Spatial and Temporal Evolution of Population-Weighted PM2.5 Concentration and Its Influencing Factors in China from 2000 to 2021

Zhe Wei, Shaoxiong Wu
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Abstract

: 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.
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2000 - 2021年中国人口加权PM2.5浓度时空演变及其影响因素
近年来,中国多次出现严重的雾霾天气,这引起了公众和政府部门对污染物对公众健康影响的越来越多的关注。为探讨中国各区域PM2.5人口暴露水平的时空分布格局和风险,基于人口加权PM2.5浓度数据,采用空间自相关分析和地理探测器方法揭示其整体时空演变格局和局部变化特征。本文还从社会经济、气候、地理环境和政策四个维度研究了中国人口加权PM2.5浓度的具体影响和相互作用。最后,根据主要影响因素对2019 - 2021年中国人口加权PM2.5浓度进行了预测。研究表明:(1)2000 - 2018年,中国人口加权PM2.5浓度呈逐年下降趋势。(2)中国人口加权PM2.5浓度空间聚类明显。(3) 2000 - 2018年中国人口加权PM2.5浓度呈现空间异质性格局。(4)在全国尺度上,对中国人口加权PM2.5浓度解释力最大的因子是平均气温。因子间的相互作用主要表现为两种类型的相互作用:双因子增强和非线性增强。(5)建立了中国人口加权PM2.5浓度预测模型,对2019 - 2021年中国人口加权PM2.5浓度进行了高精度预测。
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