***** EO12866 Review -**  **Draft -**  **Do Not Cite, Quote, or Release During Review *****



### 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$579 $72 - $2 $4 $653 2020 $599 $98 - $3 $4 $697 2021 $630 $97 - $5 $4 $725***** EO12866 Review -**  **Draft -**  **Do Not Cite, Quote, or Release During Review *****



### 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 

### 2022 $640 $121 - $6 $4 $758 2023 $639 $116 - $8 $4 $751 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.***** EO12866 Review -**  **Draft -**  **Do Not Cite, Quote, or Release During Review *****



### 8 - 3 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. A comparison of the annual fuel consumption values used in the proposal and this final rule 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 program are listed in Table 8 - 2 .***** EO12866 Review -**  **Draft -**  **Do Not Cite, Quote, or Release During Review *****



### 8 - 4 **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 

### 201 6 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 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 th e 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 Califo rnia.***** EO12866 Review -**  **Draft -**  **Do Not Cite, Quote, or Release During Review *****



### 8 - 5 8.1.2 Quantified and Monetized Health and Environmental Impacts This section presents EPA’s anal ys is of the criteria pollutant - related health and environmental impacts that would occur as a result of the final Tier 3 standards . The vehicles and fuels sub

ject 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. Emissions of NO X 

### (a precur sor 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 PM 2.5 

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

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

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

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

### and SO 2 

### ? 2. What is the economic value of the se effects? For the final rulemaking, we have quantified and monetized the health and environmental impacts in 2030, representing projected impacts associated with a ye ar 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. The estimated decrease in population - weighted national average PM 2.5 

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

### is 0.04 μg/m 3 

### in 2030). A 

### The estimated 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 by 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 will yi eld between $ 6.7 and $7.4 billion in total annual benefits , depending on the discount

A Note that the national, population - weighted PM 2.5 and ozone air quality metrics pre sented 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.***** EO12866 Review -**  **Draft -**  **Do Not Cite, Quote, or Release During Review *****



### 8 - 6 rate used . B 

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

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

### results in approximately

2, 0 00 premature mortalities avoided in 2030 and will yi eld 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 are projected to 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 ytical process for the final rul emaking . For this reason, the modeled 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.

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 ). The estimated 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 estimated de crease in population - weighted national average ozone exposure results in decreases in ozone - related health and environm ental impacts (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 ben efits methods 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

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 - illness values that reflect lost earnings and direct medical c osts over a 5 - year period following a nonfatal MI.***** EO12866 Review -**  **Draft -**  **Do Not Cite, Quote, or Release During Review *****



### 8 - 7 Analy sis Program version 4.0 65 (BenM AP). C 

### BenMAP is a computer program developed b y the U.S. EPA that integrates a number of the modeling elements used in previous analyses (e.g., interpolation functions, population projections, health impact functions, valuation functions, anal ys is and poo ling methods) to translate modeled air concentration estimates into health effects incidence estimates and monetized benefits estimates. The range of total monetized ozone - and PM - related health impacts projected 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.***** EO12866 Review -**  **Draft -**  **Do Not Cite, Quote, or Release During Review *****



### 8 - 8 **Table 8**  **- 4**  **: Estimated 2030 Monetized PM**  **- and Ozone**  **- Related Health Benefits**  2030 Total Ozone and PM Benefits

– PM Mortality Derived from Ame rican 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 estimated benefits. Range wa s developed by adding the estimate from the ozone 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 e stimates 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 unquantifie d health effects is provided in 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 o f 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 refl ect EPA’s work to update PM - related 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***** EO12866 Review -**  **Draft -**  **Do Not Cite, Quote, or Release During Review *****



### 8 - 9  Incorporation of the newest American Cancer Society (ACS) mortality study a nd newest Harvard Six Cities mortality 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 respirat ory 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 Assessm ent 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 cardiovascular 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. Ba sed on recent data from Agency

for Healthcare Research and Quality ’s Healthcare

D Refer to the 2012 PM NAAQS RIA for more detail regarding the studies themselves.***** EO12866 Review -**  **Draft -**  **Do Not Cite, Quote, or Release During Review *****



### 8 - 10 Utilization Project National Inpatient Sample database, 23 

### we identified death rates f or adults hospitalized with acute myocardial infarction stratified by age. These rates repla ce the survival rates from Rosamond 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 by 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 estimated human health impacts we are able to quantify

and monetize at this time. However, the full compl ement 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 available or which do not provide easil y 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 underestimates of the total benefits attributable to the final program .

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

BEEN MONETIZED MORE

INFORMATION **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 Hosp ital admissions

—cardiovascular (age >20)   PM NAAQS RIA, Section 5.6 Emergency department visits for asthma (all ages)   PM NAAQS RIA, Section 5.6***** EO12866 Review -**  **Draft -**  **Do Not Cite, Quote, or Release During Review *****



### 8 - 11 BENEFITS CATEGORY SPECIFIC EFFECT EFFECT HA S BEEN QUANTIFIED EFFECT HAS

BEEN MONETIZED MORE

INFORMATION Ac ute bronchitis (age 8

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

– 14)   PM NAAQS RIA, Section 5.6 Upper respiratory symptoms (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 restric ted - activity days (age 18

– 65)   PM NAAQS RIA, Section 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 disea se (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 Prematur e mortality based on short term study estimates (all 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***** EO12866 Review -**  **Draft -**  **Do Not Cite, Quote, or Release During Review *****



### 8 - 12 BENEFITS CATEGORY SPECIFIC EFFECT EFFECT HA S BEEN QUANTIFIED EFFECT HAS

BEEN MONETIZED MORE

INFORMATION Hospital admissions

—

respiratory causes (age <2)   Ozone ISA Emergency department visits for asthma (all ages)   Ozone ISA Minor restricted - activity days (age 18

– 65)   Ozone ISA School absence days (age 5

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

– 65)   Ozone ISA Other respiratory effects (e.g., premature 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,

formaldehyde, 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 lymphoc yte counts (benzene) Reproductive and developmental effects (1,3 -butadiene) Irritation of eyes and mucus membranes (formaldehyde) Respiratory irritation (formaldehyde) Asthma attacks in asthmatics (formaldehyde) Asthma - like symptoms in non asthmatics (formaldehyde) Irritation of the eyes, skin, and respiratory tract (acetaldehyde) Upper respiratory tract irritation and congestion (acrolein) — — IRIS a,b***** EO12866 Review -**  **Draft -**  **Do Not Cite, Quote, or Release During Review *****



### 8 - 13 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 li mitations for this analysis. 

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

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

Board specificall y commented in their review of the 1996 NATA that these tools were not yet ready for use in a national - scale benefits analy sis, 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 of the benefits and costs of the Clean Air Act , 27 

### EPA conducted a case study anal ys is of the estimated health effects associated with reducing exposure to benzene in Houston from implementation of the Cle an 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 anal ys is of the final program . E 

### The reduction in air pollution emissions that will result from the final program is projected to 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

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/EPA 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 o f 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 standards will reduce nitrous oxide (N 2 O) and methane (CH 4 ) emissi ons 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.***** EO12866 Review -**  **Draft -**  **Do Not Cite, Quote, or Release During Review *****



### 8 - 14 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 anal ys is 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 imp acts in the 48 contiguous U.S. states associated with the final Tier 3 program . For each endpoint presented in Table 8 - 6 and Table 8 - 7 , we provide both the point estimate and the 90 percent confi dence 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 avo ided PM 2.5 

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

anal ys is was conducted to understand the impact of alternative concentration response functions suggested b y experts in the field. 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 inc onsistent 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 excluding 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 estimate of PM - related mortality is also provided.***** EO12866 Review -**  **Draft -**  **Do Not Cite, Quote, or Release During Review *****



### 8 - 15 In 203 0, the effect estimates of nine of the twelve experts included in the elicitation panel fall within the empirically- derived range provided by the ACS and Six - Cities studies. Only one expert falls below this range , while two of the experts are above this range. Although the overall range across experts is summarized in these tables, the full uncertainty in th e 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.***** EO12866 Review -**  **Draft -**  **Do Not Cite, Quote, or Release During Review *****



### 8 - 16 **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 ( Krew ski 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) Poole d 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

– 91,000) 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 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 altern ative estimates of adult mortality and should not be summed. PM - related infant mortality based 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 cardiovascular 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.***** EO12866 Review -**  **Draft -**  **Do Not Cite, Quote, or Release During Review *****



### 8 - 17 **Table 8**  **- 7**  **: Estimated Ozone -**  **Related Health Impacts** Health Effect 2030 Annual Reduction in Incidence (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. (2005)

– 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 a dmissions - respiratory causes (adult, 65 and older) c 740 (87

– 1,400) Hospital admissions - respiratory 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 - 6 5) 600,000 (290,000

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

– 300,000) a Incidence is rounded to two significant digits. Estimates represent incidence within the 48 contiguous U.S. b Estimates of ozone - related premature mortality are based upon incidence estimates derived from several alternative studies: Bell et al. (2004); Huang et al. (2005); Schwartz (2005) ; Bell et al. (2005); Ito et al. (2005); Levy et al. (2005). The estimates of ozone - related premature mortality should therefore not be s ummed. c Respiratory hospital admissions for ozone include admissions for all respiratory causes and subcategories for COPD and pneumonia. d The negative estimate at the 5th percentile confidence estimate for this morbidity endpoint reflects the statistic al 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 Elicitat** **ion: Annual Reductions in Premature**

**Mortality in 2030 Associated with the Final**  **Program** Source 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***** EO12866 Review -**  **Draft -**  **Do Not Cite, Quote, or Release During Review *****



### 8 - 18 Source of Mortality Estimate 2030 Tier 3 Control 5th Percentile Mean 95th Percentile Expert D 58 950 1,500 Expert E 1,100 2,200 3,300 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 Impacts Table 8 - 9 p resents the estimated monetary value of changes in the incidence of ozone and PM 2.5 

### - related health and environmental effects. T otal aggregate monetized benefits are presented in Table 8 - 10 . 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 2030. G 

### The monetized value of PM 2.5 

### - related mortality also accounts for a twenty- ye ar segmented cessation 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 us e 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 welf are effects could be quantified or monetized. The estimate of total monetized health benefits of the final program is thus equal to

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 real incomes increase. Benefits are therefore adjusted by multiplying the unadjusted benefits by the appropriate adjustment factor to account for income growth over time. For grow th between 2000 and 2030, this factor is 1.23 for long - term mortality, 1.27 for chronic health impacts, and 1.08 for minor health impacts. For a complete discussion of how these adjustment factors were derived, we refer the reader to the PM NAAQS regulatory impact analysis . 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. 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 realization of changes in health effects. Within the context of b enefits 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 co st - of - illness values that reflect lost earnings and direct medical costs over a 5 - year period following a nonfatal MI.***** EO12866 Review -**  **Draft -**  **Do Not Cite, Quote, or Release During Review *****



### 8 - 19 the subset of monetized PM 2.5 

### - and ozone - related health impacts we are able to quantify plus the sum of the nonmonetized he alth 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 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 th e 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, minor 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 individuals. 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 incid ences 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 common, 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 willingne ss - to - pay ( e.g. , cost - of

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

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

– Derived from Epidemiology Studies b,c Adult, age 30+ - ACS study (Krew ski et al., 2009) 3% discount rate 7% disco unt rate $6,100 ($910 - $14,000) $5,500 ($820 - $13,000) Adult, age 25+ - Six - Cities study (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 ye ar – (Woodruff et al. 1997) $13 ($1.8 - $32) Non - fatal acute myocardial infarctions Peters et al., 2001 3% discount rate $96 ($21 - $230)***** EO12866 Review -**  **Draft -**  **Do Not Cite, Quote, or Release During Review *****



### 8 - 20 7% discount rate Pooled estimate of 4 studies 3% discount rate 7% discount rate $93 ($19 - $220) $10 ($2.6 - $27) $10 ($2.4 - $27) 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 - $0.2 9) 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 $1 2 ($11

– $14) Minor restricted - activity days (MRADs) $34 ($20 - $49) **Ozone -**  **Related Health Effects** 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) Sc hwartz, 2005 $1,700 ($220 - $4,400) Premature Mortality, All ages

– Derived from Meta - analyses 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 (children, 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 significant digits for ease of presentation and computation. PM and ozone benefits are nationwide. b Monetary benefits adjusted to account for growth in real GDP per c apita between 1990 and the analysis year (2030).***** EO12866 Review -**  **Draft -**  **Do Not Cite, Quote, or Release During Review *****



### 8 - 21 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 r esults in additional health impacts. 

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



### 8 - 22 endpoints (specific effects that can be associated with changes in air quality) and as signs values to those changes assuming independence of the values for those individual endpoints. 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 quality can be directly

valued. In other cases, such as for changes in ozone and PM, an impact anal ys is must first be conducted to convert air quality changes into eff

ects that can be assigned dollar values. For the purposes of this RIA, the health impacts anal ys is (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 impac t anal ys es, our estimates are based on the best available methods of benefits transfer. Benefits 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 Huma n 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 th e retrospective or prospective change in adverse health impacts expected to result from population - 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 functions and population projections

— provided that key

input data are available, including air quality

estimates and risk coefficients. 43 

### Analy sts have applied the HIA app roach to estimate human health impacts resulting from hypothetical changes in pollutant levels. 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 affe cting 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.***** EO12866 Review -**  **Draft -**  **Do Not Cite, Quote, or Release During Review *****



### 8 - 23 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 pollutant measure. There are other functional forms, but the basic elements remain the same. The following subsections describe the sources for each of the first three elements: size of the potentially

affected populations; PM 2.5 

### and ozone effect estimates; and 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 d escribes 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 impacts 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- level projections of population by age, sex, and race out to 2040, relative to a baseline using the 201 0 Census data. Projections in each county are determined simultaneously with every

other county

in the United States to take into account patterns of economic 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, such as outputs from mining, agriculture, and manufacturing with the national economy .

J Projections of ambient PM 2.5 concentrations for this analysis were generated using the Community Multiscale Air Quality model (CMAQ). See Chapter 7 of this RIA for more information on the air quality modeling.***** EO12866 Review -**  **Draft -**  **Do Not Cite, Quote, or Release During Review *****



### 8 - 24 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 projected for each economic area based on net migration rates derived from employ

ment opportunities and fol lowing a cohort - component method based on fertility

and mortality

in each area.

 

### Fourth, emplo ym ent and population projections are repeated for counties, using 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, fertility , 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 replace previous Criteria Documents), with input and advice from the SAB - HES, a scientific review pane l specifically established to provide advice on the use of the scientific literature in developing benefits analyses for the EPA’s Report to Congress on The Benefits and Costs of the Clean Air Act 1990 to 2020 . 51 

### Inaddition, we have included more recent ep idemiology

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 plausibility 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) rather than physiological responses (such as changes in clinical measures like Forced Expiratory Volume [F EV1]). 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 c linical trials, and observational epidemiology studies. All of these data sources provide important contributions to the weight of evidence surrounding a particular health impact. However, only epidemiology studies provide direct concentration - response rel ationships that can be used to evaluate population - level impacts of reductions in ambient pollution levels in a health impact assessment. For the data - derived 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, among other considerations. In general, the use of concentration - response functions from more than a sin gle study can provide a more representative distribution of the effect estimate. However, there are often differences between studies examining the same endpoint, making it difficult to pool the results in a consistent manner. For example, studies may exam ine different pollutants or different age groups. For this reason, we consider very carefully***** EO12866 Review -**  **Draft -**  **Do Not Cite, Quote, or Release During Review *****



### 8 - 25 the set of studies available examining each endpoint and select 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 studies 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 between - study variability , due, for example, to differences in population susceptibility . We used the f ixed 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 use the random effects model. K 

### Pooled impact functions are used to estimate hospital admissions and a sthma 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 urb an 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 more 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 single effect estimate and those defined b y a pooling of multiple effect estimates. Although the effec t estimate may , in fact, vary from one location to another (e.g., because of differences in population susceptibilities or differences in the composition of 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 c hronic exposure (e.g., over a year or longer), prospective cohort studies are preferred over ecological studies because they control for important individual - level confounding variables that cannot be controlled for in ecological studies. Study Period St udies examining a relatively longer period of time (and therefore having more data) are

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, whi le the version used here calculated sample variance. This change did not affect the selection of random or fixed effects for the pooled incidence estimates between the proposal and final RIA.***** EO12866 Review -**  **Draft -**  **Do Not Cite, Quote, or Release During Review *****



### 8 - 26 preferred, because they have greater statistical power to detect effects. 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 cov er the entire sensitive population but allow for heterogeneity across age or other relevant demographic factors. In the absence of effect estimates specific to age, sex, preexisting condition status, or other relevant factors, it may be appropriate to sele ct effect estimates that cover the broadest population to match with the desired outcome of the analysis, which is total national - level health impacts. When available, multi - city studies are preferred to single city studies because they provide a more gene ralizable representation of the concentration - response function. Study Size Studies examining a relatively large sample are preferred because they generally 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 asthmatic children). Study Location U.S. studies are more desirable than non - U.S. studies because of pote ntial differences in pollution characteristics, exposure patterns, medical care system, population behavior, and lifestyle. National estimates are most appropriate 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) jointly, 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 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 individual health endpoint may be analyzed separately, care must be exercised in selecting health endpo ints 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 to separately quantify all of the possible PM and ozone health effects that have been reported in the literature for three reasons: (1) the possibility of double counting (such as hospital admissions for specific respiratory diseases versus hospital admissions for all or a sub - set of respiratory diseases); (2) uncertainties in applying effect relationships that are based on clinical studies to the potentially affected population; or (3) the lack of an established concentration - response (CR) relationship. Table 8 - 12 lists the health endpoints included in this analy sis.***** EO12866 Review -**  **Draft -**  **Do Not Cite, Quote, or Release During Review *****



### 8 - 27 **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 Expert Elicitation (IEc, 2006) 63 >24 years Premature mortality

— all - cause PM 2.5 Woodruff et al. (1997) 64 Infant (<1 year) **Chronic Illness** Nonfatal heart attacks PM 2.5 Peters et al. (2001) 65 Pooled estimate: Pope et al. (2006) 66 Sullivan et al. (2005) 67 Zanobetti et al. (2009) 68 Zanobetti and Schwartz (2006) 69 Adults (>18 years)

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



### 8 - 28 **ENDPOINT POLLUTANT**  **STUDY STUDY POPULATION** 

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 - 414; 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 **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 appli ed 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).***** EO12866 Review -**  **Draft -**  **Do Not Cite, Quote, or Release During Review *****



### 8 - 29 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 adverse 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 bas eline pollutant levels in that location. To derive the total baseline incidence per ye ar, this rate must be multiplied by

the corresponding population number. For example, if the baseline incidence rate is the number of cases per year per million people, that number must be multiplied by the millions of people in the total population. Table 8 - 13 summarizes the sources 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 individual age groups and then summed over the relevant age range to provide an estimate of total populati on 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 b enefits. For some studies, however, the onl y available incidence information comes from the studies themselves; in these cases, incidence in the study population is assumed to represent ty pical incidence at the national level. County , state and regional in cidence rates are available for hospital admissions, and county- level data are available for premature mortality . We projected mortality

rates such that future 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 basel ine 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 emergency department for air pollution - related***** EO12866 Review -**  **Draft -**  **Do Not Cite, Quote, or Release During Review *****



### 8 - 30 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 geographic 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 likely to be unscheduled. If air pollution - related hospital admissions are scheduled, this assumption woul d underestimate these benefits. For the set of endpoints affecting the asthmatic population, in addition to baseline incidence rates, prevalence rates of asthma 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 assume 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 Infarction (heart attacks) Daily nonfatal myocardial infarction incidence rate per person, 18+ Age - , region - , state - , and county - specific rate 2007 HCUP data fi les ; a adjusted by

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, Hendersho t, and

Marano, 1999, Table 41); 106 U.S. Bureau of the Census (2000) 107***** EO12866 Review -**  **Draft -**  **Do Not Cite, Quote, or Release During Review *****



### 8 - 31 Endpoint Parameter Rates Value Source School Loss Days Rate per person per year, assuming 180 school days per year 9.9 National 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 (HCUP) database contains individual level, state and regional - level hospital and emergency department dis charges 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 Analysi**  **s** 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 0.0719 45

– 64 0.0745 65+ 0.0716 African American, 5 to 17 0.1776 American Lung Association (2010, Table 9) African American, <18 0.1553 American Lung Association a Notes: a b Calculated by ALA for U.S. EPA, based on NHIS data (CDC, 2008) . 110



### 8.1.2.4.5 Economic Values for Health Outcomes After quantifying the change in adverse health impacts, we estimate the econ omic 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 (before the effect has occurred) or ex post (after the effect has occurred). Reductions in ambient co ncentrations of air pollution generall y lower the risk of future adverse health effects b y a small amount for a large population. The appropriate economic measure 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 ch anges 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 stati stical 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***** EO12866 Review -**  **Draft -**  **Do Not Cite, Quote, or Release During Review *****



### 8 - 32 rele vant population. The same ty pe of calculation can produce values for statistical incidences of other health endpoints. For some health effects, such as hospital 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 increases. Many of the valuation studies used in this analysis were conducted in the late 1980s and earl y 1990s. Because real income has grown since the studies were conducted, people’s willingness to pay

for reductions in the risk of premature death and disease likely has grown as well. We did not adjust cost of illness - based values because they are based on current costs. Similarly, we did not adjust the value of school absences, because that value is based on current wage rates. For details on valuation estimates for PM - related endpoints, see the the RIA that accompanied the final revisions to the Natio nal Ambient Air Quality Standards (NAAQS) for Particulate Matter. 114 

### For details on valuation estimates for ozone - related endpoints, see the RIAs for the 2008 Ozone NAAQS RIA and the 2010 ozone NAAQS reconsideration . 115 , 116***** EO12866 Review -**  **Draft -**  **Do Not Cite, Quote, or Release During Review *****



### 8 - 33 **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 underl ying studies, the distribution parameters, and other useful information are available in Appendix B of EPA's current Guidelines for Preparing Economic Analyses (U.S. EPA, 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 20,00 0 $ 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 estim ates 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) $ 22,000 $ 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 COPD 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 we ighted 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 hospital stay, and weighted share of total cardiovascular category illnesses) reported in Agency for He althcare Research and Quality (200 7 ) (www.ahrq.gov). All respiratory (ages $ 37,000 $ 37,000 No distributions available. The COI point estimates (lost earnings plus***** EO12866 Review -**  **Draft -**  **Do Not Cite, Quote, or Release During Review *****



### 8 - 34 65+) direct medical costs) are based on ICD - 9 code level information ( e.g. , average hospita l care costs, average length of hospital stay, and weighted share of total COPD category illnesses) reported in Agency for Healthcare Research and Quality, 200 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 hospital 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 COI values: (1) $311.55, from Smith et al. (1997) 122 and (2) $260.67, from Stanford et al. (1999). 123 **R espiratory Ailments Not Requiring Hospitalization**  Upper Respiratory Symptoms (URS) $3 2 $3 4 Com binations of the three symptoms for which WTP estimates are available that closely match those listed by Pope et al. result in seven different “symptom clusters, ” each describing a “type” of URS. A dollar value was derived for each type of URS, using mid - range estimates of WTP (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 was derived for each type of LRS, using mid - range estimates of WTP (Neumann et al. , 1994) to avoid e ach symptom in the cluster and assuming additivity of WTPs. The dollar value for LRS is the average of the dollar values for the 11 different types of LRS. In the absence of information surrounding the frequency with which each of the 11 types of LRS occur s 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 valued at $45 per incidence, based on the mean of average WTP estimates for the four severity definitio ns 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 b elieved 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. Variable

(U.S. No distribution available. Point estimate is based on county - specific

median annual wages divided by 50 (assuming 2 weeks of vacation)***** EO12866 Review -**  **Draft -**  **Do Not Cite, Quote, or Release During Review *****



### 8 - 35 median = $ 150 ) median =

$ 150 ) and then by 5

—to get median daily wage. U.S. Year 2000 Census,

compiled by Geolytics, Inc. (Ge oly tics, 2002) 126 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 benefits are adjusted by multiplying the unadjusted benefits by the appropriate adjustment factor to ac count 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 assume constant WTP from 2024 through 2030. This results in an underestimate of benefits occurring be tween 2024 and 2030. For grow th between 2000 and 2024, this factor is 1.23 for long - term mortality, 1.27 for chronic health impacts, and 1.08 for minor health 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 reducti ons estimated to occur due to the implementation of the program . We estimate ambient PM 2.5 

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

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

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



### 8 - 36 To estimate ozone - related health effects for the contiguous United States, full - season ozone data are required for every BenMAP grid - c ell. Given available ozone monitoring data, we generated full - season ozone profiles for each location in two steps: (1) we combined monitored 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 obser ved monitor data. CMAQ generates predictions of hourly PM species concentrations for every grid. The species include a primary

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

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

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

### and PM 2.5 

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

### concentrations was created by interpolating Federal Reference Monitor ambient data and IMPROVE ambient data. Gridded fields o f 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 potenti al benefits through exposure changes to these populations. **Table 8**  **- 16**  **: Summary of CMAQ**  **- Derived Population**  **- Weighted Ozone and PM**  **2.5** 

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

### Baseline Change b 

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

### Daily Maximum 8 - Hour Average 43.65 0.32

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 Averaging (EVNA) spatial interpolation. See the BenMAP manual for technical details, available for dow nload at http://www.epa.gov/air/benmap.***** EO12866 Review -**  **Draft -**  **Do Not Cite, Quote, or Release During Review *****



### 8 - 37 Concentration PM 2.5 

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

### ) Annual Average Concentration 7.9 4 0.04 Notes: a Ozone and PM 2.5 metrics are calculated at the CMAQ grid - cell level for use in health effects estimates. Ozone metrics are calculated over relevant time periods during the daylight hours of the “ozone season” ( i.e. , May through September). Note that the national, population - weighted PM 2.5 and ozone air quality metrics presented in this c hapter represent an average for the entire, gridded U . S . CMAQ domain. These are different than the population - weighted PM 2.5 and ozone design value metric s 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 populatio n and the estimated CMAQ grid cell seasonal ozone concentration and then dividing by the total population. 

### Emissions and air quality modeling decisions are made early in the anal ytical process. For this reason, the emission control scenarios used in the air quality and benefits modeling are slightly different than the final emission inventories estimated for the 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 h ealth 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 likel 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 epidemi ology studies, economic data for monetizing benefits, and assumptions regarding the future state of the world (i.e., regulations, technology , and human behavior). Each of these inputs may be uncertain and would affect the benefits estimate. When the uncert ainties 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 Nation al 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 quantitativ e 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***** EO12866 Review -**  **Draft -**  **Do Not Cite, Quote, or Release During Review *****



### 8 - 38 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. 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 PM 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 economic 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 - term studies: three multi - city studies and three meta anal ys es of the existing literature; 5 . An analy sis 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 for characterizing random sampling error associated with the concentration response functions from ep idemiological studies and random effects modeling to characterize both sampling error and variability across the economic valuation functions. The Monte Carlo 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 epidemiolog ical studies determined the distributions for individual effect estimates for endpoints estimated using a single study . For endpoints estimated using a pooled estimate of multiple studies, the confidence intervals reflect both the standard errors and the v ariance across studies. The confidence intervals around the monetized benefits incorporate***** EO12866 Review -**  **Draft -**  **Do Not Cite, Quote, or Release During Review *****



### 8 - 39 the epidemiology

standard errors as well as the distribution of the 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.

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 knowled ge 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 ran ge 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) reco mmended 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 cohort - 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 estimates 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 no t yet include methods for***** EO12866 Review -**  **Draft -**  **Do Not Cite, Quote, or Release During Review *****



### 8 - 40 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 inte rvals 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. 8.1.2.5.3 Concentration Benchmark Analysis fo r 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 benchmarks, 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 eviden ce 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 me an PM 2.5 

### levels. The LMLapproach compares the percentage of avoided premature deaths estimated to occur above and below the minimum observed air quality level 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 the estimated number of premature deaths avoided (but not in the existence of a causal relationship b etween PM and premature mortality) diminishes as we estimate these impacts in locations where PM 2.5 

### levels are below the LML. This interpretation is consistent 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 appreciabl y less

P F or a summary of the scientific review statements regarding the lack of a threshold in the PM 2.5 - mo rtality relationship, see the Technical Support Document (TSD) entitled Sum mary of Expert Opinions on the Existence of a Threshold in the Concentration -Response Function for PM 2.5 - related Mortality (U.S. EPA, 2010f).***** EO12866 Review -**  **Draft -**  **Do Not Cite, Quote, or Release During Review *****



### 8 - 41 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 reporte d associations becomes appreciably less, and the scientific evidence provides no clear dividing line. 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 magnitude 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 concentration benchmarks as a

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

the mean falls between the 25 th and 10 th percentiles.***** EO12866 Review -**  **Draft -**  **Do Not Cite, Quote, or Release During Review *****



### 8 - 42 concentration threshold below which we would not quantify health bene fits 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. (2012)) are the best measures because they reflect the full range of modeled air quality concentration s 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 f

or 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 co ncentration benchmarks 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 concentrati on 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 co ncentration 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 LML 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 these 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 Morta**  **lity Occurring Above and Below Various Concentration Benchmarks in the Underlying Epidemiology**  **Studies a** 

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

**Reduced Mortality**  **Incidence Allocation 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 digi ts, 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 percentile. For Krew ski, the LML is 5.8 μg/m 3 and one standard deviation below the mean is***** EO12866 Review -**  **Draft -**  **Do Not Cite, Quote, or Release During Review *****



### 8 - 43 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 no t support the existence of a level below which health effects from exposure to PM 2.5 do not occur.***** EO12866 Review -**  **Draft -**  **Do Not Cite, Quote, or Release During Review *****



### 8 - 44 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 i**  **n 2030 According to the Baseline Level of PM 2.5**  

### and the Lowest Measured Air **Quality Levels of Each Mortality Study******* EO12866 Review -**  **Draft -**  **Do Not Cite, Quote, or Release During Review *****



### 8 - 45 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 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 -**  **4 .**  **Number of Premature PM 2.5**  

### - **related Deaths Avoided for the Final Tie r 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 health 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 representation of one key difference between the two studies.***** EO12866 Review -**  **Draft -**  **Do Not Cite, Quote, or Release During Review *****



### 8 - 46 8.1.2.5.4 Alternative Concentration -Response Functi ons 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 like ly 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 ytic 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 b enefits 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 with in 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 component. 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 monetiz ed 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.***** EO12866 Review -**  **Draft -**  **Do Not Cite, Quote, or Release During Review *****



### 8 - 47 8.1.2.5.6 Qualitative Assessment of Uncertainty and Other Analysis Limitations Although we strive to incorporate as many quantitative assessments of uncertainty as possible, there are several aspe cts 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 benefit estimates presented in this chapter are based on our interpretation of the best available scientific literature and methods and supported by the EPA’s independent SAB (Health Effects Subcommittee) (SAB - HES) and the National Academies of Science (NAS). 153 , 154 

### The benefit estimates are subject to a number of assumptions and uncert ainties. For example, the key assumptions underlying the estimates for premature mortality , which account for over 98% of the total monetized benefits in this analy sis, include the following: 1. We assume that all fine particles, regardless of their chem ical

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 esti mates b y particle type. The 2009 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 linked 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 log - 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 uncertainty in the relationship betwe en PM 2.5 

### and premature mortality , we include a s et 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***** EO12866 Review -**  **Draft -**  **Do Not Cite, Quote, or Release During Review *****



### 8 - 48 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 ozone benefits associated with the final standards is estimated based on the risk o f 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 oz one 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 previously

described, we strive to monetize as many of the benefits anticipated from th e 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 unc ertainties , 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 benefits 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 ytical 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 aspe ct 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 - benefit 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

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



### 8 - 49 estimate that the social costs of the program are equivalent to the estimated vehicle and fuel compliance costs of the program. While vehicle manufacturers and fuel producers w ould see their 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 final consumers of these goods. The social benefits of the program are represented by

the monetized value of health and welfare improvements experienced by the U.S. population. Table 8 - 18 contains the estimated social costs and the estimated monetized benefit s of the program. 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 disc ount 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 there 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 Estimat ed 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 es timates 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 estim ate 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 .***** EO12866 Review -**  **Draft -**  **Do Not Cite, Quote, or Release During Review *****



### 8 - 50 d Annual benefits analysis results reflect the use of a 3 percent and 7 percent discount rate in the valuation of premature mortality and nonfatal myocardial infarctions, consistent with EPA and OMB guidelines for preparing economic analyses. e Valuation of premature mortality based on long - term PM exposure assumes d iscounting 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 ( December, 2012 ). fNot all possible benefits or disbenefits are quantified and monetize d in this analysis. Potential benefit categories that have not been quantified and monetized are listed in Table 8 - 5 . 

### 8.1.4 Illustrative Anal ys is of Estimated Quantified and Monetized Impacts Associated with the Rule in 201 8 For illustrative purposes, this sec tion 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) fle et. 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 ). The decrease in population weighted national average PM 2.5 

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

### - related human health and environmental imp acts (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**  **and Table**  **8 -**  **20 present the annual PM**  **2.5** 

### and ozo **ne 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 estimate 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 v alue 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, es timates account for growth in real gross domestic product (GDP) per capita between 2000 and***** EO12866 Review -**  **Draft -**  **Do Not Cite, Quote, or Release During Review *****



### 8 - 51 201 8 . The monetized value of PM 2.5 

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

### To discount the value of premature mortality that o ccurs at different points in the 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 with the final standards in 201 8 , using the ACS and Six - Cities PM mortal ity 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 discount 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 201 8 , as the vehicle standards phase in after 201 8 and as the fleet turn s 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+, AC S 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) 200 (74

– 330)

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 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 Re quest on 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 age - specific cost - of - illness values that reflect lost earnings and direct medical costs over a 5 - year period following a nonfatal MI.***** EO12866 Review -**  **Draft -**  **Do Not Cite, Quote, or Release During Review *****



### 8 - 52 Pooled estimate of 4 studies 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) Upper respiratory symptoms (asthmatic children, age 9 - 18) 5,000 (1,600

– 8,500) Asthma ex acerbation (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 i ncidence 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 tw o 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 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.

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 State s. Environmental Health Perspectives 105(6):608 - 612.***** EO12866 Review -**  **Draft -**  **Do Not Cite, Quote, or Release During Review *****



### 8 - 53 **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 asthma (all ages) 140 ( - 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 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 th erefore 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 Epidemiology 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)***** EO12866 Review -**  **Draft -**  **Do Not Cite, Quote, or Release During Review *****



### 8 - 54 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) Hospital admissions for cardiovascular causes $2.6 ($1.3 - $4.4) Emergency room

visits for asthma d $0.045 ( - $0.007 - $0.087) 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) Asthm a 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)***** EO12866 Review -**  **Draft -**  **Do Not Cite, Quote, or Release During Review *****



### 8 - 55 Hospital admissions - respiratory causes (children, under 2) $1.6 ($0.74 - $2.5) Emergency room visit for asthma (all ages) $0.061 ( - $0.008 - $0.18) Min or 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 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 (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. 

### Table **8 -**  **22 :**  **Total Estimated**  **Monetized Ozone and PM -**  **related Benefits Associated with the**  **Final Program in 201**  **8 Tot**  **al 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 $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***** EO12866 Review -**  **Draft -**  **Do Not Cite, Quote, or Release During Review *****



### 8 - 56 **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 - AEO 2013&table=20 - AEO2013®ion=0 - 0&cases=ref2013 - d102312a . 3 U.S. Energy Information Administration. Prime Supplier Sales Volumes. Last vie we d on October 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 mo rtality. 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, Lade n 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 Protecti on 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. Environmental Protection Agency. (2013). Dr aft Regulatory Impact Analysis: Ti er 3 Motor Vehicle Emissi on 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.***** EO12866 Review -**  **Draft -**  **Do Not Cite, Quote, or Release During Review *****



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–2020 . EPA - COUNCIL - 08 - 001. July. Available at <http://yosemite.epa.gov/sab/sabproduct.nsf/D4D7EC9DAEDA8A548525748600728A83/$File/EPA - COUNCIL 08 - 001 - unsigned.pdf>. 29 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 . 30 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 . 31 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 . 32 Schwartz, J. (2005) How sensitive is the association between ozone and daily deaths to control for temperature? Am. J. Respir. Crit. Care Med. 171: 627 - 631. EPA - HQ - OAR - 2009 - 0472 - 1678 .***** EO12866 Review -**  **Draft -**  **Do Not Cite, Quote, or Release During Review *****



### 8 - 58 33 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 . 34 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 . 35 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 . 36 National Research Council (NRC). 2008. Estimating Mortality Risk Reduction and Economic Benefits from Co ntrolling Ozone Air Pollution. National Academies Press. Washington, DC. 37 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. 38 Levy JI, Baxter LK, Schwartz J. 2009. “Uncertainty and variability in health - related damages from coal - fired power plants in the United States.” Risk Anal. 29(7) 1000 - 1014. 39 Fann N, Roman HR, Fulc her CF, Gentile M, Wess on K, Hubbell BJ, Levy JI. 2012 . “Maximizing Health Benefits and Minimizing Inequality: Incorporating Local Scale Data in the Design and Evaluation of Air Quality Policies.” Risk Analysis. 32(6): 908

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—922. 47 U.S. Environmenta l Protection Agency (U.S. EPA). 2011d. 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>. 48 W oods & Poole Economic s Inc. 2012 . Population by Single Year of Age. CD - ROM. Woods & Poole Economics, Inc. Washington, D.C. 49 Hollman, F.W., T.J. Mulder, and J.E. Kallan. 2000. Methodology and Assumptions for the Population Projections of the United States: 1999 to 2100. Population Division Working Paper No. 38, Population Projections Branch, Population Division, U.S. Census Bureau, Department of Commerce. January. Available on the Internet at <http://www.census.gov/population/www/documentation/twps0038/tw ps0038.html>.***** EO12866 Review -**  **Draft -**  **Do Not Cite, Quote, or Release During Review *****



### 8 - 59 50 U.S. Bureau of Economic Analysis (U.S. BEA). 2004. BEA Economic Areas (EAs). U.S. Department of Commerce. Available on the Internet at <http://www.bea.gov/regional/docs/econlist.cfm>. 51 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. Available on the Internet at <http://www.epa.gov/oar/sect812/feb11/fullreport.pdf>. 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

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—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/BenMAPA ppendicesOct2012.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 di stributed 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 sensitive is the association between ozone and daily deaths to control for temperature? Am. J . Respir. Crit. Care Med. 171: 627 - 631. EPA - HQ - OAR - 2009 - 0472 - 1678 . 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 - OAR - 2009 - 0472 - 1661 . 63 Industrial Economics, Incorporated (IEc). (2006). Expanded Expe rt 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. Augus t. 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.***** EO12866 Review -**  **Draft -**  **Do Not Cite, Quote, or Release During Review *****



### 8 - 60 67 Sullivan, 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 components 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 - 2005).” Environ Health Perspect 116: 1614 - 1619. 69 Zanobetti A. and Schwartz, J. 200 6. “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 hospital 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 - 1673 . 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 admissions 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 Admis sions 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 admissi ons 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, et 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 Jo urnal 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 . Vol. 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. Vol. 22 (6): 445 - 8.Mar, T. F., T. V. Larson, et al. 2004. “An analysis of th e association between respiratory symptoms in subjects with asthma and daily air pollution in Spokane, Washington.” Inhal Toxicol 16(13): 809 - 15.***** EO12866 Review -**  **Draft -**  **Do Not Cite, Quote, or Release During Review *****



### 8 - 61 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

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### 8 - 62 101 U.S. Environmental Pro tection 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/airtransport/pdfs/FinalRIA.pdf>. 102 Centers for Disease Control: Wide - ranging OnLine D ata for Epidemiologic Research (CDC Wonder) (data from years 1996 - 1998), Centers for Disease Control and Prevention (CDC), U.S. Department of Health and Human Services, Available on the Internet at <http://wonder.cdc.gov>. 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>. 104 Rosamond, W., G. Broda, E. Kawalec, S. Rywik, A. Pajak, L. Cooper, and L. Chambless. 1999. “Comparison of M edical 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 A ssociation, 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. Population Projections Program, Population Division, U.S. Census B ureau, 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 Tard iness. 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 Unit Research and Program Services Division. February. Table 7. Ba sed 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. National Center for Health Statistics. National Health Interview S urvey, 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. Portney. 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 Economic 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 Quality 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 - supplemental_analysis_full.pdf .***** EO12866 Review -**  **Draft -**  **Do Not Cite, Quote, or Release During Review *****



### 8 - 63 117 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. Available 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. Discussion 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. 121 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>. 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. Okamo to. 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 Health Effects Estimation and Morbidity Valuation in the Section 81 2 Analysis

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— Executive Summary.” Prepared by Energy and Resource Consultants, Inc. Report to the U. S. Environmental Protection Agency, Office of Policy Analysis. EPA - 230 - 09 - 86 - 018. Washington, DC. 126 GeoLytics Inc. 2002. GeoLytics CensusCD® 2000 Short Form Blocks. CD - ROM Release 1.0. GeoLytics, Inc. East Brunswick, NJ. Available: http://www.geolytics.co m/ [accessed 29 September 2004]. 127 Tolley, G.S. et al. 1986. Valuation of Reductions in Human Health Symptoms and Risks. University of Chicago. Final Report for the U.S. Environmental Protection Agency, Office of Policy Analysis. January. Available at <htt p://yosemite.epa.gov/ee/epa/eerm.nsf/a7a2ee5c6158cedd852563970080ee30/eaa2842b3d2bc2798525644d0053 bf48!OpenDocument#_Section6>. 128 U.S. EPA, (2001), “Guidance for Demonstrating Attainment of Air Quality Goals for PM2.5 and Regional Haze”, http://www.epa.gov/ttn/scram/guidance_sip.htm, Modeling Guidance, DRAFT - PM . 129 National Research Council (NRC). 2002. Estimating the Public Health Benefits of Proposed Air Pollution Regulations. Washington, DC: The National Academies Press. 130 National Research Council (NRC ). 2008. Estimating Mortality Risk Reduction and Economic Benefits from Controlling Ozone Air Pollution. National Academies Press. Washington, DC. 131 U.S. Environmental Pro tection Agency (U.S. EPA). 2010 . Quantitative Health Risk Assessment for Particul ate Matter

—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/standards/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. Available on the Internet at <http://www.epa.gov/oar/sect812/feb11/fullreport.pdf>.***** EO12866 Review -**  **Draft -**  **Do Not Cite, Quote, or Release During Review *****



### 8 - 64 133 World Health Organization (WHO). 2008. Part 1: Guidance Document on Characterizing and Communicating Uncertainty in Exposure Assessment, Harmonization Project Document No. 6. Published 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, Ty ra 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 Industria l 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 Planning and Standards, U.S. Environmental Protection Agency, Re search 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 Agency 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 Internet at <http://yosemite.epa.gov/sab/sabproduct.nsf/0/43B91173651AED9E85257487004EA6CB/$File/EPA COUNCIL - 08 - 002 - unsigned.pdf>. 137 Roman, Henry

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—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_final_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?dei d=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 Methods Plan for Health Risk and Exposure Assessment. EPA - COUNCI L - 09 - 009. May. Available on the Internet at <http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb85256ead006be86e/723FE644C5D758DF852 575BD00763A32/$File/EPA - CASAC - 09 - 009 - unsigned.pdf>. 142 U.S. Environmental Protection Agency - Science Advisory Boar d (U.S. EPA - SAB). 2009. Review of EPA’s Integrated Science Assessment for Particulate Matter (First External Review Draft, December 2008). EPA COUNCIL - 09 - 008. May. Available on the Internet at <http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954 fcb85256ead006be86e/73ACCA834AB44A1085 2575BD0064346B/$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 Standar ds, 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/airtransport/pdfs/FinalRIA.pdf>.***** EO12866 Review -**  **Draft -**  **Do Not Cite, Quote, or Release During Review *****



### 8 - 65 145 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. E PA). 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 Protectio n Agency Science Advi sory Board (U.S. EPA - SAB). 2010 . CASAC Review of Policy Assessment for the Review of the PM NAAQS

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