Assessment of German population exposure levels to PM10 based on multiple spatial-temporal data
Particulate matter is the key to increasing urban air pollution, and research into pollution exposure assessment is an important part of environmental health. In order to classify PM 10 air pollution and to investigate the population exposure to the distribution of PM 10 , daily and monthly PM 10 co...
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| Veröffentlicht in: | Environmental science and pollution research international 2020-02, Vol.27 (6), p.6637-6648 |
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| creator | Liu, Xiansheng Huang, Haiying Jiang, Yiming Wang, Tao Xu, Yanling Abbaszade, Gülcin Schnelle-Kreis, Jürgen Zimmermann, Ralf |
| description | Particulate matter is the key to increasing urban air pollution, and research into pollution exposure assessment is an important part of environmental health. In order to classify PM
10
air pollution and to investigate the population exposure to the distribution of PM
10
, daily and monthly PM
10
concentrations of 379 air pollution monitoring stations were obtained for a period from 01/01/2017 to 31/12/2017. Firstly, PM
10
concentrations were classified using the head/tail break clustering algorithm to identify locations with elevated PM
10
levels. Subsequently, population exposure levels were calculated using population-weighted PM
10
concentrations. Finally, the power-law distribution was used to test the distribution of PM
10
polluted areas. Our results indicate that the head/tail break algorithm, with an appropriate segmentation threshold, can effectively identify areas with high PM
10
concentrations. The distribution of the population according to exposure level shows that the majority of people is living in polluted areas. The distribution of heavily PM
10
polluted areas in Germany follows the power-law distribution well, but their boundaries differ from the boundaries of administrative cities; some even cross several administrative cities. These classification results can guide policymakers in dividing the country into several areas for pollution control. |
| doi_str_mv | 10.1007/s11356-019-07071-0 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2364154749</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2364154749</sourcerecordid><originalsourceid>FETCH-LOGICAL-c433t-e61fe22eecac4ddee800ba8d3a4cf349e1b3aadb1426bbdf1ea50c15429910cd3</originalsourceid><addsrcrecordid>eNp9kDFv2zAQhYkiQew4-QMZCgKd2d6RlGSOhtE4BVK0QzITlHgKbEiiSkpF8-_D1G6zZbrhvvce8DF2g_AZAaovCVEVpQA0AiqoUMAHtsQStai0MWdsCUZrgUrrBbtM6QAgwcjqgi0UrqtCmmLJ7CYlSqmnYeKh5TuKvRv4GMa5c9M-DJz-jCHNkXhHv6lLfAr853cEXrtEnmegn7tpP3bE05gTrhMT9WOIruPeTe6KnbeuS3R9uiv2ePv1YXsn7n_svm0396LRSk2CSmxJSqLGNdp7ojVA7dZeOd20ShvCWjnna9SyrGvfIrkCGiy0NAah8WrFPh17xxh-zZQmewhzHPKklarUmcxOMiWPVBNDSpFaO8Z97-KzRbCvTu3Rqc1O7V-nFnLo46l6rnvy_yP_JGZAHYGUX8MTxbftd2pfAHKag3s</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2364154749</pqid></control><display><type>article</type><title>Assessment of German population exposure levels to PM10 based on multiple spatial-temporal data</title><source>MEDLINE</source><source>SpringerLink Journals - AutoHoldings</source><creator>Liu, Xiansheng ; Huang, Haiying ; Jiang, Yiming ; Wang, Tao ; Xu, Yanling ; Abbaszade, Gülcin ; Schnelle-Kreis, Jürgen ; Zimmermann, Ralf</creator><creatorcontrib>Liu, Xiansheng ; Huang, Haiying ; Jiang, Yiming ; Wang, Tao ; Xu, Yanling ; Abbaszade, Gülcin ; Schnelle-Kreis, Jürgen ; Zimmermann, Ralf</creatorcontrib><description>Particulate matter is the key to increasing urban air pollution, and research into pollution exposure assessment is an important part of environmental health. In order to classify PM
10
air pollution and to investigate the population exposure to the distribution of PM
10
, daily and monthly PM
10
concentrations of 379 air pollution monitoring stations were obtained for a period from 01/01/2017 to 31/12/2017. Firstly, PM
10
concentrations were classified using the head/tail break clustering algorithm to identify locations with elevated PM
10
levels. Subsequently, population exposure levels were calculated using population-weighted PM
10
concentrations. Finally, the power-law distribution was used to test the distribution of PM
10
polluted areas. Our results indicate that the head/tail break algorithm, with an appropriate segmentation threshold, can effectively identify areas with high PM
10
concentrations. The distribution of the population according to exposure level shows that the majority of people is living in polluted areas. The distribution of heavily PM
10
polluted areas in Germany follows the power-law distribution well, but their boundaries differ from the boundaries of administrative cities; some even cross several administrative cities. These classification results can guide policymakers in dividing the country into several areas for pollution control.</description><identifier>ISSN: 0944-1344</identifier><identifier>EISSN: 1614-7499</identifier><identifier>DOI: 10.1007/s11356-019-07071-0</identifier><identifier>PMID: 31875295</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Air monitoring ; Air Pollutants ; Air pollution ; Air Pollution - statistics & numerical data ; Algorithms ; Aquatic Pollution ; Atmospheric Protection/Air Quality Control/Air Pollution ; Boundaries ; Cities ; Clustering ; Earth and Environmental Science ; Ecotoxicology ; Environment ; Environmental Chemistry ; Environmental Exposure - statistics & numerical data ; Environmental Health ; Environmental Monitoring ; Environmental science ; Exposure ; Germany ; Health risk assessment ; Levels ; Particulate emissions ; Particulate Matter ; Pollution control ; Pollution monitoring ; Population ; Power law ; Research Article ; Segmentation ; Spatiotemporal data ; Waste Water Technology ; Water Management ; Water Pollution Control</subject><ispartof>Environmental science and pollution research international, 2020-02, Vol.27 (6), p.6637-6648</ispartof><rights>Springer-Verlag GmbH Germany, part of Springer Nature 2019</rights><rights>Environmental Science and Pollution Research is a copyright of Springer, (2019). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c433t-e61fe22eecac4ddee800ba8d3a4cf349e1b3aadb1426bbdf1ea50c15429910cd3</citedby><cites>FETCH-LOGICAL-c433t-e61fe22eecac4ddee800ba8d3a4cf349e1b3aadb1426bbdf1ea50c15429910cd3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11356-019-07071-0$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11356-019-07071-0$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27903,27904,41467,42536,51297</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31875295$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Liu, Xiansheng</creatorcontrib><creatorcontrib>Huang, Haiying</creatorcontrib><creatorcontrib>Jiang, Yiming</creatorcontrib><creatorcontrib>Wang, Tao</creatorcontrib><creatorcontrib>Xu, Yanling</creatorcontrib><creatorcontrib>Abbaszade, Gülcin</creatorcontrib><creatorcontrib>Schnelle-Kreis, Jürgen</creatorcontrib><creatorcontrib>Zimmermann, Ralf</creatorcontrib><title>Assessment of German population exposure levels to PM10 based on multiple spatial-temporal data</title><title>Environmental science and pollution research international</title><addtitle>Environ Sci Pollut Res</addtitle><addtitle>Environ Sci Pollut Res Int</addtitle><description>Particulate matter is the key to increasing urban air pollution, and research into pollution exposure assessment is an important part of environmental health. In order to classify PM
10
air pollution and to investigate the population exposure to the distribution of PM
10
, daily and monthly PM
10
concentrations of 379 air pollution monitoring stations were obtained for a period from 01/01/2017 to 31/12/2017. Firstly, PM
10
concentrations were classified using the head/tail break clustering algorithm to identify locations with elevated PM
10
levels. Subsequently, population exposure levels were calculated using population-weighted PM
10
concentrations. Finally, the power-law distribution was used to test the distribution of PM
10
polluted areas. Our results indicate that the head/tail break algorithm, with an appropriate segmentation threshold, can effectively identify areas with high PM
10
concentrations. The distribution of the population according to exposure level shows that the majority of people is living in polluted areas. The distribution of heavily PM
10
polluted areas in Germany follows the power-law distribution well, but their boundaries differ from the boundaries of administrative cities; some even cross several administrative cities. These classification results can guide policymakers in dividing the country into several areas for pollution control.</description><subject>Air monitoring</subject><subject>Air Pollutants</subject><subject>Air pollution</subject><subject>Air Pollution - statistics & numerical data</subject><subject>Algorithms</subject><subject>Aquatic Pollution</subject><subject>Atmospheric Protection/Air Quality Control/Air Pollution</subject><subject>Boundaries</subject><subject>Cities</subject><subject>Clustering</subject><subject>Earth and Environmental Science</subject><subject>Ecotoxicology</subject><subject>Environment</subject><subject>Environmental Chemistry</subject><subject>Environmental Exposure - statistics & numerical data</subject><subject>Environmental Health</subject><subject>Environmental Monitoring</subject><subject>Environmental science</subject><subject>Exposure</subject><subject>Germany</subject><subject>Health risk assessment</subject><subject>Levels</subject><subject>Particulate emissions</subject><subject>Particulate Matter</subject><subject>Pollution control</subject><subject>Pollution monitoring</subject><subject>Population</subject><subject>Power law</subject><subject>Research Article</subject><subject>Segmentation</subject><subject>Spatiotemporal data</subject><subject>Waste Water Technology</subject><subject>Water Management</subject><subject>Water Pollution 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Jürgen</au><au>Zimmermann, Ralf</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Assessment of German population exposure levels to PM10 based on multiple spatial-temporal data</atitle><jtitle>Environmental science and pollution research international</jtitle><stitle>Environ Sci Pollut Res</stitle><addtitle>Environ Sci Pollut Res Int</addtitle><date>2020-02-01</date><risdate>2020</risdate><volume>27</volume><issue>6</issue><spage>6637</spage><epage>6648</epage><pages>6637-6648</pages><issn>0944-1344</issn><eissn>1614-7499</eissn><abstract>Particulate matter is the key to increasing urban air pollution, and research into pollution exposure assessment is an important part of environmental health. In order to classify PM
10
air pollution and to investigate the population exposure to the distribution of PM
10
, daily and monthly PM
10
concentrations of 379 air pollution monitoring stations were obtained for a period from 01/01/2017 to 31/12/2017. Firstly, PM
10
concentrations were classified using the head/tail break clustering algorithm to identify locations with elevated PM
10
levels. Subsequently, population exposure levels were calculated using population-weighted PM
10
concentrations. Finally, the power-law distribution was used to test the distribution of PM
10
polluted areas. Our results indicate that the head/tail break algorithm, with an appropriate segmentation threshold, can effectively identify areas with high PM
10
concentrations. The distribution of the population according to exposure level shows that the majority of people is living in polluted areas. The distribution of heavily PM
10
polluted areas in Germany follows the power-law distribution well, but their boundaries differ from the boundaries of administrative cities; some even cross several administrative cities. These classification results can guide policymakers in dividing the country into several areas for pollution control.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>31875295</pmid><doi>10.1007/s11356-019-07071-0</doi><tpages>12</tpages></addata></record> |
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| subjects | Air monitoring Air Pollutants Air pollution Air Pollution - statistics & numerical data Algorithms Aquatic Pollution Atmospheric Protection/Air Quality Control/Air Pollution Boundaries Cities Clustering Earth and Environmental Science Ecotoxicology Environment Environmental Chemistry Environmental Exposure - statistics & numerical data Environmental Health Environmental Monitoring Environmental science Exposure Germany Health risk assessment Levels Particulate emissions Particulate Matter Pollution control Pollution monitoring Population Power law Research Article Segmentation Spatiotemporal data Waste Water Technology Water Management Water Pollution Control |
| title | Assessment of German population exposure levels to PM10 based on multiple spatial-temporal data |
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