The repeated occurrences of severe haze in China in recent years have drawn increased attention from the public and government departments to the impact of pollutants on public health. In order to investigate the spatial and temporal distribution patterns and risks of PM2.5 population exposure levels in various regions of China, based on population-weighted PM2.5 concentration data, spatial autocorrelation analysis and geographic detector methods are used to reveal its overall spatial and temporal evolution patterns and local variation characteristics. The specific influences and interactions of population-weighted PM2.5 concentrations in China are also studied in four dimensions: socio-economic, climatic, geographic environment and policy. Finally, projections of population-weighted PM2.5 concentrations in China from 2019 to 2021 are made based on the main influencing factors. The study shows that (1) China's population-weighted PM2.5 concentrations decreased year by year from 2000 to 2018. (2) Spatial clustering of population-weighted PM2.5 concentrations in China is evident. (3) The population-weighted PM2.5 concentration in China showed a spatially heterogeneous pattern between 2000 and 2018. (4) At the national scale, the factor with the greatest explanatory power of China's population-weighted PM2.5 concentration is the average temperature. The interactions between the factors mainly show two types of interactions: two-factor enhancement and non-linear enhancement. (5) A prediction model of China's population-weighted PM2.5 concentration was established to predict the population-weighted PM2.5 concentration from 2019 to 2021 with high accuracy.

Zheng Wei, Shaoxiong Wu. Spatial and Temporal Evolution of Population-Weighted PM2.5 Concentration and Its Influencing Factors in China from 2000 to 2021. Academic Journal of Mathematical Sciences (2023) Vol. 4, Issue 2: 1-9. https://doi.org/10.25236/AJMS.2023.040201.

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