| 京津冀及其周边地区PM2.5和臭氧的时空变化及多尺度社会经济驱动因素分析 |
| 摘要点击 4120 全文点击 699 投稿时间：2023-11-01 修订日期：2024-02-21 |
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| 中文关键词 PM2.5 臭氧（O3） 地理探测 时空地理加权回归（GTWR）模型 驱动因素 |
| 英文关键词 PM2.5 ozone（O3） geographical detector geographically and temporally weighted regression（GTWR） model driving factors |
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| 中文摘要 |
| 基于2015~2020年京津冀及周边地区PM2.5和O3遥感浓度数据,利用趋势分析、地理探测和时空地理加权回归方法定量研究多尺度PM2.5和O3浓度的时空分异特征及关键社会经济驱动因素影响机制. 结果表明： ①PM2.5浓度变化的变化斜率在-12.93~0.43 μg·（m3·a）-1之间,O3浓度变化斜率在0.70~14.90 μg·（m3·a）-1之间,PM2.5冬季下降幅度最大,O3夏季上升幅度最大；②PM2.5和O3浓度均具有空间相关性,PM2.5的H-H聚集位于河北省南部和河南省北部；O3的空间聚类格局变化较大. ③从区域尺度来看,GDP、人口密度和民用汽车保有量对PM2.5解释力较强,GDP、城镇化和民用汽车保有量对O3解释力较强. 2016年和2020年的主导交互因子相同,分别为人口密度∩第二产业比例和城镇化∩路网密度. ④从城市尺度来看,人口密度、工业氮氧化物排放量和用电量对PM2.5和O3污染主要呈正效应,成为城市尺度控制PM2.5和O3污染需要重点关注的社会经济驱动因素. |
| 英文摘要 |
| 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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