Revisiting the impact of temperature on ground-level ozone: A causal inference approach

被引:2
作者
Chen, Baihua [1 ]
Zhen, Ling [1 ,3 ]
Wang, Lin [1 ]
Zhong, Haobin [1 ]
Lin, Chunshui [2 ]
Yang, Lin [1 ,4 ]
Xu, Wei [1 ,2 ]
Huang, Ru-Jin [1 ]
机构
[1] Chinese Acad Sci, Inst Urban Environm, Ctr Excellence Reg Atmospher Environm, Xiamen, Peoples R China
[2] Chinese Acad Sci, Inst Earth Environm, Ctr Excellence Quaternary Sci & Global Change, State Key Lab Loess & Quaternary Geol SKLLQG, Xian 710061, Peoples R China
[3] Univ Chinese Acad Sci, Beijing, Peoples R China
[4] Univ Nottingham Ningbo, Sch Environm Sci & Technol, Ningbo, Peoples R China
关键词
Ambient air quality; Ozone; Temperature; Machine learning; Causal inference; SURFACE OZONE; METEOROLOGICAL INFLUENCES; EMISSIONS; POLLUTION; CHINA; VARIABILITY; DRIVERS;
D O I
10.1016/j.scitotenv.2024.176062
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
It has been widely acknowledged that high temperatures and heatwaves promote ozone concentration, worsening the ambient air quality. However, temperature can impact ozone via multiple pathways, and quantifying each path is challenging due to environmental confounders. In this study, we frame the problem as a treatmentoutcome issue and utilize a machine learning-aided causal inference technique to disentangle the impact of temperature on ozone formation. Our approach reveals that failing to account for the covariations of solar radiation and other meteorological factors leads to an overestimation of the O-3-temperature response. Through process evaluation, we find that temperature influences local ozone formation mainly by accelerating chemical reactions and enhancing precursor production and changing boundary layer heights. The O-3 response to temperature via enhancing soil NOx and changing relative humidity and wind field is however observable. A better appreciation of O-3 -temperature response is critical for improving air quality regulation in the warming future.
引用
收藏
页数:8
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