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Air quality changes during the COVID-19 lockdown over the Yangtze River Delta Region: An insight into the impact of human activity pattern changes on air pollution variation.

Identifieur interne : 002682 ( Main/Corpus ); précédent : 002681; suivant : 002683

Air quality changes during the COVID-19 lockdown over the Yangtze River Delta Region: An insight into the impact of human activity pattern changes on air pollution variation.

Auteurs : Li Li ; Qing Li ; Ling Huang ; Qian Wang ; Ansheng Zhu ; Jian Xu ; Ziyi Liu ; Hongli Li ; Lishu Shi ; Rui Li ; Majid Azari ; Yangjun Wang ; Xiaojuan Zhang ; Zhiqiang Liu ; Yonghui Zhu ; Kun Zhang ; Shuhui Xue ; Maggie Chel Gee Ooi ; Dongping Zhang ; Andy Chan

Source :

RBID : pubmed:32413621

English descriptors

Abstract

The outbreak of COVID-19 has spreaded rapidly across the world. To control the rapid dispersion of the virus, China has imposed national lockdown policies to practise social distancing. This has led to reduced human activities and hence primary air pollutant emissions, which caused improvement of air quality as a side-product. To investigate the air quality changes during the COVID-19 lockdown over the YRD Region, we apply the WRF-CAMx modelling system together with monitoring data to investigate the impact of human activity pattern changes on air quality. Results show that human activities were lowered significantly during the period: industrial operations, VKT, constructions in operation, etc. were significantly reduced, leading to lowered SO2, NOx, PM2.5 and VOCs emissions by approximately 16-26%, 29-47%, 27-46% and 37-57% during the Level I and Level II response periods respectively. These emission reduction has played a significant role in the improvement of air quality. Concentrations of PM2.5, NO2 and SO2 decreased by 31.8%, 45.1% and 20.4% during the Level I period; and 33.2%, 27.2% and 7.6% during the Level II period compared with 2019. However, ozone did not show any reduction and increased greatly. Our results also show that even during the lockdown, with primary emissions reduction of 15%-61%, the daily average PM2.5 concentrations range between 15 and 79 μg m-3, which shows that background and residual pollutions are still high. Source apportionment results indicate that the residual pollution of PM2.5 comes from industry (32.2-61.1%), mobile (3.9-8.1%), dust (2.6-7.7%), residential sources (2.1-28.5%) in YRD and 14.0-28.6% contribution from long-range transport coming from northern China. This indicates that in spite of the extreme reductions in primary emissions, it cannot fully tackle the current air pollution. Re-organisation of the energy and industrial strategy together with trans-regional joint-control for a full long-term air pollution plan need to be further taken into account.

DOI: 10.1016/j.scitotenv.2020.139282
PubMed: 32413621
PubMed Central: PMC7211667

Links to Exploration step

pubmed:32413621

Le document en format XML

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<name sortKey="Wang, Yangjun" sort="Wang, Yangjun" uniqKey="Wang Y" first="Yangjun" last="Wang">Yangjun Wang</name>
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<name sortKey="Zhang, Xiaojuan" sort="Zhang, Xiaojuan" uniqKey="Zhang X" first="Xiaojuan" last="Zhang">Xiaojuan Zhang</name>
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<name sortKey="Ooi, Maggie Chel Gee" sort="Ooi, Maggie Chel Gee" uniqKey="Ooi M" first="Maggie Chel Gee" last="Ooi">Maggie Chel Gee Ooi</name>
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<name sortKey="Zhang, Dongping" sort="Zhang, Dongping" uniqKey="Zhang D" first="Dongping" last="Zhang">Dongping Zhang</name>
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<nlm:affiliation>School of Environmental and Chemical Engineering, Shanghai University, Shanghai, 200444, China; Key Laboratory of Organic Compound Pollution Control Engineering (MOE), Shanghai University, Shanghai 200444, China. Electronic address: Lily@shu.edu.cn.</nlm:affiliation>
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<name sortKey="Zhu, Ansheng" sort="Zhu, Ansheng" uniqKey="Zhu A" first="Ansheng" last="Zhu">Ansheng Zhu</name>
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<name sortKey="Xu, Jian" sort="Xu, Jian" uniqKey="Xu J" first="Jian" last="Xu">Jian Xu</name>
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<name sortKey="Liu, Ziyi" sort="Liu, Ziyi" uniqKey="Liu Z" first="Ziyi" last="Liu">Ziyi Liu</name>
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<name sortKey="Li, Hongli" sort="Li, Hongli" uniqKey="Li H" first="Hongli" last="Li">Hongli Li</name>
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</affiliation>
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<name sortKey="Shi, Lishu" sort="Shi, Lishu" uniqKey="Shi L" first="Lishu" last="Shi">Lishu Shi</name>
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<nlm:affiliation>School of Environmental and Chemical Engineering, Shanghai University, Shanghai, 200444, China; Key Laboratory of Organic Compound Pollution Control Engineering (MOE), Shanghai University, Shanghai 200444, China.</nlm:affiliation>
</affiliation>
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<name sortKey="Li, Rui" sort="Li, Rui" uniqKey="Li R" first="Rui" last="Li">Rui Li</name>
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<nlm:affiliation>School of Environmental and Chemical Engineering, Shanghai University, Shanghai, 200444, China; Key Laboratory of Organic Compound Pollution Control Engineering (MOE), Shanghai University, Shanghai 200444, China.</nlm:affiliation>
</affiliation>
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<name sortKey="Azari, Majid" sort="Azari, Majid" uniqKey="Azari M" first="Majid" last="Azari">Majid Azari</name>
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<nlm:affiliation>Department of Civil Engineering, University of Nottingham Malaysia, Semenyih 43500, Selangor, Malaysia.</nlm:affiliation>
</affiliation>
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<name sortKey="Wang, Yangjun" sort="Wang, Yangjun" uniqKey="Wang Y" first="Yangjun" last="Wang">Yangjun Wang</name>
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<nlm:affiliation>School of Environmental and Chemical Engineering, Shanghai University, Shanghai, 200444, China; Key Laboratory of Organic Compound Pollution Control Engineering (MOE), Shanghai University, Shanghai 200444, China.</nlm:affiliation>
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<name sortKey="Zhang, Xiaojuan" sort="Zhang, Xiaojuan" uniqKey="Zhang X" first="Xiaojuan" last="Zhang">Xiaojuan Zhang</name>
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<name sortKey="Liu, Zhiqiang" sort="Liu, Zhiqiang" uniqKey="Liu Z" first="Zhiqiang" last="Liu">Zhiqiang Liu</name>
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<nlm:affiliation>School of Environmental and Chemical Engineering, Shanghai University, Shanghai, 200444, China; Key Laboratory of Organic Compound Pollution Control Engineering (MOE), Shanghai University, Shanghai 200444, China.</nlm:affiliation>
</affiliation>
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<name sortKey="Zhu, Yonghui" sort="Zhu, Yonghui" uniqKey="Zhu Y" first="Yonghui" last="Zhu">Yonghui Zhu</name>
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<nlm:affiliation>School of Environmental and Chemical Engineering, Shanghai University, Shanghai, 200444, China; Key Laboratory of Organic Compound Pollution Control Engineering (MOE), Shanghai University, Shanghai 200444, China.</nlm:affiliation>
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<name sortKey="Zhang, Kun" sort="Zhang, Kun" uniqKey="Zhang K" first="Kun" last="Zhang">Kun Zhang</name>
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<nlm:affiliation>School of Environmental and Chemical Engineering, Shanghai University, Shanghai, 200444, China; Key Laboratory of Organic Compound Pollution Control Engineering (MOE), Shanghai University, Shanghai 200444, China.</nlm:affiliation>
</affiliation>
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<name sortKey="Xue, Shuhui" sort="Xue, Shuhui" uniqKey="Xue S" first="Shuhui" last="Xue">Shuhui Xue</name>
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<nlm:affiliation>School of Environmental and Chemical Engineering, Shanghai University, Shanghai, 200444, China; Key Laboratory of Organic Compound Pollution Control Engineering (MOE), Shanghai University, Shanghai 200444, China.</nlm:affiliation>
</affiliation>
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<name sortKey="Ooi, Maggie Chel Gee" sort="Ooi, Maggie Chel Gee" uniqKey="Ooi M" first="Maggie Chel Gee" last="Ooi">Maggie Chel Gee Ooi</name>
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<nlm:affiliation>Department of Civil Engineering, University of Nottingham Malaysia, Semenyih 43500, Selangor, Malaysia; Institute of Climate Change (IPI), National University of Malaysia (UKM), 43600 Bangi, Selangor, Malaysia.</nlm:affiliation>
</affiliation>
</author>
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<name sortKey="Zhang, Dongping" sort="Zhang, Dongping" uniqKey="Zhang D" first="Dongping" last="Zhang">Dongping Zhang</name>
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<nlm:affiliation>School of Environmental and Chemical Engineering, Shanghai University, Shanghai, 200444, China; Key Laboratory of Organic Compound Pollution Control Engineering (MOE), Shanghai University, Shanghai 200444, China.</nlm:affiliation>
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<name sortKey="Chan, Andy" sort="Chan, Andy" uniqKey="Chan A" first="Andy" last="Chan">Andy Chan</name>
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<nlm:affiliation>Department of Civil Engineering, University of Nottingham Malaysia, Semenyih 43500, Selangor, Malaysia. Electronic address: Andy.Chan@nottingham.edu.my.</nlm:affiliation>
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<title level="j">The Science of the total environment</title>
<idno type="eISSN">1879-1026</idno>
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<term>Air Pollutants (MeSH)</term>
<term>Air Pollution (MeSH)</term>
<term>Betacoronavirus (MeSH)</term>
<term>COVID-19 (MeSH)</term>
<term>China (MeSH)</term>
<term>Coronavirus Infections (MeSH)</term>
<term>Environmental Monitoring (MeSH)</term>
<term>Human Activities (MeSH)</term>
<term>Humans (MeSH)</term>
<term>Pandemics (MeSH)</term>
<term>Particulate Matter (MeSH)</term>
<term>Pneumonia, Viral (MeSH)</term>
<term>SARS-CoV-2 (MeSH)</term>
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<term>Air Pollutants</term>
<term>Particulate Matter</term>
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<term>China</term>
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<keywords scheme="MESH" xml:lang="en">
<term>Air Pollution</term>
<term>Betacoronavirus</term>
<term>COVID-19</term>
<term>Coronavirus Infections</term>
<term>Environmental Monitoring</term>
<term>Human Activities</term>
<term>Humans</term>
<term>Pandemics</term>
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<div type="abstract" xml:lang="en">The outbreak of COVID-19 has spreaded rapidly across the world. To control the rapid dispersion of the virus, China has imposed national lockdown policies to practise social distancing. This has led to reduced human activities and hence primary air pollutant emissions, which caused improvement of air quality as a side-product. To investigate the air quality changes during the COVID-19 lockdown over the YRD Region, we apply the WRF-CAMx modelling system together with monitoring data to investigate the impact of human activity pattern changes on air quality. Results show that human activities were lowered significantly during the period: industrial operations, VKT, constructions in operation, etc. were significantly reduced, leading to lowered SO
<sub>2</sub>
, NO
<sub>x</sub>
, PM
<sub>2.5</sub>
and VOCs emissions by approximately 16-26%, 29-47%, 27-46% and 37-57% during the Level I and Level II response periods respectively. These emission reduction has played a significant role in the improvement of air quality. Concentrations of PM
<sub>2.5</sub>
, NO
<sub>2</sub>
and SO
<sub>2</sub>
decreased by 31.8%, 45.1% and 20.4% during the Level I period; and 33.2%, 27.2% and 7.6% during the Level II period compared with 2019. However, ozone did not show any reduction and increased greatly. Our results also show that even during the lockdown, with primary emissions reduction of 15%-61%, the daily average PM
<sub>2.5</sub>
concentrations range between 15 and 79 μg m
<sup>-3</sup>
, which shows that background and residual pollutions are still high. Source apportionment results indicate that the residual pollution of PM
<sub>2.5</sub>
comes from industry (32.2-61.1%), mobile (3.9-8.1%), dust (2.6-7.7%), residential sources (2.1-28.5%) in YRD and 14.0-28.6% contribution from long-range transport coming from northern China. This indicates that in spite of the extreme reductions in primary emissions, it cannot fully tackle the current air pollution. Re-organisation of the energy and industrial strategy together with trans-regional joint-control for a full long-term air pollution plan need to be further taken into account.</div>
</front>
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<DateCompleted>
<Year>2020</Year>
<Month>06</Month>
<Day>15</Day>
</DateCompleted>
<DateRevised>
<Year>2021</Year>
<Month>01</Month>
<Day>20</Day>
</DateRevised>
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<Journal>
<ISSN IssnType="Electronic">1879-1026</ISSN>
<JournalIssue CitedMedium="Internet">
<Volume>732</Volume>
<PubDate>
<Year>2020</Year>
<Month>Aug</Month>
<Day>25</Day>
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<Title>The Science of the total environment</Title>
<ISOAbbreviation>Sci Total Environ</ISOAbbreviation>
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<ArticleTitle>Air quality changes during the COVID-19 lockdown over the Yangtze River Delta Region: An insight into the impact of human activity pattern changes on air pollution variation.</ArticleTitle>
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<Abstract>
<AbstractText>The outbreak of COVID-19 has spreaded rapidly across the world. To control the rapid dispersion of the virus, China has imposed national lockdown policies to practise social distancing. This has led to reduced human activities and hence primary air pollutant emissions, which caused improvement of air quality as a side-product. To investigate the air quality changes during the COVID-19 lockdown over the YRD Region, we apply the WRF-CAMx modelling system together with monitoring data to investigate the impact of human activity pattern changes on air quality. Results show that human activities were lowered significantly during the period: industrial operations, VKT, constructions in operation, etc. were significantly reduced, leading to lowered SO
<sub>2</sub>
, NO
<sub>x</sub>
, PM
<sub>2.5</sub>
and VOCs emissions by approximately 16-26%, 29-47%, 27-46% and 37-57% during the Level I and Level II response periods respectively. These emission reduction has played a significant role in the improvement of air quality. Concentrations of PM
<sub>2.5</sub>
, NO
<sub>2</sub>
and SO
<sub>2</sub>
decreased by 31.8%, 45.1% and 20.4% during the Level I period; and 33.2%, 27.2% and 7.6% during the Level II period compared with 2019. However, ozone did not show any reduction and increased greatly. Our results also show that even during the lockdown, with primary emissions reduction of 15%-61%, the daily average PM
<sub>2.5</sub>
concentrations range between 15 and 79 μg m
<sup>-3</sup>
, which shows that background and residual pollutions are still high. Source apportionment results indicate that the residual pollution of PM
<sub>2.5</sub>
comes from industry (32.2-61.1%), mobile (3.9-8.1%), dust (2.6-7.7%), residential sources (2.1-28.5%) in YRD and 14.0-28.6% contribution from long-range transport coming from northern China. This indicates that in spite of the extreme reductions in primary emissions, it cannot fully tackle the current air pollution. Re-organisation of the energy and industrial strategy together with trans-regional joint-control for a full long-term air pollution plan need to be further taken into account.</AbstractText>
<CopyrightInformation>Copyright © 2020 Elsevier B.V. All rights reserved.</CopyrightInformation>
</Abstract>
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<CoiStatement>Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.</CoiStatement>
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<Month>05</Month>
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