In recent years, air pollution caused by PM2.5 in China has become increasingly severe. This study applied a Bayesian space-time hierarchy model to reveal the spatiotemporal heterogeneity of the PM2.5 concentrations in China. In addition, the relationship between meteorological and socioeconomic factors and their interaction with PM2.5 during 2000-2018 was investigated based on the GeoDetector model. Results suggested that the concentration of PM2.5 across China first increased and then decreased between 2000 and 2018. Geographically, the North China Plain and the Yangtze River Delta were high PM2.5 pollution areas, while Northeast and Southwest China are regarded as low-risk areas for PM2.5 pollution. Meanwhile, in Northern and Southern China, the population density was the most important socioeconomic factor affecting PM2.5 with q values of 0.62 and 0.66, respectively; the main meteorological factors affecting PM2.5 were air temperature and vapor pressure, with q values of 0.64 and 0.68, respectively. These results are conducive to our in-depth understanding of the status of PM2.5 pollution in China and provide an important reference for the future direction of PM2.5 pollution control.
Keywords: GeoDetector; Northern and Southern China; PM2.5 pollution; impact factors; spatiotemporal heterogeneity.