{"title":"[Spatiotemporal Pattern and Driving Mechanism of PM<sub>2.5</sub> Population Exposure Risk in Urban Agglomerations in China].","authors":"Jun Zhang, Lei-Yu Liu, Teng-Fei Zhang, Ya-Ni Geng","doi":"10.13227/j.hjkx.202405319","DOIUrl":null,"url":null,"abstract":"<p><p>At present, China's urban agglomerations are high-risk and high-risk clusters of PM<sub>2.5</sub> population exposure. Based on the remote sensing data of PM<sub>2.5</sub> from 2000 to 2021, this study analyzed the temporal and spatial evolution characteristics of PM<sub>2.5</sub> population exposure risk in urban agglomerations in China by using the population exposure risk model and spatial autocorrelation method and used seven factors such as average temperature, annual precipitation, and per capita GDP as independent variables, combined with geographic detectors and spatiotemporal geographically weighted regression models to explore the spatial differentiation mechanism of PM<sub>2.5</sub> population exposure risk. The results showed that: ① From 2000 to 2021, the temporal range of PM<sub>2.5</sub> exposure risk in urban agglomerations in China was small. ② From 2000 to 2021, the PM<sub>2.5</sub> population exposure risk of China's urban agglomerations changed significantly in space, and the high-risk areas of PM<sub>2.5</sub> population exposure were concentrated in the Beijing-Tianjin-Hebei urban agglomeration, the Yangtze River Delta urban agglomeration, and the central Shanxi urban agglomeration, and the PM<sub>2.5</sub> population exposure risk in China's urban agglomerations showed a marked positive correlation in space, and the spatial agglomeration characteristics were obvious. ③ The exposure risk of urban agglomerations with low population density was greatly affected by annual precipitation and annual average temperature, while urban agglomerations with high population density were greatly affected by population density and environmental regulatory factors. Industrial structure and population density factors played a positive role in enhancing the population exposure risk of PM<sub>2.5</sub> in urban agglomerations, energy consumption and environmental regulation factors played a negative inhibiting effect, and annual average wind speed and annual precipitation factors mainly played a positive role in enhancing and negatively inhibiting the population exposure risk of the urban agglomeration on the northern slope of the Tianshan Mountains. The results of this study provide a scientific basis for atmospheric environment management and pollution prevention and control in urban agglomerations in China.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"46 8","pages":"5000-5012"},"PeriodicalIF":0.0000,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"环境科学","FirstCategoryId":"1087","ListUrlMain":"https://doi.org/10.13227/j.hjkx.202405319","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Environmental Science","Score":null,"Total":0}
引用次数: 0
Abstract
At present, China's urban agglomerations are high-risk and high-risk clusters of PM2.5 population exposure. Based on the remote sensing data of PM2.5 from 2000 to 2021, this study analyzed the temporal and spatial evolution characteristics of PM2.5 population exposure risk in urban agglomerations in China by using the population exposure risk model and spatial autocorrelation method and used seven factors such as average temperature, annual precipitation, and per capita GDP as independent variables, combined with geographic detectors and spatiotemporal geographically weighted regression models to explore the spatial differentiation mechanism of PM2.5 population exposure risk. The results showed that: ① From 2000 to 2021, the temporal range of PM2.5 exposure risk in urban agglomerations in China was small. ② From 2000 to 2021, the PM2.5 population exposure risk of China's urban agglomerations changed significantly in space, and the high-risk areas of PM2.5 population exposure were concentrated in the Beijing-Tianjin-Hebei urban agglomeration, the Yangtze River Delta urban agglomeration, and the central Shanxi urban agglomeration, and the PM2.5 population exposure risk in China's urban agglomerations showed a marked positive correlation in space, and the spatial agglomeration characteristics were obvious. ③ The exposure risk of urban agglomerations with low population density was greatly affected by annual precipitation and annual average temperature, while urban agglomerations with high population density were greatly affected by population density and environmental regulatory factors. Industrial structure and population density factors played a positive role in enhancing the population exposure risk of PM2.5 in urban agglomerations, energy consumption and environmental regulation factors played a negative inhibiting effect, and annual average wind speed and annual precipitation factors mainly played a positive role in enhancing and negatively inhibiting the population exposure risk of the urban agglomeration on the northern slope of the Tianshan Mountains. The results of this study provide a scientific basis for atmospheric environment management and pollution prevention and control in urban agglomerations in China.