Chemical Characteristics of Air Pollution with Significant Emission Changes During 2019-2021 in a Typical Industrial City of Eastern China

19 Pages Posted: 17 Nov 2023

See all articles by Xiangpeng Huang

Xiangpeng Huang

Fudan University

Changping Li

Suzhou Environmental Monitoring Center

Chao Pan

China Energy Science and Technology Research Institute Co. Ltd

Yue’e Li

Suzhou Environmental Monitoring Center

Wei Zheng

Southern University of Science and Technology

Guangwu Lin

Fudan University

Haiwei Li

Nanjing University of Information Science and Technology

Yunjiang Zhang

Nanjing University of Information Science and Technology

Junfeng Wang

Nanjing University of Information Science and Technology

Yali Lei

East China Normal University (ECNU)

Xinlei Ge

Nanjing University of Information Science and Technology

Jianhuai Ye

Southern University of Science and Technology

Hongliang Zhang

Fudan University

Abstract

Restrictions on emission controls were implemented in China to cope with the outbreak of the Coronavirus Disease 2019 (COVID-19), providing a chance to study the impacts of anthropogenic emissions on air quality. Subsequently, three-year measurements of PM2.5 chemical composition and light absorption were conducted in a typical megacity (Suzhou) of the Yangtze River Delta in China from 2019 to 2021. Overall, the annual observation period is divided into four stages: Normal Days, Chinese New Year, Lockdown, and Work Resumption. Our results show that the concentrations of chemical species (such as NO3-, SO42-, OC, EC) in PM2.5 and other gas species, including SO2, NO2, and CO from 2019 to 2021 decreased gradually, which were associated with emission reduction. During the four stages, the COVID-19 lockdown period had the lowest concentration of BC and PM2.5, indicating that strict emission control could significantly improve the air quality. The significant increase of O3 during the COVID-19 lockdown period is likely due to the reduced emission of NO from the traffic, which could weaken the effect of NO on O3 formation. The Normal Days in 2020 has the highest mass loadings of SO42-, NO3-, NH4+, OC, and EC, while the mass concentrations of SO42-, NO3-, NH4+, Cl-, and Na+ in the Lockdown were the lowest. Meanwhile, the aerosol light absorption has decreased yearly, and the significant decreases in light absorption were observed during the COVID-19 lockdown when compared to other stages. The highest frequency of Absorption Ångström exponent (AAE) ranges from 1.3 to 1.4, indicating that brown carbon played critical contributions to the light absorption. The mass concentration and fraction of fuel fossil were significantly higher than that of biomass burning during the whole campaign. The remarkable decline of fuel fossil during the COVID-19 lockdown can be attributed to the closure of factories and the reduction of vehicle emissions. The potential contribution source function analysis suggested that there was a significant regional difference in the potential sources of fossil fuel and biomass combustion. Our results highlight the strict emission control could provide a positive direction for air pollution mitigation campaigns.

Keywords: COVID-19, Yangtze River Delta, chemical composition, optical properties, source apportionment

Suggested Citation

Huang, Xiangpeng and Li, Changping and Pan, Chao and Li, Yue’e and Zheng, Wei and Lin, Guangwu and Li, Haiwei and Zhang, Yunjiang and Wang, Junfeng and Lei, Yali and Ge, Xinlei and Ye, Jianhuai and Zhang, Hongliang, Chemical Characteristics of Air Pollution with Significant Emission Changes During 2019-2021 in a Typical Industrial City of Eastern China. Available at SSRN: https://ssrn.com/abstract=4629016 or http://dx.doi.org/10.2139/ssrn.4629016

Xiangpeng Huang

Fudan University ( email )

Beijing West District Baiyun Load 10th
Shanghai, 100045
China

Changping Li

Suzhou Environmental Monitoring Center ( email )

Chao Pan

China Energy Science and Technology Research Institute Co. Ltd ( email )

Yue’e Li

Suzhou Environmental Monitoring Center ( email )

Wei Zheng

Southern University of Science and Technology ( email )

No 1088, xueyuan Rd.
Xili, Nanshan District
Shenzhen, 518055
China

Guangwu Lin

Fudan University ( email )

Beijing West District Baiyun Load 10th
Shanghai, 100045
China

Haiwei Li

Nanjing University of Information Science and Technology ( email )

Nanjing
China

Yunjiang Zhang

Nanjing University of Information Science and Technology ( email )

Nanjing
China

Junfeng Wang

Nanjing University of Information Science and Technology ( email )

Nanjing
China

Yali Lei

East China Normal University (ECNU) ( email )

Xinlei Ge

Nanjing University of Information Science and Technology ( email )

Nanjing
China

Jianhuai Ye

Southern University of Science and Technology ( email )

No 1088, xueyuan Rd.
Xili, Nanshan District
Shenzhen, 518055
China

Hongliang Zhang (Contact Author)

Fudan University ( email )

Beijing West District Baiyun Load 10th
Shanghai, 100045
China

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