Spatiotemporal variability of COVID-19 pandemic in relation to air pollution, climate and socioeconomic factors in Pakistan
Information on the spatiotemporal variability of respirable suspended particulate pollutant matter concentrations, especially of particles having size of 2.5 μm and climate are the important factors in relation to emerging COVID-19 cases around the world. This study aims at examining the association...
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| Veröffentlicht in: | Chemosphere (Oxford) 2021-05, Vol.271, p.129584-129584, Article 129584 |
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| creator | Mehmood, Khalid Bao, Yansong Abrar, Muhammad Mohsin Petropoulos, George P. Saifullah Soban, Ahmad Saud, Shah Khan, Zalan Alam Khan, Shah Masud Fahad, Shah |
| description | Information on the spatiotemporal variability of respirable suspended particulate pollutant matter concentrations, especially of particles having size of 2.5 μm and climate are the important factors in relation to emerging COVID-19 cases around the world. This study aims at examining the association between COVID-19 cases, air pollution, climatic and socioeconomic factors using geospatial techniques in three provincial capital cities and the federal capital city of Pakistan. A series of relevant data was acquired from 3 out of 4 provinces of Pakistan (Punjab, Sindh, Khyber Pakhtunkhwa (KPK) including the daily numbers of COVID-19 cases, PM2.5 concentration (μgm−3), a climatic factors including temperature (°F), wind speed (m/s), humidity (%), dew point (%), and pressure (Hg) from June 1 2020, to July 31 2020. Further, the possible relationships between population density and COVID-19 cases was determined. The generalized linear model (GLM) was employed to quantify the effect of PM2.5, temperature, dew point, humidity, wind speed, and pressure range on the daily COVID-19 cases. The grey relational analysis (GRA) was also implemented to examine the changes in COVID-19 cases with PM2.5 concentrations for the provincial city Lahore. About 1,92, 819 COVID-19 cases were reported in Punjab, Sindh, KPK, and Islamabad during the study period. Results indicated a significant relationship between COVID-19 cases and PM2.5 and climatic factors at p |
| doi_str_mv | 10.1016/j.chemosphere.2021.129584 |
| format | Article |
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•Relationship between air pollution, climate, and socioeconomic factors on COVID-19 cases.•Modeling of COVID-19 with PM2.5, climatic, socioeconomic factors in provincial cities.•Vulnerable PM2.5 conc. identified for COVID-19 spread in provincial capital (Lahore).•Relationship of PM2.5, COVID-19 and climatic factors used to predict risky cities in 2021.</description><identifier>ISSN: 0045-6535</identifier><identifier>EISSN: 1879-1298</identifier><identifier>DOI: 10.1016/j.chemosphere.2021.129584</identifier><identifier>PMID: 33482526</identifier><language>eng</language><publisher>England: Elsevier Ltd</publisher><subject>Air Pollutants - analysis ; Air Pollution - analysis ; Cities ; Climate factors ; Correlation ; COVID-19 ; Geoinformation ; GLM model ; GRA ; Humans ; Pakistan - epidemiology ; Pandemics ; Particulate Matter - analysis ; PM2.5 ; SARS-CoV-2 ; Socioeconomic Factors</subject><ispartof>Chemosphere (Oxford), 2021-05, Vol.271, p.129584-129584, Article 129584</ispartof><rights>2021 Elsevier Ltd</rights><rights>Copyright © 2021 Elsevier Ltd. All rights reserved.</rights><rights>2021 Elsevier Ltd. All rights reserved. 2021 Elsevier Ltd</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c549t-b0d52aee9eead24f0ab21671640e0b4a643a4b19a55b332e72dfd61afefa8ad13</citedby><cites>FETCH-LOGICAL-c549t-b0d52aee9eead24f0ab21671640e0b4a643a4b19a55b332e72dfd61afefa8ad13</cites><orcidid>0000-0003-1442-1423 ; 0000-0002-6829-4338 ; 0000-0002-7274-9534</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.chemosphere.2021.129584$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>230,314,778,782,883,3539,27911,27912,45982</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33482526$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Mehmood, Khalid</creatorcontrib><creatorcontrib>Bao, Yansong</creatorcontrib><creatorcontrib>Abrar, Muhammad Mohsin</creatorcontrib><creatorcontrib>Petropoulos, George P.</creatorcontrib><creatorcontrib>Saifullah</creatorcontrib><creatorcontrib>Soban, Ahmad</creatorcontrib><creatorcontrib>Saud, Shah</creatorcontrib><creatorcontrib>Khan, Zalan Alam</creatorcontrib><creatorcontrib>Khan, Shah Masud</creatorcontrib><creatorcontrib>Fahad, Shah</creatorcontrib><title>Spatiotemporal variability of COVID-19 pandemic in relation to air pollution, climate and socioeconomic factors in Pakistan</title><title>Chemosphere (Oxford)</title><addtitle>Chemosphere</addtitle><description>Information on the spatiotemporal variability of respirable suspended particulate pollutant matter concentrations, especially of particles having size of 2.5 μm and climate are the important factors in relation to emerging COVID-19 cases around the world. This study aims at examining the association between COVID-19 cases, air pollution, climatic and socioeconomic factors using geospatial techniques in three provincial capital cities and the federal capital city of Pakistan. A series of relevant data was acquired from 3 out of 4 provinces of Pakistan (Punjab, Sindh, Khyber Pakhtunkhwa (KPK) including the daily numbers of COVID-19 cases, PM2.5 concentration (μgm−3), a climatic factors including temperature (°F), wind speed (m/s), humidity (%), dew point (%), and pressure (Hg) from June 1 2020, to July 31 2020. Further, the possible relationships between population density and COVID-19 cases was determined. The generalized linear model (GLM) was employed to quantify the effect of PM2.5, temperature, dew point, humidity, wind speed, and pressure range on the daily COVID-19 cases. The grey relational analysis (GRA) was also implemented to examine the changes in COVID-19 cases with PM2.5 concentrations for the provincial city Lahore. About 1,92, 819 COVID-19 cases were reported in Punjab, Sindh, KPK, and Islamabad during the study period. Results indicated a significant relationship between COVID-19 cases and PM2.5 and climatic factors at p < 0.05 except for Lahore in case of humidity (r = 0.175). However, mixed correlations existed across Lahore, Karachi, Peshawar, and Islamabad. The R2 value indicates a moderate relationship between COVID-19 and population density. Findings of this study, although are preliminary, offers the first line of evidence for epidemiologists and may assist the local community to expedient for the growth of effective COVID-19 infection and health risk management guidelines. This remains to be seen.
•Relationship between air pollution, climate, and socioeconomic factors on COVID-19 cases.•Modeling of COVID-19 with PM2.5, climatic, socioeconomic factors in provincial cities.•Vulnerable PM2.5 conc. identified for COVID-19 spread in provincial capital (Lahore).•Relationship of PM2.5, COVID-19 and climatic factors used to predict risky cities in 2021.</description><subject>Air Pollutants - analysis</subject><subject>Air Pollution - analysis</subject><subject>Cities</subject><subject>Climate factors</subject><subject>Correlation</subject><subject>COVID-19</subject><subject>Geoinformation</subject><subject>GLM model</subject><subject>GRA</subject><subject>Humans</subject><subject>Pakistan - epidemiology</subject><subject>Pandemics</subject><subject>Particulate Matter - analysis</subject><subject>PM2.5</subject><subject>SARS-CoV-2</subject><subject>Socioeconomic Factors</subject><issn>0045-6535</issn><issn>1879-1298</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqNkU9v1DAQxS0EotvCV0DmThbbsZP4goS2UCpVKhJ_rtbEnrBekjiy3ZUqvjyOFqpy42TZ835vZvwIec3ZljPevD1s7R6nkJY9RtwKJviWC606-YRseNfqqty6p2TDmFRVo2p1Rs5TOjBWYKWfk7O6lp1QotmQX18WyD5knJYQYaRHiB56P_p8T8NAd7ffry8rrukCs8PJW-pnGnFcmZnmQMFHuoRxvFsf3lA7-gky0qKmKVgf0IY5rNwANoeYVv4z_PQpw_yCPBtgTPjyz3lBvn388HX3qbq5vbrevb-prJI6Vz1zSgCiRgQn5MCgF7xpeSMZsl5CI2uQPdegVF_XAlvhBtdwGHCADhyvL8i7k-9y10_oLM65rGqWWGaN9yaAN_9WZr83P8LRtK1umaiLgT4Z2BhSijg8sJyZNRFzMI8SMWsi5pRIYV89bv5A_o2gCHYnAZYvOHqMJlmPs0XnI9psXPD_0eY3rfCneg</recordid><startdate>20210501</startdate><enddate>20210501</enddate><creator>Mehmood, Khalid</creator><creator>Bao, Yansong</creator><creator>Abrar, Muhammad Mohsin</creator><creator>Petropoulos, George P.</creator><creator>Saifullah</creator><creator>Soban, Ahmad</creator><creator>Saud, Shah</creator><creator>Khan, Zalan Alam</creator><creator>Khan, Shah Masud</creator><creator>Fahad, Shah</creator><general>Elsevier Ltd</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0003-1442-1423</orcidid><orcidid>https://orcid.org/0000-0002-6829-4338</orcidid><orcidid>https://orcid.org/0000-0002-7274-9534</orcidid></search><sort><creationdate>20210501</creationdate><title>Spatiotemporal variability of COVID-19 pandemic in relation to air pollution, climate and socioeconomic factors in Pakistan</title><author>Mehmood, Khalid ; Bao, Yansong ; Abrar, Muhammad Mohsin ; Petropoulos, George P. ; Saifullah ; Soban, Ahmad ; Saud, Shah ; Khan, Zalan Alam ; Khan, Shah Masud ; Fahad, Shah</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c549t-b0d52aee9eead24f0ab21671640e0b4a643a4b19a55b332e72dfd61afefa8ad13</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Air Pollutants - analysis</topic><topic>Air Pollution - analysis</topic><topic>Cities</topic><topic>Climate factors</topic><topic>Correlation</topic><topic>COVID-19</topic><topic>Geoinformation</topic><topic>GLM model</topic><topic>GRA</topic><topic>Humans</topic><topic>Pakistan - epidemiology</topic><topic>Pandemics</topic><topic>Particulate Matter - analysis</topic><topic>PM2.5</topic><topic>SARS-CoV-2</topic><topic>Socioeconomic Factors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Mehmood, Khalid</creatorcontrib><creatorcontrib>Bao, Yansong</creatorcontrib><creatorcontrib>Abrar, Muhammad Mohsin</creatorcontrib><creatorcontrib>Petropoulos, George P.</creatorcontrib><creatorcontrib>Saifullah</creatorcontrib><creatorcontrib>Soban, Ahmad</creatorcontrib><creatorcontrib>Saud, Shah</creatorcontrib><creatorcontrib>Khan, Zalan Alam</creatorcontrib><creatorcontrib>Khan, Shah Masud</creatorcontrib><creatorcontrib>Fahad, Shah</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Chemosphere (Oxford)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Mehmood, Khalid</au><au>Bao, Yansong</au><au>Abrar, Muhammad Mohsin</au><au>Petropoulos, George P.</au><au>Saifullah</au><au>Soban, Ahmad</au><au>Saud, Shah</au><au>Khan, Zalan Alam</au><au>Khan, Shah Masud</au><au>Fahad, Shah</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Spatiotemporal variability of COVID-19 pandemic in relation to air pollution, climate and socioeconomic factors in Pakistan</atitle><jtitle>Chemosphere (Oxford)</jtitle><addtitle>Chemosphere</addtitle><date>2021-05-01</date><risdate>2021</risdate><volume>271</volume><spage>129584</spage><epage>129584</epage><pages>129584-129584</pages><artnum>129584</artnum><issn>0045-6535</issn><eissn>1879-1298</eissn><abstract>Information on the spatiotemporal variability of respirable suspended particulate pollutant matter concentrations, especially of particles having size of 2.5 μm and climate are the important factors in relation to emerging COVID-19 cases around the world. This study aims at examining the association between COVID-19 cases, air pollution, climatic and socioeconomic factors using geospatial techniques in three provincial capital cities and the federal capital city of Pakistan. A series of relevant data was acquired from 3 out of 4 provinces of Pakistan (Punjab, Sindh, Khyber Pakhtunkhwa (KPK) including the daily numbers of COVID-19 cases, PM2.5 concentration (μgm−3), a climatic factors including temperature (°F), wind speed (m/s), humidity (%), dew point (%), and pressure (Hg) from June 1 2020, to July 31 2020. Further, the possible relationships between population density and COVID-19 cases was determined. The generalized linear model (GLM) was employed to quantify the effect of PM2.5, temperature, dew point, humidity, wind speed, and pressure range on the daily COVID-19 cases. The grey relational analysis (GRA) was also implemented to examine the changes in COVID-19 cases with PM2.5 concentrations for the provincial city Lahore. About 1,92, 819 COVID-19 cases were reported in Punjab, Sindh, KPK, and Islamabad during the study period. Results indicated a significant relationship between COVID-19 cases and PM2.5 and climatic factors at p < 0.05 except for Lahore in case of humidity (r = 0.175). However, mixed correlations existed across Lahore, Karachi, Peshawar, and Islamabad. The R2 value indicates a moderate relationship between COVID-19 and population density. Findings of this study, although are preliminary, offers the first line of evidence for epidemiologists and may assist the local community to expedient for the growth of effective COVID-19 infection and health risk management guidelines. This remains to be seen.
•Relationship between air pollution, climate, and socioeconomic factors on COVID-19 cases.•Modeling of COVID-19 with PM2.5, climatic, socioeconomic factors in provincial cities.•Vulnerable PM2.5 conc. identified for COVID-19 spread in provincial capital (Lahore).•Relationship of PM2.5, COVID-19 and climatic factors used to predict risky cities in 2021.</abstract><cop>England</cop><pub>Elsevier Ltd</pub><pmid>33482526</pmid><doi>10.1016/j.chemosphere.2021.129584</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0003-1442-1423</orcidid><orcidid>https://orcid.org/0000-0002-6829-4338</orcidid><orcidid>https://orcid.org/0000-0002-7274-9534</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Air Pollutants - analysis Air Pollution - analysis Cities Climate factors Correlation COVID-19 Geoinformation GLM model GRA Humans Pakistan - epidemiology Pandemics Particulate Matter - analysis PM2.5 SARS-CoV-2 Socioeconomic Factors |
| title | Spatiotemporal variability of COVID-19 pandemic in relation to air pollution, climate and socioeconomic factors in Pakistan |
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