5 - 1 **CHAPTER 5 HEALTH AND WELFARE C**  **O -**  **BENEFITS Synopsis**  Implementation of HAP emissions controls required by this rule is expected to have ancillary co - benefits, including lower overall ambient concentrations of SO 2, NO 2, PM 2.5 and ozone across the U.S. Pollutants such as SO 2, NO x, and direct PM 2.5 contribute to ambient PM 2.5 levels in the atmosphere, and NOx contributes to ambient ozone concentrations. Furthermore, this rule is expected to redu ce CO 2 emissions affecting climate change . The se health and welfare co - benefits comprise a significant share of the total monetized benefits from this rule. This chapter provides estimates for this subset of the expected annual health and climate co benefi ts of this rule in 2016. Due to limits in available air quality modeling, the quantified co - benefits of this rule consist of only PM 2.5 - related health co - benefits from reductions in SO 2 (a precursor to PM 2.5 formation) and direct PM 2.5 and climate co - benefits from reductions in CO 2 . These co - benefits are estimated by applying a benefit - per–ton (BPT) approach described below to estimated reductions in SO 2 and direct PM 2.5 emissions reported in Chapter 3. The monetized co - benefits assessment omits sev eral important categories of benefits, including health and ecological co benefits from reducing exposure to ozone, ecosystem co - benefits for reducing nitrogen and sulfate deposition, and the direct health co - benefits from reducing exposure to ozone, SO 2 a nd NO 2 . We describe these co - benefits qualitatively in Section 5.5. We estimate the monetized health and climate co - benefits of MATS to be $ 37 billion to $ 90 billion at a 3% discount rate and $ 33 billion to $ 81 billion at a 7% discount rate in 2016 , depend ing on the epidemiological function used to estimate reductions in premature mortality . All estimates are in 2007$. **5 .1**  **Overview** The analysis in this chapter aims to characterize the co - benefits of the Mercury and Air Toxics Standards by answering two key questions: 1. What are the health effects of changes in ambient particulate matter (PM 2.5 ) resulting from reductions in directly- emitted PM 2.5 and SO 2 ? 2. What is the economic value of these effects?5 - 2

Additionally, this chapter describ es health effects that are not quantified for this rule, unquantified welfare effects, and visibility co - benefits . In implementing these rules, emission controls may lead to reductions in ambient PM 2.5 below the National Ambient Air Quality Standards (NAAQS) for PM in some areas and assist other areas with attaining the PM NAAQS. Because the PM NAAQS RIAs also calculate PM benefits, there are important differences worth noting in the design and analytical objectives of each RIA. The NAAQS RIAs illustrate th e potential costs and benefits of attaining a new air quality standard nationwide based on an array of emission control st rategies for different sources. In short, NAAQS RIAs hypothesize, but do not predict, the con trol strategies that States may choose to enact when implementing a NAAQS. The setting of a NAAQS does not directly result in costs or benefits, and as such, the NAAQS RIAs are merely illustrative and are not intended to be added to the costs and benefits of other regulations that result in speci fic costs of control and emission reductions. However, some costs and benefits estima ted in this RIA account for the same air quality improvements as estimated in the illustrative PM 2.5 NAAQS RIA. By contrast, the emission reductions for this rule are from a specific class of well characterized sources. In general, EPA is more confident in the magnitude and location of the emission reductions for these rules. It is important to note that emission reductions anticipated from these rules do not result in emis sion increases elsewhere (other than potential energy disbenefits). Emission reductions achieved under these and other promulgated rules will ultimately be reflected in the baseline of future NAAQS analyses, which would reduce the incremental costs and ben efits associated with attaining the NAAQS. EPA remains forward looking towards the next iteration of the 5 -year review cycle for the NAAQS, and as a result does not issue updated RIAs for existing NAAQS that retroactively update the baseline for NAAQS impl ementation. For more information on the relationship between the NAAQS and rules such as analyzed here, please see Section 1.2.4 of the SO 2 NAAQS RIA ( U.S. EPA, 2010 a ). To estimate a subset of th e co - benefits from reducing PM 2.5 exposure, EPA used an approach that is consistent with the approach utilized to estimate the co - benefits of the proposed MATS (U.S. EPA 2011a ) and the Cross - State Air Pollution Rule (U.S. EPA 2011b ). In this analysis we consider an array of health impacts attributable to changes in PM 2.5 air quality. The 2009 PM 2.5 Integrated Science Assessment (U.S. EPA, 2009 a ) identif ied the human health effects associated with these ambient pollutants, which include prematu re mortality and a variety of morbidity effects associated with acute and chronic exposures.5 - 3

Table 5 - 1 summarizes the total monetized co - benefits of the rule in 2016. This table reflects the economic value of the change in PM 2.5 - related human health impacts and the monetized value of CO 2 reductions occurring as a result of the Mercury and Air Toxics Standards . **Table 5**  **- 1**  **. Estimated**  **Monetized Co -**  **benefits of the Mercury and Air Toxics Standards**  **in**

**2016 (billions of 2007$)**  **a Benefits Estimate**  **Eastern U.S. b**  **Western U.S. Total**  Pope et al. (2002) PM 2.5 mortality estimate Using a 3% discount rate $3 5 +B ($2.8 – $110) $1.1 +B ($0.0 3 – $ 3 . 4 ) $37 +B ($3.2 – $110) Using a 7% discount rate $32 +B ($2.5 – $ 9 8 ) $1.0 +B ($0.0 3 – $3.1) $3 3 +B ($2.9 – $100) Laden et al. (2006) PM 2.5 mortality estimate Using a 3% discount rate $8 7 +B ($7. 5 – $2 5 0) $2.7 +B ($0.1 – $7.9 ) $9 0 +B ($8. 0 – $260) Using a 7% discount rate $7 8 +B ($6. 8 – $230) $2.4 +B ($0.1 – $7. 2 ) $8 1 +B ($7.3 – $240) a For notational purposes, unquantified benefits are indicated with a “ B ” to represent the sum of additional monetary benefits and disbenefits. Data limitations prevented us from quantifying these endpoints, and as such, these benefits are inherent ly more uncertain than those benefits that we were able to quantify. A detailed listing of unquantified health and welfare effects is provided in Tables 5 - 2 and 5 - 3. Estimates here are subject to uncertainties discussed further in the body of the document. Estimates are rounded to two significant figures. Value of total co - benefits includes CO 2 - related co benefits discounted at 3%. b Includes Texas and those states to the north and east. Table s 5 - 2 and 5 - 3 summarize the human health and environmental co - ben efits categories contained within the total monetized benefits estimate, and those categories that were unquantified due to limited data or time. It is important to emphasize that the list of unquantified benefit categories is not exhaustive, nor is quanti fication of each effect complete. In order to identify the most meaningful human health and environmental co - benefits , we excluded effects not identified as having at least a causal, likely causal, or suggestive relationship with the affected pollutants in the most recent comprehensive scientific assessment, such as an Integrated Science Assessment. This does not imply that additional relationships between these and other human health and environmen tal co - benefits and the affected pollutants do not exist. Due to this decision criterion, some effects that were identified in previous lists of unquantified benefits in other RIAs have been dropped (e.g., UVb exposure). In addition, some quantified effect s represent only a partial accounting of likely impacts due to limitations in the currently available data (e.g., climate effects from CO 2, etc).5 - 4

**Table 5**  **- 2.**  **Human Health Effects of Pollutants Affected by the Mercury and Air Toxics** 

**Standards Benefits**  **Category Specific Effect**  **Effect Has Been**  **Quantified Effect Has** 

**Been Monetized**  **More Information Improved Human Health**  Reduced incidence of premature mortality from exposure to PM 2.5 Adult premature mortality based on cohort study estimates and expert elicitation estimates (age >25 or age >30) 

 

 Section 5.4 Infant mortality (age <1) 

 

 Section 5.4 Reduced incidence of morbidity from exposure to PM 2.5 Non -fatal heart attacks (age > 18) 

 

 Section 5.4 Hospital admissions — respiratory (all ages) 

 

 Section 5.4 Hospital admissions — cardiovascular (age >18) 

 

 Section 5.4 Emergency room visits for asthma (<18) 

 

 Section 5.4 Acute bronchitis (age 8 - 12) 

 

 Section 5.4 Lower respiratory symptoms (age 7– 14) 

 

 Section 5.4 Upper respiratory symptoms (asthmatics age 9– 11) 

 

 Section 5.4 Asthma exacerbation (asthmatics age 6– 18) 

 

 Section 5.4 Lost work days (age 18–65) 

 

 Section 5.4 Minor restricted - activity days (age 18–65) 

 

 Section 5.4 Chronic Bronchitis (age >26) 

 

 Section 5.4 Other cardiovascular effects (e.g., other ages) — — PM ISA b Other respiratory effects (e.g., pulmonary function, non - asthma ER visits, non bronchitis chronic diseases, other ages and populations) — — PM ISA b
Reproductive and developmental effects (e.g., low birth weight, pre -term births, etc) — — PM ISA b,c Cancer, mutagenicity, and genotoxicity effects — — PM ISA b,c Reduced incidence of mortality from exposure to ozone Premature mortality based on short -term study estimates (all ages) — — Ozone CD, Draft Ozone ISA a Premature mortality based on long -term study estimates (age 30–99) — — Ozone CD, Draft Ozone ISA a Reduced incidence of morbidity from exposure to ozone Hospital admissions — respiratory causes (age > 65) — — Ozone CD, Draft Ozone ISA a Hospital admissions — respiratory causes (age <2) — — Ozone CD, Draft Ozone ISA a Emergency room visits for asthma (all ages) — — Ozone CD, Draft Ozone ISA a Minor restricted - activity days (age 18–65) — — Ozone CD, Draft Ozone ISA a (continued)5 - 5

**Table 5**  **- 2.**  **Human Health Effects of Pollutants Affected by the Mercury and Air Toxics** 

**Standards (continued) Benefits Category**  **Specific Effect Effect Has**  **Been Quantified**  **Effect Has**

**Been Monetized**  **More Information** School absence days (age 5 – 17) — — Ozone CD, Draft Ozone ISA a Decreased outdoor worker productivity (age 18–65) — — Ozone CD, Draft Ozone ISA a Other respiratory effects (e.g., premature aging of lungs) — — Ozone CD, Draft Ozone ISA b Cardiovascular and nervous system effects — — Ozone CD, Draft Ozone ISA c Reproductive and developmental effects — — Ozone CD, Draft Ozone ISA c Reduced incidence of morbidity from exposure to NO 2 Asthma hospital admissions (all ages) — — NO 2 ISA a Chronic lung disease hospital admissions (age > 65) — — NO 2 ISA a Respiratory emergency department visits (all ages) — — NO 2 ISA a Asthma exacerbation (asthmatics age 4– 18) — — NO 2 ISA a Acute respiratory symptoms (age 7– 14) — — NO 2 ISA a Premature mortality — — NO 2 ISA b,c Other respiratory effects (e.g., airway hyperresponsiveness and inflammation, lung function, other ages and populations) — — NO 2 ISA b,c Reduced incidence of morbidity from exposure to SO 2 Respiratory hospital admissions (age > 65) — — SO 2 ISA a Asthma emergency room visits (all ages) — — SO 2 ISA a Asthma exacerbation (asthmatics age 4– 12) — — SO 2 ISA a Acute respiratory symptoms (age 7– 14) — — SO 2 ISA a Premature mortality — — SO 2 ISA b,c Other respiratory effects (e.g., airway hyperresponsiveness and inflammation, lung function, other ages and populations) — — SO 2 ISA b,c Reduced incidence of morbidity from exposure to methylmercury (through reduced mercury deposition as well as the role of sulfate in methylation) Neurologic effects - IQ loss   IRIS; NRC, 2000 a Other neurologic effects (e.g., developmental delays, memory, behavior) — — IRIS; NRC, 2000 b Cardiovascular effects — — IRIS; NRC, 2000 b,c Genotoxic, immunologic, and other toxic effects — — IRIS; NRC, 2000 b,c

a We assess these co - benefits qualitative ly due to time and resource limitations for this analysis. b We assess these co - benefits qualitatively because we do not have sufficient confidence in available data or methods. c We assess these co - benefits qualitatively because current evidence is only sug gestive of causality or there are other significant concerns over the strength of the association.5 - 6

**Table 5**  **- 3.**  **Environmental Effects of Pollutants Affected by the Mercury and Air Toxics** 

**Standards**
**Benefits Category Specific Effect**  **Effect Has Been**  **Quantified Effect Has** 

**Been Monetized**  **More**

**Information Improved Environment**  Reduced visibility impairment Visibility in Class I areas in SE, SW, and

CA regions — — PM ISA a Visibility in Class I areas in other regions — — PM ISA a Visibility in residential areas — — PM ISA a Reduced climate effects Global climate impacts from CO 2 —  Section 5.6

Climate impacts from ozone and PM — — Section 5.6 Other climate impacts (e.g., other GHGs, other impacts) — — IPCC b Reduced effects on materials Household soiling — — PM ISA b

Materials damage (e.g., corrosion, increased wear) — — PM ISA b Reduced effects from PM deposition (metals and organics) Effects on Individual organisms and

ecosystems — — PM ISA b Reduced vegetation and ecosystem effects from exposure to ozone Visible foliar injury on vegetation — — Ozone CD, Draft Ozone ISA b Reduced vegetation growth and reproduction — — Ozone CD, Draft Ozone ISA a Yield and quality of commercial forest products and crops — — Ozone CD, Draft Ozone ISA a,c Damage to urban ornamental plants — — Ozone CD, Draft Ozone ISA b Carbon sequestration in terrestrial ecosystems — — Ozone CD, Draft Ozone ISA b Recreational demand associated with forest aesthetics — — Ozone CD, Draft Ozone ISA b Other non - use effects Ozone CD, Draft Ozone ISA b Ecosystem functions (e.g., water cycling, biogeochemical cycles, net primary productivity, leaf-gas exchange, community composition) — — Ozone CD, Draft Ozone ISA b (continued)5 - 7

**Table 5 -**  **3. Environmental Effects**  **of Pollutants Affected by the Mercury and Air Toxics**

**Standards (continued)**
**Benefits Category Specific Effect**  **Effect Has Been**  **Quantified Effect Has** 

**Been Monetized**  **More**

**Information** Reduced effects from acid deposition Recreational fishing — — NOx SOx ISA a Tree mortality and decline — — NOx SOx ISA b Commercial fishing and forestry effects — — NOx SOx ISA b Recreational demand in terrestrial and aquatic ecosystems — — NOx SOx ISA b Other non - use effects NOx SOx ISA b Ecosystem functions (e.g., biogeochemical cycles) — — NOx SOx ISA b Reduced effects from nutrient enrichment Species composition and biodiversity in terrestrial and estuarine ecosystems — — NOx SOx ISA b Coastal eutrophication — — NOx SOx ISA b Recreational demand in terrestrial and estuarine ecosystems — — NOx SOx ISA b Other non - use effects NOx SOx ISA b Ecosystem functions (e.g., biogeochemical cycles, fire regulation) — — NOx SOx ISA b Reduced vegetation effects from ambient exposure to SO 2 and NO x Injury to vegetation from SO 2 exposure — — NOx SOx ISA b Injury to vegetation from NO x exposure — — NOx SOx ISA b Reduced ecosystem effects from exposure to methylmercury (through reduced mercury deposition as well as the role of sulfate in methylation) Effects on fish, birds, and mammals (e.g., reproductive effects) — — Mercury Study RTC b,c Commercial, subsistence and recreational fishing — — Mercury Study RTC b

a We assess these co - benefits qualitative ly due to time and resource limitations for this analys is. b We assess these co - benefits qualitatively because we do not have sufficient confidenc e in available data or methods. c We assess these co - benefits qualitatively because current evidence is only suggestive of causality or there are other significant conce rns over the strength of the association. The co - benefits analysis in this chapter relies on an array of data inputs — including air quality modeling, health impact functions and valuation functions among others — which are themselves subject to uncertainty an d may also contribute to the overall uncertainty in this analysis. As a means of characterizing this uncertainty we employ two primary techniques. First,5 - 8

we use Monte Carlo methods for characterizing random sampling error associated with the concentration response functions from epidemiological studies and economic valuation functions. Second, because this characterization of random statistical error may omit important sources of uncertainty we also employ the results of an expert elicitation on the relatio nship between premature mortality and ambient PM 2.5 concentration (Roman et al., 2008) . T his provides additional insight into the likelihood of different outcomes and about the state of knowledge regarding the co - benefits estimates. Both approaches have different strengths and weaknesses, which are fully described in Chapter 5 of the PM NAAQS RIA (U.S. EPA, 2006 a ). While the contributions from additional data inputs to uncertainty in the results are not quantified here , this analysis employs best practices in every aspect of its development. Given that co - benefits of reductions in premature mortality are a domina nt share of the overall monetized co - benefits, more focus on uncertainty in mortality- rela ted co - benefits gives us greater confidence in our uncertainty characterization surrounding total PM 2.5 - related co benefits. Additional sensitivity analyses have been performed for the 2006 PM NAAQS RIA, and were not specifically included here as the resu lts would be similar and would not change the conclusions of the analyses to support this rule. In particular, these analyses characterized the sensitivity of the monetized co - benefits to the specification of alternate cessation lags and income growth adj ustment factors. As shown in these RIAs, t he estimated co - benefits increased or decreased in proportion to the specification of alternate income growth adjustments and cessation lags . Therefore, readers can infer the sensitivity of the results in this RIA to these parameters by referring to the sensitivity analyses in the PM NAAQS RIA (2006d) and Ozone NAAQS RIA (2008a) . For example, based on the results from previous analyses, the use of an alter nate lag structure would change the PM 2.5 - related mortality co - benefits discounted at 3% discounted by between 10.4% and –27%; when discounted at 7%, these co - benefits change by between 31% and -49%. When applying higher and lower income growth adjustments , the monetary value of PM 2.5 - related premature changes between 30% and - 10%; the value of chronic endpoints change between 5% and - 2% and the value of acute endpoints change between 6% and - 7%. Additionally, in this RIA we binned the estimated population exposed to projected future baseline PM 2.5 air quality levels for comparison against the “ Lowest Measured Level ” (LML) of PM 2.5 air quality in the mortality studies. The purpose of this analysis is to show whether the estimated premature deaths associated with reduced PM 2.5 exposure occur at or above the range of ambient PM 2.5 observations studied in Pope et al. (2002) and Laden et al. (2006) , which are the two epidemiological studies that E PA uses to estimate PM 2.5 - related5 - 9

premature mortality co - benefits . We found that a significant proportion of the avoided PM related premature deaths we estimated in this analysis occurred among populations exposed at or above the LML of each study in the b aseline, increasing our confidence in our estimate of the magnitude of the PM - related premature deaths avoided . Approximately 11 % of the avoided premature deaths occur at or above an annual mean PM 2.5 level of 10 μg/m 3 (the LML of the Laden et al. 2006 study), and about 73 % occur at or above an annual mean PM 2.5 level of 7.5 μg/m 3 (the LML of the Pope et al. 2002 study). As we model avoided premature deaths among populations exposed to levels of PM 2.5 that are successively lower than the LML of each stud y our confidence in the results diminishes. **5.2 Benefits Analysis Methods**  We follow a “ damage -function ” approach in calculating health co - benefits of the modeled changes in environmental quality. This approach estimates changes in individual health and we lfare endpoints (specific effects that can be associated with changes in air quality) and estimates values of those changes assuming independence between the values of individual endpoints. Total benefits are calculated simply as the sum of the values for all non overlapping health and welfare 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 analyses (Levy et al., 2009 ; Hubbell et al., 2009 ; Tagaris et al., 2009 ). 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, as is the case for changes in visibility. In other cases, such as for changes in ozone and PM, a health and welfare impact analysis must first b e conducted to convert air quality changes into effects that can be assigned dollar values. We note at the outset that EPA rarely has the time or resources to perform extensive new research to measure directly either the health outcomes or their values for regulatory analyses. Thus, similar to Kunzli et al. (2000) and other recent health impact analyses, our estimates are based on the best available methods of benefits transfer. Benefits transfer is a means of adapting primary r esearch 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.5 - 10

**5.2.1 Health Impact Assessment**  Health Impact Assessment (HIA) quantifies changes in the incidence of adverse health impacts resulting from changes in huma n exposure to specific pollutants, such as PM 2.5 . HIAs are a well - established approach for estimating the retrospective or prospective change in adverse health impacts expected to result from population - level changes in exposure to pollutants (Levy et al. 2009 ). PC- based tools such as the environmental Ben efits M apping and A nalysis P rogram (BenMAP) can systematize health impact analyses by applying a database of key input parameters, including health impact functions and population projections. Analysts have applied the HIA approach to estimate human health impacts resulting from hypothetical changes in pollutant levels (Hubbell et al. 2005 ; Davidson et al. 2007 , Tagaris et al. 2009 ). EPA and others have relied upon this method to predict future changes in health impacts expected to result from the implementation of regulations affecting air quality ( e.g. U.S. EPA, 2008a ). For this assessment, the HIAs are limited to those health effects that are directly linked to ambient PM 2.5 concentrations . There may be other indirect health impacts associated with implementing emissions controls, such as occupational health impacts for coal miners. The HIA approach used in this analysis involves three basic steps: (1) utilizing CAMx generated projections of PM 2.5 and ozone air quality 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 applying concentration - response relationships dra wn from the epidemiological literature (Hubbell et al. 2009 ) to this change in population exposure. A typical health impact function might look as follows:



### ∆ = ∙ ∙ ∆



### − 1 ∙ where y0 is the baseline incidence rate for the health endpoint being quantified (for example, a health impact function quantifying changes in mortality would use the baseline, or background, mortality rate for the given population of interest); Pop is the population affected by the change in air q uality; 

x is the change in air quality; and β is the effect coefficient drawn from the epidemiological study. Tools such as BenMAP can systematize the HIA calculation process, allowing users to draw upon a library of existing air quality monitoring data, population data and health impact functions. Figure 5 - 1 provides a simplified overview of this approach.**Figure 5**  **- 1**  **. Illustration of BenMAP Approach** 

**5.2.2 Economic Valuation of Health Impacts**  After quantifying the change in adverse health impacts, the final step is to estimate the economic value of these avoided impacts. The appropriate economic value for a change in a health effect depends on whether the health effect is viewed occurred) or ex post (after the effect has occurred). Reductions in ambient concentrations of air pollution generally lower the risk of future adverse health effects by a small amount for a large population. The appropriate economic measure for changes in risk. However, epidemiological studies generally provide estimates of the relative risks of a particular health effect avoided due to a reduction in air pollution. A convenient way to use this d ata in a consistent framework is to convert probabilities to units of avoided statistical incidences. This measure is calculated by dividing individual WTP for a risk reduction by the related observed change in risk. For example, suppose a measure is able risk of premature mortality from 2 in 10,000 to 1 in 10,000 (a reduction of 1 in 10,000). If individual WTP for this risk reduction is $100, then the WTP for an avoided statistical premature mortality amounts to $1 million ($100/0.0001 change the affected population is automatically taken into account by the number of incidences predicted by epidemiological studies applied to the relevant population. The same type of calculation can produce values for
5 - 11

**Illustration of BenMAP Approach Economic Valuation of Health Impacts**  After quantifying the change in adverse health impacts, the final step is to estimate the economic value of these avoided impacts. The appropriate economic value for a change in a health effect depends on whether the health effect is viewed ex ante (before the effect has

(after the effect has occurred). Reductions in ambient concentrations of air pollution generally lower the risk of future adverse health effects by a small amount for a large population. The appropriate economic measure is therefore ex ante Willingness to Pay (WTP) for changes in risk. However, epidemiological studies generally provide estimates of the relative risks of a particular health effect avoided due to a reduction in air pollution. A convenient way

ata in a consistent framework is to convert probabilities to units of avoided statistical incidences. This measure is calculated by dividing individual WTP for a risk reduction by the related observed change in risk. For example, suppose a measure is able risk of premature mortality from 2 in 10,000 to 1 in 10,000 (a reduction of 1 in 10,000). If individual WTP for this risk reduction is $100, then the WTP for an avoided statistical premature mortality amounts to $1 million ($100/0.0001 change in risk). Using this approach, the size of the affected population is automatically taken into account by the number of incidences predicted by epidemiological studies applied to the relevant population. The same type of calculation can produce values for statistical incidences of other health endpoints. After quantifying the change in adverse health impacts, the final step is to estimate the economic value of these avoided impacts. The appropriate economic value for a change in a the effect has (after the effect has occurred). Reductions in ambient concentrations of air pollution generally lower the risk of future adverse health effects by a small amount for a large Willingness to Pay (WTP) for changes in risk. However, epidemiological studies generally provide estimates of the relative risks of a particular health effect avoided due to a reduction in air pollution. A convenient way

ata in a consistent framework is to convert probabilities to units of avoided statistical incidences. This measure is calculated by dividing individual WTP for a risk reduction by the related observed change in risk. For example, suppose a measure is able to reduce the risk of premature mortality from 2 in 10,000 to 1 in 10,000 (a reduction of 1 in 10,000). If individual WTP for this risk reduction is $100, then the WTP for an avoided statistical premature in risk). Using this approach, the size of the affected population is automatically taken into account by the number of incidences predicted by epidemiological studies applied to the relevant population. The same type of statistical incidences of other health endpoints.5 - 12

For some health effects, such as hospital admissions, WTP estimates are generally not available. In these cases, we use the cost of treating or mitigating the effect as a primary estimate. 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 generally (although not 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. We use the BenMAP model version 4 (Abt Associates, 2010 ) to estimate the health impacts and monetized health co - benefits for the Mercury and Air Toxics Standards . Figure 5 - 2 shows the data inputs and outputs for the BenMAP model.

**Figure 5**  **- 2**  **. Data Inputs and Outputs for the BenMAP Model** 5 - 13

**5.2.3 A**  **djusti**

**ng the Results of the PM 2.5**  **co -** **benefits Analysis to Account for the Emission**  **Reductions in the Final**  **Mercury and Air Toxics Standards**  As described in C hapter 3 of this RIA, EPA finalized the rule requirements after the completion of the air quality modeling for this rule. These changes to the rule affected both the overall level and distribution of PM 2.5 precursor emissions across the U.S., which in turn affect the level of PM 2.5 co - benefits. We determined that the geographic distribution of emissions reductions resulting from the final rule requirements were sufficiently similar to the modeled interim emissions reductions that we could adjust our co - benefits estimates to reflect these emi ssion changes by applying benefit per-ton estimates generated using the modeled air quality changes . Benefit per-ton (BPT) estimates quantify the health impacts and monetized human health co - benefits of an incremental change in air pollution precursor emis sions. In circumstances where we are unable to perform air quality modeling because of resource or time constraints, this approach can provide a reasonable estimate of the co - benefits of emission reduction s . EPA has used the BPT technique in previous RIAs, including the recent Ozone NAAQS RIA (U.S. EPA, 2008 a ), the NO 2 NAAQS RIA (U.S. EPA, 2010b ) , the proposed Mercury and Air Toxics Standards RIA (U.S. EPA 2011a), and the Cross - State Air Pollution Rule (U.S. EP A, 201 1 b ). For this co - benefits analysis we created per-ton estimates of PM 2.5 - related incidence and monetized co - benefits based on the co - benefits of the air quality modeled scenario. Our approach here is methodologically consiste nt with the technique reported in Fann, Fulcher & Hubbell (2009) , but adjusted for this analysis to better match the spatial distribution of air quality changes expected under the Mercury and Air Toxics Standards . To derive the BPT estimates for this analysis, we: 1. Quantified the PM2.5- related human and monetized health co - benefits of SO2 and direct PM2.5 changes for Eastern and Western states . We first estimated the health impacts and monetized co - benefits of reductions in directly emitted PM 2.5 and particulate sulfate. 1 MATS is expected to reduce both SO 2 and NO x emissions. In general SO 2 is a precursor to particulate sulfate and NO x is a precursor to particulate nitrate. However, there are also several in teractions between the PM 2.5 precursors which cannot be easily quantified. For example, under conditions in which SO 2 levels are reduced by a substantial margin, “ nitrate replacement” may occur. This occurs

1 Consistent with advice from the Health Effects Subcommittee of the Science Advisory Board (U.S. EPA- SAB, 2010) , we assume that each PM specie s is equally toxic. We quantify the change in incidence for each PM component by applying risk coefficients based on undifferentiated PM 2.5 mass .5 - 14 when particulate ammonium sulfate concentrations are reduc ed, thereby freeing up excess gaseous ammonia. The excess ammonia is then available to react with gaseous nitric acid to form particulate nitrate when meteorological conditions are conducive (cold temperatures and high humidity). The impact of nit rate replacement is also affected by concurrent NO x reductions. NO x reductions can lead to decreases in nitrate, which competes with the process of nitrate replacement. NO x reductions can also lead to reductions in photochemical by- products which can reduc e bot h particulate sulfate and secondary organic carbon PM concentrations. We found that reductions in NO x and SO x resulting from MATS led to significant decreases in particulate sulfate and small increases in particulate nitrate in some locations , indicating that nitrate replacement limited the nitrate decreases from NO x reductions in some locations . Reductions in directly emitted crustal and carbonaceous PM 2.5 (elemental carbon and organic carbon) were fairly modest. Carbonaceous PM 2.5 decreased slightly in the eastern US but did not significant ly change in the western US. We elected not to generate a NO x BPT for three reasons: (a) reductions in NO x emissions for this rule were relatively small; (b) previous EPA modeling indicates that PM 2.5 forma tion is less sensitive to NO x emission reductions on a per- μg/m 3 basis (Fann, Fulcher and Hubbell, 2009 ); and (c) particulate nitrate formation is governed by complex non - linear chemistry that is difficult to charact erize using BPT estimates that are derived from a single air quality modeling run which includes both NO x and SO 2 reductions . Additional modeling runs with SO 2 and NO x emissions changes modeled separately can provide information that can be used to estima te NOx benefits, and these runs have been conducted for other sectors, but have not been conducted for this rule. For the modeled scenario, sulfate reductions contributed 95% of the health co - benefits of all PM 2.5 components, with an additional 5% from di rect PM 2.5 reductions (see Appendix 5 C). Health co - benefits of sulfate reductions were two orders of magnitude larger than the health disbenefits of nitrate increases. Thus, the SO 2 emission reductions are the main driver for the health co - benefits of this rule. 2. Divided the health impacts and monetized co - benefits by the emission reduction for the air quality modeling in the corresponding geographic area. For the reasons described above, we quantified BPT estimates for SO 2 and directly emitted PM 2.5 (separately for carbonaceous and crustal). For SO 2, w e generated an array of eastern and western BPT estimates by dividing the particulate sulfate - related co - benefits in the eastern and western U.S. by the total SO 2 - related emission reductions in these two areas. As the chemistry of nitrate formation is complex and non - linear, nitrate impacts were excluded from the BPT analysis. Nitrates can be reduced when NO x emissions are reduced or increased when SO 2 emissions are reduced. The increased nitrate hea lth impacts in the modeled interim scenario were two orders of magnitude smaller than the sulfate health benefits. Thus, we estimate that i ncluding nitrate health impacts on the calculation for SO 2 BPT would reduce the SO 2 BPT by 1 - 2%, with a similar magni tude impact on the total health benefits of the rule.5 - 15 Carbonaceous and crustal PM 2.5 BPT estimates were similarly generated using the co - benefits and emissions of those species. The resulting BPT estimates (listed in Table 5C - 3) were then multiplied by the projected SO 2 (1.33 million tons) , carbonaceous PM 2.5 ( 6, 1 00 tons) , and crustal PM 2.5 ( 39 ,000 tons) emission reductions for the final policy to produce an estimate of the PM 2.5 - related health impacts and monetized co - benefits. Due to time limitations, direct PM 2.5 benefits are based on direct PM 2.5 emission reductions from an earlier policy scenario. However, since direct PM 2.5 benefits contribute only approximately 5% to the total PM 2.5 health co - benefits of this rule, and di fferences between direct PM 2.5 emission reductions between the earlier and final policy scenarios are expected to be modest, use of earlier PM 2.5 emission changes is unlikely to materially affect the results. Additional details on the BPT methodology and derivation are given in Appendix 5C. An implicit assumption in our approach is that the size and distribution of SO 2 emissions, and the relative levels of NO x and SO 2 emissions, are fairly similar in the modeled and revised policy cases. In general , the modeled and revised policy case s achieve roughly similar levels of SO 2 reductions (1.42 versus 1.3 3 million tons, respectively) with a similar distribution among states. However, for some states (notably Alabama, Colorado, Louisiana, Michigan, Missouri, North Dakota, Oklahoma, and Texas), SO 2 emission reductions were lower for the final case versus the interim case. By far, the greatest difference in SO 2 emission reductions was in Michigan where the final case emission reduction was 70% lower than for the interim case. In a few states (notably Arkansas, Ohio, and South Carolina), SO 2 emission reductions were slightly larger for the final case versus the interim case. Since differences between the interim and final cases are not concentrated in any particul ar region of the country and the overall distribution of emission reductions is similar, we conclude that it is reasonable to apply BPT values derived from the interim case to the final case. While NO x emissions reductions decreased by 70% between the inte rim and final cases (141,000 vs. 4 6 ,000 tons), the impact of NO x on PM 2.5 concentrations and mortality is very minor relative to the impact of SO 2 emission reductions. Therefore, differences in the magnitude and distribution of NO x emission reductions are likely to have only a minor effect on results. We did not develop ozone BPT estimate s for this rule for two reasons. First, the overall level of ozone - related co - benefits in the modeled case is relatively small compared to those associated with PM 2.5 reductions, due in part to the modest NO x emission reductions. Second, the complex non - linear chemistry of ozone formation introduces uncertainty to the5 - 16

development and application of BPT estimate s . Taken together, these factors argued against developing ozone BPT estimate s for this RIA. As t here is no analogous approach for estimating visibility co - benefits using the BPT approach, visibility co - benefits are calculated for the modeled interim policy scenario only and are not included in estimate of co - benefits for the final rule . However, since the magnitude of SO 2 emission reductions did not significantly change in the visibility study areas between the interim and final emissions scenarios, we expect the visibil ity benefit for the final policy scenario would be similar to that calculated for the interim policy scenario ($1.1 billion in total for the U.S., using 2007$; see Appendix 5 C). **5.3 Uncertainty Characterization**  As for any complex analysis using estimated parameters and inputs from numerous models, there are likely to be many sources of uncertainty affecting estimated results , including emission inventories, air quality models (with their associated parameters and inputs), epidemiological health effect esti mates, estimates of values (both from WTP and COI studies), population estimates, income estimates, and estimates of the future state of the world (i.e., regulations, technology, and human behavior). Each of these inputs may be uncertain and, depending on its role in the co - benefits analysis, may have a disproportionately large impact on estimates of total monetized co - benefits . For example, emissions estimates are used in the first stage of the analysis. As such, any uncertainty in emissions estimates will be propagated through the entire analysis. When compounded with uncertainty in later stages, small uncertainties in emission levels can lead to large impacts on total monetized co - benefits . The National Research Council (NRC) (2002, 2008) highlighted the need for EPA to conduct rigorous quantitative analysis of uncertainty in its benefits estimates and to present these estimates to decision makers in ways that foster an appropriate appreciation of th eir inherent uncertainty. In general, the NRC concluded that EPA’ s methodology for calculating the benefits of reducing air pollution is reasonable and informative in spite of inherent uncertainties. Since the publication of these reports, EPA continues to improve the characterization of uncertainties for both health incidence and benefits estimates. W e use both Monte Carlo analysis and expert - derived concentration - response functions to assess uncertainty quantitatively, as well as to provide a qualitative assessment for those aspects that we are unable to address quantitatively. First, we used Monte Carlo methods to characterize both sampling error and variability across the economic valuation functions , including random sampling error associated with the5 - 17

c oncentration response functions from epidemiological studies and random effects modeling. Monte Carlo simulation uses random sampling from distributions of parameters to characterize the effects of uncertainty on output variables, such as incidence of prem ature mortality. Specifically, we used Monte Carlo methods to generate confidence intervals around the estimated health impact and dollar benefits. The reported standard errors in the epidemiological studies determined the distributions for individual effe ct estimates. Second, because characterization of random statistical error omits important sources of uncertainty (e.g., in the functional form of the model — e.g., whether or not a threshold may exist), we also incorporate the results of an expert elicitati on on the relationship between premature mortality and ambient PM 2.5 concentration (Roman et al., 2008 ). Use of the expert elicitation and incorporation of the standard errors approaches provide insights into the likelihood of di fferent outcomes and about the state of knowledge regarding the benefits estimates. However, there remain significant unquantified uncertainties present in upstream inputs including emission and air quality. Both uncertainty characterization approaches hav e different strengths and weaknesses, as detailed in Chapter 5 of the PM NAAQS RIA (U.S. EPA, 2006 a ). In benefit analyses of air pollution regulations conducted to date, the estimated impact of reductions in premature mortality has accounted for 85% to 95% of total monetized benefits. Therefore, it is particularly important to attempt to characterize the uncertainties associated with reductions in premature mortality. The health impact functions used to estimate avoided premature dea ths associated with reductions in ozone have associated standard errors that represent the statistical errors around the effect estimates in the underlying epidemiological studies. In our results, we report credible intervals based on these standard errors , reflecting the uncertainty in the estimated change in incidence of avoided premature deaths. We also provide multiple estimates, to reflect model uncertainty between alternative study designs. For premature mortality associated with exposure to PM, we fo llow the same approach used in the RIA for 2006 PM NAAQS (U.S. EPA, 2006 a ), presenting two empirical estimates of premature deaths avoided, and a set of twelve estimates based on results of the expert elicitation study. Even these mu ltiple characterizations, including confidence intervals, omit the contribution to overall uncertainty of uncertainty in air quality changes, baseline incidence rates, populations exposed and transferability of the effect estimate to diverse locations. Furthermore, the approach presented here does not include methods for addressing correlation between input parameters and the identification of reasonable upper and lower bounds for input distributions characterizing uncertainty in additional model elements. As a5 - 18

result, the reported confidence intervals and range of estimates give an incomplete picture about the overall uncertainty in the estimates. This information should be interpreted within the context of the larger uncertainty surrounding the entire anal ysis. EPA estimates PM - related mortality without assuming a health effect threshold at low concentrations, based on the current body of scientific literature (U.S. EPA- SAB, 2009 a, U.S. EPA- SAB, 2009b ). However, a s we model mortal ity impacts among populations exposed to levels of PM 2.5 that are successively lower than the lowest measured level (LML) in each epidemiology study our confidence in the results diminishes. In addition to the uncertainty analyses described above, we therefore include an assessment of the mortality benefits accruing to populations exposed to baseline PM 2.5 concentrations above the LML in the two main epidemiology studies used to quantify benefits (see Section 5.7). Based on the modeled interim baseline whic h is approximately equivalent to the final baseline (see Appendix 5 A), 11 % and 73% of the estimated avoided mortality impacts occur at or above an annual mean PM 2.5 level of 10 μg/m 3 (the LML of the Laden et al. 2006 study) and 7.5 μg/m 3 (the LML of the Po pe et al. 2002 study) , respectively . K ey sources of uncertainty in the PM 2.5 health impact assessment include :

 gaps in scientific data and inquiry;

 variability in estimated relationships, such as epidemiological effect estimates, introduced through differ ences in study design and statistical modeling;

 errors in measurement and projection for variables such as population growth rates;

 errors due to misspecification of model structures, including the use of surrogate variables, such as using PM 10 when PM 2.5 is not available, excluded variables, and simplification of complex functions;

 biases due to omissions or other research limitations ; and

 additional uncertainties from benefits transfer method using BPT estimates. In Table 5 - 4 we summarize some of the ke y uncertainties in the benefits analysis.5 - 19

**Table 5**  **- 4**  **. Primary Sources of Uncertainty in the Benefits Analysis**  1. Uncertainties Associated with Impact Functions  The value of the ozone or PM effect estimate in each impact function.  Application of a single impact function to pollutant changes and populations in all locations.  Similarity of future -year impact functions to current impact functions.  Correct functional form of each impact function.  Extrapolation of effect estimates beyond the range of ozone or P M concentrations observed in the source epidemiological study.  Application of impact functions only to those subpopulations matching the original study population. 2. Uncertainties Associated with CAMx- Modeled Ozone and PM Concentrations  Responsiveness of the models to changes in precursor emissions from the control policy.  Projections of future levels of precursor emissions, especially ammonia and crustal materials.  Lack of ozone and PM 2.5 monitors in all rural areas requires extrapolation of observed o zone data from urban to rural areas. 3. Uncertainties Associated with PM Mortality Risk  Limited scientific literature supporting a direct biological mechanism for observed epidemiological evidence.  Direct causal agents within the complex mixture of PM ha ve not been identified.  The extent to which adverse health effects are associated with low - level exposures that occur many times in the year versus peak exposures.  The extent to which effects reported in the long -term exposure studies are associated with historically higher levels of PM rather than the levels occurring during the period of study.  Reliability of the PM 2.5 monitoring data in reflecting actual PM 2.5 exposures. 4. Uncertainties Associated with Possible Lagged Effects  The portion of the PM - related long -term exposure mortality effects associated with changes in annual PM levels that would occur in a single year is uncertain as well as the portion that might occur in subsequent years. 5. Uncertainties Associated with Baselin e Incidence Rates  Some baseline incidence rates are not location specific (e.g., those taken from studies) and therefore may not accurately represent the actual location - specific rates.  Current baseline incidence rates may not approximate well baseline in cidence rates in 2016 .  Projected population and demographics may not represent well future -year population and demographics. 6. Uncertainties Associated with Economic Valuation  Unit dollar values associated with health and welfare endpoints are only estimates of mean WTP and therefore have uncertainty surrounding them.  Mean WTP (in constant dollars) for each type of risk reduction may differ from current estimates because of differences in income or other factors. 7. Uncertainties Associated with Agg regation of Monetized Benefits  Health and welfare benefits estimates are limited to the available impact functions. Thus, unquantified or unmonetized benefits are not included.
PM 2.5 mortality benefits represent a substantial proportion of total monetized co benefits (over 90%), and these estimates have following key assumptions and uncertainties. 1. The PM 2.5 - related co - benefits were derived through a benefit per-ton approach, which does not fully reflect local variability in population density, meteorology, exposure, baseline health incidence rates, or other local factors that might lead to5 - 20 an over- estimate or under- estimate of the actual co - benefits of controlling PM precursors. I n addition, differences in the distribution of emissions reductions across states between the modeled scenario and the final rule scenario add uncertainty to the final benefits estimates. 2. We assume that all fine particles, regardless of their chemical c omposition, are equally potent in causing premature mortality. This is an important assumption, because PM 2.5 produced via transported precursors emitted from EGUs may differ significantly from direct PM 2.5 released from diesel engines and other industrial sources, but the scientific evidence is not yet sufficient to allow differential effects estimates by particle type. 3. We assume that the health impact function for fine particles is linear within the ran ge of ambient concentrations under consideration. Thus, the estimates include health co - benefits from reducing fine particles in areas with varied concentrations of PM 2.5 , including both regions that are in attainment with fine particle standard and those that do not meet the standard down to the lowest modeled concentrations. **5.4 Benefits Analysis Data Inputs**  In Figure 5 - 2, we summarized the key data inputs to the health impact and economic valuation estimate. Below we summarize the data sources for each of these inputs, including demographic projections, effect coefficients, incidence rates and economic valuation. Our approach here is generally consistent with the Regulatory Impact Analysis for the Cross - State Air Pollution Rule (U.S. EPA, 201 1b ). **5.4.1 Demographic D** **ata** Quantified and monetized human health impacts depend on the demographic characteristics of the population, including age, location, and income. We use projections based on economic forecasting models developed by Woo ds and Poole, Inc . (Woods and Poole, 2008 ). The Woods and Poole (WP) database contains county- level projections of population by age, sex, and race out to 2030. Projections in each county are determined simultaneously with eve ry 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 estimat es (Hollman et al., 2000 ). According to WP, linking county- level growth projections together and constraining to a national - level total growth avoids potential errors introduced by forecasting each county independently. County projections are developed in a four- stage process: 1. First, national - level variables such as income, employment, and populations are forecasted.5 - 21

2. Second, employment projections are made for 172 economic areas defined by the Bureau of Economic Analysis, using an “ export - base ” approach, which relies on linking industrial - sector production of non - locally consumed production items, such as outputs from mining, agriculture, and manufacturing with the national economy. The export - based approach requires estima tion of demand equations or calculation of historical growth rates for output and employment by sector. 3. Third, population is projected for each economic area based on net migration rates derived from employment opportunities and following a cohort - compo nent method based on fertil ity and mortality in each area. 4. Fourth, employment 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 by sex and race for each year through 2016 based on historical rates of mortality, fertility, and migration. **5.4.2 Effect Coefficients**  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 EPA’ s Integrated Science Assessments (which replace the Criteria Document), with input and advice from the EPA Science Advisory Board - Health Effects Subcommittee (SAB - HES), a scientific review panel specifically established to provide advice on the use of the scientific literature in developing benefits analyses for air pollution regulations (http://www.epa.gov/sab/). In general, we follow a weight of evidence approa ch, 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 (FEV1)). There are several types of data that can support the determination of types and magnitude of health effects associated with air pollution exposures. These sources of data include toxicological studies (including animal and cellular studies), human clinical trials, and observational epidemiology studies. All of these data sources provide important contributions t o the weight of evidence surrounding a particular health impact. However, only epidemiology studies provide direct concentration - response relationships which can be used to evaluate population - level impacts of reductions in ambient pollution levels in a he alth impact assessment.5 - 22

For the data - derived estimates, we relied on the published scientific literature to ascertain the relationship between PM and adverse human health effects. We evaluated epidemiological studies using the selection criteria summarized in Table 5 - 5 . 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 consi derations. The selection of C - R functions for the benefits analysis is guided by the goal of achieving a balance between comprehensiveness and scientific defensibility. In general, the use of results from more than a single study can provide a more robust estimate of the relationship between a pollutant and a given health effect. 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 examine d ifferent pollutants or different age groups. For this reason, we consider very carefully 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. **Table 5**  **- 5**  **. 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 Type Among studies that consider chronic exposure (e.g., over a year or longer), prospective cohort studies are preferred over ecological studies because they control for important individual - level confounding variables that cannot be controlled for in ecologic al studies. Study Period Studies examining a relatively longer period of time (and therefore having more data) are preferred, because they have greater statistical power to detect effects. More recent studies are also preferred because of possible change s 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. (continued)5 - 23

**Table 5**  **- 5**  **. Criteria Used when Selecting C** **- R Functions**  **(continued) Consideration**  **Comments** Population Attributes The most technically appropriate measures of benefits would be based on impact functions that cover the entire sensitive population but allow for heterogeneity across age or other relevant demographic factors. In the absence of effect estimates specific to age, sex, preexisting condition status, or other relevant factors, it may be appropriate to select effect estimates that cover the broadest population to match with the desired outcome of the analysis, which is total nation al - level health impacts. When available, multi - city studies are preferred to single city studies because they provide a more generalizable representation of the C - R function. Study Size Studies examining a relatively large sample are preferred because the y generally have more power to detect small magnitude effects. A large sample can be obtained in several ways, either 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 potential differences in pollution characteristics, exposure patterns, medical care system, population behavior, and lifestyle. Pollutants Included in M odel When modeling the effects of ozone and PM (or other pollutant combinations) jointly, it is important 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 qual ity 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 tha n 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 endpoints to include in the overall benefits analysis because of the possibil ity of double - counting of benefits. 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 Technical Appendices provides details of the procedure s used to combine multiple impact functions (Abt Associates, 2010 ). In general, we used fixed or random effects models to pool estimates from different studies of the same endpoint. Fixed effects pooling simply weights each stu dy’ 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 populati on susceptibility. We used the fixed effects model as our null hypothesis and then5 - 24

determined whether the data suggest that we should reject this null hypothesis, in which case we would use the random effects model. Pooled impact functions are used to esti mate hospital admissions and asthma exacerbations. For more details on methods used to pool incidence estimates, see the BenMAP Manual Appendices (Abt Associates, 2010 ), which are available with the BenMAP software at http://www.epa.gov/benmap.html. Effect estimates selected for a given health endpoint were applied consistently across all locations nationwide. This applies to both impact functions defined by a single effect estimate and those defined by a pooling of multiple effect estimates. Although the effect estimate may, in fact, vary from one location to another (e.g., because of differences in population susceptibilities or differences in the composition of PM), location - specific effect estimates are generally not availabl e. The specific studies from which effect estimates for the primary analysis are drawn are included in Table 5 - 6 . In all cases where effect estimates are drawn directly from epidemiological studies, standard errors are used as a partial representation of the uncertainty in the size of the effect estimate. Below we provide the basis for selecting these studies. 5.4.2.1 PM2.5 Premature Mortality Effect Coefficients Both long - and short -term exposures to ambient levels of PM 2.5 air pollution have been associa ted with increased risk of premature mortality. The size of the mortality risk estimates from epidemiological studies, the serious nature of the effect itself, and the high monetary value ascribed to prolonging life make mortality risk reduction the most s ignificant health endpoint quantified in this analysis. **Table 5**  **- 6**  **. Health Endpoints and Epidemiological Studies Used to Quantify Health Impacts**  **a**

**Endpoint Pollutant**  **Study Study**  **Population Premature Mortality**  Premature mortality — cohort study, all - cause PM 2.5 (annual avg) Pope et al. (2002)

Laden et al. (2006) >29 years >25 years Premature mortality, total exposures PM 2.5 (annual avg) Expert Elicitation (Roman et al., 2008 ) >24 years Premature mortality — all cause PM 2.5 (annual avg) Woodruff et al. (2006) Infant (<1 year) (continued)5 - 25

**Table 5**  **- 6**  **. Health Endpoints and Epidemiological Studies Used to Quantify Health Impacts**  **a**

**(continued) Endpoint**  **Pollutant Study**  **Study Population**  **Chronic Illness** Chronic bronchitis PM 2.5 (annual avg) Abbey et al. (1995) >26 years Non -fatal heart attacks PM 2.5 (24 - hour avg) Peters et al. (2001) Adults (>18 years) Hospital Admissions Respiratory PM 2.5 (24 - hour avg) Pooled estimate:

Moolgavkar (2003) — ICD 490–496 (COPD) Ito (2003) — ICD 490–496 (COPD) >64 years
PM 2.5 (24 - hour avg) Moolgavkar (2000) — ICD 490–496 (COPD) 20–64 years PM 2.5 (24 - hour avg) Ito (2003) — ICD 480–486 (pneumonia) >64 years PM 2.5 (24 - hour avg) Sheppard (2003) — ICD 493 (asthma) <65 years Cardiovascular PM 2.5 (24 - hour avg) Pooled estimate:

Moolgavkar (2003) — ICD 390–429 (all cardiovascular) Ito (2003) — ICD 410–414, 427–428 (ischemic heart disease, dysrhythmia, heart failure) >64 years

PM 2.5 (24 - hour avg) Moolgavkar (2000) — ICD 390–429 (all

cardiovascular) 20–64 years Asthma - related ER visits PM 2.5 (24 - hour avg) Norris et al. (1999) 0– 18 years **Other Health Endpoints** Acute bronchitis PM 2.5 (annual avg) Dockery et al. (1996) 8– 12 years Upper respiratory symptoms PM 10 (24 - hour avg) Pope et al. (1991) Asthmatics, 9– 11 years Lower respiratory symptoms PM 2.5 (24 - hour avg) Schwartz and Neas (2000) 7– 14 years Asthma exacerbations PM 2.5 (24 - hour avg) Pooled estimate:

Ostro et al. (2001) (cough, wheeze and shortness of breath) Vedal et al. (1998) (cough) 6– 18 years b

(continued)5 - 26

**Table 5**  **- 6**  **. Health Endpoints**  **and Epidemiological Studies Used to Quantify Health Impacts a** 

**(continued) Endpoint**  **Pollutant Study**  **Study Population**  Work loss days PM 2.5 (24 - hour avg) Ostro (1987) 18–65 years Minor Restricted Activity Days (MRADs) PM 2.5 (24 - hour avg) Ostro and Rothschild (1989) 18–65 years

a Studies or air quality metrics highlighted in blue represent updates incorporated since the 2005 CAIR RIA b The original study populations were 8 to 13 for the Ostro et al. (2001) study and 6 to 13 for the Vedal et al. (1998) study. Based on advice from the Science Advisory Board Health Effects Subcommittee (SAB - HES), we extended the applied population to 6 to 18, reflecting the common biological basis for the effect in children in the broader age group. See: U.S. Science Advi sory Board. 2004. Advisory Plans for Health Effects Analysis in the Analytical Plan for EPA’ s Second Prospective Analysis – Benefits and Costs of the Clean Air Act, 1990 — 2020. EPA SAB - COUNCIL-ADV - 04 - 004. See also National Research Council (NRC). 2002 . Estimating the Public Health Benefits of Proposed Air Pollution Regulations . Washington, DC: The National Academies Press. Although a number of uncertainties remain to be addressed by continued research (NRC, 2002 ), a subst antial body of published scientific literature documents the correlation between elevated PM 2.5 concentrations and increased mortality rates (U.S. EPA, 2009 a ). Time series methods have been used to relate short -term (often day-to - da y) changes in PM 2.5 concentrations and changes in daily mortality rates up to several days after a period of elevated PM 2.5 concentrations. Cohort methods have been used to examine the potential relationship between community- level PM exposures over multip le years (i.e., long-term exposures) and community- level annual mortality rates. Researchers have found statistically significant associations between PM 2.5 and premature mortality using both types of studies. In general, the risk estimates based on the co hort studies are larger than those derived from time - series studies. Cohort analyses are thought to better capture the full public health impact of exposure to air pollution over time, because they account for the effects of long -term exposures and possibl y some component of short -term exposures (Kunzli et al., 2001 ; NRC, 2002 ). This section discusses some of the issues surrounding the estimation of PM 2.5 - related premature mortality. To demonstrate the sensitivi ty of the benefits estimates to the specific sources of information regarding the impact of PM 2.5 exposures on the risk of premature death, we are providing estimates in our results tables based on studies derived from the epidemiological literature and fr om the EPA sponsored expert elicitation. The epidemiological studies from which these estimates are drawn are described below. The expert elicitation project and the derivation of effect estimates from the expert elicitation results are described in the 20 06 PM 2.5 NAAQS RIA and Roman et al. (2008) . In the interest of brevity we do not repeat those details here.5 - 27

However, Figure 5 - 1 3 summarizes the estimated PM 2.5 - related premature mortalities avoided using risk estimates drawn fro m the expert elicitation. Over a dozen epidemiological studies have found significant associations between various measures of long-term exposure to PM and elevated rates of annual mortality, beginning with Lave and Seskin (1977) . Most of the published studies found positive (but not always statistically significant) associations with available PM indices such as total suspended particles (TSP). However, exploration of alternative model specifications sometimes raised question s about causal relationships (e.g., Lipfert et al., 1989 ). These early “ ecological cross sectional ” studies (Lave and Seskin, 1977 ; Ozkaynak and Thurston, 1987 ) were criticized for a number of methodological limitations, particularly for inadequate control at the individual level for variables that are potentially important in causing mortality, such as wealth, smoking, and diet. Over the last 17 years, several st udies using “ prospective cohort” designs have been published that appear to be consistent with the earlier body of literature. These new “ prospective cohort” studies reflect a significant improvement over the earlier work because they include individual le vel information with respect to health status and residence. The most extensive analyses have been based on data from two prospective cohort groups, often referred to as the Harvard “Six- Cities Study” (Dockery et al., 1993 ; Lad en et al., 2006 ) and the “American Cancer Society or ACS study” (Pope et al., 1995 ; Pope et al., 2002 ; Pope et al., 2004, Krewski et al. 2009 ); these studies have found consistent relationships between fine particle indicators and premature mortality across multiple locations in the United States. A third major data set comes from the California - based 7th Day Adventist Study ( e.g., Abbey et al., 1999 ), which reported associations between long -term PM exposure and mortality in men. Results from this cohort, however, have been inconsistent, and the air quality results are not geographically representati ve of most of the United States, and the lifestyle of the population is not reflective of much of the U.S. population. Analysis is also available for a cohort of adult male veterans diagnosed with hypertension has been examined (Lipfert et al., 2000 ; Lipfert et al., 2003, 2006 ). The characteristics of this group differ from the cohorts in the Six- Cities, ACS, and 7th Day Adventist studies with respect to income, race, health status, and smoking status. Unlike previous long -term analyses, this study found some associations between mortality and ozone but found inconsistent results for PM indicators. Because of the selective nature of the population in the veteran ’ s cohort, we have chosen not to i nclude any effect estimates from the Lipfert et al. (2000) study in our co - benefits assessment.5 - 28

Given their consistent results and broad geographic coverage, and importance in informing the NAAQS development process, the Six- C ities and ACS data have been particularly important in benefits analyses. The credibility of these two studies is further enhanced by the fact that the initial published studies (Pope et al., 1995 and Dockery et al., 1993 ) were subject to extensive reexamination and reanalysis by an independent team of scientific experts commissioned by the Health Effect Institute (HEI) (Krewski et al., 2000 ). The final results of the reanal ysis were then independently peer reviewed by a Special Panel of the HEI Health Review Committee. The results of these reanalyses confirmed and expanded the conclusions of the original investigators. While the HEI reexamination lends credibility to the ori ginal studies, it also highlights sensitivities concerning the relative impact of various pollutants, such as SO 2, the potential role of education in mediating the association between pollution and mortality, and the influence of spatial correlation modeli ng. Further confirmation and extension of the findings of the 1993 Six City Study and the 1995 ACS study were recently completed using more recent air quality and a longer follow - up period for the ACS cohort was published over the past several years (Pope et al., 2002, 2004; Laden et al., 2006 , Krewski et al. 2009 ). The follow up to the Harvard Six City Study both confirmed the effect size from the first analysis and prov ided additional confirmation that reductions in PM 2.5 are likely to result in reductions in the risk of premature death. This additional evidence stems from the observed reductions in PM 2.5 in each city during the extended follow - up period. Laden et al. (2 006) found that mortality rates consistently went down at a rate proportionate to the observed reductions in PM 2.5 . A number of additional analyses have been conducted on the ACS cohort data (J e rrett et al., 2009 ; Pope et al., 2009 ). These studies have continued to find a strong significant relationship between PM 2.5 and mortality outcomes and life expectancy. Specifically, much of the recent research has suggested a stronger relationship between cardiovascular mortality and lung cancer mortality with PM 2.5 , and a less significant relationship between respiratory- related mortality and PM 2.5 . The extended analyses of the ACS cohort data (Krewski et al. 2009 ) provides additional refinements to the analysis of PM - related mortality by (a) extend the follow- up period by 2 years to the year 2000, for a total of 18 years; (b) incorporate ecological., or neighborhood - level co -variates so as to better estimat e personal exposure; (c) perform an extensive spatial analysis using land use regression modeling. These additional refinements may make this analysis well - suited for the assessment of PM - related mortality for EPA benefits analyses. In developing and improving the methods for estimating and valuing the potential reductions in mortality risk over the years, EPA consulted with the SAB - HES. That panel5 - 29

recommended using long -term prospective cohort studies in estimating mortality ris k reduction (U.S. EPA- SAB, 1 999 ). This recommendation has been confirmed by a report from the National Research Council, which stated that “ it is essential to use the cohort studies in benefits analysis to capture all important effects from air pollution exposure ” (NRC, 2002, p. 108 ). More specifically, the SAB recommended emphasis on the ACS study because it includes a much larger sample size and longer exposure interval and covers more locations (e.g., 50 cities compared to the Six Cities Study) than other studies of its kind. Because of the refinements in the extended follow- up analysis, the SAB - HES recommended using the Pope et al. (2002) study as the basis for the primary mortality estimate for adult s and suggests that alternate estimates of mortality generated using other cohort and time - series studies could be included as part of the sensitivi ty analysis (U.S. EPA- SAB, 2004 a ). The PM NAAQS Risk and Exposure Assessment (U. S . EPA, 2010 c ) utilized risk coefficients drawn from the Krewski et al. (2009) study. In a December of 2009 consultation with the SAB - HES, the Agency proposed utilizing the Krewski et al. (2009) extended analysis of the ACS cohort data. The panel is scheduled to issue an advisory in early 2010. As noted above, since 2004 SAB review, an extended follow - up of the Harvard Six cities study has been published (Laden et al., 2006 ) and in recent RIAs (see for example the Cross State Air Pollution Rule RIA, U.S. EPA 2011b ), we have included this estimate of mortality impacts based on application of t he C- R function derived from this study. We use this specific estimate to represent the Six Cities study because it both reflects among the most up -to - date science and was cited by many of the experts in their elicitation responses. It is clear from the ex pert elicitation that the results published in Laden et al. (2006) are potentially influential, and in fact the expert elicitation results encompass within their range the estimates from both the Pope et al. (2002) and Laden et al. (2006) studies (see Figure 5 - 3 ). These are logical choices for anchor points in our presentation because, while both studies are well designed and peer reviewed, there are strengths and weaknesses inherent in each, which we believe argues for using both studies to generate benefits estimates. 5.4.2.2 Chronic Bronchitis (CB) CB is characterized by mucus in the lungs and a persistent wet cough for at least 3 months a year for several years in a row. CB affects an estimated 5 percent of the U.S. population (American Lung Association, 1999 ). A limited number of studies have estimated the impact of air pollution on new incidences of CB. Schwartz (1993) and Abbey et al. (1995) provide evidence that long -term PM exposure gives rise to the development of CB in the United States. Because PM 2.5 reductions are expected from MATS, this analysis uses only the Abbey et5 - 30

al. (1995) study, because it is the only study focusing on the relationship between PM 2.5 and new incidences of CB. 5.4.2.3 Non -

fatal Myocardial Infarctions (Heart Attacks) Non -fatal heart attacks have been linked with short -term exposures to PM 2.5 in the United States (Peters et al., 2001 ) and other countries (Poloniecki et al., 1997 ). We used a recent study by Peters et al. (2001) as the basis for the impact function estimating the relationship between PM 2.5 and non -fatal heart attacks. Peters et al. is the only available U.S. study to provide a specific estimate for heart attacks. Other studies, such as Samet et al. (2000) and Moolgavkar (2000) , show a consistent relationship between all cardiovascular hospital admissions, including those for non -fatal heart attacks, and PM. Given the lasting impact of a heart attack on long -term health costs and earnings, we provide a separate estimate for non fatal heart attacks. The estimate used in the MATS analysis is based on the single available U.S. effect estimate. The finding of a sp ecific impact on heart attacks is consistent with hospital admission and other studies showing relationships between fine particles and cardiovascular effects both within and outside the United States. Several epidemiologic studies (Liao et al., 1999 ; Gold et al., 2000 ; Magari et al., 2001 ) have shown that heart rate variability (an indicator of how much the heart is able to speed up or slow down in response to momentary stresse s) is negatively related to PM levels. Heart rate variability is a risk factor for heart attacks and other coronary heart diseases (Carthenon et al., 2002 ; Dekker et al., 2000 ; Liao et al., 1997 ; Tsuji et al., 1996 ). As such, significant impacts of PM on heart rate variability are consistent with an increased risk of heart attacks. 5.4.2.4 Hospital and Emergency Room Admissions Because of the avai lability of detailed hospital admission and discharge records, there is an extensive body of literature examining the relationship between hospital admissions and air pollution. Because of this, many of the hospital admission endpoints use pooled impact fu nctions based on the results of a number of studies. In addition, some studies have examined the relationship between air pollution and emergency room visits. Since most emergency room visits do not result in an admission to the hospital (the majority of p eople going to the emergency room are treated and return home), we treat hospital admissions and emergency room visits separately, taking account of the fraction of emergency room visits that are admitted to the hospital. The two main groups of hospital ad missions estimated in this analysis are respiratory admissions and cardiovascular admissions. There is not much evidence linking ozone or PM5 - 31

with other types of hospital admissions. The only type of emergency room visits that have been consistently linked to ozone and PM in the United States are asthma - related visits. To estimate avoided incidences of cardiovascular hospital admissions associated with PM 2.5 , we used studies by Moolgavkar (2003) and Ito (2003) . Additional published studies show a statistically significant relationship between PM 10 and cardiovascular hospital admissions. However, given that the control options we are analyzing are expected to reduce primarily PM 2.5 , we focus on the two studies tha t examine PM 2.5 . Both of these studies provide an effect estimate for populations over 65, allowing us to pool the impact functions for this age group. Only Moolgavkar (2000) provided a separate effect estimate for populations 20 to 64. 2 Total cardiovascular hospital admissions are thus the sum of the pooled estimate for populations over 65 and the single study estimate for populations 20 to 64. Cardiovascular hospital admissions include admissions for myocardial infarctions. To av oid double - counting benefits from reductions in myocardial infarctions when applying the impact function for cardiovascular hospital admissions, we first adjusted the baseline cardiovascular hospital admissions to remove admissions for myocardial infarctio ns. To estimate total avoided incidences of respiratory hospital admissions, we used impact functions for several respiratory causes, including chronic obstructive pulmonary disease (COPD), pneumonia, and asthma. As with cardiovascular admissions, addition al published studies show a statistically significant relationship between PM 10 and respiratory hospital admissions. We used only those focusing on PM 2.5 . Both Moolgavkar (2000) and Ito (2003) provide effect estimates for COPD in populations over 65, allow ing us to pool the impact functions for this group. Only Moolgavkar (2000) provides a separate effect estimate for populations 20 to 64. Total COPD hospital admissions are thus the sum of the pooled estimate for populations over 65 and the single study est imate for populations 20 to 64. Only Ito (2003) estimated pneumonia and only for the population 65 and older. In addition, Sheppard (2003) provided an effect estimate for asthma hospital admissions for populations under age 65. Tota l avoided incidences of PM - related respiratory- related hospital admissions are the sum of COPD, pneumonia, and asthma admissions.

2 N ote that the Moolgavkar (2000) study has not been updated to reflect the more stringent GAM convergence criteria. However, given that no other estimates are available for this age group, we chose to use the existing study. Given the very small (<5 percent) difference in the effect estimates for people 65 and older with cardiovascular hospital admissions betw een the original and reanalyzed results, we do not expect this choice to introduce much bias.5 - 32

To estimate the effects of PM air pollution reductions on asthma - related ER visits, we use the effect estimate from a study of children 18 and under by Norris et al. (1999) . As noted earlier, there is another study by Schwartz examining a broader age group (less than 65), but the Schwartz study focused on PM 10 rather than PM 2.5 . We selected the Norris et al. (1999) effect estimate because it better matched the pollutant of interest. Because children tend to have higher rates of hospitalization for asthma relative to adults under 65, we will likely capture the majority of the impact of PM 2.5 on asthma e mergency room visits in populations under 65, although there may still be significant impacts in the adult population under 65. To estimate avoided incidences of respiratory hospital admissions associated with ozone, we used a number of studies examining h ospital admissions for a range of respiratory illnesses, including pneumonia and COPD. Two age groups, adults over 65 and children under 2, were examined. For adults over 65, Schwartz (1995) provides effect estimates for two differe nt cities relating ozone and hospital admissions for all respiratory causes (defined as ICD codes 460–519). Impact functions based on these studies were pooled first before being pooled with other studies. Two studies (Moolgavkar et al., 1997 ; Schwartz, 1994a ) examine ozone and pneumonia hospital admissions in Minneapolis. One additional study (Schwartz, 1994b ) examines ozone and pneumonia hospital admissions in Detroit . The impact functions for Minneapolis were pooled together first, and the resulting impact function was then pooled with the impact function for Detroit. This avoids assigning too much weight to the information coming from one city. For COPD hospital admi ssions, two studies are available: Moolgavkar et al. (1997) , conducted in Minneapolis, and Schwartz (1994b) , conducted in Detroit. These two studies were pooled together. To estimat e total respiratory hospital admissions for adults over 65, COPD admissions were added to pneumonia admissions, and the result was pooled with the Schwartz (1995) estimate of total respiratory admissions. Burnett et al. (2001) is the only study providing an effect estimate for respiratory hospital admissions in children under 2. We used two studies as the source of the concentration - response functions we used to estimate the effects of ozone exposure on asthma - related emergency room (ER) visits: Peel et al. (2005) and Wilson et al. (2005) . We estimated the change in ER visits using the effect estimate(s) from each study and then pooled the results us ing the random effects pooling technique (see Abt, 2005 ). The Peel et al. (2005) study estimated asthma - related ER visits for all ages in Atlanta, using air quality data from 1993 to 2000. Using Poisson generali zed estimating equations, the authors found a marginal association between the maximum daily 8 - hour average ozone level and ER visits for asthma over a 3 - day moving average (lags of 0, 1, and 2 days) in a single pollutant model. Wilson et al. (2005) examined the relationship between ER5 - 33

visits for respiratory illnesses and asthma and air pollution for all people residing in Portland, Maine from 1998–2000 and Manchester, New Hampshire from 1996–2000. For all models used in the ana lysis, the authors restricted the ozone data incorporated into the model to the months ozone levels are usually measured, the spring - summer months (April through September). Using the generalized additive model, Wilson et al. (2005) found a significant association between the maximum daily 8 - hour average ozone level and ER visits for asthma in Portland, but found no significant association for Manchester. Similar to the approach used to generate effect estimates for hospital adm issions, we used random effects pooling to combine the results across the individual study estimates for ER visits for asthma. The Peel et al. (2005) and Wilson et al. (2005) Manchester estimates were not significant at the 95 percent level, and thus, the confidence interval for the pooled incidence estimate based on these studies includes negative values. This is an artifact of the statistical power of the studies, and the ne gative values in the tails of the estimated effect distributions do not represent improvements in health as ozone concentrations are increased. Instead, these should be viewed as a measure of uncertainty due to limitations in the statistical power of the s tudy. We included both hospital admissions and ER visits as separate endpoints associated with ozone exposure because our estimates of hospital admission costs do not include the costs of ER visits and most asthma ER visits do not result in a hospital admi ssion. 5.4.2.5 Acute Health Events and School/Work Loss Days In addition to mortality, chronic illness, and hospital admissions, a number of acute health effects not requiring hospitalization are associated with exposure to ambient levels of ozone and PM. The sources for the effect estimates used to quantify these effects are described below. Around 4 percent of U.S. children between the ages of 5 and 17 experience episodes of acute bronchitis annually (American Lung Association, 2002c ). Acute bronchitis is characterized by coughing, chest discomfort, slight fever, and extreme tiredness, lasting for a number of days. According to the MedlinePlus medical encyclopedia, 3 with the exception of cough, most acute bronchitis symptoms abate within 7 to 10 days. Incidence of episodes of acute bronchitis in children between the ages of 5 and 17 were estimated using an effect estimate developed from Dockery et al. (1996) .

3 See http://www.nlm.nih.gov/medlineplus/ency/article/000124.htm, accessed January 2002.5 - 34

Incidences of lower respirat ory symptoms (e.g., wheezing, deep cough) in children aged 7 to 14 were estimated using an effect estimate from Schwartz and Neas (2000) . Because asthmatics have greater sensitivity to stimuli (including air pollution), chi ldren with asthma can be more susceptible to a variety of upper respiratory symptoms (e.g., runny or stuffy nose; wet cough; and burning, aching, or red eyes). Research on the effects of air pollution on upper respiratory symptoms has thus focused on effects in asthmatics. Incidences of upper respiratory symptoms in asthmatic children aged 9 to 11 are estimated using an effect estimate developed from Pope et al. (1991) . Health effects from air pollution can also result in missed d ays of work (either from personal symptoms or from caring for a sick family member). Days of work lost due to PM 2.5 were estimated using an effect estimate developed from Ostro (1987) . Children may also be absent from school because of respiratory or other diseases caused by exposure to air pollution. Most studies examining school absence rates have found little or no association with PM 2.5 , but several studies have found a significant association between ozone levels and school absence rates. We used two recent studies, Gilliland et al. (2001) and Chen et al. (2000) , to estimate changes in absences (school loss days) due to changes in ozone levels. The Gilliland et al. study est imated the incidence of new periods of absence, while the Chen et al. study examined absence on a given day. We converted the Gilliland estimate to days of absence by multiplying the absence periods by the average duration of an absence. We estimated an average duration of school absence of 1.6 days by dividing the average daily school absence rate from Chen et al. (2000) and Ransom and Pope (1992) by the episodic absence rate from Gilliland et al. (2001) . This provides estimates from Chen et al. (2000) and Gilliland et al. (2001), which can be pooled to provide an overall estimate. Minor Restricted Activity Days ( MRAD ) occur when individuals reduce most usual daily activities and replace them with less strenuous activities or rest, yet not to the point of missing work or school. For example, a mechanic who would usually be doing physical work most of the day will instead spend the day at a desk doing paper and phone work because of difficulty b reathing or chest pain. The effect of PM 2.5 and ozone on MRAD was estimated using an effect estimate derived from Ostro and Rothschild (1989) . For this analysis , we have followed the SAB - HES recommendations regarding ast hma exacerbations in developing the primary estimate. To prevent double - counting, we focused the estimation on asthma exacerbations occurring in children and excluded adults from the5 - 35

calculation. 4 Asthma exacerbations occurring in adults are assumed to be captured in the general population endpoints such as work loss days and MRADs. Consequently, if we had included an adult - specific asthma exacerbation estimate, we would likely double - count incidence for this endpoint. However, because the general populatio n endpoints do not cover children (with regard to asthmatic effects), an analysis focused specifically on asthma exacerbations for children (6 to 18 years of age) could be conducted without concern for double - counting. To characterize asthma exacerbations in children, we selected two studies (Ostro et al., 2001 ; Vedal et al., 1998 ) that followed panels of asthmatic children. Ostro et al. (2001) followed a group of 138 African -American children in Los Ang eles for 13 weeks, recording daily occurrences of respiratory symptoms associated with asthma exacerbations (e.g., shortness of breath, wheeze, and cough). This study found a statistically significant association between PM 2.5 , measured as a 12 - hour averag e, and the daily prevalence of shortness of breath and wheeze endpoints. Although the association was not statistically significant for cough, the results were still positive and close to significance; consequently, we decided to include this endpoint, alo ng with shortness of breath and wheeze, in generating incidence estimates (see below). Vedal et al. (1998) followed a group of elementary school children, including 74 asthmatics, located on the west coast of Vancouver Island for 18 months including measur ements of daily peak expiratory flow (PEF) and the tracking of respiratory symptoms (e.g., cough, phlegm, wheeze, chest tightness) through the use of daily diaries. Association between PM 10 and respiratory symptoms for the asthmatic population was only rep orted for two endpoints: cough and PEF. Because it is difficult to translate PEF measures into clearly

4 Estimating asthma exacerbations associated with air pollution exposure s is difficult, due to concerns about double counting of benefits. Concerns over double counting stem from the fact that studies of the general population also include asthmatics, so estimates based solely on the asthmatic population cannot be directly add ed to the general population numbers without double counting. In one specific case (upper respiratory symptoms in children), the only study available is limited to asthmatic children, so this endpoint can be readily included in the calculation of total ben efits. However, other endpoints, such as lower respiratory symptoms and MRADs, are estimated for the total population that includes asthmatics. Therefore, to simply add predictions of asthma - related symptoms generated for the population of asthmatics to th ese total population - based estimates could result in double counting, especially if they evaluate similar endpoints. The SAB - HES, in commenting on the analytical blueprint for 812, acknowledged these challenges in evaluating asthmatic symptoms and appropri ately adding them into the primary analysis (SAB - HES, 2004). However, despite these challenges, the SAB - HES recommends the addition of asthma - related symptoms (i.e., asthma exacerbations) to the primary analysis, provided that the studies use the panel stu dy approach and that they have comparable design and baseline frequencies in both asthma prevalence and exacerbation rates. Note also, that the SAB - HES, while supporting the incorporation of asthma exacerbation estimates, does not believe that the associat ion between ambient air pollution, including ozone and PM, and the new onset of asthma is sufficiently strong to support inclusion of this asthma - related endpoint in the primary estimate.5 - 36

defined health endpoints that can be monetized, we only included the cough - related effect estimate from this study in quantifying asthma exacerbations. W e employed the following pooling approach in combining estimates generated using effect estimates from the two studies to produce a single asthma exacerbation incidence estimate. First, we pooled the separate incidence estimates for shortness of breath, wh eeze, and cough generated using effect estimates from the Ostro et al. study, because each of these endpoints is aimed at capturing the same overall endpoint (asthma exacerbations) and there could be overlap in their predictions. The pooled estimate from the Ostro et al. study is then pooled with the cough related estimate generated using the Vedal study. The rationale for this second pooling step is similar to the first; both studies are attempting to quantify the same overall endpoint (asthma exacerbatio ns). **5.4.3 Baseline Incidence Estimates**  Epidemiological studies of the association between pollution levels and adverse health effects generally provide a direct estimate of the relationship of air quality changes to the relative risk of a health effect, r ather than estimating the absolute number of avoided cases. For example, a typical result might be that a 10 ppb decrease in daily ozone levels might, in turn, decrease hospital admissions by 3 percent. The baseline incidence of the health effect is necess ary 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 year in the assessment location, as it corresponds to baseline pollutant levels in that location. To deriv e the total baseline incidence per year, 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 5 - 7 summarizes the sources of baseline incidence rates and provides average incidence rates for the endpoints included in the analysis. 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 population benefits. Rates for mortality, hospitalizations, asthma ER visits, and non -fatal myocardi al infarction (heart attacks) have been updated sin ce the MATS Proposal RIA, consistent with the Cross - State Air Pollution Rule RIA (U.S. EPA 2011b) .5 - 37

**Table 5**  **- 7**  **. 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 2015 Age -, cause -, and countyspecific rate CDC Wonder (2004–2006 ) U.S. Census bureau Hospitalizations Daily hospitalization rate Age -, region -, state -, county- and cause specific rate 2007 HCUP data files a
Asthma ER Visits Daily asthma ER visit rate Age -, region -, state -, county- and cause specific rate 2007 HCUP data files a
Chronic Bronchitis Annual prevalence rate per person  Aged 18–44  Aged 45 –64  Aged 65 and older 0.0367 0.0505 0.0587 1999 NHIS (American Lung

Association, 2002b , Table 4)
Annual incidence rate per person 0.00378 Abbey et al. (1995 , Table 3) (continued)5 - 38

**Table 5**  **- 7**  **. Baseline Incidence Rates and Population Prevalence Rates for Use in Impact** 

**Functions, General Population (continued)**  **Endpoint Parameter**  **Rates Value**  **Source** Non -fatal Myocardial Infarction (heart attacks) Daily non -fatal myocardial infarction incidence rate per person, 18+ Age -, region -, state -, and county- specific rate 2007 HCUP data files a ; adjusted by 0.93 for probability of surviving after 28 days (Rosamond et al. , 1999 ) Asthma Exacerbations Incidence among asthmatic African -American children  daily wheeze  daily cough  daily dyspnea 0.076 0.067 0.037 Ostro et al. (2001 )

Acute Bronchitis Annual bronchitis incidence rate, children 0.043 American Lung Association (2002c, Table 11) Lower Respiratory Symptoms Daily lower respiratory symptom incidence among children b 0.0012 Schwartz et al. (1994, Table 2) Upper Respiratory Symptoms Daily upper respiratory symptom incidence among asthmatic children 0.3419 Pope et al. (1991 , Table 2) Work Loss Days Daily WLD incidence rate per person (18–65)  Aged 18–24  Aged 25 –44  Aged 45 –64 0.00540 0.00678 0.00492 1996 HIS (Adams,

Hendershot, and Marano, 1999 , Table 41); U.S. Bureau of the Census (2000 ) School Loss Days Rate per person per year, assuming 180 school days per year 9.9 National Center for Education Statistics (1996) and 1996 HIS (Adams et al., 1999 , Table 47); Minor Restricted Activity Days Daily MRAD inciden ce rate per person 0.02137 Ostro and Rothschild (1989 , p. 243)
a Healthcare Cost and Utilization Program (HCUP) database contains individual level, state and regional - level hospital and emergency department discharges for a variety of ICD codes. b Lower respiratory symptoms are defined as two or more of the following: cough, chest pain, phlegm, and wheeze. The baseline incidence rates for hospital and emergency department visits that we applied in this analysis are an impro vement over the rates we used in the proposal analysis in two ways. First, these data are newer, and so are a more recent representation of the rates at which populations of different ages, and in different locations, visit the hospital and emergency5 - 39

department for illnesses that may be air pollution related . Second, these newer data are also more spatially refined. For many locations within the U.S., these data are resolved at the county- or state - level, providing a better characterization of the geographi c distribution of hospital and emergency department visits. Newer and more spatially resolved incidence rates are likely to yield a more reliable estimate of air pollution - related hospitalizations and emergency department visits. Consistent with the propos al RIA, we continue to use countylevel mortality rates. We have projected mortality rates such that future mortality rates are consistent with our projections of population growth (Abt Associates, 2010 ). For the set of endpoin ts 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 5 - 8 lists the prevalence rates used to determine the applicable population fo r asthma symptom endpoints. Note that these reflect current asthma prevalence and assume no change in prevalence rates in future years. These rates have all been updated since the MATS proposal RIA, consistent with the Cross - State Air Pollution Rule (U.S. EPA 2011b). **Table 5**  **- 8**  **. Asthma Prevalence Rates Used for this Analysis**  a
**Population Group Asthma Prevalence Rates**  **Value Source**  All Ages 0.0780 American Lung Association (2010, Table 7) < 18 0.0941 5– 17 0.1070 18–44 0.0719 45–64 0.0745 65+ 0.0716 African American, 5 to 17 0.1776 American Lung Association (2010, Table 9) African American, <18 0.1553 American Lung Association b

a See ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/NHIS/2000/ . b Calculated by ALA for U.S. EPA, based on NHIS data (CDC, 200 9 ) **5.4.4 Economic Valuation Estimates**  Reductions in ambient concentrations of air pollution generally lower th e risk of future adverse health effects for a large population. Therefore, the appropriate economic measure is WTP for changes in risk of a health effect rather than WTP for a health effect that would occur5 - 40

with certainty (Freeman, 1993 ). Epidemiological studies generally provide estimates of the relative risks of a particular health effect that is avoided because of a reduction in air pollution. We converted those to units of avoided statistical incidence for ease of presentation. We calculated the value of avoided statistical incidences by dividing individual WTP for a risk reduction by the related observed change in risk. 5 WTP estimates generally are not available for some health effects, such as hospital admissions. In these cases, we used the cost of treating or mitigating the effect as a primary estimate. These cost - of- illness (COI) estimates generally understate the true value of reducing the risk of a health effect, because they reflect the direct expenditures related to treatmen t, but not the value of avoided pain and suffering (Harrington and Portney, 1987 ; Berger, 1987 ). We provide unit values for health endpoints (along with information on the distribution of the unit value) in Table s 5 - 10 through 5 - 12 . All values are in constant year 2006 dollars, adjusted for growth in real income out to 2016 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 early 1990s. Because real income has grown since the studies were conducted, people ’ s willingness to pay for reductions i n 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 wa ge rates. For these two reasons, these cost of illness estimates may underestimate the economic value of avoided health impacts in 2016. The discussion below provides additional details on ozone and PM 2.5 - related related endpoints. 5.4.4.1 Mortality Valuation Following the advice of the EEAC of the SAB, EPA currently uses the VSL approach in calculating the primary estimate of mortality co - benefits , because we believe this calculation

5 To comply with Circular A-4, EPA provides monetized benefits usin g discount rates of 3% and 7% (OMB, 2003). These benefits are estimated for a specific analysis year (i.e., 201 6 ), and most of the PM benefits occur within that year with two exceptions: acute myocardial infarctions (AMIs) and premature mortality. For AMIs , we assume 5 years of follow- up medical costs and lost wages. For premature mortality, we assume that there is a “cessation” lag between PM exposures and the total realization of changes in health effects. Although the structure of the lag is uncertain, E PA follows the advice of the SAB - HES to assume a segmented lag structure characterized by 30% of mortality reductions in the first year, 50% over years 2 to 5, and 20% over the years 6 to 20 after the reduction in PM 2.5 (U.S. EPA- SAB, 2004 c ). Changes in th e lag assumptions do not change the total number of estimated deaths but rather the timing of those deaths. Therefore, discounting only affects the AMI costs after the analysis year and the valuation of premature mortalities that occur after the analysis y ear. As such, the monetized benefits using a 7% discount rate are only approximately 10% less than the monetized benefits using a 3% discount rate.5 - 41

provides the most reasonable single estimate of an individual ’ s willingne ss to trade off money for reductions in mortality risk (U.S. EPA- SAB, 2000 ). The VSL approach is a summary measure for the value of small changes in mortality risk experienced by a large number of people. For a period of time (2004- 2008), the Office of Air and Radiation (OAR) valued mortality risk reductions using a value of statistical life (VSL) estimate derived from a limited analysis of some of the available studies. OAR arrived at a VSL using a range of $1 million to $10 million (2000$) consistent with two meta - analyses of the wage - risk literature. The $1 million value represented the lower end of the interquartile range from the Mrozek and Taylor (2002) meta - analysis of 33 studies. The $10 million value represented the upper end of the interquartile range from the Viscusi and Aldy (2003) meta - analysis of 43 studies. The mean estimate of $5.5 million (2000$) was also consistent with the mean VSL of $5.4 million estimated in the Kochi et al. (2006) meta
analysis. Howe ver, the Agency neither changed its official guidance on the use of VSL in rule makings nor subjected the interim estimate to a scientific peer- review process through the Science Advisory Board (SAB) or other peer- review group. During this time, the Agency continued work to update its guidance on valuing mortality risk reductions, including commissioning a report from meta - analytic experts to evaluate methodological questions raised by EPA and the SAB on combining estimates from the various data sources. In addition, the Agency consulted several times with the Science Advisory Board Environmental Economics Advisory Committee (SAB - EEAC) on the issue. With input from the meta - analytic experts, the SAB - EEAC advised the Agency to update its guidance using specif ic, appropriate meta - analytic techniques to combine estimates from unique data sources and different studies, including those using different methodologies (i.e., wage - risk and stated preference) (U.S. EPA- SAB, 2007 ). Until updat ed guidance is available, the Agency determined that a single, peer- reviewed estimate applied consistently best reflects the SAB - EEAC advice it has received. Therefore, the Agency has decided to apply the VSL that was vetted and endorsed by the SAB in the Guidelines for Preparing Economic Analyses (U.S. EPA, 2000 ) 6 while the Agency continues its efforts to update its guidance on this issue. This approach calculates a mean value across VSL estimates derived from 26 labor market and con tingent valuation studies published between 1974 and

6 In EPA’s recently revised Economic Guidelines (U.S. EPA, 20 10d ), EPA retained the VSL endorsed by the SAB with the understanding that further updates to the mortality risk valuation guidance would be forthcoming in the near future. Therefore, this report does not represent final agency policy.5 - 42

1991. The mean VSL across these studies is $6.3 million (2000$). 7 The Agency is committed to using scientifically sound, appropriately reviewed evidence in valuing mortality risk reductions and has made significant progress in responding to the SAB - EEAC’ s specific recommendations. The Agency anticipates presenting results from this effort to the SAB - EEAC in Spring 2010 and that draft guidance will be available shortly thereafter. As indicated in the previous section on quantification of premature mortality benefits, we assumed for this analysis that some of the incidences of premature mortality related to PM exposures occur in a distributed fashion over the 20 years following exposure. To take this into account in the valuation of reductions in premature mortality, we applied an annual 3% discount rate to the value of premature mortality occurring in future years. 8 The economics literature concerning the appropriate method for valuing reductions in p remature mortality risk is still developing. The adoption of a value for the projected reduction in the risk of premature mortality is the subject of continuing discussion within the economics and public policy analysis community. EPA strives to use the be st economic science in its analyses. Given the mixed theoretical finding and empirical evidence regarding adjustments to VSL for risk and population characteristics, we use a single VSL for all reductions in mortality risk. Although there are several diffe rences between the labor market studies EPA uses to derive a VSL estimate and the PM air pollution context addressed here, those differences in the affected populations and the nature of the risks imply both upward and downward adjustments. Table 5 - 9 lists some of these differences and the expected effect on the VSL estimate for air pollution - related mortality. In the absence of a comprehensive and balanced set of adjustment factors, EPA believes it is reasonable to continue to use the $6.3 million value wh ile acknowledging the significant limitations and uncertainties in the available literature.

7 In this analysis, we adjust the VSL to account for a different currency year (200 7 $) and to account for income growth to 201 6 . After applying these adjustments to the $6. 3 million value, the VSL is $ 8.9 M. 8 The choice of a discount rate, and its associated conceptual basis, is a topic of ongoing discussion within the federal government. EPA adopted a 3% discount rate for its base estimate in this case to reflect reliance on a “ social rate of time preference ” discounting concept. We have also calculated benefits and costs using a 7% rate consistent with an “ opportunity cost of capital ” concept to reflect the time value of resources directed to meet regulatory requirements. In this case, the benefit and cost estimates were not significantly affected by the choice of discount rate. Further discussion of this topic appears in EPA’ s Guidelines for Preparing Economic Analyses (EPA, 20 10 ).5 - 43

**Table 5**  **- 9**  **. Expected Impact on Estimated Benefits of Premature Mortality Reductions of** 

**Differences Between Factors Used in Developing Applied VSL and Theoretically Appropriate VSL**  **Attribute Expected Direction of Bias**  Age Uncertain, perhaps overestimate Life Expectancy/Health Status Uncertain, perhaps overestimate Attitudes Toward Risk Underestimate Income Uncertain Voluntary vs. Involuntary Uncertain, perhaps underestimate Catastrophic vs. Protracted Death Uncertain, perhaps underestimate The SAB - EEAC has reviewed many potential VSL adjustments and the state of the economics literature. The SAB - EEAC advised EPA to “ continue to use a wage - risk- based VSL as its primary estimate, including appropriate sensitivity analyses to reflect the uncertainty of these estimates, ” and that “the only risk characteristic for which adjustments to the VSL can be made is the timing of the risk” (U.S. EPA, 2000 ). In developing our primary estimate of the co benefits of premature mortality reductions, we have followed this advice and discounted over the lag period between exposure and premature mortality. Uncertainties Specific to Premature Mortality Valuation . The economic co - benefits associated with reductions in the risk of premature mortality are the largest category of monetized co - benefits of the MATS . In addition, in prior analyses, EPA has identified valuation of mortality- related benefits as the largest contributor to the range of uncertainty in monetized benefits (U.S. EPA, 1999 ). 9 Because of the uncertainty in estimates of the value of reducing premature mortality risk, it is important to adequately charact erize and understand the various types of economic approaches available for valuing reductions in mortality risk. Such an assessment also requires an understanding of how alternative valuation approaches reflect that some individuals may be more susceptibl e to air pollution - induced mortality or reflect differences in the nature of the risk presented by air pollution relative to the risks studied in the relevant economics literature.

9 This conclusion was based on an assessment of uncertainty based on statistical error in epidemiological effect estimates and economic valuation estimates. Additional sources of model error such as those examined in the PM mortality expert elicitation may result in different conclusions about the relative contribution of sources of uncertainty.5 - 44

The health science literature on air pollution indicates that several human characteristics affect the degree to which mortality risk affects an individual. For example, some age groups appear to be more susceptible to air pollution than others (e.g., the elderly and children). Health status prior to exposure also affects suscept ibility. An ideal benefits estimate of mortality risk reduction would reflect these human characteristics, in addition to an individual ’ s WTP to improve one ’ s own chances of survival plus WTP to improve other individuals ’ survival rates. The ideal measure would also take into account the specific nature of the risk reduction commodity that is provided to individuals, as well as the context in which risk is reduced. To measure this value, it is important to assess how reductions in air pollution reduce the r isk of dying from the time that reductions take effect onward and how individuals value these changes. Each individual ’ s survival curve, or the probability of surviving beyond a given age, should shift as a result of an environmental quality improvement. F or example, changing the current probability of survival for an individual also shifts future probabilities of that individual ’ s survival. This probability shift will differ across individuals because survival curves depend on such characteristics as age, health state, and the current age to which the individual is likely to survive. Although a survival curve approach provides a theoretically preferred method for valuing the benefits of reduced risk of premature mortality associated with reducing air pollut ion, the approach requires a great deal of data to implement. The economic valuation literature does not yet include good estimates of the value of this risk reduction commodity. As a result, in this study we value reductions in premature mortality risk us ing the VSL approach. Other uncertainties specific to premature mortality valuation include the following:

 Across-study variation: There is considerable uncertainty as to whether the available literature on VSL provides adequate estimates of the VSL for ri sk reductions from air pollution reduction. Although there is considerable variation in the analytical designs and data used in the existing literature, the majority of the studies involve the value of risks to a middle - aged working population. Most of the studies examine differences in wages of risky occupations, using a hedonic wage approach. Certain characteristics of both the population affected and the mortality risk facing that population are believed to affect the average WTP to reduce the risk. The appropriateness of a distribution of WTP based on the current VSL literature for valuing the mortality- related benefits of reductions in air pollution concentrations therefore depends not only on the quality of the studies (i.e., how well they measure what they are trying to measure), but also on the extent to which the risks5 - 45 being valued are similar and the extent to which the subjects in the studies are similar to the population affected by changes in pollution concentrations.

 Level of risk reduction: The transferability of estimates of the VSL from the wage - risk studies to the context of the PM NAAQS analysis rests on the assumption that, within a reasonable range, WTP for reductions in mortality risk is linear in risk reduction. For example, suppose a st udy provides a result that the average WTP for a reduction in mortality risk of 1/100,000 is $50, but that the actual mortality risk reduction resulting from a given pollutant reduction is 1/10,000. If WTP for reductions in mortality risk is linear in risk reduction, then a WTP of $50 for a reduction of 1/100,000 implies a WTP of $500 for a risk reduction of 1/10,000 (which is 10 times the risk reduction valued in the study). Under the assumption of linearity, the estimate of the VSL does not depend on the particular amount of risk reduction being valued. This assumption has been shown to be reasonable provided the change in the risk being valued is within the range of risks evaluated in the underlying studies (Rowlatt et al., 1998 ).

 Voluntariness of risks evaluated: Although job - related mortality risks may differ in several ways from air pollution - related mortality risks, the most important difference may be that job - related risks are incurred voluntarily, or generally assumed to b e, whereas air pollution - related risks are incurred involuntarily. Some evidence suggests that people will pay more to reduce involuntarily incurred risks than risks incurred voluntarily. If this is the case, WTP estimates based on wage - risk studies may un derstate WTP to reduce involuntarily incurred air pollution - related mortality risks.

 Sudden versus protracted death: A final important difference related to the nature of the risk may be that some workplace mortality risks tend to involve sudden, catastrop hic events, whereas air pollution - related risks tend to involve longer periods of disease and suffering prior to death. Some evidence suggests that WTP to avoid a risk of a protracted death involving prolonged suffering and loss of dignity and personal control is greater than the WTP to avoid a risk (of identical magnitude) of sudden death. To the extent that the mortality risks addressed in this assessment are associated with longer periods of illness or greater pain and suffering than are the risks add ressed in the valuation literature, the WTP measurements employed in the present analysis would reflect a downward bias.5 - 46  Self -selection and skill in avoiding risk: Recent research (Shogren and Stamland, 2002 ) suggests that VSL estimates based on hedonic wage studies may overstate the average value of a risk reduction. This is based on the fact that the risk- wage trade off revealed in hedonic studies reflects the preferences of the marginal worker (i.e., that worker who deman ds the highest compensation for his risk reduction). This worker must have either a higher workplace risk than the average worker, a lower risk tolerance than the average worker, or both. However, the risk estimate used in hedonic studies is generally base d on average risk, so the VSL may be upwardly biased because the wage differential and risk measures do not match.

 Baseline risk and age: Recent research (Smith, Pattanayak, and Van Houtven, 2006 ) finds that because individuals reevaluate their baseline risk of death as they age, the marginal value of risk reductions does not decline with age as predicted by some lifetime consumption models. This research supports findings in recent stated preference studies that suggest only small reductions in the value of mortality risk reductions with increasing age. 5.4.4.2 Chronic Bronchitis Valuation The best available estimate of WTP to avoid a case of CB comes from Viscusi, Magat, and Huber (1991 ). The Viscusi, Magat, and Huber study, however, describes a severe case of CB to the survey respondents. We therefore employ an estimate of WTP to avoid a pollution related case of CB, based on adjusting the Viscusi, Magat, and Huber (1991 ) estimate of the WTP to avoid a severe case. This is done to account for the likelihood that an average case of pollution - related CB is not as severe. The adjustment is made by applying the elasticity of WTP with resp ect to severity reported in the Krupnick and Cropper (1992 ) study. Details of this adjustment procedure are provided in the Benefits Technical Support Document (TSD) for the Nonroad Diesel rulemaking (Abt Associates, 2003 ). We use the mean of a distribution of WTP estimates as the central tendency estimate of WTP to avoid a pollution - related case of CB in this analysis. The distribution incorporates uncertainty from three sources: the WTP to avoid a case of severe CB, as described by Viscusi, Magat, and Huber; the severity level of an average pollution - related case of CB (relative to that of the case described by Viscusi, Magat, and Huber); and the elasticity of WTP with respect to severity of the i llness. Based on assumptions about the distributions of each of these three uncertain components, we derive a distribution of WTP to avoid a pollution - related case of CB by statistical uncertainty analysis techniques. The expected value (i.e., mean) of thi s5 - 47

distribution, which is about $340,000 (2006$), is taken as the central tendency estimate of WTP to avoid a PM - related case of CB. 5.4.4.3 Non -

fatal Myocardial Infarctions Valuation We were not able to identify a suitable WTP value for reductions in the risk of non -fatal heart attacks. Instead, we use a COI unit value with two components: the direct medical costs and the opportunity cost (lost earnings) associated with the illness eve nt. Because the costs associated with a myocardial infarction extend beyond the initial event itself, we consider costs incurred over several years. Using age - specific annual lost earnings estimated by Cropper and Krupnick (1990 ) and a 3% discount rate, we estimated a present discounted value in lost earnings (in 2006$) over 5 years due to a myocardial infarction of $8,774 for someone between the ages of 25 and 44, $12,932 for someone between the ages of 45 and 54, and $74,7 46 for someone between the ages of 55 and 65. The corresponding age - specific estimates of lost earnings (in 2006$) using a 7% discount rate are $7,855, $11,578, and $66,920, respectively. Cropper and Krupnick (1990 ) do not provide lost earnings estimates for populations under 25 or over 65. As such, we do not include lost earnings in the cost estimates for these age groups. We found three possible sources in the literature of estimates of the direct medical costs of myocardi al infarction:

 Wittels et al. (1990 ) estimated expected total medical costs of myocardial infarction over 5 years to be $51,211 (in 1986$) for people who were admitted to the hospital and survived hospitalization. (There does not appear to be any discounting used.) Wittels et al. was used to value coronary heart disease in the 812 Retrospective Analysis of the Clean Air Act. Using the CPI - U for medical care, the Wittels estimate is $144,111 in year 2006$. This estimated cost is ba sed on a medical cost model, which incorporated therapeutic options, projected outcomes, and prices (using “ knowledgeable cardiologists ” as consultants). The model used medical data and medical decision algorithms to estimate the probabilities of certain e vents and/or medical procedures being used. The authors note that the average length of hospitalization for acute myocardial infarction has decreased over time (from an average of 12.9 days in 1980 to an average of 11 days in 1983). Wittels et al. used 10 days as the average in their study. It is unclear how much further the length of stay for myocardial infarction may have decreased from 1983 to the present. The average length of stay for ICD code 410 (myocardial infarction) in the year- 2000 Agency for Hea lthcare Research and Quality (AHRQ) HCUP database is 5.5 days.5 - 48 However, this may include patients who died in the hospital (not included among our non -fatal myocardial infarction cases), whose length of stay was therefore substantially shorter than it woul d be if they had not died.

 Eisenstein et al. (2001 ) estimated 10 -year costs of $44,663 in 1997$, or $64,003 in 2006$ for myocardial infarction patients, using statistical prediction (regression) models to estimate inpatient costs. Only inpatient costs (physician fees and hospital costs) were included.

 Russell et al. (1998 ) estimated first -year direct medical costs of treating non -fatal myocardial infarction of $15,540 (in 1995$) and $1,051 annually thereafter. Converting to year 2006$, that would be $30,102 for a 5
-year period (without discounting) or $38,113 for a 10
-year period. In summary, the three different studies provided significantly different values (see Table 5 - 10 ). **Table 5**  **- 10**  **. Alternative**  **Direct Medical Cost of Illness Estimates for Non**  **- fatal**  **Heart Attacks**

**Study Direct Medical Costs (2006$)**  **Over an x -** **Year Period, for x =** Wittels et al. (1990 ) 144,111 a 5 Russell et al. (1998 ) 30,102 b 5 Eisenstein et al. (2001 ) 64,003 b 10 Russell et al. (1998 ) $38,113 b 10 a Wittels et al. (1990 ) did not appear to discount costs incurred in future years. b Using a 3% discount rate. Discounted values as reported in the study. As noted above, the estimates from these three studies are substantially different, and we have not adequately resolved the sources of differences in the estimates. Because the wage - rel ated opportunity cost estimates from Cropper and Krupnick (1990 ) cover a 5 -year period, we used estimates for medical costs that similarly cover a 5 -year period (i.e., estimates from Wittels et al. (1990 ) and Russell et al. (1998 ). We used a simple average of the two 5 -year estimates, or $65,902, and added it to the 5
-year opportunity cost estimate. The resulting estimates are given in Table 5 - 1 1 .5 - 49

**Table 5**  **- 11**  **. Estimated Costs Over a 5**  **- Year Period (in 2006$) of a**  **Non -** **fatal Myocardial** 

**Infarction Age Group**  **Cost, $ Opportunity**  **Medical a**  **Total** 0–24 0 84,955 84,955 25–44 10,757 b 84,955 95,713 45–54 15,855 b 84,955 100,811 55–65 91,647 b 84,955 176,602 > 65 0 84,955 84,955 a An average of the 5 -year costs estimated by Wittels et al. (1990 ) and Russell et al. (1998 ). b From Cropper and Krupnick (1990 ), using a 3% discount rate. **5.4.5 Hospital Admissions Valuation**  In the absence of estimates of societal WTP to avoid hospital visits/admissions for specific illnesses, estimates of total cost of illness (total medical costs plus the value of lost productivity) typically are use d as conservative, or lower bound, estimates. These estimates are biased downward, because they do not include the willingness -to - pay value of avoiding pain and suffering. The International Classification of Diseases (ICD - 9, WHO 1977 ) code - specific COI estimates used in this analysis consist of estimated hospital charges and the estimated opportunity cost of time spent in the hospital (based on the average length of a hospital stay for the illness). We based all estimates of hospital ch arges and length of stays on statistics provided by the Agency for Healthcare Research and Quality (AHRQ 2000 ). We estimated the opportunity cost of a day spent in the hospital as the value of the lost daily wage, regardless of whether th e hospitalized individual is in the workforce. To estimate the lost daily wage, we divided the 1990 median weekly wage by five and inflated the result to year 2006$ using the CPI - U “ all items. ” The resulting estimate is $127.93. The total cost
- of- illness e stimate for an ICD code - specific hospital stay lasting n days, then, was the mean hospital charge plus $127.93 multiplied by n. 5.4.5.1 Asthma - Related Emergency Room Visits Valuation To value asthma emergency room visits, we used a simple average of two estimates from the health economics literature. The first estimate comes from Smith et al. (1997) , who reported approximately 1.2 million asthma - related emergency room visits in 1987, at a total cost of $186.5 million (1987$). The average cost per visit that year was $155; in 2006$, that cost5 - 50

was $400.88 (using the CPI
- U for medical care to adjust to 2006$). The second estimate comes from Stanford et al. (1999), who reported the cost of an average asthma - related emergency room visi t at $335.14, based on 1996– 1997 data. A simple average of the two estimates yields a (rounded) unit value of $368. 5.4.5.2 Minor Restricted Activity Days Valuation No studies are reported to have estimated WTP to avoid a minor restricted activity day. How ever, one of EPA’ s contractors, IEc (1994) has derived an estimate of willingness to pay to avoid a minor respiratory restricted activity day, using estimates from Tolley et al. (1986) of WTP for avoiding a c ombination of coughing, throat congestion and sinusitis. The IEc estimate of WTP to avoid a minor respiratory restricted activity day is $38.37 (1990$), or about $62.04 (2006$).5 - 51

**Table 5**  **- 12**  **. Unit Values for Economic Valuation of Health Endpoints (2006$)**  **a**

**Health Endpoint Central Estimate of Value Per**  **Statistical Incidence, Income Level**  **Derivation of Distributions of Estimates 2000**  **2016** Premature Mortality (Value of a Statistical Life) $6,300,000 $8,600,000 EPA currently recommends a central VSL of $6.3m (2000$) based on a Weibull distribution fitted to 26 published VSL estimates (5 contingent valuation and 21 labor market studies). The underlying studies, the distribution parameters, and other useful informa tion are available in Appendix 5 B of EPA’ s current Guidelines for Preparing Economic Analyses (U.S. EPA, 2000 ). Chronic Bronchitis (CB) $340,000 $470,000 The WTP to avoid a case of pollution - related CB is calculated as where x is the severity of an average CB case, WTP 13 is the WTP for a severe case of CB, and $ is the parameter relating WTP to severity, based on the regression results reported in Krupnick and Cropper (1992) . The distribution of WTP for an average severity - level case of CB was generated by Monte Carlo methods, drawing from each of three distributions: (1) WTP to avoid a severe case of CB is assigned a 1/9 probability of being each of the first nine deciles of the distribution of WTP responses in Viscusi et al. (1991) ; (2) the severity of a pollution - related case of CB (relative to the case described in the Viscusi study) is assumed to have a triangular distribution, with the most likely value at severi ty level 6.5 and endpoints at 1.0 and 12.0; and (3) the constant in the elasticity of WTP with respect to severity is normally distributed with mean = 0.18 and standard deviation = 0.0669 (from Krupnick and Cropper [1992] ). This process and the rationale for choosing it is described in detail in the Costs and Benefits of the Clean Air Act, 1990 to 2010 (U.S. EPA, 1999 ). (continued)5 - 52

**Table 5**  **- 12**  **. Unit Values for Economic Valuation of Health**  **Endpoints (2006$) (continued)** 

**Health Endpoint Central Estimate of Value Per**  **Statistical Incidence, Income Level**  **Derivation of Distributions of Estimates 2000**  **2016** Non -fatal 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 $79,685 $88,975 $93,897 $167,532 $79,685 $77,769 $87,126 $91,559 $157,477 $77,769 $79,685

$88,975 $93,897 $167,532 $79,685 $77,769 $87,126 $91,559 $157,477 $77,769 No distributional information available. Age specific cost - of- illness values reflect lost earnings and direct medical costs over a 5 -year period following a non -fatal MI. Lost earnings estimates are based on Cropper and Krupnick (1990) . Direct medical costs are based on simple average of estimates from Russell et al. (1998) and Wittels et al. (1990) . Lost earnings: Cropper and Krupnick (1990) . Present discounted value of 5 years of lost earnings: age of onset: at 3% at 7% 25–44 $8,774 $7,855 45–54 $12,932 11,578 55–65 $74,746 66,920 Direct medical expenses: An average of: 1. Wittels et al. (1990) ($102,658 — no discounting) 2. Russell et al. (1998) , 5 -year period ($22,331 at 3% discount rate; $21,113 at 7% discount rate) **Hospital Admissions** Chronic Obstructive Pulmonary Disease (COPD) $16,606 $16,606 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 illne sses) reported in Agency for Healthcare Research and Quality (2000) (www.ahrq.gov). Asthma Admissions $8,900 $8,900 No distributional information available. The COI estimates (lost earnings plus direct medical costs) are based on ICD - 9 code - level information (e.g., average hospital care costs, average length of hospital stay, and weighted share of total asthma category illnesses) reported in Agency for Healthcare Research and Quality (2000) (www.a hrq.gov). (continued)5 - 53

**Table 5**  **- 12**  **. Unit Values for Economic Valuation of Health Endpoints (2006$)**  **(continued)**

**Health Endpoint Central Estimate of Value Per**  **Statistical Incidence, Income Level**  **Derivation of Distributions of Estimates 2000**  **2016** All Cardiovascular $24,668 $24,668 No distributional information available. The COI estimates (lost earnings plus direct medical costs) are based on ICD - 9 code - level information (e.g., average hospital care costs, average length of hospital stay, and weighted share of total cardiovascular category illnesses) reported in Agency for Healthcare Research and Quality (2000) (www.ahrq.gov). All respiratory (ages 65+) $24,622 $24,622 No distributions available. The COI point estimates (lost earnings plus direct medical costs) are based on ICD - 9 code level information (e.g., average hospital care costs, average length of hospital stay, and weighted share of total COPD category illnesses) reported in Agency for Healthcare Research and Quality, 2000 (www.ahrq.gov). All respiratory (ages 0–2) $10,385 $10,385 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 wei ghted share of total COPD category illnesses) reported in Agency for Healthcare Research and Quality, 2000 (www.ahrq.gov). Emergency Room Visits for Asthma $384 $384 No distributional information available. Simple average of two unit COI values: (1) $311.55, from Smith et al. (1997) and (2) $260.67, from Stanford et al. (1999) . (continued)5 - 54

**Table 5**  **- 12**  **. Unit Values for Economic Valuation of Health Endpoints (2006$)**  **(continued)**

**Health Endpoint C**  **entral Estimate of Value Per Statistical Incidence,**  **Income Level Derivation of Distributions of Estimates**  **2000 2016**  **Respiratory Ailments Not Requiring Hospitalization** Upper Respiratory Symptoms (URS) $30 $30 Combinations 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 mi d - range estimates of WTP (IEc, 1994) 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 types of URS occurs within the URS symptom complex, we assumed a uniform distribution between $9.2 and $43.1. Lower Respiratory Symptoms (LRS) $16 $19 Combinations 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, ” ea ch describing a “type ” of LRS. A dollar value was derived for each type of LRS, using mid - range estimates of WTP (IEc, 1994) to avoid each symptom in the cluster and assuming additivity of WTPs. The dollar value for LRS is the average o f 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 occurs within the LRS symptom complex, we assumed a uniform distribution between $6.9 and $24.46. Asthma Exacerbations $43 $53 Asthma exacerbations are valued at $45 per incidence, based on the mean of average WTP estimates for the four severity definitions of a “ bad asthma day, ” described in Rowe and Chestnut (1986) . This stu dy 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 $15.6 and $70.8. (continued)5 - 55

**Table 5**  **- 12**  **. Unit Values for Economic Valuation of Health Endpoints (2006$)**  **(continued)**

**Health Endpoint Central Estimate of Value Per**  **Statistical Incidence, Income Level**  **Derivation of Distributions of Estimates 2000**  **2016** Acute Bronchitis $360 $440 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 symptoms believed to be assoc iated with acute bronchitis: coughing and chest tightness. The high daily estimate was taken to be twice the value of a minor respiratory restricted - activity day, or $110. Work Loss Days (WLDs) Variable (U.S. median = $130) Variable (U.S.

median = $130) No distribution available. Point estimate is based

on county- specific median annual wages divided by 5 2 and then by 5 — to get median daily wage. U.S. Year 2000 Census, compiled by Geolytics, Inc. Minor Restricted Activity Days (MRADs) $51 $62 Median WTP estimate to avoid one MRAD from Tolley et al. (1986) . Distribution is assumed to be triangular with a minimum of $22 and a maximum of $83, with a most likely value of $52. Range is based on assumption that value should exceed WTP for a single mild symptom (the highest estimate for a single symptom — for eye irritation — is $16.00) and be less than that for a WLD. The triangular distribution acknowledges that the actual value is likely to be closer to the point estimate than either e xtreme.
a Values reported in this table are in 2006$, but we used 2007$ for this analysis. Inflating to 2007$ would increase the values approximately 2.8% for WTP estimates up to 4.4% for COI estimates. Although Ostro and Rothschild (1989) statistically linked ozone and minor restricted activity days, it is likely that most MRADs associated with ozone exposure are, in fact, minor respiratory restricted activity days. For the purpose of valuing this health endpoint, we us ed the estimate of mean WTP to avoid a minor respiratory restricted activity day. 5.4.5.3 Growth in WTP Reflecting National Income Growth Over Time Our analysis accounts for expected growth in real income over time. Economic theory argues that WTP for most goods (such as environmental protection) will increase if real incomes5 - 56

increase. There is substantial empirical evidence that the income elasticity 10 of WTP for health risk reductions is positive, although there is uncertainty about its exact value. Thus, as real income increases, the WTP for environmental improvements also increases. Although many analyses assume that the income elasticity of WTP is u nit elastic (i.e., a 10% higher real income level implies a 10% higher WTP to reduce risk changes), empirical evidence suggests that income elasticity is substantially less than one and thus relatively inelastic. As real income rises, the WTP value also ri ses but at a slower rate than real income. The effects of real income changes on WTP estimates can influence benefits estimates in two different ways: through real income growth between the year a WTP study was conducted and the year for which benefits are estimated, and through differences in income between study populations and the affected populations at a particular time. Empirical evidence of the effect of real income on WTP gathered to date is based on studies examining the former. The Environmental E conomics Advisory Committee (EEAC) of the Science Advisory Board (SAB) advised EPA to adjust WTP for increases in real income over time but not to adjust WTP to account for cross - sectional income differences “ because of the sensitivity of making such disti nctions, and because of insufficient evidence available at present” (U.S. EPA- SAB, 2000 ). A recent advisory by another committee associated with the SAB, the Advisory Council on Clean Air Compliance Analysis, has provided confli cting advice. While agreeing with “the general principle that the willingness to pay to reduce mortality risks is likely to increase with growth in real income (U.S. EPA- SAB, 20 04 b , p. 52 ) ” and that “The same increase sho uld be assumed for the WTP for serious non -fatal hea lth effects (U.S. EPA- SAB, 2004 b , p. 52 ), ” they note that “given the limitations and uncertainties in the available empirical evidence, the Council does not support the use of the proposed adjustments for aggregate income growth as part of the primary analysis ” (U.S. EPA- SAB, 2004 b , p. 53 ). Until these conflicting advisories have been reconciled, EPA will continue to adjust valuation est imates to reflect income growth using the methods described below, while providing sensitivity analyses for alternative income growth adjustment factors. Based on a review of the available income elasticity literature, we adjusted the valuation of human he alth benefits upward to account for projected growth in real U.S. income. Faced with a dearth of estimates of income elasticities derived from time - series studies, we applied estimates derived from cross - sectional studies in our analysis. Details of the pr ocedure can be

10 Income elasticity is a common economic measure equal to the percentage cha nge in WTP for a 1% change in income.5 - 57

found in Kleckner and Neumann (1999 ). An abbreviated description of the procedure we used to account for WTP for real income growth between 1990 and 2016 is presented below. Reported income elasticities suggest that the severity of a health effect is a primary determinant of the strength of the relationship between changes in real income and WTP. As such, we use different elasticity estimates to adjust the WTP for minor health effects, severe and chronic health effects, and premature mortality. Note that because of the variety of empirical sources used in deriving the income elasticities, there may appear to be inconsistencies in the magnitudes of the income elasticities relative to the severity of the effects ( a priori one might expect that more severe outcomes would show less income elasticity of WTP). We have not imposed any additional restrictions on the empirical estimates of income elasticity. One explanation for the seeming inconsistency is the diffe rence in timing of conditions. WTP for minor illnesses is often expressed as a short term payment to avoid a single episode. WTP for major illnesses and mortality risk reductions are based on longer term measures of payment (such as wages or annual income) . Economic theory suggests that relationships become more elastic as the length of time grows, reflecting the ability to adjust spending over a longer time period. Based on this theory, it would be expected that WTP for reducing long term risks would be mo re elastic than WTP for reducing short term risks. We also expect that the WTP for improved visibility in Class I areas would increase with growth in real income. The relative magnitude of the income elasticity of WTP for visibility compared with those for health effects suggests that visibility is not as much of a necessity as health, thus, WTP is more elastic with respect to income. The elasticity values used to adjust estimates of benefits in 2016 are presented in Table 5 - 1 3 . **Table 5**  **- 13**  **. Elasticity Value**  **s Used to Account for Projected Real Income Growth a** 

**Benefit Category Central Elasticity Estimate**  Minor Health Effect 0.14 Severe and Chronic Health Effects 0.45 Premature Mortality 0.40 Visibility 0.90 a Derivation of estimates can be found in Kleckner and Neumann (1999 ) and Chestnut (1997 ). COI estimates are assigned an adjustment factor of 1.0. In addition to elasticity estimates, projections of real gross domestic product (GDP) and populations from 1990 to 2020 are needed to adjust benefits to reflect real per capita income growth. For consistency with the emissions and benefits modeling, we used national population5 - 58

estimates for the years 1990 to 1999 based on U.S. Census Bureau est imates (Hollman, Mulder, and Kallan, 2000 ). These population estimates are based on application of a cohort - component model applied to 1990 U.S. Census data projections (U.S. Bureau of Census, 2000 ). For the years between 2000 and 2016, we applied growth rates based on the U.S. Census Bureau projections to the U.S. Census estimate of national population in 2000. We used projections of real GDP provided in Kleckner and Neumann (1999 ) for the years 1990 to 2010. 11 We used projections of real GDP (in chained 1996 dollars) provided by Standard and Poor’ s (2000 ) for the years 2010 to 2016. 12 Using the method outlined in Kleckner and Neumann (1999 ) and the population and income data described above, we calculated WTP adjustment factors for each of the elasticity estimates listed in Table 5 - 1 4 . Benefits for each of the categories (minor health effects, severe and chronic health effects, premature mortality, and visibility) are adjusted by multiplying the unadjusted benefits by the appropriate adjustment factor. Note that, for premature mortality, we applied the income adjustment factor to the present discounted value of the stream of avoided mortalities occurring over the lag period. Also note that because of a lack of data on the dependence of COI and income, and a lack of data on projected growth in average wages, no adjustments are made to benefits based on the COI approach or to work loss days and worker productivity. This assumption leads us to underpredict benefits in future years because it is likely that increases in real U.S. income would also result in increased COI (due, for example, to incr eases in wages paid to medical workers) and increased cost of work loss days and lost worker productivity (reflecting that if worker incomes are higher, the losses resulting from reduced worker production would also be higher).

11 U.S. Bureau of Economic Analysis, Table 2A (1992$) (available at http://www.bea.doc.gov/bea/dn/0897nip2/ tab2a.htm.) and U.S. Bureau of Economic Analysis, Economics and Budget Outlook. Note that projections for 2007 to 2010 are based on average GDP growth rates between 1999 and 2007. 12 In previous analyses, we used the Standard and Poor’ s projections of GDP directly. This led to an apparent discontinuity in the adjustment factors between 2010 and 2011. We refined the method by applying the relative growth rates for GDP derived from the Standard and Poor’ s projections to the 2010 projected GDP based on the Bureau of Economic Analysis projections.5 - 59

**Table 5**  **- 14**  **. Adjustment Facto**  **rs Used to Account for Projected Real Income Growth a**  **Benefit Category 2016**  Minor Health Effect 1.06 Severe and Chronic Health Effects 1.19 Premature Mortality 1.16 Visibility 1.41 a Based on elasticity values reported in Table 5 - 1 3, U.S. Census population projections, and projections of real GDP per capita. **5.5 Unquantified Health and Welfare Benefits**  This analysis is limited by the available data and resources. As such, we are not able to quantify several welfare benefit categories, as shown in Tables 5 - 3 and 5 -4. This section provides an overview of what is meant by ecosystem services as well as a description of visibility benefits, which are typically assessed and monetized in relevant RIAs but that were not quantified in this benefits analysis . The RIA for the final Cross - State Air Pollution Rule (U.S. EPA, 2011 b ) provides more information on additional major health and welfare benefit categories associated with reducing NO 2 and SO 2 emissions including: health and ecosystem benefits of reducing nitrogen and sulfur emissions and deposition; vegetation benefits from reducing ozone; mercury benefits associated with reducing mercury emissions; and the role of sulfate deposition in mercury methylation. While we are unable to quantify these benefits, previous relevant EPA assessments show that these benefits could be substantial (U.S. EPA, 2008 a ; U.S. EPA, 2009 a ; U.S. EPA, 2007 ; U.S. EPA, 1999 , U.S. EPA, 2011 b ). The omission of these endpoints from the monetized results should not imply that the impacts are small or unimportant. **5.5.1 Visibility Valuation**  Reductions in NO 2 an d SO 2 emissions along with the secondary formation of PM 2.5 would improve the level of visibility throughout the United States because these suspended particles and gases degrade visibility by scattering and absorbing light (U.S. EPA, 2009 a ). Visibility has direct significance to people ’ s enjoyment of daily activities and their overall sen se of wellbeing (U.S. EPA, 2009 a ). Individuals value visibility both in the places they live and work, in the places they travel to for recreational purposes, and at sites of unique public value, such as the Great Smokey Mountains National Park. This section discusses the measurement of the economic benefits of improved visibility. As t here is no analogous approach for estima ting visibility benefits using the BPT approach, visibility benefits are calculated for the modeled interim policy scenario only and are not included in the co - benefits estimate of the final policy . However,5 - 60

since the magnitude of SO 2 emission reductions d id not significantly change in the visibility study areas between the interim and final emissions scenarios, we expect the visibility benefit for the final policy scenario would be similar to that calculated for the interim policy scenario ($1.1 billion in total for the U.S., using 2007$; see Appendix 5 C). Visual air quality (VAQ) is commonly measured as either light extinction, which is defined as the loss of light per unit of distance in terms of inverse megameters (Mm - 1 ) or the deciview (dv) metric (Pit chford and Malm, 1993 ), which is a logarithmic function of extinction. Extinction and deciviews are physical measures of the amount of visibility impairment (e.g., the amount of “ haze ” ), with both extinction and deciview in creasing as the amount of haze increases. Pitchford and Malm characterize a change of one deciview as “a small but perceptible scenic change under many circumstances. ” Light extinction is the optical characteristic of the atmosphere that occurs when light is either scattered or absorbed, which converts the light to heat. Particulate matter and gases can both scatter and absorb light. Fine particles with significant light - extinction efficiencies include sulfates, nitrates, organic carbon, elemental carbon, a nd soil (Sisler, 1996 ). The extent to which any amount of light extinction affects a person ’ s ability to view a scene depends on both scene and light characteristics. For example, the appearance of a nearby object (i.e. a building) is generally less sensitive to a change in light extinction than the appearance of a similar object at a greater distance. See Figure 5 - 3 for an illustration of the important factors affecting visibility. In conjunction with the U.S. National Park Service, th e U.S. Forest Service, other Federal land managers, and State organizations in the U.S., the U.S. EPA has supported visibility monitoring in national parks and wilderness areas since 1988. The monitoring network known as IMPROVE (Interagency Monitoring of Protected Visual Environments) now includes 150 sites that represent almost all of the Class I areas across the country (see Figure 5 - 4) (U.S. EPA, 2009 a ).Annual average visibility conditions (reflecting light extinction due to bot h anthropogenic and non - anthropogenic sources) vary regionally across the U.S. (U.S. EPA, 2009 a ). The rural East generally has higher levels of impairment than remote sites in the West, with the exception of urban - influenced sites s uch as San Gorgonio Wilderness (CA) and Point Reyes National Seashore (CA), which have annual average levels comparable to certain sites in the Northeast (U.S. EPA, 2004 ). Higher visibility impairment levels in the East are due to ge nerally higher concentrations of fine particles, particularly sulfates, and higher average relative humidity levels. While visibility trends have improved in most Class I areas, the recent data show that these areas continue to suffer from visibility impai rment. In eastern parks,5 - 61

average visual range has decreased from 90 miles to 15 - 25 miles, and in the West, visual range has decreased from 140 miles to 35 - 90 miles (U.S. EPA, 2004; U.S. EPA, 1 999 ).

**Figure 5**  **- 3**  **. Important Factors Involved in Seeing a Scenic Vista (Malm, 1999**  **)**5 - 62

**Figure 5**  **- 4**  **. Mandatory Class I Areas in the U.S.**  EPA distinguishes benefits from two categories of visibility changes: residential visibility and recreational visibility. In both cases economic benefits are believed to consist of use values and nonuse values. Use values include the aesthetic benefits of better visibility, improved road and air safety, and enhanced recreation in activities like hunting and birdwa tching. Nonuse values are based on people ’ s beliefs that the environment ought to exist free of human - induced haze. Nonuse values may be more important for recreational areas, particularly national parks and monuments. Residential visibility benefits are those that occur from visibility changes in urban, suburban, and rural areas. In previous assessments, EPA used a study on residential visibility valuation conducted in 1990 (McClelland et al., 1993 ). Subsequently, EPA designa ted the McClelland et al. study as significantly less reliable for regulatory benefit - cost analysis consistent with SAB advice (U.S. EPA- SAB, 1999 ). Although a wide range of published, peerreview literature supp orts a non - zero value for residential visibility (Brookshire et al., 1982 ; Rae, 1983 ; Tolley et al., 1986 ; Chestnut and Rowe, 1990c ; McC le lland e t al., 1993 ; Loehman et al., 1994 ), the residential visibility benefits have not been calculated in this analysis.5 - 63

For recreational visibility, only one existing study provides defensible monetary estimates of the value of visibility changes in a 1988 survey on recreational visibility value (Chestnut and Rowe, 1990a; 1990b ). Although there are a number of other studies in the literature, they were conducted in the early 1980s and did not use methods that are considered defensible by current standards. The Chestnut and Rowe study uses the CV method. There has been a great deal of controversy and significant development of both theoretical and empirical kno wledge about how to conduct CV surveys in the past decade. In EPA’ s judgment, the Chestnut and Rowe study contains many of the elements of a valid CV study and is sufficiently reliable to serve as the basis for monetary estimates of the benefits of visibil ity changes in recreational areas. 13 This study serves as an essential input to our estimates of the benefits of recreational visibility improvements in the primary benefits estimates. F or the purposes of th e analysis of the visibility benefits of the model ed interim policy (Appendix 5C) , recreational visibility improvements are defined as those that occur specifically in federal Class I areas. 14 A key distinction between recreational and residential benefits is that only those people living in residential ar eas are assumed to receive benefits from residential visibility, while all households in the United States are assumed to derive some benefit from improvements in Class I areas. Values are assumed to be higher if the Class I area is located close to their home. 15 The Chestnut and Rowe study measured the demand for visibility in Class I areas managed by the National Park Service (NPS) in three broad regions of the country: California, the Southwest, and the Southeast. Respondents in five states were asked abo ut their WTP to protect national parks or NPS - managed wilderness areas within a particular region. The survey used photographs reflecting different visibility levels in the specified recreational areas. The visibility levels in these photographs were later converted to deciviews for the current analysis. The survey data collected were used to estimate a WTP equation for improved visibility. In addition to the visibility change variable, the estimating equation also included household income as an explanatory variable.

13 I n SAB advisory letter indicates that “ many members of the Council beli eve that the Chestnut and Rowe study is the best available ” (EPA- SAB - COUNCIL-ADV- 00 - 002, 1999 , p. 13). However, the committee did not formally approve use of these estimates because of concerns about the peer- reviewed status of the study. EPA believes the study has received adequate review and has been cited in numerous peer - reviewed publications (Chestnut and Dennis, 1997 ). 14 T he Clean Air Act designates 156 national parks and wilderness areas as Class I areas for visibility protection. 15 For details of the visibility estimates discussed in this chapter, please refer to the Benefits TSD for the Nonroad Diesel rulemaking (Abt Associates, 2003 ).5 - 64

The Chestnut and Rowe study did not measure values for visibility improvement in Class I areas outside the three regions. Their study covered 86 of the 156 Class I areas in the United States. We can infer the value of visibility changes in the other Class I areas by transferring values of visibility changes at Class I areas in the study regions. A complete description of the benefits transfer method used to infer values for visibility changes in Class I areas outside the study regions is provide d in the Benefits TSD for the Nonroad Diesel rulemaking (Abt Associates, 2003 ). The Chestnut and Rowe study (Chestnut and Rowe, 1990a; 1990b ), although representing the best available estimate s, has a number of limitations. These include the following:

 The age of the study (late 1980s) will increase the uncertainty about the correspondence of the estimated values to those that might be provided by current or future populations.

 The survey focus ed only on populations in five states, so the application of the estimated values to populations outside those states requires that preferences of populations in the five surveyed states be similar to those of non - surveyed states.

 There is an inherent diff iculty in separating values expressed for visibility improvements from an overall value for improved air quality. The Chestnut and Rowe study attempted to control for this by informing respondents that “other households are being asked about visibility, hu man health, and vegetation protections in urban areas and at national parks in other regions. ” However, most of the respondents did not feel that they were able to segregate visibility at national parks entirely from residential visibility and health effects.

 It is not clear exactly what visibility improvements the respondents to the Chestnut and Rowe survey were valuing. The WTP question asked about changes in average visibility, but the survey respondents were shown photographs of only summertime conditio ns, when visibility is generally at its worst. It is possible that the respondents believed those visibility conditions held year - round, in which case they would have been valuing much larger overall improvements in visibility than what otherwise would be the case. For the purpose of the benefits analysis for this rule, EPA assumed that respondents provided values for changes in annual average visibility. Because most policies will result in a shift in the distribution of visibility (usually5 - 65 affecting the worst days more than the best days), the annual average may not be the most relevant metric for policy analysis.

 The survey did not include reminders of possible substitutes (e.g., visibility at other parks) or budget constraints. These reminders are consi dered to be best practice for stated preference surveys.

 The Chestnut and Rowe survey focused on visibility improvements in and around national parks and wilderness areas. The survey also focused on visibility improvements of national parks in the southwes t United States. Given that national parks and wilderness areas exhibit unique characteristics, it is not clear whether the WTP estimate obtained from Chestnut and Rowe can be transferred to other national parks and wilderness areas, without introducing ad ditional uncertainty. In general, the survey design and implementation reflect the period in which the survey was conducted. Since that time, many improvements to the stated preference methodology have been developed. As future survey efforts are completed , EPA will incorporate values for visibility improvements reflecting the improved survey designs. The estimated relationship from the Chestnut and Rowe study is only directly applicable to the populations represented by survey respondents. EPA used benefit s transfer methodology to extrapolate these results to the population affected by the reductions in precursor emissions associated with this rule. A general WTP equation for improved visibility (measured in deciviews) was developed as a function of the bas eline level of visibility, the magnitude of the visibility improvement, and household income. The behavioral parameters of this equation were taken from analysis of the Chestnut and Rowe data. These parameters were used to calibrate WTP for the visibility changes resulting from this rule. The method for developing calibrated WTP functions is based on the approach developed by Smith et al. (2002 ). Available evidence indicates that households are willing to pay more for a given visibi lity improvement as their income increases (Chestnut, 1997 ). The benefits estimates here incorporate Chestnut’ s estimate that a 1% increase in income is associated with a 0.9% increase in WTP for a given change in visibility. A more detailed explanation of the visibility benefits methodology is provided in Appendix I of the PM NAAQS RIA (U.S. EPA, 2006 a ). One major source of uncertainty for the visibility benefits estimate is the benefits transfer process used. Ju dgments used to choose the functional form and key parameters of the estimating equation for WTP for the affected population could have significant effects on5 - 66

the size of the estimates. Assumptions about how individuals respond to changes in visibility tha t are either very small or outside the range covered in the Chestnut and Rowe study could also affect the results. In addition, our estimate of visibility benefits of the modeled interim policy in Appendix 5C is incomplete. For example, we anticipate impro vement in visibility in residential areas for which we are currently unable to monetize benefits, such as the Northeastern and Central regions of the U.S. The value of visibility benefits in areas where we were unable to monetize benefits could also be sub stantial. EPA requests public comment on the approach taken here to quantify the monetary value of changes in visibility in Class I areas. **5.5.2 Ecosystem Services**  Ecosystem services can be generally defined as the benefits that individuals and organizatio ns obtain from ecosystems. EPA has defined ecological goods and services as the “ outputs of ecological functions or processes that directly or indirectly contribute to social welfare or have the potential to do so in the future. Some outputs may be bought and sold, but most are not marketed ” (U.S. EPA, 2006b ). Figure 5 - 5 provides the Millennium Ecosystem Assessment’ s schematic demonstrating the connections between the categories of ecosystem services and human well - being. The interre latedness of these categories means that any one ecosystem may provide multiple services. Changes in these services can affect human well being by affecting security, health, social relationships, and access to basic material goods (MEA, 2005 ). In the Millennium Ecosystem Assessment (MEA, 2005), ecosystem services are classified into four main categories: 1. Provisioning: Products obtained from ecosystems, such as the production of food and water 2. Regulating: Benefits obtained from the r egulation of ecosystem processes, such as the control of climate and disease 3. Cultural: Nonmaterial benefits that people obtain from ecosystems through spiritual enrichment, cognitive development, reflection, recreation, and aesthetic experiences 4. Supp orting: Services necessary for the production of all other ecosystem services, such as nutrient cycles and crop pollination5 - 67

**Figure 5**  **- 5**  **. Linkages**  **Between Categories of Ecosystem Services and Components of Human**  **Well -**  **Being from Millennium Ecosystem Assessm ent (MEA, 2005)**  The monetization of ecosystem services generally involves estimating the value of ecological goods and services based on what people are willing to pay (WTP) to increase ecological services or by what people are willing to accept (WTA) in compensation for reductions in them (U.S. EPA, 2006b ). There are three primary approaches for estimating the monetary value of ecosystem services: market- based approaches, revealed preference methods, and stated preference methods ( U.S. EPA, 2006b). Because economic valuation of ecosystem services can be difficult, nonmonetary valuation using biophysical measurements and concepts also can be used. An example of a nonmonetary valuation method is the use of relative -value indicators (e .g., a flow chart indicating uses of a water body, such as boatable, fishable, swimmable, etc.). It is necessary to recognize that in the analysis of the environmental responses associated with any particular policy or environmental management action, only a subset of the ecosystem services likely to be affected are readily identified. Of those ecosystem services that are identified, only a subset of the changes can be quantified. Within those services whose changes can be quantified, only a few will likely be monetized, and many will remain nonmonetized. The stepwise concept leading up to the valuation of ecosystems services is graphically depicted in Figure 5 - 6.5 - 68

**Figure 5**  **- 6**  **. Schematic of the Benefits Assessment Process (U.S. EPA, 2006b)**  **5.5.3 Ecosystem**  **Benefits of Reduced Nitrogen and Sulfur Deposition** 5.5.3.1 Science of Deposition Nitrogen and sulfur emissions occur over large regions of North America. Once these pollutants are lofted to the middle and upper troposphere, they typically have a much longe r lifetime and, with the generally stronger winds at these altitudes, can be transported long distances from their source regions. The length scale of this transport is highly variable owing to differing chemical and meteorological conditions encountered a long the transport path (U.S. EPA, 2008 b ). Sulfur is primarily emitted as SO 2, and nitrogen can be emitted as NO, NO 2, or NH 3 . Secondary particles are formed from NO x and SO X gaseous emissions and associated chemical reactions in th e atmosphere. Deposition can occur in either a wet (i.e., rain, snow, sleet, hail, clouds, or fog) or dry form (i.e., gases or particles). Together these emissions are deposited onto terrestrial and aquatic ecosystems across the U.S., contributing to the p roblems of acidification, nutrient enrichment, and methylmercury production as represented in Figure 5 - 7. Although there is some evidence that nitrogen deposition may have positive effects on agricultural and forest output through passive fertilization, it is likely that the overall value is very small relative to other health and welfare effects.5 - 69

**Figure 5**  **- 7**  **. Schematics of Ecological Effects of Nitrogen and Sulfur Deposition**  The lifetimes of particles vary with particle size. Accumulation - mode particles such as sulfates are kept in suspension by normal air motion s and have a lower deposition velocity than coarse - mode particles; they can be transported thousands of kilometers and remain in the atmosphere for a number of days. They are removed from the atmosphere primarily by cloud processes. Particulates affect aci d deposition by serving as cloud condensation nuclei and contribute directly to the acidification of rain. In addition, the gas - phase species that lead to the dry deposition of acidity are also precursors of particles. Therefore, reductions in NO 2 and SO 25 - 70

emissions will decrease both acid deposition and PM concentrations, but not necessarily in a linear fashion. (U.S. EPA, 2008 b ). Sulfuric acid is also deposited on surfaces by dry deposition and can contribute to environmental effects (U.S. EPA, 2008 b ). 5.5 .3.2 Ecological Effects of Acidification Deposition of nitrogen and sulfur can cause acidification, which alters biogeochemistry and affects animal and plant life in terrestrial and aquatic ecosystems across the U.S. Soil acidification is a natural process , but is often accelerated by acidifying deposition, which can decrease concentrations of exchangeable base cations in soils (U.S. EPA, 2008 b ). Major terrestrial effects include a decline in sensitive tree species, such as red spruce ( Picea rubens ) and sug ar maple (Acer saccharum ) (U.S. EPA, 2008 b ). Biological effects of acidification in terrestrial ecosystems are generally linked to aluminum toxicity and decreased ability of plant roots to take up base cations (U.S. EPA, 2008 b ). Decreases in the acid neutr alizing capacity and increases in inorganic aluminum concentration contribute to declines in zooplankton, macro invertebrates, and fish species richness in aquatic ecosystems (U.S. EPA, 2008 b ). Geology (particularly surficial geology) is the principal fact or governing the sensitivity of terrestrial and aquatic ecosystems to acidification from nitrogen and sulfur deposition (U.S. EPA, 2008 b ). Geologic formations having low base cation supply generally underlie the watersheds of acid - sensitive lakes and streams. Other factors contribute to the sensitivity of soils and surface waters to acidifying deposition, including topography, soil chemistry, land use, and hydrologic flow path (U.S. EPA, 2008 b ). 5.5.3.3 Aquatic Ecosystems Aquatic effects of acidificati on have been well studied in the U.S. and elsewhere at various trophic levels. These studies indicate that aquatic biota have been affected by acidification at virtually all levels of the food web in acid sensitive aquatic ecosystems. Effects have been most clearly documented for fish, aquatic insects, other invertebrates, and algae. Biological effects are primarily attributable to a combination of low pH and high inorganic aluminum concentrations. Such conditions occur more frequently during rainfall and s nowmelt that cause high flows of water and less commonly during low -flow conditions, except where chronic acidity conditions are severe. Biological effects of episodes include reduced fish condition factor 16, changes in species composition and declines in a quatic species richness

16 C ondition factor is an index that describes the relationship between fish weight and length, and is one measure of sublethal acidification stress that has been used to quantify effects of acidification on an individual fish (U.S.EPA, 2008 b ).5 - 71

across multiple taxa, ecosystems and regions. These conditions may also result in direct fish mortality (Van Sickle et al., 1996 ). Biological effects in aquatic ecosystems can be divided into two majo r categories: effects on health, vigor, and reproductive success; and effects on biodiversity. Surface water with ANC values greater than 50 μeq/L generally provides moderate protection for most fish (i.e., brook trout, others) and other aquatic organisms (U.S. EPA, 2009c ). Table 5 - 1 5 provides a summary of the biological effects experienced at various ANC levels. **Table 5**  **- 15**  **. Aquatic Status Categories**  **Category Label ANC Levels Expected Ecological Effects**  **Acute Concern**  <0 micro

equivalent per Liter (μeq/L) Near complete loss of fish populations is expected. Planktonic communities

have extremely low diversity and are dominated by acidophilic forms. The number of individuals in plankton species that are present is greatly reduced. **Severe Concern**  0–20 μeq/L Highly sensitive to episodic acidification. During episodes of high acidifying deposition, brook trout populations may experience lethal effects. Diversity and distribution of zooplankton communities decline sharply. **Elevated Concern**  20–50 μeq/L Fish species richness is greatly reduced (i.e., more than half of expected species can be missing). On average, brook trout populations experience sublethal effects, including loss of health, reproduction capacity, and fitness. Diversity and distribution of zooplankton communities decline. **Moderate Concern**  50– 100 μeq/L Fish species richness begins to decline (i.e., sensitive species are lost from lakes). Brook trout populations are sensitive and variable, with possible sublethal effects. Diversity and distribution of zooplankton communities also begin to decline as species that are sensitive to acidifying deposition are affected. **Low Concern**  >100 μeq/L Fish species richness may be unaffected. Reproducing brook trout populations are expected where habitat is suitable. Zooplankton communities are unaffected and exhibit expected diversity and distribution. A number of national and regional assessments have been conducted to estimate the distribution and extent of surface water acidity in the U.S. (U.S. EPA, 2008 b ). As a result, several regions of the U.S. have been identified as containing a large number of lakes and streams that are seriously impacted by acidification. Figure 5 - 8 illustrates those areas of the U.S. where aquatic ecosystems are at risk from acidification.5 - 72

**Figure 5**  **- 8**  **. Areas Potentially Sensitive to Aquatic Acidification (U.S. EPA, 2008**  **b )**  Because acidification primarily affects the diversity and abundance of aquatic biota, it also affects the ecosystem services that are derived from the fish and other aquatic life found in these surface waters. While acidification is unlikely to have seriou s negative effects on, for example, water supplies, it can limit the productivity of surface waters as a source of food (i.e., fish). In the northeastern United States, the surface waters affected by acidification are not a major source of commercially rai sed or caught fish; however, they are a source of food for some recreational and subsistence fishermen and for other consumers. For example, there is evidence that certain population subgroups in the northeastern United States, such as the Hmong and Chippe wa ethnic groups, have particularly high rates of self- caught fish consumption (Hutchison and Kraft, 1994 ; Peterson et al., 1994 ). However, it is not known if and how their consumption patterns are affected by the reductions in available fish populations caused by surface water acidification. Inland surface waters support several cultural services, including aesthetic and educational services and recreational fishing. Recreational fishing in lakes an d streams is among5 - 73

the most popular outdoor recreational activities in the northeastern United States. Based on studies conducted in the northeastern United States, Kaval and Loomis (2003) estimated average consumer surp lus values per day of $36 for recreational fishing (in 2007 dollars); therefore, the implied total annual value of freshwater fishing in the northeastern United States was $5.1 billion in 2006. 17 For recreation days, consumer surplus value is most commonly measured using recreation demand, travel cost models. Another estimate of the overarching ecological benefits associated with reducing lake acidification levels in Adirondacks National Park can be derived from the contingent valuation (CV) survey (Banzhaf et al., 2006 ), which elicited values for specific improvements in acidification - related water quality and ecological conditions in Adirondack lakes. The survey described a base version with minor improvements said to result fro m the program, and a scope version with large improvements due to the program and a gradually worsening status quo. After adapting and transferring the results of this study and converting the 10 -year annual payments to permanent annual payments using disc ount rates of 3% and 5%, the WTP estimates ranged from $48 to $107 per year per household (in 2004 dollars) for the base version and $54 to $154 for the scope version. Using these estimates, the aggregate annual benefits of eliminating all anthropogenic so urces of NO x and SO x emissions were estimated to range from $291 million to $829 million (U.S. EPA, 2009 b ). 18 In addition, inland surface waters provide a number of regulating services associated with hydrological and climate regulat ion by providing environments that sustain aquatic food webs. These services are disrupted by the toxic effects of acidification on fish and other aquatic life. Although it is difficult to quantify these services and how they are affected by acidification, some of these services may be captured through measures of provisioning and cultural services. 5.5.3.4 Terrestrial Ecosystems Acidifying deposition has altered major biogeochemical processes in the U.S. by increasing the nitrogen and sulfur content of soi ls, accelerating nitrate and sulfate leaching from soil to drainage waters, depleting base cations (especially calcium and magnesium) from soils, and increasing the mobility of aluminum. Inorganic aluminum is toxic to some tree roots. Plants affected by hi gh levels of aluminum from the soil often have reduced root growth, which

17 These estimat es reflect the total value of the service, not the marginal change in the value of the service as a result of the emission reductions achieved by this rule. 18 These estimates reflect the total value of the service, not the marginal change in the value of the service as a result of the emission reductions achieved by this rule.5 - 74

restricts the ability of the plant to take up water and nutrients, espe cially calcium (U. S. EPA, 2008 b ). These direct effects can, in turn, influence the re sponse of these plants to climatic stresses such as droughts and cold temperatures. They can also influence the sensitivity of plants to other stresses, including insect pests and disease (Joslin et al., 1992 ) leading to increas ed mortality of canopy trees. In the U.S., terrestrial effects of acidification are best described for forested ecosystems (especially red spruce and sugar maple ecosystems) with additional information on other plant communities, including sh rubs and liche n (U.S. EPA, 2008 b ).Certain ecosystems in the continental U.S. are potentially sensitive to terrestrial acidification, which is the greatest concern regarding nitrogen and sulfur deposition U.S. EPA (2008 b ). Figure 5 - 9 depicts the areas across the U.S. th at are potentially sensitive to terrestrial acidification.

**Figure 5**  **- 9**  **. Areas Potentially Sensitive to Terrestrial Acidification (U.S. EPA, 2008**  **b )**  Both coniferous and deciduous forests throughout the eastern U.S. are experiencing gradual losses of base cation nutrients from the soil due to accelerated leaching from acidifying deposition. This change in nutrient availability may reduce the quality of forest nutrition over the long term. Evidence suggests that red spruce and sugar maple in some areas in th e eastern U.S. have experienced declining health because of this deposition. For red spruce, ( Picea5 - 75

rubens ) dieback or decline has been observed across high elevation landscapes of the northeastern U.S., and to a lesser extent, the southeastern U.S., and acidifying deposition has been implicated as a causal factor (DeHayes et al., 1999 ). Figure 5 - 10 shows the distribution of red spruce (brown) and sugar maple (green) in the eastern U.S.

**Figure 5**  **- 10**  **. Distribution of Red Spruce**  **( Pink**  **) and Sugar Maple ( Green**  **) in the Eastern U.S. (U.S. EPA, 2008**  **b )**  Terrestrial acidification affects several important ecological endpoints, including declines in habitat for threatened and endangered species (cultural), declines in forest aesthetics ( cultural), declines in forest productivity (provisioning), and increases in forest soil erosion and reductions in water retention (cultural and regulating). Forests in the northeastern United States provide several important and valuable provisioning servi ces in the form of tree products. Sugar maples are a particularly important commercial hardwood tree species, providing timber and maple syrup. In the United States, sugar maple saw timber was nearly 900 million board feet in 2006 (USFS, 2006 ), and annual production of maple syrup was nearly 1.4 million gallons, accounting for approximately 19% of worldwide production. The total annual value of U.S. production in these years was approximately $160 million (NASS, 2008 ). Re d spruce is also used in a variety of products including lumber, pulpwood, poles, plywood, and musical instruments. The total removal of red5 - 76

spruce saw timber from timberland in the United States was over 300 million board feet in 2006 (USFS, 2006). Forest s in the northeastern United States are also an important source of cultural ecosystem services — nonuse (i.e., existence value for threatened and endangered species), recreational, and aesthetic services. Red spruce forests are home to two federally listed species and one delisted species:

 Spruce -fir moss spider ( Microhexura montivaga ) — endangered

 Rock gnome lichen ( Gymnoderma lineare ) — endangered

 Virginia northern flying squirrel ( Glaucomys sabrinus fuscus ) — delisted, but important Forestlands support a wide variety of outdoor recreational activities, including fishing, hiking, camping, off- road driving, hunting, and wildlife viewing. Regional statistics on recreational activities that are specifically forest based are not available; however, more general data on outdoor recreation provide some insights into the overall level of recreational services provided by forests. More than 30% of the U.S. adult population visited a wilderness or primitive area during the previous year and engaged in day hiking (Cordell et al., 2008 ). From 1999 to 2004, 16% of adults in the northeastern United States participated in off- road vehicle recreation, for an average of 27 days per year (Cordell et al., 2005 ). The average cons umer surplus value per day of off- road driving in the United States was $25 (in 2007 dollars), and the implied total annual value of off- road driving recreation in the northeastern United States was more than $9 billion (Kaval and Loomis, 2003 ). More than 5% of adults in the northeastern United States participated in nearly 84 million hunting days (U.S. FWS and U.S. Census Bureau, 2007 ). Ten percent of adults in northeastern states participated in wildlife viewing away from home on 122 million days in 2006. For these recreational activities in the northeastern United States, Kaval and Loomis (2003) estimated average consumer surplus values per day of $52 for hunting and $34 for wildlife viewing (in 2007 dollars). The implied total annual value of hunting and wildlife viewing in the northeastern United States was, therefore, $4.4 billion and $4.2 billion, respectively, in 2006. As previously mentioned, it is difficult to estimate the portion of these recreational services that are specifically attributable to forests and to the health of specific tree species. However, one recreational activity that is directly dependent on forest conditions is fall colo r viewing. Sugar maple trees, in particular, are known for their bright colors and are, therefore,5 - 77

an essential aesthetic component of most fall color landscapes. A survey of residents in the Great Lakes area found that roughly 30% of residents reported at least one trip in the previous year involving fall color viewing (Spencer and Holecek, 2007) . In a separate study conducted in Vermont, Brown (2002) reported that more than 22% of households visiting Ver mont in 2001 made the trip primarily for viewing fall colors. Two studies estimated values for protecting high - elevation spruce forests in the southern Appalachian Mountains. Kramer et al. (2003) conducted a contingent valuatio n study estimating households ’ WTP for programs to protect remaining high - elevation spruce forests from damages associated with air pollution and insect infestation. Median household WTP was estimated to be roughly $29 (in 2007 dollars) for a smaller progr am, and $44 for the more extensive program. Jenkins et al. (2002) conducted a very similar study in seven Southern Appalachian states on a potential program to maintain forest conditions at status quo levels. The overall mean annual WTP for the forest protection programs was $208 (in 2007 dollars) . Multiplying the average WTP estimate from these studies by the total number of households in the seven - state Appalachian region results in an aggregate annual range of $470 million t o $3.4 billion for avoiding a significant decline in the health of high - elevation spruce forests in the Southern Appalachian region. 19 Forests in the northeastern United States also support and provide a wide variety of valuable regulating services, includi ng soil stabilization and erosion control, water regulation, and climate regulation . The total value of these ecosystem services is very difficult to quantify in a meaningful way, as is the reduction in the value of these services assoc iated with total nitrogen and sulfur deposition. As terrestrial acidification contributes to root damages, reduced biomass growth, and tree mortality, all of these services are likely to be affected; however, the magnitude of these impacts is currently very uncertain. **5.5.4 Ecological Effects Associated with Gaseous Sulfur Dioxide**  Uptake of gaseous sulfur dioxide in a plant canopy is a complex process involving adsorption to surfaces (leaves, stems, and soil) and absorption into leaves. SO 2 penetrates into leaves through to the stomata, although there is evidence for limited pathways via the cuticle. Pollutants must be transported from the bulk air to the leaf boundary layer in order to get to the stomata. When the stomata are closed, as occurs under dark or drought conditions, resistance to gas uptake is very high and the plant has a very low degree of susceptibility to

19 These estimates reflect the marginal value of the service for the hypothetical program described in the survey, not the marginal change in the value of the service as a result of the emission reductions achieved by this rule.5 - 78

injury. In contrast, mosses and lichens do not have a protective cuticle barrier to gaseous pollutants or stomates and are generally more se nsitive to gaseous sulfur and nitrogen than vascular plants (U.S. EPA, 2008 b ). Acute foliar injury usually happens within hours of exposure, involves a rapid absorption of a toxic dose, and involves collapse or necrosis of plant tissues. Another type of vi sible injury is termed chronic injury and is usually a result of variable SO 2 exposures over the growing season. Besides foliar injury, chronic exposure to low SO 2 concentrations can result in reduced photosynthesis, growth, and yield of plants (U.S. EPA, 2008 b ). These effects are cumulative over the season and are often not associated with visible foliar injury. As with foliar injury, these effects vary among species and growing environment. SO 2 is also considered the primary factor causing the death of li chens in many urban and industrial areas (Hutchinson et al., 1996 ). In addition to the role of sulfate deposition on methylation, the technologies installed to reduce emissions of NO x and SO 2 associated with this rule would also reduce mercury emissions. EPA recently commissioned an information collection request that will soon provide greatly improved power industry mercury emissions estimates that will enable the Agency to better estimate mercury emissions changes from its air emissions control actions. For this reason, the Agency did not estimate Hg changes in this rule and will instead wait for these new data which will be available in the near future. Due to time and resource limitations, we were unable in any event to mo del mercury dispersion, deposition, methylation, bioaccumulation in fish tissue, and human consumption of mercury- contaminated fish that would be needed in order to estimate the human health benefits from reducing these mercury emissions. **5.5.5 Nitrogen En**  **richment** 5.5.5.1 Aquatic Enrichment One of the main adverse ecological effects resulting from N deposition, particularly in the Mid -Atlantic region of the United States, is the effect associated with nutrient enrichment in estuarine waters. A recent assess ment of 141 estuaries nationwide by the National Oceanic and Atmospheric Administration (NOAA) concluded that 19 estuaries (13%) suffered from moderately high or high levels of eutrophication due to excessive inputs of both N and phosphorus, and a majority of these estuaries are located in the coastal area from North Carolina to Massachusetts (NOAA, 2007 ). For estuaries in the Mid -Atlantic region, the contribution of atmospheric distribution to total N loads is estimated to range between 10% and 58% (Valigura et al., 2001 ).5 - 79

Eutrophication in estuaries is associated with a range of adverse ecological effects. The conceptual framework developed by NOAA emphasizes four main types of eutrophication effects — low disso lved oxygen (DO), harmful algal blooms (HABs), loss of submerged aquatic vegetation (SAV), and low water clarity. Low DO disrupts aquatic habitats, causing stress to fish and shellfish, which, in the short -term, can lead to episodic fish kills and, in the long-term, can damage overall growth in fish and shellfish populations. Low DO also degrades the aesthetic qualities of surface water. In addition to often being toxic to fish and shellfish, and leading to fish kills and aesthetic impairments of estuaries, HABs can, in some instances, also be harmful to human health. SAV provides critical habitat for many aquatic species in estuaries and, in some instances, can also protect shorelines by reducing wave strength; therefore, declines in SAV due to nutrient enr ichment are an important source of concern. Low water clarity is the result of accumulations of both algae and sediments in estuarine waters. In addition to contributing to declines in SAV, high levels of turbidity also degrade the aesthetic qualities of t he estuarine environment. Estuaries in the eastern United States are an important source of food production, in particular fish and shellfish production. The estuaries are capable of supporting large stocks of resident commercial species, and they serve as the breeding grounds and interim habitat for several migratory species. To provide an indication of the magnitude of provisioning services associated with coastal fisheries, from 2005 to 2007, the average value of total catch was $1.5 billion per year. It is not known, however, what percentage of this value is directly attributable to or dependent upon the estuaries in these states. In addition to affecting provisioning services through commercial fish harvests, eutrophication in estuaries may also affect the demand for seafood. For example, a well publicized toxic pfiesteria bloom in the Maryland Eastern Shore in 1997, which involved thousands of dead and lesioned fish, led to an estimated $56 million (in 2007 dollars) in lost seafood sales for 360 seafood firms in Maryland in the months following the outbreak (Lipton, 1999 ). Estuaries in the United States also provide an important and substantial variety of cultural ecosystem services, including water- based recreational and aesthetic services. The water quality in the estuary directly affects the quality of these experiences. For example, there were 26 million days of saltwater fishing coastal states from North Carolina to Massachusetts in 2006 (FWA and Census, 2007 ). Assuming an average consumer surplus value for a fishing day at5 - 80

$36 (in 2007 dollars) in the Northeast and $87 in the Southeast (Kaval and Loomis, 2003 ), the aggregate value was approximately $1.3 billion (in 2007 do llars). 20 In addition, almost 6 million adults participated in motorboating in coastal states from North Carolina to Massachusetts, for a total of nearly 63 million days annually during 1999 –2000 (Leeworthy and Wiley, 2001) . Using a national daily value estimate of $32 (in 2007 dollars) for motorboating (Kaval and Loomis (2003) , the aggregate value of these coastal motorboating outings was $2 billion per year. 21 Almost 7 million participated i n birdwatching for 175 million days per year, and more than 3 million participated in visits to non - beach coastal waterside areas. Estuaries and marshes have the potential to support a wide range of regulating services, including climate, biological, and w ater regulation; pollution detoxification; erosion prevention; and protection against natural hazards from declines in SAV (MEA, 2005 ). SAV can help reduce wave energy levels and thus protect shorelines against excessive erosion, which inc reases the risks of episodic flooding and associated damages to near- shore properties or public infrastructure or even contribute to shoreline retreat. 5.5.5.2 Terrestrial Enrichment Terrestrial enrichment occurs when terrestrial ecosystems receive N loadings in excess of natural background levels, either through atmospheric deposition or direct application. Evidence presented in the Integrated Science Assessment (U.S. EPA, 2008 b ) supports a causal relationship between atmospheric N deposition and biogeochemical cycling and fluxes of N and carbon in terrestrial systems. Furthermore, evidence summarized in the report supports a causal link between atmospheric N deposition and changes in the types and number of species and biodiversity in terrestrial systems. Nitrogen enrichment occurs over a long time period; as a result, it may take as much as 50 years or more to see changes in ecosystem conditions and indicators. This long time scale also affects the timing of the ecosystem service changes. One of the main provisioning services potentially affected by N deposition is grazing opportunities offered by grasslands for livestock production in the Central U.S. Although N deposition on these grasslands can offer supplementary nutritive valu e and promote overall grass production, there are concerns that fertilization may favor invasive grasses and shift the species composition away from native grasses. This process may ultimately reduce the

20 These estimates reflect the total value of the service, not the marginal change in the value of the service as a result of the emission reductions achieved by this rule. 21 These estimates reflect the total value of the service, not the marginal change in the value of the service as a result of the emission reductions achieved by this rule.5 - 81

productivity of grasslands for livestock production. Losses due to invasive grasses can be significant; for example, based on a bioeconomic model of cattle grazing in the upper Great Plains, Leitch, Leistritz, and Bangsund (1996) and Leistritz, Bangsund, and Ho dur (2004) estimated $130 million in losses due to a leafy spurge infestation in the Dakotas, Montana, and Wyoming. 22 However, the contribution of N deposition to these losses is still uncertain. **5.5.6 Benefits**  **of Reducing Ozone Effects on Vegetation and Ecosystems** Ozone causes discernible injury to a wide array of vegetation (U.S. EPA, 2006 c ; Fox and Mickler, 1996 ). In terms of forest productivity and ecosystem diversity, ozone may be the pollutant with the greatest potential for regional - scale forest impacts (U.S. EPA, 2006 c ). Studies have demonstrated repeatedly that ozone concentrations commonly observed in polluted areas can have s ubstantial impacts on plant function (De Steiguer et al., 1990 ; Pye, 1988 ). When ozone is present in the air, it can enter the leaves of plants, where it can cause significant cellular damage. Like carbon dioxide (CO 2 ) and other gaseous substances, ozone enters plant tissues primarily through the stomata in leaves in a process called “ uptake ” (Winner and Atkinson, 1986 ). Once sufficient levels of ozone (a highly reactive su bstance), or its reaction products, reaches the interior of plant cells, it can inhibit or damage essential cellular components and functions, including enzyme activities, lipids, and cellular membranes, disrupting the plant ’ s osmotic (i.e., water) balance and energy utilization patterns (U.S. EPA, 2006 c ; Tingey and Taylor, 1982 ). With fewer resources available, the plant reallocates existing resources away from root growth and storage, above ground gr owth or yield, and reproductive processes, toward leaf repair and maintenance, leading to reduced growth and/or reproduction. Studies have shown that plants stressed in these ways may exhibit a general loss of vigor, which can lead to secondary impacts tha t modify plants ’ responses to other environmental factors. Specifically, plants may become more sensitive to other air pollutants, or more susceptible to disease, pest infestation, harsh weather (e.g., drought, frost) and other environmental stresses, whic h can all produce a loss in plant vigor in ozone - sensitive species that over time may lead to premature plant death. Furthermore, there is evidence that ozone can interfere with the formation of mycorrhiza, essential symbiotic fungi associated with the roo ts of most terrestrial plants, by reducing the amount of carbon available for transfer from the host to the symbiont (U.S. EPA, 2006 c ).

22 These estimates reflect the total value of the service, not the marginal change in the value of the service as a re sult of the emission reductions achieved by this rule.5 - 82

This ozone damage may or may not be accompanied by visible injury on leaves, and likewise, visib le foliar injury may or may not be a symptom of the other types of plant damage described above. Foliar injury is usually the first visible sign of injury to plants from ozone exposure and indicates impaired physiological processes in the leaves (Grulke, 2 003 ). When visible injury is present, it is commonly manifested as chlorotic or necrotic spots, and/or increased leaf senescence (accelerated leaf aging). Because ozone damage can consist of visible injury to leaves, it can also reduce the aesthetic value of ornamental vegetation and trees in urban landscapes, and negatively affects scenic vistas in protected natural areas. Ozone can produce both acute and chronic injury in sensitive species depending on the concentration level and the duration of the exposure. Ozone effects also tend to accumulate over the growing season of the plant, so that even lower concentrations experienced for a longer duration have the potential to create chronic stress on sensitive vegetation. Not all plants, h owever, are equally sensitive to ozone. Much of the variation in sensitivity between individual plants or whole species is related to the plant ’ s ability to regulate the extent of gas exchange via leaf stomata (e.g., avoidance of ozone uptake through closu re of stomata) (U.S. EPA, 2006 c ; Winner, 1994 ). After injuries have occurred, plants may be capable of repairing the damage to a limited extent (U.S. EPA, 2006 c ). Because of the differing sensitivities among plants to ozone, ozone pollution can also exert a selective pressure that leads to changes in plant community composition. Given the range of plant sensitivities and the fact that numerous other environmental factors modify plant uptak e and response to ozone, it is not possible to identify threshold values above which ozone is consistently toxic for all plants. Because plants are at the base of the food web in many ecosystems, changes to the plant community can affect associated organis ms and ecosystems (including the suitability of habitats that support threatened or endangered species and below ground organisms living in the root zone). Ozone impacts at the community and ecosystem level vary widely depending upon numerous factors, incl uding concentration and temporal variation of tropospheric ozone, species composition, soil properties and c limatic factors (U.S. EPA, 2006 c ). In most instances, responses to chronic or recurrent exposure in forested ecosystems are subtle and not observabl e for many years. These injuries can cause stand - level forest decline in sensitive ecosystems (U.S. EPA, 2006 c, McBride et al., 1985 ; Miller et al., 1982 ). It is not yet possi ble to predict ecosystem responses to ozone with much certainty; however, considerable knowledge of potential ecosystem responses has been acquired through long -term observations in highly damaged forests in the United States ( U.S. EPA, 2006 c ).5 - 83

5.5.6.1 Ozone Effects on Forests Air pollution can affect the environment and affect eco logical systems, leading to changes in the ecological com munity and influencing the diversity, health, and vigor of individual species (U.S . EPA, 2006 c ). Ozone has been shown in numerous studies to have a strong effect on the health of many plants, including a variety of commercial and ecologically important forest tree species throughout the United States (U.S. EPA, 2007 ). In the U.S., this data comes from the U.S. Department of Agriculture (USDA) Forest Service Forest Inventory and Analysis (FIA) program. As part of its Phase 3 program, formerly known as Forest Health Monitoring, FIA examines ozone injury to ozone - sensitive plant species at ground moni toring sites in forestland across the country (excluding woodlots and urban trees). FIA looks for damage on the foliage of ozone - sensitive forest plant species at each site that meets certain minimum criteria. Because ozone injury is cumulative over the course of the growing season, examinations are conducted in July and August, when ozone injury is typically highest. Monitoring of ozone injury to plants by the USDA Forest Service has expanded over the last 10 years from monitoring si tes in 10 states in 1994 to nearly 1,000 monitoring sites in 41 states in 2002. The data underlying the indictor in Figure 5 - 11 are based on averages of all observations collected in 2002, the latest year for which data are publicly available at the time t he study was conducted, and are broken down by U.S. EPA Regions. Ozone damage to forest plants is classified using a subjective five - category biosite index based on expert opinion, but designed to be equivalent from site to site. Ranges of biosite values t ranslate to no injury, low or moderate foliar injury (visible foliar injury to highly sensi tive or moderately sensitive plants, respectively), and high or severe foliar injury, which would be expected to result in tree - level or ecosystem - level responses, respectively (U.S. EPA, 2006 c ; Coulston, 2004 ). The highest percentages of observed high and severe foliar injury, which are most likely to be associated with tree or ecosystem - level responses, are primarily found in the Mid -Atlantic and Southeast regions.5 - 84

**Figure 5**  **- 11**  **. Ozone Injury to Forest Plants in U.S. by EPA Regions, 2002**  Assessing the impact of ground - level ozone on forests in the eastern United States involves understanding the risks to sensitive tree species from ambient ozone concentrations and accounting for the prevalence of those species within the forest. As a way to quantify the risks to particular plants from ground - level ozone, scientists have developed ozone exposure/tree
- response functions b y exposing tree seedlings to different ozone levels and measuring reductions in growth as “ biomass loss. ” Typically, seedlings are used because they are easy to manipulate and measure their growth loss from ozone pollution. The mechanisms of susceptibility to ozone within the leaves of seedlings and mature trees are identical, and the decreases predicted using the seedlings should be related to the decrease in overall plant fitness for mature trees, but the magnitude of the effect may be higher or lower dep ending on the tree species (Chappelka and Samuelson, 1998 ). In areas where certain ozone - sensitive5 - 85

species dominate the forest community, the biomass loss from ozone can be sig nificant. Si gnificant biomass loss can be defined as a more than 2% annual biomass loss, which would cause long term ecological harm as the short -term negative effects on seedlings compound to affect long -term forest health (Heck, 1997 ). Some of the common tree species in the U nited States that are sensitive to ozone are black cherry ( Prunus serotina ), tulip - poplar ( Liriodendron tulipifera) , and eastern white pine ( Pinus strobus) . Ozone - exposure/tree
- response functions have been developed for each of these tree species, as well as for aspen ( Populus tremuliodes ), and ponderosa pine ( Pinus ponderosa ) (U.S. EPA, 2007 ). Other common tree species, such as oak ( Quercus spp .) and hickory ( Carya spp.), are not as sensitive to ozone. Consequently, with knowledge of the distribution of sensitive species and the level of ozone at particular locations, it is possible to estimate a “ biomass loss” for each species across their range. As shown in Figure 5 - 12, current ambient levels of ozone are associated with significan t biomass loss across large geographic areas (U.S. EPA, 2009b ). However, this information is unavailable for this rule.

**Figure 5**  **- 12**  **. Estimated Black Cherry, Yellow Poplar, Sugar Maple, Eastern White Pine, Virginia**  **Pine, Red Maple, and Quaking Aspen Biomass Loss due to Current Ozone Exposure, 2006** **- 2008 (U.S. EPA, 2009b)** 5 - 86

To estimate the biomass loss for forest ecosystems across the eastern Unit ed States, the biomass loss for each of the seven tree spe cies was calculated using the three - month, 12 - hour W126 exposure metric at each location, along with each tree ’ s individual C - R functions. The W126 exposure metric was calculated using monitored ozone data from CASTNET and AQS sites, and a three -year average was used to mitigate the effect of variations in meteorological and soil moisture conditions. The biomass loss estimate for each species was then multiplied by its prevalence in the forest community using the U.S. Department of Agriculture (USDA) Forest Service IV index of tree abun dance calculated from Forest Inventory and Analysis (FIA) measurements (Prasad, 2003 ). Sources of uncertainty include the ozone - exposure/plant
response functions, the tree abundance index, and other factors (e.g., soil moisture). Altho ugh these factors were not considered, they can affect ozone damage (Chappelka, 1998 ). Ozone damage to the plants including the trees and understory in a forest can affect the ability of the forest to sustain suitable habitat for as sociated species particularly threatened and endangered species that have existence value — a nonuse ecosystem service — for the public. Similarly, damage to trees and the loss of biomass can affect the forest ’ s provisioning services in the form of timber for various commercial uses. In addition, ozone can cause discoloration of leaves and more rapid senescence (early shedding of leaves), which could negatively affect fall color tourism because the fall foliage would be less available or less attractive. Beyond the aesthetic damage to fall color vistas, forests provide the public with many other recreational and educational services that may be impacted by reduced forest health including hiking, wildlife viewing (including bird watching), camping, picnicking, an d hunting. Another potential effect of biomass loss in forests is the subsequent loss of climate regulation service in the form of reduced ability to sequester carbon (Felzer et al., 2005 ). 5.5.6.2 Ozone Effects on Crops and Urb an Ornamentals Laboratory and field experiments have also shown reductions in yields for agronomic crops exposed to ozone, including vegetables (e.g., lettuce) and field crops (e.g., cotton and wheat). Damage to crops from ozone exposures includes yield lo sses (i.e., in terms of weight, number, or size of the plant part that is harvested), as well as changes in crop quality (i.e., physical appearance, chemical composition, or the ability to wit hstand storage) (U.S. EPA, 2007 ). The most extensive field exper iments, conducted under the National Crop Loss Assessment Network (NCLAN) examined 15 species and numerous cultivars. The NCLAN results show that “ several economically important crop species are sensitive to ozone levels typical of those found in the Unite d States ” (U.S. EPA, 2006 c ). In addition, economic studies have shown reduced economic benefits as a result of predicted reductions in crop yields, directly affecting5 - 87

the amount and quality of the provisioning service provided by the crops in question, associated with observed ozone levels (Kopp et al., 1985 ; Adams et al., 1986 ; Adams et al., 1989 ). According to the Ozone Staff Paper, there has been no evidence that crops are becoming more to lerant of ozone (U.S. EPA, 2007 ). Using the Agriculture Simulation Model (AGSIM) (Taylor, 1994 ) to calculate the agricultural benefits of reductions in ozone exposure, U.S . EPA estimated that meeting a W126 standard of 21 ppm - hr would produce monetized benefits of approximately $160 million to $300 million (inflated t o 2006 dollars) (U.S. EPA, 2007 ). 23 Urban ornamentals are an additional vegetation c ategory likely to experience some degree of negative effects associated with exposure to ambient ozone levels. Because ozone causes visible foliar injury, the aesthetic value of ornamentals (such as petunia, geranium, and poinsettia) in urban landscapes w o uld be reduced (U.S. EPA, 2007 ). Sensitive ornamental species would require more frequent replacement and/or increased maintenance (fertilizer or pesticide application) to maintain the desired appearance because of exposure t o ambie nt ozone (U.S. EPA, 2007 ). In addition, many businesses rely on healthy- looking vegetation for their livelihoods (e.g., horticulturalists, landscapers, Christmas tree growers, farmers of leafy crops, etc.) and a variety of ornamental species have been list ed as sensitive to ozone (Abt Associates, 2010 ). The ornamental landscaping industry is valued at more than $30 billion (inflated to 2006 dollars) annually, by both private property owners/tenants and by governmental units resp onsible for public areas (Abt Associates, 2010 ). Therefore, urban ornamentals represent a potentially large unquantified benefit category. This aesthetic damage may affect the enjoyment of urban parks by the public and homeowners ’ enjoyment of their landscaping and gardening activities. In the absence of adequate exposure - response functions and economic damage functions for the potential range of effects relevant to these types of vegetation, we cannot conduct a quantitative analys is to estimate these effects. **5.5.7 Unquantified SO** **2 and NO** **2 -** **Related Human Health Benefits** Following an extensive evaluation of health evidence from epidemiologic and laboratory studies, the Integrated Science Assessment for Sulfur Dioxide concluded that t here is a causal relationship between respiratory health effects and short -term exposure to SO 2 (U.S. EPA, 2008 c ). The immediate effect of SO 2 on the respiratory system in humans is bronchoconstriction. Asthmatics are more sensitive to the effects of SO 2 likely resulting from preexisting inflammation associated with this disease. A clear concentration - response

23 These estimates illustrate the value of vegetation effects from a substantial reduction of ozone concentrations, not the marginal change in ozone concentrations anticipated a result of the emission reductions achieved by this rule.5 - 88

relationship has been demonstrated in laboratory studies following exposures to SO 2 at concentrations between 20 and 100 ppb, both in terms of increasing severity of effect and percentage of asthmatics adversely affected. Based on our review of this information, we identified four short -term morbidity endpoints that the SO 2 ISA identified as a “ causal relationship ” : asthma exacer bation, respiratory- related emergency department visits, and respiratory- related hospitalizations. The differing evidence and associated strength of the evidence for these different effects is described in detail in the SO 2 ISA. The SO 2 ISA also concluded that the relationship between short -term SO 2 exposure and premature mortality was “ suggestive of a causal relationship ” because it is difficult to attribute the mortality risk effects to SO 2 alone. Although the SO 2 ISA stated that studies are generally con sistent in reporting a relationship between SO 2 exposure and mortality, there was a lack of robustness of the observed associations to adjustment for pollutants. We did not quantify these benefits due to time constraints. Epidemiological researchers have a ssociated NO 2 exposure with adverse health effects in numerous toxicological, clinical and epidemiological studies, as described in the Integrated Science Assessment for Oxides of Nitrogen - Health Criteria (Final Report) (U.S. EPA, 2008c ). The NO 2 ISA provides a comprehensive review of the current evidence of health and environmental effects of NO 2 . The NO 2 ISA concluded that the evidence “ is sufficient to infer a likely causal relationship between short -term NO 2 exposure and advers e effects on the respiratory system ” (ISA, section 5.3.2.1). These epidemiologic and experimental studies encompass a number of endpoints including [Emergency Department (ED)] visits and hospitalizations, respiratory symptoms, airway hyperresponsiveness, airway inflammation, and lung function. Effect estimates from epidemiologic studies conducted in the United States and Canada generally indicate a 2 - 20% increase in risks for ED visits and hospital admissions and higher risks for respiratory symptoms (ISA, section 5.4). The NO 2 ISA concluded that the relationship between short -term NO 2 exposure and premature mortality was “ suggestive but not sufficient to infer a causal relationship ” because it is difficult to attribute the mortality risk effects to NO 2 alo ne. Although the NO 2 ISA stated that studies consistently reported a relationship between NO 2 exposure and mortality, the effect was generally smaller than that for other pollutants such as PM. We did not quantify these co - benefits due to time constraints. **5.6 Social Cost of Carbon and Greenhouse Gas**  **Co -**  **Benefits** EPA has assigned a dollar value to reductions in carbon dioxide (CO 2 ) emissions using recent estimates of the “social cost of carbon ” (SCC). The SCC is an estimate of the monetized damages associate d with an incremental increase in carbon emissions in a given year. It is5 - 89

intended to include (but is not limited to) changes in net agricultural productivity, human health, property damages from increased flood risk, and the value of ecosystem services du e to climate change. The SCC estimates used in this analysis were developed through an interagency process that included EPA and other executive branch entities, and concluded in February 2010. EPA first used these SCC estimates in the benefits analysis fo r the final joint EPA/DOT Rulemaking to establish Light - Duty Vehicle Greenhouse Gas Emission Standards and Corporate Average Fuel Economy Standards; see the rule ’ s preamble for discussion about application of SCC (75 FR 25324; 5/7/10). The SCC Technical Su pport Document (SCC TSD) provides a complete discussion of the methods used to develop these SCC estimates. 24 The interagency group selected four SCC values for use in regulatory analyses, which we have applied in this analysis: $5 .9 , $2 4.3 , $3 9 , and $ 74.4 per metric ton of CO 2 emissions 25 in 201 6 , in 2007 dollars. The first three values are based on the average SCC from three integrated assessment models, at discount rates of 2.5 %, 3 %, and 5 %, respectively. SCCs at several discount rates are included because the literature shows that the SCC is quite sensitive to assumptions about the discount rate, and because no consensus exists on the appropriate rate to use in an intergenerational context. The fourth value is the 95th percentile of the SCC from all three models at a 3 % discount rate. It is included to represent higher-than - expected impacts from temperature change further out in the tails of the SCC distribution. Low probability, high impact events are incorporated into all of the SCC values through explici t consideration of their effects in two of the three models as well as the use of a probability density function for equilibrium climate sensitivity. Treating climate sensitivity probabilistically results in more high temperature outcomes, which in turn le ad to higher projections of damages. The SCC increases over time because future emissions are expected to produce larger incremental damages as physical and economic systems become more stressed in response to greater climatic change. Note that the interag ency group estimated the growth rate of the SCC directly using the three integrated assessment models rather than assuming a constant annual

24 Docket ID EPA- HQ- OAR - 2009 - 0472 - 114577, Technical Support Document: Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866, Interagency Working Group on Social Cost of Carbon, with participation by Council of Economic Advisers, Council on Environmental Quality, Department of Agriculture, Department of Commerce, Department of Energy, Department of Transportation, Environmental Protection Agency, National Economic Council, Office of Energy and Clim ate Change, Office of Management and Budget, Office of Science and Technology Policy, and Departme nt of Treasury (February 2010). Also available at http://www.epa.gov/otaq/climate/regulations.ht m 25 Note that upstream and downstream emission changes were not considered for this rule. For example, t here may be changes in greenhouse gas emissions (in particular, methane) due to changes in fossil fuel extraction and transport in response to this proposal , but those emission changes were not quantified.5 - 90

growth rate. This helps to ensure that the estimates are internally consistent with other modeling assumptions. The SCC estimates for the analysis year of 2016 in 2007 $ are provided in Table 5 - 16 . **Table 5**  **- 16**  **. Social Cost of Carbon (SCC) Estimates (per tonne of CO**  **2 ) for 2016 (in 2007$)**  **a Discount Rate and Statistic**  **SCC Estimate, $** 5% Average 5.9 3% Average 24.3 2.5% Average 39.0 3% 95 th percentile 74.4 a The SCC values are dollar-year and emissions -year specific. SCC values represent only a partial accounting of climate impacts. When attempting to assess the incremental economic impacts of carbon dioxide emissions, the analyst faces a number of serious challenges. A recent report from the National Academies of Science (NRC 200 9 ) points out that any assessment will suffer from uncertainty, speculation, and lack of information about (1) future emissio ns of greenhouse gases, (2) the effects of past and future emissions on the climate system, (3) the impact of changes in climate on the physical and biological environment, and (4) the translation of these environmental impacts into economic damages. As a result, any effort to quantify and monetize the harms associated with climate change will raise serious questions of science, economics, and ethics and should be viewed as provisional. The interagency group noted a number of limitations to the SCC analysis , including the incomplete way in which the integrated assessment models capture catastrophic and non catastrophic impacts, their incomplete treatment of adaptation and technological change, uncertainty in the extrapolation of damages to high temperatures, and assumptions regarding risk aversion. Current integrated assessment models do not assign value to all of the important physical, ecological, and economic impacts of climate change because models understandably lag behind the most recent research. The l imited amount of research linking climate impacts to economic damages makes the interagency modeling exercise even more difficult. The interagency group hopes that over time researchers and modelers will work to fill these gaps and that the SCC estimates used for regulatory analysis by the federal government will continue to evolve with improvements in modeling. Additional details on these limitations are discussed in the SCC TSD.5 - 91

In light of these limitations, the interagency group has committed to updating the current estimates as the science and economic understanding of climate change and its impacts on society improves over time. Specifically, the interagency group has set a preliminary goal of revisiting the SCC values in the next few years or at such time as substantially updated models become available, and to continue to support research in this area. Applying the global SCC estimates shown in Table 5 - 1 6 to the estimated reductions in annual CO 2 emissions of 15 million metric tons for the policy scenario, we estimate the dollar value of the climate related co - benefits captured by the models for 2016 using th re e discount rate s 5%, 3%, and 2.5% rather than 3% and 7%. 26 These climate co - benefit estimates are provided in Table 5 - 1 7 . The CO 2 emission reductions associated with the policy scenario were developed using IPM and result largely from projected increases in electricity genera tion from natural gas sources and reductions in coal -fired generation by 2016 . Even within the coal generation fleet, there are likely some modest generation shifts away from the least efficient units towards units that are more efficient to operate. The s e CO 2 emission reductions are net of any CO 2 emission increases associated with the energy usage for control technologies required by the rule. **Table 5**  **- 1**  **7 .**  **Monetized Co**  **- Benefits of CO**  **2 Emissions Reductions in 2016 (in millions of** 

**2007$) a**  **,b,c,d Discount**  **Rate and Statistic SCC Estimate, $**  5% Average 8 9 3% Average 36 0 2.5% Average 5 9 0 3% 95 th percentile 1,100 a All estimates have been rounded. b The SCC values are dollar-year and emissions -year specific. c SCC values represent only a partial accounting of climate impacts. d Three discount rates are used to estimate the dollar value of the climate related co - benefits. As noted above, there are a number of limitations associated with the SCC and its use to assess the climate benefits of regulati ons. Beyond the SCC’ s incomplete treatment of impacts associated with CO 2 emissions, it is important to note that SCC is limited to assessing the

26 See SCC TSD for more information about discount rate selection. Also, i t is possible that other benefits or costs of proposed regulations unrelated to CO 2 emissions will be discounted at rates th at differ from those used to develop the SCC estimates.5 - 92

climate benefits associated with changes in CO 2 emissions only. However this rule will have an impact on the emissions of other pollutants that will affect the climate. These other pollutants include other greenhouse gases, aerosols and aerosols precursors such as black carbon, organic carbon, sulfur dioxide and nitrogen oxides, and ozone precursors such as nitro gen oxides and volatile organic carbon compounds. Changes in these pollutants (both increases and decreases) can be a direct result of changes in electricity generation, including but not limited to the changes in SO 2, NO x, and filterable particulate matte r identified in Chapter 3 of the RIA, but can also result from upstream changes in emissions due to changes in fossil fuel extraction and transport or downstream emission changes for secondary market impacts (not calculated for this rule). Reductions in bl ack carbon or ozone precursors would lead to further cooling, but reductions in the other aerosol species and precursors would lead to warming. Therefore, changes in non - CO 2 pollutants could potentially augment or offset the climate benefits calculated her e. These pollutants can act in different ways and on different timescales than carbon dioxide. For example, aerosols reflect (and in the case of black carbon, absorb) incoming radiation, whereas greenhouse gases absorb outgoing infrared radiation. These ae rosols can also affect climate indirectly by altering properties of clouds. Black carbon can also deposit on snow and ice, darkening these surfaces and accelerating melting. In terms of lifetime, while carbon dioxide emissions can increase concentrations i n the atmosphere for hundreds to thousands of years, many of these other pollutants are short lived and remain in the atmosphere for short periods of time ranging from days to weeks and can therefore exhibit large spatial and temporal variability. The clim ate impacts of these other pollutants can be complex and have not been calculated for this rule. **5.7 Co**  **- Benefits Results**  Applying the impact and valuation functions described previously in this chapter to the estimated changes in ambient PM yields estimate s of the changes in physical damages (e.g. , premature deaths, hospital admissions). Since the air quality modeling performed for this RIA does not reflect the changes in emissions of PM 2.5 precursors associated with the final emissions control requirements of the rule, we extrapolate the co - benefits of the final rule from the co - benefits of the air quality modeled emissions (see Appendi ces 5 A and 5 B). From these modeled co - benefits , we calculate BPT values for SO 2 and direct PM (carbonaceous and crustal), s eparately for Eastern and Western states, following the general methodology described by Fann et al. (2009) . We then apply the BPT values to the final emission changes associated with the revised policy scenario. Since the geogra phic distribution of emission changes did not change dramatically from the modeled emission scenarios to the final policy scenario, extrapolating co - benefits using the BPT approach reasonably approximates the co - benefits of5 - 93

the final policy scenario . However, there is additional uncertainty in the extrapolated benefits estimates relative to the benefits estimated for the air quality modeled emissions. This section summarizes the health co - benefits estimated for the final policy scenario in 2016. C o - b enefits associated with the modeled air quality changes are described in Appendix 5 C. Although extrapolating recreational visibility impacts to the final revised policy scenario is not possible, we estimate th at visibility co - benefits add $1.1 billion to t he total monetized benefits of the modeled interim policy scenario (see Appendix 5 C). Visibility benefits are not included in the co - benefits estimate for the final policy. Table 5 - 18 presents health impacts among eastern and western states. Monetized valu es for both health and welfare endpoints are presented in Table 5 - 19. All monetary benefits are in constant -year 2007$. Not all known health and welfare co - benefits for non - HAP pollutants could be quantified or monetized in this analysis . The monetized va lue of these unquantified effects is represented by adding an unknown “ B ” to the aggregate total. The estimate of total monetized co - benefits is thus equal to the subset of monetized PM - and CO 2 - related health and welfare co - benefits plus B, the sum of the non - monetized health and welfare; this B represents both uncertainty and a bias in this analysis, as it reflects those co - benefits categories that we are unable quantify in this analysis. This assessment estimates that in 2016 MATS will result in between 4,200 and 11,000 PM 2.5 - related avoided premature deaths annually. The total monetized health and climate co benefits of MATS in 2016 are between $ 37 billion and $ 90 billion using a 3% discount rate and between $ 33 billion and $ 81 using a 7% discount rate. As shown in Appendix 5C, 95% of the health co - benefits result from reduced exposure to sulfate particles. Mortality co - benefits account for approximately 93% to 97 % of total monetized co - benefits depending on the PM 2.5 estimates used, in part because we are unable to quantify most of the non - health co - benefits . The next largest benefit is for reductions in chronic illness (CB and non -fatal heart attacks), although this value is more than an order of magnitude lower than for premature mortality. Hospital a dmissions for respiratory and cardiovascular causes, visibility, MRADs and work loss days account for the majority of the remaining co - 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. Figure 5 - 13 summarizes an array of PM 2.5 - related monetized co - benefits estimates based on alternative epidemiology and expert - derived PM - mortality estimate. A comparison of the incidence table to th e monetary co - benefits table reveals that there is not always a close5 - 94

correspondence between the number of incidences avoided for a given endpoint and the monetary value associated with that endpoint. For example, there are over 100 times more work loss da ys than premature mortalities, yet work loss days account for only a very small fraction of total monetized co - benefits . This reflects the fact that many of the less severe health effects, while more common, are valued at a lower level than the more severe health effects. Also, some effects, such as hospital admissions, are valued using a proxy measure of WTP. As such, the true value of these effects may be higher than that reported in Table 5 - 1 9 .5 - 95

**Table 5 -**  **18. Estimated Reduction in Incidence of Adverse Health Effects of the Mercury and** 

**Air Toxics Standards in 2016**  **(95% confidence intervals) a**  **,b Impact**  **Eastern U.S.** c **Western U.S. Total**  Premature death Pope et al. (2002) (age >30) 4,100 (1,100 – 7, 0 00) 1 3 0 (30 – 220) 4, 2 00 (1,200 – 7, 200 ) Laden et al. (2006) (age >25) 10,0 00 (4, 8 00 – 16,000) 3 2 0 (140 – 5 1 0) 11,000 (5,000 – 17,000) Infant (< 1 year) 19 ( - 2 1 – 59 ) 1 ( - 1 – 2) 20 ( - 22 – 61 ) Chronic bronchitis 2,7 00 ( 89 – 5,400) 1 0 0 ( - 1 – 210) 2, 8 00 (88 – 5,600) Non -fatal heart attacks (age > 18) 4, 6 00 (1,200 – 8 , 1 00) 120 (2 5 – 210) 4, 700 ( 1,200 – 8, 3 00) Hospital admissions — respiratory (all ages) 820 ( 320 – 1,300) 17 (6 – 27) 83 0 (330 – 1,300) Hospital admissions — cardiovascular (age > 18) 1,800 (1,200 – 2, 1 00) 4 2 (2 7 – 5 0 ) 1,800 (1, 2 00 – 2,200) Emergency room visits for asthma (age < 18) 3, 0 00 (1, 500 – 4,500) 1 1 0 ( 52 – 160) 3, 1 00 (1,600 – 4,700) Acute bronchitis (age 8 - 12) 6, 0 00 ( - 1,400 – 13,000) 250 ( - 69 – 5 6 0) 6,300 ( - 1,400 – 14,000) Lower respiratory symptoms (age 7 - 14) 7 7 ,000 (30,000 – 1 2 0,000) 3, 1 00 (1,100 – 5, 2 00) 8 0,000 (31,000 – 130,000) Upper respiratory symptoms (asthmatics age 9 - 18) 5 8 ,000 (11,000 – 1 10,000) 2,400 ( 360 – 4, 4 00) 60,000 (11,000 – 110,000) Asthma exacerbation (asthmatics 6 - 18) 130,000 (4, 5 00 – 43 0,000) 5, 2 00 ( - 6 – 18,000) 130,000 (4,500 – 450,000) Lost work days (ages 18 - 65) 5 2 0,000 (44 0,000 – 6 0 0,000) 21,000 (1 8 ,000 – 2 4,000) 5 4 0,000 (460,000 – 6 2 0,000) Minor restricted - activity days (ages 18 - 65) 3,100,000 (2,500,000 – 3,700,000) 120,000 ( 9 9 ,000 – 150,000) 3,200,000 (2,600,000 – 3,800,000) a Estimates rounded to two significant figures; column values will not sum to total value. b The negative estimates for certain endpoints are the result of the weak statistical power of the study used to calculate these health impacts and do not suggest that increases in air pollution exposure result in decreased health impacts. c Includes Texas and those states to the nort h and east.5 - 96

**Table 5 -**  **19. Estimated Economic Value of Health and Welfare**  **co -**  **benefits of the Mercury and** 

**Air Toxics Standards in 2016**  **(95% confidence intervals, billions of 2007$) a**  **Impact Pollutant**  **Eastern U.S. b**  **Western U.S. Total**  Adult premature death (Pope et al. 2002 PM mortality estimate) 3% discount rate PM 2.5 $3 3 ($2.6
- $ 99 ) $1.0

(<$0.01
- $3. 1 ) $34

($2. 6 - $100) 7% discount rate PM 2.5 $30 ($2. 3 - $9 0 ) $0.9

(<$0.01
- $2.8) $ 30 ($2.4
- $ 92 ) Adult premature death (Laden et al. 2006 PM mortality estimate) 3% discount rate PM 2.5 $8 4 ($7. 4 - $2 4 0) $2.6

($0.1
- $7. 6 ) $8 7

($7. 5 - $250) 7% discount rate PM 2.5 $7 6 ($6.7
- $220) $2. 3

($0.1
- $6. 9 ) $7 8

($6.8
- $230) Infant premature death PM 2.5 $0.2 ( $
- 0.2 – $0.8) <$0.01 $0.2 ($
- 0.2 - $0.8) Chronic Bronchitis PM 2.5 $1.3 ($0.1
- $6. 1 ) $0.1

(<$0.01
- $0.2) $1.4 ($0.1
- $6.4) Non -fatal heart attacks 3% discount rate PM 2.5 $0.5 ($0.1
- $1.3) <$0.01 $0.5 ($0.1
- $1.3) 7% discount rate PM 2.5 $0.4 ($0.1
- $1.0) <$0.01 $0.4 ($0.1
- $1.0) Hospital admissions — respiratory PM 2.5 $0.01 (<$0.01
- $0.02) <$0.01 $0.01 ($0.01
- $0.02) Hospital admissions — cardiovascular PM 2.5 $0.03 (<$0.01
- $0.05) <$0.01 $0.03 (<$0.01
- $0.05) Emergency room visits for asthma PM 2.5 <$0.01 <$0.01 <$0.01 Acute bronchitis PM 2.5 <$0.01 <$0.01 <$0.01 Lower respiratory symptoms PM 2.5 <$0.01 <$0.01 <$0.01 Upper respiratory symptoms PM 2.5 <$0.01 <$0.01 <$0.01 Asthma exacerbation PM 2.5 <$0.01 <$0.01 <$0.01 Lost work days PM 2.5 $0.1 ($0.1
- $0.1) <$0.01 $0.1 ($0.1
- $0.1) Minor restricted - activity days PM 2.5 $0.2 ($0.1
- $0.3) <$0.01 $0.2 ($0.1
- $0.3) CO 2 - related co - benefits (3% discount rate) CO 2 $0.3 6 (continued)5 - 97

**Table 5 -**  **19. Estimated Economic Value of Health and**  **Welfare co**  **- benefits**  **of the Mercury and**

**Air Toxics Standards in 2016**  **(95% confidence intervals, billions of 2007$) a**  **(continued)** Total Monetized co - benefits (Pope et al. 2002 PM 2.5 mortality estimate) 3% discount rate $3 5 +B ($2.8
- $110) $1.1+B

($0.0 3 - $3. 4 ) $37+B

($3.2
- $110) 7% discount rate $32+B ($2.5
- $9 8 ) $1.0+B

($0.0 3 - $3.1) $33+B

($2.9
- $100) Total Monetized Benefits ( Laden et al. 200 6 PM 2.5 mortality estimate) 3% discount rate $8 7 +B ($7. 5 - $2 5 0) $2.7+B

($0.1
- $7.9 ) $9 0 +B

($8. 0 - $260) 7% discount rate $7 8 +B ($6. 8 - $230) $2.4+B

($0.1
- $7. 2 ) $8 1 +B

($7.3
- $240)
a Estimates rounded to two significant figures. The negative estimates for certain endpoints are the result of the weak statistical power of the study used to calculate these health impacts and do not suggest that increases in air pollution exposure result in decreased health impacts. Confidence intervals reflect random sampling error and not the additional uncertainty asso ciated with co - benefits scaling described above. The net present value of reduced CO 2 emissions are calculated differently than other co - benefits . The same discount rate used to discount the value of damages from future emissions (SCC at 5, 3, 2.5 percent) is used to calculate net present value of SCC for internal consistency. This table shows monetized CO 2 co - benefits at discount rates at 3 and 7 percent that were calculated using the global average SCC estimate at a 3% discount rate because the interagency workgroup on this topic deemed this marginal value to be the central value. In S ection 5 .6 we also report CO 2 co - benefits using discount rates of 5 percent (average), 2.5 percent (average), and 3 percent (95 th percentile). b Includes Texas and those stat es to the north and east. PM 2.5 mortality benefits represent a substantial proportion of total monetized co benefits (over 90%), and these estimates have the following key assumptions and uncertainties. 1. The PM 2.5 - related co - benefits were derived through a benefit per-ton approach, which does not fully reflect local variability in population density, meteorology, exposure, baseline health incidence rates, or other local factors that might lead to an over- estimate or under- estimate of the actual co - benefits of controlling PM precursors. In addition, differences in the distribution of emissions reductions across states between the modeled scenario and the final rule scenario add uncertainty to the final benefits estimates. 2. This rule is expected to reduce emissions of SO 2, NO x, and directly emitted PM 2.5 . We assume that all fine particles, regardless of their chemical composition, are equally potent in causing premature mortality. This is an important assumption, because PM 2.5 produced via transported precursors emitted from EGUs may differ significantly from direct PM 2.5 released from diesel engines and other industrial5 - 98 sources, but the scientific evidence is not yet sufficient to allow differential effects estimates by particl e type. 3. We assume that the health impact function for fine particles is linear within the range of ambient concentrations under consideration. Thus, the estimates include health co - benefits from reducing fine particles in areas with varied concentratio ns of PM 2.5 , including both regions that are in attainment with fine particle standard and those that do not meet the standard down to the lowest modeled concentrations. Based on our review of the current body of scientific literature, EPA estimated PM re lated mortality without applying an assumed concentration threshold. EPA’ s Integrated Science Assessment for Particulate Matter (U.S. EPA, 2009 a ), which was reviewed by EPA’ s Clean Air Scientific Advisory Committee (U.S. EPA- SAB, 20 09a ; U.S. EPA- SAB, 2009b ), concluded that the scientific literature consistently finds that a no -threshold log - linear model most adequately portrays the PM - mortality concentration - response relationshi p while also recognizing potential uncertainty about the exact shape of the concentration - response function. Consistent with this finding, we incorporated a “ Lowest Measured Level ” (LML) assessment, which is a method EPA has employed in several recent RIA’ s including the Cross State Air Pollution Rule (U.S. EPA, 201 1b ). This approach summarizes the distribution of avoided PM mortality impacts according to the baseline (i.e. pre - MATS ) PM 2.5 levels experienced by the population receivi ng the PM 2.5 mortality benefit (Figures 5 - 1 4 and 5 - 1 5 ). We identify on this figure the lowest air quality levels measured in each of the two primary epidemiological studies EPA use d to quantify PM - related mortality. This information allows readers to determine the portion of PM - related premature deaths avoided occurring at or above the LML of each study; in general, our confidence in the estimated PM - related premature deaths avoided decreases as we consider air quality levels further below the LML in the two epidemiological studies. While the LML analysis provides some insight into the level of uncertainty in the estimated PM mortality co - benefits , EPA does not view the LML as a thres hold and continues to quantify PM - related mortality impacts using a full range of modeled air quality concentrations. F or a summary of the scientific review statements regarding the lack of a threshold in the PM 2.5 - mortality relationship, see the Technical Support Document (TSD) entitled Summary of Expert Opinions on the Existence of a Threshold in the Concentration - Response Function for PM2.5- related Mortality (U.S. EPA, 2010e ), which is provided in Appendix 5E of this RIA. While this figure describes the relationship between baseline PM 2.5 exposure and avoided premature deaths for the modeled air quality scenario , we expect the distribution of mortality impacts to be fairly similar between the two cases.**Figure 5**  **- 13**  **. Economic Value of Estimated PM** 

**and Air Toxics Standards in 2016**  **Risk Estimate** a ,b
a Ba sed on the modeled interim baseline, which is approximately equivalent to the final baseline (see Appen 5 A) . b Column total equals sum of PM 2.5 - related mortality and morbidity

5 - 99 **Economic Value of Estimated PM 2.5** **- Related**  **Health co**  **- benefits**  **of the Mercury**

**in 2016 According to Epidemiology or Expert**  **- Derived PM Mortality**  sed on the modeled interim baseline, which is approximately equivalent to the final baseline (see Appen related mortality and morbidity co - benefits . **of the Mercury Derived PM Mortality**  sed on the modeled interim baseline, which is approximately equivalent to the final baseline (see Appendix5 - 100

**Figure 5**  **- 14**  **. Percentage of Total PM**  **- Related Mortalities**  **of the Mercury and Air Toxics Standards**  **in 2016 Avoided by Baseline Air Quality Level**  **a**
a Based on the modeled interim baseline, which is approximately equivalent to the final baseline (see Appendix 5 A) Some proportion of the avoided PM - related impacts we estimate in this analysis occur among populations exposed at or above the LML of the Laden et al. (2006) study, while a majority of the impacts occur at or above the LML of the Pope et al. (2002) study (Figure 5 - 1 4 ), increasing our confidence in the PM - related premature mortality analysis. Based on the modeled interim baseline which is approxim ately equivalent to the final baseline (see Appendix 5A), 11 % and 73% of the estimated avoided premature deaths occur at or above an annual mean PM 2.5 level of 10 μg/m 3 (the LML of the Laden et al. 2006 study) and 7.5 μg/m 3 (the LML of the Pope et al. 2002 study) , respectively . Using these percentages derived from the modeled interim baseline, Table 5 - 20 shows the allocation of reduced incidence above and below the LMLs of Laden et al. (2006) and Pope et al. (2002). As we model avoided premature deaths amo ng populations exposed to levels of PM 2.5 , we have lower confidence in levels below the LML for each study .5 - 101

Table 5 - 20 . Estimated Reduction in Incidence of Premature Adult Mortality due to the Mercury and Air Toxics Standards in 2016 Occurring Above and Below the Lowest Measured Levels in the Underlying Epidemiology Studies a

a These estimates are rounded to two significant digits. It is important to emphasize that although we have lower levels of confidence in levels below the LML for each study, the scientific evidence does not support the existence of a level below which health effects from exposure to PM 2.5 do not occur. See Appendix 5E for more information. A large fraction of the PM 2.5 - related benefits associated with this rule occur below the level of the National Ambient Air Quality Standard (NAAQS) for annual PM 2.5 at 15 μg/m 3 , which was set in 2006. It is important to emphasize that NAAQS are not set at a level of zero risk. Instead, the NAAQS reflect the level determined by the Administrator to be protective of public health within an adequate margin of safety, taking into consideration effects on susceptible populations. While benefits occurring below the standard may be less certain than those occurring above the standard, EPA conside rs them to be legitimate components of the total benefits estimate.

**Study and Lowest Measured Level (LML)**  **Total Reduced Mortality Incidence**  **Allocation of Reduced Mortality Incidence Below LML**  **At or Above LML** Pope et al. (2002), 7.5 μg/m 3 4,200 1,100 3,100 Laden et al. (2006), 10 μg/m 3 11,000 9,600 1,2005 - 102

**Figure 5**  **- 15**  **. Cumulative Percentage of Total PM**  **- Related Mortalities**  **of the Mercury and Air Toxics Standards**  **in 2016 Avoided by Baseline Air Quality Level**  **a**
a Based on the modeled interim baseline, which is approximately equivalent to the final baseline (see Appendix 5 A) While the LML of each study is important to consider when characterizing and interpreting the overall level PM 2.5 - related co - benefits , as discussed earlier in this chapter, EPA believes that both cohort - based mortality estimates are suitable for use in air pollution health impact analyses. When estimating PM - related premature deaths avoided using risk coefficients drawn from the Laden e t al. (2006) analysis of the Harvard Six Cities and the Pope et al. (2002) analysis of the American Cancer Society cohorts there are innumerable other attributes that may affect the size of the reported risk estimates — including differences in population de mographics, the size of the cohort, activity patterns and particle composition among others. The LML assessment presented here provides a limited representation of one key difference between the two studies.5 - 103

**5.8 Discussion**  This analysis demonstrates the si gnificant health and welfare co - benefits of MATS . We estimate that in 2016 the rule will have reduced the number of PM 2.5 - related premature deaths by between 4,200 and 11,000 and produce substantial non - mortality co - benefits . We estimate the monetized heal th and climate co - benefits of MATS to be $ 37 billion to $ 90 billion at a 3% discount rate and $ 33 billion to $ 81 billion at a 7% discount rate in 2016, depending on the epidemiological function used to estimate reductions in premature mortality. All estimates are in 2007$. Health co - benefits comprise approximately 99% of these total monetized co - benefits. Th is co - benefits assessment omits several important categories of co - benefits that we were unable to quantify, including health and ecological co - be nefits from reducing exposure to ozone, ecosystem co - benefits for reducing nitrogen and sulfate deposition, the direct health co benefits from reducing exposure to SO 2 and NO 2, and reduced visibility impairment in recreational areas. Inherent in any comple x RIA such as this one are multiple sources of uncertainty. Some of these we characterized through our quantification of statistical error in the concentration response relationships and our use of the expert elicitation - derived PM 2.5 mortality functions. Others are unquantified , including the projection of atmospheric conditions and source - level emissions, the projection of baseline morbidity rates, incomes and technological development. The emissions scenarios for the RIA reflects the Cross - State Air Pol lution Rule (CSAPR) as finalized in July 2011 and the emissions reductions of SOx, NOx, directly emitted PM , and CO 2 are consistent with application of federal rules, state rules and statutes, and other binding, enforceable commitments in place by December 2010 for the analysis timeframe 27 . EPA has proposed minor modifications to the state level SO 2 budgets in the Cross State Air Pollution Rule ( CSAPR; see http://www.epa.gov/airtransport/actions.html ). These modifications are expected to result in small changes in the levels of SO 2 emission reductions expected in a number of states, with the largest impact expected in Texas. EPA expects that these changes will slightly reduce the benefits of CSAPR, and will have a small impact on the baseline emissions fo r MATS. Because of the change in the baseline SO 2 emissions for MATS, the MACT controls may result in slightly larger reductions in SO 2 and other emissions, and consequently slightly higher benefits. It is important to note that the total monetized benefit s of both rules is not expected to change significantly, rather, the allocation of the SO 2 emissions reductions and

27 Consistent with the mercury risk deposition modeling for MATS, EPA did not model non -federally enforceable mercury- specific emissions reduction rules in the base case or MATS policy case (see preamb le S ection III. A. ). Note that this approach does not significantly affect SO 2 and NO x projections underlying the cost and benefit results presented in this RIA.5 - 104

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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. Woodruff TJ, Parker JD, Schoendorf KC. 2006. Fine particulate matter (PM 2.5 ) air pollution and selected causes of postneonatal infant mortality in California. Environmental Health Perspectives 114(5):786 - 90. Woods & Poole Economics Inc. 2008. Population by Single Year of Age CD. CD - ROM. Woods & Poole Economics, Inc. Washington, D.C. World Health Organization (WHO). 1977. International Classification of Diseases, 9th revision (ICD - 9). Geneva:World Health Organization.5A- 1 **APPENDIX 5 A**  **IMPACT OF THE INTERI M POLICY SCENARIO ON**  **EMISSIONS 5A.1**  **Introduction** This section summarizes the emissions inventories that are used to create emissions inputs to the air quality modeling performed for this rule. A summary of the emissions reductions that were modeled for this rule is provided. Note that the emissions proce ssing and corresponding air quality modeling were used to develop BPT scaling factors for the benefits calculation as described in this RIA. More information on this approach can be found in Appendix 5C. The emissions inventories were processed into the fo rm required by the Community Multi - scale Air Quality ( CMAQ) model. CMAQ simulates the numerous physical and chemical processes involved in the formation, transport, and destruction of ozone, particulate matter and air toxics. As part of the analysis for t his rulemaking, the modeling system was used to calculate daily and annual PM 2.5 concentrations, 8 - hr maximum ozone and visibility impairment. Model predictions of PM 2.5 and ozone are used in a relative sense to estimate scenario - specific, future year desi gn values of PM 2.5 and ozone. These are combined with monitoring data to estimate population - level exposures to changes in ambient concentrations for use in estimating health and welfare effects. In the remainder of this section we provide an overview of ( 1) the emissions components of the modeling platform, (2) the development of the 2005 base year emissions, (3) the development of the future year baseline emissions, and (4) the development of the future year control case emissions. **5A.2 Overview of Model**  **ing Platform and Emissions Processing Performed** A modeling platform is the collection of the inputs to an air quality model, including the settings and data used for the model, including emissions data, meteorology, initial conditions, and boundary conditi ons. The 2005 - based air quality modeling platform used for this RIA includes 2005 base year emissions and 2005 meteorology for modeling ozone and PM 2.5 with CMAQ. In support of this rule, EPA modeled the air quality in the Eastern and the Western United St ates using two separate model runs, each with a horizontal grid resolution of 12 km x 12 km. These 12 km modeling domains were “nested” within a modeling domain covering the remainder of the lower 48 states and surrounding areas using a grid resolution of 36 x 36 km. The results from the 36 - km modeling were used to provide incoming “boundary” for the 12km grids. Additional details on the non - emissions portion of the 2005v4.3 modeling platform used for this RIA are described in the air quality modeling secti on (Appendix 5B).5A- 2

The 2005 - based air quality modeling platform used in support of this RIA is version 4.3 and is referred to as the 2005v4.3 platform. It is an update to the 2005 - based platform, version 4.1 (i.e., 2005v4.1) used for the proposal modeling a nd for the appropriate and necessary finding. The Technical Support Document “ Preparation of Emissions Inventories for the Version 4.1, 2005 - b ased Platform ” (see http://www.epa.gov/ttn/chief/emch/index.html#toxics ) provides information on the platform used for the proposed version of this rule and for the appropriate and necessary finding . The 2005v4.3 platform builds upon the 2005 - based platform, version 4.2 which was the version of the platform used for the final Cross - State Air Pollution Rule and incorporated changes made in response to public comments on the proposed version of that rule. Detailed documentation about the 2005v4.3 platform emissions inventories used for this rule can b e found in the “ Emissions Modeling for the Final Mercury and Air Toxics Standards Technical Support Document ”. **5A.3 Development of 2005 Base Year Emissions**  Emissions inventory inputs representing the year 2005 were developed to provide a base year for for ecasting future air quality. The emission source sectors and the basis for current and future -year inventories include Electric Generating Utility point sources, non - EGU point sources, and the following types of sources with inventories primarily at the co unty level: onroad mobile, nonroad mobile, nonpoint, and fires. The specific sectors used for modeling are listed and defined in detail in the “ Emissions Modeling for the Final Mercury and Air Toxics Standards Technical Support Document ”. The inventories u sed include emissions of criteria pollutants , and for some sectors the pollutants benzene, formaldehyde, acetaldehyde and methanol are used to speciate VOC into the chemical species needed by CMAQ. The 2005v4 platform was the initial starting point for the 2005v4.3 platform used for this modeling. There were two intermediate versions: the version used for the MATS proposal modeling (2005v4.1), and the version used for the final Cross - State Air Pollution Rule modeling (2005v4.2). The basis of the 2005v4 p latform and subsequent versions is the U.S. inventory is the 2005 National Emission Inventory (NEI), version 2 from October 6, 2008 ( http://www.epa.gov/ttn/chief/net/2005inventory.html ). The 2005 NEI v2 includes 2005 - specific data for point and mobile sources, while most nonpoint data were carried forward from version 3 of the 2002 NEI. Emissions for point sources were primarily from the 2005 NEI v2 inventory, consisting mostly of 2005 value s with some 2002 emissions values used where 2005 data were not available. The point sources are split into “ EGU ” (aka “ ptipm ” ) and “ Non - EGU ” (aka5A- 3

“ ptnonipm ” ) sectors for modeling purposes based on the matching of the unit level data in the NEI units in th e National Electric Energy Database System (NEEDS) version 4.10 database. All units that matched NEEDS were included in the EGU sector so that the future year emissions could easily be taken from the Integrated Planning Model (IPM) as its outputs are also based on the NEEDS units. E ffort s made to ensure the quality of the 2005 EGU inventory included ensuring t hat there were not duplicate emissions (e.g., data pulled forward from earlier inventories) , account ing for plants or units that shutdown prior to 200 5, add ing estimates for ethanol plants, and accounting for installed emissions control devices . The 2005 annual NO X and SO 2 emissions for sources in the EGU sector are based primarily on data from EPA’ s Clean Air Markets Division ’ s Continuous Emissions Mon itoring (CEM) program, with other pollutants estimated using emission factors and the CEM annual heat input. For EGUs without CEMs, emissions were obtained from the state - submitted data in the NEI. For the 2005 base year, the annual EGU NEI emissions were allocated to hourly emissions values needed for modeling based on the 2004, 2005, and 2006 CEM data. The NO X CEM data were used to create NO X- specific profiles, the SO 2 data were used to create SO 2 - specific profiles, and the heat input data were used to allocate all other pollutants. The three years of data were used to create monthly profiles by state, while the 2005 data were used to create state - averaged profiles for allocating monthly emissions to daily. These daily values were input into SMOKE, which utilized state - averaged 2005 - based hourly profiles to allocate to hourly values. This approach to temporal allocation was used for all base and control cases modeled to provide a temporal consistency between the years modeled without tying the temporaliza tion to the events of a single year. For nonpoint sources, t he 2002 NEI v2 inventory was augmented with updated oil and gas exploration emissions for Texas and Oklahoma ( for CO, NO X, PM, SO 2, VOC). These oil and gas exploration emissions were in addition t o oil and gas data previously available in the 2005 v4 platform that includes emissions within the following states: Arizona, Colorado, Montana, Nevada, New Mexico, North Dakota, Oregon, South Dakota, Utah, and Wyoming. The commercial marine category 3 (C3 ) vessel emissions portion of the nonroad sector used point - based gridded 2005 emissions that reflect the final projections developed for the category 3 commercial marine Emissions Control Area (ECA) proposal to the International Maritime Organization (EPA- 420 - F - 10 - 041, August 2010). These emissions include Canada as part of the ECA, and were updated using region - specific growth rates and thus contain Canadian province codes. The state/federal water boundaries were based on a file available5A- 4

from the Mineral Manag ement Service (MMS) that specifies boundaries ranging from three to ten nautical miles from the coast. The onroad emissions were primarily based on the version of the Motor Vehicle Emissions Simulator (MOVE S ) ( http://www.epa.gov/otaq/models/moves/ ) used for the Tier 3 proposed rule. The first step was to run MOVES to output emission factors for a set of counties with characteristics representative of the counties within the continental United States. Data for each representative county included county- specific fuels, vehicle age distribution, inspection and maintenance programs, temperatures and relative humidity. The emission factors produced by MOVES were then combined by SMOKE with county- based act ivity data (vehicle miles traveled, speed data, and vehicle population) and gridded temperature data to create hourly, gridded emissions. Additional information on th is approach is available in the “ Emissions Modeling for the Final Mercury and Air Toxics S tandards Technical Support Document”. The nonroad emissions utilized the National Mobile Inventory Model ( NMIM ) to run the NONROAD model for all states to create county/month emissions, updated from the annual emissions in the 2005 NEI v2 with some improvements . For this case, NMIM was run using representing county mode, with improved fuels , an improved toxics emission factor (1,3 - butadiene for 2 - stroke snowmobiles), and a small coding change that enable d NONROAD to process 15% ethanol ( E1 5 ) fuels. Other emissions inventories used included average -year county- based inventories for emissions from wildfires and prescribed burning. These emissions are intended to be representative of both base and future years and are held constant for each . As a result, post processing techniques minimize their impact on the modeling results. The 2005v4.3 platform utilizes the same 2006 Canadian inventory and a 1999 Mexican inventory as were used since the v4 platform, as these were the latest available data f rom these countries. Once developed, the emissions inventories were processed to provide the hourly, gridded emissions for the model - species needed by CMAQ. Details on this processing are further described in the “ Emissions Modeling for the Final Mercury a nd Air Toxics Standards Technical Support Document ”. Table 5A- 1 provides summaries of the 2005 U.S. emission s inventories modeled for this rul e by sector . Tables 5A- 2 through 5A- 3 provide state - level summaries of SO 2, and PM 2.5 by sector . Note that the non road columns include emissions from traditional nonroad sources that are found “on - land,” along with commercial marine sources. The nonpoint columns include area fugitive dust, agriculture, and other nonpoint emissions.5A- 5

**Table 5A** **- 1**  **. 2005**  **US Emissions by Sec** **tor**
**Emissions Sector 2005 NO**  **X**

**[tons/yr] 2005 SO**  **2**

**[tons/yr] 2005 PM**  **2.5**

**[tons/yr] 2005 PM**  **10**

**[tons/yr] 2005 NH**  **3**

**[tons/yr] 2005 CO** 

**[tons/yr] 2005 VOC** 

**[tons/yr]** Agriculture 3,251,990 Area fugitive dust 1,030,391 8,858,992 Average fires 189,428 49,094 684,035 796,229 36,777 8,554,551 1,958,992 Commercial marine Category 3 (US) 130,164 97,485 10,673 11,628 11,862 4,570 EGU 3,729,161 10,380,883 496,877 602,236 21,995 603,788 41,089 Locomotive/marine 1,922,723 153,068 56,666 59,342 773 270,007 67,690 Non - EGU p oint 2,213,471 2,030,759 433,346 647,873 158,342 3,201,418 1,279,308 Nonpoint 1,696,902 1,216,362 1,079,906 1,349,639 133,962 7,410,946 7,560,061 Nonroad 2,031,527 196,277 201,406 210,767 1,971 20,742,873 2,806,422 Onroad 8,235,002 168,480 301,073 369,911 144,409 41,117,658 3,267,931 **US Total 20,148,378**  **14,292,410 4,294,373**  **12,906,616 3,750,218**  **81,913,104 16,986,064**  **Table 5A** **- 2**  **. 2005 Base Year SO**  **2 Emissions (tons/year) for States by Sector**  **State EGU**  **Non -**  **EGU Nonpoint**  **Nonroad Onroad**  **Fires Total**  Alabama 460,123 66,373 52,325 5,622 3,554 983 588,980 Arizona 52,733 23,966 2,571 6,151 3,622 2,888 91,931 Arkansas 66,384 13,039 27,260 5,678 1,918 728 115,008 California 601 33,097 77,672 40,222 4,526 6,735 162,852 Colorado 64,174 1,550 6,810 4,897 2,948 1,719 82,098 (continued)5A- 6

**Table 5A** **- 2**  **. 2005 Base Year SO**  **2 Emissions (tons/year) for States by Sector**  **(continued) State**  **EGU Non**  **- EGU**  **Nonpoint Nonroad**  **Onroad Fires**  **Total** Connecticut 10,356 1,831 18,455 2,557 1,337 4 34,540 Delaware 32,378 34,859 1,030 2,657 486 6 71,416 District of Columbia 1,082 686 1,559 414 205 0 3,947 Florida 417,321 57,429 70,490 31,190 12,388 7,018 595,836 Georgia 616,063 52,827 56,829 9,224 6,939 2,010 743,893 Idaho 0 17,151 2,915 2,304 902 3,845 27,117 Illinois 330,382 131,357 5,395 19,305 6,881 20 493,339 Indiana 878,979 86,337 59,775 9,437 4,641 24 1,039,194 Iowa 130,264 41,010 19,832 8,838 2,036 25 202,004 Kansas 136,520 12,926 36,381 8,035 1,978 103 195,943 Kentucky 502,731 25,808 34,229 6,943 3,240 364 573,315 Louisiana 109,875 165,705 2,378 25,451 2,902 892 307,202 Maine 3,887 18,512 9,969 1,625 963 150 35,106 Maryland 283,205 34,988 40,864 9,353 3,016 32 371,458 Massachusetts 84,234 19,620 25,261 6,524 2,669 93 138,402 Michigan 349,877 76,510 42,066 14,626 8,253 91 491,423 Minnesota 101,678 24,603 14,747 10,409 2,934 631 155,002 Mississippi 75,047 29,892 6,796 5,930 2,590 1,051 121,306 Missouri 284,384 78,308 44,573 10,464 4,901 186 422,816 Montana 19,715 11,056 2,600 3,813 874 1,422 39,480 Nebraska 74,955 7,910 7,659 9,199 1,510 105 101,337 Nevada 53,363 2,253 12,477 2,880 656 1,346 72,975 New Hampshire 51,445 3,155 7,408 789 746 38 63,580 New Jersey 57,044 7,639 10,726 13,321 3,038 61 91,830 New Mexico 30,628 7,831 3,193 3,541 1,801 3,450 50,445 New York 180,847 58,426 125,158 15,666 6,258 113 386,468 North Carolina 512,231 59,433 22,020 8,766 6,287 696 609,433 North Dakota 137,371 9,582 6,455 5,986 533 66 159,994 Ohio 1,116,095 115,155 19,810 15,425 7,336 22 1,273,843 (continued)5A- 7

**Table 5A** **- 2**  **. 2005 Base Year SO**  **2 Emissions (tons/year) for States by Sector**  **(continued) State**  **EGU Non**  **- EGU**  **Nonpoint Nonroad**  **Onroad Fires**  **Total** Oklahoma 110,081 40,482 8,556 5,015 3,039 469 167,642 Oregon 12,304 9,825 9,845 5,697 1,790 4,896 44,357 Pennsylvania 1,002,203 83,375 68,349 11,999 6,266 32 1,172,224 Rhode Island 176 2,743 3,365 816 254 1 7,354 South Carolina 218,781 31,495 13,489 7,719 3,589 646 275,719 South Dakota 12,215 1,702 10,347 3,412 623 498 28,797 Tennessee 266,148 65,693 32,714 6,288 5,538 277 376,659 Texas 534,949 223,625 115,192 34,944 16,592 1,178 926,480 Tribal 3 1,511 0 0 0 0 1,515 Utah 34,813 9,132 3,577 2,439 1,890 1,934 53,784 Vermont 9 902 5,385 385 342 49 7,073 Virginia 220,287 69,401 32,923 10,095 4,600 399 337,705 Washington 3,409 24,211 7,254 18,810 3,343 407 57,433 West Virginia 469,456 46,710 14,589 2,133 1,378 215 534,481 Wisconsin 180,200 66,807 6,369 7,163 3,647 70 264,256 Wyoming 89,874 22,321 6,721 2,674 721 1,106 123,417 **Total 10,380,883**  **2,030,759 1,216,362**  **446,831 168,480**  **49,094 14,292,410**  **Table 5A** **- 3**  **. 2005 Base Year PM**  **2.5 Emissions (** **tons/year) for States by Sector State**  **EGU Non**  **- EGU**  **Nonpoint Nonroad**  **Onroad Fires**  **Total** Alabama 23,366 19,498 35,555 4,142 5,775 13,938 102,273 Arizona 7,418 3,940 21,402 4,486 6,920 37,151 81,316 Arkansas 1,688 10,820 34,744 3,803 3,102 10,315 64,472 California 347 21,517 94,200 22,815 26,501 97,302 262,682 Colorado 4,342 7,116 25,340 3,960 4,377 24,054 69,189 Connecticut 562 224 11,460 1,740 2,544 56 16,586 Delaware 2,169 1,810 1,590 818 922 87 7,397 District of Columbia 17 172 589 277 367 0 1,421 (continued)5A- 8

**Table 5A** **- 3**  **. 2005 Base Year PM**  **2.5 Emissions (** **tons/year) for States by Sector (continued) State**  **EGU Non**  **- EGU**  **Nonpoint Nonroad**  **Onroad Fires**  **Total** Florida 24,217 25,193 52,955 15,035 16,241 99,484 233,125 Georgia 28,057 12,666 63,133 6,504 12,449 24,082 146,892 Idaho 0 2,072 41,492 2,140 1,402 52,808 99,914 Illinois 16,585 15,155 74,045 12,880 12,574 277 131,516 Indiana 34,439 14,124 74,443 6,515 7,585 344 137,450 Iowa 8,898 6,439 54,312 6,969 3,468 349 80,436 Kansas 5,549 7,387 138,437 5,719 3,109 1,468 161,669 Kentucky 19,830 10,453 31,245 4,762 5,566 5,155 77,010 Louisiana 5,599 32,201 28,164 9,440 4,288 12,647 92,339 Maine 52 3,783 15,037 1,363 1,759 2,127 24,120 Maryland 15,417 6,768 23,323 3,410 5,504 531 54,952 Massachusetts 3,110 2,245 31,116 3,293 5,913 1,324 47,001 Michigan 11,022 12,926 47,722 8,561 13,006 1,283 94,520 Minnesota 3,262 10,538 73,990 8,541 6,842 8,943 112,116 Mississippi 2,029 10,602 34,217 4,133 4,195 14,897 70,074 Missouri 6,471 6,966 76,419 7,230 7,665 2,636 107,388 Montana 2,398 2,729 30,096 2,654 1,347 17,311 56,536 Nebraska 1,246 2,340 45,661 5,848 2,620 1,483 59,198 Nevada 3,341 4,095 9,920 2,212 1,290 19,018 39,876 New Hampshire 2,586 568 13,316 907 1,512 534 19,423 New Jersey 4,625 2,588 13,623 5,042 5,963 865 32,707 New Mexico 5,583 1,460 50,698 1,959 2,861 48,662 111,224 New York 9,648 4,994 48,540 8,607 11,139 1,601 84,529 North Carolina 16,967 12,665 49,551 6,272 8,939 9,870 104,264 North Dakota 6,397 598 41,504 4,552 976 934 54,962 Ohio 53,572 12,847 52,348 9,847 11,785 316 140,715 Oklahoma 1,411 6,246 90,047 3,765 4,559 6,644 112,672 Oregon 412 8,852 58,145 3,741 3,375 65,350 139,874 Pennsylvania 55,547 16,263 44,607 7,565 11,058 454 135,494 (continued)5A- 9

**Table 5A** **- 3**  **. 2005 Base Year PM**  **2.5 Emissions (** **tons/year) for States by Sector (continued) State**  **EGU Non**  **- EGU**  **Nonpoint Nonroad**  **Onroad Fires**  **Total** Rhode Island 10 256 1,289 394 577 14 2,540 South Carolina 14,455 4,779 26,598 3,491 5,061 9,163 63,548 South Dakota 390 2,982 33,678 2,910 1,056 7,062 48,079 Tennessee 12,856 21,912 32,563 5,072 8,514 3,934 84,851 Texas 21,464 37,563 194,036 21,361 29,859 21,578 325,861 Tribal 0 1,569 0 0 0 0 1,569 Utah 5,055 3,595 14,761 1,627 2,703 27,412 55,153 Vermont 37 337 6,943 479 605 696 9,098 Virginia 12,357 11,455 38,140 5,968 6,661 5,659 80,241 Washington 2,396 4,618 45,599 6,697 6,721 4,487 70,519 West Virginia 26,377 5,154 14,778 1,702 1,930 3,050 52,991 Wisconsin 5,233 7,967 37,277 6,083 6,783 994 64,337 Wyoming 8,068 10,298 31,645 1,455 1,103 15,686 68,254 **Total 496,877**  **433,346 2,110,298**  **268,745 301,073**  **684,035 4,294,373** 
**5A.4 Development of Future year baseline Emissions**  The future year baseline scenario , also known as the “reference case”, represent s predicted emissions including adjustments for known promulgated federal measures for all sectors as of the year 2017, which is assumed to be representative of 2016 . T he EGU and mobile sectors reflect projected economic and fuel usage changes . Emissions from non - EGU stationary sectors have previously been shown to not be well correlated with economic forecasts, and therefore econom ic impacts were not included for non - EGU stationary sources. Like the 2005 base case, these emissions cases include criteria pollutants and for some sectors, benzene, formaldehyde, acetaldehyde and methanol from the inventory is used in VOC speciation. The future year baseline scenario represents predicted emissions in the absence of any further controls beyond those Federal measures already promulgated. For EGUs, all state and other programs available at the time of modeling have been included. For mobile sources, all national measures promulgated at the time of modeling have been included. Additional details on the future year baseline (i.e., reference case) emissions modeling can be found in the5A- 10

“ Emissions Modeling for the Final Mercury and Air Toxics Sta ndards Technical Support Document”. The future year baseline EGU emissions were obtained using version 4.10 Final of the Integrated Planning Model (IPM) ( http://www.epa.gov/airmarkt/pro gsregs/epa
ipm/index.html ). The IPM is a multiregional, dynamic, deterministic linear programming model of the U.S. electric power sector. Version 4.10 Final reflects state rules and consent decrees through December 1, 2010, information obtained from the 2 010 Information Collection Request (ICR), and information from comments received on the IPM - related Notice of Data Availability (NODA) published on September 1, 2010. Notably, IPM 4.1 Final included the addition of over 20 GW of existing Activated Carbon I njection (ACI) for coal -fired EGUs reported to EPA via the ICR. Additional unit - level updates that identified existing pollution controls (such as scrubbers) were also made based on the ICR and on comments from the IPM NODA. Units with SO 2 or NO X advanced controls (e.g., scrubber, SCR) that were not required to run for compliance with Title IV, New Source Review (NSR), state settlements, or state - specific rules were modeled by IPM to either operate those controls or not based on economic efficiency parameters. The IPM run for future year baseline case modeled with CMAQ assumed that 100% of the HCl found in the coal was emitted into the atmosphere. However, in the final IPM results for the rule, neutralization of 75% of the available HCl was include d based on recent findings. Further details on the future year baseline EGU emissions inventory used for this rule can be found in the IPM v.4.10 Documentation, available at http: //www.epa.gov/airmarkets/progsregs/epa
- ipm/transport.html . The future year baseline modeled in IPM for this rule includes estimates of emissions reductions that will result from the Cross - State Air Pollution Rule. However, reductions from the Boiler MACT r ule were not represented this modeling because the rule was stayed at the time the modeling was performed. A complete list of state regulations, NSR settlements, and state settlements included in the IPM modeling is given in Appendices 3 - 2, 3 - 3, and 3 -4 be ginning on p. 68 of http://www.epa.gov/airmarkets/progsregs/epa
ipm/CSAPR/docs/DocSuppv410_FTransport.pdf . For the future year baseline EGU emissions, the I PM outputs for 2020 , which are also representative of the year 2017, were used as part of the 2017 reference case modeling. These emissions were very similar to the year 2015 emissions output from the same IPM modeling case. I nventories of onroad mobile em issions for the future year baseline and control cases were created using the MOVES model with an approach consistent with the 2005 base year. As with the 2005 emissions, the future year onroad emissions were based on emission factors5A- 11

developed using the T ier 3 version of MOVES processed through the SMOKE - MOVES interface . Future -year vehicle miles travelled (VMT) were projected from the 2005 NEI v2 VMT using growth rates from the 2009 Annual Energy Outlook (AEO) data. The VMT for heavy duty diesel vehicles class 8a and 8b was updated based on data from Oak Ridge National Laboratory. The future year onroad emissions reflect control program implementation through 2017 and include the Light - Duty Vehicle Tier 2 Rule, the Onroad Heavy- Duty Rule, the Mobile Source Air Toxics (MSAT) final rule, and the Renewable Fuel Standard ve rsion 2 (RFS2) . Future year n onroad mobile emissions were created using NMIM to run NONROAD in a consistent manner as was done for 2005, but with future -year equipment population estimates , fuels, and control programs. The fuels in 2017 are assumed to be E10. Emissions for locomotives and category 1 and 2 (C1 and C2) commercial marine vessels were derived based on emissions published in the Final Locomotive Marine Rule, Regulatory Impact Asse ssment, Chapter 3 (see http://www.epa.gov/otaq/locomotives.htm#2008final). The future year baseline nonroad mobile emissions reductions include emissions reductions to locomotives, various nonroad engines including diesel engines and various marine engine types, fuel sulfur content, and evaporative emissions standards, including the category 3 marine residual and diesel fuelled engines and International Maritime Organization standards which include the establishment of emission control areas for these ships . A summary of the onroad and nonroad mobile source control programs included in the projected future year baseline is shown in Table 5A- 4. **T able 5A** **- 4.**  **Summary of Mobile Source Control Programs Included in the Future Year** 

**Baseline National Onroad Rules:**  Tier 2 r ule (Signature date: February 28, 2000) Onroad heavy- duty rule (February 24, 2009) Final mobile source air toxics rule (MSAT2) (February 9, 2007) Renewable fuel standard Version 2 (March 26, 2010) Light duty greenhouse gas standards (May, 2010) Corp orate Average Fuel Economy (CAFE ) standards for 2008–2011 **Local Onroad Programs:** National low emission vehicle program (NLEV) (March 2, 1998) Ozone transport commission (OTC) LEV Program (January, 1995) (continued)5A- 12

**T able 5A** **- 4.**  **Summary of Mobile Source Control Programs Included in the Future Year** 

**Baseline (continued)**  **National Nonroad Controls:** Tier 1 nonroad diesel rule (June 17, 2004) Phase 1 nonroad SI rule (July 3, 1995) Marine SI rule (October 4, 1996) Nonroad large SI and recreational engine rule (November 8, 2002) Clean Air Nonroad Diesel Rule — Tier 4 (June 29, 2004) Locomotive and marine rule (May 6, 2008) Nonroad SI rule (October 8, 2008) **Aircraft:** Itinerant (ITN) operations at airports adjusted to year 2017 **Locomotives:** Locomotive Emissions Final Rulemaking (December 17, 1997) Clean Air nonroad diesel final rule — Tier 4 (June 29, 2004) Locomotive rule (April 16, 2008) Locomotive and marine rule (May 6, 2008) **Commercial Marine:** Locomotive and marine rule (May 6, 2008) EIA fuel consumption projections for diesel -fueled vessels Clean Air Nonroad Diesel Final Rule – Tier 4 Emissions Standards for Commercial Marine Diesel Engines (December 29,1999) Tier 1 Marine Diesel Engines (February 28, 2003) Category 3 marine diesel engines Clean Air Act and International Maritime Organization standards (April, 30, 2010)
For non - EGU point sources, emissions were projected by including emissions reductions and increases from a variety of source data. Other than for certain large municipal waste combustors and airports, non - EGU point source emissions were not grown using economic growth projections, but rather were held constant at the emissions levels in 2005. Emissions reductions were applied to non - EGU point source to reflect final federal measures, known plant closures, and consent decrees. The starting inventories for this rule were the projected5A- 13

emission inventories developed for the 2005v4.2 platform for the final Cross - State Air Pollution Rule (see http://www.epa.gov/ttn/chief/emch/index.html#final ). The most significant updates to the emission projections for this rule are the addition of future year ethanol, biodiesel and cellulosic plants that account for increased ethanol production from the Renewable Fuel Standard Rule that is incorporated in to the base case for 2017. Since aircraft at airports were treated as point emissions sources in the 2005 NEI v2, we developed future year baseline emissions for airports by applying projection factors based on activity growth projected by the Federal Aviation Administration Terminal Area Forecast (TAF) system, published in January 2010 for these sources. Emissions from stationary nonpoint sources were projected using procedures specific to individual source categories. Refueling emissions were projected using refueling emissions from MOVES inventory mode runs. Portable fuel container emissions were projected using estimates from previous rulemaking in ventories compiled by the Office of Transportation and Air Quality (OTAQ). Emissions of ammonia and dust from animal operations were projected based on animal population data from the Department of Agriculture and EPA. Residential wood combustion emissions were projected by replacement of obsolete woodstoves with new woodstoves and a 1 percent annual increase in fireplaces. Landfill emissions were projected using MACT controls. Other nonpoint sources were held constant between the 2005 and future year scena rios. A summary of all rules and growth assumptions impacting non - EGU stationary sources is provided in Table 5A- 5, along with the affected pollutants. Note that reductions associated with the Boiler MACT are not included in the future year baseline. **Table 5A** **- 5.**  **Control Strategies and/or Growth Assumptions Included in the Future Year**

**Baseline for Non**  **- EGU Stationary Sources**  MACT rules, national, VOC: national applied by SCC, MACT VOC Consent decrees and settlements, including refinery consent decrees, and s ettlements for: Alcoa, TX and Premcor (formerly MOTIVA), DE All Municipal waste combustor reductions — plant level PM Hazardous waste combustion PM Hospital/medical/infectious waste incinerator regulations NO x, PM, SO 2 Large municipal waste combustors — growth applied to specific plants All (continued)5A- 14

**Table 5A** **- 5.**  **Control Strategies and/or Growth Assumptions Included in the Future Year**

**Baseline for Non**  **- EGU Stationary Sources**  **(continued)** MACT rules, plant - level, VOC: auto plants VOC MACT rules, plant - level, PM & SO 2 : lime manufacturing PM, SO 2 MACT rules, plant - level, PM: taconite ore PM Municipal waste landfills: projection factor of 0.25 applied All Livestock emissions growth from year 2002 to year 2017 NH 3, PM Residential wood combustion growth and change - outs from years 2005 to year 2017 All Gasoline Stage II growth and control via MOVES from year 2005 to year 2017 VOC Portable fuel container mobile source air toxics rule 2: inventory growth and control from year 2005 to year 2017 VOC NESHAP: Portland Cement (09/09/10) — plant level based on industrial sector integrated solutions (ISIS) policy emissions in 2013. The ISIS results are from the ISIS cement model runs for the NESHAP and NSPS analysis of July 28, 2010 and include closures. Hg, NO X, SO 2, PM, HCl

New York ozone SIP standards VOC, HAP VOC, NO X Additional plant and unit closures provided by state, regional, and EPA agencies All Emission reductions resulting from controls put on specific boiler units (not due to MACT) after 200 5, identified through analysis of the control data gathered from the ICR from the ICI boiler NESHAP. NO X, SO 2, HCL NESHAP: Reciprocating Internal Combustion Engines (RICE). NO X, CO, PM, SO 2 RICE controls applied to Phase II WRAP 2018 oil and gas emissio ns VOC, SO 2, NO X, CO RICE controls applied to 2008 Oklahoma and Texas Oil and gas emissions VOC, SO 2, NO X, CO, PM Ethanol plants that account for increased ethanol due to RFS2 All State fuel sulfur content rules for fuel oil — effective in 2017, only in Maine, New Jersey, and New York SO 2 In all future year cases, the same emissions were used for Canada and Mexico as were used in the 2005 base case because appropriate future year emissions for sources in these countries were not available. Future year emissions need to reflect expected percent reductions or increases between the base year and the future year to be considered appropriate for this type of modeling and such emissions were not available. Table 5A- 6 shows a summary of the 2005 and modeled fu ture year baseline emissions for the lower 48 states. Tables 5A- 7 and 5A- 8 below provide summaries of SO 2 and PM 2.5 in the5A- 15

2017 baseline for each sector by state. Table 5A- 9 shows the future year baseline EGU emissions by state for all criteria air polluta nts. **T able 5A** **- 6**  **. Summary of Modeled Base Case Annual Emissions (tons/year) for 48 States by** 

**Sector: SO**  **2 and**  **PM 2.5**  **Source Sector SO 2**  **Emissions 2005**  **2017** EGU p oint 10,380,883 3,281,364 Non - EGU p oint 2,030,759 1,534,991 Nonpoint 1,216,362 1,125,985 Nonroad 446,831 15,759 On - road 168,480 29,288 Average fire 49,094 49,094 **Total SO 2** **, all sources 14,292,410**  **6,036,480**

**Source Sector PM 2.5**  **Emissions 2005**  **2017** EGU point 496,877 276,430 Non - EGU point 433,346 411,437 Nonpoint 2,110,298 1,912,757 Nonroad 268,745 150,221 On - road 301,073 129,416 Average fire 684,035 684,035 **Total PM 2.5** **, all sources 4,294,373**  **3,564,296 Table**  **5A -**  **7 .**  **Future Year Baseline SO**  **2 Emissions (tons/year) for States by Sector**  **State EGU**  **Non -**  **EGU Nonpoint**  **Nonroad Onroad**  **Fires Total**  Alabama 186,084 63,053 52,341 146 569 983 303,177 Arizona 36,996 24,191 2,467 59 724 2,888 67,324 Arkansas 92,804 12,160 26,801 123 314 728 132,929 California 5,346 21,046 67,846 3,311 2,087 6,735 106,370 Colorado 74,255 1,415 6,210 50 532 1,719 84,181 Connecticut 3,581 1,833 18,149 100 311 4 23,978 (continued)5A- 16

**Table 5A** **- 7**  **. Future Year Baseline**  **SO 2**  **Emissions (tons/year) for States by Sector (continued)**  **State EGU**  **Non -**  **EGU Nonpoint**  **Nonroad Onroad**  **Fires Total**  Delaware 2,835 4,770 1,018 500 91 6 9,220 District of Columbia 5 686 1,505 3 38 0 2,237 Florida 117,702 49,082 70,073 1,255 2,111 7,018 247,241 Georgia 96,712 44,248 55,946 192 1,158 2,010 200,266 Idaho 182 17,133 2,894 23 162 3,845 24,240 Illinois 118,217 81,683 5,650 295 1,107 20 206,971 Indiana 200,969 73,930 59,771 150 760 24 335,604 Iowa 85,178 22,865 19,929 86 324 25 128,407 Kansas 45,740 10,288 36,140 57 294 103 92,622 Kentucky 116,927 23,530 33,852 215 463 364 175,350 Louisiana 142,447 129,730 2,669 1,449 447 892 277,634 Maine 2,564 14,285 2,007 72 149 150 19,226 Maryland 29,786 33,562 40,642 494 593 32 105,110 Massachusetts 15,133 17,077 24,907 266 565 93 58,041 Michigan 163,168 48,697 42,185 448 995 91 255,584 Minnesota 52,380 24,742 14,635 220 558 631 93,164 Mississippi 34,865 24,284 6,635 208 396 1,051 67,440 Missouri 178,143 33,757 44,680 191 722 186 257,679 Montana 24,018 7,212 1,875 25 106 1,422 34,657 Nebraska 70,910 6,885 7,899 58 202 105 86,058 Nevada 14,140 2,132 12,028 27 200 1,346 29,873 New Hampshire 6,719 2,471 7,284 21 137 38 16,671 New Jersey 9,042 6,700 9,528 686 757 61 26,774 New Mexico 10,211 7,813 2,719 26 262 3,450 24,480 New York 14,653 45,222 71,060 659 1,466 113 133,173 North Carolina 71,113 58,517 21,713 197 890 696 153,125 North Dakota 105,344 9,915 5,559 36 71 66 120,991 Ohio 180,935 93,600 19,777 373 1,093 22 295,799 (continued)5A- 17

**Table 5A** **- 7**  **. Future Year Baseline**  **SO 2**  **Emissions (tons/year) for States by Sector (continued)**  **State EGU**  **Non -**  **EGU Nonpoint**  **Nonroad Onroad**  **Fires Total**  Oklahoma 141,433 27,873 7,731 49 501 469 178,056 Oregon 13,211 9,790 9,508 218 361 4,896 37,985 Pennsylvania 126,316 64,697 67,650 427 1,060 32 260,183 Rhode Island 0 2,745 3,338 33 85 1 6,202 South Carolina 103,694 28,536 13,310 294 500 646 146,980 South Dakota 29,711 1,655 10,301 23 86 498 42,273 Tennessee 33,080 59,145 32,624 154 757 277 126,037 Texas 249,748 129,667 108,633 1,146 2,483 1,178 492,855 Tribal 0 676 0 0 0 0 676 Utah 34,912 6,599 3,365 27 291 1,934 47,128 Vermont 264 902 5,283 8 129 49 6,634 Virginia 51,004 50,387 32,439 275 849 399 135,353 Washington 5,569 19,780 6,885 881 633 407 34,156 West Virginia 84,344 32,458 14,322 64 178 215 131,582 Wisconsin 50,777 61,080 6,260 122 633 70 118,941 Wyoming 48,198 20,491 5,944 18 87 1,106 75,844 **Total 3,281,364**  **1,534,991 1,125,985**  **15,759 29,288**  **49,094 6,036,480**  **Table 5A -**  **8 .**  **Future Year Baseline PM**  **2.5 Emissions**  **(tons/year) for States by Sector State**  **EGU Non**  **- EGU**  **Nonpoint Nonroad**  **Onroad Fires**  **Total** Alabama 13,154 17,052 33,235 2,403 2,217 13,938 81,999 Arizona 3,889 3,809 20,214 2,674 2,762 37,151 70,498 Arkansas 2,838 10,527 33,486 2,042 1,242 10,315 60,450 California 475 20,693 73,607 14,875 13,492 97,302 220,443 Colorado 3,845 7,037 19,868 2,350 2,387 24,054 59,540 Connecticut 400 222 6,838 1,038 1,414 56 9,968 Delaware 434 772 1,207 383 375 87 3,259 (continued)5A- 18

**Table 5A -**  **8 .**  **Future Year Baseline PM**  **2.5 Emissions**  **(tons/year) for States by Sector (continued)**  **State EGU**  **Non -**  **EGU Nonpoint**  **Nonroad Onroad**  **Fires Total**  District of Columbia 1 172 536 139 154 0 1,002 Florida 12,723 24,620 50,472 8,100 7,652 99,484 203,050 Georgia 13,445 12,105 59,412 3,803 4,863 24,082 117,711 Idaho 36 2,076 40,288 1,186 714 52,808 97,108 Illinois 8,587 13,471 70,775 6,885 4,926 277 104,922 Indiana 22,354 13,570 72,501 3,491 3,380 344 115,640 Iowa 4,298 7,000 51,684 3,348 1,519 349 68,198 Kansas 3,199 6,895 136,633 2,872 1,268 1,468 152,335 Kentucky 12,078 10,353 26,811 2,717 2,059 5,155 59,173 Louisiana 3,093 30,865 27,082 5,107 1,673 12,647 80,467 Maine 355 3,543 8,213 881 750 2,127 15,869 Maryland 3,969 6,382 18,960 1,975 2,492 531 34,310 Massachusetts 1,465 2,123 23,729 1,914 2,590 1,324 33,145 Michigan 8,102 11,688 43,055 4,696 4,949 1,283 73,773 Minnesota 2,598 9,867 68,121 4,483 2,882 8,943 96,893 Mississippi 2,201 10,492 31,474 2,337 1,525 14,897 62,926 Missouri 7,061 6,384 69,722 3,954 3,059 2,636 92,816 Montana 3,870 2,562 28,479 1,332 492 17,311 54,048 Nebraska 2,358 2,834 44,904 2,967 919 1,483 55,465 Nevada 2,505 4,032 9,351 1,319 857 19,018 37,083 New Hampshire 1,130 464 8,981 576 663 534 12,348 New Jersey 2,452 2,520 8,559 2,929 3,244 865 20,569 New Mexico 3,153 1,442 49,789 1,148 1,103 48,662 105,298 New York 2,331 4,859 44,334 5,032 6,723 1,601 64,879 North Carolina 9,983 12,656 43,398 3,583 3,521 9,870 83,011 North Dakota 5,870 795 40,802 2,126 383 934 50,910 Ohio 18,920 12,353 47,811 5,302 5,013 316 89,715 Oklahoma 3,530 5,695 88,862 2,029 2,006 6,644 108,767 (continued)5A- 19

**Table 5A -**  **8 .**  **Future Year Baseline**  **PM 2.5**  **Emissions (tons/year) for States by Sector**  **(continued) State**  **EGU Non**  **- EGU**  **Nonpoint Nonroad**  **Onroad Fires**  **Total** Oregon 381 8,869 39,503 2,148 1,627 65,350 117,877 Pennsylvania 16,727 14,874 38,523 4,582 4,854 454 80,014 Rhode Island 4 256 1,070 222 383 14 1,949 South Carolina 9,997 4,527 23,430 1,932 1,929 9,163 50,978 South Dakota 737 2,399 32,697 1,339 416 7,062 44,650 Tennessee 5,053 21,553 28,449 2,939 3,057 3,934 64,985 Texas 21,677 34,648 187,604 11,901 9,289 21,578 286,698 Tribal 1 1,568 0 0 0 0 1,569 Utah 4,524 3,530 13,978 963 1,318 27,412 51,724 Vermont 67 336 4,930 307 653 696 6,989 Virginia 4,529 10,165 32,254 3,507 3,446 5,659 59,561 Washington 1,444 4,421 35,706 3,328 2,874 4,487 52,259 West Virginia 13,602 4,281 12,951 1,048 762 3,050 35,695 Wisconsin 5,323 7,853 27,656 3,161 3,148 994 48,135 Wyoming 5,662 10,225 30,812 850 392 15,686 63,626 **Total 276,430**  **411,437 1,912,757**  **150,221 129,416**  **684,035 3,564,296**  **Table 5A -**  **9 .**  **Future Year Baseline EGU CAP Emissions**  **(tons/year) by State State**  **CO NO**  **X VOC**  **SO 2**  **NH 3**  **PM 10**  **PM 2.5**  Alabama 27,024 64,064 1,524 186,084 1,472 16,686 13,154 Arizona 16,797 36,971 825 36,996 1,163 5,038 3,889 Arkansas 9,925 36,297 658 92,804 560 3,507 2,838 California 45,388 20,910 1,031 5,346 2,519 580 475 Colorado 9,006 50,879 636 74,255 398 4,605 3,845 Connecticut 9,180 2,738 139 3,581 313 431 400 Delaware 4,256 2,452 132 2,835 119 580 434 District of Columbia 67 11 2 5 3 1 1 (continued)5A- 20

**Table 5A -**  **9 .**  **Future Year Baseline EGU CAP Emissions**  **(tons/year) by State (continued) State**  **CO NO**  **X VOC**  **SO 2**  **NH 3**  **PM 10**  **PM 2.5**  Florida 72,915 83,174 2,253 117,702 3,997 19,098 12,723 Georgia 16,537 43,778 1,293 96,712 903 18,668 13,445 Idaho 1,532 613 41 182 57 38 36 Illinois 51,862 56,128 3,091 118,217 1,437 9,926 8,587 Indiana 30,587 106,881 2,295 200,969 1,317 33,816 22,354 Iowa 8,316 42,698 791 85,178 452 5,735 4,298 Kansas 5,066 25,163 683 45,740 305 3,996 3,199 Kentucky 37,287 71,259 1,604 116,927 928 16,279 12,078 Louisiana 32,626 33,509 852 142,447 1,427 3,677 3,093 Maine 12,789 6,121 306 2,564 269 366 355 Maryland 13,446 17,933 533 29,786 301 5,322 3,969 Massachusetts 7,128 7,991 279 15,133 395 1,915 1,465 Michigan 25,856 66,846 1,497 163,168 874 11,056 8,102 Minnesota 9,365 36,867 746 52,380 460 3,034 2,598 Mississippi 9,704 27,319 440 34,865 469 3,113 2,201 Missouri 16,499 52,464 1,714 178,143 740 9,093 7,061 Montana 5,266 20,946 338 24,018 198 6,117 3,870 Nebraska 4,691 28,898 542 70,910 292 2,948 2,358 Nevada 9,677 15,627 438 14,140 953 3,095 2,505 New Hampshire 5,667 4,908 206 6,719 207 1,234 1,130 New Jersey 25,831 11,178 823 9,042 747 2,948 2,452 New Mexico 9,079 65,189 574 10,211 570 3,833 3,153 New York 19,731 21,172 731 14,653 1,076 3,248 2,331 North Carolina 17,367 44,141 1,076 71,113 654 13,368 9,983 North Dakota 7,437 53,778 867 105,344 383 6,757 5,870 Ohio 33,481 93,150 2,005 180,935 1,317 25,688 18,920 Oklahoma 26,165 47,454 957 141,433 1,073 4,457 3,530 Oregon 5,905 10,828 203 13,211 381 446 381 (continued)5A- 21

**Table 5A -**  **9 .**  **Future Year Baseline EGU CAP Emissions**  **(tons/year) by State (continued) State**  **CO NO**  **X VOC**  **SO 2**  **NH 3**  **PM 10**  **PM 2.5**  Pennsylvania 38,767 123,501 2,023 126,316 1,522 22,117 16,727 Rhode Island 1,748 456 44 0 136 7 4 South Carolina 10,305 37,516 726 103,694 515 14,469 9,997 South Dakota 742 14,293 129 29,711 48 764 737 Tennessee 10,693 16,982 862 33,080 406 6,313 5,053 Texas 78,317 145,182 4,975 249,748 5,304 31,404 21,677 Tribal 601 73 15 0 47 2 1 Utah 5,632 67,476 526 34,912 279 5,843 4,524 Vermont 1,868 458 52 264 25 69 67 Virginia 30,205 39,408 821 51,004 1,115 5,404 4,529 Washington 7,183 14,284 326 5,569 346 1,706 1,444 West Virginia 15,496 54,247 1,320 84,344 658 18,415 13,602 Wisconsin 19,247 35,179 1,137 50,777 649 6,503 5,323 Wyoming 9,087 71,380 970 48,198 481 7,385 5,662 **Total 873,344**  **1,930,769 46,050**  **3,281,364 40,259**  **371,101 276,430**  Note: Emission estimates apply to all fossil Electric Generating Units, including those with capacity < 25MW . **5A.5 Development of Future Year**  **Control Case Emissions for Air Quality Modeling** For the future year control case ( i.e., policy case) air quality modeling**,** the emissions for all sectors were unchanged from the base case modeling except for those from EGUs. The IPM model was used to prepar e the future year policy case for EGU emissions . The air quality modeling for MATS relied on EGU emission projections from an interim IPM platform based on the Cross - state Air Pollution Rule version 4.10_FTransport , and was subsequently updated during the rulemaking process. The updates made include: updated assumptions regarding the removal of HCl by alkaline fly ash in subbituminous and lignite coals; an update to the fuel based mercury emission factor for petroleum coke, which was corrected based on re e xamination of the 1999 ICR data; updated capital cost for new nuclear capacity and nuclear life extension costs; corrected variable operating and maintenance cost (VOM) for ACI retrofits; adjusted coal rank availability for some units, consistent with EIA From 923 (2008); updated state rules in Washington and Colorado; and numerous unit - level revisions based on comments received through the notice and comment process. In particular, the policy case modeled with5A- 22

CMAQ did not include the neutralization of 75% of HCl as did the final policy case. Additional details on the version of IPM used to develop the control case are available in Chapter 3. The changes in EGU SO 2, and PM 2.5 emissions as a result of the policy case for the lower 48 sta tes are summarized i n Table 5A- 10 . Table 5A- 11 shows the CAP emissions for the modeled MATS control case by State. State - specific difference summaries of EGU SO 2 and PM 2.5 for the sum of the lower 48 states are shown in Tables 5A- 1 2 and 5A- 1 3 , respectively. **Ta ble 5A** **- 10**  **. Summ**  **ary of Emissions Changes for the MATS AQ Modeling**  **in the Lower 48**

**States Future Year EGU Emissions**  **SO 2**  **PM 2.5**  Base case EGU emissions (tons) 3,281,364 276,430 Control case EGU emissions (tons) 1,866,247 223,320 Reductions to base case in control case (tons) 1,415,117 53,110 Percentage reduction of base EGU emissions 43% 19%

**Total Man -**  **M ade Emissions**  **a** Total base case emissions (tons) 6,036,480 3,564,296 Total control case emissions (tons) 4,621,363 3,511,186 Percentage reduction of all man - made emissions 23% 1%

a In this table, man - made emissions includes average fires. **Ta ble 5A** **- 11**  **. EGU**  **Emissions Totals**  **for the Modeled MATS**  **Control Case in the Lower 48 States**  **State CO**  **NO X**  **VOC SO**  **2 NH**  **3 PM**  **10 PM**  **2.5** Alabama 20,873 61,863 1,313 68,517 1,235 9,734 7,844 Arizona 13,238 34,804 749 23,459 921 4,264 3,494 Arkansas 9,036 35,788 642 35,112 490 1,696 1,593 California 56,360 27,159 1,307 5,041 2,548 1,057 942 Colorado 8,219 44,409 582 19,564 358 3,492 2,859 Connecticut 8,017 2,800 136 1,400 313 439 412 Delaware 1,312 2,527 67 4,160 93 3,056 1,455 District of Columbia Florida 66,378 61,676 2,055 64,791 3,482 16,434 11,377 (continued)5A- 23

**Ta ble 5A** **- 11**  **. EGU**  **Emissions Totals**  **for the Modeled MATS**  **Control Case in the Lower 48 States** 

**(continued) State**  **CO NO**  **X VOC**  **SO 2**  **NH 3**  **PM 10**  **PM 2.5**  Georgia 14,217 41,006 1,197 78,197 790 11,165 9,742 Idaho 1,523 609 41 182 56 38 36 Illinois 24,365 50,655 2,353 103,867 1,050 7,309 6,588 Indiana 17,061 102,045 1,872 156,781 1,110 29,683 20,388 Iowa 7,340 41,247 747 48,030 410 3,318 2,947 Kansas 4,683 22,136 623 22,767 282 2,504 2,263 Kentucky 25,911 70,126 1,476 125,430 882 12,544 10,635 Louisiana 28,171 31,655 767 30,509 1,261 2,003 1,899 Maine 10,992 5,683 302 1,372 267 342 331 Maryland 4,283 16,554 400 18,091 211 3,851 3,143 Massachusetts 5,408 7,211 226 5,033 344 1,702 1,267 Michigan 18,792 60,982 1,215 82,834 718 8,261 6,893 Minnesota 8,699 34,942 709 33,214 430 3,332 2,936 Mississippi 8,782 20,749 410 15,975 397 1,949 1,720 Missouri 12,249 52,755 1,605 95,965 686 5,216 4,809 Montana 2,223 19,758 264 6,399 133 2,637 1,727 Nebraska 4,493 28,180 533 34,631 277 2,152 1,828 Nevada 7,178 14,382 336 6,372 725 2,626 2,073 New Hampshire 6,781 4,862 232 2,102 232 1,336 1,264 New Jersey 8,350 7,699 315 6,404 546 2,020 1,583 New Mexico 7,987 64,922 545 9,984 554 2,961 2,750 New York 18,725 20,863 699 28,174 1,086 3,123 2,350 North Carolina 15,195 35,309 1,033 59,551 602 8,885 7,988 North Dakota 7,266 53,267 858 23,889 371 5,940 5,051 Ohio 29,956 85,565 1,852 139,208 1,229 19,599 15,823 Oklahoma 26,687 44,725 892 44,602 970 2,293 2,056 Oregon 6,002 9,671 198 3,565 379 241 233 Pennsylvania 24,865 104,906 1,645 93,606 1,349 17,330 14,080 (continued)5A- 24

**Ta ble 5A** **- 11**  **. EGU**  **Emissions Totals**  **for the Modeled MATS**  **Control Case in the Lower 48 States** 

**(continued) State**  **CO NO**  **X VOC**  **SO 2**  **NH 3**  **PM 10**  **PM 2.5**  Rhode Island 1,721 443 43 0 134 7 4 South Carolina 9,826 37,849 725 40,901 459 9,627 6,963 South Dakota 641 14,290 117 2,483 41 260 245 Tennessee 5,551 16,931 723 42,666 334 6,721 5,272 Texas 71,475 138,086 4,444 105,958 4,774 25,359 17,601 Tribal 266 32 7 0 21 1 1 Utah 4,003 65,286 474 17,007 241 4,755 3,896 Vermont 1,868 458 52 264 25 69 67 Virginia 26,778 37,255 707 33,704 748 5,306 4,506 Washington 6,334 3,834 179 854 254 183 176 West Virginia 13,923 47,836 1,263 66,857 632 14,321 11,572 Wisconsin 16,124 32,865 1,012 28,322 578 4,725 3,969 Wyoming 7,516 71,135 932 28,456 467 5,946 4,671 **Total 707,640**  **1,789,790 40,875**  **1,866,247 35,493**  **281,811 223,320** 
**T able 5A** **- 12**  **. State Specific Changes in Annual EGU SO**  **2 for the Lower 48 States** 

**State Future Year** 

**Baseline SO 2**  **(tons) Future Year Policy** 

**Case SO 2**  **(tons) EGU SO**  **2 Reduction** 

**(tons) EGU SO**  **2 Reduction**  **(%)** Alabama 186,084 68,517 117,568 63% Arizona 36,996 23,459 13,537 37% Arkansas 92,804 35,112 57,692 62% California 5,346 5,041 305 6% Colorado 74,255 19,564 54,690 74% Connecticut 3,581 1,400 2,181 61% Delaware 2,835 4,160 - 1,324 -47% District of Columbia 5 0 5 100% (continued)5A- 25

**T able**  **5A -**  **12 .**  **State Specific Changes in Annual EGU SO 2**  **for the Lower 48 States (continued)** 

**State Future Year** 

**Baseline SO 2**  **(tons) Future Year Policy** 

**Case SO 2**  **(tons) EGU SO**  **2 Reduction** 

**(tons) EGU SO**  **2 Reduction**  **(%)** Florida 117,702 64,791 52,911 45% Georgia 96,712 78,197 18,515 19% Idaho 182 182 0 0% Illinois 118,217 103,867 14,350 12% Indiana 200,969 156,781 44,189 22% Iowa 85,178 48,030 37,148 44% Kansas 45,740 22,767 22,973 50% Kentucky 116,927 125,430 - 8,503 - 7% Louisiana 142,447 30,509 111,938 79% Maine 2,564 1,372 1,191 46% Maryland 29,786 18,091 11,695 39% Massachusetts 15,133 5,033 10,100 67% Michigan 163,168 82,834 80,334 49% Minnesota 52,380 33,214 19,165 37% Mississippi 34,865 15,975 18,890 54% Missouri 178,143 95,965 82,177 46% Montana 24,018 6,399 17,618 73% Nebraska 70,910 34,631 36,279 51% Nevada 14,140 6,372 7,768 55% New Hampshire 6,719 2,102 4,618 69% New Jersey 9,042 6,404 2,638 29% New Mexico 10,211 9,984 228 2% New York 14,653 28,174 - 13,521 - 92% North Carolina 71,113 59,551 11,562 16% North Dakota 105,344 23,889 81,455 77% Ohio 180,935 139,208 41,727 23% Oklahoma 141,433 44,602 96,831 68% (continued)5A- 26

**T able 5A** **- 12**  **. State Specific Changes in Annual EGU SO**  **2 for the Lower 48 States**  **(continued)**

**State Future Year** 

**Baseline SO 2**  **(tons) Future Year Policy** 

**Case SO 2**  **(tons) EGU SO**  **2 Reduction** 

**(tons) EGU SO**  **2 Reduction**  **(%)** Oregon 13,211 3,565 9,646 73% Pennsylvania 126,316 93,606 32,710 26% Rhode Island 0 0 0 N/A South Carolina 103,694 40,901 62,793 61% South Dakota 29,711 2,483 27,228 92% Tennessee 33,080 42,666 - 9,586 - 29% Texas 249,748 105,958 143,790 58% Tribal 0 0 0 N/A Utah 34,912 17,007 17,905 51% Vermont 264 264 0 0% Virginia 51,004 33,704 17,300 34% Washington 5,569 854 4,716 85% West Virginia 84,344 66,857 17,488 21% Wisconsin 50,777 28,322 22,454 44% Wyoming 48,198 28,456 19,742 41% **Total 3,281,364**  **1,866,247 1,415,117** 
**T able 5A** **- 13**  **. State Specific Changes in Annual EGU**  **PM 2.5**  **for the Lower 48 States**

**State Future Year** 

**Baseline PM 2.5**  **(tons) Future**  **Year Policy**

**Case PM 2.5**  **(tons) EGU PM**  **2.5**

**Reduction (tons)**  **EGU PM 2.5** 

**Reduction (%)**  Alabama 13,154 7,844 5,310 40% Arizona 3,889 3,494 395 10% Arkansas 2,838 1,593 1,246 44% California 475 942 -467 - 98% Colorado 3,845 2,859 985 26% Connecticut 400 412 - 12 - 3% (continued)5A- 27

**T able 5A** **- 13**  **. State Specific Changes in Annual EGU**  **PM 2.5**  **for the Lower 48 States (continued)** 

**State Future Year** 

**Baseline PM 2.5**  **(tons) Future Year Policy** 

**Case PM 2.5**  **(tons) EGU PM**  **2.5**

**Reduction (tons)**  **EGU PM 2.5** 

**Reduction (%)**  Delaware 434 1,455 - 1,021 - 235% District of Columbia 1 0 1 100% Florida 12,723 11,377 1,346 11% Georgia 13,445 9,742 3,703 28% Idaho 36 36 0 0% Illinois 8,587 6,588 2,000 23% Indiana 22,354 20,388 1,966 9% Iowa 4,298 2,947 1,351 31% Kansas 3,199 2,263 936 29% Kentucky 12,078 10,635 1,443 12% Louisiana 3,093 1,899 1,193 39% Maine 355 331 24 7% Maryland 3,969 3,143 826 21% Massachusetts 1,465 1,267 198 14% Michigan 8,102 6,893 1,210 15% Minnesota 2,598 2,936 - 339 - 13% Mississippi 2,201 1,720 481 22% Missouri 7,061 4,809 2,252 32% Montana 3,870 1,727 2,143 55% Nebraska 2,358 1,828 530 22% Nevada 2,505 2,073 432 17% New Hampshire 1,130 1,264 - 134 - 12% New Jersey 2,452 1,583 868 35% New Mexico 3,153 2,750 403 13% New York 2,331 2,350 - 19 - 1% North Carolina 9,983 7,988 1,995 20% North Dakota 5,870 5,051 819 14% (continued)5A- 28

**T able 5A** **- 13**  **. State Specific Changes in Annual EGU**  **PM 2.5**  **for the Lower 48 States (continued)** 

**State Future Year** 

**Baseline PM 2.5**  **(tons) Future Year Policy** 

**Case PM 2.5**  **(tons) EGU**  **PM 2.5** 

**Reduction (tons)**  **EGU PM 2.5** 

**Reduction (%)**  Ohio 18,920 15,823 3,097 16% Oklahoma 3,530 2,056 1,474 42% Oregon 381 233 148 39% Pennsylvania 16,727 14,080 2,646 16% Rhode Island 4 4 0 2% South Carolina 9,997 6,963 3,033 30% South Dakota 737 245 492 67% Tennessee 5,053 5,272 - 219 -4% Texas 21,677 17,601 4,077 19% Tribal 1 1 1 56% Utah 4,524 3,896 627 14% Vermont 67 67 0 0% Virginia 4,529 4,506 24 1% Washington 1,444 176 1,268 88% West Virginia 13,602 11,572 2,031 15% Wisconsin 5,323 3,969 1,354 25% Wyoming 5,662 4,671 991 17% **Total 276,430**  **223,320 53,110** 5B - 1 **APPENDIX 5B IMPACT OF THE INTERI**  **M POLICY SCENARIO ON AIR QUALITY**  **5B.1 Air Quality Modeling Platform**  This document describes the air quality modeling performed by EPA in support of the final National Emissions Standard for Hazardous Air Pollutants (NESHAP) related to electrical generating utilities. A national scale air quality modeling analysis was performed to estimate the impact of the sector emissio ns changes on future year annual and 24 - hour PM 2.5 concentrations, 8 - hr maximum ozone, as well as visibility impairment. Air quality benefits are estimated with the Community Multi - scale Air Quality ( CMAQ) model. CMAQ simulates the numerous physical and ch emical processes involved in the formation, transport, and destruction of ozone, particulate matter and other air pollutants. In addition to the CMAQ model, the modeling platform includes the emissions, meteorology, and initial and boundary condition data which are inputs to this model. Emissions and air quality modeling decisions are made early in the analytical process. For this reason, it is important to note that the inventories used in the air quality modeling may be slightly different than the final u tility sector inventories presented in the RIA. However, the air quality inventories and the final rule inventories are generally consistent, so the air quality modeling adequately reflects the effects of the rule. Similarly, the projected future year inve ntory used for this analysis is generally representative of several years around 2017 such as 2016. Photochemical grid models use state of the science numerical algorithms to estimate pollutant formation, transport, and deposition over a variety of spatial scales that range from urban to continental. Emissions of precursor species are injected into the model where they react to form secondary species such as ozone and then transport around the modeling domain before ultimately being removed by deposition or chemical reaction. The 2005 - based CMAQ modeling platform was used as the basis for the ai r quality modeling for this rule. This platform represents a structured system of connected modeling - related tools and data that provide a consistent and transparent basis for assessing the air quality response to projected changes in emissions. The base year of data used to construct this platform includes emiss ions and meteorology for 2005. This modelin g platform is described in more detail in the modeling technical support document for this rule (USEPA, 2011).5.B.1.1 Photochemical Model Background

The Community Multi - scale Air Quality (CMAQ) model v4.7.1 (www.cmaq a state of the science three - dimensional Eularian model used to estimate air quality (Appel et al., 2008; Appel et al., 2007; Byun and Schere, 2006). CMAQ simulates the formation and fate of photochemical oxidants, ozone, primary and secondary PM concentrations, and air toxics over regional and urban sp input sets of meteorological conditions and emissions. CMAQ is applied with the AERO5 aerosol module, which includes the ISORROPIA inorganic chemistry (Nenes et al., 1998) and a secondary organic aerosol module (Carlton et al., 2010) and organic oxidation aqueous phase chemistry (Carlton et al., 2008) and the carbon (CB05) gas - phase chemistry module (Gery et al., 1989). 5.B.1.2 Model Setup, Application, and Post The modeling analyses were performed for a domain covering the continental United States, as shown in Figure 5B - 1. finer- scale 12 km grids over portions of the eastern and western U.S. vertica lly from the surface to 100 millibars (approximately 15 km) using a sigma coordinate system. Air quality conditions at the outer boundary of the 36 km domain were taken from a global model and vary in time and space. The 36 km grid was only used t establish the incoming air quality concentrations along the boundaries of the 12 km grids. the finer grid data were used in determining provides geographic information about the photochemical model do

**Figure 5B** **- 1**  **. Map of the Photochemical Modeling Domains.**  **36 km national modeling domain; the red inner box is the 12 km western U.S. grid; and the blue inner box i**  **s the 12 km eastern U.S. grid.**

5B - 2 Photochemical Model Background

scale Air Quality (CMAQ) model v4.7.1 (www.cmaq dimensional Eularian “ one - atmosphere ” photochemical transpor used to estimate air quality (Appel et al., 2008; Appel et al., 2007; Byun and Schere, 2006). CMAQ simulates the formation and fate of photochemical oxidants, ozone, primary and secondary PM concentrations, and air toxics over regional and urban sp atial scales for given input sets of meteorological conditions and emissions. CMAQ is applied with the AERO5 aerosol module, which includes the ISORROPIA inorganic chemistry (Nenes et al., 1998) and a secondary organic aerosol module (Carlton et al., 2010) . The CMAQ model is applied with sulfur and organic oxidation aqueous phase chemistry (Carlton et al., 2008) and the carbon

phase chemistry module (Gery et al., 1989). Model Setup, Application, and Post - Processing

analyses were performed for a domain covering the continental United

This domain has a parent horizontal grid of 36 km with two scale 12 km grids over portions of the eastern and western U.S. The model extends lly from the surface to 100 millibars (approximately 15 km) using a sigma

Air quality conditions at the outer boundary of the 36 km domain were taken from a global model and vary in time and space. The 36 km grid was only used t establish the incoming air quality concentrations along the boundaries of the 12 km grids. the finer grid data were used in determining the impacts of the emissions changes. Table provides geographic information about the photochemical model do mains.

**Map of the Photochemical Modeling Domains. The black outer box denotes the**  **36 km national modeling domain; the red inner box is the 12 km western U.S. grid; and the**

**s the 12 km eastern U.S. grid.**

scale Air Quality (CMAQ) model v4.7.1 (www.cmaq - model.org) is photochemical transport used to estimate air quality (Appel et al., 2008; Appel et al., 2007; Byun and Schere, 2006). CMAQ simulates the formation and fate of photochemical oxidants, ozone, primary and atial scales for given input sets of meteorological conditions and emissions. CMAQ is applied with the AERO5 aerosol module, which includes the ISORROPIA inorganic chemistry (Nenes et al., 1998) and a . The CMAQ model is applied with sulfur and organic oxidation aqueous phase chemistry (Carlton et al., 2008) and the carbon - bond 2005

analyses were performed for a domain covering the continental United

This domain has a parent horizontal grid of 36 km with two The model extends lly from the surface to 100 millibars (approximately 15 km) using a sigma - pressure

Air quality conditions at the outer boundary of the 36 km domain were taken from a global model and vary in time and space. The 36 km grid was only used t o establish the incoming air quality concentrations along the boundaries of the 12 km grids. Only changes. Table 5B - 1

**The black outer box denotes the 36 km national modeling domain; the red inner box is the 12 km western U.S. grid; and the** 5B - 3

**Table 5B** **- 1**  **. Geographic Elements of Domains**  **Used in Photochemical Modeling Photochemical Modeling Configuration**  **National Grid Western U.S. Fine Grid**  **Eastern U.S. Fine Grid** Map Projection Lambert Conformal Projection Grid Resolution 36 km 12 km 12 km Coordinate Center 97 deg W, 40 deg N True Latitudes 33 deg N and 45 deg N Dimensions 148 x 112 x 14 213 x 192 x 14 279 x 240 x 14 Vertical extent 14 Layers: Surface to 100 millibar level The 36 km and both 12 km modeling domains were modeled for the entire year of 2005. Data from the entire year were utilized when looking at the estimation of PM 2.5 and visibility impacts from the regulation. Data from April through October is used to estimate ozone impacts. As part of the analysis for this rulemaking, the modeli ng system was used to calculate daily and annual PM 2.5 concentrations, 8 - hr maximum ozone, and visibility impairment. Model predictions are used to estimate future -year design values of PM 2.5 and ozone. Specifically, we compare a 2017 reference scenario to a 2017 control scenario. This is done by calculating the simulated air quality ratios between any particular future year simulation and the 2005 base. These predicted ratios are then applied to ambient base year design values. The design value projection methodology used here followed EPA guidance for such analyses (USEPA, 2007). 5.B.1.3 Emissions Input Data The emissions data used in the base year and future reference and future emissions adjustment case are based on the 2005 v4.1 platform. Emissions are processed to photochemical model inputs with the SMOKE emissions modeling system (Houyoux et al., 2000). The 2017 reference case is intended to represent the emissions associated with growth and controls in that year projected from the 2005 simulation yea r. The United States EGU point source emissions estimates for the future year reference and control case are based on an Integrated Planning Model (IPM) run for criteria pollutants. Both control and growth factors were applied to a subset of the 2005 non - E GU point and non - point emissions to create the 2017 reference case. The 2005 v4 platform projection factors were the starting point for most5B - 4

of the 2017 SMOKE - based projections. The estimated total anthropogenic emissions and emissions for the utility sect or used in this modeling assessment are shown in Appendix 5A. Other North American emissions are based on a 2006 Canadian inventory and 1999 Mexican inventory. Both inventories are not grown or controlled when used as part of future year inventories. Globa l emissions of criteria pollutants are included in the modeling system through boundary condition inflow. More details on these emissions are available in Appendix 5A. **5B.2 I**  **mpacts of Sector on Future Annual PM 2.5**  **Levels** This section summarizes the result s of our modeling of annual average PM 2.5 air quality impacts in the future due to reductions in emissions from this sector. Specifically, we compare a 2017 baseline scenario to a 2017 control scenario. The modeling assessment indicates a decrease up t o 1. 03 μg/m 3 in annual PM 2.5 design values is possible given an area ’ s proximity to controlled sources. The median reduction in annual PM 2.5 design value ov er all monitor locations is 0.36 μg/m 3

. The change in future year projected design value is shown in Figure 5B - 2. Negative changes indicate an improvement in air quality. An annual PM 2.5 design value is the concentration that determines whether a monitoring site meets the annual NAAQS for PM 2.5 . The full details involved in calculating an annual PM 2.5 de sign value are given in appendix N of 40 CFR part 50. Projected air quality benefits are estimated using procedures outlined by United States Environmental Protection Agency modeling guidance (USEPA, 2007) .5B - 5

**Figure 5B** **- 2.**  **Change in Design Values Between th e**  **2017 Baseline and**  **2017 Control**  **Simulations. Negative numbers indicate lower (improved) design values in the contro l case**  **compared to the baseline 5B.3**  **Impacts of Sector on Future 24 -**  **hour PM 2.5**  **Levels** This section summarizes the results of our modeling o f 24 - hr average PM 2.5 air quality impacts in the future due to reductions in emissions from this sector. Specifically, we compare a 2017 baseline scenario to a 2017 control scenario. A decrease up to 1.9 μg/m 3 in 24 - hr average PM 2.5 design value at monitor locations in the United States is possible given an area ’ s proximity to controlled sources and the amount of reduced emissions from those sources. A median decrease of 0.6 μg/m 3 in 24 - hr average PM 2.5 design value at monitor locations in the United States is possible given an area ’ s proximity to controlled sources and the amount of reduced emissions from those sources. The change in future year projected design value is shown in Figure 5B - 3. Negative changes indicate an improvement in air quality. A 24- ho ur PM 2.5 design value is the concentration that determines whether a monitoring site meets the 24- hour NAAQS for PM 2.5 . The full details involved in calculating a 24 - hour PM 2.5 design value are given in appendix N of 40 CFR part 50. Projected air quality benefits are estimated using procedures outlined by United States Environmental Protection Agency modeling guidance (USEPA, 2007) .5B - 6

**Figure 5B** **- 3.**  **Change in Design**  **Values Betwee n the 2017 Base Cas**  **e and 2017**  **Control Simulations.**  **Negative numbers indicate lower (improved) design values in the contro l case**  **compared to the baseline. 5B.4**  **Impacts of Sector on Future Visibility Levels** Air quality modeling conducted for this rule was u sed to project visibility conditions in 138 mandatory Class I federal areas across the U.S. in 2017 (USEPA, 2007) . The level of visibility impairment in an area is based on the light - extinction coefficient and a unitless visibility index, called a “ decivie w,” which is used in the valuation of visibility. The deciview metric provides a scale for perceived visual changes over the entire range of conditions, from clear to hazy. Under many scenic conditions, the average person can generally perceive a change of one deciview. Higher deciview values are indicative of worse visibility. Thus, an improvement in visibility is a decrease in deciview value. The modeling assessment indicates a media n visibility improvement of 0.09 deciviews in annual 20% worst visibility days over all Class I area monitors. An improvement in visibility up to 0.97 deciviews on the 20% worst visibility days at C lass I monitor locations in the United States is possible given an area ’ s proximity to controlled sources and the amount of reduce d emissions5B - 7

from these sources. The change in future year projected visibility is shown in Figure 5B -4. Negative changes indicate an improvement in air quality.

**Figure 5B** **- 4**  **. Change in**  **20% Worst Days**  **Between the**  **2017 Baseline**  **and 2017**  **Control Simulations**  **. Negative numbers indicat e lower (improved) visibility**  **expressed in deciviews in**  **the contro l case compared to the baseline**  **5B.5 Impacts of Sector on Future Ozone Levels**  This section summarizes the results of our modeling of 8 - hr maximum ozone air quality impacts in the future due to reductions in emissions from this sector. Specifically, we compare a 2017 baseline scenario to a 2017 control scenario. The modeling assessme nt indicates a decrease of up to 3.5 ppb in 8 - hr averaged ozone design value is possible given an area ’ s proximity to controlled sources and the amount of reduced emissions from these sources. A median decrease across all monitors of 0.20 ppb in 8 - hr avera ged ozone design value is possible given an area ’ s proximity to controlled sources and the amount of reduced emissions from these sources. The change in future year projected design value is shown in Figure 5B - 5. Negative changes indicate an improvement in air quality. The full details involved in calculating5B - 8

design value are given in appendix P of 40 CFR part 50. Projected air quality benefits are estimated using procedures outlined by United States Environmental Protection Agency modeling guidance (USEPA, 2007) .

**Figure 5B** **- 5**  **. Change in Design Values Between the 2017**  **Baseline and 2017**  **Control Simulations. Negative numbers indicate lower (improved) design values in the contro**  **l case compared to the baseline**  **5B.6 References**  Appel, K.W., Bhave, P.V., Gillil and, A.B., Sarwar, G., Roselle, S.J., 2008. Evaluation of the community multiscale air quality (CMAQ) model version 4.5: Sensitivities impacting model performance; Part II — particulate matter. Atmospheric Environment 42, 6057 - 6066. Appel, K.W., Gilliland, A .B., Sarwar, G., Gilliam, R.C., 2007. Evaluation of the Community Multiscale Air Quality (CMAQ) model version 4.5: Sensitivities impacting model performance Part I — Ozone. Atmospheric Environment 41, 9603 - 9615.5B - 9

Byun, D., Schere, K.L., 2006. Review of the go verning equations, computational algorithms, and other components of the models - 3 Community Multiscale Air Quality (CMAQ) modeling system. Applied Mechanics Reviews 59, 51 - 77. Carlton, A.G., Bhave, P.V., Napelenok, S.L., Edney, E.D., Sarwar, G., Pinder, R. W., Pouliot, G.A., Houyoux, M., 2010. Model Representation of Secondary Organic Aerosol in CMAQv4.7. Environmental Science & Technology 44, 8553 - 8560. Carlton, A.G., Turpin, B.J., Altieri, K.E., Seitzinger, S.P., Mathur, R., Roselle, S.J., Weber, R.J., 200 8. CMAQ Model Performance Enhanced When In - Cloud Secondary Organic Aerosol is Included: Comparisons of Organic Carbon Predictions with Measurements. Environmental Science & Technology 42, 8798 - 8802. Gery, M.W., Whitten, G.Z., Killus, J.P., Dodge, M.C., 198 9. A Photochemical Kinetics Mechanism for Urban and Regional Scale Computer Modeling. Journal of Geophysical Research Atmospheres 94, 12925 - 12956. Houyoux, M.R., Vukovich, J.M., Coats, C.J., Wheeler, N.J.M., Kasibhatla, P.S., 2000. Emission inventory devel opment and processing for the Seasonal Model for Regional Air Quality (SMRAQ) project. Journal of Geophysical Research -Atmospheres 105, 9079 - 9090. Nenes, A., Pandis, S.N., Pilinis, C., 1998. ISORROPIA: A new thermodynamic equilibrium model for multiphase m ulticomponent inorganic aerosols. Aquatic Geochemistry 4, 123 - 152. USEPA, 2007. Guidance on the Use of Models and Other Analyses for Demonstrating Attainment of Air Quality Goals for Ozone, PM 2.5 , and Regional Haze, RTP. USEPA, 2011. Air Quality Modeling Technical Support Document: Final EGU NESHAP (EPA454/R
- 11 - 009), Research Triangle Park, North Carolina.5C- 1 **APPENDIX 5C HEALTH AND WELFARE C**  **O -**  **BENEFITS OF THE MODE LED INTERIM POLICY S**  **CENARIO** In this appendix t o the co - benefits chapter we report the estimates of the benefits of reductions in emissions of SO 2 and directly emitted PM 2.5 based on air quality modeling of an interim policy scenario . As noted in Chapter 5 of the RIA, the air quality modeling performe d for the RIA does not reflect the emission changes associated with the final rule requirements. To estimate the benefits of those emissions changes for the final rule, we developed BPT estimates for SO 2 and directly emitted PM 2.5 based on air quality mode ling of an interim policy scenario. These BPT values we re used to adjust benefits estimates for changes in the emission reductions resulting from the final policy scenario. This appendix reports the results of the benefits analysis associated with the modeled interim policy scenario described in Appendix 5A and 5B , along with the derivation of BPT values used to estimate the health benefits of the final policy scenario . As described in the benefits chapter, the chief difference between the modeled and revised scenarios relate s to the magnitude and distribution of SO 2 emission reductions (Figure 5C- 1) . In general, the modeled and revised policy case s achieve roughly simila r levels of SO 2 reductions (1.42 versus 1.3 3 million tons, respectively) with a similar distribution among states. However, for some states (notably Alabama, Colorado, Louisiana, Michigan, Missouri, North Dakota, Oklahoma, and Texas), SO 2 emission reductio ns were lower for the final case versus the interim case. By far, the greatest difference in SO 2 emission reductions was in Michigan where the final case emission reduction was 70% lower than for the interim case. In a few states (notably Arkansas, Ohio, a nd South Carolina), SO 2 emission reductions were slightly larger for the final case versus the interim case. Since differences between the interim and final cases are not concentrated in any particular region of the country and the overall distribution of emission reductions is similar, we conclude that it is reasonable to apply BPT values derived from the interim case to the final case. While NO x emissions reductions decreased by 70% between the interim and final cases (141,000 vs. 4 6,000 tons), the impact of NO x on PM 2.5 concentrations and mortality is very minor relative to the impact of SO 2 emission reductions. Therefore, differences in the magnitude and distribution of NO x emission reductions are likely to have only a minor effect on results.5C- 2

**5C.1 PM**  **2.5 -**  **Related Health Impacts and Monetized Benefits of Reductions in Emissions of SO**  **2 and Directly Emitted PM**  **2.5 for the Air Quality Modeled Interim Policy Scenario**  Health benefits of the interim policy scenario are calculated using the modeled changes in PM 2.5 concentration described in Appendix 5 B, which result from the emission changes described in Appendix 5 A. Concentration changes are input into BenMAP to calculate the changes in incidence of an array of health endpoints, along with their associated monetary value. BenMAP is described in more detail in Chapter 5. In addition, more information can be found at http://www.epa.gov/air/benmap/ . Tables 5 C- 1 and 5 C- 2 summarize the PM 2.5 - related health impacts and monetized benefits of the air quality modeled interi m policy scenario . N ot all known PM - and ozone related health and welfare effects could be quantified or monetized. The monetized value of these unquantified effects is represented by adding an unknown “ B ” to the aggregate total. The estimate of total monetized health benefits is thus equal to the subset of monetized PM - and ozone - related health and welfare benefits plus B, the sum of the nonmonetized health and welfare benefits; this B represents both uncertainty and a bias in this analysis, as it reflects those benefits categories that we are unable quantify in this analysis. Figure 5 C- 2 illustrates the distribution of avoided PM - related deaths by county across the U.S. Methods for quantifying recreational visibility are describ ed in Section 5.5.1. V isibility benefits are calculated for the modeled interim policy scenario only since t here is no analogous approach for estimating visibility benefits using the BPT approach. However, the magnitude of SO 2 emission reductions did not significantly change in the visibility study areas between the interim and final emissions scenarios. Therefore, we expect the visibility benefit for the final policy scenario would be similar to that calculated for the interim policy scenario ($1.1 billion in total for the U.S., using 2007$). These benefits are not included in the co - benefits estimate of the final policy.**Figure 5C -**  **1. Comparison of state**

**scenario and the final policy.** 

5C - 1

**1. Comparison of state -**  **level SO 2**  **emission changes between the interim modeled**5C- 2

**Table 5**  **C -**  **1. Estimated Reduction in Incidence of Adverse Health Effects of the**  **Interim Modeled**  **Mercury and Air Toxics Standards in 2016**  **(95% confidence intervals) a,b** 

**Impact Eastern U.S.**  c **Western U.S. Total**  Premature Mortality Pope et al. (2002) (age >30) 4,200 ( 1,200 — 7,300 ) 120 ( 19 — 220 ) 4,400

( 1,200 — 7 , 5 00) Laden et al. (2006) (age >25) 11,000 ( 5,000 — 17,000 ) 310 ( 110 — 510 ) 11 ,000

( 5,100 — 17 ,000) Infant (< 1 year) 20 ( - 22 — 61 ) 1 ( - 1 — 2 ) 20

( - 23 — 63 ) Chronic Bronchitis 2,800 ( 94 — 5,500 ) 100 ( - 12 — 210 ) 2,900 ( 82 — 5,800 ) Non -fatal heart attacks (age > 18) 4,800 ( 1,200 — 8,400 ) 110 ( 13 — 220 ) 4,9 00

( 1,200 — 8,6 00) Hospital admissions — respiratory (all ages) 840 ( 340 — 1,300 ) 16 ( 5 — 27) 860

( 340 — 1,400 ) Hospital admissions — cardiovascular (age > 18) 1,800 ( 1,200 — 2,200 ) 41 (2 6 — 51 ) 1,900

( 1,300 — 2,200 ) Emergency room visits for asthma (age < 18) 3,100 ( 1,600 — 4,700 ) 100 ( 44 — 160 ) 3,200

( 1,700 — 4,800 ) Acute bronchitis (age 8 - 12) 6,200 ( - 1,400 — 14,000 ) 240 ( - 100 — 5 70) 6,500

( - 1,500 — 14,000 ) Lower respiratory symptoms (age 7 - 14) 80,000 ( 31 ,000 — 130,000) 3 ,000 ( 860 — 5 ,200) 83 ,000

( 32 ,000 — 130,000) Upper respiratory symptoms (asthmatics age 9 - 18) 60,000 ( 11 ,000 — 110,000) 2,300 ( 130 — 4,400 ) 62 ,000

( 11 ,000 — 110,000) Asthma exacerbation (asthmatics 6 - 18) 130,000 (4, 7 00 — 4 5 0,000) 5,000 ( - 520 — 17,000 ) 140,000 ( 4,200 — 460,000) Lost work days (ages 18 - 65) 540,000 ( 460,000 — 620,000) 20,000 ( 16,000 — 24,000) 560,000

( 470,000 — 640,0 00) Minor restricted - activity days (ages 18 - 65) 3,200,000 ( 2 ,600,000 — 3,8 00,000) 120,000 ( 93 ,000 — 140,000) 3,300,000

( 2,700,000 — 3,900,000)
a Estimates rounded to two significant figures; column values will not sum to total value. b The negative estimates for certain endpoints are the result of the weak statistical power of the study used to calculate these health impacts and do not suggest that increases in air pollution exposure result in decreased health impacts. c Includes Texas and those states to the north and east.5C- 3

**Table 5**  **C -**  **2 .**  **Estimated Economic Value of Health and Welfare Benefits of the**  **Interim**

**Modeled Mercury and Air Toxics Standards**  **in 2016 (95% confidence intervals,**  **billions of 200 7**  **$) Impact**  **Pollutant Eastern U.S.**  a **Western U.S. Total**  Premature mortality (Pope et al. 2002 PM mortality estimate) 3% discount rate PM 2.5 $ 34 ($ 2.7 — $ 100 ) $ 1.0

($0.1 — $ 3.1 ) $ 35

($ 2.8 — $ 100 ) 7% discount rate PM 2.5 $ 31 ($ 2.4 — $ 93 ) $0. 9

($0.1 — $ 2.8 ) $ 32

($ 2.5 — $ 96 ) Premature mortality (Laden et al. 2006 PM mortality estimate) 3% discount rate PM 2.5 $ 87 ($ 7.7 — $ 250 ) $ 2.5

($ 0.2 — $ 7.5 ) $ 90

($ 7.9 — $ 260 ) 7% discount rate PM 2.5 $ 79 ($ 6.9 — $ 230 ) $ 2.3

($0. 2 — $ 6.7 ) $ 81

($ 7.1 — $ 240 ) Infant mortality PM 2.5 $0. 2 ($
- 0. 2 — $ 0.8 ) <$0.01 $0. 2 ($
- 0. 2 — $ 0.8 ) Chronic bronchitis PM 2.5 $ 1.4 ($0.1 — $ 6.4 ) $0.0 5

( < $0.01 — $0. 23 ) $ 1.4

($0.1 — $ 6.6 ) Non -fatal heart attacks 3% discount rate PM 2.5 $ 0.5 ($0. 1 — $ 1 .3) $0.0 1

(<$0.01 — $0.0 3 ) $ 0.6

($0. 1 — $ 1 .3) 7% discount rate PM 2.5 $ 0.4 ($0. 1 — $ 1.0 ) $0.01

(<$0.01 — $0.0 3 ) $ 0.4

($0. 1 — $ 1.0 ) Hospital admissions — respiratory PM 2.5 $0.0 1 ($0.01 — $0.0 2 ) <$0.01 $0.0 1 ($0.01 — $0.0 2 ) Hospital admissions — cardiovascular PM 2.5 $0. 03 ( < $0. 0 1 — $0. 05 ) <$0.01 $0. 03 ( < $0. 0 1 — $0. 06 ) Emergency room visits for asthma PM 2.5 <$0.01 <$0.01 <$0.01 Acute bronchitis PM 2.5 <$0.01 <$0.01 <$0.01 Lower respiratory symptoms PM 2.5 <$0.01 <$0.01 <$0.01 Upper respiratory symptoms PM 2.5 <$0.01 <$0.01 <$0.01 Asthma exacerbation PM 2.5 <$0.01 <$0.01 <$0.01 Lost work days PM 2.5 $0.1 ($0.1 — $0.1) <$0.01 $0.1 ($0.1 — $0.1) Minor restricted - activity days PM 2.5 $0. 2 ($0. 1 — $0. 3 ) <$0.01 $0. 2 ($0. 1 — $0. 3 ) Recreational visibility, Class I areas PM 2.5 $0.9 $0.2 $1.1 Social cost of carbon (3% discount rate, 2016 value) CO 2 (continued)**Table 5**  **C -**  **2 .**  **Estimated Economic Value of Health and Welfare Benefits Modeled**  **Mercury and Air Toxics Standards billions of 200**  **7 $)**  **(continued) Impact**  **Pollutant** Total Monetized benefits (Pope et al. 2002 PM 3% discount rate 7% discount rate Total Monetized benefits (Laden et al. 2006 PM 3% discount rate 7% discount rate

a Includes Texas and those states to the north and east.

**Figure 5**  **C -**  **2 . Estimated Reduction in Excess PM**  **Occur in Each County in 2016 as a Result of the Standards** 

5C - 4

**Estimated Economic Value of Health and Welfare Benefits of the**  **Mercury and Air Toxics Standards in 2016**  **(95% confidence intervals, (continued)**  **Pollutant Eastern U.S.**  **Western U.S.** (Pope et al. 2002 PM 2.5 mortality estimates) $37+B ($3.7 — $110) $1.2+B

($0.2 — $3.6) ($4.0 $34+B ($3.5 — $100) $1.1+B

($0.2 — $3.3) ($3.7 (Laden et al. 2006 PM 2.5 mortality estimates) $91+B ($8.7 — $260) $2.8+B

($0.4 — $8.0) ($9.1 $82+B ($7.9 — $240) $2.5+B

($0.4 — $7.2) ($8.3 Includes Texas and those states to the north and east.

**. Estimated Reduction in Excess PM 2.5** **- Related Premature Deaths Estimated to**  **Occur in Each County in 2016 as a Result of the Interim Modeled**  **Mercury and Air Toxics of the**  **Interim (95% confidence intervals,** 

**Total** $39+B ($4.0 — $120) $35+B ($3.7 — $110) $93+B ($9.1 — $270) $84+B ($8.3 — $240)

**Related Premature Deaths Estimated to**

**Mercury and Air Toxics**5C- 5

**5C.2 Derivation of the BPT Values Used to Calculate the Health Benefits of the Final Policy**  **Scenario** The health benefit s summarized in Tables C - 1 and C- 2 include impacts of changes in sulfate, nitrate, and direct PM 2.5 . To quantify the health benefits of the final policy scenario reported in Chapter 5, we calculate the health benefits per ton of emission reduced, separatel y for eastern and western states, and separately for SO 2, directly emitted carbonaceous PM 2.5 , and directly emitted crustal PM 2.5 . This calculation is shown by Equation 1:

, , = , , , ∆ , , where BPT is the BPT for a particular pollutant i (SO 2, directly emitted carbonaceous PM 2.5 , or directly emitted crustal PM 2.5 ), region j (Eastern U.S. or Western U.S.), health endpoint k (e.g. adult mortality, infant mortality, etc.), and the interim baseline and policy scenario (denoted as “1”) . As described in Chapter 5, we do not generate BPT values for NO x . Nitrate increases in the modeled policy scenario were two orders of magnitude smaller than the sulfate decreases and were not included in the BPT estima tes . Including nitrate in the SO 2 BPT estimate would reduce the SO 2 BPT by 1 - 2%, with a corresponding impact on the total health benefits of the rule. Furthermore, as described in Appendix 5 A, NO x emission changes resulting from this rule were 75% smaller for the final policy scenario relative to the interim modeled policy scenario. Therefore, excluding the impacts of NO x emission changes is unlikely to materially impact the final benefit results. Table 5 C- 3 reports the economic value of the adult mortality benefits resulting from reductions in SO 2, directly emitted carbonaceous PM 2.5 , and directly emitted crustal PM 2.5 for the modeled interim policy scenario, along with the BPT values derived from these benefits. Only adult mortality benefits are shown here as they contribute 93 - 97% of the total health benefits, however, BPT values were calculated and applied separately for each health endpoint. Since premature mortality is discounted after BPT valu es are applied to the final emissions, the values reported in Table 5 C- 3 are not discounted. Sulfate reductions resulting from SO 2 emission reductions contribute approximately 95% of the benefits of SO 2 and directly emitted PM 2.5 combined. In some location s, directly emitted carbonaceous PM 2.5 increased slightly in the Western U.S. for the interim policy scenario relative to the interim baseline, which overall resulted in negative BPT values for the West . However, since the magnitudes of the emission and c oncentration changes are small relative to the changes in SO 2 emissions and sulfate concentrations, the resulting increase in premature mortality is only 0.04% of the total health impact of the rule .5C- 6

**Table 5**  **C -**  **3 .**  **Estimated Economic Value of Adult Mortality**  **Benefits by Pollutant, in Total and** 

**Per Ton of Emissions Reduced Interim Modeled**  **Mercury and Air Toxics Standard in 2016**  **(95% confidence intervals, 200 7**  **$)**

**Pollutant and Source of Adult Mortality Estimate**  **Total Monetized Benefits (billions)**  **BPT (thousands) Eastern U.S.**  a **Western U.S. Eastern U.S.**  a **Western U.S.** SO 2 emissions (tons) b 1,268,961 146,155 Pope et al. (2002) estimate $ 36 ($ 2.9 — $ 110 ) $ 1.2

($0.1 — $ 3.7 ) $29

($2.3
- $87) $8.3

($0.1
- $25) Laden et al. (2006) estimate $ 93 ($ 8.2 — $ 270 ) $ 3.1

($ 0.3 — $ 9.0 ) $73

($6.4
- $210) $21

($1.9
- $62) Carbonaceous PM 2.5 emissions (tons) b 5,860 231 Pope et al. (2002) estimate c $ 1.3 ($ 0.1 — $ 3.9 ) < - $0.01 $220 ($17
- $670) - $66

( - $450
- $210) Laden et al. (2006) estimate c $ 3.3 ($ 0.3 — $ 9.6 ) < - $0.01 $560 ($49
- $1,600) - $170

( - $960
- $350) Crustal PM 2.5 emissions (tons) b 34,742 29,148 Pope et al. (2002) estimate $ 0.6 ( < $ 0.01 — $ 1.9 ) $ 0.1

( < $0. 0 1 — $ 0.2 ) $18

($1.4
- $55) $ 9.6

($0.1
- $ 31 ) Laden et al. (2006) estimate $ 1.6 ($ 0.1 — $ 4.7 ) $ 0.1

( < $ 0.01 — $ 0.4 ) $47

($4.1
- $140) $ 25

($ 2.1 - $ 74 )
a Includes Texas and those states to the north and east. b Emission reductions are reported for the modeled interim policy case, from which the BPT values were generated. C Directly emitted carbonaceous PM 2.5 increased slightly in some locations in the Western U.S. for the interim policy scenario relative to the interim baseline, which overall resulted in n egative BPT values for the West . However, since the magnitudes of the emission and concentration changes are small relative to the changes in SO 2 emissions and sulfate concentrations, the resulting increase in premature mortality is only 0.04% of the total health impact of the rule.
The BPT values reported in Table 5 C- 3, along with those calculated for the other health endpoints listed in Table 5 C- 2, are applied to the final emission changes described in Chapter 3, resulting in the final benefit values summarized in Chapter 5. This calculation is shown by Equation 2:

, , , = ∆ , , , , where 2 refers to the final baseline and policy scenarios.5D - 1 **APPENDIX 5 D**  **PM 2.5**  **CO -**  **BENEFITS OF THE FINA L RULE BY STATE**  **5 D**  **.1 Introduction**  This appendix describes the distribution of the health - related PM 2.5 co - benefits associated with this rule by state. We describe our approach for allocating the national - level PM 2.5 - related mortality and monetized benefits to the state - level. W e also summarize the results of this analysis and describe the limitations and uncertainties associated with our approach. This rule is expected to achieve PM 2.5 - related health benefits in all states, resulting from both emission reductions in that state and reduced transport of PM 2.5 between states. A key limitation of our approach is that it does not account for differences in the distribution of SO 2 and direct PM 2.5 emission reductions between the modeled interim scenario and the f inal policy (see Appendix 5C) . PM 2.5 - related co - benefits may therefore be under- or over- estimated for certain states. **5D.2 Methods**  As described in Appendix 5C, the PM 2.5 health co - benefits of the final rule are calculated using a BPT approach. The BPT values are derived from air quality modeling of an interim emissions scenario. Since the distribut ion of the SO 2 emission reductions in the interim modeled scenario and the final policy were generally consistent, applying BPT values from the interim modeled scenario to the final policy reasonably approximates the total monetized benefits of the final p olicy. However, this approach requires aggregation of benefits in the interim scenario to larger spatial scales to account for transport of pollution across state boundaries. Therefore, the final rule benefits described in Appendix 5C are estimated for t he eastern and western US, the same resolution at which BPT values were generated from the interim scenario . Since spatially resolved estimates of the co - benefits are useful for understanding how the expected benefits of this rule are distributed across the U.S., we developed an approximating approach for allocating national - level PM 2.5 co - benefits estimated for the final policy to the state level. This approach follows three steps. First, we quantified the state - level mortality and monetized health co - b enefits of the air quality modeled scenario using the BenMAP software. From these results, we calculated the percentage of national health benefits occurring in each state. Finally, these percentages were used to scale the national health benefits of the final policy down to the state level.5D - 2

As another approach, EPA considered scaling the health co - benefits of the final policy scenario by the percentage of the national total SO 2 emission change occurring in each state, since the distribution of emission changes across the U.S. changed between the modeled interim scenario and the final policy. However, such an approach would not account for the population in each state which is a main driver for air pollution health impacts, nor would it account for tran sport of pollution across state lines. Therefore, EPA judged that scaling national health co - benefits of the final rule by the state distribution of the co - benefits of the interim modeled scenario is a more appropriate approach. **5D.3 Limitations and unce**  **rtainties** The method described above adds unique uncertainties and limitations beyond those already described in detail in Chapter 5. A key limitation of this approach is that the distribution of SO 2 and direct PM 2.5 emissions changed between the modeled interim scenario and the final policy (see Appendix 5C). Differences in the emission changes would have an effect on the percentage of health co - benefits occurring in each state for the final policy. However, our approach necessarily assumes that the sta te distribution of health co - benefits for the final policy is equivalent to th at of the modeled interim scenario. PM 2.5 - related health co benefits for this rule could therefore be under- or over- estimated for certain states. **5D.4 Results**  The reduction in incidence of adult premature PM 2.5 - related mortality for the final rule by state is shown in Table 5D - 1. Additional non - mortality heath benefits are also expected in each state but are not included here . The greatest percentage of interim mortality benefits in any one state is in Texas (10.8%), followed by Florida (6.7%). For the final policy, 460 to 1 , 200 avoided premature deaths are estimated to be avoided in Texas, and 2 8 0 to 7 5 0 in Florida, depending on the concentration - response factor. Altho ugh SO 2 emissions in some states (e.g. Kentucky, New York, Tennessee) increase between the interim baseline and interim policy scenario (see Appendix 5A), mortality decreases in these states due to reduced transport of pollution from other states. All sta tes, therefore, experience health benefits from the interim scenario and the final rule. Table 5D - 2 shows the estimated economic value of health and welfare benefits by state for the final rule. Approximately $4.0 to $9. 7 billion (2007$, 3% discount rate ) in benefits are expected to occur in Texas, and $2.4 to $6.0 billion in Florida, depending on the concentration - response function used for adult mortality.5D - 3

**Table 5D -**  **1. Estimated Reduction in Incidence of Premature Adult Mortality for the Mercury**  **and Ai r Toxics Standards**  **in 2016 by State**  **a ,b**  **State Percent of total** 

**interim benefits Final policy benefits**  **– adult mortality** 

**Pope et al. (2002) estimate Laden et al. (2006) estimate**  Alabama 3.34 140 3 6 0 Arizona 0.32 14 3 5 Arkansas 2.28 96 2 5 0 California 0.13 6 1 4 Colorado 1.26 53 140 Connecticut 0.84 35 9 0 Delaware 0.30 13 3 2 DC 0.14 6 1 5 Florida 6.73 280 7 3 0 Georgia 4.53 190 49 0 Idaho 0.06 3 6 Illinois 5.31 220 5 7 0 Indiana 2.64 110 29 0 Iowa 1.45 61 160 Kansas 1.43 60 160 Kentucky 1.97 83 2 1 0 Louisiana 2.68 110 29 0 Maine 0.19 8 2 0 Maryland 1.99 84 220 Massachusetts 1.23 52 1 3 0 Michigan 3.78 160 4 1 0 Minnesota 1.34 57 150 Mississippi 2.21 93 2 4 0 Missouri 3.79 160 4 1 0 Montana 0.07 3 8 Nebraska 0.67 28 7 2 Nevada 0.09 4 10 New Hampshire 0.23 10 2 5 New Jersey 2.96 130 3 2 0 New Mexico 0.22 9 24 New York 4.11 170 44 0 North Carolina 4.42 190 4 8 0 North Dakota 0.17 7 19 Ohio 5.19 220 5 6 0 Oklahoma 2.82 120 3 0 0 Oregon 0.11 5 12 Pennsylvania 4.91 210 5 3 0 Rhode Island 0.27 11 29 South Carolina 3.01 130 3 3 0 South Dakota 0.25 11 2 7 Tennessee 3.38 140 3 7 0 Texas 10.82 460 1200 Utah 0.20 8 22 Vermont 0.09 4 10 Virginia 2.76 120 3 0 0 Washington 0.28 12 3 15D - 4

West Virginia 0.89 38 96 Wisconsin 2.06 87 2 2 0 Wyoming 0.05 2 6 National Total 4, 200 11,0 00 a State level benefits of the final rule are scaled by the distribution of mortality benefits simulated from the interim scenario described in Appendices 5A, 5B, and 5C. b Estimates rounded to two significant figures; column values will not sum to total value. These estimates do not include confidence intervals.5D - 5

**Table 5D -**  **2. Estimated Economic Value of Health Benefits of the Mercury and Air Toxics**  **Standard in 2016 by State (billions of 2007$** **, 3% discount rate )**  **a ,b** 
**State Percent of**  **total**

**interim benefits H**  **ealth benefits**  **( billions of**  **2007$, 3% discount rate)**

**Pope et al. (2002) estimate Laden et al. (2006) estimate**  Alabama 3.32 $ 1.20 $ 3.00 Arizona 0.32 $ 0.12 $ 0.29 Arkansas 2.27 $ 0.82 $ 2.00 California 0.13 $ 0.05 $ 0.12 Colorado 1.28 $ 0.46 $ 1.10 Connecticut 0.84 $ 0.3 0 $ 0.75 Delaware 0.30 $ 0.11 $ 0.27 DC 0.14 $ 0.05 $ 0.12 Florida 6.68 $ 2.40 $ 6.00 Georgia 4.56 $ 1.70 $ 4.10 Idaho 0.06 $ 0.02 $ 0.05 Illinois 5.34 $ 1.90 $ 4. 7 0 Indiana 2.64 $ 0.96 $ 2.40 Iowa 1.45 $ 0.5 2 $ 1.30 Kansas 1.43 $ 0.52 $ 1.30 Kentucky 1.97 $ 0.71 $ 1.80 Louisiana 2.67 $ 0.97 $ 2.40 Maine 0.19 $ 0.07 $ 0.17 Maryland 1.99 $ 0.72 $ 1.80 Massachusetts 1.23 $ 0.45 $ 1.10 Michigan 3.78 $ 1.40 $ 3.40 Minnesota 1.35 $ 0.49 $ 1.20 Mississippi 2.20 $ 0.80 $ 2.00 Missouri 3.78 $ 1.40 $ 3.40 Montana 0.07 $ 0.03 $ 0.06 Nebraska 0.67 $ 0.24 $ 0.60 Nevada 0.09 $ 0.03 $ 0.08 New Hampshire 0.23 $ 0.08 $ 0.21 New Jersey 2.96 $ 1.10 $ 2.60 New Mexico 0.22 $ 0.08 $ 0.20 New York 4.12 $ 1.50 $ 3.70 North Carolina 4.41 $ 1.60 $ 3.90 North Dakota 0.17 $ 0.06 $ 0.1 5 Ohio 5.17 $ 1.90 $ 4.60 Oklahoma 2.81 $ 1.00 $ 2.50 Oregon 0.11 $ 0.04 $ 0.10 Pennsylvania 4.87 $ 1.80 $ 4.40 Rhode Island 0.27 $ 0.10 $ 0.24 South Carolina 2.99 $ 1.10 $ 2.70 South Dakota 0.25 $ 0.09 $ 0.23 Tennessee 3.38 $ 1.20 $ 3.00 Texas 10.95 $ 4.00 $ 9.70 Utah 0.20 $ 0.07 $ 0.18 Vermont 0.09 $ 0.03 $ 0.08 Virginia 2.77 $ 1.00 $ 2.50 Washington 0.28 $ 0.10 $ 0.255D - 6

West Virginia 0.89 $ 0.32 $ 0.79 Wisconsin 2.06 $ 0.75 $ 1.80 Wyoming 0.05 $ 0.02 $ 0.05 National Totalc $ 36.00 $ 89.00 a State level benefits of the final rule are scaled by the distribution of mortality benefits simulated from the interim scenario described in Appendices 5A, 5B, and 5C. b Estimates rounded to two significant figures; column values will not sum to total value. These est imates do not include confidence intervals. c While climate benefits are included in the total co - benefits of this rule as described in Chapter 5, only health benefits (sum of mortality and morbidity endpoints) are included in the national total here.5E - 1 **APPENDIX 5E TECHNICAL SUPPORT DO**  **CUMENT: SUMMARY OF EXPERT OP**  **INIONS ON THE EXISTE NCE OF A THRESHOLD I**  **N THE CONCENTRATION**  **- RESPONSE FUNCTION FO**  **R PM 2.5**  **- RELATED MORTALITY** **28**

28 U.S. Environmental Protection Agency. 2010. Technical Support Document: Summary of Expert O pinions on the Existence of a Threshold in the Concentration - Response Function for PM 2.5 - related Mortality. Research Triangle Park, NC. June. Available on the Internet at: < www.epa.gov/ttn/ecas/regdata/Benefits/thresholdstsd.pdf>.5E - 2



## Summary of Expert Opinions on the Existence of a **Threshold in the Concentration**  **- Response Function for PM** **2.5**

## -**related Mortality**

**Technical Support Document (TSD)**

**June 2010**
Compiled by : U.S. Environmental Protection Agency Office of Air Quality

Planning and Standards Health and Environmental Impact Division Air Benefit- Cost Group Research Triangle Park, North Carolina **Contents:** A. HES comments on 812 Analysis (2010) B. American Heart Association Scientific Statement (2010) C. Integrated Science Assessment for Particulate Matter (2009) D. CAS AC comments on PM I SA and REA (2009) E. Krewski et al. (2009) F. Schwartz et al. (2008) G. Expert Elicitation on PM Mortality (2006, 2008) H. CASAC comments on PM Staff Paper (2005) I. HES comments on 812 Analysis (2004) J. NRC (2002)5E - 3 **A. HES Comments on 8 12 Analysis (2010)**  **U.S. Environmental Protection Agency -**  **Science Advisory Board (U.S. EPA -**  **SAB). 2010. Review of EPA’s DRAFT Health Benefits of the Second Section 812 Prospective Study of**  **the Clean Air Act. EPA -**  **COUNCIL -**  **10 -** **001. June. Available on the Inter net at**  **<http://yosemite.epa.gov/sab/sabproduct.nsf/0/72D4EFA39E48CDB28525774500738776/$ File/EPA** **- COUNCIL**  **- 10**  **- 001**  **- unsigned.pdf>.**  Pg 2: “The HES generally agrees with other decisions made by the EPA project team with respect to PM, in particular, the PM mortality effect threshold model, the cessation lag model, the inclusion of infant mortality estimation, and differential toxicity of PM. ” Pg 2: “Further, the HES fully supports EPA’s use of a no -threshold model to estimate the mortality reductions associated with reduced PM exposure. ” Pg 6: “The HES also supports the Agency ’s choice of a no -threshold model for PM -related effects. ” Pg 13: “The HES fully supports EPA’s decision to use a no -threshold model to estimate mortality reductions. This decision is supported by the data, which are quite consistent in showing effects down to the lowest measured levels. Analyses of cohorts using data from more recent years, during which time PM concentrations have fallen, continue to report strong associations with mortality. Therefore, there is no evidence to support a truncation of the CRF.” **HES Panel Members Dr. John Bailar**  , Chair of the Health Effects Subcommittee, Scholar in Residence, The National Academies, Washington, DC **Dr. Michelle Bell** , Associate Professor, School of Forestry and Environmental Studies, Yale University, New Haven, CT **Dr. James K. Hammitt**, Professor, Department of Health Policy and Management, Harvard School of Public Health, Boston, MA **Dr. Jonathan Levy** , Associate Professor, Department of Environmental Health, Harvard School of Public Health, Boston, MA **Dr. C. Arden Pope , III**  Professor, Department of Economics, Brigham Young University, Provo, UT **Mr. John Fintan Hur ley**  , Research Director, Institute of Occupational Medicine (IOM), Edinburgh, United Kingdom, UK **Dr. Patrick Kinney** , Professor, Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY **Dr. Michael T. Kl einman**  , Professor, Department of Medicine, Division of Occupational and Environmental Medicine, University of California, Irvine, Irvine, CA **Dr. Bart Ostro** , Chief, Air Pollution Epidemiology Unit, Office of Environmental Health Hazard Assessment, Californi a Environmental Protection Agency, Oakland, CA5E - 4

**Dr. Rebecca Parkin** , Professor and Associate Dean, Environmental and Occupational Health, School of Public Health and Health Services, The George Washington University Medical Center, Washington, DC5E - 5 **B. Scienti fic Statement from American Heart Association (2010)**  **Brook RD, Rajagopalan S, Pope CA 3rd, Brook JR, Bhatnagar A, Diez -** **Roux AV, Holguin F, Hong Y, Luepker RV, Mittleman MA, Peters A, Siscovick D, Smith SC Jr, Whitsel L,**  **Kaufman JD; on behalf of the America n Heart Association Council on Epidemiology and**  **Prevention, Council on the Kidney in Cardiovascular Disease, and Council on Nutrition, Physical Activity and Metabolism. (2010). “Particulate matter air pollution and**  **cardiovascular disease: an update to the scientific statement from the American Heart**  **Association.” Circulation**  **. 121: 2331 -** **2378.** Pg 2338: “Finally, there appeared to be no lower- limit threshold below which PM 10 was not associated with excess mortality across all regions.” Pg 2350: “There also appears to be a monotonic (eg, linear or log- linear) concentration-response relationship between PM 2.5 and mortality risk observed in cohort studies that extends below present- day

regulations of 15 μg/m3 for mean annual levels, without a discernable “safe” threshold.” (cites Pope 2004, Krewski 2009, and Schwartz 2008) Pg 2364: “The PM2.5 concentration

– cardiovascular risk relationships for both short- and longterm exposures appear to be monotonic, extending below 15 μg/m3 (the 2006 annual NAAQS level) without a discernable “safe” threshold.” Pg 2365: “This updated review by the AHA writing group corroborates and strengthens the conclusions of the initial scientific statement. In this context, we agree with the concept and continue to support measures based on scientific evidence, such as the US EPA NAAQS, that seek to control PM levels to protect the public health. Because the evidence reviewed supports that there is no safe threshold, it appears that pub lic health benefits would accrue from lowering PM2.5 concentrations even below present- day annual (15 μg/m3 ) and 24 -hour (35 μg/m3 ) NAAQS, if feasible, to optimally protect the most susceptible populations.” Pg 2366: “Although numerous insights have greatly enhanced our understanding of the PM cardiovascular relationship since the first AHA statement was published, the following list represents broad strategic avenues for future investigation: ... Determine whether any “safe” PM threshold concentration exists that eliminates both acute and chronic cardiovascular effects in healthy and susceptible individuals and at a population level.” **Scientific Statement Authors Dr. Robert D. Brook** , MD **Dr. Sanjay Rajagopalan** , MD **Dr. C. Arden Pope** , PhD **Dr. Jeffrey R. Brook**, PhD **Dr. Aruni Bhatnagar** , PhD, FAHA **Dr. Ana V. Diez -** **Roux** , MD, PhD, MPH **Dr. Fernando Holguin** , MD5E - 6

**Dr. Yuling Hong** , MD, PhD, FAHA **Dr. Russell V. Luepker** , MD, MS, FAHA **Dr. Murray A. Mittleman** , MD, DrPH, FAHA **Dr. Annette Peters** , PhD **Dr. David Siscovick**, MD, MPH, FAHA **Dr. Sidney C. Smith, Jr** , MD, FAHA **Dr. Laurie Whitsel,** PhD **Dr. Joel D. Kaufman** , MD, MPH5E - 7 **C. Integrated Science Assessment for Particulate Matter (2009) U.S. Environmental Protection Agency (U.S. EPA).**  **2009. Integrated Science Assessment for Particulate Matter (Final Report). EPA** **- 600**  **- R** **- 08** **- 139F. National Center for**  **Environmental Assessment**
**– RTP Division. December. Available on the Internet at**  **<http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=2165 46>.**  Pg 1 -22: “An important consideration in characterizing the public health impacts associated with exposure to a pollutant is whether the concentration-response relationship is linear across the full concentration range encountered, or if nonlinear relationships exist along any part of this range. Of particular interest is the shape of the concentration-response curve at and below the level of the current standards. The shape of the concentration-response curve varies, depending on the ty pe of health outcome, underlying biological mechanisms and dose. At the human population level, however, various sources of variability and uncertainty tend to smooth and “linearize” the concentration-response function (such as the low data density in the lower concentration range, possible influence of measurement error, and individual differences in susceptibility to air pollution health effects). In addition, many chemicals and agents may act by perturbing naturally occurring background processes that lead to disease, which also linearizes population concentration-response relationships (Clewell and Crump, 2005, 156359 ; Crump et al., 1976, 003192 ; Hoel, 1980, 156555 ). These attributes of population dose -response may explain why

the available human data at ambient concentrations for some environmental pollutants (e.g., PM, O 3 , lead [Pb], ETS, radiation) do not exhibit evident thresholds for health effects, even though likely mechanisms include nonlinear processes for some key events. These attributes of human population dose -response relationships have been extensively discussed in the broader epidemiologic literature (Rothman and Greenland, 1998, 086599 ).” Pg 2 - 16: “In addition, cardiovascular hospital admission and mortality studies that examined the PM 10 concentration-response relationship found evidence of a log- linear no -threshold relationship between PM exposure and cardiovascular-related morbidity (Section 6.2) and mortality (Section 6.5).” Pg 2 -25: “2.4.3. PM Concentration-Response Relationship An important consideration in characterizing the PM -morbidity and mortality

association is whether the concentration-response relationship is linear across the full concentration range that is encountered or if there are concentration ranges where there are departures from linearity (i.e., nonlinearity). In this I SA studies have been identified that attempt to characterize the shape of the concentration-response curve along with possible PM “thresholds” (i.e., levels which PM concentrations must exceed in order to elicit a health response). The epidemiologic studies evaluated that examined the shape of the concentration-response curve and the potential presence of a threshold have focused on cardiovascular hospital admissions and ED visits and mortality associated with short-term exposure to PM 10 and mortality associated with long-term exposure to PM2.5 . “A limited number of studies have been identified that examined the shape of the PM cardiovascular hospital admission and ED visit concentration-response relationship. Of the se studies, some conducted an exploratory analys is during model selection to determine if a linear5E - 8

curve most adequately represented the concentration-response relationship; whereas, only one study conducted an extensive analys is to examine the shape of the concentration-response curve at different concentrations (Section 6.2.10.10). Overall, the limited evidence from the studies evaluated supports the use of a no -threshold, log- linear model, which is consistent with the observations made in studies that examined the PM -mortality relationship. “Although multiple studies have previously examined the PM -mortality concentration-response relationship and whether a threshold exists, more complex statistical analyses continue to be developed to analyze this association. Using a variety of methods and models, most of the studies evaluated support the use of a no -threshold, log- linear model; however, one study did observe heterogeneity

in the shape of the concentration-response curve across cities (Section 6.5). Overall, the studies evaluated further support the use of a no -threshold log- linear model, but additional issues such as the influence of heterogeneity in estimates between cities, and the effect of seasonal and regional differences in PM on the concentration-response relationship still require further investigation. “In addition to examining the concentration-response relationship between short-term exposure to PM and mortality, Schwartz et al. (2008, 156963) conducted an analys is of the shape of the concentration-response relationship associated with long-term exposure to PM. Using a variety of statistical methods, the concentration-response curve was found to be indistinguishable from linear, and, therefore, little evidence was observed to suggest that a threshold exists in the association between long-term exposure to PM2.5 and the risk of death (Section 7.6).” Pg 6 - 75: “6.2.10.10. Concentration Response The concentration-response relationship has been extensively analyz ed primarily

through studies that examined the relationship between PM and mortality. These studies, which have focused on short- and long-term exposures to PM have consistently found no evidence for deviations from linearity or a safe threshold (Daniels et al., 2004, 087343 ; Samoli et al., 2005, 087436 ; Schwartz, 2004, 078998 ; Schwartz et al., 2008, 156963 ) (Sections 6.5.2.7 and 7.1.4). Although on a more limited basis, studies that have examined PM effects on cardiovascular hospital admissions and ED visits have also analyzed the PM concentration-response relationship, and contributed to the overall body of evidence which suggests a log- linear, no -threshold PM concentration-response relationship. “The results from the three multicity studies discussed above support no -threshold log- linear models, but issues such as the possible influence of exposure error and heterogeneity of shapes across cities remain to be resolved. Also, given the pattern of seasonal and regional differences in PM risk estimates depicted in recent multicity study results (e.g., Peng et al., 2005, 087463 ), the very concept of a concentration-response relationship estimated across cities and for all - ye ar data may not be very

informative.” Pg 6 - 197: “6.5.2.7. Investigation of Concentration-Response Relationship The results from large multicity studies reviewed in the 2004 PM AQCD (U.S. EPA, 2004, 056905 ) suggested that strong evidence did not exist for a clear threshold for PM mortality effects. However, as discussed in the 2004 PM AQCD (U.S. EPA, 2004, 056905 ), there are several challenges in determining and interpreting the shape of PM-mortality concentration-5E - 9

response functions and the presence of a threshold, including: (1) limited range of available concentration levels (i.e., sparse data at the low and high end); (2) heterogeneity of susceptible populations; and (3) investigate the PM -mortality concentration-response relationship. “Daniels et al. (2004, 087343 ) evaluated three concentration-response models: (1) log- linear models (i.e., the most commonly used approach, from which the majority of risk estimates are derived); (2) spline models that allow data to fit possibly non- linear relationship; and (3) threshold models, using PM 10 data in 20 cities from the 1987 - 1994 NMMAPS data. They reported that the spline model, combined across the cities, showed a linear relation without indicating a threshold for the relative risks of death for all - causes and for cardiovascularrespiratory

causes in relation to PM 10 , but “the other cause” deaths (i.e., all cause minus c ardiovascular-respiratory) showed an apparent threshold at around 50 μg/m3 PM 10 , as shown in Figure 6 - 35. For all - cause and cardio -respiratory deaths, based on the Akaike’s Information Criterion (AIC), a log- linear model without threshold was preferred to the threshold model and to the spline model. “The HEI review committee commented that interpretation of these results required caution, because (1) the measurement error could obscure any threshold; (2) the city- specific concentration-response curves exhibited a variety of shapes; and (3) the use of AIC to choose among the models might not be appropriate due to the fact it was not designed to assess scientific theories of etiology. Note, however, that there has been no etiologically credible reason suggested thus far to choose one model over others for aggregate outcomes. Thus, at least statistically, the result of Daniels et al. (2004, 087343 ) suggests that the log- linear model is appropriate in describing the relationship between PM10 and mortality. “The Schwartz (2004, 078998 ) analys is of PM 10 and mortality in 14 U.S. cities, described in Section 6.5.2.1, also examined the shape of the concentration-response relationship by including indicator variables for days when concentrations were between 15 and 25 μg/m3 , between 25 and 34 μg/m3 , between 35 and 44 μg/m3 , and 45 μg/m3 and above. In the model, days with concentrations below 15 μg/m3 served as the reference level. This model was fit using the single stage method, combining strata across all cities in the case - crossover design. Figure 6 - 36 shows the resulting relationship, which does not provide sufficient evidence to suggest that a threshold exists. The authors did not examine city-to - city variation in the concentration-response relationship in this study. “PM 10 and mortality

in 22 European cities (and BS in 15 of the cities) participating in the APHEA project. In nine of the 22 cities, PM10 levels were estimated using a regression model relating co - located PM10 to BS or TSP. They used regression spline models with two knots (30 and 50 μg/m3 ) and then combined the individual city estimates of the splines across cities. The investigators concluded that the association between PM and mortality in these cities could be adequately estimated using the log- linear model. However, in an ancillary analys is of the concentration-response curves for the largest cities in each of the three distinct geographic areas (western, southern, and eastern European cities): London, England; Athens, Greece; and Cracow, Poland, S amoli et al. (2005, 087436 ) observed a difference in the shape of the concentrationresponse curve across cities. Thus, while the combined curves (Figure 6 - 37) appear to support5E - 10

no -threshold relationships between PM 10 and mortality, the heterogeneity of the shapes across cities makes it difficult to interpret the biological relevance of the shape of the combined curves. “The results from the three multicity studies discussed above support no -threshold log- linear models, but issues such as the possible influence of exposure error and heterogeneity of shapes across cities remain to be resolved. Also, given the pattern of seasonal and regional differences in PM risk estimates depicted in recent multicity study results (e.g., Peng et al., 2005, 087463 ), the very concept of a concentration-response relationship estimated across cities and for all - ye ar data may not be very

informative.” **Authors of ISA Dr. Lindsay Wichers Stanek**  (PM Team Leader)

— National Center for Environmental Assessment (NCEA), U.S. Environmental Protection Agency (U.S. EPA), Research Triangle Park, NC **Dr. Jeffrey Arnold**

—

NCEA, U.S. EPA, Research Triangle Park, NC (now at Institute for Water Resources, U.S. Army C orps of Engineers, Washington, D.C) **Dr. Christal Bowman**

— NCEA, U.S. EPA, Research Triangle Park, NC **Dr. James S. Brown**

—

NCEA, U.S. EPA, Research Triangle Park, NC **Dr. Barbara Buckley**

—

NCEA, U.S. EPA, Research Triangle Park, NC **Mr. Allen Davis**

—

NCEA, U.S. EPA, Research Triangle Park, NC **Dr. Jean -** **Jacques Dubois**

— NCEA, U.S. EPA, Research Triangle Park, NC **Dr. Steven J. Dutton**

—

NCEA, U.S. EPA, Research Triangle Park, NC **Dr. Tara Greaver**

—

NCEA, U.S. EPA, Research Triangle Park, NC **Dr. Erin Hines**

—

NCEA, U.S. EPA, Research Triangle Park, NC **Dr. Douglas Johns**

—

NCEA, U.S. EPA, Research Triangle Park, NC **Dr. Ellen Kirrane**

—

NCEA, U.S. EPA, Research Triangle Park, NC **Dr. Dennis Kotchmar**

— NCEA, U.S. EPA, Research Triangle Park, NC **Dr. Thomas Long**

—

NCEA, U.S. EPA, Research Tri angle Park, NC **Dr. Thomas Luben**

—

NCEA, U.S. EPA, Research Triangle Park, NC **Dr. Qingyu Meng**

—Oak Ridge Institute for Science and Education, Postdoctoral Research Fellow to NCEA, U.S. EPA, Research Triangle Park, NC **Dr. Kristopher Novak**

— NCEA, U.S. EPA, Research Triangle Park, NC **Dr. Joseph Pinto**

—

NCEA, U.S. EPA, Research Triangle Park, NC **Dr. Jennifer Richmond -** **Bryant**

— NCEA, U.S. EPA, Research Triangle Park, NC **Dr. Mary Ross**

—

NCEA, U.S. EPA, Research Triangle Park, NC **Mr. Jason Sacks**

—

NCEA, U.S. EPA, Research Triangl e Park, NC5E - 11

**Dr. Timothy J. Sullivan**

—E&S Environmental Chemistry, Inc., Corvallis, OR **Dr. David Svendsgaard**

— NCEA, U.S. EPA, Research Triangle Park, NC **Dr. Lisa Vinikoor**

—

NCEA, U.S. EPA, Research Triangle Park, NC **Dr. William Wilson**

—

NCEA, U.S. EPA, Research Triangle Park, NC **Dr. Lori White**

— NCEA, U.S. EPA, Research Triangle Park, NC (now at National Institute for Environmental Health Sciences, Research Triangle Park, NC) **Dr. Christy Avery**

—

University

of North Carolina, Chapel Hill, NC **Dr. Kathleen Belanger**

—Center for Perinatal, Pediatric and Environmental Epidemiology, Yale University, New Haven, CT **Dr. Michelle Bell**

—School of Forestry & Environmental Studies, Yale University, New Haven, CT **Dr. William D. Bennett**

—Center for Environmental Medicine, Asthma and Lung Biology, University

of North Carolina, Chapel Hill, NC **Dr. Matthew J. Campen**

—Lovelace Respiratory Research Institute, Albuquerque, NM **Dr. Leland B. Deck**

— Stratus Consulting, Inc., Washington, DC **Dr. Janneane F. Gent**

—Center for Perinatal, Pediatric and Environmental Epidemiology, Yale University, New Haven, CT **Dr. Yuh -**  **Chin Tony Huang**

—Department of Medicine, Division of Pulmonary Medicine, Duke University

Medical Center, Durham, NC **Dr. Kazuhiko Ito**

—

Nelson Institute of Environmental Medicine, NYU School of Medicine, Tuxedo, NY **Mr. Marc Jackson**

—Integrated Laboratory Systems, Inc., Research Triangle Park, NC **Dr. Michael Kleinman**

—Department of Community and Environmental Medicine, University of California, Irvine **Dr. Sergey Napelenok**

— National Exposure Research Laboratory, U.S. EPA, Research Triangle Park, NC **Dr. Marc Pitchford**

—

National Oceanic and Atmospheric Administration, Las Vegas, NV **Dr. Les Recio**

—Genetic Toxicology Division, Integrated Laboratory Systems, Inc., Research Triangle Park, NC **Dr. David Quincy Rich** 

—Department of Epidemiology, University of Medicine and Dentistry of New Jersey, Piscataway, NJ **Dr. Timothy Sullivan**

— E&S Environmental Chemistry, Inc., Corvallis, OR **Dr. George Thurston**

—Department of Environmental Medicine, NYU, Tuxedo, NY **Dr. Gregory Wellenius** 

—Cardiovascular Epidemiology Research Unit, Beth Israel Deaconess Medical Center, Boston, MA5E - 12

**Dr. Eric Whitsel**

—Departments of Epidemiology and Medicine, University of North Carolina, Chapel Hill, NC **Peer Reviewers Dr. Sara Dubowsky Adar**  , Department of Epidemiology, University of Washington, Seattle, WA **Mr. Chad Bailey** , Office of Transportation and Air Quality, Ann Arbor, M I **Mr. Richard Baldauf**

, Office of Transportation and Air Quality, Ann Arbor, MI **Dr. Prakash Bhave** , National Exposure Research Lab oratory, U.S. EPA, Research Triangle Park, NC **Mr. George Bowker** , Office of Atmospheric Programs, U.S. EPA, Washington, D.C. **Dr. Judith Chow** , Division of Atmospheric Sciences, Desert Research Institute, Reno, NV **Dr. Dan Costa** , U.S. EPA, Research Triangle P ark, NC **Dr. Ila Cote** , NCEA, U.S. EPA, Research Triangle Park, NC **Dr. Robert Devlin** , National Health and Environmental Effects Research Laboratory, U.S. EPA, Research Triangle Park, NC **Dr. David DeMarini** , National Health and Environmental Effects Research Laboratory, U.S. EPA, Research Triangle Park, NC **Dr. Neil Donahue** , Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, PA **Dr. Aimen Farraj** , National Health and Environmental Effects Research Laboratory, U.S. EPA, Research Triangle Park, NC **Dr. Mark Frampton** , Department of Environmental Medicine, University of Rochester Medical Center, Rochester, NY **Mr. Neil Frank**, Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle Park, NC **Mr. Tyler Fox** , Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle Park, NC **Dr. Jim Gauderman** , Department of Environmental Medicine, Department of Preventive Medicine, University of Southern California, Los Angeles, CA **Dr. Barbara Glenn** , National Center for Environmental Research, U.S. EPA, Washington, D.C. **Dr. Terry Gordon** , School of Medicine, New York University, Tuxedo, NY **Mr. Tim Hanley** , Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle Park, NC **Dr. Jack Harkema** , Department of Pathobiology and Diagnostic Investigation, Michigan State University, East Lansing, MI **Ms. Beth Hassett -**  **Sipple** , Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle Park, NC5E - 13

**Dr. Amy Herring** , Department of Biostatistics, University of North Carolina, Chapel Hill, NC **Dr. Israel Jirak**, Department of Meteorology, Embry-Riddle Aeronautical University, Prescott, AZ **Dr. Mike Kleeman** , Department of Civil and Environmental Engineering, University of California, Davis, CA **Dr. Petros Koutrakis** , Exposure, Epidemiology and Risk Program, Harvard School of Public Health, Boston, MA **Dr. Sagar Krupa** , Department of Plant Pathology, University of Minnesota, St. Paul, MN **Mr. John Langstaff**

, Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle Park, NC **D r. Meredith Lassiter**  , Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle Park, NC **Mr. Phil Lorang** , Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle Park, NC **Dr. Karen Martin** , Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle Park, NC **Ms. Connie Meacham** , NCEA, U.S. EPA, Research Triangle Park, NC **Mr. Tom Pace** , Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle Park, NC **Dr. Jennifer Peel** , Department of Environmental and Radiological Health Sciences, College of Veterinary

Medicine and Biomedical Sciences, Colorado State University, Fort Collins, CO **Dr. Zackary Pekar** , Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle Park, NC **Mr. Rob Pinder,** National Exposure Research Laboratory, U.S. EPA, Research Triangle Park, NC **Mr. Norm Possiel** , Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle Park, NC **Dr. Sanjay Rajagopalan** , Division of Cardiovascular Medicine, Ohio State University, Columbus, OH **Dr. Pradeep Rajan** , Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle Park, NC **Mr. Venkatesh Rao** , Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle Park, NC **Ms. Joann Rice** , Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle Park, NC **Mr. Harvey Richmond** , Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle Park, NC5E - 14

**Ms. Victoria Sandiford** , Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle Park, NC **Dr. Stefanie Sarnat** , Department of Environmental and Occupational Health, Emory University, Atlanta, GA **Dr. Frances Silverman** , Gage Occupational and Environmental Health, University of Toronto, Toronto, ON **Mr. Steven Silverman** , Office of General Council, U.S. EPA, Washington, D.C. **Dr. Barbara Turpin** , Department of Environmental Sciences, Rutgers University, New Brunswick, NJ **Dr. Robert Vanderpool,** National Exposure Research Laboratory, U.S. EPA, Research Triangle Park, NC **Dr. Joh n Vandenberg (Director)** 
**—** NCEA -RTP Division, U.S. EPA, Research Triangle Park, NC **Dr. Alan Vette** , National Exposure Research Laboratory, U.S. EPA, Research Triangle Park, NC **Ms. Debra Walsh** (Deputy Director)

—

NCEA-RTP Division, U.S. EPA, Research Triangle Par k, NC **Mr. Tim Watkins** , National Exposure Research Laboratory, U.S. EPA, Research Triangle Park, NC **Dr. Christopher Weaver** , NCEA, U.S. EPA, Research Triangle Park, NC **Mr. Lewis Weinstock**, Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle Park, NC **Ms. Karen Wesson** , Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle Park, NC **Dr. Jason West** , Department of Environmental Sciences and Engineering, University of North Carolina, Chapel Hill, NC **Mr. Ronald Williams** , Nati onal Exposure Research Laboratory, U.S. EPA, Research Triangle Park, NC **Dr. George Woodall,** NCEA, U.S. EPA, Research Triangle Park, NC **Dr. Antonella Zanobetti** , Department of Environmental Health, Harvard School of Public Health, Boston, MA5E - 15 **D. CASAC commen ts on PM ISA and REA (2009)**  **U.S. Environmental Protection Agency -**  **Science Advisory Board (U.S. EPA -**  **SAB). 2009. Review of EPA’s Integrated Science Assessment for Particulate Matter (First External**  **Review Draft, December 2008). EPA -**  **COUNCIL -** **09 -** **008. May . Available on the Internet**  **at <http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb85256ead006be86e/7**  **3ACCA834AB44A10852575BD0064346B/$File/EPA -**  **CASAC -** **09 -** **008 -** **unsigned.pdf>.** Pg 9: “There is an appropriate discussion of the time - series studies, but this section needs to have an explicit finding that the evidence supports a relationship between PM and mortality that is seen in these studies. This conclusion should be followed by the discussion of statistical methodology

and the identification of any threshold that may

exist.” **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** **- COUNCIL**  **- 09**  **- 009. May. Available on the Internet at**  **<http://yosemite.epa.gov/sab/SABPRODUCT.NSF/ 81e39f4c09954fcb85256ead006be86e/7**  **23FE644C5D758DF852575BD00763A32/$File/EPA -**  **CASAC -** **09 -** **009 -** **unsigned.pdf>.** Pg 6: “On the issue of cut-points raised on 3 - 18, the authors should be prepared to offer a scientifically

cogent reason for selection of a specific cut-point, and not simply try different cutpoints to see what effect this has on the analys is. The draft I SA was clear that there is little evidence for a population threshold in the C -R function.” **U.S. Environmental Protection Agency -**  **Science Advisory B oard (U.S. EPA** **- SAB). 2009. Review**  **of Integrated Science Assessment for Particulate Matter (Second External Review Draft, July 2009)**  **. EPA** **- CASAC**  **- 10** **- 001. November. Available on the Internet at**  **<http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb8 5256ead006be86e/151B1F8**  **3B023145585257678006836B9/$File/EPA -**  **CASAC -**  **10 -**  **001 -** **unsigned.pdf>.** Pg 2: “The paragraph on lines 22 - 30 of page 2 - 37 is not clearly written. Twice in succession it states that the use of a no -threshold log- linear model is supported, but then cites other studies that suggest otherwise. It would be good to revise this paragraph to more clearly state

– well, I’m not sure what. Probably that more research is needed.” **CASAC Panel Members Dr. Jonathan M. Samet,**  Professor and Chair, Department of Preventive Medicine, University of Southern California, Los Angeles, CA **Dr. Joseph Brain**  , Philip Drinker Professor of Environmental Physiology, Department of Environmental Health, Harvard School of Public Health, Harvard University, Boston, MA **Dr. Ellis B. Cowling** , University Distinguished Professor At-Large Emeritus, Colleges of Natural Resources and Agriculture and Life Sciences, North Carolina State University, Raleigh, NC **Dr. James Crapo** , Professor of Medicine, Department of Medicine, National Jewish Medical and Research Center, Denver, CO5E - 16

**Dr. H. Christopher Frey** , Professor, Department of Civil, Construction and Environmental Engineering, College of Engineering, North Carolina State University, Raleigh, NC **Dr. Armistead (Ted) Russell** , Professor, Department of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA **Dr. Lowell Ashbaugh,** Associate Research Ecologist, Crocker Nuclear Lab, University of California, Davis, Davis, CA **Prof. Ed Avol,** Professor, Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA **Dr. Wa yne Cascio,**  Professor, Medicine, Cardiology, Brody School of Medicine at East Carolina University, Greenville, NC **Dr. David Grantz,** Director, Botany and Plant Sciences and Air Pollution Research Center, Riverside Campus and Kearney Agricultural Center, University of California, Parlier, CA **Dr. Joseph Helble,** Dean and Professor, Thayer School of Engineering, Dartmouth College, Hanover, NH **Dr. Rogene Henderson** , Senior Scientist Emeritus, Lovelace Respiratory Research Institute, Albuquerque, NM **Dr. Philip Hopke**  , Bayard D. Clarkson Distinguished Professor, Department of Chemical Engineering, Clarkson University, Potsdam, NY **Dr. Morton Lippmann** , Professor, Nelson Institute of Environmental Medicine, New York University School of Medicine, Tuxedo, NY **Dr. Helen Suh MacIntosh**  , Associate Professor, Environmental Health, School of Public Health, Harvard University, Boston, MA **Dr. William Malm** , Research Physicist, National Park Service Air Resources Division, Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO **Mr. Charles Thomas (Tom) Moore, Jr** ., Air Quality Program Manager, Western Governors' Association, Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO **Dr. Robert F. Phalen**  , Professor, Department of Community & Environmental Medicine; Director, Air Pollution Health Effects Laboratory; Professor of Occupational & Environmental Health, Center for Occupation & Environment Health, College of Medicine, University of Cali fornia Irvine, Irvine, CA **Dr. Kent Pinkerton** , Professor, Regents of the University of California, Center for Health and the Environment, University of California, Davis, CA **Mr. Richard L. Poirot** , Environmental Analyst, Air Pollution Control Division, Dep artment of Environmental Conservation, Vermont Agency of Natural Resources, Waterbury, VT **Dr. Frank Speizer** , Edward Kass Professor of Medicine, Channing Laboratory, Harvard Medical School, Boston, MA **Dr. Sverre Vedal** , Professor, Department of Environmental and Occupational Health Sciences, School of Public Health and Community Medicine, University of Washington, Seattle, WA **Dr. Donna Kenski,** Data Analysis Director, Lake Michigan Air Directors Consortium, Rosemont, IL **Dr. Kathy Weathers** , Senior Scientist, Cary Institute of Ecosystem Studies, Millbrook, NY5E - 17 **E. Krewski et al. (2009) Krewski, Daniel, Michael Jerrett, Richard T. Burnett, Renjun Ma, Edward Hughes, Yuanli**  **Shi, Michelle C. Turner, C. Arden Pope III, George Thurston, Eugenia E. Calle, and Michael**  **J. Thun with Bernie Beckerman, Pat DeLuca, Norm Finkelstein, Kaz Ito, D.K. Moore, K. Bruce Newbold, Tim Ramsay, Zev Ross, Hwashin Shin, and Barbara**  **Tempalski. (2009). Extended follow -** **up and spatial analysis of the American Cancer Society study linking par** **ticulate air pollution and mortality. HEI Research Repor** **t, 140, Health Effects Institute, Boston, MA.**  Pg 119: [About Pope et al. (2002)] “Each 10 - μg/m3 increase in longterm average ambient PM2.5 concentrations was associated with approximately a 4%, 6%, or 8% increase in risk of death from all causes, cardiopulmonary disease, and lung cancer, respectively. There was no evidence of a threshold exposure level within the range of observed PM2.5 conc entrations. “ **Krewski (2009). Letter from Dr. Daniel Krewski to HEI’s Dr. Kate Adams (dated July July 7, 2009) regarding “EPA queries regarding HEI Report 140”. Dr. Adams then forwarded**  **the letter on July 10, 2009 to EPA’s Beth Hassett -**  **Sipple. (letter pla ced in docket #EPA** **** **HQ -**  **OAR -** **2007 -** **0492).** Pg 4: “6. The Health Review Committee commented that the Updated Analysis completed by Pope et al. 2002 reported “no evidence of a threshold exposure level within the range of observed PM

2.5 concentrations” (p. 119). In the Extended Follow
- Up study, did the analyses provide continued support for a no
- threshold response or was there evidence of a threshold? “Response: As noted above, the HEI Health Review Committee commented on the lack of evidence for a threshold exposure level in Pope et al. (2002) with follow-up through the ye ar 1998. The present report, which included follow-up through the ye ar 2000, also does not appear to demonstrate the existence of a threshold in the exposure -response function within the range of observed PM2.5 concentrations.” **HEI Health Review Committee Members Dr. Homer A. Boushey, MD**  , Chair, Professor of Medicine, Department of Medicine, University

of California

– San Francisco **Dr. Ben Armstrong,** Reader, in Epidemiological Statistics, Dep artment of Public Health and Policy, London School of Hygi ene and Tropical Medicine, United Kingdom **Dr. Michael Brauer, ScD** , Professor, School of Environmental Health, University of British Columbia, Canada **Dr. Bert Brunekreef, PhD** , Professor of Environmental Epidemiology, Institute of Risk Assessment Sciences, University of Utrecht, The Netherlands **Dr. Mark W. Frampton, MD** , Professor of Medicine & Environmental Medicine, University of Rochester Medical Center, Rochester, NY **Dr. Stephanie London, MD, Ph D**  , Senior Investigator, Epidemiology Branch, National Institute of Environmental Health Sciences5E - 18

**Dr. William N. Rom, MD, MPH** , Sol and Judith Bergstein Professor of Medicine and Environmental Medicine and Director of Pulmonary and Critical Care Medicine, New York University

Medical Center **Dr. Armistead Russell** , Georgia Power Distinguished Professor of Environmental Engineering, School of Civil and Environmental Engineering, Georgia Institute of Technology **Dr. Lianne Sheppard, PhD** , Professor, Department of Biostatistics, University of Washington5E - 19 **F. Schwartz et al. (2008) Schwartz J, Coull B, Laden F. (2008). The Effect of Dose and Timing of Dose on the**  **Association between Airborne Particles and Survival. Environmental Health Perspectives** **. 116: 64**  **- 69.**  Pg 67: “A key

finding of this study

is that there is little evidence for a threshold in the association between exposure to fine particles and the risk of death on follow-up, which continues well below the U.S. EPA standard of 15 μg/m3 .” Pg 68: “In conclusion, penalized spline smoothing and model averaging represent reasonable, feasible approaches to addressing questions of the shape of the exposure

– response curve, and can provide valuable information to decisionmakers. In this example, both approaches are consistent, and suggest that the association of particles with mortality has no threshold down to close to background levels.”5E - 20 **G. Expert Elicitation on PM -** **Mortality (2006, 2008) Industrial Economics, Inc., 2006.**  **Expanded Expert Judgment Assessment of the**  **Concentration -** 

**Response Relationship Between PM 2.5**  **Exposure and Mortality . Prepared**  **for the U.S.EPA, Office of Air Quality Planning and Standards, September. Available on the Internet at <http://www.epa.gov/ttn/ecas/regdata/Uncertainty/pm_ee_report.** **pdf>.** Pg v: “Each expert was given the option to integrate their judgments about the likelihood of a causal relationship and/or threshold in the C -R function into his distribution or to provide a distribution "conditional on" one or both of these factors. ” Pg vii: “Only

one of 12 experts explicitly incorporated a threshold into his C -R function. 3 The rest believed there was a lack of empirical and/or theoretical support for a population threshold. However, three other experts gave differing effect estimate distributions above and below some cut- off concentration. The adjustments these experts made to median estimates and/or uncertainty at lower PM2.5 concentrations were modest.” “ 3 Expert K indicated that he was 50 percent sure that a threshold existed. If there were a threshold, he thought that there was an 80 percent chance that it would be less than or equal to 5 μg/m3 , and a 20 percent chance that it would fall between 5 and 10 μg/m3 .” Pg ix: “Compared to the pilot study, experts in this study were in general more confident in a causal relationship, less likely to incorporate thresholds, and reported higher mortality effect estimates. The differences in results compared with the pilot appear to reflect the influence of new research on the interpretation of the key epidemiological studies that were the focus of both elicitation studies, more than the influence of changes to the structure of the protocol.” Pg 3 -25: “3.1.8 THRESHOLDS The protocol asked experts for their judgments regarding whether a threshold exists in the PM2.5 mortality C -R function. The protocol focused on assessing expert judgments regarding theory and evidential support for a population threshold (i.e., the concentration below which no member of the study

population would experienc e an increased risk of death). 32 If an expert wished to incorporate a threshold in his characterization of the concentration-response relationship, the team then asked the expert to specify the threshold PM2.5 concentration probabilistically, incorporating his uncertainty about the true threshold level. “From a theoretical and conceptual standpoint, all experts generally believed that individuals exhibit thresholds for PM -related mortality. However, 11 of them discounted the idea of a population threshold in the C -R function on a theoretical and/or empirical basis. Seven of these experts noted that theoretically one would be unlikely

to observe a population threshold due to the variation in susceptibility at any given time in the study

population resulting from combinations of genetic, environmental, and socioeconomic factors. 33 All 11 thought that there was insufficient empirical support for a population threshold in the C -R function. In addition, two experts (E and L) cited analys es of the ACS cohort data in Pope et al. (2002) and another (J) cited Krewski et al. (2000a & b) as supportive of a linear relationship in the study range.5E - 21

“Seven of the experts favored epidemiological studies as ideally the best means of addressing the population threshold issue , because they are best able to evaluate the full range of susceptible individuals at environmentally relevant exposure levels. However, those who favored epidemiologic studies generally acknowledged that definitive studies addressing thresholds would be difficult or impossible to conduct, because they would need to include a very large and diverse population with wide variation in exposure and a long follow-up period. Furthermore, two experts (B and I) cited studies documenting difficulties in detecting a threshold using epidemiological studies (Cakmak et al. 1999, and Brauer et al., 2002, respectively). The experts generally thought that clinical and toxicological studies are best suited for researching mechanisms and for addressing thresholds in very narrowly defined groups. One expert, B, thought that a better understanding of the detailed biological mechanism is critical to addressing the question of a threshold. “One expert, K, believed it was possible to make a conceptual argument for a population threshold. He drew an analogy with smoking, indicating that among heavy smokers, only

a proportion of them gets lung cancer or demonstrates an accelerated decline in lung function. He thought that the idea that there is no level that is biologically safe is f undamentally

at odds with toxicological theory. He did not think that a population threshold was detectable in the currently available epidemiologic studies. He indicated that some of the cohort studies showed greater uncertainty in the shape of the C -R function at lower levels, which could be indicative of a threshold. “Expert K chose to incorporate a threshold into his C -R function. He indicated that he was 50 percent sure that a threshold existed. If there were a threshold, he thought that there was an 80 percent chance that it would be less than or equal to 5 μg/m3 , and a 20 percent chance that it would fall between 5 and 10 μg/m3 .” **Roman, Henry A., Katherine D. Walker, Tyra L. Walsh, Lisa Conner, Harvey M. Richmond, Bryan J. Hubbell, and Patrick L. K** **inney. (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.** Pg 2271: “Eight experts thought the true C -R function relating mortality to changes in annual average PM2.5 was log- linear across the entire study range (ln(mortality) ) β × PM). Four experts (B, F, K, and L) specified a “piecewise” log- linear function, with different β coefficients for PM concentrations above and below an expert- specified break point. This approach allowed them to express increased uncertainty in mortality

effects seen at lower concentrations in major epidemiological studies. Expert K thought the relationship would be log- linear above a threshold.” Pg 2271: “Expert K also a pplied a threshold, T, to his function, which he described probabilistically. He specified P(T > 0) = 0.5. Given T > 0, he indicated P(T ≤ 5 μg/m3 ) = 0.8 and P(5 μg/m3 < T ≤ 10 μg/m3 ) = 0.2. Figure 3 does not include the impact of applying expert K’s threshold, as the size of the reduction in benefits will depend on the distribution of baseline PM levels in a benefits analysis.”5E - 22

**Experts: Dr. Doug W. Dockery**  , Harvard School of Public Health **Dr. Kazuhiko Ito,** Nelson Institute of Environmental Medicine, NYU School of Medicine, Tuxedo, NY **Dr. Dan Krewski** , University of Ottawa **Dr. Nino Künzli** , University of Southern California Keck School of Medicine **Dr. Morton Lippmann** , Professor, Nelson Institute of Environmental Medicine, New York University School of Medicine, Tuxedo, NY **Dr. Joe Mauderly** , Lovelace Respiratory Research Institute **Dr. Bart Ostro** , Chief, Air Pollution Epidemiology Unit, Office of Environmental Health Hazard Assessment, California Environmental Protection Agency, Oakland, CA **Dr. Arden Pope** , Professor, Department of Economics, Brigham Young University, Provo, UT **Dr. Richard Schlesinger** , Pace University **Dr. Joel Schwartz** , Harvard School of Public Health **Dr. George Thurston**

—Department of Environmental Medicine, NYU, Tuxedo, NY **Dr. Mark Utell** , University of Rochester School of Medicine and Dentistry5E - 23 **H. CASAC comments on PM Staff Paper (2005) U.S. Environmental Protection Agency**  **- Science Advisory Board (U.S. EPA** **- SAB). 2005.**  **EPA’s Review of the National Ambient Air Quality Standards for Particu late Matter**  **(Second Draft PM Staff Paper, January 2005). EPA -**  **SAB -**  **CASAC -** **05 -** **007. June. Available on the Internet at**  **<http://yosemite.epa.gov/sab/sabproduct.nsf/E523DD36175EB5AD8525701B007332AE/$Fil e/SAB**  **- CASAC**  **- 05**  **- 007_unsigned.pdf>.**  Pg 6: “A second concern is with methodological issues. The issue of the selection of concentration-response (C -R) relationships based on locally- derived coefficients needs more discussion. The Panel did not agree with EPA staff in calculating the burden of associated incidence in their risk assessment using either the predicted background or the lowest measured level (LML) in the utilized epidemiological analys is. The available epidemiological database on daily mortality and morbidity

does not establish either the presence or absence of threshold concentrations for adverse health effects. Thus, in order to avoid emphasizing an approach that assumes effects that extend to either predicted background concentrations or LML , and to standardize the approach acros s cities, for the purpose of estimating public health impacts, the Panel favored the primary use of an assumed threshold of 10 μg/m3 . The original approach of using background or LML, as well as the other postulated thresholds, could still be used in a sensitivity

analys is of threshold assumptions. “The analyses in this chapter highlight the impact of assumptions regarding thresholds, or lack of threshold, on the estimates of risk. The uncertainty associated with threshold or nonlinear models needs more thorough discussion. A major research need is for more work to determine the existence and level of any thresholds that may

exist or the shape of nonlinear concentrationresponse curves at low levels of exposure that may exist, and to reduce uncertainty in estimated risks at the lowest PM concentrations.” **CASAC Panel Members Dr. Rogene Henderson**  , Scientist Emeritus, Lovelace Respiratory Research Institute, Albuquerque, NM **Dr. Ellis Cowling** , University Distinguished Professor-at-Large, North Carolina State University, Colleges of Natural Resources and Agriculture and Life Sciences, North Carolina State University, Raleigh, NC **Dr. James D. Crapo** , Professor, Department of Medicine, Biomedical Research and PatientCare, National Jewish Medical and Research Center, Denver, CO **Dr. Philip Hopke** , Bayard D. Clarkson Distinguished Professor, Department of Chemical Engineering, Clarkson University, Potsdam, NY **Dr. Jane Q. Koenig** , Professor, Department of Environmental Health, School of Public Health and Community Medicine, University of Washington, Seattle, WA **Dr. Petros Koutrakis** , Professor of Environmental Science, Environmental Health , School of Public Health, Harvard University (HSPH), Boston, MA **Dr. Allan Legge** , President, Biosphere Solutions, Calgary, Alberta5E - 24

**Dr. Paul J. Lioy** , Associate Director and Professor, Environmental and Occupational Health Sciences Institute, UMDNJ - Robert Wood Johnson Medical School, NJ **Dr. Morton Lippmann** , Professor, Nelson Institute of Environmental Medicine, New York University School of Medicine, Tuxedo, NY **Dr. Joe Mauderly** , Vice President, Senior Scientist, and Director, National Environmental Respiratory Center, Lovelace Respiratory Research Institute, Albuquerque, NM **Dr. Roger O. McClellan** , Consultant, Albuquerque, NM **Dr. Frederick J. Miller** , Consultant, Cary, NC **Dr. Gu nter Oberdorster**  , Professor of Toxicology, Department of Environmental Medicine, School of Medicine and Dentistry, University of Rochester, Rochester, NY **Mr. Richard L. Poirot** , Environmental Analyst, Air Pollution Control Division, Department of Environmental Conservation, Vermont Agency of Natural Resources, Waterbury, VT **Dr. Robert D. Rowe** , President, Stratus Consulting, Inc., Boulder, CO **Dr. Jonathan M. Samet** , Professor and Chair, Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD **Dr. Frank Speizer** , Edward Kass Professor of Medicine, Channing Laboratory, Harvard Medical School, Boston, MA **Dr. Sverre Vedal** , Professor of Medicine, School of Public Health and Community Medicine University of Washington, Seattle, WA **Mr. Ronald White** , Research Scientist, Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD **Dr. Warren H. White** , Visiting Professor, Crocker Nuclear Laboratory, University of California -Davis, Davis, CA **Dr. George T. Wolff**

, Principal Scientist, General Motors Corporation, Detroit, MI **Dr. Barbara Zielinska** , Research Professor, Division of Atmospheric Science, Desert Research Institute, Reno, NV5E - 25 **I. HES Comments on 812 Analysis (2004) U.S. Environmental**  **Protection Agency -**  **Science Advisory Board (U.S. EPA -**  **SAB). 2004. Advisory on Plans for Health Effects Analysis in the Analytical Plan for EPA’s Second**  **Prospective Analysis**
**– Benefits and Costs of the Clean Air Act, 1990**  **- 2020. Advisory by**  **the Health Effect s Subcommittee of the Advisory Council on Clean Air Compliance**  **Analysis. EPA -**  **SAB -**  **COUNCIL -** **ADV -** **04 -** **002. March. Available on the Internet at <http://yosemite.epa.gov/sab%5CSABPRODUCT.NSF/08E1155AD24F871C85256E5400**  **433D5D/$File/council_adv_04002.pdf>.** Pg 20: “The Subcommittee agrees that the whole range of uncertainties, such as the questions of causality, shape of C -R functions and thresholds, relative toxicity, years of life lost, cessation lag structure, cause of death, biologic pathways, or susceptibilities may be viewed differently

for acute effects versus long-term effects. “For the studies of long-term exposure, the HES notes that Krewski et al. (2000) have conducted the most careful work on this issue. They report that the associations between PM2.5 and both all cause and cardiopulmonary mortality were near linear within the relevant ranges, with no apparent threshold. Graphical analys es of these studies (Dockery et al., 1993, Figure 3 and Krewski et al., 2000, page 162) also suggest a continuum of effects down to lower levels. Therefore, it is reasonable for EPA to assume a no threshold model down to, at least, the low end of the concentrations reported in the studies.” **HES Panel Members Dr. Bart Ostro**  , California Office of Environmental Health Hazard Assessment (OEHHA), Oakland, CA **Mr. John Fintan Hurley** , Institute of Occupational Medicine (IOM), Edinburgh, Scotland **Dr. Patrick Kinney** , Columbia University, New York, NY **Dr. Michael Kleinman** , University of California, Irvine, CA **Dr. Nino Künzli** , University of Southern California, Los Angeles, CA **Dr. Morton Lippmann** , New York University School of Medicine, Tuxedo, NY Dr. Rebecca Parkin, The George Washington University, Washington, DC **Dr. Trudy Cameron** , University of Oregon, Eugene, OR **Dr. David T. Al len**  , University of Texas, Austin, TX **Ms. Lauraine Chestnut** , Stratus Consulting Inc., Boulder, CO **Dr. Lawrence Goulder** , Stanford University, Stanford, CA **Dr. James Hammitt** , Harvard University, Boston, MA **Dr. F. Reed Johnson** , Research Triangle Institute, Research Triangle Park, NC **Dr. Charles Kolstad** , University of California, Santa Barbara, CA **Dr. Lester B. Lave** , Carnegie Mellon University, Pittsburgh, PA5E - 26

**Dr. Virginia McConnell** , Resources for the Future, Washington, DC **Dr. V. Kerry Smith** , North Carolina State University, Raleigh, NC **Other Panel Members** Dr. John Evans, Harvard University, Portsmouth, NH Dr. Dale Hattis, Clark University, Worcester, MA Dr. D. Warner North, NorthWorks Inc., Belmont, CA Dr. Thomas S. Wallsten, University

of Maryland, College Park, MD5E - 27

**J. NRC**
**– Committee on Estimating the Health Risk Reduction Benefits of Proposed Air**  **Pollution Regulations (2002) National Research Council (NRC). 2002. Estimating the Public Health Benefits of**  **Proposed Air Pollution Regulatio ns. Washington, DC: The National Academies Press.**  Pg 109: “**Linearity and Thresholds** “The shape of the concentration-response functions may influence the overall estimate of benefits. The shape is particularly important for lower ambient air pollution concentrations to which a large portion of the population is exposed. For this reason, the impact of the existence of a threshold may

be considerable. “In epidemiological studies, air pollution concentrations are usually measured and modeled as continuous variables. Thus, it may be feasible to test linearity and the existence of thresholds, depending on the study

design. In time - series studies with the large number of repeated measurements, linearity

and thresholds have been formally addressed with reasonable statistical power. For pollutants such as PM 10 and PM2.5 , there is no evidence for any departure of linearity in the observed range of exposure, nor any indication of a threshold. For example, examination of the mortality

effects of short-term exposure to PM 10 in 88 cities indicates that the concentration-response functions are not due to the high concentrations and that the slopes of these functions do not appear to increase at higher concentrations (Samet et al. 2000). Many other mortality

studies have examined the shape of the concentration-response function and indicated that a linear (nonthreshold) model fit the data well (Pope 2000). Furthermore, studies conducted in cities with very low ambient pollution concentrations have similar effects per unit change in concentration as those studies conducted in cities with higher concentrations. Again, this finding suggests a fairly linear concentration-re sponse function over the observed range of exposures. “Regarding the studies of long-term exposure, Krewski et al. (2000) found that the assumption of a linear concentration-response function for mortality outcomes was not unreasonable. However, the statistical power to assess the shape of these functions is weakest at the upper and lower end of the observed exposure ranges. Most of the studies examining the effects of long-term exposure on morbidity compare subjects living in a small number of communities (Dockery et al. 1996; Ackermmann-Liebrich 1997; Braun-Fahrländer et al. 1997). Because the number of long-term effects studies are few and the number of communities studied is relatively small (8 to 24), the ability to test formally the absence or existenc e of a no -effect threshold is not feasible. However, even if thresholds exist, they may not be at the same concentration for all health outcomes. “A review of the time - series and cohort studies may lead to the conclusion that although a threshold is not apparent at commonly observed concentrations, one may exist at lower levels. An important point to acknowledge regarding thresholds is that for health benefits analysis a key threshold is the population threshold (the lowest of the individual thresholds). However, the population threshold would be very difficult to observe empirically through epidemiology, because epidemiology integrates information from very large groups of people (thousands). Air pollution regulations affect even larger groups of people (millions). It is reasonable to assume that among such large groups susceptibility to air pollution health effects varies considerably across individuals and depends on a large set of underlying factors, including5E - 28

genetic makeup, age, exposure measurement error, preexisting disease, and simultaneous exposures from smoking and occupational hazards. This variation in individual susceptibilities and the resulting distribution of individual thresholds underlies the concentration-response function observed in epidemiology. Thus, until biologically based models of the distribution of individual thresholds are developed, it may be productive to assume that the population concentration-response function is continuous and to focus on finding evidence of changes in its slope as one approaches lower concentrations. EPA’s Use of Thresholds “In EPA’s benefits analyses, threshold issues were discussed and interpreted. For the PM and ozone National Ambient Air Quality Standards (NAAQS), EPA investigated the effects of a potential threshold or reference value below which health consequences were assumed to be zero (EPA 1997). Specifically, the high-end benefits estimate assumed a 12 -microgram per cubic meter (μg/m3 ) mean threshold for mortality associated with long-term exposure to PM2.5 . The low-end benefits estimate assumed a 15 - μg/m3 threshold for all PM -related health effects. The studies, however, included concentrations as low as 7.5 μg/m3 . For the Tier 2 rule and the HD engine and diesel - fuel rule, no threshold was assumed (EPA 1999, 2000). EPA in these analyses acknowledged that there was no evidence for a threshold for PM. “Several points should be noted regarding the threshold assumptions. If a threshold is assumed where one was not apparent in the original study, then the data should be refit and a new curve generated with the assumption of a zero slope over a segment of the concentration-response function that was originally found to be positively sloped. The assumption of a zero slope over a portion of the curve will f orce the slope in the remaining segment of the positively sloped concentration-response function to be greater than was indicated in the original study. A new concentration-response function was not generated for EPA’s benefits analysis for the PM and ozone NAAQS for which threshold assumptions were made. The generation of the steeper slope in the remaining portion of the concentration-response function may fully offset the effect of assuming a threshold. These aspects of assuming a threshold in a benefits analysis where one was not indicated in the original study should be conveyed to the reader. The committee notes that the treatment of thresholds should be evaluated in a consistent and transparent framework by using different explicit assumptions in the f ormal uncertainty analyses (see Chapter 5 ).” Pg 117: “Although the assumption of no thresholds in the most recent EPA benefits analyses was appropriate, EPA should evaluate threshold assumptions in a consistent and transparent framework using several alternative assumptions in the formal uncertainty analysis.” Pg 136: “Two additional illustrative examples are thresholds for adverse effects and lag structures. 2 EPA considers implausible any threshold for mortality in the particulate matter (PM) exposure ranges under consideration (EPA 1999a, p. 3 - 8). Although the agency conducts sensitivity analyses incorporating thresholds, it provides no judgment as to their relative plausibility. In a probabilistic uncertainty analysis, EPA could assign appropriate weights to various threshold models. For PM-related mortality in the Ti er 2 analysis, the committee expects that this approach would have resulted in only a slight widening of the probability distribution for avoided mortality and a slight reduction in the mean of that distribution, thus reflecting EPA’s views about the implausibility of thresholds. The committee finds that such formal incorporation of EPA’s expert judgments about the plausibility of thresholds into its primary analysis would have been an improvement. “Uncertainty about thresholds is a special aspect of uncertainty about the shape of concentration-response functions. Typically, EPA and authors of epidemiological studies assume that these functions are linear on some scale. Often, the scale is a logarithmic transformation of the risk or rate of the health outcome,5E - 29

but when a rate or risk is low, a linear function on the logarithmic scale is approximately linear on the scale of the rate or risk itself. Increasingly, epidemiological investigators are employing analytic methods that permit the estimation of nonlinear shapes for concentration-response functions (Greenland et al. 1999). As a consequence, EPA will need to be prepared to incorporate nonlinear concentration-response functions from epidemiological studies into the agency’s health benefits analyses. Any sou rce of error or bias that can distort an epidemiological association can also distort the shape of an estimated concentration -response function, as can variation in individual susceptibility (Hattis and Burmaster 1994; Hattis et al. 2001).” Pg 137: “In principle, many components of the health benefits model need realistic probabilistic models (see Table 5 - 1 for a listing of such components), in addition to concentration-response thresholds and time lags between exposure and response. For example, additional features of the concentration-response function

—such as projection of the results from the study population to the target populations (which may have etiologically relevant characteristics outside the range seen in the study population) and the projection of baseline frequencies of morbidity and mortality into the future

— must be characterized probabilistically. Other uncertainties that might affect the probability distributions are the estimations of population exposure (or even concentration) from emis sions, estimates of emissions themselves, and the relative toxicity of various classes of particles. Similarly, many aspects of the analysis of the impact of regulation on ambient concentrations and on population exposure involve considerable uncertainty a nd, therefore, may be beneficially modeled in this way. Depending on the analytic approach used, joint probability distributions will have to be specified to incorporate correlations between model components that are structurally dependent upon each other, or the analysis will have to be conducted in a sequential fashion that follows the model for the data- generating process. “EPA should explore alternative options for incorporating expert judgment into its probabilistic uncertainty analyses. The agency pos sesses considerable internal expertise, which should be employed as fully as possible. Outside experts should also be consulted as needed, individually or in panels. In all cases, when expert judgment is used in the construction of a model component, the experts should be identified and the rationales and empirical bases for their judgments should be made available.” **NRC members Dr. JOHN**  **C. BAILAR, III** ( Chair), (emeritus) University of Chicago, Chicago, Illinois **Dr. HUGH ROSS**  **ANDERSON** , University of London, London, England **Dr. MAUREEN L. CROPPER** , University of Maryland, College Park **Dr. JOHN S. EVANS**  , Harvard University, Boston, Massachusetts **Dr. DALE B. HATTIS**  , Clark University, Worcester, Massachusetts **Dr. ROGENE F. HENDERSON**  , Lovelace Respiratory Research Institute, Albuquerque, New Mexico **Dr. PATRICK L. KINNEY**  , Columbia University, New York, New York **Dr. NINO KÜNZLI**  , University of Basel, Basel, Switzerland; as of September 2002, University of Southern California, Los Angeles **Dr. BART D. OSTRO**  , California Environmental Protection Agency, Oakland **Dr. CHARLES POOLE,**  University of North Carolina, Chapel Hill **Dr. KIRK R. SMITH**  , University of California, Berkeley **Dr. PETER A. VALBERG** , Gradient Corporation, Cambridge, Massachusetts5E - 30

**Dr. SCOTT L. ZEGER** , Johns Hopkins University, Baltimore, Maryland