Benefits of the Proposed Inter State Air Quality Rule EPA 452/ 03 001 January 2004 Benefits of the Proposed Inter State Air Quality Rule U. S. Environmental Protection Agency Office of Air and Radiation Office of Air Quality Planning and Standards Air Quality Strategies and Standards Division Innovative Strategies and Economics Group Research Triangle Park, North Carolina iii CONTENTS Section Page 1 Executive Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1 1.1 Benefit Cost Comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 3 2 Introduction and Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1 2.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1 2.2 Regulated Entities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2 2.3 Control Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2 2.4 Cost of Emission Controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2 2.5 Organization of this Report . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 3 3 Emissions and Air Quality Impacts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1 3.1 Emissions Inventories and Estimated Emissions Reductions . . . . . . . . 3 1 3.2 Air Quality Impacts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 2 3.2.1 PM Air Quality Estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 4 <IP_ADDRESS> Modeling Domain . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 6 <IP_ADDRESS> Simulation Periods . . . . . . . . . . . . . . . . . . . . . . . . . . 3 7 <IP_ADDRESS> Model Inputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 8 iv <IP_ADDRESS> Model Performance for Particulate Matter ( PM) . . . . 3 9 <IP_ADDRESS> Converting REMSAD Outputs to Benefits Inputs . . 3 11 <IP_ADDRESS> PM Air Quality Results . . . . . . . . . . . . . . . . . . . . . . 3 12 3.2.2 Ozone Air Quality Estimates . . . . . . . . . . . . . . . . . . . . . . . . . 3 13 <IP_ADDRESS> Modeling Domain . . . . . . . . . . . . . . . . . . . . . . . . . 3 15 <IP_ADDRESS> Simulation Periods . . . . . . . . . . . . . . . . . . . . . . . . . 3 15 <IP_ADDRESS> Non emissions Modeling Inputs . . . . . . . . . . . . . . . 3 16 <IP_ADDRESS> Model Performance for Photochemical Ozone . . . . 3 17 <IP_ADDRESS> Converting CAMx Outputs to Full Season Profiles for Benefits Analysis . . . . . . . . . . . . . . . . . 3 19 <IP_ADDRESS> Ozone Air Quality Results . . . . . . . . . . . . . . . . . . . 3 19 3.2.3 Visibility Degradation Estimates . . . . . . . . . . . . . . . . . . . . . . 3 20 <IP_ADDRESS> Residential Visibility Improvements . . . . . . . . . . . . 3 22 <IP_ADDRESS> Recreational Visibility Improvements . . . . . . . . . . . 3 22 4 Benefits Analysis and Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1 4.1 Benefit Analysis Data and Methods . . . . . . . . . . . . . . . . . . . . . . . . . 4 10 4.1.1 Valuation Concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 15 4.1.2 Growth in WTP Reflecting National Income Growth Over Time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 18 4.1.3 Methods for Describing Uncertainty . . . . . . . . . . . . . . . . . . . 4 20 4.1.4 Demographic Projections . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 25 4.1.5 Health Benefits Assessment Methods . . . . . . . . . . . . . . . . . . 4 26 <IP_ADDRESS> Selecting Health Endpoints and Epidemiological Effect Estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 27 v <IP_ADDRESS> Uncertainties Associated with Health Impact Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 43 <IP_ADDRESS> Baseline Health Effect Incidence Rates . . . . . . . . . . 4 47 <IP_ADDRESS> Accounting for Potential Health Effect Thresholds . 4 51 <IP_ADDRESS> Selecting Unit Values for Monetizing Health Endpoints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 54 <IP_ADDRESS> Unquantified Health Effects . . . . . . . . . . . . . . . . . . 4 68 4.1.6 Human Welfare Impact Assessment . . . . . . . . . . . . . . . . . . . . 4 69 <IP_ADDRESS> Visibility Benefits . . . . . . . . . . . . . . . . . . . . . . . . . . 4 69 <IP_ADDRESS> Agricultural, Forestry and other Vegetation Related Benefits . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 72 <IP_ADDRESS> Benefits from Reductions in Materials Damage . . . . 4 75 <IP_ADDRESS> Benefits from Reduced Ecosystem Damage . . . . . . 4 75 4.2 Benefits Analysis   Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 76 4.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 79 5 Qualitative Assessment of Nonmonetized Benefits . . . . . . . . . . . . . . . . . . . . . 5 1 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1 5.2 Atmospheric Deposition of Sulfur and Nitrogen   Impacts on Aquatic,

Forest, and Coastal Ecosystems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1 5.2.1 Freshwater Acidification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2 <IP_ADDRESS> Water/ Watershed Modeling . . . . . . . . . . . . . . . . . . . 5 4 <IP_ADDRESS> Description of the MAGIC Model and Methods . . . . 5 5 <IP_ADDRESS> Model Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 5 <IP_ADDRESS> Model Implementation . . . . . . . . . . . . . . . . . . . . . . . 5 6 vi <IP_ADDRESS> Calibration Procedure . . . . . . . . . . . . . . . . . . . . . . . . 5 7 <IP_ADDRESS> MAGIC Modeling Results . . . . . . . . . . . . . . . . . . . . 5 9 5.2.2 Forest Ecosystems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 10 5.2.3 Coastal Ecosystems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 11 5.3 Benefits of Reducing Mercury Emissions . . . . . . . . . . . . . . . . . . . . . 5 12 6 Comparison of Benefits and Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . R 1 vii LIST OF FIGURES Number Page 2 1 States Identified as Having Significant Contribution to PM2.5 . . . . . . . . . . . . 2 3 2 2 States Identified as Having Significant Contribution to Ozone . . . . . . . . . . . . 2 4 3 1 REMSAD Modeling Domain for Continental United States . . . . . . . . . . . . . . 3 7 3 2 Example of REMSAD 36 x 36km Grid cells for Maryland Area . . . . . . . . . . . 3 8 3 3 CAMx Eastern U. S. Modeling Domain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 16 3 4 Recreational Visibility Regions for Continental U. S. . . . . . . . . . . . . . . . . . . . 3 23 4 1 Key Steps in Air Quality Modeling Based Benefits Analysis . . . . . . . . . . . . . . 4 3 4 2 Visibility Improvements in Southeastern Class I Areas . . . . . . . . . . . . . . . . . . 4 3 5 1 How Emissions of Mercury Can Affect Human Health and Ecosystems . . . . 5 14 5 2 Mercury Reductions by State after IAQR . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 16 5 3 Concentrations of Mercury in Blood of Women of Child Bearing Age . . . . . 5 17 viii LIST OF TABLES Number Page 1 1 Estimated Reductions in Incidence of Health Effects . . . . . . . . . . . . . . . . . . . . 1 2 1 2 Estimated Monetary Value of Reductions in Incidence of Health and Welfare Effects ( millions of 1999$) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 4 1 3 Summary of Benefits, Costs, and Net Benefits of the Inter State Air Quality Rule . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 5 1 4 Additional Nonmonetized Benefits of the Inter State Air Quality Rule . . . . . . 1 6 3 1 Emissions Sources and Basis for Current and Future Year Inventories . . . . . . 3 2 3 2 Summary of Modeled Baseline Emissions for Lower 48 States . . . . . . . . . . . . 3 3 3 3 Summary of Modeled Emissions Changes for the Proposed Transport Rule . . 3 4 3 4 Model Performance Statistics for REMSAD PM 2.5 Species Predictions: 1996 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 11 3 5 Summary of Base Case PM Air Quality and Changes due to Proposed Interstate Air Quality Rule: 2010 and 2015 . . . . . . . . . . . . . . . . . . . . . . . . . 3 13 3 6 Distribution of PM 2.5 Air Quality Improvements over Population due to Proposed Interstate Air Quality Rule: 2010 and 2015 . . . . . . . . . . . . . . . . . 3 14 3 7 Model Performance Statistics for Hourly Ozone in the Eastern U. S.

CAMx Ozone Simulations: 1995 Base Case . . . . . . . . . . . . . . . . . . . . . . . . 3 18 ix 3 8 Summary of CAMx Derived Population Weighted Ozone Air Quality Metrics Due to Proposed Transport Rule for Health Benefits Endpoints:

Eastern U. S. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 20 3 9 Distribution of Populations Experiencing Visibility Improvements due to Proposed Interstate Air Quality Rule: 2010 and 2015 . . . . . . . . . . . . . . . . . . . . . . . . . 3 21 3 10 Summary of Baseline Residential Visibility and Changes by Region:

2010 and 2015 ( annual average deciviews) . . . . . . . . . . . . . . . . . . . . . . . . . . 3 22 3 11 Summary of Baseline Recreational Visibility and Changes by Region:

2010 and 2015 ( annual average deciviews) . . . . . . . . . . . . . . . . . . . . . . . . . . 3 23 4 1 Estimated Monetized Benefits of the Proposed IAQR . . . . . . . . . . . . . . . . . . 4 10 4 2 Human Health and Welfare Effects of Pollutants Affected by the Proposed IAQR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 11 4 3 Elasticity Values Used to Account for Projected Real Income Growth . . . . . 4 19 4 4 Adjustment Factors Used to Account for Projected Real Income Growth . . . 4 21 4 5 Primary Sources of Uncertainty in the Benefit Analysis . . . . . . . . . . . . . . . . . 4 22 4 6 Summary of Considerations Used in Selecting C R Functions . . . . . . . . . . . . 4 28 4 7 Endpoints and Studies Used to Calculate Total Monetized Health Benefits . . 4 32 4 8 Studies Examining Health Impacts in the Asthmatic Population Evaluated for Use in the Benefits Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 44 4 9 Baseline Incidence Rates and Population Prevalence Rates for Use in Impact Functions, General Population . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 49 4 10 Asthma Prevalence Rates Used to Estimate Asthmatic Populations in Impact Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 52 4 11 Unit Values Used for Economic Valuation of Health Endpoints ( 1999$) . . . . 4 55 x 4 12 Expected Impact on Estimated Benefits of Premature Mortality Reductions of Differences Between Factors Used in Developing Applied VSL and Theoretically Appropriate VSL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 60 4 13 Alternative Direct Medical Cost of Illness Estimates for Nonfatal Heart Attacks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 66 4 14 Estimated Costs Over a 5 Year Period ( in 2000$) of a Nonfatal Myocardial Infarction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 66 4 15 Women with Children: Number and Percent in the Labor Force, 2000, and Weighted Average Participation Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 68 4 16 Reductions in Incidence of Adverse Health Effects Associated with Reductions in Particulate Matter and Ozone Associated with the Proposed IAQR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 77 4 17 Results of Human Health and Welfare Benefits Valuation for the Proposed IAQR ( millions of 1999 dollars) . . . . . . . . . . . . . . . . . . . . . . . . . . 4 78 5 1 Acidification Changes in Water Bodies as a Result of the Inter State Air Quality Rule . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 10 6 1 Summary of Benefits, Costs, and Net Benefits of the Inter State Air Quality Rule . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2 1 1 SECTION 1 EXECUTIVE SUMMARY The Clean Air Act ( CAA) contains a number of requirements to address nonattainment of the fine particulate matter ( PM 2.5) and the 8 hour ozone national ambient air quality standards ( NAAQS), including requirements that States address interstate transport contributing to such nonattainment. CAA Section 110( a)( 2)( D) requires that the State Implementation Plans ( SIPs) necessary to meet these standards contain adequate provisions to prohibit air pollutant emissions within those States from " contribut[ ing] significantly to nonattainment in, or interfer[ ing] with maintenance by," a downwind State. The EPA is proposing a rule to reduce interstate transport of fine particulate matter and ozone ( Inter State Air Quality Rule hereinafter referred to as IAQR) in 29 States and the District of Columbia to ensure that SIPs provide for necessary regional reductions of emissions of sulfur dioxide ( SO 2) and/ or nitrogen oxides ( NO x), that are important precursors of PM 2.5 ( NO x and SO 2) and ozone ( NO x). The EPA is proposing that emissions reductions be implemented in two phases, with the first phase in 2010 and the second phase in 2015.

This document presents the health and welfare benefits of the IAQR and compares the benefits of this proposal to the estimated costs of implementing the rule in 2010 and 2015.

Significant health and welfare benefits are likely to occur as a result of this rule. Thousands of deaths and other serious health effects would be prevented each year. The EPA is able to monetize annual benefits of approximately $ 58 billion in 2010 and approximately $ 84 billion in 2015. Table 1 1 presents the primary estimates of reduced incidence of PM and ozonerelated health effects for the years 2010 and 2015 for the regulatory control strategy. In interpreting the results, it is important to keep in mind the limited set of effects we are able to monetize. Specifically, the table lists the PM and ozone related benefits associated with the reduction of ambient PM and ozone levels. These benefits are substantial both in incidence and dollar value. In 2010, we estimate that reduction in exposure to PM 2.5 will result in approximately 9,600 fewer premature deaths annually associated with PM 2.5, as well as 5,200 fewer cases of chronic bronchitis, 13,000 fewer nonfatal heart attacks ( acute myocardial infarctions), 8,900 fewer hospitalizations ( for respiratory and cardiovascular disease combined), and significant reductions in days of restricted activity due to respiratory illness 1 2 ( with an estimate of 6.4 million fewer cases). We also estimate substantial health improvements for children from reductions in upper and lower respiratory illnesses, acute bronchitis, and asthma attacks. Ozone health related benefits are expected to occur during the summer ozone season ( usually ranging from May to September in the eastern U. S.). Based on modeling for 2010, ozone related health benefits are expected to include 1,000 fewer hospital admissions for respiratory illnesses, 120 fewer emergency room admissions for asthma,

280,000 fewer days with restricted activity levels, and 180,000 fewer days where children are absent from school because of illnesses. In addition, recent reports by Thurston and Ito ( 2001) and the World Health Organization ( WHO) support an independent ozone mortality impact, and the EPA Science Advisory Board has recommended that the EPA reevaluate the ozone mortality literature for possible inclusion in the estimate of total benefits. Based on these new analyses and recommendations, EPA is sponsoring three independent meta analyses of the ozone mortality epidemiology literature to inform a determination on inclusion of this important health endpoint. Upon completion and peer review of the meta analyses, EPA will Table 1 1. Estimated Reductions in Incidence of Health Effects Endpoint Constituent 2010 Estimated Reduction 2015 Estimated Reduction Premature Mortality adult PM2.5 9,600 13,000 Mortality infant PM2.5 22 29 Chronic bronchitis PM2.5 5,200 6,900 Acute myocardial infarction total PM2.5 13,000 18,000 Hospital admissions  respiratory PM2.5, O3 5,200 8,100 Hospital admissions  cardiovascular PM2.5 3,700 5,000 Emergency room visits, respiratory PM2.5, O3 7,100 9,400 Acute bronchitis PM2.5 12,000 16,000 Lower respiratory symptoms PM2.5 140,000 190,000 Upper respiratory symptoms PM2.5 490,000 620,000 Asthma exacerbation PM2.5 190,000 240,000 Acute respiratory symptoms ( MRADs) PM2.5, O3 6,400,000 8,500,000 Work loss days PM2.5 1,000,000 1,300,000 School loss days O3 180,000 390,000 MRADs = minor restricted activity days 1 3 determine whether benefits of reductions in ozone related mortality will be included in the benefits analysis for the final IAQR.

Table 1 2 presents the estimated monetary value of reductions in the incidence of health and welfare effects. PM related health benefits and ozone benefits are estimated to be approximately $ 56.9 billion and $ 82.4 billion annually in 2010 and 2015, respectively.

Estimated annual visibility benefits in Southeastern Class I areas brought about by the IAQR are estimated to be $ 880 million in 2010 and $ 1.4 billion in 2015. All monetized estimated values are stated in 1999$. Table 1 3 presents the total annual monetized benefits for the years 2010 and 2015. This table also indicates with a " B" those additional health and environmental effects that we were unable to quantify or monetize. These effects are additive to the estimate of total benefits, and the EPA believes there is considerable value to the public of the benefits that could not be monetized. A listing of the benefit categories that could not be quantified or monetized in our estimate is provided in Table 1 4. Major benefits not quantified for this proposed rule include the value of increases in yields of agricultural crops and commercial forests, value of improvements in visibility in places where people live and work and recreational areas outside of federal Class I areas, and value of reductions in nitrogen and acid deposition and the resulting changes in ecosystem functions.

In summary, EPA's primary estimate of the annual benefits of the rule is approximately $ 58 + B billion in 2010. In 2015, total monetized annual benefits are approximately $ 84 + B billion. These estimates account for growth in the willingness to pay for reductions in environmental health risks with growth in real gross domestic product ( GDP) per capita between the present and the years 2010 and 2015.

1.1 Benefit Cost Comparison The estimated annual social benefits of the rule are compared to the annual estimated cost to implement the proposed rule in Table 1 3. Estimates of the annual costs of implementing the rule are $ 3 and $ 4 billion in 2010 and 2015, respectively ( 1999$). For further information concerning the costs of the proposed rule, please see " Preliminary Analysis of the Costs of the Inter State Air Quality Rule   January 2004." 1 4 Table 1 2. Estimated Monetary Value of Reductions in Incidence of Health and Welfare Effects ( millions of 1999$)

Endpoint Constituent 2010 Estimated Monetary Value of Reductions 2015 Estimated Monetary Value of Reductions Preamature Mortality adult PM2.5 $ 53,000 $ 77,000 Chronic bronchitis PM2.5 $ 1,900 $ 2,700 Acute myocardial infarction PM2.5 $ 1,100 $ 1,500 Acute respiratory symptoms ( MRADs) PM2.5, O3 $ 320 $ 440 Work loss days PM2.5 $ 140 $ 170 Mortality infant PM2.5 $ 130 $ 180 Hospital admissions, respiratory PM2.5, O3 $ 85 $ 130 Hospital admissions, cardiovascular PM2.5 $ 78 $ 110 School loss days O3 $ 13 $ 28 Worker productivity O3 $ 8.0 $ 17 Asthma exacerbation PM2.5 $ 8.0 $ 11 Acute bronchitis PM2.5 $ 4.3 $ 5.7 Lower respiratory symptoms PM2.5 $ 2.3 $ 3.0 Upper respiratory symptoms PM2.5 $ 13 $ 17 Emergency room visits, respiratory PM2.5, O3 $ 2.0 $ 2.6 Visibility, Southeastern Class I areas Light extinction $ 880 $ 1,400 TOTAL + B* $ 58,000 $ 84,000 MRADs= minor restricted activity days B= nonmonetized benefits * Note total dollar benefits are rounded to the nearest billion and column totals may not add due to rounding. 1 5 Thus, the annual net benefit ( social benefits minus social costs) of the program is approximately $ 55 + B billion in 2010 and $ 80 + B billion in 2015. Therefore,

implementation of the proposed rule is expected to provide society with a net gain in social welfare based on economic efficiency criteria. As Table 1 2 shows, although mortality benefits account for over 90 percent of total monetized benefits, the economic value of morbidity benefits alone exceed the cost of the proposed rule. As discussed in section IX of the notice for this rulemaking, we did not complete air quality modeling that precisely matches the IAQR region. We anticipate that any differences in the estimates presented due to the modeling region analyzed will be small.

Every benefit cost analysis examining the potential effects of a change in environmental protection requirements is limited to some extent by data gaps, limitations in model capabilities Table 1 3. Summary of Annual Benefits, Costs, and Net Benefits of the Inter State Air Quality Rule Description 2010 ( billions of 1999 dollars) 2015 ( billions of 1999 dollars)

Social costsa $ 2.9 $ 3.7 Social benefits b, c Ozone related benefits $ 0.1 $ 0.1 PM related health benefits $ 56.8 + B $ 82.3 + B Visibility benefits $ 0.9 $ 1.4 Net benefits ( benefits costs) c, d $ 55 + B $ 80 + B a Note that costs are the annual total costs of reducing pollutants including NOx and SO2.

b As the table indicates, total benefits are driven primarily by PM related health benefits. The reduction in premature fatalities each year accounts for over 90 percent of total benefits. Benefits in this table are associated with NOx and SO2 reductions.

c Not all possible benefits or disbenefits are quantified and monetized in this analysis. B is the sum of all unquantified benefits and disbenefits. Potential benefit categories that have not been quantified and monetized are listed in Table 1 4.

d Net benefits are rounded to the nearest billion. Columnar totals may not sum due to rounding. 1 6 Table 1 4. Additional Nonmonetized Benefits of the Inter State Air Quality Rule Pollutant Unquantified Effects Ozone Health Premature mortalitya Increased airway responsiveness to stimuli Inflammation in the lung Chronic respiratory damage Premature aging of the lungs Acute inflammation and respiratory cell damage Increased susceptibility to respiratory infection Non asthma respiratory emergency room visits Ozone Welfare Decreased yields for commercial forests Decreased yields for fruits and vegetables Decreased yields for commercial and non commercial crops Damage to urban ornamental plants Impacts on recreational demand from damaged forest aesthetics Damage to ecosystem functions PM Health Low birth weight Changes in pulmonary function Chronic respiratory diseases other than chronic bronchitis Morphological changes Altered host defense mechanisms Non asthma respiratory emergency room visits PM Welfare Visibility in many Class I areas Residential and recreational visibility in non Class I areas Soiling and materials damage Damage to ecosystem functions Nitrogen and Sulfate Deposition Welfare Impacts of acidic sulfate and nitrate deposition on commercial forests Impacts of acidic deposition to commercial freshwater fishing Impacts of acidic deposition to recreation in terrestrial ecosystems Reduced existence values for currently healthy ecosystems Impacts of nitrogen deposition on commercial fishing, agriculture, and forests Impacts of nitrogen deposition on recreation in estuarine ecosystems Damage to ecosystem functions Mercury Health Neurological disorders Learning disabilities Developmental delays Potential cardiovascular effects* Altered blood pressure regulation* Increased heart rate variability* Myocardial infarction* Potential reproductive effects*

Mercury Deposition Welfare Impact on birds and mammals ( e. g., reproductive effects) Impacts to commercial, subsistence, and recreational fishing Reduced existence values for currently healthy ecosystems a Premature mortality associated with ozone is not separately included in this analysis. * These are potential effects as the literature is either contradictory or incomplete. 1 7 ( such as geographic coverage), and uncertainties in the underlying scientific and economic studies used to configure the benefit and cost models. Deficiencies in the scientific literature often result in the inability to estimate quantitative changes in health and environmental effects, such as potential increases in fish populations due to reductions in nitrogen loadings in sensitive estuaries. Deficiencies in the economics literature often result in the inability to assign economic values even to those health and environmental outcomes that can be quantified. Although these general uncertainties in the underlying scientific and economics literatures ( that can cause the valuations to be higher or lower) are discussed in detail in the economic analyses and its supporting documents and references, the key uncertainties that have a bearing on the results of the benefit cost analysis of this proposed rule include the following:

the exclusion of potentially significant benefit categories ( such as health and ecological benefits of reductions in mercury emissions),

errors in measurement and projection for variables such as population growth and baseline incidence rates,

uncertainties in the estimation of future year emissions inventories and air quality,

variability in the estimated relationships of health and welfare effects to changes in pollutant concentrations,

uncertainties in exposure estimation,

uncertainties in the size of the effect estimates linking air pollution and health endpoints,

uncertainties about relative toxicity of different components within the complex mixture, and

uncertainties associated with the effect of potential future actions to limit emissions.

Despite these uncertainties, we believe the benefit cost analysis provides a reasonable indication of the expected economic benefits of the proposed rulemaking in future years under a set of reasonable assumptions.

In addition, in valuing reductions in premature fatalities associated with PM, we used a value of $ 5.5 million per statistical life. This represents a central value consistent with a range of values from $ 1 to $ 10 million suggested by recent meta analyses of the wage risk value of statistical life ( VSL) literature. 1 8 The benefits estimates generated for the Proposed IAQR are subject to a number of assumptions and uncertainties, which are discussed throughout the document. As Table 1 2 indicates, total benefits are driven primarily by the reduction in premature fatalities each year,

which account for over 90 percent of total benefits. For example, key assumptions underlying the primary estimate for the mortality category include the following:

( 1) Inhalation of fine particles is causally associated with premature death at concentrations near those experienced by most Americans on a daily basis.

Although biological mechanisms for this effect have not yet been definitively established, the weight of the available epidemiological evidence supports an assumption of causality.

( 2) All fine particles, regardless of their chemical composition, are equally potent in causing premature mortality. This is an important assumption, because PM produced via transported precursors emitted from EGUs may differ significantly from direct PM released from automotive engines and other industrial sources, but no clear scientific grounds exist for supporting differential effects estimates by particle type.

( 3) The C R function for fine particles is approximately linear within the range of ambient concentrations under consideration. Thus, the estimates include health benefits from reducing fine particles in areas with varied concentrations of PM,

including both regions that are in attainment with fine particle standard and those that do not meet the standard.

Although recognizing the difficulties, assumptions, and inherent uncertainties in the overall enterprise, these analyses are based on peer reviewed scientific literature and up to date assessment tools, and we believe the results are highly useful in assessing this proposal.

We were unable to quantify or monetize a number of health and environmental effects. A full appreciation of the overall economic consequences of the proposed rule requires consideration of all benefits and costs expected to result from the proposed rule, not just those benefits and costs that could be expressed here in dollar terms. A listing of the benefit categories that could not be quantified or monetized in our estimate is provided in Table 1 4.

These effects are denoted by " B" in Table 1 3 above and are additive to the estimates of benefits. 1 9 We are unable to quantify changes in levels of methylmercury contamination in fish associated with reductions in mercury emissions for this proposal. However, this proposal is anticipated to decrease annual EGU mercury emissions nationwide by 10.6 tons in 2010 or approximately 23.5 percent, by 11.8 tons in 2015 or 26.3 percent, and by 14.3 tons or 32 percent in 2020. Emission reduction percentage decreases are based upon expected mercury emissions changes from fossil fired EGUs larger than 25 megawatt capacity. In a separate action, EPA is proposing to regulate mercury and nickel from certain types of electric generating units using the maximum achievable control technology ( MACT) provisions of section 112 of the CAA or, in the alternative, using the performance standards provisions under section 111 of the CAA. This proposal will have implications for mercury reductions,

and potential interactions may exist between the rulemakings. Information concerning potential interactions in the two rulemakings is discussed in the notice for proposed rulemaking for the CAA Section 112 proposal and in the document Benefit Analysis of the CAA Section 112 Proposal to Reduce Mercury Emissions included in the docket for the rulemaking. 2 1 SECTION 2 INTRODUCTION AND BACKGROUND For this rulemaking, the EPA has assessed the role that transported emissions from upwind States play in contributing to unhealthy levels of PM 2.5 and 8 hour ozone in downwind States. Based on this assessment, the EPA is proposing emissions reduction requirements that would apply to upwind States under the Clean Air Act. This report assesses the health and welfare benefits of the proposed rule. This document presents the health and welfare benefits of the IAQR and compares the benefits of this proposal to the estimated costs of implementing the rule in 2010 and 2015. Significant health and welfare benefits are likely to occur as a result of this rule, and these benefits are enumerated in this document. This chapter contains background information relative to the rule and an outline of the chapters of the report.

2.1 Background Congress recognized that interstate pollution transport from upwind States can contribute to unhealthy pollution levels in downwind States. Therefore, the CAA contains provisions in section 110( a)( 2)( D) that require upwind States to eliminate emissions that contribute significantly to nonattainment downwind. Under section 110( a)( 2) States are required to submit plans to the EPA within 3 years of issuance of a revised National Ambient Air Quality Standard. Among other requirements, these plans are required to prohibit emissions in the State that contribute significantly to nonattainment downwind.

The EPA's proposal finds that 29 States and the District of Columbia contribute significantly to nonattainment, or interfere with maintenance, of the NAAQS for PM 2.5 and/ or 8 hour ozone in downwind States. The EPA is proposing to require these upwind States to revise their State Implementation Plans to include control measures to reduce emissions of SO 2 and/ or NO x. SO 2 is a precursor to PM 2.5 formation, and NO x is a precursor to both ozone and PM 2.5 formation. Reducing upwind precursor emissions will assist the downwind PM 2.5 and 8 hour ozone nonattainment areas in achieving the NAAQS. Moreover, attainment would be achieved in a more equitable, cost effective manner than if each nonattainment area attempted to achieve attainment by implementing local emissions reductions alone. The 2 2 relevant regions for PM2.5 and ozone significant contribution are depicted in Figures 2 1 and 2 2, respectively.

2.2 Regulated Entities This action does not propose to directly regulate emissions sources. Instead, it proposes to require States to revise their SIPs to include control measures to reduce emissions of NO x and SO 2. The proposed emission reduction requirements that would be assigned to the States are based on controls that are known to be highly cost effective for electric generation units ( EGUs). However, States would have the flexibility to choose what sources to control.

While the EPA is soliciting comments on the potential for pollution control from other sources, the analysis conducted assumes controls for EGUs only.

2.3 Control Scenario The analysis conducted assumes that a cap and trade program will be used to achieve the level of emission control requirements desired. The EPA would establish regional emission budget determinations for SO 2 and NO x to address the transport problem. In this proposal,

these requirements would effectively establish emission caps in 2010 for SO 2 and NO x of 3.9 million tons and 1.6 million tons, respectively. These budgets would be lowered in 2015 to provide SO 2 and NO x emission caps of 2.7 million tons and 1.3 million tons, respectively in the proposed control region. These quantities were derived by calculating the amount of emissions of SO 2 and NO x that the EPA believes can be controlled from large EGUs in a highly cost effective manner. When fully implemented, this would result in nationwide SO 2 emissions of approximately 3.5 million tons. This is significantly lower than the 8.95 million tons of SO 2 emissions allowed under the current Title IV Acid Rain SO 2 Trading Program.

The EPA expects that States will elect to join a regional cap and trade program for these pollutants.

2.4 Cost of Emission Controls The EPA analyzed the costs of IAQR using the Integrated Planning Model ( IPM). The EPA has used the IPM to analyze the impacts of regulations on the power sector. A description of the methodology used to model the costs and economic impacts to the power sector may be obtained in " Preliminary Analysis of the Costs of the Inter State Air Quality Rule" January 2004. It is estimated that the annual cost of implementing this proposal in 2010 is $ 2.9 billion and in 2015 is $ 3.7 billion in the transport region ( 1999$). 2 3 States where NO x control is not needed for ozone, only PM.

States where NO x control is needed for ozone and PM Figure 2 1. States Identified as Having Significant Contribution to PM2.5 2.5 Organization of this Report This document describes the health and welfare benefits of the proposed rule. The document is organized as follows:

Chapter 3, Emissions and Air Quality Impacts, describes emission inventories and air quality modeling that are essential inputs into the benefits assessment. 2 4 Figure 2 2. States Identified as Having Significant Contribution to Ozone

Chapter 4, Benefits Analysis and Results, describes the methodology and results of the benefits analysis.

Chapter 5, Qualitative Assessment of Nonmonetized Benefits, describes benefits that are not monetized for this rulemaking.

Chapter 6, Comparison of Benefits and Costs, provides a comparison of the monetized benefits and estimated annual costs of the proposed control scenario. 3 1 SECTION 3 EMISSIONS AND AIR QUALITY IMPACTS This chapter summarizes the emissions inventories and air quality modeling that serve as the inputs to the benefits analysis of this proposed rule as detailed in Chapter 4. In summary,

given baseline and post control emissions inventories for the emission species expected to impact ambient air quality, we use sophisticated photochemical air quality models to estimate baseline and post control ambient concentrations of ozone and PM and deposition of nitrogen and sulfur for each year. The estimated changes in ambient concentrations are then combined with monitoring data to estimate population level exposures to changes in ambient concentrations for use in estimating health effects. Modeled changes in ambient data are also used to estimate changes in visibility and changes in other air quality statistics that are necessary to estimate welfare effects.

The initial section of this chapter provides a summary of the baseline emissions inventories and the emissions reductions that were modeled for this rule. The next section provides a summary of the methods for and results of estimating air quality for the 2010 and 2015 base cases and control scenarios for the purposes of the benefit analysis. There are separate sections for PM, ozone, and visibility.

3.1 Emissions Inventories and Estimated Emissions Reductions The technical support document for emissions inventories discusses the development of the 2001, 2010 and 2015 baseline emissions inventories for the benefits analysis of this proposed rule. The emission sources and the basis for current and future year inventories are listed in Table 3 1. Tables 3 2 and 3 3 summarize the baseline emissions of NO X and SO 2 and the change in the emissions from EGUs that were used in modeling air quality changes. 3 2 3.2 Air Quality Impacts This section summarizes the methods for and results of estimating air quality for the 2010 and 2015 base cases and control scenarios for the purposes of the benefit analysis. EPA has focused on the health, welfare, and ecological effects that have been linked to air quality changes. These air quality changes include the following:

1. Ambient particulate matter ( PM 10 and PM 2.5)   as estimated using a national scale version of the REgional Modeling System for Aerosols and Deposition ( REMSAD);

2. Ambient ozone   as estimated using regional scale applications of the Comprehensive Air Quality Model with Extensions ( CAMx); and Table 3 1. Emissions Sources and Basis for Current and Future Year Inventories Emissions Source 2001 Base Year Future Year Base Case Projections Utilities 2001 CEM data Integrated Planning Model ( IPM)

Non Utility Point and Area sources Straight line projections from 1996 NEI Version 3.12 ( point)

Version 3.11 ( area) BEA growth projections Highway vehicles MOBILE5b model with MOBILE6 adjustment factors for VOC and NOX;

PART5 model for PM VMT projection data Nonroad engines ( except locomotives, commercial marine vessels, and aircraft) NONROAD2002 model BEA and Nonroad equipment growth projections Note: Full description of data, models, and methods applied for emissions inventory development and modeling are provided in Emissions Inventory TSD ( EPA, 2003a). 3 3 3. Visibility degradation ( i. e., regional haze), as developed using empirical estimates of light extinction coefficients and efficiencies in combination with REMSAD modeled reductions in pollutant concentrations. 3 4 Table 3 2. Summary of Modeled Baseline Emissions for Lower 48 States Pollutant Emissions ( tons)

Source NOX SO2 2001 Baseline EGUs 4,824,967 10,714,558 Non EGUs 3,180,835 3,696,048 Area 2,220,728 1,379,810 Mobile 8,694,038 261,526 Nonroad 4,059,278 531,203 Total, All Sources 22,979,846 16,583,145 2010 Base Case EGUs 3,943,438 9,856,926 Non EGUs 3,228,201 3,799,163 Area 2,225,898 1,367,643 Mobile 4,931,947 29,790 Nonroad 3,404,962 236,446 Total, All Sources 17,734,447 15,289,969 2015 Base Case EGUs 4,008,241 9,222,097 Non EGUs 3,307,415 3,893,813 Area 2,235,712 1,369,925 Mobile 3,458,279 32,551 Nonroad 2,903,048 232,644 Total, All Sources 15,912,695 14,751,030 3 5 The air quality estimates in this section are based on the emission changes summarized in the preceding section. These air quality results are in turn associated with human populations and ecosystems to estimate changes in health and welfare effects. In Section 3.2.1, we describe the estimation of PM air quality using REMSAD, and in Section 3.2.2, we cover the estimation of ozone air quality using CAMx. Lastly, in Section 3.2.3, we discuss the estimation of visibility degradation.

3.2.1 PM Air Quality Estimates We use the emissions inputs summarized above with a national scale version of the REgional Model System for Aerosols and Deposition ( REMSAD) to estimate PM air quality in the contiguous U. S. REMSAD is a three dimensional grid based Eulerian air quality model designed to estimate annual particulate concentrations and deposition over large spatial scales ( e. g., over the contiguous U. S.). Consideration of the different processes that affect primary ( directly emitted) and secondary ( formed by atmospheric processes) PM at the regional scale Table 3 3. Summary of Modeled Emissions Changes for the Proposed Interstate Air Quality Rule: 2010 and 2015 Pollutant Item NOX SO2 2010 Emission Changesa Absolute Tons 1,373,919 3,750,219 Percentage of EGU Emissions 34.8% 38.1%

Percentage of All Manmade Emissions 7.8% 24.5%

2015 Emission Changesa Absolute Tons 1,704,065 3,820,393 Percentage of EGU Emissions 42.5% 41.4%

Percentage of All Manmade Emissions 10.7% 25.9%

a Note that the emission changes only occur within the affected transport region; however, the percent reductions reflect the change as a share of baseline emissions for the lower 48 states as presented in Table 3 2. 1Given the focus of this rule on secondarily formed particles it is important to employ a Eulerian model such as REMSAD. The impact of secondarily formed pollutants typically involves primary precursor emissions from a multitude of widely dispersed sources, and chemical and physical processes of pollutants are best addressed using an air quality model that employs an Eulerian grid model design.

3 6 in different locations is fundamental to understanding and assessing the effects of proposed pollution control measures that affect ozone, PM and deposition of pollutants to the surface. 1 Because it accounts for spatial and temporal variations as well as differences in the reactivity of emissions, REMSAD is useful for evaluating the impacts of the proposed rule on U. S. PM concentrations.

REMSAD was peer reviewed in 1999 for EPA as reported in " Scientific Peer Review of the Regulatory Modeling System for Aerosols and Deposition" ( Seigneur et al., 1999).

Earlier versions of REMSAD have been employed for the EPA's Prospective 812 Report to Congress, EPA's Heavy Duty ( HD) Engine/ Diesel Fuel rule, and EPA's air quality assessment of the Clear Skies Initiative. Version 7.06 of REMSAD was employed for this analysis and is fully described in the air quality modeling technical support document ( EPA, 2003b). This version reflects updates in the following areas to improve performance and address comments from the 1999 peer review:

1. Gas phase chemistry updates to " micro CB4" mechanism including new treatment for the NO 3 and N 2 O 5 species and the addition of several reactions to better account for the wide ranges in temperature, pressure, and concentrations that are encountered for regional and national applications.

2. PM chemistry updates to calculate particulate nitrate concentrations through use of the MARS A equilibrium algorithm and internal calculation of secondary organic aerosols from both biogenic ( terpene) and anthropogenic ( estimated aromatic)

VOC emissions.

3. Aqueous phase chemistry updates to incorporate the oxidation of SO 2 by O3 and O2 and to include the cloud and rain liquid water content from MM5 meteorological data directly in sulfate production and deposition calculations.

4. Calculation of the production of secondary organic aerosols ( SOA) due to atmospheric chemistry processes has been added for both anthropogenic and biogenic organics. 3 7 As discussed in the Air Quality Modeling TSD, the model tends to underestimate observed PM 2.5 concentrations nationwide.

Our analysis applies the modeling system to the entire U. S. for the six emissions scenarios:

a 1996 baseline year for performance evaluation, a 2001 baseline projection, a 2010 baseline projection and a 2010 projection with controls, a 2015 baseline projection and a 2015 projection with controls. REMSAD simulates every hour of every day of the year and, thus,

requires a variety of input files that contain information pertaining to the modeling domain and simulation period. These include gridded, 1 hour average emissions estimates and meteorological fields, initial and boundary conditions, and land use information. As applied to the contiguous U. S., the model segments the area within the region into square blocks called grids ( roughly equal in size to counties), each of which has several layers of air conditions.

Using this data, REMSAD generates predictions of 1 hour average PM concentrations for every grid. As discussed in the Air Quality Modeling TSD, we use the relative predictions from the model by combining the 2001 base year and each future year scenario with speciated ambient air quality observations to determine the expected change in 2010 or 2015 concentrations due to the rule. After completing this process, we then calculated daily and seasonal PM air quality metrics as inputs to the health and welfare C R functions of the benefits analysis. The following sections provide a more detailed discussion of each of the steps in this evaluation and a summary of the results.

<IP_ADDRESS> Modeling Domain The PM air quality analyses employed the modeling domain used previously in support of Clear Skies air quality assessment. As shown in Figure 3 1, the modeling domain encompasses the lower 48 States and extends from 126 degrees to 66 degrees west longitude and from 24 degrees north latitude to 52 degrees north latitude. The model contains horizontal grid cells across the model domain of roughly 36 km by 36 km. There are 12 vertical layers of atmospheric conditions with the top of the modeling domain at 16,200 meters. The 36 by 36 km horizontal grid results in a 120 by 84 grid ( or 10,080 grid cells) for each vertical layer. Figure 3 2 illustrates the horizontal grid cells for Maryland and surrounding areas. 3 8 Figure 3 1. REMSAD Modeling Domain for Continental United States Note: Gray markings define individual grid cells in the REMSAD model.

<IP_ADDRESS> Simulation Periods For use in this benefits analysis, the simulation periods modeled by REMSAD included separate full year application for each of the six emissions scenarios, i. e., 1996 and 2001 baseline years and the 2010 and 2015 base cases and control scenarios. 3 9 Figure 3 2. Example of REMSAD 36 x 36km Grid cells for Maryland Area <IP_ADDRESS> Model Inputs REMSAD requires a variety of input files that contain information pertaining to the modeling domain and simulation period. These include gridded, 1 hour average emissions estimates and meteorological fields, initial and boundary conditions, and land use information.

Separate emissions inventories were prepared for the 1996 and 2001 baseline years and each of the future year base cases and control scenarios. All other inputs were specified for the 1996 baseline model application and remained unchanged for each future year modeling scenario.

REMSAD requires detailed emissions inventories containing temporally allocated emissions for each grid cell in the modeling domain for each species being simulated. The previously described annual emission inventories were preprocessed into model ready inputs 3 10 through the SMOKE emissions preprocessing system. Details of the preprocessing of emissions through SMOKE as provided in the emissions inventory TSD. Meteorological inputs reflecting 1996 conditions across the contiguous U. S. were derived from Version 5 of the Mesoscale Model ( MM5). These inputs included horizontal wind components ( i. e., speed and direction), temperature, moisture, vertical diffusion rates, and rainfall rates for each grid cell in each vertical layer. Details of the annual 1996 MM5 modeling are provided in Olerud ( 2000).

A postprocessor called MM5REMSAD was developed to convert the MM5 data into the appropriate REMSAD grid coordinate systems and file formats. This postprocessor was used to develop the hourly average meteorological input files from the MM5 output.

Documentation of the MM5REMSAD code and further details on the development of the input files are contained in Mansell ( 2000). A more detailed description of the development of the meteorological input data is provided in the Air Quality TSD, which is located in the docket for this rule.

The modeling specified initial species concentrations and lateral boundary conditions to approximate background concentrations of the species; for the lateral boundaries the concentrations varied ( decreased parabolically) with height. These initial conditions reflect relatively clean background concentration values. Terrain elevations and land use information was obtained from the U. S. Geological Survey database at 10 km resolution and aggregated to the roughly 36 km horizontal resolution used for this REMSAD application. The development of model inputs is discussed in greater detail in the Air Quality TSD, which is available in the docket for this rule.

<IP_ADDRESS> Model Performance for Particulate Matter ( PM)

The purpose of the base year PM air quality modeling was to reproduce the atmospheric processes resulting in formation and dispersion of fine particulate matter across the U. S. An operational model performance evaluation for PM 2.5 and its related speciated components ( e. g., sulfate, nitrate, elemental carbon etc.) for 1996 was performed in order to estimate the ability of the modeling system to replicate base year concentrations.

This evaluation is comprised principally of statistical assessments of model versus observed pairs. The robustness of any evaluation is directly proportional to the amount and quality of the ambient data available for comparison. Unfortunately, there are few PM 2.5 monitoring networks with available data for evaluation of the PM modeling. Critical limitations of the 1996 databases are a lack of urban monitoring sites with speciated 3 11 measurements and poor geographic representation of ambient concentration in the Eastern U. S.

The largest available ambient database for 1996 comes from the Interagency Monitoring of PROtected Visual Environments ( IMPROVE) network. IMPROVE is a cooperative visibility monitoring effort between EPA, federal land management agencies, and state air agencies. Data is collected at Class I areas across the United States mostly at National Parks,

National Wilderness Areas, and other protected pristine areas ( IMPROVE 2000). There were approximately 60 IMPROVE sites that had complete annual PM 2.5 mass and/ or PM 2.5 species data for 1996. Using the 100th meridian to divide the eastern and western U. S., 42 sites were located in the West and 18 sites were in the East.

As presented in Table 3 4, the observed IMPROVE data used for the performance evaluation consisted of PM 2.5 total mass, sulfate ion, nitrate ion, elemental carbon, organic aerosols, and crustal material ( soils). The REMSAD model output species were postprocessed in order to achieve compatibility with the observation species. The principal evaluation statistic used to evaluate REMSAD performance is the " ratio of the means." It is defined as the ratio of the average predicted values over the average observed values. The annual average ratio of the means was calculated for five individual PM2.5 species as well as for total PM2.5 mass. The metrics were calculated for all IMPROVE sites across the country as well as for the East and West individually. The following table shows the ratio of the annual means. Numbers greater than 1 indicate overpredictions compared to ambient observations ( e. g., 1.23 is a 23 percent overprediction). Numbers less than 1 indicate underpredictions.

When considering annual average statistics ( e. g., predicted versus observed), which are computed and aggregated over all sites and all days, REMSAD underpredicted fine particulate mass ( PM 2.5), by 18 percent. PM 2.5 in the Eastern U. S. was underpredicted by 2 percent,

while PM 2.5 in the West was underpredicted by 33 percent. All PM 2.5 component species were underpredicted in the west. In the East, nitrate and crustal material are overestimated.

Elemental carbon shows neither over or underprediction in the east with a bias near 0 percent.

Eastern sulfate is slightly underpredicted with a bias of 12 percent. Organic aerosols show little or no bias in the East and West.

Given the state of the science relative to PM modeling, it is inappropriate to judge PM model performance using criteria derived for other pollutants, like ozone. Still, the performance of the IAQR PM modeling is very encouraging, especially considering that the 3 12 results may be limited by our current knowledge of PM science and chemistry, by the emissions inventories for primary PM and secondary PM precursor pollutants, by the relatively sparse ambient data available for comparisons to model output, and by uncertainties in monitoring techniques. The model performance for sulfate is quite reasonable, which is key to the analysis due to the importance of SO 2 emissions reductions in the IAQR control strategy. Additional details, including comparisons to other monitoring networks, can be found in the Air Quality Modeling TSD.

<IP_ADDRESS> Converting REMSAD Outputs to Benefits Inputs REMSAD generates predictions of hourly PM concentrations for every grid. The particulate matter species modeled by REMSAD include a primary coarse fraction ( corresponding to PM in the 2.5 to 10 micron size range), a primary fine fraction ( corresponding to PM less than 2.5 microns in diameter), and several secondary particles ( e. g., sulfates, nitrates, and organics). PM 2.5 is calculated as the sum of the primary fine fraction and all of the secondarily formed particles. Future year estimates of PM 2.5 were calculated using relative reduction factors ( RRFs) applied to 2000 2002 PM 2.5 design values ( EPA, 2003b). The procedures for determining the RRFs are similar to those in EPA's draft guidance for modeling the PM 2.5 standard ( EPA, 1999a). The guidance recommends that model predictions be used in a relative sense to estimate changes expected to occur in each Table 3 4. Model Performance Statistics for REMSAD PM2.5 Species Predictions: 1996 Ratio of the Means ( annual average concentrations)

IMPROVE PM Species Nationwide Eastern U. S. Western U. S.

PM2.5, total mass 0.82 0.98 0.67 Sulfate ion 0.79 0.88 0.59 Nitrate ion 1.55 2.66 0.69 Elemental carbon 0.86 1.01 0.71 Organic aerosols 1.00 1.04 0.97 Soil/ Other 1.33 3.08 0.81 Note: The dividing line between the West and East was defined as the 100th meridian. 3 13 major PM 2.5 species. These species are sulfate, nitrate, organic carbon, elemental carbon,

crustal and un attributed mass which is defined as the difference between measured PM 2.5 and the sum of the other five components. The procedure for calculating future year PM 2.5 design values is called the " Speciated Modeled Attainment Test ( SMAT)". EPA previously used this procedure to estimate the ambient impact of the Clear Skies Act emissions controls.

The SMAT procedure was performed using the base year 2001 scenario and each of the future year scenarios. The SMAT approach uses temporally scaled speciated PM2.5 monitor data from 2001 2002, reconstructed into total PM2.5 mass based on 2000 2002 design values, and kriged to 12 kilometer grids ( nested within the standard 36 km REMSAD grid structure). Temporal scaling is based on ratios of future modeled REMSAD data to 2001 REMSAD model data, using REMSAD modeling conducted at the 36 km grid resolution.

SMAT output files include both quarterly mean and annual mean PM2.5 mass results, which are then manipulated within SAS to produce a BenMAP input file containing 364 daily values ( created by replicating the quarterly mean values for each day of the appropriate season).

<IP_ADDRESS> PM Air Quality Results Table 3 5 provides a summary of the predicted ambient PM 2.5 concentrations for the 2010 and 2015 base cases and changes associated with proposed rule. The REMSAD results indicate that the predicted change in PM concentrations is composed almost entirely of reductions in fine particulates ( PM 2.5) with little or no reduction in coarse particles ( PM 10 less PM 2.5). Therefore, the observed changes in PM 10 are composed primarily of changes in PM 2.5. In addition to the standard frequency statistics ( e. g., minimum, maximum, average), we provide the population weighted average which better reflects the baseline levels and predicted changes for more populated areas of the nation. This measure, therefore, better reflects the potential benefits of these predicted changes through exposure changes to these populations. As shown, the average annual mean concentrations of PM 2.5 across populated eastern U. S. grid cells declines by roughly 5.6 percent ( or 0.6  g/ m3) and 7.5 percent ( or 0.8  g/ m3) in 2010 and 2015, respectively. The population weighted average mean concentration declined by 6.1 percent ( or 0.74  g/ m3) in 2010 and 7.9 percent ( or 0.94  g/ m3) in 2015,

which is much larger in absolute terms than the spatial average for both years. This indicates the proposed rule generates greater absolute air quality improvements in more populated,

urban areas. 3 14 Table 3 5. Summary of Base Case PM Air Quality and Changes Due to Proposed Interstate Air Quality Rule: 2010 and 2015 2010 2015 Statistic Base Case Changea Percent Change Base Case Changea Percent Change PM2.5 (

g/ m3)

Minimum Annual Mean 5.24  0.33  6.3% 5.13  0.33  6.4%

Maximum Annual Mean 16.88  0.86  5.1% 16.79  1.19  7.1%

Average Annual Mean 10.82  0.61  5.6% 10.67  0.80  7.5%

Pop Weighted Average Annual Mean b 12.19  0.74  6.1% 11.99  0.94  7.9%

a The change is defined as the control case value minus the base case value.

b Calculated by summing the product of the projected REMSAD grid cell population and the estimated PM concentration, for that grid cell and then dividing by the total population.

Table 3 6 provides information on the populations in 2010 and 2015 that will experience improved PM air quality. There are significant populations that live in areas with meaningful reductions in annual mean PM 2.5 concentrations resulting from the proposed rule. As shown,

in 2015, almost 40 percent of the U. S. population located in the eastern 37 state modeling domain are predicted to experience reductions of greater than 1.0  g/ m3. This is an increase from the 20 percent of the U. S. population that are expected to experience such reductions in 2010. Furthermore, over 7 percent of this population will benefit from reductions in annual mean PM 2.5 concentrations of greater than 1.5  g/ m3 and almost 2 percent will live in areas with reductions of greater than 1.75  g/ m3.

3.2.2 Ozone Air Quality Estimates We use the emissions inputs summarized earlier in this chapter with a regional scale version of CAMx to estimate ozone air quality in the Eastern and Western U. S. CAMx is an Eulerian three dimensional photochemical grid air quality model designed to calculate the concentrations of both inert and chemically reactive pollutants by simulating the physical and chemical processes in the atmosphere that affect ozone formation. Version 3.10 of the CAMx model was employed for these analyses. Because it accounts for spatial and temporal variations as well as differences in the reactivity of emissions, CAMx is useful for evaluating 3 15 the impacts of the proposed rule on U. S. ozone concentrations. Although the model tends to underestimate observed ozone, it exhibits less bias and error than any past regional ozone Table 3 6. Distribution of PM2.5 Air Quality Improvements Over Population Due to Proposed Interstate Air Quality Rule: 2010 and 2015 Change in Annual Mean PM 2.5 Concentrations (

g/ m3) 2010 Populationb 2015 Population Number ( millions) Percent (%) Number ( millions) Percent (%)

0 >   PM2.5 Conc  0.25 6.1 2.7% 0.0 0.0%

0.25 >   PM2.5 Conc  0.5 59.0 26.1% 29.9 12.8%

0.5 >   PM2.5 Conc  0.75 57.1 25.3% 52.9 22.6%

0.75 >   PM2.5 Conc  1.0 59.3 26.2% 60.6 25.9%

1.0 >   PM2.5 Conc  1.25 22.5 9.9% 34.6 14.8%

1.25 >   PM2.5 Conc  1.5 11.2 4.9% 38.0 16.2%

1.5 >   PM2.5 Conc  1.75 9.0 4.0% 13.9 5.9%

  PM2.5 Conc > 1.75 2.0 0.9% 4.2 1.8%

a The change is defined as the control case value minus the base case value.

b Population counts and percentages are for the fraction of the national population located in the eastern 37 state modeling domain considered in modeling health benefits for the rule.

modeling application conducted by EPA ( i. e., OTAG, On highway Tier 2, and HD Engine/ Diesel Fuel).

Our analysis applies the modeling system separately to the Eastern U. S. for six emissions scenarios: a 1995 baseline projection, a 2001 baseline projection, a 2020 baseline projection and a 2020 projection with controls, a 2030 baseline projection and a 2030 projection with controls. The model was applied and evaluated over three episodes that occurred during the summer of 1995 base year. Subsequently, episodic ozone model runs were made for the 2001 base year scenario and the 2010 and 2015 base and control case scenarios for all episodes.

Further discussion of this modeling, including evaluations of model performance relative to predicted future air quality, is provided in the air quality modeling TSD. As discussed in chapter 4, we use the relative predictions from the model by combining the 2001 base year and each future year scenario with current ambient air quality observations to determine the expected change in 2010 or 2015 ozone concentrations due to the rule ( Abt Associates,

2003). These results are used solely in the benefits analysis. 3 16 The CAMx modeling system requires a variety of input files that contain information pertaining to the modeling domain and simulation period. These include gridded, day specific emissions estimates and meteorological fields, initial and boundary conditions, and land use information. As applied to the Eastern U. S., the model segments the area into square blocks called grids ( roughly equal in size to counties), each of which has several layers of air conditions that are considered in the analysis. Using this data, the CAMx model generates predictions of hourly ozone concentrations for every grid. We then calibrate the results of this process to develop 2010 and 2015 ozone profiles at monitor sites by normalizing the observations to the observed ozone concentrations at each monitor site. For areas ( grids)

without ozone monitoring data, we interpolated ozone values using data from monitors surrounding the area. After completing this process, we calculated daily and seasonal ozone metrics to be used as inputs to the health and welfare C R functions of the benefits analysis.

The following sections provide a more detailed discussion of each of the steps in this evaluation and a summary of the results.

<IP_ADDRESS> Modeling Domain The modeling domain representing the Eastern U. S. is the same as that used previously for OTAG and the On highway Tier 2 rulemaking. As shown in Figure 3 3, this domain encompasses most of the Eastern U. S. from the East coast to mid Texas and consists of two grids with differing resolutions. The modeling domain extends from 99 degrees to 67 degrees west longitude and from 26 degrees to 47 degrees north latitude. The inner portion of the modeling domain shown in Figure 3 3 uses a relatively fine grid of 12 km consisting of nine vertical layers. The outer area has less horizontal resolution, as it uses a 36 km grid with the same nine vertical layers. The vertical height of the modeling domain is 4,000 meters above ground level for both areas.

<IP_ADDRESS> Simulation Periods For use in this benefits analysis, the simulation periods modeled by CAMx included several multi day periods when ambient measurements recorded high ozone concentrations. A simulation period, or episode, consists of meteorological data characterized over a block of days that are used as inputs to the air quality model. A simulation period is selected to characterize a variety of ozone conditions including some days with high ozone concentrations in one or more portions of the U. S. and observed exceedances of the 1 hour NAAQS for ozone being recorded at monitors. We focused on the summer of 1995 for selecting the episodes to model because it is a recent time period for which we had model ready 3 17 Figure 3 3. CAMx Eastern U. S. Modeling Domain Note: The inner area represents fine grid modeling at 12 km resolution, while the outer area represents the coarse grid modeling at 36 km resolution.

meteorological inputs and this timeframe contained several periods of elevated ozone over the Eastern U. S. As detailed in the air quality modeling TSD, this analysis used three multi day meteorological scenarios during the summer of 1995 for the model simulations over the eastern U. S.: June 12 24, July 5 15, and August 7 21. Each of the six emissions scenarios ( 1995 base year, 2001 base year, 2010 base and control, 2015 base and control) were simulated for the selected episodes. These episodes include a three day " ramp up" period to initialize the model, but the results for these days are not used in this analysis.

<IP_ADDRESS> Non emissions Modeling Inputs The meteorological data required for input into CAMx ( wind, temperature, vertical mixing, etc.) were developed by separate meteorological models. The gridded meteorological 3 18 data for the three historical 1995 episodes were developed using the Regional Atmospheric Modeling System ( RAMS), version 3b. This model provided needed data at every grid cell on an hourly basis. These meteorological modeling results were evaluated against observed weather conditions before being input into CAMx and it was concluded that the model fields were adequate representations of the historical meteorology. A more detailed description of the settings and assorted input files employed in these applications is provided in the Air Quality TSD, which is located in the docket for this rule.

The modeling assumed background pollutant levels at the top and along the periphery of the domain as in Tier 2. Additionally, initial conditions were assumed to be relatively clean as well. Given the ramp up days and the expansive domains, it is expected that these assumptions will not affect the modeling results, except in areas near the boundary ( e. g.,

Dallas Fort Worth TX). The other non emission CAMx inputs ( land use, photolysis rates,

etc.) were developed using procedures employed in the Tier 2/ OTAG regional modeling. The development of model inputs is discussed in greater detail in the Air Quality TSD, which is available in the docket for this rule.

<IP_ADDRESS> Model Performance for Photochemical Ozone The purpose of the 1995 base year photochemical ozone modeling was to reproduce the atmospheric processes resulting in the observed ozone concentrations over these domains and episodes. One of the fundamental assumptions in air quality modeling is that a model which adequately replicates observed pollutant concentrations in the base year can be used to assess the effects of future year emissions controls. A series of performance statistics was calculated for the Eastern U. S. domain as well as the four quadrants and multiple subregions. The model performance evaluation consisted solely of comparisons against ambient surface ozone data.

There was insufficient data available in terms of ozone precursors or ozone aloft to allow for a more complete assessment of model performance. Three primary statistical metrics were used to assess the overall accuracy of the base year modeling simulations.

Mean normalized bias is defined as the average difference between the hourly model predictions and observations ( paired in space and time) at each monitoring location, normalized by the magnitude of the observations.

Mean normalized gross error is defined as the average absolute difference between the hourly model predictions and observations ( paired in space and time) at each monitoring location, normalized by the magnitude of the observations. 3 19

Average accuracy of the peak is defined as the average difference between peak daily model predictions and observations at each monitoring location, normalized by the magnitude of the observations.

In general, the model tends to underestimate observed ozone. When all hourly observed ozone values greater than a 60 ppb threshold are compared to their model counterparts for the 30 episode modeling days in the eastern domain, the mean normalized bias is  1.1 percent and the mean normalized gross error is 20.5 percent. As shown in Table 3 7, the model generally underestimates observed ozone values for the June and July episodes, but predicts higher than observed amounts for the August episode.

At present, there are no guidance criteria by which one can determine if a regional ozone modeling exercise is exhibiting adequate model performance. These base case simulations were determined to be acceptable based on comparisons to previously completed model rulemaking analyses ( e. g., OTAG, Tier 2, and Heavy Duty Engine). The modeling completed for this proposal exhibits less bias and error than any past regional ozone modeling application done by EPA. Thus, the model is considered appropriate for use in projecting changes in future year ozone concentrations and the resultant health/ economic benefits due to the proposed emissions reductions.

In addition, the CAMx modeling results were also evaluated at a " local" level to ensure that areas determined to need the emissions reductions based on projected exceedances of the ozone standard were not unduly influenced by local overestimation of ozone in the model base year. As detailed in the Air Quality Modeling TSD, performance statistics were computed for each of 51 local subregions within the modeling domain. These performance statistics were compared to the recommended performance ranges for urban attainment modeling ( EPA, 1991). The results indicate that model performance for the June episode was within Table 3 7. Model Performance Statistics for Hourly Ozone in the Eastern U. S. CAMx Ozone Simulations: 1995 Base Case Episode Average Accuracy of the Peak Mean Normalized Bias Mean Normalized Gross Error June 1995   7.3   8.8 19.6 July 1995   3.3   5.0 19.1 August 1995 9.6 8.6 23.3 2The ozone season for this analysis is defined as the 5 month period from May to September; however, to estimate certain crop yield benefits, the modeling results were extended to include months outside the 5 month ozone season.

3Based on AIRS, there were 961 ozone monitors with sufficient data, i. e., 50 percent or more days reporting at least 9 hourly observations per day ( 8 am to 8 pm) during the ozone season.

4The 8 km grid squares contain the population data used in the health benefits analysis model, BenMAP. See Chapter 4 for a discussion of this model.

5This approach is a generalization of planar interpolation that is technically referred to as enhanced Voronoi Neighbor Averaging ( EVNA) spatial interpolation ( See Abt Associates ( 2003) for a more detailed description).

3 20 the recommended ranges for 69 percent of the local areas examined. For the July and August episodes, the percent of local areas with performance within the recommended ranges was 80 percent and 61 percent, respectively.

<IP_ADDRESS> Converting CAMx Outputs to Full Season Profiles for Benefits Analysis This study extracted hourly, surface layer ozone concentrations for each grid cell from the standard CAMx output file containing hourly average ozone values. These model predictions are used in conjunction with the observed concentrations obtained from the Aerometric Information Retrieval System ( AIRS) to generate ozone concentrations for the entire ozone season. 2,3 The predicted changes in ozone concentrations from the future year base case to future year control scenario serve as inputs to the health and welfare C R functions of the benefits analysis, i. e., the Environmental Benefits Mapping and Analysis Program ( BenMAP).

In order to estimate ozone related health and welfare effects for the contiguous U. S.,

fullseason ozone data are required for every BenMAP grid cell. Given available ozone monitoring data, we generated full season ozone profiles for each location in the contiguous 48 States in two steps: ( 1) we combine monitored observations and modeled ozone predictions to interpolate hourly ozone concentrations to a grid of 8 km by 8 km population grid cells, and ( 2) we converted these full season hourly ozone profiles to an ozone measure of interest, such as the daily average. 4,5 These methods are described in detail in the benefits analysis technical support document ( Abt Associates, 2003).

<IP_ADDRESS> Ozone Air Quality Results This section provides a summary the predicted ambient ozone concentrations from the CAMx model for the 2010 and 2015 base cases and changes associated with the proposed 3 21 rule. Table 3 8 provides those ozone metrics for grid cells in the Eastern U. S. that enter the concentration response functions for health benefits endpoints. The population weighted average reflects the baseline levels and predicted changes for more populated areas of the Table 3 8. Summary of CAMx Derived Population Weighted Ozone Air Quality Metrics for Health Benefits Endpoints Due to Proposed Interstate Air Quality Rule: Eastern U. S.

2010 2015 Statistic a Base Case Change b Percent Change b Base Case Change b Percent Change b Population Weighted Average ( ppb) d Daily 1 Hour Maximum Concentration 53.32   0.51   0.95% 52.10   1.05   2.02%

Daily 5 Hour Average Concentration 44.51   0.42   0.93% 43.65   0.87   2.00%

Daily 8 Hour Average Concentration 43.81   0.41   0.93% 42.97   0.86   1.99%

Daily 12 Hour Average Concentration 41.28   0.38   0.92% 40.56   0.80   1.98%

Daily 24 Hour Average Concentration 31.20   0.28   0.89% 30.83   0.59   1.91%

a These ozone metrics are calculated at the CAMX grid cell level for use in health effects estimates based on the results of spatial and temporal Voronoi Neighbor Averaging. Except for the daily 24 hour average, these ozone metrics are calculated over relevant time periods during the daylight hours of the " ozone season," i. e., May through September. For the 5 hour average, the relevant time period is 10 am to 3 pm; for the 8 hr average, it is 9 am to 5 pm; and, for the 12 hr average it is 8 am to 8 pm.

b The change is defined as the control case value minus the base case value. The percent change is the " Change" divided by the " Base Case," and then multiplied by 100 to convert the value to a percentage.

d Calculated by summing the product of the projected CAMx grid cell population and the estimated CAMx grid cell seasonal ozone concentration, and then dividing by the total population.

nation. This measure, therefore, will better reflect the potential benefits of these predicted changes through exposure changes to these populations.

3.2.3 Visibility Degradation Estimates Visibility degradation is often directly proportional to decreases in light transmittal in the atmosphere. Scattering and absorption by both gases and particles decrease light transmittance. To quantify changes in visibility, our analysis computes a light extinction coefficient, based on the work of Sisler ( 1996), which shows the total fraction of light that is decreased per unit distance. This coefficient accounts for the scattering and absorption of light by both particles and gases, and accounts for the higher extinction efficiency of fine particles compared to coarse particles. Fine particles with significant light extinction efficiencies include sulfates, nitrates, organic carbon, elemental carbon ( soot), and soil ( Sisler,

1996). 3 22 Based upon the light extinction coefficient, we also calculated a unitless visibility index,

called a " deciview," 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. The higher the deciview value, the worse the visibility. Thus, an improvement in visibility is a decrease in deciview value.

Table 3 9 provides the distribution of visibility improvements across 2010 and 2015 populations resulting from this proposed rule. The majority of the 2015 U. S. population live in areas with predicted improvement in annual average visibility of greater than 0.6 deciviews resulting from the proposed rule. As shown, almost 72 percent of the 2015 U. S. population are predicted to experience improved annual average visibility of greater than 0.4 deciviews.

Furthermore, roughly 25 percent of the 2015 U. S. population will benefit from reductions in annual average visibility of greater than 1 deciviews.

Because the visibility benefits analysis distinguishes between general regional visibility degradation and that particular to Federally designated Class I areas ( i. e., national parks,

forests, recreation areas, wilderness areas, etc.), we separated estimates of visibility Table 3 9. Distribution of Populations Experiencing Visibility Improvements due to Proposed Interstate Air Quality Rule: 2010 and 2015 2010 Population 2015 Population Improvements in Visibilitya( annual average deciviews) Number ( millions) Percent (%) Number ( millions) Percent (%)

0 >   Deciview

0.2 75.6 24.9% 74.8 23.6%

0.2 >   Deciview

0.4 24.1 7.9% 15.2 4.8%

0.4 >   Deciview

0.6 46.5 15.3% 25.3 8.0%

0.6 >   Deciview

0.8 87.7 28.8% 64.7 20.4%

0.8 >   Deciview

1.0 56.0 18.4% 57.8 18.2%

  Deciview > 1.0 14.3 4.7% 79.1 25.0% 6 The visibility calculations presented in this section are changes in the annual average visibility for the purpose of generating monetized benefits. There improvements in visibility should not be confused with the requirements under the Regional Haze rule to show " reasonable progress" for the 20% best and 20% worst days to each Class I area. Example Regional Haze calculations for the 20% best and worst days are contained in the AQMTSD.

3 23 degradation into " residential" and " recreational" categories. 6 The estimates of visibility degradation for the " recreational" category apply to Federally designated Class I areas, while estimates for the " residential" category apply to non Class I areas. Deciview estimates are estimated using outputs from REMSAD for the 2010 and 2015 base cases and control scenarios.

<IP_ADDRESS> Residential Visibility Improvements Air quality modeling results predict that the proposed Interstate Air Quality Rule will create improvements in visibility through the country. In Table 3 10, we summarize residential visibility improvements across the Eastern U. S. in 2010 and 2015. The baseline annual average visibility for eastern U. S. counties is 21.61 deciviews in 2010. The mean improvement across eastern U. S. counties is 0.69 deciviews, or almost 3.2 percent. In urban areas with a population of 250,000 or more, the mean improvement in annual visibility was similar at 0.71 deciviews in 2010 and ranged from 0.17 to 1.64 deciviews. In rural areas, the mean improvement in visibility was 0.68 deciviews in 2010 and ranged from 0.19 to 1.69 deciviews.

Table 3 10. Summary of Baseline Residential Visibility and Changes by Region: 2010 and 2015 ( annual average deciviews)

2010 2015 Regionsa Base Case Changeb Percent Change Base Case Changeb Percent Change Eastern U. S. 21.61 0.69 3.17% 21.31 0.85 3.95%

Urban 22.78 0.71 3.14% 22.50 0.88 3.95%

Rural 21.14 0.68 3.18% 20.84 0.84 3.96%

a The dividing line between the Eastern and Western U. S. was defined as the 100th meridian.

b An improvement in visibility is a decrease in deciview value. The change is defined as the control case deciview level minus the base case deciview level. 3 24 Northwest Rocky Mountain Northeast/ Midwest Southeast Southwest California Study Region Transfer Region Figure 3 4. Recreational Visibility Regions for Continental U. S.

Note: Study regions were represented in the Chestnut and Rowe ( 1990a, 1990b) studies used in evaluating the benefits of visibility improvements, while transfer regions used extrapolated study results. <IP_ADDRESS> Recreational Visibility Improvements In Table 3 11, we summarize recreational visibility improvements in 2010 and 2015 in Federal Class I areas located in the eastern U. S. These recreational visibility regions are shown in Figure 3 4. As shown, the improvement in visibility for Federal Class I areas in the Eastern U. S. increases from 3.8 percent, or 0.77 deciviews, in 2010. The predicted absolute improvement of 0.94 deciviews in 2015 reflects a 4.6 percent change from 2015 baseline visibility of 20.38 deciviews.

Table 3 11. Summary of Baseline Recreational Visibility and Changes by Region: 2010 and 2015 ( annual average deciviews)

2010 2015 Class I Visibility Regionsa Base Case Changeb Percent Change Base Case Changeb Percent Change Eastern U. S. 20.59 0.77 3.75% 20.38 0.94 4.61%

Southeast 22.04 0.91 4.11% 21.80 1.17 5.35%

Northeast/ Midwest 19.28 0.65 3.38% 19.11 0.74 3.85%

a Regions are pictured in Figure VI 5 and are defined in the technical support document ( see Abt Associates,

2003).

b An improvement in visibility is a decrease in deciview value. The change is defined as the control case deciview level minus the base case deciview level. 3 25 4 1 SECTION 4 BENEFITS ANALYSIS AND RESULTS This chapter reports the EPA's analysis of a subset of the public health and welfare impacts and associated monetized benefits to society of the proposed IAQR. The EPA is required by Executive Order 12866 to estimate the benefits and costs of major new pollution control regulations. Accordingly, the analysis presented here attempts to answer three questions: 1) what are the physical health and welfare effects of changes in ambient air quality resulting from reductions in precursors to particulate matter ( PM) including ( NOx) and sulfur dioxide ( SO 2) emissions? 2) how much are the changes in these effects attributable to the proposed rule worth to U. S. citizens as a whole in monetary terms? and 3) how do the monetized benefits compare to the costs? It constitutes one part of the EPA's thorough examination of the relative merits of this proposed regulation.

The analysis presented in this chapter uses a methodology generally consistent with benefits analyses performed for the recent analysis of Nonroad Diesel Engines Tier 4 Standards and the proposed Clear Skies Act of 2003 ( EPA, 2003). The benefits analysis relies on three major modeling components:

1) Calculation of the impact that a set of preliminary emissions standards for EGUs based on a state level cap and trade program would have on the national inventory of precursors to PM including SO 2 and NOx.

2) Air quality modeling for 2010 and 2015 to determine changes in ambient concentrations of ozone and particulate matter, reflecting baseline and post control emissions inventories.

3) A benefits analysis to determine the changes in human health and welfare, both in terms of physical effects and monetary value, that result from the projected changes in ambient concentrations of various pollutants for the modeled standards.

A wide range of human health and welfare effects are linked to the emissions of NOx and SOx from EGUs and the resulting impact on ambient concentrations of ozone and PM. 7Short term exposure to ambient ozone has also been linked to premature death. The EPA is currently evaluating the epidemiological literature examining the relationship between ozone and premature mortality, sponsoring three independent meta analyses of the literature. Once this evaluation has been completed and peer reviewed, the EPA will consider including ozone related premature mortality in the primary benefits analysis for the final rule.

4 2 Potential human health effects linked to PM2.5 range from mortality linked to long term exposure to PM, to a range of morbidity effects linked to long term ( chronic) and shorterterm ( acute) exposures ( e. g., respiratory and cardiovascular symptoms resulting in hospital admissions, asthma exacerbations, and acute and chronic bronchitis [ CB]). Exposure to ozone has also been linked to a variety of respiratory effects including hospital admissions and illnesses resulting in school absences. 7 Welfare effects potentially linked to PM include materials damage and visibility impacts, while ozone can adversely affect the agricultural and forestry sectors by decreasing yields of crops and forests. Although methods exist for quantifying the benefits associated with many of these human health and welfare categories,

not all can be evaluated at this time due to limitations in methods and/ or data. Table 4 1 lists the full complement of human health and welfare effects associated with PM and ozone and identifies those effects that are quantified for the primary estimate, are quantified as part of the sensitivity analysis ( to be completed for the supplemental analysis), and remain unquantified because of to current limitations in methods or available data.

Figure 4 1 illustrates the major steps in the benefits analysis. Given baseline and postcontrol emissions inventories for the emission species expected to affect ambient air quality,

we use sophisticated photochemical air quality models to estimate baseline and post control ambient concentrations of ozone and PM, and deposition of nitrogen and sulfur for each year.

The estimated changes in ambient concentrations are then combined with monitoring data to estimate population level exposures to changes in ambient concentrations for use in estimating health effects. Modeled changes in ambient data are also used to estimate changes in visibility,

and changes in other air quality statistics that are necessary to estimate welfare effects.

Changes in population 4 3 Emissions inventories ( 2001 CEM, 1996 NEI,

MOBILE 5b and 6 PART5 model, NONROAD2002)

Air quality monitoring data AIRS ( ozone), FRM ( total PM), STN ( speciated PM) Model baseline and post control ambient air quality ( REMSAD, CAM X)

Model population exposure to changes in ambient concentrations Estimate expected changes in human health outcomes Estimate monetary value of changes in human health Adjust monetary values for growth in real income to year of analysis Sum health and welfare monetary values to obtain total monetary benefits Concentration response functions Incidence and prevalence rates for health endpoints Population and demographic data ( with growth projections)

Valuation functions Interpolation of projected air concentration surfaces ( base and control) Estimate expected changes in welfare ( visibility)

Estimate monetary value of changes in welfare effects Valuation functions ° BenMAP derived ( ozone)

° SMAT derived ( PM2.5) BenMAP integrated model Income elasticities GDP projections PROCESSES INPUTS INPUTS Figure 4 1. Key Steps in Air Quality Modeling Based Benefits Analysis 8The term " impact function" as used here refers to the combination of ( a) an effect estimate obtained from the epidemiological literature, ( b) the baseline incidence estimate for the health effect of interest in the modeled population, ( c) the size of that modeled population, and ( d) the change in the ambient air pollution metric of interest. These elements are combined in the impact function to generate estimates of changes in incidence of the health effect. The impact function is distinct from the concentration response ( C R) function, which strictly refers to the estimated equation from the epidemiological study relating incidence of the health effect and ambient pollution. We refer to the specific value of the relative risk or estimated coefficients in the epidemiological study as the " effect estimate." In referencing the functions used to generate changes in incidence of health effects for this RIA, we use the term impact function rather than C R function because " impact function" includes all key input parameters used in the incidence calculation.

4 4 exposure to ambient air pollution are then input to impact functions8 to generate changes in incidence of health effects, or changes in other exposure metrics are input to dose response functions to generate changes in welfare effects. The resulting effects changes are then assigned monetary values, taking into account adjustments to values for growth in real income out to the year of analysis ( values for health and welfare effects are in general positively related to real income levels). Finally, values for individual health and welfare effects are summed to obtain an estimate of the total monetary value of the changes in emissions.

On September 26, 2002, the National Academy of Sciences ( NAS) released a report on its review of the Agency's methodology for analyzing the health benefits of measures taken to reduce air pollution. The report focused on the EPA's approach for estimating the health benefits of regulations designed to reduce concentrations of airborne PM.

In its report, the NAS said that the EPA has generally used a reasonable framework for analyzing the health benefits of PM control measures. It recommended, however, that the Agency take a number of steps to improve its benefits analysis. In particular, the NAS stated that the Agency should

include benefits estimates for a range of regulatory options;

estimate benefits for intervals, such as every 5 years, rather than a single year;

clearly state the projected baseline statistics used in estimating health benefits, including those for air emissions, air quality, and health outcomes;

examine whether implementation of proposed regulations might cause unintended impacts on human health or the environment;

when appropriate, use data from non U. S. studies to broaden age ranges to which current estimates apply and to include more types of relevant health outcomes; and 4 5

begin to move the assessment of uncertainties from its ancillary analyses into its base analyses by conducting probabilistic, multiple source uncertainty analyses. This assessment should be based on available data and expert judgment.

Although the NAS made a number of recommendations for improvement in the EPA's approach, it found that the studies selected by the Agency for use in its benefits analysis were generally reasonable choices. In particular, the NAS agreed with the EPA's decision to use cohort studies for estimating premature mortality benefits. It also concluded that the Agency's selection of the American Cancer Society ( ACS) study for the evaluation of PMrelated premature mortality was reasonable, although it noted the publication of new cohort studies that the Agency should evaluate. Since the publication of the NAS report, the EPA has reviewed new cohort studies, including reanalyses of the ACS study data and has carefully considered these new study data in developing the analytical approach for the IAQR ( see below).

In addition to the NAS report, the EPA has also received technical guidance and input regarding its methodology for conducting PM and ozone related benefits analysis from two additional sources, including the Health Effects Subgroup ( HES) of the SAB Council reviewing the 812 blueprint ( SAB HES, 2003) and the Office of Management and Budget ( OMB) through ongoing discussions regarding methods used in conducting regulatory impact analyses ( RIAs). The SAB HES recommendations include the following ( SAB HES, 2003):

use of the updated ACS Pope et al. ( 2002) study rather than the ACS Krewski et al. study to estimate mortality for the primary analysis;

dropping the alternative estimate used in earlier RIAs and instead including a primary estimate that incorporates consideration of uncertainty in key effects categories such as mortality directly into the estimates ( e. g., use of the standard errors from the Pope et al. [ 2002] study in deriving confidence bounds for the adult mortality estimates);

addition of infant mortality ( children under the age of one) into the primary estimate, based on supporting evidence from the World Health Organization Global Burden of Disease study and other published studies that strengthen the evidence for a relationship between PM exposure and respiratory inflamation and infection in children leading to death;

inclusion of asthma exacerbations for children in the primary estimate;

expansion of the age groups evaluated for a range of morbidity effects beyond the narrow band of the studies to the broader ( total) age group ( e. g., expanding a 9Note that the SAB HES comments were made in the context of a review of the methods for the Section 812 analysis of the costs and benefits of the Clean Air Act. This context is pertinent to our interpretation of the SAB HES comments on the selection of effect estimates for hospital admissions associated with PM ( SABHES 2003). The Section 812 analysis is focused on a broad set of air quality changes, including both the coarse and fine fractions of PM10. As such, impact functions that focus on the full impact of PM10 are appropriate. However, for the IAQR, which is expected to affect primarily the fine fraction ( PM2.5) of PM10, impact functions that focus primarily on PM2.5 are more appropriate.

4 6 study population for 7 to 11 year olds to cover the entire child age range of 6 to 18 years).

inclusion of new endpoints ( school absences [ ozone], nonfatal heart attacks in adults [ PM], hospital admissions for children under two [ ozone]), and suggestion of a new meta analysis of hospital admissions ( PM10) rather than using a few PM2.5 studies; 9 and

updating of populations and baseline incidences.

Recommendations from OMB regarding RIA methods have focused on the approach used to characterize uncertainty in the benefits estimates generated for RIAs, as well as the approach used to value mortality estimates. The EPA is currently in the process of developing a comprehensive integrated strategy for characterizing the impact of uncertainty in key elements of the benefits modeling process ( e. g., emissions modeling, air quality modeling,

health effects incidence estimation, valuation) on the results that are generated. A subset of this effort, which is currently underway, involves an expert elicitation designed to characterize uncertainty in the estimation of PM related mortality resulting from both short term and longer term exposure. The EPA will be evaluating the results of this elicitation to determine its usefulness in characterizing uncertainty in our estimates of PM related mortality benefits.

As elements of this uncertainty analysis strategy are finalized, it may be possible to integrate them into later iterations of the analysis completed for the IAQR ( e. g., the supplemental analysis and final rule).

We are also altering the value of a statistical life ( VSL) used in the analysis to reflect new information in the ongoing academic debate over the appropriate characterization of the value of reducing the risk of premature mortality. In previous analyses, we used a distribution of VSL based on 26 VSL estimates from the economics literature. For this analysis, we are characterizing the VSL distribution in a more general fashion, based on two recent metaanalyses of the wage risk based VSL literature. The new distribution is assumed to be normal, with a mean of $ 5.5 million and a 95 percent confidence interval between $ 1 and $ 10 4 7 million. The EPA welcomes public comment on the appropriate methodology for valuing reductions in the risk of premature death.

The EPA has addressed many of the comments received from the NAS, the SAB HES,

and OMB in developing the analytical approach for the IAQR. We have also reflected advances in data and methods in air quality modeling, epidemiology, and economics in developing this analysis. Updates to the assumptions and methods used in estimating PM 2.5

related and ozone related benefits since completion of the Proposed Nonroad Diesel Rule include the following:

Air Quality

Use of the Simulated Modeled Attainment Test ( SMAT) approach for developing PM2.5 air modeling results. The nonroad diesel rule used spatially and temporally scaled total PM2.5 mass based on monitoring data from 1999 to 2001 ( averaged by season). For the nonroad diesel rule, spatial scaling was based on 1996 modeled REMSAD data at a 36 km grid resolution, while temporal scaling was based on the ratios of future modeled REMSAD data to 1996 modeled REMSAD data. All scaling was conducted internally by BenMAP ( see below) using the monitor and model relative grid creation option. Resulting gridded outputs were for binned daily PM2.5 averages. For the IAQR, we used the SMAT approach, which uses temporally scaled speciated PM2.5 monitor data from 2001 2002, reconstructed into total PM2.5 mass based on 2000 2002 design values and kriged to 12 kilometer grids ( nested within the standard 36 km REMSAD grid structure). Temporal scaling is based on ratios of future modeled REMSAD data to 2001 REMSAD model data, using REMSAD modeling conducted at the 36 km grid resolution. SMAT output files include both quarterly mean and annual mean PM2.5 mass results, which are then manipulated within SAS to produce a BenMAP input file containing 364 daily values ( created by replicating the quarterly mean values for each day of the appropriate season). For more details on the SMAT approach and REMSAD modeling, see the air quality chapter of this document.

For both PM and ozone, the interstate air quality analysis domain will include only the eastern United States, focusing on 37 States believed to contribute significantly to the long range transport of precursors in the formation of PM2.5.

Health Endpoints 4 8

Incorporation of updated impact functions to reflect updated time series studies of hospital admissions to correct for errors in application of the generalized additive model ( GAM) functions in S plus. More information on this issue is available at http:// www. healtheffects. org.

The primary analysis will use an all cause mortality effect estimate based on the Pope et al. ( 2002) reanalysis of the ACS study data. In addition, we will provide a breakout for two major cause of death categories   cardiopulmonary and lung cancer.

Infant mortality will be included in the primary analysis.

Asthma exacerbations are incorporated into the primary analysis. Although the Nonroad Diesel Rule included asthma exacerbations as a separate endpoint outside of the base case analysis, for the IAQR, we will include asthma exacerbations in children 6 to 18 years of age as part of the primary analysis.

Valuation

In generating the monetized benefits for mortality in the primary analysis, the VSL will be entered as a mean ( best estimate) of 5.5 million. Unlike the Nonroad Diesel Rule, the IAQR will not include a value of statistical life year ( VSLY) estimate.

In response to comments from the SAB HES as well as the NAS panel, rather than including an alternative estimate in the IAQR, the EPA will investigate the impact of key assumptions on mortality and morbidity estimates through a series of sensitivity analyses ( to be completed for the supplemental analysis).

The benefits estimates generated for the Proposed IAQR are subject to a number of assumptions and uncertainties, which are discussed throughout the document. For example,

key assumptions underlying the primary estimate for the mortality category include the following:

( 1) Inhalation of fine particles is causally associated with premature death at concentrations near those experienced by most Americans on a daily basis.

Although biological mechanisms for this effect have not yet been definitively established, the weight of the available epidemiological evidence supports an assumption of causality.

( 2) All fine particles, regardless of their chemical composition, are equally potent in causing premature mortality. This is an important assumption, because PM produced via transported precursors emitted from EGUs may differ significantly 4 9 from direct PM released from automotive engines and other industrial sources, but no clear scientific grounds exist for supporting differential effects estimates by particle type.

( 3) The C R function for fine particles is approximately linear within the range of ambient concentrations under consideration. Thus, the estimates include health benefits from reducing fine particles in areas with varied concentrations of PM,

including both regions that are in attainment with fine particle standard and those that do not meet the standard.

( 4) The forecasts for future emissions and associated air quality modeling are valid.

Although recognizing the difficulties, assumptions, and inherent uncertainties in the overall enterprise, these analyses are based on peer reviewed scientific literature and up to date assessment tools, and we believe the results are highly useful in assessing this proposal.

In addition to the quantified and monetized benefits summarized above, a number of additional categories are not currently amenable to quantification or valuation. These include reduced acid and particulate deposition damage to cultural monuments and other materials,

reduced ozone effects on forested ecosystems, and environmental benefits due to reductions of impacts of acidification in lakes and streams and eutrophication in coastal areas.

Additionally, we have not quantified a number of known or suspected health effects linked with PM and ozone for which appropriate health impact functions are not available or which do not provide easily interpretable outcomes ( i. e., changes in forced expiratory volume [ FEV1]). As a result, monetized benefits generated for the primary estimate may underestimate the total benefits attributable to the proposed regulatory option.

Benefits estimates for the Proposed IAQR were generated using BenMAP, which is a computer program developed by the EPA that integrates a number of the modeling elements used in previous RIAs ( e. g., interpolation functions, population projections, health impact functions, valuation functions, analysis and pooling methods) to translate modeled air concentration estimates into health effects incidence estimates and monetized benefits estimates. BenMAP provides estimates of both the mean impacts and the distribution of impacts.

In general, the chapter is organized around the steps illustrated in Figure 4 1. In Section 4.1, we provide an overview of the data and methods that are used to quantify and value health and welfare endpoints and discuss how we incorporate uncertainty into our analysis. In 4 10 Section 4.2, we report the results of the analysis for human health and welfare effects ( the overall benefits estimated for the Proposed IAQR are summarized in Table 4 1). Details on the emissions inventory and air modeling are presented in Chapter 3.0.

4.1 Benefit Analysis Data and Methods Environmental and health economists have a number of methods for estimating the economic value of improvements in ( or deterioration of) environmental quality. The method used in any given situation depends on the nature of the effect and the kinds of data, time, and resources that are available for investigation and analysis. This section provides an overview of the methods we selected to quantify and monetize the benefits included in this RIA.

Given changes in environmental quality ( ambient air quality, visibility, nitrogen, and sulfate deposition), the next step is to determine the economic value of those changes. We follow a " damage function" approach in calculating total benefits of the modeled changes in environmental quality. This approach estimates changes in individual health and welfare endpoints ( specific effects that can be associated with changes in air quality) and assigns values to those changes assuming independence of the individual values. Total benefits are calculated simply as the sum of the values for all nonoverlapping health and welfare endpoints.

This imposes no overall preference structure and does not account for potential income or substitution effects ( i. e., adding a new endpoint will not reduce the value of changes in other Table 4 1. Estimated Monetized Benefits of the Proposed IAQR Total Benefitsa, b ( billions 1999$)

2010 2015 Using a 3% discount rate $ 58+ B $ 84+ B Using a 7% discount rate $ 54+ B $ 79+ B a For notational purposes, unquantified benefits are indicated with a " B" to represent the sum of additional monetary benefits and disbenefits. A detailed listing of unquantified health and welfare effects is provided in Table 4 2.

b Results reflect the use of two different discount rates: a 3 percent rate, which is recommended by the EPA's Guidelines for Preparing Economic Analyses ( EPA, 2000c), and 7 percent, which is recommended by OMB Circular A 94 ( OMB, 1992). Results are rounded to two significant digits. 4 11 endpoints). The " damage function" approach is the standard approach for most cost benefit analyses of environmental quality programs and has been used in several 4 12 Table 4 2. Human Health and Welfare Effects of Pollutants Affected by the Proposed IAQR Pollutant/ Effect Quantified and Monetized in Base Estimatesa Quantified and/ or Monetized Effects in Sensitivity Analyses Unquantified Effects Ozone/ Health Hospital admissions: respiratory Emergency room visits for asthma Minor restricted activity days School loss days Asthma attacks Cardiovascular emergency room visits Premature mortality: acute exposuresb Acute respiratory symptoms Increased airway responsiveness to stimuli Inflammation in the lung Chronic respiratory damage Premature aging of the lungs Acute inflammation and respiratory cell damage Increased susceptibility to respiratory infection Nonasthma respiratory emergency room visits Ozone/ Welfare Decreased outdoor worker productivity Decreased yields for commercial crops ( selected species)

Decreased eastern commercial forest productivity ( selected species) Decreased western commercial forest productivity Decreased eastern commercial forest productivity ( other species)

Decreased yields for fruits and vegetables Decreased yields for other commercial and noncommercial crops Damage to urban ornamental plants Impacts on recreational demand from damaged forest aesthetics Damage to ecosystem functions ( continued) 4 13 Table 4 2. Human Health and Welfare Effects of Pollutants Affected by the Proposed IAQR ( continued)

Pollutant/ Effect Quantified and Monetized in Base Estimatesa Quantified and/ or Monetized Effects in Sensitivity Analyses Unquantified Effects PM/ Health Premature mortality: long term exposures Bronchitis: chronic and acute Hospital admissions: respiratory and cardiovascular Emergency room visits for asthma Non fatal heart attacks ( myocardial infarction)

Lower and upper respiratory illness Minor restricted activity days Work loss days Asthma exacerbations ( asthmatic population) Respiratory symptoms ( asthmatic population)

Infant mortality Premature mortality: short term exposures Low birth weight Changes in pulmonary function Chronic respiratory diseases other than chronic bronchitis Morphological changes Altered host defense mechanisms Nonasthma respiratory emergency room visits 4 14 Table 4 2. Human Health and Welfare Effects of Pollutants Affected by the Proposed IAQR ( continued)

Pollutant/ Effect Quantified and Monetized in Base Estimatesa Quantified and/ or Monetized Effects in Sensitivity Analyses Unquantified Effects Nitrogen and Sulfate Deposition/

Welfare Impacts of acidic sulfate and nitrate deposition on commercial forests Impacts of acidic deposition on commercial freshwater fishing Impacts of acidic deposition on recreation in terrestrial ecosystems Impacts of nitrogen deposition on commercial fishing, agriculture, and forests Impacts of nitrogen deposition on recreation in estuarine ecosystems Reduced existence values for currently healthy ecosystems SO2/ Health Hospital admissions for respiratory and cardiac diseases Respiratory symptoms in asthmatics NOX/ Health Lung irritation Lowered resistance to respiratory infection Hospital admissions for respiratory and cardiac diseases ( continued) 4 15 Table 4 2. Human Health and Welfare Effects of Pollutants Affected by the Proposed IAQR ( continued)

Pollutant/ Effect Quantified and Monetized in Base Estimatesa Quantified and/ or Monetized Effects in Sensitivity Analyses Unquantified Effects Mercury Deposition/

Health Neurological disorders Learning disabilities Retarded development Potential cardiovascular effects *

Altered blood pressure regulation *

Increased heart rate variability *

Myocardial infarctions *

Potential reproductive effects *

Mercury Deposition/

Welfare Impacts on birds and mammals ( e. g., reproductive effects) Impacts to commercial, subsistence, and recreational fishing Reduced existence values for currently healthy ecosystems

These are potential effects as the literature is either contradictory or incomplete.

a Primary quantified and monetized effects are those included when determining the primary estimate of total monetized benefits of the IAQR. See Section C 2 for a more complete discussion of presentation of benefits estimates.

b Premature mortality associated with ozone is not currently included in the primary analysis. Recent evidence suggests that short term exposures to ozone may have a significant effect on daily mortality rates, independent of exposure to PM. The EPA is currently conducting a series of meta analyses of the ozone mortality epidemiology literature and will reevaluate inclusion of ozone related mortality in the primary analysis once the meta analyses have been completed. 4 16 recent published analyses ( Banzhaf et al., 2002; Levy et al., 2001; Levy et al., 1999; Ostro and Chestnut, 1998).

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 be conducted to convert air quality changes into effects that can be assigned dollar values.

For the purposes of this RIA, the health impacts analysis is limited to those health effects that are directly linked to ambient levels of air pollution and specifically to those linked to ozone and PM. There may be other, indirect health impacts associated with implementing controls to meet the preliminary control options, such as occupational health impacts for equipment operators. These impacts may be positive or negative, but in general, for this set of control options, they are expected to be small relative to the direct air pollution related impacts.

The welfare impacts analysis is limited to changes in the environment that have a direct impact on human welfare. For this analysis, we are limited by the available data to examining impacts of changes in visibility. We also provide qualitative discussions of the impact of changes in other environmental and ecological effects, for example, changes in deposition of nitrogen and sulfur to terrestrial and aquatic ecosystems, but we are unable to place an economic value on these changes.

We note at the outset that the EPA rarely has the time or resources to perform extensive new research to measure either the health outcomes or their values for this analysis. 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 the science and art of adapting primary research from similar contexts to obtain the most accurate measure of benefits for the environmental quality change under analysis. Where appropriate, adjustments are made for the level of environmental quality change, the sociodemographic and economic characteristics of the affected population, and other factors to improve the accuracy and robustness of benefits estimates. 4 17 4.1.1 Valuation Concepts In valuing health impacts, we note that reductions in ambient concentrations of air pollution generally lower the risk of future adverse health affects by a fairly small amount for a large population. The appropriate economic measure is therefore willingness to pay ( WTP)

for changes in risk prior to the regulation ( Freeman, 1993). In general, economists tend to view an individual's WTP for an improvement in environmental quality as the appropriate measure of the value of a risk reduction. An individual's willingness to accept ( WTA)

compensation for not receiving the improvement is also a valid measure. However, WTP is generally considered to be a more readily available and conservative measure of benefits.

Adoption of WTP as the measure of value implies that the value of environmental quality improvements depends on the individual preferences of the affected population and that the existing distribution of income ( ability to pay) is appropriate. 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. These cost of illness ( COI)

estimates generally understate the true value of reductions in risk of a health effect, reflecting the direct expenditures related to treatment but not the value of avoided pain and suffering from the health effect ( Harrrington and Portnoy, 1987; Berger, 1987).

For many goods, WTP can be observed by examining actual market transactions. For example, if a gallon of bottled drinking water sells for $ 1, it can be observed that at least some people are willing to pay $ 1 for such water. For goods not exchanged in the market, such as most environmental " goods," valuation is not as straightforward. Nevertheless, a value may be inferred from observed behavior, such as sales and prices of products that result in similar effects or risk reductions ( e. g., nontoxic cleaners or bike helmets). Alternatively, surveys can be used in an attempt to directly elicit WTP for an environmental improvement.

One distinction in environmental benefits estimation is between use values and nonuse values. Although no general agreement exists among economists on a precise distinction between the two ( see Freeman [ 1993]), the general nature of the difference is clear. Use values are those aspects of environmental quality that affect an individual's welfare more or less directly. These effects include changes in product prices, quality, and availability; changes in the quality of outdoor recreation and outdoor aesthetics; changes in health or life expectancy; and the costs of actions taken to avoid negative effects of environmental quality changes. 10Concerns about the reliability of value estimates from CV studies arose because research has shown that bias can be introduced easily into these studies if they are not carefully conducted. Accurately measuring WTP for avoided health and welfare losses depends on the reliability and validity of the data collected. There are several issues to consider when evaluating study quality, including but not limited to 1) whether the sample estimates of WTP are representative of the population WTP; 2) whether the good to be valued is comprehended and accepted by the respondent; 3) whether the WTP elicitation format is designed to minimize strategic responses; 4) whether WTP is sensitive to respondent familiarity with the good, to the size of the change in the good, and to income; 5) whether the estimates of WTP are broadly consistent with other estimates of WTP for similar goods; and 6) the extent to which WTP responses are consistent with established economic principles.

4 18 Nonuse values are those for which an individual is willing to pay for reasons that do not relate to the direct use or enjoyment of any environmental benefit but might relate to existence values and bequest values. Nonuse values are not traded, directly or indirectly, in markets.

For this reason, the measurement of nonuse values has proved to be significantly more difficult than the measurement of use values. The air quality changes produced by the IAQR cause changes in both use and nonuse values, but the monetary benefit estimates are almost exclusively for use values.

More frequently than not, the economic benefits from environmental quality changes are not traded in markets, so direct measurement techniques cannot be used. There are three main nonmarket valuation methods used to develop values for endpoints considered in this analysis: stated preference ( or contingent valuation [ CV]), indirect market ( e. g., hedonic wage), and avoided cost methods.

The stated preference or CV method values endpoints by using carefully structured surveys to ask a sample of people what amount of compensation is equivalent to a given change in environmental quality. There is an extensive scientific literature and body of practice on both the theory and technique of stated preference based valuation. The EPA believes that well designed and well executed stated preference studies are valid for estimating the benefits of air quality regulations. 10 Stated preference valuation studies form the basis for valuing a number of health and welfare endpoints, including the value of mortality risk reductions, CB risk reductions, minor illness risk reductions, and visibility improvements.

Indirect market methods can also be used to infer the benefits of pollution reduction. The most important application of this technique for our analysis is the calculation of the VSL for use in estimating benefits from mortality risk reductions. No market exists where changes in the probability of death are directly exchanged. However, people make decisions about occupation, precautionary behavior, and other activities associated with changes in the risk of 11Income elasticity is a common economic measure equal to the percentage change in WTP for a 1 percent change in income.

4 19 death. By examining these risk changes and the other characteristics of people's choices, it is possible to infer information about the monetary values associated with changes in mortality risk ( see Section <IP_ADDRESS>.1).

Avoided cost methods are ways to estimate the costs of pollution by using the expenditures made necessary by pollution damage. For example, if buildings must be cleaned or painted more frequently as levels of PM increase, then the appropriately calculated increment of these costs is a reasonable lower bound estimate ( under most conditions) of true economic benefits when PM levels are reduced. Avoided costs methods are also used to estimate some of the health related benefits related to morbidity, such as hospital admissions ( see Section 4.1.5).

4.1.2 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 incomes increase. There is substantial empirical evidence that the income elasticity11 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 unit elastic ( i. e., 10 percent higher real income level implies a 10 percent 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 rises but at a slower rate than real income.

The effects of real income changes on WTP estimates can influence benefit 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 Economics Advisory Committee ( EEAC) of the SAB advised the 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 distinctions, and because of insufficient evidence available at present" ( EPA SAB EEAC 00

013). 4 20 Based on a review of the available income elasticity literature, we adjust the valuation of human health 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 procedure can be 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 2010 and 2015 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. We also expect that the WTP for improved visibility in Class I areas would increase with growth in real income. The elasticity values used to adjust estimates of benefits in 2010 and 2015 are presented in Table 4 3.

In addition to elasticity estimates, projections of real gross domestic product ( GDP) and populations from 1990 to 2010 and 2015 are needed to adjust benefits to reflect real per capita income growth. For consistency with the emissions and benefits modeling, we use national population estimates for the years 1990 to 1999 based on U. S. Census Bureau estimates ( 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. Table 4 3. Elasticity Values Used to Account for Projected Real Income Growtha Benefit Category Central Elasticity Estimate Minor Health Effect 0.14 Severe and Chronic Health Effects 0.45 Premature mortality 0.40 Visibilityb 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.

b No range was applied for visibility because no ranges were available in the current published literature. 12U. S. Bureau of Census. Annual Projections of the Total Resident Population, Middle Series, 1999 2100. ( Available on the internet at http:// www. census. gov/ population/ www/ projections/ natsum T1. html)

13U. S. Bureau of Economic Analysis, Table 2A ( 1992$). ( Available on the internet 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.

14Standard and Poor's. 2000. " The U. S. Economy: The 25 Year Focus." Winter.

15In 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.

4 21 Bureau of Census, 2000). 12 For the years between 2000 and 2015, we applied growth rates based on the U. S. Census Bureau projections to the U. S. Census estimate of national population in 2000. We use projections of real GDP provided in Kleckner and Neumann ( 1999) for the years 1990 to 2010.13 We use projections of real GDP ( in chained 1996 dollars) provided by Standard and Poor's14 ( 2000) for the years 2010 to 2015.15 Using the method outlined in Kleckner and Neumann ( 1999) and the population and income data described above, we calculate WTP adjustment factors for each of the elasticity estimates listed in Table 4 4. Benefits for each of the categories ( minor health effects, severe and chronic health effects, premature mortality, and visibility) will be adjusted by multiplying the unadjusted benefits by the appropriate adjustment factor. Table 4 4 lists the estimated adjustment factors. Note that, for premature mortality, we apply the income adjustment factor ex post to the present discounted value of the stream of avoided mortalities occurring over the lag period. Also note that no adjustments will be made to benefits based on the COI approach or to work loss days and worker productivity. This assumption will also lead 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 increases 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). 4 22 4.1.3 Methods for Describing Uncertainty In any complex analysis using estimated parameters and inputs from numerous models,