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Air Quality Modeling of Traffic-related Air Pollutants for the NEXUS Study
Citation:
Isakov, V., D. Heist, J. Burke, K. Dionisio, S. Bereznicki, M. Snyder, S. Arunachalam, AND S. Batterman. Air Quality Modeling of Traffic-related Air Pollutants for the NEXUS Study. Presented at 2014 ISES Conference, Cincinnati, OH, Cincinnati, OH, October 12 - 16, 2014.
Impact/Purpose:
The National Exposure Research Laboratory (NERL) Atmospheric Modeling and Analysis Division (AMAD) conducts research in support of EPA mission to protect human health and the environment. AMAD research program is engaged in developing and evaluating predictive atmospheric models on all spatial and temporal scales for forecasting the air quality and for assessing changes in air quality and air pollutant exposures, as affected by changes in ecosystem management and regulatory decisions. AMAD 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 AMAD 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:
The paper presents the results of the model applications to estimate exposure metrics in support of an epidemiologic study in Detroit, Michigan. A major challenge in traffic-related air pollution exposure studies is the lack of information regarding pollutant exposure characterization. Air quality modeling can provide spatially and temporally varying exposure estimates for examining relationships between traffic-related air pollutants and adverse health outcomes. This paper presents a hybrid air quality modeling approach and its application in NEXUS in order to provide spatial and temporally varying exposure estimates and identification of the mobile source contribution to the total pollutant exposure. The exposure metrics were evaluated in their ability to characterize the spatial and temporal variations of multiple ambient air pollutants compared to measurements across the study area. Preliminary results of the epidemiologic analyses using model-based exposure estimates indicate a potential to help discern relationships between air quality and health outcomes.