Environmental Health Perspectives • VOLUME 110 | NUMBER 12 | December 2002 1253

The Importance of Population Susceptibility for Air Pollution RiskAssessment: A Case Study of Power Plants Near Washington, DC

Jonathan I. Levy, Susan L. Greco, and John D. Spengler

Department of Environmental Health, Harvard School of Public Health, Boston, Massachusetts, USA

The issue of subpopulation susceptibility tofine particulate matter (< 2.5 µm in aerody-namic diameter; PM2.5) has been givenincreased attention by researchers in recentyears, motivated in part by the research priori-ties articulated by the National Academy ofSciences (1). Understanding patterns of suscep-tibility not only would help identify and pro-tect sensitive subpopulations, but also wouldcontribute to the understanding of mechanismsby which PM2.5 might influence human health.

Often, air pollution policies are informedby risk assessments or benefit–cost analyses,which generally focus on the total health ben-efits of alternative emission control strategies(2–5). Because relevant susceptibility evi-dence is limited, differential effects on suscep-tible subpopulations are rarely incorporated.Typically, the same relative risks are appliedto all individuals in an “at-risk” age group,and baseline rates of disease or health care useare assumed to be uniform across large geo-graphic areas (often national averages).

However, it is likely that the effects of airpollution vary widely across subpopulations,depending on demographics, behavior patterns,income, access to health care, and other factors.Differences could exist either in relative risks (ifan increment of air pollution yields a differentpercentage increase in different populations) or

in absolute risks (if there are differences in base-line disease patterns by subpopulation, inde-pendent of air pollution). For a benefitsassessment, if policy makers were concernedabout distributional issues or if the ultimate val-uation of benefits depended on populationcharacteristics, the incorporation of susceptibil-ity could potentially influence the conclusions.

One current policy issue for which infor-mation on susceptibility could be influential isthe regulation of emissions from older powerplants. To date, older power plants have notbeen required to meet the same control require-ments as new sources, helping to extend theuseful lifetime of older facilities (6–8). Thesefacilities contribute a substantial fraction ofnational power sector emissions. In 1999, coal-fired power plants contributed approximately86% of nitrogen oxide (NOx) emissions and93% of sulfur dioxide emissions from the util-ity sector, largely from facilities exempted fromnew source standards (9).

At the time this article was written (2001),several states (including Massachusetts,Connecticut, and Texas) had introduced mul-tipollutant regulations or legislation to requireolder power plants to meet emission levelscommensurate with the application of “BestAvailable Control Technology” (BACT; tech-nology required under the Clean Air Act for

new or modified sources in attainment areas).Pollutants considered typically included NOx

and SO2, as well as mercury and carbon diox-ide. Multipollutant power plant legislationwas also being debated at the federal level, butno bills or regulations had been passed at thetime of our analysis.

From both a state and a federal perspec-tive, the question of how the benefits of emis-sion controls would be distributed could beimportant. Policy makers may be concernedabout providing benefits to high-risk commu-nities, communities near power plants, orother subpopulations. If these questions areimportant, population susceptibility couldinfluence the policy choices (e.g., emissiontrading vs. mandatory on-site controls).

In this article, we develop a model to esti-mate the health benefits associated with emis-sion reductions at older fossil-fueled powerplants. We focus on both primary PM2.5 andsecondary sulfate and nitrate particles formedthrough emissions of SO2 and NOx. Here weconsider a case study of all older power plantslocated within a 50-mile (80-km) radius ofWashington, DC. We calculated three healthend points—premature mortality, cardiovas-cular hospital admissions (CHA) in theelderly, and pediatric asthma emergencyroom visits (ERV)—both using conventionalassumptions and then considering availableevidence for differential effects on susceptiblesubpopulations. Our goal was both to quan-tify the health benefits associated with theimplementation of BACT at the selectedpower plants and to consider whether intro-duction of susceptibility models might affectthe interpretation of our findings.

Case Study Setting

For this analysis, our goal was to select a geo-graphic area that had multiple older powerplants nearby and geographic heterogeneity in

Address correspondence to J.I. Levy, Landmark CenterRoom 404K, 401 Park Drive, PO Box 15677, Boston,MA 02215 USA. Telephone: (617) 384-8808. Fax:(617) 384-8859. E-mail: jilevy@hsph.harvard.edu

We thank D. Hlinka, D. Sullivan, and D. Moonfor their assistance in the atmospheric dispersionmodeling; B. Egan, R. Paine, D. Heinold, F.Cameron, J. Samet, and J. Scire for providing help-ful feedback on related manuscripts; and R.Tangirala for bringing an error to our attention.

This study was commissioned by the Clean AirTask Force and prepared with support from the PewCharitable Trusts and the Kresge Center forEnvironmental Health (NIEHS ES000002).

Received 4 November 2001; accepted 21 May 2002.

Articles

In evaluating risks from air pollution, health impact assessments often focus on the magnitude ofthe impacts without explicitly considering the distribution of impacts across subpopulations. Inthis study, we constructed a model to estimate the magnitude and distribution of health benefitsassociated with emission controls at five older power plants in the Washington, DC, area. Weused the CALPUFF atmospheric dispersion model to determine the primary and secondary fine-particulate-matter (< 2.5 µm in aerodynamic diameter) concentration reductions associated withthe hypothetical application of “Best Available Control Technology” to the selected power plants.We combined these concentration reductions with concentration–response functions for mortalityand selected morbidity outcomes, using a conventional approach as well as considering susceptiblesubpopulations. Incorporating susceptibility had a minimal effect on total benefits, with centralestimates of approximately 240 fewer premature deaths, 60 fewer cardiovascular hospital admis-sions (CHA), and 160 fewer pediatric asthma emergency room visits (ERV) per year. However,because individuals with lower education appear to have both higher background mortality ratesand higher relative risks for air-pollution–related mortality, stratifying by educational attainmentimplies that 51% of the mortality benefits accrue among the 25% of the population with less thanhigh school education. Similarly, diabetics and African Americans bear disproportionate shares ofthe CHA and ERV benefits, respectively. Although our ability to characterize subpopulations isconstrained by the available information, our analysis demonstrates that incorporation of suscepti-bility information significantly affects demographic and geographic patterns of health benefits andenhances our understanding of individuals likely to benefit from emission controls. Key words:asthma emergency department visits, cardiovascular hospital admissions, diabetes, education, mor-tality, particulate matter, power plant, risk assessment, susceptibility. Environ Health Perspect110:1253–1260 (2002). [Online 29 October 2002]http://ehpnet1.niehs.nih.gov/docs/2002/110p1253-1260levy/abstract.html

factors that might influence relative risks,baseline health status, or health care use (e.g.,socioeconomic status). Washington, DC, andits surrounding suburbs provide an exampleof such a region. According to 1990 U.S.Census data (10), median household incomein Washington, DC, ranged from less than$10,000 to more than $150,000 across cen-sus tracts. Washington, DC, is also quiteracially divided, with few African Americansresiding in the western half of the city andmostly African Americans residing in the east-ern half of the city.

In addition, within a 50-mile (80-km)radius of Washington, DC, there are five fos-sil-fueled power plants grandfathered underthe Clean Air Act—Benning, Chalk Point,Dickerson, Possum Point, and PotomacRiver (Table 1). The choice of these fivepower plants is somewhat artificial becauseany single regulation would not affect onlythese plants. However, our analysis is meantto be illustrative, and these five plants arelikely the greatest contributors to heterogene-ity in power-plant–related exposures in thearea. Inclusion of additional power plantswould increase the total benefits but decreasethe relative concentration gradient across theWashington, DC, area.

Methods

To quantify the magnitude and distributionof health benefits, we estimated the emissionreductions of key pollutants, applied anatmospheric dispersion model to determineincremental concentration reductions, andderived concentration–response functions.Any such analysis involves numerous bound-ary decisions and contains substantial uncer-tainties. In this article, we focus largely onissues related to susceptible subpopulationsand the resulting implications. We do notextensively address the complexities of otherelements of the model, nor do we provide aformal analysis of uncertainties. We also donot consider the economic valuation dimen-sion of a benefits assessment. Additionalinformation about parametric uncertainties inour atmospheric model (4,11) and issuesrelated to differential particle toxicity or alter-native interpretations of the health evidence(4) can be found elsewhere.

Quantification of emissions. We esti-mated emissions of PM2.5 and its precursors(NOx and SO2) following the model structurein our earlier analyses (4,11) and supportedby the fact that PM2.5 has dominated aggre-gate benefits in past air pollution risk assess-ments (2,3). This omits any benefitsassociated with ozone, air toxics, or otherimpact pathways from the power sector. Ofnote, most proposed regulations considerNOx and SO2 but do not directly requirecontrols for primary PM2.5 (although many

NOx and SO2 control strategies would affectprimary PM2.5).

We used 1999 as the base year for ouranalysis, evaluating the concentration andhealth benefits that would have been obtainedhad lower target emission rates been achieved.This is not identical to the future benefitsthat might be obtained through pending reg-ulation, because some facilities have ongoingor near-term plans for repowering or emissioncontrols.

Emissions of SO2 and NOx were takenfrom the 1999 acid rain program emissionsscorecard from the U.S. EnvironmentalProtection Agency (EPA) (12). To captureseasonality in emissions, we incorporatedquarterly average emission rates whenreported. When no data on seasonal emis-sions were available, we assumed constantemissions per unit of heat input. For filterablePM2.5, total plant emissions were taken fromthe U.S. EPA National Emission Trendsdatabase (13). We estimated condensablePM2.5 emissions given fuel type and sulfurcontent, using AP-42 air pollution emissionfactors from the U.S. EPA (14).

We selected lower target emissions to cor-respond to the levels proposed in multipleregulations, which correspond to the applica-tion of BACT. This resulted in target emis-sion rates of 0.3 lb/MMBTU (million Britishthermal units) of SO2, 0.15 lb/MMBTU ofNOx, and 0.01 lb/MMBTU of filterable par-ticulate matter. Lower target condensable par-ticulate emissions were taken from AP-42,given assumed application of control tech-nologies. Because both Dickerson andBenning power plants have actual filterablePM2.5 emissions less than the lower targetrate, we set the lower target filterable PM2.5

emission rate equal to actual emissions forthese plants.

Atmospheric modeling. We established areceptor grid covering a 400-km (250-mile)radius around Washington, DC (centered at38.9°N, 77°W), to capture a significant frac-tion of total benefits without extending thedispersion modeling boundaries excessively(Figure 1). Because of our focus on spatialpatterns, it was important to determineconcentration reductions at small geographic

scales close to the sources. We selected censustracts within 100 km of Washington, DC,because they are relatively small (generallybetween 2,500 and 8,000 people) and weretheoretically designed to be socioeconomi-cally homogeneous. Beyond 100 km, we usedcounty-level resolution, resulting in a nestedreceptor grid with 1,908 receptors. Using1990 Census data (10) (the most recent dataavailable at the time of our study), our recep-tor grid contained 47 million individuals, 7million of whom live within 100 km ofWashington, DC.

We conducted our atmospheric modelingusing CALPUFF (CALMET version 5.2000602a, CALPUFF version 5.4-000602-1,CALPOST version 5.2 991104b; Earth Tech,Concord, MA). CALPUFF is a regional-scaleLagrangian puff model that has been recom-mended by the U.S. EPA for long-rangetransport modeling (15), given that it hasbeen shown to be relatively unbiased at dis-tances out to 200 km (16). In general, limita-tions in the atmospheric chemistry make thesecondary pollutant estimates relatively moreuncertain than the primary PM2.5 estimates,given the nonlinearities associated with sul-fate and nitrate formation.

Our methodology to generate meteoro-logic files for CALMET was similar to theapproach in our past applications and isdescribed in depth elsewhere (4,11). We com-bined National Oceanic and AtmosphericAdministration (NOAA) prognostic modeloutputs with mesoscale data assimilation sys-tems for each hour across our case study year(January 1999–January 2000). This involvedcombining lower-resolution upper air data(40-km grid spacing) generated throughNOAA’s Rapid Update Cycle (RUC2)model (17) with Aviation Routine WeatherReport (METAR) surface observations andcloud cover data available at 15 km resolu-tion (18). These data sources were combinedusing the Advanced Regional PredictionSystem (ARPS) Data Assimilation System(ADAS) and provided hourly CALMETwindfields within eight vertical layers.Precipitation data were taken from allNational Climatic Data Center stationswithin the receptor region, with CALMET

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1254 VOLUME 110 | NUMBER 12 | December 2002 • Environmental Health Perspectives

Table 1. Characteristics of five power plants in Washington, DC, case study (1999 data).

Possum PotomacCharacteristics Benning Chalk Point Dickerson Point River

Initial year of commercial operation 1968 1964 1959 1948 1949Nameplate capacity (megawatts) 580 2,046 588 1,373 514Heat input (MMBTU) 3,304,107 85,352,274 33,592,811 28,930,805 32,100,184Emissions, tons (% per quarter)

SO2 1,432 57,630 30,637 19,497 17,627(2, 21, 76, 2) (21, 25, 31, 23) (30, 17, 34, 18) (24, 22, 32, 23) (22, 28, 29, 21)

NOx 447 25,222 10,709 5,116 6,893(2, 22, 74, 1) (20, 24, 30, 26) (30, 17, 34, 18) (25, 22, 32, 21) (21, 28, 30, 21)

PM2.5 12 304 14 156 106(2, 22, 74, 2) (21, 27, 33, 20) (30, 17, 34, 18) (23, 20, 37, 20) (21, 28, 29, 22)

defaults used for interpolation between sta-tions. The primary difference from our previ-ous applications was the inclusion of 50evenly spaced “soundings” based on columnsof the ADAS data, to more accurately providea reasonable high-resolution temperature fieldand subsequent planetary boundary-layerdepth estimates.

In CALPUFF, we adopted recommendedmodeling assumptions that were used in ourpast applications (4,11). We used theMESOPUFF II chemical transformationmechanism, which is generally preferred inurban settings. Wet and dry deposition wereincorporated using precipitation data andCALPUFF default deposition rates. Hourlybackground ozone concentrations were takenfrom five U.S. EPA Clean Air Status andTrends Network (CASTNET) stationsspaced throughout our receptor region(Prince George’s County, MD; MercerCounty, NJ; Elk County, PA; Prince EdwardCounty, VA; Gilmer County, WV), and weassumed a background ammonia concentra-tion of 1 ppb.

For brevity’s sake, in this article we donot provide sensitivity or uncertainty analy-ses for our atmospheric modeling. In ourpast analyses (4,11), we found total benefitsto be reasonably stable given single paramet-ric changes in CALPUFF, including thechemical conversion mechanism, background

ammonia concentration, and treatment ofwet and dry deposition. In addition, we con-cluded that any bias associated with eitherhypothetical CALPUFF overestimationbeyond 200 km or exclusion of long-rangeexposures is relatively small in comparisonwith other model uncertainties. A compre-hensive risk assessment would need to incor-porate these uncertainties in an evaluation ofoverall model uncertainty.

Health evidence. Although numeroushealth outcomes have been incorporated intopast analyses (2), here we focus on a subset forwhich some evidence exists for differentialeffects on susceptible subpopulations. Thechoice of outcomes as well as the subpopula-tions considered therefore depends entirely onthe current literature and is not meant to becomprehensive. Furthermore, we restrictedthe health evidence to epidemiologic studiesconducted in the United States, because pat-terns of health care use and the relationshipbetween demographics and health status likelyvary across countries. Given these criteria, weevaluated premature mortality (stratified byeducation), CHA for the elderly (stratified bydiabetic status and age), and asthma ERV forchildren (stratified by race and age). For eachoutcome, we both describe a conventionalapproach and construct a susceptibility model.Our goal is not to consider the complete arrayof susceptible subpopulations, but rather to

select one example for each outcome forwhich epidemiologic evidence and popula-tion data exist.

Premature mortality. For premature mor-tality, we derived a central estimate from thefollow-up analysis of the American CancerSociety (ACS) cohort study (19). Several othercohort studies are available (20,21), but theACS study has the largest and most geograph-ically diverse population, with relative risksbounded by other studies and a statisticalapproach suggested by a detailed reanalysis(22). For all-cause mortality, the authors cal-culated a relative risk of 1.04 [95% confidenceinterval (CI), 1.01–1.08] for a 10 µg/m3

increase in annual mean PM2.5 concentrations(using 1979–1983 concentrations). The rela-tive risk was slightly higher (1.06) using morerecent pollution data, but we use the lowerfigure to be conservative and because Pope etal. (19) presented stratified estimates based onthe 1979–1983 concentrations.

Relative risks did not vary substantiallyacross most demographic factors except edu-cational attainment. Educational attainmentappeared to be a strong effect modifieracross all causes of mortality. The relativerisk for a 10 µg/m3 increase in annual meanPM2.5 concentrations was 1.085 (95% CI,1.031–1.142) for individuals with less thanhigh school education, 1.045 (95% CI,1.004–1.087) for individuals with highschool education, and 1.003 (95% CI,0.967–1.040) for individuals with morethan high school education.

There are numerous uncertainties relatedto the application of this stratified relativerisk. The ACS cohort is somewhat more edu-cated than the population at large, and corre-lated terms such as race and poverty statushave not been significant in time-series mor-tality or hospital admissions studies (23–25).In addition, the statistical approach impliesthat we are modeling the effect of educationcontrolling for smoking and other factors,which would ideally be included to model theinfluence of all risk factors correlated witheducational attainment. Regardless, we usethe education-stratified values to determinethe implications of the reported relationship.

For background mortality rates, the stan-dard approach is to apply county-level aver-ages to individuals 30 or more years old [theage range considered in the ACS study (19)].We used this as our baseline approach, butfor our susceptibility model, we consideredwhether mortality rates vary as a function ofeducation while still averaging to the reportedcounty-level rates.

There is a strong and consistent negativerelationship between socioeconomic statusand all-cause mortality (26). Socioeconomicstatus can be measured by occupation,income, education, or some combination of

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Environmental Health Perspectives • VOLUME 110 | NUMBER 12 | December 2002 1255

Figure 1. Receptor grid and power plant locations for Washington, DC, case study.

BenningPotomac RiverChalk PointPossum Point

Dickerson

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these terms. It is generally believed that bothincome (27) and educational attainment (28)are independent predictors of mortality,although the bases for these relationships arenot well understood. Some argue that those inlower socioeconomic classes display high-riskbehaviors, such as smoking, being overweight,and not exercising (29), producing highermortality rates. However, only a small fractionof the increased mortality can be explained bya higher prevalence of high-risk behaviors (30),so there must be other contributing factors. Inany case, it is clear that those in low educationor income categories represent a susceptiblesubpopulation for all-cause mortality.

Educational attainment is a useful predic-tor of mortality because it typically does notchange after adulthood. Additionally, this termis available for all segments of the adult popu-lation, even those not in the work force.Although it may be a proxy for other factors,various hypotheses have been presented forwhy lower education might be a causal factorfor mortality. Education may be a marker forfactors (e.g., intelligence and good health inearly childhood) that allow for both educa-tional attainment and good health in adult-hood, for acquired knowledge that can be usedto obtain positive health outcomes, for relativestatus in society, or for the development ofpositive social networks (31). The protectiveeffect of higher education has been seen in theUnited States (31) and worldwide (32,33).

We selected our baseline mortality riskratios from a study that evaluated risks for all-cause mortality as a function of both educa-tion and annual income among a cohort25–64 years old, drawn from the NationalLongitudinal Mortality Study (31). The rela-tionship between education and mortalitywas best described by a trichotomy (less thanhigh school education, high school diplomaor greater but no college diploma, or a collegediploma or greater). When compared withthe highest education group, the annual mor-tality relative risk for men was 1.7 for lessthan high school education and 1.5 for highschool diploma or greater but no collegediploma. For women, the corresponding rela-tive risks were 1.5 and 1.2. The attenuationin women has been documented previouslyand can be attributed largely to the marriedsubpopulation of women (34). We appliedthese relative risks to all individuals morethan 30 years old, although there is some evi-dence that socioeconomic differences play lessof a role in determining mortality ratesamong the aged (35).

Cardiovascular hospital admissions.Several studies in the United States have eval-uated the relationship between particulatematter exposure and CHA among individuals65 or more years old (24,25,36–43). Mostcentral estimates from these studies fall in the

range of a 0.5–1% increase in CHA for a 10µg/m3 increase in daily concentrations of par-ticulate matter < 10 µm in aerodynamicdiameter (PM10). Using a typical PM2.5:PM10

ratio of 60%, we would consider appropriatea central estimate of an approximate 1%increase in CHA per 10 µg/m3 increase indaily PM2.5 concentrations. As a baseline, weapplied this percentage to the average back-ground rate of 0.084 CHA per year perindividual ≥ 65 years old (44).

Although numerous factors might influ-ence either the baseline risk or the relative riskof an air-pollution–related CHA, we focusedon diabetes to illustrate the influence of a riskfactor that varies demographically and mightinfluence both risks. To estimate the numberof diabetic and nondiabetic CHA in a countyor census tract, we considered two relation-ships: the risk factors for diabetes among theelderly and the differential risk for a CHAgiven the presence of diabetes.

In those > 65 years old, noninsulin-depen-dent diabetes mellitus (NIDDM) accounts forvirtually all of the diabetic caseload. There arenumerous risk factors for NIDDM, includingage, obesity, family history, and sedentarylifestyle. Although lifestyle variables are thestrongest predictors of diabetic status[accounting for as much as 90% of populationattributable risk (45)], we cannot estimatethese variables at the census tract level frompublicly available data. In the absence of thisinformation, we estimated NIDDM preva-lence as a function of gender, age, and race.According to a national survey (46), NIDDMprevalence in individuals > 65 years old ishigher among African Americans andMexican Americans than in non-Hispanicwhites, ranging from 10.9% for non-Hispanicwhite males 65–74 years old to 29% forMexican-American females 65–74 years old.We applied these estimates to our study popu-lations, despite the limitations in applyingnational relationships based on race to a spe-cific geographic setting. The relationshipbetween race and common risk factors likelyvaries widely across regions and within smallgeographic areas, a feature that is not capturedby our model.

Regarding risks for a CHA, it has beenwell established that diabetics have anincreased risk of heart disease. Several studiesalso indicate that diabetics are admitted to thehospital more frequently than are nondiabet-ics (47,48). Thus, it is not surprising thatCHA rates are elevated in diabetic popula-tions. According to a national diabetes sur-veillance report (49), as of 1996, the annualCHA rate was 0.20 admissions per year perdiabetic 65–74 years old and 0.27 for diabet-ics ≥ 75 years old. In contrast, the rates forthe population as a whole are 0.06 (ages65–74 years) and 0.11 (≥ 75 years) (44).

Using these two rates and the estimated dia-betes prevalence across our study population,we can calculate the CHA rate for nondiabet-ics. Clearly, there are several appreciableassumptions underlying these estimates.Although we know that marked differencescan exist in CHA rates among states andcommunities, we assume that tract-specificrates vary only as a function of the estimatednumber of diabetics, with CHA rates invari-ant for nondiabetics. This likely underesti-mates the degree of spatial and demographicvariability in CHA rates.

On the relative risk side, a time-seriesstudy in Chicago (38) found a 2% increase inCHA for diabetic individuals > 65 years oldfor a 10 µg/m3 increase in PM10, versus a0.9% increase for nondiabetics. In contrast,the studies that evaluated factors such as race,education, or poverty (24,37,43) found nosignificant effect modification for CHA rela-tive risks. To ensure that our concentration–response function agrees with our nonstrati-fied estimate, we assumed that a factor of twodifference exists between diabetics and non-diabetics and calculated the concentration–response function given the estimated numberof CHA in diabetics and nondiabetics in ourstudy population. The result is a 0.7%increase in CHA per 10 µg/m3 increase inPM2.5 for nondiabetics, with a 1.5% increasefor diabetics.

Pediatric asthma ERV. Many studies haveassociated ERV for numerous respiratory andcardiovascular causes with particulate matter,but to date only two studies in the UnitedStates have considered asthma-related visitsamong children (defined here as ≤ 18 yearsold). In Seattle (50), an 11.6 µg/m3 increase inPM10 was associated with a 14% increase inasthma ERV (95% CI, 5–23%), and a 9.5µg/m3 increase in PM2.5 was associated with a15% increase. This study found the relativerisk to be similar in high-use and low-use areas(a proxy for socioeconomic status). In Atlanta(51), a 4% increase in pediatric asthma ERVwas estimated for a 15 µg/m3 increase in PM10

concentrations (95% CI, 0.4–7%). As inSeattle, there did not appear to be effect modi-fication due to race or socioeconomic status.Simply pooling these two studies using a ran-dom effects model (52) provides a central esti-mate of a 0.7% increase in asthma ERV permicrogram per cubic meter increase in PM10,which we translate into an approximate 1%increase in asthma ERV per microgram percubic meter increase in daily PM2.5. This canbe applied to a background asthma ERV rateof 0.012 for children 0–4 years old, 0.0081for children 5–14 years old, and 0.0069 forchildren ≥ 15 years old (53).

Although the published studies did notidentify susceptible subpopulations from arelative risk perspective, the background rate

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of asthma ERV would be anticipated to differwidely across subpopulations. This would bea function both of trends in asthma preva-lence and of patterns in health care use acrosspopulations.

The prevalence of asthma has increasedsubstantially in recent years (53), with lower-income individuals and minorities dispropor-tionately affected by the disease (54–58). Manyof the significant predictors of childhoodasthma, such as cockroach presence in thehome (59) and maternal education (60), arerelated to socioeconomic status. Furthermore,patterns of health care use are strongly relatedto income. The ratio of anti-inflammatory tobeta-agonist medication is lower in low-incomecommunities and is inversely correlated withhospitalization rates (61), and lower-incomepopulations lacking health insurance often useemergency services as a means of primary care.Thus, it would be expected that low-incomepopulations would have somewhat higher pedi-atric asthma ERV rates.

Data on pediatric asthma ERV rates as afunction of income were limited, but substan-tial racial differences have been documented.According to data from the National HospitalAmbulatory Medical Care Survey (53), acrossall ages, the asthma ERV rate for AfricanAmericans is nearly five times greater thanthat for whites (0.023 and 0.0049 per capita,respectively). No data were provided onasthma ERV rates stratified across both ageand race, but a study of 3-year-olds in theUnited States found a racial differential ofsimilar magnitude but with some indepen-dent effects of both race and income (54).

Given available information, we estimatedbaseline pediatric asthma ERV rates as a func-tion of age and race, assuming the racial dispar-ity to exist in all age groups. This encompassesdifferences both in prevalence and in healthcare use. As with our diabetes estimates, thereare some substantial limitations in using onlyrace as a predictor, because the relationship

between race and asthma ERV risk factorsvaries by income, urban/rural status, and otherfactors. Regardless, the consistent relationshipbetween race and ERV and the ability togather racial information at the census tractlevel make this the best available covariate.

Results

Concentration reductions. With our atmos-pheric dispersion model, the emission reduc-tions at the five selected power plants wouldlead to annual average PM2.5 (primary plussecondary) concentration reductions rangingfrom 0.009 to 0.9 µg/m3 in our receptorregion (Figure 2C). By way of comparison,according to U.S. EPA AIRS data (62),annual average PM2.5 concentrations inWashington, DC, were approximately 14–18µg/m3 in 1999. The maximum annual aver-age PM2.5 concentration reduction is foundwithin Washington, DC, as might be antici-pated by the power plant selection criteriaand the inclusion of primary PM2.5.

The geographic distribution of benefitsvaries somewhat across particle types, powerplants, and seasons. Annual average primaryPM2.5 concentration reductions peak closer tothe plants and decrease more rapidly with dis-tance than secondary sulfates or nitrates(Figure 2). As a result, a greater fraction oftotal exposure reduction (defined as the sumacross receptors of the product of concentra-tion reduction and population assigned to thereceptor) occurs closer to the power plants forprimary than for secondary PM2.5 (Figure 3).However, there is tremendous variability inthe distribution of total exposure reduction,caused principally by variations in sourcelocations and pollutant type (primary vs. sec-ondary). In addition, total exposure reductionper unit emissions displayed expected sea-sonal patterns, with slightly higher values forprimary PM2.5 in the winter and fall (relatedin part to lower mixing heights) and highervalues for sulfates and lower values for

nitrates in the summer due to the effect oftemperature on relative conversion rates.

Health benefits. For premature mortality,using nonstratified relative risks and homoge-neous baseline mortality rates within coun-ties, our central estimate is that emissionreductions from the five power plants wouldlead to 210 fewer deaths per year (Table 2).The estimated impact under the currentemissions scenario is 270 deaths per year. Ofthe total mortality benefits, approximately25% occur in individuals with less than highschool education (identical to the proportionin the population). Approximately 16% ofmortality benefits accrue within 50 km of thepower plants, largely related to the substantialcontribution of secondary sulfates (62%) andnitrates (19%) to total PM2.5 exposures.

In our susceptibility model, with bothbaseline mortality rates and PM2.5 relativerisks stratified by educational attainment, ourunderstanding of the affected subpopulationschanges substantially (Table 2). The totalmortality benefit is largely unaffected, with aslight increase associated with differences in educational attainment between theWashington, DC, area and the ACS cohort.However, 51% of the estimated mortalitybenefits now accrue among individuals withless than high school education, double theprediction in the homogeneous risk model.

Although stratification by education doesnot significantly influence the broad geo-graphic patterns of benefits (i.e., the fraction ofbenefits within 50 km), at the census tract levelbenefits differ by as much as a factor of 13between the models. Figure 4 depicts the geo-graphic patterns of benefits under both thebaseline and susceptibility models, focusingsolely on census tracts in Washington, DC, forsimplicity. Using the baseline model, the mor-tality risk reductions in Washington are rea-sonably homogeneous, ranging from 36 to 67fewer deaths per year per million individuals> 30 years old. Under the education-stratified

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Environmental Health Perspectives • VOLUME 110 | NUMBER 12 | December 2002 1257

Figure 2. Combined concentration reductions (annual average, µg/m3) from hypothetical emission controls at five power plants: (A) primary PM2.5; (B) secondaryPM2.5; (C) total PM2.5.

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36–80 –79 –78 –77 –76 –75 –74 –80 –79 –78 –77 –76 –75 –74 –80 –79 –78 –77 –76 –75 –74

A B C0.450.420.390.360.330.300.270.240.210.180.150.120.090.060.030.00

0.450.420.390.360.330.300.270.240.210.180.150.120.090.060.030.00

0.850.800.750.700.650.600.550.500.450.400.350.300.250.200.150.100.050.00

Longitude Longitude Longitude

µg/

m3

µg/

m3

µg/

m3

model, the range broadens considerably andthe distribution is more complex, with percapita benefits now varying by more than afactor of 10 across census tracts. The mortalitybenefits are generally increased in southeasternWashington, DC, the lowest-income area ofthe city.

When we consider CHA among theelderly, our baseline model estimates 59 fewerCHA per year. Although it seems counterin-tuitive that the mortality numbers couldexceed the morbidity numbers, this is relatedto the limited focus on CHA because of onlyshort-term exposures among the elderly (vs.all-cause mortality from long-term exposuresamong individuals ≥ 30 years old). Using aconventional model that assumes diabetics donot differ in any way from nondiabetics, 13%of the CHA benefits are estimated to occuramong diabetics, whereas 80% are foundamong non-Hispanic whites (Table 2). Thegeographic distribution of CHA benefits issimilar to the exposure reduction and mortal-ity benefits, with differences reflecting the rel-ative number of individuals 65–74 years oldand ≥ 75 years old within census tracts.

As expected, incorporating the diabetes-based information has a minimal impact onaggregate benefits but dramatically alters theprofile of the affected individuals (Table 2).Using this model, 54% of the CHA benefitsare found among diabetics, with 76% amongnon-Hispanic whites. Because we haveassumed that baseline CHA risk for nondia-betics does not differ as a function of race orincome, the CHA estimates under the suscep-tibility model are closer to those from thebaseline model than are those for mortality(Figure 4). However, even considering onlydiabetes-related susceptibility changes thecensus tract-level benefits by as much as 40%.

Finally, we estimate 140 fewer pediatricasthma ERV per year using our nonstratifiedmodel (38% in children 0–4 years old, 46% inchildren 5–14 years old). Twenty-seven percentof benefits occur in African-American children(who represent 21% of the study population).When we stratify asthma ERV risk by race, thetotal benefits increase to 160 fewer visits peryear, with significant changes in the geographicand demographic distributions (Table 2). Thecensus-tract–level risk reduction varies by anorder of magnitude across Washington, DC,with the benefits increased by more than afactor of two in the eastern half of the city(Figure 4). The proportion of benefits amongAfrican-American children is increased to 64%,commensurate with the assumption of greaterbaseline asthma ERV rates.

Discussion

Our analytical approach demonstrates twoimportant points. First, given an interpreta-tion of the epidemiologic evidence that

assumes that ambient concentrations in theWashington, DC, area exceed any potentialpopulation threshold for PM2.5 health effects,emission controls at older fossil-fueled powerplants would provide tangible and quantifi-able health benefits. Second, when we takeaccount of susceptible subpopulations anddifferences in both relative risk and baselinedisease rates across these populations, thesmall-scale geographic and demographic dis-tributions of those benefits are stronglyaffected. For the example of premature mor-tality, if educational attainment influencesboth the relative risk of air pollution and thebaseline mortality risk, then more than half ofthe mortality benefits accrue among the 25%of our study population with less than highschool education. Similarly, for pediatricasthma ERV, the fact that background ratesare substantially greater in African Americansimplies that most ERV benefits accrue in21% of the population, even given identicalrelative risks from air pollution. The relativelysmaller differences found for CHA whendiabetes is considered illustrates that evidence

for differential effects on a relatively smallfraction of the population has a smaller effectthan a population-wide model.

There are clearly some barriers in bothinterpretation of the study findings and appli-cation of our model to other settings. Oneimportant uncertainty is related to the strati-fied risk models we selected. For all healthoutcomes, we used stratification variables(such as race) that might have independenteffects on baseline health but likely are prox-ies for numerous socioeconomic end points.If the stratification variables represent otherfactors, this adds to the uncertainty in a site-specific stratified analysis.

In general, we have applied susceptibilitymodels based on national data to a smallnumber of states, which has multiple inherentlimitations. Clearly, it would be preferable touse local health data, but data at small geo-graphic scales for a large region are difficult toobtain and are rarely stratified across alldemographic variables of interest. In addi-tion, the reliance on national data increasesthe generalizability of our findings. Despite

Articles • Levy et al.

1258 VOLUME 110 | NUMBER 12 | December 2002 • Environmental Health Perspectives

Figure 3. Cumulative distribution of total exposure reduction as a function of distance from the source, bypower plant and pollutant type.

100

90

80

70

60

50

40

30

20

10

0

Perc

ent o

f tot

al e

xpos

ure

redu

ctio

n

0 50 100 150 200 250 300 350 400 450 500

Distance from source (km)

Primary Chalk Point Primary Dickerson Primary BenningPrimary Potomac River Primary Possum Point Secondary Chalk PointSecondary Dickerson Secondary Benning Secondary Potomac RiverSecondary Possum Point

Table 2. Magnitude and distribution of health benefits associated with modeled emission reductions at fivepower plants near Washington, DC.

Health outcome and Baseline model Full susceptibility modelstratification covariate (No stratification) (Stratification by listed covariate)

Deaths/yearTotal 210 240< High school education 52 120≥ High school education 150 120

CHA/yearTotal 59 60Diabetic 8 33Nondiabetic 51 27

Asthma ERV/yearTotal 140 160African American 38 100Non-African American 100 57

Data presented are rounded to two significant figures; sums may not add because of rounding.

these issues, our models demonstrate thatsimple assumptions about susceptibility canbe influential in our understanding of healthrisks and benefits. The alternative is anassumption of homogeneity, which itselfintroduces implicit uncertainty and may con-tribute to biases in selected settings.

Another limitation of our study is the factthat we have devoted limited attention touncertainty analysis, a crucial element ininterpreting sensitive and complex findings.

Drawing on the uncertainty analyses in ourearlier work (4,11), most parametric changesin CALPUFF led to changes to aggregatebenefits of less than a factor of two, whereasvariations in concentration–response assump-tions (particularly for mortality) could influ-ence estimates by as much as a factor of five.The influence of population susceptibility isgenerally at the lower end of this range, evenfor small geographic scales. However, suscep-tibility information has a greater influence on

the relative distribution of benefits than doother assumptions, many of which tend toaffect all populations identically (e.g., thepopulation-averaged concentration–responsefunction). Furthermore, a broader view ofareas of heterogeneity or susceptibility [e.g.,assumptions regarding particle size andchemical composition, time–activity data, orphysiologic factors (63)] could increase theimportance of this evidence. Further analysisthat considers the full array of uncertaintiesand evaluates which (if any) would be influ-ential in policy decisions would be warranted.

In addition, although we have focused onpower plants (partly because of pending regu-latory decisions at the time of our analysis),the issue of susceptible subpopulations islikely more significant for motor vehicle pol-lution. Given that motor vehicles have lowstack heights and have a strong presence inurban street canyons with high populationdensity, it is likely that aggregate impactswould be spread over a smaller populationthan for power plants. If the exposed popula-tion had demographic differences from theUnited States average, assumptions of homo-geneity would bias the risk calculations.

Finally, any assessment of impacts from alimited number of sources is somewhatimpaired by the relatively small reductionswhen compared with baseline concentrations.This makes field validation of model results dif-ficult and implies that an ultimate comparisonof the costs and benefits of taking action wouldbe required to determine if action is warranted.

Despite these limitations, our analysisillustrates that emission controls at older fos-sil-fueled power plants could lead to quantifi-able concentration and health benefits andthat susceptibility information informs theinterpretation of those benefits. Although theindividual benefits represent a small incre-ment over baseline risks, the number of peo-ple affected because of long-range pollutiontransport implies aggregate benefits that arerelevant for policy evaluation. As the healthliterature develops additional informationabout differences in relative and absolute riskacross populations, risk assessments and bene-fit–cost analyses should take advantage of thisinformation to provide more interpretableinformation to decision makers.

Conclusions

We have evaluated the health benefits of emis-sion controls at five older fossil-fueled powerplants in the Washington, DC, area, usingconventional risk assessment assumptions andincorporating available information about sus-ceptible subpopulations. We found that thegeographic and demographic distributions ofbenefits differ substantially between the twoapproaches. If robust and causal, our suscepti-bility models identify subpopulations that

Articles • Population susceptibility and air pollution risk

Environmental Health Perspectives • VOLUME 110 | NUMBER 12 | December 2002 1259

Figure 4. Distribution of health benefits by census tract in Washington, DC (no color indicates zero at-riskpopulation).

9.86–33.1

33.2–57.5

57.6–77.5

77.6–113

0.20–0.73

0.74–1.17

1.18–1.44

1.45–1.87

31.4–45.1

45.2–58.0

58.1–69.3

69.4–90.1

0.97–1.05

1.06–1.15

1.16–1.23

1.24–1.37

23.7–66.3

66.4–130

131–167

168–209

0.56–1.00

1.01–1.64

1.65–2.27

2.28–2.65

Uniform risk Education-stratified risk

Uniform risk Diabetes-stratified risk

Uniform risk Race-stratified risk

Annual reduction in mortality per million people > age 30

Annual reduction in CHA per million people > age 65

Annual reduction in asthma ERV per million people < age 18

Ratio

Ratio

Ratio

bear a disproportionate air pollution burdenand account for a substantial fraction of thebenefits of emission controls (lower-educatedindividuals for mortality, diabetics for CHA,and African Americans for asthma ERV). Thecharacterization of high-risk subpopulationscan help both in the interpretation of the riskassessment and in targeting future exposureassessment or epidemiologic efforts.

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