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基于中国气候区划的 PM2.5 浓度时空演变机制。

The spatial-temporal evolution mechanism of PM2.5 concentration based on China's climate zoning.

机构信息

College of Geography and Environment, Shandong Normal University, Jinan, China.

School of International and Public Affairs, Shanghai Jiao Tong University, Shanghai, China; SJTU-UNIDO Joint Institute of Inclusive and Sustainable Industrial Development, Shanghai Jiao Tong University, Shanghai, China; China Institute for Urban Governance, Shanghai Jiao Tong University, Shanghai, China.

出版信息

J Environ Manage. 2023 Jan 1;325(Pt B):116671. doi: 10.1016/j.jenvman.2022.116671. Epub 2022 Nov 4.

Abstract

Increasing attention has been given to the impact of PM2.5 concentration on human health. Exploring the influential factors of PM2.5 is conducive to improving air quality. Most existing studies explore the factors that influence the PM2.5 concentration from the perspective of cities or urban agglomerations, while few studies are conducted from the perspective of climate zones. We used the standard deviation ellipse and spatial autocorrelation analysis to explore the spatial-temporal evolution of the PM2.5 concentration in different climate zones in China during 2000-2018. We used differentiated EKC to construct panel regression models to explore the differences in the influential factors of the PM2.5 concentration in three climate zones. The number of cities with PM2.5 concentration less than 35 μg/m increased in the different climate zones. The center of gravity of the PM2.5 concentration has remained at the junction of the temperate and subtropical monsoon climate zones. The PM2.5 concentration had a high positive spatial autocorrelation in the different climate zones. The high-high clustering areas were located in the south of the temperate monsoon climate zone and the north of the subtropical monsoon climate zone. There was an inverted "U-shaped" curve between the PM2.5 concentration and economic development in China that varied in different climate zones. Identifying the differences in the influential factors of PM2.5 concentration in different climate zones will help to accelerate the implementation of the EKC inflection point.

摘要

人们越来越关注 PM2.5 浓度对人类健康的影响。探索 PM2.5 的影响因素有助于改善空气质量。大多数现有研究从城市或城市群的角度探讨影响 PM2.5 浓度的因素,而很少从气候带的角度进行研究。本研究采用标准差椭圆和空间自相关分析方法,探讨了 2000-2018 年中国不同气候带 PM2.5 浓度的时空演变。利用差异化 EKC 构建面板回归模型,探讨了三种气候带 PM2.5 浓度影响因素的差异。不同气候带 PM2.5 浓度小于 35μg/m3 的城市数量有所增加。PM2.5 浓度重心一直位于温带和亚热带季风气候带的交界处。不同气候带 PM2.5 浓度具有较高的正空间自相关。高-高聚类区位于温带季风气候带南部和亚热带季风气候带北部。中国 PM2.5 浓度与经济发展之间存在倒“U 型”曲线,不同气候带的曲线存在差异。识别不同气候带 PM2.5 浓度影响因素的差异将有助于加快 EKC 拐点的实现。

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