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### 8 - 1 

# Chapter 8 **Com parison of**  **Program Costs to**  **Pr ogram**  **Em ission** 

**Reductions and Air** 

**Quality Benefits** EPA traditionally

evaluates the effectiveness of a proposal in terms of net benefits and in terms of cost - effectiveness. Section 8 . 1 below presents the cost - benefit analysis of the proposal, while Section 8 . 2 presents the cost - effectiveness. **8.1 Cost** **- Benefit Analysis**  The net benefits of the proposed Tier 3 program are determined b y the effects of the program on the costs to comply with the vehicle and fuel aspects of the program along with the benefits of improved air quality on health and the environment. 8.1.1 Program - Wide Costs The costs that would be incurred from our proposed program fall into three categories - for the Tier 3 exhaust standards, Tier 3 evaporative standards, and reductions in sulfur content of gasoline. While we present these three categories of costs separatel y in this section, for purposes of the calculation of cost - effectiveness we have summed them to represent the estimated costs of the proposed program. All costs represent the fleet - weighted average of light - duty vehicles and trucks. All costs are represented in 2010 dollars.

8.1.1.1 Vehicle Costs The vehicle costs include the technology costs projected to meet the proposed exhaust and evaporative standards, a s detailed in draft RIA Chapter 2 and shown in Table 8 - 1 . The fleet mix of light - duty

vehicles, light duty

trucks, and medium - duty trucks represents the 2016 MY fleet used in the 2012 - 2016MY light - duty GHG final rulemaking.

**Table 8**  **- 1**  **: Annual Vehicle Technology Costs, 2010$** Year Vehicle Exhaust Emission Control Costs ($Million) Vehicle Evaporative

Emission Control Costs ($Million) Facility Costs

($Million) Total Proposed

Vehicle Progr am Costs ($Million) 2016 $ - $ - $ 22.5 

### $22.5 2017 $705 

### $79.4 $ 3.75 

### $788 2018 $1,300 

### $190 $ 3.75 

### $1,494 2019 $1,410 

### $180 $ 3.75 

### $1,594 2020 $1,530 

### $245 $ 3.75 

### $1,779 2021 $1,670 

### $237 $ 3.75 

### $1,911 2022 $ 1,810 

### $301 $ 3.75 

### $2,115 2023 $1,840 

### $288 $ 3.75 

### $2,132 2024 $1,960 

### $293 $ 3.75 

### $2,257

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### 8 - 2 Year Vehicle Exhaust Emission Control Costs ($Million) Vehicle Evaporative

Emission Control Costs ($Million) Facility Costs

($Million) Total Proposed

Vehicle Progr am Costs ($Million) 2025 $2,080 

### $281 $ 3.75 

### $2,365 2026 $2,070 

### $281 $ 3.75 

### $2,355 2027 $2,040 

### $281 $ 3.75 

### $2,325 2028 $2,040 

### $281 $ 3.75 

### $2,325 2029 $2,000 

### $2 81 $ 3.75 

### $2,285 2030 $1,990 

### $281 $ 3.75 

### $2,275 8.1.1.2 Fuel Costs The fuel costs consist of the additional operating costs and capital costs to the refiners to meet the proposed sulfur average of 10 ppm. The sulfur control costs, as described in detail in draft RIA Chapter 5, assume a cost of 0. 89 cents per gallon which includes the refinery operating and capital costs. The projected annual fuel consumption and annual fuel cost s of the proposed program are listed in Table 8 - 2 . **Table 8**  **- 2**  **: Annual Fuel Costs, 2010$** Year Annual Fuel Consumption (million gallons) Fuel Sulfur

Control Costs ($Million) 2016 36,208 $ 322 2017 144,878 $ 1,289 2018 144,710 $ 1,288 2019 144,435 $ 1,285 2020 144,324 $ 1,284 2021 144,562 $ 1,287 2022 144,838 $ 1,289 20 23 144,774 $ 1,288 2024 144,812 $ 1,289 2025 145,057 $ 1,291 2026 145,476 $ 1,295 2027 145,988 $ 1,299 2028 146,761 $ 1,306 2029 147,280 $ 1,311 2030 148,295 $ 1,320 8.1.1.3 Total Costs The sum of the vehicle technology costs to control exhaust and evaporative emissions, in addition to the costs to control the sulfur level in the fuel, represent the total costs of the proposed program, as shown in Table 8 - 3 .

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### 8 - 3 **Table 8**  **- 3**  **: Total Annual Vehicle and Fuel Control Costs, 2010$** Year Total Proposed Vehicle Program Costs ($Million) Fuel Sulfur

Control Costs ($Million) Total Prop osed

Program Costs ($Million) 2016 $22.5 $ 322 $345 2017 $788 $ 1,289 $2,078 2018 $1,494 $ 1,288 $2,782 2019 $1,594 $ 1,285 $2,879 2020 $1,779 $ 1,284 $3,063 2021 $1,911 $ 1,287 $3,197 2022 $2,115 $ 1,289 $3,404 2023 $2,132 $ 1,288 $3,420 2024 $2,257 $ 1,289 $3,546 2025 $2,365 $ 1,291 $3,656 2026 $2,355 $ 1,295 $3,649 2027 $2,325 $ 1,299 $3,624 2028 $2,325 $ 1,306 $3,631 2029 $2,285 $ 1,311 $3 ,596 2030 $2,275 $ 1,320 $3,595 8.1.2 Quantified and Monetized Health and Env ironmental Impacts This section presents EPA’s anal ys is of the criteria pollutant - related health and environmental impacts that would occur as a result of the proposed Tier 3 s tandards . The vehicles and fuels subject to the proposed standards are significant sources of mobile source air pollution such as direct PM, NO X 

### , SO X 

### , VOCs and air toxics. The standards would affect exhaust and evaporative emissions of these pollutants f rom vehicles. Emissions of NO X 

### (a precursor to ozone formation and secondaril y- formed PM 2.5 

### ), SO X 

### (a precursor to secondarilyformed PM 2.5 

### ) , VOCs (a precursor to ozone formation and, to a lesser degree, secondarilyformed PM 2.5 

### ) and directly- emitted PM 2.5 

### contribute to ambient concentrations of PM 2.5 

### and ozone. Exposure to ozone and PM 2.5 

### is linked to adverse human health impacts such as premature deaths as well as other important public health and environmental effects. The anal ys is in this section aims to characterize the benefits of the proposed standard b y answering two key

questions: 1. What are the health and welfare effects of changes in ambient particulate matter (PM 2.5 

### ) and ozone air quality resulting from reductions in precursors including NO X 

### an d SO 2 

### ? 2. What is the economic value of these effects? For the proposal, we have quantified and monetized the health and environmental impacts in 2030, representing impacts associated with a year when the program is fully implemented and most of the fleet is turned over . Overall, we estimate that the proposed standards would lead to a net decrease in PM 2.5 

### - and ozone - related health impacts in 2030 . The

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### 8 - 4 decrease in population - weighted national average PM 2.5 

### exposure results in a net decrease in adverse PM - related human health impacts (the decrease in national population - weighted annual average PM 2.5 

### is 0.05 μg/m 3 

### in 2030). A 

### The de crease in population - weighted national average ozone exposure results in a net decrease in ozone - related health impacts (population - weighted maximum 8 - hour average ozone de creases b y 0.52 ppb in 2030 ). Using the most conservative p remature mortality estimates (Pope et al., 2002 for PM 2.5 

### and Bell et al., 2004 for ozone), B , C 

### we estimate that by 2030, implementation of the proposed standards w ould reduce approximately 970 premature mortalities annuall y and yield approximately

$9.5 bil lion in total annual benefits. The upper end of the range of avoided premature mortality

estimates associated with the proposed standards (based on Laden et al., 2006 for PM 2.5 

### and Levy et al., 2005 for ozone) D , E 

### results in approximately 2,800 premature m ortalities avoided in 2030 and yield s approximately $ 27 billion in total benefits. Thus, even taking the most conservative premature mortality assumptions, the health impacts of the proposed standards presented in this rule are clearly substantial. We not e that of necessity decisions on the emissions and other elements used in the air quality modeling were made earl y in the anal ytical process for this proposal . For this reason, the Tier 3 emission control scenario used in the air quality and benefits mode ling include s emission reductions from Tier 3 across the nation, assuming no reductions associated with California's LEV III program (as opposed to including California’s LEV III program and its associated emission reductions in the baseline scenario). This was because EPA had not granted California a waiver of preemption under CAA section 209 for the LEV III program at the time EPA conducted the air quality

modeling. EPA did include California’s fuel program , which independent of LEV III was already res ulting in average gasoline sulfur levels of 10 ppm, in the baseline scenario. Since then, EPA granted a waiver for California's LEV III program (78 FR 2112, January

9, 2013). Based on this change in circumstances, we will conduct new air quality modeling for the final rule that will include emission reductions from California’s LEV III program in the baseline scenario. Had we modeled the California LEV III emission impacts in the Tier 3 air quality baseline, we estimate that benefits would decrease by approximately

12 - 16 percent, depending

A Note that the national, population - weighted PM 2.5 and ozone air quality metrics presented in this Chapter represent an average for the entire, gridded U.S. CMAQ domain. These are di fferent than the population - weighted PM 2.5 and ozone design value metrics presented in Chapter 7, w hich represent the average for areas with a current air quality mo nitor. B Pope, C.A., III, R.T. Burnett, M.J. Thun, E.E. Calle, D. Krewski, K. Ito, and G.D. Thurston. (2002). Lung Cancer, Cardiopulmonary Mortality, and Long - term Exposure to Fine Particulate Air Pollution. Journal of the American Medical Association, 287, 1132 - 1141. C Bell, M.L., et al. (2004). Ozone and short - term mortality in 95 US urban communities, 1987 - 2000. Journal of the American Medical Association , 292(19) , 2372 - 2378. D Laden, F., J. Schwartz, F.E. Speizer, and D.W. Dockery. (2006). Reduction in Fine Particulate Air Pollution and Mortality. American Journal of Respiratory and Cri tical Care Medicine . 173 , 667 - 672. E Levy, J.I., S.M. Chemerynski, and J.A. Sarnat. (2005). Ozone exposure and mortality: an empiric bayes metaregression analysis. Epidemiology. 16(4) , 458 - 68.

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### 8 - 5 on the particular health impact functions used to characterize both PM - and ozone - related premature mortality . F 

### As a result, we estimate that in 2030, using the most conservative premature mortality

estimates (Pope et al., 2002 for PM 2.5 

### and Bell et al., 2004 for ozone), G , H 

### the proposed standards w ould reduce approximately 820 premature mortalities annually and yield approximately

$8.0 billion in total annual benefits. The upper end of the range of avoided premature mo rtality

estimates associated with the proposed standards (based on Laden et al., 2006 for PM 2.5 

### and Levy et al., 2005 for ozone) I , J 

### results in approximately 2,4 00 premature mortalities avoided in 2030 and yield s approximately $ 23 billion in total benefits. Th ese are rough estimate s since, without new photochemical air quality modeling to reflect the revised baseline and control scenarios, we are unable to account for cro ss - state transport of pollution. However, we believe this is a reasonable characterization of the small reduction in benefits had we modeled California in the baseline; our overall cost - benefit conclusions do not materially change with or without the inclusion of California emissions in our anal ys is. W e will conduct new air quality modelin g for the final rule that will include emission reductions from California’s LEV III program in the baseline scenario . The rest of this Chapter presents benefits that include California LEV III emission reductions .

8.1.2.1 Overview This analysis reflects the impac t s of the proposed Tier 3 rule in 2030 compared to a future - ye ar reference scenario without the program in place. Overall, we estimate that the proposed rule w ould lead to a net decrease in PM 2.5 

### - related health and environmental impacts (see Section 7.2.5 for more information about the air quality modeling results ). The decrease in population - weighted national average PM 2.5 

### exposure results in a net decrease in adverse PM related human health and environmental impacts (the decrease in national population weighted annual average PM 2.5 

### is 0.05 μg/m3 in 2030 ). The air quality modeling also projects decreases in ozone concentrations (see Section 7.2.5 ). The overall de crease in population - weighted national average ozone exposure results in decreases in ozone - related health and environmental impacts (population weighted maximum 8 - hour average ozone decreases b y 0.52 ppb in 2030 ).

F To conduct this sensitivity analysis, we simply assumed no air quality change in the California portion of the CMAQ domain. We then exported the reference and control air quality surfaces to be used as inputs to BenMAP. Note that this simple approach is unable to account for legitimate emissions impacts related to cross - state transport of pollution. G Pope, C.A., III, R.T. Burnett, M.J. Thun, E.E. Calle, D. Krewski, K. Ito, and G.D. Thurston. (2002). Lung Cancer, Cardiopulmonary Mortality, and Long - term Exposure to Fine Particulate Air Pollution. Journal of th e American Medical Association, 287, 1132 - 1141. H Bell, M.L., et al. (2004). Ozone and short - term mortality in 95 US urban communities, 1987 - 2000. Journal of the American Medical Association , 292(19) , 2372 - 2378. I Laden, F., J. Schwartz, F.E. Speizer, and D.W. Dockery. (2006). Reduction in Fine Particulate Air Pollution and Mortality. American Journal of Respiratory and Critical Care Medicine . 173 , 667 - 672. J Levy, J.I., S.M. Chemerynski, and J.A. Sarnat. (2005). Ozone exposure and mortality: an empiri c bayes metaregression analysis. Epidemiology. 16(4) , 458 - 68.

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### 8 - 6 We base our analysis of the program’s impact on human health and the environment on peer- reviewed studies of air quality and human he alth effects. 1 , 2 

### Our benefits methods are also consistent with rulemaking anal ys es such as the final 2012 - 2016 MY Light - Duty Vehicle Rule, 3 

### the final Portland Cement National Emissions Standards for Hazardou s Air Pollutants (NESHAP) RIA , 4 

### , and the final 2017 - 2025 MY Light - Duty Vehicle Rule. 5 

### To model the ozone and PM air quality

impacts of th e proposal , we used the Community Multiscale Air Quality (CMAQ) model (see Section 7.2.2 ). The modeled ambient air quality data serves as an input to the Environmental Benefits Mapping and Analy sis Program version 4.0 (BenMAP). K 

### BenMAP is a computer program developed b y the U.S. EPA that integrates a number of the modeling elements used in previous analyses ( e.g. , interpolation functions, population projections, health impact functions, valuation functions, anal ys is and pooling methods) to translate modeled air concentration estimates into health effects incidence estimates and monetized benefits estimates. The range of total monetized ozone - and PM - related health impacts in 2030 is presented in Table 8 - 4 . We present total benefits based on the PM - and ozone - related premature mortality function used. The benefits ranges therefore reflect the addition of each estimate of ozone related premature mortality ( each with its own row in) to estimates of PM - related premature mortality . The anal ys is of the proposed standards reflects EPA’s work to characterize benefits prior to the most recent PM NAAQS. L 

### EPA will update its benefits anal ys is, and relat ed uncertainty analysis, to be consistent with the final PM NAAQS for the final Tier 3 regulatory impact anal ys is . **Table 8**  **- 4**  **: Estimated 2030 Monetized PM**  **- and Ozone**  **- Related Health Benefits**  **a ,d**  2030 Total Ozone and PM Benefits

– PM Mortality Derived from American Cancer Society Analysis and Six - Cities Analysis a Premature Ozone Mortality Function Reference Total Benefits (Billions, 2010 $, 3% Discount Rate) b,c Total Benefits (Billions, 2010 $, 7% Discount Rate) b ,c Multi - city analyses Bell et al., 2004 Total: $9.5 - $21 PM: $7.7 - $19 Ozone: $1.8 Total: $8.7 - $19 PM: $7.0 - $17 Ozone: $1.8 Huang et al., 2005 Total: $10 - $21 PM: $7.7 - $19 Ozone: $2.6 Total: $9.5 - $20 PM: $7.0 - $17 Ozone: $2.6 Schwartz, 20 05 Total: $10 - $22 PM: $7.7 - $19 Ozone: $2.7 Total: $9.6 - $20 PM: $7.0 - $17 Ozone: $2.7 Meta - analyses Bell et al., 2005 Total: $13 - $24 PM: $7.7 - $19 Ozone: $5.5 Total: $12 - $23 PM: $7.0 - $17 Ozone: $5.5 Ito et al., 2005 Total: $15 - $26 PM: $7. 7 - $19 Ozone: $7.5 Total: $15 - $25 PM: $7.0 - $17 Ozone: $7.5

K Information on BenMAP, including downloads of the software, can be found at http://www.epa.gov/ttn/ecas/ benmodels.html. L US EPA (2012). National Ambient Air Quality Standards for Particulat e Matter. http://www.epa.gov/PM/2012/finalrule.pdf

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### 8 - 7 Levy et al., 2005 Total: $15 - $27 PM: $7.7 - $19 Ozone: $7.7 Total: $15 - $25 PM: $7.0 - $17 Ozone: $7.7 Notes: a Total includes premature mortality - related and morbidity - related ozone and PM 2.5 benefits. Range was developed by adding the estimate from the ozone premature mortality function to the estimate of PM 2.5 - related premature mo rtality derived from either the ACS study (Pope et al., 2002) or the Six - Cities study (Laden et al., 2006) . b Note that total benefits presented here do not include a number of unquantified benefits categories. A detailed listing of unquantified health and welfare effects is provided in Table 8 - 5 . c Results reflect the us e of both a 3 and 7 percent discount rate, as recommended by EPA’s Guidelines for Preparing Economic Analyses and OMB Circular A - 4. Results are rounded to two significant digits for ease of presentation and computation. Totals may not sum due to rounding . 

### The benefits in Table 8 - 4 include all of the human health impacts we are able to quantify and monetize at this time. However, the full complement of human health and welfare effects associated with PM and ozone remain unquantified because of current limitations in methods or available data. We have not quantified a number of known or suspected health effects linked with ozone and PM for which appropriate health impact functions are not available or which do not provide easil y inter

pretable outcomes ( e.g. , changes in heart rate variability ). Additionally , we are unable to quantify a number of known welfare effects, including reduced acid and particulate deposition damage to cultural monuments and other materials, and environmental b enefits due to reductions of impacts of eutrophication in coastal areas. The se are listed in Table 8 - 5 . As a result, the health benefits quantified in this section are likel y underestimates of the total benefits attributable to the proposed program . **Table 8**  **- 5**  **: Human Health and Welfare Effects of Pollutants Affected by the Proposed**  **Tier 3 Program** Pollutant/ Effect Quantified and monetized in primary estimate Unquantified **PM: health a**  Premature mortality based on cohort study

estimates b and expert elicitation estimates Hospital admissions: respiratory and cardiovascular Emergency room visits for asthma Nonfatal heart attacks (myocardial infarctions) Lo wer and upper respiratory illness Minor restricted activity days Work loss days Asthma exacerbations (among asthmatic populations Respiratory symptoms (among asthmatic populations) Infant mortality Lo w birth weight, pre - term birth and other reproductive outcomes Pulmonary func tion Chronic respiratory diseases other than chronic bronchitis Non - asthma respiratory emergency room visits UVb exposure (+/ - ) c

**PM: welfare** Visibility in Class I areas in SE, SW, and CA regions Household soiling Visibility in residential areas Visibility in non - class I areas and class 1 areas in NW, NE, and Central regions UVb exposure (+/ - ) c Global climate impacts c

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### 8 - 8 Pollutant/ Effect Quantified and monetized in primary estimate Unquantified **Ozone: health** Premature mortality based on short - term

study estimates Hospital admissions: respiratory Emergency room visits for asthma Minor restricted activity days School loss days Chronic respiratory damage Premature aging of the lungs Non - asthma respiratory emergency room visits UVb exposure (+/ - ) c **Ozone: welfare**  Decreased outdoor worker productivity Yields for:

-- Commercial forests

- - Fruits and vegetables, and

-- Other commercial and noncommercial crops Damage to urban ornamental plants Recreational demand from damaged forest aesthetics Ecosystem functions UVb exposure (+/ - ) c Climate impacts **CO: health** Behavioral effects **Nitrate Dep**  **osition: welfare**  Commercial fishing and forestry from acidic deposition effects Commercial fishing, agriculture and forestry from nutrient deposition effects Recreation in terrestrial and estuarine ecosystems from nutrient deposition effects Other ecosys tem services and existence values for currently healthy ecosystems Coastal eutrophication from nitrogen deposition effects **Sulfate Deposition:**  **welfare** Commercial fishing and forestry from acidic deposition effects Recreation in terrestrial and aquatic ec osystems from acid deposition effects Increased mercury methylation **HC/Toxics: health**  **d** Cancer (benzene, 1,3 -butadiene, formaldehyde, acetaldehyde) Anemia (benzene) Disruption of production of blood components (benzene) Reduction in the number of blood pl atelets (benzene) Excessive bone marrow

formation (benzene) Depression of lymphocyte counts (benzene) Reproductive and developmental effects (1,3 - butadiene) Irritation of eyes and mucus membranes (formaldehyde) Respiratory irritation (formaldehyde) Asthma attacks in asthmatics (formaldehyde) Asthma - like symptoms in non - asthmatics (formaldehyde) Irritation of the eyes, skin, and respiratory tract (acetaldehyde) Upper respiratory tract irritation and congestion (acrolein)

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### 8 - 9 Pollutant/ Effect Quantified and monetized in primary estimate Unquantified **HC/Toxics: welfare**  Direct toxic eff ects to animals Bioaccumulation in the food chain Damage to ecosystem function Odor Notes: a In addition to primary economic endpoints, there are a number of biological responses that have been associated with PM health effects including morphological changes and altered host defense mechanisms. The public health im pact of these biological responses may be partly represented by our quantified endpoints. b Cohort estimates are designed to examine the effects of long term exposures to ambient pollution, but relative risk estimates may also incorporate some effects due to shorter term exposures (see Kunzli et al., 2001 for a discussion of this issue). 6 While some of the effects of short term exposure are likely to be captured by the cohort estimates, there may be additional premature mortality from short term PM exposure not captured in the cohort estimates included in the primary analysis. c May result in benefits or disbenefits. d Many of the key hydrocarbons related to this action are als o hazardous air pollutants listed in the CAA. 

### While there w ould be impacts associated with air toxic pollutant emission changes that result from the proposed program , we do not attempt to monetize those impacts. This is primarily because currently

availa ble tools and methods to assess air toxics risk from mobile sources at the national scale are not adequate for extrapolation to incidence estimations or benefits assessment. The best suite of tools and methods currentl y available for assessment at the national scale are those used in the National - Scale Air Toxics Assessment (NATA). The EPA Science Advisory

Board specificall y commented in their review of the 1996 NATA that these tools were not yet ready for use in a national - scale benefits analysis, becaus e they did not consider the full distribution of exposure and risk, or address sub - chronic health effects. 7 

### While EPA has since improved the se tools, there remain critical limitations for estimating incidence and assessing benefits of reducing mobile source air toxics. As part of the second prospective anal ys is of the benefits and costs of the Clean Air Act , 8 

### EPA conducted a case study anal ys is of the health effects associated with reducing exposure to benzene in Houston from implementation of the Clean Air Act . While reviewing the draft report, EPA’s Advisory

Council on Clean Air Compliance Anal ys is concluded that “the challenges for assessing progress in health improvement as a result of reductions in emissions of hazardous air pollutants (HAPs) are daunting...due to a lack of exposure - response functions, uncertainties in emissions inventories and background levels, the difficulty of extrapolating risk estimates to low doses and the challenges of tracking health progress for diseases, such as cancer, that have long latency periods .” 9 

### EPA continues to work to address these limitations; however, we did not have the methods and tools available for national - scale application in time for the anal ys is of the proposed program . M

M In April, 2009, EPA hosted a workshop on estimating the benefits or reducing hazardous air pollutants. This workshop built upon the work accom plished in the June 2000 Science Advisory Board/EPA Worksho p on the Benefits of Reductions in Exposure to Hazardous Air Pollutants, which generated thoughtful discussion on approaches to estimating human health benefits from reductions in air toxics exposure, but no consensus was reached on methods that could be imp lemented in the near term for a broad selection of air toxics. Please visit http://epa.gov/air/toxicair/2009workshop.html for more information about the workshop and its associated materials.

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### 8 - 10 8.1.2.1.1 Human Health Impacts Table 8 - 6 and Table 8 - 7 present EPA’s preferred estimates of the annual PM 2.5 

### and ozone health impacts in the 48 contiguous U.S. states associated with the proposed Tier 3 program . For each endpoint presented in Table 8 - 6 and Table 8 - 7 , we provide both the point estimate and the 90 percent confidence interval. Table 8 - 8 presents the sensitivity anal ys is. Using EPA’s preferred estimates, based on the American Cancer Society (ACS) and Six Cities studies and no threshold assumption in the model of mortality , we estimate that the proposed program would result in between 800 and 2,100 cases of avoided PM 2.5 

### - related premature deaths annually in 2030. A sensitivity

anal ys is was conducted to understand the impact of alternative concentration response functions suggested b y experts in the field . When the range of expert opinion is used, we estimate between 270 and 2,700 fewer premature mortalities in 2030. The range of ozone impacts is based on changes in risk estimated using several sources of ozone - related mortality effect estimates. This analysis presents six alternative estimates for the association based upon different functions reported in the scientific literature, derived from both the National Morbidity , Mortality , and Air Pollution Study (NMMAPS) (Bell et al., 2004 ; Huang et al., 2005; Schwartz, 2005 ) and from a series of meta - anal ys es (Bell et al., 2005, Ito et al., 2005, and Levy et al., 2005). This approach is not inconsistent with recommendations provided by the NRC in their report (NRC, 2008) on the estimation of ozone - related mortality risk reductions, “The committee recommends that the greatest emphasis be placed on estimates from new s ys tematic multicity anal ys es that use national databases of air pollution and mortality , such as in the NMMAPS, without excluding consideration of meta - anal ys es of previously published studies.” 10 

### For ozone - related prematur e mortality in 2030 , we estimate a range of between 170 to 770 fewer premature mortalities. Following these tables, we also provide a more comprehensive presentation of the distributions of incidence generated using the available information from empiric al studies and expert elicitation. Table 8 - 8 present s the distributions of the reduction in PM 2.5 

### - related premature mortality based on the C - R distributions provided by each expert, as well as that from the data - derived health impact functions, based on the statistical error associated with the ACS study (Pope et al., 2002) and the Six - C ities study (Laden et al., 2006). The 90 percent confidence interval for each separate estimate of PM - related mortality is also provided. In 203 0, the effect estimates of nine of the twelve experts included in the elicitation panel fall within the empirically- derived range provided by the ACS and Six - Cities studies. Only one expert falls below this range , while two of the experts are above this rang e. Although the overall range across experts is summarized in these tables, the full uncertainty in the estimates is reflected b y the results for the full set of 12 experts. The twelve experts’ judgments as to the likely mean effect estimate are not evenly distributed across the range illustrated by arraying the highest and lowest expert means.

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### 8 - 11 **Table 8**  **- 6**  **: Estimated PM**  **2.5** 

### - **Related Health Impacts a**  Health Effect 2030 Annual Reduction in Incidence (5 th

- 95 th pe rcentile) Premature Mortality

– Derived from epidemiology literature b Adult, age 30+, ACS Cohort Study (Pope et al., 2002) Adult, age 25+, Six - Cities Study (Laden et al., 2006) Infant, age <1 year (Woodruff et al., 1997) 800 (310

– 1,300) 2,100 ( 1 ,100

– 3,000) 3 (0

– 8) Chronic bronchitis (adult, age 26 and over) 560 (100

– 1,000) Non - fatal myocardial infarction (adult, age 18 and over) 980 (360

– 1,600) Hospital admissions - respiratory (all ages) c 160 (77

– 230) Hospital admissions - cardiova scular (adults, age >18) d 380 (270

– 440) Emergency room visits for asthma (age 18 years and younger) 600 (350

– 850) Ac ute bronchitis, (children, age 8 - 12) 1,300 (0

– 2,500) Lo wer respiratory symptoms (children, age 7 - 14) 16,000 (7,700

– 24,000) Upp er respiratory symptoms (asthmatic children, age 9 - 18) 12,000 (3,800

– 20,000) Asthma exacerbation (asthmatic children, age 6 - 18) 27,000 (3,000

– 74,000) Work loss days 100,000 (88,000

– 110,000) Minor restricted activity days (adults age 18 - 65) 600,000 (500,000

– 690,000) Notes: a Incidence is rounded to two significant digits. Estimates represent incidence within the 48 contiguous United States. b PM - related adult mortality based upon the American Cancer Society (ACS) Cohort Study (Pope et al., 2002) and the Six - Cities Study (Laden et al., 2006). Note that these are two alternative estimates of adult mortality and should not be summed. PM - related infant mortality based upon a study by Woodruff, Grillo, and Schoendorf, (1997). N c Respiratory hospital admissions for PM include admissions for chronic obstructive pulmonary disease (COPD), pneumonia and asthma. d Cardiovascular hospital admissions for PM include total cardiovascular and subcategories for ischemic heart disease, dysrhythmias, and heart fai lure.

N Woodruff, T.J., J. Grillo, and K.C. Schoendorf. 1997. “Th e Relationship Between Selected Causes of Postneonatal Infant Mortality and Particulate Air Pollution in the United States.” Environmental Health Perspectives 105(6):608 - 612.

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### 8 - 12 **Table 8**  **- 7**  **: Estimated Ozone -**  **Related Health Impacts a**  Health Effect 2030 Annual Reduction in Incidence (5 th

- 95 th percentile) Premature Mortality, All ages b Multi - City Analyses Bell et al. (2004)

– Non - accidental Huang et al. (2005)

– Cardiopulmonary Schwartz (2005)

– Non - accidental Meta - analyses: Bell et al. (2005)

– All cause Ito et al. (2005)

– Non - accidental Levy et al. (2005)

– All cause 170 (73

– 260) 250 (120

– 380) 260 (110

– 410) 540 (300

– 780) 750 ( 500

– 1,000) 770 (560

– 970) Hospital admissions - respiratory causes (adult, 65 and older)c 1,200 (160

– 2,200) Hospital admissions - respiratory causes (children, under 2) 550 (290

– 810) Emergency room visit for asthma (all ages) 580 (0

– 1,500) Minor restricted activity days (adults, age 18 - 65) 970,000 (490,000

– 1,500,000) School absence days 350,000 (150,000

– 490,000) Notes: a Incidence is rounded to two significant digits. Estimates represent incidence within the 48 contiguous U.S. b Estimates of ozone - related premature mortality are based upon incidence estimates derived from several alternative studies: Bell et al. (2004); Huang et al. (2005); Schwartz (2005) ; Bell et al. (2005); Ito et al. (2005); Levy et al. (20 05). The estimates of ozone - related premature mortality should therefore not be summed. c Respiratory hospital admissions for ozone include admissions for all respiratory causes and subcategories for COPD and pneumonia. 

### Table **8 -**  **8 : Results of Application of Expert Elicitation: Annual Reductions in Premature** 

**Mortality in 2030 Associated with the Proposed Program**  Source of Mortality Estimate 2030 Tier 3 Control 5th Percentile Mean 95th Percentile Pope et al . (2002) 310 800 1,300 Laden et al. (2006) 1,100 2,100 3,000 Expert A 400 2,200 4,000 Expert B 170 1,600 3,600 Expert C 300 1,700 3,600 Expert D 240 1,200 1,900 Expert E 1,400 2,700 4,100 Expert F 1,000 1,500 2,100 Expert G 0 960 1,800 Expert H 4 1,200 2,800 Expert I 260 1,600 2,900

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### 8 - 13 Source of Mortality Estimate 2030 Tier 3 Control 5th Percentile Mean 95th Percentile Expert J 390 1,300 2,900 Expert K 0 270 1,300 Expert L 65 1,000 2,300 

### 8.1.2.1.2 Monetized Estimates ofHuman Health and Environmental Impacts Table 8 - 9 p resents the estimated monetary value of changes in the incidence of ozone and PM 2.5 

### - related health and environmental effects. Total aggregate monetized benefits are presented in Table 8 - 10 . All monetized estimates are presented in 2010 $ . Where appropriate, estimates account for growth in real gross domestic product (GDP) per capita between 2000 and 2030. O 

### The monetized value of PM 2.5 

### - related mortality also accounts for a twenty- ye ar segmented cessation lag. P 

### To discount the value of premature mortality that occurs at different points in the future, we apply both a 3 and 7 percent discount rate. We also use both a 3 and 7 percent discount rate to value PM - related nonfatal heart attacks (myocardial infarctions). Q 

### In addition to omitted benefits categories such as air toxics and various welfare effects, not all known PM 2.5 

### - and ozone - related health and welfare effects could be quantified or monetized. The estimate of total monetized health benefits of the final program is thus equal to the subset of monetized PM 2.5 

### - and ozone - re lated health impacts we are able to quantify plus the sum of the nonmonetized health and welfare benefits. Our estimate of total monetized benefits in 203 0 for the proposed program , using the ACS and Six - Cities PM mortality studies and the range of ozone mortality assumptions, is between $ 9.5 and $ 27 billion , assuming a 3 percent discount rate, or between $ 8.7 and $ 25 billion , assuming a 7 percent discount rate. As the results indicate, total benefits are driven primaril y b y the reduction in PM 2.5 

### - and oz one - related

O Our analysis accounts for expected grow th in real income over time. Economic theory argues that WTP for most goods (such as environmental protection) will increase if real incomes increase. Benefits are therefore adjusted by multiplying the unadjusted benefits by the appropriate adjustment factor to account for income growth over time. For grow th between 2000 and 2030, this factor is 1.23 for long - term mortality, 1.27 for chronic health impacts, and 1.08 for minor health impacts. For a complete discussion of how these adjustment factors were derived, we refer the reader to the PM NAAQS regulatory impact analysis. 9 Note that similar adjustments do not exist for cost - of - illness based unit values. For these, we apply the same unit value regardless of the future year of analysis. P Based in part on prior SAB advice, EPA has typicall y assumed that there is a time lag between changes in pollution exposures and the total realization of changes in health effects. Within the context of benefits analyses, this term is often referred to as “cessation lag”. The existence of such a lag is i mp ortant for the valuation of premature mortality incidence because economic theory suggests that benefits occurring in the future should be discounted. In this analysis, we apply a twenty - year distributed lag to PM mortality reductions. This method is c onsistent with the most recent recommendation by the EPA’s Science Advisory Board. Refer to: EPA

– Science Advisory Board, 2004. Advisory Council on Clean Air Compliance Analysis Response to Agency Request on Cessation Lag. Letter from the Health Effects Subcommittee to the U.S. Environmental Protection Agency Administrator, December. Q Nonfatal myocardial infarctions (MI) are valued using age - specific cost - of - illness values that reflect lost earnings and direct medical costs over a 5 - year period following a nonfatal MI.

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### 8 - 14 premature fatalities each year and represent the benefits of the Tier 3 program anticipated to occur annually

when the program is fully implemented and most of the fleet turned over. The next largest benefit is for reductions in chronic illness (chronic bronchitis and nonfatal heart attacks), although this value is more than an order of magnitude lower than for premature mortality . Hospital admissions for respiratory and cardiovascular causes, minor restricted activity

days, and work loss days account for the majority of the remaining benefits. The remaining categories each account for a small percentage of total benefit; however, they represent a large number of avoided incidences affecting many individuals. A comparison of the incidence tabl e to the monetary benefits table reveals that there is not always a close correspondence between the number of incidences avoided for a given endpoint and the monetary value associated with that endpoint. For example, there are many more work loss days th an PM - related premature m ortalities, ye t work loss day s account for onl y a very small fraction of total monetized benefits. This reflects the fact that many of the less severe health effects, while more common, are valued at a lower level than the more severe health effects. Also, some effects, such as hospital admissions, are valued using a proxy measure of willingness - to -pay ( e.g. , cost - of

- illness). As such, the true value of these effects may be higher than that reported here. **Table 8**  **- 9**  **: Estimated Monetary Value of Changes in Incidence of Health and Welfare Effects (millions of**  **2010 $)**  **a,b** 2030 PM 2.5 - Related Health Effect (5 th and 95 th Percentile) Premature Mortality

– Derived from Epidemiology Studies c,d Adult, age 30+ - ACS study (Pope et al., 2002) 3% discount rate 7% discount rate $7,200 ($920

– $18,000) $6,500 ($830 - $17,000) Adult, age 25+ - Six - C ities study (Laden et al., 2006) 3% discount rate 7% discount rate $18,000 ($2,600 - $45,000) $17,000 ($2,300 - $41,000) Infant Mortality, <1 year

– (Woodruff et al. 1997) $27 ($0 - $100) Chronic bronchitis (adults, 26 and over) $310 ($25 - $1,000) Non - fatal acute myocardial infarctions 3% discount rate 7% discount rate $110 ($24 - $260) $90 ($19 - $210) Hospital admissions for respiratory causes $2.5 ($1.2

– $3.6) Hospital admissions for cardiovascular causes $5.5 ($1.2 - $10) Emergency room visits for asthma $0.24 ($0.13 - $0.36)

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### 8 - 15 Ac ute bronchitis (children, age 8

– 12) $0.61 ($0.00 - $1.5) Lo wer respiratory symptoms (children, 7

– 14) $0.34 ($0.13 - $0.63) Upper respiratory symptoms (asthma, 9

– 11) $0.40 ($0.12 - $0.89) Asthma exacerbations $1.6 ($0.17 - $4.4) Work loss day s $16 ($14 - $19) Minor restricted - activity days (MRADs) $41 ($24 - $59) Ozone - related Health Effect Premature Mortality, All ages

– Derived from Multi - city analyses Bell et al., 2004 $1,700 ($220 - $4,200) Huang et al., 2005 $2,500 ($340 - $6,200) Schwartz, 2005 $2,600 ($330 - $6,500) Premature Mortality, All ages

– Derived from Meta - analyses Bell et al., 2005 $5,400 ($760 - $13,000) Ito et al., 2005 $7,400 ($1,100 - $18,000) Levy et al., 2005 $7,600 ($1,100 - $18,000) Hospital admissions - respiratory causes (adult, 65 and older) $32 ($4.2 - $56) Hospital admissions - respiratory causes (children, under 2) $6.0 ($3.1 - $8.9) Emergency room visit for asthma (all ages) $0.23 ($0.0 - $0.57) Minor restricted activity days (adults, age 18 - 65) $67 ($31 - $110) School absence days $34 ($15 - $48) Notes: a Monetary benefits are rounded to two significant digits for ease of presentation and computation. PM and ozone benefits are nationwide. b Monetary benefits adjusted to account for growth in real GDP per capita between 1990 and the analysis year (2030) . c Valuation assumes discounting over the SAB recommended 20 year segmented lag structure. Results reflect the use of 3 percent and 7 percent discount rates consistent with EPA and OMB guidelines for preparing economic analyses.

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### 8 - 16 **Table 8**  **- 10**  **: Total Monetized Ozone and PM**  **- related Benefits Associated with the**  **Proposed Program**  **in 2030 Total Ozone and PM Benefits (billions,**  **2010 $)**  **– PM Mortality Derived f**  **ro m**  **the ACS and Six -**  **Cities Studies 3% Discount Rate**  **7% Discount Rate** Ozone Mortality Function Reference Mean Total Benefits Ozone

Mortality Function Reference Mean Total Benefits Multi - city Bell et al.,

2004 $9.5 - $21 Multi - city Bell et al., 2004 $8.7 - $19 Huang et al., 2005 $10 - $21 Huang et al., 2005 $9.5 - $20 Schwartz, 2005 $10 - $22 Schwartz, 2005 $9.6 - $20 Meta - analysis Bell et al.,

2005 $13 - $24 Meta - analysis Bell et al., 2005 $12 - $23 Ito et al., 2005 $15 - $26 Ito et al., 2005 $15 - $25 Levy et al., 2005 $15 - $27 Levy et al., 2005 $15 - $25 **Total Ozone and PM Benefits (billions, 2010**  **$) –**  **PM Mortality Derived from Expert Elicitation (Lowest and Highest Estimate)**  **3% Discount Rate 7% Discount Rate**  Ozone Mortality Function Reference Mean Total Benefits Ozone

Mortality Function Reference Mean Total Benefits Multi - city Bell et al.,

2004 $4.6 - $27 Multi - city Bell et al., 2004 $4.4 - $24 Huang et al., 2005 $5.4 - $27 Huang et al., 2005 $5.2 - $25 Schwartz, 2005 $5.5 - $28 Schwartz, 2005 $5.3 - $25 Meta - analysis Bell et al.,

2005 $8.3 - $30 Meta - analysis Bell et al., 2005 $8.1 - $28 Ito et al., 2005 $10 - $32 Ito et al., 2005 $10 - $30 Levy et al., 2005 $11 - $33 Levy et al., 2005 $10 - $30 

### 8.1.2.1.3 Methodology 8.1.2.1.3.1 Human Health Impact Functions 

### Health impact functions measure the change in a health endpoint of interest, such as hospital admissions, for a given change in ambient ozone or PM concentration. Health impact functions are derived from primary epidemiology

studies, m eta - anal ys es of multiple epidemiology

studies, or expert elicitations. A standard health impact function has four components: ( 1) an effect estimate from a particular study ; ( 2) a baseline incidence rate for the health effect (obtained from either the epi demiology study or a source of public health statistics such as the Centers for Disease Control); ( 3) the size of the potentially affected population; and (4) the estimated change in the relevant ozone or PM summary measures.

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### 8 - 17 A ty pical health impact functi on might look like:



#   1 0 

###     

 x 

### e y y  

### , where y0 

### is the baseline incidence (the product of the baseline incidence rate times the potentially

affected population), β is the effect estimate, and Δx is the estimated change in the summary pollutant m easure. There are other functional forms, but the basic elements remain the same. The following subsections describe the sources for each of the first three elements: size of the potentially

affected populations; PM 2.5 

### and ozone effect estimates; and ba seline incidence rates. We also describe the treatment of potential thresholds in PM - related health impact functions . Section 8. 2 describes the ozone and PM air quality inputs to the health impact functions. 8.1.2.1.3.2 Potentially Affected Populations 

### The starting point for estimating the size of potentially affected populations is the 2000 U.S. Census block level dataset. 11 

### Benefits Modeling and Anal ys is Program (BenMAP) incorporates 250 age/gender/race categories to match specific populations potentially affected by ozone and other air pollutants. The software constructs specific populations matching the populations in each epidemiological study by accessing the appropriate age - specific populations from the overall population database. BenMAP projects population s to 203 0 using growth factors based on economic projections. 12



### 8.1.2.1.3.3 Effect Estimate Sources 

### The most significant quantifiable benefits of reducing ambient concentrations of ozone and PM are attributable to reductions in human health risks. EPA’s Ozone and PM C riteria Documents 13 , 14 

### and the World Health Organization’s 2003 and 2004 15 , 16 

### reports outline numerous human health effects known or suspected to be linked to exposure to ambient ozone and PM. EPA evaluated the ozone and PM literature for use in the benefits anal ys is for the final 2008 Ozone NAAQS and final 2006 PM NAAQS anal ys es. We use the same literature in this anal ys is; for more information on the studies that underlie the health impacts quantified in this RIA, please refer to those documents. It is impo rtant to note that we are unable to separately quantify all of the possible PM and ozone health effects that have been reported in the literature for three reasons: (1) the possibility of double counting (such as hospital admissions for specific respiratory diseases versus hospital admissions for all or a sub - set of respiratory diseases); (2) uncertainties in applying effect relationships that are based on clinical studies to the potentially affected population; or (3) the lack of an established concentrati on - response (CR) relationship. Table 8 - 11 lists the health endpoints included in this analysis.

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### 8 - 18 **Table 8**  **- 11**  **: Health Impact Functions Used in BenMAP to Estimate Impact**  **s of PM 2.5**  

### and **Ozone Reductions ENDPOINT**  **POLLUTANT STUDY**  **STUDY POPULATION Prem**  **ature Mortality** Premature mortality

– daily time series O 3 Multi - city Bell et al (2004) (NMMAPS study) 17

– Non accidental Huang et al (2005) 18

- Cardiopulmonary Schwartz (2005) 19

– Non - accidental Meta - analyses: Bell et al (2005) 20

– All cause Ito et al (2005) 21

– Non - accidental Levy et al (2005) 22

– All cause All ages

Premature mortality

—cohort study, all cause PM 2.5 Pope et al. (2002) 23 Laden et al. (2006) 24 >29 years >25 years Pr emature mortality, total exposures PM 2.5 Expert Elicitation (IEc, 2006) 25 >24 years Premature mortality

— all - cause PM 2.5 Woodruff et al. (1997) 26 Infant (<1 year) **Chronic Illness** Chronic bronchitis PM 2.5 Abbey et al. (1995) 27 >26 years Nonfatal heart attacks PM 2.5 Peters et al. (2001) 28 Adults (>18 years) **Hospital Admissions** Respiratory O 3 Pooled estimate: Schwartz (1995) - ICD 460 - 519 (all resp) 29 Schwartz (1994a; 1994b) - ICD 480 - 486 (pneumonia) 30 , 31 Moolgavkar et al. (1997) - ICD 480 - 487 (pneumonia) 32 Schwartz (1994b) - ICD 491 - 492, 494 - 496 (COPD) Moolgavkar et al. (1997)

– ICD 490 - 496 (COPD) >64 years

Burnett et al. (2001) 33 <2 years PM 2.5 Pooled estimate:

Moolgavkar (2003)

—ICD 490 - 496 (COPD) 34 Ito (2003)

—ICD 490 - 496 (COPD) 35 >64 years PM 2.5 Mo olgavkar (2000)

—ICD 490 - 496 (COPD) 36 20

– 64 years PM 2.5 Ito (2003)

—ICD 480 - 486 (pneumonia) >64 years PM 2.5 Sheppard (2003)

—ICD 493 (asthma) 37 <65 years Cardiovascular PM 2.5 Pooled estimate: Moolgavkar (2003)

—ICD 390 - 429 (all cardiovascular) Ito (2003)

—ICD 410 - 414, 427 - 428 (ischemic heart disease, dysrhythmia, heart failure) >64 years

PM 2.5 Moolgavkar (2000)

—ICD 390 - 429 (all

cardiovascular) 20

– 64 years

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### 8 - 19 **ENDPOINT POLLUTANT**  **STUDY STUDY POPULATION**  Asthma - related ER visits O 3 Pooled estimate: Peel et al (2005) 38 Wilson et al (2005) 39 All ages All ages Asthma - related ER visits (cont ’d) PM 2.5 Norris et al. (1999) 40 0

– 18 years **Other Health Endpoints** Ac ute bronchitis PM 2.5 Dockery et al. (1996) 41 8

– 12 years Upper respiratory symptoms PM 2.5 Pope et al. (1991) 42 Asthmatics, 9

– 11 years Lo wer respiratory symptoms PM 2.5 Schwartz and Neas (2000) 43 7

– 14 years Asthma exacerbations PM 2.5 Pooled estimate: Ostro et al. (2001) 44 (cough, wheeze and shortness of breath) Vedal et al. (1998) 45 (cough) 6

– 18 years a Work loss days PM 2.5 Ostro (1987) 46 18

– 65 years School absence days O 3 Pooled estimate: Gilliland et al. (2001) 47 Chen et al. (2000) 48 5

– 17 years b Minor Restricted Activity Days (MRADs) O 3 Ostro and Rothschild (1989) 49 18

– 65 years

PM 2.5 Ostro and Rothschild (1989) 18

– 65 years Notes: a The original study populations were 8 to 13 for the Ostro et al. (2001) study and 6 to 13 for the Vedal et al. (1998) study. Based on advice from the Science Advisory Board Health Effects Subcommittee (SAB - HES), we extended the applied population to 6 to 18, reflecting the common biological basis for the effect in children in the broader age group. See: U.S. Science Advisory Board. 2004. Advisory Plans for Health Effects Analysis in the Analytical Plan for EPA’s Second Prospective Analysis

–Benefits and Costs of the C lean Air Act, 1990

—2020. EPA - SAB COUNCIL - ADV - 04 - 004. See also National Research Council (NRC). 2002. Estimating the Public Health Benefits of Proposed Air Pollution Regulations . Washington, DC: The National Academies Press. b Gilliland et al. (2001) st udied children aged 9 and 10. Chen et al. (2000) studied children 6 to 11. Based on advice from the National Research Council and the EPA SAB - HES, we have calculated reductions in school absences for all school - aged children based on the biological simil arity between children aged 5 to 17. 

### In selecting epidemiological studies as sources of effect estimates, we applied several criteria to develop a set of studies that is likely to provide the best estimates of impacts in the U.S. To account for the potential impacts of different health care s ys tems or underl ying health status of populations, we give preference to U.S. studies over non - U.S. studies. In addition, due to the potential for confounding b y co -pollutants, we give preference to effect estimates f rom models including both ozone and PM over effect estimates from single -pollutant models. 50 , 51



### 8.1.2.1.3.4 Baseline Incidence Rates 

### Epidemiological studies of the association between pollution levels and adverse health effects generall y provide a direct estimate of th e relationship of air quality changes to the relative risk of a health effect, rather than estimating the absolute number of avoided cases. For example, a ty pical result might be that a 100 ppb decrease in daily ozone levels might, in turn, decrease hospi tal admissions by

3 percent. The baseline incidence of the health effect is necessary to

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### 8 - 20 convert this relative change into a number of cases. A baseline incidence rate is the estimate of the number of cases of the health effect per ye ar in the assessment location, as it corresponds to baseline pollutant levels in that location. To derive the total baseline incidence per ye ar, this rate must be multiplied by

the corresponding population number. For example, if the baseline incidence rate is the number of cases per year per 100,000 people, that number must be multiplied by

the number of 100,000s in the population. Table 8 - 12 summarizes the sources of baseline incidence rates and provides average incidence rates for the endpoints included in the analy sis. Table 8 - 13 presents the asthma prevalence rates used in this analy sis. For both baseline incidence and prevalence data, we used age - specific rates where available. We applied concentration - r esponse functions to individual age groups and then summed over the relevant age range to provide an estimate of total population benefits. In most cases, we used a single national incidence rate, due to a lack of more spatially

disaggregated data. Whenever possible, the national rates used are national averages, because these data are most applicable to a national assessment of benefits. For some studies, however, the only available incidence information comes from the studies themselves; in these cases , incidence in the study population is assumed to represent ty pical incidence at the national level. Regional incidence rates are available for hospital admissions, and county- level data are available for premature mortality . We have projected mortality rates such that future mortality rates are consistent with our projections of population growth. 52 

### Table **8 -**  **12 : Baseline Incidence Rates and Population Prevalence Rates for Use in Impact**  **Functions, General Popu lation**  Endpoint Parameter Rates Value Source Mortality Daily or annual mortality rate projected to 2020 Age - , cause - , and county - specific rate CDC Wonder (2006

–2008) 53 U.S. Census bureau Hospitalizations Daily hospitalization rate Age - , region - , state - , county - and cause specific rate 2007 HCUP data files a, 54 Asthma ER Visits Daily asthma ER visit rate Age - , region - , state - , county - and cause specific rate 2007 HCUP data files a Chronic Bronchitis Annual prevalence rate per person Aged 18

–44 Aged 45

– 6 4 Aged 65 and older 0.0367 0.0505 0.0587 1999 NHIS (American Lung

Association, 2002, Table 4) 55 Annual incidence rate per person 0.00378 Abbey et al. (1993, Table 3) Nonfatal Myocardial Infarction (heart attacks) Daily nonfatal myocardial infarction i ncidence rate per person, 18+ Age - , region - , state - , and county - specific rate 2007 HCUP data files a ; adjusted by

0.93 for probability of surviving after 28 days (Rosamond et al., 1999) Asthma Exacerbations Incidence among asthmatic African - American children daily wheeze daily cough daily dyspnea 0.076 0.067 0.037 Ostro et al. (2001)

Ac ute Bronchitis Annual bronchitis incidence rate, children 0.043 American Lung Association (2002, Table 11) 56

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### 8 - 21 Endpoint Parameter Rates Value Source Lo wer Respiratory Symptoms Daily lower respiratory symptom inc idence among children

b 0.0012 Schwartz et al. (1994, Table 2) Upper Respiratory Symptoms Daily upper respiratory symptom incidence among asthmatic children 0.3419 Pope et al. (1991, Table 2) Work Loss Days Daily WLD incidence rate per person (18

– 65) Aged 18

–24 Aged 25

–44 Aged 45

– 64 0.00540 0.00678 0.00492 1996 HIS (Adams, Hendershot, and

Marano, 1999, Table 41); 57 U.S. Bureau of the Census (2000) 58 School Loss Days Rate per person per year, assuming 180 school days per year 9.9 National Center for Education Statistics (1996) 59 and 1996 HIS (Adams et al., 1999, Table 47); Minor Restricted Activity Days Daily MRAD incidence rate per person 0.02137 Ostro and Rothschild (1989, p. 243) Notes: a Healthcare Cost and Utilization Program (HCUP) database contains individual level, state and regional - level hospital and emergency department discharges for a variety of ICD codes. b Lo wer respiratory symptoms are defined as two or more of the following: cough, chest pain, phlegm, and wheeze.



### Table **8 -**  **13 : Asthma**  **Prevalence Rates Used for this Analysis**  Population Group Asthma Prevalence Rates Value Source All Ages 0.0780 American Lung Association (2010, Table 7) < 18 0.0941 5

– 17 0.1070 18

–44 0.0719 45

– 64 0.0745 65+ 0.0716 African American, 5 to 17 0.1776 American Lung Association (2010, Table 9) African American, <18 0.1553 American Lung Association b Notes: a See ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/NHIS/2000/ . b Calculated by ALA for U.S. E PA, based on NHIS data (CDC, 2008) . 60



### 8.1.2.1.3.5 PM 2.5 

### -Related Premature Mortality “Lowest Measured Level” (LML) Assessment 

### Based on our review of the current body of scientific literature, EPA estimated PM related mortality without applying an assumed concentration threshold. EPA’s Integrated Science Assessment for Particulate Matter (U.S. EPA, 2009), 61 

### which was reviewed b y EPA’s Clean Air Scientific Advisory Committee (U.S. EPA - SAB, 2009; U.S. EPA - SAB, 2009), 62 , 63 

### concluded that the scientific literature consistently f inds that a no - threshold log - linear model most adequatel y portrays the PM - mortality concentration - response relationship while recognizing potential uncertainty about the exact shape of the concentration - response function. Consistent with this finding, we h ave conformed the threshold sensitivity analysis to the current state of the PM science and improved upon our previous approach for estimating the sensitivity

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### 8 - 22 of the benefits estimates to the presence of an assumed threshold by incorporating a new “Lowest Measured Level” (LML) assessment. This approach summarizes the distribution of avoided PM mortality impacts according to the baseline ( i.e. pre - Tier 3 Program) PM 2.5 

### levels experienced b y the population receiving the PM 2.5 

### mortality

benefit (see Figure 8 - 1 ) . We identify on this figure the lowest air quality levels measured in each of the two primary epidemiological studies EPA uses to quantify PM - related mortality . This information allows readers to determine the portion of PM - related mortality benefits occurring above or below the LML of each study ; in general, our confidence in the estimated PM mortality

decreases as we consider air quality levels further below the LML in the two epidemiological studies. While the LMLanalysis pro vides some insight into the level of uncertainty in the estimated PM mortality benefits, EPA does not view the LML as a threshold and continues to quantify PM - related mortality impacts using a full range of modeled air quality concentrations. The large proportion of the avoided PM - related impacts we estimate in this analysis occur among populations exposed at or above the LML of each study (Figure 8 - 1 ) , increasing our confidence in the PM mortality anal ys is. Approximately 25 percent of the avoided impacts occur at or above an annual mean PM 2.5 

### level of 10 μg/m 3 

### (the LMLof the Laden et al. 2006 study); about 81 percent occur at or above an annual mean PM 2.5 

### level of 7.5 μg/m 3 

### (the LMLof the Pope et al. 2002 study). As we model mortality impacts among populations exposed to levels of PM 2.5 

### that are successively lower than the LML of each study our confidence in the results diminishes. However, the anal ys is above confirms that the great majority of the impacts occur at or above the Pope et al. LM L. As an example, when considering mortality impacts among populations living in areas with an annual mean PM level of 8 μ g/m 3 

### , we would place greater confidence in estimates drawn from the Pope et al. 2002 study , as this air quality level is a bove the LML of this study . Conversel y, we would place equal confidence when estimating mortality impacts among populations living in locations where the annual mean PM levels are above 10 μ g/m 3 

### because this value is at or above the LMLof each study . Whi le the LMLof each study is important to consider when characterizing and interpreting the overall level PM - related benefits, EPA believes that both cohort - based mortality estimates are suitable for use in air pollution health impact anal ys es. When estimating PM mortality impacts using risk coefficients drawn from the L aden et al. anal ysis of the Harvard Six Cities and the Pope et al. anal ys is of the American Cancer Society cohorts there are innumerable other attributes that may

affect the size of the reported risk estimates

—including differences in population demographics, the size of the cohort, activity patterns and particle composition among others. The LML assessment presented here provides a limited representation of one key difference between the two studies.

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### 8 - 23 **Figure 8**  **- 1**  **Percentage of Total PM -**  **related Mortalities Avoided by Baseline Air Quality Level**  8.1.2.1.3.6 Economic Values for Health Outcomes 

### Reductions in ambient concentrations of air pollution generall y lower the risk of future adverse health effects for a large population. Therefore, the appropriate economic measure is willingness - to -pay

(WTP) for changes in risk of a health effect rather than WTP for a health effect that would occur with certainty (Freeman, 1993). 64 

### Epidemiological studies generally provide estimates of the relative risks of a particular health effect that is avoided because of a reduction in air pollution. We converted those to units of avoided statistical incidence for ease of presentation . We calculated the value of avoided statistical incidences b y dividing individual WTP for a risk reduction by the related observed change in risk. For example, suppose a pollution - reduction regulation is able to reduce the risk of premature mortality fro m 2 in 10,000 to 1 in 10,000 (a reduction of 1 in 10,000). If individual WTP for this risk reduction is $100, then the WTP for an avoided statistical premature death is $1 million ($100/0.0001 change in risk). WTP estimates generally are not available for some health effects, such as hospital admissions. In these cases, we used the cost of treating or mitigating the effect as a primary estimate. These cost - of

- illness (COI) estimates generall y understate the true value of reducing the risk of a health effe ct, because they reflect the direct expenditures related to treatment, but not the value of avoided pain and suffering (Harrington and Portney , 1987; Berger, 1987). 65 , 66

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### 8 - 24 We provide unit values for health endpoints (along with information on the distribution of the unit value) in Table 8 - 14 . All values are in constant year 2010 dollars, adjusted for growth in real income out to 2030 using projections provided by Standard and Poor’s. Economic theory argues that WTP f

or most goods (such as environmental protection) will increase if real income increases. Many of the valuation studies used in this analysis were conducted in the late 1980s and earl y 1990s. Because real income has grown since the studies were conducted, people’s willingness to pay

for reductions in the risk of premature death and disease likely has grown as well. We did not adjust cost of illness - based values because they are based on current costs. Similarly, we did not adjust the value of school absences, because that value is based on current wage rates. For details on valuation estimates for PM - related endpoints, see the 2006 PM NAAQS RIA. 67 

### For details on valuation estimates for ozone - related endpoints, see the 2008 Ozone NAAQS RIA. 68 

### Table **8 -**  **14 : Unit Values for Economic Valuation of Health Endpoints (** **2010 $)**  **Health Endpoint Central Estimate of**  **Value Per Statistical Incidence**  **2000 Income**  **Level 2030** 

**Income Level**  **Derivation of Distributions of Estimates** Premature Mortality (Value of a Statistical Life) $ 8 , 0 00,000 $9, 9 00,000 EPA currently recommends a central VSL of $6.3m (2000$) based on a Weibull distribution fitted to 26 published VSL estimates (5 contingent valuation and 21 labor market studies). Th e underlying studies, the distribution parameters, and other useful information are available in Appendix B of EPA's current Guidelines for Preparing Economic Analyses (U.S. EPA, 2000). Chronic Bronchitis (CB) $4 5 0,000 $5 5 0,000 The WTP to avoid a case of pollution - related CB is calculated as where x is the severity of an average CB case, WTP 13 is the WTP for a severe case of CB, and $ is the parameter relating WTP to severity, based on the regression results reported in Krupnick and Cropper (1992). The distribution of WTP for an average severity - level case of CB was generated by Monte Carlo methods, drawing from each of three distributions: (1) WTP to avoid a severe case of CB is assigned a 1/9 probability of being each of the first nine deciles of the d istribution of WTP responses in Viscusi et al. (1991); (2) the severity of a pollution related case of CB (relative to the case described in the Viscusi study) is assumed to have a triangular distribution, with the most likely value at severity level 6.5 and endpoints at 1.0 and 12.0; and (3) the constant in the elasticity of WTP with respect to severity is normally distributed with mean = 0.18 and standard deviation = 0.0669 (from Krupnick and Cropper [1992]). This process and the rationale for choosing it is described in detail in the Costs and Benefits of the Clean Air Act, 1990 to 2010 (U.S. EPA, 1999).

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### 8 - 25 Nonfatal Myocardial Infarction (heart attack) 3% discount rate Age 0

–24 Age 25

–44 Age 45

– 54 Age 55

– 65 Age 66 and over 7% discount rate Age 0

–24 Age 25

–44 Age 45

– 54 Age 55

– 65 Age 66 and over $8 9 , 373

$ 100,690 $1 0 6 , 053 $1 85,785 $8 9,373 $8 8,547 $9 8,680 $ 103,481 $1 74,866 $8 8,548 $8 9 , 373

$ 100,690 $10 6,053 $1 85,785 $8 9,373 $8 8,547 $9 8,680 $ 103,481 $1 74,866 $8 8,548 No distributional in formation available. Age - specific cost - of - illness values reflect lost earnings and direct medical costs over a 5 - year period following a nonfatal MI. Lost earnings estimates are based on Cropper and Krupnick (1990). Direct medical costs are based on simple average of estimates from Russell et al. (1998) and Wittels et al. (1990). Lost earnings: Cropper and Krupnick (1990). Present discounted value of 5 years of lost earnings: age of onset: at 3% at 7% 25

–44 $8,774 $7,855 45

– 54 $12,932 $11,578 55

– 65 $74,746 $66,920 Direct medical expenses: An average of: 1. Wittels et al. (1990) ($102,658

— no discounting) 2. Russell et al. (1998), 5 - year period ($22,331 at 3% discount rate; $21,113 at 7% discount rate) **Hosp ital Admissions**  Chronic Obstructive Pulmonary Disease (COPD) $17, 996 $17, 996 No distributional information available. The COI estimates (lost earnings plus direct medical costs) are based on ICD - 9 code - level information ( e.g. , average hospital care cos ts, average length of hospital stay, and weighted share of total COPD category illnesses) reported in Agency for Healthcare Research and Quality (2000) (www.ahrq.gov). Asthma Admissions $11, 957 $11, 957 No distributional information available. The COI estimates (lost earnings plus direct medical costs) are based on ICD - 9 code - level information ( e.g. , average hospital care costs, average length of hospital stay, and weighted share of total asthma category illnesses) reported in Agency for Healthcare Researc h and Quality (2000) (www.ahrq.gov). All Cardiovascular $ 30,256 $ 30,256 No distributional information available. The COI estimates (lost earnings plus direct medical costs) are based on ICD - 9 code - level information ( e.g. , average hospital care costs, average length of hospital stay, and weighted share of total cardiovascular category illnesses) reported in Agency for Healthcare Research and Quality (2000) (www.ahrq.gov). All respiratory (ages 65+) $2 5,413 $2 5,413 No distributions available. The COI point estimates (lost earnings plus direct medical costs) are based on ICD - 9 code level information ( e.g. , average hospital care costs, average length of hospital stay, and weighted share of total COPD category illnesses) reported in Agency for Healthcare Rese arch and Quality, 2000 (www.ahrq.gov). All respiratory (ages 0

–2) $10, 943 $10, 943 No distributions available. The COI point estimates (lost earnings plus direct medical costs) are based on ICD - 9 code level information ( e.g. , average hospital care costs, average length of hospital stay, and weighted share of total COPD category illnesses) reported in Agency for Healthcare Research and Quality, 2000 (www.ahrq.gov). Emergency Room Visits for Asthma $ 40 5 $ 40 5 No distributional information available. Simple av erage of two unit COI values: (1) $311.55, from Smith et al. (1997) and (2) $260.67, from Stanford et al. (1999).

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### 8 - 26 **Respiratory Ailments Not Requiring Hospitalization** Upper Respiratory Symptoms (URS) $3 2 $3 4 Com binations of the three symptoms for which WTP estimates are available that closely match those listed by Pope et al. result in seven different “symptom clusters,” each describing a “type” of URS. A dollar value was derived for each type of URS, using mid - range estimates of WTP (IEc, 1994) to avoid ea ch symptom in the cluster and assuming additivity of WTPs. In the absence of information surrounding the frequency with which each of the seven types of URS occurs within the URS symptom complex, we assumed a uniform distribution between $9.2 and $43.1. L ower Respiratory Symptoms (LRS) $ 20 $2 1 Com binations of the four symptoms for which WTP estimates are available that closely match those listed by Schwartz et al. result in 11 different “symptom clusters,” each describing a “type” of LRS. A dollar value wa s derived for each type of LRS, using mid - range estimates of WTP (IEc, 1994) to avoid each symptom in the cluster and assuming additivity of WTPs. The dollar value for LRS is the average of the dollar values for the 11 different types of LRS. In the absenc e of information surrounding the frequency with which each of the 11 types of LRS occurs within the LRS symptom complex, we assumed a uniform distribution between $6.9 and $24.46. Asthma Exacerbations $5 5 $5 7 Asthma exacerbations are valued at $45 per inc idence, based on the mean of average WTP estimates for the four severity definitions of a “bad asthma day,” described in Rowe and Chestnut (1986). This study surveyed asthmatics to estimate WTP for avoidance of a “bad asthma day,” as defined by the subject s. For purposes of valuation, an asthma exacerbation is assumed to be equivalent to a day in which asthma is mo derate or worse as reported in the Rowe and Chestnut (1986) study. The value is assumed have a uniform distribution between $15.6 and $70.8. Ac ute Bronchitis $4 52 $4 94 Assumes a 6 - day episode, with the distribution of the daily value specified as uniform with the low and high values based on those recommended for related respiratory symptoms in Neumann et al. (1994). The low

daily estimate of $10 is the sum of the mid - range values recommended by IEc 1994 for two sym ptoms believed to be associated with acute bronchitis: coughing and chest tightness. The high daily estimate was taken to be twice the value of a minor respiratory restricted - activity day, or $110. Work Loss Days (WLDs) Variable (U.S. median = $13 7 ) Variable

(U.S. median = $13 7 ) No distribution available. Point estimate is based on county - specific

median annual wages divided by 50 (assuming 2 weeks of vacation) and then by 5

—to get medi an daily wage. U.S. Year 2000 Census, compiled by Geolytics, Inc. Minor Restricted Activity Days (MRADs) $6 4 $6 9 Median WTP estimate to avoid one MRAD from Tolley et al. (1986). Distribution is assumed to be triangular with a minimum of $22 and a maximum of $83, with a most likely value of $52. Range is based on assumption that value should exceed WTP for a single mild symptom (the highest estimate for a single symptom

— for eye irritation

—is $16.00) and be less than that for a WLD. The triangular distributi on acknowledges that the actual value is likely to be closer to the point estimate than either extreme. School Absence Days $9 5 $9 5 No distribution available

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### 8 - 27 8.1.2.1.3.7 Manipulating Air Quality Modeling Data for Health Impacts Analysis 

### In Section 7.2 , we summarize d the methods for and results of estimating air quality for the program . These air quality results are in turn associated with human populations to estimate changes in health effects. For the purposes of this anal ys is, we focus on the health effects that have been linked to ambient changes in ozone and PM 2.5 

### related to emission reductions estimated to occur due to the implementation of the program . We estimate ambient PM 2.5 

### and ozone concentrations using the Community Multiscale Air Quality

model (CMAQ). This section describes how we converted the CMAQ modeling output into full - season profiles suitable for the health impacts anal ys is. General Methodology First, we extracted hourly , surface - layer PM and ozone concentrations for each grid cell from the st andard CMAQ output files. For ozone, these model predictions are used in conjunction with the observed concentrations obtained from the Aerometric Information Retrieval System (AIRS) to generate ozone concentrations for the entire ozone season. R , S 

### The predicted changes in ozone concentrations from the future - ye ar base case to future - ye ar control scenario serve as inputs to the health and welfare impact functions of the benefits anal ys is ( i.e. , BenMAP). To estimate ozone - related health effects for the co ntiguous United States, full - season ozone data are required for every BenMAP grid - cell. Given available ozone monitoring data, we generated full - season ozone profiles for each location in two steps: (1) we combined monitored observations and modeled ozon e predictions to interpolate hourl y ozone concentrations to a grid of 12 - km by

12 - km population grid cells for the contiguous 48 states, and (2) we converted these full - season hourl y ozone profiles to an ozone measure of interest, such as the daily 8 - hour maximum. T , U 

### For PM 2.5 

### , we also use the model predictions in conjunction with observed monitor data. CMAQ generates predictions of hourly PM species concentrations for every grid. The species include a primary

coarse fraction (corresponding to PM in the 2.5 to 10 micron size range), a primary fine fraction (corresponding to PM less than 2.5 microns in diameter), and several secondary particles ( e.g. , sulfates, nitrates, and organics). PM 2.5 

### is calculated as the sum of the primary fine fraction and all of the secondarily formed particles. Future - ye ar estimates of PM 2.5 

### were calculated using relative reduction factors (RRFs) applied to 200 5 ambient PM 2.5 

### and PM 2.5 

### species concentrations. A gridded field of PM 2.5 

### concentrations was created by interpolating Federal Reference Monitor ambient data and IMPROVE ambient data. Gridded fields of PM 2.5

R The ozone season for this analysis is defined as the 5 - month period from May to September. S Based on AIRS, there were 961 ozone monitors with sufficient data ( i.e. , 50 percent or more days reporting at least nine hourly observations p er day [8 am to 8 pm] during the ozone season). T The 12 - km

grid squares contain the population data used in the health benefits analysis model, BenMAP. U This approach is a generalization of planar interpolation that is technically referred to as enhance d Voronoi Neighbor Averaging (EVNA) spatial interpolation. See the BenMAP manual for technical details, available for dow nload at http://www.epa.gov/air/benmap.

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### 8 - 28 species concentrations were created b y interpolating EPA speciation network (ESPN) ambient data and IMPROVE data. The ambient data were interpolated to the CMAQ 12 km grid. The procedures for determining the RRFs are similar to those in EPA’s draft guidance for modeling the PM 2.5 

### standard (EPA, 2001 ). 69 

### The guidance recommends that model predictions be used in a relative sense to estimate changes expected to occur in each major PM 2.5 

### species. The procedure for calculating future - ye ar PM 2.5 

### design values is called the “Speciated Modeled Attainment Test (SMAT).” EPA used this procedure to estimate the ambient impacts of the final program . Table 8 - 15 provides those ozone and PM 2.5 

### metrics for grid cells in the modeled domain that enter the health impact functions for health benefits endpoints. The population - weighted average reflects the baseline levels and predicted change s for more populated areas of the nation. This measure better reflects the potential benefits through exposure changes to these populations. **Table 8**  **- 15**  **: Summary of CMAQ**  **- Derived Population**  **- Weighted Ozone and P**  **M 2.5**  

### Air **Quality Metrics for Health Benefits Endpoints Associated with the Proposed Tier 3**  **Program** 2030 Statistic a 

### Baseline Changeb 

### Ozone Metric: National Population - Weighted Average (ppb) c 

### Daily Maximum 8 - Hour Average Concentration 42.8652 0.5235 PM 2.5 

### Metric: National Population - Weighted Average ( μ g/m 3 

### ) Annual Average Concentration 8.3941 0.0479 Notes: a Ozone and PM 2.5 metrics are calculated at the CMAQ grid - cell level for use in health effects estimates. Ozone metrics are calculated over relevant time periods during the daylight hours of the “ozone season” ( i.e. , May through September). Note that the national, population - weighted PM 2.5 and ozone air quality metrics presented in this c hapter represent an average for the entire, gridded U . S . CMAQ domain. These are different than the population - weighted PM 2.5 and ozone design value metrics presented in Chapter 7, which represent the average for areas with a current air quality monitor. b The change is defined as the base - case value minus the control - case value. c Calculated by summing the product of the projected CMAQ grid - cell population and the estimated CMAQ grid cell seasonal ozone concentration and then dividing by the total population. 

### Emissions and air quality modeling decisions are made e arly in the anal ytical process. For this reason, the emission control scenarios used in the air quality and benefits modeling are slightly different than the final emission inventories estimated for the proposed program . Please refer to Section 7.2.1 for more information about the inventories used in the air quality modeling that supports the health impacts anal ys is.

8.1.2.1.4 Methods for Describing Uncertainty In any complex anal ys is using estimated parameters and inputs from numerous models, there are likel y to be many sources of uncertainty

and this analysis is no exception. As outlined both in this and preceding chapters, many inputs were used to derive the estimate of benefits for

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### 8 - 29 the proposal , including emission inventories, air quality models (with their a ssociated parameters and inputs), epidemiological health effect estimates, estimates of values (both from WTP and COIstudies), population estimates, income estimates, and estimates of the future state of the world ( i.e. , regulations, technology , and human behavior). Each of these inputs may be uncertain and, depending on its role in the benefits anal ys is, may have a disproportionately large impact on estimates of total benefits. For example, emissions estimates are used in the first stage of the anal ys is . As such, any

uncertainty in emissions estimates will be propagated through the entire anal ys is. When compounded with uncertainty in later stages, small uncertainties in emission levels can lead to large impacts on total benefits. The National Research Council (NRC) (2002, 2008) 70 , 71 

### highlighted the need for EPA to conduct rigorous quantitative anal ys is of uncertainty in its benefits estimates and to present these estimates to decision makers in ways that foster an appropriate appreciation of their inherent uncertainty. In general, the NRC concluded that EPA’s general methodology for calculating the benefits of reducing air pollution is reasonable and informative in spite of inherent uncertainties. Since the publication of these reports, EPA’s Office of Air and Radiation (OAR) continues to make progress toward the goal of characterizing the aggregate impact of uncertainty in key modeling elements on both health incidence and benefits estimates in two key ways: Monte Carlo anal ys is and expert - derived concentration - response functions. In this anal ys is, we use both of these two methods to assess uncertainty quantitatively , as well as provide a qualitative assessment for those aspects that we are unable to address quantitativel y. First, we used Monte Carlo me thods for characterizing random sampling error associated with the concentration response functions from epidemiological studies and random effects modeling to characterize both sampling error and variability across the economic valuation functions. Monte Carlo simulation uses random sampling from distributions of parameters to characterize the effects of uncertainty on output variables, such as incidence of premature mortality . Specifically , we used Monte Carlo methods to generate confidence intervals around the estimated health impact and dollar benefits. The reported standard errors in the epidemiological studies determined the distributions for individual effect estimates. Second, because characterization of random statistical error omits important sourc es of uncertainty ( e.g. , in the functional form of the model

—e.g. , whether or not a threshold may exist), we also incorporate the results of an expert elicitation on the relationship between premature mortality

and ambient PM 2.5 

### concentration (Roman et al. , 2008). 72 

### Use of the expert elicitation and incorporation of the standard errors approaches provide insights into the likelihood of different outcomes and about the state of knowledge regarding the benefits estimates. However, there are significant unquantified uncertainties present in upstream inputs including emission and air quality . Both approaches have different strengths and weaknesses, which are fully

described in Chapter 5 of the PM NAAQS RIA (U.S. EPA, 2006). In benefit anal ys es of air pollution regulations conducted to date, the estimated impact of reductions in premature mortality has accounted for 85 to 95 percent of total monetized benefits. Therefore, it is particularly important to attempt to characterize the uncertainties associated with re ductions in premature mortality . The health impact functions used to estimate avoided premature deaths associated with reductions in ozone have associated standard errors that represent the statistical errors around the effect estimates in the underl ying epidemiological

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### 8 - 30 studies. In our results, we report credible intervals based on these standard errors, reflecting the uncertainty in the estimated change in incidence of avoided premature deaths. We also provide multiple estimates, to reflect model uncertainty between alternative study designs. For premature mortality

associated with exposure to PM, we follow the same approach used in the RIA for 2006 PM NAAQS (U.S. EPA, 2006), presenting two empirical estimates of premature deaths avoided, and a set of twelve estimates based on results of the expert elicitation study . Even these multiple characterizations, including confidence intervals, omit the contribution to overall uncertainty of uncertainty i n air quality changes, baseline incidence rates, populations exposed and transferability of the effect estimate to diverse locations. 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 f or input distributions characterizing uncertainty in additional model elements. As a result, the reported confidence intervals and range of estimates give an incomplete picture about the overall uncertainty in the estimates. This information should be interpreted within the context of the larger uncertainty surrounding the entire anal ys is. In 2006 the EPA requested an NAS study to evaluate the extent to which the epidemiological literature to that point improved the understanding of ozone - related mortality . The NAS found that short - term ozone exposure was likely to contribute to ozone - related mortality (NRC, 2008) and issued a series of recommendations to EPA, including that the Agency should: 1. Present multiple short - term ozone mortality estimates, including those based on multi city anal ys es such as the National Morbidity , Mortality and Air Pollution Study (NMMAPS) as well as meta - analy tic studies. 2. Report additional risk metrics, including the percentage of baseline mortality attributable to short - term expos ure. 3. Remove reference to a no - causal relationship between ozone exposure and premature mortality . The quantification and presentation of ozone - related premature mortality in this chapter is responsive to these NRC recommendations . Some key sources of uncertainty

in each stage of both the PM and ozone health impact assessment are the following:  gaps in scientific data and inquiry ;  variability

in estimated relationships, such as epidemiological effect estimates, introduced through differences in study design and statistical modeling;  errors in measurement and projection for variables such as population growth rates;

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### 8 - 31  errors due to misspecification of model structures, including the use of surrogate variables, such as using PM 10 

### when PM 2.5 

### is not available , excluded variables, and simplification of complex functions; and  biases due to omissions or other research limitations. In Table 8 - 16 we summarize some of the key uncertainties in the benefits anal ys is. **Table 8**  **- 16**  **: Primary Sources of Uncertainty in the Benefits Analysis** 1. Uncertainties Associated with Impact Functions

- The value of the ozone or PM effect estimate in each impact function.

- Application of a single impact function to pollutant changes and populations in all locations.

- Similarity of future - year impact functions to current impact functions.

- Correct functional form of each impact function.

- Extrapolation of effect estimates beyond the range of ozone or PM concentrations observed in the source epidemiological study.

- Application of impact functions only to those subpopulations matching the original study population. 2. Uncertainties Associated with CMAQ-Modeled Ozone and PM Concentrations

- Responsiveness of the models to changes in precursor emissions from the control policy.

- Projections of future levels of precursor emissions, especially ammonia and crustal materials.

- Lack of ozone and PM 2.5 mo nitors in all rural areas requires extrapolation of observed ozone data from urban to rural areas. 3. Uncertainties Associated with PM Mortality Risk

- Limited scientific literature supporting a direct biological mechanism for observed epidemiological evidence.

- Direct causal agents within the complex mixture of PM have not been i dentified.

- The extent to which adverse health effects are associated with low - level exposures that occur many times in the year versus peak exposures.

- The extent to which effects reported in the long - term exposure studies are associated with historically higher levels of PM rather than the levels occurring during the period of study.

- Reliability of the PM 2.5 mo nitoring data in reflecting actual PM 2.5 exposures. 4. Uncertainties Associated with Possible Lagged Effects

- The portion of the PM - related long - te rm exposure mortality effects associated with changes in annual PM levels that would occur in a single year is uncertain as well as the portion that might occur in subsequent years. 5. Uncertainties Associated with Baseline Incidence Rates

- Some baseline incidence rates are not location specific ( e.g. , those taken from studies) and therefore may not accurately represent the actual location - specific rates.

- Current baseline incidence rates may not approximate well baseline incidence rates in 20 30 .

- Projected population and demographics may not represent well future - year population and demographics. 6. Uncertainties Associated with Economic Valuation

- Unit dollar values associated with health and welfare endpoints are only estimates of mean WTP and therefore have uncertainty surrounding them.

- Mean WTP (in constant dollars) for each type of risk reduction may differ from current estimates because of differences in income or other factors. 7. Uncertainties Associated with Aggregation of Monetized Benefits

- He alth and welfare benefits estimates are limited to the available impact functions. Thus, unquantified or unmonetized benefits are not included. 

### 8.1.3 C omparison of Costs and Benefits This section presents the cost - benefit comparison related to the expected impacts of the proposed Tier 3 program . In estimating the net benefits of the program , the appropriate cost

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### 8 - 32 measure is ‘social costs.’ Social costs represent the welfare costs of a rule to society and do not consider transfer pay

ments (such as taxes) that are simply redistributions of wealth. For this anal ys is, we estimate that the social costs of the program are equivalent to the estimated vehicle and fuel compliance costs of the program. While vehicle manufacturers and fuel producers w ould see their cos ts increase b y the amount of those compliance costs, they are expected to pass them on in their entirety

to vehicle and fuel consumers in the form of increased prices . Ultimately , these costs will be borne by the final consumers of these goods. The social benefits of the program are represented by the monetized value of health and welfare improvements experienced b y the U.S. population. Table 8 - 17 contains the estimated social costs and the estimated monetized benefit s of the program . The results in Table 8 - 17 suggest that the 20 3 0 monetized benefits of the proposed standards are greater than the expected costs. Specificall y, the annual benefits of the total program will range between $ 9.5 to $ 27 billio n annually in 20 3 0 using a three percent discount rate, or between $ 8.7 to $ 25 billion assuming a 7 percent discount rate, compared to estimated social costs of approximately $ 3. 6 billion in that same year. Though there are a number of health and environm ental effects associated with the proposed standards that we are unable to quantify or monetize (see Table 8 - 5 ), the benefits of the proposed standards outweigh the projected costs. Using a conservative benefits estimate, the 20 3 0 benefits outweigh the costs by a factor of 2. 4 . Using the upper end of the benefits range, the benefits could outweigh the costs by a factor of 7. 5 . Thus, even taking the most conservative benefits assumptions, benefits of the proposed standards clearly outweigh the costs. **Table 8**  **- 17**  **: Summary of Annual Benefts and Costs Associated with the Proposed Tier 3**  **Program (Billions, 2010$) a**  

### Description **2030** Vehicle Program Costs Fuels Program Costs Total Estimated Costs b 

### $2. 3 $1.3 $3. 6 Total Estimated Health Benefits c,d,e,f 

### 3 percent discount rate 7 percent discount rate $9.5 - $27 $8.7 - $25 Annual Net Benefits (Total Benefits

– Total Costs) 3 percent discount rate 7 percent discount rate $ 5.9 - $ 23 $ 5.1 - $ 21 Notes: a All estimates represent annual benefits and costs anticipated for the year 2030. Totals are rounded to two significant digits and may not sum due to rounding. b The calculation of annual costs does not require amortization of costs over time. Therefore, the estimates of annual cost do not include a discount rate or rate of return assumption (see Chapter 2 of the draft RIA for more information on vehicle costs, Chapter 5 for fuel costs, and Section 8.1.1 for a summary of total program costs ). c Total includes ozone and PM 2.5 benefits. Range was developed by adding the estimate from the Bell et al., 200 4 ozone premature mortality function to PM 2.5 - related premature mortality derived from the American Cancer S ociety cohort study (Pope et al., 2002) for the low estimate and ozone premature mortality derived from the Levy et al., 2005 study to PM 2.5 - related premature mortality derived from the Six - Cities (Laden et al., 2006) study for the high estimate .

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### 8 - 33 d Annual benefits analysis results reflect the use of a 3 percent and 7 percent discount rate in the valuation of premature mortality and nonfatal myocardial infarctions, consistent with EPA and OMB guidelines for preparing economic analyses. e Valuation of premature mortality based on long - term PM exposure assumes discounting over the SAB recommended 20 - year segmented lag structure described in the Regulatory Impact Analysis for the 2006 PM National Ambient Air Quality Standard s ( September, 2006 ). fNot all poss ible benefits or disbenefits are quantified and monetized in this analysis. Potential benefit categories that have not been quantified and monetized are listed in Table 8 - 5 . 

### 8.1.4 Illustrative Anal ys is of Quantified and Monetized Impacts Associated with the Proposal in 2017 For illustrative purposes, this section present s the quantified and monetized impacts associated with the proposed standards in 2017. As presented in Section 7.1.5 , the emissions impacts of the proposed standards in 2017 are primarily due to the effects of sulfur on the existing (pre - Tier 3) fleet. For reasons explained in Section 7 . 1.3.2. 2 , our analysis of the air quality impacts in 2017 reflect s an increase in direct PM emissions from vehicles (along with reductions in NO X 

### , VOCs and other pollutants). This emissions increase results from a series of conservative assumptions and uncertainties related to fuel parameters in 2017, and is not expected to occur in reality . Because our air quality modeling assumes this increase, as well as incre ased direct PM emissions due to an emissions processing error (see Section 7 .2 .1.1.2 ), our illustrative benefits anal ysis in 2017 overestimates ambient concentrations of PM and underestimates the benefits of the proposed Tier 3 standards. This analysis r eflects the impact s of the proposed Tier 3 rule in 2017 compared to a future - ye ar reference scenario without the program in place. Overall, we estimate that the proposed rule w ould lead to a net decrease in PM 2.5 

### - related health and environmental impacts in 2017 (see Section 7.2.5 for more information about the air quality modeling results ). The decrease in population - weighted national average PM 2.5 

### exposure results in a net decrease in adverse PM 2.5 

### - related human health and environmental impacts (the decrease in national population - weighted annual average PM 2.5 

### is 0.003 μg/m 3 

### in 2017). The air quality modeling also projects decreases in ozone concentrations. The overall de crease in population - weighted national average ozone exposure results in decreases in ozone - related health and environmental impacts (population - weighted maximum 8 - hour average ozone decreases by 0.17 ppb in 2017). Table 8 - 18 and Table 8 - 19 present the annual PM 2.5 

### and ozone health impacts in the 48 contiguous U.S. states associated with the proposed T ier 3 program . For each endpoint presented in Table 8 - 18 and Table 8 - 19 , we provide both the point estimate an d the 90 percent confidence interval. Using EPA’s preferred estimates, based on the American C ancer S ociety (ACS) and Six - Cities studies and no threshold assumption in the model of mortality , we estimate that the proposed standards would result in between 57 and 150 cases of avoided PM 2.5 

### - related premature mortalities annually in 2017 . For ozone - related premature mortality in 2017, we estimate a range of between 49 to 230 fewer premature mortalities. Table 8 - 20 p resents the estimated monetary value of changes in the incidence of ozone and PM 2.5 

### - related health and environmental effects. Total aggregate monetized benefits are presented in Table 8 - 21 . All monetized estimates are presented in 2010 $ . Where appropriate, estimates account for growth in real gross domestic product (GDP) per capita b etween 2000 and

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### 8 - 34 2017 . The monetized value of PM 2.5 

### - related mortality also accounts for a twenty- ye ar segmented cessation lag. V 

### To discount the value of premature mortality that occurs at different points in the future, we appl y both a 3 and 7 percent dis count rate. We also use both a 3 and 7 percent discount rate to value PM 2.5 

### - related nonfatal heart attacks (myocardial infarctions). W 

### In addition to omitted benefits categories such as air toxics and various welfare effects, not all known PM 2.5 

### - and ozo ne - related health and welfare effects could be quantified or monetized. The estimate of total monetized health benefits of the proposed program is thus equal to the subset of monetized PM 2.5 

### - and ozone - related health impacts we are able to quantify plus the sum of the nonmonetized health and welfare benefits. Our estimate of total monetized benefits associated with the proposed standards in 2017, using the ACS and Six - Cities PM mortality studies and the range of ozone mortality assumptions, is between $1. 0 and $3.4 billion, assuming a 3 percent discount rate, or between $1.0 and $3.3 billion, assuming a 7 percent discount rate. Had our ambient air quality modeling of PM 2.5 

### not included the increase in direct PM emissions, we estimate that benefits would increase b y a range of $400 to $970 million (assuming a 3 percent discount rate) or increase b y a range of $360 to $880 million (assuming a 7 percent discount rate), using current EPA benefit -per- ton estimates for direct PM. X 

### The results in Table 8 - 21 demon strate that the gasoline sulfur standards provide large immediate benefits in the program’s first year, related to emission reductions from existing gasoline vehicle s . Accounting for the removal of the increase in direct PM emissions in the 2017 air quali ty

modeling , total monetized benefits increase even more, to between $1.4 and $4.3 billion, assuming a 3 percent discount rate, or between $1.3 and $4.2 billion, assuming a 7 percent discount rate. The benefits increase substantially after 2017, as the v ehicle standards phase in after 2017 and as the fleet turns over.

V Based in part on prior SAB advice, EPA has typically assumed that there is a time lag betwee n changes in pollution exposures and the total realization of changes in health effects. Within the context of benefits analyses, this term is often referred to as “cessation lag”. The existence of such a lag is important for the valuation of premature mortality incidence because economic theory suggests that benefits occurring in the future should be discounted. In this analysis, we apply a twenty - year distributed lag to PM mortality reductions. This method is consistent with the most recent recommendation by the EPA’s Science Advisory Board. Refer to: EPA

– Science Advisory Board, 2004. Advisory Council on Clean Air Compliance Analysis Response to Agency Request on Cessation Lag. Letter from the Health Effects Subcommittee to the U.S. Environmental P rotection Agency Administrator, December. W Nonfatal myocardial infarctions (MI) are valued using age - specific cost - of - illness values that reflect lost earnings and direct medical costs over a 5 - year period following a nonfatal MI. X The PM 2.5 - related be nefit -per - ton estimates provide the total monetized human health benefits (the sum of premature mortality and premature morbidity) of reducing one ton of directly emitted PM 2.5 . The benefit per - ton technique has been used in previous analyses, including the 2012 - 2016 Light - Duty Greenhouse Gas Rule (U.S. EPA, 2010. Regulatory Impact Analysis, Final Rulemaking to Establish Light - Duty Vehicle Greenhouse Gas Emission Standards and Corporate Average Fuel Economy Standards. Office of Transportation and Air Qual ity. April. Available at http://www.epa.gov/otaq/climate/regulations/420r10009.pdf. EPA - 420 - R - 10 - 009). The benefits per - ton values are available at http://www.epa.gov/oaqps001/benmap/bpt.html . Note that the values on the website are presented in year 2006$; the values underlying the estimates here have been adjusted to 2010$ using the CPI - U “all” index.

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### 8 - 35 **Table 8**  **- 18**  **: Estimated PM**  **2.5** 

### - **Related Health Impacts a**  Health Effect 2017 Annual Reduction in Incidence ( 5 th

- 95 th percentile ) Premature Mortal ity

– Derived from epidemiology literature b Adult, age 30+, ACS Cohort Study (Pope et al., 2002) Adult, age 25+, Six - Cities Study (Laden et al., 2006) Infant, age <1 year (Woodruff et al., 1997) 57 (9

– 110) 150 (54

– 240) 0 (0

– 1) Chronic bronc hitis (adult, age 26 and over) 38 ( - 7

– 84) Non - fatal myocardial infarction (adult, age 18 and over) 64 (7

– 120) Hospital admissions - respiratory (all ages) c 10 (3

– 17) Hospital admissions - cardiovascular (adults, age >18) d 23 14

– 28) Emergency room

visits for asthma (age 18 years and younger) 40 (15

– 64) Ac ute bronchitis, (children, age 8 - 12) 87 ( - 45

– 220) Lo wer respiratory symptoms (children, age 7 - 14) 1,100 (270

– 1,900) Upper respiratory symptoms (asthmatic children, age 9 - 18) 830 (0

– 1 ,700) Asthma exacerbation (asthmatic children, age 6 - 18) 1,800 ( - 360

– 5,100) Work loss days 7,300 (5,900

– 8,700) Minor restricted activity days (adults age 18 - 65) 43,000 (33,000

– 53,000) Notes: a Incidence is rounded to two significant digits. Estimates represent incidence within the 48 contiguous United States. b PM - related adult mortality based upon the American Cancer Society (ACS) Cohort Study (Pope et al., 2002) and the Six - Cities Study (Laden et al., 2006). Note that these are two alternative estimates of adult mortality and should not be summed. PM - related infant mortality based upon a study by Woodruff, Grillo, and Schoendorf, (1997). Y c Respiratory hospital admissions for PM include admissions for chronic obstructive pulmonary disease (COP D), pneumonia and asthma. d Cardiovascular hospital admissions for PM include total cardiovascular and subcategories for ischemic heart disease, dysrhythmias, and heart failure.

Y Woodruff, T.J., J. Grillo, and K.C. Schoendorf. 1997. “The Relationship Between Selected Causes of Postneonatal Infant Mortality and Particulate Air Pollution in the United States.” Environmental Health Perspectives 105(6):608 - 612.

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### 8 - 36 **Table 8**  **- 19**  **: Estimated Ozone -**  **R elated Health Impacts**  **a**

Health Effect 2017 Annual Reduction in Incidence (5 th

- 95 th percentile) Premature Mortality, All ages b Multi - City Analyses Bell et al. (2004)

– Non - accidental Huang et al. (2005)

– Cardiopulmonary Schwartz (2005)

– Non - a ccidental Meta - analyses: Bell et al. (2005)

– All cause Ito et al. (2005)

– Non - accidental Levy et al. (2005)

– All cause 49 (21

– 77) 71 (33

– 110) 75 (31

– 120) 160 (88

– 230) 220 (140

– 290) 230 (160

– 290) Hospital admissions - respiratory causes (adult, 65 and older)c 290 (37

– 520) Hospital admissions - respiratory causes (children, under 2) 170 (85

– 250) Emergency room visit for asthma (all ages) 170 ( - 3

– 441) Minor restricted activity days (adults, age 18 - 65) 300,000 (150,000

– 440, 000) School absence days 98,000 (43,000

– 140,000) Notes: a Incidence is rounded to two significant digits. Estimates represent incidence within the 48 contiguous U.S. b Estimates of ozone - related premature mortality are based upon incidence estimates d erived from several alternative studies: Bell et al. (2004); Huang et al. (2005); Schwartz (2005) ; Bell et al. (2005); Ito et al. (2005); Levy et al. (2005). The estimates of ozone - related premature mortality should therefore not be summed. c Respiratory hospital admissions for ozone include admissions for all respiratory causes and subcategories for COPD and pneumonia. 

### Table **8 -**  **20 : Estimated Monetary Value of Changes in Incidence of Health and Welfare**  **Effe cts (millions of**  **2010 $)**  **a,b** 2017 PM 2.5 - Related Health Effect (5 th and 95 th Percentile) Premature Mortality

– Derived from Epidemiology Studies c,d Adult, age 30+ - ACS study (Pope et al., 2002) 3% discount rate 7% discount rate $490 ($41 - $1,300) $440 ($37 - $1,200) Adult, age 25+ - Six - C ities study (Laden et al., 2006) 3% discount rate 7% discount rate $1,300 ($160 - $3,200) $1,100 ($140 - $2,900)

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### 8 - 37 Infant Mortality, <1 year

– (Woodruff et al. 1997) $2 .5 ( - $3.3 - $11) Chronic bronchitis (adults, 26 and over) $20 ( - $2.6 - $70) Non - fatal acute myocardial infarctions 3% discount rate 7% discount rate $7.4 ($0.80 - $20) $5.9 ($0.60 - $15) Hospital admissions for respiratory causes $0.16 ($0.05 - $0.23) Hospital admissions for cardiovascular causes $0.38 ($0.01 - $0.74) Emergency room visits for asthma $0.016 ($0.006 - $0.026) Ac ute bronchitis (children, age 8

– 12) $0.041 ( - $0.021 - $0.12) Lo wer respiratory symptoms (children, 7

– 14) $0.023 ($0.005 - $0.047) Upper respiratory symptoms (asthma, 9

– 11) $0.028 ($0 - $0.070) Asthma exacerbations $0.10 ( - $0.02 - $0.30) Work loss days $1.2 ($0.96 - $1.4) Minor restricted - activity days (MRADs) $2.9 ($1.7 - $4.3) Ozone - related Health E ffect Premature Mortality, All ages

– Derived from Multi - city analyses Bell et al., 2004 $460 ($60 - $1,200) Huang et al., 2005 $670 ($90 - $1,700) Schwartz, 2005 $700 ($91 - $1,800 Premature Mortality, All ages

– Derived from Meta - analyses Bell et al., 2005 $1,500 ($210 - $3,700) Ito et al., 2005 $2,100 ($300 - $4,900) Levy et al., 2005 $2,100 ($320 - $5,000) Hospital admissions - respiratory causes (adult, 65 and older) $7.6 ($0.97 - $14) Hospital admissions - respiratory causes (children, under 2) $1.8 ($0.93 - $2.7) Emergency room visit for asthma (all ages) $0.07 ($0 - $0.17) Minor restricted activity days (adults, age 18 - 65) $20 ($9.0 - $34) School absence days $9.4 ($4.1 - $13) Notes: a Monetary benefits are rounded to two significant di gits for ease of presentation and computation. PM and ozone benefits are nationwide.

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### 8 - 38 b Monetary benefits adjusted to account for growth in real GDP per capita between 1990 and the analysis year (2017 ) . c Valuation assumes discounting over the SAB recomm ended 20 year segmented lag structure. Results reflect the use of 3 percent and 7 percent discount rates consistent with EPA and OMB guidelines for preparing economic analyses. 

### Table **8 -**  **21 :**  **Total Monetized O zone and PM**  **- related Benefits Associated with the**  **Proposed Program in 2017**  **Total Ozone and PM Benefits (billions, 2010**  **$) –**  **PM Mortality Derived from the ACS and Six** **- Cities Studies**  **3% Discount Rate 7% Discount Rate**  Ozone Mortality Function Reference Mean Total Benefits Ozone

Mortality Function Reference Mean Total Benefits Multi - city Bell et al.,

2004 $1.0 - $1.8 Multi - city Bell et al., 2004 $0.96 - $1.7 Huang et al., 2005 $1.2 - $1.9 Huang et al., 2005 $1.2 - $1.9 Schwartz, 2005 $1.3 - $2.0 Schwartz, 2005 $1.2 - $1.9 Meta - analysis Bell et al.,

2005 $2.1 - $2.8 Meta - analysis Bell et al., 2005 $2.0 - $2.7 Ito et al., 2005 $2.6 - $3.4 Ito et al., 2005 $2.6 - $3.3 Levy et al., 2005 $2.7 - $3.4 Levy et al., 2005 $2.6 - $3.3 

### 8.2 **C ost** **- Effectiveness**  This section will present the cost - effectiveness analysis we completed for the proposed combined Tier 3 vehicle and fuel standards. This anal ys is relies in part on cost information from draft RIA Chapters 2 and 5 and emissions information from draft RIA C hapter 8 to estimate the dollars per ton ($/ton) of total NO X 

### + NMOG emission reductions after the proposed Tier 3 standards would have been fully implemented. We have calculated the cost - effectiveness on an aggregate basis to provide a comprehensive mean s for capturing the effectiveness of the proposed program on vehicles and fuels. This chapter also compares the cost - effectiveness of the proposed provisions with the cost - effectiveness of other NO X 

### and NMOG control strategies from previous and potential future EPA emission control programs. 8.2.1 Overview We have calculated the aggregate cost - effectiveness which uses the costs and emission reductions for calendar years 2017 and 2030, consistent with the years that we evaluated for air quality and the cost - benefit analysis. All of our results are presented and discussed in Section 8.2.5 below .

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### 8 - 39 8.2.2 Baselines An average approach to cost - effectiveness requires that we compare the costs and emission reductions associated with the proposed standards to those for the p revious set of standards that are being met by manufacturers. In this case, the $/ton values represent the full range of control from the last applicable standard to our proposed standards. Since today's program includes both proposed fuel standards and proposed vehicle standards, it was necessary for us to define a baseline for both fuels and vehicles from which to calculate reductions in emissions and increases in costs. For sulfur content of fuel and vehicle emission s, the previous standards were set under the Tier 2 program. The baseline sulfur level in the fuel is therefore 30 ppm and the baseline vehicle exhaust standard is 0.07 g/mi NO X 

### . The baseline vehicle evaporative standard s are listed below in Table 8 - 22 . **Table 8**  **- 22**  **: Light** **- Duty Vehicle, Light** **- Duty Truck, and Medium**  **- Duty Passenger Vehicle**  **Evaporative Standards a**  

### Vehicle T ype 3 Day Diurnal + Hot Soak (g/test) 2 Day Diurnal + Hot Soak (g/test) LDV 0.50 0.65 LLDT 0.65 0.85 MDPV 1.00 1.25 a 7 2 Federal Register at 8471 (February 26, 2007) 

### 8.2.3 Costs Costs that would be incurred from our proposed program would be due to the proposed Tier 3 exhaust standards, Tier 3 evaporative standards, and reductions in sulfur content of g asoline , as discussed above in Section 8 . 1.1 . The sum of the vehicle technology costs to control exhaust and evaporative emissions, in addition to the costs to control the sulfur level in the fuel, are as shown in Table 8 - 23 . Al l costs represent the fleet - weighted average of light - duty vehicles and trucks. All costs are represented in 2010 dollars.

**Table 8**  **- 23**  **: Total Annual Vehicle and Fuel Control Costs, 2010$**

Total Vehicle and Fu el Control Costs

($Million) 2017 $ 2,0 78 20 30 $3, 59 5 8.2.4 Emission Reductions In order to determine the overall cost - effectiveness of the standards we are proposing, it was necessary

to calculate the tons of each pollutant reduced on an aggregate basis. Our proposed standards are intended primaril y to reduce emissions of NO X 

### and NMOG. As a result, we have determined that the cost - effectiveness of our standards should be determined for both NO X 

### and NMOG. It is true that our program does include new proposed standards for PM. However, as discussed in Chapter 2, we believe that the efforts manufacturers make to meet the

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### 8 - 40 NO X

### +NMOG standards will also result in sufficient PM reductions to meet our proposed PM standards. Thus we estimate that manufacturers would incur no additional costs to comply with the Tier 3 PM standard and a cost - effectiveness analysis of the PM standards is therefore unnecessary .

8.2.4.1 NO X 

### and NMOG Several past rulemakings which produced reductions in both NO X 

### and NMOG have taken an approach to cost - effectiveness that sums the NO X 

### and NMOG emission reductions. This approach leads to $/ton NO X

### +NMOG. In addition, many standards for mobile sources have been established in terms of NO X

### +NMOG caps, including the previous Tier 2 vehicle standards. Thus we believe that this approach to cost - effectiveness is appropriate for our Tier 3 standards as well. The projected annual reductions in NO X 

### and NMOG in 2017 and 2030 are included in Table 8 - 24 .

**Table 8**  **- 24**  **: Annual**  **NO X**  

### and VOC Reductions (tons) **in 2017 and 2030** NO X 

### Reductions (tons) VOC Reductions (tons) Total NO X

### +VOC Reductions (tons 2017 284,381 44,782 329,162 2030 524,790 226,028 750,818 8.2.5 Results The results of our cost - effec tiveness anal ys is are provided in Table 8 - 25 . Costs are provided above in Table 8 - 23 . The tons reduced are from the values in Table 8 - 24 as the difference between our Tier 2 ba seline at our baseline fuel sulfur level of 30 ppm, and our Tier 3 standards at our fuel sulfur standard of 10 ppm. The costs of the proposed program would be higher immediatel y after it is implemented than they w ould be after several ye ars, since both v ehicle manufacturers and refiners can take advantage of decreasing capital and operating costs over time. Inaddition, the reductions in NO X 

### and VOC emissions will become greater as a greater percentage of the fleet contains the technologies required to m eet the proposed standards. **Table 8**  **- 25**  **C ost** **- E**  **ffectiveness of the Proposed Vehicle and Fuel S**  **tandards**

Total Proposed Program Cost ($million, 2010$) Total NO X 

### + VOC Reductions (tons) Cost Effectiveness ($/ton) 2017 $ 2,0 78 329,162 $ 6, 3 12 2030 $3, 595 750,818 $4 , 78 8

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### 8 - 41 **References** 1 U.S. Environmental Protection Agency. (2006). Final Regulatory Impact Analysis ( RIA) for the Proposed National Ambient Air Quality Standards for Particulate Matter . Prepared by: Office of Air and Radiation. Retrieved March, 26, 2009 at http://www.epa.gov/ttn/ecas/ria.html . EPA - HQ - OAR- 2009 - 0472 - 0240 2 U.S. Environmental Protection Agency. (2008). Final Ozone NAAQS Regulatory Impact Analysis . Prepared by: Office of Air and Radiation, Office of Air Quality Planning and Standards. Retrieved March, 26, 2009 at http://www.epa.gov/ttn/ecas/ria.html . EPA - HQ - OAR- 2009 - 0472 - 0238 3 U.S. Environmental Protection Agency. (2010). Final Rulemaking to Establish Light-Duty Vehicle Greenhouse Gas Emission Standards and Corporate Average Fuel Econ omy Standards: Regulatory Impact Analysis , Assessment and Standards Division, Office of Transportation and Air Quality, EPA - 420 - R - 10 - 009, April 2010. Available on the internet: http://www.epa.gov/otaq/climate/regulations/420r10009.pdf 4 U.S. Environmental Protection Agency (U.S. EPA). 2010. Regulatory Impact Analysis: National Emission Standards for Hazardous Air Pollutants from the Portland Cement Manufacturing Industry. Office of Air Quality Planning and Standards, Research Triangle Park, NC. Augues. Available on the Internet at < http://www.epa.gov/ttn/ecas/regdata/RIAs/portlandcementfinalria.pdf >. EPA - HQ - OAR - 2009 - 0472 - 0241 5 U.S. Environmental Protection Agency. (2012 ). Regulatory Impact Analysis: Final Rulemaking for 2017- 2025 Li ght-Duty Vehicle Greenhouse Gas E mission Standards and Corporate Average Fuel Economy Standards , Assessment and Standards Division, Office of Transportati on and Air Quality, EPA - 420 - R - 12 - 016, August 2012 . Available on the internet: http://www.epa.gov/otaq/climate/docume nts/420r12016.pdf 6 Kunzli, N., S. Medina, R. Kaiser, P. Quenel, F. Horak Jr, and M. Studnicka. 2001. Assessment of Deaths Attributable to Air Pollution: Should We Use Risk Estimates Based on Time Series or on Cohort Studies? American Journal of Epidemiolo gy 153(11):1050 - 55. 7 Science Advisory Board. 2001. NATA

– Evaluating the National - Scale Air Toxics Assessment for 1996

– an SAB Advisory. http://www.epa.gov/ttn/atw/sab/sabrev.html. 8 U.S. Environmental Protection Agency (U.S. EPA). 2011. The Benefits and Costs of the Clean Air Act from 1990 to 2020 . Office of Air and Radiation, Washington, DC. March. Available on the Internet at <http://www.epa.gov/air/sect812/feb11/fullreport.pdf>. 9 U.S. Environmental Protection Agency

— Science Advisory Board (U.S. EPA - SAB). 2008. Benefits of Reducing Benzene Emissions in Houston, 1990

–2020 . EPA - COUNCIL - 08 - 001. July. Available at <http://yosemite.epa.gov/sab/sabproduct.nsf/D4D7EC9DAEDA8A548525748600728A83/$File/EPA - COUNCIL 08 - 001 -unsigned.pdf>. 10 National Research Co uncil (NRC). 2008. Estimating Mortality Risk Reduction and Economic Benefits from Controlling Ozone Air Pollution. National Academies Press. Washington, DC. 11 GeoLytics Inc. (2002). Geolytics CensusCD® 2000 Short Form Blocks. CD - ROM Release 1.0. GeoLytics, Inc. East Brunswick, NJ. Available: http://www.geolytics.com/ [accessed 29 September 2004] 12 Woods & Poole Economics Inc. 2008. Population by Single Year of Age CD. CD - ROM. Woods & Poole Economics, Inc. Washington, D.C. EPA - HQ - OAR- 2009 - 0472 - 0011 13 U.S. Environmental Protection Agency. (2006). Air quality criteria for ozone and related photochemical oxidants (second external review draft). Research Triangle Park, NC: National Center for Environmental Assessment; report no. EPA/600R- 05/004aB - c B, 3v. Available: http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=137307[March 2006] EPA - HQ - OAR- 2009 - 0472 - 0099, EPA - HQ - OAR - 2009 - 0472 - 0100, EPA - HQ - OAR - 2009 - 0472 - 0101 14 U.S. Environmental Protection Agency, 2004. Air Quality Criteria for Particulate Ma tter Volume II of II. National Center for Environmental Assessment, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC EPA/600/P - 99/002bF. EPA - HQ - OAR - 2009 - 0472 - 0097

### *** **Working Comments on Draft Language under**  **E.O. 13563 and 12866 Interagency Review. Subject** 

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### 8 - 42 15 World Health Organization (WHO). (2003 ). Health Aspects of Air Pollution with Particulate Matter, Ozone and Nitrogen Dioxide: Report on a WHO Working Group. World Health Organization. Bonn, Germany. EUR/03/5042688. 16 Anderson HR, Atkinson RW, Peacock JL, Marston L, Konstantinou K. (2004). Meta - analysis of time -series studies and panel studies of Particulate Matter (PM) and Ozone (O3): Report of a WHO task group. Copenhagen, Denmark: World Health Organization. 17 Bell, M.L., et al. (2004). Ozone and short - term mortality in 95 U.S. urban c ommunities, 1987 - 2000. JAMA , 2004. 292(19): p. 2372 - 8. EPA - HQ - OAR - 2009 - 0472 - 1662 18 Huang, Y.; Dominici, F.; Bell, M. L. (2005) Bayesian hierarchical distributed lag models for summer ozone exposure and cardio - respiratory mortality. Environmetrics . 16: 547 - 562. EPA - HQ - OAR - 2009 - 0472 - 0233 19 Schwartz, J. (2005) How sensitive is the association between ozone and daily deaths to control for temperature? Am. J. Respir. Crit. Care Med. 171: 627 - 631. EPA - HQ - OAR - 2009 - 0472 - 1678 20 Bell, M.L., F. Dominici, and J.M. Same t. (2005). A meta - analysis of time - series studies of ozone and mortality with comparison to the national morbidity, mortality, and air pollution study. Epidemiology . 16(4): p. 436 - 45. EPA HQ - OAR - 2009 - 0472 - 0222 21 Ito, K., S.F. De Leon, and M. Lippmann (2005 ). Associations between ozone and daily mortality: analysis and meta - analysis. Epidemiology . 16(4): p. 446 - 57. EPA - HQ - OAR - 2009 - 0472 - 0231 22 Levy, J.I., S.M. Chemerynski, and J.A. Sarnat. (2005). Ozone exposure and mortality: an empiric bayes metaregression analysis. Epidemiology . 16(4): p. 458 - 68. EPA - HQ - OAR - 2009 - 0472 - 0236 23 Pope, C.A., III, R.T. Burnett, M.J. Thun, E.E. Calle, D. Krewski, K. Ito, and G.D. Thurston. (2002). “Lung Cancer, Cardiopulmonary Mortality, and Long - term Exposure to Fine Particulat e Air Pollution.” Journal of the American Medical Association 287:1132 - 1141. EPA - HQ - OAR - 2009 - 0472 - 0263 24 Laden, F., J. Schwartz, F.E. Speizer, and D.W. Dockery. (2006). Reduction in Fine Particulate Air Pollution and Mortality. American Journal of Resp iratory and Critical Care Medicine . 173: 667 - 672. EPA - HQ - OAR - 2009 - 0472 - 1661 25 Industrial Economics, Incorporated (IEc). (2006). Expanded Expert Judgment Assessment of the Concentration Response Relationship Between PM2.5 Exposure and Mortality. Peer Re view Draft. Prepared for: Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC. August. EPA HQ - OAR - 2009 - 0472 - 0242 26 Woodruff, T.J., J. Grillo, and K.C. Schoendorf. (1997). The Relationship Betwee n Selected Causes of Postneonatal Infant Mortality and Particulate Air Pollution in the United States. Environmental Health Perspectives. 105(6):608 - 612. EPA - HQ - OAR - 2009 - 0472 - 0382 27 Abbey, D.E., B.L. Hwang, R.J. Burchette, T. Vancuren, and P.K. Mills. (1 995). Estimated Long - Term Am bient Concentrations of PM(10) and Development of Respiratory Symptoms in a Nonsmoking Population. Archives of Environmental Health. 50(2): 139 - 152. EPA - HQ - OAR- 2009 - 0472 - 0432 28 Peters, A., D.W. Dockery, J.E. Muller, and M.A. M ittleman. (2001). Increased Particulate Air Pollution and the Triggering of Myocardial Infarction. Circulation. 103:2810 - 2815. EPA - HQ - OAR - 2009 - 0472 - 0239 29 Schwartz J. (1995). Short term fluctuations in air pollution and hospital admissions of the elderly for respiratory disease. Thorax. 50(5):531 - 538. 30 Schwartz J. (1994a). PM(10) Ozone, and Hospital Admissions For the Elderly in Minneapolis St Paul, Minnesota. Arch Environ Health. 49(5):366 - 374. EPA - HQ - OAR- 2009 - 0472 - 1673 31 Schwartz J. (1994b). Air Pollution and Hospital Admissions For the Elderly in Detroit, Michigan. Am J Respir Crit Care Med. 150(3):648 - 655. EPA - HQ - OAR- 2009 - 0472 - 1674 32 Moolgavkar SH, Luebeck EG, Anderson EL. (1997). Air pollution and hospital admissions for respiratory causes in Minneapolis St. Paul and Birmingham. Epidemiology . 8(4):364 - 370. EPA - HQ - OAR- 2009 - 0472 - 1673

### *** **Working Comments on Draft Language under**  **E.O. 13563 and 12866 Interagency Review. Subject** 

**to Further Policy Review. E.O. 12866 Review**  **– Revised Version**  **– Do Not Cite, Quote, or Release**  **During Review *****



### 8 - 43 33 Burnett RT, Smith - Doiron M, Stieb D, Raizenne ME, Brook JR, Dales RE, et al. (2001). Association between ozone and hospitalization for acute respiratory dis eases in children less than 2 years of age. Am J Epidemiol . 153(5):444 - 452. EPA - HQ - OAR- 2009 - 0472 - 0223 34 Moolgavkar, S.H. (2003). “Air Pollution and Daily Deaths and Hospital Admissions in Los Angeles and Cook Counties.” In Revised Analyses of Time - Seri es Studies of Air Pollution and Health . Special Report. Boston, MA: Health Effects Institute. 35 Ito, K. (2003). “Associations of Particulate Matter Components with Daily Mortality and Morbidity in Detroit, Michigan.” In Revised Analyses of Time - Series Studies of Air Pollution and Health . Special Report. Health Effects Institute, Boston, MA. EPA - HQ - OAR - 2009 - 0472 - 1674 36 Moolgavkar, S.H. (2000). Air Pollution and Hospital Admissions for Diseases of the Circulatory System in Three U.S. Metropolitan Area s. Journal of the Air and Waste Management Association 50:1199 - 1206. EPA - HQ OAR - 2009 - 0472 - 1664 37 Sheppard, L. (2003). Ambient Air Pollution and Nonelderly Asthma Hospital Admissions in Seattle, Washington, 1987 - 1994. In Revised Analyses of Time - Series Studies of Air Pollution and Health . Special Report. Boston, MA: Health Effects Institute. EPA - HQ - OAR - 2009 - 0472 - 0318 38 Peel, J. L., P. E. Tolbert, M. Klein, et al. (2005). Ambient air pollution and respiratory emergency department visits. Epidemiology . Vol. 16 (2): 164 - 74. EPA - HQ - OAR - 2009 - 0472 - 1663 39 Wilson, A. M., C. P. Wake, T. Kelly, et al. (2005). Air pollution, weather, and respiratory emergency room visits in two northern New England cities: an ecological time - series study. Environ Res . Vol. 97 (3) : 312 - 21. EPA - HQ OAR - 2009 - 0472 - 0246 40 Norris, G., S.N. YoungPong, J.Q. Koenig, T.V. Larson, L. Sheppard, and J.W. Stout. (1999). An Association between Fine Particles and Asthma Emergency Department Visits for Children in Seattle. Environmental Health Perspectives 107(6):489 - 493. EPA - HQ - OAR - 2009 - 0472 - 0318 41 Dockery, D.W., J. Cunningham, A.I. Damokosh, L.M. Neas, J.D. Spengler, P. Koutrakis, J.H. Ware, M. Raizenne, and F.E. Speizer. (1996). Health Effects of Acid Aerosols On North American Children - Resp iratory Symptoms. Environmental Health Perspectives 104(5):500 - 505. EPA - HQ - OAR - 2009 - 0472 - 0225 42 Pope, C.A., III, D.W. Dockery, J.D. Spengler, and M.E. Raizenne. (1991). Respiratory Health and PM 10 Pollution: A Daily Time Series Analysis. American Revi ew of Respiratory Diseases 144:668 - 674. EPA - HQ - OAR 2009 - 0472 - 1672 43 Schwartz, J., and L.M. Neas. (2000). Fine Particles are More Strongly Associated than Coarse Particles with Ac ute Respiratory Health Effects in Schoolchildren. Epidemiology 11:6 - 10. 44 Ostro, B., M. Lipsett, J. Mann, H. Braxton - Owens, and M. White. (2001). Air Pollution and Exacerbation of Asthma in African - American Children in Los Angeles. Epidemiology 12(2):200 - 208. 45 Vedal, S., J. Petkau, R. White, and J. Blair. (1998). Acute Ef fects of Ambient Inhalable Particles in Asthmatic and Nonasthmatic Children. American Journal of Respiratory and Critical Care Medicine 157(4):1034 - 1043. EPA HQ - OAR - 2009 - 0472 - 1671 46 Ostro, B.D. (1987). Air Pollution and Morbidity Revisited: A Specification Test. Journal of Environmental Economics Management 14:87 - 98. EPA - HQ - OAR- 2009 - 0472 - 1670 47 Gilliland FD, Berhane K, Rappaport EB, Thomas DC, Avol E, Gauderman WJ, et al. (2001). The effects of am bient air pollution on school absenteeism due to respir atory illnesses. Epidemiology 12(1):43 - 54. EPA - HQ OAR - 2009 - 0472 - 1675 48 Chen L, Jennison BL, Yang W, Omaye ST. (2000). Elementary school absenteeism and air pollution. Inhal Toxicol 12(11):997 - 1016. EPA - HQ - OAR - 2009 - 0472 - 0224 49 Ostro, B.D. and S. Rothschild. (1989). Air Pollution and Acute Respiratory Morbidity: An Observational Study of Multiple Pollutants. Environmental Research 50:238 - 247. EPA - HQ - OAR - 2009 - 0472 - 0364

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**to Further Policy Review. E.O. 12866 Review**  **– Revised Version**  **– Do Not Cite, Quote, or Release**  **During Review *****



### 8 - 44 50 U.S. Science Advisory Board. (2004). Advisory Plans for Health Effects Analysis in the Analytical Plan for EPA’s Second Prospective Analysis

– Benefits and Costs of the Clean Air Act, 1990

—2020. EPA - SAB - COUNCIL ADV - 04 - 004. EPA - HQ - OAR - 2009 - 0472 - 4664 51 National Research Council (NRC). (2002). Estimating the Public Health Benefits of Pro posed Air Pollution Regulations. Washington, DC: The National Academies Press. 52 Abt Associates, Inc. October 2005. Methodology for County- level Mortality Rate Projections. Memorandum to Bryan Hubbell and Zachary Pekar, U.S. EPA. 53 Centers for Diseas e Control: Wide - ranging OnLine Data for Epidemiologic Research (CDC Wonder) (data from years 1996 - 1998), Centers for Disease Control and Prevention (CDC), U.S. Department of Health and Human Services, Available on the Internet at <http://wonder.cdc.gov>. 54 Agency for Healthcare Research and Quality (AHRQ). 2000. HCUPnet, Healthcare Cost and Utilization Project. 55 American Lung Association. 1999. Chronic Bronchitis. Available on the Internet at <http://www.lungusa.org/diseases/lungchronic.html>. 56 American Lung Association. 2002. Trends in Asthma Morbidity and Mortality. American Lung Association, Best Practices and Program Services, Epidemiology and Statistics Unit. Available on the Internet at <http://www.lungusa.org/data/asthma/ASTHMAdt.pdf>. 57 Adams PF , Hendershot GE, Marano MA. 1999. Current Estimates from the National Health Interview Survey, 1996. Vital Health Stat 10(200):1 - 212. 58 U.S. Bureau of Census. 2000. Population Projections of the United States by Age, Sex, Race, Hispanic Origin and Nativity : 1999 to 2100. Population Projections Program, Population Division, U.S. Census Bureau, Washington, DC. Available on the Internet at <http://www.census.gov/population/projections/nation/summary/np - t.txt>. 59 National Center for Education Statistics (NCHS). 1996. The Condition of Education 1996, Indicator 42: Student Absenteeism and Tardiness. U.S. Department of Education. Washington, DC. 60 Centers for Disease Control: Wide - ranging OnLine Data for Epidemiologic Research (CDC Wonder) (data from years 1996 - 199 8), Centers for Disease Control and Prevention (CDC), U.S. Department of Health and Human Services, Available on the Internet at <http://wonder.cdc.gov>. 61 U.S. Environmental Protection Agency (U.S. EPA). 2009. Integrated Science Assessment for Particulate Matter (Final Report). EPA - 600 - R- 08 - 139F. National Center for Environmental Assessment

– RTP Division. December. Available on the Internet at <http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=216546>. 62 U.S. Environmental Protection Agency - Scienc e Advisory Board (U.S. EPA - SAB). 2009. Consultation on EPA’s Particulate Matter National Ambient Air Quality Standards: Scope and Methods Plan for Health Risk and Exposure Assessment. EPA - COUNCIL - 09 - 009. May. Available on the Internet at <http://yose mite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb85256ead006be86e/723FE644C5D758DF852 575BD00763A32/$File/EPA - CASAC - 09 - 009 -unsigned.pdf>. 63 U.S. Environmental Protection Agency - Science Advisory Board (U.S. EPA - SAB). 2009. Review of EPA’s Integrated Scie nce Assessment for Particulate Matter (First External Review Draft, December 2008). EPA COUNCIL - 09 - 008. May. Available on the Internet at <http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb85256ead006be86e/73ACCA834AB44A1085 2575BD0064346B/$File/EPA - CASAC - 09 - 008 - unsigned.pdf>. 64 Freeman(III), AM. 1993. The Measurement of Environmental and Resource Values: Theory and Methods. Washington, DC: Resources for the Future. 65 Harrington, W., and P.R. Portney. 1987. Valuing the Benefits of Health and Safe ty Regulation. Journal of Urban Economics 22:101 - 112. 66 Berger, M.C., G.C. Blomquist, D. Kenkel, and G.S. Tolley. 1987. Valuing Changes in Health Risks: A Com parison of Alternative Measures. The Southern Economic Journal 53:977 - 984.

### *** **Working Comments on Draft Language under**  **E.O. 13563 and 12866 Interagency Review. Subject** 

**to Further Policy Review. E.O. 12866 Review**  **– Revised Version**  **– Do Not Cite, Quote, or Release**  **During Review *****



### 8 - 45 67 U.S. Environmental Protection Agency (U.S. EPA). 2006. Regulatory Impact Analysis, 2006 National Ambient Air Quality Standards for P articulate Matter, Chapter 5. Office of Air Quality Planning and Standards, Research Triangle Park, NC. October. Available on the Internet at <http://www.epa.gov/ttn/ecas/regdata/RIAs/Chapter%205 -- Benefits.pdf>. 68 U.S. Environmental Protection Agency (U .S. EPA). 2008. Regulatory Impact Analysis, 2008 National Ambient Air Quality Standards for Ground - level Ozone, Chapter 6. Office of Air Quality Planning and Standards, Research Triangle Park, NC. March. Available at <http://www.epa.gov/ttn/ecas/regdata/RIAs/6 - ozoneriachapter6.pdf>. 69 U.S. EPA, (2001), “Guidance for Demonstrating Attainment of Air Quality Goals for PM2.5 and Regional Haze”, http://www.epa.gov/ttn/scram/guidance_sip.htm, Modeling Guidance, DRAFT - PM 70 National Research Council (NRC). 20 02. Estimating the Public Health Benefits of Proposed Air Pollution Regulations. Washington, DC: The National Academies Press. 71 National Research Council (NRC). 2008. Estimating Mortality Risk Reduction and Economic Benefits from Controlling Ozone Air Pollution. National Academies Press. Washington, DC. 72 Roman, Henry A., Katherine D. Walker, Tyra L. Walsh, Lisa Conner, Harvey M. Richmond, Bryan J. Hubbell, and Patrick L. Kinney. 2008. Expert Judgment Assessment of the Mortality Impact of Changes in Ambient Fine Particulate Matter in the U.S. Environ. Sci. Technol., 42(7):2268 - 2274.