Spring festival and COVID-19 lockdown: disentangling PM sources in major Chinese cities

Qili Dai; Linlu Hou; Bowen Liu; Yufen Zhang; Congbo Song; Zongbo Shi; Philip K. Hopke; Yinchang Feng

doi:10.1029/2021GL093403

Spring festival and COVID-19 lockdown: disentangling PM sources in major Chinese cities

Qili Dai, Linlu Hou, Bowen Liu, Yufen Zhang, Congbo Song, Zongbo Shi, Philip K. Hopke, Yinchang Feng^*

^*Corresponding author for this work

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Abstract

Responding to the 2020 COVID-19 outbreak, China imposed an unprecedented lockdown producing reductions in air pollutant emissions. However, the lockdown driven air pollution changes have not been fully quantified. We applied machine learning to quantify the effects of meteorology on surface air quality data in 31 major Chinese cities. The meteorologically normalized NO₂, O₃, and PM_2.5 concentrations changed by −29.5%, +31.2%, and −7.0%, respectively, after the lockdown began. However, part of this effect was also associated with emission changes due to the Chinese Spring Festival, which led to ∼14.1% decrease in NO₂, ∼6.6% increase in O₃ and a mixed effect on PM_2.5 in the studied cities that largely resulted from festival associated fireworks. After decoupling the weather and Spring Festival effects, changes in air quality attributable to the lockdown were much smaller: −15.4%, +24.6%, and −9.7% for NO₂, O₃, and PM_2.5, respectively.

Original language	English
Article number	e2021GL093403
Number of pages	12
Journal	Geophysical Research Letters
Volume	48
Issue number	11
Early online date	1 Jun 2021
DOIs	https://doi.org/10.1029/2021GL093403
Publication status	Published - 16 Jun 2021

Bibliographical note

Funding Information:
This work was funded by the Ministry of Science and Technology of the People's Republic of China (2016YFC0208505).

Publisher Copyright:
© 2021. The Authors.

Keywords

air quality
COVID-19
machine learning
meteorological normalization
source
spring festival

ASJC Scopus subject areas

Geophysics
Earth and Planetary Sciences(all)

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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DaiQ2021Spring Final published version, 495 KBLicence: Creative Commons: Attribution-NonCommercial-NoDerivs (CC BY-NC-ND)

Cite this

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title = "Spring festival and COVID-19 lockdown: disentangling PM sources in major Chinese cities",

abstract = "Responding to the 2020 COVID-19 outbreak, China imposed an unprecedented lockdown producing reductions in air pollutant emissions. However, the lockdown driven air pollution changes have not been fully quantified. We applied machine learning to quantify the effects of meteorology on surface air quality data in 31 major Chinese cities. The meteorologically normalized NO2, O3, and PM2.5 concentrations changed by −29.5%, +31.2%, and −7.0%, respectively, after the lockdown began. However, part of this effect was also associated with emission changes due to the Chinese Spring Festival, which led to ∼14.1% decrease in NO2, ∼6.6% increase in O3 and a mixed effect on PM2.5 in the studied cities that largely resulted from festival associated fireworks. After decoupling the weather and Spring Festival effects, changes in air quality attributable to the lockdown were much smaller: −15.4%, +24.6%, and −9.7% for NO2, O3, and PM2.5, respectively.",

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note = "Funding Information: This work was funded by the Ministry of Science and Technology of the People's Republic of China (2016YFC0208505). Publisher Copyright: {\textcopyright} 2021. The Authors.",

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AU - Dai, Qili

AU - Hou, Linlu

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AU - Zhang, Yufen

AU - Song, Congbo

AU - Shi, Zongbo

AU - Hopke, Philip K.

AU - Feng, Yinchang

PY - 2021/6/16

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N2 - Responding to the 2020 COVID-19 outbreak, China imposed an unprecedented lockdown producing reductions in air pollutant emissions. However, the lockdown driven air pollution changes have not been fully quantified. We applied machine learning to quantify the effects of meteorology on surface air quality data in 31 major Chinese cities. The meteorologically normalized NO2, O3, and PM2.5 concentrations changed by −29.5%, +31.2%, and −7.0%, respectively, after the lockdown began. However, part of this effect was also associated with emission changes due to the Chinese Spring Festival, which led to ∼14.1% decrease in NO2, ∼6.6% increase in O3 and a mixed effect on PM2.5 in the studied cities that largely resulted from festival associated fireworks. After decoupling the weather and Spring Festival effects, changes in air quality attributable to the lockdown were much smaller: −15.4%, +24.6%, and −9.7% for NO2, O3, and PM2.5, respectively.

AB - Responding to the 2020 COVID-19 outbreak, China imposed an unprecedented lockdown producing reductions in air pollutant emissions. However, the lockdown driven air pollution changes have not been fully quantified. We applied machine learning to quantify the effects of meteorology on surface air quality data in 31 major Chinese cities. The meteorologically normalized NO2, O3, and PM2.5 concentrations changed by −29.5%, +31.2%, and −7.0%, respectively, after the lockdown began. However, part of this effect was also associated with emission changes due to the Chinese Spring Festival, which led to ∼14.1% decrease in NO2, ∼6.6% increase in O3 and a mixed effect on PM2.5 in the studied cities that largely resulted from festival associated fireworks. After decoupling the weather and Spring Festival effects, changes in air quality attributable to the lockdown were much smaller: −15.4%, +24.6%, and −9.7% for NO2, O3, and PM2.5, respectively.

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Spring festival and COVID-19 lockdown: disentangling PM sources in major Chinese cities

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

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