Ozone pollution mitigation in guangxi (south China) driven by meteorology and anthropogenic emissions during the COVID-19 lockdown.
Identifieur interne : 000E96 ( Main/Corpus ); précédent : 000E95; suivant : 000E97Ozone pollution mitigation in guangxi (south China) driven by meteorology and anthropogenic emissions during the COVID-19 lockdown.
Auteurs : Shuang Fu ; Meixiu Guo ; Linping Fan ; Qiyin Deng ; Deming Han ; Ye Wei ; Jinmin Luo ; Guimei Qin ; Jinping ChengSource :
- Environmental pollution (Barking, Essex : 1987) [ 1873-6424 ] ; 2020.
Abstract
With the implementation of COVID-19 restrictions and consequent improvement in air quality due to the nationwide lockdown, ozone (O3) pollution was generally amplified in China. However, the O3 levels throughout the Guangxi region of South China showed a clear downward trend during the lockdown. To better understand this unusual phenomenon, we investigated the characteristics of conventional pollutants, the influence of meteorological and anthropogenic factors quantified by a multiple linear regression (MLR) model, and the impact of local sources and long-range transport based on a continuous emission monitoring system (CEMS) and the HYSPLIT model. Results show that in Guangxi, the conventional pollutants generally declined during the COVID-19 lockdown period (January 24 to February 9, 2020) compared with their concentrations during 2016-2019, while O3 gradually increased during the resumption (10 February to April 2020) and full operation periods (May and June 2020). Focusing on Beihai, a typical Guangxi region city, the correlations between the daily O3 concentrations and six meteorological parameters (wind speed, visibility, temperature, humidity, precipitation, and atmospheric pressure) and their corresponding regression coefficients indicate that meteorological conditions were generally conducive to O3 pollution mitigation during the lockdown. A 7.84 μg/m3 drop in O3 concentration was driven by meteorology, with other decreases (4.11 μg/m3) explained by reduced anthropogenic emissions of O3 precursors. Taken together, the lower NO2/SO2 ratios (1.25-2.33) and consistencies between real-time monitored primary emissions and ambient concentrations suggest that, with the closure of small-scale industries, residual industrial emissions have become dominant contributors to local primary pollutants. Backward trajectory cluster analyses show that the slump of O3 concentrations in Southern Guangxi could be partly attributed to clean air mass transfer (24-58%) from the South China Sea. Overall, the synergistic effects of the COVID-19 lockdown and meteorological factors intensified O3 reduction in the Guangxi region of South China.
DOI: 10.1016/j.envpol.2020.115927
PubMed: 33143981
PubMed Central: PMC7588315
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Ozone pollution mitigation in guangxi (south China) driven by meteorology and anthropogenic emissions during the COVID-19 lockdown.</title><author><name sortKey="Fu, Shuang" sort="Fu, Shuang" uniqKey="Fu S" first="Shuang" last="Fu">Shuang Fu</name><affiliation><nlm:affiliation>School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.</nlm:affiliation></affiliation></author><author><name sortKey="Guo, Meixiu" sort="Guo, Meixiu" uniqKey="Guo M" first="Meixiu" last="Guo">Meixiu Guo</name><affiliation><nlm:affiliation>Beihai Ecology and Environment Agency, Beihai, Guangxi, 536000, China.</nlm:affiliation></affiliation></author><author><name sortKey="Fan, Linping" sort="Fan, Linping" uniqKey="Fan L" first="Linping" last="Fan">Linping Fan</name><affiliation><nlm:affiliation>School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.</nlm:affiliation></affiliation></author><author><name sortKey="Deng, Qiyin" sort="Deng, Qiyin" uniqKey="Deng Q" first="Qiyin" last="Deng">Qiyin Deng</name><affiliation><nlm:affiliation>College of Environment, Hohai University, Nanjing, Jiangsu, 210098, China.</nlm:affiliation></affiliation></author><author><name sortKey="Han, Deming" sort="Han, Deming" uniqKey="Han D" first="Deming" last="Han">Deming Han</name><affiliation><nlm:affiliation>School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.</nlm:affiliation></affiliation></author><author><name sortKey="Wei, Ye" sort="Wei, Ye" uniqKey="Wei Y" first="Ye" last="Wei">Ye Wei</name><affiliation><nlm:affiliation>Beihai Ecology and Environment Agency, Beihai, Guangxi, 536000, China.</nlm:affiliation></affiliation></author><author><name sortKey="Luo, Jinmin" sort="Luo, Jinmin" uniqKey="Luo J" first="Jinmin" last="Luo">Jinmin Luo</name><affiliation><nlm:affiliation>Beihai Ecology and Environment Agency, Beihai, Guangxi, 536000, China.</nlm:affiliation></affiliation></author><author><name sortKey="Qin, Guimei" sort="Qin, Guimei" uniqKey="Qin G" first="Guimei" last="Qin">Guimei Qin</name><affiliation><nlm:affiliation>School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.</nlm:affiliation></affiliation></author><author><name sortKey="Cheng, Jinping" sort="Cheng, Jinping" uniqKey="Cheng J" first="Jinping" last="Cheng">Jinping Cheng</name><affiliation><nlm:affiliation>School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China. Electronic address: jpcheng@sjtu.edu.cn.</nlm:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2020">2020</date><idno type="RBID">pubmed:33143981</idno><idno type="pmid">33143981</idno><idno type="doi">10.1016/j.envpol.2020.115927</idno><idno type="pmc">PMC7588315</idno><idno type="wicri:Area/Main/Corpus">000E96</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Corpus" wicri:corpus="PubMed">000E96</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Ozone pollution mitigation in guangxi (south China) driven by meteorology and anthropogenic emissions during the COVID-19 lockdown.</title><author><name sortKey="Fu, Shuang" sort="Fu, Shuang" uniqKey="Fu S" first="Shuang" last="Fu">Shuang Fu</name><affiliation><nlm:affiliation>School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.</nlm:affiliation></affiliation></author><author><name sortKey="Guo, Meixiu" sort="Guo, Meixiu" uniqKey="Guo M" first="Meixiu" last="Guo">Meixiu Guo</name><affiliation><nlm:affiliation>Beihai Ecology and Environment Agency, Beihai, Guangxi, 536000, China.</nlm:affiliation></affiliation></author><author><name sortKey="Fan, Linping" sort="Fan, Linping" uniqKey="Fan L" first="Linping" last="Fan">Linping Fan</name><affiliation><nlm:affiliation>School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.</nlm:affiliation></affiliation></author><author><name sortKey="Deng, Qiyin" sort="Deng, Qiyin" uniqKey="Deng Q" first="Qiyin" last="Deng">Qiyin Deng</name><affiliation><nlm:affiliation>College of Environment, Hohai University, Nanjing, Jiangsu, 210098, China.</nlm:affiliation></affiliation></author><author><name sortKey="Han, Deming" sort="Han, Deming" uniqKey="Han D" first="Deming" last="Han">Deming Han</name><affiliation><nlm:affiliation>School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.</nlm:affiliation></affiliation></author><author><name sortKey="Wei, Ye" sort="Wei, Ye" uniqKey="Wei Y" first="Ye" last="Wei">Ye Wei</name><affiliation><nlm:affiliation>Beihai Ecology and Environment Agency, Beihai, Guangxi, 536000, China.</nlm:affiliation></affiliation></author><author><name sortKey="Luo, Jinmin" sort="Luo, Jinmin" uniqKey="Luo J" first="Jinmin" last="Luo">Jinmin Luo</name><affiliation><nlm:affiliation>Beihai Ecology and Environment Agency, Beihai, Guangxi, 536000, China.</nlm:affiliation></affiliation></author><author><name sortKey="Qin, Guimei" sort="Qin, Guimei" uniqKey="Qin G" first="Guimei" last="Qin">Guimei Qin</name><affiliation><nlm:affiliation>School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.</nlm:affiliation></affiliation></author><author><name sortKey="Cheng, Jinping" sort="Cheng, Jinping" uniqKey="Cheng J" first="Jinping" last="Cheng">Jinping Cheng</name><affiliation><nlm:affiliation>School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China. Electronic address: jpcheng@sjtu.edu.cn.</nlm:affiliation></affiliation></author></analytic><series><title level="j">Environmental pollution (Barking, Essex : 1987)</title><idno type="eISSN">1873-6424</idno><imprint><date when="2020" type="published">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">With the implementation of COVID-19 restrictions and consequent improvement in air quality due to the nationwide lockdown, ozone (O<sub>3</sub>) pollution was generally amplified in China. However, the O<sub>3</sub> levels throughout the Guangxi region of South China showed a clear downward trend during the lockdown. To better understand this unusual phenomenon, we investigated the characteristics of conventional pollutants, the influence of meteorological and anthropogenic factors quantified by a multiple linear regression (MLR) model, and the impact of local sources and long-range transport based on a continuous emission monitoring system (CEMS) and the HYSPLIT model. Results show that in Guangxi, the conventional pollutants generally declined during the COVID-19 lockdown period (January 24 to February 9, 2020) compared with their concentrations during 2016-2019, while O<sub>3</sub> gradually increased during the resumption (10 February to April 2020) and full operation periods (May and June 2020). Focusing on Beihai, a typical Guangxi region city, the correlations between the daily O<sub>3</sub> concentrations and six meteorological parameters (wind speed, visibility, temperature, humidity, precipitation, and atmospheric pressure) and their corresponding regression coefficients indicate that meteorological conditions were generally conducive to O<sub>3</sub> pollution mitigation during the lockdown. A 7.84 μg/m<sup>3</sup> drop in O<sub>3</sub> concentration was driven by meteorology, with other decreases (4.11 μg/m<sup>3</sup>) explained by reduced anthropogenic emissions of O<sub>3</sub> precursors. Taken together, the lower NO<sub>2</sub>/SO<sub>2</sub> ratios (1.25-2.33) and consistencies between real-time monitored primary emissions and ambient concentrations suggest that, with the closure of small-scale industries, residual industrial emissions have become dominant contributors to local primary pollutants. Backward trajectory cluster analyses show that the slump of O<sub>3</sub> concentrations in Southern Guangxi could be partly attributed to clean air mass transfer (24-58%) from the South China Sea. Overall, the synergistic effects of the COVID-19 lockdown and meteorological factors intensified O<sub>3</sub> reduction in the Guangxi region of South China.</div></front></TEI><pubmed><MedlineCitation Status="Publisher" Owner="NLM"><PMID Version="1">33143981</PMID><DateRevised><Year>2020</Year><Month>11</Month><Day>06</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1873-6424</ISSN><JournalIssue CitedMedium="Internet"><PubDate><Year>2020</Year><Month>Oct</Month><Day>27</Day></PubDate></JournalIssue><Title>Environmental pollution (Barking, Essex : 1987)</Title><ISOAbbreviation>Environ Pollut</ISOAbbreviation></Journal><ArticleTitle>Ozone pollution mitigation in guangxi (south China) driven by meteorology and anthropogenic emissions during the COVID-19 lockdown.</ArticleTitle><Pagination><MedlinePgn>115927</MedlinePgn></Pagination><ELocationID EIdType="pii" ValidYN="Y">S0269-7491(20)36616-1</ELocationID><ELocationID EIdType="doi" ValidYN="Y">10.1016/j.envpol.2020.115927</ELocationID><Abstract><AbstractText>With the implementation of COVID-19 restrictions and consequent improvement in air quality due to the nationwide lockdown, ozone (O<sub>3</sub>) pollution was generally amplified in China. However, the O<sub>3</sub> levels throughout the Guangxi region of South China showed a clear downward trend during the lockdown. To better understand this unusual phenomenon, we investigated the characteristics of conventional pollutants, the influence of meteorological and anthropogenic factors quantified by a multiple linear regression (MLR) model, and the impact of local sources and long-range transport based on a continuous emission monitoring system (CEMS) and the HYSPLIT model. Results show that in Guangxi, the conventional pollutants generally declined during the COVID-19 lockdown period (January 24 to February 9, 2020) compared with their concentrations during 2016-2019, while O<sub>3</sub> gradually increased during the resumption (10 February to April 2020) and full operation periods (May and June 2020). Focusing on Beihai, a typical Guangxi region city, the correlations between the daily O<sub>3</sub> concentrations and six meteorological parameters (wind speed, visibility, temperature, humidity, precipitation, and atmospheric pressure) and their corresponding regression coefficients indicate that meteorological conditions were generally conducive to O<sub>3</sub> pollution mitigation during the lockdown. A 7.84 μg/m<sup>3</sup> drop in O<sub>3</sub> concentration was driven by meteorology, with other decreases (4.11 μg/m<sup>3</sup>) explained by reduced anthropogenic emissions of O<sub>3</sub> precursors. Taken together, the lower NO<sub>2</sub>/SO<sub>2</sub> ratios (1.25-2.33) and consistencies between real-time monitored primary emissions and ambient concentrations suggest that, with the closure of small-scale industries, residual industrial emissions have become dominant contributors to local primary pollutants. Backward trajectory cluster analyses show that the slump of O<sub>3</sub> concentrations in Southern Guangxi could be partly attributed to clean air mass transfer (24-58%) from the South China Sea. Overall, the synergistic effects of the COVID-19 lockdown and meteorological factors intensified O<sub>3</sub> reduction in the Guangxi region of South China.</AbstractText><CopyrightInformation>Copyright © 2020 Elsevier Ltd. All rights reserved.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Fu</LastName><ForeName>Shuang</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Guo</LastName><ForeName>Meixiu</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>Beihai Ecology and Environment Agency, Beihai, Guangxi, 536000, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Fan</LastName><ForeName>Linping</ForeName><Initials>L</Initials><AffiliationInfo><Affiliation>School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Deng</LastName><ForeName>Qiyin</ForeName><Initials>Q</Initials><AffiliationInfo><Affiliation>College of Environment, Hohai University, Nanjing, Jiangsu, 210098, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Han</LastName><ForeName>Deming</ForeName><Initials>D</Initials><AffiliationInfo><Affiliation>School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Wei</LastName><ForeName>Ye</ForeName><Initials>Y</Initials><AffiliationInfo><Affiliation>Beihai Ecology and Environment Agency, Beihai, Guangxi, 536000, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Luo</LastName><ForeName>Jinmin</ForeName><Initials>J</Initials><AffiliationInfo><Affiliation>Beihai Ecology and Environment Agency, Beihai, Guangxi, 536000, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Qin</LastName><ForeName>Guimei</ForeName><Initials>G</Initials><AffiliationInfo><Affiliation>School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Cheng</LastName><ForeName>Jinping</ForeName><Initials>J</Initials><AffiliationInfo><Affiliation>School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China. Electronic address: jpcheng@sjtu.edu.cn.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2020</Year><Month>10</Month><Day>27</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>Environ Pollut</MedlineTA><NlmUniqueID>8804476</NlmUniqueID><ISSNLinking>0269-7491</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">CEMS</Keyword><Keyword MajorTopicYN="N">COVID-19</Keyword><Keyword MajorTopicYN="N">Lockdown</Keyword><Keyword MajorTopicYN="N">MLR model</Keyword><Keyword MajorTopicYN="N">Ozone</Keyword><Keyword MajorTopicYN="N">South China</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>08</Month><Day>06</Day></PubMedPubDate><PubMedPubDate PubStatus="revised"><Year>2020</Year><Month>10</Month><Day>23</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2020</Year><Month>10</Month><Day>23</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>11</Month><Day>4</Day><Hour>5</Hour><Minute>33</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2020</Year><Month>11</Month><Day>5</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>11</Month><Day>5</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>aheadofprint</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">33143981</ArticleId><ArticleId IdType="pii">S0269-7491(20)36616-1</ArticleId><ArticleId 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