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

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Spring festival and COVID-19 lockdown : disentangling PM sources in major Chinese cities. / Dai, Qili; Hou, Linlu; Liu, Bowen; Zhang, Yufen; Song, Congbo; Shi, Zongbo; Hopke, Philip K.; Feng, Yinchang.

In: Geophysical Research Letters, Vol. 48, No. 11, e2021GL093403, 16.06.2021.

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Dai, Qili ; Hou, Linlu ; Liu, Bowen ; Zhang, Yufen ; Song, Congbo ; Shi, Zongbo ; Hopke, Philip K. ; Feng, Yinchang. / Spring festival and COVID-19 lockdown : disentangling PM sources in major Chinese cities. In: Geophysical Research Letters. 2021 ; Vol. 48, No. 11.

Bibtex

@article{a1a8aa463e8c49b7a0fe33c61bb8564e,
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.",
keywords = "air quality, COVID-19, machine learning, meteorological normalization, source, spring festival",
author = "Qili Dai and Linlu Hou and Bowen Liu and Yufen Zhang and Congbo Song and Zongbo Shi and Hopke, {Philip K.} and Yinchang Feng",
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.",
year = "2021",
month = jun,
day = "16",
doi = "10.1029/2021GL093403",
language = "English",
volume = "48",
journal = "Geophysical Research Letters",
issn = "0094-8276",
publisher = "American Geophysical Union",
number = "11",

}

RIS

TY - JOUR

T1 - Spring festival and COVID-19 lockdown

T2 - disentangling PM sources in major Chinese cities

AU - Dai, Qili

AU - Hou, Linlu

AU - Liu, Bowen

AU - Zhang, Yufen

AU - Song, Congbo

AU - Shi, Zongbo

AU - Hopke, Philip K.

AU - Feng, Yinchang

N1 - 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.

PY - 2021/6/16

Y1 - 2021/6/16

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.

KW - air quality

KW - COVID-19

KW - machine learning

KW - meteorological normalization

KW - source

KW - spring festival

UR - http://www.scopus.com/inward/record.url?scp=85107735215&partnerID=8YFLogxK

U2 - 10.1029/2021GL093403

DO - 10.1029/2021GL093403

M3 - Article

AN - SCOPUS:85107735215

VL - 48

JO - Geophysical Research Letters

JF - Geophysical Research Letters

SN - 0094-8276

IS - 11

M1 - e2021GL093403

ER -