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Combining Regional- and Local-Scale Air Quality Models with Exposure Models for Use in Environmental Health Studies
Citation:
ISAKOV, V., J. TOUMA, J. M. BURKE, D. T. LOBDELL, T. PALMA, A. Rosenbaum, AND H. A. OZKAYNAK. Combining Regional- and Local-Scale Air Quality Models with Exposure Models for Use in Environmental Health Studies. JOURNAL OF AIR AND WASTE MANAGEMENT. Air & Waste Management Association, Pittsburgh, PA, 59(4):461-472, (2009).
Impact/Purpose:
The National Exposure Research Laboratory′s (NERL′s) Atmospheric Modeling Division (AMD) conducts research in support of EPA′s mission to protect human health and the environment. AMD′s research program is engaged in developing and evaluating predictive atmospheric models on all spatial and temporal scales for forecasting the Nation′s air quality and for assessing changes in air quality and air pollutant exposures, as affected by changes in ecosystem management and regulatory decisions. AMD is responsible for providing a sound scientific and technical basis for regulatory policies based on air quality models to improve ambient air quality. The models developed by AMD are being used by EPA, NOAA, and the air pollution community in understanding and forecasting not only the magnitude of the air pollution problem, but also in developing emission control policies and regulations for air quality improvements.
Description:
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 vs. 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 (CMAQ chemical transport model added to the AERMOD dispersion model) and linking the resulting hourly concentrations to population exposure models (HAPEM or SHEDS) 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, which can vary by almost a factor of two across the urban area studied. At the high end of the exposure distribution (95th percentile), exposures are higher in the central district then 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 two differences due to 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, 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.
