2 22 Table 2 1.

Health and Welfare Effects of Pollutants Affected by the Proposed Utility MACT Standard Pollutant/ Effect Quantified and Monetized Potential Unquantified Effects PM/ Health Premature mortality  adults Premature mortality  infants 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 Asthma exacerbations Minor restricted activity days Work loss days 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 Changes in cardiac function ( e. g. heart rate variability)

Allergic responses ( to diesel exhaust)

PM/ Welfare Visibility in Class I areas Visibility in residential and non Class I areas Household soiling Ozone/ Health 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 Hospital admissions  respiratory Emergency room visits for asthma Minor restricted activity days School loss days Asthma attacks Cardiovascular emergency room visits Premature mortality   acute exposures Acute respiratory symptoms Pollutant/ Effect Quantified and Monetized Potential Unquantified Effects 2 23 Ozone/ Welfare Decreased commercial forest productivity 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 Decreased outdoor worker productivity Nitrogen and Sulfate Deposition/

Welfare Costs of nitrogen controls to reduce eutrophication in selected eastern estuaries 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 SO 2/ Health Hospital admissions for respiratory and cardiac diseases Respiratory symptoms in asthmatics NO x/ Health Lung irritation Lowered resistance to respiratory infection Hospital Admissions for respiratory and cardiac diseases Mercury Health Neurological disorders Learning disabilities Neonatal development delays Potential Cardiovascular effects*

Altered blood pressure regulation*

Increased heart rate variability*

Myocardial infarctions*

Potential Reproductive effects*

Mercury Deposition Welfare Deposition Impacts on birds and mammals ( e. g. reproductive effects)

Impacts to commercial, subsistence, and recreational fishing Reduced existence values for currently healthy ecosystems 2 24 * These are potential effects as the literature is either contradictory or incomplete. 2 25 2.1 Emission Changes Expected to Result from Implementation of the Proposed Standard The proposed standards have various cost and emission related components, as described in the " Economic and Energy Impact Analysis for the Utility MACT Proposed Rulemaking" memo available in the docket for this proposal. The controls and emission reductions are expected to be implemented by 2008. Our benefits analysis provide a snapshot of the expected human health impacts and dollar benefits in 2010. We chose 2010 due to the availability of air quality modeling for Clear Skies in 2010.

Table 2 2 summarizes the expected changes in emissions of SO 2 and NO x, based on the IPM modeling for 2010. Over 99 percent of emission reductions for SO 2 and NO x are predicted to occur in the Eastern U. S. As such, our omission of benefits occurring in the Western U. S. will not result in a large downward bias in our national benefits estimate.

Table 2 2. Summary of 2010 Reductions in Emissions of SO2 and NOx Predicted from Utility MACT IPM Modeling Tons Reduced (% of baseline)

NO x SO 2 Eastern U. S. 899,179 590,846 26.8% 6.3%

Western U. S. 2,742 592 0.5% 0.2%

Total 901,921 591,438 23.2% 6.1%

2.2 Development of Benefits Scaling Factors Based on Differences in Emission Impacts Between Proposed MACT and Clear Skies The Clear Skies benefits analysis was based on the pattern of reductions in emissions of SO 2 and NO x occurring as a result of a nationwide cap and trade program. Under the Clear Skies proposal, emissions of NO x were expected to be reduced by about 1.7 million tons, while SO 2 emissions were expected to be reduced by 3.5 million tons. The pattern of projected emission reductions for the proposed MACT standard is somewhat different than that for Clear Skies. The main difference is that the MACT standard are expected to see over 85 percent of the emissions 2 26 reductions for NO x and SO 2 in the Ohio Valley, the Southeast, and the Mid Atlantic ( 48 percent of NO x reductions and 75 percent of SO 2 reductions were in the Ohio Valley alone). Very little emissions reductions are predicted for the Midwest or Western states. In contrast, only 57 percent of NO x emission reductions and 74 percent of SO 2 emission reductions occurred in these regions based on Clear Skies, and even within these regions, were much more spread out. We have attempted to minimize these differences somewhat by focusing only on the results for the Eastern U. S., however, it is likely that our benefits estimates will have some remaining biases due to the differences in emission reductions patterns in the Eastern U. S. Because the reductions under the Utility MACT are more concentrated in areas that are upwind of major population centers, we expect that benefits per ton of emissions reduced will be somewhat higher on average for the Utility MACT than for Clear Skies. As such, we are likely to underestimate the benefits of the Utility MACT by transferring benefits from the Clear Skies analysis. However, we are not able to account for this quantitatively in our estimates. Table 2 3 summarizes the reductions in emissions of NO x and SO 2 from baseline for Clear Skies and the proposed standard, the difference between the two, and the ratio of emissions reductions from the proposed standard to Clear Skies. The ratios presented in the last column of Table 2 3 are the basis for the benefits scaling approach discussed below.

Table 2 3. Comparison of Modeled Emission Reductions in 2010 Between Clear Skies and the Proposed Utility MACT Standard ( Eastern U. S. Only)

Emissions Species Reduction from Baseline Difference in Reductions ( Proposed MACTClear Skies) Ratio of Reductions ( Proposed MACT/ Clear Skies) Clear Skies Proposed MACT NO x 1,764,882 901,918  862,964 0.511 SO 2 3,526,491 591,459  2,935,032 0.168 2.3 Summary of Modeled Benefits and Apportionment Method Based on the air quality modeling conducted for the Clear Skies analysis, we conducted a benefits analysis to determine human health benefits resulting from the reductions in emissions of NO x and SO 2. We used the air quality modeling results from the Clear Skies assessment. However, we have updated the health impact and valuation approaches to be consistent with those used in the upcoming proposed Interstate Air Quality rule analysis. The Clear Skies analysis is available on the internet at http:// www. epa. gov/ clearskies. The benefits analysis for the proposed Interstate Air Quality rule is documented in U. S. EPA, 2003b.

The reductions in emissions of NO x and SO 2 from fossil fuel fired utilities in the United States are expected to result in wide spread overall reductions in ambient concentrations of PM 2.5. 2 27 These improvements in air quality are expected to result in substantial health benefits, based on the body of epidemiological evidence linking PM with health effects such as premature mortality, cardiovascular disease, chronic lung disease, hospital admissions, and acute respiratory symptoms. Based on modeled changes in ambient concentrations of PM 2.5, we estimate changes in the incidence of each health effect using health impact functions derived from the epidemiological literature with appropriate baseline populations and incidence rates. We then apply estimates of the dollar value of each health effect to obtain a monetary estimate of the total PM related health benefits of the rule.

2.3.1 Overview of Analytical Approach This section summarizes our analysis of the modeled air quality changes from the Clear Skies assessment to determine the changes in human health and welfare, both in terms of physical effects and monetary value that result from modeled changes in PM 2.5. The methodology closely follows that used in the analyses of the proposed Nonroad Diesel rule and proposed Interstate Air Quality rule. Details of the analytical approach can be found in the Regulatory Impact Analyses for these rules ( U. S. EPA, 2003a, 2003b) and in the User's Manual for the environmental Benefits Mapping and Analysis Program ( BenMAP) ( Abt Associates, 2003).

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 non overlapping 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 endpoints. The " damage function" approach is the standard approach for most cost benefit analyses of regulations affecting environmental quality, and it has been used in several recent published analyses ( Banzhaf et al., 2002; Levy et al, 2001; Kunzli et al, 2000; Levy et al, 1999; Ostro and Chestnut, 1998). Time and resource constraints prevented us from performing extensive new research to measure either the health outcomes or their values for this analysis. Thus, similar to these studies, 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 available for the environmental quality change under analysis.

There are significant categories of PM related benefits that cannot be monetized ( or in many cases even quantified), and thus they are not included in our accounting of health and welfare benefits. These unquantified effects include low birth weight, changes in pulmonary function, chronic respiratory diseases other than chronic bronchitis, morphological changes, altered host defense mechanisms, non fatal cancers, and non asthma respiratory emergency room visits. A complete discussion of PM related health effects can be found in the PM Criteria Document ( U. S. EPA, 1996). Since many health effects overlap, such as minor restricted activity days and asthma symptoms, we made assumptions intended to reduce the chances of " double 2 28 counting" health benefits, which may result in an underestimate of the total health benefits of the pollution controls.

2.3.2 Health Impact Functions Health impact functions are derived from the epidemiology literature. A standard health impact function has four components: an effect estimate from a particular epidemiological study, a baseline incidence rate for the health effect ( obtained from either the epidemiology study or a source of public health statistics like the Centers for Disease Control), the affected population, and the estimated change in the relevant ozone summary measure.

A typical health impact function might look like:

    y y e x =       0 1 ( ),   where y 0 is the baseline incidence, equal to the baseline incidence rate times the potentially affected population,   is the effect estimate, and   x is the estimated change in the summary PM 2.5 measure. There are other functional forms, but the basic elements remain the same.

Integral to the estimation of the impact functions are reasonable estimates of future population projections. The underlying data used to create county level 2010 population projections is based on county level allocations of national population projections from the U. S. Census Bureau ( Hollman, Mulder and Kallan, 2000). County level allocations of populations by age, race, and sex are based on economic forecasting models developed by Woods and Poole, Inc ( WP), which account for patterns of economic growth and migration.

The WP projections of county level population are based on historical population data from 1969 1999, and do not include the 2000 Census results. Given the availability of detailed 2000 Census data, we constructed adjusted county level population projections for each future year using a two stage process. First, we constructed ratios of the projected WP populations in a future year to the projected WP population in 2000 for each future year by age, sex, and race. Second, we multiplied the block level 2000 Census population data by the appropriate age, sex, and race specific WP ratio for the county containing the census block, for each future year. This results in a set of future population projections that is consistent with the most recent detailed census data.

Specific populations matching the study populations in each epidemiological study are constructed by accessing the appropriate age specific projections from the overall population database. For some endpoints, such as asthma attacks, we further limit the population by applying disease prevalence rates to the overall population. We do not have sufficient information to quantitatively characterize uncertainty in the population estimates.

Fundamental to the estimation of health benefits was our utilization of the PM 2 29 epidemiology literature. We rely upon effect estimates derived from published epidemiological studies that relate health effects to ambient concentrations of PM. The specific studies from which effect estimates are drawn are listed in Table 2 4. While a broad range of serious health effects have been associated with exposure to elevated PM levels, we include only a subset of health effects in this benefit analysis due to limitations in available effect estimates and concerns about double counting of overlapping effects ( U. S. EPA, 1996). For the most part, we use the same set of effect estimates as we used in the analysis of the proposed Nonroad Diesel Engines rule. However, based on recent advice from the Science Advisory Board, we use an updated effect estimate for premature mortality and include two additional health effects, infant mortality and asthma exacerbations. Because of their significance in the analysis, we provide a more detailed discussion of premature mortality and chronic illness endpoints below. Complete details on the effect estimates used in the analysis can be found in the benefits analysis for the proposed Interstate Air Quality rule ( U. S. EPA, 2003) and the BenMAP User's Manual ( Abt Associates, 2003).

To generate health outcomes, projected changes in ambient PM concentrations were entered into BenMAP, a customized geographic information system based program. BenMAP aggregates populations to air quality model grids and calculates changes in air pollution metrics ( e. g., daily averages) for input into health impact functions. BenMAP uses grid cell level population data and changes in pollutant concentrations to estimate changes in health outcomes for each grid cell. Details on the BenMAP program can be found in the BenMAP User's Manual ( Abt Associates, 2003).

The baseline incidences for health outcomes used in our analyses are selected and adapted to match the specific populations studied. For example, we use age and county specific baseline total mortality rates in the estimation of PM related premature mortality. County level incidence rates are not available for other endpoints. We used national incidence rates whenever possible, because these data are most applicable to a national assessment of benefits. However, for some studies, the only available incidence information comes from the studies themselves; in these cases, incidence in the study population is assumed to represent typical incidence at the national level. Sources of baseline incidence rates are reported in Table 2 5.

In this assessment we made analytical judgements affecting both the selection of effect estimates and the application of those estimates in formulating health impact functions. In general, we selected effect estimates that 1) most closely match the pollutants of interest, i. e. PM 2.5) cover the broadest potentially exposed population ( i. e. all ages functions would be preferred to adults 27 to 35), 3) have appropriate model specification ( e. g. control for confounding pollutants), 4) have been peer reviewed, and 5) are biologically plausible. Other factors may also affect our selection of effect estimates for specific endpoints, such as premature mortality. Some of the more important of these relating to premature mortality and chronic illness are discussed below. Alternative assumptions about these judgements may lead to substantially different results.

While there is a consistent body of evidence supporting a relationship between a number 2 30 of adverse health effects and ambient PM levels, there is often only a single study of a specific endpoint covering a specific age group. There may be multiple estimates examining subgroups ( i. e. asthmatic children). However, for the purposes of assessing national population level benefits, we chose the most broadly applicable effect estimate to more completely capture health benefits in the general population.

Based on a review of the recent literature on health effects of PM exposure ( Daniels et al., 2000; Pope et al, 2002; Rossi et al., 1999; Schwartz, 2000), we chose for the purposes of this analysis to assume that PM related health effects occur down to natural background ( i. e. there is no health effects threshold). We assume that all of the health impact functions are continuous and differentiable down to natural background levels. Our assumptions regarding thresholds are supported as being plausible by the National Research Council in its recent review of methods for estimating the public health benefits of air pollution regulations. In their review, the National Research Council did not find evidence for departing from linearity in the observed range of exposure to PM 10 or PM 2.5, nor any indication of a threshold. They cite the weight of evidence available from both short and long term exposure models and the similar effects found in cities with low and high ambient concentrations of PM.

Premature Mortality As recommended by the SAB HES ( 2003), we focus on the prospective cohort long term exposure studies in deriving the health impact function for our base estimate of premature mortality. We selected an effect estimate from the extended analysis of the American Cancer Society ( ACS) cohort ( Pope et al., 2002). This effect estimate quantifies the relationship between annual mean PM 2.5 levels and all cause mortality in adults 30 and older. We selected the effect estimate based on the measure of PM representing average exposure over the follow up period, calculated as the average of 1979 1984 and 1999 2000 PM2.5 levels. EPA is investigating ways of characterizing the uncertainty in the concentration response function estimates.

In previous analyses, infant mortality has not been evaluated as part of the primary analysis. Instead, benefits estimates related to reduced infant mortality have been included as part of the sensitivity analyses. However recently published studies have strengthened the case for an association between PM exposure and respiratory inflamation and infection leading to premature mortality in infants under five years of age. Specifically, the SAB's Health Effects Subcommittee ( HES) noted the release of the World Health Organization Global Burden of Disease Study focusing on ambient air which cites several recently published time series studies relating daily PM exposure to mortality in children ( EPA SAB COUNCIL ADV 03 00x). The HES also cites the study by Belanger et al., ( 2003) as corroborating findings linking PM exposure to increased respiratory inflamation and infections in children. With regard to the cohort study conducted by Woodruff et al. ( 1997), the HES notes several strengths of the study including the use of a larger cohort drawn from a large number of metropolitan areas and efforts to control for a variety of individual risk factors in children ( e. g., maternal educational level, maternal ethnicity, parental marital status and maternal smoking status). We follow the HES recommendation to include infant mortality in the primary benefits estimate using the effect estimate from the Woodruff et al. 2 31 ( 1997) study.

Chronic Illness Although there are several studies examining the relationship between PM of different size fractions and incidence of chronic bronchitis, we use a study by Abbey et al ( 1995) to obtain our estimate of avoided incidences of chronic bronchitis in adults aged 25 and older, because Abbey et al ( 1995) is the only available estimate of the relationship between PM 2.5 and chronic bronchitis. Based on the Abbey et al study, we estimate the number of new chronic bronchitis cases that will " reverse" over time and subtract these reversals from the estimate of avoided chronic bronchitis incidences. Reversals refer to those cases of chronic bronchitis that were reported at the start of the Abbey et al. survey, but were subsequently not reported at the end of the survey. Since we assume that chronic bronchitis is a permanent condition, we subtract these reversals. Given the relatively high value assigned to chronic bronchitis, this ensures that we do not overstate the economic value of this health effect.

Non fatal heart attacks have been linked with short term exposures to PM 2.5 in the U. S. ( Peters et al, 2001) and other countries ( Poloniecki et al, 1997). We use a recent study by Peters et al. ( 2001) as the source for the effect estimate quantifying the relationship between PM 2.5 and non fatal heart attacks in adults. Peters et al is the only available U. S. study to provide a specific estimate for heart attacks. Other studies, such as Samet et al ( 2000) and Moolgavkar et al ( 2000) show a consistent relationship between all cardiovascular hospital admissions, including for nonfatal heart attacks, and PM. Given the lasting impact of a heart attack on longer term health costs and earnings, we choose to provide a separate estimate for non fatal heart attacks based on the single available U. S. effect estimate. The finding of a specific impact on heart attacks is consistent with hospital admission and other studies showing relationships between fine particles and cardiovascular effects both within and outside the U. S. These studies provide a weight of evidence for this type of effect. Several epidemiologic studies ( Liao et al, 1999; Gold et al, 2000; Magari et al, 2001) have shown that heart rate variability ( an indicator of how much the heart is able to speed up or slow down in response to momentary stresses) is negatively related to PM levels. Heart rate variability is a risk factor for heart attacks and other coronary heart diseases ( Carthenon et al, 2002; Dekker et al, 2000; Liao et al, 1997, Tsuji et al. 1996). As such, significant impacts of PM on heart rate variability is consistent with an increased risk of heart attacks.

2.3.3 Economic Values for Health Outcomes 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). 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 costs of illness ( COI) estimates generally understate the true value of 4 The SAB HES has recently recommended that EPA rethink the use of a 5 year lag. They recommend that a more complex lag structure be considered incorporation components dealing with short term ( 0 6 months), intermediate ( 1 2 years) and long term ( 15 25 years) exposures. EPA is evaluating techniques for characterizing lag structures and will incorporate new methods as they become available.

2 32 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 ( Harrington and Portney, 1987; Berger, 1987). Unit values for health endpoints are provided in Table 2 6. All values are in constant year 1999 dollars.

The size of the delay between changes in chronic PM exposures and changes in mortality rates is unknown. The size of such a time lag is important for the valuation of premature mortality incidences as economic theory suggests benefits occurring in the future should be discounted relative to benefits occurring today. Although there is no specific scientific evidence of the size of PM effects lag, current scientific literature on adverse health effects associated with smoking and the difference in the effect size between chronic exposure studies and daily mortality studies suggest that all incidences of premature mortality reduction associated with a given incremental change in PM exposure would not occur in the same year as the exposure reduction. This literature implies that lags of a few years or longer are plausible. For our analysis, we have assumed a five year distributed lag structure, with 25 percent of premature deaths occurring in the first year, another 25 percent in the second year, and 16.7 percent in each of the remaining three years4. To account for the preferences of individuals for current risk reductions relative to future risk reductions, we discount the value of avoided premature mortalities occurring beyond the analytical year ( 2010) using three and seven percent discount rates.

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. The economics literature suggests that the severity of a health effect is a primary determinant of the strength of the relationship between changes in real income and WTP ( Alberini, 1997; Miller, 2000; Evans and Viscusi, 1993). As such, we use different factors to adjust the WTP for minor health effects, severe and chronic health effects, and premature mortality. Adjustment factors used to account for projected growth in real income from 1990 to 2010 are 1.03 for minor health effects, 1.11 for severe and chronic health effects, and 1.10 for premature mortality.

2.3.4 Treatment of Uncertainty In any complex analysis, there are likely to be many sources of uncertainty. This analysis is no exception. Many inputs are used to derive the final estimate of economic benefits, including emission inventories, air quality models ( with their associated parameters and inputs), epidemiological effect estimates, estimates of values, population estimates, income estimates, and estimates of the future state of the world ( i. e., regulations, technology, and human behavior). Some of the key uncertainties in the benefits analysis are presented in Table 2 7. For some parameters or inputs it may be possible to provide a statistical representation of the underlying 2 33 uncertainty distribution. For other parameters or inputs, the necessary information is not available.

In addition to uncertainty, the annual benefit estimates presented in this analysis are also inherently variable due to the truly random processes that govern pollutant emissions and ambient air quality in a given year. Factors such as electricity demand and weather display constant variability regardless of our ability to accurately measure them. As such, the estimates of annual benefits should be viewed as representative of the magnitude of benefits expected, rather than the actual benefits that would occur every year.

A key source of uncertainty for this analysis is the scaling approach used to estimate the benefits associated with the emission reductions for the Utility MACT. As noted earlier, while we believe this to be a valid approach, we are unable to quantify any uncertainties related to the scaling approach. To the extent that the effectiveness in reducing ambient PM 2.5 of each ton of NO x and SO 2 reduced by the Utility MACT over or understates the effectiveness of the tons reduced by Clear Skies, the benefits of the Utility MACT will be over or underestimated.

2.3.5 Results of Revised Clear Skies Analysis In order to generate benefits estimates consistent with the analytical assumptions underlying the benefits estimates for the Interstate Air Quality Rule, we have revised the Clear Skies benefits analysis to use a consistent set of assumptions. Based on the application of the health impact functions to the modeled changes in ambient PM2.5, we estimated the change in incidence and economic value of health effects for the updated set of health endpoints listed in Table 2 4. The results of the estimation are provided in Table 2 8. Compared to the original Clear Skies benefits analysis, the updated estimates show an increase in avoided cases of premature mortality due to the change in the effect estimate and slight changes in other endpoints, due to minor changes in the set of air quality monitoring data used in defining the change in ambient PM 2.5. 2 34 Table 2 4. Endpoints and Studies Used to Calculate Total Monetized Health Benefits Endpoint Study Study Population Premature Mortality Premature Mortality  Adult, all cause Pope et al. ( 2002) > 29 years Premature Mortality  Infant Woodruff et al. ( 1997) < 1 Chronic Illness Chronic Bronchitis Abbey, et al. ( 1995) > 26 years Non fatal Heart Attacks Peters et al. ( 2001) Adults Hospital Admissions Respiratory Pooled estimate: Moolgavkar ( 2003)  ICD 490 496 ( COPD) Ito ( 2003)  ICD 490 496 ( COPD) > 64 years Moolgavkar ( 2000)  ICD 490 496 ( COPD) 20 64 years Ito ( 2003)  ICD 480 486 ( pneumonia) > 64 years Sheppard, et al. ( 2003)  ICD 493 ( asthma) < 65 years Cardiovascular Pooled estimate: Moolgavkar ( 2003)  ICD 390 429 ( all cardiovascular) Ito ( 2003)  ICD 410 414, 427 428 ( ischemic heart disease, dysrhythmia, heart failure) > 64 years Moolgavkar ( 2000)  ICD 390 429 ( all cardiovascular) 20 64 years Asthma Related ER Visits Norris et al. ( 1999) 0 18 years Other Health Endpoints Acute Bronchitis Dockery et al. ( 1996) 8 12 years Upper Respiratory Symptoms Pope et al. ( 1991) Asthmatics, 9 11 years Lower Respiratory Symptoms Schwartz and Neas ( 2000) 7 14 years Asthma Exacerbations Pooled estimate: Ostro et al. ( 2001) Cough Ostro et al. ( 2001) Wheeze Ostro et al. ( 2201) Shortness of breath Vedal et al. ( 1998) Cough 6 18 yearsA Work Loss Days Ostro ( 1987) 18 65 years Minor Restricted Activity Days Ostro and Rothschild ( 1989) 18 65 years B The original study populations were 8 13 for the Ostro et al ( 2001) study and 6 13 for the Vedal et al. ( 1998) study. Based on advice from the SAB HES, we have extended the applied population to 6 18, reflecting the 2 35 common biological basis for the effect in children in the broader age group. 2 36 Table 2 5 Baseline Incidence Rates and Population Prevalence Rates for Use in Impact Functions Endpoint Parameter Rates Value Source1 Mortality Daily or annual mortality rate Age, cause, and county specific rate CDC Wonder ( 1996 1998)

Hospitalizations Daily hospitalization rate Age, region, cause specific rate 1999 NHDS public use data files2 Asthma ER visits Daily asthma ER visit rate Age, Region specific visit rate 2000 NHAMCS public use data files3; 1999 NHDS public use data files2 Chronic Bronchitis Annual prevalence rate per person Age 18 44 Age 45 64 Age 65 and older 0.0367 0.0505 0.0587 1999 HIS ( American Lung Association, 2002b, Table 4)

Annual incidence rate per person 0.00378 Abbey et al. ( 1993, Table 3)

Nonfatal MI ( heart attacks) Daily nonfatal myocardial infarction incidence rate per person, 18+ Northeast Midwest South West 0.0000159 0.0000135 0.0000111 0.0000100 1999 NHDS public use data files2; adjusted by 0.93 for prob. of surviving after 28 days ( Rosamond et al., 1999)

Asthma Exacerbations Incidence ( and prevalence) among asthmatic African American children  daily wheeze  daily cough  daily dyspnea 0.076 ( 0.173) 0.067 ( 0.145) 0.037 ( 0.074) Ostro et al. ( 2001)

Prevalence among asthmatic children  daily wheeze  daily cough  daily dyspnea 0.038 0.086 0.045 Vedal et al. ( 1998)

Acute Bronchitis Annual bronchitis incidence rate, children 0.043 American Lung Association ( 2002a, Table 11)

Lower Respiratory Symptoms Daily lower respiratory symptom incidence among children4 0.0012 Schwartz ( 1994, Table 2)

Upper Respiratory Symptoms Daily upper respiratory symptom incidence among asthmatic children 0.3419 Pope et al. ( 1991, Table 2)

Work Loss Days Daily WLD incidence rate per person ( 18 65) Age 18 24 Age 25 44 Age 45 64 0.00540 0.00678 0.00492 1996 HIS ( Adams et al., 1999, Table 41); U. S. Bureau of the Census ( 2000) Endpoint Parameter Rates Value Source1 2 37 Minor Restricted Activity Days Daily MRAD incidence rate per person 0.02137 Ostro and Rothschild ( 1989, p. 243)

1. The following abbreviations are used to describe the national surveys conducted by the National Center for Health Statistics: HIS refers to the National Health Interview Survey; NHDS  National Hospital Discharge Survey; NHAMCS  National Hospital Ambulatory Medical Care Survey. 2. See ftp:// ftp. cdc. gov/ pub/ Health_ Statistics/ NCHS/ Datasets/ NHDS/ 3. See ftp:// ftp. cdc. gov/ pub/ Health_ Statistics/ NCHS/ Datasets/ NHAMCS/ 4. Lower Respiratory Symptoms are defined as

2 of the following: cough, chest pain, phlegm, wheeze 2 38 Table 2 6 Unit Values Used for Economic Valuation of Health Endpoints ( 2000$)

Health Endpoint Central Estimate of Value Per Statistical Incidence Derivation of Estimates 1990 Income Level 2010 Income Level Premature Mortality $ 5,500,000 $ 6,100,000 Point estimate is the mean of a normal distribution with a 95% confidence interval between $ 1 and $ 10 million. Confidence interval is based on two meta analyses of the wage risk VSL literature. $ 1 million represents the lower end of the interquartile range from the Mrozek and Taylor ( 2000)

metaanalysis $ 10 million represents the upper end of the interquartile range from the Viscusi and Aldy ( 2003) meta analysis. The VSL represents the value of a small change in mortality risk aggregated over the affected population.

Chronic Bronchitis ( CB) $ 340,000 $ 370,000 Base value is the mean of a generated distribution of WTP to avoid a case of pollution related CB. WTP to avoid a case of pollution related CB is derived by adjusting WTP ( as described in Viscusi et al., 1991) to avoid a severe case of CB for the difference in severity and taking into account the elasticity of WTP with respect to severity of CB. Health Endpoint Central Estimate of Value Per Statistical Incidence Derivation of Estimates 1990 Income Level 2010 Income Level 2 39 Non fatal Myocardial Infarction ( heart attack)

3% discount rate Age 0 24 Age 25 44 Age 45 54 Age 55 65 Age 66 and over 7% discount rate Age 0 24 Age 25 44 Age 45 54 Age 55 65 Age 66 and over $ 66,902 $ 74,676 $ 78,834 $ 140,649 $ 66,902 $ 65,293 $ 73,149 $ 76,871 $ 132,214 $ 65,293 $ 66,902 $ 74,676 $ 78,834 $ 140,649 $ 66,902 $ 65,293 $ 73,149 $ 76,871 $ 132,214 $ 65,293 Age specific cost of illness values reflecting lost earnings and direct medical costs over a 5 year period following a non fatal MI. Lost earnings estimates based on Cropper and Krupnick ( 1990). Direct medical costs based on simple average of estimates from Russell et al. ( 1998) and Wittels et al. ( 1990).

Lost earnings:

Cropper and Krupnick ( 1990). Present discounted value of 5 yrs of lost earnings:

age of onset: at 3% at 7%

25 44 $ 8,774 $ 7,855 45 54 $ 12,932 $ 11,578 55 65 $ 74,746 $ 66,920 Direct medical expenses: An average of:

1. Wittels et al., 1990 ($ 102,658   no discounting)

2. Russell et al., 1998, 5 yr period. ($ 22,331 at 3% discount rate; $ 21,113 at 7% discount rate)

Hospital Admissions Chronic Obstructive Pulmonary Disease ( COPD)

( ICD codes 490 492, 494 496) $ 12,378 $ 12,378 The COI estimates ( lost earnings plus direct medical costs) are based on ICD 9 code level information ( e. g., average hospital care costs, average length of hospital stay, and weighted share of total COPD category illnesses) reported in Agency for Healthcare Research and Quality, 2000 ( www. ahrq. gov).

Pneumonia ( ICD codes 480 487) $ 14,693 $ 14,693 The COI estimates ( lost earnings plus direct medical costs) are based on ICD 9 code level information ( e. g., average hospital care costs, average length of hospital stay, and weighted share of total pneumonia category illnesses)

reported in Agency for Healthcare Research and Quality, 2000 ( www. ahrq. gov). Health Endpoint Central Estimate of Value Per Statistical Incidence Derivation of Estimates 1990 Income Level 2010 Income Level 2 40 Asthma admissions $ 6,634 $ 6,634 The COI estimates ( lost earnings plus direct medical costs) are based on ICD 9 code level information ( e. g., average hospital care costs, average length of hospital stay, and weighted share of total asthma category illnesses) reported in Agency for Healthcare Research and Quality, 2000 ( www. ahrq. gov).

All Cardiovascular ( ICD codes 390 429) $ 18,387 $ 18,387 The COI estimates ( lost earnings plus direct medical costs) are based on ICD 9 code level information ( e. g., average hospital care costs, average length of hospital stay, and weighted share of total cardiovascular category illnesses)

reported in Agency for Healthcare Research and Quality, 2000 ( www. ahrq. gov).

Emergency room visits for asthma $ 286 $ 286 Simple average of two unit COI values:

( 1) $ 311.55, from Smith et al., 1997, and ( 2) $ 260.67, from Stanford et al., 1999.

Respiratory Ailments Not Requiring Hospitalization Asthma Exacerbations $ 42 $ 43 Asthma exacerbations are valued at $ 42 per incidence, based on the mean of average WTP estimates for the four severity definitions of a " bad asthma day,"

described in Rowe and Chestnut ( 1986). This study surveyed asthmatics to estimate WTP for avoidance of a " bad asthma day," as defined by the subjects.

For purposes of valuation, an asthma attack is assumed to be equivalent to a day in which asthma is moderate or worse as reported in the Rowe and Chestnut ( 1986) study.

Upper Respiratory Symptoms ( URS) $ 25 $ 26 Combinations of the 3 symptoms for which WTP estimates are available that closely match those listed by Pope, et al. result in 7 different " symptom clusters," each describing a " type" of URS. A dollar value was derived for each type of URS, using mid range estimates of WTP ( IEc, 1994) to avoid each symptom in the cluster and assuming additivity of WTPs. The dollar value for URS is the average of the dollar values for the 7 different types of URS. Health Endpoint Central Estimate of Value Per Statistical Incidence Derivation of Estimates 1990 Income Level 2010 Income Level 2 41 Lower Respiratory Symptoms ( LRS) $ 16 $ 17 Combinations of the 4 symptoms for which WTP estimates are available that closely match those listed by Schwartz, et al. result in 11 different " symptom clusters," each describing a " type" of LRS. A dollar value was derived for each type of LRS, using mid range estimates of WTP ( IEc, 1994) to avoid each symptom in the cluster and assuming additivity of WTPs. The dollar value for LRS is the average of the dollar values for the 11 different types of LRS.

Acute Bronchitis $ 360 $ 370 Assumes a 6 day episode, with daily value equal to the average of low and high values for related respiratory symptoms recommended in Neumann, et al.

1994.

Restricted Activity and Work Loss Days Work Loss Days ( WLDs) Variable ( national median = $ 115 ) Variable ( national median =

$ 115) County specific median annual wages divided by 50 ( assuming 2 weeks of vacation) and then by 5   to get median daily wage. U. S. Year 2000 Census,

compiled by Geolytics, Inc.

Minor Restricted Activity Days ( MRADs) $ 51 $ 53 Median WTP estimate to avoid one MRAD from Tolley, et al. ( 1986) . Table 2 7 Primary Sources of Uncertainty in the Benefit Analysis 1. Uncertainties Associated With Health Impact Functions S The value of the PM effect estimate in each impact function.

S Application of a single effect estimate to pollutant changes and populations in all locations.

S Similarity of future year effect estimates to current effect estimates.

S Correct functional form of each impact function.

S Application of effect estimates to changes in PM outside the range of PM concentrations observed in the study.

S Application of effect estimates only to those subpopulations matching the original study population.

2. Uncertainties Associated With PM Concentrations S Responsiveness of the models to changes in precursor emissions.

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

S Model chemistry for the formation of ambient nitrate concentrations.

S Use of separate air quality models for ozone and PM does not allow for a fully integrated analysis of pollutants and their interactions.

S Comparison of model predictions of particulate nitrate with observed rural monitored nitrate levels indicates that REMSAD overpredicts nitrate in some parts of the Eastern US and underpredicts nitrate in parts of the Western US.

3. Uncertainties Associated with PM Mortality Risk S Limited scientific literature supporting a direct biological mechanism for observed epidemiological evidence.

S Direct causal agents within the complex mixture of PM have not been identified.

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

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

S Reliability of the limited ambient PM 2.5 monitoring data in reflecting actual PM 2.5 exposures.

4. Uncertainties Associated With Possible Lagged Effects S The portion of the PM related long term exposure mortality effects associated with changes in annual PM levels would occur in a single year is uncertain as well as the portion that might occur in subsequent years.

5. Uncertainties Associated With Baseline Incidence Rates S Some baseline incidence rates are not location specific ( e. g., those taken from studies) and may therefore not accurately represent the actual location specific rates.

S Current baseline incidence rates may not approximate well baseline incidence rates in 2010.

S Projected population and demographics may not represent well future year population and demographics.

6. Uncertainties Associated With Economic Valuation S Unit dollar values associated with health endpoints are only estimates of mean WTP and therefore have uncertainty surrounding them.

S Mean WTP ( in constant dollars) for each type of risk reduction may differ from current estimates due to differences in income or other factors.

7. Uncertainties Associated With Aggregation of Monetized Benefits S Health benefits estimates are limited to the available effect estimates. Thus, unquantified or unmonetized benefits are not included. 2 43 Table 2 8. Results of Revised Clear Skies Benefits Analysis Endpoint Cases AvoidedA Economic Value ( Millions of 2000$)

Premature mortality

Long term exposure ( adults, 30 and over) B 9,800 $ 60,000 Long term exposure ( infant, < 1 yr) 23 $ 140 Chronic bronchitis ( adults, 26 and over) 5,300 $ 2,000 Non fatal myocardial infarctions ( adults, 18 and older) C 13,000 $ 1,100 Hospital admissions   Respiratory ( all ages) D 4,300 $ 76 Hospital admissions   Cardiovascular ( adults, 20 and older) E 3,700 $ 82 Emergency Room Visits for Asthma ( 18 and younger) 7,400 $ 2.1 Acute bronchitis ( children, 8 12) 12,000 $ 4.5 Lower respiratory symptoms ( children, 7 14) 150,000 $ 2.4 Upper respiratory symptoms ( asthmatic children, 9 11) 110,000 $ 3.0 Asthma exacerbations 190,000 $ 8.5 Work loss days ( adults, 18 65) 1,000,000 $ 130 Minor restricted activity days ( adults, age 18 65) 6,200,000 $ 320 Total Economic Value of Health BenefitsF $ 64,000 A Incidences and values are rounded to two significant digits. B Economic value calculated using a 3 percent discount rate. Economic value using a 7 percent discount rate is $ 57,000 million. C Economic value calculated using a 3 percent discount rate. Economic value using a 7 percent discount rate is also $ 1,100 million ( difference is within the margin of rounding). D Respiratory hospital admissions for PM includes admissions for COPD, pneumonia, and asthma. E Cardiovascular hospital admissions for PM includes total cardiovascular and subcategories for ischemic heart disease, dysrhythmias, and heart failure. F Total economic value calculated using 3 percent discount rate results. Total economic value using a 7 percent discount rate is $ 61,000 million.

2.3.6 Apportionment of Benefits to NOx and SO2 Emissions Reductions In order to develop benefits estimates for the set of emission reductions expected to result from the proposed MACT standard, it is necessary for us to scale the Clear Skies based benefits to reflect the difference in emissions reductions between the proposed MACT standards and the Clear Skies analysis. In order to do so, however, we must first apportion total benefits to the NO x 2 44 and SO 2 reductions for the modeled Clear Skies scenario. This apportionment is necessary due to the differential contribution of each emission species to the total change in ambient PM and total benefits.

PM is a complex mixture of particles of varying species, including nitrates, sulfates, and primary particles, including organic and elemental carbon. These particles are formed in complex chemical reactions from emissions of precursor pollutants, including NO x, SO 2, ammonia, hydrocarbons, and directly emitted particles. Different emissions species contribute to the formation of PM in different amounts, so that a ton of emissions of NO x contributes to total ambient PM mass differently than a ton of SO 2. As such, it is inappropriate to scale benefits by simply scaling the sum of all precursor emissions. A more appropriate scaling method is to first apportion total PM benefits to the changes in underlying emission species and then scale the apportioned benefits.

PM formation relative to any particular reduction in an emission species is a highly nonlinear process, depending on meteorological conditions and baseline conditions, including the amount of available ammonia to form ammonium nitrate and ammonium sulfate. Given the limited air quality modeling conducted for this analysis, we make several simplifying assumptions about the contributions of emissions reductions for specific species to changes in particle species. For this exercise, we assume that changes in sulfate particles are attributable to changes in SO 2 emissions, and changes in nitrate and secondary organic particles are attributable to changes in NO x emissions. These assumptions essentially assume independence between SO 2 and NO x in the formation of ambient PM. This is a potentially significant source of uncertainty, as SO 2 and NO x emissions interact with other compounds in the atmosphere to form PM 2.5. For example, ammonia reacts with SO 2 first to form ammonium sulfate. If there is remaining ammonia, it reacts with NO x to form ammonium nitrate. When SO 2 alone is reduced, ammonia is freed to react with any NO x that has not been used in forming ammonium nitrate. If NO x is also reduced, then there will be less available NO x to form ammonium nitrate from the newly available ammonia. Thus, reducing SO 2 can potentially lead to decreased ammonium sulfate and increased nitrate, so that overall ambient PM benefits are less than the reduction in sulfate particles. If NO x alone is reduced, there will be a direct reduction in ammonium nitrate, although the amount of reduction depends on whether an area is ammonia limited. If there is not enough ammonia in an area to use up all of the available NO x, then NO x reductions will only have an impact if they reduce emissions to the point where ammonium nitrate formation will be affected. NO x reductions will not result in any offsetting increases in ambient PM under most conditions. The implications of this for apportioning benefits between NO x and SO 2, is that some of the sulfate related benefits will be offset by reductions in nitrate benefits, so benefits from SO 2 reductions will be overstated, while NO x benefits will be understated. It is not immediately apparent the size of this bias.

The measure of change in ambient particle mass that is most related to health benefits is the population weighted change in PM 2.5 µ g/ m3, because health benefits are driven both by the size of the change in PM 2.5 and the populations exposed to that change. We calculate the proportional share of total change in mass accounted for by sulfate particles and the sum of nitrate and secondary organic particles. Results of these calculations for the 2010 Clear Skies REMSAD 2 45 modeling analysis are presented in Table 2 9. The sulfate percentage of total change is used to represent the SO 2 contribution to health benefits and the nitrate plus secondary organics percentage is used to represent the NO x contribution to health benefits. These percentages are then applied to the PM related health benefits estimates from the analysis of the Clear Skies PM 2.5 air quality modeling and combined with the emission scaling factors developed in section 2.2 to estimate benefits for the proposed Utility MACT standard.

Table 2 9. Apportionment of Population Weighted Change in Ambient PM2.5 to Nitrate, Sulfate, and Secondary Organic Particles Population weighted Change ( µ g/ m3) Percent of Total Change Total PM2.5 0.610 Sulfate 0.520 85.2%

Nitrate 0.081 13.4%

Secondary Organic 0.008 1.4%

2.4 Estimated Benefits of Proposed MACT Standard in 2010 To estimate the benefits of the NO x and SO 2 emission reductions from the proposed standard in 2010, we apply the emissions scaling factors derived in section 2.2 and the apportionment factors described in section 2.3.6 to the benefits estimates for 2010 estimated using the Clear Skies PM 2.5 air quality modeling. The scaled avoided incidence estimate for any particular health endpoint is calculated using the following equation:

, Scaled Incidence Modeled Incidence R A i i i =

*

where Modeled Incidence is the estimated change in incidence of the health effect from the updated Clear Skies analysis from Table 2 8, R i is the emissions ratio for emission species i from Table 2 3, and A i is the health benefits apportionment factor for emission species i, from Table 2 9. Essentially, benefits are scaled using a weighted average of the species specific emissions ratios. For example, the calculation of the avoided incidence of premature mortality in 2010 is:

Scaled Premature Mortality Incidence = 9,800 * ( 0.852* 0.168 + 0.147* 0.574) = 2,200 The economic value for each endpoint is obtained by scaling the estimated Clear Skies economic value from Table 2 8 using the same function. The estimated changes in incidence and economic value of PM related health effects in 2010 for the proposed Utility MACT standard based on application of the weighted scaling factors are presented in Table 2 10. 2 46 The benefits estimates generated for the proposed rule are subject to a number of assumptions and uncertainties, which are discussed throughout the document. As the table indicates, total benefits are driven primarily by the reduction in premature fatalities each year, which account for over 90 percent of total benefits. 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. 2 47 Table 2 10. Estimated PM related Health Benefits of the Proposed Utility MACT Standards in 2010 Endpoint Cases AvoidedA Economic Value ( Millions of 1999$)

Premature mortality

Long term exposure ( adults, 30 and over) B 2,200 $ 14,000 Long term exposure ( infant, < 1 yr) 5 $ 32 Chronic bronchitis ( adults, 26 and over) 1,200 $ 460 Non fatal myocardial infarctions ( adults, 18 and older) C 2,900 $ 250 Hospital admissions   Respiratory ( all ages) D 980 $ 17 Hospital admissions   Cardiovascular ( adults, 20 and older) E 850 $ 19 Emergency Room Visits for Asthma ( 18 and younger) 1,700 $ 0.48 Acute bronchitis ( children, 8 12) 2,800 $ 1.0 Lower respiratory symptoms ( children, 7 14) 33,000 $ 0.54 Upper respiratory symptoms ( asthmatic children, 9 11) 25,000 $ 0.69 Asthma exacerbations 43,000 $ 1.9 Work loss days ( adults, 18 65) 240,000 $ 31 Minor restricted activity days ( adults, age 18 65) 1,400,000 $ 74 Total Economic Value of Health BenefitsF $ 15,000 A Incidences and values are rounded to two significant digits. B Economic value calculated using a 3 percent discount rate. Economic value using a 7 percent discount rate is $ 13,000 million. C Economic value calculated using a 3 percent discount rate. Economic value using a 7 percent discount rate is $ 250 million. D Respiratory hospital admissions for PM includes admissions for COPD, pneumonia, and asthma. E Cardiovascular hospital admissions for PM includes total cardiovascular and subcategories for ischemic heart disease, dysrhythmias, and heart failure. F Total economic value calculated using 3 percent discount rate results. Total economic value using a 7 percent discount rate is $ 14,000 million.

2.5 Welfare Effects There are a number of environmental resources which may be adversely affected by emissions of NO x and SO 2 or ambient PM 2.5 Changes in these environmental resources may affect human welfare, but due to a lack of appropriate physical effects or valuation methods, we are unable to quantify or monetize these effects for our analysis of the proposed MACT standard. Qualitative discussions of these benefits are provided in Section 1. A brief discussion of some of 2 48 the benefits which are known to have significant economic value is provided below.

Changes in the level of ambient particulate matter caused by the reduction in emissions from fossil fuel fired utility sources will change the level of visibility in much of the U. S. Visibility directly affects people's enjoyment of a variety of daily activities. Individuals value visibility both in the places they live and work, in the places they travel to for recreational purposes, and at sites of unique public value, such as the Grand Canyon.

The effects of air pollution on the health and stability of ecosystems are potentially very important, but are at present poorly understood and difficult to measure. The reductions in NO x caused by the proposed rule could produce significant benefits. Excess nutrient loads, especially of nitrogen, cause a variety of adverse consequences to the health of estuarine and coastal waters. These effects include toxic and/ or noxious algal blooms such as brown and red tides, low ( hypoxic) or zero ( anoxic) concentrations of dissolved oxygen in bottom waters, the loss of submerged aquatic vegetation due to the light filtering effect of thick algal mats, and fundamental shifts in phytoplankton community structure ( Bricker et al., 1999). Direct impact functions relating changes in nitrogen loadings to changes in estuarine benefits are not available. The preferred WTP based measure of benefits depends on the availability of these impact functions and on estimates of the value of environmental responses. Because neither appropriate impact functions nor sufficient information to estimate the marginal value of changes in water quality exist at present, calculation of a WTP measure is not possible.

Reductions in NO x emissions will also reduce nitrogen deposition on agricultural land and forests. There is some evidence that nitrogen deposition may have positive effects on agricultural output through passive fertilization. Holding all other factors constant, farmers' use of purchased fertilizers or manure may increase as deposited nitrogen is reduced. Estimates of the potential value of this possible increase in the use of purchased fertilizers are not available, but it is likely that the overall value is very small relative to other health and welfare effects.

The proposed Utility MACT standard are also expected to produce economic benefits in the form of reduced materials damage. There are two important categories of these benefits. Household soiling refers to the accumulation of dirt, dust, and ash on exposed surfaces. Criteria pollutants also have corrosive effects on commercial/ industrial buildings and structures of cultural and historical significance. The effects on historic buildings and outdoor works of art are of particular concern because of the uniqueness and irreplaceability of many of these objects.

Previous EPA benefit analyses have been able to provide quantitative estimates of household soiling damage. Consistent with SAB advice, we determined that the existing data ( based on consumer expenditures from the early 1970' s) are too out of date to provide a reliable enough estimate of current household soiling damages ( EPA SAB Council ADV 003, 1998) to include in our primary estimate. We are also unable to estimate any benefits to commercial and industrial entities from reduced materials damage. Nor is EPA able to estimate the benefits of reductions in PM related damage to historic buildings and outdoor works of art. Existing studies of damage to this latter category in Sweden ( Grosclaude and Soguel, 1994) indicate that these 2 49 benefits could be an order of magnitude larger than household soiling benefits.

If better models of ecological effects can be defined, EPA believes that progress can be made in estimating WTP measures for ecosystem functions. For example, if nitrogen or sulfate loadings can be linked to measurable and definable changes in fish populations or definable indexes of biodiversity, then CV studies can be designed to elicit individuals' WTP for changes in these effects. This is an important area for further research and analysis, and will require close collaboration among air quality modelers, natural scientists, and economists.

2.6 Comparison of Costs and Benefits Table 2 11 summarizes the results of the benefit cost analysis of the proposed section 112 MACT scenario and compares them with estimates of the range of potential costs and benefits associated with an alternative scenario that addresses combined implementation of section 111 Hg requirements in coordination with proposed SO2 and NOx requirements in the proposed IAQR. The potential influence of such a combined scenario is illustrated in the second column of Table 2 11, which assumes the proposed section 111 requirements are implemented in combination with the IAQR. The IAQR analysis projects that the Hg reductions associated with implementing the SO2/ NOx requirements in the Eastern U. S. in 2010 would be approximately 10.6 tons per year, which is almost identical to those estimated from the proposed section 112 MACT only scenario.

If the goal for the proposed section 111 program in 2010 is limited to these co control reductions, there might be no additional costs or benefits to the program, over those achieved by the IAQR   this is indicated in the lower portion of the ranges in Table 2 11. By contrast, if the proposed section 111 regulation adopts a 2010 goal similar to the Phase I Clear Skies Hg cap, additional Hg reductions would be required over those forecast for the IAQR. Based on a multipollutant analyses conducted for Clear Skies ( p D 9, Technical appendix D, at www. epa. gov/ airmarkets/ epa ipm), power generators would likely opt for some additional SO2 and NOx controls beyond those needed for the IAQR, as well as considering additional direct Hg controls. Although the actual results are uncertain, the Clear Skies results suggest that the costs and benefits associated with a section 112 MACT only approach may reflect a reasonable lower bound for the additional costs and benefits. These potential additional costs and benefits related to additional Hg controls are reflected in the upper end of the ranges in Table 2 11. In the decade beyond 2010, the proposed section 111 program would establish a 15 ton cap for Hg in 2018, similar to Clear Skies. Based on Clear Skies analyses, this would result in further Hg controls, which would likely include at least some additional SO2/ NOx controls as well as direct Hg controls. The IAQR program alone produces only small additional reductions in Hg emissions in 2020. The Hg reductions estimated for the proposed section 112 MACT and the proposed section 111 and proposed IAQR programs are summarized in Table 2 12. These forecasts are based on IPM analyses of the proposed section 112 MACT scenario outlined above, the proposed IAQR analysis, and estimates derived from earlier analyses of the Clear Skies program.

Every benefit cost analysis examining the potential effects of a change in environmental 2 50 protection requirements is limited, to some extent, by data gaps, limitations in model capabilities ( 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 changes in health and environmental effects. 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. While these general uncertainties in the underlying scientific and economics literatures are discussed in detail in the RIA and its supporting documents and references, the key uncertainties which have a bearing on the results of the benefitcost analysis of today's action are the following:

1. The exclusion of potentially significant benefit categories ( e. g., health and ecological benefits of reduction in hazardous air pollutants emissions);

2. Errors in measurement and projection for variables such as population growth;

3. Uncertainties in the estimation of future year emissions inventories and air quality;

4. Uncertainties associated with the extrapolation of air quality monitoring data to some unmonitored areas required to better capture the effects of the standards on the affected population;

5. Variability in the estimated relationships of health and welfare effects to changes in pollutant concentrations; and 6. Uncertainties associated with the benefit transfer approach.

Despite these uncertainties, we believe the benefit cost analysis provides a reasonable indication of the expected economic benefits of the proposed actions under a given set of assumptions.

Based on estimated compliance costs ( control + administrative costs associated with Paperwork Reduction Act requirements associated with the proposed rule and predicted changes in the price and output of electricity), the estimated social costs of the proposed section 112 MACT only scenario are $ 1.6 billion ( 1999$). Social costs are different from compliance costs in that social costs take into account the interactions between affected producers and the consumers of affected products in response to the imposition of the compliance costs. In this action, coalfired utilities are the affected producers and users of electricity are the consumers of the affected product.

As explained above, we estimate $ 15 billion in benefits from the proposed section 112 MACT, compared to less than $ 2 billion in costs. It is important to put the results of this analysis in the proper context. The large benefit estimate is not attributable to reducing human and environmental exposure to Hg. It arises from ancillary reductions in SO2 and NOx that result from controls aimed at complying with the proposed MACT. Although consideration of ancillary 2 51 benefits is reasonable, we note that these benefits are not uniquely attributable to Hg regulation. Under the IAQR, coal fired units would achieve much larger reductions in SO2 and NOx emissions than they would under the proposed section 112 MACT. In the years ahead, as the Agency and the States develop rules, guidance and policies to implement the new air quality standards for ozone and PM, coal fired power plants will be required to implement additional controls to reduce SO2 and NOx ( e. g., scrubbers, SCR units, year round NOx controls in place of summertime only controls, conversion to low sulfur coals, and so forth). Thus, most or all of the ancillary benefits of Hg control would be achieved anyway, regardless of whether a section 112 MACT is promulgated. Based on analysis of the Clear Skies legislation, EPA believes that the proposed 2018 Hg cap in the proposed section 111 rule would result in additional SO2 and NOx reductions beyond those that would be required under the proposed IAQR. Thus, the section 111 approach, unlike the section 112 approach, may achieve SO2 and NOx reduction benefits beyond those that would be achieved under the IAQR. We believe, however, that even if no Hg controls were imposed, most major coal fired units would still have to reduce their SO2 and NOx emissions as part of the efforts to bring the nation into attainment with the new air quality standards. In light of these considerations, the Agency believes that the key rationale for controlling Hg is to reduce public and environmental exposure to Hg, thereby reducing risk to public health and wildlife. Although the available science does not support quantification of these benefits at this time, the Agency believes the qualitative benefits are large enough to justify substantial investment in Hg emission reductions.

It should be recognized, however, that this analysis does not account for many of the potential benefits that may result from these actions. The net benefits would be greater if all the benefits of the Hg, Ni, and other pollutant reductions could be quantified. Notable omissions to the net benefits include all benefits of HAP reductions, including reduced cancer incidences, toxic morbidity effects, and cardiovascular and CNS effects, and all health and welfare effects from reduction of ambient NO x and SO 2.

Table 2 11. Summary of Monetized Benefits, Costs, and Net Benefits under the Proposed Section 112 MACT Standard, and the Proposed Section 111 Rule and the Proposed IAQRA ($ billions/ yr)

MACT only Scenario Sec. 111 plus IAQR CombinedD Social CostsB $ 1.6 $ 2.9 to 4.5+

Social BenefitsC: PM related Health benefits $ 15+ B $ 58 to 73+ B Net Benefits ( Benefits Costs) C $ 13+ B $ 55 to $ 68+ B A All costs and benefits are rounded to two significant digits.

B Note that costs are the total costs of reducing all pollutants, including Hg and other metallic air toxics, as well as NOx and SO2. Benefits in this table are associated only with NOx and SO2 reductions. 2 52 C Not all possible benefits or disbenefits are quantified and monetized in this analysis. In particular, ozone health and welfare and PM welfare benefits are omitted. Other potential benefit categories that have not been quantified and monetized are listed in Table 2 1. B is the sum of all unquantified benefits and disbenefits.

D Estimated combined benefits of Sec. 111 plus IAQR costs and benefits in 2010. Ranges do not reflect actual analyses of combined programs. Rough estimates based on consideration of available IAQR, MACT, and Clear Skies analyses. See text. 2 53 Table 2 12. Forecast Mercury Emissions under the Proposed Section 112 MACT, and the Proposed Section 111 Rule and the Proposed IAQRA Program/ Year 2010 2020 MACT only 34 31 IAQR only 34 30 IAQR and section 111 caps B 18  22 A Annual reductions from base case forecast under current programs to reduce Utility Unit emissions. MACT only value for 2015 based on interpolation of 2010 and 2015. Lower bound of IAQR and section 111 caps in 2010 assumes Hg cap is set at co control level achieved by IAQR. Upper bound in 2010 and ranges thereafter estimates derived from Clear Skies analyses. B Mercury emissions will reflect the level of emissions resulting from the co benefits of controlling SO2 and NOx. See section IV. B. 1 for a detailed discussion. 2 54 2.7 References Abbey, D. E., B. L. Hwang, R. J. Burchette, T. Vancuren, and P. K. Mills. 1995. Estimated Long Term Ambient Concentrations of PM( 10) and Development of Respiratory Symptoms in a Nonsmoking Population. Archives of Environmental Health 50( 2): 139 152.

Abbey, D. E., F. Petersen, P. K. Mills, and W. L. Beeson. 1993. Long Term Ambient Concentrations of Total Suspended Particulates, Ozone, and Sulfur Dioxide and Respiratory Symptoms in a Nonsmoking Population. Archives of Environmental Health 48( 1): 33 46.

Abbey, D. E., S. D. Colome, P. K. Mills, R. Burchette, W. L. Beeson and Y. Tian. 1993. Chronic Disease Associated With Long Term Concentrations of Nitrogen Dioxide. Journal of Exposure Analysis and Environmental Epidemiology. Vol. 3( 2): 181 202.

Abbey, D. E., N. Nishino, W. F. McDonnell, R. J. Burchette, S. F. Knutsen, W. Lawrence Beeson and J. X. Yang. 1999. Long term inhalable particles and other air pollutants related to mortality in nonsmokers [ see comments]. Am J Respir Crit Care Med. Vol. 159( 2): 373 82.

Abt Associates, Inc. 2003. BenMAP User's Manual. Prepared for EPA Office of Air Quality Planning and Standards, November.

Adams, P. F., G. E. Hendershot and M. A. Marano. 1999. Current Estimates from the National Health Interview Survey, 1996. Vital Health Stat. Vol. 10( 200): 1 212.

Agency for Healthcare Research and Quality. 2000. HCUPnet, Healthcare Cost and Utilization Project.

American Lung Association, 1999. Chronic Bronchitis. Web site available at: http:// www. lungusa. org/ diseases/ lungchronic. html.

Anderson, J; Sherwood, T; Comparison of EPA and Other Estimates of Mobile Source Rule Costs to Actual Price Changes; Society of Automotive Engineers; SAE 2002 01 1980; May 14, 2002.

Alberini, A., M. Cropper, T. Fu, A. Krupnick, J. Liu, D. Shaw, and W. Harrington. 1997. Valuing Health Effects of Air Pollution in Developing Countries: The Case of Taiwan. Journal of Environmental Economics and Management. 34: 107 126.

American Lung Association. 2002a. Trends in Morbidity and Mortality: Pneumonia, Influenza, and Acute Respiratory Conditions. American Lung Association, Best Practices and Program Services, Epidemiology and Statistics Unit.

American Lung Association. 2002b. Trends in Chronic Bronchitis and Emphysema: Morbidity and Mortality. American Lung Association, Best Practices and Program Services, Epidemiology and Statistics Unit.

American Lung Association. 2002c. Trends in Asthma Morbidity and Mortality. American Lung Association, Best Practices and Program Services, Epidemiology and Statistics Unit.

Banzhaf, S., D. Burtraw, and K. Palmer. 2002. Efficient Emission Fees in the U. S. Electricity Sector. Resources for the Future Discussion Paper 02 45, October.

Berger, M. C., G. C. Blomquist, D. Kenkel, and G. S. Tolley. 1987. Valuing Changes in Health 2 55 Risks: A Comparison of Alternative Measures. The Southern Economic Journal 53: 977 984.

Bricker, S. B., C. G. Clement, D. E. Pirhalla, S. P. Orlando and D. R. G. Farrow. 1999. National Estuarine Eutrophication Assessment: Effects of Nutrient Enrichment in the Nation's Estuaries. National Oceanic and Atmospheric Administration, National Ocean Service, Special Projects Office and the National Centers for Coastal Ocean Science. Silver Spring, Maryland. 71p Burnett RT, Smith Doiron M, Stieb D, Raizenne ME, Brook JR, Dales RE, Leech JA, Cakmak S, Krewski D. 2001. Association between ozone and hospitalization for acute respiratory diseases in children less than 2 years of age. Am J Epidemiol 153: 444 52 Carnethon MR, Liao D, Evans GW, Cascio WE, Chambless LE, Rosamond WD, Heiss G. 2002. Does the cardiac autonomic response to postural change predict incident coronary heart disease and mortality? The Atherosclerosis Risk in Communities Study. American Journal of Epidemiology, 155( 1): 48 56 Chen, L., B. L. Jennison, W. Yang and S. T. Omaye. 2000. Elementary school absenteeism and air pollution. Inhal Toxicol. Vol. 12( 11): 997 1016.

Chestnut, L. G. 1997. Draft Memorandum: Methodology for Estimating Values for Changes in Visibility at National Parks. April 15.

Chestnut, L. G. and R. L. Dennis. 1997. Economic Benefits of Improvements in Visibility: Acid Rain Provisions of the 1990 Clean Air Act Amendments. Journal of Air and Waste Management Association 47: 395 402.

Chestnut, L. G. and R. D. Rowe. 1990a. Preservation Values for Visibility Protection at the National Parks: Draft Final Report. Prepared for Office of Air Quality Planning and Standards, US Environmental Protection Agency, Research Triangle Park, NC and Air Quality Management Division, National Park Service, Denver, CO.

Chestnut, L. G., and R. D. Rowe. 1990b. A New National Park Visibility Value Estimates. In Visibility and Fine Particles, Transactions of an AWMA/ EPA International Specialty Conference, C. V. Mathai, ed. Air and Waste Management Association, Pittsburgh.

CMS ( 2002). Centers for Medicare and Medicaid Services. Conditions of Participation: Immunization Standards for Hospitals, Long Term Care Facilities, and Home Health Agencies. 67 FR 61808, October 2, 2002.

Cody, R. P., C. P. Weisel, G. Birnbaum and P. J. Lioy. 1992. The effect of ozone associated with summertime photochemical smog on the frequency of asthma visits to hospital emergency departments. Environ Res. Vol. 58( 2): 184 94.

Crocker, T. D. and R. L. Horst, Jr. 1981. Hours of Work, Labor Productivity, and Environmental Conditions: A Case Study. The Review of Economics and Statistics. Vol. 63: 361 368.

Cropper, M. L. and A. J. Krupnick. 1990. The Social Costs of Chronic Heart and Lung Disease. Resources for the Future. Washington, DC. Discussion Paper QE 89 16 REV.

Daniels MJ, Dominici F, Samet JM, Zeger SL. 2000. Estimating particulate matter mortality dose response curves and threshold levels: an analysis of daily time series for the 20 largest US cities. Am J Epidemiol 152( 5): 397 406 Dockery, D. W., C. A. Pope, X. P. Xu, J. D. Spengler, J. H. Ware, M. E. Fay, B. G. Ferris and F. E. 2 56 Speizer. 1993. An association between air pollution and mortality in six U. S. cities. New England Journal of Medicine 329( 24): 1753 1759.

Dockery, D. W., J. Cunningham, A. I. Damokosh, L. M. Neas, J. D. Spengler, P. Koutrakis, J. H. Ware, M. Raizenne and F. E. Speizer. 1996. " Health Effects of Acid Aerosols On North American Children Respiratory Symptoms." Environmental Health Perspectives. 104( 5): 500 505.

Dominici F, McDermott A, Zeger SL, Samet JM. 2002. On the use of generalized additive models in time series studies of air pollution and health. Am J Epidemiol 156( 3): 193 203 Dekker J. M., R. S. Crow, A. R. Folsom, P. J. Hannan, D. Liao, C. A. Swenne, and E. G. Schouten. 2000. Low Heart Rate Variability in a 2 Minute Rhythm Strip Predicts Risk of Coronary Heart Disease and Mortality From Several Causes : The ARIC Study. Circulation 2000 102: 1239 1244.

Eisenstein, E. L., L. K. Shaw, K. J. Anstrom, C. L. Nelson, Z. Hakim, V. Hasselblad and D. B. Mark. 2001. Assessing the clinical and economic burden of coronary artery disease: 1986 1998. Med Care. Vol. 39( 8): 824 35.

EPA SAB COUNCIL ADV 99 05, 1999. An SAB Advisory on the Health and Ecological Effects Initial Studies of the Section 812 Prospective Study: Report to Congress: Advisory by the Health and Ecological Effects Subcommittee, February.

EPA SAB COUNCIL ADV 98 003, 1998. Advisory Council on Clean Air Compliance Analysis Advisory on the Clean Air Act Amendments ( CAAA) of 1990 Section 812 Prospective Study: Overview of Air Quality and Emissions Estimates: Modeling, Health and Ecological Valuation Issues Initial Studies.

EPA SAB COUNCIL ADV 99 012, 1999. The Clean Air Act Amendments ( CAAA) Section 812 Prospective Study of Costs and Benefits ( 1999): Advisory by the Health and Ecological Effects Subcommittee on Initial Assessments of Health and Ecological Effects: Part 1. July.

EPA SAB COUNCIL ADV 00 001, 1999. The Clean Air Act Amendments ( CAAA) Section 812 Prospective Study of Costs and Benefits ( 1999): Advisory by the Health and Ecological Effects Subcommittee on Initial Assessments of Health and Ecological Effects: Part 2. October, 1999.

EPA SAB COUNCIL ADV 00 002, 1999. The Clean Air Act Amendments ( CAAA) Section 812 Prospective Study of Costs and Benefits ( 1999): Advisory by the Advisory Council on Clean Air Compliance Analysis: Costs and Benefits of the CAAA. Effects Subcommittee on Initial Assessments of Health and Ecological Effects: Part 2. October, 1999.

EPA SAB EEAC 00 013, 2000. An SAB Report on EPA's White Paper Valuing the Benefits of Fatal Cancer Risk Reduction. July.

EPA SAB COUNCIL ADV 01 004. 2001. Review of the Draft Analytical Plan for EPA's Second Prospective Analysis  Benefits and Costs of the Clean Air Act 1990 2020: An Advisory by a Special Panel of the Advisory Council on Clean Air Compliance Analysis. September.

EPA SAB COUNCIL ADV 03 00x. 2003. Advisory on Plans for Health Effects Analysis in the Analytical Plan for EPA's Second Prospective Analysis   Benefits and Costs of the Clean 2 57 Air Act, 1990 2020: An Advisory by the Health Effects Subcommittee of the Advisory Council for Clean Air Compliance Analysis. December.

Evans, William N., and W. Kip Viscusi. 1993. Income Effects and the Value of Health. Journal of Human Resources 28( 3): 497 518.

Fox, S., and R. A. Mickler, 1995. Impact of Air Pollutants on Southern Pine Forests Ecological Studies 118. Springer Verlag: New York.

Freeman, A. M. III. 1993. The Measurement of Environmental and Resource Values: Theory and Methods. Resources for the Future, Washington, D. C.

Garcia, P., Dixon, B. and Mjelde, J. ( 1986): Measuring the benefits of environmental change using a duality approach: The case of Ozone and Illinios cash grain farms. Journal of Environmental Economics and Management.

Gilliland, F. D., K. Berhane, E. B. Rappaport, D. C. Thomas, E. Avol, W. J. Gauderman, S. J. London, H. G. Margolis, R. McConnell, K. T. Islam and J. M. Peters. 2001. The effects of ambient air pollution on school absenteeism due to respiratory illnesses. Epidemiology. Vol. 12( 1): 43 54.

Gold DR, Litonjua A, Schwartz J, Lovett E, Larson A, Nearing B, Allen G, Verrier M, Cherry R, Verrier R. 2000. Ambient pollution and heart rate variability. Circulation 101( 11): 1267 73 Greenbaum, D. 2002a. Letter. Health Effects Institute. May 30. Available online at : http:// www. healtheffects. org/ Pubs/ NMMAPSletter. pdf . Accessed March 20, 2003.

Grosclaude, P. and N. C. Soguel. 1994. " Valuing Damage to Historic Buildings Using a Contingent Market: A Case Study of Road Traffic Externalities." Journal of Environmental Planning and Management 37: 279 287.

Guo, Y. L., Y. C. Lin, F. C. Sung, S. L. Huang, Y. C. Ko, J. S. Lai, H. J. Su, C. K. Shaw, R. S. Lin, D. W. Dockery. 1999. Climate, Traffic Related Air Pollutants, and Asthma Prevalence in Middle School Children in Taiwan. Environmental Health Perspectives 107: 1001 1006.

Harrington, W. and P. R. Portney. 1987. Valuing the Benefits of Health and Safety Regulation. Journal of Urban Economics 22: 101 112.

Hollman, F. W., T. J. Mulder, and J. E. Kallan. 2000. Methodology and Assumptions for the Population Projections of the United States: 1999 to 2100. Population Division Working Paper No. 38, Population Projections Branch, Population Division, U. S. Census Bureau, Department of Commerce. January.

HRSA ( 1998). Health Resources and Services Administration: Procurement and Transplantation Network; Final Rule. 63 FR 16295, April 2, 1998.

Ibald Mulli, A., J. Stieber, H. E. Wichmann, W. Koenig, and A. Peters. 2001. Effects of Air Pollution on Blood Pressure: A Population Based Approach. American Journal of Public Health. 91: 571 577.

Industrial Economics, Incorporated ( IEc). 1994. Memorandum to Jim DeMocker, Office of Air and Radiation, Office of Policy Analysis and Review, US Environmental Protection Agency, March 31.

Ito, K. and G. D. Thurston. 1996. Daily PM10/ mortality associations: an investigations of at risk subpopulations. Journal of Exposure Analysis and Environmental Epidemiology. Vol. 2 58 6( 1): 79 95.

JAMA ( 2003). Journal of the American Medical Association. Vol. 289( 13): 1667 1674.

Jones Lee, M. W., M. Hammerton and P. R. Philips. 1985. The Value of Safety: Result of a National Sample Survey. Economic Journal. 95( March): 49 72.

Jones Lee, M. W. 1989. The Economics of Safety and Physical Risk. Oxford: Basil Blackwell.

Jones Lee, M. W., G. Loomes, D. O'Reilly, and P. R. Phillips. 1993. The Value of Preventing Non fatal Road Injuries: Findings of a Willingness to pay National Sample Survey. TRY Working Paper, WP SRC2.

Kleckner, N. and J. Neumann. 1999. " Recommended Approach to Adjusting WTP Estimates to Reflect Changes in Real Income. Memorandum to Jim Democker, US EPA/ OPAR, June 3.

Krewski D, Burnett RT, Goldbert MS, Hoover K, Siemiatycki J, Jerrett M, Abrahamowicz M, White WH. 2000. Reanalysis of the Harvard Six Cities Study and the American Cancer Society Study of Particulate Air Pollution and Mortality. Special Report to the Health Effects Institute, Cambridge MA, July 2000 Krupnick, A. J. and M. L. Cropper. 1992. " The Effect of Information on Health Risk Valuations." Journal of Risk and Uncertainty 5( 2): 29 48.

Krupnick, A., M. Cropper., A. Alberini, N. Simon, B. O'Brien, R. Goeree, and M. Heintzelman. 2002. Age, Health and the Willingness to Pay for Mortality Risk Reductions: A Contingent Valuation Study of Ontario Residents, Journal of Risk and Uncertainty, 24, 161 186.

Kunzli, N., R. Kaiser, S. Medina, M. Studnicka, O. Chanel, P. Filliger, M. Herry, F. Horak Jr., V. Puybonnieux Texier, P. Quenel, J. Schneider, R. Seethaler, J C Vergnaud, and H. Sommer. 2000. Public health Impact of Outdoor and Traffic related Air Pollution: A European Assessment. The Lancet, 356: 795 801.

Kunzli N, Medina S, Kaiser R, Quenel P, Horak F Jr, Studnicka M. 2001. Assessment of deaths attributable to air pollution: should we use risk estimates based on time series or on cohort studies? Am J Epidemiol 153( 11): 1050 5 Lareau, T. J. and D. A. Rae. 1989. Valuing WTP for Diesel Odor Reductions: An Application of Contingent Ranking Techniques, Southern Economic Journal, 55: 728 742.

Lave, L. B. and E. P. Seskin. 1977. Air Pollution and Human Health. Johns Hopkins University Press for Resources for the Future: Baltimore.

Levy, J. I., J. K. Hammitt, Y. Yanagisawa, and J. D. Spengler. 1999. Development of a New Damage Function Model for Power Plants: Methodology and Applications. Environmental Science and Technology, 33: 4364 4372.

Levy, J. I., T. J. Carrothers, J. T. Tuomisto, J. K. Hammitt, and J. S. Evans. 2001. Assessing the Public Health Benefits of Reduced Ozone Concentrations. Environmental Health Perspectives. 109: 1215 1226.

Liao D, Cai J, Rosamond WD, Barnes RW, Hutchinson RG, Whitsel EA, Rautaharju P, Heiss G. 1997. Cardiac autonomic function and incident coronary heart disease: a population based case cohort study. The ARIC Study. Atherosclerosis Risk in Communities Study. American Journal of Epidemiology, 145( 8): 696 706. 2 59 Liao D, Creason J, Shy C, Williams R, Watts R, Zweidinger R. 1999. Daily variation of particulate air pollution and poor cardiac autonomic control in the elderly. Environ Health Perspect 107: 521 5 Lipfert, F. W., S. C. Morris and R. E. Wyzga. 1989. Acid Aerosols  the Next Criteria Air Pollutant. Environmental Science & Technology. Vol. 23( 11): 1316 1322.

Lipfert, F. W. ; H. Mitchell Perry Jr ; J. Philip Miller ; Jack D. Baty ; Ronald E. Wyzga ; Sharon E. Carmody 2000. The Washington University EPRI Veterans' Cohort Mortality Study: Preliminary Results, Inhalation Toxicology, 12: 41 74 Lippmann, M., K. Ito, A. Nádas, and R. T. Burnett. 2000. Association of Particulate Matter Components with Daily Mortality and Morbidity in Urban Populations. Health Effects Institute Research Report Number 95, August.

Magari SR, Hauser R, Schwartz J, Williams PL, Smith TJ, Christiani DC. 2001. Association of heart rate variability with occupational and environmental exposure to particulate air pollution. Circulation 104( 9): 986 91 McClelland, G., W. Schulze, D. Waldman, J. Irwin, D. Schenk, T. Stewart, L. Deck, and M. Thayer. 1993. Valuing Eastern Visibility: A Field Test of the Contingent Valuation Method. Prepared for Office of Policy, Planning and Evaluation, US Environmental Protection Agency. September.

McConnell, R., K. Berhane, F. Gilliland, S. J. London, H. Vora, E. Avol, W. J. Gauderman, H. G. Margolis, F. Lurmann, D. C. Thomas, and J. M. Peters. 1999. Air Pollution and Bronchitic Symptoms in Southern California Children with Asthma. Environmental Health Perspectives, 107( 9): 757 760.

McConnell R, Berhane K, Gilliland F, London SJ, Islam T, Gauderman WJ, Avol E, Margolis HG, Peters JM. 2002. Asthma in exercising children exposed to ozone: a cohort study. Lancet 359( 9309): 896.

McDonnell, W. F., D. E. Abbey, N. Nishino and M. D. Lebowitz. 1999. Long term ambient ozone concentration and the incidence of asthma in nonsmoking adults: the ahsmog study. Environmental Research. 80( 2 Pt 1): 110 21.

Miller, T. R. 2000. Variations between Countries in Values of Statistical Life. Journal of Transport Economics and Policy. 34: 169 188.

Moolgavkar SH, Luebeck EG, Anderson EL. 1997. Air pollution and hospital admissions for respiratory causes in Minneapolis St. Paul and Birmingham. Epidemiology 8: 364 70 Moolgavkar, S. H. 2000. Air pollution and hospital admissions for diseases of the circulatory system in three U. S. metropolitan areas. J Air Waste Manag Assoc 50: 1199 206 Moore and Viscusi ( 1988). The Quality Adjusted Value of Life. Economic Inquiry. 26( 3): 369 388.

Mrozek, JR and Taylor, LO ( 2002). What Determines the Value of Life? A Meta Analysis. Journal of Policy Analysis and Management, Vol 21, No. 2, 253 270.

National Center for Education Statistics. 1996 The Condition of Education 1996, Indicator 42: Student Absenteeism and Tardiness. U. S. Department of Education National Center for Education Statistics. Washington DC.

National Research Council ( NRC). 1998. Research Priorities for Airborne Particulate Matter: I. 2 60 Immediate Priorities and a Long Range Research Portfolio. The National Academies Press: Washington, D. C.

National Research Council ( NRC). 2002. Estimating the Public Health Benefits of Proposed Air Pollution Regulations. The National Academies Press: Washington, D. C.

NCLAN. 1988. Assessment of Crop Loss from Air Pollutants. ( Eds. Walter W. Heck, O. Clifton Taylor and David T. Tingey) Elsevier Science Publishing Co.: New York, Pp. 1 5. ( ERL, GB 639).

Neumann, J. E., M. T. Dickie, and R. E. Unsworth. 1994. Linkage Between Health Effects Estimation and Morbidity Valuation in the Section 812 Analysis  Draft Valuation Document. Industrial Economics Incorporated ( IEc) Memorandum to Jim DeMocker, U. S. Environmental Protection Agency, Office of Air and Radiation, Office of Policy Analysis and Review. March 31.

Norris, G., S. N. YoungPong, J. Q. Koenig, T. V. Larson, L. Sheppard and J. W. Stout. 1999. An association between fine particles and asthma emergency department visits for children in Seattle. Environ Health Perspect. Vol. 107( 6): 489 93.

Ostro, B. D. 1987. Air Pollution and Morbidity Revisited: a Specification Test. Journal of Environmental Economics Management. 14: 87 98.

Ostro, B. and L. Chestnut. 1998. Assessing the Health Benefits of Reducing Particulate Matter Air Pollution in the United States. Environmental Research, Section A, 76: 94 106.

Ostro B. D. and S. Rothschild. 1989. Air Pollution and Acute Respiratory Morbidity: An Observational Study of Multiple Pollutants. Environmental Research 50: 238 247.

Ostro, B. D., M. J. Lipsett, M. B. Wiener and J. C. Selner. 1991. Asthmatic Responses to Airborne Acid Aerosols. Am J Public Health. Vol. 81( 6): 694 702.

Ostro, B., M. Lipsett, J. Mann, H. Braxton Owens and M. White. 2001. Air pollution and exacerbation of asthma in African American children in Los Angeles. Epidemiology. Vol. 12( 2): 200 8.

Ozkaynak, H. and G. D. Thurston. 1987. Associations between 1980 U. S. mortality rates and alternative measures of airborne particle concentration. Risk Anal. Vol. 7( 4): 449 61.

Peters A, Dockery DW, Muller JE, Mittleman MA. 2001. Increased particulate air pollution and the triggering of myocardial infarction. Circulation. 103: 2810 2815.

Poloniecki JD, Atkinson RW, de Leon AP, Anderson HR. 1997. Daily time series for cardiovascular hospital admissions and previous day's air pollution in London, UK. Occup Environ Med 54( 8): 535 40.

Pope, C. A. 2000. Invited Commentary: Particulate Matter Mortality Expsoure Response Relations and Thresholds. American Journal of Epidemiology, 152: 407 412.

Pope, C. A., III, R. T. Burnett, M. J. Thun, E. E. Calle, D. Krewski, K. Ito, G. D. Thurston. 2002. Lung Cancer, Cardiopulmonary Mortality, and Long term Exposure to Fine Particulate Air Pollution. Journal of the American Medical Association. 287: 1132 1141.

Pope, C. A., III, M. J. Thun, M. M. Namboodiri, D. W. Dockery, J. S. Evans, F. E. Speizer, and C. W. Heath, Jr. 1995. Particulate Air Pollution as a Predictor of Mortality in a Prospective Study of U. S. Adults. American Journal of Respiratory Critical Care Medicine 151: 669 674. 2 61 Pope, C. A., III, D. W. Dockery, J. D. Spengler, and M. E. Raizenne. 1991. Respiratory Health and PM 10 Pollution: a Daily Time Series Analysis American Review of Respiratory Diseases 144: 668 674.

Ransom, M. R. and C. A. Pope. 1992. Elementary School Absences and PM( 10) Pollution in Utah Valley. Environmental Research. Vol. 58( 2): 204 219.

Rosamond, W., G. Broda, E. Kawalec, S. Rywik, A. Pajak, L. Cooper and L. Chambless. 1999. Comparison of medical care and survival of hospitalized patients with acute myocardial infarction in Poland and the United States. American Journal of Cardiology. 83: 1180 5.

Rossi G, Vigotti MA, Zanobetti A, Repetto F, Gianelle V, Schwartz J. 1999. Air pollution and cause specific mortality in Milan, Italy, 1980 1989. Arch Environ Health 54( 3): 158 64 Rowlatt et al. 1998. Valuation of Deaths from Air Pollution. NERA and CASPAR for DETR.

Russell, M. W., D. M. Huse, S. Drowns, E. C. Hamel and S. C. Hartz. 1998. Direct medical costs of coronary artery disease in the United States. Am J Cardiol. Vol. 81( 9): 1110 5.

Samet, J. M., S. L. Zeger, J. E. Kelsall, J. Xu and L. S. Kalkstein. 1997. Air Pollution, Weather, and Mortality in Philadelphia 1973 1988. Health Effects Institute. Cambridge, MA. March.

Samet JM, Zeger SL, Dominici F, Curriero F, Coursac I, Dockery DW, Schwartz J, Zanobetti A. 2000. The National Morbidity, Mortality and Air Pollution Study: Part II: Morbidity, Mortality and Air Pollution in the United States. Research Report No. 94, Part II. Health Effects Institute, Cambridge MA, June 2000.

Schwartz, J., Dockery, D. W., Neas, L. M., Wypij, D., Ware, J. H., Spengler, J. D., Koutrakis, P., Speizer, F. E., and Ferris, Jr., B. G. 1994. Acute Effects of Summer Air Pollution on Respiratory Symptom Reporting in Children American Journal of Respiratory Critical Care Medicine 150: 1234 1242.

Schwartz J, Laden F, Zanobetti A. 2002. The concentration response relation between PM( 2.5) and daily deaths. Environmental Health Perspectives 110: 1025 9 Schwartz J. 2000. The distributed lag between air pollution and daily deaths. Epidemiology. 2000 May; 11( 3): 320 6.

Schwartz, J. 2000. Assessing confounding, effect modification, and thresholds in the association between ambient particles and daily deaths. Environmental Health Perspectives 108( 6): 563 8.

Schwartz, J. 1995. Short term fluctuations in air pollution and hospital admissions of the elderly for respiratory disease. Thorax 50( 5): 531 8 Schwartz, J. 1993. Particulate Air Pollution and Chronic Respiratory Disease Environmental Research 62: 7 13.

Schwartz J, Dockery DW, Neas LM. 1996. Is daily mortality associated specifically with fine particles? J Air Waste Manag Assoc. 46: 927 39.

Schwartz J and Zanobetti A. 2000. Using meta smoothing to estimate dose response trends across multiple studies, with application to air pollution and daily death. Epidemiology. 11: 666 72.

Schwartz J, Neas LM. 2000. Fine particles are more strongly associated than coarse particles with 2 62 acute respiratory health effects in schoolchildren. Epidemiology 11: 6 10.

Seigneur, C., G. Hidy, I. Tombach, J. Vimont, and P. Amar. 1999. Scientific Peer Review of the Regulatory Modeling System for Aerosols and Deposition ( REMSAD). Prepared for the KEVRIC Company, Inc.

Sheppard, L., D. Levy, G. Norris, T. V. Larson and J. Q. Koenig. 1999. Effects of ambient air pollution on nonelderly asthma hospital admissions in Seattle, Washington, 1987 1994. Epidemiology. Vol. 10: 23 30.

Shogren, J. and T. Stamland. 2002. Skill and the Value of Life. Journal of Political Economy. 110: 1168 1197.

Sisler, J. F. 1996. Spatial and Seasonal Patterns and Long Term Variability of the Composition of the Haze in the United States: An Analysis of Data from the IMPROVE Network. Cooperative Institute for Research in the Atmosphere, Colorado State University; Fort Collins, CO July.

Smith, D. H., D. C. Malone, K. A. Lawson, L. J. Okamoto, C. Battista and W. B. Saunders. 1997. A national estimate of the economic costs of asthma. Am J Respir Crit Care Med. 156( 3 Pt 1): 787 93.

Smith, V. K., G. Van Houtven, and S. K. Pattanayak. 2002. Benefit Transfer via Preference Calibration. Land Economics. 78: 132 152.

Stanford, R., T. McLaughlin and L. J. Okamoto. 1999. The cost of asthma in the emergency department and hospital. Am J Respir Crit Care Med. Vol. 160( 1): 211 5.

Stieb, D. M., R. T. Burnett, R. C. Beveridge and J. R. Brook. 1996. Association between ozone and asthma emergency department visits in Saint John, New Brunswick, Canada. Environmental Health Perspectives. Vol. 104( 12): 1354 1360.

Stieb DM, Judek S, Burnett RT. 2002. Meta analysis of time series studies of air pollution and mortality: effects of gases and particles and the influence of cause of death, age, and season. J Air Waste Manag Assoc 52( 4): 470 84 Taylor, C. R., K. H. Reichelderfer, and S. R. Johnson. 1993. Agricultural Sector Models for the United States: Descriptions and Selected Policy Applications. Iowa State University Press: Ames, IA.

Thurston, G. D. and K. Ito. 2001. Epidemiological studies of acute ozone exposures and mortality. J Expo Anal Environ Epidemiol. Vol. 11( 4): 286 94.

Tolley, G. S. et al. 1986. Valuation of Reductions in Human Health Symptoms and Risks. University of Chicago. Final Report for the US Environmental Protection Agency. January.

Tsuji H, Larson MG, Venditti FJ Jr, Manders ES, Evans JC, Feldman CL, Levy D. 1996. Impact of reduced heart rate variability on risk for cardiac events. The Framingham Heart Study. Circulation 94( 11): 2850 5 US Bureau of the Census. 2002. Statistical Abstract of the United States: 2001. Washington DC.

US Department of Commerce, Bureau of Economic Analysis. BEA Regional Projections to 2045: Vol. 1, States. Washington, DC US Govt. Printing Office, July 1995. 2 63 US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Health Statistics. 1999. National Vital Statistics Reports, 47( 19).

US Environmental Protection Agency. 2002. Third External Review Draft of Air Quality Criteria for Particulate Matter ( April, 2002): Volume II. EPA/ 600/ P 99/ 002aC US Environmental Protection Agency. 2003a. Emissions Inventory Technical Support Document for the Proposed Nonroad Diesel Engines Rule.

US Environmental Protection Agency. 2003b. Air Quality Technical Support Document for the Proposed Nonroad Diesel Engines Rule.

US Environmental Protection Agency, 1996a. Review of the National Ambient Air Quality Standards for Ozone: Assessment of Scientific and Technical Information. Office of Air Quality Planning and Standards, Research Triangle Park, NC EPA report no. EPA/ 4521R 96 007.

US Environmental Protection Agency, 1996b. Review of the National Ambient Air Quality Standards for Particulate Matter: Assessment of Scientific and Technical Information. Office of Air Quality Planning and Standards, Research Triangle Park, NC EPA report no. EPA/ 4521R 96 013.

US Environmental Protection Agency, 1999. The Benefits and Costs of the Clean Air Act, 1990 2010. Prepared for US Congress by US EPA, Office of Air and Radiation/ Office of Policy Analysis and Review, Washington, DC, November; EPA report no. EPA 410 R 99 001.

US Environmental Protection Agency, 1993. External Draft, Air Quality Criteria for Ozone and Related Photochemical Oxidants. Volume II. US EPA, Office of Health and Environmental Assessment. Research Triangle Park, NC, EPA/ 600/ AP 93/ 004b. 3v.

US Environmental Protection Agency, 2000a. Regulatory Impact Analysis: Heavy Duty Engine and Vehicle Standards and Highway Diesel Fuel Sulfur Control Requirements. Prepared by: Office of Air and Radiation. Available at http:// www. epa. gov/ otaq/ diesel. htm Accessed March 20, 2003.

US Environmental Protection Agency, 2000b. Valuing Fatal Cancer Risk Reductions. White Paper for Review by the EPA Science Advisory Board.

US Environmental Protection Agency 2000c. Guidelines for Preparing Economic Analyses. EPA 240 R 00 003. September.

US Environmental Protection Agency, 1997. The Benefits and Costs of the Clean Air Act, 1970 to 1990. Prepared for US Congress by US EPA, Office of Air and Radiation/ Office of Policy Analysis and Review, Washington, DC U. S. Environmental Protection Agency ( 1997). Regulatory Impact Analysis for Particulate Matter and Ozone National Ambient Air Quality Standards and Proposed Regional Haze Rule. July 1997.

U. S. Environmental Protection Agency, 2003a. Technical Addendum: Methodologies for the Benefit Analysis of the Clear Skies Act of 2003. September. Available online at http:// www. epa. gov/ air/ clearskies/ tech_ adden. pdf. Accessed December 9, 2003.

U. S. Environmental Protection Agency, 2003b. Benefits Analysis for the Proposed Interstate Air Quality Rule. December. 2 64 U. S. FDA ( 1995). U. S. Food and Drug Administration: Procedures for the Safe and Sanitary Processing and Importing of Fish and Fishery Products; Final Rule. 60 FR 65095, December 18, 1995.

U. S. FDA ( 1996). U. S. Food and Drug Administration: Regulations Restricting the Sale and Distribution of Cigarettes and Smokeless Tobacco to Protect Children and Adolescents, Final Rule. 61 FR 44395, August 28, 1996.

U. S. FDA ( 1997). U. S. Food and Drug Administration: Quality Mammography Standards, Final Rule. 62 FR 55851, October 28, 1997.

U. S. FDA ( 1998). U. S. Food and Drug Administration: Food Labeling, Warning and Notice Statement, Labeling of Juice Products, Final Rule. 63 FR 37029, July 1998.

U. S. FDA ( 1999). U. S. Food and Drug Administration: Food Labeling, Trans Fatty Acids in Nutrition Laveling, Nutrient Content Claims, and Health Claims, Proposed Rule. 64 FR 62746, November 17, 1999.

U. S. FDA ( 2000). U. S. Food and Drug Administration: Food Labeling, Safe Handling Statements, Laveling of Shell Eggs, Refrigeration of Shell Eggs Held for Retail Distribution, Final Rule. 65 FR 76091, December 5, 2000.

U. S. FDA ( 2001). U. S. Food and Drug Administration: Hazard Analysis and Critical Control Point, Procedures for the Safe and Sanitary Processing and Importing of Juice, Final Rule. 66 FR 6137, January 19, 2001.

US Office of Management and Budget. 1992. Guidelines and Discount Rates for Benefit Cost Analysis of Federal Programs. Circular No. A 94. October.

Vedal, S., J. Petkau, R. White and J. Blair. 1998. Acute effects of ambient inhalable particles in asthmatic and nonasthmatic children. American Journal of Respiratory and Critical Care Medicine. Vol. 157( 4): 1034 1043.

Viscusi, W. K. 1992. Fatal Tradeoffs: Public and Private Responsibilities for Risk. ( New York: Oxford University Press).

Viscusi, W. K., W. A. Magat, and J. Huber. 1991. " Pricing Environmental Health Risks: Survey Assessments of Risk Risk and Risk Dollar Trade Offs for Chronic Bronchitis" Journal of Environmental Economics and Management, 21: 32 51.

Weisel, C. P., R. P. Cody and P. J. Lioy. 1995. Relationship between summertime ambient ozone levels and emergency department visits for asthma in central New Jersey. Environ Health Perspect. Vol. 103 Suppl 2: 97 102.

Whittemore, A. S. and E. L. Korn. 1980. Asthma and Air Pollution in the Los Angeles Area. American Journal of Public Health. 70: 687 696.

Wittels, E. H., J. W. Hay and A. M. Gotto, Jr. 1990. Medical costs of coronary artery disease in the United States. Am J Cardiol. Vol. 65( 7): 432 40.

Woodruff, T. J., J. Grillo and K. C. Schoendorf. 1997. The relationship between selected causes of postneonatal infant mortality and particulate air pollution in the United States. Environmental Health Perspectives. Vol. 105( 6): 608 612.

Woods & Poole Economics Inc. 2001. Population by Single Year of Age CD. Woods & Poole Economics, Inc. 2 65 Yu, O., L. Sheppard, T. Lumley, J. Q. Koenig and G. G. Shapiro. 2000. Effects of Ambient Air Pollution on Symptoms of Asthma in Seattle Area Children Enrolled in the CAMP Study. Environ Health Perspect. Vol. 108( 12): 1209 1214.

Zanobetti, A., J. Schwartz, E. Samoli, A. Gryparis, G. Touloumi, R. Atkinson, A. Le Tertre, J. Bobros, M. Celko, A. Goren, B. Forsberg, P. Michelozzi, D. Rabczenko, E. Aranguez Ruiz and K. Katsouyanni. 2002. The temporal pattern of mortality responses to air pollution: a multicity assessment of mortality displacement. Epidemiology. Vol. 13( 1): 87 93. TECHNICAL REPORT DATA ( Please read Instructions on reverse before completing)

1. REPORT NO.

EPA 452/ R 03 021 2. 3. RECIPIENT'S ACCESSION NO.

4. TITLE AND SUBTITLE Benefit Analysis for the Section 112 Utility Rule 5. REPORT DATE January 2004 6. PERFORMING ORGANIZATION CODE 7. AUTHOR( S) 8. PERFORMING ORGANIZATION REPORT NO.

9. PERFORMING ORGANIZATION NAME AND ADDRESS U. S. Environmental Protection Agency Office of Air Quality Planning and Standards Air Quality Strategies and Standards Division Research Triangle Park, NC 27711 10. PROGRAM ELEMENT NO.

11. CONTRACT/ GRANT NO.

12. SPONSORING AGENCY NAME AND ADDRESS Director Office of Air Quality Planning and Standards Office of Air and Radiation U. S. Environmental Protection Agency Research Triangle Park, NC 27711 13. TYPE OF REPORT AND PERIOD COVERED 14. SPONSORING AGENCY CODE EPA/ 200/ 04 15. SUPPLEMENTARY NOTES 16. ABSTRACT This document is a benefits analysis of the Section 112 ( MACT) proposal to reduce mercury emissions from power plants. The analysis provided qualitative and quantitative estimates of the benefits from the emission reductions, which include reductions of nitrogen oxides and sulfur dioxide as well as mercury. Extensive background on the health effects from mercury exposure is also included.

17. KEY WORDS AND DOCUMENT ANALYSIS a. DESCRIPTORS b. IDENTIFIERS/ OPEN ENDED TERMS c. COSATI Field/ Group Benefits Health Effects Emissions Air Pollution Control 18. DISTRIBUTION STATEMENT Release Unlimited 19. SECURITY CLASS ( Report)

Unclassified 21. NO. OF PAGES 71 20. SECURITY CLASS ( Page)

Unclassified 22. PRICE EPA Form 2220 1 ( Rev. 4 77) PREVIOUS EDITION IS OBSOLETE United States Office of Air Quality Planning and Standards Publication No. EPA 452/ R 03 021 Environmental Protection Air Quality Strategies and Standards Division January 2004 Agency Research Triangle Park, NC 27711