Accurate assessment of human exposures is an important part of environmental health effects research. However, most air pollution epidemiology studies rely upon imperfect surrogates of personal exposures, such as information based on available central-site outdoor concentration monitoring or modeling data. In this paper, we examine the limitations of using outdoor concentration predictions instead of modeled personal exposures for over 30 gaseous and particulate hazardous air pollutants (HAPs) in the US. The analysis uses the results from an air quality dispersion model (the ASPEN or Assessment System for Population Exposure Nationwide model) and an inhalation exposure model (the HAPEM or Hazardous Air Pollutant Exposure Model, Version 5), applied by the US. Environmental protection Agency during the 1999 National Air Toxic Assessment (NATA) in the US. Our results show that the total predicted chronic exposure concentrations of outdoor HAPs from all sources are lower than the modeled ambient concentrations by about 20% on average for most gaseous HAPs and by about 60% on average for most particulate HAPs (mainly, due to the exclusion of indoor sources from our modeling analysis and lower infiltration of particles indoors). On the other hand, the HAPEM/ASPEN concentration ratio averages for onroad mobile source exposures were found to be greater than 1 (around 1.20) for most mobile-source related HAPs (e.g. 1, 3-butadiene, acetaldehyde, benzene, formaldehyde) reflecting the importance of near-roadway and commuting environments on personal exposures to HAPs. The distribution of the ratios of personal to ambient concentrations was found to be skewed for a number of the VOCs and reactive HAPs associated with major source emissions, indicating the importance of personal mobility factors. We conclude that the increase in personal exposures from the corresponding predicted ambient levels tends to occur near locations where there are either major emission sources of HAPs or when individuals are exposed to either on-or nonroad sources of HAPs during their daily activities. These findings underscore the importance of applying exposure-modeling methods, which incorporate information on time-activity, commuting, and exposure factors data, for the purposes of assigning exposures in air pollution health studies. Keywords: air toxics, commuting, hazardous air pollutants, microenvironment, modeling, population exposure, time-activity. IntroductionEstimating human exposure is a critical component of most environmental risk assessments. Human exposures to hazardous air pollutants (HAPs) can result from contact with air, water, soils, and food. Exposures may be dominated by contact with a single medium (e.g., ambient air) or result from concurrent contacts with multiple media (e.g., air, water, soil). The nature and extent of such exposures depend largely on two things: human factors and the concentration of a pollutant in the relevant exposure media. Human factors include behavioral, sociological, and physiological characteristic...

Population-based human exposure models predict the distribution of personal exposures to pollutants of outdoor origin using a variety of inputs, including air pollution concentrations; human activity patterns, such as the amount of time spent outdoors versus indoors, commuting, walking, and indoors at home; microenvironmental infiltration rates; and pollutant removal rates in indoor environments. Typically, exposure models rely upon ambient air concentration inputs from a sparse network of monitoring stations. Here we present a unique methodology for combining multiple types of air quality models (the Community Multi-Scale Air Quality [CMAQ] chemical transport model added to the AERMOD dispersion model) and linking the resulting hourly concentrations to population exposure models (the Hazardous Air Pollutant Exposure Model [HAPEM] or the Stochastic Human Exposure and Dose Simulation [SHEDS] model) to enhance estimates of air pollution exposures that vary temporally (annual and seasonal) and spatially (at census-block-group resolution) in an urban area. The results indicate that there is a strong spatial gradient in the predicted mean exposure concentrations near roadways and industrial facilities that can vary by almost a factor of 2 across the urban area studied. At the high end of the exposure distribution (95th percentile), exposures are higher in the central district than in the suburbs. This is mostly due to the importance of personal mobility factors whereby individuals living in the central area often move between microenvironments with high concentrations, as opposed to individuals residing at the outskirts of the city. Also, our results indicate 20-30% differences due to commuting patterns and almost a factor of 2 difference because of near-roadway effects. These differences are smaller for the median exposures, indicating the highly variable nature of the reflected ambient concentrations. In conjunction with local data on emission sources, microenvironmental factors, and behavioral and socioeconomic characteristics, the combined source-to-exposure modeling methodology presented in this paper can improve the assessment of exposures in future community air pollution health studies.

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A hybrid air quality model has been developed and applied to estimate annual concentrations of 40 hazardous air pollutants (HAPs) across the continental United States (CONUS) to support the 2011 calendar year National Air Toxics Assessment (NATA). By combining a chemical transport model (CTM) with a Gaussian dispersion model, both reactive and nonreactive HAPs are accommodated across local to regional spatial scales, through a multiplicative technique designed to improve mass conservation relative to previous additive methods. The broad scope of multiple pollutants capturing regional to local spatial scale patterns across a vast spatial domain is precedent setting within the air toxics community. The hybrid design exhibits improved performance relative to the stand alone CTM and dispersion model. However, model performance varies widely across pollutant categories and quantifiably definitive performance assessments are hampered by a limited observation base and challenged by the multiple physical and chemical attributes of HAPs. Formaldehyde and acetaldehyde are the dominant HAP concentration and cancer risk drivers, characterized by strong regional signals associated with naturally emitted carbonyl precursors enhanced in urban transport corridors with strong mobile source sector emissions. The multiple pollutant emission characteristics of combustion dominated source sectors creates largely similar concentration patterns across the majority of HAPs. However, reactive carbonyls exhibit significantly less spatial variability relative to nonreactive HAPs across the CONUS.

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