Modeling high-resolution air pollution concentrations is essential to accurately assess exposure for population studies. The aim of this study is to establish an advanced exposure model to predict spatiotemporal changes in fine particulate matter (PM2.5), nitrogen dioxides (NO2), and ozone (O3) concentrations in Shanghai, China. The model is constructed on a geo-statistical modeling framework that incorporates a dimension reduction regression approach and a spatial smoothing function to deal with fine-scale exposure variations. We used a dataset with comprehensive observational and predictor variables that included monitoring data from both national and local agencies from 2013 to 2019, a high-resolution geographical dataset of predictor variables, and a full-coverage weekly satellite data of the aerosol optical depth at a 1 × 1 km2 resolution. Our model performed well in terms of the spatial and temporal prediction ability assessed by cross-validation (CV) for PM2.5 (spatial R2 = 0.89, temporal R2 = 0.91), NO2 (R2 = 0.49, 0.78), and O3 (R2 = 0.67, 0.81) at the national monitors over seven years according to the leave-one-out CV. For the predictions at the local agency monitoring stations, the overall CV R2 was between 0.77 and 0.89 across the air pollutants. We visualized the long-term and seasonal averaged predictions of the PM2.5, NO2, and O3 exposure on maps with a spatial resolution of 100 × 100 m2. Our study provides a useful tool to accurately estimate air pollution exposure with high spatial and temporal resolution at the urban scale. These model predictions will be useful to assess both short-term and long-term air pollution exposure for health studies.
Keywords: NO(2); O(3); PM(2.5); Shanghai; Spatiotemporal model.
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