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Wednesday, April 26, 2017 1-2pm
Statistical Data Integration Methods for Environmental Exposures
Dr. Howard Chang
Accurate and reliable exposure estimates are crucial to the success of any environmental health study. However, monitoring measurements are often available sparsely in space and time. One approach to improve exposure assessment is by supplementing measurements with additional data sources, such as computer model simulations and satellite imagery. I will discuss methods development for three statistical approaches to perform data integration: statistical downscaling, ensemble averaging, and quantile mapping. One important advantage of statistical methods for data integration is the ability to incorporate and quantify various sources of uncertainty. These methods are applied to (1) estimate daily fine particulate matter concentration at fine spatial resolution, (2) bias-correct climate model projections, and (3) estimate source-contributions to air pollution. Results from population-based health studies utilizing these data fusion products will also be presented.