Meng Wang, PhD
Department of Epidemiology and Environmental Health
Exposure modeling is crucial of estimating human exposure to air pollution in environmental health studies, especially in cohort studies with large population. This project aims to develop nationwide PM2.5 and NO2 exposure models that incorporate land-use regression, satellite-derived observations and spatial smoothing to improve prediction accuracy for multiple NIH and US EPA funded epidemiological studies. We will apply a novel modeling framework developed in our group and will leverage a big dataset of national monitoring network as well as cohort-specific monitors to estimate PM2.5 and NO2 concentrations with high spatiotemporal resolution from 1999 to 2016 in the United States. Our preliminary results suggest that the models can make accurate point predictions of PM2.5 and NO2 concentrations at both short and long time scales when utilizing additional data from a large number of fine-scale monitors and satellite technology. These models will eventually be used to support the PATHWAYS study for children's health research (1UG30D023271-01) and the MESA Air Next Study (RD-83830001) for cardiovascular health research.