The spatiotemporal variation of PM2.5-O3 association and its influencing factors across China: Dynamic Simil-Hu lines

Chenru Chen; Bingbo Gao; Miaoqing Xu; Shuyi Liu; Dehai Zhu; Jianyu Yang; Ziyue Chen

doi:10.1016/j.scitotenv.2023.163346

The spatiotemporal variation of PM_2.5-O₃ association and its influencing factors across China: Dynamic Simil-Hu lines

Sci Total Environ. 2023 Jul 1:880:163346. doi: 10.1016/j.scitotenv.2023.163346. Epub 2023 Apr 7.

Authors

Chenru Chen¹, Bingbo Gao², Miaoqing Xu³, Shuyi Liu⁴, Dehai Zhu⁴, Jianyu Yang⁴, Ziyue Chen⁵

Affiliations

¹ College of Surveying and Geographic Informatics, Tongji University, Shanghai 200092, China.
² College of Land Science and Technology, China Agricultural University, Beijing 100091, China. Electronic address: gaobingbo@cau.edu.cn.
³ College of Global and Earth System Sciences, Beijing Normal University, Beijing 100875, China.
⁴ College of Land Science and Technology, China Agricultural University, Beijing 100091, China.
⁵ College of Global and Earth System Sciences, Beijing Normal University, Beijing 100875, China. Electronic address: zychen@bnu.edu.cn.

PMID: 37031933
DOI: 10.1016/j.scitotenv.2023.163346

Abstract

In recent years, PM_2.5 and O₃ composite airborne pollution has become one of the most severe environment issues in China. To get a better understanding and tackle these problems, we employed multi-year data to explore the spatiotemporal variation of the PM_2.5-O₃ relationship in China and investigated its major driving factors. Firstly, interesting patterns were found that named dynamic Simil-Hu lines, which presented a combined effect of natural and anthropogenic influences, were closely related to the spatial patterns of PM_2.5-O₃ association across seasons. Furthermore, regions with lower altitudes, higher humidity, higher atmospheric pressure, higher temperature, fewer sunshine hours, more accumulated precipitation, denser population and higher GDP often show positive PM_2.5-O₃ associations, regardless of seasonal variations. Amongst these factors, humidity, temperature and precipitation were dominant factors. This research suggests that the collaborative governance of composite atmospheric pollution should be implemented dynamically, in consideration of geographical locations, meteorological conditions and socioeconomic conditions.

Keywords: Collaborative governance of atmospheric pollutants; Correlation analysis; Geographical detector; PM(2.5) and O(3); Random forest.