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京津冀及周边地区细颗粒物与臭氧时空变化及多尺度社会经济驱动因素分析

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

作者信息

Yan Li, Song Xiao-Han, Lei Yu, Tian He-Zhong

机构信息

School of Environment, Beijing Normal University, Beijing 100875, China.

Chinese Academy for Environmental Planning, Beijing 100043, China.

出版信息

Huan Jing Ke Xue. 2024 Nov 8;45(11):6207-6218. doi: 10.13227/j.hjkx.202311002.

Abstract

Based on PM and O 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 and O concentrations. The results indicated that: ① The changing slope of PM concentration ranged from -12.93 to 0.43 μg·(m·a), and the changing slope of O concentration ranged from 0.70 to 14.90 μg·(m·a). The decreasing slope of PM concentration was the largest in winter, and the increasing slope of O concentration was the largest in summer. ② The concentrations of PM and O were spatially correlated, and the H-H concentrations of PM were located in the southern Hebei Province and the northern Henan Province. The spatial clustering pattern of O changed greatly. ③ From the perspective of urban agglomeration, the GDP, population density, and civilian car ownership had a strong explanatory power for PM, while GDP, urbanization rate, and civilian car ownership had a strong explanatory power for O. 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 and O pollution and became the socio-economic driving factors that need to be focused on to control PM and O co-pollution.

摘要

基于2015—2020年京津冀及其周边地区的细颗粒物(PM)和臭氧(O)遥感浓度数据,我们运用趋势分析、地理探测器和地理时空加权回归模型,探究了多尺度PM和O浓度的时空特征以及关键驱动社会经济因素。结果表明:①PM浓度变化斜率范围为-12.93至0.43μg·(m·a),O浓度变化斜率范围为0.70至14.90μg·(m·a)。PM浓度下降斜率在冬季最大,O浓度上升斜率在夏季最大。②PM和O浓度存在空间相关性,PM的高高浓度区位于河北省南部和河南省北部。O的空间聚类模式变化较大。③从城市群角度来看,国内生产总值(GDP)、人口密度和民用汽车保有量对PM有较强的解释力,而GDP、城市化率和民用汽车保有量对O有较强的解释力。2016年和2020年的主导交互作用因素分别是人口密度∩第二产业比重和城市化率∩道路网密度。④从单个城市角度来看,人口密度、工业氮氧化物排放量和用电量对PM和O污染主要有正向影响,成为控制PM和O复合污染需重点关注的社会经济驱动因素。

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