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APPENDIX 5 B: ADDITIONAL SENSITIVITY ANALYSES RELATED TO THE OZONE HEALTH BENEFITS ANALYSIS
Overview The benefits analysis presented in Chapter 5 of this RIA is based on our current interpretation of the scientific and economic literature. That interpretation requires judgments regarding the best available data, models, and analytical methodologies and the assumptions that are most appropriate to adopt in the face of important uncerta inties. The majority of the analytical assumptions used to develop the main estimates of benefits have been reviewed and supported by EPA’s independent Science Advisory Board (SAB). Both EPA and the SAB recognize that data and modeling limitations as well as simplifying assumptions can introduce significant uncertainty into the estimates of benefits and that alternative choices exist for some inputs to the analysis, such as the concentration

- response functions for mortality. This appendix assesses the sensi tivity of the core benefits to: (a) the potential impact of thresholds in long

- term ozone exposure
- related mortality in incidence and benefits estimates (section 5B.2), (b) alternative response functions developed through expert elicitation (EE) for long

- t erm PM 2.5 

### exposure - related mortality (section 5B.3), and (c) alternative assumptions regarding income elasticity on benefits derived using willingness

- to
- pay (WTP) functions (section 5 B .3). For the core analysis, we estimated incidence and dollar benefits for two scenarios: 2025 and post

- 2025 (see section 5.2 and 5.4.3). However, in conducting these sensitivity analyses, we use d the 2025 scenario as the basis for making our calculations, since sensitivity analysis findings for this scenario would generally hold for the post

- 2025 scenario. I n addition to the three sensitivity analyses covered in this appendix, we also included two sensitivity analyses which are covered in detail in the body of the chapter. The first of these are estimates of dollar benefits a ssociated with mortality resulting from long

- term exposure to ozone. As discussed in section 5.2, while we felt that we had sufficient confidence to include incidence estimates associated with long

- term ozone exposure (based on effect estimates obtained fr om Jerrett et al., 2009) in the core analysis, limitations in our ability to specify an

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appropriate lag for reductions in this endpoint meant that we could not include dollar benefit estimates in the core analysis. Instead, we have included those values as sensitivity analyses, including consideration of alternative lag structures (they are presented in Tables 5

- 20 and 5

- 27, respectively for the 2025 and post

- 2025 scenarios). 1 

### The second of the sensitivity analyses already covered in the chapter address alt ernative models for short

- term ozone exposure
- related mortality. As discussed in section 5.6.3.1, in addition to the two epidemiological studies (Smith et al., 2009 and Zanobetti and Sc h wartz 2008) which provide effect estimates for the core benefits estim ates, we have also considered seven additional epidemiological studies as sensitivity analyses. These additional studies including a mix of multi

- city and meta

- analysis designs. Results of this sensitivity analysis are presented in Tables 5

- 19 and 5

- 20 (in cidence and benefits, respectively for the 2025 scenario) and Tables 5

- 26 and 5

- 27 (incidence and benefits, respectively for the post

- 2025 scenario). 5B.1 Threshold Sensitivity Analysis for Premature Mortality Incidence and Benefits from Long - term Exposur e to O 3 

### In estimating long

- term ozone mortality , we employ ed a continuous non

- threshold C

- R function relating O 3 

### exposure to premature death . However, as discussed in section 5.6.3.1, there is uncertainty regarding the potential existence and location of a threshold in the C

- R function relating mortality and long

- term O 3 

### concentrations. Thus, we have included a sensitivity analysis exploring the impact of potential thresholds in the C

- R relationship on estimates of long

- term exposure - related mortality that were evaluated in Jerrett et al. (2009), consistent with advice from CASAC ( Frey, 2014 ). In their memo clarifying the results of their study (Sasser, 2014), the authors note that in terms of goodness of fit, long

- term health risk models including O 3 

### clear ly performed better than models without O 3 

### , indicating the improved predictions of respiratory mortality when O 3 

### is included. In exploring different functional forms, the authors report that the model including a threshold at 56 ppb had the lowest log

- like lihood value of all models evaluated (i.e., linear models and models including thresholds ranging from 40

- 60 ppb), and thus provided the best

1 The sensitivity analysis

- related benefits estimates presented in these tables include (a) benefits estimates reflecting application of a zero lag model and (b) estimates reflecting application of the same 20

- year segmented lag used in modeling benefits fo r PM2.5, together with application of a 3% and 7% discount rate). See sections 5.6.3 and 5.6.4 for additional discussion of lags in relation to long

- term ozone
- related mortality.

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overall statistical fit to the data. However, they also note that it is not clear whether the 56 ppb threshold mod el is a better predictor of respiratory mortality than when using a linear (no

threshold) model for this dataset. Using one statistical test, the model with a threshold at 56 ppb was determined to be statistically superior to the linear model. Using anothe r, more stringent test, none of the threshold models considered were statistically superior to the linear model. Under the less stringent test, although the threshold model produces a statistically superior prediction than the linear model, there is uncert ainty about the specific location of the threshold, if one exists. This is because the confidence intervals on the model predictions indicate that a threshold could exist anywhere from 0 to 60 ppb. The authors conclude that considerable caution should be e xercised in using any specific threshold, particularly when the more stringent statistical test indicates there is no significantly improved prediction. Based on this additional information from the authors, we have chosen to reflect the uncertainty about the existence and location of a potential threshold by estimating mortality attributable to long

- term O 3 

### exposures using a range of threshold

- based effect estimates as sensitivity analyses. Specifically, we estimate long

- term O 3 

### mortality benefits using un ique risk coefficients that include a range of thresholds from 40 ppb to 60 ppb in 5 ppb increments, while also including a model with a threshold equal to 56 ppb, which had the lowest log likelihood value for all models examined. 2 

### Table 5B

- 1 provides the results of these sensitivity analyses (based on modeling incidence) for 60, ppb, 65 ppb and 70 ppb. We note that the same pattern in terms of relative reductions across thresholds (relative to the core estimate) would hold for the dollar benefit estimates generated for this endpoint. Table 5B - 1. Long - term O3 Mortality Incidence at Various Assumed Thresholds a Threshold Concentration 70 ppb 65 ppb 60 ppb No threshold (core model) 680 2,100 3,900 60 ppb 520 1,600 2,900 56 ppb 410 1,100 1,700 55 ppb 120 280 510 50 ppb 5.6 58 140 45 ppb 3.3 47 105 40 ppb <1 10 13
a All estimates rounded to two significant digits.

2 There is a separate effect estimate (and associated standard error) for eac h of the fitted threshold models estimated in Jerrett et al. (2009). As a result, the sensitivity of estimated mortality attributable to long

- term O 3 concentrations is affected by both the assumed threshold level (below which there is no effect of O 3 ) and the effect estimate applied to O 3 concentrations above the threshold.

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

- based risk coefficients suggests that threshold models can result in substantially lower estimates of O 3 

### - attributable long

- term mortality. For example, estimated incidence and dollar benefits for long

term mortality using a model that includes a 55 ppb threshold are approximately 70% less than long

- term mortality benefits estimated using the core co

- pollutant non

- threshold model. Generally, estimated long

- term mortality benefits are progressively reduced when using models with increasing thresholds, with the highest threshold considered (60ppb) removing virtually all of the estimated incidence reduction and associated benefits. 5B.2 Alternative Concentration - Response Functions for PM 2.5 

### – Related Mortality In modeling PM 2.5 

### cobenefits, we estimate that total dollar benefits are driven largely by reductions in mortality (see Table 5

- 22 and 5

- 29). Therefore, it is particularly important to attempt to characterize the uncertainties associated with reductions in premature mortal ity as modeled in the PM 2.5 

### cobenefits analysis. To better understand the concentration

- response relationship between PM 2.5 

### exposure and premature mortality, the EPA conducted an expert elicitation in 2006 (Roman et al., 2008; IEc, 2006). 3 

### In general, the results of the expert elicitation support the conclusion that the benefits of PM 2.5 

### control are very likely to be substantial. Alternative concentration

- response functions are useful for assessing uncertainty beyond random statistical error, including unc ertainty in the functional form of the model or alternative study design. In this analysis, we present the results derived from the expert elicitation as indicative of the uncertainty associated with a major component of the health impact functions, and we provide the independent estimates derived from each of the twelve experts to better characterize the degree of variability in the expert responses. In previous RIAs, the EPA presented benefits estimates using concentration response functions derived from the PM 2.5 

### Expert Elicitation (Roman et al., 2008) as a range from the lowest expert value (Expert K) to the highest expert value (Expert E). However, this approach did not indicate the agency’s judgment on what the best estimate of PM 2.5 

### benefits may be, a nd

3 Expert elicitation is a formal, highly structured and well documented process whereby expert judgments, usually of multiple experts, are obtained (Ayyub, 2002).

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the EPA’s independent Science Advisory Board (SAB) recommended refinements to the way EPA presented the results of the elicitation (U.S. EPA

- SAB, 2008). Therefore, we began to present the cohort

- based studies (Krewski et al., 2009; Laden et al., 2006) 4 

### as our core estimates in the proposal RIA for the Portland Cement NESHAP (U.S. EPA, 2009a). Using alternate relationships between PM2.5 and premature mortality supplied by experts, higher and lower benefits estimates are plausible, but most of the expert

- b ased estimates of the mean PM 2.5 

### effect on mortality fall between the two epidemiology

- based estimates (Roman et al., 2008). In addition to these studies, we have included a discussion or other recent multi

- state cohort studies conducted in North America, but we have not estimated benefits using the effect coefficients from these studies (see Appendix 5D). Please note that the benefits estimates results presented are not the direct results from the studies or expert elicitation; rather, the estimates are ba sed in part on the effect coefficients provided in those studies or by experts. In addition, because we are using a dollar

- per - ton approach in modeling PM2.5 cobenefits in this RIA, we cannot generate confidence intervals reflecting the statistical fit cha racterized in the expert elicitation

- based functions. Even these multiple characterizations based on application of the range of expert elicitation

- based effect estimates omit the contribution to overall uncertainty from uncertainty in air quality changes, baseline incidence rates, and populations exposed. Furthermore, the approach presented here does not yet include methods for addressing correlation between input parameters and the identification of reasonable upper and lower bounds for input distribution s characterizing uncertainty in additional model elements. The PM 2.5 

### expert elicitation and the derivation of effect estimates from the expert elicitation results (used in generating these alternative dollar

- per - ton estimates) are described in detail in t he 2006 PM 2.5 

### NAAQS RIA (U.S. EPA, 2006 ), the elicitation summary report (IEc, 2006) and Roman et al. (2008), and consequently, we do not present those effect estimates (and associated functional forms) here.

4 We have since updated the the Harvard Six Cities cohort study from Laden et al. (2006) to use the most recent follow

- up publication of this cohort (Lepeule et al, 2012). This study is reflected in the dollar

- per - ton values used in this RIA.

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Table 5B

- 2 presents the results of this sensiti vity analysis as completed for the 2025 scenario (overall conclusions generated from this analysis are transferable to the post

- 2025 scenario). The alternative mortality estimates presented in Table 5B

- 2 were generated similar to core cobenefits PM 2.5 

### mort ality estimates, but applying dollar

- per - ton values (for each expert elicitation

- based effect estimate) to the sector

- level estimates of NOx reductions associated with the post

- 2025 scenario. We have also included the core cobenefits estimates for each alt ernative standard to facilitate comparison against these alternative sensitivity analysis estimates. Because application of these effect estimates (using a dollar

- per - ton approach) represents a linear calculation, results in terms of the relative ranking o f the core estimates compared with expert elicitation estimates will remain the same for all alternative standards evaluated. Therefore, we only present estimates for the 70ppb alternative standard, observing that observations drawn from this sensitivity a nalysis would hold for other alternative standards considered (and for the post
- 2025 scenario

– for all alternative standards

– as well). Table 5B - 2. Application of Alternative (Expert Elicitation - Based Effect Estimates) to the

Modeling of PM 2.5 

### Co - benefit Estimates for PM 2.5 

### (avoided incidence)

C - R Function (and effect estimate) b 70 ppb Krewski et al., (2012) (core model) 45 Lepeule et al., (2012) (core model) 100

Expert K 10 Expert G 54 Expert L 63 Expert D 65 Expert H 67 Expert J 75 Expert F 88 Expert C 92 Expert I 92 Expert B 95 Expert A 120 Expert E 150
a All estimates rounded to two significant digits. b Expert elicitation

- based values ordered by magnitude of incidence reduction 

### The values presented in Table 5B

- 2 suggest that the two core incidence estimate fall within the range of alternative C

- R function based estimates obtained through expert elicitation. This increases overall confidence in the core estimates with regard to th e form of the functions

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and magnitude of the effect estimates. We would expect the relationship between the core estimates and the expert

- derived estimates to remain constant for the remaining scenarios as well
. 5B .3 Income Elasticity of Willingness to Pay As discussed in Chapter 5, our estimates of monetized benefits account for growth in real GDP per capita by adjusting the WTP for individual endpoints based on the central estimate of the adjustment factor for each of the categories (minor health effects, severe and chronic health effects, premature mortality, and visibility). We examined how sensitive the estimate of total benefits is to alternative estimates of the income elasticities. Income growth projections are only currently available in BenMAP thro ugh 2024, so both the 2025 and post

- 2025 scenario estimates use income growth only through 2024 and are therefore likely underestimates. Table 5 B

- 4 lists the ranges of elasticity values used to calculate the income adjustment factors, while Table 5 B

- 5 lists the ranges of corresponding adjustment factors. The results of this sensitivity analysis, giving the monetized benefit subtotals for the four benefit categories, are presented in Table 5 B

- 6.
Table 5 B - 3. Ranges of Elasticity Values Used to Account for Projected Real Income

Growth a Benefit Category Lower Sensitivity Bound Upper Sensitivity Bound Minor Health Effect b 0.04 0.30 Premature Mortality 0.08 1.00
a Derivation of these ranges can be found in Kleckner and Neumann (1999). COI estimates are assigned an adjustment factor of 1.0. b Minor health effects included in this RIA and valued using WTP

- based functions include: upper and lower respiratory symptoms, asthma exacerbations, minor restricted activity days, and acute bronchitis. 

### Table 5 B - 4. Ranges of Adjustment Factors Used to Account for Projected Real Income

Growth to 2024 a

Benefit Category Lower Sensitivity Bound Upper Sensitivity Bound Minor Health Effect b 1.021 1.170 Premature Mortality 1.043 1.705
a Based on elasticity values reported in Table C

- 4, U.S. Census population projections, and projections of real GDP per capita. b Minor health effects included in this RIA and valued using WTP

- based functions include: upper and lower respiratory symptoms, asthma exacerbations, minor restricted activity days, and acute bronchitis.

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Table 5 B - 5. Sensitivity of Monetized O 3 

### Benefits to Alternative Income Elasticities in

2025 (Millions of 2011$) a Benefit Category No adjustment Lower Sensitivity Bound Upper Sensitivity Bound 70 ppb 65 ppb 70 ppb 65 ppb 70 ppb 65 ppb Minor Health Effect b $66 $200 $67 $210 $77 $240 Premature Mortality c $2,000 $6,400 $2,100 $6,600 $3,500 $11,000
a All estimates rounded to two significant digits. Only reflects income growth to 2024. b For purposes of completing this sensitivity analysis, we have included minor restricted activity days (MRADS) based resulting from short

- term ozone exposure as the min or health effect evaluated here. c Using short

- term mortality effect estimate from Smith et al. (2009) and 3% discount rate. Results using other short

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

### Consistent with th e impact of mortality on total benefits, the adjustment factor for mortality has the largest impact on total benefits. The value of mortality in 2025 ranges from 86% to 133% of the main estimate for mortality based on the lower and upper sensitivity bounds on the mortality income adjustment factor. The effect on the value of minor health effects is much less pronounced, ranging from 96% to 108% of the main estimate for minor effects. These observations (in terms of relative impact from alternative elasticit ies) hold for all three of the alternative standard levels evaluated under both the 2025 and post

- 205 scenarios. 5B . 4 References Frey, C. 2014. CASAC Review of the EPA’s Health Risk and Exposure Assessment for Ozone (Second External Review Draft

– February, 2014). U.S. Environmental Protection Agency Science Advisory Board. EPA

CASAC

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- XXX.Gent, J.F.; E.W. Triche; T.R. Holford; K. Belanger; M.B. Bracken; W.S. Beckett, et al. 2003. Association of low

- level ozone and fine particles with respiratory s ymptoms in children with asthma. Journal of the American Medical Association. 290(14):1859

- 1867. Industrial Economics, Incorporated (IEc). 2006. Expanded Expert Judgment Assessment of the Concentration

Response Relationship Between PM 2.5 Exposure and Mortality . Prepared for: Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC. September. Available at <http://www.epa.gov/ttn/ecas/regdata/Uncertainty/pm_ee_tsd_expert_interview_summa ries.pdf>. Jerrett M, Burnett RT, Pope CA, III, et al. 2009. “Long

- Term Ozone Exposure and Mortality.” New England Journal of Medicine 360:1085

- 95. Krewski D, Jerrett M, Burnett RT, Ma R, Hughes E, Shi, Y, et al. 2009. Extended follow

- up and spatial analys is of the American Cancer Society study linking particulate air pollution and mortality
. HEI Research Report, 140, Health Effects Institute, Boston, MA. Kleckner, N., and J. Neumann. 1999. “Recommended Approach to Adjusting WTP Estimates to Reflect Changes in Real Income.” Memorandum to Jim DeMocker, US EPA/OPAR. June 3, Available on the Internet at <http://www.epa.gov/ttn/ecas/regdata/Benefits/background/klecknerandneumann1999.pdf>. Lepeule J, Laden F, Dockery D, Schwartz J. 2012. “Chronic Exposure to Fine Particles and Mortality: An Extended Follow

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Roman, Henry A., Katherine D. Walker, Tyra L. Walsh, Lisa Conner, Harvey M. Richmond, Bryan J. Hubbell, an d Patrick L. Kinney. 2008. “ Expert Judgment Assessment of the Mortality Impact of Changes in Ambient Fine Particulate Matter in the U.S. ” Environ mental Sci ence & Technol ogy 42(7):2268

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- term mortality in U.S. urban communities. Inhalation Toxicology. 21:37

- 61. U.S. Environmental Pro tection Agency (U.S. EPA). 2009
. Regulatory Impact Analysis: National Emission Standards for Hazardous Air Pollutants fro m the Portland Cement Manufacturing Industry . Office of Air Quality Planning and Standards, Research Triangle Park, NC. April. Available at <http://www.epa.gov/ttn/ecas/regdata/RIAs/portlandcementria_4

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- 09.pdf>. U.S. Environmental Protection Agency (U.S. EPA). 2012. Regulatory Impact Analysis for the Final Revisions to the National Ambient Air Quality Standards for Particulate Matter . EPA

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- 003. Office of Air Quality Planning and Standards, Health and Environmental Impacts Division. December. Avail able at: <http://www.epa.gov/pm/2012/finalria.pdf>. U.S. Environmental Protection Agency

— Science Advisory Board (EPA

- SAB). 1999a. The Clean Air Act Amendments (CAAA) Section 812 Prospective Study of Costs and Benefits (1999): Advisory by the Health and Ec ological Effects Subcommittee on Initial Assessments of Health and Ecological Effects. Part 1. EPA

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COUNCIL

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. U.S. Environmental Protection Agency

— Science Advisory Board (EPA

- SAB). 1999b. The Clean Air Act Amendments (CAAA) Section 812 Prospective Study of Costs and Benefits (1999): Advisory by the Health and Ecological Effects Subcommittee on Initial Assessment s of Health and Ecological Effects. Part 2. EPA

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COUNCIL

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- 001. October. Available on the Internet at <http://yosemite.epa.gov/sab%5CSABPRODUCT.NSF/7D54675178C12703852571B90044B4F8/$File/coua0001 .pdf>. U.S. Environmental Protection Agency

— Science A dvisory Board (EPA

- SAB). 2004a. Advisory on Plans for Health Effects Analysis in the Analytical Plan for EPA’s Second Prospective Analysis

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- SAB). 2004b. Advisory Council on Clean Air Compliance Analysis Response to Agency Request on Cessation Lag . EPA

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- 001 December. Available on the Internet at <http://yosemite.epa.gov/sab%5CSABPRODUCT.NSF/39F44B098DB49F3C 85257170005293E0/$File/council_l tr_05_001.pdf>. U.S. Environmental Protection Agency Science Advisory Board (U.S. EPA

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