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In developing a strategy for mitigatingundesirable health outcomes, risk managers and health sci-entists should include an assessment of the extent to whichhumans are exposed to pollutants. Human exposure assess-ment involves understanding how individuals or targetedgroups will contact environmental chemicals or agents andthe inter- and intra-personal variability in their exposure andresultant intake dose. Differences in exposure and intake dosefor pollutants are largely due to variations in the concentra-tions of chemicals where a person is physically located (e.g.,microscale—at home, the office, or school; macroscale—ur-ban, suburban, or rural), or due to exposure factors, such asthe fundamental physiology associated with activities per-formed (e.g., breathing rates), and other anthropometricattributes (e.g., age, gender, body weight). An individual’slifestyle and/or life-stage may dictate their proximity to sourcesof pollutants, such as emissions from automobiles while driv-ing or emissions from household products, as well as theduration and intensity of their daily activities. Thus, humanexposure assessments are strongly influenced by the level ofdetail used to characterize the spatial and temporal variationof pollutant concentrations and how human contact occurs.

Progress has been made in understanding human expo-sure through exposure measurement programs. Data fromexposure studies have also been used to develop popula-tion-based exposure models that combine measurementsof pollutant concentrations with human activity pattern dataand census demographics to simulate actual human expo-sures. These models can provide estimates of the range ofexposures for a population of interest and, if desired, the

percent above a level of concern. As monitoring networksare reduced in size and frequency due to their expense,exposure and risk assessments will have to rely more oftenon air quality model concentrations as input to exposuremodels. To help identify air pollutant sources of greatestrisk to humans, integration of modeling tools is essential toestimating air concentrations from sources and the result-ant exposures for a population of interest.

The appropriate combination of human exposure and airquality models for producing scientifically credible estimatesof the distribution of exposures will depend on many factorsdue to the complex nature of environmental transport andfate of chemicals and human contact with air pollutants. Airconcentrations may vary significantly for certain pollutantswithin an urban area based on the source of emission and/orby time of day or season of the year. Human activities also varyin space and time. Depending on the magnitude of the vari-ability of air concentrations and human activities, the relation-ship between the two may be important to preserve. The level

Vlad Isakov is a physical scientist in the National Oceanic andAtmospheric Administration’s (NOAA) Atmospheric Sciences

Modeling Division, Research Triangle Park, NC. StephenGraham and Janet Burke are research physical scientists in the

U.S. Environmental Protection Agency’s (EPA), Office ofResearch and Development (ORD) National Exposure Research

Laboratory (NERL), Research Triangle Park, NC.Halûk Özkaynak is a senior research environmental scientist

in EPA/ORD, Research Triangle Park, NC.E-mail: isakov.vlad@epa.gov.

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of refinement needed in both types ofmodels will also depend on the ques-tions asked, such as those borne outof a screening-level assessment and cov-ering a broad geographic region, thoseregarding an assessment focused on adiscrete location, or those as definedby a specific pollutant. The couplingand appropriate application of thesemodels will improve estimates,demonstrate utility in environmentalhealth accountability programs, assistin the development of risk mitigationstrategies, and improve communityhealth or epidemiology studies.

AIR QUALITY MODELINGFor exposure assessments, air qual-ity modeling should include local-scale features, long-range transport,and photochemistry to provide the best estimates of air con-centrations. Generally speaking, there are two major typesof air quality models: source-based dispersion models andgrid-based chemical transport models. Chemical transportmodels, such as the Community Multi-scale Air Quality(CMAQ) model,1 can resolve photochemistry, but not local-level details. CMAQ provides volume-average concentrationvalues for each grid cell in the modeling domain, given statedconditions that can change hourly. Emissions are assumedto be instantaneously well mixed within the cell they areemitted.

While grid-models are the model platform of choice forsimulation of chemically reactive airborne pollutants, thereare various dispersion models (e.g., AERMOD2) that havebeen developed to simulate the fate of chemically stable air-borne pollutants. Not having to incorporate complex atmos-pheric chemistry in generating output, these dispersionmodels can provide detailed resolution of the spatial varia-tions in hourly-average concentrations of airborne pollut-ants. It would be desirable to combine the capabilities ofgrid-models and dispersion models into one model, but thisis still an evolving area of research and development.3 Oneoption is a hybrid approach,4 where a regional grid modeland a local plume model are run independently. The re-gional grid model provides the regional background con-centrations and urban-scale photochemistry, and the localplume dispersion model provides the air toxics concentra-tions due to local emission sources. Then, the results of bothmodel simulations are combined to provide the total ambi-ent air toxics concentrations for use in exposure models (seeFigure 1). Care is required to avoid double counting of emis-sions when combining the two simulations.

EXPOSURE MODELINGAir quality models estimate pollutant concentrations for agiven time period at a given location. If used for estimatingexposure, the general assumption would be that all indi-viduals living in that location are outdoors breathing the

ambient concentration level predicted by an air quality dis-persion model during the time period. Realistically, mostpeople do not spend their entire day outdoors; a majority oftime is spent in indoors (e.g., the home, workplace, school,vehicle), where air concentrations can be quite differentthan those outdoors due to local sources or decay and depo-sition processes during and following penetration. Also, thetime spent in these locations by individuals or groups of simi-lar individuals is variable from one day to the next.5,6

Human exposure models have been developed to accountfor these factors.

The Stochastic Human Exposure and Dose Simulation(SHEDS) model7 has been developed by the U.S. Environ-mental Protection Agency’s National Exposure ResearchLaboratory. The general structure of SHEDS includes sev-eral input databases, algorithms, and outputs, as shown inFigure 1. SHEDS can estimate sequential exposure and in-take dose of a pollutant for the selected population throughthree principal routes—inhalation, dermal, and dietary—over the course of a specific year. The generation of thetime-series involves stochastic processes using numerical Monte-Carlo sampling techniques to characterize the variability withinan individual and between individuals across a population.SHEDS is capable of using time-varying ambient concentra-tions, including hourly, daily, or other time-averaged data.

In estimating inhalation exposures, ambient pollutant con-centrations are related to concentrations of pollutants in spe-cific locations (termed “microenvironmental concentrations”).These algorithms typically take the form of proportions, lin-ear regressions, and steady-state mass balance equations. Mostof the factors or coefficients in these algorithms are providedin the form of a distribution that retains the inherent variabil-ity (and optionally the uncertainty) in these factors. For eachsimulated individual, a yearlong time series of exposure andintake doses are calculated from the microenvironmental con-centration estimates and activity-specific inhalation rates basedon the assigned human activity diaries. Daily-averagedexposure and total daily intake dose results for individuals in

Figure 1. Approach to integrate air quality and human exposure modeling for air pollutants.

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the simulated population can be combined to produce theestimated population distribution of exposure and dose, whichdescribes the variability in the population over a user-definedbroad geographic region or down to a census tract level.

The current model design incorporates advanced exposurealgorithms specific to mobile source pollutant exposure sce-narios and also considers important sources of the pollutants.7For example, in-vehicle exposure and intake dose estimationfor the population addresses the spatial and temporal varia-tion encompassed by individuals’ commuting patterns, non-work-related locations visited, and various roadway types traveledthat account for variability in daily and monthly traffic density.In addition, estimates of benzene exposures originating fromattached garages and emissions from passive cigarette smokeare included as sources to indoor residential exposure and per-sonal exposure, while refueling an automobile is estimated forthose performing the activity and for passengers of the vehicle.

INTEGRATED AIR QUALITY/EXPOSUREMODELING IN PHILADELPHIATo illustrate how air quality models can be used to provideinputs to human exposure models, consider a 100-by-100-kmarea that includes Philadelphia County and several surround-ing counties. Figure 2a displays a roadway network and loca-tions of 380 census tract centroids in Philadelphia County.CMAQ was used to simulate ambient concentrations of severalair toxics.8 Traditionally, CMAQ has been used for criteria pol-lutant and regional applications to support regulations. In thisstudy, CMAQ results for benzene were used as input to theSHEDS model. The CMAQ modeling system was run for anannual period in a nested mode at 36-, 12-, and 4-km horizon-tal grid dimensions, using the 1999 National Emission Inven-tory9 and meteorological outputs from 2001 using the MM5meteorological model.10 The 4-km grid mesh encompassesPhiladelphia, PA, and part of New Jersey (see Figure 2b).

Since CMAQ provides average concentrations for grid vol-umes, they do not reflect the fine-scale details in the concen-tration pattern that might occur within a grid volume. The

number of census tracts in a4-by-4-km CMAQ grid cellranges from a few tracts in sub-urban areas up to 30 tracts indowntown Philadelphia. Thissuggests that both populationdensity and concentration val-ues will likely vary within a typi-cal CMAQ grid cell, such thatif the variations in concentra-tion are not adequately ad-dressed, results of an exposureassessment may be more un-certain. To provide sufficientdetail in air quality inputs forexposure modeling, a methodthat allows CMAQ to provideregional background concen-tration values and contribu-tions from chemically reactive

pollutants was used, and local details in relatively inert pollut-ants concentrations were provided by a dispersion model.

The results of this hybrid approach is illustrated in Figure3a. SHEDS was applied to provide annual exposures for a non-smoking population by accounting for the actual demographiccharacteristics of persons in the region, and simulating humanactivities performed and locations visited using the hourlyambient concentrations from the CMAQ-AERMOD hybridsimulation. Figure 3b displays the corresponding annual aver-age exposure concentrations across individuals residing in eachPhiladelphia County census tract estimated by SHEDS. Thespatial pattern in benzene exposures is consistent with thespatial pattern of the ambient concentrations used as input,but indicates that exposures from other sources (e.g., attachedgarages, refueling) can contribute an additional 25–150% toannual average benzene exposure.

ADVANTAGES AND CHALLENGES OF CONDUCTINGINTEGRATED AIR QUALITY/EXPOSURE MODELINGEnvironmental public health protection requires a good un-derstanding of the types and locations of emissions of pollut-ants of health concern and the extent and duration ofindividuals’ exposures to these pollutants. Integrated air qual-ity/human exposure modeling provides the means to evaluatethe potential health risks from air pollution and the basis todetermine optimum risk management strategies, while con-sidering scientific, social, and economic factors. Ideally, emis-sions control strategies aim not only to reduce the emissionsfrom principal sources of targeted pollutants but also to iden-tify those sources and microenvironments that contribute togreatest portion of personal or population exposures.

Currently, most federal and state air quality implementa-tion plans rely heavily on modeling study results for targetingemissions reductions. However, for protecting the public againstadverse health impacts from short-term exposures to reactiveoutdoor pollutants like ozone, many environmental healthagencies advocate exposure mitigation approaches to mini-mize the health risks (e.g., health advisories on high ozone

Figure 2. (a) Philadelphia study domain, topography, roads, and census tract centroids, and(b) annual average benzene concentrations [µg/m3] from CMAQ, Philadelphia County, PA.

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days recommending asthmatics to limit their activities outdoorsor shelter-in-place warnings during a chemical spill event). Thus,an optimum source control program incorporates informa-tion from all facets of a combined source-emissions-transportand fate-exposure-health effects modeling assessment. Clearly,such a combined modeling analysis affords many advantagesnot only for carrying out informed risk management decisions,but also for conducting enhanced air pollution health studiesand community-level air accountability studies. Moreover,results from these studies can provide valuable insight intoprioritizing future modeling research and data collectionactivities that improve the limitations of current models,modeling approaches, and their inputs.

Although highly desirable, conducting an integratedair quality/exposure modeling application is not devoidof technical and practical challenges. Both air quality andexposure models require reliable spatially- and temporally-resolved emissions inventory information. Unfortunately,existing emissions inventories, though continually beingupdated, lack the required specificity for many of the primary em

REFERENCES1. Byun, D.W.; Schere, K. Review of

the Governing Equations, Compu-tational Algorithms, and OtherComponents of the Models-3 Com-munity Multiscale Air Quality(CMAQ) Modeling System; Appl.Mechanics Rev. 2006, 59, 51-77.

2. Cimorelli, A.J.; Perr y, S.G.;Venkatram, A.; Weil, J.C.; Paine,R.J.; Wilson, R.B.; Lee, R.F.; Peters,W.D.; Brode, R.W. AERMOD: ADispersion Model for IndustrialSource Applications. Part I: GeneralModel Formulation and BoundaryLayer Characterization; J. Appl.Meteorol. 2005, 44, 682–693.

3. Touma, J.S.; Isakov, V.; Ching, J.;Seigneur, C. Air Quality Modelingof Hazardous Pollutants: CurrentStatus and Future Directions; J. Air& Waste Manage. Assoc. 2006, 56,547-558.

4. Isakov, V.; Irwin, J.S.; Ching, J. Us-ing CMAQ for Exposure Modelingand Characterizing the Sub-GridVariability for Exposure Estimates;J. Appl. Meteorol. 2006 (in review).

5. McCurdy, T.; Graham, S.E. UsingHuman Activity Data in ExposureModels: Analysis of DiscriminatingFactors; J. Exp. Anal. Environ.Epidemiol. 2003, 13, 294-317.

6. Graham, S.E.; McCurdy, T. Devel-oping Meaningful Cohorts for Hu-man Exposure Models; J. Exp. Anal.Environ. Epidemiol. 2004, 14, 23-43.

7. Graham, S.E.; Burke, J.M. Microen-vironmental Exposures to Benzene: ACritical Review and Probabilistic ModelInput Distribution Development; EPA/600/J-03/008; U.S. EnvironmentalProtection Agency, Office of Re-search and Development: ResearchTriangle Park, NC, 2003.

8. Luecken, D.J.; Hutzell, W.T.;Gipson, G.J. Development andAnalysis of Air Quality ModelingSimulations for Hazardous Air Pol-lutants; Atmos. Environ. 2006, 40,5087-5096.

9. National Emissions Inventory. Seewww.epa.gov/ttn/chief/net/1999inventory.html.

10. Grell, G.A.; Dudhia, J.; Stauffer,D.R. A Description of the Fifth-Genera-tion Penn State/NCAR MesoscaleModel (MM5); NCAR Tech. Note.TN-398+STR, 1995; available atw w w. m m m . u c a r. e d u / m m 5 /doc1.html.

and secondary air pollutantsof interest. In particular,emissions information ondifferent air toxics or PMspecies, which is necessaryfor assessing cumulativehealth risks from exposuresto multiple pollutants, is notyet sufficient for many spe-cies. A major limitation ofexposure models is theavailability of exposure fac-tors information at regional,local, or neighborhoodscale, such as indoor air ex-change rates, ambient pol-lutant penetration factors forthe different microenviron-ments, and indoor sourcestrengths. Furthermore, thecurrent exposure modelswould greatly benefit fromcollection of multiday timeactivity diaries from specialor vulnerable populationgroups, such as individualswith preexisting health con-ditions, genetic susceptibili-ties, children and agingpopulations, and individualsliving in communities differ-entially impacted by pollu-tion. Ultimately, withimproved emissions, expo-sure factors, and populationmobility/time-activity data,coupled air quality and ex-posure models for pollutantsthat are linked with acuteand chronic health effects,will provide useful tools nec-essary for assessing healthrisks and various risk man-agement options. Thesemodels will also serve the ever-growing need to provide moreaccurate air quality and exposure forecasts, and to quantifythe health and economic benefits of emissions reductionsprograms as part of air accountability studies.

ACKNOWLEDGMENTSThe research presented here was performed under aMemorandum of Understanding between the U.S. Environ-mental Protection Agency (EPA) and the U.S. Departmentof Commerce’s National Oceanic and Atmospheric Admin-istration (NOAA) and under agreement numberDW13921548. This work constitutes a contribution to theNOAA Air Quality Program. It does not necessarily reflectagency policies or views.

Figure 3. (a) Modeled annual average benzene concentrations and(b) exposures in [µg/m3] from SHEDS, Philadelphia County, PA.

Copyright 2006 Air & Waste Management Association