Spatiotemporal distribution characteristics of PM2.5 concentration in China from 2000 to 2018 and its impact on population

PM2.5 is an important indicator reflecting changes in air quality. In recent years, affected by climate change and human activities, the problem of environmental pollution has become more and more prominent. In this study, the PM2.5 data from 2000 to 2018 obtained by satellite remote sensing inversi...

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Veröffentlicht in:Journal of environmental management 2022-12, Vol.323, p.116273-116273, Article 116273
Hauptverfasser: Jin, Haoyu, Zhong, Ruida, Liu, Moyang, Ye, Changxin, Chen, Xiaohong
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Zhong, Ruida
Liu, Moyang
Ye, Changxin
Chen, Xiaohong
description PM2.5 is an important indicator reflecting changes in air quality. In recent years, affected by climate change and human activities, the problem of environmental pollution has become more and more prominent. In this study, the PM2.5 data from 2000 to 2018 obtained by satellite remote sensing inversion algorithm were selected to analyze the temporal and spatial distribution of PM2.5 in China. The results show that the areas with higher PM2.5 concentrations were mainly in the North China, the Sichuan Basin, and the Tarim Basin. The areas with a significant increase in PM2.5 were mainly in the Northeast China, while the areas with a significant decrease were mainly in the Sichuan Basin and southeastern Gansu. The change of PM2.5 in southern China was not significantly correlated with the change of population and economy, while PM2.5 in Northeast China increases with the increase of population and economy. In 2000, 2005, 2010, and 2015, the proportion of the population polluted by PM2.5 was 8.65%, 7.2%, 22.99%, and 9.75%, respectively. The year with the highest percentage (37.63%) of population when air quality reached EXCELLENT was 2015. When the PM2.5 spatial cluster number was six, it can better reflect the PM2.5 spatial distribution state. The places with large changes in PM2.5 spatial clustering were mainly in the Northeast China, Sichuan Basin, and Tarim Basin, which were also areas with large changes in PM2.5. This study provides an important reference for atmospheric environmental monitoring and protection. •The temporal and spatial variation patterns of PM2.5 in China from 2000 to 2018 were obtained.•The relationship between PM2.5 and population and GDP was obtained.•The change rule of the population affected by PM2.5 was obtained.•We used cluster analysis method to obtain PM2.5 spatial clustering status.
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In recent years, affected by climate change and human activities, the problem of environmental pollution has become more and more prominent. In this study, the PM2.5 data from 2000 to 2018 obtained by satellite remote sensing inversion algorithm were selected to analyze the temporal and spatial distribution of PM2.5 in China. The results show that the areas with higher PM2.5 concentrations were mainly in the North China, the Sichuan Basin, and the Tarim Basin. The areas with a significant increase in PM2.5 were mainly in the Northeast China, while the areas with a significant decrease were mainly in the Sichuan Basin and southeastern Gansu. The change of PM2.5 in southern China was not significantly correlated with the change of population and economy, while PM2.5 in Northeast China increases with the increase of population and economy. In 2000, 2005, 2010, and 2015, the proportion of the population polluted by PM2.5 was 8.65%, 7.2%, 22.99%, and 9.75%, respectively. The year with the highest percentage (37.63%) of population when air quality reached EXCELLENT was 2015. When the PM2.5 spatial cluster number was six, it can better reflect the PM2.5 spatial distribution state. The places with large changes in PM2.5 spatial clustering were mainly in the Northeast China, Sichuan Basin, and Tarim Basin, which were also areas with large changes in PM2.5. This study provides an important reference for atmospheric environmental monitoring and protection. •The temporal and spatial variation patterns of PM2.5 in China from 2000 to 2018 were obtained.•The relationship between PM2.5 and population and GDP was obtained.•The change rule of the population affected by PM2.5 was obtained.•We used cluster analysis method to obtain PM2.5 spatial clustering status.</description><identifier>ISSN: 0301-4797</identifier><identifier>EISSN: 1095-8630</identifier><identifier>DOI: 10.1016/j.jenvman.2022.116273</identifier><language>eng</language><publisher>Elsevier Ltd</publisher><subject>Cluster analysis ; GDP ; PM2.5 ; Population ; Spatiotemporal change</subject><ispartof>Journal of environmental management, 2022-12, Vol.323, p.116273-116273, Article 116273</ispartof><rights>2022 Elsevier Ltd</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c342t-7eb6fec402a45d653ef78df84c2abdd21513977148f8226de102f346f57577873</citedby><cites>FETCH-LOGICAL-c342t-7eb6fec402a45d653ef78df84c2abdd21513977148f8226de102f346f57577873</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.jenvman.2022.116273$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids></links><search><creatorcontrib>Jin, Haoyu</creatorcontrib><creatorcontrib>Zhong, Ruida</creatorcontrib><creatorcontrib>Liu, Moyang</creatorcontrib><creatorcontrib>Ye, Changxin</creatorcontrib><creatorcontrib>Chen, Xiaohong</creatorcontrib><title>Spatiotemporal distribution characteristics of PM2.5 concentration in China from 2000 to 2018 and its impact on population</title><title>Journal of environmental management</title><description>PM2.5 is an important indicator reflecting changes in air quality. In recent years, affected by climate change and human activities, the problem of environmental pollution has become more and more prominent. In this study, the PM2.5 data from 2000 to 2018 obtained by satellite remote sensing inversion algorithm were selected to analyze the temporal and spatial distribution of PM2.5 in China. The results show that the areas with higher PM2.5 concentrations were mainly in the North China, the Sichuan Basin, and the Tarim Basin. The areas with a significant increase in PM2.5 were mainly in the Northeast China, while the areas with a significant decrease were mainly in the Sichuan Basin and southeastern Gansu. The change of PM2.5 in southern China was not significantly correlated with the change of population and economy, while PM2.5 in Northeast China increases with the increase of population and economy. In 2000, 2005, 2010, and 2015, the proportion of the population polluted by PM2.5 was 8.65%, 7.2%, 22.99%, and 9.75%, respectively. The year with the highest percentage (37.63%) of population when air quality reached EXCELLENT was 2015. When the PM2.5 spatial cluster number was six, it can better reflect the PM2.5 spatial distribution state. The places with large changes in PM2.5 spatial clustering were mainly in the Northeast China, Sichuan Basin, and Tarim Basin, which were also areas with large changes in PM2.5. This study provides an important reference for atmospheric environmental monitoring and protection. •The temporal and spatial variation patterns of PM2.5 in China from 2000 to 2018 were obtained.•The relationship between PM2.5 and population and GDP was obtained.•The change rule of the population affected by PM2.5 was obtained.•We used cluster analysis method to obtain PM2.5 spatial clustering status.</description><subject>Cluster analysis</subject><subject>GDP</subject><subject>PM2.5</subject><subject>Population</subject><subject>Spatiotemporal change</subject><issn>0301-4797</issn><issn>1095-8630</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><recordid>eNqFkE1r3DAURUVJoZO0P6GgZTZ2pCdb8qxKGfoRSEig7VpopCeiwZYcSRNof32VTPZZPbjce-AdQj5z1nPG5dWhP2B8WkzsgQH0nEtQ4h3ZcLYdu0kKdkY2TDDeDWqrPpDzUg6MMQFcbci_X6upIVVc1pTNTF0oNYf9sWWR2geTja2YWxhsocnT-1voR2pTtBhrNi-1EOnuIURDfU4LhcamNbXLJ2qio6EWGpa1gWgrr2k9zi-7j-S9N3PBT6_3gvz5_u337md3c_fjevf1prNigNop3EuPdmBghtHJUaBXk_PTYMHsnQM-crFVig-TnwCkQ87Ai0H6UY1KTUpckMsTd83p8Yil6iUUi_NsIqZj0aBAboFLyVp1PFVtTqVk9HrNYTH5r-ZMP7vWB_3qWj-71ifXbffltMP2x1PArIsN2By5kNFW7VJ4g_AfTDmKHQ</recordid><startdate>20221201</startdate><enddate>20221201</enddate><creator>Jin, Haoyu</creator><creator>Zhong, Ruida</creator><creator>Liu, Moyang</creator><creator>Ye, Changxin</creator><creator>Chen, Xiaohong</creator><general>Elsevier Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope></search><sort><creationdate>20221201</creationdate><title>Spatiotemporal distribution characteristics of PM2.5 concentration in China from 2000 to 2018 and its impact on population</title><author>Jin, Haoyu ; Zhong, Ruida ; Liu, Moyang ; Ye, Changxin ; Chen, Xiaohong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c342t-7eb6fec402a45d653ef78df84c2abdd21513977148f8226de102f346f57577873</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Cluster analysis</topic><topic>GDP</topic><topic>PM2.5</topic><topic>Population</topic><topic>Spatiotemporal change</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Jin, Haoyu</creatorcontrib><creatorcontrib>Zhong, Ruida</creatorcontrib><creatorcontrib>Liu, Moyang</creatorcontrib><creatorcontrib>Ye, Changxin</creatorcontrib><creatorcontrib>Chen, Xiaohong</creatorcontrib><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of environmental management</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Jin, Haoyu</au><au>Zhong, Ruida</au><au>Liu, Moyang</au><au>Ye, Changxin</au><au>Chen, Xiaohong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Spatiotemporal distribution characteristics of PM2.5 concentration in China from 2000 to 2018 and its impact on population</atitle><jtitle>Journal of environmental management</jtitle><date>2022-12-01</date><risdate>2022</risdate><volume>323</volume><spage>116273</spage><epage>116273</epage><pages>116273-116273</pages><artnum>116273</artnum><issn>0301-4797</issn><eissn>1095-8630</eissn><abstract>PM2.5 is an important indicator reflecting changes in air quality. In recent years, affected by climate change and human activities, the problem of environmental pollution has become more and more prominent. In this study, the PM2.5 data from 2000 to 2018 obtained by satellite remote sensing inversion algorithm were selected to analyze the temporal and spatial distribution of PM2.5 in China. The results show that the areas with higher PM2.5 concentrations were mainly in the North China, the Sichuan Basin, and the Tarim Basin. The areas with a significant increase in PM2.5 were mainly in the Northeast China, while the areas with a significant decrease were mainly in the Sichuan Basin and southeastern Gansu. The change of PM2.5 in southern China was not significantly correlated with the change of population and economy, while PM2.5 in Northeast China increases with the increase of population and economy. In 2000, 2005, 2010, and 2015, the proportion of the population polluted by PM2.5 was 8.65%, 7.2%, 22.99%, and 9.75%, respectively. The year with the highest percentage (37.63%) of population when air quality reached EXCELLENT was 2015. When the PM2.5 spatial cluster number was six, it can better reflect the PM2.5 spatial distribution state. The places with large changes in PM2.5 spatial clustering were mainly in the Northeast China, Sichuan Basin, and Tarim Basin, which were also areas with large changes in PM2.5. This study provides an important reference for atmospheric environmental monitoring and protection. •The temporal and spatial variation patterns of PM2.5 in China from 2000 to 2018 were obtained.•The relationship between PM2.5 and population and GDP was obtained.•The change rule of the population affected by PM2.5 was obtained.•We used cluster analysis method to obtain PM2.5 spatial clustering status.</abstract><pub>Elsevier Ltd</pub><doi>10.1016/j.jenvman.2022.116273</doi><tpages>1</tpages></addata></record>
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subjects Cluster analysis
GDP
PM2.5
Population
Spatiotemporal change
title Spatiotemporal distribution characteristics of PM2.5 concentration in China from 2000 to 2018 and its impact on population
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