Whereas air pollution in many Chinese cities has reached epidemic levels in recent years, limited research has explored the spatial and temporal patterns of fine air particles such as PM2.5, or particulate matter with diameter smaller than 2.5 μm, using nationally representative data. This article applied spatial statistical approaches including spatial interpolation and spatial regression to the analysis of ground-level PM2.5 observations for 190 Chinese cities in 2014 obtained from the Chinese Air Quality Online Monitoring Platform. Results of this article suggest that most Chinese cities included in the dataset recorded severe levels of PM2.5 in excess of the WHO's interim target and cities in the North China Plain had the highest levels of PM2.5 regardless of city size. Spatially interpolated maps of PM2.5 and population-weighted PM2.5 indicate vast majority of China's land and population was exposed to disastrous levels of PM2.5 concentrations. The regression results suggest that PM2.5 in a city was positively related to its population size, amount of atmospheric pollutants, and emissions from nearby cities, but inversely related to precipitation and wind speed. Findings from this research can shed new light on the complex spatiotemporal patterns of PM2.5 throughout China and provide insights into policies aiming to mitigate air pollution in China.
Keywords: China; Exposure; Ground-level PM(2.5); Population-weighted; Spatial dependence; Spatial interpolation.
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