Geospatial analysis of COVID-19 lockdown effects on air quality in the South and Southeast Asian region.
Identifieur interne : 000410 ( Main/Exploration ); précédent : 000409; suivant : 000411Geospatial analysis of COVID-19 lockdown effects on air quality in the South and Southeast Asian region.
Auteurs : Sanjoy Roy [Bangladesh] ; Monojit Saha [Bangladesh] ; Bandhan Dhar [Bangladesh] ; Santa Pandit [Japon] ; Rubaiya Nasrin [Bangladesh]Source :
- The Science of the total environment [ 1879-1026 ] ; 2021.
Descripteurs français
- KwdFr :
- MESH :
- Wicri :
- geographic : Bangladesh.
English descriptors
- KwdEn :
- MESH :
- chemical , analysis : Air Pollutants, Particulate Matter.
- geographic : Bangladesh, Cities.
- analysis : Air Pollution.
- COVID-19, Communicable Disease Control, Environmental Monitoring, Humans, Pandemics, SARS-CoV-2.
Abstract
The COVID-19 pandemic, induced by the novel Coronavirus worldwide outbreak, is causing countries to introduce different types of lockdown measures to curb the contagion. The implementation of strict lockdown policies has had unprecedented impacts on air quality globally. This study is an attempt to assess the effects of COVID-19 induced lockdown measures on air quality in both regional, country, and city scales in the South and Southeast Asian region using open-source satellite-based data and software frameworks. We performed a systematic review of the national lockdown measures of 19 countries of the study area based on publicly available materials. We considered two temporal settings over a period of 66 days to assess and compare the effects of lockdown measures on air quality levels between standard business as usual and current situation COVID-19 lockdown. Results showed that compared to the same period of 2019, atmospheric NO2, SO2, PM2.5, and CO levels decreased by an average of 24.16%, 19.51%, 20.25%, and 6.88%, respectively during the lockdown, while O3 increased by a maximum of 4.52%. Among the 19 studied cities, Dhaka, Kathmandu, Jakarta, and Hanoi experienced the highest reduction of NO2 (40%-47%) during the lockdown period compared to the corresponding period of 2019. The methodological framework applied in this study can be used and extended to future research in the similar domain such as understanding long-term effects of COVID-19 mitigation measures on the atmospheric pollution at continental-scale or assessing the effects of the domestic emissions during the stay-at-home; a standard and effective COVID-19 lockdown measure applied in most of the countries.
DOI: 10.1016/j.scitotenv.2020.144009
PubMed: 33250248
PubMed Central: PMC7833964
Affiliations:
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The implementation of strict lockdown policies has had unprecedented impacts on air quality globally. This study is an attempt to assess the effects of COVID-19 induced lockdown measures on air quality in both regional, country, and city scales in the South and Southeast Asian region using open-source satellite-based data and software frameworks. We performed a systematic review of the national lockdown measures of 19 countries of the study area based on publicly available materials. We considered two temporal settings over a period of 66 days to assess and compare the effects of lockdown measures on air quality levels between standard business as usual and current situation COVID-19 lockdown. Results showed that compared to the same period of 2019, atmospheric NO<sub>2</sub>, SO<sub>2</sub>, PM<sub>2.5</sub>, and CO levels decreased by an average of 24.16%, 19.51%, 20.25%, and 6.88%, respectively during the lockdown, while O<sub>3</sub> increased by a maximum of 4.52%. Among the 19 studied cities, Dhaka, Kathmandu, Jakarta, and Hanoi experienced the highest reduction of NO<sub>2</sub> (40%-47%) during the lockdown period compared to the corresponding period of 2019. The methodological framework applied in this study can be used and extended to future research in the similar domain such as understanding long-term effects of COVID-19 mitigation measures on the atmospheric pollution at continental-scale or assessing the effects of the domestic emissions during the stay-at-home; a standard and effective COVID-19 lockdown measure applied in most of the countries.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" IndexingMethod="Automated" Owner="NLM"><PMID Version="1">33250248</PMID><DateCompleted><Year>2021</Year><Month>01</Month><Day>01</Day></DateCompleted><DateRevised><Year>2021</Year><Month>01</Month><Day>29</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1879-1026</ISSN><JournalIssue CitedMedium="Internet"><Volume>756</Volume><PubDate><Year>2021</Year><Month>Feb</Month><Day>20</Day></PubDate></JournalIssue><Title>The Science of the total environment</Title><ISOAbbreviation>Sci Total Environ</ISOAbbreviation></Journal><ArticleTitle>Geospatial analysis of COVID-19 lockdown effects on air quality in the South and Southeast Asian region.</ArticleTitle><Pagination><MedlinePgn>144009</MedlinePgn></Pagination><ELocationID EIdType="pii" ValidYN="Y">S0048-9697(20)37540-9</ELocationID><ELocationID EIdType="doi" ValidYN="Y">10.1016/j.scitotenv.2020.144009</ELocationID><Abstract><AbstractText>The COVID-19 pandemic, induced by the novel Coronavirus worldwide outbreak, is causing countries to introduce different types of lockdown measures to curb the contagion. The implementation of strict lockdown policies has had unprecedented impacts on air quality globally. This study is an attempt to assess the effects of COVID-19 induced lockdown measures on air quality in both regional, country, and city scales in the South and Southeast Asian region using open-source satellite-based data and software frameworks. We performed a systematic review of the national lockdown measures of 19 countries of the study area based on publicly available materials. We considered two temporal settings over a period of 66 days to assess and compare the effects of lockdown measures on air quality levels between standard business as usual and current situation COVID-19 lockdown. Results showed that compared to the same period of 2019, atmospheric NO<sub>2</sub>, SO<sub>2</sub>, PM<sub>2.5</sub>, and CO levels decreased by an average of 24.16%, 19.51%, 20.25%, and 6.88%, respectively during the lockdown, while O<sub>3</sub> increased by a maximum of 4.52%. Among the 19 studied cities, Dhaka, Kathmandu, Jakarta, and Hanoi experienced the highest reduction of NO<sub>2</sub> (40%-47%) during the lockdown period compared to the corresponding period of 2019. The methodological framework applied in this study can be used and extended to future research in the similar domain such as understanding long-term effects of COVID-19 mitigation measures on the atmospheric pollution at continental-scale or assessing the effects of the domestic emissions during the stay-at-home; a standard and effective COVID-19 lockdown measure applied in most of the countries.</AbstractText><CopyrightInformation>Copyright © 2020 Elsevier B.V. All rights reserved.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Roy</LastName><ForeName>Sanjoy</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>Bengal Institute for Architecture, Landscapes and Settlements, Dhaka, Bangladesh. Electronic address: sroy.du@gmail.com.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Saha</LastName><ForeName>Monojit</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>Bengal Institute for Architecture, Landscapes and Settlements, Dhaka, Bangladesh; Dept. of Geography and Environment, University of Dhaka, Bangladesh. Electronic address: monojits76@gmail.com.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Dhar</LastName><ForeName>Bandhan</ForeName><Initials>B</Initials><AffiliationInfo><Affiliation>Bengal Institute for Architecture, Landscapes and Settlements, Dhaka, Bangladesh; Dept. of Geography and Environment, University of Dhaka, Bangladesh. Electronic address: bandhangeo@gmail.com.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Pandit</LastName><ForeName>Santa</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>United Nations University, Institute for the Advanced Study of Sustainability, Tokyo, Japan. Electronic address: pandit@unu.edu.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Nasrin</LastName><ForeName>Rubaiya</ForeName><Initials>R</Initials><AffiliationInfo><Affiliation>Bengal Institute for Architecture, Landscapes and Settlements, Dhaka, Bangladesh; Urban and Regional Planning, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh. Electronic address: nasrinrubaiya@gmail.com.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D000078182">Systematic Review</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2020</Year><Month>11</Month><Day>21</Day></ArticleDate></Article><MedlineJournalInfo><Country>Netherlands</Country><MedlineTA>Sci Total Environ</MedlineTA><NlmUniqueID>0330500</NlmUniqueID><ISSNLinking>0048-9697</ISSNLinking></MedlineJournalInfo><ChemicalList><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D000393">Air Pollutants</NameOfSubstance></Chemical><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D052638">Particulate Matter</NameOfSubstance></Chemical></ChemicalList><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000393" MajorTopicYN="Y">Air Pollutants</DescriptorName><QualifierName UI="Q000032" MajorTopicYN="N">analysis</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D000397" MajorTopicYN="Y">Air Pollution</DescriptorName><QualifierName UI="Q000032" MajorTopicYN="N">analysis</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D001459" MajorTopicYN="N" Type="Geographic">Bangladesh</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000086382" MajorTopicYN="Y">COVID-19</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D002947" MajorTopicYN="N" Type="Geographic">Cities</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D003140" MajorTopicYN="N">Communicable Disease Control</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D004784" MajorTopicYN="N">Environmental Monitoring</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D058873" MajorTopicYN="N">Pandemics</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D052638" MajorTopicYN="N">Particulate Matter</DescriptorName><QualifierName UI="Q000032" MajorTopicYN="N">analysis</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D000086402" MajorTopicYN="N">SARS-CoV-2</DescriptorName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Air pollution</Keyword><Keyword MajorTopicYN="N">Lockdown setting</Keyword><Keyword MajorTopicYN="N">SARS-CoV-2</Keyword><Keyword MajorTopicYN="N">Satellite-based data</Keyword></KeywordList><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></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2020</Year><Month>09</Month><Day>12</Day></PubMedPubDate><PubMedPubDate 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IdType="pubmed">32015508</ArticleId></ArticleIdList></Reference></ReferenceList></PubmedData></pubmed><affiliations><list><country><li>Bangladesh</li><li>Japon</li></country><region><li>Région de Kantō</li></region><settlement><li>Tokyo</li></settlement></list><tree><country name="Bangladesh"><noRegion><name sortKey="Roy, Sanjoy" sort="Roy, Sanjoy" uniqKey="Roy S" first="Sanjoy" last="Roy">Sanjoy Roy</name></noRegion><name sortKey="Dhar, Bandhan" sort="Dhar, Bandhan" uniqKey="Dhar B" first="Bandhan" last="Dhar">Bandhan Dhar</name><name sortKey="Nasrin, Rubaiya" sort="Nasrin, Rubaiya" uniqKey="Nasrin R" first="Rubaiya" last="Nasrin">Rubaiya Nasrin</name><name sortKey="Saha, Monojit" sort="Saha, Monojit" uniqKey="Saha M" first="Monojit" last="Saha">Monojit Saha</name></country><country name="Japon"><region name="Région de Kantō"><name sortKey="Pandit, Santa" sort="Pandit, Santa" uniqKey="Pandit S" first="Santa" last="Pandit">Santa 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