived from expert 

elicitation is discussed in Appendix 6B (section 6B.4). 

PM2.5 Mortality Effect Coefficients for Infants. In addition to the adult mortality 

studies described above, several studies show an association between PM exposure and 
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premature mortality in children under 5 years of age.21 The PM ISA states that less evidence is 

available regarding the potential impact of PM2.5 exposure on infant mortality than on adult 

mortality and the results of studies in several countries include a range of findings with some 

finding significant associations. Specifically, the PM ISA concluded that evidence exists for a 

stronger effect at the post-neonatal period and for respiratory-related mortality, although this 

trend is not consistent across all studies. In addition, compared to avoided premature 

mortalitydeaths estimated for adults mortality, avoided premature mortalitydeaths for infants are 

significantly smaller because the number of infants in the population is much smaller than the 

number of adults and the epidemiology studies on infant mortality provide smaller risk 

coefficients associated with exposure to PM2.5. 

In 2004, the SAB-HES noted the release of the WHO Global Burden of Disease Study 

focusing on ambient air, which cites several recently published time-series studies relating daily 

PM exposure to mortality in children (U.S. EPA-SAB, 2004). With regard to the cohort study 

conducted by Woodruff et al. (1997), the SAB-HES noted several strengths of the study, 

including the use of a larger cohort drawn from a large number of metropolitan areas and efforts 

to control for a variety of individual risk factors in infants (e.g., maternal educational level, 

maternal ethnicity, parental marital status, and maternal smoking status). Based on these 

findings, the SAB-HES recommended that the EPA incorporate infant mortality into the primary 

benefits estimate and that infant mortality be evaluated using an impact function developed from 

the Woodruff et al. (1997) study (U.S. EPA-SAB, 2004). 

In 2010, the SAB-HES again noted the increasing body of literature relating infant 

mortality and PM exposure and supported the inclusion of infant mortality in the monetized 

benefits (U.S. EPA-SAB, 2010a). The SAB-HES generally supported the approach of estimating 

infant mortality based on Woodruff et al. (1997) but also noted that a more recent study by 

Woodruff et al. (2006) continued to find associations between PM2.5 and infant mortality in 

California. The SAB-HES also noted, “when PM10 results are scaled to estimate PM2.5 impacts, 

the results yield similar risk estimates.” Consistent with The Benefits and Costs of the Clean Air 

 

21 For the purposes of this analysis, we only calculate benefits for infants age 0–1, not all children under 5 years old. 
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Act 1990 to 2020 (U.S. EPA, 2011a), we continue to rely on the earlier 1997 study in part due to 

the national–scale of the earlier study. 

6.6.3.4 Hospital Admissions and Emergency Department Visits 

We pool together the incidence estimates using several different studies for many of the 

hospital admission endpoints. Some studies have examined the relationship between air 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 emergency 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. 

The ozone ISA states that studies consistently found positive associations between shortterm ozone exposures and asthma and COPD hospital admissions and ED visits, with more 

limited evidence for pneumonia (U.S. EPA, 2013a). The ISA found no evidence of a threshold 

between short term ozone exposure and respiratory hospital admissions and ED visits, although 

as discussed in the preamble, there is increasing uncertainty at lower ozone concentrations 

particularly under 40 ppb . The ISA also observes that effect estimates remained robust to 

copollutants (U.S. EPA 2013a). 

Considering these observation from the ISA and a thoroughly reviewing available 

epidemiological studies, we estimate respiratory hospital admissions (for 65-99 year olds) using 

an effect estimate obtained from Katsouyanni et al. (2009) and asthma-related emergency room 

visits (for all ages) using several single-city studies. Although Katsouyanni et al. (2009) provides 

effect estimates specific to the summer season, we adjusted the 1-hour max metric to the 

equivalent 8-hour max effect estimates.22 The study provides summer season single pollutant 

effect estimates based both on natural and penalized splines. We have re-evaluated the choice of 

these models in this final RIA. In contrast to the proposal RIA, where we averaged both the 

 

22 Given that Katsouyanni et al. (2009) included a larger number of cities (14), rather than constructing an air metric 

adjustment ratio based on this set of urban study areas, we used a national ratio to adjust effect estimates to 

represent the 8-hour metric. 
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penalized and natural spline estimates, we decided to focus on the penalized spline model 

because it displays a higher degree of precision, thus less potential for random error. 23 While 

Katsouyanni et al. (2009) included a set of effect estimates based on co-pollutant modeling (with 

PM10), but we could not use them because they were based on the full year rather than the 

summer season. 

A number of studies are available to model respiratory ED visits. Because we do not yet 

have the information needed to value this endpoint, we focused on the narrower category of 

asthma-related ED visits We used a set of single city studies together with random-effects 

pooling to generate a single pooled estimate. The set of single city studies used in this 

calculation include: Peel et al. (2005) and Sarnat et al. (2013) both for Atlanta, Wilson et al. 

(2005) and Mar and Koenig (2009) for Seattle, Wilson et al. (2005) for Portland, Maine,, Ito et 

al. (2007) for New York City, and Glad et al. (2012) for Pittsburgh. We note that of these single 

city studies, only the Ito et al. (2007) study included a co-pollutant model (for PM2.5).24 In 

addition, two of the studies required adjustments to reflect the 8-hour max air metric. 

Specifically, Glad et al. (2012) uses the 1-hour max air metric, while Sarnat et al. (2013) used the 

24-hour average metric. Each required the use of air metric ratios to adjust their betas. In 

generating a single pooled benefit estimate for this endpoint, we used random/fixed effects 

pooling to combine estimates across these single city studies. 

The two main groups of hospital admissions estimated in this analysis for PM2.5 are: 

respiratory admissions and cardiovascular admissions. There is not sufficient evidence linking 

PM2.5 with other types of hospital admissions. Both asthma- and cardiovascular-related visits 

have been linked to exposure to PM2.5 in the United States, though as we note below, we are able 

to assign an economic value to asthma-related events only. To estimate the effects of PM2.5 air 

pollution reductions on asthma-related ER visits, we use the effect estimates from studies of 

children 18 and under by Mar et al. (2010), Slaughter et al. (2005), and Glad et al. (2012). The 

 

23 We evaluated the impact from this change (i.e., use of the penalized spline model alone as contrasted with an 

average of estimates generated using both the natural and penalized splines), and this change made a negligible 

(<<1%) difference in monetized ozone benefits due to the similarity in effect coefficients between the two 

models. 24 While we have included co-pollutant models as sensitivity analyses for mortality, we did not include separate copollutant models for any of the morbidity endpoints as sensitivity analyses because morbidity endpoint represent a 

small fraction of the total monetized benefits. . 
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first two studies examined populations 0 to 99 in Washington, while Glad et al. examined 

populations 0-99 in Pittsburgh, Pennsylvania. Mar and colleagues perform their study in 

Tacoma, while Slaughter and colleagues base their study in Spokane. We apply random/fixed 

effects pooling to combine evidence across these two studies. 

To estimate avoided incidences of cardiovascular hospital admissions associated with 

exposure to PM2.5, we used studies by Moolgavkar (2000), Zanobetti et al. (2009), Peng et al. 

(2008, 2009) and Bell et al. (2008). Only Moolgavkar (2000) provided a separate effect estimate 

for adults 20 to 64, while the remainder estimate risk among adults over 64. Total cardiovascular 

hospital admissions are the sum of the pooled estimate for adults over 64 and the single study 

estimate for adults 20 to 64. To avoid double-counting benefits from reductions in myocardial 

infarctions when applying the impact function for cardiovascular hospital admissions, we first 

adjusted the baseline cardiovascular hospital admissions to remove admissions for myocardial 

infarctions. We applied equal weights pooling to the multi-city studies assessing risk among 

adults over 64 because these studies already incorporated pooling across the city-level estimates. 

One potential limitation of our approach is that while the Zanobetti et al. (2009) study assesses 

all cardiovascular risk, the Bell et al. (2008), and Peng et al. (2008, 2009) studies estimate a 

subset of cardiovascular hospitalizations as well as certain cerebro- and peripheral-vascular 

diseases. To address the potential for the pooling of these four studies to produce a biased 

estimate, we match the pooled risk estimate with a baseline incidence rate that excludes cerebroand peripheral-vascular disease. An alternative approach would be to use the Zanobetti et al. 

(2009) study alone, though this would prevent us from drawing upon the strengths of the three 

multi-city studies. 

To estimate avoided incidences of respiratory hospital admissions associated with PM2.5, 

we used a number of studies examining total respiratory hospital admissions as well as asthma 

and chronic lung disease. We estimated impacts among three age groups: adults 65 and over, 

adults 18 to 64 and children 0 to 17. For adults over 65, the multi-city studies by Zanobetti et al. 

(2009) and Kloog et al. (2012) provide effect coefficients for total respiratory hospital 

admissions. We pool these two studies using equal weights. Moolgavkar et al. (2003) examines 

PM2.5 and chronic lung disease hospital admissions (not including asthma) in Los Angeles, 

California among adults 18 to 64. For children 0 to 18, we pool two studies using random/fixed 
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effects. The first is Babin et al. (2007), which assessed PM2.5 and asthma hospital admissions in 

Washington, DC among children 1 to 18; we adjusted the age range for this study to apply to 

children 0 to 18. The second is Sheppard et al. (2003), which assessed PM2.5 and asthma 

hospitalizations in Seattle, Washington, among children 0 to 18. 

6.6.3.5 Acute Health Events 

A number of acute health effects not requiring hospitalization are also associated with 

exposure to ozone and PM2.5. The sources for the effect estimates used to quantify these 

endpoints are described below. 

Asthma exacerbations. For this RIA, we followed the SAB-HES recommendations 

regarding asthma exacerbations in developing the core estimate (U.S. EPA-SAB, 2004a). 

Although certain studies of acute respiratory events characterize these impacts among only 

asthmatic populations, others consider the full population, including both asthmatics and nonasthmatics. For this reason and to avoid double counting impacts, incidence estimates derived 

from studies focused only on asthmatics cannot be added to estimates from studies that consider 

the full population. To prevent such double-counting, we estimated the exacerbation of asthma 

among children and excluded adults from the calculation. Asthma exacerbations occurring in 

adults are assumed to be captured in the general population endpoints, such as work loss days 

and minor restricted activity days (MRADs). Finally, we note the important distinction between 

the exacerbation of asthma among asthmatic populations and the onset of asthma among 

populations not previously suffering 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 (U.S. 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 exacerbations in children from exposure to ozone, for the 

proposed RIA, we selected three multi-city studies (Mortimer et al., 2002; O’Connor et al., 2008; 

Schildcrout et al., 2006). All three of these studies required the application of air metric ratios to 
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adjust effect estimates to represent the 8-hour max metric.25 In this final RIA, following a final 

review of our technical approach, we removed O’Connor (2008) due to concerns about potential 

exposure measurement error and residual confounding from meteorological variables in its 19-

day lag structure. Current evidence in the ozone ISA suggests a more immediate effect with 

exposure to ozone for respiratory-related effects, such as hospital admissions and emergency 

department visits, with additional supporting evidence from studies of respiratory symptoms 

(U.S. EPA, 2013a). Consequently, the 19-day lag structure reflected in the O’Connor (2008) 

study is not as strongly supported by the evidence as the shorter lag structures associated with the 

other two asthma exacerbation studies.26 In generating the asthma exacerbation estimate, we pool 

estimates from Mortimer et al. (2012) and Schildcrout et al. (2006) using a random/fixed effects 

approach applied within BenMAP-CE (U.S. EPA, 2015a).27

To characterize asthma exacerbations in children from exposure to PM2.5, we selected 

two studies (Ostro et al., 2001; Mar et al., 2004) that followed panels of asthmatic children. 

Ostro et al. (2001) followed a group of 138 African-American children in Los Angeles for 13 

weeks, recording daily occurrences of respiratory symptoms associated with asthma 

exacerbations (e.g., shortness of breath, wheeze, and cough). This study found a statistically 

significant association between exposure to PM2.5, measured as a 12-hour average, and the daily 

prevalence of shortness of breath and wheeze endpoints. Although the association was not 

statistically significant for cough, the results were still positive and close to significance; 

consequently, we decided to include this endpoint, along with shortness of breath and wheeze, in 

generating asthma exacerbation benefits.28

Mar et al. (2004) studied the effects of various size fractions of particulate matter on 

respiratory symptoms of adults and children with asthma, monitored over many months. The 

 

25 Mortimer et al. (2002) had effect estimates based on an 8-hour mean metric, O’Connor et al. (2008) used a 24-

hour metric, and Schildcrout et al. (2006) was based on a 1-hour max metric. 26 We evaluated the impact from excluding O’Connor (2008) in estimating asthma exacerbations, and the change 

made a negligible (<1%) difference in monetized ozone benefits. Furthermore, the combined changes in 

estimating asthma exacerbations and respiratory hospital admissions resulted in only a 1% change in monetized 

ozone benefits compared to the approaches used in the proposal RIA (U.S. EPA, 2014c). 27 BenMAP-CE applies a chi-squared test to determine whether a fixed or random effect pooling approach should be 

used (for additional detail see U.S. EPA (2015b), p. 206). 28 The random effects pooling used in BenMAP (U.S. EPA, 2015b) to generate a single estimate for asthma 

exacerbations weights estimates based on variance, and consequently those estimates with less statistical 

significance and wider confidence intervals will be down-weighted in generating the total aggregated estimate. 
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study was conducted in Spokane, Washington, a semi-arid city with diverse sources of 

particulate matter. Data on respiratory symptoms and medication use were recorded daily by the 

study’s subjects, while air pollution data was collected by the local air agency and Washington 

State University. Subjects in the study consisted of 16 adults—the majority of whom participated 

for over a year—and nine children, all of whom were studied for over eight months. Among the 

children, the authors found a strong association between cough symptoms and several metrics of 

particulate matter, including PM2.5. However, the authors found no association between 

respiratory symptoms and exposure to PM for any metric in adults. Mar et al. therefore 

concluded that the discrepancy in results between children and adults was due either to the way 

in which air quality was monitored, or a greater sensitivity of children than adults to increased 

levels of PM air pollution. 

We employed the following pooling approach in combining estimates generated using 

effect estimates from the two studies to produce a single estimate for PM-related asthma 

exacerbation incidence. First, we used random/fixed effects pooling to combine the Ostro and 

Mar estimates for shortness of breath and cough. Next, we pooled the Ostro estimate of wheeze 

with the pooled cough and shortness of breath estimates to derive an overall estimate of asthma 

exacerbation in children. 

Acute Respiratory Symptoms. We estimate one type of acute respiratory symptom 

related to ozone exposure. Minor Restricted Activity Days (MRADs) result when individuals 

reduce most usual daily activities and replace them with less strenuous activities or rest, yet not 

to the point of missing work or school. For example, a mechanic who would usually be doing 

physical work most of the day will instead spend the day at a desk doing paper work and phone 

work because of difficulty breathing or chest pain. 

For ozone, we modeled MRADs using Ostro and Rothschild (1989). This study provides 

a co-pollutant model (with PM2.5) based on a national sample of 18-64 year olds. The original 

study used a 24-hour average metric and included control for PM2.5, which necessitated the use 

of an air metric ratio to convert the effect estimate to an 8-hour max equivalent. 

We estimate three types of acute respiratory symptoms related to PM2.5 exposure: lower 

respiratory symptoms, upper respiratory symptoms, and MRAD. Incidences of lower respiratory 
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symptoms (e.g., wheezing, deep cough) in children aged 7 to 14 were estimated for PM2.5 using 

an effect estimate from Schwartz and Neas (2000). Incidences of upper respiratory symptoms in 

asthmatic children aged 9 to 11 are estimated for PM2.5 using an effect estimate developed from 

Pope et al. (1991). Because asthmatics have greater sensitivity to stimuli (including air 

pollution), children with asthma can be more susceptible to a variety of upper respiratory 

symptoms (e.g., runny or stuffy nose; wet cough; and burning, aching, or red eyes). Research on 

the effects of air pollution on upper respiratory symptoms has focused on effects in asthmatics. 

The effect of PM2.5 on MRAD was estimated using an effect estimate derived from Ostro 

and Rothschild (1989). More recently published literature examining the relationship between 

short-term PM2.5 exposure and acute respiratory symptoms was available in the PM ISA (U.S. 

EPA, 2009b), but proved to be unsuitable for use in this benefits analysis. In particular, the best 

available study (Patel et al., 2010) specified a population aged 13–20, which overlaps with the 

population in which we assess asthma exacerbation. As we describe in detail below, to avoid the 

chance of double-counting impacts, we do not estimate changes in acute respiratory symptoms 

and asthma exacerbation among populations of the same age. 

School loss days (absences). Children may be absent from school due to respiratory or 

other acute diseases caused, or aggravated by, exposure to air pollution. Several studies have 

found a significant association between ozone levels and school absence rates. We use two 

studies (Gilliland et al., 2001; Chen et al., 2000) to estimate changes in school absences resulting 

from changes in ozone levels. The Gilliland et al. study estimated the incidence of new periods 

of absence, while the Chen et al. study examined daily absence rates. We converted the Gilliland 

et al. estimate to days of absence by multiplying the absence periods by the average duration of 

an absence. We estimated 1.6 days as the average duration of a school absence, the result of 

dividing the average daily school absence rate 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 of school age children, ages five to 17. This 

is consistent with recent peer-reviewed literature on estimating the impact of ozone exposure on 

school absences (Hall et al., 2003). We estimated the change in school absences using both Chen 
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et al. (2000) and Gilliland et al. (2001) and then pooled the results using the random effects 

pooling procedure. 

Acute Bronchitis. Approximately 4% of U.S. children between the ages of 5 and 17 

experience episodes of acute bronchitis annually (ALA, 2002). Acute bronchitis is characterized 

by coughing, chest discomfort, slight fever, and extreme tiredness, lasting for a number of days. 

According to the MedlinePlus medical encyclopedia,29 with the exception of cough, most acute 

bronchitis symptoms abate within 7 to 10 days. Incidence of episodes of acute bronchitis in 

children between the ages of 5 and 17 were estimated using an effect estimate developed from 

Dockery et al. (1996). 

Work Loss Days. Health effects from air pollution can also result in missed days of 

work (either from personal symptoms or from caring for a sick family member). Days of work 

lost resulting from exposure to PM2.5 were estimated using an effect estimate developed from 

Ostro (1987). Ostro (1987) estimated the impact of PM2.5 on the incidence of work loss days in a 

national sample of the adult working population, ages 18 to 65 living in metropolitan areas. 

Ostro reported that two-week average PM2.5 levels were significantly linked to work loss days, 

but there was some year-to-year variability in the results. 

6.6.3.6 Nonfatal Acute Myocardial Infarctions (AMI) (Heart Attacks) 

Nonfatal heart attacks have been linked with short-term exposures to PM2.5 in the United 

States (Mustafić et al., 2012; Peters et al., 2001; Sullivan et al., 2005; Pope et al., 2006; 

Zanobetti and Schwartz, 2006; Zanobetti et al., 2009) and other countries (Poloniecki et al., 

1997; Barnett et al., 2006; Peters et al., 2005). In previous health impact assessments, we have 

relied upon a study by Peters et al. (2001) as the basis for the impact function estimating the 

relationship between PM2.5 and nonfatal heart attacks. The Peters et al. (2001) study exhibits a 

number of strengths. In particular, it includes a robust characterization of populations 

experiencing AMIs. The researchers interviewed patients within 4 days of their AMI events and, 

for inclusion in the study, patients were required to meet a series of criteria including minimum 

 

29 See http://www.nlm.nih.gov/medlineplus/ency/article/001087.htm, accessed April 2012. 
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kinase levels, an identifiable onset of pain or other symptoms, and the ability to indicate the time, 

place and other characteristics of their AMI pain in an interview. 

Since the publication of Peters et al. (2001), a number of other single and multi-city 

studies have appeared in the literature, including: Sullivan et al. (2005), which considered the 

risk of PM2.5-related hospitalization for AMIs in King County, Washington; Pope et al. (2006), 

based in Wasatch Range, Utah; Zanobetti and Schwartz (2006), based in Boston, Massachusetts; 

and, Zanobetti et al. (2009), a multi-city study of 26 U.S. communities. Each of these single and 

multi-city studies, with the exception of Pope et al. (2006), measure AMIs using hospital 

discharge rates. Conversely, the Pope et al. (2006) study is based on a large registry with 

angiographically characterized patients—arguably a more precise indicator of AMI. Because the 

Pope et al. (2006) study reflected both myocardial infarctions and unstable angina, this produces 

a more comprehensive estimate of acute ischemic heart disease events than the other studies. 

However, unlike the Peters study (Peters et al., 2006), Pope and colleagues did not measure the 

time of symptom onset, and PM2.5 data were not measured on an hourly basis. 

As a means of recognizing the strengths of the Peters study while also incorporating the 

newer evidence found in the four single and multi-city studies, we present a range of AMI 

estimates. The upper end of the range is calculated using the Peters study, while the lower end of 

the range is the result of an equal-weights pooling of these four newer studies. It is important to 

note that when calculating the incidence of nonfatal AMI, the fraction of fatal heart attacks is 

subtracted to ensure that there is no double-counting with premature mortality estimates. 

Specifically, we apply an adjustment factor in the C-R function to reflect the probability of 

surviving a heart attack. Based on recent data from the Agency for Healthcare Research and 

Quality’s Healthcare Utilization Project National Inpatient Sample database (AHRQ, 2009), we 

identified mortalitydeath rates for adults hospitalized with acute myocardial infarction stratified 

by age (e.g., 1.852% for ages 18–44, 2.8188% for ages 45–64, and 7.4339% for ages 65+). These 

rates show a clear downward trend over time between 1994 and 2009 for the average adult and 

replace the 7% survival rate previously applied across all age groups from Rosamond et al. 

(1999). 
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6.6.3.7 Worker Productivity 

The EPA last quantified the effect of ozone on outdoor agricultural worker productivity 

in the final Regulatory Impact Analysis accompanying the Transport Rule (U.S. EPA, 2011c); 

that analysis reported the value of worker productivity in the core benefits analysis. That RIA 

applied information reported in Crocker and Horst (1981), which observed that reducing ozone 

by 10 percent translated to a 1.4 increase in income among outdoor citrus workers. The RIA 

accompanying the proposed Ozone NAAQS (US EPA, 2014c) noted that, due in part to the 

vintage of the data used in this study, the Agency omitted this analysis in subsequent RIA’s. 

A recent study by Graff Zivin, and Neidell (2012) provides new evidence of the effect of 

ozone exposure on productivity among outdoor agricultural workers which we use in a 

supplemental analysis. Specifically, the study combined individual-level daily harvest rates for 

outdoor agricultural workers on a 500-acre farm in the Central Valley of California, with groundlevel ozone data. The authors observed that a 10 ppb increase in work-day ozone concentrations 

(from 6:00 am to 3:00 pm) was associated with a 5.5% decrease in productivity among outdoor 

agricultural workers on a given day. Additional detail on the worker productivity supplemental 

analysis (including results) is presented in Appendix 6B (section 6B.8). 

6.6.3.8 Unquantified Human Health Effects 

Attaining a revised ozone NAAQS would reduce emissions of NOx and VOC. Although 

we have quantified many of the health benefits associated with reducing exposure to ozone and 

PM2.5, as shown in Table 6-3, we are unable to quantify the health benefits associated with 

reducing direct exposure to NO2 or VOC because of the absence of air quality modeling data for 

these pollutants. In addition, we are unable to quantify the effects of VOC emission reductions 

on ambient PM2.5 and associated health effects. Below we provide a qualitative description of 

these health benefits. In general, previous analyses have shown that the monetized value of these 

additional health benefits is much smaller than ozone and PM2.5-related benefits (U.S. EPA, 

2010a, 2010c, 2010d). 

Epidemiological researchers have associated NO2 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 
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NO2 ISA provides a comprehensive review of the current evidence of health and environmental 

effects of NO2. The NO2 ISA concluded that the evidence “is sufficient to infer a likely causal 

relationship between short-term NO2 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 from 

epidemiologic studies conducted in the United States and Canada generally indicate a 2–

20 percent increase in risks for ED visits and hospital admissions and higher risks for respiratory 

symptoms. The NO2 ISA concluded that the relationship between short-term NO2 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 NO2 alone. Although the NO2 ISA stated that 

studies consistently reported a relationship between NO2 exposure and mortality, the effect was 

generally smaller than that for other pollutants such as PM. We did not quantify these benefits 

due to data constraints. 

6.6.4 Economic Valuation Estimates 

Reductions in ambient concentrations of air pollution generally lower the risk of future 

adverse health effects for a large population. 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 estimates 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 statistical 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 individual 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 number 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. 
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WTP estimates generally are not available for some health effects, such as hospital 

admissions. In these cases, we instead used the cost of treating or mitigating the effect to 

estimate the economic value. Cost-of-illness (COI) estimates generally (although not necessarily 

in all cases) understate the true value of reducing the risk of a health effect, because they reflect 

the direct expenditures related to treatment, but not the value of avoided pain and suffering 

(Harrington and Portney, 1987; Berger, 1987). 

We provide unit values for health endpoints (along with information on the distribution 

of the unit value) in Table 6-10. All values are in constant year 2011$, adjusted for growth in real 

income for WTP estimates out to 2024 using projections provided by Standard and Poor’s, which 

is discussed in further detail below.30 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 process 

of reviewing the literature to update these unit values. The discussion below provides additional 

details on valuing specific PM2.5-related related endpoints. 

6.6.4.1 Mortality Valuation 

Following the advice of the SAB’s Environmental Economics Advisory 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 willingness 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 the 

wage-risk literature. The $1 million value represented the lower end of the interquartile range 

 

30 Income growth projections are only currently available in BenMAP through 2024, so both the 2025 and 2038 

estimates use income growth through 2024 and are therefore likely underestimates. Currently, BenMAP does not 

have an inflation adjustment to 2011$. We ran BenMAP for a currency year of 2010$ and calculated the benefitper-ton estimates in 2010$. We then adjusted the resulting benefit-per-ton estimates to 2011$ using the Consumer 

Price Index (CPI-U, all items). This approach slightly underestimates the inflation for medical index and wage 

index between 2010 and 2011, which affects COI estimates and wage-based estimates. 
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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) metaanalysis of 43 studies. The mean estimate of $5.5 million (2000$) was also consistent with the 

mean VSL of $5.4 million estimated in the Kochi et al. (2006) meta-analysis. However, the EPA 

neither changed its official guidance on the use of VSL in rule-makings nor subjected the interim 

estimate to a scientific peer-review process through SAB or other peer-review group. 
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Table 6-10. Unit Values for Economic Valuation of Health Endpoints (2011$) a

Health Endpoint 

Central Estimate of Value Per Statistical 

Incidence 

1990 Income Level 2024 Income Level Derivation of Distributions of Estimates 

Premature Mortality (Value of a Statistical Life) 

$8,300,000 $10,000,000 The EPA currently recommends a central VSL of $4.8 million 

(1990$, 1990 income) based on a Weibull distribution fitted to 26 published VSL estimates (5 contingent valuation and 21 labor market studies). The underlying studies, the distribution parameters, and other useful information are available in Appendix B 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 simple average of estimates from Russell et al. (1998) and Wittels et al. (1990). Lost earnings: Cropper and Krupnick (1990). Present discounted value of 5 years of lost earnings in 2000$: age of onset: at 3% at 7%  25–44 $9,000 $8,000  45–54 $13,000 $12,000  55–65 $77,000 $69,000 Direct medical expenses (2000$): 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) 
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Table 6-10. Unit Values for Economic Valuation of Health Endpoints (2011$) a (continued) 

Health Endpoint 

Central Estimate of Value Per Statistical 

Incidence 

2000 Income Level 2024 Income Level Derivation of Distributions of Estimates 

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 weighted 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 Healthcare 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) 
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Table 6-10. Unit Values for Economic Valuation of Health Endpoints (2011$) a (continued) 

Health Endpoint 

Central Estimate of Value Per Statistical 

Incidence 

2000 Income Level 2024 Income Level Derivation of Distributions of Estimates 

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 frequency 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 LRS symptom complex, we assumed a uniform distribution between $6.9 and $25 (2000$). 

Asthma Exacerbations $56 $60 Asthma exacerbations are valued at $45 per incidence, based on the 

mean of average WTP estimates for the four severity definitions of a “bad asthma day,” described in Rowe and Chestnut (1986). This study surveyed asthmatics to estimate WTP for avoidance of a “bad asthma day,” as defined by the subjects. For purposes of valuation, an asthma exacerbation is assumed to be equivalent to a day in which asthma is moderate or worse as reported in the Rowe and Chestnut (1986) study. The value is assumed to have a uniform distribution between $16 and $71 (2000$). 

(continued) 
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Table 6-10. Unit Values for Economic Valuation of Health Endpoints (2011$) a (continued) 

Health Endpoint 

Central Estimate of Value Per Statistical Incidence 

2000 Income Level 2024 Income Level Derivation of Distributions of Estimates 

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-range 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, compiled 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 actual 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 BenMAP user manual (U.S. EPA, 

2015). Income growth projections are only currently available in BenMAP through 2024, so both the 2025 and 2038 estimates use income growth through 2024 and are therefore likely underestimates. Currently, BenMAP does not have an inflation adjustment to 2011$. We ran BenMAP for a currency year of 2010$ and 

calculated the benefit-per-ton estimates in 2010$. We then adjusted the resulting benefit-per-ton estimates to 2011$ using the Consumer Price Index (CPI-U, all 

items). This approach slightly underestimates the inflation for medical index and wage index between 2010 and 2011, which affects COI estimates and wagebased estimates. 
6-62 

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 Economic Analyses (U.S. EPA, 2000)31 while the Agency continues its efforts to 

update its guidance on this issue. This approach calculates a mean value across VSL estimates 

derived from 26 labor market and contingent valuation studies published between 1974 and 

1991. The mean VSL across these studies is $4.8 million (1990$) or $6.3 million (2000$).32 The 

Agency is committed to using scientifically sound, appropriately reviewed evidence in valuing 

mortality risk reductions and has made significant progress in responding to the SAB-EEAC’s 

specific recommendations. In the process, the Agency has identified a number of important 

issues to be considered in updating its mortality risk valuation estimates. These are detailed in a 

white paper on “Valuing Mortality Risk Reductions in Environmental Policy,” which underwent 

review by the SAB-EEAC. A meeting with the SAB on this paper was held on March 14, 2011 

and formal 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 

the risk of premature mortality is the subject of continuing discussion within the economics and 

 

31 In the updated Guidelines for Preparing Economic Analyses (U.S. EPA, 2010e), EPA retained the VSL endorsed 

by the SAB with the understanding that further updates to the mortality risk valuation guidance would be 

forthcoming in the near future. 

32 In this analysis, we adjust the VSL to account for a different currency year (2011$) and to account for income 

growth to 2024. After applying these adjustments to the $6.3 million value, the VSL is $10 million. 
6-63 

public policy analysis communities. The EPA strives to use the best economic science in its 

analyses. Given the mixed theoretical finding and empirical evidence regarding adjustments to 

VSL for risk and population characteristics (e.g., Smith et al., 2004; Alberini et al., 2004; Aldy 

and Viscusi, 2008), we use a single VSL for all reductions in mortality risk. 

Although there are several differences between the studies the EPA uses to derive a VSL 

estimate and the ozone and PM2.5 air pollution context addressed here, those differences in the 

affected populations and the nature of the risks imply both upward and downward adjustments. 

Table 6-11 lists some of these differences and the expected effect on the VSL estimate for air 

pollution-related mortality. In the absence of a comprehensive and balanced set of adjustment 

factors, the EPA believes it is reasonable to 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 6-11. Influence of Applied VSL Attributes on the Size of the Economic Benefits of 

Reductions in the Risk of Premature MortalityDeath (U.S. EPA, 2006a) 

Attribute Expected Direction of Bias 

Age Uncertain, perhaps overestimate 

Life Expectancy/Health Status Uncertain, perhaps overestimate 

Attitudes Toward Risk Underestimate 

Income Uncertain 

Voluntary vs. Involuntary Uncertain, perhaps underestimate 

Catastrophic vs. Protracted Death Uncertain, perhaps underestimate 

The SAB-EEAC has reviewed many potential VSL adjustments and the state of the 

economics literature. The SAB-EEAC advised the EPA to “continue to use a wage-risk-based 

VSL as its primary estimate, including appropriate sensitivity analyses to reflect the uncertainty 

of these estimates,” and that “the only risk characteristic for which adjustments to the VSL can 

be made is the timing of the risk” (U.S. EPA-SAB, 2000). In developing our core estimate of the 

benefits of premature mortality reductions, we followed this advice. 

For PM2.5-related premature mortality, we assume that there is a “cessation” lag between 

exposures and the total realization of changes in health effects. For PM2.5, we assumed that some 

of the incidences of premature mortality related to PM2.5 exposures occur in a distributed fashion 

over the 20 years following exposure and discounted over the period between exposure and 
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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 reduction in PM2.5 (U.S. EPA-SAB, 2004c). To take this into account in the valuation of 

reductions in premature mortality, we discount the value of premature mortality occurring in 

future years using rates of 3 percent and 7 percent.33 Changes in the cessation lag assumptions do 

not change the total number of estimated premature mortalitydeaths but rather the timing of those 

deaths. As such, the monetized PM2.5 co-benefits using a 7 percent discount rate are only 

approximately 10 percent less than the monetized benefits using a 3 percent discount rate. 

Further discussion of this topic appears in the EPA’s 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 that prevented us monetizing these benefits. In the sensitivity analysis, we include both 

an assumption of zero lag and the PM lag structure (i.e., the SAB 20-year segmented lag). 

Inclusion of the zero lag reflects consideration of the possibility that the long-term respiratory 

mortality estimate primarily captures an accumulation of short-term mortality effects across the 

ozone season.34 The use of the 20-year segmented lag reflects consideration of advice provided 

by the SAB-HES (U.S. EPA-SAB, 2010a), where they state that, “[i]f Alternative 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 assumptions as for particulate matter when utilizing 

 

33 The choice of a discount rate, and its associated conceptual basis, is a topic of ongoing discussion within the 

federal government. To comply with OMB Circular A-4, EPA provides monetized benefits using discount rates of 

3% and 7% (OMB, 2003). A 3% discount reflects reliance on a “social rate of time preference” discounting 

concept. A 7% rate is consistent with an “opportunity cost of capital” concept to reflect the time value of 

resources directed to meet regulatory requirements. 

34 The ozone HREA noted,: “The effect estimates used in modeling long-term O3-attributable mortality, utilize a 

seasonal average of peak (1-hr maximum) measurements. These long-term exposure metrics can be viewed as 

long-term exposures to daily peak O3 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 

O3-attributable mortality estimates together with the short-term O3-attributable mortality estimates, in order to 

avoid double counting.” (U.S. EPA, 2014b). 
6-65 

cohort mortality evidence for ozone.” Monetized benefit estimates generated using both lag 

assumptions are presented as sensitivity analyses (see section 6.7.3.1). 

Uncertainties Specific to Premature Mortality Valuation. The economic benefits 

associated with reductions in the risk of premature mortality are the largest category of 

monetized benefits in this RIA. In addition, in prior analyses, the EPA identified valuation of 

mortality-related benefits as the largest contributor to the range of uncertainty in monetized 

benefits (Mansfield et al., 2009).35 Because of the uncertainty in estimates of the value of 

reducing premature mortality risk, it is important to adequately characterize and understand the 

various types of economic 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 mortality 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 degree to which mortality risk affects an individual. For example, some age groups 

appear to be more susceptible to air pollution than others (e.g., the elderly and children). Health 

status prior to exposure also affects susceptibility. An ideal benefits estimate of mortality risk 

reduction would reflect these human characteristics, in addition to an individual’s WTP to 

improve one’s own chances of survival along with WTP to improve other individuals’ survival 

rates. The ideal measure would also take into account the specific nature of the risk reduction 

commodity that is provided to individuals, as well as the context in which risk is reduced. To 

measure this value, it is important to assess how reductions in air pollution reduce the risk of 

dying from the time that reductions take effect onward and how individuals value these changes. 

Each individual’s survival curve, or the probability of surviving beyond a given age, should shift 

as a result of an environmental quality improvement. For example, changing the current 

probability of survival for an individual also shifts future probabilities of that individual’s 

survival. This probability shift will differ across individuals because survival curves depend on 

 

35 This conclusion was based on an assessment of uncertainty based on statistical error in epidemiological effect 

estimates and economic valuation estimates. Additional sources of model error such as those examined in the 

PM2.5 mortality expert elicitation (Roman et al., 2008) may result in different conclusions about the relative 

contribution of sources of uncertainty. 
6-66 

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 

analysis we value reductions in premature mortality risk using the VSL approach. 

Other uncertainties specific to premature mortality valuation include the following: 

 Across-study variation: There is considerable uncertainty as to whether the available 

literature on VSL provides adequate estimates of the VSL for risk reductions from air 

pollution reduction. Although there is considerable variation in the analytical designs and 

data used in the existing literature, the majority of the studies involve the value of risks to 

a middle-aged working population. Most of the studies examine differences in wages of 

risky occupations, using a hedonic wage approach. Certain characteristics of both the 

population affected and the mortality risk facing that population are believed to affect the 

average WTP to reduce the risk. The appropriateness of a distribution of WTP based on 

the current VSL literature for valuing the mortality-related benefits of reductions in air 

pollution concentrations therefore 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 average WTP for a reduction in mortality risk 

of 1/100,000 is $50, but that the actual mortality risk reduction resulting from a given 

pollutant reduction is 1/10,000. If WTP for reductions in mortality risk is linear in risk 

reduction, then a WTP of $50 for a reduction of 1/100,000 implies a WTP of $500 for a 

risk reduction of 1/10,000 (which is 10 times the risk reduction valued in the study). 

Under the assumption of linearity, the estimate of the VSL does not depend on the 

particular amount of risk reduction being valued. This assumption has been shown to be 

reasonable provided the change in the risk being valued is within the range of risks 

evaluated in the underlying studies (Rowlatt et al., 1998). 

 Voluntariness of risks evaluated: Although job-related mortality risks may differ in 

several ways from air pollution-related mortality risks, the most important difference may 

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

pollution-related risks are incurred involuntarily. Some evidence suggests that people will 

pay more to reduce involuntarily incurred risks than risks incurred voluntarily (e.g., 
6-67 

Lichtenstein and Slovic, 2006). If this is the case, WTP estimates based on wage-risk 

studies may understate WTP to reduce involuntarily incurred air pollution-related 

mortality risks. 

 Sudden versus protracted death: A final important difference related to the nature of 

the risk may be that some workplace mortality risks tend to involve sudden, traumatic 

events, whereas air pollution-related risks tend to involve longer periods of disease and 

suffering prior to death. Some evidence suggests that WTP to avoid a risk of a protracted 

death involving prolonged suffering and loss of dignity and personal control is greater 

than the WTP to avoid a risk (of identical magnitude) of sudden death (e.g., Tsuge et al., 

2005; Alberini and Scasny, 2011). To the extent that the mortality risks addressed in this 

assessment are associated with longer periods of illness or greater pain and suffering than 

are the risks addressed in the valuation literature, the WTP measurements employed in 

the present analysis would reflect a downward bias. 

 Self-selection and skill in avoiding risk: Recent research (Shogren and Stamland, 2002) 

suggests that VSL estimates based on hedonic wage studies may overstate the average 

value of a risk reduction. This is because the risk-wage trade-off revealed in hedonic 

studies reflects the preferences of the marginal worker (i.e., that worker who demands the 

highest compensation for his risk reduction for a given job). This worker must have either 

a higher workplace risk than the average worker in a given occupation, a lower risk 

tolerance than the average worker in that occupation, or both. Conversely, the marginal 

worker should have a higher risk tolerance than workers employed in less-risky sectors. 

However, the risk estimate used in hedonic studies is generally based on average risk, so 

the VSL may be biased, in an ambiguous direction, because the wage differential and risk 

measures do not match. 

 Baseline risk and age: Recent research (Smith, Pattanayak, and Van Houtven, 2006) 

finds that because individuals reevaluate their baseline risk of death as they age, the 

marginal value of risk reductions does not decline with age as predicted by some lifetime 

consumption models. This research supports findings in recent stated preference studies 

that suggest only small reductions in the value of mortality risk reductions with 

increasing age (e.g., Alberini et al., 2004). 

6.6.4.2 Hospital Admissions and Emergency Department Valuation 

In the absence of estimates of societal WTP to avoid hospital visits/admissions for 

specific illnesses, we derive COI estimates for use in the benefits analysis. The International 

Classification of Diseases (ICD) (WHO, 1977) code-specific COI estimates used in this analysis 

consist of estimated hospital charges and the estimated opportunity cost of time 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, 
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2007). We estimated the opportunity cost of a day spent in the hospital as the value of the lost 

daily wage, regardless of whether the hospitalized individual is in the workforce. To estimate the 

lost daily 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” (U.S. EPA, 2015). 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 provide the rounded unit values in 2011$ for the COI 

functions used in this analysis in Table 6-12. 

Table 6-12. Unit Values for Hospital Admissions a

End Point ICD Codes 

Age Range Mean Hospital 

Charge 

(2011$) 

Mean 

Length of 

Stay (days) 

Total Cost of 

Illness (unit 

value in 2011$) min. max. 

HA, Chronic Lung Disease 490–496 18 64 $20,000 3.9 $22,000 

HA, Asthma 493 0 64 $15,000 3.0 $16,000 

HA, All Cardiovascular 390–429 18 64 $41,000 4.1 $44,000 

HA, All Cardiovascular 390–429 65 99 $38,000 4.9 $42,000 

HA, All Respiratory 460–519 65 99 $32,000 6.1 $37,000 

a

 All estimates rounded to two significant digits. Unrounded estimates in 2000$ are available in Appendix J of the 

BenMAP user manual (U.S. EPA, 2015). 

To value asthma emergency department visits, we used a simple average of two estimates 

from the health economics literature. The first estimate comes from Smith et al. (1997), who 

reported approximately 1.2 million asthma-related emergency department visits in 1987, at a 

total cost of $186 million (1987$). The average cost per visit that year was $155; in 2011$, that 

cost is $480 (using the CPI-U for medical care to adjust to 2011$). The second estimate comes 

from Stanford et al. (1999), who reported the cost of an average asthma-related emergency 

department visit based on 1996–1997 data at $400 (using the CPI-U for medical care to adjust to 

2011$). A simple average of the two estimates yields a unit value of $440 (2011$). 

6.6.4.3 Nonfatal Myocardial Infarctions Valuation 

We were not able to identify a suitable WTP value for reductions in the risk of nonfatal 

heart attacks. Instead, we use a COI unit value with two components: the direct medical costs 

and the opportunity cost (lost earnings) associated with the illness event. Because the costs 
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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% discount rate, we estimated a rounded present discounted value in lost 

earnings (in 2000$) over 5 years due to a myocardial infarction of $8,800 for someone between 

the ages of 25 and 44, $13,000 for someone between the ages of 45 and 54, and $75,000 for 

someone between the ages of 55 and 65. The rounded corresponding age-specific estimates of 

lost earnings (in 2000$) using a 7% discount rate are $7,900, $12,000, and $67,000, respectively. 

Cropper and Krupnick (1990) do not provide lost earnings estimates for populations under 25 or 

over 65. As such, we do not include lost earnings in the cost estimates for 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 6-13): 

 Wittels et al. (1990) estimated expected total medical costs of myocardial infarction over 

5 years to be $51,000 (rounded in 1986$) for people who were admitted to the hospital 

and survived hospitalization. (There does not appear to be any discounting used.) This 

estimated cost is based on a medical cost model, which incorporated therapeutic options, 

projected outcomes, and prices (using “knowledgeable cardiologists” as consultants). The 

model used medical data and medical decision algorithms to estimate the probabilities of 

certain events and/or medical procedures 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 

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 as a result whose length of stay was substantially 

shorter. 

 Eisenstein et al. (2001) estimated 10-year costs of $45,000 in rounded 1997$ (using a 3% 

discount rate) for myocardial infarction patients, using statistical prediction (regression) 

models to estimate inpatient costs. Only inpatient costs (physician fees and hospital costs) 

were included. 
6-70 

Table 6-13. Alternative Direct Medical Cost of Illness Estimates for Nonfatal Heart 

Attacks a

Study Direct Medical Costs (2011$) Over an x-Year Period, for x = 

Wittels et al. (1990) $170,000 b 5 

Russell et al. (1998) $34,000 c 5 

Average (5-year) costs $100,000 5 

Eisenstein et al. (2001) $76,000 c

 10 

a

 All estimates rounded to two significant digits. Unrounded estimates in 2000$ are available in appendix J of the 

BenMAP user manual (U.S. EPA, 2015). 

b

 Wittels et al. (1990) did not appear to discount costs incurred in future years. c

 Using a 3% discount rate. Discounted values as reported in the study. 

As noted above, the estimates from these three studies are substantially different, and we 

have not adequately resolved the sources of differences in the estimates. Because the wagerelated opportunity cost estimates from Cropper and Krupnick (1990) cover a 5-year period, we 

used estimates for medical costs that similarly cover a 5-year period (i.e., 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 6-14. 

Table 6-14. Estimated Costs Over a 5-Year Period of a Nonfatal Myocardial Infarction 

(in 2011$) a

Age Group Opportunity Cost Medical Cost b Total Cost 

0–24 $0 $100,000 $100,000 

25–44 $12,000 c $100,000 $110,000 

45–54 $18,000 c

 $100,000 $120,000 

55–65 $100,000 c

 $100,000 $210,000 

> 65 $0 $100,000 $100,000 

a

 All estimates rounded to two significant digits, so estimates may not sum across columns. Unrounded estimates in 

2000$ are available in appendix J of the BenMAP user manual (U.S. EPA, 2015). 

b

 An average of the 5-year costs estimated by Wittels et al. (1990) and Russell et al. (1998). 

c

 From Cropper and Krupnick (1990), using a 3% discount rate for illustration. 

6.6.4.4 Valuation of Acute Health Events 

Asthma Exacerbation Valuation. 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. 
6-71 

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 day, using estimates 

from Tolley et al. (1986) of WTP for avoiding a combination of coughing, throat congestion and 

sinusitis. This estimate of WTP to avoid a minor respiratory restricted 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 PM2.5

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 absence, we: (1) estimated the 

probability that if a school child stays home from school, a parent will have to stay home from 

work to care for the child; and (2) valued the lost productivity at the parent’s wage. To do this, 

we estimated the number of families with school-age children in which both 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 

about the wages of the parent staying home with the child. Finally, this method assumes that 

parents are unable to work from home. If this is not a valid assumption, 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 United 

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 children. From these 

two sets of statistics, we calculated a weighted average participation rate of 72.85 percent. Our 
6-72 

estimate of daily lost wage (wages lost if a mother must stay at home with a sick child) is based 

on the year 2000 median weekly wage among women ages 25 and older (U.S. Census Bureau, 

2001). This median weekly wage is $551 (2000$). Dividing by five gives an estimated median 

daily wage of $103 (2000$). To estimate the expected lost wages on a day when a mother has to 

stay home with a school-age child, we first estimated the probability that the mother is in the 

workforce then multiplied that estimate by the daily wage she would lose by missing a workday: 

72.85 percent times $103, for a total loss of $75 (2000$). This valuation approach is similar to 

that used by Hall et al. (2003). 

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 Valuation. 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). 

6.6.4.5 Growth in WTP Reflecting National Income Growth over Time 

Our analysis accounts for expected growth in real income over time. This is a distinct 

concept from inflation and currency year. Economic theory argues that WTP for most goods 

(such as environmental protection) will increase if real incomes increase. There is substantial 

empirical evidence that the income elasticity36 of WTP for health risk reductions is positive, 

although there is uncertainty about its exact value. Thus, as real income increases, the WTP for 

environmental improvements also increases. Although many analyses assume that the income 

elasticity of WTP is unit elastic (i.e., a 10% higher real income level implies a 10% higher WTP 

to reduce risk changes), empirical evidence suggests that income elasticity is substantially less 

 

36 Income elasticity is a common economic measure equal to the percentage change in WTP for a 1 percent change 

in income. 
6-73 

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 changes 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 study populations and the affected populations at a particular time. The SABEEAC advised the EPA to adjust WTP for increases in real income over time but not to adjust 

WTP to account for cross-sectional income differences “because of the sensitivity of making 

such distinctions, and because of insufficient evidence available at present” (U.S. EPA-SAB, 

2000). An advisory by another committee associated with the SAB, the Advisory Council on 

Clean Air Compliance Analysis (SAB-Council), has provided conflicting advice. While agreeing 

with “the general principle that the willingness to pay to reduce mortality risks is likely to 

increase with 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). More recently, in response to questions related to the adjustment for income growth over 

time, the SAB-EEAC noted that “EPA should adjust for changes in income in evaluating benefits 

of risk reduction” (U.S. EPA-SAB, 2011). Until these conflicting advisories can be reconciled, 

the EPA will continue to adjust valuation estimates to reflect income growth using the methods 

described below, while providing sensitivity analyses for alternative income growth adjustment 

factors. 

Based on a review of the available income elasticity literature, we adjusted the valuation 

of human health benefits upward to account for projected growth in real U.S. income. Faced with 

a dearth of estimates of income elasticities derived from time-series studies, we applied estimates 

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

Kleckner and Neumann (1999). We note that the literature has evolved since the publication of 

this memo and that an array of newer studies identifying potentially suitable income elasticity 

estimates are available (IEc, 2012). The EPA anticipates seeking a peer review in the near future 

of these studies and its approach to adjusting WTP estimates to account for changes in personal 
6-74 

income. As such, these newer studies have not yet been incorporated into the benefits analysis. 

An abbreviated description of the procedure we used to account for WTP for real income growth 

between 1990 and 2024 is presented below. 

Reported income elasticities suggest that the severity of a health effect is a primary 

determinant of the strength of the relationship between changes in real income and WTP. As 

such, we use different 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 empirical 

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 short-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 adjust estimates of benefits in 2024 are 

presented in Table 6-15. 

Table 6-15. 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 1990 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 
6-75 

estimates for the years 1990 to 1999 based on U.S. Census Bureau estimates (Hollman, Mulder, 

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) provided by Standard and Poor’s (2000) for 

the years 2010 to 2024.38

Using the method outlined in Kleckner and Neumann (1999) and the population and 

income data described above, we calculated WTP adjustment factors for each of the elasticity 

estimates listed in Table 6-16. 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. 

There is some uncertainty regarding 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 

 

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. 
6-76 

incomes are higher, the losses resulting from 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 total economic value of 

avoided health impacts. 

Table 6-16. Adjustment Factors Used to Account for Projected Real Income Growth a

Benefit Category 2024 

Minor Health Effect 1.07 

Severe and Chronic Health Effects 1.22 

Premature Mortality 1.20 

a

 Based on elasticity values reported in Table 6-15, U.S. Census population projections, and projections of real GDP 

per capita. 

6.6.5 Benefit per Ton Estimates Used in Modeling PM2.5-Related Co-benefits 

This section presents the benefit-per-ton estimates (dimensioned by mortality study and 

simulation year) used as inputs in generating PM2.5 co-benefits estimates including (Table 6-17).

Benefit-per-ton estimates were generated for each of the long-term exposure-related mortality 

studies used in generating core benefits estimates for this RIA including Krewski et al. (2009) 

and Lepeule et al. (2012) (see section 6.6.3.3). Estimates were available for 2025 and 2030, with 

those being used to model co-benefits for the 2025 scenario and post-2025 scenario, respectively. 

For additional detail on the approach used to generate PM2.5 co-benefits estimates and the role 

played by these two types of inputs, see Section 6.4.4.39

 

39 Sector-level NOx reductions (for each alternative standard level) were generated using methods described in 

Chapter 4, section 4.2 and 4.3. As noted in section 5.4.4, NOx emissions reductions associated with alternative 

standard levels considered for this NAAQS review involved seven of the 17 sectors for which we had benefit-per 

ton values and consequently, the co-benefits PM2.5 estimates are based on simulated benefits for those seven 

sectors.
6-77 

Table 6-17. Summary of PM2.5 Benefit-per-ton Estimates a a Benefit-per-ton estimates reflect application of the 20-year segmented lag at either a 3% or 7% discount rate. In addition, separate sets of benefit-per-ton 

estimates were generate for 2025 and 2030, reflecting application of appropriate projected demographic and baseline incidence data. 

b Benefit-per-ton estimates for the non-specified source category were generated as a weighted average of values for the 17 source categories, with weighting 

based on sector-specific NOx emissions for 2005 obtained from Fann et al. (2012b).

air,

locamotive

and

marine

cement

kilns

coke

ovens

EGU

point

electric

arc

furnaces

ferro

alloys

integrated

iron and

steel

iron and

steel

non‐EGU

point

other

non‐

point

other nonroad onroad

pulp and

paper refineries

residenti

al wood

taconite

mining

ocean

going

vessels

Non‐

specified

source

b

2025 at 7% social discount

Krewski et al., 2009 $7,200 $5,700 $10,000 $5,200 $9,600 $4,400 $13,000 $17,000 $6,300 $7,900 $7,000 $7,600 $3,700 $6,900 $14,000 $6,000 $2,000 $6,400

Lepeule et al., 2012 $16,000 $13,000 $24,000 $12,000 $22,000 $9,800 $30,000 $38,000 $14,000 $18,000 $16,000 $17,000 $8,500 $16,000 $31,000 $14,000 $4,600 $15,000

2025 at 3% social discount

Krewski et al., 2009 $8,000 $6,300 $12,000 $5,800 $11,000 $4,800 $15,000 $19,000 $7,000 $8,700 $7,700 $8,400 $4,200 $7,700 $15,000 $6,600 $2,300 $7,100

Lepeule et al., 2012 $18,000 $14,000 $26,000 $13,000 $24,000 $11,000 $34,000 $42,000 $16,000 $20,000 $17,000 $19,000 $9,400 $17,000 $34,000 $15,000 $5,100 $16,000

2030 at 7% social discount

Krewski et al., 2009 $7,800 $6,100 $11,000 $5,600 $10,000 $4,600 $14,000 $18,000 $6,800 $8,500 $7,600 $8,200 $4,000 $7,500 $15,000 $6,400 $2,300 $6,900

Lepeule et al., 2012 $18,000 $14,000 $25,000 $13,000 $23,000 $10,000 $32,000 $41,000 $15,000 $19,000 $17,000 $19,000 $9,100 $17,000 $33,000 $14,000 $5,100 $16,000

2030 at 3% social discount

Krewski et al., 2009 $8,700 $6,800 $12,000 $6,200 $11,000 $5,100 $16,000 $20,000 $7,600 $9,400 $8,400 $9,100 $4,500 $8,300 $16,000 $7,100 $2,500 $7,700

Lepeule et al., 2012 $20,000 $15,000 $28,000 $14,000 $26,000 $12,000 $36,000 $46,000 $17,000 $21,000 $19,000 $21,000 $10,000 $19,000 $37,000 $16,000 $5,600 $17,000

Long‐term mortality

study

Emissionssector
6-78 

6.7 Benefits Results 

We estimated the benefits of attaining the revised and alternative ozone standard levels 

across the U.S. in 2025 except California. We estimated the benefits of attaining these standard 

levels in California in 2038. We report the 2025 and 2038 estimates separately because deriving 

a summed estimate would require us to calculate the Present Value (PV) of the stream of benefits 

occurring between those two years, which is not possible with the available data. Additional 

analyses (section 6.7.3) inform the interpretation of these core analyses. 

Applying the impact and valuation functions described above to the estimated changes in 

ozone concentrations yields estimates of the changes in physical damages (e.g., premature 

deathsmortality, cases of hospital admissions) and the associated monetary values for those 

changes. Not all known ozone and PM health effects could be quantified or monetized, and the 

monetized value of these unquantified effects is represented by adding an unknown “B” to the 

aggregate total. Values are rounded to two significant figures and so totals may not sum across 

columns or rows. 

6.7.1 Benefits of Attaining a Revised Ozone Standard in 2025 

This section presents the avoided health impacts and monetized benefits of attaining a 

more stringent ozone standard in 2025. Table 6-18 shows the population-weighted air quality 

change for the revised and alternative standard levels averaged across the continental U.S. Table 

6-19 summarizes the tons of NOx emissions required to simulate attainment of the revised and 

alternative standard levels (further differentiated by geographic region including east, west and 

California). Tables 6-20 through 6-25 present the benefits results for the standard levels 

analyzed. Table 6-25 summarizes total benefits by geographic region (including east, west minus 

California, and California). 

 In addition, Figure 6-4 presents a sensitivity analysis for short-term ozone-related 

benefits using additional C-R functions for premature mortality, and Figure 6-5 presents a 

sensitivity analysis for PM2.5-related co-benefits using additional C-R functions for premature 

mortality. See sections 6.6.3.2 and 6.6.3.3, respectively for additional discussion of the 

alternative effect estimates used in each sensitivity analysis. 

Commented [A16]: For future ozone RIA, reviewer 

recommends that it is useful to quantify the percentage 

of the benefits that occur in areas that do and do not 

meet the NAAQS at baseline, as well as the percentage 

of the benefits associated with premature mortality 

occurring on days when the 8-hour max is less than 40 

ppb. 
6-79 

Table 6-18. Population-Weighted Air Quality Change for the Revised and Alternative 

Annual Primary Ozone Standards Relative to the Analytical Baseline in 2025a

Standard 

Population-Weighted Summer Season Ozone Concentration Change 

(8-hour max)b

70 ppb 0.2574 

65 ppb 1.278 

a

 Because we used benefit-per-ton estimates for the PM2.5 co-benefits, population-weighted PM2.5 changes are not 

available. 

b

 Population weighting based on all ages (demographic used in modeling short-term exposure-related mortality for 

ozone) for 2025. 

Table 6-19. Sector-Specific NOx Emissions Reductions for the Revised and Alternative 

Standard Levelsa

a

All values are tons of NOx reductions (75 ppb vs revised and alternative standard levels). Results are presented 

both for “CA NOx” (emissions in CA only – used in post-2025 scenario PM2.5 cobenefits modeling) and “nonCA 

NOx” (emissions reductions outside of CA – used in 2025 scenario PM2.5 cobenefits modeling).

CA NOX

Emis Rdxn

nonCA NOX

Emis Rdxn

CA NOX

Emis Rdxn

nonCA NOX

Emis Rdxn

Aircraft, locomotives and marine vessels               ‐ ‐                 ‐             ‐                

Area sources              ‐          ‐ 32,224                     69,576

Cement kilns              ‐          ‐ 19,285                     31,963

Electricity Generating Units              ‐          ‐ 47,507                   113,678

Industrial pointsources              ‐        ‐ 134,763                   319,484

Non‐road mobile sources              ‐            ‐ 2,832                       8,791

On‐road mobile sources              ‐

Pulp and paperfacilities              ‐                  ‐ 42                           265

Refineries            ‐ 3,134                       7,735

Residential wood combustion

Unknown sector       51,000         46,542     99,500       862,803

TOTAL       51,000         286,330       99,500     1,414,296

Emissions Sector

70ppb 65ppb

Revised and Alternative Standard Levels
6-80 

Table 6-20. Estimated Number of Avoided Ozone-Related Health Impacts for the 

Revised and Alternative Standard Levels (Incremental to the Baseline) for the 

2025 Scenario (nationwide benefits of attaining the standards in the U.S. except 

California) a, b

a

 All incidence estimates are rounded to whole numbers with a maximum of two significant digits. 

b All incidence estimates are based on ozone-only models unless otherwise noted. 

d

 The negative estimates at the 5th percentile confidence estimates for these morbidity endpoints reflect the statistical 

power of the studies used to calculate these health impacts. These results do not suggest that reducing air pollution 

results will adversely affect health, but rather, that we are less confident in the magnitude of the expected benefits 

for this endpoint. 

70 ppb 65 ppb

Avoided Short-Term Mortality

96 490

(47 to 140) (240 to 740)

160 820

(86 to 240) (440 to 1,200)

Avoided Long-term Respiratory Mortality

340 1,700

(110 to 560) (580 to 2,800)

Avoided Morbidity

180 920

(-42 to 400) (-220 to 2,000)

510 2,700

(47 to 1,600) (250 to 8,300)

220,000 1,100,000

(-67,000 to 440,000) (-330,000 to 2,100,000)

450,000 2,200,000

(190,000 to 720,000) (920,000 to 3,500,000)

160,000 790,000

(57,000 to 360,000) (280,000 to 1,700,000)

Health Effectb

Revised and Alterative Standard Levels

(95th percentile confidence intervals)

multi-city studies

Smith et al. (2009) (all ages)

Zanobetti and Schwartz (2008) 

(all ages)

multi-city study

Jerrett et al. (2009) (30-99yrs) 

copollutants model (PM2.5) 

Hospital admissions - respiratory 

(age 65+)

d

Emergency department visits for 

asthma (all ages)

Asthma exacerbation (age 6-18)d

Minor restricted-activity days 

(age 18-65)

School Loss Days (age 5-17)
6-81 

Table 6-21. Total Monetized Ozone-Related Benefits for the Revised and Alternative 

Annual Ozone Standards (Incremental to the Baseline) for the 2025 Scenario 

(nationwide benefits of attaining the standards everywhere in the U.S. except 

California) (millions of 2011) a

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. 

Figure 6-4. Sensitivity Analysis for Short-Term Ozone-Related Mortality Benefits 

70 ppb 65 ppb

Avoided Short-Term Mortality - Core Analysis

$1,000 $5,300

($99 to $2,900) ($500 to $15,000)

1,700 8,700

($160 to $4,800) ($800 to $24,000)

Health Effectb

(95th percentile confidence intervals)

multi-city 

studies

Smith et al. (2009) (all ages)

Zanobetti and Schwartz (2008) (all 

ages)

Revised and Alterative Standard Levels

$0

$500

$1,000

$1,500

$2,000

$2,500

$3,000

$3,500

$4,000

Millions (2011$)
6-82 

Table 6-22. Estimated Number of Avoided PM2.5-Related Health Impacts for the Revised 

and Alternative Annual Ozone Standards (Incremental to the Baseline) for the 

2025 Scenario (Nationwide Benefits of Attaining the Standards 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 95th

percentile confidence interval for the health impact function alone ranges from approximately ±30 percent for 

mortality incidence based on Krewski et al. (2009) and ±46 percent based on Lepeule et al. (2012). 

70ppb 65ppb

Avoided PM2.5-related Mortality

Krewski et al. (2009) (adult mortality age 30+) 220 1,100

Lepeule et al. (2012) (adult mortality age 25+) 500 2,500

Woodruff et al. (1997) (infant mortality) <1 2

Avoided PM2.5-related Morbidity

Non-fatal heart attacks

 Peters et al. (2001) (age >18) 260 1,300

 Pooled estimate of 4 studies (age >18) 28 140

Hospital admissions—respiratory (all ages) 66 330

Hospital admissions—cardiovascular (age > 18) 80 400

Emergency department visits for asthma (all ages) 120 600

Acute bronchitis (ages 8–12) 340 1,700

Lower respiratory symptoms (ages 7–14) 4,400 22,000

Upper respiratory symptoms (asthmatics ages 9–11) 6,300 31,000

Asthma exacerbation (asthmatics ages 6–18) 7,000 42,000

Lost work days (ages 18–65) 28,000 140,000

Minor restricted-activity days (ages 18–65) 170,000 830,000

Revised and Alterative Standard Levels

Health Effectb
6-83 

Table 6-23. Monetized PM2.5-Related Health Co-Benefits for the Revised and Alternative 

Annual Ozone Standards (Incremental to Baseline) for the 2025 Scenario 

(Nationwide Benefits of Attaining the Standards in the U.S. except California) 

(Millions of 2011) a,b,c 

Monetized Benefits 

Revised and Alternative Standard Levels 

70 ppb 65 ppb 

3% Discount Rate 

Krewski et al. (2009) (adult mortality age 30+) $2,100 $10,000 

Lepeule et al. (2012) (adult mortality age 25+) $4,700 $23,000 

7% Discount Rate 

Krewski et al. (2009) (adult mortality age 30+) $1,900 $9,300 

Lepeule et al. (2012) (adult mortality age 25+) $4,200 $21,000 

a

 All estimates are rounded to two significant digits. Because these estimates were generated using benefit-per-ton 

estimates, confidence intervals are not available. In general, the 95th percentile confidence interval for monetized 

PM2.5 benefits ranges from approximately -90 percent to +180 percent of the central estimates based on Krewski et 

al. (2009) and Lepeule et al. (2012). 

b

 The reduction in premature fatalities each year accounts for over 98% of total monetized benefits in this analysis. 

Mortality risk valuation assumes discounting over the SAB-recommended 20-year segmented lag structure. 

Figure 6-5. Sensitivity Analysis Long-Term PM2.5-Related Mortality Co-Benefits 

$0

$1,000

$2,000

$3,000

$4,000

$5,000

$6,000

$7,000

$8,000

Millions (2011$)

Dollar benefit (7%) Dollar benefit (3%)
6-84 

Table 6-24. Estimated Monetized Ozone and PM2.5 Benefits for Revised and Alternative 

Annual Ozone Standards Incremental to the Baseline for the 2025 Scenario 

(Nationwide Benefits of Attaining the Standards in the U.S. Except California) –

Identified + Unidentified Control Strategies (combined) and Identified Control 

Strategies Only (billions of 2011$) a

Discount 

Rate 

Revised and Alternative Standard Levels 

70 ppb 65 ppb 

Identified + Unidentified Control Strategies

Ozone-only Benefits (range reflects Smith et 

al. (2009) to Zanobetti and Schwartz (2008)) b $1.0 to $1.7 $5.3 to $8.7 

PM2.5 Co-benefits (range reflects Krewski et 

al. (2009) to Lepeule et al. (2012)

3% $2.1 to $4.7 $10 to $23 

7% $1.9 to $4.2 $9.3 to $21 

Total Benefits 3% $3.1 to $6.4 +B $16 to $32 +B 

7% $2.9 to $5.9 +B $15 to $30 +B 

Identified Control Strategies Only 

Ozone-only Benefits (range reflects Smith et 

al. (2009) to Zanobetti and Schwartz (2008)) b $0.86 to $1.4 $2.2 to $3.5 

PM2.5 Co-benefits (range reflects Krewski et 

al. (2009) to Lepeule et al. (2012)

3% $1.7 to $3.9 $4.0 to $9.0 

7% $1.6 to $3.5 $3.6 to $8.1 

Total Benefits 3% $2.6 to $5.3 +B $6.1 to $12 +B 

7% $2.4 to $4.9 +B $5.7 to $12 +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 PM2.5 assumes discounting over the SABrecommended 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 

quantifying these endpoints, and as such, these benefits are inherently more uncertain than those benefits that we 

were able to quantify. These estimates reflect the economic value of avoided morbidities and premature mortality 

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 6-25. Regional Breakdown of Monetized Ozone-Specific Benefits Results for the 

2025 Scenario (Nationwide Benefits of Attaining the Standards in the U.S. except 

California) – Identified + Unidentified Control Strategies a, b 

Region Revised and Alterative Standard Levels 

70 ppb 65 ppb 

East c 98% 96% 

California ~0% ~0% 

Rest of West 2% 4% 

a

 Because we use benefit-per-ton estimates to calculate the PM2.5 co-benefits, a regional breakdown for the cobenefits is not available. Therefore, this table only reflects the ozone benefits. 

b These regional breakdown results reflect application of identified and unidentified control strategies. Regional 

breakdown results are the same for benefits based on application of identified control strategies only. 

c

 Includes Texas and those states to the north and east. 
6-85 

6.7.2 Benefits of the Post-2025 Scenario 

This section presents the estimated number and economic value of avoided ozone- and 

PM2.5-related effects associated with attaining a revised ozone standard after 2025 (note, sectorspecific NOx emissions reductions levels used in modeling benefits for the post-2025 scenario 

are presented earlier in Table 6-19 - see entries under “CA NOx”). In addition, general trends 

and observations drawn from the sensitivity analyses presented in Figures 6-4 and 6-5 hold for 

the post-2025 scenario and for that reason separate plots for these sensitivity analyses (for the 

post-2025 scenario) are not presented (see section 6.7.3 for further discussion). While simulated 

attainment of standard levels for the 2025 scenario involved application of both identified and 

unidentified controls outside of California, simulated attainment of standard levels for the post2025 scenario involved application exclusively of unidentified controls within California (see 

Table 6-18 and section 2.2.2). For that reason, results tables in this section represent exclusively, 

benefits based on application of unidentified controls. 

Table 6-26. Population-Weighted Air Quality Change for the Revised and Alternative 

Annual Primary Ozone Standards Relative to Baseline for Post-2025 a

Standard 

Population-Weighted Ozone Season Ozone Concentration Change 

(8-hour max)b

70 ppb 0.1708 

65 ppb 0.3464 a

 Because we used benefit-per-ton estimates for the PM2.5 co-benefits, population-weighted PM2.5 changes are not 

available. 

b

 Population weighting based on all ages (demographic used in modeling short-term exposure-related mortality for 

ozone) for 2025. 
6-86 

Table 6-27. Estimated Number of Avoided Ozone-Related Health Impacts for the 

Revised and Alternative Annual Ozone Standards (Incremental to the Baseline) 

for the Post-2025 Scenario (Nationwide Benefits of Attaining the Standards 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. 

d

 The negative estimates at the 5th percentile confidence estimates for these morbidity endpoints reflect the statistical 

power of the studies used to calculate these health impacts. These results do not suggest that reducing air pollution 

results in additional health impacts. 

70 ppb 65 ppb

Avoided Short-Term Mortality

72 150

(35 to 110) (71 to 220)

120 240

(64 to 180) (130 to 350)

Avoided Long-term Respiratory Mortality

290 590

(98 to 480) (200 to 970)

Avoided Morbidity

140 270

(-32 to 300) (-65 to 610)

360 720

(33 to 1,100) (67 to 2,200)

160,000 330,000

(-49,000 to 320,000) (-100,000 to 650,000)

320,000 660,000

(130,000 to 510,000) (270,000 to 1,000,000)

120,000 240,000

(42,000 to 260,000) (85,000 to 530,000)

Asthma exacerbation (age 6-18)d

Minor restricted-activity days 

(age 18-65)

School Loss Days (age 5-17)

Hospital admissions - respiratory 

(age 65+)d

Emergency department visits for 

asthma (all ages)

multi-city study

Health Effectb

Revised and Alterative Standard Levels

(95th percentile confidence intervals)

multi-city studies

Smith et al. (2009) (all ages)

Zanobetti and Schwartz (2008) 

(all ages)

Jerrett et al. (2009) (30-99yrs) 

copollutants model (PM2.5) 
6-87 

Table 6-28. Total Monetized Ozone-Only Benefits for the Revised and Alternative 

Annual Ozone Standards (Incremental to the Baseline) for the Post-2025 

Scenario (Nationwide Benefits of Attaining the Standards just in California) 

(millions of 2011) a 

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 

70 ppb 65 ppb

Avoided Short-Term Mortality - Core Analysis

$790 $1,600

($74 to $2,200) ($150 to $4,500)

1,300 2,600

($120 to $3,600) ($240 to $7,200)

multi-city 

studies

Smith et al. (2009) (all ages)

Zanobetti and Schwartz (2008) (all 

ages)

Health Effectb

(95th percentile confidence intervals)

Revised and Alterative Standard Levels
6-88 

Table 6-29. Estimated Number of Avoided PM2.5-Related Health Impacts for the Revised 

and Alternative Annual Ozone Standards (Incremental to the Baseline) for the 

Post-2025 Scenario (Nationwide Benefits of Attaining the Standards 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 95th

percentile confidence interval for the health impact function alone ranges from approximately ±30 percent for 

mortality incidence based on Krewski et al. (2009) and ±46 percent based on Lepeule et al. (2012). 

70 ppb 65 ppb

Avoided PM2.5-related Mortality

Krewski et al. (2009) (adult mortality age 30+) 43 84

Lepeule et al. (2012) (adult mortality age 25+) 98 190

Woodruff et al. (1997) (infant mortality) <1 <1

Avoided PM2.5-related Morbidity

Non-fatal heart attacks

 Peters et al. (2001) (age >18) 51 100

 Pooled estimate of 4 studies (age >18) 6 11

Hospital admissions—respiratory (all ages) 13 26

Hospital admissions—cardiovascular (age > 18) 16 31

Emergency department visits for asthma (all ages) 23 44

Acute bronchitis (ages 8–12) 64 130

Lower respiratory symptoms (ages 7–14) 820 1,600

Upper respiratory symptoms (asthmatics ages 9–11) 1,200 2,300

Asthma exacerbation (asthmatics ages 6–18) 1,900 3,600

Lost work days (ages 18–65) 5,300 10,000

Minor restricted-activity days (ages 18–65) 31,000 61,000

Revised and Alterative Standard Levels

Health Effectb
6-89 

Table 6-30. Monetized PM2.5-Related Health Co-Benefits for the Revised and Alternative 

Annual Ozone Standards (Incremental to Baseline) for the Post-2025 

Scenario (Nationwide Benefits of Attaining the Standards just in California) 

(Millions of 2011$) a,b

 Revised and Alterative Standard Levels

Monetized Benefits 70 ppb 65 ppb 

3% Discount Rate 

Krewski et al. (2009) (adult mortality age 30+) $400 $790 

Lepeule et al. (2012) (adult mortality age 25+) $910 $1,800 

7% Discount Rate 

Krewski et al. (2009) (adult mortality age 30+) $370 $710 

Lepeule et al. (2012) (adult mortality age 25+) $820 $1,600 

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 

PM2.5 benefits ranges from approximately -90 percent to +180 percent of the central estimates based on Krewski et 

al. (2009) and Lepeule et al. (2012). 

b

 The reduction in premature fatalities each year accounts for over 98% of total monetized benefits in this analysis. 

Mortality risk valuation assumes discounting over the SAB-recommended 20-year segmented lag structure. 

Table 6-31. Estimate of Monetized Ozone and PM2.5 Benefits for Revised and Alternative 

Annual Ozone Standards Incremental to the Baseline for the Post-2025 

Scenario (Nationwide Benefits of Attaining the Standards just in California) 

– Identified + Unidentified Control Strategies (billions of 2011$) a 

Discount 

Rate 

Revised and Alternative Standard Levels 

70 ppb 65 ppb 

Identified + Unidentified Control Strategies

Ozone-only Benefits (range reflects Smith 

et al. (2009) to Zanobetti and Schwartz 

(2008))

b $0.79 to $1.3 $1.6 to $2.6 

PM2.5 Co-benefits (range reflects Krewski 

et al. (2009) to Lepeule et al. (2012)

3% $0.40 to $0.91 $0.79 to $1.8 

7% $0.37 to $0.82 $0.71 to $1.6 

Total Benefits 3% $1.2 to $2.2 +B $2.4 to $4.4 +B 

7% $1.2 to $2.1 +B $2.3 to $4.2 +B 

a

 Rounded to two significant figures. The reduction in premature fatalities each year accounts for over 98% of total 

monetized benefits in this analysis. Mortality risk valuation for PM2.5 assumes discounting over the SABrecommended 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 

quantifying these endpoints, and as such, these benefits are inherently more uncertain than those benefits that we 

were able to quantify. These estimates reflect the economic value of avoided morbidities and premature 

mortalitydeaths 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. 
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Table 6-32. Regional Breakdown of Monetized Ozone-Specific Benefits Results for the 

Post-2025 Scenario (Nationwide Benefits of Attaining the Standards just in 

California) – Identified + Unidentified Control Strategies a

Region Revised and Alterative Standard Levels 

70 ppb 65 ppb 

East b 3% 2% 

California 90% 91% 

Rest of West 7% 7% 

a

 Because we use benefit-per-ton estimates to calculate the PM2.5 co-benefits, a regional breakdown for the cobenefits is not available. Therefore, this table only reflects the ozone benefits. 

b

 Includes Texas and those states to the north and east. 

6.7.3 Uncertainty in Benefits Results (including Results of Sensitivity Analyses and 

Supplemental Analyses) 

Avoided ozone and PM2.5 related premature deathmoralitys account for 94% to 96% 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 remaining categories each account for a small percentage of total monetized 

benefits; however, these outcomes occur among a significantly larger population. Comparing an 

incidence table to the monetized benefits table reveals that the number of incidences avoided and 

the unit value for that endpoint do not always closely correspond. For example, for ozone we 

estimate almost 1,000 times more cases of exacerbated ozone would be avoided than premature 

mortalitydeaths, yet cases of exacerbated asthma account for only a very small fraction (<1%) of 

total monetized benefits (see Table 6-20). This is because many of the less severe health effects, 

while more common, are valued at a lower level than the more severe health effects. Also, some 

effects, such as hospital admissions, are valued using a proxy measure of WTP. As such, the true 

value of these effects may be higher than that reported in the tables above. Sources of uncertainty 

associated with both the modeling of ozone-related benefits and PM2.5-related co-benefits are 

discussed qualitatively in Appendix 6A. 

PM2.5 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 PM2.5

Commented [A17]: Reviewer recommends 

converting this paragraph into a set of bullets that 

characterize the key sources of uncertainty in the short 

term ozone-premature morality relationship, akin to 

those that follow this paragraph for PM2.5. 
6-91 

produced varies considerably in composition across sources, but the scientific evidence 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 PM2.5

can be linked with multiple health effects, and the evidence is not yet sufficient to allow 

differentiation of those constituents or sources that are more 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 PM2.5, including both areas that do not 

meet the fine particle standard and those areas that are in attainment, down to the lowest 

modeled concentrations. 

 We assume that there is a “cessation” lag between the change in PM exposures and the 

total realization of changes in mortality effects. Specifically, we assume that some of the 

incidences of premature mortality related to PM2.5 exposures occur in a distributed 

fashion over the 20 years following exposure based on the advice of the SAB-HES (U.S. 

EPA-SAB, 2004c), which affects the valuation of mortality benefits at different discount 

rates. 

 We recognize uncertainty associated with application of the benefit-per-ton approach 

used in modeling PM2.5 co-benefits. 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. (2012b). Consequently, these estimates may not reflect local variability in 

population density, meteorology, exposure, baseline health incidence rates, or other local 

factors associated with the current ozone NAAQS review. Therefore, use of these benefitper-ton values to estimate co-benefits may lead to higher or lower benefit estimates than 

if co-benefits were calculated based on direct air quality modeling. 

6.7.3.1 Sensitivity Analyses 

This RIA includes a number of sensitivity analyses. Given that the size of the monetized 

benefits is sensitive to the number of estimated ozone and PM2.5 related premature 

mortalitydeaths, the sensitivity analyses focus 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. See Appendix 6B for more detailed results. 

 Short-term ozone-exposure related mortality (alternative epidemiological studies 

and C-R functions): We estimated the number of premature mortalitydeaths using seven 

additional effect estimates including four multi-city studies and three meta-analysis 

studies. This sensitivity analysis showed that the two core incidence and benefits 
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estimates fall within (and towards the lower end of) the broader range resulting from 

application of the seven alternative effect estimates (see Figure 6-4). 

 Economic value of avoided premature deathsmortality from long-term exposure to 

ozone: We estimated the economic value of long-term ozone mortality using two 

cessation lag structures: 20-year segment lag (as used for PM2.5) and a zero lag. The 

sensitivity analysis suggests that if included in the core benefit estimate, long-term ozone 

exposure-related mortality could add substantially to the overall benefits. Additionally, 

use of a 20-year segment lag can reduce benefits by 10-20% (relative to a zero lag) 

depending on the discount rate applied. 

 Long-term ozone-exposure related respiratory mortality and potential thresholds:

We evaluated the impact of several assumed thresholds ranging from 40-60 ppb. This 

sensitivity analysis suggested that a threshold of 50 ppb or greater could have a 

substantial impact on estimated benefits, while thresholds below this range have a 

relatively minor impact. 

 Long-term PM2.5 exposure-related mortality and alternative C-R functions (based 

on the Expert Elicitation): We applied the set of expert elicitation-based functions to 

generate an alternative set of PM2.5 benefit estimates (see Figure 6-5). The estimates 

based on Krewski et al. (2009) and Lepeule et al. (2012) fall within the range of estimates 

based on the functions from the 2006 expert elicitation. 

 Income elasticity for mortality and certain morbidity endpoints: 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 WTP functions used for 

mortality and for morbidity endpoints. That sensitivity analysis suggests that alternative 

assumptions regarding income elasticity could result in a moderate impact on mortality 

benefits (values ranging from ~90% to ~130% of the core estimate depending on the 

assumption regarding elasticity). Income elasticity was found to have a far more modest 

impact on morbidity endpoints modeled using WTP functions. 

6.7.3.2 Supplemental Analyses 

In addition to the sensitivity analyses, we also included a number of supplemental 

analyses intended to examine other attributes of the core risk estimates. These are briefly 

described below with additional detail found in Appendix 6B. 

 Ozone-related premature deathsmortality by age: These analyses report: (a) the 

number of avoided ozone-related premature mortalitydeaths by age range; (b) the 

estimated number of life years gained by age range; and (c) the percent reduction in 

baseline mortality by age range. We observe that about half of the ozone-attributable 

premature mortalitydeaths occur in populations age 75–99, but half of the life years 

would occur in populations younger than 65. This is because the younger populations 

have the potential to lose more life years per death than older populations. The percent 

reduction in total all-cause premature mortality attributable to simulated attainment of 
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both the revised and alternative standards are relatively small and fairly consistent across 

age ranges (see Appendix B, Table 6B-11). However, estimates of total ozoneattributable mortality associated with attainment of both standards is not evenly 

distributed across age ranges with the vast majority of projected avoided mortalitydeaths

occurring in older age groups (see Appendix B, Table 6B-9). 

 Avoided premature mortalitydeaths according to baseline pollutant concentrations:

We describe the summer season average 8-hour max ozone levels at which the avoided 

deathspremature mortality occurred. We found that: (a) the vast majority of avoided 

short-term ozone-related premature mortalitydeaths occur in grid cells with mean 8-hour 

max baseline levels between 35 and 55 ppb; and (b) virtually all of the avoided premature 

mortalitydeaths is are associated with ozone levels above the 20-40 ppb range, where we 

are less confident in the shape of the C-R function for ozone mortality (ozone ISA, 

section 2.5.4.4). For PM2.5 premature mortality, we evaluated the lowest measured level 

(LML) associated with the epidemiological studies providing the effect estimates. We are 

less confident in specifying the shape of the C-R function at exposure levels below the 

range observed in the epidemiological study (i.e., below the LML). In the supplemental 

analysis, we found that, depending on the mortality study, between 67% and 93% of the 

premature mortality estimate is based on modeling involving PM2.5 levels above the 

LML. 

 Value of increased productivity among outdoor agricultural workers due to reduced 

exposure to ozone: Using information from the Graff Zivin and Neidell (2012) study, we 

estimate the economic value of improved productivity among outdoor non-livestock 

workers for the ozone standards in 2025. We estimate the monetized worker productivity 

benefits of attaining a 70 ppb standard would be about $1.7 million and a 65 ppb standard 

would yield monetized benefits of about $8.9 million. 

6.8 Discussion 

This analysis demonstrates the potential for significant health benefits of the illustrative 

emissions controls applied to simulate attainment with the revised and alternative primary ozone 

standard levels. We estimate that by 2025, the emissions reductions to reach the revised and 

alternative standard levels everywhere except California, would have reduced the number of 

ozone- and PM2.5-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 rule would also 

yield significant welfare impacts as well (see Chapter 7). Even considering the quantified and 

unquantified uncertainties identified in this chapter, we believe that the revised and alternative 

standards would have substantial public health benefits that are likely to outweigh the costs of 

the control strategies for the revised and alternative standard levels analyzed (see Chapter 4). 

Commented [A18]: The summary of results of these 

analyses would be integrated with the uncertainty 

bullets, and the figures and text associated with this 

analysis, which currently appear in the middle of the 

Appendix 6B would be moved to comprise the final 

section of Chapter 6. Perhaps titled “Distribution of 

Premature Mortality Avoided.” 
6-94 

Inherent in any complex RIA such as this one are multiple sources of uncertainty. Some 

of these we characterized through our quantification of statistical error in the C-R relationships 

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 useful 

information regarding the public health benefits associated with the revised and alternative 

standard levels. 

As discussed in Chapter 1, 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, 2014a). Setting a NAAQS does not directly result in costs or benefits. The 

NAAQS RIAs illustrate the potential costs and benefits of the revised and alternative air quality 

standards nationwide based on an array of emissions reduction strategies for different sources, 

incremental to implementation of existing regulations and controls needed to attain the current 

standards. In short, NAAQS RIAs hypothesize, but do not predict, the emissions reduction 

strategies that States may choose to enact when implementing a revised NAAQS, and as such, by 

contrast, the emissions reductions from implementation rules are generally for specific, wellcharacterized sources, such as the recent MATS rule (U.S. EPA, 2011c). In general, the EPA is 

more confident in the magnitude and location of the emissions reductions for implementation 

rules. As such, emissions reductions achieved under promulgated implementation rules, such as 

MATS, have been reflected in the baseline of this NAAQS analysis.40 For this reason, the 

benefits estimated provided in this RIA and all other NAAQS RIAs should not be added to the 

benefits estimated for implementation rules. Subsequent implementation rules will be reflected 

in the baseline for the next ozone NAAQS review. 

In setting the NAAQS, the EPA considers that ozone concentrations vary over space and 

time. While the standard is designed to limit concentrations at the highest monitor in an area, it is 

understood that emissions controls put in place to reduce concentrations at the highest monitor 

will simultaneously result in lower ozone concentrations throughout the entire area. In fact, the 

 

40 The full set of rules reflected in the baseline are presented in Chapter 2, Section 2.1.3. 
6-95 

ozone HREA (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 requisite to protect public health with an 

adequate margin of safety, taking into consideration effects on susceptible populations based on 

the scientific literature. The risk analysis prepared in support of this ozone NAAQS reported 

risks below these levels, while acknowledging that the confidence in those effect estimates is 

higher at levels closer to the standard (U.S. EPA, 2014b). While benefits occurring below the 

standard may be somewhat more uncertain than those occurring above the standard, the EPA 

considers these to be legitimate components of the total benefits estimate. Though there are 

greater uncertainties at lower ozone and PM2.5 concentrations, there is no evidence of a threshold 

in short-term ozone or PM2.5-related health effects in the epidemiology literature. Given that the 

epidemiological literature in most cases has not provided estimates based on threshold models, 

there would be additional uncertainties imposed by assuming thresholds or other non-linear C-R 

functions for the purposes of benefits analysis. 

The estimated benefits shown here are in addition to the substantial benefits estimated for 

several recent air quality rules (U.S. EPA, 2009a, 2011c, 2014a). Emissions reductions from 

rules such as Tier 3 will have substantially reduced ambient ozone concentrations by 2025 in the 

East, such that few additional controls would be needed to reach 70 ppb. These rules that have 

already been promulgated have tremendous combined benefits that explain why the number of 

avoided premature mortalitydeaths associated with this NAAQS revision are smaller than were 

estimated in the previous ozone NAAQS RIA (U.S. EPA, 2006) for the year 2020 and even 

smaller than the mortality risks estimated for the current year in the ozone HREA (U.S. EPA, 

2014b). 
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