{"title":"[Analysis of Spatiotemporal Changes and Multi-scale Socio-economic Driving Factors of PM<sub>2.5</sub> and Ozone in Beijing-Tianjin-Hebei and Its Surroundings].","authors":"Li Yan, Xiao-Han Song, Yu Lei, He-Zhong Tian","doi":"10.13227/j.hjkx.202311002","DOIUrl":null,"url":null,"abstract":"<p><p>Based on PM<sub>2.5</sub> and O<sub>3</sub> remote sensing concentration data in Beijing-Tianjin-Hebei and its surrounding areas from 2015 to 2020, we used trend analysis, geographic detectors, and a geographically and temporally weighted regression model to explore the spatiotemporal characteristics and key driving socio-economic factors of multi-scale PM<sub>2.5</sub> and O<sub>3</sub> concentrations. The results indicated that: ① The changing slope of PM<sub>2.5</sub> concentration ranged from -12.93 to 0.43 μg·(m<sup>3</sup>·a)<sup>-1</sup>, and the changing slope of O<sub>3</sub> concentration ranged from 0.70 to 14.90 μg·(m<sup>3</sup>·a)<sup>-1</sup>. The decreasing slope of PM<sub>2.5</sub> concentration was the largest in winter, and the increasing slope of O<sub>3</sub> concentration was the largest in summer. ② The concentrations of PM<sub>2.5</sub> and O<sub>3</sub> were spatially correlated, and the H-H concentrations of PM<sub>2.5</sub> were located in the southern Hebei Province and the northern Henan Province. The spatial clustering pattern of O<sub>3</sub> changed greatly. ③ From the perspective of urban agglomeration, the GDP, population density, and civilian car ownership had a strong explanatory power for PM<sub>2.5</sub>, while GDP, urbanization rate, and civilian car ownership had a strong explanatory power for O<sub>3</sub>. The dominant interaction factors of 2016 and 2020 were the population density∩the proportion of the secondary industry and urbanization rate∩road network density, respectively. ④ From the perspective of single city, population density, industrial nitrogen oxide emissions, and electricity consumption had mainly positive effects on PM<sub>2.5</sub> and O<sub>3</sub> pollution and became the socio-economic driving factors that need to be focused on to control PM<sub>2.5</sub> and O<sub>3</sub> co-pollution.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"45 11","pages":"6207-6218"},"PeriodicalIF":0.0000,"publicationDate":"2024-11-08","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.202311002","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
Based on PM2.5 and O3 remote sensing concentration data in Beijing-Tianjin-Hebei and its surrounding areas from 2015 to 2020, we used trend analysis, geographic detectors, and a geographically and temporally weighted regression model to explore the spatiotemporal characteristics and key driving socio-economic factors of multi-scale PM2.5 and O3 concentrations. The results indicated that: ① The changing slope of PM2.5 concentration ranged from -12.93 to 0.43 μg·(m3·a)-1, and the changing slope of O3 concentration ranged from 0.70 to 14.90 μg·(m3·a)-1. The decreasing slope of PM2.5 concentration was the largest in winter, and the increasing slope of O3 concentration was the largest in summer. ② The concentrations of PM2.5 and O3 were spatially correlated, and the H-H concentrations of PM2.5 were located in the southern Hebei Province and the northern Henan Province. The spatial clustering pattern of O3 changed greatly. ③ From the perspective of urban agglomeration, the GDP, population density, and civilian car ownership had a strong explanatory power for PM2.5, while GDP, urbanization rate, and civilian car ownership had a strong explanatory power for O3. The dominant interaction factors of 2016 and 2020 were the population density∩the proportion of the secondary industry and urbanization rate∩road network density, respectively. ④ From the perspective of single city, population density, industrial nitrogen oxide emissions, and electricity consumption had mainly positive effects on PM2.5 and O3 pollution and became the socio-economic driving factors that need to be focused on to control PM2.5 and O3 co-pollution.