### 5 C

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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 exposure

related mortality (including total avoided incid ence, 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
- rel ated mortality and long

- term PM 2.5 

### exposure
- related mortality (section 5C.2) 1 

### and (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 Aspec ts of Short
- Term Ozone Exposure
- Related Mortality In their 2008 review of the EPA’s approach to estimating O 3 

### - related mortality benefits, 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 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 a). To address these recommendations, we use simplifying assumptions to estimate the number of life

1 The plot of long

- term PM 2.5 exposure
- related mortality incidence versus PM 2.5 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).

### 5 C

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years that might be gained. We also estimate the reduction in the percentage of deaths attributed to O 3 

### resulting from the illustrative emissio n reduction strategies to reach the proposed and alternative primary standards. The EPA included similar estimates of life years gained in a previous assessment of O 3 

### and/or PM 2.5 

### benefits (U.S. EPA, 2006, 2010c, 2011b), the latter of which was peer review ed 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 pas t 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, estima ted 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 a ge minus this person’s age at death).
Due to the use of benefit

- per
- ton estimates for the PM 2.5 

### co
- benefits, we are unable to estimate the life years gained by reducing exposure to PM 2.5 

### in this analysis. Instead, we refer the reader to the 2012 PM NAAQs R IA for more information about the avoided life years lost from PM 2.5 

### 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.

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Estimated Life Years Gained For estimating the potential life years gained by reducing exposure to O 3 

### 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 expos ure. 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 foll owing formula:

Total Life Years = ∑ LE i 

### × M i n i = 1 

### (5.2)

where LE i 

### is the average remaining life expectancy for age interval i, M i 

### is the estimated change in number of deaths in age interval i, and n is the number of age intervals. To get M i 

### (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 mor tality 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 d eaths 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 O 3 

### for each alternative standard evaluated for the 2025 scenario. We then calculated the average number of life years gained per avoided premature mortality.

### 5 C

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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 2 025 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

### 5 C

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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 O 3 

### exposure for the 2025 scenario. We estimate that the ave rage individual who would otherwise have died prematurely from O 3 

### 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 ai r 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 O 3 

### - related Mortality Reduced To estimate the percentage reduction in all

- c ause mortality attributed to reduced O 3 

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

### from the equation above, dividing the number of excess deaths estimated for each alternative standard by the tot al number of deaths in each county. Table 5C

- 3 shows the reduction in all

cause mortality attributed to reducing O 3 

### 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).

### 5 C

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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 smal l 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 exposure related mortality and long
- term PM2.5 exposure
- related mortality Analysis of baseline ozone levels used in modeling short

- term ozon e exposure
- related mortality Our review of the current body of scientific literature indicates that a log

- linear no

threshold model provides the best estimate of O 3 

### - related short

- term mortality. The O 3 

### 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 O 3 

### exposure. Our confidence in the estimated number of premature deaths avoided (but not in the existence of a causal relationsh ip between O 3 

### 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 appr eciably less, and the scientific evidence provides no clear dividing line. The O 3 

### ISA noted that the studies indicate reduced certainty in specifying the shape of the C

- R function specifically for short

- term O 3 

### - attributable respiratory morbidity and mortal ity, in the range generally below 20 ppb (for these reasons, the ≤ 20 ppb range discussed in the O 3 

### 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 sh ort

- 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, inc luding older individuals and individuals with existing health conditions which place them at greater risk for O 3 - 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.

### 5 C

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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 th ese 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. 3 

### In addition, each figure includes sep arate 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 45ppb 4



### .

Figure 5C
- 1. Premature O 3 

### - related Deaths Avoided for the Alternative Standards (2025

scenario) According to the Baseline O 3 

### 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 i s 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.

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Figure 5C
- 2. Cumulative Probability Plot of Premature O 3 

### - related Deaths Avoided for the

Alternative Standards (2025 scenario) According to the Baseline O 3 

### Concentrations When interpreting these results, it is important to understand that the avoided O 3 

### - related deaths are estimated to occur from O 3 

### 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 st andard and use an air quality interpolation technique to simulate the change in O 3 

### concentrations surrounding that monitor. This technique tends to simulate the greatest air quality changes nearest the monitor. We estimate benefits using modeled air qualit y data with 12 km grid cells, which is important because the grid cells are often substantially smaller than counties and O 3 

### concentrations vary spatially within a county. Therefore, there may be a small number of grid cells with concentrations slightly gr eater 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,

em ission 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 alternativ e standard being

0 0.2 0.4 0.6 0.8 1 1.2

0 to 15 15 to 20 20 to 25 25 to 30 30 to 35 35 to 40 40 to 45 45 to 50 50 to 55 55 to 60 60 to 65 65 to 70 70 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 75-60

### 5 C

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evaluated. Emission reduction strategies designed to reduce O 3 

### 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 emis sion 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 onl y 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 e ndpoint, 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 esti mate 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 estima tes 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 m ortality 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 Oz one ISA as being associated with less confidence in specifying the nature of the C

- R function for ozone mortality (O 3 

### 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 earlie r, 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 composit e 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.

### 5 C

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Concentration Benchmark Analysis for PM 2.5 

### Benefit

- per
- ton Estimates In general, we are more confident in the magnitude of the risks we estimate from simulated PM 2.5 

### 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 sim ulated PM 2.5 

### 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 PM 2.5 

### levels at or above different concentrations, which provides some insight into the level of uncertainty in the estimated PM 2.5 

### mortality benefits. In this analysis, we apply two concentrat ion 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 qua ntify 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 PM 2.5 

### exposures and mortality is causal and that overall the studies support the use of a

no
- threshold log

- linear model to estimate P M

- 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 o f premature mortality associated with the emission reductions at each PM 2.5 

### 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 distr ibution 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 thre shold in the PM 2.5 - mortality relationship, see the TSD entitled Summary of Expert Opinions on the Existence of a Threshold in the Concentration

- Response Function for PM 2.5 - related Mortality (U.S. EPA, 2010b).

### 5 C

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the percentage of the population exposed at each PM 2.5 

### 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. 7 

### 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 qualit y. 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 fo r 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 eac h 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 PM 2.5 concentrations in the LML assessment are higher than would be expected if MATS was reflected.

### 5 C

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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 10 th and 25 th percentile. For Krewski, the LML is 5.8 μg/m 3 and one standard deviation below the mean is 11.0 μg/m 3 . For Lepeule et al., the LML is 8 μg/m 3 and we do not have the data for one standard deviation below the mea n. 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 PM 2.5 do not occur.



### Figure 5C
- 3. Percentage of Adult Population (age 30+) by Annual Mean PM 2.5 

### 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 PM 2.5 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). 

### A mong the populations exposed to PM 2.5 

### in the baseline:
93% are exposed to PM 2.5 levels at or above the LML of the Krewski et al. (2009) study 67% are exposed to PM 2.5 levels at or above the LML of the Lepeule et al. (2012) study

### 5 C

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Figure 5C
- 4. Cumulative Distribution of Adult Population (age 30+) by Annual Mean

PM 2.5 

### E xposure 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 model ing show premature mortality impacts at each PM 2.5 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 PM 2.5 

### in the baseline: 93% are exposed to PM 2.5 levels at or above the LML of the Krewski et al. (2009) study 67% are exposed to PM 2.5 levels at or above the LML of the Lepeule et al. (2012) study

### 5 C

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5C.3 Core Incidenc e 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 simulat ing 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 PM 2.5 

### (Table 5C

- 8) and core dollar benefits estimates associated with PM 2.5 

### reductions (Table 5C

- 9). A summary of overall core benefits associated with application of kn own 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% PM 2.5 Co

- benefits 76% 59% 33% Total Benefits 77% 59% 34%

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Table 5C
- 6. Estimated Number of Avoided O 3 

### Health Impacts for the Alternative Annual

Primary O 3 

### Standards (Incremental to the Analytical Baseline) for the P artial Attainment of the 2025 S cenario (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 ass essment of potential thresholds, which was completed for the 2025 scenario (see Table 5

- 19)

. Care should be taken in applying the r esults of that sensitivity analysis to the partial 2025 scenario, although general patterns of impact across the thresholds may apply.

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Table 5C
- 7. Total Monetized O 3 

### Benefits for the Alternative Annual Primary O 3



### Standards (Incremental to the Analytical B aseline) for the P artial Attainment of the 2025 S cenario (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, sectio n 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 incl ude a 95 th percentile confidence interval range

– these are the values within parentheses). d The range (outside of the parentheses) within each cell results from application of a 7% and 3% discount rates in the context of applying the 20year segmented lag (with the 7% resulting in the lower estimate and the 3% the higher estimate). The range presented within the parentheses reflects consideration for the 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).

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Table 5C
- 8. Estimated Number of Avoided PM 2.5 

### Health Impacts for the Alternative

Annual Primary O 3 

### Standards (Incremental to the Analytical Baseline) for the P artial Attainment of the 2025 S cenario (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 95 th 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).

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Table 5C
- 9. Monetized PM 2.5 

### Health Co
- Benefits for the Alternative Annual Primary O 3 

### Standards (Incremental to Analytical Baseline) for the P artial Attainment of the 2025 S cenario (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 PM 2.5 benefits ranges from approximately

- 90 percent to +180 percent of the central estimates based on Krewski et al. (2009) and Lepeule et al. (2012). Estimates do not include unquantified health benefits noted in Table 5

- 2 or Section 5.6.5 or welfare co

- benefits noted in Chapter 6. b The reduction in premature fatalities each year accounts for over 98% of total moneti zed 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 O 3 

### and PM 2.5 

### Benefits for the Alternative

Annual Primary O 3 

### Standards for th e P artial Attainment of the 2025 S cenario (using known controls) (billions of 2011$) a,b

Benefits Estimate 70 ppb 65 ppb 60 ppb Economic value for avoided O 3 - related morbidities and premature deaths using O 3 mortality estimate from Smith et al., (2009) and Zanobetti and Schwartz (2008) plus PM 2.5 - related morbidities and premature deaths using PM 2.5 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 O 3 - related morbidities and premature deaths using O 3 mortality estimate from Smith et al., (2009) and Zanobetti and Schwartz (2008) plus PM 2.5 - related morbidities and premature deaths using PM 2.5 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 PM 2.5 assumes discounting over the SAB

recommended 20

- year segmented lag s tructure. 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 m ore uncertain than those benefits that we were able to quantify.

### 5 C

- 19

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