Estimating exposures to PM2.5 within urban areas requires surface PM2.5 concentrations at high temporal and spatial resolutions. We developed a mixed effects model to derive daily estimations of surface PM2.5 levels in Beijing, using the 3 km resolution satellite aerosol optical depth (AOD) calibrated daily by the newly available high-density surface measurements. The mixed effects model accounts for daily variations of AOD-PM2.5 relationships and shows good performance in model predictions (R(2) of 0.81-0.83) and cross-validations (R(2) of 0.75-0.79). Satellite derived population-weighted mean PM2.5 for Beijing was 51.2 μg/m(3) over the study period (Mar 2013 to Apr 2014), 46% higher than China's annual-mean PM2.5 standard of 35 μg/m(3). We estimated that more than 19.2 million people (98% of Beijing's population) are exposed to harmful level of long-term PM2.5 pollution. During 25% of the days with model data, the population-weighted mean PM2.5 exceeded China's daily PM2.5 standard of 75 μg/m(3). Predicted high-resolution daily PM2.5 maps are useful to identify pollution "hot spots" and estimate short- and long-term exposure. We further demonstrated that a good calibration of the satellite data requires a relatively large number of ground-level PM2.5 monitoring sites and more are still needed in Beijing.

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The accuracy in estimated fine particulate matter concentrations (PM2.5), obtained by fusing of station-based measurements and satellite-based aerosol optical depth (AOD), is often reduced without accounting for the spatial and temporal variations in PM2.5 and missing AOD observations. In this study, a city-specific linear regression model was first developed to fill in missing AOD data. A novel interpolation-based variable, PM2.5 spatial interpolator (PMSI2.5), was also introduced to account for the spatial dependence in PM2.5 across grid cells. A Bayesian hierarchical model was then developed to estimate spatiotemporal relationships between AOD and PM2.5. These methods were evaluated through a city-specific 10-fold cross-validation procedure in a case study in North China in 2014. The cross validation R(2) was 0.61 when PMSI2.5 was included and 0.48 when PMSI2.5 was excluded. The gap-filled AOD values also effectively improved predicted PM2.5 concentrations with an R(2) = 0.78. Daily ground-level PM2.5 concentration fields at a 12 km resolution were predicted with complete spatial and temporal coverage. This study also indicates that model prediction performance should be assessed by accounting for monitor clustering due to the potential misinterpretation of model accuracy in spatial prediction when validation monitors are randomly selected.

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Particulate matter with an aerodynamic diameter of 2.5 micrometers or less (PM 2.5 ) is a primary pollutant in most cities in China. PM 2.5 poses a significant human health risk, especially in the most densely populated urban areas. We used observations of PM 2.5 and backward air mass trajectories modeled by HYSPLIT-4. We characterize how air movement patterns influence pollution levels in four large cities of China. Then we developed a method to evaluate regional and local sources and contributions of PM 2.5 . For Beijing and Shanghai, PM 2.5 concentrations are sensitive to air moving direction, indicating significant influence of air movement on PM 2.5 pollution. In Beijing, PM 2.5 concentrations were higher when the air masses were from the south and the east. In Shanghai, pollution was greater with northerly air mass flows. Regional contributions of PM 2.5 in Beijing during 2013 were 46, 62, 52, and 39% in spring, summer, autumn and winter, respectively. In Shanghai, regional contributions over four seasons were 36, 39, 45, and 35%. In Guangzhou and Chengdu, PM 2.5 concentrations were more sensitive to speed rather than direction of air mass movements, indicating weaker pollution pathways. In Guangzhou, regional contributions were smaller over the four seasons: 15, 28, 16, and 22% while in Chengdu, they are 21, 52, 28, and 14%. These results are comparable to previous results obtained using complex atmospheric chemical transport models.