A1092
Title: Spatial-temporal data integration to improve the assessment of population exposure and health risks to PM2.5
Authors: Zhengyuan Zhu - Iowa State University (United States) [presenting]
Abstract: Monitoring and forecasting PM2.5 is important for countries where air pollution is a serious public health issue. Current PM2.5 forecasts are mostly based on observations from monitoring stations, which have high temporal frequency but sparse and uneven spatial distribution. Aerosol Optical Depth (AOD) data from satellites such as MODIS has better spatial coverage but low temporal frequency. The fusion of PM2.5 data from stations and AOD data from satellites to provide hourly high-resolution PM2.5 data is useful for forecasting and epidemiology studies. The aim is to propose a novel data fusion framework using spatial functional data analysis tools. Efficient algorithms are developed to estimate the non-stationary mean and covariance structure using Bivariate spline and PACE. Estimates from the AOD data are then used to improve the spatial prediction of PM2.5. The proposed method is applied to data in the Beijing area in China, and the proposed approach outperforms several existing data fusion methods.
