5C-1

APPENDIX 5C: SUPPLEMENTAL ANALYSES RELATED TO THE OZONE HEALTH 

BENEFITS ANALYSIS

Overview

A number of additional analyses have been completed to supplement the core estimates 

generated for the RIA. These analyses give greater insight to the manner in which these impacts 

are distributed among populations of different ages, the ambient levels of ozone at which the 

avoided deaths are estimated to occur and the benefits attributable to the known emission control 

measures. These supplemental analyses, which are presented in detail here (and summarized in 

section 5.7.3.2) include: (a) age group-differentiated aspects of short-term ozone exposurerelated mortality (including total avoided incidence, life years gained and percent reduction in 

baseline mortality - section 5C.1), (b) evaluation of mortality impacts relative to the baseline 

pollutant concentrations (used in generating those mortality estimates) for both short-term ozone 

exposure-related mortality and long-term PM2.5 exposure-related mortality (section 5C.2)1and 

(c) presentation of core incidence and benefits estimates reflecting application of known controls 

for the 2025 scenario (section 5C.4). 

5C.1 Age Group-Differentiated Aspects of Short-Term Ozone Exposure-Related 

Mortality

In their 2008 review of the EPA’s approach to estimating O3-related mortality benefits, 

NRC indicated, “EPA should consider placing greater emphasis on reporting decreases in agespecific 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 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, 2004a). To 

address these recommendations, we use simplifying assumptions to estimate the number of life 

 

1 The plot of long-term PM2.5 exposure-related mortality incidence versus PM2.5 levels is taken from previous 

RIAs and reflects the benefits simulation used to generate the dollar-per-ton estimates used in deriving PM2.5-

related cobenefits for this analysis (i.e., these plots are not derived using new data specific to this RIA) (see section 

5C.3).
5C-2

years that might be gained. We also estimate the reduction in the percentage of deaths attributed 

to O3 resulting from the illustrative emission reduction strategies to reach the proposed and 

alternative primary standards. The EPA included similar estimates of life years gained in a 

previous assessment of O3 and/or PM2.5 benefits (U.S. EPA, 2006, 2010c, 2011b), the latter of 

which was peer reviewed by the SAB-HES (U.S. EPA-SAB, 2010a).

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

thus it is important to distinguish these two very different metrics at the outset. Life expectancy 

varies by age. 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, 2001). 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 PM NAAQs RIA for more information about the avoided life years lost from 

PM2.5 exposure (U.S. EPA, 2012b). This analysis 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, this analysis found that the average individual 

who would otherwise have died prematurely from PM exposure would gain 16 additional years 

of life.
5C-3

Estimated Life Years Gained

For estimating the potential life years gained by reducing exposure to O3 in the U.S. adult 

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

have not estimated the change in average life expectancy at birth in this RIA. Because life 

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), average 

life expectancy changes associated with air pollution exposure would be expected to always be 

significantly smaller than the average number of life years lost by an individual who is 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:

Total Life Years = ∑ LEi × Mi

n

i=1

(5.2)

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 estimate the 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 5C-1 

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

alternative standard for the 2025 scenario. Table 5C-2 summarizes the modeled number of life 

years gained (for each age range) by reducing O3 for each alternative standard evaluated for the 

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

premature mortality. 
5C-4

Table 5C-1. Potential Reduction in Premature Mortality by Age Range from Attaining 

Alternate Ozone Standards (2025 scenario) a, b

Age Range b Standard Alternative

70 ppb 65 ppb 60 ppb

0-4 0.65 1.9 3.6

5-9 0.14 0.44 0.81

10-14 0.15 0.46 0.85

15-19 0.21 0.65 1.2

20-24 0.3 0.9 1.7

25–29 0.61 1.8 3.4

30–34 0.63 1.9 3.5

35–44 3.4 11 19

45–54 8.6 27 49

55–64 22 67 120

65–74 45 140 260

75–84 60 190 340

85–99 59 190 340

Total ozone-attributable mortality 200 630 1,100

a Estimates rounded to two significant figures.

b Effects calculated using the core Smith et al. (2009) effect estimate for the 2025 scenario

Table 5C-2. Potential Years of Life Gained by Age Range from Attaining Alternate O3 

Standards (2025 Scenario) a,b

Age Range b Standard Alternative

70 ppb 65 ppb 60 ppb

0-4 51 150 280 

5-9 10 30 56 

10-14 10 30 55 

15-19 13 39 71 

20-24 16 49 90 

25–29 30 91 170 

30–34 28 86 160 

35–44 120 380 690 

45–54 230 720 1,300 

55–64 410 1,300 2,300 

65–74 550 1,700 3,100 

75–84 390 1,200 2,300 

85–99 130 430 790 

Total life years gained 2,000 6,200 11,000 

Average life years gained per individual 10.0 9.93 9.92 

a Estimates rounded to two significant figures (except for average life years gained – presented to three significant 

figures to allow differences across values to be evident.

b Effects calculated using the core Smith et al. (2009) effect estimate for the 2025 scenario

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 5C-1), but half of the life years would occur in populations younger than 
5C-5

65 (see Table 5C-2). This is because the younger populations have the potential to lose more life 

years per death than older populations based on changes in O3 exposure for the 2025 scenario. 

We estimate that the average individual who would otherwise have died prematurely from O3

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 in and of itself caused by air pollution exposure (for 

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

Percent of O3-related Mortality Reduced

To estimate the percentage reduction in all-cause mortality attributed to reduced O3

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

Mi from the equation above, dividing the number of excess deaths estimated for each alternative 

standard by the total number of deaths in each county. Table 5C-3 shows the reduction in allcause mortality attributed to reducing O3 exposure to the proposed primary standards for the 

2025 scenario.

Table 5C-3. Estimated Percent Reduction in All-Cause Mortality Attributed to the 

Proposed Primary Ozone Standards (2025 Scenario) a

Age Range b

Standard Alternative

70 ppb 65 ppb 60 ppb

0-4 0.0183% 0.0547% 0.101%

5-9 0.0176% 0.0538% 0.100%

10-14 0.0178% 0.0542% 0.100%

15-19 0.0180% 0.0546% 0.101%

20-24 0.0177% 0.0536% 0.099%

25–29 0.0177% 0.0535% 0.099%

30–34 0.0176% 0.0535% 0.098%

35–44 0.0174% 0.0535% 0.099%

45–54 0.0174% 0.0536% 0.099%

55–64 0.0179% 0.0554% 0.101%

65–74 0.0179% 0.0557% 0.102%

75–84 0.0176% 0.0549% 0.101%

85–99 0.0164% 0.0522% 0.096%

a

In order to illustrate the slight variations in percent reductions across age ranges (for a given alternative standard 

level) we have presented results to three significant figures (rather than two as is typically done for other estimates 

in this RIA). 
5C-6

Results presented in Table 5C-3 highlight that when reductions in ozone-attributable 

mortality (in going from baseline to an alternative standard level) are considered as a percentage 

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

across age ranges. However, it is important to point out that estimates of total ozone-attributable 

mortality represent a substantially larger fraction of all-cause baseline mortality.

5C.2 Evaluation of mortality impacts relative to the baseline pollutant concentrations 

(used in generating those mortality estimates) for both short-term ozone exposurerelated mortality and long-term PM2.5 exposure-related mortality

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 O3-related short-term mortality. The O3 ISA (U.S. 

EPA, 2013a), which was reviewed by the EPA’s Clean Air Scientific Advisory Committee. 

Consistent with this finding, we estimate benefits associated with the full range of O3 exposure. 

Our confidence in the estimated number of premature deaths avoided (but not in the existence of 

a causal relationship between O3 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 O3-attributable 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).2

 

2 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 which place them at greater risk for O3-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.
5C-7

Figures 5C-1 and 5C-2 compare the distribution of short-term ozone exposure-related 

mortality to the underlying distribution of 8hr max baseline ozone levels used in generating those 

estimates (with these two plots present probability and cumulative probability plots, 

respectively). Both figures are based on the core estimate of short-term mortality generated using 

effect estimates obtained from Smith et al., 2009.3In addition, each figure includes separate plots 

for the three alternative standard levels being considered (with all being based on the 2025 

scenario). If we look at Figure 5C-1, we see that approximately 45% of the mortality benefit 

estimated for the 65ppb alternative standard is associated with days having baseline ozone level 

of between 40 and 45ppb4.

Figure 5C-1. Premature O3-related Deaths Avoided for the Alternative Standards (2025 

scenario) According to the Baseline O3 Concentrations

 

3 The set of 12km-level mortality estimates (and associated 8hr max baseline values) generated using BenMAP 

forms the basis for the plots. 

4 As noted later in this section, this baseline range is actually for the mean across the ozone season of 8hr max values 

within a given grid cell, so the actual distribution of baseline 8hr max values associated with this segment of benefits 

reductions is likely wider than the 40-45ppb range.

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 75-60
5C-8

Figure 5C-2. Cumulative Probability Plot of Premature O3-related Deaths Avoided for the 

Alternative Standards (2025 scenario) According to the Baseline O3

Concentrations

When interpreting these results, it is important to understand that the avoided O3-related 

deaths are estimated to occur from O3 reductions in the baseline air quality simulation, which 

assumes that 75 ppb is already met. When simulating attainment with proposed and alternative 

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

that standard and use an air quality interpolation technique to simulate the change in O3

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 O3 concentrations vary spatially within a county. Therefore, there may be a small number of 

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

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

below the level of a standard even though the highest monitor value is above that standard. Thus, 

emission 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 simulating 

attainment can lead to benefits in counties that are below the alternative standard being 

0

0.2

0.4

0.6

0.8

1

1.2

0 to 15

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

Cumulative percentage of reduction in short- term ozone related mortality

Ozone season mean daily 8hr max baseline (ppb)

75-70 75-65 75-60
5C-9

evaluated. Emission reduction strategies designed to reduce O3 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 emission reduction strategies, it is appropriate to include 

all the benefits occurring as a result of the emission 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 alternative standards because it would omit benefits attributable 

to emission 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 which 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.

Consideration for the plots presented in Figures 5C-1 and 5C-2 results in a number of 

observations. The vast majority of reductions in short-term exposure-related mortality for ozone 

occur in grid cells with mean 8hr max baseline levels (across the ozone season) between 35 and 

55ppb. Importantly, virtually all of the mortality reductions are associated with ozone levels 

above the <20ppb range identified within the Ozone ISA as being associated with less 

confidence in specifying the nature of the C-R function for ozone mortality (O3 ISA, section 

2.5.4.4).5 We also note, that as we compare patterns across the three alternative standard levels, 

we see that, as expected, the upper end of the distribution is being shifted downwards as 

increasingly lower standard level are considered (see Figure 5C-2). 

 

5 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 (<20ppb) identified in the Ozone ISA. The region of reduced confidence 

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

short-term mortality studies, while the ozone levels summarized in Figures 5C-1 and 5C-2 are the mean (across the 

ozone season) of daily 8hr 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.
5C-10

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, 2011d). 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.6 Rather, the co-benefits 

estimates reported in this RIA are the best estimates because they reflect the full range of air 

quality concentrations associated with the emission 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, 2009b). 

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

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

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

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

EPA, 2011b, 2012a). 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 

 

6 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).
5C-11

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.7It is important to note that baseline exposure 

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

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

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

health impacts as a result of a specific emission 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 implementing the proposed guidelines. 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 5C-4 provides the percentage of the population exposed above and below two 

concentration benchmarks (i.e., LML and one standard deviation below the mean) in the 

modeled baseline for the sector modeling. Figure 5C-3 shows a bar chart of the percentage of the 

population exposed to various air quality levels in the baseline, and Figure 5C-4 shows a 

cumulative distribution function of the same data. Both figures identify the LML for each of the 

major cohort studies.

 

7 As noted above, the modeling used to generate the benefit-per-ton estimates does not reflect emission 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.
5C-12

Table 5C-4. Population Exposure in the Baseline Sector Modeling (used to generate the 

benefit-per-ton estimates) Above and Below Various Concentrations 

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, the LML is 5.8 μg/m3and one standard deviation below the mean is 11.0 μg/m3. For 

Lepeule et al., the LML is 8 μg/m3and 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 5C-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)

Percentage of Population Exposed by Baseline Air Quality Level

LML of Lepeule et al. 

(2012) study

LML of Krewski et 

al. (2009) study
5C-13

Figure 5C-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).

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%

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)

Cumulative Percentage of Population Exposed by Baseline Air Quality 

Level

LML of Krewski et 

al. (2009) study

LML of Lepeule et 

al. (2012) study
5C-14

5C.3 Core Incidence and Dollar Benefits Estimates Reflecting Application of Known 

Controls for the 2025 Scenario

This section presents a subset of the core incidence and dollar benefits estimates for the 

2025 scenario reflecting only application of known controls in simulating each of the alternative 

standard levels (i.e., partial-2025 scenario estimates). The presentation of these estimates 

parallels results summarized for the 2025 and post-2025 scenario in Section 5.7 and the reader is 

referred to that section for further explanation of the tables and types of estimates included in 

those tables. However, before presenting detailed benefits summary tables for the partial-2025 

scenario, we first (in Table 5C-5) present an overview of the percentage of benefits associated 

with known control for each of the alternative standards considered. Then, following that 

overview table, we present a set of more detailed tables including: core incidence attributable to 

reductions in ozone (Table 5C-6), core dollar benefits associated with ozone reductions (Table 

5C-7), core incidence estimates associated with reductions in PM2.5 (Table 5C-8) and core dollar 

benefits estimates associated with PM2.5 reductions (Table 5C-9). A summary of overall core 

benefits associated with application of known controls is presented in Table 5C-10.

Table 5C-5. Fraction of Total Core Benefits Associated with Partial Attainment 

(application of known controls) (2025 Scenario)

Category of Benefit

Percentage of Benefits Resulting from Application of Known 

Controls

70 ppb 65 ppb 60 ppb

Ozone Benefits 82% 59% 35%

PM2.5 Co-benefits 76% 59% 33%

Total Benefits 77% 59% 34%
5C-15

Table 5C-6. Estimated Number of Avoided O3 Health Impacts for the Alternative Annual 

Primary O3 Standards (Incremental to the Analytical Baseline) for the Partial

Attainment of the 2025 Scenario (using known controls) a,b

a All incidence estimates are rounded to whole numbers with a maximum of two significant digits. 

b All incidence estimates are based on ozone-only models unless otherwise noted.

c The sensitivity analysis for long-term exposure-related mortality included an assessment of potential thresholds, 

which was completed for the 2025 scenario (see Table 5-19). Care should be taken in applying the results of that 

sensitivity analysis to the partial 2025 scenario, although general patterns of impact across the thresholds may apply.

70 ppb 65 ppb 60 ppb

Avoided Short-Term Mortality - Core Analysis

160 370 400

(79 to 250) (180 to 550) (200 to 610)

270 620 680

(150 to 400) (330 to 900) (360 to 990)

Avoided Long-term Respiratory Mortality - Core Analysis

550 1,200 1,400

(190 to 910) (420 to 2,100) (460 to 2,300)

Avoided Short-Term Mortality - Sensitivity Analysis

130 290 320

(-36 to 300) (-80 to 660) (-88 to 730)

200 460 500

(63 to 340) (140 to 770) (160 to 850)

190 430 480

(72 to 310) (160 to 710) (180 to 780)

130 300 330

(44 to 220) (99 to 490) (110 to 550)

430 960 1,100

(200 to 650) (460 to 1,500) (510 to 1,600)

590 1,300 1,500

(360 to 830) (800 to 1,900) (880 to 2,100)

600 1,400 1,500

(410 to 790) (930 to 1,800) (1,000 to 2,000)

Avoided Long-term Respiratory Mortality - Sensitivity Analysis c

370 840 920

(110 to 640) (240 to 1,400) (260 to 1,600)

400 900 990

(140 to 660) (320 to 1,500) (350 to 1,600)

Avoided Morbidity - Core Analysis

300 680 740

(-79 to 670) (-180 to 1,500) (-200 to 1,700)

930 2,100 2,400

(87 to 2,900) (200 to 6,600) (230 to 7,500)

240,000 550,000 600,000

(-360,000 to 740,000) (-800,000 to 1,600,000) (-880,000 to 1,800,000)

760,000 1,700,000 1,900,000

(310,000 to 1,200,000) (700,000 to 2,700,000) (780,000 to 3,000,000)

270,000 600,000 660,000

(95,000 to 600,000) (210,000 to 1,300,000) (230,000 to 1,500,000)

Proposed and Alterative Standards

(95th percentile confidence intervals)

multi-city 

studies

Smith et al. (2009) (all ages)

Zanobetti and Schwartz (2008) (all 

ages)

metaanalyses

Bell et al. (2005) (all ages)

Ito et al. (2005) (all ages)

multi-city 

study

School Loss Days (age 5-17)

Jerrett et al. (2009) (age 30-99) (96 

cites) (ozone-only)

Levy et al. (2005) (all ages)

Jerrett et al. (2009) (age 30-99) (86 

cities) (ozone-only)

Hospital admissions - respiratory 

(age 65+)

Emergency department visits for 

asthma (all ages)

Asthma exacerbation (age 6-18)

Minor restricted-activity days (age 

18-65)

Smith et al. (2009) (all ages) 

copollutants model (PM10)

Schwartz (2005) (all ages)

Bell et al. (2004) (all ages)

Health Effectb

multi-city 

study

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

copollutants model (PM2.5) 

Huang et al. (2005) 

(cardiopulmonary)

multi-city 

studies
5C-16

Table 5C-7. Total Monetized O3 Benefits for the Alternative Annual Primary O3

Standards (Incremental to the Analytical Baseline) for the Partial Attainment of 

the 2025 Scenario (using known controls) a,b

a All benefits estimates are rounded to whole numbers with a maximum of two significant digits. The monetized 

value of the ozone-related morbidity benefits are included in the estimates shown in this table for each mortality 

study (and when combined account for from 4-6% of the total benefits, depending on the total mortality estimate 

compared against. Note that asthma exacerbations accounts for <<1% of the total).

b The sensitivity analysis for long-term exposure-related mortality included an assessment of potential thresholds 

however this assessment was implemented using incidence estimates (see Table 5-19). Observations from that 

analysis (in terms of fractional impacts on incidence can be directly applied to these benefit results) (see Appendix 

5B, section 5B.1)

c A single central-tendency value is provided in each cell, since the zero-lag model used here did not require 

application of a 3% and 7% discount rates (see footnote d below) (note, however that as with all other entries in this 

study, we do include a 95th percentile confidence interval range – these are the values within parentheses).

d The range (outside of the parentheses) within each cell results from application of a 7% and 3% discount rates in 

the context of applying the 20year segmented lag (with the 7% resulting in the lower estimate and the 3% the higher 

estimate). The range presented within the parentheses reflects consideration for the 95th% confidence interval 

generated for each of these estimates and ranges from a low value (2.5th% CI for the 7% discount-based dollar 

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

70 ppb 65 ppb 60 ppb

Avoided Short-Term Mortality - Core Analysis

$1,700 $3,700 $4,100

($150 to $4,700) ($330 to $11,000) ($360 to $12,000)

2,800 6,300 6,900

($250 to $7,900) ($560 to $18,000) ($610 to $20,000)

Avoided Short-Term Mortality - Sensitivity Analysis

$1,300 $3,000 $3,300

(-$310 to $4,800) (-$710 to $11,000) (-$780 to $12,000)

$2,100 $4,700 $5,100

($160 to $6,300) ($370 to $14,000) ($410 to $16,000)

$2,000 $4,400 $4,900

($160 to $5,800) ($370 to $13,000) ($400 to $14,000)

1,300 3,000 3,300

($110 to $4,000) ($240 to $9,100) ($270 to $10,000)

4,400 9,800 11,000

($380 to $12,000) ($860 to $28,000) ($950 to $31,000)

$6,000 $14,000 $15,000

($550 to $17,000) ($1,200 to $38,000) ($1,400 to $42,000)

$6,100 $14,000 $15,000

($570 to $17,000) ($1,300 to $38,000) ($1,400 to $41,000)

Avoided Long-term Respiratory Mortality - Sensitivity Analysis

$5,600 $13,000 $14,000

($460 to $17,000) ($1,000 to $38,000) ($1,100 to $41,000)

$4,600 to $5,100 $10,000 to $11,000 $11,000 to $13,000

($370 to $15,000) ($840 to $34,000) ($920 to $37,000)

Proposed and Alterative Standards

Health Effectb

(95th percentile confidence intervals)

Smith et al. (2009) (all ages) 

copollutants model (PM10)

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

copollutants model (PM2.5) 20 yr 

segmented lag d

multi-city 

study

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

copollutants model (PM2.5) no lag c

Schwartz (2005) (all ages)

Bell et al. (2004) (all ages)

Bell et al. (2005) (all ages)

Huang et al. (2005) 

(cardiopulmonary)

multi-city 

studies

metaanalyses

Levy et al. (2005) (all ages)

Ito et al. (2005) (all ages)

multi-city 

studies

Smith et al. (2009) (all ages)

Zanobetti and Schwartz (2008) (all 

ages)
5C-17

Table 5C-8. Estimated Number of Avoided PM2.5 Health Impacts for the Alternative 

Annual Primary O3 Standards (Incremental to the Analytical Baseline) for the 

Partial Attainment of the 2025 Scenario (using known controls) a,b

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).

60ppb 65ppb 70ppb

Avoided PM2.5-related Mortality

Krewski et al. (2009) (adult mortality age 30+) 390 860 880

Lepeule et al. (2012) (adult mortality age 25+) 870 1,900 2,000

Woodruff et al. (1997) (infant mortality) 1 1 1

Avoided PM2.5-related Morbidity

Non-fatal heart attacks

 Peters et al. (2001) (age >18) 450 1,000 1,000

 Pooled estimate of 4 studies (age >18) 49 110 110

Hospital admissions—respiratory (all ages) 120 260 260

Hospital admissions—cardiovascular (age > 18) 140 310 320

Emergency department visits for asthma (all ages) 210 470 480

Acute bronchitis (ages 8–12) 600 1,300 1,400

Lower respiratory symptoms (ages 7–14) 7,700 17,000 17,000

Upper respiratory symptoms (asthmatics ages 9–11) 11,000 24,000 25,000

Asthma exacerbation (asthmatics ages 6–18) 11,000 25,000 26,000

Lost work days (ages 18–65) 49,000 110,000 110,000

Minor restricted-activity days (ages 18–65) 290,000 640,000 660,000

Health Effectb

Proposed and Alterative Standards
5C-18

Table 5C-9. Monetized PM2.5 Health Co-Benefits for the Alternative Annual Primary O3

Standards (Incremental to Analytical Baseline) for the Partial Attainment of the 2025

Scenario (using known controls) a,b,c 

Monetized Benefits

Proposed and Alterative Standards

70 ppb 65 ppb 60 ppb

3% Discount Rate

Krewski et al. (2009) (adult mortality age 30+) $3,600 $8,000 $8,300

Lepeule et al. (2012) (adult mortality age 25+) $8,200 $18,000 $19,000

7% Discount Rate

Krewski et al. (2009) (adult mortality age 30+) $3,300 $7,200 $7,400

Lepeule et al. (2012) (adult mortality age 25+) $7,400 $16,000 $17,000

a All estimates are rounded to two significant digits. Because these estimates were generated using benefit-per-ton 

estimates, confidence intervals are not available. In general, the 95th percentile confidence interval for monetized 

PM2.5 benefits ranges from approximately -90 percent to +180 percent of the central estimates based on Krewski et 

al. (2009) and Lepeule et al. (2012). Estimates do not include unquantified health benefits noted in Table 5-2 or 

Section 5.6.5 or welfare co-benefits noted in Chapter 6. 

b The reduction in premature fatalities each year accounts for over 98% of total monetized benefits in this analysis. 

Mortality risk valuation assumes discounting over the SAB-recommended 20-year segmented lag structure.

Table 5C-10. Combined Estimate of Monetized O3 and PM2.5 Benefits for the Alternative 

Annual Primary O3 Standards for the Partial Attainment of the 2025 Scenario

(using known controls) (billions of 2011$) a,b

Benefits Estimate 70 ppb 65 ppb 60 ppb

Economic value for avoided O3-related morbidities and premature deaths using O3 mortality estimate from Smith 

et al., (2009) and Zanobetti and Schwartz (2008) plus PM2.5-related morbidities and premature deaths using PM2.5

mortality estimate from Krewski et al. (2009)

3% discount rate $5.3 to $6.4 + B $12 to $14 + B $12 to $15 + B

7% discount rate $5 to $6.1 + B $11 to $14 + B $12 to $14 + B

Economic value for avoided O3-related morbidities and premature deaths using O3 mortality estimate from Smith 

et al., (2009) and Zanobetti and Schwartz (2008) plus PM2.5-related morbidities and premature deaths using PM2.5

mortality estimate from Lepeule et al. (2012)

3% discount rate $9.9 to $11 + B $22 to $24 + B $23 to $26 + B

7% discount rate $9.1 to $10 + B $20 to $23 + B $21 to $24 + 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. 
5C-19

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