Variability of PM2.5 and O3 concentrations and their driving forces over Chinese megacities during 2018-2020

Tianyi Xu; Chengxin Zhang; Cheng Liu; Qihou Hu

doi:10.1016/j.jes.2021.10.014

Variability of PM_2.5 and O₃ concentrations and their driving forces over Chinese megacities during 2018-2020

J Environ Sci (China). 2023 Feb:124:1-10. doi: 10.1016/j.jes.2021.10.014. Epub 2022 Feb 2.

Authors

Tianyi Xu¹, Chengxin Zhang², Cheng Liu³, Qihou Hu⁴

Affiliations

¹ School of Environmental Science and Optoelectronic Technology, University of Science and Technology of China, Hefei 230026, China; Key Laboratory of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China.
² Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei 230026, China. Electronic address: zcx2011@ustc.edu.cn.
³ Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei 230026, China; Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; Key Laboratory of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China; Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei 230026, China. Electronic address: chliu81@ustc.edu.cn.
⁴ Key Laboratory of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China.

PMID: 36182119
DOI: 10.1016/j.jes.2021.10.014

Abstract

Recently, air pollution especially fine particulate matters (PM_2.5) and ozone (O₃) has become a severe issue in China. In this study, we first characterized the temporal trends of PM_2.5 and O₃ for Beijing, Guangzhou, Shanghai, and Wuhan respectively during 2018-2020. The annual mean PM_2.5 has decreased by 7.82%-33.92%, while O₃ concentration showed insignificant variations by -6.77%-4.65% during 2018-2020. The generalized additive models (GAMs) were implemented to quantify the contribution of individual meteorological factors and their gas precursors on PM_2.5 and O₃. On a short-term perspective, GAMs modeling shows that the daily variability of PM_2.5 concentration is largely related to the variation of precursor gases (R = 0.67-0.90), while meteorological conditions mainly affect the daily variability of O₃ concentration (R = 0.65-0.80) during 2018-2020. The impact of COVID-19 lockdown on PM_2.5 and O₃ concentrations were also quantified by using GAMs. During the 2020 lockdown, PM_2.5 decreased significantly for these megacities, yet the ozone concentration showed an increasing trend compared to 2019. The GAMs analysis indicated that the contribution of precursor gases to PM_2.5 and O₃ changes is 3-8 times higher than that of meteorological factors. In general, GAMs modeling on air quality is helpful to the understanding and control of PM_2.5 and O₃ pollution in China.

Keywords: Air pollution; COVID-19 lockdown; Meteorology; PM(2.5) and O(3) trends.

MeSH terms

Air Pollutants* / analysis
Air Pollution* / analysis
COVID-19*
China
Cities
Communicable Disease Control
Environmental Monitoring
Humans
Ozone* / analysis
Particulate Matter / analysis

Substances

Air Pollutants
Particulate Matter
Ozone