[Analysis of Spatiotemporal Changes and Multi-scale Socio-economic Driving Factors of PM2.5 and Ozone in Beijing-Tianjin-Hebei and Its Surroundings].

Q2 Environmental Science 环境科学 Pub Date : 2024-11-08 DOI:10.13227/j.hjkx.202311002
Li Yan, Xiao-Han Song, Yu Lei, He-Zhong Tian
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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.

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京津冀及周边地区PM2.5和臭氧时空变化及多尺度社会经济驱动因素分析[j]。
基于2015 - 2020年京津冀及周边地区PM2.5和O3遥感浓度数据,采用趋势分析、地理探测器和地理时间加权回归模型,探讨了多尺度PM2.5和O3浓度的时空特征及关键驱动因素。结果表明:①PM2.5浓度变化斜率为-12.93 ~ 0.43 μg·(m3·a)-1, O3浓度变化斜率为0.70 ~ 14.90 μg·(m3·a)-1。PM2.5浓度的下降斜率在冬季最大,O3浓度的上升斜率在夏季最大。②PM2.5与O3浓度具有空间相关性,PM2.5的H-H浓度分布在河北省南部和河南省北部;O3的空间聚类格局变化较大。③从城市群角度看,GDP、人口密度、民用汽车保有量对PM2.5有较强的解释力,GDP、城镇化率、民用汽车保有量对O3有较强的解释力。2016年和2020年的主导交互因素分别是人口密度∩第二产业比重和城镇化率∩路网密度。④从单城市角度看,人口密度、工业氮氧化物排放和用电量对PM2.5和O3污染的正向影响主要存在,成为控制PM2.5和O3共污染需要重点关注的社会经济驱动因素。
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来源期刊
环境科学
环境科学 Environmental Science-Environmental Science (all)
CiteScore
4.40
自引率
0.00%
发文量
15329
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