PM[sub.2.5] Pollution in Six Major Chinese Urban Agglomerations: Spatiotemporal Variations, Health Impacts, and the Relationships with Meteorological Conditions
To investigate the spatiotemporal patterns of fine particulate matter (PM[sub.2.5] ) under years of control measures in China, a comprehensive analysis including statistical analysis, geographical analysis, and health impact assessment was conducted on millions of hourly PM[sub.2.5] concentrations d...
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description | To investigate the spatiotemporal patterns of fine particulate matter (PM[sub.2.5] ) under years of control measures in China, a comprehensive analysis including statistical analysis, geographical analysis, and health impact assessment was conducted on millions of hourly PM[sub.2.5] concentrations data during the period of 2017–2020 in six typical major urban agglomerations. During the period of 2017–2020, PM[sub.2.5] concentrations in the Beijing–Tianjin–Hebei urban agglomeration (BTH-UA), Central Plains urban agglomeration (CP-UA), Yangtze River Delta urban agglomeration (YRD-UA), Triangle of Central China urban agglomeration (TC-UA), Chengdu–Chongqing urban agglomeration (CY-UA), and Pearl River Delta urban agglomeration (PRD-UA) decreased at a rate of 6.69, 5.57, 5.45, 3.85, 4.66, and 4.1 µg/m[sup.3] /year, respectively. PM[sub.2.5] concentration in BTH-UA decreased by 30.5% over four years, with an annual average of 44.6 µg/m[sup.3] in 2020. CP-UA showed the lowest reduction ratio (22.1%) among the six regions, making it the most polluted urban agglomeration. In southern BTH-UA, northeastern CP-UA, and northwestern TC-UA, PM[sub.2.5] concentrations with high levels formed a high–high agglomeration, indicating pollution caused by source emission in these areas was high and hard to control. Atmospheric temperature, pressure, and wind speed have important influences on PM[sub.2.5] concentrations. RH has a positive correlation with PM[sub.2.5] concentration in north China but a negative correlation in south China. We estimated that meteorological conditions can explain 16.7–63.9% of the PM[sub.2.5] changes in 129 cities, with an average of 33.4%, indicating other factors including anthropogenic emissions dominated the PM[sub.2.5] changes. Among the six urban agglomerations, PM[sub.2.5] concentrations in the CP-UA were most influenced by the meteorological change. Benefiting from the reduction in PM[sub.2.5] concentration, the total respiratory premature mortalities in six regions decreased by 73.1%, from 2017 to 2020. The CP-UA had the highest respiratory premature mortality in six urban agglomerations. We suggested that the CP-UA needs more attention and stricter pollution control measures. |
doi_str_mv | 10.3390/atmos13101696 |
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During the period of 2017–2020, PM[sub.2.5] concentrations in the Beijing–Tianjin–Hebei urban agglomeration (BTH-UA), Central Plains urban agglomeration (CP-UA), Yangtze River Delta urban agglomeration (YRD-UA), Triangle of Central China urban agglomeration (TC-UA), Chengdu–Chongqing urban agglomeration (CY-UA), and Pearl River Delta urban agglomeration (PRD-UA) decreased at a rate of 6.69, 5.57, 5.45, 3.85, 4.66, and 4.1 µg/m[sup.3] /year, respectively. PM[sub.2.5] concentration in BTH-UA decreased by 30.5% over four years, with an annual average of 44.6 µg/m[sup.3] in 2020. CP-UA showed the lowest reduction ratio (22.1%) among the six regions, making it the most polluted urban agglomeration. In southern BTH-UA, northeastern CP-UA, and northwestern TC-UA, PM[sub.2.5] concentrations with high levels formed a high–high agglomeration, indicating pollution caused by source emission in these areas was high and hard to control. Atmospheric temperature, pressure, and wind speed have important influences on PM[sub.2.5] concentrations. RH has a positive correlation with PM[sub.2.5] concentration in north China but a negative correlation in south China. We estimated that meteorological conditions can explain 16.7–63.9% of the PM[sub.2.5] changes in 129 cities, with an average of 33.4%, indicating other factors including anthropogenic emissions dominated the PM[sub.2.5] changes. Among the six urban agglomerations, PM[sub.2.5] concentrations in the CP-UA were most influenced by the meteorological change. Benefiting from the reduction in PM[sub.2.5] concentration, the total respiratory premature mortalities in six regions decreased by 73.1%, from 2017 to 2020. The CP-UA had the highest respiratory premature mortality in six urban agglomerations. We suggested that the CP-UA needs more attention and stricter pollution control measures.</description><identifier>ISSN: 2073-4433</identifier><identifier>EISSN: 2073-4433</identifier><identifier>DOI: 10.3390/atmos13101696</identifier><language>eng</language><publisher>MDPI AG</publisher><subject>Air quality management ; Distribution ; Environmental aspects ; Environmental monitoring ; Health aspects ; Particles ; Photochemical smog</subject><ispartof>Atmosphere, 2022-10, Vol.13 (10)</ispartof><rights>COPYRIGHT 2022 MDPI AG</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,864,27922,27923</link.rule.ids></links><search><creatorcontrib>Li, Zhuofan</creatorcontrib><creatorcontrib>Zhang, Xiangmin</creatorcontrib><creatorcontrib>Liu, Xiaoyong</creatorcontrib><creatorcontrib>Yu, Bin</creatorcontrib><title>PM[sub.2.5] Pollution in Six Major Chinese Urban Agglomerations: Spatiotemporal Variations, Health Impacts, and the Relationships with Meteorological Conditions</title><title>Atmosphere</title><description>To investigate the spatiotemporal patterns of fine particulate matter (PM[sub.2.5] ) under years of control measures in China, a comprehensive analysis including statistical analysis, geographical analysis, and health impact assessment was conducted on millions of hourly PM[sub.2.5] concentrations data during the period of 2017–2020 in six typical major urban agglomerations. During the period of 2017–2020, PM[sub.2.5] concentrations in the Beijing–Tianjin–Hebei urban agglomeration (BTH-UA), Central Plains urban agglomeration (CP-UA), Yangtze River Delta urban agglomeration (YRD-UA), Triangle of Central China urban agglomeration (TC-UA), Chengdu–Chongqing urban agglomeration (CY-UA), and Pearl River Delta urban agglomeration (PRD-UA) decreased at a rate of 6.69, 5.57, 5.45, 3.85, 4.66, and 4.1 µg/m[sup.3] /year, respectively. PM[sub.2.5] concentration in BTH-UA decreased by 30.5% over four years, with an annual average of 44.6 µg/m[sup.3] in 2020. CP-UA showed the lowest reduction ratio (22.1%) among the six regions, making it the most polluted urban agglomeration. In southern BTH-UA, northeastern CP-UA, and northwestern TC-UA, PM[sub.2.5] concentrations with high levels formed a high–high agglomeration, indicating pollution caused by source emission in these areas was high and hard to control. Atmospheric temperature, pressure, and wind speed have important influences on PM[sub.2.5] concentrations. RH has a positive correlation with PM[sub.2.5] concentration in north China but a negative correlation in south China. We estimated that meteorological conditions can explain 16.7–63.9% of the PM[sub.2.5] changes in 129 cities, with an average of 33.4%, indicating other factors including anthropogenic emissions dominated the PM[sub.2.5] changes. Among the six urban agglomerations, PM[sub.2.5] concentrations in the CP-UA were most influenced by the meteorological change. Benefiting from the reduction in PM[sub.2.5] concentration, the total respiratory premature mortalities in six regions decreased by 73.1%, from 2017 to 2020. The CP-UA had the highest respiratory premature mortality in six urban agglomerations. We suggested that the CP-UA needs more attention and stricter pollution control measures.</description><subject>Air quality management</subject><subject>Distribution</subject><subject>Environmental aspects</subject><subject>Environmental monitoring</subject><subject>Health aspects</subject><subject>Particles</subject><subject>Photochemical smog</subject><issn>2073-4433</issn><issn>2073-4433</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid/><recordid>eNqVjMFOwzAMhiMEEhPsyN0PwEqbdJvKbZpA41BpYsAFIZS1buspjaskEzwOj0rGOHDF_8G_f3-2EFdZmihVpDc69OwzlaXZrJidiJFM52qS50qd_vHnYuz9Lo2VF0qqfCS-1uWr328TmUzfYM3G7AOxBbKwoU8o9Y4dLDuy6BGe3VZbWLSt4R6dPoD-FjbDwQXsB3bawIt2dFxdwwq1CR089IOuQpy1rSF0CI9ojkhHg4cPikyJAdmx4Zaq-GXJtqYf5FKcNdp4HP_2C5Hc3z0tV5NWG3wn23BwuoqqsaeKLTYU88U8n0opM1Wofx98A9Qwa7M</recordid><startdate>20221001</startdate><enddate>20221001</enddate><creator>Li, Zhuofan</creator><creator>Zhang, Xiangmin</creator><creator>Liu, Xiaoyong</creator><creator>Yu, Bin</creator><general>MDPI AG</general><scope/></search><sort><creationdate>20221001</creationdate><title>PM[sub.2.5] Pollution in Six Major Chinese Urban Agglomerations: Spatiotemporal Variations, Health Impacts, and the Relationships with Meteorological Conditions</title><author>Li, Zhuofan ; Zhang, Xiangmin ; Liu, Xiaoyong ; Yu, Bin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-gale_infotracacademiconefile_A7452221393</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Air quality management</topic><topic>Distribution</topic><topic>Environmental aspects</topic><topic>Environmental monitoring</topic><topic>Health aspects</topic><topic>Particles</topic><topic>Photochemical smog</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Li, Zhuofan</creatorcontrib><creatorcontrib>Zhang, Xiangmin</creatorcontrib><creatorcontrib>Liu, Xiaoyong</creatorcontrib><creatorcontrib>Yu, Bin</creatorcontrib><jtitle>Atmosphere</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Li, Zhuofan</au><au>Zhang, Xiangmin</au><au>Liu, Xiaoyong</au><au>Yu, Bin</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>PM[sub.2.5] Pollution in Six Major Chinese Urban Agglomerations: Spatiotemporal Variations, Health Impacts, and the Relationships with Meteorological Conditions</atitle><jtitle>Atmosphere</jtitle><date>2022-10-01</date><risdate>2022</risdate><volume>13</volume><issue>10</issue><issn>2073-4433</issn><eissn>2073-4433</eissn><abstract>To investigate the spatiotemporal patterns of fine particulate matter (PM[sub.2.5] ) under years of control measures in China, a comprehensive analysis including statistical analysis, geographical analysis, and health impact assessment was conducted on millions of hourly PM[sub.2.5] concentrations data during the period of 2017–2020 in six typical major urban agglomerations. During the period of 2017–2020, PM[sub.2.5] concentrations in the Beijing–Tianjin–Hebei urban agglomeration (BTH-UA), Central Plains urban agglomeration (CP-UA), Yangtze River Delta urban agglomeration (YRD-UA), Triangle of Central China urban agglomeration (TC-UA), Chengdu–Chongqing urban agglomeration (CY-UA), and Pearl River Delta urban agglomeration (PRD-UA) decreased at a rate of 6.69, 5.57, 5.45, 3.85, 4.66, and 4.1 µg/m[sup.3] /year, respectively. PM[sub.2.5] concentration in BTH-UA decreased by 30.5% over four years, with an annual average of 44.6 µg/m[sup.3] in 2020. CP-UA showed the lowest reduction ratio (22.1%) among the six regions, making it the most polluted urban agglomeration. In southern BTH-UA, northeastern CP-UA, and northwestern TC-UA, PM[sub.2.5] concentrations with high levels formed a high–high agglomeration, indicating pollution caused by source emission in these areas was high and hard to control. Atmospheric temperature, pressure, and wind speed have important influences on PM[sub.2.5] concentrations. RH has a positive correlation with PM[sub.2.5] concentration in north China but a negative correlation in south China. We estimated that meteorological conditions can explain 16.7–63.9% of the PM[sub.2.5] changes in 129 cities, with an average of 33.4%, indicating other factors including anthropogenic emissions dominated the PM[sub.2.5] changes. Among the six urban agglomerations, PM[sub.2.5] concentrations in the CP-UA were most influenced by the meteorological change. Benefiting from the reduction in PM[sub.2.5] concentration, the total respiratory premature mortalities in six regions decreased by 73.1%, from 2017 to 2020. The CP-UA had the highest respiratory premature mortality in six urban agglomerations. We suggested that the CP-UA needs more attention and stricter pollution control measures.</abstract><pub>MDPI AG</pub><doi>10.3390/atmos13101696</doi></addata></record> |
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subjects | Air quality management Distribution Environmental aspects Environmental monitoring Health aspects Particles Photochemical smog |
title | PM[sub.2.5] Pollution in Six Major Chinese Urban Agglomerations: Spatiotemporal Variations, Health Impacts, and the Relationships with Meteorological Conditions |
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