Development of Model-Based Air Pollution Exposure Metrics for Use in Epidemiologic Studies

Abstract

Population-based epidemiological studies of air pollution have traditionally relied upon imperfect surrogates of personal exposures, such as area-wide ambient air pollution levels based on readily available concentrations from central monitoring sites. U.S. EPA in collaboration with University of Michigan is developing and evaluating several types or tiers of exposure metrics for traffic-related and regional pollutants that differ in their modeling approaches for addressing the spatial and temporal heterogeneity of pollutant concentrations. We hypothesize that using more refined exposure estimates will provide greater power to detect associations with health outcomes, particularly for traffic-related pollutants that can vary considerably over short distances and time scales. The Near-road Exposures to Urban air pollutant Study (NEXUS) design is focused on determining if children in Detroit, MI with asthma living in close proximity to major roadways have greater health impacts associated with air pollutants than those living farther away, particularly for children living near roadways with high diesel traffic. One tier for estimating exposures to traffic-generated pollutants uses local-scale dispersion modeling. Temporally and spatially-resolved pollutant concentrations, associated with local variations of emissions and meteorology, were estimated using a combination of the AERMOD and RLINE 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. Hourly pollutant concentrations for CO, NO_x, PM_2.5 and its components (EC and OC) were predicted at each study participant location (n = 160). The exposure metrics were evaluated in their ability to characterize the spatial and temporal variations of multiple ambient air pollutants across the study area. This research will be used for improving exposure assessments in future air pollution epidemiology studies, and for informing future multipollutant exposure analyses.