4
Exposure and Response

This chapter discusses three key components of benefits analyses: exposure assessment, health outcomes, and concentration-response functions. The exposure assessment section begins with an overview of exposure assessment considerations, including issues related to exposure assessments in the epidemiological studies that are frequently used to estimate health benefits of air pollution reductions. A general overview of air-quality modeling and its role in benefits analysis follows. The selection and interpretation of health outcomes are then discussed. Finally, the concentration-response section explores the sources and selection of these functions and issues associated with the existence of thresholds, analysis of population subgroups, and assumptions regarding effects lags (the temporal relationship between changes in exposure and resulting changes in health outcomes).

EXPOSURE ASSESSMENT

Estimating changes in population exposures to air pollutants is an essential component of EPA’s benefits analyses, providing the link between anticipated emissions changes and resulting changes in health outcomes. Because it is not possible to observe population exposures to air pollution under different regulatory options, exposure assessment in benefits analysis uses models to simulate air pollution exposures that might occur as a result of



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Estimating the Public Health Benefits of Proposed Air Pollution Regulations 4 Exposure and Response This chapter discusses three key components of benefits analyses: exposure assessment, health outcomes, and concentration-response functions. The exposure assessment section begins with an overview of exposure assessment considerations, including issues related to exposure assessments in the epidemiological studies that are frequently used to estimate health benefits of air pollution reductions. A general overview of air-quality modeling and its role in benefits analysis follows. The selection and interpretation of health outcomes are then discussed. Finally, the concentration-response section explores the sources and selection of these functions and issues associated with the existence of thresholds, analysis of population subgroups, and assumptions regarding effects lags (the temporal relationship between changes in exposure and resulting changes in health outcomes). EXPOSURE ASSESSMENT Estimating changes in population exposures to air pollutants is an essential component of EPA’s benefits analyses, providing the link between anticipated emissions changes and resulting changes in health outcomes. Because it is not possible to observe population exposures to air pollution under different regulatory options, exposure assessment in benefits analysis uses models to simulate air pollution exposures that might occur as a result of

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Estimating the Public Health Benefits of Proposed Air Pollution Regulations those options. Exposure modeling is a complex process that depends on many assumptions about the future, including pollution emissions reductions resulting from the proposed regulation; changes in emissions due to factors other than the proposed regulation; meteorological conditions; the physical and chemical processes in the atmosphere affecting pollution dispersion, transformations, and deposition; and the nature and degree of pollutant contact with future human populations. As in all other stages of the benefits analysis, the assumptions and methods used in the exposure assessment should be well-justified and clearly described, with careful attention paid to assessing and communicating key sources of uncertainty. EPA’s exposure assessment methods have evolved considerably over time, as is evident in the health benefits analyses reviewed by the committee. This evolution is due to continued improvements in modeling capabilities and to a marked increase in available air-monitoring data for many pollutants. Because the most recent EPA analysis reviewed by the committee (the benefits analysis for the heavy-duty (HD) engine and diesel-fuel rule) uses current data and exposure assessment methods, it serves as an illustrative example throughout this exposure assessment discussion. The committee considers that the exposure assessment methods used in the analysis for the HD engine and diesel-fuel rule represent an appropriate and reasonably thorough application of available data and models. Although limitations, as noted in following sections, exist, they are primarily due to limitations of available scientific knowledge and, ultimately, the limited time and staff resources available for analysis rather than flawed analytical methods. Exposure to air pollution has been defined as the intersection in time and space of a concentration of pollution in the air and the presence of a human being (NRC 1991; Ott 1995). For benefits analyses, exposure is typically assessed at the population level by geographically linking estimates of outdoor pollution concentrations with projected population numbers; these together represent the necessary input to population concentration-response functions for calculating health impacts. The use of ambient air concentrations to represent population exposures is justifiable when the health findings underlying the benefits analysis are similarly based on ambient concentration data and when the outdoor concentrations are correlated with personal exposures, as is the case for particulate matter (PM).

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Estimating the Public Health Benefits of Proposed Air Pollution Regulations Exposure Assessment in Epidemiological Studies The health benefits analyses reviewed by the committee have depended heavily on the estimated mortality impacts of PM. To better understand the role of and uncertainties in exposure assessment for such benefits analyses, it is important to examine characteristics of the exposure assessments used in the epidemiological studies on which the PM mortality effects were based. Two classes of study designs have been used to assess mortality effects: time-series and prospective cohort studies (Kinney 1999). The time-series studies examine day-to-day associations between citywide mean daily outdoor PM concentrations and citywide daily death counts. This approach addresses the relationship between acute exposure and health. For example, deaths on a given day are related to PM concentrations on the same day or on a few previous days. In contrast, the prospective cohort studies examine differences between cities in mortality among individuals followed over an extended period and the variations in annual (or longer) mean outdoor PM concentrations. These studies are believed to address the relationship between chronic exposure and mortality. (See the Concentration-Response Function section for a further discussion of time-series and cohort studies.) Population exposures are assessed in both designs using outdoor citywide average PM concentrations derived from regulatory air-quality monitoring data collected from a small number of sites in each city. Uncertainties may arise in using a citywide average to represent exposures of persons at risk because of spatial variations in ambient concentrations across a city, differences in penetration of ambient air pollution indoors, and the wide range in activity patterns of persons at risk. However, in the single-city time-series studies, central-site fine-particle measurements have been shown to correlate well over time with average population personal exposures (Rojas-Bracho et al. 2000; Sarnat et al. 2000). These findings support the validity of daily ambient PM measurements in capturing variations over time in population exposures to fine particles and strengthen the reliability of benefits estimates of acute health effects that depend on ambient PM concentrations. Less is known about the reliability of central-site, long-term average, ambient PM concentrations in characterizing variations between cities in

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Estimating the Public Health Benefits of Proposed Air Pollution Regulations average population exposures. The relationship between population exposures to pollutants of outdoor origin and ambient concentrations measured at central sites may differ across cities because of differences in local sources, indoor penetration efficiency, activity patterns, housing characteristics, and other geographic factors. For example, recent exposure studies highlighted variations across cities in the penetration of ambient PM to indoor environments as a result of weather-related factors, such as the prevalence of air-conditioner use (Rojas-Bracho et al. 2000; Sarnat et al. 2000; Janssen et al. 2002). This result implies geographic differences in the ability of ambient air-monitoring data to characterize population exposures accurately. This uncertainty will affect analyses that estimate benefits in diverse locations and in future years when housing characteristics that affect air-exchange rates may change. As more data become available, EPA should examine how this uncertainty affects benefits estimates and attempt to incorporate this source of uncertainty in an overall uncertainty analysis. Another important characteristic of the exposure assessments in the epidemiological studies that evaluate PM mortality is their dependence on relatively simple measures of airborne PM, notably PM10 (most time-series studies) or PM2.5 (most cohort studies). These size classifications incorporate a heterogeneous mixture of particles varying in size, composition, and source of origin. Furthermore, particle characteristics vary to some extent across locations and time. Because of this heterogeneity, the toxicity of different mixtures may vary. Potential differential toxicity is especially important in a benefits analysis in which PM exposures and resulting health impacts are modeled in diverse locations and at future times, which may result in evaluating particle compositions that differ from those observed in the epidemiological studies used as a basis for analysis. The issue of differential toxicity is an area of active research. Although information is currently inadequate for determining the relative toxicity of different particle types, recent efforts to apportion the relative impacts of different source categories to observed health effects in the epidemiological setting show promise (Laden et al. 2000; Janssen et al. 2002). Lacking information on the relative potencies of different particle types, EPA has made the assumption of constant potency across particle types in its benefits analyses. As data become available, EPA should consider a range of alternative assumptions regarding relative toxicity and incorporate these assumptions in sensitivity or uncertainty analyses.

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Estimating the Public Health Benefits of Proposed Air Pollution Regulations Regarding the collection of data, most epidemiological studies of air pollution health effects use routinely collected compliance monitoring data on a limited set of criteria pollutants for which toxicity is already well-established. To resolve issues of differential toxicity, EPA will need to expand its air-monitoring network to collect data for species other than the criteria pollutants. An improvement in the air-monitoring network should facilitate generation of more specific effect coefficients, and therefore the estimation of more reliable benefits estimates. Determining the responsible toxic components in the particle mix would also result in more effective regulations, because regulations could be better designed to control the sources responsible for generation of these components. One exposure-related issue not typically considered explicitly in benefits analyses is that different categories of emissions sources may vary dramatically in their particle intake fractions, which are the fractions of material emitted that are actually inhaled by the population (Smith 1993; Bennett et al. 2002). Differences in intake fractions between sources may be much larger than the relative impacts of the source categories on ambient PM concentrations. For example, a kilogram of primary particle emissions from diesel vehicles may have an order of magnitude or greater impact on actual population exposure than a kilogram from stationary sources, even though they have similar impacts on ambient PM concentrations, because diesel exhaust is typically emitted closer to people (Marshal et al. 2001). EPA should develop standard methods and validation procedures for evaluating intake fractions for major source categories in different locations and conditions for use in benefits estimation. Over time, such information would also help to make effect coefficients derived from epidemiological studies more specific to actual exposures. When effect coefficients from epidemiological studies are used to derive benefits estimates, they should be applied at the same spatial scales used in the original studies to avoid biased benefits estimates. EPA followed this approach in the benefits analysis for the HD engine and diesel-fuel rule, matching pollution concentrations with population estimates within grid areas similar in scale to metropolitan areas. However, the accuracy and reliability of a central-site monitor in representing human exposures may vary among population subgroups, resulting in differences in exposure misclassification across groups. Furthermore, exposure misclassification is likely to differ by pollutant, because a central-site monitor better represents citywide concentrations for pollutants that exhibit greater spatial homogene-

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Estimating the Public Health Benefits of Proposed Air Pollution Regulations ity, such as PM2.5 and sulfate, than for pollutants that exhibit small-scale spatial variations, such as coarse and ultrafine PM. In summary, several important uncertainties in the use of exposure assessment in benefits analysis arise from the characteristics and interpretations of exposure assessment in the epidemiological studies. These uncertainties include the assumption that ambient concentrations consistently represent population exposures across locations and at future times, the assumption that sources affect population exposures in the same way that they affect modeled ambient concentrations, and the availability of health information only for aggregate PM measures, such as PM10. Other important uncertainties in exposure assessment for benefits analysis result from methods used to model air quality under alternative regulatory scenarios. Air quality models are discussed in the following section. Air-Quality Modeling A critical link in determining the benefits of air pollution controls is to determine how emissions changes impact air quality. This determination is traditionally done using air-quality models of varying complexity. Models can be as simple as ones that assume a direct relationship between emissions and pollutant concentrations such that a 50% reduction in emissions results in a 50% reduction in ambient concentrations. These models are called linear rollback models. Air-quality models can also be considerably more complex, attempting to represent all the processes that have an important influence on ambient pollutant concentrations, including meteorology, emissions, chemistry, and physics across a broad three-dimensional region as a function of time. These models are generally called airshed models and have a wide range of capabilities and complexity. For pollutants that undergo complex nonlinear transformation, such as ozone and many components of PM, airshed models are often used, and EPA used these models in its more recent benefits analyses. Airshed models solve the mathematical equations governing the physics and chemistry of pollutants in the atmosphere, such as the conservation of chemical species, that characterize the chemical production, chemical destruction, and transport by wind and diffusion. Hundreds of compounds are in the atmosphere; thus, the system of equations to solve could be very large and also nonlinear. Airshed models generally use a subset of all the species

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Estimating the Public Health Benefits of Proposed Air Pollution Regulations and chemical reactions because not all the compounds are well-characterized. A difference in models is the complexity of the chemistry reflected in the model. For example, typically 20 to 80 species are used when modeling ozone. The number of species used has grown as computer capabilities have expanded. The actual representation of the chemistry used by a model is called a chemical mechanism. For most regulatory modeling, the mechanism used is carbon bond IV mechanism (CB-IV), which is a relatively more streamlined approach than other modeling mechanisms available (Gery et al. 1989). Another aspect of models is the spatial resolution or grid size. Most recent models allow the modeler to define the resolution. For example, a model might have a horizontal grid size of 80 kilometer (km) in one application and 36 km in another application. Newer models can also vary resolution in a single application, such as by using nested grids, and some can use grid scales as fine as 1 or 2 km. Finer resolution should improve model results and allow more accurate determination of exposure changes, especially for sources, such as mobile sources, that exhibit strong spatial gradients over fine spatial scales. However, the degree of improvement that can be achieved is limited by the resolution of the input data, such as the emissions inventory data. EPA has recently used two air-quality models for ozone analyses: the regional oxidant model (ROM) and the urban airshed model variable (UAM-V). The latter model was used in the benefits analysis for the HD engine and diesel-fuel rule. ROM is an older model that uses a nonvariable grid resolution and has relatively little vertical resolution. In addition, ROM uses an early version of CB-IV, which does not have some of the most recent updates. UAM-V has a variable grid that uses nesting and a more recent version of CB-IV and allows for a more comprehensive treatment of meteorology. However, neither ROM nor UAM-V develops the meteorological fields internally; instead, they are provided by an external meteorological model. To model PM, EPA has recently relied on the Lagrangian particle model (LPM), the climatological regional dispersion model (CRDM), the regional particulate model (RPM), and the regulatory modeling system for aerosols and deposition (REMSAD), which was used in the benefits analysis for the HD engine and diesel-fuel rule. The LPM and CRDM are relatively simple, describing the dispersion of pollutants without chemistry,

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Estimating the Public Health Benefits of Proposed Air Pollution Regulations whereas the RPM and REMSAD are built on ozone models and include chemistry and some aerosol processes. Currently, EPA is assessing the use of the community multiscale air-quality model (CMAQ). This model can be considered a state-of-the-science, “one-atmosphere” air-quality model and is to be used in regulatory and research applications. One atmosphere refers to inclusion of all relevant processes that determine the evolution of pollutants and their interactions. The one-atmosphere approach is particularly useful because it allows integrated study of all pollutants that are important to a specific region. One problem with CMAQ is that it requires extensive resources, staff, and computer time. How well a model works in a specific application is determined by two factors: the fidelity of the model itself and the quality of the model application. The latter is currently the more dominant factor. Thus, the credibility of the model results is determined by the modeling process. A good model application will use and evaluate the most appropriate model inputs, including emissions, meteorology, and topography. EPA relied on the best model inputs that were available at the time in the benefits analysis for the HD engine and diesel-fuel rule. Emissions are believed to have the greatest role in air-quality model uncertainty, followed by meteorology. Significant strides have been made to improve our understanding of emissions, and many of the biases in older inventories are believed to have been remedied. At this time, the ammonia emissions inventory is believed to be the most uncertain. Ammonia is important in PM and ozone modeling because it limits the production of secondary ammonium sulfate and ammonium nitrate. Considerable research is being dedicated to this issue and is viewed as an important step in reducing uncertainties associated with these secondary products. It is difficult to make broad generalizations regarding the accuracy of model predictions. The accuracy will depend on the model used, the pollutant modeled, the quality of the application, the available data, the spatial and temporal resolution used, the averaging times, and the areas of interest. Model accuracy should be determined empirically by comparing model estimates to actual observations in a recent period. For the HD engine and diesel-fuel rule, EPA presented fairly extensive and appropriate data on the agreement between modeled and monitored concentrations of ozone. For example, EPA reported mean normalized biases (the average difference between model predictions and observations normalized by the observa-

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Estimating the Public Health Benefits of Proposed Air Pollution Regulations tions) for ozone in the eastern United States ranging from -20% to +12%, depending on the region (northeast or southeast) and specific month (June, July, or August 1995) being modeled (EPA 2000, see Table 2A-1). Poor model performance (consistent negative biases of 30-50%) in the western United States led EPA to eliminate the western United States from the benefits analysis (EPA 2000, see p. 7-12). Although extensive evaluation of PM2.5 estimates has not been possible to date due to the lack of monitoring data, this limitation may be readily addressed in future analyses with the recent establishment of a nationwide PM2.5 monitoring program. To increase the accuracy of modeling predictions, air-quality models are typically calibrated by comparing current air quality to model predictions for current conditions. Specifically, the model is used to calculate the fractional change in pollutant concentrations between a recent time period for which data exist (the base case) and a hypothetical future time period after emissions are controlled (the control case). The fractional change is then applied to the observed pollutant level for the recent time period to derive predictions of future concentrations when proposed emissions controls have been implemented. For example, if the current observed peak ozone level is 140 parts per billion (ppb), the simulated base case is 120 ppb, and the simulated control case is 90 ppb, the ratio of the modeled quantities (90:120 or 0.75, which is known as the correction factor) is multiplied by the observed ozone level (140 ppb) to yield a predicted future ozone concentration of 105 ppb for the control case. This approach may help reduce the bias introduced by modeling errors and, therefore, may be more accurate than using model results directly (absolute values) to estimate future pollutant levels. The committee recognizes that EPA appropriately used this approach for ozone for the benefits analysis for the HD engine and diesel-fuel rule but did not do so for PM2.5, citing the lack of available PM2.5 monitoring data. The above discussion suggests that there are still significant uncertainties in model applications. Although these uncertainties are poorly characterized, they may be decreasing with time. The models that have been used in past benefits analyses noted above are subject to many uncertainties, the older ones more so than the newer ones. Many deficiencies of the older models have been remedied in the newest model, CMAQ, which may yield improved results. However, until tests are conducted that demonstrate the expected improvements in performance, CMAQ results will have to be treated as if they carry similar levels of uncertainty to current models.

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Estimating the Public Health Benefits of Proposed Air Pollution Regulations One final point regarding models is that resource constraints often prevent simultaneously estimating concentration fields with fine spatial resolution over long periods and broad areas, such as the continental United States. Compromises must be made in one or more of these dimensions (area, time, or spatial resolution). Thus, models tend to be used to estimate concentrations over low-resolution grids, such as 36 × 36 km squares, for a few days or weeks. Such large spatial scales are more appropriate for secondary pollutants (such as ozone), which exhibit relatively smooth spatial variations, than for primary pollutants (such as diesel particles), which show strong spatial gradients. Using large spatial scales limits one’s ability to assess differential exposure within urban areas and, therefore, risks to population subgroups. Although an evaluation of differential exposures would be valuable, it can only be accomplished if source emissions or air-monitoring data are available at similar or finer scales and if sufficient resources are allocated to the task. Resource constraints have also limited the periods of air quality that have been modeled in recent benefits analyses. The temporal resolution of the model outputs in days or weeks is well-suited for modeling of episodic excursions in the standards implementation context, which is the purpose for development of most models, but relatively less useful for benefits analysis, for which longer exposure records would result in more reliable health benefits estimates. For the HD engine and diesel-fuel rule, full benefits analyses were conducted only for the year 2030, although exposure modeling results were also given for two intermediate time periods (2007 and 2020). Given the need for long-term exposure estimates and the national importance of the benefits analyses, the committee recognizes that overcoming the resource constraints is a critical need. HEALTH OUTCOMES Air pollution may give rise to health outcomes depending on specific pollutants and their concentration or exposure levels. The appropriate selection and interpretation of health outcomes is integral to any assessment of health benefits. Overall, the health effects of air pollution can be described on three levels. The first level is the way that air pollution adversely affects biochemical, physiological, and pathological mechanisms. The second level concerns the way these mechanisms translate into recognized

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Estimating the Public Health Benefits of Proposed Air Pollution Regulations health effects (symptoms, impairment of activity, pain, or death). The third level involves translation of health effects into public health terms (incidence, prevalence, and mortality rates). The pathophysiological effects will, if sufficiently severe, become manifest in individuals as illness (symptoms, impairment, pain, disability, death) and be attributed to certain clinical diagnoses, such as asthma or pneumonia. These effects may be associated with the use of medical services or medications. However, the health effects of many air pollutants, such as PM and ozone, lack specificity. In other words, the manifestations of these air pollutants may have other causes and cannot be understood independently from risk factors with the same outcomes. The primary health effects resulting from air pollution have been observed in the respiratory and cardiovascular systems (ATS 1996, 2000). There is also growing evidence that air pollution exposure may cause reproductive and developmental effects (Brunekreef 1999). Short-term effects are typically minor and reversible at the levels of air pollution generally encountered in the United States, unless there is a preexisting condition that has already reduced the reserve or adaptability of the individual (ATS 1996, 2000). For example, certain air pollutants may cause a transient mild cough or eye irritation in a healthy person with plenty of functional reserve. However, for an older person who has advanced chronic respiratory disease and who is acutely ill with a respiratory infection, exposure to air pollution might result in death or some other clinically observable outcome, such as hospital admission. If the person would die soon regardless of the exposure to air pollution, the additional effect of the air pollution could be small in terms of life-years lost.1 On the other hand, if the person would otherwise recover from the respiratory infection, the loss of life-years could be appreciable. Regarding the development of chronic disease, such as chronic obstructive pulmonary disease or asthma, the effects of air pollution are likely to act together with other risk factors, such as exposure to environmental tobacco smoke. Most of the wide range of health outcomes described by the World Health Organization (WHO 2001) were considered by EPA for its benefits analyses (see Tables 2-1 and 2-5). However, many health outcomes were not quantified (EPA 1999, 2000; see Table 7-1) and included in the primary 1   This scenario is referred to as short-term mortality displacement or harvesting.

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Estimating the Public Health Benefits of Proposed Air Pollution Regulations The time course relating exposure to outcome is an important assumption in benefits analysis, especially when long-term mortality effects dominate the analysis, as occurs in PM analyses. It is important because health benefits that occur far into the future may count less based on the way the benefits are monetized. In EPA’s benefits analyses for the Tier 2 rule and the HD engine and diesel-fuel rule, EPA assumed a weighted 5-year time course of benefits in which 25% of the PM-related mortality benefits were assumed to occur in the first and second year, and 16.7% were assumed to occur in each of the remaining 3 years. Although recommended by EPA’s Science Advisory Board, the committee found little justification for a 5-year time course and recommends that future benefits analyses more fully account for the uncertainty regarding lags in health effects by incorporating a range of assumptions and probabilities on the temporal relationship. CONCLUSIONS EPA’s approaches to exposure assessment have evolved considerably over time because of the continued improvement in the models and the marked increase in available monitoring data for key pollutants. Overall, the methods used in the most recent EPA analysis reviewed by the committee (heavy-duty engine and diesel-fuel analysis) represent an appropriate and reasonably thorough application of the available data and models for exposure assessment. Many uncertainties associated with exposure assessment need to be addressed more fully as more data become available. These uncertainties include the assumptions that ambient pollutant concentrations consistently represent population exposures across locations and at future times, that sources affect actual exposures in the same way that they affect ambient concentrations, and that all particle types have a constant potency. The appropriate selection and definition of adverse health outcomes is integral to any assessment of health benefits. A wide range of health effects associated with exposure to air pollution has been described and most of them have been carefully considered by EPA. However, many health outcomes are not quantified because of insufficient data or because of the potential for double-counting. Data for many health outcomes are restricted to a specific age group, and EPA did not extrapolate those data beyond the age ranges pro-

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Estimating the Public Health Benefits of Proposed Air Pollution Regulations vided in the studies. However, recent studies conducted outside the United States provide information on certain health outcomes with broader age ranges and on outcomes, such as use of the primary care system, not evaluated by EPA. EPA used concentration-response functions from epidemiological studies. The committee supports this approach because using epidemiological studies avoids many of the problems encountered with animal toxicity and human clinical studies. The studies selected by EPA for use in its benefits analyses were generally reasonable choices. However, the criteria and the process by which EPA reached its decisions are often not clearly articulated. For the analysis of mortality, EPA used cohort studies to derive benefits estimates in the analyses reviewed by the committee. The committee supports this approach. Compared with time-series studies, cohort studies give a more complete assessment of the long-term, cumulative effects of air pollution. Furthermore, the particular advantage of cohort studies is that they provide data to estimate the number of life-years lost in a population, not just the number of lives lost, thus allowing for several valuation methods to be used. RECOMMENDATIONS As in all other stages of the benefits analysis, EPA should justify and clearly describe the assumptions and methods used to assess exposure, choose health outcomes, and select studies and concentration-response functions, paying careful attention to assessing and communicating key sources of uncertainty. Because pollution modeling rarely addresses the smaller-scale issue of how local concentrations from specific source categories interact with human time-activity patterns, EPA should examine how different major source categories, for example, mobile versus large stationary sources, affect total exposures per unit emissions. EPA has typically made the assumption of equivalent potency across particle types because of insufficient scientific information. As more data become available, EPA should strengthen its benefits analyses by evaluating a range of alternative assumptions regarding relative particle toxicity and incorporate these assumptions in sensitivity or uncertainty analyses.

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Estimating the Public Health Benefits of Proposed Air Pollution Regulations The lack of clear categorization of severity of certain health outcomes in benefits analyses has implications for the quantification and the valuation of these outcomes. Although EPA has made some attempt to recognize this issue, it should continue to develop and improve methods used to reconcile differences between the severity of disease described in air pollution epidemiology and that commonly used to develop estimates of background disease prevalence and incidence. EPA should consider data from U.S. and non-U.S. studies to extrapolate beyond the age groups evaluated and incorporate other relevant outcomes not evaluated in its current benefits analyses. EPA should give more emphasis to the assessment, presentation, and communication of changes in morbidity. Although often difficult to quantify, these factors may begin to play a more dominant role in benefits analysis if the value assigned to mortality decreases. EPA provided little information in the benefits analyses reviewed by the committee on causal association between particular types of air pollution and adverse health outcomes. EPA should summarize the evidence for causality to justify the inclusion or exclusion of the health outcomes and to assess the uncertainty associated with the assumption of causality. EPA should investigate and, if necessary, develop methods of evaluating causal uncertainty relating to key outcomes so that this uncertainty can be represented in the final benefits estimates. Although the committee believes the use of the ACS study to derive premature mortality estimates was reasonable, EPA should thoroughly review its selection of the best estimate for long-term effects of air pollution on mortality. Several new studies have been published since the ACS study, including an extended analysis of the ACS study, a new U.S. cohort study, and other non-U.S. studies. EPA should also consider whether the derivation of a weighted mean estimate from the cohort studies is appropriate following review of the database. To evaluate short-term effects of air pollution, EPA should use concentration-response functions from studies that integrate over several days or weeks the exposure period and the time period to the event (cumulative or distributed lag models) rather than those that restrict these time periods to 1 or 2 days. Although the assumption of no thresholds in the most recent EPA benefits analyses was appropriate, EPA should evaluate threshold assump-

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Estimating the Public Health Benefits of Proposed Air Pollution Regulations tions in a consistent and transparent framework using several alternative assumptions in the formal uncertainty analysis. The committee found little justification for the 5-year time course of exposure and outcome assumed in the more recent EPA analyses and recommends that EPA more fully account for the uncertainty regarding lags in health effects by incorporating a range of assumptions and probabilities on the temporal relationship. EPA is encouraged to estimate and report benefits by age, sex, and other demographic factors. The committee recognizes, however, that evaluating the differences for various subgroups adds complexity and uncertainty to the analysis and that caution must be exercised in the interpretation of such results. REFERENCES Abbey, D.E., B.L. Hwang, and R.J. Burchette. 1995. Estimated long-term ambient concentrations of PM10 and development of respiratory symptoms in a nonsmoking population. Arch. Environ. Health 50(2):139-152. Abbey, D.E., N. Nishino, W.F. McDonnel, R.J. Burchette, S.F. Knutsen, W.L. Beeson, and J.X. Yang. 1999. Long-term inhalable particles and other air pollutants related to mortality in nonsmokers. Am. J. Respir. Crit. Care Med. 159(2):373-382. Ackermann-Liebrich, U., P. Leuenberger, J. Schwartz, C. Schindler, C. Monn, G. Bolognini, J.P. Bongard, O. Brandli, G. Domenighetti, S. Elsasser, L. Grize, W. Karrer, R. Keller, H. Keller-Wossidlo, N. Künzli, B.W. Martin, T.C. Medici, A.P. Perruchoud, M.H. Schoni, J.M. Tschopp, B. Villiger, B. Wuthrich, J.P. Zellweger, and E. Zemp. 1997. Lung function and long-term exposure to air pollutants in Switzerland. Study on air pollution and lung disease in adult (SAPALDIA) team. Am. J. Respir. Crit. Care Med. 155(1):122-129. ATS (American Thoracic Society). 1996. Health effects of outdoor air pollution. Committee of the Environmental and Occupational Health Assembly of the American Thoracic Society. Am. J. Respir. Crit. Care Med. 153(1):3-50. ATS (American Thoracic Society). 2000. What constitutes an adverse health effect of air pollution? Official statement of the American Thoracic Society. Am. J. Respir. Crit. Care Med. 161(2 Pt. 1):665-673. Bates, D.V. 1992. Health indices of the adverse effects of air pollution: The question of coherence. Environ. Res. 59(2):336-349. Bennett, D.H., T.E. McKone, J.S. Evans, W.M. Nazaroff, M.D. Margni, O. Jolliet, and K.R. Smith. 2002. Defining intake fraction. Environ. Sci. Technol. 36(9):207A-211A.

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