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CHAPTER 5: HUMAN HEALTH BENEFITS ANALYSIS APPROACH AND RESULTS

5.1 Synopsis

This chapter presents the estimated human health benefits for the proposed range of 

National Ambient Air Quality Standards (NAAQS) for ozone. In this chapter, we quantify the 

health-related benefits of the ozone air quality improvements resulting from the illustrative 

emission control scenarios that reduce emissions of the ozone precursor pollutants nitrogen 

oxides (NOx) and volatile organic compounds (VOCs) to reach the set of alternative ozone 

NAAQS levels being considered. This chapter also estimates the health co-benefits of the fine 

particulate matter (PM2.5)-related air quality improvements that would occur as a result of 

reducing NOx emissions.1 The EPA Administrator is proposing to revise the level of the primary 

ozone standard to within a range of 65 to 70 ppb and is soliciting comment on alternative 

standard levels below 65 ppb, and as low as 60 ppb. In the Regulatory Impact Analysis (RIA) 

we analyze the following alternative standard levels: 70, 65 and 60 ppb.

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

most areas of the U.S. to meet a revised ozone standard by 2025. Benefits are estimated 

incremental to attainment of the existing standard of 75 ppb. In estimating the incremental costs 

and benefits of potential alternative standards, we recognize that there are several areas that the 

Act does not require to meet the existing ozone standard of 75 ppb by 2025. The Clean Air Act 

provides areas with more significant air quality problems with additional time to reach the 

existing standard. Several areas in California are not expected to meet the existing standard by 

2025 and may not be required to meet a revised standard until 2038. 

We estimated the benefits of California attaining a revised standard in 2038 to account 

for the fact that many locations in this state must attain a revised standard at a later date than the 

rest of the U.S. We assume that projected nonattainment areas everywhere in the U.S. excluding 

California will be designated such that they attain a revised standard by 2025, and we develop 

our projected baseline emissions, air quality, and population estimates for 2025. We also assume 

 

1 VOC reductions associated with simulated attainment of alternative ozone standards also have the potential to 

impact PM2.5 concentrations, but we are not able to model those effects at this time.
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that the projected nonattainment areas in California will be designated such that they reach 

attainment by approximately 2038. 

We were not able to project baseline emissions and air quality levels beyond 2025 for 

California for sectors other than mobile sources. We account for changes in mobile source 

precursor emissions expected to occur between 2025 and 2030. While there is uncertainty about 

the precise timing of emissions reductions and related costs for California, we assume costs

occur through 2038. We also model benefits for California accounting for population growth to 

2038 (see section 5.6.1). Because we were unable to account for the change in emissions from 

other sectors our projected baseline may under- or over-estimate the post-2025 ozone levels in 

California. In this analysis, we refer to estimates of nationwide benefits of attaining an 

alternative standard everywhere in the U.S. except California as the 2025 scenario. The post2025 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. Consequently, in presenting both incidence and dollar benefit estimates in this 

chapter, we present and discuss the 2025 scenario and post-2025 scenario in separate sections 

(see sections 5.7.1 and 5.7.2, respectively). 

Benefits estimated for the 2025 and post-2025 scenarios are relative to an analytical 

baseline in which the nation attains the current primary ozone standard (i.e., 4th highest daily 

maximum 8-hour ozone concentration of 75 ppb) and incorporates promulgated national 

regulations and illustrative emission controls to simulate attainment with 75 ppb. Table 5-1 

summarizes the total estimated monetized benefits of attaining alternative ozone standards of 70 

ppb, 65 ppb, and 60 ppb for the 2025 scenario (i.e., nationwide benefits of attaining everywhere 

in the U.S. but California). Table 5-2 presents the same types of benefit estimates for the post2025 scenario (i.e., nationwide benefits of attaining just in California). These estimates reflect 

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

exposure to ozone and fine particulate matter (PM2.5). There are additional unquantified benefits 

which are described in Section 5.2. The estimated benefits for attaining the proposed standards 
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are incremental to the substantial benefits estimated for several recent implementation rules (e.g., 

U.S. EPA, 2011e, 2014a). 

Table 5-1. Estimated Monetized Benefits of Attainment of the Alternative Ozone Standards 

for the 2025 Scenario (nationwide benefits of attaining each alternative standard 

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

Benefits Estimate 70 ppb 65 ppb 60 ppb

Economic value of avoided ozone-related morbidities and premature deaths using ozone mortality modeled using 

risk coefficients from Smith et al., (2009) and Zanobetti and Schwartz (2008) plus PM2.5-related premature deaths 

using PM2.5 mortality modeled using risk coefficients from Krewski et al. (2009)

3% discount rate $6.9 to $8.2 + B $20 to $24 + B $37 to $44 + B

7% discount rate $6.4 to $7.7 + B $19 to $23 + B $34 to $42 + B

Economic value of avoided ozone-related morbidities and premature deaths using ozone mortality modeled using 

risk coefficients from Smith et al., (2009) and Zanobetti and Schwartz (2008) plus PM2.5-related premature deaths 

using PM2.5 mortality modeled using risk coefficients from Lepeule et al. (2012)

3% discount rate $13 to $14 + B $37 to $41 + B $68 to $75 + B

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

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.

Table 5-2. Estimated Monetized Benefits of Attainment of the Alternative Ozone Standards 

for the Post-2025 Scenario (nationwide benefits of attaining each alternative 

standard just in California) – Full Attainment (billions of 2011$) a

Benefits Estimate 70 ppb 65 ppb 60 ppb

Economic value of avoided ozone-related morbidities and premature deaths using ozone mortality modeled using 

risk coefficients from Smith et al., (2009) and Zanobetti and Schwartz (2008) plus PM2.5-related premature deaths 

using PM2.5 mortality modeled using risk coefficients from Krewski et al. (2009)

3% discount rate $1.1 to $1.5 + B $2.3 to $3.2 + B $3.4 to $4.7 + B

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

Economic value of avoided ozone-related morbidities and premature deaths using ozone mortality modeled using 

risk coefficients from Smith et al., (2009) and Zanobetti and Schwartz (2008) plus PM2.5-related premature deaths 

using PM2.5 mortality modeled using risk coefficients from Lepeule et al. (2012)

3% discount rate $1.6 to $2 + B $3.3 to $4.2 + B $4.8 to $6.2 + B

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

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.

In addition to ozone and PM2.5 benefits, implementing emissions controls to reach some 

of the alternative ozone standards would reduce other ambient pollutants, such as 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 not able to quantify the cobenefits of reduced exposure to these pollutants. In addition, due to data and methodology 
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limitations, we were unable to estimate some anticipated health benefits associated with 

exposure to ozone and PM2.5.

5.2 Overview

This chapter presents estimated health benefits for three alternative ozone standards (70, 

65 and 60ppb) that the EPA could quantify, given the available resources, data and methods. 

Separate set of benefits are presented for the 2025 scenario, representing nationwide benefits of 

attaining an alternative standard everywhere in the U.S. but California in 2025 and the post-2025 

scenario, representing nationwide benefits of attaining an alternative standard just in California

in 2038. This chapter characterizes the benefits of implementing new ozone standards by 

answering three key questions:

1. What health effects are avoided by reducing ambient ozone levels to attain a 

revised ozone standard?

2. What is the economic value of these effects?

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

reductions in emissions of ozone precursors (specifically NOx)?

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

PM2.5. 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 death and a variety of illnesses associated with acute (dayslong) 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 death and a variety of 

illnesses associated with acute and chronic exposures. Air pollution can affect human health in a 

variety of ways, and in Table 5-3 we summarize the “categories” of effects and describe those 

that we could quantify in our “core” benefits estimates and those we were unable to quantify 

because we lacked the data, time or techniques. 

This list of unquantified benefit categories is not exhaustive and we are not always able 

to quantify each effect completely. Endpoints that the ozone and PM ISAs classified as causal or 
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likely causal we quantified with confidence. We excluded from quantification effects not 

identified as having at least a causal, likely causal, or suggestive relationship with the affected 

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

other human health and environmental effects. Following this criterion, we 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. In designing this benefits analysis, including the identification of 

endpoints to include in the core estimate, we also considered the design of the Health Risk and 

Exposure Assessment (HREA) completed as part of this ozone NAAQS review (USEPA, 

2014b). The design and implementation of the HREA was subjected to rigorous peer review by 

the Science Advisory Board’s (SAB’s) Clean Air Science Advisory Committee (CASAC) with 

the results of that review being presented in letter form (Frey, and Samet 2012 for the first draft 

and Frey, 2014 for the second draft of the HREA). The overall design of the HREA, including 

the health endpoints selected for modeling was supported by the CASAC.2 

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

modeled ozone air quality, 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 5.5 and 5.7.3.

Table 5-3. Human Health Effects of Pollutants Potentially Affected by Strategies to Attain 

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

More 

Information

Improved Human Health

Reduced incidence 

of premature 

mortality from 

exposure to ozone

Premature mortality based on short-term 

exposure (all ages)

 

Section 5.6 Premature respiratory mortality based on 

long-term exposure (age 30–99)

 a

Hospital admissions—respiratory causes 

(age > 65)

 

 

2 The CASAC expressed their support for the overall design of the HREA, including endpoints selected and 

epidemiological studies used in supplying the effect estimates used to model those endpoints (Samet and Frey, 2012, 

p. 15 and Frey, 2014, p. 9).
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Benefits Category Specific Effect

Effect Has 

Been 

Quantified

Effect Has 

Been 

Monetized

More 

Information

Reduced incidence 

of morbidity from 

exposure to ozone

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., mediation 

use, pulmonary inflammation, decrements 

in lung functioning)

— —

ozone ISA d Cardiovascular (e.g., hospital admissions, 

emergency department visits)

— —

Reproductive and developmental effects 

(e.g., reduced birthweight, restricted fetal 

growth)

— —

Reduced incidence 

of premature 

mortality from 

exposure to PM2.5

Adult premature mortality based on 

cohort study estimates and expert 

elicitation estimates (age >25 or age >30)

 

See section 

5.6 and 

Appendix 5D

Infant mortality (age <1)  

Reduced incidence 

of morbidity from 

exposure to 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)

— —

Other cardiovascular effects (e.g., other 

ages)

— —

PM ISA Other respiratory effects (e.g., pulmonary c

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

— —

Asthma hospital admissions (all ages) — — NO2 ISA e
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Benefits Category Specific Effect

Effect Has 

Been 

Quantified

Effect Has 

Been 

Monetized

More 

Information

Reduced incidence 

of morbidity from 

exposure to NO2

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 Other respiratory effects (e.g., airway c,d

hyperresponsiveness and inflammation, 

lung function, other ages and populations)

— —

a Due to concerns over translating incidence estimates into dollar benefits, for long-term ozone exposure-related 

respiratory mortality, we included estimates of reduced incidence as part of the core analysis, but included 

associated dollar benefits as a sensitivity analysis (see section 5.3).

b We are in the process of considering an update to the worker productivity analysis for ozone based on more recent 

literature (see section 5.6.3.4).

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.

As described in Chapter 1 of this RIA, there are important differences worth noting in the 

design and analytical objectives of NAAQS RIAs compared to RIAs for rules that implement 

technology standards, such as Tier 3 (U.S. EPA, 2014a). The NAAQS RIAs illustrate the 

potential costs and benefits of attaining a revised air quality standard nationwide. These analyses 

simulate an array of strategies to reduce emissions at different sources and may model wellestablished emission control technologies for sectors and emission controls for which the control 

technology has not yet been developed (i.e., “unknown” controls). This type of RIA accounts for 

existing regulations and controls needed to attain the current standards and so estimated benefits 

and costs are incremental to attaining the current standard. In short, NAAQS RIAs hypothesize, 

but do not predict, the emission reduction strategies that States may enact when implementing a 

revised NAAQS. Setting a NAAQS does not result directly in costs or benefits. By contrast, the 

emission reductions from implementation rules are generally for specific, well-characterized 

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

generating units (U.S. EPA, 2011e). In general, the EPA is more confident in the magnitude and 

location of the emission reductions for implementation rules. As such, emission reductions 

achieved under promulgated implementation rules such as MATS have been reflected in the 

baseline of this NAAQS analysis (the full set of rules reflected in baseline are presented in 

section 3.1.3). Subsequent implementation rules will be reflected in the baseline for the next 
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ozone NAAQS review. For this reason, the benefits estimated provided in this RIA and all other 

NAAQS RIAs should not be added to the benefits estimated for implementation rules.

5.3 Updated Methodology Presented in this RIA

The benefits analysis presented in this chapter incorporates an array of policy and 

technical changes that the Agency has adopted since the previous review of the ozone standards 

in 2008 and the proposed reconsideration in 2010. Below we note the aspects of this analysis that 

differ from the reconsideration RIA (U.S. EPA, 2010d):

1. The population demographic data in BenMAP-CE (U.S. EPA, 2014d) reflects the 2010 

Census and future projections based on economic forecasting models developed by 

Woods and Poole, Inc. (Woods and Poole, 2012). These data replace the earlier 

demographic projection data from Woods and Poole (2007). This update was introduced 

in the final PM NAAQS RIA (U.S. EPA, 2012b).

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

and emergency department visits and the asthma prevalence rates were updated to replace 

the earlier rates. This update was introduced in the final CSAPR (U.S. EPA, 2011d). 

3. We updated the median wage data in the cost-of-illness studies. This update was 

introduced in the final PM NAAQS RIA (U.S. EPA, 2012b).

4. Updates for ozone-related effects:

a. Incorporated new mortality studies. We include two new multi-city studies to 

estimate deaths attributable to short-term exposure for the core analysis (Smith et al., 

2009 and Zanobetti and Schwartz 2008). We also estimate long-term respiratory 

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

long-term respiratory deaths in the core analysis, limitations in our ability to specify a 

lag between exposure and the onset of death (i.e. the cessation lag that is required for 

valuing these deaths) prevents us estimating dollar benefits in the core analysis. Both 

the new short-term and long-term mortality studies were included in the HREA 

completed in support of this NAAQS review with the overall design of that HREA 

(including inclusion of these new studies) being subjected to rigorous review by 

CASAC (Frey, and Samet 2012; Frey, 2014).

b. Incorporated new morbidity studies. The ozone ISA (U.S. EPA, 2013a) identifies 

several new epidemiological studies examining the association between short-term 

ozone exposure and respiratory hospitalizations, respiratory emergency department 

visits, and exacerbated asthma. Upon carefully evaluating this new literature, we 

added several new studies to our health impact assessment. Several of these studies 

were also included in the HREA, which as noted earlier, underwent rigorous review 

by CASAC.
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c. 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 

introduce this expanded assessment in this RIA (see sections 5.5 and 5.7.3).

5. Updates for PM2.5-related effects

a. Incorporated new mortality studies. We updated the American Cancer Society cohort 

study to Krewski et al. (2009) and updated the Harvard Six Cities cohort study to 

Lepeule et al. (2012). The effect coefficient for Krewski et al. (2009) is identical to 

the previous coefficient, and the Lepeule et al. (2012) is roughly similar to the 

previous coefficient. Both studies show narrower confidence intervals. The update for 

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

NAAQS review (U.S. EPA, 2012b) and the update for the Harvard Six Cities cohort 

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

b. Incorporated new morbidity studies. The epidemiological literature has produced 

several recent studies examining the association between short-term PM2.5 exposure 

and respiratory and cardiovascular hospitalizations, respiratory and cardiovascular 

emergency department visits, and stroke. Upon careful evaluation of new literature in 

the PM ISA and Provisional Assessment, we added several new studies and health 

endpoints to our health impact assessment. These updates were introduced in the 

proposal and final RIAs for the PM NAAQS review (U.S. EPA, 2012b).

c. Updated the survival rates for non-fatal acute myocardial infarctions. Based on recent 

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

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

for adults hospitalized with acute myocardial infarction stratified by age. These rates 

replaced the survival rates from Rosamond et al. (1999). This update was introduced 

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

d. Expanded uncertainty assessment. We clarified the comprehensive assessment of the 

various uncertain parameters and assumptions within the benefits analysis and 

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

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

(U.S. EPA, 2012b).

Although the list above identifies the major changes implemented since the 2010 ozone 

reconsideration RIA, the EPA has also updated several additional components of the benefits 

analysis since the 2008 ozone NAAQS RIA (U.S. EPA, 2008a), which were reflected in the 

reconsideration RIA. In the Portland Cement NESHAP proposal RIA (U.S. EPA, 2009a), the 

Agency no longer assumed a concentration threshold in the concentration-response function for 

PM2.5-related health effects and began estimating the benefits derived from the two major cohort 

studies of PM2.5 and mortality as the core benefits estimates, while still including a range of 
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sensitivity estimates based on the EPA’s PM2.5 mortality expert elicitation. In the NO2 NAAQS 

proposal RIA (U.S. EPA, 2009a), we revised the estimate used for the value-of-a-statistical life 

to be consistent with Agency guidance. 

5.4 Human Health Benefits Analysis Methods

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

changes in environmental quality.3 This approach estimates changes in individual health 

endpoints (specific effects that can be associated with changes in air quality) and assigns values 

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

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

The “damage-function” approach is the standard method for assessing costs and benefits of 

environmental quality programs and has been used in several recent published analyses (Levy et 

al., 2009; Fann et al., 2012a; Tagaris et al., 2009).

To assess economic values in a damage-function framework, the changes in 

environmental quality must be translated into effects on people or on the things that people 

value. In some cases, the changes in environmental quality can be directly valued, as is the case 

for changes in visibility. In other cases, such as for changes in ozone and PM, an impact analysis 

must first be conducted to convert air quality changes into effects that can be assigned dollar 

values. For the purposes of this RIA, the health impacts analysis (HIA) is limited to those health 

effects that are directly linked to ambient levels of air pollution and specifically to those linked to 

ozone 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 under analysis. Adjustments are made 

 

3 The damage function approach is a more comprehensive method of estimating total benefits than the hedonic price 

approach applied to housing prices, which requires homebuyers to be knowledgeable of the full magnitude of 

health risks associated with their home purchase. 
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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 

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

incidence of specific health endpoints. In generating ozone benefits estimates for the two 

scenarios considered in the RIA (2025 and post-2025), we actually utilized three distinct benefits 

simulations including one completed for 2025 and two completed for 2038. The way in which 

these three benefits simulations were used to generate estimates for the two time periods reported 

in the RIA (2025 and post-2035) is described in section 5.4.3. 

In contrast to ozone, we used a benefit-per-ton (reduced form) approach in modeling PM2.5

co-benefits. With this approach, we utilize the results of previous benefits analysis simulations 

focusing on PM2.5 to derive benefits-per-ton estimates for NOx.4 We then combine these dollarper-ton estimates with projected reductions in NOx associated with meeting a given alternative 

standard to project co-benefits associated with PM2.5. We acknowledge increased uncertainty 

associated with the dollar-per-ton approach for PM2.5, relative to explicitly modeling benefits 

using gridded PM2.5 surfaces specific to the baseline and alternative scenarios being considered 

in this review (see Appendix 5A, Table 5A-1 for additional discussion). 

Sections 5.4.1 and 5.4.2 describe respectively, the underlying basis for the health and 

economic valuation estimates. Section 5.4.3 describes the procedure used to combine the three 

benefits simulations referenced above in order to generate benefits for the two time periods 

considered in the RIA (2025 and post-2025). Finally, section 5.4.4 provides an overview of the 

benefit-per-ton estimates used to estimate the PM2.5 co-benefits from NOx emission reductions in 

this RIA.

 

4

In addition to dollar-per-ton estimates for NOx, we also utilized incidence-per-ton values (also for NOx) for 

specific health endpoints in order to generate incidence reduction estimates associated with the dollar benefits. 
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5.4.1 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, 2014d). 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, 2014d). 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 implementing emissions controls, such as occupational health exposures.

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

projections of ozone air quality5and estimating the change in the spatial distribution of the 

ambient air quality; (2) determining the subsequent change in population-level exposure; (3) 

calculating health impacts by applying concentration-response relationships drawn from the 

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

A typical health impact function might look as follows:

∆y = 1 − (e

β∙∆x

)yo ∙ Pop (5.1)

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 

 

5 Projections of ambient ozone concentrations for this analysis were generated by applying emissions reductions 

described in chapters 3 and 4 to gridded surfaces of recent-year ozone concentrations. The full methodology 

which incorporates information both from ambient measurements and from photochemical modeling simulations 

is described in section 3.4.1 of this RIA.
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background, mortality rate for the given population of interest); Pop is the population affected by 

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

from the epidemiological study. Figure 5-1 provides a simplified overview of this approach.

Figure 5-1. Illustration of BenMAP-CE Approach

5.4.2 Economic Valuation of Health Impacts

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

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

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

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

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

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

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

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

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

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 1.0.8 (U.S. EPA, 2014d) to estimate the health impacts 

and monetized health benefits for the proposed standard range. Figure 5-2 shows the data inputs 

and outputs for the BenMAP-CE program.6

 

6 The environmental Benefits Mapping and Analysis Program—Community Edition (BenMAP-CE) is an opensource PC-based tool that quantifies the number and economic value of air pollution-related deaths and illnesses. 

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

algorithms and input data to calculate incidence counts and dollar values—for a 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; (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 Ozone Risk and Exposure Assessment completed in support of the current review.
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Figure 5-2. Data Inputs and Outputs for the BenMAP-CE Program

5.4.3 Estimating Benefits for the 2025 and Post-2025 Scenarios

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

The need for these two distinct time periods reflects the fact that, while most of the U.S. will 

have attained both the current and any alternative standard by 2025, there are portions of the 

country with more significant air quality problems (including several areas in California) that 

may not be required to meet both the current and any alternative standard until 2038. 

Consequently, for each alternative standard we model a 2025 scenario reflecting the nationwide 

benefits of attaining that standard everywhere in the U.S. except California. We then model a 

post-2025 scenario, which represents nationwide benefits from attaining that same standard in 

California. Due to the temporal disconnect between these two scenarios, we do not attempt to 

sum these two estimates, but instead present each estimate in separate sections of this document 

(sections 5.7.1 and 5.7.2, respectively).

Our approach for estimating the benefits of attaining alternate ozone standards post-2025 

is illustrated in Figure 5-3; in this figure, Simulation A represents our approach for estimating the 

Modeled Baseline 

and Post-Control 

Ambient ozone

ozone Health 

Functions

Economic 

Valuation 

Functions

2025 Population 

Projections

ozone Incremental 

Air Quality Change

ozone-Related 

Health Impacts

Woods & Poole 

Population 

Projections

Background 

Incidence and 

Prevalence Rates

Monetized ozonerelated 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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5-16

benefits of attaining alternate ozone standards in every state except California in 2025. We first 

estimated the benefits occurring in 2038 from all areas (including California) attaining each 

alternative standard (Simulation C). Next, we simulated the nationwide benefits of attaining each 

alternate ozone standard in 2038 for every state except California (Simulation B). Subtracting 

Simulation B from Simulation C calculates the benefits of attaining each alternate ozone 

standard after 2025— that is, the nationwide benefits from California alone attaining the standard 

in 2038. There are important caveats associated with this approach mentioned in Section 5.1 and 

discussed further in section 5.7.3. 

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

Scenarios

5.4.4 Benefit-per-ton estimates for PM2.5

We used a “benefit-per-ton” approach to estimate the PM2.5 co-benefits in this RIA. EPA 

has applied this approach in several previous RIAs (e.g., U.S. EPA, 2014a). These benefit-perton estimates provide the total monetized human health co-benefits (the sum of premature 

75 ppb baseline 

reductions in Texas

(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 

California and Texas (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 

reductions in Texas

(CA does not attain 75)

B

BenMAP Benefits Simulations

Benefits year = 2025

Cost year = 2025

Benefits year = 2038

Cost year = 2038

Benefits year = 2038

Cost year = 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
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mortality), of reducing one ton of NOx (as a PM2.5 precursor) from a specified source.7In 

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

economic valuation), which are described further below, 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 acknowledge that this approach has greater uncertainty relative to explicitly modeling 

benefits for PM2.5 based on application of gridded surfaces specifically generated for the baseline 

and alternative standard levels being considered in this ozone NAAQS review (see Appendix 5A, 

Table 5A-1). However, resource and time constraints prevented us from completing detailed 

PM2.5 modeling as part of this review.

We used a method to calculate the regional benefit-per-ton estimates that is a slightly 

modified version of the national benefit-per-ton estimates described in the TSD: Estimating the 

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

estimates used in this NAAQS review were derived using the approach published in Fann et al. 

(2012c), but they have since been updated to reflect the epidemiology studies and Census 

population data first applied in the final PM NAAQS RIA (U.S. EPA, 2012b). The approach in 

Fann et al. (2012c) is similar to the work previously published by Fann et al. (2009), but the 

newer study includes improvements that provide more refined estimates of 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 5.6 below, we describe all of the data inputs for the benefits analysis portion of 

the benefit-per-ton calculation, including the demographic data, baseline incidence, and 

valuation functions. The specification of effect estimates (including selection of epidemiology 

 

7

In generating these estimates, we first use incidence-per-ton values to generate estimates of reductions in morbidity 

and mortality incidence for core endpoints (see Table 5-3). Then in estimating dollar values associated with these 

reductions in incidence, we use dollar-per-ton values for mortality only, noting that this is likely to provide 

coverage for upwards of 97% of the total dollar benefits (i.e., morbidity endpoints provide less than 3% of total 

benefits – see RIA from last PM review, USEPA, 2012, Table 5-19).
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studies) used in the derivation of the benefit-per-ton values for PM2.5 is described in detail in 

Appendix 5D. Additional information on the source apportionment modeling for each of the 

sectors can be found in Fann et al. (2012c) and the TSD (U.S. EPA, 2013b). Specifically for this 

analysis, we applied the benefit-per-ton estimates for 2025 and 2030 in generating PM2.5 cobenefit estimates for the 2025 and post-2025 time periods, respectively.8

5.5 Characterizing Uncertainty

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

there are likely to be many sources of uncertainty. This analysis is no exception. As outlined both 

in this and preceding chapters, this analysis includes many data sources as inputs, including 

emission inventories, air quality data from models (with their associated parameters and inputs), 

population data, population estimates, health effect estimates from epidemiology studies, 

economic data for monetizing benefits, and assumptions regarding the future state of the world 

(i.e., regulations, technology, and human behavior). Each of these inputs may be uncertain and 

would affect the benefits estimate. When the uncertainties from each stage of the analysis are 

compounded, even small uncertainties can have large effects on the total quantified benefits.

After reviewing the EPA’s approach, the National Research Council (NRC) (2002, 2008), 

which is part of the National Academies of Science, concluded that the EPA’s general 

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 has continued work to improve the characterization of uncertainty in both 

health incidence and benefits estimates. In response to these recommendations, we have 

expanded our previous analyses to incorporate additional quantitative and qualitative 

characterizations of uncertainty. Although we have not yet been able to make as much progress 

towards a full, probabilistic uncertainty assessment as envisioned by the NAS as we had hoped, 

we have added a number of additional quantitative and qualitative analyses to highlight the 

 

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

the 2038 because the population grows and ages over time.
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impact that uncertain assumptions may have on the benefits estimates. These additional analyses 

focusing primarily on uncertainty related to the mortality endpoint (for both ozone and PM2.5) 

since mortality is the driver for dollar benefits. In addition, for some inputs into the benefits 

analysis, such as the air quality data, it is difficult to address uncertainty probabilistically due to 

the complexity of the underlying air quality models and emission inputs. Therefore, we decline 

to construct alternative assumptions simply for the purpose of probabilistic uncertainty 

characterization when there is no 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; 2011b), and uses a tiered 

approach developed by the World Health Organization (WHO) for characterizing uncertainty 

(WHO, 2008). We present this tiered assessment as well as an assessment of the potential impact 

and magnitude of each aspect of uncertainty in Appendix 5A (results of these assessments are 

summarized in section 5.7.3). Data limitations prevent us from treating each source of 

uncertainty quantitatively and from reaching a full-probabilistic simulation of our results, but we 

were able to consider the influence of uncertainty in the risk coefficients and economic valuation 

functions by incorporating several quantitative analyses described in more detail below:

1. A Monte Carlo assessment that accounts for random sampling error and between study 

variability in the epidemiological and economic valuation studies for ozone-related health 

effects. See section 5.5.1 for additional detail on the Monte Carlo assessment.

2. A series of sensitivity analyses primarily focused on the mortality endpoint (for both 

ozone and PM2.5). We focus on mortality in conducting sensitivity analyses reflecting the 

important role that this endpoint plays in driving both ozone-related and PM2.5 (cobenefit) related dollar benefits. These sensitivity analyses address factors related to (a) 

estimating incidence (e.g., multiple epidemiology studies providing alternative effect 

estimates, shape of the C-R function including potential for thresholds) and (b) estimating 

associated dollar benefits (e.g., income elasticity related to willingness to pay functions 

and uncertainty in specifying lag structures for long-term exposure-related mortality). See 

section 5.5.2 for additional detail on the set of sensitivity analyses completed for this 

RIA.

3. Supplemental analyses which allow us to consider additional factors related to the 

benefits analysis. These include an assessment of the age-related differentiation of shortterm ozone exposure-related mortality (including life year saved and how estimates of 

avoided mortality are distributed across age ranges). In addition, we also looked at the 

relationship between estimates of mortality and the underlying baseline ambient air levels 

used in their derivation. These analyses allow us to consider which range of baseline 
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levels drive the benefits estimates. Finally, we considered the fraction of benefit estimates 

(for the 2025 scenario) which are associated with application of (higher-confidence) 

known emissions controls. See section 5.5.3 for additional detail on the supplemental 

analyses completed for this RIA.

5.5.1 Monte Carlo Assessment

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

sampling error associated with the concentration response functions from epidemiological 

studies and random effects modeling to characterize both sampling error and variability across 

the 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.

In this RIA, we provide confidence intervals for ozone-related benefits, but we are unable 

to provide confidence intervals for PM2.5-related co-benefits due to the use of benefit-per-ton 

estimates.

5.5.2 Sensitivity Analysis Addressing Both Incidence and Dollar Benefit Valuation

We assign the greatest economic value to the reduction in mortality risk. Therefore, it is 

particularly important to characterize to a reasonable extent the uncertainties associated with 

reductions in premature mortality, including both incidence estimation and the translation of 

reduced mortality into equivalent dollar benefits. Each of the sensitivity analyses completed for 
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this RIA are briefly described below. The reader is referred to section 5.7.3.1 for discussion of 

the results and observations stemming from these sensitivity analyses. 

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

Alternative concentration-response functions are useful for assessing uncertainty beyond 

random statistical error, including uncertainty in the functional form of the model or 

alternative study designs. For ozone we have included two multi-city studies (Smith et 

al., 2009 and Zanobetti and Schwartz 2008) in our core estimate of the range for shortterm exposure-related mortality. For the sensitivity analysis addressing this endpoint, we 

have included additional multi-city and meta-analysis studies utilized in RIAs completed 

for previous ozone NAAQS reviews (Bell et al., 2004 and 2005, Huang, 2005, Ito et al., 

2005 and Levy et al., 2005), as well as alternative model specifications from the Smith et 

al (2009) study. The selection of studies for the core and sensitivity analyses, reflects 

consideration for recommendations made both by the NAS in relation to modeling ozone 

benefits (p. 80, NRC, 2008) and the CASAC in their review of the HREA completed in 

support of this ozone NAAQS review. We also considered information and 

recommendations provided in the latest ozone ISA. Additional detail on the selection and 

use of studies in modeling short-term exposure-related mortality for the core analysis is 

presented in section 5.6.3.1.

 Impact of potential thresholds on the modeling of long-term ozone exposure-related 

respiratory mortality: Consistent with the HREA, we estimate counts of respiratory 

deaths from long-term exposure to ozone in our core analysis. As discussed in detail in 

section 5.6.3.1, the Jerrett et al., 2009 study from which the mortality effect estimate was 

derived included an exploration of potential thresholds in the concentration-response 

function. To provide a more comprehensive picture of potential benefits associated with 

long term ozone exposures, we use the results of the threshold analysis conducted by 

Jerrett et al, 2009 to conduct a sensitivity analysis evaluating models with a range of 

potential thresholds in addition to a non-threshold model (see section 5.7.3.1 and 

Appendix 5B, section 5B.1). 

 Considering alternative C-R functions in estimating long-term PM2.5 exposurerelated mortality: In modeling co-benefits related to reductions in long-term exposure to 

PM2.5, our dollar-per-ton approach relies on estimates based on two studies (Krewski et 

al., 2009 and Lepeule et al., 2012 – see Appendix 5D, section 5D.1). To better understand 

the concentration-response relationship between PM2.5 exposure and premature mortality, 

the EPA conducted an expert elicitation in 2006 (Roman et al., 2008; IEc, 2006).9In 

general, the results of the expert elicitation support the conclusion that the benefits of 

PM2.5 control are very likely to be substantial. Using alternate relationships between 

PM2.5 and premature mortality supplied by experts, higher and lower benefits estimates 

are plausible, but most of the expert-based estimates of the mean PM2.5 effect on 

mortality fall between the two epidemiology-based estimates (Roman et al., 2008). 

Application of the expert elicitation-based effect estimates (as part of characterizing 

 

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

multiple experts, are obtained (Ayyub, 2002).
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uncertainty) is covered in section 5.7.3.1 and Appendix 5B, section 5B.2. In addition to 

these studies, we have included a discussion or other recent multi-state cohort studies 

conducted in North America, but we have not estimated benefits using the effect 

coefficients from these studies (see Appendix 5D, section 5D.1).

 Specifying the cessation lag for long-term PM2.5 exposure-related respiratory 

mortality: As discussed in section 5.1 and 5.6.4.1, uncertainty in projecting the cessation 

lag for long-term PM2.5 exposure-related respiratory mortality prevents us from 

estimating dollar benefits associated with projected reductions in mortality. In the 

absence of clear evidence pointing to a particular lag structure, we have decided to use 

two lag structures (the 20 year segmented lag used for PM2.5 and an assumption of zero 

lag – see section 5.6.4.1). The range of dollar benefits that result have been included in 

as sensitivity analyses and not in the core analysis.

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

mortality and morbidity endpoints: There is uncertainty in specifying the degree to 

which the WTP function used in valuing mortality and some morbidity endpoints tracks 

projected increase in income over time (i.e., the income elasticity for WTP). We 

completed a sensitivity analysis to evaluate the potential impact of this factor on dollar 

estimates generated for mortality (and a subset of morbidity endpoints – see section 

5.6.4.1).

Even these multiple estimates (including confidence intervals in the case of estimates 

generated for ozone) 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 the identification of reasonable 

upper and lower bounds for input distributions characterizing uncertainty in additional model 

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

incomplete picture about the overall uncertainty in the estimates. Thus, confidence intervals 

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

the larger uncertainty surrounding the entire analysis.

5.5.3 Supplemental Analyses

We have also conducted a number of supplemental analyses designed to provide 

additional perspectives on the core mortality estimates generated for this RIA. These analyses 

(the results of which are described in section 5.7.3.2) include:

 Age group-differentiated aspects of short-term ozone exposure-related mortality:

We examined several risk metrics intended to characterize how mortality risk reductions 
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are distributed across different age ranges. These include (a) estimated reduction in life 

years lost, (b) distribution of mortality incidence reductions across age ranges and (c) 

estimated reductions in baseline mortality incidence rates by age group.

 Analysis of baseline ozone levels used in modeling short-term ozone exposurerelated mortality: We assess the relationship between short-term exposure-related 

mortality for ozone and the distribution of baseline (i.e., reflecting attainment of the 

current standard) 8hr max daily values used in deriving those estimates (see section 

5.7.3.2 and Appendix 5C, section 5C.2). This analysis allows us to explore how estimates 

of ozone- attributable mortality are distributed with regard to projected ambient ozone 

levels, including the fraction of overall mortality that falls within specific ozone ranges. 

We note that, while the latest ozone ISA did not provide support for a threshold in 

relation to short-term exposure-related mortality, it did note that there is reduced 

confidence in specifying the nature of the concentration-response function at lower ozone 

levels (in the range of 20ppb and below) (ozone ISA, U.S. EPA, 2013a, section 2.5.4.4). 

We use the distribution of short-term mortality across ozone levels to determine the 

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

range.10

 Analysis of baseline PM2.5 levels used in modeling short-term ozone exposurerelated mortality: We also include a similar plot of the baseline annual PM2.5 levels used 

in modeling long-term PM2.5 exposure-related mortality (see section 5.7.3.2 and 

Appendix 5C, section 5C.2). However, we are using a reduced form dollar-per-ton 

approach in modeling PM2.5 cobenefits, we do not have spatially differentiated PM2.5

values (and associated mortality estimates) with which to derive this type of distributional 

plot specifically for this RIA and consequently, we have reproduced a plot from the 

earlier analysis used to generate the benefit-per-ton values.

 Fraction of core ozone and PM2.5 (cobenefit) estimates associated with application of 

known emissions controls for the 2025 scenario: This analysis estimates the fraction of 

core incidence and associated dollar benefits that are associated with application of the 

set of known (higher confidence) emissions control measures. Note that this analysis is 

only completed for the 2025 scenario since application of controls in California 

(associated with the post-2025 scenario) exclusively involves application of unknown 

controls. 

 

10 However, care must be taken in interpreting this range of reduced confidence since benefits estimates are based on 

the average daily 8hr max across the ozone season and not on a true daily time series of 8hr metrics within each 

grid cell. The use of a seasonal mean 8hr max (rather than the more temporally differentiated daily time series) 

has been shown to generate nearly identical benefit estimates at the national-level due to underlying linearity in 

the benefits model being used. However, the use of the seasonal average 8hr metric, rather than a full daily time 

series, does decrease overall temporal variability in a plot of mortality versus ozone level which introduces 

uncertainty into the interpretation of these plots.
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5.5.4 Qualitative Assessment of Uncertainty and Other Analysis Limitations

Although we strive to incorporate as many quantitative assessments of uncertainty as 

possible, there are several aspects we are only able to address qualitatively. These aspects are 

important factors to consider when evaluating the relative benefits of the emission reduction 

strategies for the proposed and alternative standards.

The total monetized benefits presented in this chapter are based on our interpretation of 

the best available scientific literature and methods and supported by the EPA’s independent SAB 

(Health Effects Subcommittee of the Advisory Council on Clean Air Compliance Analysis) 

(SAB-HES) (U.S. EPA- SAB, 2010a) and the National Academies of Science (NAS) (NRC, 

2002, 2008). The benefits estimates are subject to a number of assumptions and uncertainties.

To more fully address all these uncertainties including those we cannot quantify, we 

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

provides a means for systematically linking the characterization of uncertainty to the 

sophistication of the underlying risk assessment. The EPA has applied similar approaches in 

previous analyses (U.S. EPA, 2010b, 2011b, U.S. EPA, 2012a – the HREA). Using this 

framework, we summarize the key uncertainties in the health benefits analysis, 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 uncertainty characterization are available 

in section 5.7.3 and Appendix 5A. 

As previously described, we strive to monetize as many of the benefits anticipated from 

the proposed and alternative standards as possible given data and resource limitations, but the 

monetized benefits estimated in this RIA inevitably only reflect a portion of the total health 

benefits. Data and methodological limitations prevented the EPA from quantifying or monetizing 

the benefits from several important health benefit categories from emission reduction strategies 

to attain the alternative ozone standards analyzed in this RIA, including potential co-benefits 

from reducing NO2 exposure (see section 5.6.3.6 for more information) and reductions in VOC 

exposures. 
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5.6 Benefits Analysis Data Inputs

In Figure 5-2 above, we summarized the key data inputs to the health impact and 

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

including demographic projections, incidence and prevalence rates, effect 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). 

5.6.1 Demographic Data

Quantified and monetized human health impacts depend on the demographic 

characteristics of the population, including age, location, and income. We use population 

projections based on economic forecasting models developed by Woods and Poole, Inc. (Woods 

and Poole, 2012). The Woods and Poole (WP) database contains county-level projections of 

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

the 2008 proposal 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-base” 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.
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 Fourth, employment and population projections are repeated for counties, using the 

economic region totals as bounds. The age, sex, and race distributions for each region or 

county are determined by aging the population by single year of age by sex and race for 

each year through 2040 based on historical rates of mortality, fertility, and migration.

5.6.2 Baseline Incidence and Prevalence Estimates

Epidemiological studies of the association between pollution levels and adverse health 

effects generally provide a direct estimate of the relationship of air quality changes to the relative 

risk of a health effect, rather than estimating the absolute number of avoided cases. For example, 

a typical result might be that a 5 ppb decrease in 8hr max daily ozone levels might be associated 

with a decrease in hospital admissions of three percent. The baseline incidence of the health 

effect is 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.

Table 5-4 summarizes the sources of baseline incidence rates and provides average 

incidence rates for the endpoints included in the analysis. For both baseline incidence and 

prevalence data, we used age-specific rates where available. We applied concentration-response 

functions to individual age groups and then summed over the relevant age range to provide an 

estimate of total population benefits. In most cases, we used a single national incidence rate, due 

to a lack of more spatially disaggregated data. Whenever possible, the national rates used are 

national averages, because these data are most applicable to a national assessment of benefits. 

For some studies, however, the only available incidence information comes from the 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 (Abt Associates, 2012). To perform this calculation, we began 

first with an average of 2004–2006 cause-specific mortality rates. Using Census Bureau 
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projected national-level annual mortality rates stratified by age range, we projected these 

mortality rates to 2050 in 5-year increments (Abt Associates, 2012; U.S. Bureau of the Census 

2002).

The baseline incidence rates for hospital admissions and emergency department visits 

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

have updated the baseline incidence rates for acute myocardial infarction. These updated rates 

(AHRQ, 2007) provide a better representation of the rates at which populations of different ages, 

and in different locations, visit the hospital and emergency department for air pollution-related 

illnesses. Also, the new baseline incidence rates are more spatially 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 assumption that most air pollution-related visits are likely to be 

unscheduled. If air pollution-related hospital admissions are scheduled, 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 5-5 lists the prevalence rates used to determine the applicable population for 

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

prevalence rates in future years. We updated these rates in the CSAPR RIA (U.S. EPA, 2011d).

Table 5-4. Baseline Incidence Rates and Population Prevalence Rates for Use in Impact 

Functions, General Population

Endpoint Parameter

Rates

Value Source

Mortality Daily or annual mortality 

rate projected to 2025 a

Age-, cause-, and 

county-specific rate

CDC WONDER (2004–2006)

U.S. Census bureau, 2000

Hospitalizations Daily hospitalization rate Age-, region-, state-, 

county- and 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
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Endpoint Parameter

Rates

Value Source

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 badjusted 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 DaysDaily 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 ICD codes (AHRQ, 2007).

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

Table 5-5. Asthma Prevalence Rates 

Population Group

Asthma Prevalence Rates

Value Source

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

< 18 0.0941

5–17 0.1070

18–44 0.0719

45–64 0.0745

65+ 0.0716

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

African American, <18 0.1553 American Lung Association a

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

5.6.3 Effect Coefficients

The modeling of incidence and benefits for each pollutant employ distinct and separate 

sets of effect estimates since these are obtained from epidemiological studies specific to a given 
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endpoint/pollutant combination. In the case of PM2.5, the dollar-per-ton approach being 

employed reflects application of effect estimates (for all endpoints) which have been used by the 

EPA in previous RIA’s (e.g., PM NAAQS, U.S. EPA, 2012b). Consequently, while we identify 

the studies and effect estimates reflected in the dollar-per-ton values used for PM2.5 (see Table 5-

8), we do not present a detailed discussion of those effect estimates in this section and instead 

present that more detailed discussion in Appendix 5D. By contrast, with ozone, we conducted

detailed benefits modeling using an updated set of effect estimates that reflects a combination of 

values used in previous RIAs together with updated values. For that reason, we provide a 

detailed discussion of effect estimates for ozone (including the rationale for their selection) in 

this section. 

The first step in selecting effect coefficients is to identify the health endpoints to be 

quantified. We base our selection of health endpoints on consistency with the EPA’s ISAs 

(which replace previous “Criteria Documents”), with input and advice from the HES, a scientific 

review panel specifically established to provide advice on the use of the scientific literature in 

developing benefits analyses for the EPA’s Report to Congress on The Benefits and Costs of the 

Clean Air Act 1990 to 2020 (U.S. EPA, 2011a). In addition, we have included more recent 

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

the PM Provisional Assessment (U.S. EPA, 2012c).11 In selecting health endpoints for ozone, we 

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

supported by CASAC (Frey, and Samet 2012; Frey, 2014). In general, we follow a weight of 

evidence approach, based on the biological plausibility of effects, availability of concentrationresponse 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 

 

11 The peer-reviewed studies in the Provisional Assessment have not yet undergone external review by the SAB. 
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observational epidemiology studies. All of these data sources provide important contributions to 

the weight of evidence surrounding a particular health impact. However, only epidemiology 

studies provide direct concentration-response relationships that can be used to evaluate 

population-level impacts of reductions in ambient pollution levels in a health impact assessment.

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

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

epidemiological studies using the selection criteria summarized in Table 5-6. These criteria 

include consideration of whether the study was peer-reviewed, the match between the pollutant 

studied and the pollutant of interest, the study design and location, and characteristics of the 

study population, among other considerations. In general, the use of concentration-response 

functions from more than a single study can provide a more representative distribution of the 

effect estimate. However, there are often differences between studies examining the same 

endpoint, making it 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)12 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 (Abt Associates, 2012). 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 

 

12 In the case of mortality, when we have multiple studies providing effect estimates for the core analysis, we 

include the range of the resulting incidence and dollar benefit estimates rather than pooling them in order to 

provide additional characterization of overall confidence associated with this key endpoint. However, for 

morbidity endpoints we do pool estimates as described here.
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variance and between-study variability, due, for example, to differences in population 

susceptibility. We used the fixed effect model as our null hypothesis and then determined 

whether the data suggest that we should reject this null hypothesis, in which case we would use 

the random effects model.13 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. For more details on methods used to pool 

incidence estimates, see the BenMAP Manual Appendices (Abt Associates, 2012).

Effect estimates selected for a given health endpoint were applied 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, in 

fact, 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 5-6. Criteria Used When Selecting C-R Functions

Consideration Comments

Peer-Reviewed 

Research

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

process.

 

13 EPA recently changed the algorithm BenMAP uses 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

Study Type Prospective vs. cohort: 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: In recommending approaches for modeling ozonerelated mortality, the NAS notes a number of advantages multi-city time series studies as 

compared to meta-analyses. Multi-city studies utilize a consistent model structure and 

can include factors that explain differences between effect estimates among the cities. By 

contrast, with meta-analyses given the aggregation of large sets of studies, the construct 

definitions can become imprecise and the results difficult to interpret. In addition, metaanalyses can suffer from publication bias which can result in high-biased effect estimates. 

Ultimately, the NAS recommends that the greatest emphasis be placed on estimates 

based on systematic new multi-city analyses without excluding consideration of meaanalyses (NRC, 2008). Reflecting these observations by the NAS, in modeling ozone 

benefits, we have included several newer multi-city studies in the core analysis and a 

combination of multi-city and meta analyses in the accompanying sensitivity analysis. 

Placing emphasis on these two multi-city studies was also supported by the CASAC in 

the context of the HREA (Frey, and Samet 2012; Frey, 2014).

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

preferred, because they have greater statistical power to detect effects. Studies that 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 will be included.

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

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

modeling constraints, we were not able to consider variation in ozone seasons in 

modeling benefits and instead, had to select a standard ozone season for the entire 

country (May 1st through September 31st). Consequently, in selecting effect estimates, we 

favored those values that reflected ozone seasons close to this fixed ozone season (i.e., 

we did not include effect estimates based on a full year of ozone monitoring data). 

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

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

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

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

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

the analysis, which is total national-level health impacts. When available, multi-city 

studies are preferred to single city studies because they provide a more generalizable 

representation of the concentration-response function.

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

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

ways, including through a large population or through repeated observations on a smaller 

population (e.g., through a symptom diary recorded for a panel of asthmatic children).

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. 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.
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Consideration Comments

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, when modeling the PM effect, epidemiological models specifying copollutants are preferred because this approach controls for the potential ozone effect 

while not diminishing the effective sample size available for specifying the PM effect. 

However, when modeling the ozone effect, the use of copollutants modeling (with PM) 

can substantially reduce sample size (by 1/3 to 1/6) since only days with both ozone and 

PM can be used. While these copollutants models may control for potential PM effects, 

they also result in a substantially less robust characterization of the ozone effect due to 

the reduced number of ozone measurements. For this reason, while we favor copollutants 

models in modeling PM benefits, for ozone we favor single pollutant models for the core 

estimate and reserve copollutants models for sensitivity analyses. 

Measure of PM For this analysis, 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. These health effects are not 

quantified in this analysis.

Non-overlapping 

Endpoints

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

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

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

The specific studies from which effect estimates for the core analysis related to ozone 

exposure are drawn are included in Table 5-7. Table 5-8 identifies studies reflected in the dollarper-ton analysis for PM2.5. 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 ozone 

NAAQS RIA (U.S. EPA, 2008a). In all cases where effect estimates are drawn directly from 

epidemiological studies, standard errors are used as a partial representation of the uncertainty in 

the size of the effect estimate. Table 5-9 summarizes those health endpoints and studies we have 

included as sensitivity analyses for ozone.
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Table 5-7. Health Endpoints and Epidemiological Studies Used to Quantify ozone-related 

Health Impacts in the Core Analysis a

Endpoint Study Study 

Population

Relative Risk or Effect Estimate (β)

(with 95th Percentile Confidence 

Interval or SE, respectively)

Premature Mortality

Premature mortality—

short-term

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

Zanobetti and Schwartz 

(2008) β = 0.00051 (0.00012)

Premature respiratory 

mortality-long-term 

(incidence only)

Jerrett et al. (2009) >29 years β = 0.003971 (0.00133) 

Hospital Admissions

Respiratory Pooled estimate:

 Katsouyanni et al. (2009) > 65 years β = 0.00061 (0.00041) natural splines

β = 0.00064 (0.00040) penalized splines

Asthma-related 

emergency department 

visits

Pooled estimate:

 Glad et al. (2012)

0-99 years

β = 0.00306 (0.00117)

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

 Mar and Koenig (2010) 

 

β = 0.01044 (0.00436) (0-17 yr olds)

β = 0.00770 (0.00284) (18-99 yr olds)

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

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

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

Other Health Endpoints

Asthma exacerbations 

Pooled estimate: b

Mortimer et al. (2002)

6–18 yearsc

β = 0.00929 (0.00387)

O’Connor et al. (2008) β = 0.00097 (0.00299)

Schildcrout et al. (2006) β = 0.00222 (0.00282)

School loss days Pooled estimate:

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

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

Acute respiratory 

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

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

b As discussed in sections 5.3 and 5.6.3.1, while we believe that available evidence supports inclusion of estimates of 

long-term exposure-related respiratory mortality incidence in the core analysis, uncertainty in specifying a lag 

structure for this endpoint (a key factor in valuation) prevents us from including dollar benefits in the core analysis 

and instead, these are included as part of the sensitivity analysis exploring this endpoint.

c The original study populations were 5 to 12yrs for the O’Conner et al., (2008) and Schildcrout et al., (2006) and 5-

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

to 6-18yrs for all three studies, reflecting the common biological basis for the effect in children in the broader age 

group. See: U.S. EPA-SAB (2004a) and NRC (2002)
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Table 5-8. Health Endpoints and Epidemiological Studies Used to Quantify PM2.5-related 

Health Impacts in the Core Analysis a

Endpoint Study

Study 

Population

Relative Risk or Effect Estimate (β)

(with 95th Percentile Confidence 

Interval or SE, respectively)

Premature Mortality

Premature mortality—

cohort study, all-cause

Krewski et al. (2009) > 29 years

> 24 years

RR = 1.06 (1.04–1.06) per 10 μg/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. (2008a)—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, 2012b).

b The original study populations were 8 to 13 for the Ostro et al. (2001) study and 7 to 12 for the Mar et al. (2004) 

study. Based on advice from the SAB-HES, we extended the applied population to 6-18, reflecting the common 

biological basis for the effect in children in the broader age group. See: U.S. EPA-SAB (2004a,b) and NRC (2002).

Table 5-9. Health Endpoints and Epidemiological Studies Used to Quantify ozone-related 

Health Impacts in the Sensitivity Analysis a

Endpoint Study

Study 

Population

Relative Risk or Effect Estimate (β)

(with 95th Percentile Confidence 

Interval or SE, respectively)

Premature Mortality

Premature respiratory 

mortality—long-term

Jerrett et al. (2009)-based models:

> 29 years

- non-threshold ozone only (86 

cities) β=0.002664 (0.000969)

- non-threshold ozone only (96 

cities) β=0.00286 (0.000942)

- threshold 40 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) (copollutant 

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) 

(cardiopulmonary) β=0.00026 (0.00009)

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

b All threshold models are ozone-only and based on the full 96 city dataset.
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5.6.3.1 Ozone Premature Mortality Effect Coefficients

Core Mortality Effect Coefficients for Short-term ozone Exposure. The overall body 

of evidence indicates that there is likely to be a causal relationship between short-term ozone 

exposure and premature death. The 2013 ozone ISA states that:

“The evaluation of new multi-city studies that examined the association between

short-term ozone exposure and mortality found evidence which supports the 

conclusions of the 2006 ozone AQCD. These new studies reported consistent 

positive associations between short-term ozone exposure and all-cause (nonaccidental) mortality, with associations persisting or increasing in magnitude during 

the warm season, and provide additional support for associations between ozone 

exposure and cardiovascular and respiratory mortality” (ozone ISA section 6.6.3, 

USEPA 2013). 

The ISA concludes by stating that, “Although some uncertainties still remain, the collective body 

of evidence is sufficient to conclude there is likely to be a causal relationship between short-term 

ozone exposure and total mortality.” (ozone ISA section 6.6.3, USEPA 2013a). Regarding 

potential confounding of the ozone mortality effect by PM, the ISA states, “Overall, across 

studies, the potential impact of PM indices on ozone-mortality risk estimates tended to be much 

smaller than the variation in ozone-mortality risk estimates across cities suggesting that ozone 

effects are independent of the relationship between PM and mortality.” (ozone ISA section 6.3.3, 

USEPA 2013a). However, the ISA does note that the interpretation of the potential confounding 

effects of PM on ozone-mortality risk estimates requires caution. This caution reflects in part, the 

every-3

rd

- and every-6th-day PM sampling schedule (in most cities) which limits the overall 

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

ISA section 6.3.3, USEPA 2013a). 

In their review of the HREA, the SAB’s CASAC Ozone Review Panel expressed support 

for epidemiological studies and corresponding concentration-response functions used in the 

HREA, which included effect estimates obtained from Smith et al., (2009) and Zanobetti and 

Schwartz (2008b) (Frey and Samet, 2012, p. 17-18 and Frey, 2014, p, 9). Furthermore, the 

CASAC specifically noted support for the use of multi-city studies, where available in modeling 
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the health endpoints included in the analysis (Frey and Samet, 2012, p. 15). In addition, they 

expressed support for modeling total risk, and de-emphasized the importance of estimating risk 

associated with ozone concentrations above the lowest measure level (LML) from contributing 

epidemiological studies (Frey and Samet, 2012, p. 10 and 18). 

In 2006, the EPA requested an NAS study to answer four key questions regarding ozonerelated mortality: (1) how did the epidemiological literature to that point improve our 

understanding of the size of the ozone-related mortality effect? (2) How best can EPA quantify 

the level of ozone-related mortality impacts from short-term exposure? (3) How might EPA 

estimate the change in life expectancy? (4) What methods should EPA use to estimate the 

monetary value of changes in ozone-related mortality risk and life expectancy?

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

the quantification and valuation of ozone-related short-term mortality. Chief among these was 

that “...short-term exposure to ambient ozone is likely to contribute to premature deaths” and the 

committee recommended that “ozone-related mortality be included in future estimates of the 

health benefits of reducing ozone exposures...” The NAS also recommended that “...the greatest 

emphasis be placed on the multi-city and NMMAPS studies without exclusion of the metaanalyses” (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 HES panel, and the CASAC 

panel, we include ozone-related premature mortality for short-term exposure in the core health 

effects analysis using effect coefficients from the Smith et al. (2009) NMMAPS analysis and the 

Zanobetti and Schwartz (2008) multi-city study. As discussed below, we also include several 

additional studies as sensitivity analyses. This approach with an emphasis on newer multi-city 

studies is consistent with recommendations provided by the NAS in their ozone mortality report 
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(NRC, 2008), “The committee recommends that the greatest emphasis be placed on estimates 

from new systematic multi-city analyses that use national databases of air pollution and 

mortality, such as in the NMMAPS, without excluding consideration of meta-analyses of 

previously published studies.” In selecting the Smith et al. (2009) and Zanobetti and Schwartz 

(2008b) studies, we point both to CASAC support for the use of these two studies in the context 

of the HREA completed for this NAAQS review (Samet and Frey, 2012, p. 15 and Frey, 2014, p. 

9) and the fact that both of these studies are multi-city studies published more recently (as 

compared with other multi-city studies or meta-analyses included in the sensitivity analyses – see 

discussion below).

The Smith et al., (2009) study is a reanalysis of the NMMAPS data set focused on 

evaluating the relationship between short-term ozone exposure and mortality. While this 

reproduces the core national-scale estimates presented in Bell et al., (2004), it also explores the 

sensitivity of the mortality effect to different model specifications including (a) regional versus 

national Bayes-based adjustment,14 (b) co-pollutants models considering PM10, (c) all - year 

versus ozone-season based estimates, and (d) consideration of a range of ozone-metrics, 

including the daily 8hr max which is of particular interest in the context of the RIA, given that is 

the metric that is used in the form of the ozone standard. In addition, the Smith et al. (2009) 

study does not use the trimmed mean approach employed in the Bell et al. (2004) study in 

preparing ozone monitor data, which is another advantage.15 In selecting effect estimates from 

Smith et al. (2009) for use in the core analysis, we focused on an ozone-only estimate for nonaccidental mortality based on the 8hr max metric for the warmer ozone season. In addition, for 

the sensitivity analysis, we included a copollutants model (ozone and PM10) from Smith et al. 

(2009) for all-cause mortality which also used the 8hr max ozone metric for the ozone season. As 

 

14 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 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.

15 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.
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noted in Table 5-6, the decision to use a single pollutant model for the core analysis and reserve 

the copollutants model for the sensitivity analysis, reflects our concern that the reduced sampling 

frequency for days with copollutants measurements (1/3 and 1/6) can impact characterization of 

the ozone effect which is the focus of this assessment. In addition, as noted earlier in this section, 

the latest ozone ISA states that ozone effects are likely to be independent of the relationship 

between PM and mortality, which further supports the approach of favoring single pollutant 

models in the core analysis.

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

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

cities using data collected between 1989 and 2000. The study presented single pollutant 

concentration-response functions based on shorter (0-3 day) and longer (0-20) day lag structures, 

with the comparison of effects based on these different lag structures being a central focus of the 

study. For the core analysis, we used the 0-3 day lag based concentration-response function since 

this had the strongest effect and tighter confidence interval. Note, that because the RIA utilizes 

the 8hr max ozone metric, we had to covert the effect estimate from Zanobetti and Smith (2008) 

which is based on an 8hr mean metric to an equivalent effect estimate based on an 8hr max. To 

do this, we used the ozone metric approach wherein the original effect estimate (and standard 

error) is multiplied by the appropriate ozone metric adjustment ratio.16

Core Mortality Effect Coefficient for Long-term ozone Exposure. We also estimated 

long-term ozone exposure-related respiratory mortality incidence in the core analysis. The 

available evidence did not allow us to characterize how long-term exposure to ozone related to 

the year of onset of mortality (i.e., a cessation lag), which is a necessary input to quantifying the 

discounted dollar benefits. For this reason, we report the dollar benefits of avoided long-term 

exposure-related respiratory deaths as a sensitivity analyses (see section 5.7.3.1). Support for 

 

16 These adjustment ratios are created by (a) obtaining summary air quality (composite monitor values) for each 

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

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

these urban-study area ratios.16 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., 8hr max and 8hr mean) as is the case with the Zanobetti and Schwartz (2008) study are likely to 

introduce less uncertainty that adjustments for more disparate ratios (e.g., 24hr or 1hr max ratios to 8hr max 

equivalents).
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modeling long-term exposure-related mortality incidence comes from the final ozone ISA as well 

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

ozone NAAQS review (Frey, 2014, p. 3 and 9).

The final ozone ISA references long-term respiratory mortality in section 7.2.1 (USEPA 

2013a) where they state, “The positive results from various designs and locations support a 

relationship between long-term exposure to ambient ozone concentrations and respiratory health 

effects and mortality.” Later in that chapter, the ISA states that: “The strongest evidence for an 

association between long-term exposure to ambient ozone concentrations and mortality is 

derived from associations reported in the Jerrett et al. (2009) study for respiratory mortality that 

remained robust after adjusting for PM2.5 concentrations.” (Section 7.7.1, USEPA, 2013a). In that 

same section, the authors also state that:

“Coherence and biological plausibility for this observation [the association between 

long-term exposure and respiratory mortality] is provided by evidence from 

epidemiologic, controlled human exposure, and animal toxicological studies for the 

effects of short- and long-term exposure to ozone on respiratory effects (see Sections 

6.2 and 7.2). Respiratory mortality is a relatively small portion of total mortality 

[about 7.6% of all deaths in 2010 were due to respiratory causes (Murphy et al., 

2012)], thus it is not surprising that the respiratory mortality signal may be difficult 

to detect in studies of cardiopulmonary or total mortality.” 

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

mortality and long-term ozone exposure (section 7.7.1), specifically with regard to respiratory 

health effects (including mortality), the ISA concludes that there is likely to be a causal 

association (section 7.2.8).

In their review of the HREA completed for the ozone NAAQS review and specifically 

modeling of the long-term exposure-related respiratory mortality endpoint, the CASAC states 

that, “The basis for estimating long-term mortality (respiratory) risks relies on a single study, 

Jerrett et al. (2009), and the HREA should acknowledge the uncertainty and confidence in 

modeling results from the use of a single study, albeit a good one.” And, while they comment on 

the size of the chronic obstructive pulmonary disease (COPD) mortality effect attributable to 
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ozone (page 7-68) they go on state that, “the CASAC concurs 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.” (Frey, C., 2014). This advice supersedes previous 

advice provided by the HES to include long-term ozone exposure-related mortality only as a 

sensitivity analysis.

The Jerrett et al. (2009) study was the first to explore the relationship between long-term 

ozone exposure and respiratory mortality (rather than focusing on cardiopulmonary mortality). 

Jerrett et al. (2009) exhibits a number of strengths including (a) the study is based on the 1.2 

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

Rico (included ozone data from 1977 [5 years before enrollment in the cohort began] to 2000); 

(b) it includes copollutants models that controlled for PM2.5; and (c) explored the potential for a 

threshold concentration associated with the long-term mortality endpoint. However there are 

attributes to this study that affect how we interpret the long-term exposure-related respiratory 

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

single study—and so provides the only quantitative basis for estimating this endpoint. By 

comparison, we estimate short-term exposure-related mortality risk using several studies. 

There is also the potential existence and location of a threshold in the C-R function 

relating mortality and long-term ozone concentrations. That uncertainty could greatly influence 

our quantitative risk estimates (we address the potential for a threshold in a sensitivity analysis 

below). The CASAC did address the use of the zero threshold versus threshold based models in 

the context of the HREA, stating that, “The EPA examined the threshold analysis contained in 

Jerrett et al. (2009) and found that the mortality model including a threshold at 56 ppb had the 

lowest log likelihood value of all models examined. However, it is not clear whether the 56 ppb 

threshold model is a better predictor of respiratory mortality than when using a linear model for 

the Jerrett et al. data. Different, but valid statistical tests produced different conclusions about the 

threshold versus linear models. The less stringent test judged the 56 ppb threshold model to be 

superior to the linear model, but the confidence interval indicates the threshold could occur 

anywhere from 0 to 60 ppb. Using the more stringent statistical test, none of the threshold 

models produce better predictions than the linear model. Given these results, the CASAC 

concurs with the EPA’s planned approach [as stated in the 2nd draft REA] to conduct a sensitivity 
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analysis evaluating potential thresholds in the C-R functions that relate long-term ozone 

exposures with respiratory mortality and to not make the threshold models the core analytical 

procedure in the PA.” (Frey, 2014, p. 13-14) Here, the CASAC clearly states their support for 

inclusion of the zero threshold (linear model) as the core approach, while treating the threshold 

models as sensitivities. 

Reflecting this advice provided by CASAC, we have generated the core benefit estimate 

for long-term exposure-related respiratory mortality uses a non-threshold co-pollutant model 

(with PM2.5) obtained from Jerrett et al. (2009) (see Tables 5-7 and 5-9). Using co-pollutant 

model is consistent with the fact that this study applied seasonal average metrics that are 

insensitive to co-pollutant monitoring for PM2.5; we explore the influence of co-pollutant models 

and thresholds in a sensitivity analysis below.17 The effect estimates used to model long-term 

ozone-attributable mortality are calculated using a seasonal average of peak (1-hr maximum) 

measurements. These long-term exposure metrics can be viewed as long-term exposures to daily 

peak ozone over the warmer months, as compared with annual average levels such as are used in 

long-term PM exposure calculations. This increases the need for care in attempting to combine 

estimates of long-term ozone-attributable mortality and short-term ozone-attributable mortality 

estimates, in order to avoid double counting. It is also important to keep in mind that our 

estimates of short-term ozone- attributable mortality are for all-causes, while estimates of longterm ozone-attributable mortality are focused on respiratory-related mortality. This further limits 

the ability to compare estimates of long-term and short-term exposure related mortality.

 

17 See Table 5-6 for additional discussion of the issue involving reduced sampling frequency and modeling of shortterm ozone exposure-related mortality.
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Sensitivity Analysis: Alternate Mortality Effect Coefficients for Short-term ozone 

Exposure. In addition to the ozone-related studies we use for the core estimates, we also 

evaluate several alternative studies to characterize uncertainty in the core estimates. These 

alternative studies include a mix of meta-analyses and multi-city studies which have been 

included in RIAs completed for previous ozone NAAQS reviews including the review completed 

in 2008 as well as the Reconsideration completed in 2010 (USEPA, 2008a and USEPA 2010d, 

respectively). The decision to include a mixture of meta-analyses and multi-city studies in the 

sensitivity analysis reflects the recommendation from the NRC reference earlier, that in 

modeling short-term exposure-related mortality for ozone emphasis be placed on more recent 

multi-city studies without excluding consideration for meta-analyses.

In selecting effect estimates from each study, we followed the criteria presented in 

Table 5-6. Consequently, we favored effect estimates reflecting the warmer ozone monitoring 

period, if available. We also favored effect estimates based on the 8hr max air metric if available. 

And finally, while we considered multi-pollutant models (providing some coverage for potential 

confounding by PM), we placed primary emphasis on single-pollutant models since these would 

typically have a significantly larger dataset with which to specify the ozone mortality effect. The 

decision to emphasize single-pollutant models and deemphasize copollutants models 

(specifically in modeling short-term endpoints) reflected observations in the current ozone ISA. 

In relation to short-term mortality, the authors note limitations of co-pollutant models due to the 

reduced sampling frequency association with PM in most cities. However, they also note that, 

“Together, these co-pollutant-adjusted findings across respiratory endpoints provide support for 

the independent effects of short-term exposures to ambient ozone.” (ozone ISA section 6.2.9, 

USEPA, 2013a). The set of multi-city and meta-analysis studies selected for inclusion in the 

sensitivity analysis (including effect estimates and standard errors) is presented in Table 5-9. 

Studies and effect estimates selected for the core analysis are also included in the table for 

completeness. Detailed discussions of each of these studies can be found in the current ozone 

ISA as well as the RIA for the ozone NAAQS completed in 2008.
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Threshold-Based Effect Coefficients for Long-term ozone Exposure. As discussed in 

the HREA completed as part of this NAAQS review (U.S. EPA, 2014b), the exploration of 

potential thresholds for long-term exposure-related respiratory mortality (as discussed in the 

Jerrett et al., 2009 study) deserves additional discussion.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 loglikelihood 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 

 

18 The approach we developed to explore the potential for thresholds related to long-term exposure-related mortality 

was presented in a memorandum to CASAC which was also released to the public (Sasser, 2014). That 

memorandum describes additional data obtained from the authors of Jerrett et al. (2009) to support modeling of 

potential thresholds and also lays out our proposed approach for exploring the impact of potential thresholds on 

estimates of long-term exposure-related mortality (including presentation of the threshold-based results as a 

sensitivity analysis and inclusion of the non-threshold model-based results as the core analysis). This plan, 

including these details related to presentation of the threshold and non-threshold based estimates were supported 

by CASAC (Frey, 2014, p. 13-14).
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mortality attributable to long-term ozone exposures using a range of threshold-based effect 

estimates as sensitivity analyses (see section 5.7.3.1 and Appendix 5B, section 5B.1). 

Specifically, we generate additional long-term risk results using unique risk models that include 

a range of thresholds from 40 ppb to 60 ppb in 5 ppb increments, while also including a model 

with a threshold equal to 56 ppb, which had the lowest log likelihood value for all models 

examined.19 In addition, to exploring the impact of potential thresholds, as part of the sensitivity 

analysis we also explore the impact of using ozone-only (non-threshold) models in estimating 

long-term exposure-related respiratory mortality.20

Ozone Exposure Metric. Both the NMMAPS analysis and the individual time series studies 

upon which the meta-analyses were based use the 24-hour average or 1-hour maximum ozone 

concentrations as exposure metrics. The 24-hour average is not the most relevant ozone exposure 

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

outdoors during the daylight hours and concentrations are highest during the daylight hours, the 

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

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

means that the most biologically relevant metric, and the one used in the ozone NAAQS since 

1997 is the maximum daily 8-hour average ozone. Thus, we have converted ozone mortality 

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

maximum 8-hour average ozone 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.21 The conversion 

 

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 

copollutants monitoring data for both ozone and PM2.5 (this best compared with the core estimate based on the 

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

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

21 However, it is important to note that 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 
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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 

national conversion ratios between daily average and 8-hour and 1-hour maxima, based on 

national data. 

5.6.3.2 Hospital Admissions and Emergency Department Visits

Because of the availability of detailed hospital admission and discharge records, there is 

an extensive body of literature examining the relationship between hospital admissions and air 

pollution. For this reason, we pool together the incidence estimates using several different 

studies for many of the hospital admission endpoints. In addition, some studies have examined 

the relationship between air pollution and emergency department 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.

With regard to short-term hospital admissions and ED visits, the current ozone ISA states 

that, “[c]ompared with studies reviewed in the 2006 ozone AQCD, a larger number of recent 

studies examined hospital admissions and ED visits for specific respiratory outcomes. Although 

limited in number, both single- and multi-city studies consistently found positive associations 

 

estimates for the same endpoint can result in different incidence estimates for the same location under certain 

conditions. This introduces uncertainty into the use of these ratio-adjusted effect estimates (see Appendix 5A).
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between short-term ozone exposures and asthma and COPD hospital admissions and ED visits, 

with more limited evidence for pneumonia. Consistent with the conclusions of the 2006 ozone 

AQCD, in studies that conducted seasonal analyses, risk estimates were elevated in the warm 

season compared to cold season or all-season analyses, specifically for asthma and COPD.” 

(ozone ISA section 6.2.9, USEPA, 2013a). In this same section, the ISA also addresses potential 

thresholds in effect: “Although the C-R relationship has not been extensively examined, 

preliminary examinations found no evidence of a threshold between short term ozone exposure 

and asthma hospital admissions and pediatric asthma ED visits...” Regarding the potential for 

confounding by other pollutants including PM, the ISA observes that, “Several epidemiologic 

studies of respiratory morbidity and mortality evaluated the potential confounding effects of 

copollutants, in particular, PM10, PM2.5, or NO2. In most cases, effect estimates remained robust 

to the inclusion of copollutants.” (ozone ISA section 6.2.9, USEPA 2013a).

Based on consideration for these observation from the ISA, and a thorough review of 

available epidemiological studies, for the core analysis, we model respiratory hospital 

admissions (for 65-99yr olds) using effect estimates obtained from Katsouyanni et al., 2009 and 

asthma-related emergency room visits (for all ages) using several single-city studies. The 

Katsouyanni et al., 2009 study is for all respiratory hospital admissions, and thus to avoid double 

counting, we do not provide separate estimates for specific subcategories of respiratory 

admissions such as asthma. The Katsouyanni et al., 2009 study provides effect estimates specific 

to the summer season, which is an advantage, however it also utilizes the 1hr max metric, which 

required adjustment using air metric ratios to generate equivalent 8hr max effect estimates for 

use in the RIA.22 The study provides summer season single pollutant effect estimates based both 

on natural and penalized splines. We used both of these effect estimates and pooled the results 

using equal-weight averaging. It is also important to note that, while the Katsouyanni et al., 2009 

study did include a set of effect estimates based on copollutants modeling (with PM10), these 

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

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

 

22 Given that the Katsouyanni et al., 2009 study included a larger number of cities (14), rather than constructing an 

air metric adjustment ratio based on this set of urban study areas, we used a national ratio to adjust effect 

estimates to represent the 8hr metric used in the RIA. 
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A number of studies are available to model respiratory ED visits. However, at this time 

we do not have a valuation function for this endpoint. Since we do have a valuation function 

available for the narrower category of asthma-related ED visits, for the core estimate, we have 

focused on asthma-related ED visits (for which we can estimate the economic value), using a set 

of single city studies together with random-effects pooling to generate a single pooled estimate. 

The set of single city studies used in this calculation include: Peel et al., (2005) and Sarnat et al., 

(2013) both for Atlanta, Wilson et al., (2005) and Mar and Koenig (2009) for Seattle, Wilson et 

al., (2005) for Portland ME, Ito et al., (2007) for New York City, and Glad et al., (2012) for 

Pittsburgh. We note that of these single city studies, only the Ito et al., (2007) study included a 

co-pollutant model (for PM2.5).23 In addition, two of the studies required adjustments of their 

betas to reflect the 8hr max air metric. Specifically, Glad et al., (2012) utilizes the 1hr max air 

metric, while Sarnat et al., 2013 utilized the 24hr average metric. Each required the use of air 

metric ratios to adjust their betas. In generating a single pooled benefit estimate for this endpoint, 

we used random/fixed effects pooling to combine estimates across these single city studies. 

5.6.3.3 Acute Health Events and School/Work Loss Days

In addition to mortality, chronic illness, and hospital admissions, a number of acute 

health effects not requiring hospitalization are associated with exposure to ozone and PM2.5. The 

sources for the effect estimates used to quantify these effects are described below.

Asthma exacerbations. For this RIA, we have followed the SAB-HES recommendations 

regarding asthma exacerbations in developing the core estimate (U.S. EPA-SAB, 2004a). 

Although certain studies of acute respiratory events characterize these impacts among only 

asthmatic populations, others consider the full population, including both asthmatics and nonasthmatics. For this reason, incidence estimates derived from studies focused only on asthmatics 

cannot be added to estimates from studies that consider the full population—to do so would 

double-count impacts. To prevent such double-counting, we estimated the exacerbation of 

 

23 While we have included copollutants models as sensitivity analyses for mortality, given the reduced role of 

morbidity endpoints in driving overall dollar benefits, we have not included separate copollutants models for any 

of the morbidity endpoints as sensitivity analyses. In the case of the Ito et al., (2007) study, we do note, that 

while the copollutants model does result in a somewhat smaller effect estimate for asthma ED visits as compared 

with the single pollutant (ozone-only) model, the SE for the copollutants model is also larger (as would be 

expected). 
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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 (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, we selected 

three multi-city studies (Mortimer et al., 2002, O’Connor et al., 2008, and Schildcrout et al., 

2006). Of these three, one of the studies (O’Connor et al., 2008) only included a multi-pollutant 

model (for PM2.5 and NO2) and consequently, that effect estimate was used in the core analysis. 

All three of these studies required the application of air metric ratios to adjust effect estimates to 

represent the 8hr metric used in the RIA.24 To combine these three estimates into a single pooled 

estimate, we used equal weights. 

Acute Respiratory Symptoms. We estimate one type of acute respiratory symptom related to 

ozone exposure - minor restricted activity days (MRAD). 

Minor restricted activity days result when individuals reduce most usual daily activities 

and replace them with less strenuous activities or rest, yet not to the point of missing work or 

school. For example, a mechanic who would usually be doing physical work most of the day will 

instead spend the day at a desk doing paper and phone work because of difficulty breathing or 

chest pain.

For ozone, we modeled MRADs using Ostro and Rothschild (1989). This study provides 

a copollutants model (with PM2.5) based on a national sample of 18-64yr olds. The original study 

 

24 Mortimer et al., (2002) had effect estimates based on an 8hr mean metric, O’Connor et al., (2008) utilized a 24hr 

metric and Schildcrout et al., (2006) was based on a 1hr max metric. Consequently, all three studies required the 

application of air metric ratios to produce effect estimates reflecting an 8hr max metric. 
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used a 24hr average metric and included control for PM2.5, which necessitated the use of an air 

metric ratio to convert the effect estimate to an 8hr max equivalent. 

School loss days (absences). Children may be absent from school due to respiratory or other 

acute diseases 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 

et al. (2000) and Gilliland et al. (2001) and then pooled the results using the random effects 

pooling procedure.

5.6.3.4 Unquantified Human Health Effects

The illustrative emission reduction strategies to reach the proposed and alternative 

standards described in Chapter 4 would reduce emissions of NOx and VOCs. Although we have 

quantified many of the health benefits associated with reducing exposure to ozone and PM2.5, as 

shown in Table 5-3, we are unable to quantify the health benefits associated with reducing the 

potential for NO2 or VOC exposures due to the absence of air quality modeling data for these 

pollutants in this analysis. In addition, we are unable to quantify the effects of VOC reductions 

on ambient PM2.5 and associated health effects. Although the method we applied simulated the 

impact of attaining the proposed and alternative standards on ambient levels of ozone, this 

method does not simulate how the illustrative emission reductions would affect ambient levels of 
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NO2 or VOC. 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 

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.

The USEPA last quantified the value of ozone-related worker productivity in the final 

Regulatory Impact Analysis supporting the Transport Rule (USEPA, 2011d). That analysis 

applied information reported in Crocker and Horst (1981) to relate changes in ground-level 

ozone to changes in the productivity of outdoor citrus workers. That study found that a 10 

percent reduction in ozone translated to a 1.4 increase in income. Concerned that this study 

might not adequately characterize the relationship between ground-level ozone and the 

productivity of agricultural workers because of the vintage of the underlying data, the Agency 

subsequently omitted this endpoint.
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In 2012, Graff Zivin and Neidell published “The impact of pollution on worker 

productivity” in the American Economic Review. That study combined data on individual-level 

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

to characterize changes in worker productivity. The authors used data on harvest rates from a 

500-acre farm in the Central Valley of California. That farm produced three crops (blueberries 

and two types of grapes) and the harvesting laborers were paid through piece rate contracts. The 

analyses in the paper were based on 2009 and 2010 California growing seasons. The analyses 

were not affected by: (i) endogenous ozone exposure (because there were limited local sources of 

ozone precursors); (ii) avoidance behavior (because the work has to be performed outdoors); and 

(iii) shirking (due to the nature of the piece rate contract).

Table 3 in Graff Zivin and Neidell (2012) reports the main result: A 10 ppb increase in 

work-day ozone concentration (represented by hourly measurements averaged between 6am and 

3pm) will result in a decline of 0.143 (with a standard error of 0.068) in standardized hourly 

pieces collected on a given work day. The standardized hourly pieces were “the average hourly 

productivity minus the minimum number of pieces per hour required to reach the piece rate 

regime, divided by the standard deviation of productivity for each crop” (Graff Zivin and 

Neidell, 2012; p. 3665). The range of ozone concentrations in the sample was between 10.50 ppb 

and 86.0 ppb (Table 1 in Graff Zivin and Neidell, 2012). This result is significant and robust 

under different model specifications designed to test modeling assumptions. Based on the effect 

estimate and individual-level information in their dataset, the authors estimated the effect of an 

increase in ozone concentration on the worker productivity, as measured by the average number 

of pieces collected per hour during a given work day (rather than by standardized hourly piece 

rate that was used in regression modeling). They found a decline of 5.5% in worker productivity 

due to a 10 ppb increase in average work-day ozone concentration.

While Graff Zivin and Neidell (2012) report the information needed to quantify ozonerelated worker productivity, we were unable to incorporate this endpoint into the RIA. We lacked 

the time to modify the BenMAP-CE tool so that it could quantify this impact, but look forward to 

estimating ozone-related worker productivity in the final ozone RIA. 
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5.6.4 Economic Valuation Estimates

Reductions in ambient concentrations of air pollution generally lower the risk of future 

adverse health effects for a large 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.

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 5-10.25 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, 

 

25 We note that a number of the endpoints included in Table 5-10 and discussed in this section were only modeled 

for PM2.5 and consequently could be moved to Appendix 5C (as was done with the discussion of effect estimates 

specific to PM2.5). However, given that many of the valuation functions discussed are shared between the two 

pollutants and given the relatively shorter length of discussions associated with these valuation functions, we 

have included coverage of all valuation functions (for ozone and PM2.5-related endpoints) in this section.
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which is discussed in further detail below.26 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.

5.6.4.1 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 

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 

Agency 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.

 

26 Income growth projections are only currently available in BenMAP through 2024, so both the 2025 and 2038 

estimates use income growth only through 2024 and are therefore likely underestimates. Currently, BenMAP 

does not have an inflation adjustment to 2011$. We ran BenMAP for a currency year of 2010$ and calculated the 

benefit-per-ton estimates in 2010$. We then adjusted the resulting benefit-per-ton estimates to 2011$ using the 

Consumer Price Index (CPI-U, all items). This approach slightly underestimates the inflation for medical index 

and wage index between 2010 and 2011, which affects COI estimates and wage-based estimates. 
56

Table 5-10. 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)
57

Table 5-10. 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)
58

Table 5-10. 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 have a uniform distribution 

between $16 and $71 (2000$).

(continued)
59

Table 5-10. 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 (Abt Associates, 

2012). Income growth projections are only currently available in BenMAP through 2024, so both the 2025 and 2038 estimates use income growth only through 

2024 and are therefore likely underestimates. 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. 
5-60

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)27 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 $6.3 million (2000$).28 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 

 

27 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. 

28 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. Income 

growth projections are only currently available in BenMAP through 2024, so both the 2025 and 2038 estimates 

use income growth only through 2024 and are therefore likely underestimates.
5-61

the risk of premature mortality is the subject of continuing discussion within the economics and 

public policy analysis community. The EPA strives to use the best economic science in its 

analyses. Given the mixed theoretical finding and empirical evidence regarding adjustments to 

VSL for risk and 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 labor market 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 5-11 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 5-11. Influence of Applied VSL Attributes on the Size of the Economic Benefits of 

Reductions in the Risk of Premature Death (U.S. EPA, 2006a)

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 have 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 assume that some 

of the incidences of premature mortality related to PM2.5 exposures occur in a distributed fashion 
5-62

over the 20 years following exposure and discounted over the period between exposure and 

premature mortality. Although the structure of the lag is uncertain, the EPA follows the advice of 

the SAB-HES to assume a segmented lag structure characterized by 30 percent of mortality 

reductions in the first year, 50 percent over years 2 to 5, and 20 percent over the years 6 to 20 

after the 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.29 Changes in the cessation lag assumptions do 

not change the total number of estimated deaths but rather the timing of those deaths. As such, 

the monetized 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 which prevents us from including this endpoint as a monetary benefit estimate within 

the core analysis.30 In presenting dollar benefit estimates as part of the sensitivity analysis, we 

include both an assumption of zero lag and a lag structure matching that used for the core PM2.5

estimate (the SAB 20 year segmented lag). Inclusion of the zero lag reflects consideration for the 

possibility that the long-term respiratory mortality estimate captures primarily, an accumulation 

of short term mortality effects across the ozone season.31 The use of the 20 year segmented lag 

 

29 The choice of a discount rate, and its associated conceptual basis, is a topic of ongoing discussion within the 

federal government. To comply with 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. 

30 Recall however, that we consider the estimate of reduced incidence of respiratory mortality associated with longterm ozone exposure to have sufficient support in the literature to be included as part of the core estimate. 

31 In presenting risk estimates associated with modeling long-term ozone-related respiratory mortality in the HREA, 

we noted that: “The effect estimates used in modeling long-term 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.” (USEPA, 2014b). This statement was included in the 2nd draft of the HREA that CASAC 

commented extensively on (p. 7-21) (Frey 2014). While the CASAC made recommendations on specific aspects 

of our approach for modeling this endpoint (specifically the need to include consideration for potential thresholds 

as a sensitivity analysis), they did not criticize this observation regarding the potential that this metric could 

actually represent an accumulation of short-term daily peak exposures and that care needed to be taken to avoid 
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reflects consideration for advice provided by the HES (USEPA-SAB, 2010a), where they state 

that, “[i]f 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 cohort mortality evidence for ozone.” Dollar benefit estimates 

generated using both lag assumptions are as presented as sensitivity analyses (see section 

5.7.3.1). 

Uncertainties Specific to Premature Mortality Valuation. The economic benefits 

associated with reductions in the risk of premature mortality are the largest category of 

monetized benefits in this RIA. In addition, in prior analyses, the EPA has identified valuation of 

mortality-related benefits as the largest contributor to the range of uncertainty in monetized 

benefits (Mansfield et al., 2009).32 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 plus WTP to improve other individuals’ survival rates. 

 

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

long-term exposure-related respiratory mortality could (to a significant extent) capture an accumulation of shortterm effects. Note, that we include the zero threshold model together with a 20 year segmented lag model in 

order to capture a potential rage of lag effect and both are given equal coverage in generating the dollar benefit 

estimate included as a sensitivity analysis for this endpoint.

32 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.
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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 

such characteristics as age, health state, and the current age to which the individual is likely to 

survive.

Although a survival curve approach provides a theoretically preferred method for valuing 

the benefits of reduced risk of premature mortality associated with reducing air pollution, the 

approach requires a great deal of data to implement. The economic valuation literature does not 

yet include good estimates of the value of this risk reduction commodity. As a result, in this 

study we value reductions in premature mortality risk using the VSL approach.

Other 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 
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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., 

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, catastrophic 

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 based on the fact that the risk-wage trade-off revealed in 

hedonic studies reflects the preferences of the marginal worker (i.e., that worker who 

demands the 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).
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5.6.4.2 Hospital Admissions and Emergency Department Valuation

In the absence of estimates of societal WTP to avoid hospital visits/admissions for 

specific illnesses, we derive COI estimates for use in the benefits analysis. The International 

Classification of Diseases (WHO, 1977) code-specific COI estimates used in this analysis consist 

of estimated hospital charges and the estimated opportunity cost of time spent in the hospital 

(based on the average length of a hospital stay for the illness). We based all estimates of hospital 

charges and length of stays on statistics provided by the Agency for Healthcare Research and 

Quality’s Healthcare Utilization Project National Inpatient Sample (NIS) database (AHRQ, 

2007). We estimated the opportunity 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” (Abt Associates, 2012). The resulting national average lost daily wage is $150 (2011$). 

The total cost-of-illness estimate for an ICD code-specific hospital stay lasting n days, then, was 

the mean hospital charge plus daily lost wage multiplied by n. In general, the mean length of stay 

has decreased since the 2000 database used in 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 5-12.

Table 5-12. Unit Values for Hospital Admissions a

End Point ICD Codes

Age Range Mean Hospital 

Charge 

(2011$)

Mean 

Length of 

Stay (days)

Total Cost of 

Illness (unit 

value in 2011$) min. max.

HA, Chronic Lung Disease 490–496 18 64 $20,000 3.9 $22,000

HA, Asthma 493 0 64 $15,000 3.0 $16,000

HA, All Cardiovascular 390–429 18 64 $41,000 4.1 $44,000

HA, All Cardiovascular 390–429 65 99 $38,000 4.9 $42,000

HA, All Respiratory 460–519 65 99 $32,000 6.1 $37,000

a All estimates rounded to two significant digits. Unrounded estimates in 2000$ are available in Appendix J of the 

BenMAP user manual (Abt Associates, 2012).

To value asthma emergency department visits, we used a simple average of two estimates 

from the health economics literature. The first estimate comes from Smith et al. (1997), who 

reported approximately 1.2 million asthma-related emergency department visits in 1987, at a 

total cost of $186 million (1987$). The average cost per visit that year was $155; in 2011$, that 

cost was $480 (using the CPI-U for medical care to adjust to 2011$). The second estimate comes 
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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 $440 (2011$).

5.6.4.3 Nonfatal Myocardial Infarctions Valuation

We were not able to identify a suitable WTP value for reductions in the risk of nonfatal 

heart attacks.33 Instead, we use a COI unit value with two components: the direct medical costs 

and the opportunity cost (lost earnings) associated with the illness event. Because the costs 

associated with a myocardial infarction extend beyond the initial event itself, we consider costs 

incurred over several years. Using age-specific annual lost earnings estimated by Cropper and 

Krupnick (1990) and a 3% 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 5-13):

 Wittels et al. (1990) estimated expected total medical costs of myocardial infarction over 

5 years to be $51,000 (rounded in 1986$) for people who were admitted to the hospital 

and survived hospitalization. (There does not appear to be any discounting used.) This 

estimated cost is based on a medical cost model, which incorporated therapeutic options, 

projected outcomes, and prices (using “knowledgeable cardiologists” as consultants). The 

model used medical 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 

 

33 We note that this endpoint was only modeled as part of the cobenefits analysis for PM2.5 and is not included in the 

ozone-related benefits analysis. As such, we could have moved this discussion to Appendix 5D (as was done 

with the discussion of PM2.5-related effect estimates). However, since this was the only broader discussion

related to valuation which is exclusively related to PM2.5 we left it in this section.
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Research and Quality (AHRQ) HCUP database is 5.5 days (AHRQ, 2000). However, this 

may include patients who died in the hospital (not included among our nonfatal 

myocardial infarction cases), and whose length of stay was therefore substantially shorter 

than it would be if they had not died.

 Eisenstein et al. (2001) estimated 10-year costs of $45,000 in rounded 1997$ (using a 3% 

discount rate) for myocardial infarction patients, using statistical prediction (regression) 

models to estimate inpatient costs. Only inpatient costs (physician fees and hospital costs) 

were included.

Table 5-13. Alternative Direct Medical Cost of Illness Estimates for Nonfatal Heart 

Attacks a

Study 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 (Abt Associates, 2012).

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 5-14.

Table 5-14. 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 $10,000

a All estimates rounded to two significant digits, so estimates may not sum across columns. Unrounded estimates in 

2000$ are available in appendix J of the BenMAP user manual (Abt Associates, 2012).

b An average of the 5-year costs estimated by Wittels et al. (1990) and Russell et al. (1998).

c From Cropper and Krupnick (1990), using a 3% discount rate for illustration.
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5.6.4.4 Valuation of Acute Health Events

Asthma exacerbation. Several respiratory symptoms in asthmatics or characterizations of an 

asthma episode have been associated with exposure to air pollutants. All of these can generally 

be taken as indications of an asthma exacerbation when they occur in an asthmatic. Therefore, 

we apply the same set of unit values for all of the variations of “asthma exacerbation”. 

Specifically, we use a unit value based on the mean WTP estimates for a “bad asthma day,” 

described in Rowe and Chestnut (1986). This study surveyed asthmatics to estimate WTP for 

avoidance of a “bad asthma day,” as defined by the subjects.

Minor Restricted Activity Days Valuation. No studies are reported to have estimated WTP to 

avoid a minor restricted activity day. However, Neumann et al. (1994) derived an estimate of 

willingness to pay to avoid a minor respiratory restricted activity 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 
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about the wages of the parent staying home with the child. Finally, this method assumes that 

parents are unable to work 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 

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. Lower and upper respiratory symptoms are each 

considered a complex of symptoms. A dollar value was derived for clusters of these symptoms 

that most closely match the studies used to calculate incidence (Schwartz and Neas, 2000; Pope 

et al, 1991) based on mid-range estimates from each cluster (IEc, 1994). 

5.6.4.5 Growth in WTP Reflecting National Income Growth over Time

Our analysis accounts for expected growth in real income over time. This is a distinct 

concept from inflation and currency year. Economic theory argues that WTP for most goods 

(such as environmental protection) will increase if real incomes increase. There is substantial 
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empirical evidence that the income elasticity34 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 

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). Until these conflicting advisories have been 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 

 

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

income.
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Kleckner and Neumann (1999). We note that the literature has evolved since the publication of 

this memo and that an array of newer studies identifying potentially suitable income elasticity 

estimates are available (IEc, 2012). The EPA anticipates seeking an SAB review of these studies, 

and its approach to adjusting WTP estimates to account for changes in personal income, in the 

near future. As such, these newer studies have not yet been incorporated into the benefits 

analysis. An abbreviated description of the procedure we used to account for WTP for real 

income growth between 1990 and 2024 is presented below. Income growth projections are only 

currently available in BenMAP through 2024, so both the 2025 and 2038 estimates use income 

growth only through 2024 and are therefore likely underestimates. 

Reported income elasticities suggest that the severity of a health effect is a primary 

determinant of the 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 5-15.35

 

35 We expect that the WTP for improved visibility in Class 1 areas would also increase with growth in real income 

(see Chapter 6).
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Table 5-15. Elasticity Values Used to Account for Projected Real Income Growth a

Benefit Category Central Elasticity Estimate

Minor Health Effect 0.14

Severe and Chronic Health Effects 0.45

Premature Mortality 0.40

a Derivation of estimates can be found in Kleckner and Neumann (1999). COI estimates are not adjusted for income 

growth.

In addition to elasticity estimates, projections of real gross domestic product (GDP) and 

populations from 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 

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 2010.36 We used 

projections of real GDP (in chained 1996 dollars) provided by Standard and Poor’s (2000) for 

the years 2010 to 2024.37

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 5-16. Benefits for each of the categories (minor health effects, severe 

and chronic health effects, premature mortality, and visibility) are adjusted by multiplying the 

unadjusted benefits by the appropriate adjustment factor. For premature mortality, we applied the 

income adjustment factor specific to the analysis year, but we do not adjust for income growth 

over the 20-year cessation lag. Our approach could underestimate the benefits for the later years 

of the lag. 

 

36 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.

37 In previous analyses, we used the Standard and Poor’s projections of GDP directly. This led to an apparent 

discontinuity in the adjustment factors between 2010 and 2011. We refined the method by applying the relative 

growth rates for GDP derived from the Standard and Poor’s projections to the 2010 projected GDP based on the 

Bureau of Economic Analysis projections.
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There is some uncertainty 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 

underpredict benefits in future years because it is likely that increases in real U.S. income would 

also result in increased COI (due, for example, to increases in wages paid to medical workers) 

and increased cost of work loss days and lost worker productivity (reflecting that if worker 

incomes are higher, the losses resulting 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 5-16. Adjustment Factors Used to Account for Projected Real Income Growth a

Benefit Category 2024

Minor Health Effect 1.07

Severe and Chronic Health Effects 1.22

Premature Mortality 1.20

a Based on elasticity values reported in Table 5-3, U.S. Census population projections, and projections of real GDP 

per capita.

5.7 Benefits Results

As stated in section 5.1 and described in detail in section 5.4.3, we have estimated 

nationwide benefits for 2025 associated with attainment of alternative ozone standards across the 

U.S. with the exception of California. We have also estimated the nationwide benefits of 

attaining in California for 2038. Because of the temporal disconnect between these two 

scenarios, benefit estimates for each are not totaled and instead, are presented separately (section 

5.7.1 for 2025 and section 5.7.2 for post-2025).

In addition to these core incidence and benefits estimates, we also present a number of 

additional analyses which are intended to inform interpretation of these core benefit estimates 

(see section 5.7.3). In completing these additional analyses, in many cases, we did not have to 
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generate separate assessments for both scenarios since observations from one scenario could be 

readily applied to the other (in these cases, we tended to model the 2025 scenario and then 

discuss application of those observations to the post-2025 scenario).

5.7.1 Benefits of the Proposed and Alternative Annual Primary ozone standards for the 2025 

scenario

This section presents incidence reductions and associated dollar benefit estimates 

associated with the 2025 scenario (i.e., every state apart from California – see section 5.4.3). 

Applying the impact and valuation functions described previously in this chapter to the estimated 

changes in ozone yields estimates of the changes in physical damages (e.g., premature 

mortalities, cases of hospital admissions) and the associated monetary values for those changes. 

Similarly applying the incidence per ton and dollar per ton values to the estimates of NOx 

reductions produces estimates of changes in PM-related health effect incidence and associated 

dollar benefits. Not all known ozone and PM health effects could be quantified or monetized. 

The monetized value of these unquantified effects is represented by adding an unknown “B” to 

the aggregate total. The estimate of total monetized health benefits is thus equal to the subset of 

monetized ozone and PM-related health benefits plus B, the sum of the non-monetized health 

benefits and welfare co-benefits; this B represents both uncertainty and a bias in this analysis, as 

it reflects those benefits categories that we are unable to monetize in this analysis.

We follow our standard rounding conventions in presenting these benefits results. After 

reviewing the presentation of EPA’s benefits results, NRC (2002) concluded, “EPA should strive 

to present the results of the analyses in ways that avoid conveying an unwarranted degree of 

certainty. Such ways include rounding to few significant digits, increasing the use of graphs, and 

placing less emphasis on single numbers and greater emphasis on ranges” (p. 161). Following 

this advice, we round all benefits estimates to two significant digits, and all rounding occurs after 

final summing of unrounded estimates. As such, totals may not sum across columns or rows. In 

addition, all incidence estimates are rounded to whole numbers with a maximum of two 

significant digits. 

Table 5-17 shows the population-weighted air quality change for the alternative standards 

averaged across the continental U.S. Table 5-18 summarizes the tons of VOC and NOx 
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emissions required to simulate attainment of each alternative standard (further differentiated by 

geographic region including east, west and California). Tables 5-19 through 5-23 present the 

benefits results for the ozone standards. Table 5-24 summarizes total benefits by geographic 

region (including east, west minus California, and California). Note that in presenting estimates 

related to reductions in ozone (Tables 5-19 and 5-20), we include the full set of core estimates 

together with a subset of sensitivity analysis results (specifically, alternative estimates for both 

short-term and long-ter mortality). The benefit estimates presented are relative to a 2025 

analytical baseline reflecting attainment nationwide (excluding California) of the current primary 

ozone standards (i.e., 75 ppb) that includes promulgated national regulations and illustrative 

emission controls to simulate attainment with 75 ppb. 

Table 5-17. Population-Weighted Air Quality Change for the Proposed and Alternative 

Annual Primary ozone Standards Relative to Analytical Baseline for 2025 a

Standard

Population-Weighted Summer Season 

ozone Concentration Change (8hr max)b

70 ppb 0.5285

65 ppb 1.6317

60 ppb 3.0222

a Because we used benefit-per-ton estimates for the 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 5-18. Emission Reductions in Illustrative Emission Reduction Strategies for the 

Proposed and Alternative Annual Primary ozone standards, by Pollutant and 

Region Relative to Analytical Baseline – Full Attainment (tons)a

70 ppb 65 ppb 60 ppb

NOx

East 600 1,700 2,800

West 0 110 500

CA 53 110 140

VOC

East 55 99 150

West 0 7 7

CA 0 0 0

a See Chapter 4 for more information on the illustrative emission reduction strategies. The emissions in this table 

reflect both known and unknown controls. Several recent rules such as Tier 3 will have substantially reduced ozone 

concentrations by 2025 in the East, thus few additional controls would be needed to reach 70 ppb. 
5-77

Table 5-19. Estimated Number of Avoided ozone Health Impacts for the Proposed and 

Alternative Annual Ozone Standards (Incremental to the Analytical Baseline) 

for the 2025 scenario (nationwide benefits of attaining each alternative standard 

everywhere in the U.S. except California) a, b

a All incidence estimates are rounded to whole numbers with a maximum of two significant digits. 

b All incidence estimates are based on ozone-only models unless otherwise noted.

c See Appendix 5B, section 5B.1 for additional detail on the threshold-based sensitivity analysis.

70 ppb 65 ppb 60 ppb

Avoided Short-Term Mortality - Core Analysis

200 630 1,100

(97 to 300) (310 to 940) (560 to 1,700)

340 1,000 1,900

(180 to 490) (560 to 1,500) (1,000 to 2,800)

Avoided Long-term Respiratory Mortality - Core Analysis

680 2,100 3,900

(230 to 1,100) (710 to 3,500) (1,300 to 6,400)

Avoided Short-Term Mortality - Sensitivity Analysis

160 500 920

(-44 to 360) (-140 to 1,100) (-250 to 2,100)

250 780 1,400

(77 to 420) (240 to 1,300) (440 to 2,400)

240 740 1,400

(88 to 380) (280 to 1,200) (510 to 2,200)

160 510 930

(54 to 270) (170 to 850) (310 to 1,600)

520 1,600 3,000

(250 to 800) (780 to 2,500) (1,400 to 4,600)

730 2,300 4,200

(440 to 1,000) (1,400 to 3,200) (2,500 to 5,800)

740 2,300 4,200

(510 to 970) (1,600 to 3,000) (2,900 to 5,600)

Avoided Long-term Respiratory Mortality - Sensitivity Analysis

460 1,400 2,600

(130 to 790) (410 to 2,500) (760 to 4,500)

500 1,500 2,800

(170 to 810) (550 to 2,500) (1,000 to 4,600)

60 ppb threshold 520 1,600 2,900

56 ppb threshold 410 1,100 1,700

55 ppb threshold 120 280 510

50 ppb threhsold 6 58 140

45 ppb threshold 3 47 105

40 ppb threshold <1 10 13

Avoided Morbidity - Core Analysis

360 1,100 2,100

(-97 to 820) (-310 to 2,600) (-560 to 4,700)

1,100 3,500 6,600

(100 to 3,400) (330 to 11,000) (610 to 20,000)

300,000 910,000 1,700,000

(-440,000 to 900,000) (-1,300,000 to 2,700,000) (-2,500,000 to 5,000,000)

930,000 2,900,000 5,300,000

(380,000 to 1,500,000) (1,200,000 to 4,500,000) (2,200,000 to 8,300,000)

330,000 1,000,000 1,900,000

(120,000 to 730,000) (360,000 to 2,200,000) (660,000 to 4,500,000)

multi-city 

study

Jerrett et al (2009) - copollutant (PM2.5) model with: c 

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 Loss Days (age 5-17)

metaanalyses

Bell et al. (2005) (all ages)

Ito et al. (2005) (all ages)

Levy et al. (2005) (all ages)

Jerrett et al. (2009) (age 30-99) (86 

cities) (ozone-only)

Jerrett et al. (2009) (age 30-99) (96 

cites) (ozone-only)

multi-city 

study

Jerrett et al. (2009) (30-99yrs) 

copollutants model (PM2.5) 

multi-city 

studies

Smith et al. (2009) (all ages) 

copollutants model (PM1 0)

Schwartz (2005) (all ages)

Huang et al. (2005) 

(cardiopulmonary)

Bell et al. (2004) (all ages)

Health Effectb

Proposed and Alterative Standards

(95th percentile confidence intervals)

multi-city 

studies

Smith et al. (2009) (all ages)

Zanobetti and Schwartz (2008) (all 

ages)
5-78

Table 5-20. Total Monetized ozone Benefits for the Proposed and Alternative Annual 

Ozone Standards (Incremental to the Analytical Baseline) for the 2025 scenario 

(nationwide benefits of attaining each alternative standard everywhere in the 

U.S. except California) (millions of 2011) a, b

a All benefits estimates are rounded to whole numbers with a maximum of two significant digits. The monetized 

value of the ozone-related morbidity benefits are included in the estimates shown in this table for each mortality 

study (and when combined account for from 4-6% of the total benefits, depending on the total mortality estimate 

compared against. Note that asthma exacerbations accounts for <<1% of the total).

b The sensitivity analysis for long-term exposure-related mortality included an assessment of potential thresholds 

however this assessment was implemented using incidence estimates (see Table 5-19). Observations from that 

analysis (in terms of fractional impacts on incidence can be directly applied to these benefit results) (see Appendix 

5B, section 5B.1)

c A single central-tendency value is provided in each cell, since the zero-lag model used here did not require 

application of a 3% and 7% discount rates (see footnote d below) (note, however that as with all other entries in this 

study, we do include a 95th percentile confidence interval range – these are the values within parentheses).

d The range (outside of the parentheses) within each cell results from application of a 7% and 3% discount rates in 

the context of applying the 20year segmented lag (with the 7% resulting in the lower estimate and the 3% the higher 

estimate). The range presented within the parentheses reflects consideration for the 95th% confidence interval 

generated for each of these estimates and ranges from a low value (2.5th% CI for the 7% discount-based dollar 

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

70 ppb 65 ppb 60 ppb

Avoided Short-Term Mortality - Core Analysis

$2,000 $6,400 $12,000

($180 to $5,800) ($560 to $18,000) ($1,000 to $33,000)

3,400 11,000 20,000

($300 to $9,600) ($950 to $30,000) ($1,700 to $55,000)

Avoided Short-Term Mortality - Sensitivity Analysis

$1,600 $5,100 $9,400

(-$390 to $5,900) (-$1,200 to $19,000) (-$2,200 to $34,000)

$2,500 $7,900 $15,000

($200 to $7,700) ($630 to $24,000) ($1,200 to $44,000)

$2,400 $7,500 $14,000

($200 to $7,000) ($620 to $22,000) ($1,100 to $41,000)

1,700 5,200 9,500

($130 to $4,900) ($420 to $15,000) ($760 to $28,000)

5,300 17,000 31,000

($470 to $15,000) ($1,500 to $48,000) ($2,700 to $88,000)

$7,400 $23,000 $42,000

($680 to $21,000) ($2,100 to $64,000) ($3,900 to $120,000)

$7,500 $24,000 $43,000

($700 to $20,000) ($2,200 to $64,000) ($4,000 to $120,000)

Avoided Long-term Respiratory Mortality - Sensitivity Analysis

$6,900 $22,000 $40,000

($560 to $21,000) ($1,700 to $64,000) ($3,200 to $120,000)

$5,600 to $6300 $18,000 to $20,000 $32,000 to $36,000

($460 to $19,000) ($1,400 to $58,000) ($2,600 to $110,000)

Proposed and Alterative Standards

(95th percentile confidence intervals)

multi-city 

studies

Smith et al. (2009) (all ages)

Zanobetti and Schwartz (2008) (all 

ages)

Smith et al. (2009) (all ages) 

copollutants model (PM1 0)

Schwartz (2005) (all ages)

Bell et al. (2004) (all ages)

Health Effectb

Huang et al. (2005) 

(cardiopulmonary)

multi-city 

studies

metaanalyses

Bell et al. (2005) (all ages)

Ito et al. (2005) (all ages)

Levy et al. (2005) (all ages)

multi-city 

study

Jerrett et al. (2009) (age 30-99) 

copollutants model (PM2.5) no lag c

Jerrett et al. (2009) (age 30-99) 

copollutants model (PM2.5) 20 yr 

segmented lag d
5-79

Table 5-21. Estimated Number of Avoided PM2.5 Health Impacts for the Proposed and 

Alternative Annual Ozone Standards (Incremental to the Analytical Baseline)

for the 2025 scenario (nationwide benefits of attaining each alternative standard 

everywhere in the U.S. except California) a

a All incidence estimates are rounded to whole numbers with a maximum of two significant digits. Because these 

estimates were generated using benefit-per-ton estimates, confidence intervals are not available. In general, the 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 60ppb

Avoided PM2.5-related Mortality

Krewski et al. (2009) (adult mortality age 30+) 510 1,400 2,600

Lepeule et al. (2012) (adult mortality age 25+) 1,100 3,300 6,000

Woodruff et al. (1997) (infant mortality) 1 2 5

Avoided PM2.5-related Morbidity

Non-fatal heart attacks

 Peters et al. (2001) (age >18) 600 1,700 3,100

 Pooled estimate of 4 studies (age >18) 64 180 330

Hospital admissions—respiratory (all ages) 150 430 780

Hospital admissions—cardiovascular (age > 18) 180 530 950

Emergency department visits for asthma (all ages) 280 790 1,400

Acute bronchitis (ages 8–12) 790 2,300 4,100

Lower respiratory symptoms (ages 7–14) 10,000 29,000 53,000

Upper respiratory symptoms (asthmatics ages 9–11) 14,000 41,000 75,000

Asthma exacerbation (asthmatics ages 6–18) 17,000 51,000 100,000

Lost work days (ages 18–65) 65,000 180,000 340,000

Minor restricted-activity days (ages 18–65) 380,000 1,100,000 2,000,000

Proposed and Alterative Standards

Health Effectb
5-80

Table 5-22. Monetized PM2.5 Health Co-Benefits for the Proposed and Alternative 

Annual Ozone Standards (Incremental to Analytical Baseline) for the 2025 scenario 

(nationwide benefits of attaining each alternative standard everywhere in the U.S. except 

California) (Millions of 2011)a,b,c

Monetized Benefits

Proposed and Alterative Standards

70 ppb 65 ppb 60 ppb

3% Discount Rate

Krewski et al. (2009) (adult mortality age 30+) $4,800 $14,000 $25,000

Lepeule et al. (2012) (adult mortality age 25+) $11,000 $31,000 $56,000

7% Discount Rate

Krewski et al. (2009) (adult mortality age 30+) $4,300 $12,000 $22,000

Lepeule et al. (2012) (adult mortality age 25+) $9,700 $28,000 $50,000

a All estimates are rounded to two significant digits. Because these estimates were generated using benefit-per-ton 

estimates, confidence intervals are not available. In general, the 95th percentile confidence interval for monetized 

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). Estimates do not include unquantified health benefits noted in Table 5-2 or 

Section 5.6.3.6 or welfare co-benefits noted in Chapter 6. 

b The reduction in premature fatalities each year accounts for over 98% of total monetized benefits in this analysis. 

Mortality risk valuation assumes discounting over the SAB-recommended 20-year segmented lag structure.

Table 5-23. Estimate of Monetized Ozone and PM2.5 Benefits for Proposed and 

Alternative Annual Ozone Standards Incremental to the Analytical Baseline for 

the 2025 scenario (nationwide benefits of attaining each alternative standard 

everywhere in the U.S. except California) – Full Attainment (billions of 2011$) a,b

Benefits Estimate 70 ppb 65 ppb 60 ppb

Economic value for avoided ozone-related morbidities and premature deaths using ozone mortality estimate from 

Smith et al., (2009) and Zanobetti and Schwartz (2008) plus PM2.5-related morbidities and premature deaths using 

PM2.5 mortality estimate from Krewski et al. (2009)

3% discount rate $6.9 to $8.2 + B $20 to $24 + B $37 to $44 + B

7% discount rate $6.4 to $7.7 + B $19 to $23 + B $34 to $42 + B

Economic value for avoided ozone-related morbidities and premature deaths using ozone mortality estimate from 

Smith et al., (2009) and Zanobetti and Schwartz (2008) plus PM2.5-related morbidities and premature deaths using 

PM2.5 mortality estimate from Lepeule et al. (2012)

3% discount rate $13 to $14 + B $37 to $41 + B $68 to $75 + B

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

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. 
5-81

Table 5-24. Regional Breakdown of Monetized Ozone-Specific Benefits Results for the 

2025 scenario (nationwide benefits of attaining each alternative standard 

everywhere in the U.S. except California) – Full Attainment a

Region Proposed and Alterative Standards

70 ppb 65 ppb 60 ppb

East b 99% 96% 92%

California 0% 0% 0%

Rest of West 1% 4% 7%

a Because we use benefit-per-ton estimates to calculate the 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. Several recent rules such as Tier 3 will have substantially 

reduced ozone concentrations by 2025 in the East, thus few additional controls would be needed to reach 70 ppb. 

5.7.2 Benefits of the Proposed and Alternative Annual Primary ozone standards for the post2025 scenario

This section presents incidence reductions and associated dollar benefit estimates 

associated with the post-2025 scenario (i.e., nationwide benefits estimates reflecting attainment 

of alternative standards in California – see section 5.4.3). The same rounding conventions 

described in section 5.7.1 (for the 2025 estimates) were applied in generating these estimates. As 

with estimates generated for the 2025 scenario, total monetized health benefits are equal to the 

subset of monetized ozone and PM-related health benefits plus B, the sum of the non-monetized 

health benefits and welfare co-benefits. Organization and general content of tables presented 

here for the post-2025 scenario mirrors that of tables presented in section 5.7.1 for the 2025 

scenario and the reader is referred there for further clarification. 

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

Annual Primary ozone Standards Relative to Analytical Baseline for post-2025 a

Standard

Population-Weighted Ozone Season 

ozone Concentration Change (8hr max)b

70 ppb 0.1526

65 ppb 0.3311

60 ppb 0.5007 

a Because we used benefit-per-ton estimates for the 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.
5-82

Table 5-26. Estimated Number of Avoided Ozone Health Impacts for the Proposed and 

Alternative Annual Ozone Standards (Incremental to the Analytical Baseline) 

for the Post-2025 scenario (nationwide benefits of attaining each alternative 

standard just in California) a, b

a All incidence estimates are rounded to whole numbers with a maximum of two significant digits. 

b All incidence estimates are based on ozone-only models unless otherwise noted.

c The sensitivity analysis for long-term exposure-related mortality included an assessment of potential thresholds, 

which was completed for the 2025 scenario (see Table 5-19). Care should be taken in applying the results of that 

sensitivity analysis to the post-2025 scenario, although general patterns of impact across the thresholds may apply.

70 ppb 65 ppb 60 ppb

Avoided Short-Term Mortality - Core Analysis

65 140 210

(31 to 97) (68 to 210) (100 to 320)

110 230 350

(57 to 160) (120 to 340) (190 to 510)

Avoided Long-term Respiratory Mortality - Core Analysis

260 560 840

(88 to 430) (190 to 930) (290 to 1,400)

Avoided Short-Term Mortality - Sensitivity Analysis

52 110 170

(-14 to 120) (-31 to 250) (-46 to 380)

80 170 260

(25 to 140) (54 to 290) (81 to 440)

88 190 290

(33 to 140) (71 to 310) (110 to 470)

52 110 170

(17 to 87) (38 to 190) (57 to 280)

170 360 550

(80 to 250) (170 to 550) (260 to 830)

230 510 770

(140 to 330) (300 to 710) (460 to 1,100)

240 510 770

(160 to 310) (350 to 670) (530 to 1,000)

Avoided Long-term Respiratory Mortality - Sensitivity Analysis c

180 380 570

(51 to 300) (110 to 660) (160 to 980)

190 410 620

(68 to 310) (150 to 680) (220 to 1,000)

Avoided Morbidity - Core Analysis

120 260 390

(-32 to 270) (-69 to 580) (-100 to 880)

320 690 1,000

(29 to 980) (64 to 2,100) (97 to 3,200)

97,000 210,000 310,000

(-140,000 to 290,000) (-310,000 to 620,000) (-460,000 to 930,000)

290,000 630,000 950,000

(120,000 to 460,000) (260,000 to 990,000) (390,000 to 1,500,000)

110,000 230,000 350,000

(38,000 to 240,000) (81,000 to 500,000) (120,000 to 830,000)

Health Effectb

Proposed and Alterative Standards

(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) (age 30-99) (86 

cities) (ozone-only)

Jerrett et al. (2009) (age 30-99) (96 

cites) (ozone-only)

Hospital admissions - respiratory 

(age 65+)

multi-city 

study

Jerrett et al. (2009) (30-99yrs) 

copollutants model (PM2.5) 

Huang et al. (2005) 

(cardiopulmonary)

multi-city 

studies

Smith et al. (2009) (all ages) 

copollutants model (PM1 0)

Schwartz (2005) (all ages)

Bell et al. (2004) (all ages)

metaanalyses

Bell et al. (2005) (all ages)

Ito et al. (2005) (all ages)

Levy et al. (2005) (all ages)

Emergency department visits for 

asthma (all ages)

Asthma exacerbation (age 6-18)

Minor restricted-activity days (age 

18-65)

School Loss Days (age 5-17)
5-83

Table 5-27. Total Monetized ozone Benefits for the Proposed and Alternative Annual 

Ozone Standards (Incremental to the Analytical Baseline) for the post-2025

scenario (nationwide benefits of attaining each alternative standard just in 

California) (millions of 2011) a, b

a All benefits estimates are rounded to whole numbers with a maximum of two significant digits. The monetized 

value of the ozone-related morbidity benefits are included in the estimates shown in this table for each mortality 

study (and when combined account for from 4-6% of the total benefits, depending on the total mortality estimate 

compared against. Note that asthma exacerbations accounts for <<1% of the total).

b The sensitivity analysis for long-term exposure-related mortality included an assessment of potential thresholds 

however this assessment was implemented using incidence estimates (see Table 5-19). Observations from that 

analysis (in terms of fractional impacts on incidence can be directly applied to these benefit results) (see Appendix 

5B, section 5B.1)

c A single central-tendency value is provided in each cell, since the zero-lag model used here did not require 

application of a 3% and 7% discount rates (see footnote d below) (note, however that as with all other entries in this 

study, we do include a 95th percentile confidence interval range – these are the values within parentheses).

d The range (outside of the parentheses) within each cell results from application of a 7% and 3% discount rates in 

the context of applying the 20year segmented lag (with the 7% resulting in the lower estimate and the 3% the higher 

estimate). The range presented within the parentheses reflects consideration for the 95th% confidence interval 

generated for each of these estimates and ranges from a low value (2.5th% CI for the 7% discount-based dollar 

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

70 ppb 65 ppb 60 ppb

Avoided Short-Term Mortality - Core Analysis

$660 $1,400 $2,100

($58 to $1,900) ($130 to $4,100) ($190 to $6,100)

1,100 2,400 3,600

($96 to $3,100) ($210 to $6,700) ($320 to $10,000)

Avoided Short-Term Mortality - Sensitivity Analysis

$530 $1,100 $1,700

(-$120 to $1,900) (-$270 to $4,200) (-$410 to $6,300)

$820 $1,800 $2,700

($65 to $2,500) ($140 to $5,400) ($210 to $8,100)

$900 $1,900 $2,900

($74 to $2,600) ($160 to $5,700) ($240 to $8,600)

530 1,200 1,700

($43 to $1,600) ($93 to $3,500) ($140 to $5,200)

1,700 3,700 5,600

($150 to $4,900) ($320 to $11,000) ($490 to $16,000)

$2,400 $5,200 $7,800

($220 to $6,600) ($470 to $14,000) ($710 to $22,000)

$2,400 $5,200 $7,800

($220 to $6,500) ($480 to $14,000) ($730 to $21,000)

Avoided Long-term Respiratory Mortality - Sensitivity Analysis

$2,700 $5,700 $8,600

($220 to $7,900) ($470 to $17,000) ($700 to $25,000)

$2,200 to $2,400 $4,700 to $5,200 $7,000 to $7,800

($180 to $7,200) ($380 to $15,000) ($570 to $23,000)

Proposed and Alterative Standards

(95th percentile confidence intervals)

multi-city 

studies

Smith et al. (2009) (all ages)

Zanobetti and Schwartz (2008) (all 

ages)

Health Effectb

Jerrett et al. (2009) (age 30-99) 

copollutants model (PM2.5) 20 yr 

segmented lag d

multi-city 

study

Jerrett et al. (2009) (age 30-99) 

copollutants model (PM2.5) no lag c

Smith et al. (2009) (all ages) 

copollutants model (PM1 0)

Huang et al. (2005) 

(cardiopulmonary)

multi-city 

studies

Schwartz (2005) (all ages)

Bell et al. (2004) (all ages)

metaanalyses

Bell et al. (2005) (all ages)

Ito et al. (2005) (all ages)

Levy et al. (2005) (all ages)
5-84

Table 5-28. Estimated Number of Avoided PM2.5 Health Impacts for the Proposed and 

Alternative Annual Ozone Standards (Incremental to the Analytical Baseline)

for the post-2025 scenario (nationwide benefits of attaining each alternative 

standard just in California) a

a All incidence estimates are rounded to whole numbers with a maximum of two significant digits. Because these 

estimates were generated using benefit-per-ton estimates, confidence intervals are not available. In general, the 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).

Table 5-29. Monetized PM2.5 Health Co-Benefits for the Proposed and Alternative 

Annual Ozone Standards (Incremental to Analytical Baseline) for the post-2025 scenario 

70 ppb 65 ppb 60 ppb

Avoided PM2.5-related Mortality

Krewski et al. (2009) (adult mortality age 30+) 45 89 120

Lepeule et al. (2012) (adult mortality age 25+) 100 200 280

Woodruff et al. (1997) (infant mortality) <1 <1 <1

Avoided PM2.5-related Morbidity

Non-fatal heart attacks

 Peters et al. (2001) (age >18) 54 110 140

 Pooled estimate of 4 studies (age >18) 6 11 16

Hospital admissions—respiratory (all ages) 14 27 37

Hospital admissions—cardiovascular (age > 18) 16 32 45

Emergency department visits for asthma (all ages) 24 46 64

Acute bronchitis (ages 8–12) 67 130 180

Lower respiratory symptoms (ages 7–14) 860 1,700 2,300

Upper respiratory symptoms (asthmatics ages 9–11) 1,200 2,400 3,300

Asthma exacerbation (asthmatics ages 6–18) 1,900 3,800 5,200

Lost work days (ages 18–65) 5,500 11,000 15,000

Minor restricted-activity days (ages 18–65) 32,000 64,000 88,000

Proposed and Alterative Standards

Health Effectb
5-85

(nationwide benefits of attaining each alternative standard just in California) (Millions of 

2011) a,b

Monetized Benefits Proposed and Alterative Standards

70 ppb 65 ppb 60 ppb

3% Discount Rate

Krewski et al. (2009) (adult mortality age 30+) $420 $830 $1,100

Lepeule et al. (2012) (adult mortality age 25+) $950 $1,900 $2,600

7% Discount Rate

Krewski et al. (2009) (adult mortality age 30+) $380 $750 $1,000

Lepeule et al. (2012) (adult mortality age 25+) $860 $1,700 $2,300

a All estimates are rounded to two significant digits. Because these estimates were generated using benefit-per-ton 

estimates, confidence intervals are not available. In general, the 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). Estimates do not include unquantified health benefits noted in Table 5-3 or 

Section 5.6.3.6 or welfare co-benefits noted in Chapter 6. 

b The reduction in premature fatalities each year accounts for over 98% of total monetized benefits in this analysis. 

Mortality risk valuation assumes discounting over the SAB-recommended 20-year segmented lag structure.

Table 5-30. Estimate of Monetized Ozone and PM2.5 Benefits for Proposed and Alternative 

Annual Ozone Standards Incremental to the Analytical Baseline for the post2025 scenario (nationwide benefits of attaining each alternative standard just in 

California) – Full Attainment (billions of 2011$) a

Benefits Estimate 70 ppb 65 ppb 60 ppb

Economic value for avoided ozone-related morbidities and premature deaths using ozone mortality estimate from 

Smith et al., (2009) and Zanobetti and Schwartz (2008) plus PM2.5-related morbidities and premature deaths using 

PM2.5 mortality estimate from Krewski et al. (2009)

3% discount rate $1.1 to $1.5 + B $2.3 to $3.2 + B $3.4 to $4.7 + B

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

Economic value for avoided ozone-related morbidities and premature deaths using ozone mortality estimate from 

Smith et al., (2009) and Zanobetti and Schwartz (2008) plus PM2.5-related morbidities and premature deaths using 

PM2.5 mortality estimate from Lepeule et al. (2012)

3% discount rate $1.6 to $2 + B $3.3 to $4.2 + B $4.8 to $6.2 + B

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

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. 
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Table 5-31. Regional Breakdown of Monetized Ozone-Specific Benefits Results for the 

post-2025 scenario (nationwide benefits of attaining each alternative standard 

just in California) – Full Attainment a

Region Proposed and Alterative Standards

70 ppb 65 ppb 60 ppb

East 0% 0% 0%

California 93% 94% 94%

Rest of West 6% 6% 6%

a Because we use benefit-per-ton estimates to calculate the PM2.5 co-benefits, a regional breakdown for the cobenefits is not available. Therefore, this table only reflects the ozone benefits.

5.7.3 Uncertainty in Benefits Results (including discussion of sensitivity analyses and 

supplemental analyses)

The dollar value of avoided ozone and PM2.5 related premature deaths account for 94% to 

98% of the total monetized benefits. This is true in part because we are unable to quantify many 

categories of benefits. The next largest benefit is for reducing the incidence of nonfatal heart 

attacks. The remaining categories each account for a small percentage of total benefit; however, 

they represent a large number of avoided incidences affecting many individuals. Comparing an 

incidence table to the monetary benefits table reveals that the number of incidences avoided and 

the dollar value for that endpoint do not always closely correspond. For example, for ozone we 

estimate almost 1,000 times more asthma exacerbations would be avoided than premature 

mortalities, yet asthma exacerbations account for only a very small fraction (<<1%) of total 

monetized benefits (see Tables 5-20). This reflects the fact that many of the less severe health 

effects, while more common, are valued at a lower level than the more severe health effects. 

Also, some effects, such as 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 cobenefits are discussed qualitatively in Appendix A. Key assumptions and 

uncertainties related to the modeling of ozone are presented below.

 We assume that short-term exposure to ozone is associated with mortality and that this 

relationship holds across the full range of exposure. Furthermore, we assume that longterm exposure to ozone is associated with respiratory mortality and while we favor a nothreshold linear C-R function, we consider the potential impact of thresholds ranging 

from 40ppb to 60ppb. 
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 In modeling long-term exposure-related respiratory mortality, we acknowledge 

uncertainty in specifying the nature of the cessation lag and have consequently included

two alternative lag structures including a 20 year segmented lag and a zero lag (the zero 

lag reflects the potential that estimates of long-term exposure-related mortality could 

actually be capturing the accumulation of short-term exposure-related mortality events).

In addition, we acknowledge the value in exploring the impact of potential thresholds in 

effect on the core incidence and benefits estimates.

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

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 cobenefits. The benefit-per-ton estimates used here reflect 

specific geographic patterns of emissions reductions and specific air quality and benefits 

modeling assumptions associated with the derivation of those estimates (see the TSD 

describing the calculation of the national benefit-per-ton estimates (U.S. EPA, 2013b) 

and Fann et al. (2012c)). Consequently, these estimates may not reflect local variability in 

population density, 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. 
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In order to evaluate the quantitative impact of specific sources of uncertainty on the core 

risk estimates, we completed a number of sensitivity analyses, some of which have already been 

described in presenting the core risk estimates and some of which are presented in Appendix 5B. 

A brief overview of these sensitivity analyses, including key observations resulting from those 

observations are presented below in section 5.7.3.1. In addition to these sensitivity analyses, we 

have also included several supplemental analyses intended to provide additional perspectives on 

the core incidence and benefits analyses. These supplemental analyses are presented in detail in 

Appendix 5C and are also briefly summarized below in section 5.7.3.2.

5.7.3.1 Sensitivity Analyses

A number of sensitivity analyses have been completed as part of this RIA. Given the 

importance of mortality in driving benefits estimates (both for ozone and PM2.5 cobenefits), the 

sensitivity analyses completed have been focused largely on the mortality endpoint. Each 

sensitivity analysis is briefly described below (including an overview of the approach used in 

conducting the analysis and key observations). As identified below, several of the sensitivity 

analyses have been presented earlier parallel to presentation of the core estimates, while others 

are described in Appendix B.38

 Short-term ozone-exposure related mortality (alternative epidemiological studies 

and C-R functions): As described in section 5.6.3.1, in addition to the two core effect 

estimates we estimated benefits using seven additional effect estimates including four 

multi-city studies and three meta-analysis studies. This sensitivity analysis showed that 

the two core incidence and benefits estimates fall within (and towards the lower end of) 

the broader range resulting from application of the seven alternative effect estimates (see 

Tables 5-19 and 5-20). This increased our overall confidence in the two core studies, 

particularly with regard to epidemiological study design and the characterization of the 

relationship between short-term exposure and mortality.

 Long-term ozone-exposure related respiratory mortality dollar benefits: As 

discussed in section 5.1, we could not specify a cessation lag structure specific to longterm ozone exposure-related respiratory mortality. While we included incidence estimates 

as a part of the core analysis, we present the associated dollar benefits as a sensitivity 

analysis (see Tables 5-20 and 5-27). Given uncertainty related to the lag structure, we 

also included two different lags in modeling these benefits as a sensitivity analysis – a 20 

year segment lag (as used for PM2.5) and a zero lag (see section 5.6.4.1 for additional 

 

38 Generally, we gave greater emphasis to those sensitivity analyses examining sources of potential uncertainty 

directly associated with estimates of ozone-related mortality and have included summaries of those sensitivity 

analyses results parallel to presentation of core incidence and benefits estimates in sections 5.7.1 and 5.7.2. 
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detail on the lag structures used). The sensitivity analysis suggests that if included in the 

core benefit estimate, long-term ozone exposure-related mortality could add substantially 

to the overall benefits (see Table 5-20 and 5-27). Additionally, use of a 20 year segment 

lag can 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:

As discussed in section 5.6.3.1, while we favor the no-threshold model in estimating 

benefits related to long-term ozone exposure-related respiratory mortality, as a sensitivity 

analysis, we evaluated the impact of thresholds ranging from 40-60ppb.

39 These results 

are included alongside core incidence estimates in Table 5-19 (see Appendix 5B, section 

5B1 for additional details). This sensitivity analysis suggested that a threshold of 50ppb 

or greater could have a substantial impact on estimated benefits, while thresholds below 

this range have a relatively minor impact. 

 Long-term PM2.5 exposure-related mortality and alternative CR functions (based on 

expert elicitation): As discussed in Appendix 5D (section 5D.1) in 2006 we conducted 

an expert elicitation to help better characterize uncertainty associated with long-term 

PM2.5 exposure-related mortality and specifically the C-R functions used in modeling that 

endpoint, including the shape of the functions and potential for thresholds in effect. As 

part of the sensitivity analysis for the current ozone RIA, we applied the set of expert 

elicitation-based functions to generate an alternative set of PM2.5 incidence and benefit 

estimates (see Appendix 5B, section 5B.2). That sensitivity analysis showed that the two 

core incidence estimate fall within the range of alternative C-R function based estimates 

obtained through expert elicitation (see Table 5B-2). This increases overall confidence in 

the core estimates with regard to the form of the functions and magnitude of the effect 

estimates.

 Income elasticity and the willingness to pay (WTP) values used for mortality and 

certain morbidity endpoints: As described in Appendix 5B (section 5B.3) we examined 

the impact of alternative assumptions regarding income elasticity (i.e., the degree to 

which willingness to pay changes as income changes) and the degree of impact on 

willingness to pay (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.

 

39 As noted in section 5.6.3.1, our decision to include benefit estimates based on the no-threshold model in the core 

analysis reflects recommendations on the HREA provided by CASAC (Frey, 2014).
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5.7.3.2 Supplemental Analyses

In addition to the sensitivity analyses described in section 5.7.3.1, we also included a 

number of supplemental analyses intended to examine other attributes of the core risk estimates 

(these are briefly described below with additional detail found in Appendix 5C).

 Consideration for age group-differentiated aspects of short-term ozone exposurerelated mortality (including total avoided incidence, life years gained and percent 

reduction in baseline mortality): These analyses expand on the basic mortality 

incidence estimates presented in section 5.7 by considering (a) estimates of the reduction 

in mortality incidence differentiated by age range (i.e., how the avoided deaths map to 

different age ranges) and (b) estimates of life years gained by age range and (c) percent 

reduction in baseline mortality by age range. By comparing the projected age distribution 

of the avoided premature deaths with the age distribution of life years gained, we 

observed that about half of the ozone-attributable deaths occur in populations age 75–99 

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

populations younger than 65 (see Appendix 5C, section 5C.1, Table 5C-2). This is 

because the younger populations have the potential to lose more life years per death than 

older populations based on changes in ozone exposure for the 2025 scenario. Results 

presented in Table 5C-3 highlight that when reductions in ozone-attributable mortality (in 

going from baseline to an alternative standard level) are considered as a percentage of 

total all-cause baseline mortality, the estimates are relatively small and are fairly constant 

across age ranges. However, it is important to point out that estimates of total ozoneattributable mortality represent a substantially larger fraction of all-cause baseline 

mortality than the increment attributable to the simulated reduction in ozone associated 

with an alternative standard level.

 Evaluation of mortality impacts relative to the baseline pollutant concentrations 

(used in generating those mortality estimates) for both short-term ozone exposurerelated mortality and long-term PM2.5 exposure-related mortality: In this 

supplemental analysis, we begin by comparing the distribution of short-term ozone 

exposure-related mortality against the ozone season-averaged 8hr max values used in 

deriving those estimates (for the three alternative standards modeled for the 2025 

scenario) (see section 5C.2). In making this comparison, we point out that, rather than 

using a full daily time series of 8hr max values for each grid cell, we used an ozone 

season-average 8hr max value for incidence modeling. While this simplification did not 

impact the overall core incidence estimate for this mortality endpoint, it does impact 

efforts to compare the distribution of mortality estimates against associated daily values 

(by reducing temporal variation associated with each grid cell calculation). Key 

observations resulting from this supplemental analysis are that (a) the vast majority of 

reductions in short-term exposure-related mortality for ozone occur in grid cells with 

mean 8hr max baseline levels (across the ozone season) between 35 and 55ppb and (b) 

virtually all of the mortality reductions are associated with ozone levels above the 

<20ppb range identified within the Ozone ISA as being associated with less confidence in 

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

of PM2.5 mortality estimates relative to associated PM2.5 levels used in modeling) relies 

on the concept of the lowest measured level (LML) associated with the epidemiological 

studies providing the effect estimates used in morality modeling. Specifically, we observe 

that there is reduced confidence in specifying the nature of the C-R function at exposure 

levels below the range used in the epidemiological study (i.e., below the LML). In the 

supplemental analysis, we found that, depending on the mortality study, between 67% 

and 93% of the mortality estimate is based on modeling involving PM2.5 levels above the 

LML (i.e., levels at which we have increased confidence in specifying the PM2.5-

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

 Core Incidence and Dollar Benefits Estimates Reflecting Application of Known 

Controls for the 2025 Scenario: In Appendix 5C, section 5C.3, we present a subset of 

core incidence and benefits for the 2025 scenario reflecting only application of known 

controls in modeling reductions in ozone to attain each alternative standard level. These 

known-control based estimates include both ozone-related and PM2.5 co-benefit estimates 

as well as total benefits. As expected, the percent of benefits reflecting application of 

known controls decreases as you consider more stringent alternative standards. Estimates 

presented in Appendix 5C.3 suggest that 77%, 59% 34% of total benefits are associated 

with application of known controls (for the 70, 65 and 60 ppb alternative standards, 

respectively). 

5.8 Discussion

The analysis in this Chapter demonstrates the potential for significant health benefits of 

the illustrative emission controls applied to simulate attainment with the alternative primary 

ozone standards. We estimate that by 2025, the emissions reductions to reach the alternative 

standards everywhere except California, would have reduced the number of ozone- and 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 also promises to yield 

significant welfare impacts as well (see Chapter 6). Even considering the quantified and 

unquantified uncertainties identified in this chapter, we believe that the implementing the 

alternative standards would have substantial public health benefits that are likely to outweigh the 

costs for the three alternative standards considered (see Chapter 7). 

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 concentrationresponse relationships and our use of alternate mortality functions. Others, including the 
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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 

proposed and alternative primary standards.

There are important differences worth noting in the design and analytical objectives of 

NAAQS RIAs compared to RIAs for implementation rules, such as the Tier 3 (U.S. EPA, 

2014c). The NAAQS RIAs illustrate the potential costs and benefits of a revised air quality 

standard nationwide based on an array of emission reduction strategies for different sources, 

incremental to implementation of existing regulations and controls needed to attain the current 

standards. In short, NAAQS RIAs hypothesize, but do not predict, the emission reduction 

strategies that States may choose to enact when implementing a revised NAAQS. The setting of 

a NAAQS does not directly result in costs or benefits, and as such, NAAQS RIAs are merely 

illustrative and are not intended to be added to the costs and benefits of other regulations that 

result in specific costs of control and emission reductions. By contrast, the emission reductions 

from implementation rules are generally for specific, well-characterized sources, such as the 

recent MATS rule (U.S. EPA, 2011e). In general, the EPA is more confident in the magnitude 

and location of the emission reductions for implementation rules. As such, emission reductions 

achieved under promulgated implementation rules such as Tier 3 have been reflected in the 

baseline of this NAAQS analysis. Subsequent implementation rules will be reflected in the 

baseline for the next ozone NAAQS review. For this reason, the benefits estimated provided in 

this RIA and all other NAAQS RIAs should not be added to the benefits estimated for 

implementation rules.

In setting the NAAQS, the EPA considers that ozone concentrations vary over space and 

time. While the standard is designed to limit concentrations at the highest monitor in an area, it is 

understood that emission controls put in place to meet the standard of the highest monitor will 

simultaneously result in lower ozone concentrations throughout the entire area. In fact, the 

Health Risk and Exposure Assessment for Ozone (HERA) (U.S. EPA, 2014b) shows how 

different standard levels would affect the entire distribution of ozone concentrations, and thus 
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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.40 Given that the 

epidemiological literature in most cases has not provided estimates based on threshold models, 

there would be additional uncertainties imposed by assuming thresholds or other non-linear 

concentration-response functions for the purposes of benefits analysis.

The estimated benefits for the proposed and alternative standards are in addition to the 

substantial benefits estimated for several recent implementation rules (U.S. EPA, 2009a, 2011d, 

2014c). Rules such as Tier 3 and other emission reductions will have substantially reduced 

ambient ozone concentrations by 2025 in the East, such that few additional controls would be 

needed to reach 70 ppb in the East beyond the analytical baseline. These rules that have already 

been promulgated have tremendous combined benefits that explain why the number of avoided 

premature deaths associated with this NAAQS revision are smaller than were estimated in the 

previous ozone NAAQS RIA (U.S. EPA, 2006) for the year 2025 and even smaller than the 

mortality risks estimated for the current year in the ozone HREA (U.S. EPA, 2014b). 

 

40 As discussed 5.7.3.1, our modeling of long-term ozone exposure-related respiratory mortality did include 

consideration of potential thresholds as a sensitivity analysis. However, key points need to be emphasized in the 

context of interpreting this endpoint: (a) based on recommendations from CASAC the core incidence reduction 

estimates was based on a non-threshold model (Frey, 2014) and (b) while the incidence estimates for this 

endpoint were included as core estimates, associated dollar benefit estimates were only included as sensitivity 

analyses and consequently did not factor in to the core dollar benefit estimates generated.
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