1

Chappell, Regina

From: Sasser, Erika

Sent: Thursday, September 17, 2015 8:26 AM

To: Chappell, Regina

Subject: FW: Part of E.O. 12866 Review: Ozone NAAQS RIA Chapters & Appendices RIN2060-AP38

From: Sasser, Erika  

Sent: Wednesday, September 16, 2015 6:04 PM

To: 'Frey, Nathan J.'

Subject: RE: Part of E.O. 12866 Review: Ozone NAAQS RIA Chapters & Appendices RIN2060‐AP38

You’re welcome. Also‐‐ I just sent a scheduler for a brief discussion tomorrow at 11:00.  Looks like that time will work

fine. Let me know if you did not receive it.  

Thanks

From: Frey, Nathan J. [mailto:Nathan_J._Frey@omb.eop.gov]  

Sent: Wednesday, September 16, 2015 5:50 PM

To: Sasser, Erika

Cc: Szabo, Aaron; Cooperstein, Sharon; Weatherhead, Darryl; Chappell, Regina; Langdon, Robin; Rush, Alan; Henigin,

Mary; Hetes, Bob; Pekar, Zachary

Subject: RE: Part of E.O. 12866 Review: Ozone NAAQS RIA Chapters & Appendices RIN2060‐AP38

Many thanks Erika.

From: Sasser, Erika [mailto:Sasser.Erika@epa.gov]  

Sent: Wednesday, September 16, 2015 5:46 PM

To: Frey, Nathan J. <Nathan_J._Frey@omb.eop.gov>

Cc: Szabo, Aaron <Aaron_L_Szabo@omb.eop.gov>; Cooperstein, Sharon <Cooperstein.Sharon@epa.gov>; Weatherhead,

Darryl <Weatherhead.Darryl@epa.gov>; Chappell, Regina <Chappell.Regina@epa.gov>; Langdon, Robin

<Langdon.Robin@epa.gov>; Rush, Alan <Rush.Alan@epa.gov>; Henigin, Mary <Henigin.Mary@epa.gov>; Hetes, Bob

<Hetes.Bob@epa.gov>; Pekar, Zachary <Pekar.Zachary@epa.gov>

Subject: Part of E.O. 12866 Review: Ozone NAAQS RIA Chapters & Appendices RIN2060‐AP38

Nathan,

As requested, attached please find Microsoft word versions of the Benefits chapter and associated appendices from the

draft Final Ozone NAAQS RIA.  Please let me know if you have any questions.

Thanks,

Erika
6A-1

APPENDIX 6A: COMPREHENSIVE CHARACTERIZATION OF UNCERTAINTY IN 

OZONE BENEFITS ANALYSIS

Overview

As noted in Chapter 6, the benefits analysis relies on an array of data inputs—including air 

quality modeling, health impact functions and valuation estimates among others—which are 

themselves subject to uncertainty and may contribute to the overall uncertainty in this analysis. The 

RIA employs a variety of analytic approaches designed to reduce the extent of the uncertainty and/or 

characterize the impact that uncertainty has on the final estimates. We strive to incorporate as many 

quantitative assessments of uncertainty as possible (e.g., Monte Carlo assessments, sensitivity 

analyses); however, there are some aspects we are only able to characterize qualitatively. 

To more comprehensively and systematically address these uncertainties, including those we 

cannot quantify, we adapt the World Health Organization (WHO) uncertainty framework (WHO, 

2008), which provides a means for systematically linking the characterization of uncertainty to the 

sophistication of the underlying health impact assessment. EPA has applied similar approaches in peerreviewed analyses of PM2.5-related impacts (U.S. EPA, 2010a, 2011, 2012) and ozone-related impacts 

(U.S. EPA, 2014). EPA’s Science Advisory Board (SAB) has supported using a tabular format to 

qualitatively assess the uncertainties inherent in the quantification and monetization of health benefits, 

including identifying potential bias, potential magnitude, confidence in our approach, and the level of 

quantitative assessment of each uncertainty (U.S. EPA-SAB, 1999, 2001, 2004a, 2004b, 2011a, 

2011b). The assessments presented here are largely consistent with those previous peer-reviewed 

assessments. 

This appendix focuses on uncertainties inherent in the ozone benefits estimates. For more 

information regarding the uncertainties inherent in the PM2.5 benefits estimates, please see the 2012 

PM2.5 NAAQS RIA (U.S. EPA, 2012).

6A.1 Description of Classifications Applied in the Uncertainty Characterization

Table 6A-1 catalogs the most significant sources of uncertainty in the ozone benefits analysis 

and then characterizes four dimensions of that uncertainty briefly described below. The first two 

dimensions focus on the nature of the uncertainty. The third and fourth dimensions focus on the extent 

to which the analytic approach chosen in the benefits analysis either minimizes the impact of the 

uncertainty or quantitatively characterizes its impact.
6A-2

1) The direction of the bias that a given uncertainty may introduce into the benefits 

assessment if not taken into account in the analysis approach; 

2) The magnitude of the impact that uncertainty is likely to have on the benefits estimate if 

not taken into account in the analysis approach; 

3) The extent to which the analytic approach chosen is likely to minimize the impact of 

that uncertainty on the benefits estimate; and

4) The extent to which EPA has been able to quantify the residual uncertainty after the 

preferred analytic approach has been incorporated into the benefits model. 

6A.1.1 Direction of Bias

The “direction of bias” column in Table 6A-1 is an assessment of whether, if left unaddressed, 

an uncertainty would likely lead to an underestimate or overestimate of the total monetized benefits. In 

some cases we indicate that there are reasons why the bias might go either direction, depending upon 

the true nature of the underlying relationship. Where available, we base the classification of the 

“direction of bias” on the analysis in the Integrated Science Assessment for Ozone and Related 

Photochemical Oxidants (Ozone ISA) (U.S. EPA, 2013). Additional sources of information include 

advice from SAB and the National Academies of Science (NAS), as well as studies from the peerreviewed literature. In some cases we indicate that there is not sufficient information to estimate 

whether the uncertainty would likely lead to under or overestimation of benefits; these cases are 

identified as “unable to determine.” 

6A.1.2 Magnitude of Impact

The “magnitude of impact” column in Table 6A-1 is an assessment of how much plausible 

alternative assumptions about the underlying relationship about which we are uncertain could influence 

the overall monetary benefits. EPA has applied similar classifications in previous risk and benefit 

analyses (U.S. EPA, 2010a, 2011, 2014), but we have slightly revised the category names and the cutoffs here.1 The definitions used here are provided below. 

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

“potentially major” if a plausible alternative assumption or approach could influence the overall monetary benefit 

estimate by five percent or more and “probably minor” if an alternative assumption or approach is likely to change the 

total benefit estimate by less than five percent. In the Quantitative Health Risk Assessment for Particulate Matter (U.S. 

EPA, 2010b), EPA applied classifications of “low” if the impact would not be expected to impact the interpretation of 

risk estimates in the context of the PM NAAQS review, “medium” if the impact had the potential to change the 

interpretation; and “high” if it was likely to influence the interpretation of risk in the context of the PM NAAQS review.
6A-3

• High—if the uncertainty associated with an assumption could influence the total 

monetized benefits by more than 25%.

• Medium—if the uncertainty associated with an assumption could influence the total 

monetized benefits by 5% to 25%.

• Low—if the uncertainty associated with an assumption could influence the total 

monetized benefits by less than 5%.

For each uncertainty, we provide as much quantitative information as is available in the table to 

support the classification. 

Although many of the sources of uncertainty could affect both morbidity and mortality 

endpoints, because mortality benefits comprise over 94% of the monetized benefits that we are able to 

quantify in this analysis, uncertainties that affect the mortality estimate have the potential to have 

larger impacts on the total monetized benefits than uncertainties affecting only morbidity endpoints. 

One morbidity-related uncertainty that could have a significant impact on the benefits estimate is the 

extent to which omitted morbidity endpoints are included in the benefits analysis. Including additional 

morbidity endpoints that are currently not monetized would reduce the fraction of total benefits from 

mortality. Ultimately, the magnitude classification is determined by professional judgment of EPA 

staff based on the results of available information, including other U.S. EPA assessments of 

uncertainty (U.S. EPA, 2010a, 2011).

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

are: the causal relationship between long-term and short-term ozone exposure and mortality, the shape 

of the concentration-response (C-R) function for both categories of ozone-related mortality, and the 

mortality valuation, specifically for long-term exposure-related mortality. 

6A.1.3 Confidence in Analytic Approach

The “confidence in analytic approach” column of Table 6A-1 is an assessment of the scientific 

support for the analytic approach chosen (or the inherent assumption made) to account for the 

relationship about which we are uncertain. In other words, based on the available evidence, how 

certain are we that EPA’s selected approach is the most plausible of the potential alternatives. Similar 
6A-4

classifications have been included in previous risk and benefits analyses (U.S. EPA, 2010a, 

2011).2 The three categories used to characterize the degree of confidence are: 

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

• Medium—some evidence exists to support the selected approach, but data gaps are 

present; and

• Low—limited data exists to support the selected approach.

Ultimately, the degree of confidence in the analytic approach is EPA staff’s professional 

judgment based on the volume and consistency of supporting evidence, much of which has been 

evaluated in the O3 ISA (U.S. EPA, 2013) and by SAB. The O3 ISA evaluated the entire body of 

scientific literature on ozone science and was twice peer-reviewed by EPA’s Clean Air Scientific 

Advisory Committee (CASAC). In general, we regard a conclusion in the O3 ISA or specific advice 

from SAB as supporting a high degree of confidence in the selected approach. 

Based on this assessment, we have low or low-medium confidence in the evidence available to 

assess exposure error in epidemiology studies, morbidity valuation, baseline incidence projections for 

morbidity, and omitted morbidity endpoints. However, because these uncertainties have been classified 

as having a low or low-medium impact on the magnitude of the benefits, further investment in 

improving the available evidence would not have a substantial impact on the total monetized benefits. 

6A.1.4 Uncertainty Quantification

The column of Table 6A-1 labeled “uncertainty quantification” is an assessment of the extent to 

which we were able to use quantitative methods to characterize the residual uncertainty in the benefits 

analysis, after addressing it to the extent feasible in the analytic approach for this RIA. We categorize 

the level of quantification using the four tiers used in the WHO uncertainty framework. The WHO 

uncertainty framework is a well-established approach to assess uncertainty in risk estimates that 

systematically links the characterization of uncertainty to the sophistication of the health impact 

assessment. The advantage of using this framework is that it clearly highlights the level of uncertainty 

quantification applied in this assessment and the potential sources of uncertainty that require methods 

development in order to assess quantitatively. Specifically, EPA applied this framework in multiple 

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

2011) in this analysis. In the Quantitative Health Risk Assessment for Particulate Matter (U.S. EPA, 2010b), EPA 

assessed the degree of uncertainty (low, medium, or high) associated with the knowledge-base (i.e., assessed how well 

we understand each source of uncertainty), but did not provide specific criteria for the classification.
6A-5

risk and exposure assessments (U.S. EPA, 2010a, 2014), and it has been recommended in EPA 

guidance documents assessing air toxics-related risk and Superfund site risks (U.S. EPA, 2004 and 

2001, respectively). Ultimately, the tier decision is the professional judgment of EPA staff based on the 

availability of information for this assessment. The tiers used in this assessment are defined below.

• Tier 0—screening level, generic qualitative characterization.

• Tier 1—Scenario-specific qualitative characterization.

• Tier 2—Scenario-specific sensitivity analysis.

• Tier 3—Scenario-specific probabilistic assessment of individual and combined 

uncertainty.

Within the limits of the data, we strive to use more sophisticated approaches (e.g., Tier 2 or 3) 

for characterizing uncertainties that have the largest magnitudes and could not be completely addressed 

through the analytic approach. The uncertainties for which we have conducted probabilistic (Tier 3) 

assessments in this analysis are mortality causality, the shape of the concentration-response function, 

and mortality and morbidity valuation. For lower magnitude uncertainties, we include qualitative 

discussions of the potential impact of uncertainty on risk results (WHO Tier 0/1) and/or completed 

sensitivity analyses assessing the potential impact of sources of uncertainty on risk results (WHO Tier 

2). 

6A.2 Organization of the Qualitative Uncertainty Table

Table 6A-1 is organized as follows: the uncertainties are grouped by category (i.e., 

concentration-response function, valuation, population and baseline incidence, omitted benefits 

categories, and exposure changes). 
6A-6

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

Potential Source of 

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

Uncertainties Associated with Concentration-Response Functions

Causal relationship 

between short-term 

ozone exposure and 

premature mortality

Overestimate, if short-term ozone

exposure does not have a causal 

relationship with premature 

mortality.

High High Tier 1 (qualitative)

Mortality generally dominates 

monetized benefits, so small 

uncertainties could have large 

impacts on the total monetized 

benefits.

Our approach is consistent with the O3 ISA, 

which determined that premature mortality 

has a likely causal relationship with shortterm ozone exposure based on the collective 

body of evidence (p. 6-264). In addition, the 

NAS recommended that EPA “should give 

little or no weight to the 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, 2011), 

also confirmed the NAS recommendation to 

include ozone mortality benefits (U.S. EPASAB, 2010).

Either Medium-High Medium Tier 2 (sensitivity analysis)
6A-7

Potential Source of 

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

Shape of the C-R 

functions, particularly at 

low concentrations for 

short-term ozone 

exposure-related 

mortality

The direction of bias that assuming 

a linear-no threshold model or 

alternative model introduces 

depends upon the “true” functional 

from of the relationship and the 

specific assumptions and data in a 

particular analysis. For example, if 

the true function identifies a 

threshold below which health 

effects do not occur, benefits may 

be overestimated if a substantial 

portion of those benefits were 

estimated to occur below that 

threshold. Alternately, if a 

substantial portion of the benefits 

occurred above that threshold, the 

benefits may be underestimated 

because an assumed linear nothreshold function may not reflect 

the steeper slope above that 

threshold to account for all health 

effects occurring above that 

threshold.

The magnitude of this impact 

depends on the fraction of benefits 

occurring in areas with lower 

concentrations. Mortality generally 

dominates monetized benefits, so 

small uncertainties could have 

large impacts on total monetized 

benefits.

The O3 ISA did not find any evidence that 

supports a threshold in the relationship 

between short-term exposure to ozone and 

mortality within the range of ozone 

concentrations observed in the U.S., and 

recent evidence suggests that the shape of 

the ozone-mortality C-R curve remains 

linear across the full range of ozone

concentrations (p. 6-257). Consistent with 

the O3 ISA, we assume a log-linear nothreshold model for the C-R functions for 

short-term ozone mortality. However, the 

ISA notes that there is less certainty in the 

shape of the C-R function below 20 ppb due 

to the low density of data in this range (p. 6-

254-255). 

The comparison of short-term 

mortality against the associated 

distribution of (ozone seasonaveraged) 8-hour maximum

ozone concentrations (see 

Appendix 6B, section 6B.7) 

suggests that the vast majority 

of predicted reductions in 

mortality are associated with 

days having 8-hour maximum 

concentrations that fall within 

the higher confidence range. 

Causal relationship 

between long-term 

ozone exposure and 

premature respiratory 

mortality

Overestimate, if long-term ozone

exposure does not have a causal 

relationship with premature 

mortality.

Potentially High, if included in 

monetized benefits High Tier 1 (qualitative)

Mortality generally dominates 

monetized benefits, so small 

uncertainties could have large 

impacts on the total monetized 

benefits. However, we have not 

included long-term ozone 

mortality in the monetized benefits 

for this analysis due to 

uncertainties in the cessation lag. 

While the O3 ISA concludes that evidence is 

suggestive of a causal association between 

total mortality and long-term ozone 

exposure (section 7.7.1), specifically with 

regard to respiratory health effects 

(including mortality), the ISA concludes that 

there is likely to be a causal association

(section 7.2.8). 

Shape of the C-R 

functions, particularly at 

low concentrations for 

long-term ozone 

exposure-related 

respiratory mortality

Either Potentially High, if included in 

monetized benefits Medium Tier 2 (sensitivity analysis)
6A-8

Potential Source of 

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

The direction of bias that assuming 

a linear-no threshold model or 

alternative model introduces 

depends upon the “true” functional 

form of the relationship and the 

specific assumptions and data in a 

particular analysis. For example, if 

the true function identifies a 

threshold below which health 

effects do not occur, benefits may 

be overestimated if a substantial 

portion of those benefits were 

estimated to occur below that 

threshold. Alternately, if a 

substantial portion of the benefits 

occurred above that threshold, the 

benefits may be underestimated 

because an assumed linear nothreshold function may not reflect 

the steeper slope above that 

threshold to account for all health 

effects occurring above that 

threshold.

The magnitude of this impact 

depends on the fraction of benefits 

occurring in areas with lower 

concentrations. However, we have 

not included long-term ozone 

mortality in the monetized benefits 

for this analysis due to 

uncertainties in the cessation lag.

In their memo (see Sasser 2014) clarifying 

the results of their study (Jerrett et al., 2009) 

regarding long-term ozone exposure-related 

respiratory mortality, the study authors note 

that in terms of goodness of fit, long-term 

health risk models including ozone clearly 

performed better than models without 

ozone, indicating the improved predictions 

of respiratory mortality when ozone is 

included. In the article proper, the authors 

state that, “There was limited evidence that 

a threshold model specification improved 

model fit as compared with a non-threshold 

linear model...”. Furthermore, in the memo 

referenced above, the authors conclude that 

considerable caution should be exercised in 

using any specific threshold, particularly 

when the more stringent statistical test 

indicates there is no significantly improved 

prediction. The CASAC was supportive of 

the approach EPA used in the HREA of 

using a non-threshold C-R function based on 

this study to generate core estimates and 

consider the impact of potential thresholds 

(ranging from 40-60 ppb) as a sensitivity 

analysis. 

We examined potential 

thresholds (from 40 to 60 ppb) 

in the C-R function for longterm exposure-related morality. 

That analysis suggested that 

thresholds between 55 and 60

ppb would have a substantial 

impact on overall modeled 

benefits, while thresholds 

below 50 ppb would have a 

minor impact on predicted 

benefits (see Appendix 6B, 

Table 6B-3). 

Exposure error in 

epidemiology studies

Underestimate (generally) Medium Low-Medium Tier 1 (qualitative)

The O3 ISA states that exposure 

measurement error can also be an 

important contributor to 

uncertainty in effect estimates 

associated with both short-term 

and long-term studies (p. 1xii). 

Together with other factors (e.g., 

low data density), exposure error 

can smooth the C-R functions and 

obscure potential thresholds (p. 

lxix). In addition, the O3 ISA 

states that exposure error can bias 

effect estimates toward or away 

from the null and widen 

confidence intervals (p. lxii). 

Recent analyses reported in 

Krewski et al. (2009) demonstrate 

the potentially significant effect 

that this source of uncertainty can 

have on effect estimates. These 

analyses also illustrate the 

complexity and site-specific nature 

of this source of uncertainty.

Although this underestimation is well 

documented, including in the O3 ISA, the 

SAB has not suggested an approach to 

adjust for this bias.

(No quantitative method 

available)

Unknown Medium Medium Tier 1 (qualitative)
6A-9

Potential Source of 

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

Adjustment of risk 

coefficients to 8-hour 

maximum from 24-hour 

average or 1-hour 

maximum in the 

epidemiology studies

We converted these metrics to 

maximum 8-hour average ozone 

concentration using standard 

conversion functions based on 

observed relationships in the 

underlying studies. If the 

relationships between air metrics 

reported in the studies differ 

systematically from the 

relationships seen across the 

modeling domain, then bias could 

be introduced. 

This conversion does not affect the 

relative magnitude of the health 

impact function. However, the 

pattern of 8-hour maximum

concentrations for a particular 

location over an ozone season 

could differ from the pattern of 1-

hour max or 24-hour average 

metrics for that same location. 

Consequently, monetized benefits 

could differ for a particular 

location depending on the metric

used in modeling benefits. 

This practice is consistent both with the 

available exposure modeling and with the 

form of the current ozone standard. 

However, in some cases, these conversions 

were not specific to the ozone “warm” 

season, which introduces additional 

uncertainty due to the use of effect estimates 

based on a mixture of warm season and all 

year data in the epidemiological studies.

(No quantitative method 

available)

Confounding by 

individual risk factors, 

other than 

socioeconomic status—

e.g., smoking, or 

ecologic factors, which 

represent the 

neighborhood, such as 

unemployment

Either, depending on the factor and 

study Medium Medium Tier 2 (sensitivity analysis)

Individual, social, economic, and 

demographic covariates can bias 

the relationship between 

particulate air pollution and 

mortality, particularly in cohort 

studies that rely on regional air 

pollution levels.

Because mortality dominates 

monetized benefits, even a small 

amount of confounding could have 

medium impacts on total 

monetized benefits.

To minimize confounding effects, we use 

risk coefficients that control for individual 

risk factors to the extent practical.

(Quantitative methods 

available but not assessed in 

this analysis.)

Confounding and effect 

modification by copollutants

Either, depending upon the 

pollutant. Medium Medium Tier 1 (qualitative)

Disentangling the health responses 

of combustion-related pollutants 

(i.e., PM, SOx, NOx, ozone, and 

CO) is a challenge. The PM ISA 

states that co-pollutants may 

mediate the effects of PM or PM 

may influence the toxicity of copollutants (U.S. EPA, 2009, p. 1–

16). Alternately, effects attributed 

to one pollutants may be due to 

another.

Because this uncertainty could 

affect mortality and because 

mortality generally dominates 

monetized benefits, even small 

uncertainties could have medium 

impacts on total monetized 

benefits.

The O3 ISA states that there is high 

confidence that unmeasured confounders are 

not producing the findings when multiple 

studies are conducted in various settings 

using different subjects or exposures, such 

as multi-city studies (p. lxi). When modeling 

effects of pollutants jointly (e.g., PM and 

O3), we apply multi-pollutant effect 

estimates when those estimates are available 

to avoid double-counting and satisfy other 

selection criteria. In addition, we apply 

multi-city effect estimates when available.

(No quantitative method 

available)

Application of C-R 

relationships only to the 

original study 

population

Underestimate Low High Tier 2 (sensitivity analysis)

Estimating health effects for only 

the original study population may 

underestimate the whole 

population benefits of reductions 

in pollutant exposures.

Mortality generally dominates 

monetized benefits, so further age 

range expansions for morbidity 

endpoints would have a small 

impact on total monetized benefits.

Following advice from the SAB (U.S. EPASAB, 2004a, pg. 7) and NAS (NRC, 2002, 

pg. 114), we expanded the age range for 

childhood asthma exacerbations beyond the 

original study population to ages 6-18.

(Quantitative methods 

available but not assessed in 

this analysis.)

Uncertainties Associated with Economic Valuation

Unknown High Medium Tier 3 (probabilistic)
6A-10

Potential Source of 

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

Mortality Risk 

Valuation/Value-of-aStatistical-Life (VSL)

Some studies suggest that EPA’s 

mortality valuation is too high, 

while other studies suggest that it 

is too low. Differences in age, 

income, risk aversion, altruism, 

nature of risk (e.g., cancer), and 

study design could lead to higher 

or lower estimates of mortality 

valuation.

Mortality generally dominates 

monetized benefits, so moderate 

uncertainties could have a large 

effect on total monetized benefits.

The VSL used by EPA is based on 26 labor 

market and stated preference studies 

published between 1974 and 1991. EPA is 

in the process of reviewing this estimate and 

will issue revised guidance based on the 

most up-to-date literature and 

recommendations from the SAB-EEAC in 

the near future (U.S. EPA, 2010b, U.S. 

EPA-SAB, 2011c).

Assessed uncertainty in 

mortality valuation using a 

Weibull distribution.

Cessation lag structure 

for long-term ozone

mortality

Unkonwn Medium, if included in the 

monetized benefits Low Tier 2 (sensitivity analysis)

We included both a zero (no) lag 

and 20-year segmented lag model 

in completing the sensitivity 

analysis involving dollar benefits 

for long-term respiratory mortality. 

Given that available information 

does not lead to the selection of a 

particular lag model, we are not in 

a position to classify the direction 

of potential bias associated with 

this source of uncertainty. 

Although the cessation lag does 

not affect the number of premature 

deaths attributable to long-term 

ozone exposure, it affects the 

timing of those deaths and thus the 

discounted monetized benefits. 

Mortality generally dominates 

monetized benefits, so moderate 

uncertainties could have a large 

effect on total monetized benefits.

However, we have not included 

long-term ozone mortality in the 

monetized benefits for this 

analysis due to uncertainties in the 

cessation lag.

As discussed in section 6.7.3.1, in 

presenting dollar benefit estimates as part of 

the sensitivity analysis (presenting dollar 

benefits for long-term ozone-related 

morality), we 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. The use of the 20-

year segmented lag reflects consideration for 

advice provided by the SAB-HES (USEPASAB, 2010, 2004c).

Using the 20-year segmented 

lag developed for PM2.5–

related mortality results in a 

10-20% reduction in the total 

dollar benefit (using a 3% and 

7% discount rate, respectively) 

relative to the alternative 

approach of applying no lag 

(i.e., assuming all of the 

mortality reductions occur in 

the same year).

Income growth 

adjustments

Either Medium Medium Tier 2 (sensitivity analysis)

Income growth increases 

willingness-to-pay (WTP) 

valuation estimates, including 

mortality, over time. From 1997 to 

2010, personal income and GDP 

growth have begun to diverge. If 

this trend continues, the 

assumption that per capita GDP 

growth is a reasonable proxy for 

income growth may lead to an 

overstatement of benefits. (IEc, 

2012).

Income growth from 1990 to 2020 

increases mortality valuation by 

20%. Alternate estimates for this 

adjustment vary by 20% (IEc, 

2012). Because we do not adjust 

for income growth over the 20-

year cessation lag, this approach 

could also underestimate the 

benefits for the later years of the 

lag.

Consistent with SAB recommendations 

(U.S. EPA,-SAB, 2000, pg. 16), we adjust 

WTP for income growth. It is difficult to 

forecast future income growth; however, in 

the absence of readily available income data 

projections, per capita GDP is the best 

available option.

As shown in Appendix 6B

(section 6B.5), the use of 

alternate income growth 

adjustments would result in an 

increase of from 8 to 75% in 

the dollar benefits for shortterm ozone-related mortality.

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

available
6A-11

Potential Source of 

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

Morbidity benefits such as hospital 

admissions are calculated using 

cost-of-illness (COI) estimates, 

which are generally half the WTP

to avoid the illness (Alberini and 

Krupnick, 2000). In addition, the 

morbidity costs do not reflect 

physiological responses or 

sequelae events, such as increased 

susceptibility for future morbidity.

Even if we doubled the monetized 

valuation of morbidity endpoints 

using COI valuations that are 

currently included in the RIA, the 

change would still be less than 5% 

of the monetized benefits. It is 

unknown how much including 

sequelae events could increase 

morbidity valuation.

Although the COI estimates for 

hospitalizations reflect recent data, we have 

not yet updated other COI estimates such as 

for school loss days. The SAB concluded 

that COI estimates could be used as 

placeholders where WTP estimates are 

unavailable, but it is reasonable to presume 

that this strategy typically understates WTP 

values (U.S. EPA-SAB, 2004b, pg. 3).

Assessed uncertainty in 

morbidity valuation using 

distributions specified in the 

underlying literature, where 

available (see Table 6-10).

Uncertainties Associated with Baseline Incidence and Population Projections

Population estimates 

and projections

Either Low–Medium Medium Tier 1 (qualitative)

The monetized benefits would 

change in the same direction as the 

over- or underestimate in 

population projections in areas 

where exposure changes.

Monetized benefits are 

substantially affected by 

population density. Comparisons 

using historical Census data show 

that population projections are 

±5% nationally, but projection 

accuracy can vary by locality. 

Historical error for Woods & 

Poole’s population projections has 

been ±8.1% for county-level 

projections and ±4.1% for states 

(Woods and Poole, 2012). The 

magnitude of impact on total 

monetized benefits depends on the 

specific location where ozone is 

reduced.

We use population projections for 5-year 

increments for 304 race/ethnicity/gender/age 

groups (Woods and Poole, 2012) at Census 

blocks. Population forecasting is wellestablished but projections of future 

migration due to possible catastrophic 

events are not considered. In addition, 

projections at the small spatial scales used in 

this analysis are inherently more uncertain 

than projections at the county- or state-level.

(No quantitative method 

available)

Uncertainty in 

projecting baseline 

incidence rates for 

mortality

Unknown Low-Medium Medium Tier 1 (qualitative)

Because the mortality rate 

projections for future years reflect 

changes in mortality patterns as

well as population growth, the 

projections are unlikely to be 

biased.

Because mortality generally 

dominates monetized benefits, 

small uncertainties could have 

medium impacts on total 

monetized benefits.

The county-level baseline mortality rates 

reflect recent databases (i.e., 2004–2006 

data) and are projected for 5-year 

increments for multiple age groups. This 

database is generally considered to have 

relatively low uncertainty (CDC Wonder, 

2008). The projections account for both 

spatial and temporal changes in the 

population.

(No quantitative method 

available)

Uncertainty in 

projecting baseline 

Either, depending on the health 

endpoint Low Low-Medium Tier 1 (qualitative)
6A-12

Potential Source of 

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

incidence rates and 

prevalence rates for 

morbidity

Morbidity baseline incidence is 

available for current year only (i.e., 

no projections available). 

Assuming current year levels can 

bias the benefits for a specific 

endpoint if the data has clear 

trends over time. Specifically, 

asthma prevalence rates have 

increased substantially over the

past few years while hospital 

admissions have decreased 

substantially.

The magnitude varies with the 

health endpoint, but the overall 

impact on the total benefits 

estimate from these morbidity 

endpoints is likely to be low.

We do not have a method to project future 

baseline morbidity rates, thus we assume 

current year levels will continue. While we 

try to update the baseline incidence and 

prevalence rates as frequently as practicable, 

this does not continue trends into the future. 

Some endpoints, such as hospitalizations 

and ER visits, have more recent data (i.e., 

2007) stratified by age and geographic 

location. Other endpoints, such as 

respiratory symptoms, reflect a national 

average. Asthma prevalence rates reflect 

recent increases in baseline asthma rates 

(i.e., 2008).

(No quantitative method 

available)

Uncertainties Associated with Omitted Benefits Categories

Unquantified ozone

health benefit 

categories, such as 

worker productivity and 

long-term mortality

Underestimate High Low Tier 2 (sensitivity analysis)

EPA has not included monetized 

estimates of these benefits 

categories in the core benefits 

estimate.

Although the potential magnitude 

is unknown, including all of the 

additional endpoints associated 

with ozone exposure that are 

currently not monetized could 

increase the total benefits by a 

large amount.

Current data and methods are insufficient to 

value national quantitative estimates of these 

health effects. The O3 ISA determined that 

respiratory effects (including mortality) are 

causally associated with long-term ozone

exposure (p. 2–17). The O3 ISA also 

determined that outdoor workers have an 

increased risk of ozone-related health effects 

(p. 1-15), and that studies on outdoor 

workers show consistent evidence that 

short-term increases in ambient ozone

exposure can decrease lung function in 

healthy adults (p.6-38). Additional studies 

link short-term ozone exposure to reduced 

productivity in outdoor workers (Graf Zivin 

and Neidell, 2013; Crocker and Horst, 

1981).

We include sensitivity analyses 

reflecting long-term mortality, 

which shows that this endpoint 

could add substantially to the 

monetized benefits (see 

Appendix 6B, section 6B.2). 

We have also included a 

supplemental analysis 

reflecting application of an 

updated worker productivity 

analysis (see section 6.5.3).

Uncertainties Associated with Estimated Exposure Changes

Unknown Unknown Low Tier 1 (qualitative)
6A-13

Potential Source of 

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

Spatial matching of air 

quality estimates from 

epidemiology studies to 

air quality estimates 

from air quality 

modeling

Epidemiology studies often 

assume one air quality 

concentration is representative of 

an entire urban area when 

calculating hazard ratios, while 

benefits are calculated using air 

quality modeling conducted at 12 

km spatial resolution. This spatial 

mismatch could introduce 

uncertainty.

We have not controlled for this potential 

bias, and the SAB has not suggested an

approach to adjust for this bias.

(No quantitative method 

available)
6A-14

6A.3 References

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Controlling Ozone Air Pollution. National Academies Press. Washington, DC.

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Matter—Final Report. EPA-452/R-10-005. Office of Air Quality Planning and Standards, Research Triangle 

Park, NC. September. Available on the Internet at 

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Policy: A White Paper (SAB Review Draft). December. Available on the Internet at 

<http://yosemite.epa.gov/ee/epa/eerm.nsf/vwAN/EE-0563-1.pdf/$file/EE-0563-1.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2011. The Benefits and Costs of the Clean Air Act 1990 to 

2020: EPA Report to Congress. Office of Air and Radiation, Office of Policy, Washington, DC. March. 

Available on the Internet at <http://www.epa.gov/oar/sect812/feb11/fullreport.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2012. Regulatory Impact Analysis for the Final Revisions to the 

National Ambient Air Quality Standards for Particulate Matter. EPA-452/R-12-003. Office of Air Quality 

Planning and Standards, Health and Environmental Impacts Division. December. Available at: 

<http://www.epa.gov/pm/2012/finalria.pdf>. 

U.S. Environmental Protection Agency (U.S. EPA). 2013. Integrated Science Assessment of Ozone and Related 

Photochemical Oxidants (Final Report). U.S. Environmental Protection Agency, Washington, DC, EPA/600/R10/076F. Available at: <http://cfpub.epa.gov/ncea/isa/recordisplay.cfm?deid=247492#Download>.

U.S. Environmental Protection Agency (U.S. EPA). 2014. Health Risk and Exposure Assessment for Ozone Final 

Report. EPA-452/R-14-004a. Office of Air Quality Planning and Standards, Health and Environmental Impacts 

Division. August. Available at: < http://www.epa.gov/ttn/naaqs/standards/ozone/data/20140829healthrea.pdf>. 

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 1999. An SAB Advisory: The 

Clean Air Act Section 812 Prospective Study Health and Ecological Initial Studies. EPA-SAB-COUNCILADV-99-005, Available on the Internet at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/1AFDEBB45C57F6E5852571930058C1A5/$File/coua9905.pdf>.

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2000. The Clean Air Act 

Amendments (CAAA) Section 812 Prospective Study of Costs and Benefits (1999): Advisory by the Advisory 

Council on Clean Air Compliance Analysis: Costs and Benefits of the CAAA. EPA-SAB-COUNCIL-ADV-00-

002. October. Available on the Internet at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/E992F918DF32131A852571B9004571F7/$File/coua0002.pdf>.

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2001. REVIEW OF THE 

DRAFT ANALYTICAL PLAN FOR EPA’S SECOND PROSPECTIVE ANALYSIS - BENEFITS AND 

COSTS OF THE CLEAN AIR ACT 1990- 2020. EPA-SAB-COUNCIL-ADV-01-004, Available on the 

Internet at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/572DC1989A3986CC8525718D006BAB8B/$File/councila01004.

pdf>.

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Health Effects Analysis in the Analytical Plan for EPA’s Second Prospective Analysis—Benefits and Costs of 

the Clean Air Act, 1990–2020. EPA-SAB-COUNCIL-ADV-04-002. March. Available on the Internet at 
6A-16

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il_adv_04002.pdf>.

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Analytical Plan for EPA’s Second Prospective Analysis—Benefits and Costs of the Clean Air Act, 1990-2020: 

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004.pdf>.

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Clean Air Compliance Analysis Response to Agency Request on Cessation Lag. EPA-COUNCIL-LTR-05-001. 

December. Available on the Internet at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/0/39F44B098DB49F3C85257170005293E0/$File/council_ltr_05_

001.pdf>.

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DRAFT Health Benefits of the Second Section 812 Prospective Study of the Clean Air Act. EPA-COUNCIL10-001. June. Available on the Internet at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/9288428b8eeea4c885257242006935a3/72D4EFA39E48CDB2852

5774500738776/$File/EPA-COUNCIL-10-001-unsigned.pdf>.

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Quantitative Health Risk Assessment for Particulate Matter—Second External Review Draft (February 2010). 

EPA-CASAC-10-008. Available on the Internet at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/BC4F6E77B6385155852577070002F09F/$File/EPA-CASAC-10-

008-unsigned.pdf>.

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2011b. Review of the Final 

Integrated Report for the Second Section 812 Prospective Study of the Benefits and Costs of the Clean Air Act 

(August 2010). EPA-COUNCIL-11-001. Available on the Internet at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/1E6218DE3BFF682E852577FB005D46F1/$File/EPA-COUNCIL11-001-unsigned.pdf>.

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2011c. Review of Valuing 

Mortality Risk Reductions for Environmental Policy: A White Paper (December 10, 2010). EPA-SAB-11-011. 

July. Available on the Internet at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/298E1F50F844BC23852578DC0059A616/$File/EPA-SAB-11-

011-unsigned.pdf>.

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

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

World Health Organization (WHO). 2008. Part 1: Guidance Document on Characterizing and Communicating 

Uncertainty in Exposure Assessment, Harmonization Project Document No. 6. Published under joint 

sponsorship of the World Health Organization, the International Labour Organization and the United Nations 

Environment Programme. WHO Press: Geneva, Switzerland. Available on the Internet at 

<www.who.int/ipcs/methods/harmonization/areas/uncertainty%20.pdf>. 
6B-1

APPENDIX 6B: SENSITIVITY ANALYSES RELATED TO THE BENEFITS 

ANALYSIS

Overview

The benefits analysis presented in Chapter 6 of this RIA is based on our current 

interpretation of the scientific and economic literature. That interpretation requires judgments 

regarding the best available data, models, and analytical methodologies and the assumptions that 

are most appropriate to adopt in the face of important uncertainties. The majority of the 

analytical assumptions used to develop the main estimates of benefits have been reviewed and 

supported by EPA’s independent Science Advisory Board (SAB). Both EPA and the SAB 

recognize that data and modeling limitations, as well as simplifying assumptions can introduce 

uncertainty into the estimates of benefits and that alternative choices exist for some inputs to the 

analysis, such as the concentration-response (C-R) functions for mortality.

This appendix assesses the sensitivity of the benefits results to: (a) alternative studies for 

short-term ozone exposure-related mortality (section 6B.1), (b) monetized benefits associated 

with mortality resulting from long-term exposure to ozone (section 6B.2), (c) the potential 

impact of thresholds in long-term ozone exposure-related mortality in incidence and benefits 

estimates (section 6B.3), (d) alternative response functions developed through expert elicitation 

for long-term PM2.5 exposure-related mortality (section 6B.4), and (e) alternative assumptions 

regarding income elasticity on benefits derived using willingness-to-pay (WTP) functions 

(section 6B.5). For the core analyses, we estimated benefits for two scenarios: 2025 and post2025. However, in conducting these sensitivity analyses, we used the 2025 scenario as the basis 

for making our calculations, since sensitivity analysis findings for this scenario would generally 

hold for the post-2025 scenario.

In addition to the sensitivity analyses described above, this appendix presents several 

analyses intended to supplement the core estimates generated for the RIA. These supplemental 

analyses give greater insight to the manner in which these impacts are distributed among 

populations of different ages and the ambient levels of ozone at which the avoided deaths are 

estimated to occur and include: (a) age group-differentiated aspects of short-term ozone 

exposure-related mortality (including total avoided incidence, life years gained and percent 
6B-2

reduction in baseline mortality - Section 6B.6), (b) evaluation of mortality impacts relative to the 

baseline pollutant concentrations (used in generating those mortality estimates) for both shortterm ozone exposure-related mortality and long-term PM2.5 exposure-related mortality (Section 

6B.7) and (c) a detailed discussion of the methodology used in modeling ozone-related impacts 

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

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

Ozone

Table 6B-1 presents the results of applying alternative effect estimates identified for 

modeling short-term exposure-related mortality for ozone, along with the set of core risk 

estimates (these sensitivity analysis results are also reflected in Figure 6-4 presented in the body 

of the document). The suite of effect estimates included in the sensitivity analysis reflects 

consideration for both meta-analyses and multi-city epidemiology studies. The rationale for the 

specific mix of effect estimates included in the sensitivity analysis is presented in section 6.6.3.2.

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

to Ozone a

Health Effect

Revised and Alternative Standards

(95th percentile confidence intervals)

70 ppb 65 ppb

Avoided Short-Term Mortality - Core Analysis

multi-city studies

Smith et al. (2009) (all ages) 96 490

(47 to 140) (240 to 740)

Zanobetti and Schwartz (2008) (all ages) 160 820

(86 to 240) (440 to 1,200)

Avoided Short-Term Mortality - Sensitivity Analysis

multi-city studies

Smith et al. (2009) (all ages) 

co-pollutant model with PM10

77 390

(-21 to 170) (-110 to 890)

Schwartz (2005) (all ages) 120 610

(37 to 200) (190 to 1,000)

Huang et al. (2005) (cardiopulmonary) 110 580

(42 to 180) (220 to 940)

Bell et al. (2004) (all ages) 78 400

(26 to 130) (130 to 660)

meta- analyses

Bell et al. (2005) (all ages) 250 1,300

(120 to 380) (610 to 2,000)

Ito et al. (2005) (all ages) 350 1,800

(210 to 490) (1,100 to 2,500)

Levy et al. (2005) (all ages) 350 1,800

(240 to 470) (1,200 to 2,400)

a All estimates are rounded to two significant digits.
6B-3

This sensitivity analysis showed that the two core incidence estimates fall within (and 

towards the lower end of) the broader range resulting from application of the seven alternative 

effect estimates (note that these observations based on incidence would also hold for the 

matching set of dollar benefit estimates generated for this endpoint category).

6B.2 Sensitivity Analysis for Monetized Benefits for Premature Mortality from Longterm Exposure to Ozone 

As discussed in section 6.3, due to uncertainty in specifying the temporal lag structure 

associated with reductions in long-term ozone-related respiratory mortality, we have included 

these estimates as a sensitivity analysis and not as part of the core dollar benefit estimate. Table 

6B-2 presents the dollar benefits (2011$) associated with modeled reductions in long-term 

ozone-related respiratory mortality. These benefit estimates are generated using non-threshold 

models obtained from Jerrett et al. (2009) because we present the potential threshold analysis in 

the section 6B.3. Additional detail on the effect estimates used in the core analysis and this 

sensitivity analysis is presented in section 6.6.3.2. Section 6.6.4 provides additional detail on the 

approach used in valuing these mortality estimates including how uncertainty related to cessation 

lag was addressed.
6B-4

Table 6B-2. Sensitivity Analysis for Monetized Benefits for Mortality from Long-term 

Exposure to Ozone (millions of 2011$) (2025 and post-2025 scenarios)a, d

a See section 6B.3 for the sensitivity analysis for potential thresholds in the C-R function for long-term exposurerelated mortality Observations from that analysis can be applied to these results. b The zero-lag model is not affected by discounting. The values in parentheses reflect 95th percentile confidence 

intervals. c The range (outside of the parentheses) results from application of 7% and 3% discount rates after applying the 20-

year segmented lag (i.e., ranging from the 7% discount rate up to the 3% discount rate). The range in parentheses 

reflects the 95th percentile confidence interval in combination with the discount rate (i.e., ranging from the 

2.5th percentile and the 7% discount rate up to the 97.5th percentile and the 3% discount rate). d All estimates rounded to two significant digits.

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

ozone exposure-related mortality could add substantially to the overall benefits. Additionally, 

use of a 20-year segment lag can reduce benefits by 10-20% (relative to a zero lag) depending on 

the discount rate applied.

6B.3 Threshold Sensitivity Analysis for Premature Mortality Incidence and Benefits from 

Long-term Exposure to Ozone

In estimating long-term ozone mortality (including the benefit estimates presented in the 

last section), we employed a continuous non-threshold C-R function relating ozone exposure to 

premature death. However, as discussed in Section 6.6.3.2, there is uncertainty regarding the 

70 ppb 65 ppb

Avoided Long-term Respiratory Mortality

2025 scenario

$3,400 $17,000

($280 to $10,000) ($1,400 to $52,000)

$2,800 to $3,100 $14,000 to $16,000

($250 to $8,400) ($1,200 to $47,000)

post-2025 scenario

$3,000 $6,000

($240 to $8,400) ($490 to $18,000)

$2,400 to $2,700 $4,900 to $5,400

($200 to $8,000) ($400 to $18,000)

Revised and Alterative Standard Levels

(95th percentile confidence intervals)

multi-city study

Jerrett et al. (2009) (age 30-99) 

copollutants model (PM2.5) no lag b

multi-city study

Jerrett et al. (2009) (age 30-99) 

copollutants model (PM2.5) no lag b

Jerrett et al. (2009) (age 30-99) 

copollutants model (PM2.5) 20 yr 

segmented lag c

Jerrett et al. (2009) (age 30-99) 

copollutants model (PM2.5) 20 yr 

segmented lag c

Health Effectb
6B-5

potential existence and location of a threshold in the C-R function relating mortality and longterm ozone concentrations. Thus, we have included a sensitivity analysis exploring the impact of 

potential thresholds in the C-R relationship on estimates of long-term exposure-related mortality 

that were evaluated in Jerrett et al. (2009), consistent with advice from the Clean Air Scientific 

Advisory Committee (CASAC) (U.S. EPA-SAB, 2014). 

In their memo clarifying the results of their study (Sasser, 2014), the authors note that in 

terms of goodness of fit, long-term health risk models including ozone clearly performed better 

than models without ozone, indicating the improved predictions of respiratory mortality when 

ozone is included. In exploring different functional forms, the authors report that the model 

including a threshold at 56 ppb had the lowest log-likelihood value of all models evaluated (i.e., 

linear models and models including thresholds ranging from 40-60 ppb), and thus provided the 

best overall statistical fit to the data. However, they also note that it is not clear whether the 56 

ppb threshold model is a better predictor of respiratory mortality than when using a linear (nothreshold) 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, we have chosen to reflect the uncertainty about the existence and location of a 

potential threshold by estimating mortality attributable to long-term ozone exposures using a 

range of threshold-based effect coefficient as sensitivity analyses. Specifically, we estimate longterm ozone mortality benefits using unique risk coefficients that include a range of thresholds 

from 40 ppb to 60 ppb in 5 ppb increments, while also including a model with a threshold equal 

to 56 ppb, which had the lowest log-likelihood value for all models examined.1 Table 6B-3

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

estimated in Jerrett et al. (2009). As a result, the sensitivity of estimated mortality attributable to long-term ozone
6B-6

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

70 ppb. The same pattern in terms of relative reductions across thresholds would also hold for 

the monetized benefit estimates.

Table 6B-3. Long-term Ozone Mortality Incidence at Various Assumed Thresholds a

Threshold Concentration 70 ppb 65 ppb

No threshold 340 1,700

40 ppb 260 1,300

45 ppb 200 910

50 ppb 59 210

55 ppb 6 66

56 ppb 5 60

60 ppb 3 19

a All estimates rounded to two significant digits.

The results of the sensitivity analysis based on the suite of threshold-based risk 

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

long-term mortality. For example, estimated benefits for long-term mortality using a model that 

includes a 55 ppb threshold are approximately 70% less than long-term mortality benefits 

estimated using the co-pollutant, non-threshold model. Generally, estimated long-term mortality 

benefits are progressively reduced when using models with increasing thresholds, with the 

highest threshold considered (60 ppb) removing virtually all of the estimated incidence reduction 

and associated benefits.

6B.4 Sensitivity Analysis for Alternative C-R Functions for PM2.5–Related Mortality

In estimating PM2.5 co-benefits, monetized benefits are driven largely by reductions in 

mortality. Therefore, it is particularly important to attempt to characterize the uncertainties 

associated with reductions in premature mortality. In addition to the ACS and Six Cities cohort

studies, several recent cohort studies conducted in North America provide evidence for the 

relationship between long-term exposure to PM2.5 and the risk of premature death. Many of these 

additional cohort studies are described in the PM ISA (U.S. EPA, 2009) and the Provisional 

Assessment (U.S. EPA, 2012a).2, Table 6B-4 provides the effect estimates from each of these 

 

concentrations is affected by both the assumed threshold level (below which there is no effect of ozone) and the 

effect coefficient applied to ozone concentrations above the threshold.

2 It is important to note that the newer studies in the Provisional Assessment are published in peer-reviewed journals 

and meet our study selection criteria, but they have not been assessed in the context of an Integrated Science 
6B-7

cohort studies for all-cause, cardiovascular, cardiopulmonary, and ischemic heart disease (IHD) 

mortality, as well as the lowest measured air quality level (LML) and mean concentration in the 

study. 

We also draw upon the results of the 2006 expert elicitation3 sponsored by the EPA 

(Roman et al., 2008; IEc, 2006) to demonstrate the sensitivity of the benefits estimates to 12 

expert-defined C-R functions. The PM2.5 expert elicitation and the derivation of C-R functions

from the expert elicitation results are described in detail in the 2006 PM2.5 NAAQS RIA (U.S. 

EPA, 2006), the elicitation summary report (IEc, 2006) and Roman et al. (2008), and we 

summarize the key attributes of this study relative to the interpretation of the estimates of PMrelated mortality reported here. We describe also how the epidemiological literature has evolved 

since the expert elicitation was conducted in 2005 and 2006.

 

Assessment nor gone through review by the SAB. In addition, only the ACS and Harvard Six Cities’ cohort 

studies have been recommended by the SAB as appropriate for benefits analysis of national rulemakings.

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

of multiple experts, are obtained (Ayyub, 2002).
6B-8

Table 6B-4. Summary of Effect Estimates from Recent Cohort Studies in North America

Associated with Change in Long-Term Exposure to PM2.5

Study Cohort (age)

LML 

(μg/m3)

Mean

(μg/m3)

Hazard Ratios per 10 μg/m3 Change in PM2.5

(95th percentile confidence intervals)

All Causes Cardiovascular Cardiopulmonary IHD

Pope et al. 

(2002)

ACS 

(age >30)

7.5 18.2 1.06

(1.02–1.11)

1.12

(1.08–1.15)

1.09

(1.03–1.16)

N/A

Laden et al. 

(2006)

Six Cities

(age > 25)

10 16.4 1.16

(1.07–1.26)

1.28

(1.13–1.44)

N/A N/A

Lipfert et al. 

(2006)a

Veterans

(age 39–63)

<14.1 14.3 1.15

(1.05–1.25)

N/A N/A N/A

Miller et al. 

(2007)b

WHI

(age 50–79)

3.4 13.5 N/A 1.76

(1.25–2.47)

N/A 2.21

(1.17–4.16)

Eftim et al. 

(2008)

Medicare (age > 

65)

6 13.6 1.21

(1.15–1.27)

N/A N/A N/A

Zeger et al. 

(2008)c

Medicare (age > 

65)

<9.8 13.2 1.068

(1.049–1.087)

N/A N/A N/A

Krewski et 

al. (2009)d

ACS

(age >30)

5.8 14 1.06

(1.04–1.08)

N/A 1.13

(1.10–1.16)

1.24

(1.19–1.29)

Puett et al. 

(2009)b

NHS 

(age 30–55)

5.8 13.9 1.26

(1.02–1.54)

N/A N/A 2.02

(1.07–3.78)

Crouse et al. 

(2012)

d,e

Canadian 

census

1.9 8.7 1.06

(1.01–1.10)

N/A N/A N/A

Puett et al. 

(2011)f

Health 

Professionals 

(age 40–75)

<14.4 17.8 0.86

(0.70–1.00)

1.02

(0.84–1.23)

N/A N/A

Lepeule et 

al. (2012)d

Six Cities

(age > 25)

8 15.9 1.14

(1.07–1.22)

1.26

(1.14–1.40)

N/A N/A

a Low socio-economic status (SES) men only. Used traffic proximity as a surrogate of exposure. b Women only.

c Reflects risks in the Eastern U.S. Risks in the Central U.S. were higher, but the authors found no association in the 

Western U.S.

d Random effects Cox model with individual and ecologic covariates.

e Canadian population.

f Men with high socioeconomic status only.

The primary goal of the 2006 study was to elicit from a sample of health experts 

probabilistic distributions describing uncertainty in estimates of the reduction in mortality among 

the adult U.S. population resulting from reductions in ambient annual average PM2.5 levels. 

These distributions were obtained through a formal interview protocol using methods designed to 

elicit subjective expert judgments. These experts were selected through a peer-nomination 

process and included experts in epidemiology, toxicology, and medicine. The elicitation 

interview consisted of a protocol of carefully structured questions, both qualitative and 

quantitative, about the nature of the PM2.5-mortality relationship designed to build twelve 

individual distributions for the coefficient (or slope) of the C-R function relating changes in 

annual average PM2.5 exposures to annual, adult all-cause mortality. The elicitation also 

provided useful information regarding uncertainty characterization in the PM2.5-mortality 
6B-9

relationship. Specifically, during their interviews, the experts highlighted several uncertainties 

inherent within the epidemiology literature, such as causality, concentration thresholds, effect 

modification, role of short- and long-term exposures, potential confounding, and exposure 

misclassification. For several of these uncertainties, such as causality, we are able to use the 

expert-derived functions to quantify the impacts of applying different assumptions. The 

elicitation received favorable peer review in 2006 (Mansfield and Patil, 2006).

Prior to providing a quantitative estimate of the risk of premature death associated with 

long-term PM2.5 exposure, the experts answered a series of “conditioning questions.” One such 

question asked the experts to identify which epidemiological studies they found most 

informative. The “ideal study attributes”4 according to the experts included:

1. Geographic representation of the entire U.S. (e.g., monitoring sites across the country)

2. Collection of information on individual risk factors and residential information both at 

the beginning and throughout the follow-up period

3. Large sample size that is representative of the general U.S. population

4. Collection of genetic information from cohort members to identify and assess potential 

effect modifiers

5. Monitoring of individual exposures (e.g., with a personal monitor)

6. Collection of data on levels of several co-pollutants (not only those that are monitored for 

compliance purposes)

7. Accurate characterization of outcome (i.e., cause of death)

8. Follow-up for a long period of time, up to a lifetime

9. Prospective study design

Although no single epidemiological study completely satisfies each of these criteria, the 

experts determined that the ACS and Six Cities’ cohort studies best satisfy a majority of these 

ideal attributes. To varying degrees the studies examining these two cohorts are geographically 

representative; have collected information on individual risk factors; include a large sample size; 

have collected data on co-pollutants in the case of the ACS study; have accurately characterized 

the health outcome; include a long (and growing) follow-up period; and, are prospective in 

nature. The experts also noted a series of limitations in these two cohort studies. In the case of 

the Six Cities study (Laden et al., 2006), the experts identified the “small sample size, limited 

 4 These criteria are substantively similar to EPA’s study selection criteria identified in Table 6-5 of Chapter 6.
6B-10

number of cities, and concerns about representativeness of the six cities for the U.S. as a whole” 

as weaknesses. When considering the ACS study (Pope et al., 2002), the experts indicated that 

the “method of recruitment for the study, which resulted in a group with higher income, more 

education, and a greater proportion of whites than is representative of the general U.S. 

population” represented a shortcoming. Several experts also argued that because the ACS study 

relied upon “...whatever monitors were available to the study...a single monitor represent[ed] 

exposure for an entire metropolitan area...whereas [the Six Cities study] often had exposures 

assigned at the county level.” Despite these limitations, the experts considered the Pope et al. 

(2002) extended analysis of the ACS cohort and the Laden et al. (2006) extended analysis of the 

Six Cities cohort to be particularly influential in their opinions (see Exhibit 3-3 of the elicitation 

summary report [IEc, 2006]). 

It is important to note that the benefits estimates results presented are not the direct 

results from the studies or expert elicitation; rather, the estimates are based in part on the effect 

coefficients provided in those studies or by experts. In addition, the experts provided 

distributions around their mean PM2.5 effect estimates, which provides more information 

regarding the overall range of uncertainty, and this overall range is larger than the range of the 

mean effect estimates from each of the experts.

Since the completion of the EPA’s expert elicitation in 2006, additional epidemiology 

literature has become available, including 9 new multi-state cohort studies shown in Table 6B-4.

This newer literature addresses some of the weaknesses identified in the prior literature. For 

example, in an attempt to improve its characterization of population exposure the most recent 

extended analysis of the ACS cohort Krewski et al. (2009) incorporates two case studies that 

employ more spatially resolved estimates of population exposure. 

In light of the availability of this newer literature, we have updated the presentation of 

results in the RIA. Specifically, we focus the core analysis on results derived from the two most 

recent studies of the ACS and Six Cities cohorts (Krewski et al., 2009; Lepeule et al., 2012). 

Because the other multi-state cohorts generally have limited geography and age/gender 

representativeness, these limitations preclude us from using these studies in our core benefits 

results, and we instead present the risk coefficients from these other multi-state cohorts in Table 
6B-11

6B-4. However, we have completed a sensitivity analysis based on application of the full set of 

expert-derived effect estimates. To preserve the breadth and diversity of opinion on the expert 

panel, we do not combine the expert results (Roman et. al., 2008). This presentation of the 

expert-derived results is generally consistent with SAB advice (U.S. EPA-SAB, 2008), which 

suggested that the EPA emphasize that “scientific differences existed only with respect to the 

magnitude of the effect of PM2.5 on mortality, not whether such an effect existed” and that the 

expert elicitation “supports the conclusion that the benefits of PM2.5 control are very likely to be 

substantial.” Although it is possible that the newer literature could revise the experts’ 

quantitative responses if elicited again, we believe that these general conclusions are unlikely to 

change.

Table 6B-5 presents the results of this sensitivity analysis using the expert elicitation 

results for the 2025 scenario for 70 ppb. Overall conclusions from this analysis are also 

applicable to the post-2025 scenario. 

Table 6B-5. PM2.5 Co-benefit Estimates using Two Epidemiology Studies and Functions 

Supplied from the Expert Elicitation

C-R Function 70 ppb (2025 Scenario)

Krewski et al. (2009) 220

Lepeule et al. (2012) 500

Expert K 50

Expert G 260

Expert L 310

Expert D 320

Expert H 330

Expert J 360

Expert F 430

Expert C 450

Expert I 450

Expert B 460

Expert A 570

Expert E 740

a All estimates rounded to two significant digits.

The values presented in Table 6B-5 indicate that the two core incidence estimate fall 

within the range of alternative C-R function-based estimates obtained through expert elicitation. 

Figure 6-5 in the body of the document reproduces these benefit estimates, but presents them in 

terms of the associated dollar benefits (7% discount rate in 2011$) rather than incidence 

estimates (observations presented here for the sensitivity analysis results reflected modeled 
6B-12

incidence estimates - i.e., spread in results and relative position of the two core estimates - also 

hold for dollar benefit estimates presented in Figure 4-6.

6B.5 Sensitivity Analysis for Income Elasticity of Willingness-to-Pay

As discussed in Chapter 6, our estimates of monetized benefits account for growth in real 

GDP per capita by adjusting the WTP for individual endpoints based on the central estimate of 

the adjustment factor for each of the categories (minor health effects, severe and chronic health

effects, premature mortality, and visibility). We examined how sensitive the estimate of total 

benefits is to alternative estimates of the income elasticities. Income growth projections are only 

currently available in BenMAP through 2024, so both the 2025 and post-2025 scenario estimates 

use income growth through 2024 only and are therefore likely underestimates. 

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

factors, while Table 6B-7 lists the ranges of corresponding adjustment factors. The results of this 

sensitivity analysis for the two benefit categories are presented in Table 6B-8. 

Table 6B-6. Ranges of Elasticity Values Used to Account for Projected Real Income 

Growth a

Benefit Category Lower Sensitivity Bound Upper Sensitivity Bound

Minor Health Effectb 0.04 0.30

Premature Mortality 0.08 1.00

a Derivation of these ranges can be found in Kleckner and Neumann (1999). COI estimates are assigned an 

adjustment factor of 1.0.

b Minor health effects included in this RIA and valued using WTP-based functions include: upper and lower 

respiratory symptoms, asthma exacerbations, minor restricted activity days, and acute bronchitis.

Table 6B-7. Ranges of Adjustment Factors Used to Account for Projected Real Income 

Growth to 2024 a

Benefit Category Lower Sensitivity Bound Upper Sensitivity Bound

Minor Health Effectb 1.021 1.170

Premature Mortality 1.043 1.705

a Based on elasticity values reported in Table C-4, U.S. Census population projections, and projections of real GDP 

per capita.

b Minor health effects included in this RIA and valued using WTP-based functions include: upper and lower 

respiratory symptoms, asthma exacerbations, minor restricted activity days, and acute bronchitis.
6B-13

Table 6B-8. Sensitivity of Monetized Ozone Benefits to Alternative Income Elasticities in 

2025 (Millions of 2011$) a

Benefit Category No adjustment Lower Sensitivity Bound Upper Sensitivity Bound

70 ppb 65 ppb 70 ppb 65 ppb 70 ppb 65 ppb

Minor Health Effect b $31 $150 $33 $162 $38 $186

Premature Mortality c $950 $4,100 $1,000 $5,200 $1,700 $8,500

a All estimates rounded to two significant digits. Only reflects income growth to 2024.

b For illustrative purposes, we evaluate minor restricted activity days (MRADS) resulting from short-term ozone 

exposure is the minor health effect here. c Short-term mortality using Smith et al. (2009) and 3% discount rate. Results using other short-term mortality 

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

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

mortality has the largest impact on total benefits. The value of mortality in 2025 ranges from 8% 

to 76% greater than the core estimate for mortality based on the lower and upper sensitivity 

bounds on the mortality income adjustment factor. The effect on the value of minor health effects 

is much less pronounced, ranging from 5% to 21% greater than the core estimate for minor 

effects. These observations (in terms of relative impact from alternative elasticities) hold for the 

revised and alternative standard levels analyzed under both the 2025 and post-205 scenarios.

6B.6 Supplemental Analysis: Age Group-Differentiated Aspects of Short-Term Ozone 

Exposure-Related Mortality

In their 2008 review of the EPA’s approach to estimating ozone-related mortality 

benefits, the National Research Council (NRC) indicated, “EPA should consider placing greater 

emphasis on reporting decreases in age-specific death rates in the relevant population and 

develop models for consistent calculation of changes in life expectancy and changes in number 

of deaths at all ages” (NRC, 2008). In addition, NRC noted in an earlier report that “[f]rom a 

public-health perspective, life-years lost might be more relevant than annual number of mortality 

cases” (NRC, 2002). This advice is consistent with that of the Health Effects Subcommittee of 

the Advisory Council on Clean Air Compliance Analysis (SAB-HES), which agreed that “...the 

interpretation of mortality risk results is enhanced if estimates of lost life-years can be made” 

(U.S. EPA-SAB, 2004). To address these recommendations, we use simplifying assumptions to 

estimate the number of life years that might be gained. We also estimate the reduction in the 

percentage of deaths attributed to ozone resulting from the illustrative emissions reduction 

strategies to reach the revised and alternative primary standards. The EPA included similar 

estimates of life years gained in a previous assessment of ozone and/or PM2.5 benefits (U.S. 
6B-14

EPA, 2006, 2010, 2011a), the latter of which was peer reviewed by the SAB-HES (U.S. EPASAB, 2010a).

Changes in life years and changes in life expectancy at birth are frequently conflated, 

thus it is important to distinguish these two very different metrics. Life expectancy varies by age. 

The Centers for Disease Control and Prevention (CDC) defines life expectancy as the “average 

number of years of life remaining for persons who have attained a given age” (CDC, 2011). In 

other words, changes in life expectancy refer to an average change for the entire population, and 

refer to the future. Over the past 50 years, average life expectancy at birth in the U.S. has 

increased by 8.4 years (CDC, 2011). For example, life expectancy at birth was estimated in 2007 

to be 77.9 years for an average person born in the U.S., but for people surviving to age 60, 

estimated life expectancy is 82.5 years (i.e., 4.6 years more than life expectancy at birth) (CDC, 

2011). Life years, on the other hand, measure the amount of time that an individual loses if they 

die before the age of their life expectancy. Life years refer to individuals, and refer to the past, 

e.g., when the individual has already died. If a 60-year old individual dies, we estimate that this 

individual would lose about 22.5 years of life (i.e., the average population life expectancy for an 

individual of this age minus this person’s age at death).

Due to the use of benefit-per-ton estimates for the PM2.5 co-benefits, we are unable to 

estimate the life years gained by reducing exposure to PM2.5 in this analysis. Instead, we refer the 

reader to the 2012 PM2.5 NAAQs RIA for more information about the avoided life years lost 

from PM2.5 exposure (U.S. EPA, 2012b). The analysis performed for the PM NAAQS RIA found 

that about half of the avoided PM-related deaths occur in populations age 75 to 99, but half of 

the avoided life years lost would occur in populations younger than 65 because the younger 

populations have the potential to lose more life years per death than older populations. In 

addition, that analysis found that the average individual who would otherwise have died 

prematurely from PM exposure would gain 16 additional years of life.

Estimated Life Years Gained

To estimate the potential life years gained by reducing exposure to ozone in the U.S.

adult population, we use the same general approach as Hubbell (2006) and Fann et al. (2012). 

We have not estimated the change in average life expectancy at birth in this RIA. Because life 
6B-15

expectancy is an average of the entire population (including both those whose deaths would 

likely be attributed to air pollution exposure as well as those whose deaths would not), we would 

expect average life expectancy changes associated with air pollution exposure to always be 

significantly smaller than the average number of life years lost by an individual projected to die 

prematurely from air pollution exposure.

To estimate the potential distribution of life years gained for population subgroups 

defined by the age range at which their reduction in air pollution exposure is modeled to occur,

we use standard life tables available from the CDC (2014) and the following formula:

TTTTTTTTTT LLLLLLLL YY YYYY = ∑ LLLLii × MMii nn

ii=1 (6.2)

where LEi is the average remaining life expectancy for age interval i, Mi is the estimated change 

in number of deaths in age interval i, and n is the number of age intervals. 

To get Mi (the estimated number of avoided premature deaths attributed to changes in 

ozone exposure for the 2025 scenario), we use a health impact function that incorporates risk 

coefficients estimated for the adult population in the U.S. and age-specific mortality rates. That 

is, we use risk coefficients that do not vary by age, but use baseline mortality rates that do. 

Because mortality rates for younger populations are much lower than mortality rates for older 

populations, most but not all, of the avoided deaths tend to be in older populations. Table 6B-9

summarizes the number of avoided deaths (by age range) attributable to ozone for the revised 

and alternative standards for the 2025 scenario. Table 6B-10 summarizes the modeled number of 

life years gained (for each age range) by reducing ozone for the revised and alternative standards

analyzed for the 2025 scenario. We then calculated the average number of life years gained per 

avoided premature mortality. 
6B-16

Table 6B-9. Potential Reduction in Premature Mortality by Age Range from Attaining 

the Revised and Alternative Ozone Standards (2025 scenario) a, b

Age Range b Revised and Alternative Standards

70 ppb 65 ppb

0-4 0.3 1.5

5-9 0.068 0.33

10-14 0.072 0.35

15-19 0.1 0.5

20-24 0.14 0.69

25–29 0.29 1.4

30–34 0.3 1.5

35–44 1.6 7.9

45–54 4.1 20

55–64 10 52

65–74 22 110

75–84 29 150

85–99 28 150

Total ozone-attributable mortality 96 490

a Estimates rounded to two significant digits. b Effects calculated using Smith et al. (2009).

Table 6B-10. Potential Years of Life Gained by Age Range from Attaining the Revised and 

Alternative Ozone Standards (2025 Scenario) a,b

Age Range b Revised and Alternative Standard

70 ppb 65 ppb

0-4 24 110 

5-9 5 23 

10-14 5 23 

15-19 6 30 

20-24 8 38 

25–29 14 70 

30–34 13 66 

35–44 57 280 

45–54 110 550 

55–64 200 990 

65–74 260 1,300 

75–84 190 970 

85–99 65 340 

Total life years gained 960 4,800 

Average life years gained per individual 10 10 

a Estimates rounded to two significant digits. b Effects calculated using Smith et al. (2009).

By comparing the projected age distribution of the avoided premature deaths with the age 

distribution of life years gained, we observed that about half of the deaths occur in populations 

age 75–99 (see Table 6B-9), but half of the life years would occur in populations younger than 
6B-17

65 (see Table 6B-10). This is because the younger populations have the potential to lose more 

life years per death than older populations based on changes in ozone exposure for the 2025 

scenario. We estimate that the average individual who would otherwise have died prematurely 

from ozone exposure would gain 10 additional years of life. However, this approach does not 

account for whether or not people who are older are more likely to be susceptible to the health 

effects of air pollution or whether that susceptibility was caused by air pollution exposure (for a 

more complete discussion of this issue, see Kunzli et al., 2001). 

Percent of Ozone-related Mortality Reduced

To estimate the percentage reduction in all-cause mortality attributed to reduced ozone

exposure for the 2025 scenario as a result of the illustrative emissions reduction strategies, we 

use Mi from the equation above, dividing the number of excess deaths estimated for the revised 

and alternative standards by the total number of deaths in each county. Table 6B-11 shows the 

reduction in all-cause mortality attributed to reducing ozone exposure to the revised and 

alternative primary standards for the 2025 scenario.

Table 6B-11. Estimated Percent Reduction in All-Cause Mortality Attributed to the 

Proposed Primary Ozone Standards (2025 Scenario) a

Age Range b Revised and Alternative Standards

70 ppb 65 ppb

0-4 0.0085% 0.0409%

5-9 0.0084% 0.0411%

10-14 0.0084% 0.0415%

15-19 0.0086% 0.0419%

20-24 0.0085% 0.0412%

25–29 0.0084% 0.0409%

30–34 0.0083% 0.0408%

35–44 0.0081% 0.0404%

45–54 0.0082% 0.0410%

55–64 0.0085% 0.0429%

65–74 0.0086% 0.0436%

75–84 0.0085% 0.0433%

85–99 0.0079% 0.0415%

a To illustrate the slight variations in percent reductions across age ranges, we present results rounded to three 

significant digits (rather than two as is typically done for other estimates in this RIA). 

Results presented in Table 6B-11 highlight that when reductions in ozone-attributable 

mortality (in going from baseline to the revised or alternative standard level) are considered as a 
6B-18

percentage of total all-cause baseline mortality, the estimates are relatively small and are fairly 

constant across age ranges. However, it is important to point out that estimates of total ozoneattributable mortality represent a substantially larger fraction of all-cause baseline mortality.

6B.7 Supplemental Analysis: Evaluation of Mortality Impacts Relative to the Baseline

Pollutant Concentrations for both Short-Term Ozone Exposure-Related Mortality 

and Long-Term PM2.5 Exposure-Related Mortality

Analysis of baseline ozone levels used in modeling short-term ozone exposure-related mortality 

Our review of the current body of scientific literature indicates that a log-linear nothreshold model provides the best estimate of ozone-related short-term mortality (see section 

2.5.4.4, in the O3 ISA, U.S. EPA, 2013). Consistent with this finding, we estimate benefits 

associated with the full range of ozone exposure. Our confidence in the estimated number of 

premature deaths avoided (but not in the existence of a causal relationship between ozone and 

premature mortality) diminishes as we estimate these impacts at successively lower 

concentrations. However, there are uncertainties inherent in identifying any particular point at 

which our confidence in reported associations becomes appreciably less, and the scientific 

evidence provides no clear dividing line. The O3 ISA noted that the studies indicate reduced 

certainty in specifying the shape of the C-R function specifically for short-term ozoneattributable respiratory morbidity and mortality, in the range generally below 20 ppb (for these 

reasons, the ≤ 20 ppb range discussed in the O3 ISA should be viewed as a more generalized 

range to be considered qualitatively or semi-quantitatively, along with many other factors, when 

interpreting the risk estimates rather than as a fixed, bright-line).5

Figures 6B-1 and 6B-2 compare the distribution of short-term ozone exposure-related 

mortality to the underlying distribution of the summer season 8-hour maximum ozone 

concentrations. Both figures (probability and cumulative probability) are based on Smith et al. 

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

support specification of population-level thresholds for use in the epidemiological-based risk assessment focusing 

on short-term exposure-related endpoints. The clinical studies focus on relatively small and clearly defined 

populations of healthy adults, which are not representative of the broader residential populations typically 

associated with epidemiological studies, including older individuals and individuals with existing health conditions 

that place them at greater risk for ozone-related effects. Therefore, the clinical studies are unlikely to have the 

power to capture population thresholds in a broader and more diverse urban residential population, should those 

thresholds exist.
6B-19

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

maximum concentration in the baseline. In addition, each figure includes separate plots for the 

revised and alternative standard levels. Figure 6B-1 shows that approximately 45% of the 

premature mortalities estimated for the 65 ppb alternative standard is associated with baseline 

ozone concentrations between 40 and 45 ppb. Because this baseline range is represents the mean 

across the ozone season of 8-hour max values within a given grid cell, the actual distribution of 

8-hour max values on a daily basis is likely wider than the 40-45 ppb range.

Figure 6B-1. Premature Ozone-related Deaths Avoided for the Revised and Alternative 

Standards (2025 scenario) According to the Baseline Ozone Concentrations

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

0 to 15

Percentage of 

15 to 2020 to 2525 to 3030 to 3535 to 4040 to 4545 to 5050 to 5555 to 6060 to 6565 to 7070 to 7575 to 80

reduction in short-term ozone 

related mortality 

Ozone season mean daily 8hr max baseline (ppb)

75-70 75-65
6B-20

Figure 6B-2. Cumulative Probability Plot of Premature Ozone-related Deaths Avoided for 

the Revised and Alternative Standards (2025 scenario) According to the Baseline 

Ozone Concentrations

When interpreting these results, it is important to understand that the avoided ozonerelated deaths are estimated to occur from ozone reductions in the baseline air quality simulation, 

which assumes that 75 ppb is already met. When simulating attainment with the revised and 

alternative standards, we adjust the design value at each monitor exceeding the standard to equal 

that standard and use an air quality interpolation technique to simulate the change in ozone

concentrations surrounding that monitor. This technique tends to simulate the greatest air quality 

changes nearest the monitor. We estimate benefits using modeled air quality data with 12 km 

grid cells, which is important because the grid cells are often substantially smaller than counties 

and ozone concentrations vary spatially within a county. Therefore, there may be a small number 

of grid cells with concentrations slightly greater than 75 ppb in the gridded baseline even though 

all monitors could meet an annual standard of 75 ppb. In addition, some grid cells in a county 

can be below the level of a standard even though the highest monitor value is above that 

standard. Thus, emissions reductions can lead to benefits in grid cells that are below a standard 

even within a county with a monitor that exceeds that standard. Furthermore, our approach to 

0

0.2

0.4

0.6

0.8

1

1.2

0 to 15

15 to 2020 to 2525 to 3030 to 35

35 to 4040 to 4545 to 50

50 to 5555 to 6060 to 6565 to 7070 to 75

75 to 80

Cumulative percentage of reduction in 

short-term ozone related mortality

Ozone season mean daily 8hr max baseline (ppb)

75-70 75-65
6B-21

simulating attainment can lead to benefits in counties that are below the revised or alternative 

standard being evaluated. Emissions reduction strategies designed to reduce ozone

concentrations at a given monitor will frequently improve air quality in neighboring counties. In 

order to make a direct comparison between the benefits and costs of these emissions reduction 

strategies, it is appropriate to include all the benefits occurring as a result of the emissions

reduction strategies applied, regardless of where they occur. Therefore, it is not appropriate to 

estimate the fraction of benefits that occur only in counties that exceed the revised or alternative 

standards because it would omit benefits attributable to emissions reductions in exceeding 

counties. 

One final caveat in interpreting the information presented in these figures is that in 

modeling this mortality endpoint, rather than using a true distribution of daily 8hr max ozone 

levels for each grid cell, due to resource limitations, we used a single mean value for the ozone 

season within each grid cell. While this will generate the same total ozone benefit estimate for 

each grid cell compared with application of a full distribution of daily 8hr max values, use of a 

mean daily value means that an assessment such as this one, that considers both the spatial and 

temporal association between mortality benefit estimates and ozone levels, will be limited 

somewhat in its treatment of the temporal dimension.

As shown in Figures 6B-1 and 6B-2, the vast majority of reductions in short-term 

exposure-related mortality for ozone occur in grid cells with mean 8-hour max baseline levels 

(across the ozone season) between 35 and 55 ppb. Importantly, virtually all of the mortality 

reductions are associated with ozone levels above the greater than 20 ppb range identified within 

the O3 ISA as being associated with less confidence in specifying the nature of the C-R function 

for ozone mortality (O3 ISA, section 2.5.4.4).6 Comparing patterns across the revised and 

 6 As noted earlier, care needs to be taken in interpreting these mortality vs. ozone air level distributions in the 

context of the range of reduced confidence (<20 ppb) identified in the O3 ISA. The region of reduced confidence 

identified by the ISA reflects the composite monitor daily time series values (including 8-hour max values) used in 

short-term mortality studies, while the ozone levels summarized in Figures 6B-1 and 6B-2 are the mean (across the 

ozone season) of daily 8-hour max values within each grid cell. The use of these mean values, while not impacting 

the total mortality reductions estimated, could significantly reduce variability in the spread of values presented in 

these two figures.
6B-22

alternative standard levels, the upper end of the distribution shifts downwards as increasingly 

lower standard levels are analyzed (see Figure 6B-2). 

Concentration Benchmark Analysis for PM2.5 Benefit-per-ton Estimates

In general, we are more confident in the magnitude of the risks we estimate from simulated 

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

epidemiological studies that are used to estimate the benefits. Likewise, we are less confident in 

the risk we estimate from simulated PM2.5 concentrations that fall below the bulk of the observed 

data in these studies. Concentration benchmark analyses (e.g., lowest measured level [LML], one 

standard deviation below the mean of the air quality data in the study, etc.) allow readers to 

determine the portion of population exposed to annual mean PM2.5 levels at or above different 

concentrations, which provides some insight into the level of uncertainty in the estimated PM2.5 

mortality benefits. In this analysis, we apply two concentration benchmark approaches (LML 

and one standard deviation below the mean) that have been incorporated into recent RIAs and 

EPA’s Policy Assessment for Particulate Matter (U.S. EPA, 2011c). There are uncertainties 

inherent in identifying any particular point at which our confidence in reported associations 

becomes appreciably less, and the scientific evidence provides no clear dividing line. However, 

the EPA does not view these concentration benchmarks as a concentration threshold below 

which we would not quantify health co-benefits of air quality improvements.7 Rather, the cobenefits estimates reported in this RIA are the best estimates because they reflect the full range 

of air quality concentrations associated with the emissions reduction strategies. The PM ISA 

concluded that the scientific evidence collectively is sufficient to conclude that the relationship 

between long-term PM2.5 exposures and mortality is causal and that overall the studies support 

the use of a no-threshold log-linear model to estimate PM-related long-term mortality (U.S. EPA, 

2009). 

For this analysis, policy-specific air quality data is not available, and the compliance 

strategies are illustrative of what states may choose to do. For this RIA, we are unable to 

estimate the percentage of premature mortality associated with the emissions reductions at each 

 7 For a summary of the scientific review statements regarding the lack of a threshold in the PM2.5-mortality 

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

Concentration-Response Function for PM2.5-related Mortality (U.S. EPA, 2010b).
6B-23

PM2.5 concentration, as we have done for previous rules with air quality modeling (e.g., U.S. 

EPA, 2011b, 2012b). However, we believe that it is still important to characterize the distribution 

of exposure to baseline concentrations. As a surrogate measure of mortality impacts, we provide 

the percentage of the population exposed at each PM2.5 concentration in the baseline of the 

source apportionment modeling used to calculate the benefit-per-ton estimates for this sector 

using 12 km grid cells across the contiguous U.S.8 It is important to note that baseline exposure 

is only one parameter in the health impact function, along with baseline incidence rates, 

population and change in air quality. In other words, the percentage of the population exposed to 

air pollution below the LML is not the same as the percentage of the population experiencing 

health impacts as a result of a specific emissions reduction policy. The most important aspect, 

which we are unable to quantify without rule-specific air quality modeling, is the shift in 

exposure anticipated by the revised or alternative standards. Therefore, caution is warranted 

when interpreting the LML assessment in this RIA because these results are not consistent with 

results from RIAs that had air quality modeling. 

Table 6B-12 provides the percentage of the population exposed above and below two 

concentration benchmarks in the modeled baseline for the sector modeling. Figure 6B-3 shows a 

bar chart of the percentage of the population exposed to various air quality levels in the baseline, 

and Figure 6B-4 shows a cumulative distribution function of the same data. Both figures identify 

the LML for each of the major cohort studies.

 8 As noted above, the modeling used to generate the benefit-per-ton estimates does not reflect emissions reductions 

anticipated from MATS rule. Therefore, the baseline PM2.5 concentrations in the LML assessment are higher than 

would be expected if MATS was reflected.
6B-24

Table 6B-12. Population Exposure in the Baseline Sector Modeling (used to generate the 

benefit-per-ton estimates) Above and Below Various Concentration Benchmarks 

in the Underlying Epidemiology Studies a

Epidemiology Study

Below 1 Standard 

Deviation.

Below AQ Mean

At or Above 1 

Standard Deviation 

Below AQ Mean

Below LML At or Above LML

Krewski et al. (2009) 89% 11% 7% 93%

Lepeule et al. (2012) N/A N/A 23% 67%

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

percentile. For Krewski et al., the LML is 5.8 μg/m3 and one standard deviation below the mean is 11.0 μg/m3

. 

For Lepeule et al., the LML is 8 μg/m3 and we do not have the data for one standard deviation below the mean. It 

is important to emphasize that although we have lower levels of confidence in levels below the LML for each 

study, the scientific evidence does not support the existence of a level below which health effects from exposure 

to PM2.5 do not occur.

Figure 6B-3. Percentage of Adult Population (age 30+) by Annual Mean PM2.5 Exposure 

in the Baseline Sector Modeling (used to generate the benefit-per-ton estimates)*

* This graph shows the population exposure in the modeling baseline used to generate the benefit-per-ton estimates. 

Similar graphs for analyses with air quality modeling show premature mortality impacts at each PM2.5

concentration. Therefore, caution is warranted when interpreting this graph because it is not consistent with similar 

graphs from RIAs that had air quality modeling (e.g., MATS).

Among the populations exposed to PM2.5 in the baseline:

93% are exposed to PM2.5 levels at or above the LML of the Krewski et al. (2009) study

67% are exposed to PM2.5 levels at or above the LML of the Lepeule et al. (2012) study

0%

5%

10%

15%

20%

25%

<1 1-2 2-3 3-4 4-5 5-6 6-7 7-8 8-9 9-10 10-11 11-12 12-13 13-14 14-15 15-16 16-17 17-18 18-19 19-20

Percentage of Population Exposed

Baseline Annual Mean PM2.5 Level (μg/m3)

 

LML of Lepeule et al. 

(2012) study

LML of Krewski et 

al. (2009) study
6B-25

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

PM2.5 Exposure in the Baseline Sector Modeling (used to generate the benefitper-ton estimates)*

* This graph shows the population exposure in the modeling baseline used to generate the benefit-per-ton estimates. 

Similar graphs for analyses with air quality modeling show premature mortality impacts at each PM2.5

concentration. Therefore, caution is warranted when interpreting this graph because it is not consistent with similar 

graphs from RIAs that had air quality modeling (e.g., MATS).

6B.8 Sensitivity Analysis of Ozone-related Impacts on Outdoor Worker Productivity

The EPA last quantified the value of ozone-related worker productivity in the final 

Regulatory Impact Analysis supporting the Transport Rule (U.S. EPA, 2011d). That analysis 

applied information reported in Crocker and Horst (1981) to relate changes in 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 among outdoor citrus workers.

Concerned that this study might not adequately characterize the relationship between groundAmong the populations exposed to PM2.5 in the baseline:

93% are exposed to PM2.5 levels at or above the LML of the Krewski et al. (2009) study

67% are exposed to PM2.5 levels at or above the LML of the Lepeule et al. (2012) study

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

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

Cumulative percentage of population exposed

Baseline Annual Mean PM2.5 Level (μg/m3)

 

LML of Krewski et 

al. (2009) study

LML of Lepeule et 

al. (2012) study
6B-26

level ozone and the productivity of agricultural workers because of the vintage of the underlying 

data, the Agency subsequently omitted this endpoint.

In a recent study, Graff Zivin and Neidell (2012) combined data on individual-level daily 

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

to characterize changes in worker productivity as a result of ozone exposure. The authors used 

data on harvest rates from a 500-acre farm in the 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 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 are still evaluating whether and how to most appropriately apply 

the limited evidence from this study in a national benefits assessment. An important issue is the 
6B-27

generalizability of the results to the appropriate population. Because we received no comments 

on this proposed approach, we now include the results in a sensitivity analysis. 

Below we provide the function, input data and results for the sensitivity analysis.

YY = ββ ∗ DD DDDD ∗ DD DDDDDD DD DD DD ∗ OO ∗ EEEE EE EE

Table 6B-13. Definitions of Variables Used to Calculate Changes in Worker Productivity

Variable Definition

ββ Percent change in daily outdoor worker productivity per 1ppb change in ozone 

(Graff Zivin and Neidell, 2012)

DD DDDD Summer season average of daily 9 hour average (6am to 3pm)

DD DDDDDD DD DD DD Summer season daily wage for agricultural non-livestock workers in 17 

USDA-defined regions (USDA, 2012) 

OO Summer season number of workers employed in outdoor non-livestock 

agricultural per county (USDA, 2010; US Census, 2010)

EEEE EE EE Growth in agricultural non-livestock workers to 2025 (Woods and Poole, 

2012)

Table 6B-14. Population Estimated Economic Value of Increased Productivity among 

Outdoor Agricultural Workers from Attaining the Revised and Alternative 

Ozone Standards in 2025 (millions of 2011$)

Standard Economic Value

(95th percentile confidence interval)

70 ppb $1.7

($0.1 to $3.3)

65 ppb $8.9

($0.6 to $17)

60 ppb $20

($1.4 to $39)

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6-1

CHAPTER 6: HUMAN HEALTH BENEFITS ANALYSIS APPROACH AND RESULTS

6.1 Summary

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

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

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

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

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

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

as a result of reducing NOx emissions.1

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

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

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

 

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

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

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

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

benefits of attaining an alternative standard everywhere in the U.S. except California as the 2025 

scenario. The post-2025 scenario refers to estimates of nationwide benefits of attaining an 

alternative standard just in California. Because we estimate incremental costs and benefits for 

these two distinct scenarios reflecting attainment in different years it is not appropriate to either 

sum, or directly compare, the estimates. 

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

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

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

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

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

potential to impact PM2.5 concentrations, but we were not able to estimate those effects. 2 The current standard is the 4th highest daily maximum 8-hour ozone concentration of 75 ppb.
6-2

exposure to ozone and PM2.5. Although these tables present ozone and PM2.5-related benefits 

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

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

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

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

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

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

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

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

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

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

Ozone Standards for 2025 (nationwide benefits of attaining the standards

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

Discount 

Rate 70 ppb 65 ppb

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

PM2.5 Co-benefits d 3% $2.1 to $4.7 $10 to $23

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

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

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

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

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

value of avoided morbidities and premature deaths using risk coefficients from the studies noted. b Ozone-only benefits reflect short-term exposure impacts and as such are assumed to occur in the same year as 

ambient ozone reductions. Consequently, social discounting is not applied to the benefits for this category. c Range reflects application of effect estimates from Smith et al. (2009) and Zanobetti and Schwartz (2008). d Range reflects application of effect estimates from Krewski et al. (2009) and Lepeule et al. (2012).
6-3

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

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

just in California) (billions of 2011$)a

Discount 

Rate 70 ppb 65 ppb

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

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

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

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

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

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

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

value of avoided morbidities and premature deaths using risk coefficients from the studies noted. b Ozone-only benefits reflect short-term exposure impacts and as such are assumed to occur in the same year as 

ambient ozone reductions. Consequently, social discounting is not applied to the benefits for this category. c Range reflects application of effect estimates from Smith et al. (2009) and Zanobetti and Schwartz (2008). d Range reflects application of effect estimates from Krewski et al. (2009) and Lepeule et al. (2012). 

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

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

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

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

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

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

6.2 Overview

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

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

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

identified and unidentified control strategies for the revised and alternative ozone standards by 

answering three key questions:

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

revised and alternative ozone standards?

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)?
6-4

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

PM2.5 that would be avoided by attaining a revised ozone standard. The Integrated Science 

Assessment for Ozone and Related Photochemical Oxidants (“ozone ISA”) (U.S. EPA, 2013a) 

identifies the human health effects associated with ozone exposure, which include premature 

death and a variety of illnesses associated with acute (days-long) and chronic (months to yearslong) 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. In Table 6-3 we 

summarize the “categories” of effects and describe those that we quantified for this analysis and 

those we were unable to quantify due to lack of resources, data, or methods. 

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

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

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

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

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

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

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

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

relationship with ozone. 

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

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

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

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

described in detail in sections 6.5 and 6.7.3.
6-5

Table 6-3. Human Health Effects of Pollutants Potentially Affected by Strategies to Attain 

the Primary Ozone Standards (endpoints included in the core analysis are identified 

with a red checkmark)

Benefits Category Specific Effect

Effect Has 

Been 

Quantified

Effect Has 

Been 

Monetized

Source of 

More 

Information

Improved Human Health

Reduced incidence 

of premature 

mortality from 

exposure to ozone

Premature mortality based on short-term 

exposure (all ages)

 

Section 6.6

Premature respiratory mortality based on 

long-term exposure (age 30–99)

b b

Reduced incidence 

of morbidity from 

exposure to ozone

Hospital admissions—respiratory 

(age > 65)

 

Emergency department visits for asthma 

(all ages)

 

Asthma exacerbation (age 6-18)  

Minor restricted-activity days (age 18–65)  

School absence days (age 5–17)  

Decreased outdoor worker productivity 

(age 18–65)

b b

Other respiratory effects (e.g., medication 

use, pulmonary inflammation, decrements 

in lung functioning)

— —

ozone ISAd Cardiovascular (e.g., hospital admissions, 

emergency department visits)

— —

Reproductive and developmental effects 

(e.g., reduced birthweight, restricted fetal 

growth)

— —

Reduced incidence 

of premature 

mortality from 

exposure to PM2.5

Adult premature mortality based on 

cohort study estimates and expert 

elicitation estimates (age >25 or age >30)

 

Section 6.6 

Infant mortality (age <1)  

Reduced incidence 

of morbidity from 

exposure to PM2.5

Non-fatal heart attacks (age > 18)  

Hospital admissions—respiratory (all 

ages)

 

Hospital admissions—cardiovascular (age 

>20)

 

Emergency department visits for asthma 

(all ages)

 

Acute bronchitis (age 8–12)  

Lower respiratory symptoms (age 7–14)  

Upper respiratory symptoms (asthmatics 

age 9–11)

 

Asthma exacerbation (asthmatics age 6–

18)

 

Lost work days (age 18–65)  

Minor restricted-activity days (age 18–65)  

Chronic Bronchitis (age >26) — —

Emergency department visits for 

cardiovascular effects (all ages)

— —

Strokes and cerebrovascular disease (age 

50–79)

— —
6-6

Benefits Category Specific Effect

Effect Has 

Been 

Quantified

Effect Has 

Been 

Monetized

Source of 

More 

Information

Other cardiovascular effects (e.g., other 

ages)

— —

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

function, non-asthma ER visits, nonbronchitis chronic diseases, other ages 

and populations)

— —

Reproductive and developmental effects 

(e.g., low birth weight, pre-term births, 

etc.)

— —

PM ISA c,d

Cancer, mutagenicity, and genotoxicity 

effects

— —

Reduced incidence 

of morbidity from 

exposure to NO2

Asthma hospital admissions (all ages) — —

NO2 ISA e

Chronic lung disease hospital admissions 

(age > 65)

— —

Respiratory emergency department visits 

(all ages)

— —

Asthma exacerbation (asthmatics age 4–

18)

— —

Acute respiratory symptoms (age 7–14) — —

Premature mortality — —

NO2 ISA Other respiratory effects (e.g., airway c,d

hyperresponsiveness and inflammation, 

lung function, other ages and populations)

— —

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

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

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

significant concerns over the strength of the association. e We assess these benefits qualitatively due to time and resource limitations for this analysis.

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

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

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

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

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

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

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

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

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

6.3 Updated Methodology Presented in the Proposal and Final RIAs

This RIA incorporates an array of policy and technical updates to the benefits analysis 

methods that since the previous review the ozone standards in 2008 and the proposed 
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reconsideration in 2010. These updates to the methods are summarized here in chronological 

order. 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Census and future projections based on economic forecasting models developed by 

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

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

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

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

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

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

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

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

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

4. Updates affecting estimated ozone-related effects:

a. New studies used to quantify ozone-related premature death. The ozone ISA (U.S. 

EPA, 2013a) identifies several new epidemiological studies examining the association 

between ozone exposure and premature mortality. We include two new multi-city 

studies to estimate deaths attributable to short-term exposure (Smith et al., 2009 and 

Zanobetti and Schwartz 2008). We also estimate long-term respiratory deaths using 
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Jerrett et al. (2009).3 We introduced this update in the proposal RIA (U.S. EPA, 

2014c).

b. New studies used to quantify ozone-related morbidity effects. The ozone ISA (U.S. 

EPA, 2013a) identifies several new epidemiological studies examining the association 

between short-term ozone exposure and respiratory hospitalizations, respiratory 

emergency department visits, and exacerbated asthma. We introduced this update in 

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

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 

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

5. Updates affecting estimated PM2.5-related effects:

a. New studies used to quantify PM2.5-related premature death. We updated the 

American Cancer Society cohort study to Krewski et al. (2009) and updated the 

Harvard Six Cities cohort study to Lepeule et al. (2012). The update for the American 

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

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

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

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

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

health impact assessment, including hospital admissions and emergency department 

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

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

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

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

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

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

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

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

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

various uncertain parameters and assumptions within the benefits analysis including 

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

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

2012c).

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

we do not include the monetized benefits in the core analysis. Instead, monetized benefits associated with longterm ozone-related respiratory mortality are included as a sensitivity analysis (see Appendix 5B, section 5B.2).
6-9

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

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

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

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

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

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

section 6.6.3 for further discussion).

6.4 Human Health Benefits Analysis Methods

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

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

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

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

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

endpoints. The “damage-function” approach is the standard method 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 valued directly, as is the case 

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

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

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

this RIA, the health impacts analysis (HIA) is limited to those health effects that 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 
6-10

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

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

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

robustness of benefits estimates. 

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

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

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

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

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

both approaches are described in additional detail below. 

6.4.1 Health Impact Assessment

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

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

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

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

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

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

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

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

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

resulting from hypothetical changes in pollutant levels (Hubbell et al., 2005; Tagaris et al., 2009; 

Fann et al., 2012a). The EPA and others have relied upon this method to predict future changes 

in health impacts expected to result from the implementation of regulations affecting air quality 

(e.g., U.S. EPA, 2014a). For this assessment, the HIA is limited to those health effects that are 

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

impacts associated with applying emissions controls, such as occupational health exposures.
6-11

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

of ozone air quality4 and estimating the change in the spatial distribution of the ambient air 

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

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

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

A typical health impact function might look as follows:

∆yy = 1 − �eeββ∙∆xx�yyoo ∙ PPPPPP (6.1)

where y0 is the baseline incidence rate for the health endpoint being quantified (for 

example, a health impact function quantifying changes in mortality would use the baseline, or 

background, mortality rate for the given population of interest); Pop is the population affected by 

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

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

 
6-12

Figure 6-1. Illustration of BenMAP-CE Approach

6.4.2 Economic Valuation of Health Impacts

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

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

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

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

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

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

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

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

relative 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 

Baseline Air Quality Post-Policy Scenario Air Quality

Incremental Air 

Quality

Improvement

Ozone

Reduction

Population

Ages 30 -99

Background

Incidence

Rate

Effect

Estimate

Mortality 

Reduction
6-13

risk. For some health effects, such as hospital admissions, WTP estimates are generally not 

available. In these cases, we use the costs of treating or mitigating the effect, which generally 

understate the true value of reductions in risk of a health effect because they exclude the value of 

avoided pain and suffering from the health effect.

We use the BenMAP-CE version 1.1 (U.S. EPA, 2015a, 2015b) to estimate the health 

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

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

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

quantifies the number and economic value of air pollution-related deaths and illnesses.5

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

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

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

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

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

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

Exposure Assessment for Ozone (ozone HREA) (U.S. EPA, 2014b).

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
6-14

6.4.3 Estimating Benefits for 2025 and Post-2025 Analysis Years

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

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

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

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

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

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

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

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

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

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

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

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

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

revised and alternative standards (Simulation C). Next, we simulated the nationwide benefits of 

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

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

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

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

associated with this approach mentioned in Section 6.1 and discussed further in section 6.7.3.
6-15

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

Scenarios

6.4.4 Benefit-per-ton Estimates for PM2.5

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

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

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

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

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

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

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

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

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

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

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

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

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

75 ppb baseline (CA does 

not attain 75)

70 ppb full 

attainment scenario 

everywhere but CA

70 ppb full attainment 

scenario everywhere 

including CA

75 ppb baseline reductions in 

(all locations attain 75)

A

C

2025 scenario: Nationwide benefits in 2025 

resulting from all areas in the U.S. attaining 

the alternative standard under consideration 

excluding California (here 70ppb). This is 

Simulation A

70 ppb full 

attainment scenario 

everywhere but CA

75 ppb baseline (CA does 

not attain 75)

B

BenMAP Benefits Simulations

Benefits year = 2025

Cost year = 2025

Benefits year = 2038

Cost year = 2037/2038

Benefits year = 2038

Cost year = 2037/2038

Post-2025 scenario: Nationwide benefits in 

2038 having California attain the 

alternative standard (here 70ppb). This is 

the difference between Simulation C and 

Simulation B
6-16

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

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

reductions in the various sectors. Specifically, the air quality modeling data reflect industrial 

sectors that are more narrowly defined. In addition, the updated air quality modeling data reflects 

more recent emissions data -- a 2005 baseline projected to 2016 rather than 2001 baseline 

projected to 2015 -- and has higher spatial resolution (12 km rather than 36 km grid cells). 

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

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

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

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

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

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

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

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

scenarios, respectively.7

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

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

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

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

local variability in factors associated with PM2.5-related health impacts (e.g., population density, 

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

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

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

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

 6 For unidentified controls, we use a weighted average of the benefit-per-ton estimates from all of the sectors. 7 We do not have benefit-per-ton estimates for 2038. The last year available is 2030, which is an underestimate of 

the 2038 benefits because the population grows and ages over time.
6-17

6.5 Characterizing Uncertainty

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

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

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

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

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

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

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

the uncertainties from each stage of the analysis are compounded, even small uncertainties can 

have large effects on the total quantified benefits.

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

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

methodology for calculating the benefits of reducing air pollution is reasonable and informative 

in spite of inherent uncertainties. The NRC also highlighted the need to conduct rigorous 

quantitative analyses of uncertainty and to present benefits estimates to decision makers in ways 

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

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

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

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

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

quantitative and qualitative analyses. These additional analyses characterize uncertainty related 

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

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

uncertainty probabilistically due to the complexity of the underlying air quality models and 

emissions 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; 2014b), and use a tiered 

approach developed by the World Health Organization (WHO) (WHO, 2008). We present this 
6-18

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

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

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

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

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

6.5.1 Monte Carlo Assessment

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

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

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

valuation functions. The Monte Carlo simulation in the BenMAP-CE software randomly samples 

from a distribution of incidence and valuation estimates to characterize the effects of uncertainty 

on output variables. Specifically, we used Monte Carlo methods to generate confidence intervals 

around the estimated health impact and monetized benefits. The reported standard errors in the 

epidemiological studies determined the distributions for individual effect estimates for endpoints 

estimated using a single study. For endpoints estimated using a pooled estimate of multiple 

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

The confidence intervals around the monetized benefits incorporate the epidemiology standard 

errors, as well as the distribution of the valuation function. These confidence intervals do not 

reflect other sources of uncertainty inherent within the estimates, such as baseline incidence 

rates, populations exposed and transferability of the effect estimate to diverse locations. As a 

result, the reported confidence intervals and range of estimates give an incomplete picture about 

the overall uncertainty in the benefits estimates. We report confidence intervals for ozone-related 

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

we used the benefit-per-ton estimates.

6.5.2 Sensitivity Analyses 

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

particularly important to characterize the uncertainties associated with reductions in premature 

mortality. Each of the sensitivity analyses completed for this RIA are briefly described below
6-19

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

sensitivity analyses. 

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

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

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

designs. We used two multi-city studies (Smith et al., 2009; Zanobetti and Schwartz 

2008) to estimate short-term ozone-related mortality in our core estimate. We performed 

a sensitivity analysis using effect coefficients from additional multi-city studies and 

meta-analyses utilized in prior RIAs (Bell et al., 2004; 2005, Huang, 2005; Ito et al., 

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

(2009) study (see Figure 6-4 and Appendix 6B, section 6B.1). When selecting studies for 

the core and sensitivity analyses we considered NAS (p. 80, NRC, 2008) and CASAC 

recommendations (U.S. EPA-SAB, 2012, 2014).

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

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

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

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

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

a range of potential thresholds in addition to a non-threshold (see Appendix 6B, section 

6B.3). 

• Alternative C-R functions in estimating long-term PM2.5 exposure-related 

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

Lepeule et al., 2012). To better understand the C-R relationship between PM2.5 exposure 

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

2008; IEc, 2006).8 We apply the functions from the experts as a characterization of 

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

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

know how long-term O3 exposure-related respiratory deaths are distributed over time and 

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

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

6B, section 6B.2). 

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

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

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

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

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

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

multiple experts, are obtained (Ayyub, 2002).
6-20

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

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

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

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

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

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

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

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

larger uncertainty surrounding the entire analysis.

6.5.3 Supplemental Analyses

A number of supplemental analyses provide additional perspectives on the benefits 

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

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

We examined several risk metrics intended to characterize how mortality risk reductions 

are distributed across different age ranges. These include (a) estimated reduction in life 

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

estimated reductions in baseline mortality incidence rates by age group.

• Analysis of baseline ozone concentrations used in estimating ozone mortality: We 

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

distribution of baseline ozone concentrations. 

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

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

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

benefit-per-ton values.

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

value of improved productivity among outdoor agricultural workers using Graff Zivin 

and Neidell (2012).

6.5.4 Qualitative Assessment of Uncertainty and Other Analysis Limitations

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

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

underlying risk assessment. The EPA has applied similar approaches in previous analyses (U.S. 
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EPA, 2010b, 2011a, 2012a, 2012b). Using this framework, we summarize the key uncertainties 

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

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

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

qualitative uncertainty characterization are available in Appendix 6A. 

6.6 Benefits Analysis Data Inputs

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

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

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

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

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

NAAQS Reconsideration RIA (U.S. EPA, 2010d). As noted above, only slight modifications 

have been made to the epidemiology studies used to estimate two morbidity endpoints since 

proposal, and those modifications are described in section 6.6.3.

6.6.1 Demographic Data

Quantifying the incidence and dollar value of pollution impacts requires information 

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

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

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

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

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

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

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

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

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

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

national-level total growth avoids potential errors introduced by forecasting each county 

independently. County projections are developed in a four-stage process:
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• First, national-level variables such as income, employment, and populations are

forecasted.

• Second, employment projections are made for 179 economic areas defined by the Bureau 

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

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

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

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

historical growth rates for output and employment by sector.

• Third, population is projected for each economic area based on net migration rates 

derived from employment opportunities and following a cohort-component method based 

on fertility and mortality in each area.

• Fourth, employment and population projections are repeated for counties, using the 

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

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

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

6.6.2 Baseline Incidence and Prevalence Estimates

Epidemiological studies of the association between pollution levels and adverse health 

effects generally provide an estimate of the relationship of air quality changes to the relative risk 

of a health effect, rather than estimating the absolute number of avoided cases. For example, a 5 

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

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

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

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

baseline pollutant levels in that location. To derive the total baseline incidence per year, this rate 

must be multiplied by the corresponding population number. For example, if the baseline 

incidence rate is the number of cases per year per million people, that number must be multiplied 

by the millions of people in the total population in that location.

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

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

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

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

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

spatially disaggregated data; in these cases, whenever possible we used national average rates, 
6-23

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

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

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

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

county-level data are available for premature mortality. 

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

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

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

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

2050 in 5-year increments (U.S. EPA, 2015b; U.S. Bureau of the Census, 2002).

The baseline incidence rates for hospital admissions and emergency department visits 

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

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

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

ages, and in different locations, visit the hospital and emergency department for air pollutionrelated illnesses. Also, the updated baseline incidence rates are more spatially refined. For many 

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

characterization of the 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 a portion of scheduled hospital admissions are air pollution-related, this 

assumption would underestimate these benefits.

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

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

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

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

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

2011c).
6-24

Table 6-4. Baseline Incidence Rates and Population Prevalence Rates for Use in Impact 

Functions, General Population

Endpoint Parameter

Rates

Value Source

Mortality Daily or annual mortality 

rate projected to 2025 a

Age-, cause-, and 

county-specific rate

CDC WONDER (2004–2006)

U.S. Census bureau, 2000

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

county- and causespecific rate

2007 HCUP data files b

ER Visits Daily ER visit rate for 

asthma and cardiovascular 

events

Age-, region-, state-, 

county- and causespecific rate

2007 HCUP data files b

Nonfatal Myocardial 

Infarction (heart 

attacks)

Daily nonfatal myocardial 

infarction incidence rate per 

person, 18+

Age-, region-, state-, 

and county-specific 

rate

2007 HCUP data files b adjusted by 

0.93 for probability of surviving 

after 28 days (Rosamond et al., 

1999)

Asthma Exacerbations Incidence among asthmatic 

African-American children

daily wheeze

daily cough

daily shortness of breath

0.173

0.145

0.074

Ostro et al. (2001) 

Acute Bronchitis Annual bronchitis incidence 

rate, children

0.043 American Lung Association (2002, 

Table 11)

Lower Respiratory 

Symptoms

Daily lower respiratory 

symptom incidence among 

children c

0.0012 Schwartz et al. (1994, Table 2)

Upper Respiratory 

Symptoms

Daily upper respiratory 

symptom incidence among 

asthmatic children

0.3419 Pope et al. (1991, Table 2)

Work Loss Days Daily WLD incidence rate 

per person (18–65)

Aged 18–24

Aged 25–44

Aged 45–64

0.00540

0.00678

0.00492

1996 HIS (Adams, Hendershot, 

and Marano, 1999, Table 41); U.S. 

Census Bureau (2000)

School Loss Days Rate per person per year, 

assuming 180 school days 

per year

9.9 National Center for Education 

Statistics (1996) and 1996 HIS 

(Adams et al., 1999, Table 47); 

Minor RestrictedActivity 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 International Classification of Diseases (ICD) codes 

(AHRQ, 2007).

c Lower respiratory symptoms are defined as two or more of the following: cough, chest pain, phlegm, and wheeze.
6-25

Table 6-5. Asthma Prevalence Rates 

Population Group

Asthma Prevalence Rates

Value Source

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

< 18 0.0941

5–17 0.1070

18–44 0.0719

45–64 0.0745

65+ 0.0716

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

African American, <18 0.1553 American Lung Association a

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

6.6.3 Effect Coefficients

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

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

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

EPA’s ISAs, with input and advice from the SAB-HES.9 In addition, we included more recent 

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

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

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

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

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

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

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

physiological responses (such as changes in clinical measures like Forced Expiratory Volume 

[FEV1]).

There are several types of data that can support the determination of types and magnitude 

of health effects associated with air pollution exposures. These sources of data include 

toxicological studies (including animal and cellular studies), human clinical trials, and 

observational epidemiology studies. All of these data sources provide important contributions to 

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

 9 The SAB-HES is a scientific review panel specifically established to provide advice on the use of the scientific 

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

Clean Air Act 1990 to 2020 (U.S. EPA, 2011a). 10 The peer-reviewed studies in the Provisional Assessment have not yet undergone external review by the SAB.
6-26

studies provide direct C-R relationships that can be used to evaluate population-level impacts of 

reductions in ambient pollution levels in a health impact assessment.

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

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

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

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

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

study population, among other considerations. In general, using C-R functions from more than a 

single study can provide a more representative distribution of the effect estimate. However, there 

are often differences between studies examining the same endpoint, making it difficult to pool 

the results in a consistent manner. For example, studies may examine different pollutants or 

different age groups. For this reason, we consider very carefully the set of studies available 

examining each endpoint and select a consistent subset that provides a good balance of 

population coverage and match with the pollutant of interest. In many cases, either because of a 

lack of multiple studies, consistency problems, or clear superiority in the quality or 

comprehensiveness of one study over others, a single published study is selected as the basis of 

the effect estimate.

When several effect estimates for a pollutant and a given health endpoint (with the 

exception of mortality)11 have been selected, they are quantitatively combined or pooled to 

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

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

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

estimates from different single city studies of the same endpoint. Fixed effect pooling simply 

weights each study’s estimate by the inverse variance, giving more weight to studies with greater 

statistical power (lower variance). Random effects pooling accounts for both within-study 

variance and between-study variability, due, for example, to differences in population 

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

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

 11 In the case of mortality, we do not pool results. Instead, we provide the results from each study separately. 
6-27

the random effects model.12 Pooled impact functions are used to estimate hospital admissions and 

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

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

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

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

reported in each study. 

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

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

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

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

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

available.

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

Consideration Comments

Peer-Reviewed 

Research

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

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

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

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

controlled for in ecological studies. 

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

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

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

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

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

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

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

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

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

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

studies from all years would be included.

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

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

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

preferred. 

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

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

variance.
6-28

Consideration Comments

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

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

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

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

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

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

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

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

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

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

children).

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

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

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

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

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

area.

Pollutants Included in 

Model

An important factor affecting the specification of co-pollutant models for ozone and PM 

is sampling frequency. While ozone is typically measured every hour of each day during 

the ozone season for a specific location, PM is typically measured every 3rd or 6th day. 

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

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

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

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

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

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

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

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

for ozone we generally favor single pollutant models.

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

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

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

functions are used as surrogates, recognizing that there will be potential downward 

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

Economically Valuable 

Health Effects

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

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

include these effects in benefits analyses.

Non-overlapping 

Endpoints

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

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

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

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

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

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

2008a). In all cases where effect estimates are drawn directly from epidemiological studies, 
6-29

standard errors are used as a partial representation of the uncertainty in the size of the effect 

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

sensitivity analyses.

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

Health Impacts a

Endpoint Study Study 

Population

Relative Risk or Effect Estimate (β)

(with 95th Percentile Confidence 

Interval or SE)

Premature Mortality

Premature mortality—

short-term

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

Zanobetti and Schwartz 

(2008) β = 0.00051 (0.00012)

Premature respiratory 

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

Hospital Admissions

Respiratory Pooled estimate:

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

Asthma-related 

emergency department 

visits

Pooled estimate:

 Glad et al. (2012)

0-99 years

β = 0.00306 (0.00117)

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

 Mar and Koenig (2010) 

 

β = 0.01044 (0.00436) (0-17 yr olds)

β = 0.00770 (0.00284) (18-99 yr olds)

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

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

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

Other Health Endpoints

Asthma exacerbation 

Pooled estimate: b

Mortimer et al. (2002)

Schildcrout et al. (2006)

6–18 years β = 0.00929 (0.00387)

β = 0.00222 (0.00282)

School loss days Pooled estimate:

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

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

Acute respiratory 

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

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

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

population to 6-18 years for all three studies, reflecting the common biological basis for the effect in children in the 

broader age group. See: U.S. EPA-SAB (2004a) and NRC (2002)
6-30

Table 6-8. Health Endpoints and Epidemiological Studies Used to Quantify PM2.5-Related 

Health Impacts a

Endpoint Study

Study 

Population

Relative Risk or Effect Estimate (β)

(with 95th Percentile Confidence 

Interval or SE)

Premature Mortality

Premature mortality—

cohort study, all-cause

Krewski et al. (2009) > 29 years

> 24 years

RR = 1.06 (1.04–1.06) per 10 μg/m3

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

Premature mortality—

all-cause

Woodruff et al. (1997) Infant (< 1 

year)

OR = 1.04 (1.02–1.07) per 10 μg/m3

Chronic Illness

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

years)

OR = 1.62 (1.13–2.34) per 20 μg/m3

Pooled estimate:

Pope et al. (2006) β = 0.00481 (0.00199)

Sullivan et al. (2005) β = 0.00198 (0.00224)

Zanobetti et al. (2009) β = 0.00225 (0.000591)

Zanobetti and Schwartz (2006) β = 0.0053 (0.00221)

Hospital Admissions 

Respiratory Zanobetti et al. (2009)—ICD 

460-519 (All respiratory)

> 64 years β=0.00207 (0.00446)

Kloog et al. (2012)—ICD 460-

519 (All Respiratory

β=0.0007 (0.000961)

Moolgavkar (2000)—ICD 490–

496 (Chronic lung disease)

18–64 years 1.02 (1.01–1.03) per 36 μg/m3

Babin et al. (2007)—ICD 493 

(asthma)

< 19 years β=0.002 (0.004337)

Sheppard (2003)—ICD 493 

(asthma) < 18 RR = 1.04 (1.01–1.06) per 11.8 μg/m3

Cardiovascular Pooled estimate:

Zanobetti et al. (2009)—ICD 

390-459 (all cardiovascular)

> 64 years

β=0.00189 (0.000283)

Peng et al. (2009)—ICD 426-

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

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

β=0.00068

(0.000214)

Peng et al. (2008)—ICD 426-

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

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

β=0.00071

(0.00013)

Bell et al. (2008)—ICD 426-

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

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

β=0.0008

(0.000107)

Moolgavkar (2000)—ICD 390–

429 (all cardiovascular)

20–64 years RR=1.04 (t statistic: 4.1) per 10 μg/m3

Asthma-related 

emergency department 

visits

Pooled estimate:

Mar et al. (2010)

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

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

Glad et al. (2012) β=0.00392 (0.002843)

Other Health Endpoints

Acute bronchitis Dockery et al. (1996) 8–12 years OR = 1.50 (0.91–2.47) per 14.9 μg/m3
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Asthma exacerbations Pooled estimate:

Ostro et al. (2001) (cough, 

wheeze, shortness of breath) b

6–18 years b OR = 1.03 (0.98–1.07) 

OR = 1.06 (1.01–1.11) 

OR = 1.08 (1.00–1.17) per 30 μg/m3

Mar et al. (2004) (cough, 

shortness of breath)

RR = 1.21 (1–1.47) per 

RR = 1.13 (0.86–1.48) per 10 μg/m3

Work loss days Ostro (1987) 18–65 years β=0.0046 (0.00036)

Acute respiratory 

symptoms (MRAD)

Ostro and Rothschild (1989) 

(Minor restricted activity days) 18–65 years β=0.00220 (0.000658)

Upper respiratory 

symptoms Pope et al. (1991) Asthmatics, 9–11 years 1.003 (1–1.006) per 10 μg/m3

Lower respiratory 

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

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

updates were introduced in the PM NAAQS RIA (U.S. EPA, 2012c). b The original study populations were 8 to 13 years for the Ostro et al. (2001) study and 7 to 12 years for the Mar et 

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

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

NRC (2002).

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

Health Impacts in Sensitivity Analyses a

Endpoint Study

Study 

Population

Effect Estimate (β)

(with 95th Percentile 

Confidence Interval)

Premature Mortality

Premature respiratory 

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

> 29 years

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

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

- threshold 40 ppbb β=0.00312 (0.00096)

- threshold 45 ppb β=0.00336 (0.001)

- threshold 50 ppb β=0.00356 0.00106) 

- threshold 55 ppb β=0.00417 (0.00118)

- threshold 56 ppb β=0.00432 (0.00121)

- threshold 60 ppb β=0.00402 (0.00137)

Premature mortality -

short-term

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

PM10)

All ages

β=0.00026 (0.00017)

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

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

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

Ito et al. (2005) β=0.00117 (0.00024)

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

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

a Studies highlighted in red represent updates incorporated since the 2008 ozone NAAQS RIA (U.S. EPA, 2008a). b All threshold models are ozone-only and based on the full 96 city dataset.
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6.6.3.1 Ozone Exposure Metric

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

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

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

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

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

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

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

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

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

average ozone concentration using standard conversion functions.

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

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

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

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

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

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

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

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

1-hour maximum should be half that of the effect estimate based on the 24-hour values (and the 

8-hour maximum three-quarters of the 24-hour effect estimate).

In the sensitivity analyses for this benefits analysis, we apply national effect estimates 

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

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

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

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

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

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

national data.

6.6.3.2 Ozone Premature Mortality Effect Coefficients

Mortality Effect Coefficients for Short-term Ozone Exposure. 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 concludes that the evidence suggests that 

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

However, the ISA notes that the interpretation of the potential confounding effects of PM on 

ozone-mortality risk estimates requires caution due to the PM sampling schedule (in most cities) 

which limits the overall sample size available for evaluating potential confounding of the ozone 

effect by PM (U.S. EPA 2013a).14 

In 2006, the EPA requested an NAS study to answer the following four key questions 

regarding ozone-related mortality: (1) How did the epidemiological literature to that point 

improve our understanding of the size of the 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?; and (4) What methods should EPA use to 

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

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

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

that “...short-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 

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

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

address potential uncertainty associated with this assumption of an independent ozone mortality effect.
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Subcommittee of the Advisory Council on Clean Air Compliance Analysis, while reviewing 

EPA’s The Benefits and Costs of the Clean Air Act 1990 to 2020 (U.S. EPA, 2011a), also 

confirmed the NAS recommendation to include ozone mortality benefits (U.S. EPA-SAB, 

2010a).

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

CASAC panel, we estimate ozone-related premature mortality for short-term exposure in the 

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

analysis and the Zanobetti and Schwartz (2008) multi-city study with several additional studies 

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

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

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

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

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

see discussion below).

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

short-term ozone exposure and mortality. While this study reproduces the core national-scale 

estimates presented in Bell et al. (2004), it also explored the sensitivity of the mortality effect to 

different model specifications including (a) regional versus national Bayes-based adjustment,15

(b) co-pollutant models considering PM10, (c) all-year versus ozone-season based estimates, and 

(d) consideration of a range of ozone metrics, including the daily 8-hour max. In addition, the 

Smith et al. (2009) study did not use the trimmed mean approach employed in the Bell et al. 

(2004) study in preparing ozone monitor data.16 In selecting effect estimates from Smith et al. 

(2009), we focused on an ozone-only estimate for non-accidental mortality using the 8-hour max 

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

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

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

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

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

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

across estimates. 16 There are a number of concerns regarding the trimmed mean approach including (1) the potential loss of 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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metric for the warmer ozone season. For the sensitivity analysis, we included a co-pollutant 

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

ozone metric for the ozone season. Using a single pollutant model for the core analysis and the 

co-pollutant model in the sensitivity analysis reflects our concern that the reduced sampling 

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

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

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

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

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

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

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

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

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

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

estimate based on an 8-hour max. To do this, we used the ozone metric approach wherein the 

original effect estimate (and standard error) is multiplied by the appropriate ozone metric 

adjustment ratio.17

Mortality Effect Coefficient for Long-term Ozone Exposure. We also estimated longterm ozone exposure-related respiratory mortality incidence. Support for modeling long-term 

exposure-related mortality incidence comes from the ozone ISA as well as recommendations 

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

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

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

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

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

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

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

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

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

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

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

2013a). Consistent with the ozone HREA, we use Jerrett et al. (2009) to estimate premature 

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

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

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

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

long-term ozone exposure-related mortality only as a sensitivity analysis.

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

ozone exposure and respiratory mortality (rather than other causes of mortality). Jerrett et al. 

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

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

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

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

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

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

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

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

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

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

relating mortality and long-term ozone concentrations, which can greatly affect the results. 

CASAC concluded, “it is not clear whether the 56 ppb threshold model is a better predictor of 

respiratory mortality than when using a linear model for the Jerrett et al. data” (U.S. EPA-SAB, 

2014, p. 13-14) 

Reflecting this CASAC advice, we estimate long-term exposure-related respiratory 

mortality using a non-threshold co-pollutant model (with PM2.5) from Jerrett et al. (2009). 

Because the Jerrett et al. (2009) study uses seasonal average metrics (rather than shorter single 

day or multi-day lagged models as with time series studies studies), co-pollutants models 

obtained from Jerrett et al., (2009) are not affected by the lower PM2.5 sampling rates. Using a 

co-pollutant model is consistent with this study applying seasonal average metrics that are 
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insensitive to co-pollutant monitoring for PM2.5. The effect estimates used to model long-term 

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

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

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

PM benefits. Therefore, combining long-term and short-term ozone-attributable mortality 

estimates could lead to double counting. Estimates of short-term ozone mortality are for allcauses, while estimates of long-term ozone mortality are for respiratory-related mortality only. 

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

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

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

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

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

PM10) from Smith et al. (2009).

Sensitivity Analysis: Threshold-Based Effect Coefficients for Long-term Ozone 

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

estimated ozone-related deaths to a concentration threshold using the Jerrett et al. (2009) study.18

In their memo clarifying the results of their study (see Sasser, 2014), the authors note that in 

terms of goodness of fit, long-term health risk models including ozone clearly performed better 

than models without ozone, indicating the improved predictions of respiratory mortality when 

ozone is included. In exploring different functional forms, they report that the model including a 

threshold at 56 ppb had the lowest log-likelihood value of all models evaluated (i.e., linear 

models and models including 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 (nothreshold) model for this dataset. Using one statistical test, the model with a threshold at 56 ppb 

was determined to be statistically superior to the linear model. Using another, more stringent test, 

none of the threshold models considered were statistically superior to the linear model. Under the 

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

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

authors of Jerrett et al. (2009) to support modeling potential thresholds.
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less stringent test, although the threshold model produces a statistically superior prediction than 

the linear model, there is uncertainty about the specific location of the threshold, if one exists. 

This is because the confidence intervals on the model predictions indicate that a threshold could 

exist anywhere from 0 to 60 ppb. The authors conclude that considerable caution should be 

exercised in using any specific threshold, particularly when the more stringent statistical test 

indicates there is no significantly improved prediction. 

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

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

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

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

unique risk models that include a range of thresholds from 40 ppb to 60 ppb in 5 ppb increments, 

while also including a model with a threshold equal to 56 ppb, which had the lowest log 

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

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

6.6.3.3 PM2.5 Premature Mortality Coefficients

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

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

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

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

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

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

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

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

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

concentrations is affected by both the assumed threshold level (below which there is no effect of ozone) and the 

effect estimate applied to ozone concentrations above the threshold. 20 The set of ozone-only non-threshold effect estimates include (a) a value based on the 86 cities for which there are 

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

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

cities were used in developing the threshold-based effect estimates used in the analysis). 
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nature of the effect itself, and the high monetary value ascribed to prolonging life make mortality 

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

Researchers have found statistically significant associations between PM2.5 and 

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

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

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

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

PM2.5 exposures over multiple years (i.e., long-term exposures) and community-level annual 

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

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

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

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

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

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

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

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

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

estimating the mortality impacts of reductions in PM2.5.

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

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

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

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

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

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

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

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

published studies (Pope et al., 1995; Dockery et al., 1993) were subject to extensive 

reexamination and reanalysis by an independent team of scientific experts commissioned by the 

Health Effects Institute (HEI) and by a Special Panel of the HEI Health Review Committee 
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(Krewski et al., 2000). Publication of studies confirming and extending the findings of the 1993 

Six Cities Study and the 1995 ACS study using more recent air quality data and a longer followup period for the ACS cohort provides additional validation of the findings of these original 

studies (Pope et al., 2002, 2004; Laden et al., 2006; Krewski et al., 2009; Lepeule et al., 2012). 

The SAB-HES also supported using these two cohorts for analyses of the benefits of PM 

reductions, and concluded, “the selection of these cohort studies as the underlying basis for PM 

mortality benefit estimates [is] a good choice. These are widely cited, well studied and 

extensively reviewed data sets” (U.S. EPA-SAB, 2010a). As both the ACS and Six Cities studies 

have inherent strengths and weaknesses, we present benefits estimates using relative risk 

estimates from the most recent extended reanalysis of these cohorts (Krewski et al., 2009; 

Lepeule et al., 2012). Presenting results using both ACS and Six Cities is consistent with other 

recent RIAs (e.g., U.S. EPA, 2010c, 2011a, 2011c). The PM ISA concludes that the ACS and Six 

Cities cohorts provide the strongest evidence of the association between long-term PM2.5

exposure and premature mortality with support from a number of additional cohort studies 

(described below).

The extended analyses of the ACS cohort data (Krewski et al., 2009) refined the earlier 

ACS studies by (a) extending the follow-up period by 2 years to the year 2000, for a total of 18 

years; (b) incorporating almost double the number of urban areas; (c) addressing confounding by 

spatial autocorrelation by incorporating ecological, or community-level, co-variates; and (d) 

performing an extensive spatial analysis using land use regression modeling in two large urban 

areas. These enhancements make this analysis well-suited for the assessment of mortality risk 

from long-term PM2.5 exposures for the EPA’s benefits analyses. 

In 2009, the SAB-HES again reviewed the choice of mortality risk coefficients for 

benefits analysis, concluding that “[t]he Krewski et al. (2009) findings, while informative, have 

not yet undergone the same degree of peer review as have the aforementioned studies. Thus, the 

SAB-HES recommends that EPA not use the Krewski et al. (2009) findings for generating the 

Primary Estimate” (U.S. EPA-SAB, 2010a). Since this time, the Krewski et al. (2009) has 

undergone additional peer review, which we believe strengthens the support for including this 

study in this RIA. For example, the PM ISA (U.S. EPA, 2009b) included this study among the 

key mortality studies. In addition, the risk assessment supporting the PM NAAQS (U.S. EPA, 
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2010b) used risk coefficients drawn from the Krewski et al. (2009) study, the most recent 

reanalysis of the ACS cohort data. The PM risk assessment cited a number of advantages that 

informed the selection of the Krewski et al. (2009) study as the source of the core effect 

estimates, including the extended period of observation, the rigorous examination of model 

forms and effect estimates, the coverage for ecological variables, and the large dataset with over 

1.2 million individuals and 156 MSAs (U.S. EPA, 2010b). The CASAC also provided extensive 

peer review of the PM risk assessment and supported the use of effect estimates from this study 

(U.S. EPA-SAB, 2009a, b, 2010b). 

Consistent with the PM risk assessment (U.S. EPA, 2010b) which was reviewed by the 

CASAC (U.S. EPA-SAB, 2009a, b), we use the all-cause mortality risk estimate based on the 

random-effects Cox proportional hazard model that incorporates 44 individual and 7 ecological 

covariates (RR=1.06, 95% confidence intervals 1.04–1.08 per 10 μg/m3 increase in PM2.5). The 

relative risk estimate (1.06 per 10 μg/m3 increase in PM2.5) is identical to the risk estimate drawn 

from the earlier Pope et al. (2002) study, though the confidence interval around the Krewski et 

al. (2009) risk estimate is tighter.

In the most recent Six Cities study, which was published after the last SAB-HES review, 

Lepeule et al. (2012) evaluated the sensitivity of previous Six Cities results to model 

specifications, lower exposures, and averaging time using eleven additional years of cohort 

follow-up that incorporated recent lower exposures. The authors found significant associations 

between PM2.5 exposure and increased risk of all-cause, cardiovascular and lung cancer 

mortality. The authors also concluded that the C-R relationship was linear down to PM2.5

concentrations of 8 μg/m3 and that mortality rate ratios for PM2.5 fluctuated over time, but 

without clear trends, despite a substantial drop in the sulfate fraction. We use the all-cause 

mortality risk estimate based on a Cox proportional hazard model that incorporates 3 individual 

covariates. (RR=1.14, 95% confidence intervals 1.07–1.22 per 10 μg/m3 increase in PM2.5). The 

relative risk estimate is slightly smaller than the risk estimate drawn from Laden et al. (2006), 

with relatively smaller confidence intervals.

Given that monetized benefits associated with PM2.5 are driven largely by reductions in 

premature mortality, it is important to characterize the uncertainty in this endpoint. In order to do 
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so, we utilize the results of an expert elicitation sponsored by the EPA and completed in 2006 

(Roman et al., 2008; IEc, 2006). The results of that expert elicitation can be used as a 

characterization of uncertainty in the C-R functions. The co-benefits results derived from expert 

elicitation is discussed in Appendix 6B (section 6B.4).

PM2.5 Mortality Effect Coefficients for Infants. In addition to the adult mortality 

studies described above, several studies show an association between PM exposure and 

premature mortality in children under 5 years of age.21 The PM ISA states that less evidence is 

available regarding the potential impact of PM2.5 exposure on infant mortality than on adult 

mortality and the results of studies in several countries include a range of findings with some 

finding significant associations. Specifically, the PM ISA concluded that evidence exists for a 

stronger effect at the post-neonatal period and for respiratory-related mortality, although this 

trend is not consistent across all studies. In addition, compared to avoided premature deaths 

estimated for adult mortality, avoided premature deaths for infants are significantly smaller 

because the number of infants in the population is much smaller than the number of adults and 

the epidemiology studies on infant mortality provide smaller risk coefficients associated with 

exposure to PM2.5.

In 2004, the SAB-HES noted the release of the WHO Global Burden of Disease Study 

focusing on ambient air, which cites several recently published time-series studies relating daily 

PM exposure to mortality in children (U.S. EPA-SAB, 2004). With regard to the cohort study 

conducted by Woodruff et al. (1997), the SAB-HES noted several strengths of the study, 

including the use of a larger cohort drawn from a large number of metropolitan areas and efforts 

to control for a variety of individual risk factors in infants (e.g., maternal educational level, 

maternal ethnicity, parental marital status, and maternal smoking status). Based on these 

findings, the SAB-HES recommended that the EPA incorporate infant mortality into the primary 

benefits estimate and that infant mortality be evaluated using an impact function developed from 

the Woodruff et al. (1997) study (U.S. EPA-SAB, 2004).

In 2010, the SAB-HES again noted the increasing body of literature relating infant 

mortality and PM exposure and supported the inclusion of infant mortality in the monetized 

 21 For the purposes of this analysis, we only calculate benefits for infants age 0–1, not all children under 5 years old.
6-43

benefits (U.S. EPA-SAB, 2010a). The SAB-HES generally supported the approach of estimating 

infant mortality based on Woodruff et al. (1997) but also noted that a more recent study by 

Woodruff et al. (2006) continued to find associations between PM2.5 and infant mortality in 

California. The SAB-HES also noted, “when PM10 results are scaled to estimate PM2.5 impacts, 

the results yield similar risk estimates.” Consistent with The Benefits and Costs of the Clean Air 

Act 1990 to 2020 (U.S. EPA, 2011a), we continue to rely on the earlier 1997 study in part due to 

the national–scale of the earlier study.

6.6.3.4 Hospital Admissions and Emergency Department Visits

We pool together the incidence estimates using several different studies for many of the 

hospital admission endpoints. Some studies have examined the relationship between air pollution 

and emergency department (ED) visits. Since most emergency department visits do not result in 

an admission to the hospital (i.e., most people going to the emergency department are treated and 

return home), we treat hospital admissions and emergency department visits separately, taking 

account of the fraction of emergency department visits that are admitted to the hospital. 

Specifically, within the baseline incidence rates, we parse out the scheduled hospital visits from 

unscheduled ones as well as the hospital visits that originated in the emergency department.

The ozone ISA states that studies consistently found positive associations between shortterm ozone exposures and asthma and COPD hospital admissions and ED visits, with more 

limited evidence for pneumonia (U.S. EPA, 2013a). The ISA found no evidence of a threshold 

between short term ozone exposure and respiratory hospital admissions and ED visits. The ISA 

also observes that effect estimates remained robust to copollutants (U.S. EPA 2013a).

Considering these observation from the ISA and a thoroughly reviewing available 

epidemiological studies, we estimate respiratory hospital admissions (for 65-99 year olds) using 

an effect estimate obtained from Katsouyanni et al. (2009) and asthma-related emergency room 

visits (for all ages) using several single-city studies. Although Katsouyanni et al. (2009) provides 

effect estimates specific to the summer season, we adjusted the 1-hour max metric to the
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equivalent 8-hour max effect estimates.22 The study provides summer season single pollutant 

effect estimates based both on natural and penalized splines. We have re-evaluated the choice of 

these models in this final RIA. In contrast to the proposal RIA, where we averaged both the 

penalized and natural spline estimates, we decided to focus on the penalized spline model 

because it displays a higher degree of precision, thus less potential for random error. 23 While 

Katsouyanni et al. (2009) included a set of effect estimates based on co-pollutant modeling (with 

PM10), but we could not use them because they were based on the full year rather than the 

summer season. 

A number of studies are available to model respiratory ED visits. Because we do not yet 

have the information needed to value this endpoint, we focused on the narrower category of 

asthma-related ED visits We used a set of single city studies together with random-effects 

pooling to generate a single pooled estimate. The set of single city studies used in this 

calculation include: Peel et al. (2005) and Sarnat et al. (2013) both for Atlanta, Wilson et al. 

(2005) and Mar and Koenig (2009) for Seattle, Wilson et al. (2005) for Portland, Maine,, Ito et 

al. (2007) for New York City, and Glad et al. (2012) for Pittsburgh. We note that of these single 

city studies, only the Ito et al. (2007) study included a co-pollutant model (for PM2.5).24 In 

addition, two of the studies required adjustments to reflect the 8-hour max air metric. 

Specifically, Glad et al. (2012) uses the 1-hour max air metric, while Sarnat et al. (2013) used the 

24-hour average metric. Each required the use of air metric ratios to adjust their betas. In 

generating a single pooled benefit estimate for this endpoint, we used random/fixed effects 

pooling to combine estimates across these single city studies. 

The two main groups of hospital admissions estimated in this analysis for PM2.5 are: 

respiratory admissions and cardiovascular admissions. There is not sufficient evidence linking 

 22 Given that Katsouyanni et al. (2009) included a larger number of cities (14), rather than constructing an air metric 

adjustment ratio based on this set of urban study areas, we used a national ratio to adjust effect estimates to 

represent the 8-hour metric. 23 We evaluated the impact from this change (i.e., use of the penalized spline model alone as contrasted with an 

average of estimates generated using both the natural and penalized splines), and this change made a negligible 

(<<1%) difference in monetized ozone benefits due to the similarity in effect coefficients between the two 

models. 24 While we have included co-pollutant models as sensitivity analyses for mortality, we did not include separate copollutant models for any of the morbidity endpoints as sensitivity analyses because morbidity endpoint represent a 

small fraction of the total monetized benefits. . 
6-45

PM2.5 with other types of hospital admissions. Both asthma- and cardiovascular-related visits 

have been linked to exposure to PM2.5 in the United States, though as we note below, we are able 

to assign an economic value to asthma-related events only. To estimate the effects of PM2.5 air 

pollution reductions on asthma-related ER visits, we use the effect estimates from studies of 

children 18 and under by Mar et al. (2010), Slaughter et al. (2005), and Glad et al. (2012). The 

first two studies examined populations 0 to 99 in Washington, while Glad et al. examined 

populations 0-99 in Pittsburgh, Pennsylvania. Mar and colleagues perform their study in 

Tacoma, while Slaughter and colleagues base their study in Spokane. We apply random/fixed 

effects pooling to combine evidence across these two studies.

To estimate avoided incidences of cardiovascular hospital admissions associated with 

exposure to PM2.5, we used studies by Moolgavkar (2000), Zanobetti et al. (2009), Peng et al. 

(2008, 2009) and Bell et al. (2008). Only Moolgavkar (2000) provided a separate effect estimate 

for adults 20 to 64, while the remainder estimate risk among adults over 64. Total cardiovascular 

hospital admissions are the sum of the pooled estimate for adults over 64 and the single study 

estimate for adults 20 to 64. To avoid double-counting benefits from reductions in myocardial 

infarctions when applying the impact function for cardiovascular hospital admissions, we first 

adjusted the baseline cardiovascular hospital admissions to remove admissions for myocardial 

infarctions. We applied equal weights pooling to the multi-city studies assessing risk among 

adults over 64 because these studies already incorporated pooling across the city-level estimates. 

One potential limitation of our approach is that while the Zanobetti et al. (2009) study assesses 

all cardiovascular risk, the Bell et al. (2008), and Peng et al. (2008, 2009) studies estimate a 

subset of cardiovascular hospitalizations as well as certain cerebro- and peripheral-vascular 

diseases. To address the potential for the pooling of these four studies to produce a biased 

estimate, we match the pooled risk estimate with a baseline incidence rate that excludes cerebroand peripheral-vascular disease. An alternative approach would be to use the Zanobetti et al. 

(2009) study alone, though this would prevent us from drawing upon the strengths of the three 

multi-city studies.

To estimate avoided incidences of respiratory hospital admissions associated with PM2.5, 

we used a number of studies examining total respiratory hospital admissions as well as asthma 

and chronic lung disease. We estimated impacts among three age groups: adults 65 and over, 
6-46

adults 18 to 64 and children 0 to 17. For adults over 65, the multi-city studies by Zanobetti et al. 

(2009) and Kloog et al. (2012) provide effect coefficients for total respiratory hospital 

admissions. We pool these two studies using equal weights. Moolgavkar et al. (2003) examines 

PM2.5 and chronic lung disease hospital admissions (not including asthma) in Los Angeles, 

California among adults 18 to 64. For children 0 to 18, we pool two studies using random/fixed 

effects. The first is Babin et al. (2007), which assessed PM2.5 and asthma hospital admissions in 

Washington, DC among children 1 to 18; we adjusted the age range for this study to apply to 

children 0 to 18. The second is Sheppard et al. (2003), which assessed PM2.5 and asthma 

hospitalizations in Seattle, Washington, among children 0 to 18.

6.6.3.5 Acute Health Events 

A number of acute health effects not requiring hospitalization are also associated with 

exposure to ozone and PM2.5. The sources for the effect estimates used to quantify these 

endpoints are described below.

Asthma exacerbations. For this RIA, we followed the SAB-HES recommendations 

regarding asthma exacerbations in developing the core estimate (U.S. EPA-SAB, 2004a). 

Although certain studies of acute respiratory events characterize these impacts among only 

asthmatic populations, others consider the full population, including both asthmatics and nonasthmatics. For this reason and to avoid double counting impacts, incidence estimates derived 

from studies focused only on asthmatics cannot be added to estimates from studies that consider 

the full population. To prevent such double-counting, we estimated the exacerbation of asthma 

among children and excluded adults from the calculation. Asthma exacerbations occurring in 

adults are assumed to be captured in the general population endpoints, such as work loss days 

and minor restricted activity days (MRADs). Finally, we note the important distinction between 

the exacerbation of asthma among asthmatic populations and the onset of asthma among 

populations not previously suffering from asthma. In this RIA, we quantify the exacerbation of 

asthma among asthmatic populations and not the onset of new cases of asthma.

Based on advice from the SAB-HES (U.S. EPA-SAB 2004a), regardless of the age 

ranges included in the source epidemiology studies, we extend the applied population to ages 6 
6-47

to 18, reflecting the common biological basis for the effect in children in the broader age group. 

This age range expansion is also supported by NRC (2002, pp. 8, 116).

To characterize asthma exacerbations in children from exposure to ozone, for the 

proposed RIA, we selected three multi-city studies (Mortimer et al., 2002; O’Connor et al., 2008;

Schildcrout et al., 2006). All three of these studies required the application of air metric ratios to 

adjust effect estimates to represent the 8-hour max metric.25 In this final RIA, following a final 

review of our technical approach, we removed O’Connor (2008) due to concerns about potential 

exposure measurement error and residual confounding from meteorological variables in its 19-

day lag structure. Current evidence in the ozone ISA suggests a more immediate effect with 

exposure to ozone for respiratory-related effects, such as hospital admissions and emergency 

department visits, with additional supporting evidence from studies of respiratory symptoms 

(U.S. EPA, 2013a). Consequently, the 19-day lag structure reflected in the O’Connor (2008) 

study is not as strongly supported by the evidence as the shorter lag structures associated with the 

other two asthma exacerbation studies.26 In generating the asthma exacerbation estimate, we pool 

estimates from Mortimer et al. (2012) and Schildcrout et al. (2006) using a random/fixed effects 

approach applied within BenMAP-CE (U.S. EPA, 2015a).27

To characterize asthma exacerbations in children from exposure to PM2.5, we selected 

two studies (Ostro et al., 2001; Mar et al., 2004) that followed panels of asthmatic children. 

Ostro et al. (2001) followed a group of 138 African-American children in Los Angeles for 13 

weeks, recording daily occurrences of respiratory symptoms associated with asthma 

exacerbations (e.g., shortness of breath, wheeze, and cough). This study found a statistically 

significant association between exposure to PM2.5, measured as a 12-hour average, and the daily 

prevalence of shortness of breath and wheeze endpoints. Although the association was not 

statistically significant for cough, the results were still positive and close to significance; 

 25 Mortimer et al. (2002) had effect estimates based on an 8-hour mean metric, O’Connor et al. (2008) used a 24-

hour metric, and Schildcrout et al. (2006) was based on a 1-hour max metric. 26 We evaluated the impact from excluding O’Connor (2008) in estimating asthma exacerbations, and the change 

made a negligible (<1%) difference in monetized ozone benefits. Furthermore, the combined changes in 

estimating asthma exacerbations and respiratory hospital admissions resulted in only a 1% change in monetized 

ozone benefits compared to the approaches used in the proposal RIA (U.S. EPA, 2014c). 27 BenMAP-CE applies a chi-squared test to determine whether a fixed or random effect pooling approach should be 

used (for additional detail see U.S. EPA (2015b), p. 206). 
6-48

consequently, we decided to include this endpoint, along with shortness of breath and wheeze, in 

generating asthma exacerbation benefits.28

Mar et al. (2004) studied the effects of various size fractions of particulate matter on 

respiratory symptoms of adults and children with asthma, monitored over many months. The 

study was conducted in Spokane, Washington, a semi-arid city with diverse sources of 

particulate matter. Data on respiratory symptoms and medication use were recorded daily by the 

study’s subjects, while air pollution data was collected by the local air agency and Washington 

State University. Subjects in the study consisted of 16 adults—the majority of whom participated 

for over a year—and nine children, all of whom were studied for over eight months. Among the 

children, the authors found a strong association between cough symptoms and several metrics of 

particulate matter, including PM2.5. However, the authors found no association between 

respiratory symptoms and exposure to PM for any metric in adults. Mar et al. therefore 

concluded that the discrepancy in results between children and adults was due either to the way 

in which air quality was monitored, or a greater sensitivity of children than adults to increased 

levels of PM air pollution.

We employed the following pooling approach in combining estimates generated using 

effect estimates from the two studies to produce a single estimate for PM-related asthma 

exacerbation incidence. First, we used random/fixed effects pooling to combine the Ostro and 

Mar estimates for shortness of breath and cough. Next, we pooled the Ostro estimate of wheeze 

with the pooled cough and shortness of breath estimates to derive an overall estimate of asthma 

exacerbation in children.

Acute Respiratory Symptoms. We estimate one type of acute respiratory symptom 

related to ozone exposure. Minor Restricted Activity Days (MRADs) result when individuals 

reduce most usual daily activities and replace them with less strenuous activities or rest, yet not 

to the point of missing work or school. For example, a mechanic who would usually be doing 

 28 The random effects pooling used in BenMAP (U.S. EPA, 2015b) to generate a single estimate for asthma 

exacerbations weights estimates based on variance, and consequently those estimates with less statistical 

significance and wider confidence intervals will be down-weighted in generating the total aggregated estimate. 
6-49

physical work most of the day will instead spend the day at a desk doing paper work and phone 

work because of difficulty breathing or chest pain.

For ozone, we modeled MRADs using Ostro and Rothschild (1989). This study provides 

a co-pollutant model (with PM2.5) based on a national sample of 18-64 year olds. The original 

study used a 24-hour average metric and included control for PM2.5, which necessitated the use 

of an air metric ratio to convert the effect estimate to an 8-hour max equivalent. 

We estimate three types of acute respiratory symptoms related to PM2.5 exposure: lower 

respiratory symptoms, upper respiratory symptoms, and MRAD. Incidences of lower respiratory 

symptoms (e.g., wheezing, deep cough) in children aged 7 to 14 were estimated for PM2.5 using 

an effect estimate from Schwartz and Neas (2000). Incidences of upper respiratory symptoms in 

asthmatic children aged 9 to 11 are estimated for PM2.5 using an effect estimate developed from 

Pope et al. (1991). Because asthmatics have greater sensitivity to stimuli (including air 

pollution), children with asthma can be more susceptible to a variety of upper respiratory 

symptoms (e.g., runny or stuffy nose; wet cough; and burning, aching, or red eyes). Research on 

the effects of air pollution on upper respiratory symptoms has focused on effects in asthmatics. 

The effect of PM2.5 on MRAD was estimated using an effect estimate derived from Ostro 

and Rothschild (1989). More recently published literature examining the relationship between 

short-term PM2.5 exposure and acute respiratory symptoms was available in the PM ISA (U.S. 

EPA, 2009b), but proved to be unsuitable for use in this benefits analysis. In particular, the best 

available study (Patel et al., 2010) specified a population aged 13–20, which overlaps with the 

population in which we assess asthma exacerbation. As we describe in detail below, to avoid the 

chance of double-counting impacts, we do not estimate changes in acute respiratory symptoms 

and asthma exacerbation among populations of the same age.

School loss days (absences). Children may be absent from school due to respiratory or 

other acute diseases caused, or aggravated by, exposure to air pollution. Several studies have 

found a significant association between ozone levels and school absence rates. We use two 

studies (Gilliland et al., 2001; Chen et al., 2000) to estimate changes in school absences resulting 

from changes in ozone levels. The Gilliland et al. study estimated the incidence of new periods 

of absence, while the Chen et al. study examined daily absence rates. We converted the Gilliland 
6-50

et al. estimate to days of absence by multiplying the absence periods by the average duration of 

an absence. We estimated 1.6 days as the average duration of a school absence, the result of 

dividing the average daily school absence rate 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.

Acute Bronchitis. Approximately 4% of U.S. children between the ages of 5 and 17 

experience episodes of acute bronchitis annually (ALA, 2002). Acute bronchitis is characterized 

by coughing, chest discomfort, slight fever, and extreme tiredness, lasting for a number of days. 

According to the MedlinePlus medical encyclopedia,29 with the exception of cough, most acute 

bronchitis symptoms abate within 7 to 10 days. Incidence of episodes of acute bronchitis in 

children between the ages of 5 and 17 were estimated using an effect estimate developed from 

Dockery et al. (1996). 

Work Loss Days. Health effects from air pollution can also result in missed days of 

work (either from personal symptoms or from caring for a sick family member). Days of work 

lost resulting from exposure to PM2.5 were estimated using an effect estimate developed from 

Ostro (1987). Ostro (1987) estimated the impact of PM2.5 on the incidence of work loss days in a 

national sample of the adult working population, ages 18 to 65 living in metropolitan areas. 

Ostro reported that two-week average PM2.5 levels were significantly linked to work loss days, 

but there was some year-to-year variability in the results. 

6.6.3.6 Nonfatal Acute Myocardial Infarctions (AMI) (Heart Attacks)

Nonfatal heart attacks have been linked with short-term exposures to PM2.5 in the United 

States (Mustafić et al., 2012; Peters et al., 2001; Sullivan et al., 2005; Pope et al., 2006; 

 29 See http://www.nlm.nih.gov/medlineplus/ency/article/001087.htm, accessed April 2012. 
6-51

Zanobetti and Schwartz, 2006; Zanobetti et al., 2009) and other countries (Poloniecki et al., 

1997; Barnett et al., 2006; Peters et al., 2005). In previous health impact assessments, we have 

relied upon a study by Peters et al. (2001) as the basis for the impact function estimating the 

relationship between PM2.5 and nonfatal heart attacks. The Peters et al. (2001) study exhibits a 

number of strengths. In particular, it includes a robust characterization of populations 

experiencing AMIs. The researchers interviewed patients within 4 days of their AMI events and, 

for inclusion in the study, patients were required to meet a series of criteria including minimum 

kinase levels, an identifiable onset of pain or other symptoms, and the ability to indicate the time, 

place and other characteristics of their AMI pain in an interview.

Since the publication of Peters et al. (2001), a number of other single and multi-city 

studies have appeared in the literature, including: Sullivan et al. (2005), which considered the 

risk of PM2.5-related hospitalization for AMIs in King County, Washington; Pope et al. (2006), 

based in Wasatch Range, Utah; Zanobetti and Schwartz (2006), based in Boston, Massachusetts; 

and, Zanobetti et al. (2009), a multi-city study of 26 U.S. communities. Each of these single and 

multi-city studies, with the exception of Pope et al. (2006), measure AMIs using hospital 

discharge rates. Conversely, the Pope et al. (2006) study is based on a large registry with 

angiographically characterized patients—arguably a more precise indicator of AMI. Because the 

Pope et al. (2006) study reflected both myocardial infarctions and unstable angina, this produces 

a more comprehensive estimate of acute ischemic heart disease events than the other studies. 

However, unlike the Peters study (Peters et al., 2006), Pope and colleagues did not measure the 

time of symptom onset, and PM2.5 data were not measured on an hourly basis.

As a means of recognizing the strengths of the Peters study while also incorporating the 

newer evidence found in the four single and multi-city studies, we present a range of AMI 

estimates. The upper end of the range is calculated using the Peters study, while the lower end of 

the range is the result of an equal-weights pooling of these four newer studies. It is important to 

note that when calculating the incidence of nonfatal AMI, the fraction of fatal heart attacks is 

subtracted to ensure that there is no double-counting with premature mortality estimates. 

Specifically, we apply an adjustment factor in the C-R function to reflect the probability of 

surviving a heart attack. Based on recent data from the Agency for Healthcare Research and 

Quality’s Healthcare Utilization Project National Inpatient Sample database (AHRQ, 2009), we 
6-52

identified death rates for adults hospitalized with acute myocardial infarction stratified by age 

(e.g., 1.852% for ages 18–44, 2.8188% for ages 45–64, and 7.4339% for ages 65+). These rates 

show a clear downward trend over time between 1994 and 2009 for the average adult and replace 

the 7% survival rate previously applied across all age groups from Rosamond et al. (1999).

6.6.3.7 Worker Productivity

The EPA last quantified the effect of ozone on outdoor agricultural worker productivity 

in the final Regulatory Impact Analysis accompanying the Transport Rule (U.S. EPA, 2011c); 

that analysis reported the value of worker productivity in the core benefits analysis. That RIA 

applied information reported in Crocker and Horst (1981), which observed that reducing ozone 

by 10 percent translated to a 1.4 increase in income among outdoor citrus workers. The RIA 

accompanying the proposed Ozone NAAQS (US EPA, 2014c) noted that, due in part to the 

vintage of the data used in this study, the Agency omitted this analysis in subsequent RIA’s. 

A recent study by Graff Zivin, and Neidell (2012) provides new evidence of the effect of 

ozone exposure on productivity among outdoor agricultural workers which we use in a 

supplemental analysis. Specifically, the study combined individual-level daily harvest rates for 

outdoor agricultural workers on a 500-acre farm in the Central Valley of California, with groundlevel ozone data. The authors observed that a 10 ppb increase in work-day ozone concentrations 

(from 6:00 am to 3:00 pm) was associated with a 5.5% decrease in productivity among outdoor 

agricultural workers on a given day. Additional detail on the worker productivity supplemental 

analysis (including results) is presented in Appendix 6B (section 6B.8). 

6.6.3.8 Unquantified Human Health Effects

Attaining a revised ozone NAAQS would reduce emissions of NOx and VOC. Although 

we have quantified many of the health benefits associated with reducing exposure to ozone and 

PM2.5, as shown in Table 6-3, we are unable to quantify the health benefits associated with 

reducing direct exposure to NO2 or VOC because of the absence of air quality modeling data for 

these pollutants. In addition, we are unable to quantify the effects of VOC emission reductions 

on ambient PM2.5 and associated health effects. Below we provide a qualitative description of 

these health benefits. In general, previous analyses have shown that the monetized value of these 
6-53

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.

6.6.4 Economic Valuation Estimates

Reductions in ambient concentrations of air pollution generally lower the risk of future 

adverse health effects for a large population. Therefore, the appropriate economic measure is 

willingness-to-pay (WTP) for changes in risk of a health effect rather than WTP for a health 

effect that would occur with certainty (Freeman, 1993). Epidemiological studies generally 

provide estimates of the relative risks of a particular health effect that is avoided because of a 

reduction in air pollution. We converted those changes in risk to units of avoided statistical 

incidence for ease of presentation. We calculated the value of avoided statistical incidences by 

dividing individual WTP for a risk reduction by the related observed change in risk. For 

example, suppose a measure is able to reduce the risk of premature mortality from 2 in 10,000 to 

1 in 10,000 (a reduction of 1 in 10,000). If individual WTP for this risk reduction is $100, then 
6-54

the WTP for an avoided statistical premature mortality amounts to $1 million ($100/0.0001 

change in risk). Using this approach, the size of the affected population is automatically taken 

into account by the number of 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 6-10. All values are in constant year 2011$, adjusted for growth in real 

income for WTP estimates out to 2024 using projections provided by Standard and Poor’s, which 

is discussed in further detail below.30 Economic theory argues that WTP for most goods (such as 

environmental protection) will increase if real income increases. Several of the valuation studies 

used in this analysis were conducted in the late 1980s and early 1990s, and we are in the process 

of reviewing the literature to update these unit values. The discussion below provides additional 

details on valuing specific PM2.5-related related endpoints.

6.6.4.1 Mortality Valuation

Following the advice of the SAB’s Environmental Economics Advisory Committee 

(SAB-EEAC), the EPA currently uses the value of statistical life (VSL) approach in calculating 

the core estimate of mortality benefits, because we believe this calculation provides the most 

reasonable single estimate of an individual’s willingness to trade off money for reductions in 

mortality risk (U.S. EPA-SAB, 2000). The VSL approach is a summary measure for the value of 

 30 Income growth projections are only currently available in BenMAP through 2024, so both the 2025 and 2038 

estimates use income growth through 2024 and are therefore likely underestimates. Currently, BenMAP does not 

have an inflation adjustment to 2011$. We ran BenMAP for a currency year of 2010$ and calculated the benefitper-ton estimates in 2010$. We then adjusted the resulting benefit-per-ton estimates to 2011$ using the Consumer 

Price Index (CPI-U, all items). This approach slightly underestimates the inflation for medical index and wage 

index between 2010 and 2011, which affects COI estimates and wage-based estimates.
6-55

small changes in mortality risk experienced by a large number of people. For a period of time 

(2004–2008), the Office of Air and 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 EPA

neither changed its official guidance on the use of VSL in rule-makings nor subjected the interim 

estimate to a scientific peer-review process through SAB or other peer-review group.
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Table 6-10. Unit Values for Economic Valuation of Health Endpoints (2011$) a

Health Endpoint

Central Estimate of Value Per Statistical 

Incidence

1990 Income Level 2024 Income Level Derivation of Distributions of Estimates

Premature Mortality (Value of a 

Statistical Life)

$8,300,000 $10,000,000 The EPA currently recommends a central VSL of $4.8 million

(1990$, 1990 income) based on a Weibull distribution fitted to 26 

published VSL estimates (5 contingent valuation and 21 labor market 

studies). The underlying studies, the distribution parameters, and 

other useful information are available in Appendix B of the EPA’s 

Guidelines for Preparing Economic Analyses (U.S. EPA, 2010e).

Nonfatal Myocardial Infarction 

(heart attack)

3% discount rate

Age 0–24

Age 25–44

Age 45–54

Age 55–64

Age 65 and over

7% discount rate

Age 0–24

Age 25–44

Age 45–54

Age 55–64

Age 65 and over

$100,000

$110,000

$120,000

$210,000

$100,000

$100,000

$110,000

$120,000

$190,000

$100,000

$100,000

$110,000

$120,000

$210,000

$100,000

$100,000

$110,000

$120,000

$190,000

$100,000

No distributional information available. Age-specific cost-of-illness 

values reflect lost earnings and direct medical costs over a 5-year 

period following a nonfatal MI. Lost earnings estimates are based on 

Cropper and Krupnick (1990). Direct medical costs are based on 

simple average of estimates from Russell et al. (1998) and Wittels 

et al. (1990).

Lost earnings:

Cropper and Krupnick (1990). Present discounted value of 5 years of 

lost earnings in 2000$:

age of onset: at 3% at 7%

25–44 $9,000 $8,000

45–54 $13,000 $12,000

55–65 $77,000 $69,000

Direct medical expenses (2000$): An average of:

1. Wittels et al. (1990) ($100,000—no discounting)

2. Russell et al. (1998), 5-year period ($22,000 at 3% discount rate; 

$21,000 at 7% discount rate)

(continued)
6-57

Table 6-10. Unit Values for Economic Valuation of Health Endpoints (2011$) a (continued)

Health Endpoint

Central Estimate of Value Per Statistical 

Incidence

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)
6-58

Table 6-10. Unit Values for Economic Valuation of Health Endpoints (2011$) a (continued)

Health Endpoint

Central Estimate of Value Per Statistical 

Incidence

2000 Income Level 2024 Income Level Derivation of Distributions of Estimates

Respiratory Ailments Not Requiring Hospitalization

Upper Respiratory Symptoms 

(URS)

$35 $32 Combinations of the three symptoms for which WTP estimates are 

available that closely match those listed by Pope et al. result in seven 

different “symptom clusters,” each describing a “type” of URS. A 

dollar value was derived for each type of URS, using mid-range 

estimates of WTP (IEc, 1994) to avoid each symptom in the cluster 

and assuming additivity of WTPs. In the absence of information 

surrounding the frequency with which each of the seven types of URS 

occurs within the URS symptom complex, we assumed a uniform 

distribution between $9.2 and $43 (2000$).

Lower Respiratory Symptoms 

(LRS)

$22 $21 Combinations of the four symptoms for which WTP estimates are

available that closely match those listed by Schwartz et al. result in 11 

different “symptom clusters,” each describing a “type” of LRS. A 

dollar value was derived for each type of LRS, using mid-range 

estimates of WTP (IEc, 1994) to avoid each symptom in the cluster 

and assuming additivity of WTPs. The dollar value for LRS is the 

average of the dollar values for the 11 different types of LRS. In the 

absence of information surrounding the frequency with which each of 

the 11 types of LRS occurs within the LRS symptom complex, we 

assumed a uniform distribution between $6.9 and $25 (2000$).

Asthma Exacerbations $56 $60 Asthma exacerbations are valued at $45 per incidence, based on the 

mean of average WTP estimates for the four severity definitions of a 

“bad asthma day,” described in Rowe and Chestnut (1986). This 

study surveyed asthmatics to estimate WTP for avoidance of a “bad 

asthma day,” as defined by the subjects. For purposes of valuation, an 

asthma exacerbation is assumed to be equivalent to a day in which 

asthma is moderate or worse as reported in the Rowe and Chestnut 

(1986) study. The value is assumed to have a uniform distribution 

between $16 and $71 (2000$).

(continued)
6-59

Table 6-10. Unit Values for Economic Valuation of Health Endpoints (2011$) a (continued)

Health Endpoint

Central Estimate of Value Per Statistical Incidence

2000 Income Level 2024 Income Level Derivation of Distributions of Estimates

Respiratory Ailments Not Requiring Hospitalization (continued)

Acute Bronchitis $460 $500 Assumes a 6-day episode, with the distribution of the daily value 

specified as uniform with the low and high values based on those 

recommended for related respiratory symptoms in Neumann et al. 

(1994). The low daily estimate of $10 is the sum of the mid-range 

values recommended by IEc (1994) for two symptoms believed to be 

associated with acute bronchitis: coughing and chest tightness. The 

high daily estimate was taken to be twice the value of a minor 

respiratory restricted-activity day, or $110 (2000$). 

Work Loss Days (WLDs) Variable 

(U.S. median = $150)

Variable 

(U.S. median = $150)

No distribution available. Point estimate is based on county-specific 

median annual wages divided by 52 and then by 5—to get median 

daily wage. U.S. Year 2000 Census, compiled by Geolytics, Inc. 

(Geolytics, 2002)

School Loss Days $98 $98 No distribution available. Based on (1) the probability that, if a school 

child stays home from school, a parent will have to stay home from 

work to care for the child, and (2) the value of the parent’s lost 

productivity.

Minor Restricted Activity Days 

(MRADs)

$64 $68 Median WTP estimate to avoid one MRAD from Tolley et al. (1986). 

Distribution is assumed to be triangular with a minimum of $22 and a 

maximum of $83, with a most likely value of $52 (2000$). Range is 

based on assumption that value should exceed WTP for a single mild 

symptom (the highest estimate for a single symptom—for eye 

irritation—is $16) and be less than that for a WLD. The triangular 

distribution acknowledges that the actual value is likely to be closer 

to the point estimate than either extreme.

a All estimates are rounded to two significant digits. Unrounded estimates in 2000$ are available in the Appendix J of the BenMAP user manual (U.S. EPA, 

2015). Income growth projections are only currently available in BenMAP through 2024, so both the 2025 and 2038 estimates use income growth through 2024 

and are therefore likely underestimates. Currently, BenMAP does not have an inflation adjustment to 2011$. We ran BenMAP for a currency year of 2010$ and 

calculated the benefit-per-ton estimates in 2010$. We then adjusted the resulting benefit-per-ton estimates to 2011$ using the Consumer Price Index (CPI-U, all 

items). This approach slightly underestimates the inflation for medical index and wage index between 2010 and 2011, which affects COI estimates and wagebased estimates. 
6-60

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)31 while the Agency continues its efforts to 

update its guidance on this issue. This approach calculates a mean value across VSL estimates 

derived from 26 labor market and contingent valuation studies published between 1974 and 

1991. The mean VSL across these studies is $4.8 million (1990$) or $6.3 million (2000$).32 The 

Agency is committed to using scientifically sound, appropriately reviewed evidence in valuing 

mortality risk reductions and has made significant progress in responding to the SAB-EEAC’s 

specific recommendations. In the process, the Agency has identified a number of important 

issues to be considered in updating its mortality risk valuation estimates. These are detailed in a 

white paper on “Valuing Mortality Risk Reductions in Environmental Policy,” which underwent 

review by the SAB-EEAC. A meeting with the SAB on this paper was held on March 14, 2011 

and formal recommendations were transmitted on July 29, 2011 (U.S. EPA-SAB, 2011). EPA is 

taking SAB’s recommendations under advisement.

The economics literature concerning the appropriate method for valuing reductions in 

premature mortality risk is still developing. The adoption of a value for the projected reduction in 

the risk of premature mortality is the subject of continuing discussion within the economics and 

 31 In the updated Guidelines for Preparing Economic Analyses (U.S. EPA, 2010e), EPA retained the VSL endorsed 

by the SAB with the understanding that further updates to the mortality risk valuation guidance would be 

forthcoming in the near future. 

32 In this analysis, we adjust the VSL to account for a different currency year (2011$) and to account for income 

growth to 2024. After applying these adjustments to the $6.3 million value, the VSL is $10 million. 
6-61

public policy analysis communities. The EPA strives to use the best economic science in its 

analyses. Given the mixed theoretical finding and empirical evidence regarding adjustments to 

VSL for risk and population characteristics (e.g., Smith et al., 2004; Alberini et al., 2004; Aldy 

and Viscusi, 2008), we use a single VSL for all reductions in mortality risk.

Although there are several differences between the studies the EPA uses to derive a VSL 

estimate and the ozone and PM2.5 air pollution context addressed here, those differences in the 

affected populations and the nature of the risks imply both upward and downward adjustments. 

Table 6-11 lists some of these differences and the expected effect on the VSL estimate for air 

pollution-related mortality. In the absence of a comprehensive and balanced set of adjustment 

factors, the EPA believes it is reasonable to continue to use the $4.8 million (1990$) value 

adjusted for inflation and income growth over time while acknowledging the significant 

limitations and uncertainties in the available literature.

Table 6-11. Influence of Applied VSL Attributes on the Size of the Economic Benefits of 

Reductions in the Risk of Premature 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 followed this advice. 

For PM2.5-related premature mortality, we assume that there is a “cessation” lag between 

exposures and the total realization of changes in health effects. For PM2.5, we assumed that some 

of the incidences of premature mortality related to PM2.5 exposures occur in a distributed fashion 

over the 20 years following exposure and discounted over the period between exposure and 
6-62

premature mortality. Although the structure of the lag is uncertain, the EPA follows the advice of 

the SAB-HES to assume a segmented lag structure characterized by 30 percent of mortality 

reductions in the first year, 50 percent over years 2 to 5, and 20 percent over the years 6 to 20 

after the reduction in PM2.5 (U.S. EPA-SAB, 2004c). To take this into account in the valuation of 

reductions in premature mortality, we discount the value of premature mortality occurring in 

future years using rates of 3 percent and 7 percent.33 Changes in the cessation lag assumptions do 

not change the total number of estimated 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 that prevented us monetizing these benefits. In the sensitivity analysis, we include both 

an assumption of zero lag and the PM lag structure (i.e., the SAB 20-year segmented lag). 

Inclusion of the zero lag reflects consideration of the possibility that the long-term respiratory 

mortality estimate primarily captures an accumulation of short-term mortality effects across the 

ozone season.34 The use of the 20-year segmented lag reflects consideration of advice provided 

by the SAB-HES (U.S. EPA-SAB, 2010a), where they state that, “[i]f Alternative estimates are 

derived using cohort mortality evidence, there is no evidence in the literature to support a 

different cessation lag between ozone and particulate matter. The HES therefore recommends 

using the same cessation lag structure and assumptions as for particulate matter when utilizing 

 33 The choice of a discount rate, and its associated conceptual basis, is a topic of ongoing discussion within the 

federal government. To comply with OMB Circular A-4, EPA provides monetized benefits using discount rates of 

3% and 7% (OMB, 2003). A 3% discount reflects reliance on a “social rate of time preference” discounting 

concept. A 7% rate is consistent with an “opportunity cost of capital” concept to reflect the time value of 

resources directed to meet regulatory requirements. 

34 The ozone HREA noted,: “The effect estimates used in modeling long-term O3-attributable mortality, utilize a 

seasonal average of peak (1-hr maximum) measurements. These long-term exposure metrics can be viewed as 

long-term exposures to daily peak O3 over the warmer months, as compared with annual average levels such as 

are used in long-term PM exposure calculations. This increases the need for care in interpreting these long-term 

O3-attributable mortality estimates together with the short-term O3-attributable mortality estimates, in order to 

avoid double counting.” (U.S. EPA, 2014b). 
6-63

cohort mortality evidence for ozone.” Monetized benefit estimates generated using both lag 

assumptions are presented as sensitivity analyses (see section 6.7.3.1). 

Uncertainties Specific to Premature Mortality Valuation. The economic benefits 

associated with reductions in the risk of premature mortality are the largest category of 

monetized benefits in this RIA. In addition, in prior analyses, the EPA identified valuation of 

mortality-related benefits as the largest contributor to the range of uncertainty in monetized 

benefits (Mansfield et al., 2009).35 Because of the uncertainty in estimates of the value of 

reducing premature mortality risk, it is important to adequately characterize and understand the 

various types of economic approaches available for valuing reductions in mortality risk. Such an 

assessment also requires an understanding of how alternative valuation approaches reflect that 

some individuals may be more susceptible to air pollution-induced mortality or reflect 

differences in the nature of the risk presented by air pollution relative to the risks studied in the 

relevant economics literature.

The health science literature on air pollution indicates that several human characteristics 

affect the degree to which mortality risk affects an individual. For example, some age groups 

appear to be more susceptible to air pollution than others (e.g., the elderly and children). Health 

status prior to exposure also affects susceptibility. An ideal benefits estimate of mortality risk 

reduction would reflect these human characteristics, in addition to an individual’s WTP to 

improve one’s own chances of survival along with WTP to improve other individuals’ survival 

rates. The ideal measure would also take into account the specific nature of the risk reduction 

commodity that is provided to individuals, as well as the context in which risk is reduced. To 

measure this value, it is important to assess how reductions in air pollution reduce the risk of 

dying from the time that reductions take effect onward and how individuals value these changes. 

Each individual’s survival curve, or the probability of surviving beyond a given age, should shift 

as a result of an environmental quality improvement. For example, changing the current 

probability of survival for an individual also shifts future probabilities of that individual’s 

survival. This probability shift will differ across individuals because survival curves depend on 

 35 This conclusion was based on an assessment of uncertainty based on statistical error in epidemiological effect 

estimates and economic valuation estimates. Additional sources of model error such as those examined in the 

PM2.5 mortality expert elicitation (Roman et al., 2008) may result in different conclusions about the relative 

contribution of sources of uncertainty.
6-64

such characteristics as age, health state, and the current age to which the individual is likely to 

survive.

Although a survival curve approach provides a theoretically preferred method for valuing 

the benefits of reduced risk of premature mortality associated with reducing air pollution, the 

approach requires a great deal of data to implement. The economic valuation literature does not 

yet include good estimates of the value of this risk reduction commodity. As a result, in this 

analysis we value reductions in premature mortality risk using the VSL approach.

Other uncertainties specific to premature mortality valuation include the following:

• Across-study variation: There is considerable uncertainty as to whether the available 

literature on VSL provides adequate estimates of the VSL for risk reductions from air 

pollution reduction. Although there is considerable variation in the analytical designs and 

data used in the existing literature, the majority of the studies involve the value of risks to 

a middle-aged working population. Most of the studies examine differences in wages of 

risky occupations, using a hedonic wage approach. Certain characteristics of both the 

population affected and the mortality risk facing that population are believed to affect the 

average WTP to reduce the risk. The appropriateness of a distribution of WTP based on 

the current VSL literature for valuing the mortality-related benefits of reductions in air 

pollution concentrations therefore depends not only on the quality of the studies (i.e., how 

well they measure what they are trying to measure), but also on the extent to which the 

risks being valued are similar and the extent to which the subjects in the studies are 

similar to the population affected by changes in pollution concentrations.

• Level of risk reduction: The transferability of estimates of the VSL from the wage-risk 

studies to the context of this analysis rests on the assumption that, within a reasonable 

range, WTP for reductions in mortality risk is linear in risk reduction. For example, 

suppose a study provides a result that the average WTP for a reduction in mortality risk 

of 1/100,000 is $50, but that the actual mortality risk reduction resulting from a given 

pollutant reduction is 1/10,000. If WTP for reductions in mortality risk is linear in risk 

reduction, then a WTP of $50 for a reduction of 1/100,000 implies a WTP of $500 for a 

risk reduction of 1/10,000 (which is 10 times the risk reduction valued in the study). 

Under the assumption of linearity, the estimate of the VSL does not depend on the 

particular amount of risk reduction being valued. This assumption has been shown to be 

reasonable provided the change in the risk being valued is within the range of risks 

evaluated in the underlying studies (Rowlatt et al., 1998).

• Voluntariness of risks evaluated: Although job-related mortality risks may differ in 

several ways from air pollution-related mortality risks, the most important difference may 

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

pollution-related risks are incurred involuntarily. Some evidence suggests that people will 

pay more to reduce involuntarily incurred risks than risks incurred voluntarily (e.g., 
6-65

Lichtenstein and Slovic, 2006). If this is the case, WTP estimates based on wage-risk 

studies may understate WTP to reduce involuntarily incurred air pollution-related 

mortality risks.

• Sudden versus protracted death: A final important difference related to the nature of 

the risk may be that some workplace mortality risks tend to involve sudden, traumatic

events, whereas air pollution-related risks tend to involve longer periods of disease and 

suffering prior to death. Some evidence suggests that WTP to avoid a risk of a protracted 

death involving prolonged suffering and loss of dignity and personal control is greater 

than the WTP to avoid a risk (of identical magnitude) of sudden death (e.g., Tsuge et al., 

2005; Alberini and Scasny, 2011). To the extent that the mortality risks addressed in this 

assessment are associated with longer periods of illness or greater pain and suffering than 

are the risks addressed in the valuation literature, the WTP measurements employed in 

the present analysis would reflect a downward bias.

• Self-selection and skill in avoiding risk: Recent research (Shogren and Stamland, 2002) 

suggests that VSL estimates based on hedonic wage studies may overstate the average 

value of a risk reduction. This is because the risk-wage trade-off revealed in hedonic 

studies reflects the preferences of the marginal worker (i.e., that worker who demands the 

highest compensation for his risk reduction for a given job). This worker must have either 

a higher workplace risk than the average worker in a given occupation, a lower risk 

tolerance than the average worker in that occupation, or both. Conversely, the marginal 

worker should have a higher risk tolerance than workers employed in less-risky sectors. 

However, the risk estimate used in hedonic studies is generally based on average risk, so 

the VSL may be biased, in an ambiguous direction, because the wage differential and risk 

measures do not match.

• Baseline risk and age: Recent research (Smith, Pattanayak, and Van Houtven, 2006) 

finds that because individuals reevaluate their baseline risk of death as they age, the 

marginal value of risk reductions does not decline with age as predicted by some lifetime 

consumption models. This research supports findings in recent stated preference studies 

that suggest only small reductions in the value of mortality risk reductions with 

increasing age (e.g., Alberini et al., 2004).

6.6.4.2 Hospital Admissions and Emergency Department Valuation

In the absence of estimates of societal WTP to avoid hospital visits/admissions for 

specific illnesses, we derive COI estimates for use in the benefits analysis. The International 

Classification of Diseases (ICD) (WHO, 1977) code-specific COI estimates used in this analysis 

consist of estimated hospital charges and the estimated opportunity cost of time spent in the 

hospital (based on the average length of a hospital stay for the illness). We based all estimates of 

hospital charges and length of stays on statistics provided by the Agency for Healthcare Research 

and Quality’s Healthcare Utilization Project National Inpatient Sample (NIS) database (AHRQ, 
6-66

2007). We estimated the opportunity cost of a day spent in the hospital as the value of the lost 

daily wage, regardless of whether the hospitalized individual is in the workforce. To estimate the 

lost daily wage, we divided the median weekly wage reported by the 2007 American Community 

Survey (ACS) by five and deflated the result to the correct currency year using the CPI-U “all 

items” (U.S. EPA, 2015). The resulting national average lost daily wage is $150 (2011$). The 

total cost-of-illness estimate for an ICD code-specific hospital stay lasting n days, then, was the 

mean hospital charge plus daily lost wage multiplied by n. In general, the mean length of stay 

has decreased since the 2000 database used in the previous version of BenMAP, while the mean 

hospital charge has increased. We provide the rounded unit values in 2011$ for the COI 

functions used in this analysis in Table 6-12.

Table 6-12. Unit Values for Hospital Admissions a

End Point ICD Codes

Age Range Mean Hospital 

Charge 

(2011$)

Mean 

Length of 

Stay (days)

Total Cost of 

Illness (unit 

value in 2011$) min. max.

HA, Chronic Lung Disease 490–496 18 64 $20,000 3.9 $22,000

HA, Asthma 493 0 64 $15,000 3.0 $16,000

HA, All Cardiovascular 390–429 18 64 $41,000 4.1 $44,000

HA, All Cardiovascular 390–429 65 99 $38,000 4.9 $42,000

HA, All Respiratory 460–519 65 99 $32,000 6.1 $37,000

a All estimates rounded to two significant digits. Unrounded estimates in 2000$ are available in Appendix J of the 

BenMAP user manual (U.S. EPA, 2015).

To value asthma emergency department visits, we used a simple average of two estimates 

from the health economics literature. The first estimate comes from Smith et al. (1997), who 

reported approximately 1.2 million asthma-related emergency department visits in 1987, at a 

total cost of $186 million (1987$). The average cost per visit that year was $155; in 2011$, that 

cost is $480 (using the CPI-U for medical care to adjust to 2011$). The second estimate comes 

from Stanford et al. (1999), who reported the cost of an average asthma-related emergency 

department visit based on 1996–1997 data at $400 (using the CPI-U for medical care to adjust to 

2011$). A simple average of the two estimates yields a unit value of $440 (2011$).

6.6.4.3 Nonfatal Myocardial Infarctions Valuation

We were not able to identify a suitable WTP value for reductions in the risk of nonfatal 

heart attacks. Instead, we use a COI unit value with two components: the direct medical costs 

and the opportunity cost (lost earnings) associated with the illness event. Because the costs 
6-67

associated with a myocardial infarction extend beyond the initial event itself, we consider costs 

incurred over several years. Using age-specific annual lost earnings estimated by Cropper and 

Krupnick (1990) and a 3% discount rate, we estimated a rounded present discounted value in lost 

earnings (in 2000$) over 5 years due to a myocardial infarction of $8,800 for someone between 

the ages of 25 and 44, $13,000 for someone between the ages of 45 and 54, and $75,000 for 

someone between the ages of 55 and 65. The rounded corresponding age-specific estimates of 

lost earnings (in 2000$) using a 7% discount rate are $7,900, $12,000, and $67,000, respectively. 

Cropper and Krupnick (1990) do not provide lost earnings estimates for populations under 25 or 

over 65. As such, we do not include lost earnings in the cost estimates for these age groups.

We found three possible sources in the literature of estimates of the direct medical costs 

of myocardial infarction, which provide significantly different values (see Table 6-13):

• Wittels et al. (1990) estimated expected total medical costs of myocardial infarction over 

5 years to be $51,000 (rounded in 1986$) for people who were admitted to the hospital 

and survived hospitalization. (There does not appear to be any discounting used.) This 

estimated cost is based on a medical cost model, which incorporated therapeutic options, 

projected outcomes, and prices (using “knowledgeable cardiologists” as consultants). The 

model used medical data and medical decision algorithms to estimate the probabilities of 

certain events and/or medical procedures being used. The authors note that the average 

length of hospitalization for acute myocardial infarction has decreased over time (from an 

average of 12.9 days in 1980 to an average of 11 days in 1983). Wittels et al. used 10 

days as the average in their study. It is unclear how much further the length of stay for 

myocardial infarction may have decreased from 1983 to the present. The average length 

of stay for ICD code 410 (myocardial infarction) in the year-2000 Agency for Healthcare 

Research and Quality (AHRQ) HCUP database is 5.5 days (AHRQ, 2000). However, this 

may include patients who died in the hospital (not included among our nonfatal 

myocardial infarction cases), and as a result whose length of stay was substantially 

shorter.

• Eisenstein et al. (2001) estimated 10-year costs of $45,000 in rounded 1997$ (using a 3% 

discount rate) for myocardial infarction patients, using statistical prediction (regression) 

models to estimate inpatient costs. Only inpatient costs (physician fees and hospital costs) 

were included.
6-68

Table 6-13. Alternative Direct Medical Cost of Illness Estimates for Nonfatal Heart 

Attacks a

Study Direct Medical Costs (2011$) Over an x-Year Period, for x =

Wittels et al. (1990) $170,000 b 5

Russell et al. (1998) $34,000 c 5

Average (5-year) costs $100,000 5

Eisenstein et al. (2001) $76,000 c 10

a All estimates rounded to two significant digits. Unrounded estimates in 2000$ are available in appendix J of the 

BenMAP user manual (U.S. EPA, 2015). b Wittels et al. (1990) did not appear to discount costs incurred in future years.

c Using a 3% discount rate. Discounted values as reported in the study.

As noted above, the estimates from these three studies are substantially different, and we 

have not adequately resolved the sources of differences in the estimates. Because the wagerelated opportunity cost estimates from Cropper and Krupnick (1990) cover a 5-year period, we 

used estimates for medical costs that similarly cover a 5-year period (i.e., estimates from Wittels 

et al. (1990) and Russell et al. (1998). We used a simple average of the two 5-year estimates, or 

rounded to $85,000, and added it to the 5-year opportunity cost estimate. The resulting estimates 

are given in Table 6-14.

Table 6-14. Estimated Costs Over a 5-Year Period of a Nonfatal Myocardial Infarction 

(in 2011$) a

Age Group Opportunity Cost Medical Cost b Total Cost

0–24 $0 $100,000 $100,000

25–44 $12,000 c $100,000 $110,000

45–54 $18,000 c $100,000 $120,000

55–65 $100,000 c $100,000 $210,000

> 65 $0 $100,000 $100,000

a All estimates rounded to two significant digits, so estimates may not sum across columns. Unrounded estimates in 

2000$ are available in appendix J of the BenMAP user manual (U.S. EPA, 2015). b An average of the 5-year costs estimated by Wittels et al. (1990) and Russell et al. (1998). c From Cropper and Krupnick (1990), using a 3% discount rate for illustration.

6.6.4.4 Valuation of Acute Health Events

Asthma Exacerbation Valuation. Several respiratory symptoms in asthmatics or 

characterizations of an asthma episode have been associated with exposure to air pollutants. All 

of these can generally be taken as indications of an asthma exacerbation when they occur in an 

asthmatic. Therefore, we apply the same set of unit values for all of the variations of “asthma 

exacerbation”. Specifically, we use a unit value based on the mean WTP estimates for a “bad 

asthma day,” described in Rowe and Chestnut (1986). This study surveyed asthmatics to estimate 

WTP for avoidance of a “bad asthma day,” as defined by the subjects. 
6-69

Minor Restricted Activity Days Valuation. No studies are reported to have estimated 

WTP to avoid a minor restricted activity day. However, Neumann et al. (1994) derived an 

estimate of willingness to pay to avoid a minor respiratory restricted activity day, using estimates 

from Tolley et al. (1986) of WTP for avoiding a combination of coughing, throat congestion and 

sinusitis. This estimate of WTP to avoid a minor respiratory restricted activity day is $38 

(1990$), or about $71 (2011$). Although Ostro and Rothschild (1989) statistically linked ozone 

and minor restricted activity days, it is likely that most MRADs associated with ozone and PM2.5

exposure are, in fact, minor respiratory restricted activity days. For the purpose of valuing this 

health endpoint, we used the estimate of mean WTP to avoid a minor respiratory restricted 

activity day.

School Loss Days Valuation. To value a school absence, we: (1) estimated the 

probability that if a school child stays home from school, a parent will have to stay home from 

work to care for the child; and (2) valued the lost productivity at the parent’s wage. To do this, 

we estimated the number of families with school-age children in which both parents work, and 

we valued a school-loss day as the probability that such a day also would result in a work-loss 

day. We calculated this value by multiplying the proportion of households with school-age 

children by a measure of lost wages.

We used this method in the absence of a preferable WTP method. However, this 

approach suffers from several uncertainties. First, it omits willingness to pay to avoid the 

symptoms/illness that resulted in the school absence; second, it effectively gives zero value to 

school absences that do not result in work-loss days; and third, it uses conservative assumptions 

about the wages of the parent staying home with the child. Finally, this method assumes that 

parents are unable to work from home. If this is not a valid assumption, then there would be no 

lost wages.

For this valuation approach, we assumed that in a household with two working parents, 

the female parent will stay home with a sick child. From the Statistical Abstract of the United 

States (U.S. Census Bureau, 2001), we obtained: (1) the numbers of single, married and “other” 

(widowed, divorced or separated) working women with children; and (2) the rates of 

participation in the workforce of single, married and “other” women with children. From these 

two sets of statistics, we calculated a weighted average participation rate of 72.85 percent. Our 
6-70

estimate of daily lost wage (wages lost if a mother must stay at home with a sick child) is based 

on the year 2000 median weekly wage among women ages 25 and older (U.S. Census Bureau, 

2001). This median weekly wage is $551 (2000$). Dividing by five gives an estimated median 

daily wage of $103 (2000$). To estimate the expected lost wages on a day when a mother has to 

stay home with a school-age child, we first estimated the probability that the mother is in the 

workforce then multiplied that estimate by the daily wage she would lose by missing a workday: 

72.85 percent times $103, for a total loss of $75 (2000$). This valuation approach is similar to 

that used by Hall et al. (2003).

Work Loss Days Valuation. Work loss days are valued at a day’s wage. BenMAP-CE 

calculates county-specific median daily wages from county-specific annual wages (by dividing 

the annual wage by 52 weeks multiplied by 5 work days per week), on the theory that a worker’s 

vacation days are valued at the same daily rate as work days.

Upper and Lower Respiratory Symptoms Valuation. Lower and upper respiratory 

symptoms are each considered a complex of symptoms. A dollar value was derived for clusters 

of these symptoms that most closely match the studies used to calculate incidence (Schwartz and 

Neas, 2000; Pope et al., 1991) based on mid-range estimates from each cluster (IEc, 1994). 

6.6.4.5 Growth in WTP Reflecting National Income Growth over Time

Our analysis accounts for expected growth in real income over time. This is a distinct 

concept from inflation and currency year. Economic theory argues that WTP for most goods 

(such as environmental protection) will increase if real incomes increase. There is substantial 

empirical evidence that the income elasticity36 of WTP for health risk reductions is positive, 

although there is uncertainty about its exact value. Thus, as real income increases, the WTP for 

environmental improvements also increases. Although many analyses assume that the income 

elasticity of WTP is unit elastic (i.e., a 10% higher real income level implies a 10% higher WTP 

to reduce risk changes), empirical evidence suggests that income elasticity is substantially less 

 36 Income elasticity is a common economic measure equal to the percentage change in WTP for a 1 percent change 

in income.
6-71

than one and thus relatively inelastic. As real income rises, the WTP value also rises but at a 

slower rate than real income.

The effects of real income changes on WTP estimates can influence benefits estimates in 

two different ways: through real (national average) income growth between the year a WTP 

study was conducted and the year for which benefits are estimated, and through differences in 

income between study populations and the affected populations at a particular time. The SABEEAC advised the EPA to adjust WTP for increases in real income over time but not to adjust 

WTP to account for cross-sectional income differences “because of the sensitivity of making 

such distinctions, and because of insufficient evidence available at present” (U.S. EPA-SAB, 

2000). An advisory by another committee associated with the SAB, the Advisory Council on 

Clean Air Compliance Analysis (SAB-Council), has provided conflicting advice. While agreeing 

with “the general principle that the willingness to pay to reduce mortality risks is likely to 

increase with growth in real income” and that “[t]he same increase should be assumed for the 

WTP for serious nonfatal health effects,” they note that “given the limitations and uncertainties 

in the available empirical evidence, the Council does not support the use of the proposed 

adjustments for aggregate income growth as part of the primary analysis” (U.S. EPA-SAB, 

2004b). More recently, in response to questions related to the adjustment for income growth over 

time, the SAB-EEAC noted that “EPA should adjust for changes in income in evaluating benefits 

of risk reduction” (U.S. EPA-SAB, 2011). Until these conflicting advisories can be reconciled, 

the EPA will continue to adjust valuation estimates to reflect income growth using the methods 

described below, while providing sensitivity analyses for alternative income growth adjustment 

factors.

Based on a review of the available income elasticity literature, we adjusted the valuation 

of human health benefits upward to account for projected growth in real U.S. income. Faced with 

a dearth of estimates of income elasticities derived from time-series studies, we applied estimates 

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

Kleckner and Neumann (1999). We note that the literature has evolved since the publication of 

this memo and that an array of newer studies identifying potentially suitable income elasticity 

estimates are available (IEc, 2012). The EPA anticipates seeking a peer review in the near future 

of these studies and its approach to adjusting WTP estimates to account for changes in personal 
6-72

income. As such, these newer studies have not yet been incorporated into the benefits analysis. 

An abbreviated description of the procedure we used to account for WTP for real income growth 

between 1990 and 2024 is presented below. 

Reported income elasticities suggest that the severity of a health effect is a primary 

determinant of the strength of the relationship between changes in real income and WTP. As 

such, we use different elasticity estimates to adjust the WTP for minor health effects, severe and 

chronic health effects, and premature mortality. Note that because of the variety of empirical 

sources used in deriving the income elasticities, there may appear to be inconsistencies in the 

magnitudes of the income elasticities relative to the severity of the effects (a priori one might 

expect that more severe outcomes would show less income elasticity of WTP). We have not 

imposed any additional restrictions on the empirical estimates of income elasticity. One 

explanation for the seeming inconsistency is the difference in timing of conditions. WTP for 

minor illnesses is often expressed as a short-term payment to avoid a single episode. WTP for 

major illnesses and mortality risk reductions are based on longer-term measures of payment 

(such as wages or annual income). Economic theory suggests that relationships become more 

elastic as the length of time grows, reflecting the ability to adjust spending over a longer time 

period (U.S. EPA, 2010e, p. A-9). Based on this theory, it would be expected that WTP for 

reducing long-term risks would be more elastic than WTP for reducing short-term risks. The 

relative magnitude of the income elasticity of WTP for visibility compared with those for health 

effects suggests that visibility is not as much of a necessity as health, thus, WTP is more elastic 

with respect to income. The elasticity values used to adjust estimates of benefits in 2024 are 

presented in Table 6-15.

Table 6-15. Elasticity Values Used to Account for Projected Real Income Growth a

Benefit Category Central Elasticity Estimate

Minor Health Effect 0.14

Severe and Chronic Health Effects 0.45

Premature Mortality 0.40

a Derivation of estimates can be found in Kleckner and Neumann (1999). COI estimates are not adjusted for income 

growth.

In addition to elasticity estimates, projections of real gross domestic product (GDP) and 

populations from 1990 to 2024 are needed to adjust benefits to reflect real per capita income 

growth. For consistency with the emissions and benefits modeling, we used national population 
6-73

estimates for the years 1990 to 1999 based on U.S. Census Bureau estimates (Hollman, Mulder, 

and Kallan, 2000). These population estimates are based on application of a cohort-component 

model applied to 1990 U.S. Census data projections (U.S. Bureau of Census, 2000). For the 

years between 2000 and 2024, we applied growth rates based on the U.S. Census Bureau 

projections to the U.S. Census estimate of national population in 2000. We used projections of 

real GDP provided in Kleckner and Neumann (1999) for the years 1990 to 2010.37 We used 

projections of real GDP (in chained 1996 dollars) provided by Standard and Poor’s (2000) for 

the years 2010 to 2024.38

Using the method outlined in Kleckner and Neumann (1999) and the population and 

income data described above, we calculated WTP adjustment factors for each of the elasticity 

estimates listed in Table 6-16. Benefits for each of the categories (minor health effects, severe 

and chronic health effects, premature mortality, and visibility) are adjusted by multiplying the 

unadjusted benefits by the appropriate adjustment factor. For premature mortality, we applied the 

income adjustment factor specific to the analysis year, but we do not adjust for income growth 

over the 20-year cessation lag. Our approach could underestimate the benefits for the later years 

of the lag. 

There is some uncertainty regarding the total costs of illness in the future. Specifically, 

the nature of medical treatment is changing, including a shift towards more outpatient treatment. 

Although we adjust the COI estimates for inflation, we do not have data to project COI estimates 

for the cost of treatment in the future or income growth over time, which leads to an inherent 

though unavoidable inconsistency between COI- and WTP-based estimates. This approach may 

under predict benefits in future years because it is likely that increases in real U.S. income would 

also result in increased COI (due, for example, to increases in wages paid to medical workers) 

and increased cost of work loss days and lost worker productivity (reflecting that if worker 

 37 U.S. Bureau of Economic Analysis, Table 2A—Real Gross Domestic Product (1997) and U.S. Bureau of 

Economic Analysis, The Economic and Budget Outlook: An Update, Table 4—Economic Projections for 

Calendar Years 1997 Through 2007 (1997). Note that projections for 2007 to 2010 are based on average GDP 

growth rates between 1999 and 2007.

38 In previous analyses, we used the Standard and Poor’s projections of GDP directly. This led to an apparent

discontinuity in the adjustment factors between 2010 and 2011. We refined the method by applying the relative 

growth rates for GDP derived from the Standard and Poor’s projections to the 2010 projected GDP based on the 

Bureau of Economic Analysis projections.
6-74

incomes are higher, the losses resulting from reduced worker production would also be higher). 

In addition, cost-of-illness estimates do not include sequelae costs or pain and suffering, the 

value of which would likely increase in the future. To the extent that costs would be expected to 

increase over time, this increase may be partially offset by advancement in medical technology 

that improves the effectiveness of treatment at lower costs. For these reasons, we believe that the 

cost-of-illness estimates in this RIA may underestimate (on net) the total economic value of 

avoided health impacts.

Table 6-16. Adjustment Factors Used to Account for Projected Real Income Growth a

Benefit Category 2024

Minor Health Effect 1.07

Severe and Chronic Health Effects 1.22

Premature Mortality 1.20

a Based on elasticity values reported in Table 6-15, U.S. Census population projections, and projections of real GDP 

per capita.

6.6.5 Benefit per Ton Estimates Used in Modeling PM2.5-Related Co-benefits

This section presents the benefit-per-ton estimates (dimensioned by mortality study and 

simulation year) used as inputs in generating PM2.5 co-benefits estimates including (Table 6-17).

Benefit-per-ton estimates were generated for each of the long-term exposure-related mortality 

studies used in generating core benefits estimates for this RIA including Krewski et al. (2009) 

and Lepeule et al. (2012) (see section 6.6.3.3). Estimates were available for 2025 and 2030, with 

those being used to model co-benefits for the 2025 scenario and post-2025 scenario, respectively. 

For additional detail on the approach used to generate PM2.5 co-benefits estimates and the role 

played by these two types of inputs, see Section 6.4.4.39

 39 Sector-level NOx reductions (for each alternative standard level) were generated using methods described in

Chapter 4, section 4.2 and 4.3. As noted in section 5.4.4, NOx emissions reductions associated with alternative 

standard levels considered for this NAAQS review involved seven of the 17 sectors for which we had benefit-per 

ton values and consequently, the co-benefits PM2.5 estimates are based on simulated benefits for those seven 

sectors.
6-75

Table 6-17. Summary of PM2.5 Benefit-per-ton Estimates a

a Benefit-per-ton estimates reflect application of the 20-year segmented lag at either a 3% or 7% discount rate. In addition, separate sets of benefit-per-ton 

estimates were generate for 2025 and 2030, reflecting application of appropriate projected demographic and baseline incidence data. b Benefit-per-ton estimates for the non-specified source category were generated as a weighted average of values for the 17 source categories, with weighting

based on sector-specific NOx emissions for 2005 obtained from Fann et al. (2012b).

air, 

locamotive 

and 

marine

cement 

kilns

coke 

ovens

EGU 

point

electric 

arc 

furnaces

ferro 

alloys

integrated 

iron and 

steel

iron and 

steel

non-EGU 

point 

other

nonpoint 

other nonroad onroad

pulp and 

paper refineries

residenti

al wood

taconite 

mining

ocean 

going 

vessels

Nonspecified 

source b 

2025 at 7% social discount

Krewski et al., 2009 $7,200 $5,700 $10,000 $5,200 $9,600 $4,400 $13,000 $17,000 $6,300 $7,900 $7,000 $7,600 $3,700 $6,900 $14,000 $6,000 $2,000 $6,400

Lepeule et al., 2012 $16,000 $13,000 $24,000 $12,000 $22,000 $9,800 $30,000 $38,000 $14,000 $18,000 $16,000 $17,000 $8,500 $16,000 $31,000 $14,000 $4,600 $15,000

2025 at 3% social discount

Krewski et al., 2009 $8,000 $6,300 $12,000 $5,800 $11,000 $4,800 $15,000 $19,000 $7,000 $8,700 $7,700 $8,400 $4,200 $7,700 $15,000 $6,600 $2,300 $7,100

Lepeule et al., 2012 $18,000 $14,000 $26,000 $13,000 $24,000 $11,000 $34,000 $42,000 $16,000 $20,000 $17,000 $19,000 $9,400 $17,000 $34,000 $15,000 $5,100 $16,000

2030 at 7% social discount

Krewski et al., 2009 $7,800 $6,100 $11,000 $5,600 $10,000 $4,600 $14,000 $18,000 $6,800 $8,500 $7,600 $8,200 $4,000 $7,500 $15,000 $6,400 $2,300 $6,900

Lepeule et al., 2012 $18,000 $14,000 $25,000 $13,000 $23,000 $10,000 $32,000 $41,000 $15,000 $19,000 $17,000 $19,000 $9,100 $17,000 $33,000 $14,000 $5,100 $16,000

2030 at 3% social discount

Krewski et al., 2009 $8,700 $6,800 $12,000 $6,200 $11,000 $5,100 $16,000 $20,000 $7,600 $9,400 $8,400 $9,100 $4,500 $8,300 $16,000 $7,100 $2,500 $7,700

Lepeule et al., 2012 $20,000 $15,000 $28,000 $14,000 $26,000 $12,000 $36,000 $46,000 $17,000 $21,000 $19,000 $21,000 $10,000 $19,000 $37,000 $16,000 $5,600 $17,000

Long-term mortality 

study

Emissions sector
6-76

6.7 Benefits Results

We estimated the benefits of attaining the revised and alternative ozone standard levels 

across the U.S. in 2025 except California. We estimated the benefits of attaining these standard 

levels in California in 2038. We report the 2025 and 2038 estimates separately because deriving 

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

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

analyses (section 6.7.3) inform the interpretation of these core analyses. 

Applying the impact and valuation functions described above to the estimated changes in 

ozone concentrations yields estimates of the changes in physical damages (e.g., premature 

deaths, cases of hospital admissions) and the associated monetary values for those changes. Not 

all known ozone and PM health effects could be quantified or monetized, and the monetized 

value of these unquantified effects is represented by adding an unknown “B” to the aggregate 

total. Values are rounded to two significant figures and so totals may not sum across columns or 

rows. 

6.7.1 Benefits of Attaining a Revised Ozone Standard in 2025 

This section presents the avoided health impacts and monetized benefits of attaining a 

more stringent ozone standard in 2025. Table 6-18 shows the population-weighted air quality 

change for the revised and alternative standard levels averaged across the continental U.S. Table 

6-19 summarizes the tons of NOx emissions required to simulate attainment of the revised and

alternative standard levels (further differentiated by geographic region including east, west and 

California). Tables 6-20 through 6-25 present the benefits results for the standard levels

analyzed. Table 6-25 summarizes total benefits by geographic region (including east, west minus

California, and California). 

In addition, Figure 6-4 presents a sensitivity analysis for short-term ozone-related 

benefits using additional C-R functions for premature mortality, and Figure 6-5 presents a 

sensitivity analysis for PM2.5-related co-benefits using additional C-R functions for premature 

mortality. See sections 6.6.3.2 and 6.6.3.3, respectively for additional discussion of the 

alternative effect estimates used in each sensitivity analysis.
6-77

Table 6-18. Population-Weighted Air Quality Change for the Revised and Alternative 

Annual Primary Ozone Standards Relative to the Analytical Baseline in 2025a

Standard

Population-Weighted Summer Season Ozone Concentration Change 

(8-hour max)b

70 ppb 0.2574

65 ppb 1.278

a Because we used benefit-per-ton estimates for the PM2.5 co-benefits, population-weighted PM2.5 changes are not 

available.

b Population weighting based on all ages (demographic used in modeling short-term exposure-related mortality for 

ozone) for 2025.

Table 6-19. Sector-Specific NOx Emissions Reductions for the Revised and Alternative 

Standard Levelsa

a

All values are tons of NOx reductions (75 ppb vs revised and alternative standard levels). Results are presented 

both for “CA NOx” (emissions in CA only – used in post-2025 scenario PM2.5 cobenefits modeling) and “nonCA 

NOx” (emissions reductions outside of CA – used in 2025 scenario PM2.5 cobenefits modeling).

 CA NOX 

Emis Rdxn 

 nonCA NOX 

Emis Rdxn 

 CA NOX 

Emis Rdxn 

 nonCA NOX 

Emis Rdxn 

Aircraft, locomotives and marine vessels - - - -

Area sources - 32,224 - 69,576

Cement kilns - 19,285 - 31,963

Electricity Generating Units - 47,507 - 113,678

Industrial point sources - 134,763 - 319,484

Non-road mobile sources - 2,832 - 8,791

On-road mobile sources -

Pulp and paper facilities - 42 - 265

Refineries 3,134 - 7,735

Residential wood combustion

Unknown sector 51,000 46,542 99,500 862,803

TOTAL 51,000 286,330 99,500 1,414,296

Emissions Sector

 70ppb 65ppb 

Revised and Alternative Standard Levels
6-78

Table 6-20. Estimated Number of Avoided Ozone-Related Health Impacts for the 

Revised and Alternative Standard Levels (Incremental to the Baseline) for the 

2025 Scenario (nationwide benefits of attaining the standards in the U.S. except 

California) a, b

a All incidence estimates are rounded to whole numbers with a maximum of two significant digits. 

b All incidence estimates are based on ozone-only models unless otherwise noted. d The negative estimates at the 5th percentile confidence estimates for these morbidity endpoints reflect the statistical 

power of the studies used to calculate these health impacts. These results do not suggest that reducing air pollution 

results will adversely affect health, but rather, that we are less confident in the magnitude of the expected benefits 

for this endpoint.

70 ppb 65 ppb

Avoided Short-Term Mortality

96 490

(47 to 140) (240 to 740)

160 820

(86 to 240) (440 to 1,200)

Avoided Long-term Respiratory Mortality

340 1,700

(110 to 560) (580 to 2,800)

Avoided Morbidity

180 920

(-42 to 400) (-220 to 2,000)

510 2,700

(47 to 1,600) (250 to 8,300)

220,000 1,100,000

(-67,000 to 440,000) (-330,000 to 2,100,000)

450,000 2,200,000

(190,000 to 720,000) (920,000 to 3,500,000)

160,000 790,000

(57,000 to 360,000) (280,000 to 1,700,000)

Health Effectb

Revised and Alterative Standard Levels

(95th percentile confidence intervals)

multi-city studies

Smith et al. (2009) (all ages)

Zanobetti and Schwartz (2008) 

(all ages)

multi-city study

Jerrett et al. (2009) (30-99yrs) 

copollutants model (PM2.5) 

Hospital admissions - respiratory 

(age 65+)d

Emergency department visits for 

asthma (all ages)

Asthma exacerbation (age 6-18)d

Minor restricted-activity days 

(age 18-65)

School Loss Days (age 5-17)
6-79

Table 6-21. Total Monetized Ozone-Related Benefits for the Revised and Alternative 

Annual Ozone Standards (Incremental to the Baseline) for the 2025 Scenario 

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

California) (millions of 2011) a

a All benefits estimates are rounded to whole numbers with a maximum of two significant digits. The monetized 

value of the ozone-related morbidity benefits are included in the estimates shown in this table for each mortality 

study.

Figure 6-4. Sensitivity Analysis for Short-Term Ozone-Related Mortality Benefits

70 ppb 65 ppb

Avoided Short-Term Mortality - Core Analysis

$1,000 $5,300

($99 to $2,900) ($500 to $15,000)

1,700 8,700

($160 to $4,800) ($800 to $24,000)

Health Effectb

(95th percentile confidence intervals)

multi-city 

studies

Smith et al. (2009) (all ages)

Zanobetti and Schwartz (2008) (all 

ages)

Revised and Alterative Standard Levels

$0

$500

$1,000

$1,500

$2,000

$2,500

$3,000

$3,500

$4,000

Millions (2011$)
6-80

Table 6-22. Estimated Number of Avoided PM2.5-Related Health Impacts for the Revised

and Alternative Annual Ozone Standards (Incremental to the Baseline) for the 

2025 Scenario (Nationwide Benefits of Attaining the Standards in the U.S. except 

California) a

a All incidence estimates are rounded to whole numbers with a maximum of two significant digits. Because these 

estimates were generated using benefit-per-ton estimates, confidence intervals are not available. In general, the 95th

percentile confidence interval for the health impact function alone ranges from approximately ±30 percent for 

mortality incidence based on Krewski et al. (2009) and ±46 percent based on Lepeule et al. (2012).

70ppb 65ppb

Avoided PM2.5-related Mortality

Krewski et al. (2009) (adult mortality age 30+) 220 1,100

Lepeule et al. (2012) (adult mortality age 25+) 500 2,500

Woodruff et al. (1997) (infant mortality) <1 2

Avoided PM2.5-related Morbidity

Non-fatal heart attacks

 Peters et al. (2001) (age >18) 260 1,300

 Pooled estimate of 4 studies (age >18) 28 140

Hospital admissions—respiratory (all ages) 66 330

Hospital admissions—cardiovascular (age > 18) 80 400

Emergency department visits for asthma (all ages) 120 600

Acute bronchitis (ages 8–12) 340 1,700

Lower respiratory symptoms (ages 7–14) 4,400 22,000

Upper respiratory symptoms (asthmatics ages 9–11) 6,300 31,000

Asthma exacerbation (asthmatics ages 6–18) 7,000 42,000

Lost work days (ages 18–65) 28,000 140,000

Minor restricted-activity days (ages 18–65) 170,000 830,000

Revised and Alterative Standard Levels

Health Effectb
6-81

Table 6-23. Monetized PM2.5-Related Health Co-Benefits for the Revised and Alternative 

Annual Ozone Standards (Incremental to Baseline) for the 2025 Scenario 

(Nationwide Benefits of Attaining the Standards in the U.S. except California) 

(Millions of 2011) a,b,c

Monetized Benefits

Revised and Alternative Standard Levels

70 ppb 65 ppb

3% Discount Rate

Krewski et al. (2009) (adult mortality age 30+) $2,100 $10,000

Lepeule et al. (2012) (adult mortality age 25+) $4,700 $23,000

7% Discount Rate

Krewski et al. (2009) (adult mortality age 30+) $1,900 $9,300

Lepeule et al. (2012) (adult mortality age 25+) $4,200 $21,000

a All estimates are rounded to two significant digits. Because these estimates were generated using benefit-per-ton 

estimates, confidence intervals are not available. In general, the 95th percentile confidence interval for monetized 

PM2.5 benefits ranges from approximately -90 percent to +180 percent of the central estimates based on Krewski et 

al. (2009) and Lepeule et al. (2012). b The reduction in premature fatalities each year accounts for over 98% of total monetized benefits in this analysis. 

Mortality risk valuation assumes discounting over the SAB-recommended 20-year segmented lag structure.

Figure 6-5. Sensitivity Analysis Long-Term PM2.5-Related Mortality Co-Benefits 

$0

$1,000

$2,000

$3,000

$4,000

$5,000

$6,000

$7,000

$8,000

Millions (2011$)

Dollar benefit (7%) Dollar benefit (3%)
6-82

Table 6-24. Estimated Monetized Ozone and PM2.5 Benefits for Revised and Alternative 

Annual Ozone Standards Incremental to the Baseline for the 2025 Scenario 

(Nationwide Benefits of Attaining the Standards in the U.S. Except California)

–Identified + Unidentified Control Strategies (combined) and Identified Control 

Strategies Only (billions of 2011$) a

Discount 

Rate

Revised and Alternative Standard Levels

70 ppb 65 ppb

Identified + Unidentified Control Strategies

Ozone-only Benefits (range reflects Smith et 

al. (2009) to Zanobetti and Schwartz (2008)) b $1.0 to $1.7 $5.3 to $8.7

PM2.5 Co-benefits (range reflects Krewski et 

al. (2009) to Lepeule et al. (2012)

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

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

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

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

Identified Control Strategies Only

Ozone-only Benefits (range reflects Smith et 

al. (2009) to Zanobetti and Schwartz (2008)) b $0.86 to $1.4 $2.2 to $3.5

PM2.5 Co-benefits (range reflects Krewski et 

al. (2009) to Lepeule et al. (2012)

3% $1.7 to $3.9 $4.0 to $9.0

7% $1.6 to $3.5 $3.6 to $8.1

Total Benefits 3% $2.6 to $5.3 +B $6.1 to $12 +B

7% $2.4 to $4.9 +B $5.7 to $12 +B

a Rounded to two significant figures. The reduction in premature fatalities each year accounts for over 98% of total 

monetized benefits in this analysis. Mortality risk valuation for PM2.5 assumes discounting over the SABrecommended 20-year segmented lag structure. Not all possible benefits are quantified and monetized in this 

analysis. B is the sum of all unquantified health and welfare co-benefits. Data limitations prevented us from 

quantifying these endpoints, and as such, these benefits are inherently more uncertain than those benefits that we 

were able to quantify. These estimates reflect the economic value of avoided morbidities and premature deaths using 

risk coefficients from the studies noted. b Ozone-only benefits reflect short-term exposure impacts and as such are assumed to occur in the same year as 

ambient ozone reductions. Consequently, social discounting is not applied to the benefits for this category. 

Table 6-25. Regional Breakdown of Monetized Ozone-Specific Benefits Results for the 

2025 Scenario (Nationwide Benefits of Attaining the Standards in the U.S. except 

California) – Identified + Unidentified Control Strategies a, b

Region Revised and Alterative Standard Levels

70 ppb 65 ppb

East c 98% 96%

California ~0% ~0%

Rest of West 2% 4%

a Because we use benefit-per-ton estimates to calculate the PM2.5 co-benefits, a regional breakdown for the cobenefits is not available. Therefore, this table only reflects the ozone benefits. b These regional breakdown results reflect application of identified and unidentified control strategies. Regional 

breakdown results are the same for benefits based on application of identified control strategies only. c Includes Texas and those states to the north and east.
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6.7.2 Benefits of the Post-2025 Scenario

This section presents the estimated number and economic value of avoided ozone- and 

PM2.5-related effects associated with attaining a revised ozone standard after 2025 (note, sectorspecific NOx emissions reductions levels used in modeling benefits for the post-2025 scenario 

are presented earlier in Table 6-19 - see entries under “CA NOx”). In addition, general trends 

and observations drawn from the sensitivity analyses presented in Figures 6-4 and 6-5 hold for 

the post-2025 scenario and for that reason separate plots for these sensitivity analyses (for the 

post-2025 scenario) are not presented (see section 6.7.3 for further discussion). While simulated 

attainment of standard levels for the 2025 scenario involved application of both identified and 

unidentified controls outside of California, simulated attainment of standard levels for the post2025 scenario involved application exclusively of unidentified controls within California (see 

Table 6-18 and section 2.2.2). For that reason, results tables in this section represent exclusively, 

benefits based on application of unidentified controls.

Table 6-26. Population-Weighted Air Quality Change for the Revised and Alternative 

Annual Primary Ozone Standards Relative to Baseline for Post-2025 a

Standard

Population-Weighted Ozone Season Ozone Concentration Change 

(8-hour max)b

70 ppb 0.1708

65 ppb 0.3464 a Because we used benefit-per-ton estimates for the PM2.5 co-benefits, population-weighted PM2.5 changes are not 

available.

b Population weighting based on all ages (demographic used in modeling short-term exposure-related mortality for 

ozone) for 2025.
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Table 6-27. Estimated Number of Avoided Ozone-Related Health Impacts for the 

Revised and Alternative Annual Ozone Standards (Incremental to the Baseline) 

for the Post-2025 Scenario (Nationwide Benefits of Attaining the Standards just 

in California) a, b

a All incidence estimates are rounded to whole numbers with a maximum of two significant digits. 

b All incidence estimates are based on ozone-only models unless otherwise noted. d The negative estimates at the 5th percentile confidence estimates for these morbidity endpoints reflect the statistical 

power of the studies used to calculate these health impacts. These results do not suggest that reducing air pollution 

results in additional health impacts.

70 ppb 65 ppb

Avoided Short-Term Mortality

72 150

(35 to 110) (71 to 220)

120 240

(64 to 180) (130 to 350)

Avoided Long-term Respiratory Mortality

290 590

(98 to 480) (200 to 970)

Avoided Morbidity

140 270

(-32 to 300) (-65 to 610)

360 720

(33 to 1,100) (67 to 2,200)

160,000 330,000

(-49,000 to 320,000) (-100,000 to 650,000)

320,000 660,000

(130,000 to 510,000) (270,000 to 1,000,000)

120,000 240,000

(42,000 to 260,000) (85,000 to 530,000)

Asthma exacerbation (age 6-18)d

Minor restricted-activity days 

(age 18-65)

School Loss Days (age 5-17)

Hospital admissions - respiratory 

(age 65+)d

Emergency department visits for 

asthma (all ages)

multi-city study

Health Effectb

Revised and Alterative Standard Levels

(95th percentile confidence intervals)

multi-city studies

Smith et al. (2009) (all ages)

Zanobetti and Schwartz (2008) 

(all ages)

Jerrett et al. (2009) (30-99yrs) 

copollutants model (PM2.5) 
6-85

Table 6-28. Total Monetized Ozone-Only Benefits for the Revised and Alternative 

Annual Ozone Standards (Incremental to the Baseline) for the Post-2025

Scenario (Nationwide Benefits of Attaining the Standards just in California) 

(millions of 2011) a

a All benefits estimates are rounded to whole numbers with a maximum of two significant digits. The monetized 

value of the ozone-related morbidity benefits are included in the estimates shown in this table for each mortality 

study 

70 ppb 65 ppb

Avoided Short-Term Mortality - Core Analysis

$790 $1,600

($74 to $2,200) ($150 to $4,500)

1,300 2,600

($120 to $3,600) ($240 to $7,200)

multi-city 

studies

Smith et al. (2009) (all ages)

Zanobetti and Schwartz (2008) (all 

ages)

Health Effectb

(95th percentile confidence intervals)

Revised and Alterative Standard Levels
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Table 6-29. Estimated Number of Avoided PM2.5-Related Health Impacts for the Revised

and Alternative Annual Ozone Standards (Incremental to the Baseline) for the 

Post-2025 Scenario (Nationwide Benefits of Attaining the Standards just in 

California) a

a All incidence estimates are rounded to whole numbers with a maximum of two significant digits. Because these 

estimates were generated using benefit-per-ton estimates, confidence intervals are not available. In general, the 95th

percentile confidence interval for the health impact function alone ranges from approximately ±30 percent for 

mortality incidence based on Krewski et al. (2009) and ±46 percent based on Lepeule et al. (2012).

70 ppb 65 ppb

Avoided PM2.5-related Mortality

Krewski et al. (2009) (adult mortality age 30+) 43 84

Lepeule et al. (2012) (adult mortality age 25+) 98 190

Woodruff et al. (1997) (infant mortality) <1 <1

Avoided PM2.5-related Morbidity

Non-fatal heart attacks

 Peters et al. (2001) (age >18) 51 100

 Pooled estimate of 4 studies (age >18) 6 11

Hospital admissions—respiratory (all ages) 13 26

Hospital admissions—cardiovascular (age > 18) 16 31

Emergency department visits for asthma (all ages) 23 44

Acute bronchitis (ages 8–12) 64 130

Lower respiratory symptoms (ages 7–14) 820 1,600

Upper respiratory symptoms (asthmatics ages 9–11) 1,200 2,300

Asthma exacerbation (asthmatics ages 6–18) 1,900 3,600

Lost work days (ages 18–65) 5,300 10,000

Minor restricted-activity days (ages 18–65) 31,000 61,000

Revised and Alterative Standard Levels

Health Effectb
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Table 6-30. Monetized PM2.5-Related Health Co-Benefits for the Revised and Alternative 

Annual Ozone Standards (Incremental to Baseline) for the Post-2025

Scenario (Nationwide Benefits of Attaining the Standards just in California) 

(Millions of 2011$) a,b

Revised and Alterative Standard Levels

Monetized Benefits 70 ppb 65 ppb

3% Discount Rate

Krewski et al. (2009) (adult mortality age 30+) $400 $790

Lepeule et al. (2012) (adult mortality age 25+) $910 $1,800

7% Discount Rate

Krewski et al. (2009) (adult mortality age 30+) $370 $710

Lepeule et al. (2012) (adult mortality age 25+) $820 $1,600

a All estimates are rounded to two significant digits. Because these estimates were generated using benefit-per-ton 

estimates, confidence intervals are not available. In general, the 95th percentile confidence interval for monetized 

PM2.5 benefits ranges from approximately -90 percent to +180 percent of the central estimates based on Krewski 

et al. (2009) and Lepeule et al. (2012). 

b The reduction in premature fatalities each year accounts for over 98% of total monetized benefits in this analysis. 

Mortality risk valuation assumes discounting over the SAB-recommended 20-year segmented lag structure.

Table 6-31. Estimate of Monetized Ozone and PM2.5 Benefits for Revised and Alternative 

Annual Ozone Standards Incremental to the Baseline for the Post-2025 

Scenario (Nationwide Benefits of Attaining the Standards just in California)

– Identified + Unidentified Control Strategies (billions of 2011$) a

Discount 

Rate

Revised and Alternative Standard Levels

70 ppb 65 ppb

Identified + Unidentified Control Strategies

Ozone-only Benefits (range reflects Smith 

et al. (2009) to Zanobetti and Schwartz 

(2008))

b $0.79 to $1.3 $1.6 to $2.6

PM2.5 Co-benefits (range reflects Krewski

et al. (2009) to Lepeule et al. (2012)

3% $0.40 to $0.91 $0.79 to $1.8

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

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

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

a Rounded to two significant figures. The reduction in premature fatalities each year accounts for over 98% of total 

monetized benefits in this analysis. Mortality risk valuation for PM2.5 assumes discounting over the SABrecommended 20-year segmented lag structure. Not all possible benefits are quantified and monetized in this 

analysis. B is the sum of all unquantified health and welfare co-benefits. Data limitations prevented us from 

quantifying these endpoints, and as such, these benefits are inherently more uncertain than those benefits that we 

were able to quantify. These estimates reflect the economic value of avoided morbidities and premature deaths using 

risk coefficients from the studies noted. b Ozone-only benefits reflect short-term exposure impacts and as such are assumed to occur in the same year as 

ambient ozone reductions. Consequently, social discounting is not applied to the benefits for this category. 
6-88

Table 6-32. Regional Breakdown of Monetized Ozone-Specific Benefits Results for the 

Post-2025 Scenario (Nationwide Benefits of Attaining the Standards just in 

California) – Identified + Unidentified Control Strategies a

Region Revised and Alterative Standard Levels

70 ppb 65 ppb

East b 3% 2%

California 90% 91%

Rest of West 7% 7%

a Because we use benefit-per-ton estimates to calculate the PM2.5 co-benefits, a regional breakdown for the cobenefits is not available. Therefore, this table only reflects the ozone benefits.

b Includes Texas and those states to the north and east.

6.7.3 Uncertainty in Benefits Results (including Results of Sensitivity Analyses and 

Supplemental Analyses)

Avoided ozone and PM2.5 related premature deaths account for 94% to 96% of the total 

monetized benefits. This is true in part because we are unable to quantify many categories of 

benefits. The next largest benefit is for reducing the incidence of nonfatal heart attacks. The 

remaining categories each account for a small percentage of total monetized benefits; however, 

these outcomes occur among a significantly larger population. Comparing an incidence table to 

the monetized benefits table reveals that the number of incidences avoided and the unit value for 

that endpoint do not always closely correspond. For example, for ozone we estimate almost 

1,000 times more cases of exacerbated ozone would be avoided than premature deaths, yet cases 

of exacerbated asthma account for only a very small fraction (<1%) of total monetized benefits 

(see Table 6-20). This is because many of the less severe health effects, while more common, are 

valued at a lower level than the more severe health effects. Also, some effects, such as hospital 

admissions, are valued using a proxy measure of WTP. As such, the true value of these effects 

may be higher than that reported in the tables above. Sources of uncertainty associated with both 

the modeling of ozone-related benefits and PM2.5-related co-benefits are discussed qualitatively 

in Appendix 6A. 

PM2.5 mortality co-benefits represent a substantial proportion of total monetized benefits 

(over 98% of the co-benefits), and these estimates have the following key assumptions and 

uncertainties.

• We assume that all fine particles, regardless of their chemical composition, are equally 

potent in causing premature mortality. This is an important assumption, because PM2.5
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produced varies considerably in composition across sources, but the scientific evidence is 

not yet sufficient to allow differential effects estimates by particle type. The PM ISA, 

which was twice reviewed by SAB-CASAC, concluded that “many constituents of PM2.5

can be linked with multiple health effects, and the evidence is not yet sufficient to allow 

differentiation of those constituents or sources that are more closely related to specific 

outcomes” (U.S. EPA, 2009b).

• We assume that the health impact function for fine particles is log-linear without a 

threshold in this analysis. Thus, the estimates include health benefits from reducing fine 

particles in areas with varied concentrations of PM2.5, including both areas that do not 

meet the fine particle standard and those areas that are in attainment, down to the lowest 

modeled concentrations.

• We assume that there is a “cessation” lag between the change in PM exposures and the 

total realization of changes in mortality effects. Specifically, we assume that some of the 

incidences of premature mortality related to PM2.5 exposures occur in a distributed 

fashion over the 20 years following exposure based on the advice of the SAB-HES (U.S. 

EPA-SAB, 2004c), which affects the valuation of mortality benefits at different discount 

rates. 

• We recognize uncertainty associated with application of the benefit-per-ton approach 

used in modeling PM2.5 co-benefits. The benefit-per-ton estimates used here reflect 

specific geographic patterns of emissions reductions and specific air quality and benefits 

modeling assumptions associated with the derivation of those estimates (see the TSD 

describing the calculation of the national benefit-per-ton estimates (U.S. EPA, 2013b) 

and Fann et al. (2012b). Consequently, these estimates may not reflect local variability in 

population density, meteorology, exposure, baseline health incidence rates, or other local 

factors associated with the current ozone NAAQS review. Therefore, use of these benefitper-ton values to estimate co-benefits may lead to higher or lower benefit estimates than 

if co-benefits were calculated based on direct air quality modeling. 

6.7.3.1 Sensitivity Analyses

This RIA includes a number of sensitivity analyses. Given that the size of the monetized 

benefits is sensitive to the number of estimated ozone and PM2.5 related premature deaths, the 

sensitivity analyses focus largely on the mortality endpoint. Each sensitivity analysis is briefly 

described below, including an overview of the approach used in conducting the analysis and key 

observations. See Appendix 6B for more detailed results. 

• Short-term ozone-exposure related mortality (alternative epidemiological studies 

and C-R functions): We estimated the number of premature deaths using seven 

additional effect estimates including four multi-city studies and three meta-analysis 

studies. This sensitivity analysis showed that the two core incidence and benefits 
6-90

estimates fall within (and towards the lower end of) the broader range resulting from 

application of the seven alternative effect estimates (see Figure 6-4). 

• Economic value of avoided premature deaths from long-term exposure to ozone: We 

estimated the economic value of long-term ozone mortality using two cessation lag 

structures: 20-year segment lag (as used for PM2.5) and a zero lag. The sensitivity 

analysis suggests that if included in the core benefit estimate, long-term ozone exposurerelated mortality could add substantially to the overall benefits. Additionally, use of a 20-

year segment lag can reduce benefits by 10-20% (relative to a zero lag) depending on the 

discount rate applied. 

• Long-term ozone-exposure related respiratory mortality and potential thresholds:

We evaluated the impact of several assumed thresholds ranging from 40-60 ppb. This 

sensitivity analysis suggested that a threshold of 50 ppb or greater could have a 

substantial impact on estimated benefits, while thresholds below this range have a 

relatively minor impact. 

• Long-term PM2.5 exposure-related mortality and alternative C-R functions (based 

on the Expert Elicitation): We applied the set of expert elicitation-based functions to 

generate an alternative set of PM2.5 benefit estimates (see Figure 6-5). The estimates 

based on Krewski et al. (2009) and Lepeule et al. (2012) fall within the range of estimates 

based on the functions from the 2006 expert elicitation.

• Income elasticity for mortality and certain morbidity endpoints: We examined the 

impact of alternative assumptions regarding income elasticity (i.e., the degree to which 

WTP changes as income changes) and the degree of impact on WTP functions used for 

mortality and for morbidity endpoints. That sensitivity analysis suggests that alternative

assumptions regarding income elasticity could result in a moderate impact on mortality 

benefits (values ranging from ~90% to ~130% of the core estimate depending on the 

assumption regarding elasticity). Income elasticity was found to have a far more modest 

impact on morbidity endpoints modeled using WTP functions.

6.7.3.2 Supplemental Analyses

In addition to the sensitivity analyses, we also included a number of supplemental 

analyses intended to examine other attributes of the core risk estimates. These are briefly 

described below with additional detail found in Appendix 6B.

• Ozone-related premature deaths by age: These analyses report: (a) the number of 

avoided ozone-related deaths by age range; (b) the estimated number of life years gained 

by age range; and (c) the percent reduction in baseline mortality by age range. We 

observe that about half of the ozone-attributable deaths occur in populations age 75–99, 

but half of the life years would occur in populations younger than 65. This is because the 

younger populations have the potential to lose more life years per death than older 

populations. The percent reduction in total all-cause mortality attributable to simulated 

attainment of both the revised and alternative standards are relatively small and fairly 
6-91

consistent across age ranges (see Appendix B, Table 6B-11). However, estimates of total 

ozone-attributable mortality associated with attainment of both standards is not evenly 

distributed across age ranges with the vast majority of projected avoided deaths occurring 

in older age groups (see Appendix B, Table 6B-9).

• Avoided deaths according to baseline pollutant concentrations: We describe the 

summer season average 8-hour max ozone levels at which the avoided deaths occurred. 

We found that: (a) the vast majority of avoided short-term ozone-related deaths occur in 

grid cells with mean 8-hour max baseline levels between 35 and 55 ppb; and (b) virtually 

all of the avoided deaths are associated with ozone levels above the 20-40 ppb range, 

where we are less confident in the shape of the C-R function for ozone mortality (ozone 

ISA, section 2.5.4.4). For PM2.5 mortality, we evaluated the lowest measured level 

(LML) associated with the epidemiological studies providing the effect estimates. We are 

less confident in specifying the shape of the C-R function at exposure levels below the 

range observed in the epidemiological study (i.e., below the LML). In the supplemental 

analysis, we found that, depending on the mortality study, between 67% and 93% of the 

mortality estimate is based on modeling involving PM2.5 levels above the LML.

• Value of increased productivity among outdoor agricultural workers due to reduced 

exposure to ozone: Using information from the Graff Zivin and Neidell (2012) study, we 

estimate the economic value of improved productivity among outdoor non-livestock 

workers for the ozone standards in 2025. We estimate the monetized worker productivity 

benefits of attaining a 70 ppb standard would be about $1.7 million and a 65 ppb standard 

would yield monetized benefits of about $8.9 million. 

6.8 Discussion

This analysis demonstrates the potential for significant health benefits of the illustrative 

emissions controls applied to simulate attainment with the revised and alternative primary ozone 

standard levels. We estimate that by 2025, the emissions reductions to reach the revised and 

alternative standard levels everywhere except California, would have reduced the number of 

ozone- and PM2.5-related premature mortalities and produce substantial non-mortality benefits. 

Furthermore, emissions reductions required to meet alternative standards in California post-2025 

are also likely to produce substantial reductions in these same endpoints. This rule would also

yield significant welfare impacts as well (see Chapter 7). Even considering the quantified and 

unquantified uncertainties identified in this chapter, we believe that the revised and alternative 

standards would have substantial public health benefits that are likely to outweigh the costs of 

the control strategies for the revised and alternative standard levels analyzed (see Chapter 4). 

Inherent in any complex RIA such as this one are multiple sources of uncertainty. Some 

of these we characterized through our quantification of statistical error in the C-R relationships 
6-92

and our use of alternate mortality functions. Others, including the projection of atmospheric 

conditions and source-level emissions, the projection of baseline morbidity rates, incomes and 

technological development are unquantified. When evaluated within the context of these 

uncertainties, the health impact and monetized benefits estimates in this RIA can provide useful 

information regarding the public health benefits associated with the revised and alternative 

standard levels.

As discussed in Chapter 1, there are important differences worth noting in the design and 

analytical objectives of NAAQS RIAs compared to RIAs for implementation rules, such as the 

Tier 3 (U.S. EPA, 2014a). Setting a NAAQS does not directly result in costs or benefits. The 

NAAQS RIAs illustrate the potential costs and benefits of the revised and alternative air quality 

standards nationwide based on an array of emissions reduction strategies for different sources, 

incremental to implementation of existing regulations and controls needed to attain the current 

standards. In short, NAAQS RIAs hypothesize, but do not predict, the emissions reduction 

strategies that States may choose to enact when implementing a revised NAAQS, and as such, by 

contrast, the emissions reductions from implementation rules are generally for specific, wellcharacterized sources, such as the recent MATS rule (U.S. EPA, 2011c). In general, the EPA is 

more confident in the magnitude and location of the emissions reductions for implementation 

rules. As such, emissions reductions achieved under promulgated implementation rules, such as 

MATS, have been reflected in the baseline of this NAAQS analysis.40 For this reason, the 

benefits estimated provided in this RIA and all other NAAQS RIAs should not be added to the 

benefits estimated for implementation rules. Subsequent implementation rules will be reflected 

in the baseline for the next ozone NAAQS review.

In setting the NAAQS, the EPA considers that ozone concentrations vary over space and 

time. While the standard is designed to limit concentrations at the highest monitor in an area, it is 

understood that emissions controls put in place to reduce concentrations at the highest monitor 

will simultaneously result in lower ozone concentrations throughout the entire area. In fact, the 

ozone HREA (U.S. EPA, 2014b) shows how different standard levels would affect the entire 

distribution of ozone concentrations, and thus people’s exposures and risk, across a selected set 

 40 The full set of rules reflected in the baseline are presented in Chapter 2, Section 2.1.3.
6-93

of urban areas. For this reason, it is inappropriate to use the NAAQS level as a bright line for 

health effects.

The NAAQS are not set at levels that eliminate the risk of air pollution completely. 

Instead, the Administrator sets the NAAQS at a level requisite to protect public health with an 

adequate margin of safety, taking into consideration effects on susceptible populations based on 

the scientific literature. The risk analysis prepared in support of this ozone NAAQS reported 

risks below these levels, while acknowledging that the confidence in those effect estimates is 

higher at levels closer to the standard (U.S. EPA, 2014b). While benefits occurring below the 

standard may be somewhat more uncertain than those occurring above the standard, the EPA 

considers these to be legitimate components of the total benefits estimate. Though there are 

greater uncertainties at lower ozone and PM2.5 concentrations, there is no evidence of a threshold 

in short-term ozone or PM2.5-related health effects in the epidemiology literature. Given that the 

epidemiological literature in most cases has not provided estimates based on threshold models, 

there would be additional uncertainties imposed by assuming thresholds or other non-linear C-R

functions for the purposes of benefits analysis.

The estimated benefits shown here are in addition to the substantial benefits estimated for 

several recent air quality rules (U.S. EPA, 2009a, 2011c, 2014a). Emissions reductions from 

rules such as Tier 3 will have substantially reduced ambient ozone concentrations by 2025 in the 

East, such that few additional controls would be needed to reach 70 ppb. These rules that have 

already been promulgated have tremendous combined benefits that explain why the number of 

avoided premature deaths associated with this NAAQS revision are smaller than were estimated 

in the previous ozone NAAQS RIA (U.S. EPA, 2006) for the year 2020 and even smaller than 

the mortality risks estimated for the current year in the ozone HREA (U.S. EPA, 2014b). 
6-94

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1

Chappell, Regina

From: Sasser, Erika

Sent: Wednesday, September 16, 2015 5:46 PM

To: Frey, Nathan J.

Cc: Aaron_L_Szabo@omb.eop.gov; Cooperstein, Sharon; Weatherhead, Darryl; Chappell, 

Regina; Langdon, Robin; Rush, Alan; Henigin, Mary; Hetes, Bob; Pekar, Zachary

Subject: Part of E.O. 12866 Review: Ozone NAAQS RIA Chapters & Appendices RIN2060-AP38

Attachments: chapter6.docx; appendix-6A.docx; appendix-6B.docx

Nathan,

As requested, attached please find Microsoft word versions of the Benefits chapter and associated appendices from the

draft Final Ozone NAAQS RIA.  Please let me know if you have any questions.

Thanks,

Erika