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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 s control measures. These supplemental analyses, which are presented in detail here (and summarized in S ection 5.7.3.2) include: (a) age group

- differentiated aspects of short

- term ozone exposure
- related mortality (including total avoided inc idence, life years gained and percent red uction in baseline mortality

- S ection 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

- r elated mortality and long

- term PM 2.5 

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

### and (c) presentation of core incidence and benefits estimates reflecting application of known c ontrols for the 2025 scenario (S ection 5C. 3 ).

5C.1 Age Group
- Differentiated Asp ects of Short
- Term Ozone Exposure
- Related Mortality In their 2008 review of the EPA’s approach to estimating ozone
- related mortality benefits, NRC indicated, “EPA should consider placing greater emphasis on reporting decreases in age

- specific death rates i n 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 interpretati on 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 PM 2.5 - related cobenefits for this analysis (i.e., these plots are not derived using new d ata specific to this RIA) (see S ection 5C.3).

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years that might be gained. We also estimate the re duction in the percentage of deaths attributed
to ozone resulting from the illustrative emission s reduction strategies to reach the proposed and alternative primary standards. The EPA included similar estimates of life years gained in a previous assessment of ozone and/or PM 2.5 

### 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 dist inguish 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, 2001). For example, life expecta ncy 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 oth er 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 estim ate 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 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 RIA 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 olde r 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 red ucing exposure to ozone 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 signi ficantly 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 w hich 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 = ∑ 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 d number of avoided premature deaths attributed to changes in

ozon e 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 basel ine 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 dea ths (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 ozone 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

Ozone 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 dea ths occur in populations age 75

– 99 (see Table 5C

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

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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 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 in and of itself 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 emission s reduction strategies, we use M i 

### 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 all

- cause mortality attributed to reducing ozone 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 R elative to the Baseline Pollutant C oncentrations (used in generating those mortality estimates) for both Short
- Term O zone Exposure
- Related M ortality and L ong - T erm PM 2.5 

### E xposure - Related M ortality 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 ozone
- related short

- term mortality (see section 2.5.4.4, in the

. 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 ozone exposure. Our confidence in the estimated number of premature deaths avoided (but n ot 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

con fidence in reported associations becomes appreciably 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 ozo ne
- attributable respiratory morbidity and mortality, 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, alon g 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 fo r 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 Formatted: Not Highlight

### 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 le vels used in generating those estimates (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., 2 009. 3 

### In addition, each figure includes separate plots for the three alternative standard levels being analyzed (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 65 ppb alternative standard is associated with days having baseline ozone level of between 40 and 45 ppb

. 4



### Figure 5C
- 1. Premature Ozone
- related Deaths Avoided for the Alternative Standards

(2025 scenario) According to the Baseline Ozone Concentrations

have the p ower to capture population thresholds in a broader and more diverse urban residential population, should those thresholds exist. 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 r eductions is likely wider than the 40

- 45 ppb range.

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

the Alternative Standards (2025 scenario) According to the Baseline Ozone Concentrations When interpreting these results, it is important to understand that the avoide d ozone
related 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 proposed and alternative standards, we adjust the design value at each mon itor exceeding the standard alternative 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 b e 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 tho ugh the highest monitor value is above that standard. Thus, emission s 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

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

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alternative standard being evaluated. Emission s reduction strategies designed to reduce ozone concentrations at a given monitor will frequently improve air quality in neighboring counties. In order to ma ke a direct comparison between the benefits and costs of these emission s reduction strategies, it is appropriate to include all the benefits occurring as a result of the emission s 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 s reductions in exceeding counties. One final caveat in interpreting the info rmation 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 eac h 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. Consideration for the plots presented in Figures 5C

- 1 and 5C

- 2 results in a number of observati ons. 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 associ ated with ozone levels above the <20ppb range identified within the O 3 

### 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 s are analyzed (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 O 3 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.

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Concentration Benchmark Analysis for PM 2.5 

### Benefit

- per
- to n 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 simulated 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 t he 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 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). The re 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 ben chmarks 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 concent rations associated with the emission s 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 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 ma y choose to do. For this RIA, we are unable to estimate the percentage of premature mortality associated with the emission s 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 distribution of exposure to baseline concentrations. As a surrogate measure of mortality impacts, we provide

6 For a summar y of the scientific review statements regarding the lack of a threshold 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).

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the percentage of the population exposed at each PM 2.5 

### concentration in the bas eline 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 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 s 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 standards . Therefore, caution is warranted

w hen 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 dis tribution 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 s reductions anticipated from MATS rule. Therefore, the baseline PM 2.5 concentrations in the LML assessment are higher than would be e xpected if MATS was reflected.

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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 Concentration s 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 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 l evel 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 exposu re 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 bec ause 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

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

PM 2.5 

### Exposure in the Baseline Sector Modeling (used to generate the benefit
per - ton estimate s)* * 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 i s 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 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). T he 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, be fore 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 s for each of the alternative standards considered. Then, following tha t 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 i n 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 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% PM 2.5 Co

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

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Table 5C
- 6. Estimated Number of Avoided Ozone
- Only Health Impacts for the

Alternative Annual Primary Ozone Standards (Incremental to the Analytical Baseline) for the Partial Attainment of the 2025 Scenario (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 results 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 Ozone
- Only Benefits for the Alternative Annual Primary

Ozone Standards (Incremental to the Analytical Baseline) 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 i n 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 dir ectly 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 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 

### - Related Health Impacts for the

Alternative Annual Primary Ozone Standards (Incremental to the Analy tical 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 inter val 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 

### - Related Health Co
- Benefits for the Altern ative Annual

Primary Ozone 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 confide nce 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 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 Ozone and PM 2.5 

### Benefits for the

Alternative Annual Primary Ozone Standards for the P artial Attainment of the 2025 S cenario (using known controls) (billions of 2011$) a,b Discount Rate 70 ppb 65 ppb 60 ppb Ozone
- only Benefits (range reflects Smith et al., 2009 and Zanobetti and Schwartz, 2008) b $1.7 to $2.8 +B $3.7 to $6.3 +B $4.1 to $6.9 +B
PM 2.5 Co
- benefits ( range reflects Krewski et al., 2009 and Lepeule et al. , 20 12) 3% $3.6 to $8.2 +B $8.0 to $18 +B $8.3 to $19 +B 7% $3.3 to $7.4 +B $7.2 to $16 +B $7.4 to $17 +B
Total Benefits 3% $5.3 to $11 +B $12 to $2 4 +B $12 to $26 +B 7% $5. 0 to $ 10 +B $11 to $23 +B $12 to $24 +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 morbid ities 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

### 5 C

- 19

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. 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 impact s 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.

### 5 C

- 20

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