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Hybrid Air Quality Modeling Approach For Use in the Near-Road Exposures to Urban Air Pollutant Study (NEXUS)
Isakov, V., D. Heist, J. Burke, M. Snyder, S. Arunachalam, AND S. Batterman. Hybrid Air Quality Modeling Approach For Use in the Near-Road Exposures to Urban Air Pollutant Study (NEXUS). 16th International Conference on Harmonisation ADMRD, Varna, BULGARIA, September 08 - 11, 2014.
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
The Near-road EXposures to Urban air pollutant Study (NEXUS) investigated whether children with asthma living in close proximity to major roadways in Detroit, MI, (particularly near roadways with high diesel traffic) have greater health impacts associated with exposure to air pollutants than those living farther away. A major challenge in such health and 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. Model-based exposure metrics, associated with local variations of emissions and meteorology, were estimated using a combination of the AERMOD and R-LINE dispersion models, local emission source information from the National Emissions Inventory, detailed road network locations and traffic activity, and meteorological data from the Detroit City Airport. The regional background contribution was estimated using a combination of the Community Multiscale Air Quality (CMAQ) model and the Space/Time Ordinary Kriging (STOK) model. To capture the near-road pollutant gradients, refined “mini-grids” of model receptors were placed around participant homes. Mini-grids gave anonymity to 50 or 100 m, a distance sufficient to protect participants' identity. Exposure metrics were calculated from mini-grids to produce an estimate at each home location (n=160). Exposure metrics for CO, NOx, PM2.5 and its components (EC and OC) were predicted for the following time periods: daily: 24 period; a.m. off-peak: 1-6; a.m. peak: 7-8; mid-day: 9-14; p.m. peak: 15-17; and p.m. off-peak: 18-24. These daily exposure metrics, capturing spatial and temporal variability across the health study domain (Fall 2010 – Spring 2012) were used in the epidemiologic analyses. Preliminary results of the epidemiologic analyses using model-based exposure estimates indicate a potential to help discern relationships between air quality and health outcomes. This modeling approach can be used for improving exposure assessments in future air pollution health studies.