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8 - 1 

# Chapter 8 **Com parison of**  **Program Costs to**  **Pr ogram**  **Air Quality**  **Benefits** EPA traditionally

evaluates the effectiveness of a final rule in terms of net benefits . Section 8 . 1 below presents the cost - benefit anal ys is of the final rule . **8.1 Cost**  **- Benefit Analysis**  The net bene fits of the final Tier 3 program are determined by 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 c osts that are incurred from our final 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 separately in this se ction, for purposes of the calculation of cost per ton of emissions reduced analy sis we have summed them to represent the estimated costs of the final program. All costs represent the fleet - weighted average of light - duty vehicles and trucks. All costs are represented in 201 1dollars.

8.1.1.1 Vehicle Program Costs The vehicle costs include the technology costs projected to meet the final exhaust and evaporative standards including the facility and operating cost impacts, as detailed in 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 MYs 2017 - 2025 fleet mix projected to result from the most recent GHG and fuel economy rules. The final vehicle costs are lo wer than the values projected in the proposal due to the items outlined in RIA Section 2.1 in addition to the exclusion of the vehicle sales in California and the states that have adopted the LEV III program.

**Table 8**  **- 1**  **: Annual Vehicle Program Costs, 2011$** Year Vehicle Exhaust Emission Control Costs ($Million) Vehicle Evaporative

Emission Control Costs ($Million) Operating Costs ($Million) Facility Costs

($Million) Total Vehicle

Program Costs ($Million) a 

### 2016 $0 $0 $0 $21 $21 2017 $268 $26 $0 $4 $297 2018 $539 $73 - $1 $4 $615 2019 $579 $72 - $2 $4 $653 2020 $599 $98 - $3 $4 $697 2021 $630 $97 - $5 $4 $725 2022 $640 $121 - $6 $4 $758 2023 $639 $116 - $8 $4 $751

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

Emission Control Costs ($Million) Operating Costs ($Million) Facility Costs

($Million) Total Vehicle

Program Costs ($Million) a 

### 2024 $653 $114 - $9 $4 $761 2025 $668 $113 - $11 $4 $773 2030 $664 $113 - $19 $4 $761 a These estimates include costs associated with the Tier 3 vehicle standards in all states except California and states that have adopted the LEV III program .



### 8.1.1.2 Fuel Program Costs The annual fuel costs of the Tier 3 program consist of the costs to the refiners to control sulfur on a per gallon basis and the total number of gallons consumed annually in the U.S. except in the state of California. The fuel costs associated with the additional operating and capital costs to refiners to meet the final sulfur average of 10 ppm, as described in detail in RIA Chapter 5, is 0.65 cents per gallon. The annual fuel costs of the final Tier 3 program are lower than the proposed fuel costs, 0.89 cents, due to the reduction in cost per gallon to control sulfur. The fuel consumption values used in the annual fuel cost projections are based on the U.S. Energy

Information Administration’s Annual Energy Outlook (AEO) 2013. The latest AEO projection used in the final rule cost projections c ontain lower annual fuel consumption than the values used in the Tier 3 proposal because AEO 2013 now reflects the light - duty GHG and fuel economy

standards for the 2017 - 2025 model years. The light - duty standards are projected to decrease the national gas oline fuel consumption due to more efficient vehicles. The annual fuel consumption in AEO 2013 represents the national fuel usage in terms of BTU. For the fuel program cost anal ys is, it was necessary to convert the national BTU fuel consumption to milli ons of gallons of fuel consumed in the U.S. in all states except California. This conversion process is explained here using a sample year of 2017. Based on AEO 2013, the annual fuel consumption of motor gasoline and E85 in the transportation sector is p rojected to be 15,342 trillion BTU in 2017. 1 

### AEO’s million BTU/barrel conversion factors for E85 and Motor Gasoline Average along with a conversion factor of 42 U.S. gallons of fuel per barrel were used to calculate the annual fuel consumption in terms of million gallons. 2 

### Based on these conversions, the national fuel consumption in 2017 is projected to be 128,318 million gallons. Finally , the national fuel consumption values from AEO 2013 were reduced for this anal ys is to remove the fraction of gasoline sold in California. The AEO 2013 Prime Supplier Sales Volumes of Motor Gasoline provides the sales of motor gasoline b y state. 3 

### Based on the average between 1983 and 2012, the fraction of national gasoline sales in California was 3.6 percent. After rem oving the fraction of fuel consumed in California, the annual fuel costs in 2017 are calculated based on a fuel consumption level of 123,689 million gallons. The Tier 3 fuel program provides flexibilities, including the ability for refiners to phase in the sulfur standards and earn earl y credits. The fuel program costs presented here assume a start date of 2017 and do not reflect the sulfur control program phase in.

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8 - 3 A comparison of the annual fuel consumption values used in the proposal and this final ru le is shown in Figure 8 - 1 .

0 20,000 40,000 60,000 80,000 100,000 120,000 140,000 160,000

2016 2018 2020 2022 2024 2026 2028 2030 2032**A n** **n u** **a l** **F u** **e l** **C o** **n s** **u m** **p t** **i o** **n (** **m i** **l l** **i o** **n s** **o f** **g a** **l l** **o n** **s )** 

**Ye ar** 



### Tier **3 Im pacted Annual**  **Fuel Consumption Notice**  **of Proposed**  **Ru lemaking** **v s.**  **Final Ru**  **le**

NPRM Final Rule



### Figure **8 -**  **1 : Annual Fuel Consumption Comparison between NPRM and Final Rule**  The projected annual fuel consumption and annual fuel costs of the final progr am are listed in Table 8 - 2 . **Table 8**  **- 2**  **: Annual Fuel Costs, 2011$**

Year Annual Fuel Consumption

Impacted b y Tier 3 Program (million gallons) Fuel Sulfur Control Costs ($Million) a 

### 2016 0 $0 2017 123,698 $804 2018 122,962 $799 2019 122,121 $794 2020 121,015 $787 2021 119,625 $778 2022 118,190 $768 2023 116,645 $758 2024 115,066 $748 2025 113,335 $737

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8 - 4 2030 107,065 $696 a These estimates include costs associated with the Tier 3 fuel standards in all states except California.



### 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 program, as shown in Table 8 - 3 .

**Table 8**  **- 3**  **: Total Annual Vehicle and Fuel Control Costs, 2011$** Year Total Vehicle Program Costs ($Million) Fuel Sulfur

Control Costs ($Million) Total Program Costs

($Million) a 

### 2016 $21.4 $0 $21 2017 $297 $804 $1,101 2018 $615 $799 $1,414 2019 $653 $794 $1,447 2020 $697 $787 $1,484 2021 $725 $778 $1,503 2022 $758 $768 $1,526 2023 $751 $758 $1,509 2024 $761 $748 $1,509 2025 $773 $737 $ 1,510 2030 $761 $696 $1,457 a These estimates include : (a) costs associated with the Tier 3 vehicle standards in all states except California and states that have adopted the LEV III program and (b) the Tier 3 fuel standards in all states except California. 

### 8.1.2 Quantified and Monetized Health and Environmental Impacts T his section presents EPA’s anal ys is of the criteria pollutant - related health and environmental impacts that would occur as a result of the final Tier 3 standards . The vehicles and fuels subj ect to the final standards are significant sources of mobile source air pollution such as direct PM, NO X 

### , SO X 

### , VOCs and air toxics. The standards will affect exhaust and evaporative emissions of these pollutants from vehicles. E missions of NO X 

### (a precurs or to ozone formation and secondarily- formed PM 2.5 

### ), SO X 

### (a precursor to secondaril y- formed PM 2.5 

### ) , VOCs (a precursor to ozone formation and, to a lesser degree, secondarily- formed PM 2.5 

### ) and directlyemitted PM 2.5 

### contribute to ambient concentrations of P M 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 final standards b y answering two key

questions:

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8 - 5 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 

### and SO 2 

### ? 2. What is the economic value of thes e effects? For the final rulemaking, 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 final standards will lead to a net decrease in PM 2.5 

### - and ozone - related health impacts in 2030. T he decrease in population - weighted national average PM 2.5 

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

### is 0.04 μg/m 3 

### in 2030). A 

### T he 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 0.32 ppb in 2030 ). Using the lower end of EPA’s range of preferred premature mortality estimates ( Krewski et al., 20 09 for PM 2.5 

### and Bell et al., 2004 for ozone) , 4 , 5 

### we estimate that b y 2030, implementation of the standards will reduce approximately 770 premature mortalities annually and yield between $ 6.7 and $7.4 billion in total annual benefits , depending on the discount rate used . B 

### The upper end of the range of avoided premature mortality estimates associated with the final standards (based on Lepeule et al., 20 12 for PM 2.5 

### and L ev y et al., 2005 for ozone) 6 , 7 

### results in approximately

2, 0 00 premature mortalities avoided in 2030 and yields between $ 18 and $19 billion in total benefits. Thus, even using the lower end of the range of premature mortality estimates, the health impacts of the final standards presented in this rule will clearl y be substantial. We note that of necessity , decisions on the emissions and other elements used in the air quality modeling were made earl y in the anal yt ical process for the final rulemaking . For this reason, the model ed changes in emissions used to support the air quality and benefits anal ys es are slightly

different than those used to represent the final emissions impacts of the Tier 3 standards . The magnitude of the differences is small, however, and for that reason we do not expect these differences to materially impact our cost - benefit conclusions. See Chapter 7.2.1.1 for more details.

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 different 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 The monetized value of PM 2.5 - related mortality accounts for a twenty - year segmented cessation lag. 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). Nonfatal myocardial infarctions (MI) are valued using age - specific cost - of - i llness values that reflect lost earnings and direct medical costs over a 5 - year period following a nonfatal MI.

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8 - 6 8.1.2.1 Overview This analy sis reflects the impact s of the final Tier 3 rule in 2030 compared to a future ye ar reference scenario without the program in place. Overall, we estimate that the final rule will lead to a net decrease in PM 2.5 

### - related health and environmental impacts (see Section 7.2. 4 for more information about the air quality modeling results ). T he 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 0.04 μg/m3 in 2030). The air quality modeling also projects decreases in ozone concentrations (see Section 7.2. 4 ). The overall de crease in population - weighted national average ozone exposure results in decreases in ozone - related health and environmental impa cts (population weighted maximum 8 - hour average ozone decreases b y 0.32 ppb in 2030). We base our analy sis of the program’s impact on human health and the environment on peer - reviewed studies of air quality and human health effects. 8 , 9 , 10 

### Our benefits meth ods are consistent with the RIA that accompanied the final revisions to the National Ambient Air Quality Standards (NAAQS) for Particulate Matter, the 2008 final ozone NAAQS , and the 2010 ozone NAAQS reconsideration . To model the ozone and PM air quality impacts of the final standards, we used the Community

Multiscale Air Quality

(CMAQ) model (see Chapter 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 65 (BenMAP). C 

### Ben MAP 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 metho ds) 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 Table 8 - 4 ) to estimates of PM - related premature mortality .

C Information on BenMAP, including downloads of the software, can be found at http://www.epa.gov/ttn/ecas/ benmodels.html.

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8 - 7 **Table 8**  **- 4**  **: Estimated 2030 Monetized PM**  **- and Ozone**  **- Related Health Benefits**  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, 201 1 $, 3% Discount Rate) b,c Total Benefits (Billions, 201 1 $, 7% Discount Rate) b,c Multi - city analyses Bell et al., 2004 Total: $7.4 - $15 PM: $6.0 - $14 Ozone: $1.1 Total: $6.7 - $14 PM: $5.4 - $12 Ozone: $1.1 Huang et al., 2005 Total: $7.9 - $16 PM: $6.0 - $14 Ozone: $1.7 Total: $7.3 - $14 PM: $5.4 - $12 Ozone: $1.7 Schwartz, 2005 Total: $8.0 - $16 PM: $6.0 - $14 Ozone: $1.7 Total: $7.3 - $14 PM : $5.4 - $12 Ozone: $1.7 Meta - analyses Bell et al., 2005 Total: $9.8 - $18 PM: $6.0 - $14 Ozone: $3.6 Total: $9.2 - $16 PM: $5.4 - $12 Ozone: $3.6 Ito et al., 2005 Total: $11 - $19 PM: $6.0 - $14 Ozone: $4.9 Total: $11 - $18 PM: $5.4 - $12 Ozone: $4.9 Levy et al., 2005 Total: $11 - $19 PM: $6.0 - $14 Ozone: $5.0 Total: $11 - $18 PM: $5.4 - $12 Ozone: $5.0

. a Total includes premature mortality - related and morbidity - related ozone and PM 2.5 benefits. Range was developed by adding the estimate from the o zone premature mortality function to the estimate of PM 2.5 - related premature mortality derived from either the ACS study ( Krewski et al., 200 9 ) or the Six - Cities study ( Lepeule et al., 20 12 ). Range also reflects alternative estimates of non - fatal heart attacks avoided based on either Peters et al. (2001) or a pooled estimate of four studies. b Note that total benefits presented here do not include a number of unquantified benefits categories. A detailed listing of unquantified health effects is provided i n Table 8 - 5 . c Results reflect the use 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 anal ys is presented in this chapter incorporates an array of policy and technical changes that the Agency has adopted since the Tier 3 proposal’s draft RIA. These changes reflect EPA’s work to update PM - re lated benefits reflected in the most recent PM NAAQS. 11 

### Below we note the aspects of this analysis that differ from the Tier 3 proposal’s draft RIA: 12

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8 - 8  Incorporation of the newest American Cancer Society (ACS) mortality study and newest Harvard Six Cities m ortality study. In2012, L epeule et al. published an extended anal ys is of the Six Cities cohort. 13 

### Compared to the study it replaces (Laden et al., 2006), 14 

### this new anal ys is follows the cohort for a longer time and includes more ye ars of PM 2.5 

### monitoring data. The all - cause PM 2.5 

### mortality risk coefficient drawn from Lepeule et al. is roughl y similar to the Laden et al. risk coefficient applied in the EPA’s recent anal ys es of long - term PM 2.5 

### mortality and has narrower confidence intervals. In 2009, the Health Effects Institute published an extended analy sis of the ACS cohort (Krewski et al., 2009). 15 

### Compared to the study it replaces (Pope et al., 2002), 16 

### this new anal ys is incorporates a number of methodological improvements. D 

### The all - cause PM 2.5 

### mortality risk estimate drawn from Krewski et al. (2009) is identical to the Pope et al. (2002) risk estimate applied in recent EPA anal ys es of long - term PM 2.5 

### mortality but has narrower confidence intervals.

 Updated health endpoints. We have removed the quantification of chronic bronchitis from our main anal ys is. This change is consistent with the findings of the PM Integrated Science Assessment (ISA) that the evidence for an association between long - term exposure to PM 2.5 

### and respiratory effects is more tenuous. 17



###  Updated demographic data. We updated the population demographic data in BenMAP to reflect the 2010 Census and future projections based on economic forecasting models developed by Woods and Poole, Inc. 18 

### These data replace the earlier demographic projection data from Woods and Poole (2011). 19



###  Incorporation of new morbidity studies. Since the publication of the 2004 Criteria Document for Particulate Matter, 20 

### the publication of the more recent PM ISA, 21 

### and the Provisional Assessment of Recent Studies on Health Effects of Particulate Matter Exposure (“Provisional Assessment”), 22 

### the epidemiological literature has produced several new studies examining the association between short - term PM 2.5 

### exposure and acute m yo cardial infarctions, respiratory and cardiovascul ar hospitalizations, respiratory and cardiovascular emergency department visits, acute respiratory

s ymptoms and exacerbation of asthma, respiratory and cardiovascular hospitalizations. Upon careful evaluation of this new literature, we added several new studies to our health impact assessment; in many cases we have replaced older single - city time - series studies with newer multi - city time series anal ys es.  Updated the survival rates for non - fatal acute myocardial infarctions. Based on recent data from Agency for Healthcare Research and Quality ’s Healthcare Utilization Project National Inpatient Sample database, 23 

### we identified death rates

D Refer to the 2012 P M NAAQS RIA for more detail regarding the studies themselves.

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8 - 9 f or adults hospitalized with acute m yocardial infarction stratified by age. These rates replace the survival rates from Ros amond et al. (1999). 24



###  Updated hospital cost- of - illness (COI), including median wage data. In previous benefits anal ys es, estimates of hospital charges and lengths of hospital stays were based on discharge statistics provided b y the Agency for Healthcare Research and Quality ’s Healthcare Utilization Project National Inpatient Sample (NIS) database for 2000. 25 

### The version of BenMAP (version 4.0.65) used in this analy sis updated this information to use the 2007 database. The data source for the updated median annual income is the 2007 American Community Survey . The benefits in Table 8 - 4 i nclude all of the human health impacts we are able to quantify and monetize at this time. However, the full complement of human health 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 availab le or which do not provide easily interpretable outcomes ( e.g. , changes in heart rate variability). The se are listed in Table 8 - 5 . As a result, the health benefits quantified in this analy sis are likel y underestimate s of the total benefits attributable to the final program .

**Table 8**  **- 5**  **: Quantified and U**  **nquantified Health Effects** BENEFITS CATEGORY SPECIFIC EFFECT EFFECT HAS BEEN QUANTIFIED EFFECT HAS

BEEN MONETIZED MORE

I NFORMATION **Improved Human Health** Reduced incidence of premature mo rtality and mo rbidity from exposure to PM 2.5 Adult premature mortality based on

cohort study estimates and expert elicitation estimates (age >25 or age >30)   PM NAAQS RIA, Section 5.6 Infant mortality (age <1)   PM NAAQS RIA, Section 5.6 Non - fatal heart attacks (age > 18)   PM NAAQS RIA, Section 5.6 Hospital admissions

—

respiratory (all ages)   PM NAAQS RIA, Section 5.6 Hospital admissions

—cardiovascular (age >20)   PM NAAQS RIA, Section 5.6 Emergency department visits for asthma (all ages)   PM NAAQS RIA, Section 5.6 Ac ute bronchitis (age 8

– 12)   PM NAAQS RIA, Section 5.6 Lo wer respiratory symptoms (age 7

–   PM NAAQS RIA,

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8 - 10 BENEFITS CATEGORY SPECIFIC EFFECT EFFECT HAS BEEN QUANTIFIED EFFECT HAS

BEEN MONETIZED MORE

I NFORMATION 14) Section 5.6 Upper respiratory sym

ptoms (asthmatics age 9

– 11)   PM NAAQS RIA, Section 5.6 Asthma exacerbation (asthmatics age 6

– 18)   PM NAAQS RIA, Section 5.6 Lost work days (age 18

– 65)   PM NAAQS RIA, Section 5.6 Minor restricted - activity days (age 18

– 65)   PM NAAQS RIA, Se ction 5.6 Chronic Bronchitis (age >26)

— — PM NAAQS RIA, Section 5.6 c Emergency department visits for cardiovascular effects (all ages) — — PM NAAQS RIA, Section 5.6 c Strokes and cerebrovascular disease (age 50

– 79) — — PM NAAQS RIA, Section 5.6 c Other cardiovascular effects (e.g., other ages) — — PM ISA a Other respiratory effects (e.g., pulmonary function, non - asthma ER visits, non - bronchitis chronic diseases, other ages and populations) — — PM ISA a Reproductive and developmental effects (e.g., low

birth weight, pre term births, etc.) — — PM ISA a , b Cancer, mutagenicity, and genotoxicity effects — — PM ISA a , b Reduced incidence of premature mo rtality and mo rbidity from exposure to ozone Premature mortality based on short term study estimates (a ll ages)   Ozone ISA Premature mortality based on long term study estimates (age 30

– 99) — — Ozone ISA c Hospital admissions

—

respiratory causes (age > 65)   Ozone ISA Hospital admissions

—

respiratory causes (age <2)   Ozone ISA Emergency department visits for asthma (all ages)   Ozone ISA Minor restricted - activity days (age   Ozone ISA

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8 - 11 BENEFITS CATEGORY SPECIFIC EFFECT EFFECT HAS BEEN QUANTIFIED EFFECT HAS

BEEN MONETIZED MORE

I NFORMATION 18

– 65) School absence days (age 5

– 17)   Ozone ISA Decreased outdoor worker productivity (age 18

– 65)   Ozone ISA Other respiratory effects (e.g., pr emature aging of lungs) — — Ozone ISA a Cardiovascular and nervous system effects — — Ozone ISA b Reproductive and developmental effects — — Ozone ISA b Reduced incidence of mo rbidity from exposure to air toxics Cancer (benzene, 1,3 - butadiene,

formaldehy de, acetaldehyde) Anemia (benzene) Disruption of production of blood components (benzene) Reduction in the number of blood platelets (benzene) Excessive bone marrow

formation (benzene) Depression of lymphocyte counts (benzene) Reproductive and developmenta l 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 respir atory tract (acetaldehyde) Upper respiratory tract irritation and congestion (acrolein) — — IRIS; NRC, 2000 a,b

a We assess these benefits qualitatively because we do not have sufficient confidence in available data or methods. b We assess these benefits qualitatively because current evidence is only suggestive of causality or there are other significant concerns over the strength of the association. c We assess these benefits qualitatively due to time and resource limitations for this analysis. 

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

available tools and methods to assess air toxics risk from mobile s ources at the national scale are not adequate for extrapolation to incidence estimations or benefits assessment.

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8 - 12 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 A ssessment (NATA). The EPA Science Advisory Board specifically

commented in their review of the 1996 NATA that these tools were not yet ready for use in a national - scale benefits anal ys is, because they did not consider the full distribution of exposure and risk, or address sub - chronic health effects. 26 

### 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 o f the benefits and costs of the Clean Air Act , 27 

### 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 .” 28 

### 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 analy sis of the final program . E 

### The reduction in air pollutants that will result from the final program will also have “welfare ” co - benefits in addition to human health benefits, including changes in visibility , materials damage, ecological effects from PM deposition, ecological effects from nitrogen and sulfur emissions, vegetation effects from ozone exposure, and climate effects . F 

### Despite our goal to quantify

and monetize as many

of the benefits as possible for the final rulemaking , the welfare co - benefits of the Tier 3 standard s remain unquantified and nonmonetized in this RIA due to data, methodology , and resource limitations. As a result, the benefits quantified in this analy sis are likel y underestimates of the total benefits attributable to the final program . We refer the reader to Chapter 6 of the PM NAAQS RIA for a complete discussion of these welfare co benefits. 29 

### 8.1.2.2 Human Health Impacts Table 8 - 6 and Table 8 - 7 present the core estimates of annual PM 2.5 

### and ozone health impacts in the 48 contiguous U.S. states associated with the final Tier 3 program . For each

E 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/E PA Workshop 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 implemented 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. F We project that the Tier 3 vehicle and fuel stand ards will reduce nitrous oxide (N 2 O) and methane (CH 4 ) emissions from vehicles. The reductions in these potent greenhouse gases will be offset to some degree by the increase in CO 2 emissions from refineries. The combined impact is a net de crease on a CO2 - equivalent basis and would yield a net benefit if these reductions were monetized.

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8 - 13 end point presented in Table 8 - 6 and Table 8 - 7 , we provide both the point estimate and 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 final program would result in between 660 and 1,500 cases of avoided PM 2.5 

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

anal ys is was conducted t o understand the impact of alternative concentration response functions suggested b y experts in the field. As shown in Table 8 - 8, when the range of expert opinion is used, we estimate between 130 and 2,200 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) 30 , 31 , 32 

### and from a series of meta - anal ys es. 33 , 34 , 35 

### 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 sy stematic multicity

analy ses that use national databases of air pollution and mortality , such as in the NMMAPS, without ex cluding consideration of meta - anal ys es of previously published studies.” 36 

### For ozone - related premature mortality in 2030 , we estimate a range of between 110 to 500 fewer premature mortalities. Following these tables, we also provide a more comprehensive presentation of the distributions of incidence generated using the available information from empirical 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 and the Six C ities study . The 90 percent confidence interval for each separate es timate 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 be low this range , while two of the experts are above this range. 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 evenl y distributed across the range illustrated b y arrayi ng the highest and lowest expert means.

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8 - 14 **Table 8**  **- 6**  **: Estimated PM**  **2.5** 

### - **Related Health Impacts** Health Effect 2030 Annual Reduction in Incidence (5 th % - 95 th %ile) Premature Mortality

– Derived from epidemiology literature b Adult, age 30+, ACS Cohort Study ( Krewski et al., 200 9 ) Adult, age 25+, Six - Cities Study ( Lepeule et al., 20 12 ) Infant, age < 1 year (Woodruff et al., 1997) 660 (480

– 840) 1,500 (860

– 2,100) Non - fatal myocardial infarction (adult, age 18 and over) Peters et al. (2001) Pooled estimate of 4 studies 790 (290

– 1,300) 85 (42

– 190) Hospital admissions - respiratory (all ages) c,e 210 ( - 38

– 380) Hospital admissions - cardiovascular (adults, age >18) d 250 (130

– 440) Emergency room visits for asthma (age 18 years and younger) e 340 ( - 58

– 660) Ac ute bronchitis, (children, age 8 - 12) e 980 ( - 35

– 2,000) Lo wer respiratory symptoms (children, age 7 - 14) 13,000 (6,000

– 19,000) Upper respiratory symptoms (asthmatic children, age 9 - 18) 18,000 (5,600

– 30,000) Asthma exacerbation (asthmatic children, age 6 - 18) 19,000 (2,300

– 37,000) Work loss days 81,000 (70,000

– 91,00 0) Minor restricted activity days (adults age 18 - 65) 480,000 (400,000

– 550,000)

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 most re cent American Cancer Society (ACS) Cohort Study ( Krew ski et al., 200 9 ) and the mo st recent Six - Cities Study ( Lepeule et al., 20 12 ). Note that these are two alternative estimates of adult mortality and should not be summed. PM - related infant mortality bas ed upon a study by Woodruff, Grillo, and Schoendorf, (1997). 37 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 card iovascular and subcategories for ischemic heart disease, dysrhythmias, and heart failure. e The negative estimates at the 5th percentile confidence estimates for these morbidity endpoints reflect the statistical power of the study used to calculate these health impacts. These results do not suggest that reducing air pollution results in additional health impacts.

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8 - 15 **Table 8**  **- 7**  **: Estimated Ozone -**  **Related Health Impacts** Health Effect 2030 Annual Reduction in Inciden ce (5th% - 95th%ile) 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. (20 05)

– Non - accidental Levy et al. (2005)

– All cause 110 (46

– 170) 160 (74

– 250) 170 (68

– 270) 350 (190

– 510) 490 (320

– 660) 500 (360

– 630) Hospital admissions - respiratory causes (adult, 65 and older) c 740 (87

– 1,400) Hospital admissions - r espiratory causes (children, under 2) 310 (160

– 450) Emergency room visit for asthma (all ages) d 330 ( - 8

– 990) Minor restricted activity days (adults, age 18 - 65) 600,000 (290,000

– 910,000) School absence days 210,000 (92,000

– 300,000) a Incidence i s 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. (2005). The estimates of ozone - related premature mortality should therefore not be summed. c Respiratory hospital admissions for ozone include admissions for all respiratory cau ses and subcategories for COPD and pneumonia. d The negative estimate at the 5th percentile confidence estimate for this morbidity endpoint reflects the statistical power of the study used to calculate this health impact. This result does not suggest that reducing air pollution results in additional health impacts. 

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

**Mortality in 2030 Associated with the Final**  **Program** Sourc e of Mortality Estimate 2030 Tier 3 Control 5th Percentile Mean 95th Percentile Krewski et al. (2009) 480 660 840 Lepeule et al. (2012) 860 1,500 2,100 Expert A 340 1,700 3,300 Expert B 88 1,400 2,900 Expert C 480 1,300 2,200 Expert D 58 950 1,500 Expert E 1,100 2,200 3,300

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8 - 16 Sourc e of Mortality Estimate 2030 Tier 3 Control 5th Percentile Mean 95th Percentile Expert F 790 1,200 1,700 Expert G - 1 790 1,400 Expert H - 2 980 2,300 Expert I 110 1,300 2,300 Expert J 160 1,100 2,300 Expert K 0 130 590 Expert L 1 860 1,800 

### 8.1.2.3 Monetized Estimates of Human Health and Environmental Impact s 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 mon etized estimates are presented in 201 1 $ . Where appropriate, estimates account for growth in real gross domestic product (GDP) per capita between 2000 and 2030. G 

### The monetized value of PM 2.5 

### - related mortality also accounts for a twenty- ye ar segmented cess ation lag. H 

### 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). I 

### 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 benefit s of the final 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 in 203 0 for the final program , using the ACS and Six - Cities PM mortality studies and the range of ozone mortality

assumptions, is between $ 7.4 and $ 19 billion billion , assuming a 3 percent

G 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 re al 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 f or 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 . Note that similar adjustments do not exist for cost - of - i llness based unit values. For these, we apply the same unit value regardless of the future year of analysis. H Based in part on prior SAB advice, EPA has typically assumed that there is a time lag between changes in pollution exposures and the total reali zation 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 Protection Agency Administrator, December. I 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 - 17 discount rate, or between $ 6.7 and $ 18 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 ozone - related 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 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, mino r restricted activity day s, and work loss day s 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 i ndividuals. A comparison of the incidence table to the monetary benefits table reveals that there is not alway s 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 day s than PM - related premature m ortalities, yet work loss day s account for only a very small fraction of total monetized benefits. This reflects the fact that many of the less severe health effects, while more commo n, are valued at a lower level than the more severe health effects. Also, some effects, such as hospital admissions, are valued using a prox y measure of willingness - to - pay ( e.g. , cost - of

- illness). As such, the true value of these effects may

be higher th an that reported here. **Table 8**  **- 9**  **: Estimated Monetary Value of Changes in Incidence of Health and Welfare Effects (millions of**  **2010 $)**  **HEALTH ENDPOINTS 2030**  **(5 TH**  **AND 95 TH**  **PERCENTILE) PM**  **2.5 -**  **Related Health Eff ects**  Premature Mortality

– Derived from Epidemiology Studies b,c Adult, age 30+ - ACS study (Krew ski et al., 2009) 3% discount rate 7% discount rate $6,100 ($910 - $14,000) $5,500 ($820 - $13,000) Adult, age 25+ - Six - Cities stu dy (Lepeule et al., 2012) 3% discount rate 7% discount rate $14,000 ($2,000 - $33,000) $12,000 ($1,800 - $30,000) Infant Mortality, <1 year

– (Woodruff et al. 1997) $13 ($1.8 - $32) Non - fatal acute myocardial infarctions Pe ters et al., 2001 3% discount rate 7% discount rate Pooled estimate of 4 studies 3% discount rate 7% discount rate $96 ($21 - $230) $93 ($19 - $220) $10 ($2.6 - $27) $10 ($2.4 - $27)

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8 - 18 Hospital admissions for respiratory causes d $5.9 ( - $1.6 - $11) Hospital admissions for cardiovascular causes $9.9 ($5.0 - $17) Emergency room visits for asthma d $0.15 ( - $0.02 - $0.29) Ac ute bronchitis (children, age 8

– 12) d $0.49 ( - $0.02 - $1.2) Lo wer respiratory symptoms (children, 7

– 14) $0.27 ($0.11 - $0.51) Upper respiratory symptoms (asthma, 9

– 11) $0.62 ($0.18 - $1.4) Asthma exacerbations $1.1 ($0.14 - $2.7) Work loss days $12 ($11

– $14) Minor restricted - activity days (MRADs) $34 ($20 - $49) **Ozone -**  **Related Health E ffects**  Premature Mortality, All ages

– Derived from Multi - city analyses Bell et al., 2004 $1,100 ($150 - $2,800) Huang et al., 2005 $1,600 ($220 - $4,100) Schwartz, 2005 $1,700 ($220 - $4,400) Premature Mortality, All ages

– Derived from Meta - analy ses Bell et al., 2005 $3,600 ($510 - $8,800) Ito et al., 2005 $5,000 ($740 - $12,000 Levy et al., 2005 $5,100 ($760 - $12,000) Hospital admissions - respiratory causes (adult, 65 and older) $21 ($2.5 - $39) Hospital admissions - respiratory causes (chi ldren, under 2) $3.7 ($1.9 - $5.4) Emergency room visit for asthma (all ages) $0.14 ( - $0.003 - $0.41) Minor restricted activity days (adults, age 18 - 65) $43 ($19 - $73) School absence days $21 ($9.3 - $31) a Monetary benefits are rounded to two signifi cant 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. d The negative estimate at the 5th percentile confidence estimate for this morbidity endpoint reflects the statistical power of the study used to calculate this health impact. This result does not suggest that reducing air pollution results in additional health impacts.

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8 - 19 **Table 8**  **- 10**  **: Total Monetized Ozone and PM**  **- related Benefits Associated with the**  **Final Program**  **in 2030 Total Ozone and PM Benefits (billions,**  **201 1**  **$) –**  **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 $7.4 - $15 Multi - city Bell et al., 2004 $6.7 - $14 Huang et al., 2005 $7.9 - $16 Huang et al., 2005 $7.3 - $14 Schwartz, 2005 $8.0 - $16 Schwartz, 2005 $7.3 - $14 Meta - analysis Bell et al.,

2005 $9.8 - $18 Meta - analysis Bell et al., 2005 $9.2 - $16 Ito et al., 2005 $11 - $19 Ito et al., 2005 $11 - $18 Levy et al., 2005 $11 - $19 Levy et al., 2005 $11 - $18 **Total Ozone and PM Benefits (billions,**  **201 1**  **$) –**  **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 Mul ti - city Bell et al.,

2004 $2.4 - $22 Multi - city Bell et al., 2004 $2.4 - $20 Huang et al., 2005 $2.9 - $22 Huang et al., 2005 $2.9 - $20 Schwartz, 2005 $3.0 - $22 Schwartz, 2005 $3.0 - $20 Meta - analysis Bell et al.,

2005 $4.9 - $24 Meta - analysis Bel l et al., 2005 $4.9 - $22 Ito et al., 2005 $6.3 - $26 Ito et al., 2005 $6.3 - $24 Levy et al., 2005 $6.3 - $26 Levy et al., 2005 $6.3 - $26 

### 8.1.2.4 Methodology We follow a “damage - function” approach in calculating total benefits of the modeled changes in environmental quality . This approach estimates changes in individual health endpoints (specific effects that can be associated with changes in air quality) and assigns values to those changes assuming independence of the values for those individual endpoi nts. Total benefits are calculated simply as the sum of the values for all non - overlapping health endpoints. The “damage - function” approach is the standard method for assessing costs and benefits of environmental quality

programs and has been used in several recent published anal ys es. 38 , 39 , 40 

### To assess economic value in a damage - function framework, the changes in environmental quality must be translated into effects on people or on the things that people value. In some cases, the changes in environmental quali ty can be directly

valued. In other cases, such as for

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8 - 20 changes in ozone and PM, an impact anal ys is must first be conducted to convert air quality changes into effects that can be assigned dollar values. For the purposes of this RIA, the health impacts analy sis (HIA) includes those health effects that are directl y linked to ambient levels of air pollution and specifically to those linked to ozone and PM 2.5 

### . We note at the outset that the EPA rarel y has the time or resources to perform extensive new research to measure directly either the health outcomes or their values for regulatory anal ys es. Thus, similar to Kunzli et al. (2000) 41 

### and other, more recent health impact anal ys es, our estimates are based on the best available methods of benefits transfer. Benef its transfer is the science and art of adapting primary research from similar contexts to obtain the most accurate measure of benefits for the environmental quality change under analysis. Adjustments are made for the level of environmental quality change, the socio - demographic and economic characteristics of the affected population, and other factors to improve the accuracy and robustness of benefits estimates.

8.1.2.4.1 Human Health Impact Assessment The health impact assessment (HIA) quantifies the changes in the incidence of adverse health impacts resulting from changes in human exposure to PM 2.5 

### and ozone air quality. HIAs are a well - established approach for estimating the retrospective or prospective change in adverse health impacts expected to result from popul ation - level changes in exposure to pollutants. 42 

### PC based tools such as the environmental Ben efits M apping and A nal ys is P

rogram (BenMAP) can sy stematize health impact analy ses by appl yi ng a database of key input parameters, including health impact functio ns and population projections

— provided that key

input data are available, including air quality

estimates and risk coefficients. 43 

### Analy sts have applied the HIA approach to estimate human health impacts resulting from hypothetical changes in pollutant level s. 44 , 45 

### , 46 

### The EPA and others have relied upon this method to predict future changes in health impacts expected to result from the implementation of regulations affecting air quality . 47 

### For this assessment, the HIA is limited to those health effects that are directl y linked to ambient ozone and PM 2.5 

### concentrations. The HIA approach used in this analy sis involves three basic steps: (1) utilizing projections of PM 2.5 

### air qualityJ 

### and estimating the change in the spatial distribution of the ambient air quality ; (2) determining the subsequent change in population - level exposure; (3) calculating health impacts by appl yi ng concentration - response relationships drawn from the epidemiological literature to this change in population exposure. A ty pical health impact function 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

J Projections of ambient PM 2.5 concentrations for this analysis were gener ated using the Community Multiscale Air Quality model (CMAQ). See Chapter 7 of this RIA for more information on the air quality modeling.

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8 - 21 pollutant measure. There are other functional forms, but the basic e lements 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 baseline incidence rates. We also describe the treatment of potential thresholds in PM - related health impact functions in Section 8.1.2.5.3 . Section 8. 1.2.4.6 describes the ozone and PM air quality inputs to the health impact functions. 8.1.2.4.2 Potentially Affected Populations Quantified and monetized human health impac ts depend on the demographic characteristics of the population, including age, location, and income. We use population projections based on economic forecasting models developed b y Woods and Poole, Inc. 48 

### The Woods and Poole (WP) database contains county- le vel projections of population by age, sex, and race out to 2040, relative to a baseline using the 2010 Census data. Projections in each county are determined simultaneously with every

other county

in the United States to take into account patterns of econo mic growth and migration. The sum of growth in county- level populations is constrained to equal a previously determined national population growth, based on Bureau of Census estimates. 49 

### According to WP, linking county- level growth projections together and constraining to a national - level total growth avoids potential errors introduced b y forecasting each county

independently . County

projections are developed in a four - stage process:

 

### First, national - level variables such as income, employ ment, and populations are forecasted.

 

### Second, employ

ment projections are made for 179 economic areas defined by the Bureau of Economic Analy sis, 50 

### using an “export - base” approach, which relies on linking industrial - sector production of non - locall y consumed production items, suc h as outputs from mining, agriculture, and manufacturing with the national economy . The export - based approach requires estimation of demand equations or calculation of historical growth rates for output and employ ment by sector.

 

### Third, population is projec ted for each economic area based on net migration rates derived from employ

ment opportunities and following a cohort - component method based on fertility

and mortality

in each area.

 

### Fourth, emplo ym ent and population projections are repeated for counties, us ing the economic region totals as bounds. The age, sex, and race distributions for each region or county are determined by

aging the population by

single year of age b y s ex and race for each ye ar through 2040 based on historical rates of mortality, fertili ty , and migration. 8.1.2.4.3 Effect Estimate Sources The first step in selecting effect coefficients is to identify the health endpoints to be quantified. We base our selection of health endpoints on consistency with the EPA’s Integrated Science Assessments (which r eplace previous Criteria Documents), with input and advice from the SAB - HES, a scientific review panel specifically established to provide advice on the use of the scientific literature in developing benefits analyses for the EPA’s Report to Congress on Th e Benefits and Costs of the Clean Air Act 1990 to 2020 . 51 

### Inaddition, we have included more

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8 - 22 recent epidemiology

studies from the PM I SA and the Provisional Assessment . 52 , 53 

### In general, we follow a weight of evidence approach, based on the biological plausib ility of effects, availability

of concentration - response functions from well conducted peer - reviewed epidemiological studies, cohesiveness of results across studies, and a focus on endpoints reflecting public health impacts (like hospital admissions) rathe r than ph ysiological responses (such as changes in clinical measures like Forced Expiratory Volume [FEV1]). There are several ty

pes of data that can support the determination of types and magnitude of health effects associated with air pollution exposures. These sources of data include toxicological studies (including animal and cellular studies), human clinical trials, and observational epidemiology studies. All of these data sources provide important contributions to the weight of evidence surrounding a p articular health impact. However, only epidemiology studies provide direct concentration - response relationships that can be used to evaluate population - level impacts of reductions in ambient pollution levels in a health impact assessment. For the data - deri ved estimates, we relied on the published scientific literature to ascertain the relationship between PM 2.5 , 

### , ozone, and adverse human health effects. We evaluated epidemiological studies using the selection criteria summarized in Table 8 - 11 . These criteria include consideration of whether the study was peer - reviewed, the match between the pollutant studied and the pollutant of interest, the study design and location, and characteristics of the study population, amo ng other considerations. In general, the use of concentration - response functions from more than a single study can provide a more representative distribution of the effect estimate. However, there are often differences between studies examining the same en dpoint, making it difficult to pool the results in a consistent manner. For example, studies may examine different pollutants or different age groups. For this reason, we consider very carefully the set of studies available examining each endpoint and sele ct a consistent subset that provides a good balance of population coverage and match with the pollutant of interest. In many cases, either because of a lack of multiple studies, consistency problems, or clear superiority in the quality or comprehensiveness of one study over others, a single published study is selected as the basis of the effect estimate. When several effect estimates for a pollutant and a given health endpoint have been selected, they

are quantitatively

combined or pooled to derive a more robust estimate of the relationship. The BenMAP Manual Technical Appendices provides details of the procedures used to combine multiple impact functions. 54 

### In general, we used fixed or random effects models to pool estimates from different single city studie s of the same endpoint. Fixed effects pooling simply weights each study ’s estimate by the inverse variance, giving more weight to studies with greater statistical power (lower variance). Random effects pooling accounts for both within - study variance and be tween - study variability , due, for example, to differences in population susceptibility . We used the fixed effects model as our null hy pothesis and then determined whether the data suggest that we should reject this null hy pothesis, in which case we would u se the random effects model. K 

### Pooled impact functions are used to estimate hospital admissions

K EPA recently changed the algorithm BenMAP uses to calculate study variance, which is used in the pooling process. Prior versions of the model calculated population variance, while the version used here calculated sample

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8 - 23 and asthma exacerbations. When combining evidence across multi - city studies (e.g., cardiovascular hospital admission studies), we use equal weights pooling. The effect estimates drawn from each multi - city study are themselves pooled across a large number of urban areas. For this reason, we elected to give each study an equal weight rather than weighting b y the inverse of the variance reported in each study. For m ore details on methods used to pool incidence estimates, see the BenMAP Manual Appendices. Effect estimates selected for a given health endpoint were applied consistently across all locations nationwide. This applies to both impact functions defined b y a s ingle effect estimate and those defined b y a pooling of multiple effect estimates. Although the effect estimate may , in fact, vary from one location to another (e.g., because of differences in population susceptibilities or differences in the composition o f PM), location - specific effect estimates are generally not available. **Table 8**  **- 11**  **: Criteria Used When Selecting C**  **- R Functions**  Consideration Comments Peer - Reviewed Research Peer - reviewed research is preferred to research that has not undergone the peer - review process. Study T ype Among studies that consider chronic exposure (e.g., over a year or longer), prospective cohort studies are preferred over ecological studies because they control for important individu al - level confounding variables that cannot be controlled for in ecological studies. Study Period Studies examining a relatively longer period of time (and therefore having more data) are preferred, because they have greater statistical power to detect ef fects. Studies that are mo re recent are also preferred because of possible changes in pollution mixes, medical care, and lifestyle over time. However, when there are only a few studies available, studies from all years will be included. Population Attributes The most technically appropriate measures of benefits would be based on impact functions that cover the entire sensitive population but allow for heterogeneity across age or other relevant demographic factors. In the absence of effect estimates specifi c to age, sex, preexisting condition status, or other relevant factors, it may be appropriate to select effect estimates that cover the broadest population to match with the desired outcome of the analysis, which is total national - level health impacts. Whe n available, multi - city studies are preferred to single city studies because they provide a more generalizable representation of the concentration - response function. Study Size Studies examining a relatively large sample are preferred because they general ly have mo re power to detect small magnitude effects. A large sample can be obtained in several ways, including through a large population or through repeated observations on a smaller population (e.g., through a symptom diary recorded for a panel of asthm atic children). Study Location U.S. studies are more desirable than non - U.S. studies because of potential differences in pollution characteristics, exposure patterns, medical care system, population behavior, and lifestyle. National estimates are most app ropriate when benefits are nationally distributed; the impact of regional differences may be important when benefits only accrue to a single area. Pollutants Included in Model When modeling the effects of ozone and PM (or other pollutant combinations) jo intly, it is im portant to use properly specified impact functions that include both pollutants. Using single - pollutant models in cases where both pollutants are expected to affect a health

variance. This change did not affect the selection of random or fixed effects for the pooled incidence estimates between the proposal and final RIA.

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8 - 24 outcome can lead to double - counting when pollutants are correlated. Measure of PM For this analysis, impact functions based on PM 2.5 are preferred to PM 10 because of the focus on reducing emissions of PM 2.5 precursors, and because air quality modeling was conducted for this size fraction of PM. Where PM 2.5 functions are not available, PM 10 functions are used as surrogates, recognizing that there will be potential downward (upward) biases if the fine fraction of PM 10 is more (less) toxic than the coarse fraction. Economically Valuable Health Effects Some health effects, such as forced expiratory volume and other technical measurements of lung function, are difficult to value in monetary terms. These health effects are not quantified in this analysis. Non - overlapping Endpoints Although the benefits associated with each in dividual health endpoint may be analyzed separately, care must be exercised in selecting health endpoints to include in the overall benefits analysis because of the possibility of double - counting of benefits. 

### It is important to note that we are unable t o 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 admissi ons 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 concentration - response (CR) relationship. Table 8 - 12 lists the health endpoints included in this analy sis. **Table 8**  **- 12**  **: Health Impact Functions Used in BenMAP to Estimate Impacts 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) 55

– Non accidental Huang et al (2005) 56

- Cardiopulmonary Schwartz (2005) 57

– Non - accidental Meta - analyses: Bell et al (2005) 58

– All cause Ito et al (2005) 59

– Non - accidental Levy et al (2005) 60

– All cause All ages

Premature mortality

—cohort study, all cause PM 2.5 Krewski et al. (200 9 ) 61 L epeule et al. (20 12 ) 62 >29 years >25 years Premature mortality, total exposures PM 2.5 Exp ert Elicitation (IEc, 2006) 63 >24 years Premature mortality

— all - cause PM 2.5 Woodruff et al. (1997) 64 Infant (<1 year) **Chronic Illness**

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8 - 25 **ENDPOINT POLLUTANT**  **STUDY STUDY POPULATION**  Nonfatal heart attacks PM 2.5 Peters et al. (2001) 65 Pooled estimate: Pope et al. (2006) 66 Sullivan et al. (20 05) 67 Zanobetti et al. (2009) 68 Zanobetti and Schwartz (2006) 69 Adults (>18 years)

**Hospital Admissions** Respiratory O 3 Pooled estimate: Schwartz (1995) - ICD 460 - 519 (all resp) 70 Schwartz (1994a; 1994b) - ICD 480 - 486 (pneumonia) 71 , 72 Moolgavkar et al. (1997) - ICD 480 - 487 (pneumonia) 73 Schwartz (1994b) - ICD 491 - 492, 494 - 496 (COPD) Moolgavkar et al. (1997)

– ICD 490 - 496 (COPD) >64 years

Burnett et al. (2001) 74 <2 years PM 2.5 Pooled estimate:

Zanobetti et al. (2009)

—ICD 460 - 519 (All respiratory) Kloog et al. (2012)

—ICD 460 - 519 (All Respiratory) 75 >64 years

PM 2.5 Moolgavkar (2000)

—ICD 490

–496 (Chronic

lung disease) 76 18

– 64 years PM 2.5 Pooled estimate:

Babin et al. (2007)

—ICD 493 (asthma) 77 Sheppard (2003)

—ICD 493 (asthma) 78 < 18 years Cardiovascular PM 2.5 Pooled estimate: Zanobetti et al. (2009)

—ICD 390 - 459 (all cardiovascular) Peng et al. (2009)

—ICD 426 - 427; 428; 430 - 438; 410 - 414; 429; 440 - 449 (Cardio - , cerebro and peripheral vascular disease) 79 Peng et al. (2008)

—ICD 426 - 427; 428; 430 - 438; 410 - 4 14; 429; 440 - 449 (Cardio - , cerebro and peripheral vascular disease) 80 Bell et al. (2008)

—ICD 426 - 427; 428; 430 - 438; 410 - 414; 429; 440 - 449 (Cardio - , cerebro and peripheral vascular disease) 81 >64 years

PM 2.5 Moolgavkar (2000)

—ICD 390 - 429 (all

cardiovascular) 20

– 64 years Asthma - related ER visits O 3 Pooled estimate: Peel et al (2005) 82 Wilson et al (2005) 83 All ages All ages Asthma - related ER visits (cont ’d) PM 2.5 Pooled estimate: Mar et al. (2010) 84 Slaughter et al. (2005) 85 Glad et al. (2012) 86 All ages

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8 - 26 **ENDPOINT POLLUTANT**  **STUDY STUDY POPULATION**  **Other Health Endpoints** Ac ute bronchitis PM 2.5 Dockery et al. (1996) 87 8

– 12 years Upper respiratory symptoms PM 2.5 Pope et al. (1991) 88 Asthmatics, 9

– 11 years Lo wer respiratory symptoms PM 2.5 Schwartz and Neas (2000) 89 7

– 14 years Asthma exacerbations PM 2.5 Pooled estimate: Ostro et al. (2001) 90 (cough, wheeze and shortness of breath) Mar et al. (2004) (cough, shortness of breath) 6

– 18 years a Work loss days PM 2.5 Ostro (1987) 91 18

– 65 years School absence days O 3 Pooled estimate: Gilliland et al. (2001 ) 92 Chen et al. (2000) 93 5

– 17 years b Minor Restricted Activity Days (MRADs) O 3 Ostro and Rothschild (1989) 94 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 7 to 12 for the Mar et al. (2004) study. Based on advice from the SAB - HES, we extended the applied population to 6 - 18, reflecting the common biological basis for the effect in children in the broader age group. See: U.S. EPA - SAB (2004) and NRC (2002). b Gilliland et al. (2001) studied 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 similarity between children aged 5 to 17. 

### For detailed descriptions of each of the individual studies referenced in Table 8 - 12 , please refer to the RIAs that accompanied the final revisions to the National Ambient Air Quality Standards (NAAQS) for Particulate Matter, the 2008 final ozone NAAQS, and the 2010 ozone NAAQS reconsideration. 95 , 96 , 97



### 8.1.2.4.4 Baseline Incidence Rates Epidemiological studies of the association between pollution levels and adv erse health effects generall y provide a direct estimate of the 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 10 μg/m3 decrease in daily PM2.5 levels might be associated with a decrease in hospital admissions of 3%. The baseline incidence of the health effect is necessary to 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 numb er. For example, if the baseline incidence rate is the number of cases per year per million people, that number must be multiplied by the millions of people in the total population. Table 8 - 13 summarizes the source s of baseline incidence rates and provides average incidence rates for the endpoints included in the analy sis. For both baseline incidence and prevalence data, we used age - specific rates where available. We applied concentration - response functions to indiv idual age groups and then summed over the relevant age range to provide an

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8 - 27 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 onl y available incidence information comes from the studies themselves; in these cases, incidence in the study popu lation is assumed to represent ty pical incidence at the national level. County , state and regional incidence rates are available for hospital admissions, and county- level data are available for premature mortality . We projected mortality

rates such that f

uture mortality

rates are consistent with our projections of population growth. 98 

### To perform this calculation, we began first with an average of 2004

–2006 cause - specific mortality rates. Using Census Bureau projected national - level annual mortality

rates stratified by

age range, we projected these mortality rates to 2050 in 5 - year increments. 99 , 100 

### The baseline incidence rates for hospital admissions and emergency department visits reflect the revised rates first applied in the CSAPR RIA. 101 

### In addition, we have revised the baseline incidence rates for acute myocardial infarction. These revised rates are more recent, which provides a better representation of the rates at which populations of different ages, and in different locations, visit the hospital and emerg ency department for air pollution - related illnesses. Also, the new baseline incidence rates are more spatiall y refined. For many locations within the U.S., these data are resolved at the county- or state - level, providing a better characterization of the ge ographic distribution of hospital and emergency department visits than the previous national rates. L astl y, these rates reflect unscheduled hospital admissions only , which represent a conservative assumption that most air pollution - related visits are likel y to be unscheduled. If air pollution - related hospital admissions are scheduled, this assumption would underestimate these benefits. For the set of endpoints affecting the asthmatic population, in addition to baseline incidence rates, prevalence rates of a sthma in the population are needed to define the applicable population. Table 8 - 14 lists the prevalence rates used to determine the applicable population for asthma sy mptoms. Note that these reflect current asthma prevalence and assum e no change in prevalence rates in future years. We updated these rates in the CSAPR RIA. **Table 8**  **- 13**  **: Baseline Incidence Rates and Population Prevalence Rates for Use in Impact Functions, General Population**  Endpoint Parameter Rates Value Source Mortality Daily or annual mortality rate projected to 2020 Age - , cause - , and county - specific rate CDC Wonder (2006

–2008) 102 U.S. Census bureau Hospitalizations Daily hospitalization rate Age - , region - , state - , county - and cause specific rate 2007 HCUP data files a, 103 Asthma ER Visits Daily asthma ER visit rate Age - , region - , state - , county - and cause specific rate 2007 HCUP data files a Nonfatal Myocardial Daily nonfatal myocardial Age - , region - , state - , 2007 HCUP data files ; a adjusted by

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8 - 28 Endpoint Parameter Rates Value Source Infarction (heart attacks) infarction incidence rate per person, 18+ and county - specific rate 0.93 for probability of surviving after

28 days (Rosamond et al., 1999) 104 Asthma Exacerbations Incidence among asthmatic African - American children daily wheeze daily cough daily shortness of breath 0. 173 0. 145 0. 074 Ostro et al. (2001)

Ac ute Bronchitis Annual bronchitis incidence rate, children 0.043 American Lung Association (2002, Table 11) 105 Lo wer Respiratory Symptoms Daily lower respiratory symptom incidence 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); 106 U.S. Bureau of the Census (2000) 107 School Loss Days Rate per person per year, assuming 180 school days per year 9.9 Nation al Center for Education Statistics (1996) 108 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 (HCU P) 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 -**  **14 : Asthma**  **Prevalence Rates Used for this Analysis**  Population Group Asthma Prevalence Rates Value Source All Ages 0.0780 American Lung Association (2010, Table 7) 109 < 18 0.0941 5

– 17 0.1070 18

–44 0.0 719 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 a Notes: a b Calculated by ALA for U.S. EPA, based on NHIS data (CDC, 2008) . 110



### 8.1.2.4.5 Economic Valu es for Health Outcomes After quantifying the change in adverse health impacts, we estimate the economic value of these avoided impacts. The appropriate economic value for a change in a health effect depends on whether the health effect is viewed ex ante (b efore the effect has occurred) or ex post (after the effect has occurred). Reductions in ambient concentrations of air pollution generall y lower

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8 - 29 the risk of future adverse health effects b y a small amount for a large population. The appropriate economic me asure is therefore ex ante willingness to pay (WTP) for changes in risk. 111 

### Epidemiological studies generall y provide estimates of the relative risks of a particular health effect for a given increment of air pollution (often per 10 μg/m3 for PM 2.5 

### ). These relative risks can be used to develop risk coefficients that relate a unit reduction in PM 2.5 

### to changes in the incidence of a health effect. In order to value these changes in incidence, WTP for changes in risk need to be converted into WTP per statistical incidence. This measure is calculated b y dividing individual WTP for a risk reduction b y the related observed change in risk. For example, suppose a measure is able to reduce the risk of premature mortality from 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 mortality amounts to $1 million ($100/0.0001 change in risk). Using this approach, the size of the affected population is automatically taken into account b y the number of incidences predicted by epidemiological studies applied to the relevant population. The same ty pe of calculation can produce values for statistical incidences of other health endpoints. For some health effects, such as hospi tal admissions, WTP estimates are generall y not available. In these cases, we use the cost of treating or mitigating the effect. For example, for the valuation of hospital admissions we use the avoided medical costs as an estimate of the value of avoiding the health effects causing the admission. These cost - of - illness (COI) estimates generall y (although not necessarily in every case) understate the true value of reductions in risk of a health effect. They

tend to reflect the direct expenditures related to treatment but not the value of avoided pain and suffering from the health effect . 112 , 113 

### We provide unit values for health endpoints (along with information on the distribution of the unit value) in Table 8 - 15 . All values are in constant year 201 1 dollars, adjusted for growth in real income out to 2030 using projections provided by Standard and Poor’s. Economic theory argues that WTP for most goods (such as environmental protection) will increase if real income increas es. 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 diseas e 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 es timates for PM - related endpoints, see the the RIA that accompanied the final revisions to the National Ambient Air Quality Standards (NAAQS) for Particulate Matter. 114 

### For details on valuation estimates for ozone - related endpoints, see the RIAs for the 200 8 Ozone NAAQS RIA and the 2010 ozone NAAQS reconsideration . 115 , 116

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8 - 30 **Table 8**  **- 15**  **: Unit Values for Economic Valuation of Health Endpoints ( 201**  **1 $)**  **Health Endpoint Central Estimate of**  **Value Per Statistical Incidence**  **1990 Income**  **Level 2030** 

**Income Level**  **a Derivation of Distributions of Estimates**  Premature Mortality (Value of a Statistical Life) $ 8 , 3 00,000 $ 10,2 00,000 EPA currently recommends a central VSL of $6.3m ( 2000$, 1990 income ) based on a Weibull distribution fitted to 26 published VSL estimates (5 contingent valuation and 21 labor market studies). The underlying studies, the distribution parameters, and other useful information are available in Appendix B of EPA's current Guidelines for Preparing Economic Analys es (U.S. EPA, 20 1 0). 117 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 $ 100,000 $ 1 10,000 $1 20 ,000 $ 210,000 $ 100,000 $ 100,000 $ 110,000 $ 1 10,000 $ 200,000 $ 100,000 $ 100,000

$ 1 10,000 $1 20,000 $ 210,000 $ 100,000 $ 100,000 $ 110,000 $ 1 10,000 $ 200,000 $ 100,000 No distributional information 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). 118 Direct medical costs are based on simple average of estimates from Russell et al. (1998) 119 and Wittels et al. (1990). 120 Lost earnings: Cropper and Krupnick (1990). Present discounted value of 5 years of lost earnings: age of onset: at 3% at 7% 25

–44 $ 9,000 $ 8,000 45

– 54 $ 13,000 $ 12,000 55

– 65 $ 77,000 $ 69,000 Direct medical expenses: An average of: 1. Wittels et al. (1990) ($10 0,000

— no discounting) 2. Russell et al. (1998), 5 - year period ($22, 000 at 3% discount rate; $21, 000 at 7% discount rate) **Hospital Admissions** Chronic Obstructive Pulmonary Disease (COPD) $ 2 2,000 $ 22,000 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 CO PD category illnesses) reported in Agency for Healthcare Research and Quality (200 7 ) (www.ahrq.gov). 121 Asthma Admissions $ 17,000 $ 17,000 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 Research and Quality (200 7 ) (www.ahrq.gov). All Cardiovascular Age 18 - 64 Age 65 - 99 $43,000 $42,000 $ 43,000

$42,000 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 hosp ital stay, and weighted share of total cardiovascular category illnesses) reported in Agency for Healthcare Research and Quality (200 7 ) (www.ahrq.gov). All respiratory (ages 65+) $ 37,000 $ 37,000 No distributions available. The COI point estimates (lost e arnings 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

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8 - 31 for Healthcare Research and Quality, 2 00 7 (www.ahrq.gov). All respiratory (ages 0

–2) $1 3,000 $1 3,000 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 ho spital stay, and weighted share of total COPD category illnesses) reported in Agency for Healthcare Research and Quality, 200 7 (www.ahrq.gov). Emergency Room Visits for Asthma $ 4 40 $ 4 40 No distributional information available. Simple average of two unit C OI values: (1) $311.55, from Smith et al. (1997) 122 and (2) $260.67, from Stanford et al. (1999). 123 **Respiratory Ailments Not Requiring Hospitalization** Upper Respiratory Symptoms (URS) $3 2 $3 4 Com binations of the three symptoms for which WTP estimates are av ailable 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 (Neumann et al. , 1994) 124 to avoid each symptom in the cluster and assuming additivity of WTPs. In the absence of information surrounding the frequency with which each of the seven ty pes of URS occurs within the URS symptom complex, we assumed a uniform distribution between $9.2 and $43 (2000$) . Lo wer Respiratory Symptoms (LRS) $ 2 1 $2 2 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 w as derived for each type of LRS, using mid - range estimates of WTP (Neumann et al. , 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. I n the absence 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 $2 5 (2000$) . Asthma Exacerbations $5 7 $5 9 Asthma exacerbations are valu ed at $45 per incidence, based on the mean of average WTP estimates for the four severity definitions of a “bad asthma day,” described in Rowe and Chestnut (1986). 125 This study surveyed asthmatics to estimate WTP for avoidance of a “bad asthma day,” as defined by the subjects. For purposes of valuation, an asthma exacerbation is assumed to be equivalent to a day in which asthma is moderate or worse as reported in the Rowe and Chestnut (1986) study. The value is assumed have a uniform distribution between $1 6 and $7 1 (2000$) . Ac ute Bronchitis $4 70 $ 510 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 $110 (2000$) . Work Loss Days (WLDs) Variable (U.S. median = $ 150 ) Variable

(U.S. median = $ 150 ) 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 median daily wage. U.S. Year 2000 Census, compiled by Geolytics, Inc. (Geolytics, 2002) 126

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8 - 32 Minor Restricted Activity Days (MRADs) $6 6 $ 71 Median WTP estimate to avoid one MRAD from Tolley et al. (1986). 127 Distribution is assumed to be triangular with a minimum of $22 and a maximum of $83, with a most likely value of $52 (2000$) . 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 distribution acknowledges that the actual value is likely to be closer to the point estimate than either extreme. School Absence Days $9 8 $9 8 No distribution available Note: a Willingness - to - pay - based bene fits are adjusted by multiplying the unadjusted benefits by the appropriate adjustment factor to account for income growth over time using projections provided by Standard and Poor’s. Due to a lack of reliable projections of income growth past 2024, we as sume constant WTP from 2024 through 2030. This results in an underestimate of benefits occurring between 2024 and 2030. For growth between 2000 and 2024, this factor is 1.23 for long - term mortality, 1.27 for chronic health impacts, and 1.08 for minor heal th impacts. 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.



### 8.1.2.4.6 Manipulating Air Quality Modeling Data for Health Impacts Analysis In Chapter 7 , we summarized 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 Quali ty

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 standard CMAQ output files. For ozone, these model predictions are used in conjunction with the observed concentrations obtained from the Aerometric Information Retrieval Sy stem (AIRS) to generate ozone concentrations for the entire ozone se ason. L , M 

### 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 ef fects for the contiguous 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

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

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8 - 33 observations and modeled ozone 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. N , O 

### 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 (correspondi ng 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 fra ction 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 

### 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 ). 128 

### 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 - 16 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 changes for more populated areas of the nation. This measure better reflects the potential benefits through exposure changes to these populations. **Table 8**  **- 16**  **: Summary of CMAQ**  **- Derived Population**  **- Weig**  **hted Ozone and PM 2.5**  

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

### Baseline Change b 

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

### Daily Maximum 8 - Hour Average Concentration 43.65 0.32 PM 2.5 

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

### ) Annual Average Concentration 7.94 0.04 Notes:

N The 12 - km

grid squares contain the population data used in the health benefits analysis model, BenMAP. O This approach is a generalization of planar interpolation that is technically referred to as enhanced Voronoi Neighbor Av eraging (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 - 34 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 r elevant 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 m ade early in the anal yt ical 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 Tier 3 standards . Please refer to Section 7.2. 1 .1 for more information about the inventories used in the air quality modeling that supports the health impacts analy sis.

8.1.2.5 Methods for Describing Uncertainty In any complex anal ys is using estimated parameters and inputs from numerous models, there are li kel y to be many sources of uncertainty. This analysis is no exception. As outlined both in this and preceding chapters, this analy sis includes many data sources as inputs, including emission inventories, air quality data from models (with their associated parameters and inputs), population data, population estimates, health effect estimates from epidemiology studies, economic data for monetizing benefits, and assumptions regarding the future state of the world (i.e., regulations, technology , and human behav ior). Each of these inputs may be uncertain and would affect the benefits estimate. When the uncertainties from each stage of the anal ys is are compounded, even small uncertainties can have large effects on the total quantified benefits. After reviewing the EPA’s approach, the National Research Council (NRC) (2002, 2008), 129 , 130 

### which is part of the National Academies of Science, concluded that the EPA’s general methodology

for calculating the benefits of reducing air pollution is reasonable and informative in spite of inherent uncertainties. The NRC also highlighted the need to conduct rigorous quantitative anal ys es of uncertainty and to present benefits estimates to decision makers in ways that foster an appropriate appreciation of their inherent uncertainty. Since the publication of these reports, the EPA has continued work to improve the characterization of uncertainty in both health incidence and benefits estimates. In response to these recommendations, we have expanded our previous analy ses to incorporate additional quantitative and qualitative characterizations of uncertainty . Although we have not yet been able to make as much progress towards a full, probabilistic uncertainty assessment as envisioned by the NAS as we had hoped, we have added a number of additional quantitative and qualitative anal ys es to highlight the impact that uncertain assumptions may have on the benefits estimates. In addition, for some inputs into the benefits analy sis, such as the air quality data, it is difficult to address uncertainty probabilistically

due to the complexity

of the underlying air quality models and emission inputs. Therefore, we decline to make up alternative assumptions simply for the purpose of probabilistic uncertainty characterization when there is no scientific literature to support alternate assumptions.

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8 - 35 To characterize uncertainty and variability , the 2012 PM NAAQS RIA utilized an approach that combined elements from two recent anal ys es b y the EPA, 131 , 132 

### and used a tiered approach developed b y the World Health Organization (WHO) for characterizing uncertainty. 133 

### We refer the reader to this tiered assessment as an assessment of the potential impact and magnitude of each aspect of uncertainty that is also present in the final Tier 3 RIA (see Appendix 5c of the 2012 P M NAAQS RIA). Data limitations prevent us from treating each source of uncertainty quantitatively

and from reaching a full - probabilistic simulation of our results, but we were able to consider the influence of uncertainty

in the risk coefficients and econ omic valuation functions by incorporating four quantitative anal ys es described in more detail below: 1. A Monte Carlo assessment that accounts for random sampling error and between study variability

in the epidemiological and economic valuation studies; 2. The quantification of PM - related mortality using alternative PM 2.5 

### mortality effect estimates drawn from two long - term cohort studies and an expert elicitation; 3 . A concentration benchmark assessment that characterizes the distribution of avoided PM 2.5 

### - related deaths relative to specific concentrations in the long - term epidemiological studies used to estimate PM 2.5 

### - related mortality ; 4. The quantification of ozone - related mortality using alternative ozone mortality effect estimates drawn from six short - te rm studies: three multi - city studies and three meta anal ys es of the existing literature; 5 . An anal ys is of the influence of various parameters on total monetized benefits.

8.1.2.5.1 Monte Carlo Assessment Similar to other recent RIAs, we used Monte Carlo methods fo r 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. The Monte Carl o simulation in the BenMAP software randomly samples from a distribution of incidence and valuation estimates to characterize the effects of uncertainty on output variables. Specifically, we used Monte Carlo methods to generate confidence intervals around the estimated health impact and monetized benefits. The reported standard errors in the epidemiological studies determined the distributions for individual effect estimates for endpoints estimated using a single study . For endpoints estimated using a poole d estimate of multiple studies, the confidence intervals reflect both the standard errors and the variance across studies. The confidence intervals around the monetized benefits incorporate the epidemiology

standard errors as well as the distribution of th e valuation function. These confidence intervals do not reflect other sources of uncertainty inherent within the estimates, such as baseline incidence rates, populations exposed and transferability of the effect estimate to diverse locations. As a result, the reported confidence intervals and range of estimates give an incomplete picture about the overall uncertainty in the benefits estimates.

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8 - 36 8.1.2.5.2 Alternative Concentration -Response Functions for PM 2.5 

### - Related Mortality We assign the greatest economic value to the reduction in PM 2.5 

### related mortality risk. Therefore, it is particularly important to attempt to characterize the uncertainties associated with reductions in premature mortality . To better understand the concentration - response relationship between PM 2.5 

### exposure and premature mortality, the EPA conducted an expert elicitation in 2006. 134 , 135 

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

### control are very likel y to be substantial. Alternative concentration - response functions are useful for assessing uncertainty beyond random statistical error, including uncertainty in the functional form of the model or alternative study design. Thus, we include the expert elicitation results as well as standard errors approaches to provide insights into the likelihood of different outcomes and about the state of knowledge regarding the benefits estimates. In this analy sis, we present the results derived from the expert elicitation as indicative of the uncertainty associated with a major component of the health impact functions, and we provide the independent estimates derived from each of the twelve experts to better characterize the degree of variability in the expert responses. In previous RIAs, the EPA presented benefits estimates using concentration response functions derived from the PM 2.5 

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

### benefits may be, and the EPA’s independent Science Advisory Board (SAB) recommended refinements to the way EPA presented the results of the elicitation. 136 

### As a result of this recommendation, we have presented the ACS and Six - Cities co hort - based studies as our core premature mortality estimates, such as in the RIA for the final PM NAAQS . Using alternate relationships between PM 2.5 

### and premature mortality supplied by

experts, higher and lower benefits estimates are plausible, but most of the expert - based estimates of the mean PM 2.5 

### effect on mortality fall between the two epidemiologybased estimates . Please note that the benefits estimates results presented are not the direct results from the studies or expert elicitation; rather, the es timates are based in part on the effect coefficients provided in those studies or by experts. In addition, the experts provided distributions around their mean PM 2.5 

### effect estimates, which provide more information regarding the overall range of uncertainty, and this overall range is larger than the range of the mean effect estimates from each of the experts. Even these multiple characterizations with confidence intervals omit the contribution to overall uncertainty from uncertainty in air quality changes, baseline incidence rates, and populations exposed. Furthermore, the approach presented here does not yet include methods for addressing correlation between input parameters and the identification of reasonable upper and lower bounds for input 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.

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8 - 37 8.1.2.5.3 Concentration Benchmark Analysis for PM 2.5 

### In this analy sis, we estimate the number of avoided PM 2.5 

### - related deaths occurring due to PM 2.5 

### reductions down to various PM 2.5 

### concentration benchm arks, including the Lowest Measured Level (LML) of each long - term PM 2.5 

### mortality study . This analy sis is one of several sensitivities that the EPA has historically performed that characterize the uncertainty associated with the PM - mortality

relationship and the economic value of reducing the risk of premature death. 137 , 138 , 139 

### Our review of the current body of scientific literature indicates that a log - linear no threshold model provides the best estimate of PM - related long - term mortali ty . The PM ISA , 140 

### which was twice reviewed by the EPA’s Clean Air Scientific Advisory Committee , 141 , 142 

### concluded that the evidence supports the use of a no - threshold log - linear model while also recognizing potential uncertainty about the exact shape of the concentration - response function. P 

### Consistent with this finding, we estimate benefits associated with the full range of PM 2.5 

### exposure in conjunction with sensitivity anal ys es to recognize the potential uncertainty at lower concentrations. Specifically , we incorporated a LMLassessment, a method the EPA has employed in several recent RIAs. 143 , 144 , 145 

### In addition, we have incorporated an assessment using specific concentration benchmarks identified in the EPA’s Policy Assessment for Particulate Matter . 146 

### These two approaches summarize the distribution of avoided PM 2.5 

### - related mortality impacts relative to baseline (i.e., pre - rule) annual mean PM 2.5 

### levels. The LMLapproach compares the percentage of avoided premature deaths estimated to occur above and below the minimum observed air quality lev el of each long - term cohort study we use to quantify

PM. In the air quality

benchmark approach, we summarize the impacts occurring at different points in the distribution of the air quality data used in these same epidemiology studies. Our confidence in th e estimated number of premature deaths avoided (but not in the existence of a causal relationship between PM and premature mortality) diminishes as we estimate these impacts in locations where PM 2.5 

### levels are below the LML. This interpretation is consiste nt with the Policy Assessment and advice from SAB - CASAC during their peer review. 147 

### The Policy Assessment concludes that the range from the 25 th 

### to the 10 th 

### percentile is a reasonable range of the air quality distribution below which we start to have apprec iabl y less confidence in the magnitude of the associations observed in the epidemiological studies. In general, we are more confident in the magnitude of the risks we estimate from simulated PM 2.5 

### concentrations that coincide with the bulk of the observed PM concentrations in the epidemiological studies at are used to estimate the benefits. Li kewise, we are less confident in the risk we estimate from simulated PM 2.5 

### concentrations that fall below the bulk of the observed data in these studies. However, there are uncertainties inherent in identifying any particular point at which our confidence in reported associations becomes appreciably less, and the scientific evidence provides no clear dividing line.

P F or a summary of the scientific review statements regarding the lack of a threshold in the PM 2.5 - mo rtality relati onship, see the Technical Support Document (TSD) entitled Summary of Expert Opinions on the Existence of a Threshold in the Concentration -Response Function for PM 2.5 - related Mortality (U.S. EPA, 2010f).

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8 - 38 For these reasons, we consider the LML as well as one standard deviation below the mean Q 

### air quality

levels when characterizing the distribution of mortality impacts. It is important to emphasize that “less confidence” does not mean “no confidence.” In addition, while we may have less confidence in the magnit ude of the risk, we still have high confidence that PM 2.5 

### is causall y associated with risk at those lower air quality concentrations. To clarify this concept, Figure 8 - 2 graphically

display s the spectrum of confidence using illustrative concentration benchmarks from the major epidemiology studies cited in this chapter.

**Figure 8**  **- 2**  **: Relationship**  **between the Size of the PM Mortality Estimates and the PM 2.5**  

### Concentration Observed in the **Epidemiology Study** Although these types of concentration benchmark anal ys es (e.g., LML, one standard deviation below the mean, etc.) provide some insight into the level of uncertainty in the estimated PM 2.5 

### mortality benefits, the EPA does not view these c oncentration benchmarks as a concentration threshold below which we would not quantify health benefits of air quality improvements. Rather, the core benefits estimates reported in this RIA (i.e., those based on Krewski et al. (2009) and L epeule et al. (201 2)) are the best measures because they reflect the full range of modeled air quality concentrations associated with the emission reduction strategies. In reviewing the Policy Assessment , SAB - CASAC confirmed that “[a]lthough there is increasing uncertainty at lower levels, there is no evidence of a threshold (i.e., a level below which there is no risk for adverse health effects)”. 148 

### Inaddition, in reviewing the Costs and Benefits of the Clean Air Act , 149 

### the SAB - HES noted that “[t]his [no - threshold] decision is supported by

the data, which are quite consistent in showing effects down to the lowest measured levels. Anal ys es of cohorts using data from more recent ye ars, during which time PM concentrations have fallen, continue to report strong associations with mortality”. 150 

### Therefore, the best estimate of benefits includes estimates below and above these concentration benchmarks

Q A range of one standard deviation around the mean r epresents approximately 68 percent of normally distributed data, and, below

the mean falls between the 25 th and 10 th percentiles.

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8 - 39 but uncertainty is higher in the magnitude of health benefits estimated at lower concentrations, with the lowest confidence below the LML . Estimated health impacts reflecting air quality improvements below and above these concentration benchmarks are appropriately included in the total benefits estimate. In other words, our higher confidence in the estimated benefits above these concentration benchmarks should not imply an absence of confidence in the benefits estimated below these concentration benchmarks. We estimate that most of the avoided PM - related impacts quantified in this anal ys is occur among populations exposed at or above the LM L of the Lepeule et al. (2012) study , while a majority of the impacts occur at or above the LML of the Krewski et al. (2009) study . We show the estimated reduction in incidence of premature mortality above and below the LMLor air quality benchmarks of the se studies in Table 8 - 17 , and we graphicall y display the distribution of PM 2.5 

### - related mortality

impacts for the final standard in Figure 8 - 3 and Figure 8 - 4 . **Table 8**  **- 17**  **: Estimated Reduction in Incidence of Adult Premature Mortality Occurring**  **Above and Below Various Concentration Benchmarks in the Underlying Epidemiology Studies**  **a**

**Epide miology**  **Study Total** 

**Reduced Mortality**  **Incidence A**  **llocation of Reduced Mortality Incidence**

**Below 1 Std. Dev.**  **Below AQ Me**  **an At or** 

**Above 1 Std. Dev.**  **Below AQ Me**  **an Below** 

**LM L**  **At or Above**

**LM L**  Krewski et al. (2009) 660 620 38 37 620 ( 94 %) (6%) ( 6 %) (9 4 %) Lepeule et al. (2012) 1,500 N/A N/A 630 860 ( 42 %) ( 58 %) a Mortality incidence estimates are rounded to whole numbers and two significant digits, so estimates may not sum across columns. One standard deviation below the mean is equivalent to the middle of the range between the 10 th and 25 th percenti le. For Krew ski, the LML is 5.8 μg/m 3 and one standard deviation below the mean is 11.0 μg/m3 . For Lepeule et al., the LML is 8 μg/m 3 and we do not have the data for one standard deviation below the mean. It is important to emphasize that although we have lower levels of confidence in levels below the LML for each study, the scientific evidence does not support the existence of a level below which health effects from exposure to PM 2.5 do not occur.

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8 - 40 0% 5% 10% 15% 20% 25% 30%

<1 1 - 2 2 - 3 3 - 4 4 - 5 5 - 6 6 - 7 7 - 8 8 - 9 9 - 10 10 - 11 11 - 12 12 - 13 13 - 14 14 - 15 15 - 16 16 - 17 17 - 18 18 - 19 19 - 20 **P e**  **r c**  **e n**  **t a**  **g e**  **o f**  **A v**  **o i**  **d e**  **d P**  **M 2**  **. 5**  **M o**  **r t**  **a l**  **i t**  **i e**  **s**

**Bas eline**  **Annual Mea**  **n PM**  **2.5 Le**  **ve l**  **(μ g/m**  **3 )**  LM L of L epeu le e t al. (2012) study LM L of Krew ski e t al. (2009) study

**Of total PM 2.5**  **- Related deaths avoided for 12 μg/m** **3 :**  **9 4**  **%** occur among populations exposed to PM 2.5 levels at or above the LML of the Krewski et al. (2009) study. **58 %**  occur among populations exposed to PM 2.5 levels at or above the LML of the Lepeule et al. (2012) study. 

### Figure **8 -**  **3 .**  **Number of Premature PM 2.5**  

### - **related Deaths Avoided for the Final Tier 3 Standards in 2030 According to the Baseline Level of PM**  **2.5** 

### and the Lowest Measured Air **Quality Levels of Each Mortality Study**

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8 - 41 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 **C u**  **m u**  **l a**  **t i**  **v e**  **p e**  **r c**  **e n**  **t a**  **g e**  **o f**  **a v**  **o i**  **d e**  **d P**  **M M**  **o r**  **t a**  **l i**  **t i**  **e s** 

**Base line**  **Annua l**  **Mea n**  **PM 2.**  **5 Le**  **ve l**  **(μ g/m**  **3 )**  

### Cumulative **Pe rc**  **entage of T**  **ot al**  **PM -**  **re lat**  **ed Mor**  **talities Avo**  **ided by**  **Bas eline**  **Air Qualit** **y Level**

LM L of Kr ew ski et al. (2009) study

LM L of L epeu le e t al. (2012) study

**Of total PM 2.5**  **- Related deaths avoided fo**  **r 12 μg/m 3**  **: 9**  **4 %**  occur among populations exposed to PM 2.5 levels at or above the LML of the Krewski et al. (2009) study. **58 %**  occur among populations exposed to PM 2.5 levels at or above the LML of the Lepeule et al. (2012) study. 

### Figure **8 -**  **4 .**  **Number of Premature PM 2.5**  

### - **related Deaths Avoided for the Final Tier 3 Standards in 2030 According to the Baseline Level of PM**  **2.5** 

### and the Lowest Measured Air **Quality Levels of Each Mortality Study** While the LMLof each study is important to consider when characterizing and interpreting the overall level of PM 2.5 

### - related benefits, as discussed earlier in this chapter, the EPA believes that both of the cohort - based mortality estimates are suitable for use in air pollution heal th impact analy ses. When estimating PM - related premature deaths avoided using risk coefficients drawn from the L epeule et al. (2012) anal ys is of the Harvard Six Cities and the Krewski et al. (2009) analy sis of the ACS cohorts there are innumerable other attributes that may affect the size of the reported effect estimates

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

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8 - 42 8.1.2.5.4 Alternative Concentration -Response Functions for Ozone Related Mortality 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 151 

### and issued a series of recommendations to EPA, including that the Agency should p resent 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 - anal yt ic studies. The NAS also recommended that EPA remove reference to a no - causal relationship between ozone exposure and premature mortality . The quantification and presentation of ozone - related premature mortality in this analy sis is responsive to these recommendations . 8.1.2.5.5 Influence Analysis

– Quantitative Assessment of Uncertainty In the past few years, the EPA has initiated several projects to improve the characterization of uncertainty for benefits anal ys is. In particular, the EPA recentl y completed the first phase of a quantitative uncertainty anal ys is of benefits, hereafter referred to as the “ Influence Analy sis”. 152 

### The Influence Analy sis diagramed the uncertain components of each step within the benefits analy sis process, identified plausible ranges for a sensitivity anal ys is, and assessed the sensitivity

to total benefits to changes in each co mponent. Although this anal ys is does not quite fulfill the goal of a full probabilistic assessment, it accomplished the necessary first steps and identified the challenges to accomplishing that goal. Below are some of the preliminary

observations from the first phase of the project.

 The components that contribute the most to uncertainty of the monetized benefits and mortality

incidence (in order of importance) are the value - of - a - statistical - life (VSL), the concentration - response (C - R) function for mortality, and change in PM 2.5 

### concentration.  The components that contribute the least to uncertainty of the monetized benefits and mortality

incidence are population, morbidity valuation, and income elasticity .  The choice of a C - R function for mortality affects the mortality

incidence and monetized benefits more than other sources of uncertainty within each C - R function.  Alternative cessation lag structures for mortality have a moderate effect on the monetized benefits.  Because the health impact function is essentially linear, the key

components show the same sensitivity

across all mortality

C - R functions even if the midpoints differ significantl y from one expert to another. 8.1.2.5.6 Qualitative Assessment of Uncertainty and Other Analysis Limitations

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8 - 43 Although we strive t o incorporate as many quantitative assessments of uncertainty as possible, there are several aspects we are onl y able to address qualitatively. These aspects are important factors to consider when evaluating the benefits of the final Tier 3 standards. The total monetized benefits presented in this chapter are based on our interpretation of the best available scientific literature and methods and supported b y the EPA’s independent SAB (Health Effects Subcommittee) (SAB - HES) and the National Academies of Sci ence (NAS). 153 , 154 

### The benefits estimates are subject to a number of assumptions and uncertainties. For example, the key assumptions underlying the estimates for premature mortality , which account for over 98% of the total monetized benefits in this anal ys is, include the following:

1. We assume that all fine particles, regardless of their chemical composition, are equall y potent in causing premature mortality . This is an important assumption, because PM 2.5 

### varies considerabl y in composition across sources, but the scientific evidence is not yet sufficient to allow differentiation of effect estimates b y particle type. The PM I SA, which was twice reviewed by Clean Air Scientific Advisory Committee (SAB - CASAC), concluded that “many constituents of PM 2.5 

### can be li nked with multiple health effects, and the evidence is not yet sufficient to allow differentiation of those constituents or sources that are more closel y related to specific outcomes”. 155 

### 2. We assume that the health impact function for fine particles is l og - linear without a threshold in this analy sis. Thus, the estimates include health benefits from reducing fine particles in areas with varied concentrations of PM 2.5 

### , including both areas that do not meet the fine particle standard and those areas that are in attainment, down to the lowest modeled concentrations. 3 . We assume that there is a “cessation” lag between the change in PM exposures and the total realization of changes in mortality effects. Specifically , we assume that some of the incidences of premature mortality related to PM 2.5 

### exposures occur in a distributed fashion over the 20 years following exposure based on the advice of the SAB - HES, 156 

### which affects the valuation of mortality benefits at different discount rates. 4 . To characterize the unc ertainty in the relationship betwe en PM 2.5 

### and premature mortality , we include a set of twelve estimates based on results of the expert elicitation study

in addition to our core estimates. Even these multiple characterizations omit the uncertainty in air quality

estimates, baseline incidence rates, populations exposed and transferability of the effect estimate to diverse locations. As a result, the reported confidence intervals and range of estimates give an incomplete picture about the overall uncertainty in the PM 2.5 

### estimates. This information should be interpreted within the context of the larger uncertainty surrounding the entire analy sis. 5. There is uncertainty in the magnitude of the association between ozone and premature mortality . The range of o zone benefits associated with the final

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8 - 44 standards is estimated based on the risk of several sources of ozone - related mortality effect estimates. In a report on the estimation of ozone - related premature mortality

published by

the National Research Council, a panel of experts and reviewers concluded that short - term exposure to ambient ozone is likely to contribute to premature deaths and that ozone - related mortality should be included in estimates of the health benefits of reducing ozone exposure. R 

### As previ ously

described, we strive to monetize as many of the benefits anticipated from the revised and alternative standards as possible given data and resource limitations, but the monetized benefits estimated in this RIA inevitably only reflect a portion of the benefits. Specificall y, only certain benefits attributable to the health impacts associated with exposure to ambient fine particles have been monetized in this anal ys is. Data and methodological limitations prevented the EPA from quantifying or monetizing the benefits from several important health benefit categories (see Table 8 - 5 for more information). If we could full y monetize all of the benefit categories, the total monetized benefits would exceed the costs by an even greater margin than we currentl y estimate. To more fully

address these uncertainties , including those we cannot quantify , the 2012 PM NAAQS RIA utilized a four - tiered approach using the WHO uncertainty framework, 157 

### which provides a means for sy stematically linking the characterization of uncertainty to the sophistication of the underlying risk assessment. The EPA has applied similar approaches in other anal ys es. 158 , 159 

### Using this framework, the PM NAAQS summarize d the key uncertainties in the health bene fits anal ys is, including our assessment of the direction of potential bias, magnitude of impact on the monetized benefits, degree of confidence in our anal yt ical approach, and our ability

to assess the source of uncertainty. We refer the reader to this tiered assessment as an assessment of the potential impact and magnitude of each aspect of uncertainty that is also present in the final Tier 3 RIA (see Appendix 5b of the 2012 PM NAAQS RIA). 160 

### 8.1.3 Comparison of Costs and Benefits This section presents the cost - be nefit comparison related to the expected impacts of the final Tier 3 program . In estimating the net benefits of the program , the appropriate cost 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 manufacturer s and fuel producers w ould see their costs 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 b y the fi nal consumers of these goods. The social benefits of the program are represented b y the monetized value of health and welfare improvements experienced b y the U.S. population. Table 8 - 18 contains the estimated social costs and th e estimated monetized benefit s of the program.

R National Research Council (NRC), (2008). Estimating Mortality Risk Reduction and Economic Benefits from Controlling Ozone Air Pollution. The National Academies Press: Washington, D.C.

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8 - 45 The results in Table 8 - 18 suggest that the 20 3 0 monetized benefits of the final standards are greater than the expected costs. Specifically , the annual benefits of the total program will range between $ 7.4 to $ 19 billion annually in 20 3 0 using a three percent discount rate, or between $ 6.7 to $ 18 billion assuming a 7 percent discount rate, compared to estimated social costs of approximately

$ 1.5 billion in that same year. Though th ere are a number of health and environmental effects associated with the final standards that we are unable to quantify or monetize (see Table 8 - 5 ), the benefits of the final standards outweigh the projected costs. Using a conservative benefits estimate, the 2030 benefits outweigh the costs by a factor of 4.5 . Using the upper end of the benefits range, the benefits could outweigh the costs by a factor of 13 . Thus, even taking the most conservative benefits assumptions, benefits of the final standards clearl y outweigh the costs. **Table 8**  **- 18**  **: Summary of Annual Benefts and Costs Associated with the Final Tier 3**  **Program (Billions, 2011$) a**  

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

### $0.76 $0.70 $1.5 Total Estimated Health Benefits c,d,e,f 

### 3 percent discount rate 7 percent discount rate $7.4 - $19 $6.7 - $18 Annual Net Benefits (Total Benefits

– Total Costs) 3 percent discount rate 7 percent discount rate $5.9 - $18 $5.2 - $17 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 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 Society cohort study ( Krewski et al., 200 9 ) 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 ( Lepeule et al., 20 12 ) study for the high estimate . 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 analy ses. 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 20 12 PM National Ambient Air Quality Standards ( Decemb er, 2012 ). f Not all possible 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 .

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8 - 46 8.1.4 Illustrative Anal ys is of Quantified and Monetized Impac ts Associated with the Rule in 201 8 For illustrative purposes, this section presents the quantified and monetized impacts associated with the final standards in 201 8 . As presented in Section 7.1.5 , the emissions impacts of the final standards in 201 8 are primarily due to the effects of sulfur on the existing (pre - Tier 3) fleet. This analy sis reflects the impact s of the final Tier 3 rule in 201 8 compared to a future ye ar reference scenario without the program in place. Overall, we estimate that the final rule will lead to a net decrease in PM 2.5 

### - related health and environmental impacts in 201 8 (see Section 7.2. 4 for more information about the air quality modeling results ). T he decrease in population weighted national average PM 2.5 

### exposure results in a n et 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 . 012 μg/m 3 

### in 201 8 ). 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. 15 ppb in 201 8 ). **Table 8**  **- 19**  **an d**  **Table 8**  **- 20**  **present the annual PM 2.5**  

### and ozone health impacts in the 48 contiguous U.S. **states associated with the final**  **Tier 3 program . For each endpoint presen**  **ted in Table**  **8 -**  **19 and**  Table 8 - 20 , 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 esti mate that the final standards would result in between 180 and 400 cases of avoided PM 2.5 

### - related premature mortalities annually

in 201 8 . For ozone - related premature mortality in 201 8 , we estimate a range of between 4 5 to 2 10 fewer premature mortalities. Table 8 - 21 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 - 22 . All monetized estimates are presented in 201 1 $ . Where appropriate, estimates account for growth in real gross domestic product (GDP) per capita between 2000 and 201 8 . The monetized value of PM 2.5 

### - related mortality also accounts for a twenty- ye ar segment ed cessation lag. S 

### To discount the value of premature mortality that occurs at different points in the

S Based in part on prior SAB advice, EPA has typically assumed that there is a time lag between changes in pollution exposures and the total realization of changes in health effects. Within th e 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 C ouncil on Clean Air Compliance Analysis Response to Agency Request on

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8 - 47 future, we appl y both a 3 and 7 percent discount rate. We also use both a 3 and 7 percent discount rate to value PM 2.5 

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

### 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 standards 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 w ith the final standards in 201 8 , using the ACS and Six - Cities PM mortality studies and the range of ozone mortality assumptions, is between $ 2.1 and $ 5.6 billion, assuming a 3 percent discount rate, or between $1. 9 and $ 5 .3 billion, assuming a 7 percent di scount rate . The results in Table 8 - 21 demonstrate that the gasoline sulfur standards provide large immediate benefits in the program’s first ye ar, related to emission reductions from existing gasoline vehicles. The benefits increase substantially after 2 01 8 , as the vehicle standards phase in after 201 8 and as the fleet turns over.

**Table 8**  **- 19**  **: Estimated PM**  **2.5** 

### - **Related Health Impacts a**  Health Effect 2018 Annual Reduction in Incidence (5 th % - 95 th %ile) Premature Mortality

– Derived from epidemiology literature b Adult, age 30+, ACS Cohort Study ( Krewski et al., 200 9 ) Adult, age 25+, Six - Cities Study ( Lepeule et al., 20 12 ) Infant, age <1 year (Woodruff et al., 1997) 180 (130

– 220) 400 (230

– 570) 0 (0

– 1 ) Non - fatal myocardial infarction (adult, age 18 and over) Peters et al. (2001) Pooled estimate of 4 studies 200 (74

– 330) 22 (11

– 48) Hospital admissions - respiratory (all ages) c,e 51 ( - 6

– 93) Hospital admissions - cardiovascular (adults, age >18) d 64 (33

– 110) Emergency room visits for asthma (age 18 years and younger) e 100 ( - 18

– 200) Ac ute bronchitis, (children, age 8 - 12) e 280 ( - 10

– 560) Lo wer respiratory symptoms (children, age 7 - 14) 3,500 (1,700

– 5,300) Cessation Lag. Letter from the Health Effects Subcommittee to the U.S. Environmental Protection Agency Administrator, December. T Nonfatal myocardial infarctions (MI) are valued using a ge - 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 - 48 Upper respiratory s ymptoms (asthmatic children, age 9 - 18) 5,000 (1,600

– 8,500) Asthma exacerbation (asthmatic children, age 6 - 18) 5,200 (650

– 10,000) Work loss days 23,000 (20,000

– 27,000) Minor restricted activity days (adults age 18 - 65) 140,000 (120,000

– 160,000)

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 most recent American Cancer Society (ACS) Cohort Study ( Krew ski et al., 200 9 ) and the mo st recent Six - Cities Study ( Lepeule et al., 20 12 ). 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). U 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. e The negative estimates at the 5th percentile confidence estimates for these morbidity endpoints reflect the statistical power of the study used to calculate these health impacts. These results do not suggest that reducing air pollution results in additional health impacts.



### Table **8 -**  **20 : Estimated Ozone**  **- Related Health Impacts**  **a**

Health Effect 201 8 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 45 (17

– 73) 65 ( 27

– 100) 69 (25

– 110) 150 (75

– 220) 200 (130

– 280) 210 (150

– 270) Hospital admissions - respiratory causes (adult, 65 and older)c 260 (130

– 280) Hospital admissions - respiratory causes (children, under 2) 130 (60

– 210) Emergency room visit for as thma (all ages) 140

U 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 - 49 Health Effect 201 8 Annual Reduction in Incidence (5 th

- 95 th percentile) ( - 19

– 430) Minor restricted activity days (adults, age 18 - 65) 270,000 (120,000

– 420,000) School absence days 92,000 (36,000

– 140,000) Notes: a Incidence is rounded to two significant digits. Estimates represent incidence within th e 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 . (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 -**  **21 : Estimated Monetary Value of Changes in Incidence of Health and Welfare**  **Effects (millions of 201**  **1 $)**  **a,b HEALTH ENDPOINTS**  **201 8**  **(5 TH**  **AND 95 TH**  **PERCENTILE) PM**  **2.5 -**  **Related Health Effects** Premature Mortality

– Derived from Epidemi ology Studies b,c Adult, age 30+ - ACS study (Krew ski et al., 2009) 3% discount rate 7% discount rate $1,600 ($230 - $3,700) $1,400 ($210 - $3,300) Adult, age 25+ - Six - Cities study (Lepeule et al., 2012) 3% discount rate 7% discount rate $3,500 ($510 - $8,600) $3,200 ($460 - $7,700) Infant Mortality, <1 year

– (Woodruff et al. 1997) $4.0 ($0.55 - $10) Non - fatal acute myocardial infarctions Peters et al., 2001 3% discount rate 7% discount rate Pooled estimate of 4 studies 3% discount rate 7% discount rate $26 ($5.9 - $60) $25 ($5.5 - $59) $2.8 ($0.74 - $7.3) $2.7 ($0.68 - $7.2) Hospital admissions for respiratory causes d $1.4 ( - $0.32 - $2.7) Hosp ital admissions for cardiovascular causes $2.6 ($1.3 - $4.4) Emergency room visits for asthma d $0.045 ( - $0.007 - $0.087)

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8 - 50 Ac ute bronchitis (children, age 8

– 12) d $0.14 ( - $0.005 - $0.34) Lo wer respiratory symptoms (children, 7

– 14) $0.076 ($0.030 - $0.14) Upper respiratory symptoms (asthma, 9

– 11) $0.17 ($0.049 - $0.38) Asthma exacerbations $0.31 ($0.039

– $0.74) Work loss days $3.5 ($3.1 - $4.0) Minor restricted - activity days (MRADs) $9.8 ($5.7 - $14) **Ozone -**  **Related Health Effects** Premature Mortality, All ages

– Derived from Multi - city analyses Bell et al., 2004 $440 ($56

– $1,100) Huang et al., 2005 $670 ($95 - $1,700) Schwartz, 2005 $710 ($96 - $1,800) Premature Mortality, All ages

– Derived from Meta - analyses Bell et al., 2005 $1,400 ($200 - $3 ,600) Ito et al., 2005 $2,000 ($290 - $4,800) Levy et al., 2005 $2,000 ($300 - $4,800) Hospital admissions - respiratory causes (adult, 65 and older) $7.5 ($0.62 - $14) Hospital admissions - respiratory causes (children, under 2) $1.6 ($0.74 - $2.5) E mergency room visit for asthma (all ages) $0.061 ( - $0.008 - $0.18) Minor restricted activity days (adults, age 18 - 65) $19 ($7.7 - $33) School absence days $9.4 ($3.6 - $14) a Monetary benefits are rounded to two significant digits for ease of presentati on 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 (2018 ). 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. d The negative estimate at the 5th percentile confidence estimate for this morbidity endpoint reflects the statistical power of the study used to calculate this health impact. This result does not suggest that reducing air pollution results in additional health impacts.

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8 - 51 **Table 8**  **- 22**  **: Total Monetized Ozone and PM**  **- r**  **elated Benefits Associated with the Final**  **Program in 201 8**  **Total Ozone and PM Benefits (billions, 201**  **1 $)**  **– PM Mortality Derived from**  **the ACS and Six -**  **Cities Studies 3% Discount Rate**  **7% Discount Rate** Ozone Mortality Function Reference Mean Total Benefits O zone

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

2004 $2.1 - $4.1 Multi - city Bell et al., 2004 $1.9 - $3.7 Huang et al., 2005 $2.3 - $4.2 Huang et al., 2005 $2.1 - $3.9 Schwartz, 2005 $2.3 - $4.3 Schwartz, 2005 $2.1 - $3 .9 Meta - analysis Bell et al.,

2005 $3.1 - $5.0 Meta - analysis Bell et al., 2005 $2.9 - $4.7 Ito et al., 2005 $3.6 - $5.6 Ito et al., 2005 $3.4 - $5.2 Levy et al., 2005 $3.6 - $5.6 Levy et al., 2005 $3.5 - $5.3

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8 - 52 **References** 1 U.S. Energy Information Administration. Annual Energy Outlook 2013. Energy Consumption by Sector and Source, United States, Reference Case. Last viewed on October 23, 2013 at http://www.eia.gov/oiaf/aeo/tablebrowser/#release=AEO2013&subject=2 - AEO2013&table=2 - AEO2013®ion=1 - 0&cases=ref2013 - d102312a . 2 U.S. Energy Information Administration. Annual Energy Outlook 2013. Conversion Factors, Reference Case. Last viewed on October 23, 2013 at http://www.eia.gov/oiaf/aeo/tablebrowser/#release=AEO2013&subject=0 - AEO2013&table=20 - AEO2013®ion=0 - 0&cases=ref2013 - d102312a . 3 U.S. Energy Information Administration. Prime Supplier Sales Volumes. Last vie we d on Octo ber 23, 2013 at http://www.eia.gov/dnav/pet/pet_cons_prim_a_EPM0_P00_Mgalpd_a.htm . 4 Krew ski, D., M. Jerret, R.T. Burnett, R. Ma, E. Hughes, Y. Shi, et al. (2009). Extended follow - up and spatial analysis of the American Cancer Society study linking particulate air pollution and mortality. HEI Research Report, 140, Health Effects Institute, Boston, MA. 5 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. 6 Lepeule J, Laden F, Dockery D, Schwartz J (2012). Chronic Exposure to Fine Particles and Mortality: An Extended Follow- Up of the Harvard Six Cities Study from 1974 to 2009 . Environ Health Perspect. Jul;120(7):965 - 70. 7 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. 8 U.S. Enviro nmental Protection Agency. (2012 ) . Regulatory Impact Analysis for the Final Revisions to the National Ambient Air Quality Standards for Particulate Matter . Prepared by: Office of Air and Radiation , EPA 452/R- 12 - 005 . Retrieved November 22, 2013 at http://www.epa.gov/ttn/ecas/ria.html 9 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 November 22 , 20 13 at http://www.epa.gov/ttn/ecas/ria.html . EPA - HQ - OAR- 2009 - 0472 - 0238 10 U.S. Environmental Protection Agency. (2010). Summary of the updated Regulatory Impact Analysis (RIA) for the Reconsideration of the 2008 Ozone National Ambient Air Quality Standard (NAAQS). Prepared by: Office of Air and Radiation, Office of Air Quality Plannin and Standards. Retrieved November 22, 2013 at http://www.epa.gov/ttnecas1/regdata/RIAs/s1 - supplemental_analysis_full.pdf . 11 U.S. Environmental Protection Agency. (2012). Regulatory Impact Analysis for the Final Revisions to the National Ambient Air Quality Standards for Particulate Matter. Prepared by: Office of Air and Radiation, EPA 452/R- 12 - 005. Retrieved August 14, 2013 at http://www.epa.gov/ttn/ecas/ria.html 12 U.S. E nvironmental Protection Agency. (2013). Dr aft Regulatory Impact Analysis: Ti er 3 Motor Vehicle Emission and Fuel Standards . Prepared by: Office of Air and Radiation. EPA - 420 - D - 13 - 002 . Retrieved October 18, 2013 at http://www.epa.gov/otaq/documents/tier3 /420d13002.pdf 13 Lepeule J, Laden F, Dockery D, Schwartz J. 2012. Chronic Exposure to Fine Particles and Mortality: An Extended Follow- Up of the Harvard Six Cities Study from 1974 to 2009 . Environ Health Perspect. Jul;120(7):965 - 70. 14 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. 15 Krew ski, D., M. Jerret, R.T. Burnett, R. Ma, E. Hughes, Y. Shi, et al. (2009). Extended follow - up and spatial analysis of the American Cancer Society study linking particulate air pollution and mortality. HEI Research Report, 140, Health Effects Institute, Boston, MA. 16 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.

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8 - 55 52 U.S. Environmental Pro tection 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. D ecember. Available on the Internet at <http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=216546>. 53 U.S. Environmental Pro tection Agency (U.S. EPA). 2012 . Provisional Assessment of Recent Studies on Health Effects of Particulate Matter Exposure. EPA/600/R - 12/056A. National Center for Environmental Assessment

—RTP Division. December. 54 Abt Associates, Inc. 2012. BenMAP User’s Manual Appendices. Prepared for U.S. Environmental Protection Agency Office of Air Quality Planning and Standards. Research Triangle Park, NC. September. Available on the Internet at <http://www.epa.gov/air/benmap/models/BenMAPAppendicesOct2012.pdf>. 55 Bell, M.L., et al. (2004). Ozone and short - term mortality in 95 U.S. urban communities, 1987 - 2000. JAMA , 2004. 292(19): p. 2372 - 8. EPA - HQ - OAR - 2009 - 0472 - 1662 56 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 57 Schwartz, J. (2005) How se nsitive 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 58 Bell, M.L., F. Dominici, and J.M. Samet. (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 59 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 60 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 61 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. EPA - HQ - OAR - 2009 - 0472 - 0263 62 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. EPA - HQ - O AR - 2009 - 0472 - 1661 63 Industrial Economics, Incorporated (IEc). (2006). Expanded Expert Judgment Assessment of the Concentration Response Relationship Between PM2.5 Exposure and Mortality. Peer Review 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 64 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. EPA - HQ - OAR - 2009 - 0472 - 0382 65 Peters, A., D.W. Dockery, J.E. Muller, and M.A. Mittleman. (2001). Increased Particulate Air Pollution and the Triggering of Myocardial Infarction. Circulation. 103:2810 - 2815. EPA - HQ - OAR - 2009 - 0472 - 0239 66 Pope, C. A., III, J. B. Muhlestein, et al. 2006. “Ischemic heart disease events triggered by short - term exposure to fine particulate air pollution.” Circulation 114(23): 2443 - 8. 67 S ullivan, J., L. Sheppard, et al. 2005. “Relation between short - term fine - particulate matter exposure and onset of myocardial infarction.” Epidemiology 16(1): 41 - 8. 68 Zanobetti, A., M. Franklin, et al. 2009. “Fine particulate air pollution and its component s in association with cause - specific emergency admissions.” Environmental Health 8: 58 - 60.Zeger S; Dominici F; McDermott A; Samet J. 2008. “Mortality in the Medicare population and chronic exposure to fine particulate air pollution in urban centers (2000 - 2 005).” Environ Health Perspect 116: 1614 - 1619.

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8 - 56 69 Zanobetti A. and Schwartz, J. 2006. “Air pollution and emergency admissions in Boston, MA.” J Epidemiol Community Health 60(10): 890 - 5. 70 Schwartz J. (1995). Short term fluctuations in air pollution and ho spital admissions of the elderly for respiratory disease. Thorax. 50(5):531 - 538. 71 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 - 16 73 72 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 73 Moolgavkar SH, Luebeck EG, Anderson EL. (1997). Air pollution and hospital admi ssions for respiratory causes in Minneapolis St. Paul and Birmingham. Epidemiology . 8(4):364 - 370. EPA - HQ - OAR- 2009 - 0472 - 1673 74 Burnett RT, Smith - Doiron M, Stieb D, Raizenne ME, Brook JR, Dales RE, et al. (2001). Association between ozone and hospitalization for acute respiratory diseases in children less than 2 years of age. Am J Epidemiol . 153(5):444 - 452. EPA - HQ - OAR- 2009 - 0472 - 0223 75 Kloog, I., B.A. Coull, A. Zanobetti, P. Koutrakis, J.D. Schwartz. 2012. Acute and Chronic Effects of Particles on Hospital Admissions in New - England. PLoS ONE. Vol 7 (4): 1 - 8. 76 Moolgavkar, S.H. 2000. “Air Pollution and Hospital Admissions for Diseases of the Circulatory System in Three U.S. Metropolitan Areas.” Journal of the Air and Waste Management Association 50:1199 - 1206 . 77 Babin, S. M., H. S. Burkom, et al. 2007. “Pediatric patient asthma - related emergency department visits and admissions in Washington, DC, from 2001

–2004, and associations with air quality, socio - economic status and age group.” Environ Health 6: 9. 78 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 79 Peng, R. D., M. L. Bell, et al. 2009. “Emergency admissions for cardiovascular and respiratory diseases and the chemical composition of fine particle air pollution.” Environ Health Perspect 117(6): 957 - 63. 80 Peng, R. D., H. H. Chang, e t al. 2008. “Coarse particulate matter air pollution and hospital admissions for cardiovascular and respiratory diseases among Medicare patients.” JAMA 299(18): 2172 - 9. 81 Bell, M. L., K. Ebisu, et al. 2008. “Seasonal and Regional Short - term Effects of Fine Particles on Hospital Admissions in 202 US Counties, 1999

–2005.” American Journal of Epidemiology 168(11): 1301 - 1310. 82 Peel, J. L., P. E. Tolbert, M. Klein, et al. (2005). Ambient air pollution and respiratory emergency department visits. Epidemiology . V ol. 16 (2): 164 - 74. EPA - HQ - OAR - 2009 - 0472 - 1663 83 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 84 Mar, T. F., J. Q. Koenig and J. Primomo. 2010. “Associations between asthma emergency visits and particulate matter sources, including diesel emissions from stationary generators in Tacoma, Washington.” Inhal Toxicol. V ol. 22 (6): 445 - 8.Mar, T. F., T. V. Larson, et al. 2004. “An analysis of the association between respiratory symptoms in subjects with asthma and daily air pollution in Spokane, Washington.” Inhal Toxicol 16(13): 809 - 15. 85 Slaughter, James C et al. 2005. “ Association between particulate matter and emergency room visits, hospital admissions and mortality in Spokane, Washington.” Journal of Exposure Analysis and Environmental Epidemiology (2005) 15, 153

– 159. doi:10.1038/sj.jea.7500382 86 Glad, J.A., L.L. Brink , E.O. Talbott, P.C. Lee, X. Xu, M. Saul, and J. Rager. 2012. The Relationship of Ambient Ozone and PM2.5 Levels and Asthma Emergency Department Visits: Possible Influence of Gender and Ethnicity. Archives of Environmental & Occupational Health. Vol 62 (2) : 103 - 108.

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8 - 57 87 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 - Respiratory Symptoms. Environmental Health Per spectives 104(5):500 - 505. EPA - HQ - OAR - 2009 - 0472 - 0225 88 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 Review of Respiratory Diseases 144:668 - 674. EPA - HQ - OAR 2009 - 0472 - 1672 89 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. 90 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. 91 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 92 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 respiratory illnesses. Epidemiology 12( 1):43 - 54. EPA - HQ OAR - 2009 - 0472 - 1675 93 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 94 Ostro, B.D. and S. Rothschild. (1989). Air Pollution and A cute Respiratory Morbidity: An Observational Study of Multiple Pollutants. Environmental Research 50:238 - 247. EPA - HQ - OAR - 2009 - 0472 - 0364 95 U.S. Enviro nmental Protection Agency. (2012 ). Regulatory Impact Analysis for the Final Revisions to the National Am bient Air Quality Standards for Particulate Matter . Prepared by: Office of Air and Radiation , EPA 452/R- 12 - 005 . Retrieved November 22, 2013 at http://www.epa.gov/ttn/ecas/ria.html 96 U.S. Environmental Protection Agency. (2008). Final Ozone NAAQS Regula tory Impact Analysis . Prepared by: Office of Air and Radiation, Office of Air Quality Planning and Standards. Retrieved November 22 , 20 13 at http://www.epa.gov/ttn/ecas/ria.html . EPA - HQ - OAR- 2009 - 0472 - 023 8 97 U.S. Environmental Protection Agency. (2010). Summary of the updated Regulatory Impact Analysis (RIA) for the Reconsideration of the 2008 Ozone National Ambient Air Quality Standard (NAAQS). Prepared by: Office of Air and Radiation, Office of Air Quali ty Plannin and Standards. Retrieved November 22, 2013 at http://www.epa.gov/ttnecas1/regdata/RIAs/s1 - supplemental_analysis_full.pdf . 98 Abt Associates, Inc. 2012. BenMAP User’s Manual Appendices. Prepared for U.S. Environmental Protection Agency Office of Air Quality Planning and Standards. Research Triangle Park, NC. September. Available on the Internet at <http://www.epa.gov/air/benmap/models/BenMAPAppendicesOct2012.pdf>. 99 Abt Associates, Inc. 2012. BenMAP User’s Manual Appendices. Prepared for U.S. Envi ronmental Protection Agency Office of Air Quality Planning and Standards. Research Triangle Park, NC. September. Available on the Internet at <http://www.epa.gov/air/benmap/models/BenMAPAppendicesOct2012.pdf>. 100 U.S. Bureau of the Census. 2002 . Modified Ra ce Data Summary File 2000 Technical Documentation. Census of Population and Housing: Washington DC. Available on the Internet at <http://www.census.gov/popest/archives/files/MRSF - 01 - US1.pdf>. 101 U.S. Environmental Pro tection Agency (U.S. EPA). 2011 . Regulat ory Im pact Analysis for the Federal Im plementation Plans to Reduce Interstate Transport of Fine Particulate Matter and Ozone in 27 States; Correction of SIP Approvals for 22 States. June. Available on the Internet at <http://www.epa.gov/airtransport/pdfs/F inalRIA.pdf>. 102 Centers for Disease 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 Inte rnet at <http://wonder.cdc.gov>. 103 Agency for Healthcare Research and Quality (AHRQ). 2007. HCUPnet, Healthcare Cost and Utilization Project. Rockville, MD. Available on the Internet at <http://hcupnet.ahrq.gov>.

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8 - 58 104 Rosamond, W., G. Broda, E. Kawalec, S. Rywik, A. Pajak, L. Cooper, and L. Chambless. 1999. “Comparison of Medical Care and Survival of Hospitalized Patients with Acute Myocardial Infarction in Poland and the United States.” American Journal of Cardiology 83:1180 - 1185 105 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>. 106 Adams PF, Hendershot GE, Marano MA. 1999. Current Estimates from the National Health Interview Survey, 1996. Vital Health Stat 10(200):1 - 212. 107 U.S. Bureau of Census. 2000. Population Projections of the United States by Age, Sex, Race, Hispanic Origin and Nativity: 1999 to 2100. P opulation 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>. 108 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. 109 Americ an Lung Association (ALA). 2010 . Trends in Asthma Morbidity and Mortality. American Lung Association Epidemiology and Statistics U nit Research and Program Services Division. February. Table 7. Based on NHIS data (CDC, 2008). Available at http://www.lungusa.org/finding - cures/our - research/trend - reports/asthma trend - report.pdf. 110 Centers for Disease Control and Prevention (CDC). 2008. N ational Center for Health Statistics. National Health Interview Survey, 1999 - 2008. 111 Freeman(III), AM. 1993. The Measurement of Environmental and Resource Values: Theory and Methods. Washington, DC: Resources for the Future. 112 Harrington, W., and P.R. Port ney. 1987. Valuing the Benefits of Health and Safety Regulation. Journal of Urban Economics 22:101 - 112. 113 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 Ec onomic Journal 53:977 - 984. 114 U.S. Enviro nmental Protection Agency. (2012 ). Regulatory Impact Analysis for the Final Revisions to the National Ambient Air Quality Standards for Particulate Matter . Prepared by: Office of Air and Radiation , EPA 452/R- 12 - 005 . Retrieved November 22, 2013 at http://www.epa.gov/ttn/ecas/ria.html 115 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 Qual ity Planning and Standards, Research Triangle Park, NC. March. Available at <http://www.epa.gov/ttn/ecas/regdata/RIAs/6 - ozoneriachapter6.pdf>. 116 U.S. Environmental Protection Agency. (2010). Summary of the updated Regulatory Impact Analysis (RIA) for the Reconsideration of the 2008 Ozone National Ambient Air Quality Standard (NAAQS). Prepared by: Office of Air and Radiation, Office of Air Quality Plannin and Standards. Retrieved November 22, 2013 at http://www.epa.gov/ttnecas1/regdata/RIAs/s1 - supplementa l_analysis_full.pdf . 117 U.S. Environmental Pro tection Agency (U.S. EPA). 2010 . Guidelines for Preparing Economic Analyses. EPA 240 - R- 10 - 001. National Center for Environmental Economics, Office of Policy Economics and Innovation. Washington, DC. December. Av ailable on the Internet at <http://yosemite.epa.gov/ee/epa/eerm.nsf/vwAN/EE - 0568 - 50.pdf/$file/EE - 0568 - 50.pdf>. 118 Cropper, M. L. and A. J. Krupnick. 1990. The Social Costs of Chronic Heart and Lung Disease. Resources for the Future. Washington, DC. Discuss ion Paper QE 89 - 16 - REV. 119 Russell, M.W., D.M. Huse, S. Drow ns, E.C. Hamel, and S.C. Hartz. 1998. “Direct Medical Costs of Coronary Artery Disease in the United States.” American Journal of Cardiology 81(9):1110 - 1115. 120 Wittels, E.H., J.W. Hay, and A.M. Gotto, Jr. 1990. “Medical Costs of Coronary Artery Disease in the United States.” American Journal of Cardiology 65(7):432 - 440.

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8 - 59 121 Agency for Healthcare Research and Quality (AHRQ). 2007. HCUPnet, Healthcare Cost and Utilization Project. Rockville, MD. Availab le on the Internet at <http://hcupnet.ahrq.gov>. 122 Smith, D.H., D.C. Malone, K.A. Lawson, L.J. Okamoto, C. Battista, and W.B. Saunders. 1997. “A National Estimate of the Economic Costs of Asthma.” American Journal of Respiratory and Critical Care Medicine 156(3 Pt 1):787 - 793. 123 Stanford, R., T. McLaughlin and L. J. Okamoto. 1999. “The cost of asthma in the emergency department and hospital.” Am J Respir Crit Care Med.160 (1): 211 - 5. 124 Neumann, J.E., M.T. Dickie, and R.E. Unsworth. 1994. “Linkage Between Hea lth Effects Estimation and Morbidity Valuation in the Section 812 Analysis

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—Final Report. EPA - 452/R- 10 - 005. Office of Air Quality Planning and Standards, Research Triangle Park, NC. September. Available on the Internet at <http://www.epa.gov/ttnnaaqs/standar ds/pm/data/PM_RA_FINAL_June_2010.pdf>. 132 U.S. Environmental Pro tection Agency (U.S. EPA). 2011 . The Benefits and Costs of the Clean Air Act 1990 to 2020: EPA Report to Congress. Office of Air and Radiation, Office of Policy, Washington, DC. March. Availabl e on the Internet at <http://www.epa.gov/oar/sect812/feb11/fullreport.pdf>. 133 World Health Organization (WHO). 2008. Part 1: Guidance Document on Characterizing and Communicating Uncertainty in Exposure Assessment, Harmonization Project Document No. 6. Pub lished under joint sponsorship of the World Health Organization, the International Labour Organization and the United Nations Environment Programme. WHO Press: Geneva, Switzerland. Available on the Internet at <www. who.int/ipcs/methods/harmonization/areas/ uncertainty%20.pdf>. 134 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. 135 Industrial Economics, Incorporated (IEc). 2006. Expanded Expert Judgment Assessment of the Concentration Response Relationship Between PM2.5 Exposure and Mortality. Prepared for: Office of Air Quality P lanning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC. September. Available on the Internet at <http://www.epa.gov/ttn/ecas/regdata/Uncertainty/pm_ee_tsd_expert_interview_summaries.pdf>. 136 U.S. Environmental Protection Age ncy Science Advisory Board (U.S. EPA - SAB). 2008. Characterizing Uncertainty in Particulate Matter Benefits Using Expert Elicitation. EPA - COUNCIL - 08 - 002. July. Available on the

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8 - 60 Internet at <http://yosemite.epa.gov/sab/sabproduct.nsf/0/43B91173651AED9E852574 87004EA6CB/$File/EPA COUNCIL - 08 - 002 - unsigned.pdf>. 137 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 Ambi ent Fine Particulate Matter in the U.S.” Environ. Sci. Technol., 42(7):2268 - 2274. 138 U.S. Environmental Pro tection Agency (U.S. EPA). 2006 . Regulatory Impact Analysis, 2006 National Ambient Air Quality Standards for Particulate 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>. 139 Mansfield, Carol; Paramita Sinha; Max Henrion. 2009. Influence Analysis in Support of Characterizing Uncertainty in Human Health Benefits Analysis: Final Report. Prepared for U.S. EPA, Office of Air Quality Planning and Standards. November. Available on the internet at <http://www.epa.gov/ttn/ecas/regdata/Benefits/influence_analysis_fina l_report_psg.pdf>. 140 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>. 141 U.S. Environmental Protection Agency - Science Advisory Board (U.S. EPA - SAB). 2009. Consultation on EPA’s Particulate Matter National Ambient Air Quality Standards: Scope and Me thods Plan for Health Risk and Exposure Assessment. EPA - COUNCIL - 09 - 009. May. Available on the Internet at <http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb85256ead006be86e/723FE644C5D758DF852 575BD00763A32/$File/EPA - CASAC - 09 - 009 - unsigned.pdf>. 142 U.S. Environmental Protection Agency - Science Advisory Board (U.S. EPA - SAB). 2009. Review of EPA’s Integrated Science Assessment for Particulate Matter (First External Review Draft, December 2008). EPA COUNCIL - 09 - 008. May. Available on the Intern et at <http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb85256ead006be86e/73ACCA834AB44A1085 2575BD0064346B/$File/EPA - CASAC - 09 - 008 - unsigned.pdf>. 143 U.S. Environmental Pro tection Agency (U.S. EPA). 2010 . Proposed Regulatory Impact Analysis (RIA) for the Transport Rule. Office of Air Quality Planning and Standards, Research Triangle Park, NC. January. Available on the Internet at <http://www.epa.gov/ttn/ecas/regdata/RIAs/proposaltrria_final.pdf>. 144 U.S. Environmental Prot ection Agency (U.S. EPA). 2011 . Regulatory Impact Analysis for the Federal Im plementation Plans to Reduce Interstate Transport of Fine Particulate Matter and Ozone in 27 States; Correction of SIP Approvals for 22 States. June. Available on the Internet at <http://www.epa.gov/airtranspo rt/pdfs/FinalRIA.pdf>. 145 U.S. Environmental Pro tection Agency (U.S. EPA). 2011 . Regulatory Impact Analysis for the Final Mercury and Air Toxics Standards. EPA - 452/R- 11 - 011. December. Available on the Internet at <http://www.epa.gov/ttn/ecas/regdata/RIAs/matsriafinal.pdf>. 146 U.S. Environmental Pro tection Agency (U.S. EPA). 2011 . Policy Assessment for the Review of the Particulate Matter National Ambient Air Quality Standards. EPA - 452/D - 11 - 003. April. Available on the Internet at <http://www.epa.gov/ttnnaaqs/ standards/pm/s_pm_2007_pa.html>. 147 U.S. Environmental Protection Agency Science Advi sory Board (U.S. EPA - SAB). 2010 . CASAC Review of Policy Assessment for the Review of the PM NAAQS

— Second External Revie w Draft (June 2010). EPA - CASAC 10 - 015. Available on the Internet at <http://yosemite.epa.gov/sab/sabproduct.nsf/264cb1227d55e02c85257402007446a4/CCF9F4C0500C500F8525779 D0073C593/$File/EPA - CASAC - 10 - 015 - unsigned.pdf>. 148 U.S. Environmental Protection Agency Science Advi sory Board (U.S. EPA - SAB). 2010 . CASAC Rev iew of Policy Assessment for the Review of the PM NAAQS

— Second External Revie w Draft (June 2010). EPA - CASAC 10 - 015. Available on the Internet at <http://yosemite.epa.gov/sab/sabproduct.nsf/264cb1227d55e02c85257402007446a4/CCF9F4C0500C500F8525779 D0073C593/$ File/EPA - CASAC - 10 - 015 - unsigned.pdf>.

### EO12866_Tier 3 2060 - AQ86 FRM RIA_20140123. pdf

*** EO12866 Review - Draft - Do Not Cite, Quote, or Release During Review

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