Temporal and Spatial Simulation of Atmospheric Pollutant PM2.5 Changes and Risk Assessment of Population Exposure to Pollution Using Optimization Algorithms of the Back Propagation-Artificial Neural Network Model and GIS.
Identifieur interne : 000071 ( PubMed/Curation ); précédent : 000070; suivant : 000072Temporal and Spatial Simulation of Atmospheric Pollutant PM2.5 Changes and Risk Assessment of Population Exposure to Pollution Using Optimization Algorithms of the Back Propagation-Artificial Neural Network Model and GIS.
Auteurs : Ping Zhang [République populaire de Chine] ; Bo Hong [République populaire de Chine] ; Liang He [République populaire de Chine] ; Fei Cheng [République populaire de Chine] ; Peng Zhao [République populaire de Chine] ; Cailiang Wei [République populaire de Chine] ; Yunhui Liu [République populaire de Chine]Source :
- International journal of environmental research and public health [ 1660-4601 ] ; 2015.
Descripteurs français
- Wicri :
- geographic : République populaire de Chine.
English descriptors
- KwdEn :
- Air Pollutants (analysis), Air Pollutants (toxicity), Algorithms, China, Environmental Exposure, Environmental Monitoring (methods), Environmental Policy, Health Policy, Humans, Models, Theoretical, Neural Networks (Computer), Particle Size, Particulate Matter (analysis), Particulate Matter (toxicity), Public Policy, Risk Assessment.
- MESH :
- chemical , analysis : Air Pollutants, Particulate Matter.
- chemical , toxicity : Air Pollutants, Particulate Matter.
- geographic : China.
- methods : Environmental Monitoring.
- Algorithms, Environmental Exposure, Environmental Policy, Health Policy, Humans, Models, Theoretical, Neural Networks (Computer), Particle Size, Public Policy, Risk Assessment.
Abstract
PM2.5 pollution has become of increasing public concern because of its relative importance and sensitivity to population health risks. Accurate predictions of PM2.5 pollution and population exposure risks are crucial to developing effective air pollution control strategies. We simulated and predicted the temporal and spatial changes of PM2.5 concentration and population exposure risks, by coupling optimization algorithms of the Back Propagation-Artificial Neural Network (BP-ANN) model and a geographical information system (GIS) in Xi'an, China, for 2013, 2020, and 2025. Results indicated that PM2.5 concentration was positively correlated with GDP, SO₂, and NO₂, while it was negatively correlated with population density, average temperature, precipitation, and wind speed. Principal component analysis of the PM2.5 concentration and its influencing factors' variables extracted four components that accounted for 86.39% of the total variance. Correlation coefficients of the Levenberg-Marquardt (trainlm) and elastic (trainrp) algorithms were more than 0.8, the index of agreement (IA) ranged from 0.541 to 0.863 and from 0.502 to 0.803 by trainrp and trainlm algorithms, respectively; mean bias error (MBE) and Root Mean Square Error (RMSE) indicated that the predicted values were very close to the observed values, and the accuracy of trainlm algorithm was better than the trainrp. Compared to 2013, temporal and spatial variation of PM2.5 concentration and risk of population exposure to pollution decreased in 2020 and 2025. The high-risk areas of population exposure to PM2.5 were mainly distributed in the northern region, where there is downtown traffic, abundant commercial activity, and more exhaust emissions. A moderate risk zone was located in the southern region associated with some industrial pollution sources, and there were mainly low-risk areas in the western and eastern regions, which are predominantly residential and educational areas.
DOI: 10.3390/ijerph121012171
PubMed: 26426030
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Arts, Northwest A & F University, Yangling 712100, China. hongbo@nwsuaf.edu.cn.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>College of Landscape Architecture and Arts, Northwest A & F University, Yangling 712100</wicri:regionArea></affiliation></author><author><name sortKey="He, Liang" sort="He, Liang" uniqKey="He L" first="Liang" last="He">Liang He</name><affiliation wicri:level="1"><nlm:affiliation>Xi'an Environmental Monitoring Station, Xi'an 710054, China. he121@163.com.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Xi'an Environmental Monitoring Station, Xi'an 710054</wicri:regionArea></affiliation></author><author><name sortKey="Cheng, Fei" sort="Cheng, Fei" uniqKey="Cheng F" first="Fei" last="Cheng">Fei Cheng</name><affiliation wicri:level="1"><nlm:affiliation>Forestry College, Guangxi University, Nanning 530004, China. ivan-025@163.com.</nlm:affiliation><country 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710048</wicri:regionArea></affiliation></author><author><name sortKey="Hong, Bo" sort="Hong, Bo" uniqKey="Hong B" first="Bo" last="Hong">Bo Hong</name><affiliation wicri:level="1"><nlm:affiliation>College of Landscape Architecture and Arts, Northwest A & F University, Yangling 712100, China. hongbo@nwsuaf.edu.cn.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>College of Landscape Architecture and Arts, Northwest A & F University, Yangling 712100</wicri:regionArea></affiliation></author><author><name sortKey="He, Liang" sort="He, Liang" uniqKey="He L" first="Liang" last="He">Liang He</name><affiliation wicri:level="1"><nlm:affiliation>Xi'an Environmental Monitoring Station, Xi'an 710054, China. he121@163.com.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Xi'an Environmental Monitoring Station, Xi'an 710054</wicri:regionArea></affiliation></author><author><name sortKey="Cheng, Fei" sort="Cheng, Fei" uniqKey="Cheng F" first="Fei" last="Cheng">Fei Cheng</name><affiliation wicri:level="1"><nlm:affiliation>Forestry College, Guangxi University, Nanning 530004, China. ivan-025@163.com.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Forestry College, Guangxi University, Nanning 530004</wicri:regionArea></affiliation></author><author><name sortKey="Zhao, Peng" sort="Zhao, Peng" uniqKey="Zhao P" first="Peng" last="Zhao">Peng Zhao</name><affiliation wicri:level="1"><nlm:affiliation>College of Life Sciences, Northwest University, Xi'an 710069, China. pengzhao@nwu.edu.cn.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>College of Life Sciences, Northwest University, Xi'an 710069</wicri:regionArea></affiliation></author><author><name sortKey="Wei, Cailiang" sort="Wei, Cailiang" uniqKey="Wei C" first="Cailiang" last="Wei">Cailiang Wei</name><affiliation wicri:level="1"><nlm:affiliation>School of Environmental and Chemical Engineering, Xi'an Polytechnic University, Xi'an 710048, China. mirage3000@163.com.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>School of Environmental and Chemical Engineering, Xi'an Polytechnic University, Xi'an 710048</wicri:regionArea></affiliation></author><author><name sortKey="Liu, Yunhui" sort="Liu, Yunhui" uniqKey="Liu Y" first="Yunhui" last="Liu">Yunhui Liu</name><affiliation wicri:level="1"><nlm:affiliation>College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China. liuyh@cau.edu.cn.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193</wicri:regionArea></affiliation></author></analytic><series><title level="j">International journal of environmental research and public health</title><idno type="eISSN">1660-4601</idno><imprint><date when="2015" type="published">2015</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Air Pollutants (analysis)</term><term>Air Pollutants (toxicity)</term><term>Algorithms</term><term>China</term><term>Environmental Exposure</term><term>Environmental Monitoring (methods)</term><term>Environmental Policy</term><term>Health Policy</term><term>Humans</term><term>Models, Theoretical</term><term>Neural Networks (Computer)</term><term>Particle Size</term><term>Particulate Matter (analysis)</term><term>Particulate Matter (toxicity)</term><term>Public Policy</term><term>Risk Assessment</term></keywords><keywords scheme="MESH" type="chemical" qualifier="analysis" xml:lang="en"><term>Air Pollutants</term><term>Particulate Matter</term></keywords><keywords scheme="MESH" type="chemical" qualifier="toxicity" xml:lang="en"><term>Air Pollutants</term><term>Particulate Matter</term></keywords><keywords scheme="MESH" type="geographic" xml:lang="en"><term>China</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Environmental Monitoring</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Environmental Exposure</term><term>Environmental Policy</term><term>Health Policy</term><term>Humans</term><term>Models, Theoretical</term><term>Neural Networks (Computer)</term><term>Particle Size</term><term>Public Policy</term><term>Risk Assessment</term></keywords><keywords scheme="Wicri" type="geographic" xml:lang="fr"><term>République populaire de Chine</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">PM2.5 pollution has become of increasing public concern because of its relative importance and sensitivity to population health risks. Accurate predictions of PM2.5 pollution and population exposure risks are crucial to developing effective air pollution control strategies. We simulated and predicted the temporal and spatial changes of PM2.5 concentration and population exposure risks, by coupling optimization algorithms of the Back Propagation-Artificial Neural Network (BP-ANN) model and a geographical information system (GIS) in Xi'an, China, for 2013, 2020, and 2025. Results indicated that PM2.5 concentration was positively correlated with GDP, SO₂, and NO₂, while it was negatively correlated with population density, average temperature, precipitation, and wind speed. Principal component analysis of the PM2.5 concentration and its influencing factors' variables extracted four components that accounted for 86.39% of the total variance. Correlation coefficients of the Levenberg-Marquardt (trainlm) and elastic (trainrp) algorithms were more than 0.8, the index of agreement (IA) ranged from 0.541 to 0.863 and from 0.502 to 0.803 by trainrp and trainlm algorithms, respectively; mean bias error (MBE) and Root Mean Square Error (RMSE) indicated that the predicted values were very close to the observed values, and the accuracy of trainlm algorithm was better than the trainrp. Compared to 2013, temporal and spatial variation of PM2.5 concentration and risk of population exposure to pollution decreased in 2020 and 2025. The high-risk areas of population exposure to PM2.5 were mainly distributed in the northern region, where there is downtown traffic, abundant commercial activity, and more exhaust emissions. A moderate risk zone was located in the southern region associated with some industrial pollution sources, and there were mainly low-risk areas in the western and eastern regions, which are predominantly residential and educational areas.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">26426030</PMID><DateCreated><Year>2015</Year><Month>10</Month><Day>02</Day></DateCreated><DateCompleted><Year>2016</Year><Month>05</Month><Day>27</Day></DateCompleted><DateRevised><Year>2015</Year><Month>11</Month><Day>13</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">1660-4601</ISSN><JournalIssue CitedMedium="Internet"><Volume>12</Volume><Issue>10</Issue><PubDate><Year>2015</Year><Month>Sep</Month><Day>29</Day></PubDate></JournalIssue><Title>International journal of environmental research and public health</Title><ISOAbbreviation>Int J Environ Res Public Health</ISOAbbreviation></Journal><ArticleTitle>Temporal and Spatial Simulation of Atmospheric Pollutant PM2.5 Changes and Risk Assessment of Population Exposure to Pollution Using Optimization Algorithms of the Back Propagation-Artificial Neural Network Model and GIS.</ArticleTitle><Pagination><MedlinePgn>12171-95</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.3390/ijerph121012171</ELocationID><Abstract><AbstractText>PM2.5 pollution has become of increasing public concern because of its relative importance and sensitivity to population health risks. Accurate predictions of PM2.5 pollution and population exposure risks are crucial to developing effective air pollution control strategies. We simulated and predicted the temporal and spatial changes of PM2.5 concentration and population exposure risks, by coupling optimization algorithms of the Back Propagation-Artificial Neural Network (BP-ANN) model and a geographical information system (GIS) in Xi'an, China, for 2013, 2020, and 2025. Results indicated that PM2.5 concentration was positively correlated with GDP, SO₂, and NO₂, while it was negatively correlated with population density, average temperature, precipitation, and wind speed. Principal component analysis of the PM2.5 concentration and its influencing factors' variables extracted four components that accounted for 86.39% of the total variance. Correlation coefficients of the Levenberg-Marquardt (trainlm) and elastic (trainrp) algorithms were more than 0.8, the index of agreement (IA) ranged from 0.541 to 0.863 and from 0.502 to 0.803 by trainrp and trainlm algorithms, respectively; mean bias error (MBE) and Root Mean Square Error (RMSE) indicated that the predicted values were very close to the observed values, and the accuracy of trainlm algorithm was better than the trainrp. Compared to 2013, temporal and spatial variation of PM2.5 concentration and risk of population exposure to pollution decreased in 2020 and 2025. The high-risk areas of population exposure to PM2.5 were mainly distributed in the northern region, where there is downtown traffic, abundant commercial activity, and more exhaust emissions. A moderate risk zone was located in the southern region associated with some industrial pollution sources, and there were mainly low-risk areas in the western and eastern regions, which are predominantly residential and educational areas.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Zhang</LastName><ForeName>Ping</ForeName><Initials>P</Initials><AffiliationInfo><Affiliation>School of Environmental and Chemical Engineering, Xi'an Polytechnic University, Xi'an 710048, China. miracle1891@126.com.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Hong</LastName><ForeName>Bo</ForeName><Initials>B</Initials><AffiliationInfo><Affiliation>College of Landscape Architecture and Arts, Northwest A & F University, Yangling 712100, China. hongbo@nwsuaf.edu.cn.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>He</LastName><ForeName>Liang</ForeName><Initials>L</Initials><AffiliationInfo><Affiliation>Xi'an Environmental Monitoring Station, Xi'an 710054, China. he121@163.com.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Cheng</LastName><ForeName>Fei</ForeName><Initials>F</Initials><AffiliationInfo><Affiliation>Forestry College, Guangxi University, Nanning 530004, China. ivan-025@163.com.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Zhao</LastName><ForeName>Peng</ForeName><Initials>P</Initials><AffiliationInfo><Affiliation>College of Life Sciences, Northwest University, Xi'an 710069, China. pengzhao@nwu.edu.cn.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Wei</LastName><ForeName>Cailiang</ForeName><Initials>C</Initials><AffiliationInfo><Affiliation>School of Environmental and Chemical Engineering, Xi'an Polytechnic University, Xi'an 710048, China. mirage3000@163.com.</Affiliation></AffiliationInfo></Author><Author 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UI="D052638">Particulate Matter</NameOfSubstance></Chemical></ChemicalList><CitationSubset>IM</CitationSubset><CommentsCorrectionsList><CommentsCorrections RefType="Cites"><RefSource>Environ Health Perspect. 2001 Apr;109(4):341-7</RefSource><PMID Version="1">11335181</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>J Air Waste Manag Assoc. 2002 Sep;52(9):1096-101</RefSource><PMID Version="1">12269670</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>J Neurosci. 1997 Aug 1;17(15):5900-20</RefSource><PMID Version="1">9221787</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Int J Environ Res Public Health. 2006 Mar;3(1):86-97</RefSource><PMID Version="1">16823080</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>J Environ Monit. 2007 Mar;9(3):246-52</RefSource><PMID Version="1">17344950</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Environ Health 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Version="1">25546729</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Int J Environ Res Public Health. 2015 Jun;12(6):6608-25</RefSource><PMID Version="1">26068090</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Int J Environ Res Public Health. 2015 Jun;12(6):7085-99</RefSource><PMID Version="1">26110332</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Int J Environ Res Public Health. 2015 Jul;12(7):7667-81</RefSource><PMID Version="1">26184247</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Int J Environ Res Public Health. 2015 Aug;12(8):9089-101</RefSource><PMID Version="1">26247953</PMID></CommentsCorrections></CommentsCorrectionsList><MeshHeadingList><MeshHeading><DescriptorName UI="D000393" MajorTopicYN="N">Air Pollutants</DescriptorName><QualifierName UI="Q000032" MajorTopicYN="N">analysis</QualifierName><QualifierName UI="Q000633" 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Theoretical</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D016571" MajorTopicYN="N">Neural Networks (Computer)</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D010316" MajorTopicYN="N">Particle Size</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D052638" MajorTopicYN="N">Particulate Matter</DescriptorName><QualifierName UI="Q000032" MajorTopicYN="N">analysis</QualifierName><QualifierName UI="Q000633" MajorTopicYN="Y">toxicity</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D011640" MajorTopicYN="N">Public Policy</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D018570" MajorTopicYN="N">Risk Assessment</DescriptorName></MeshHeading></MeshHeadingList><OtherID Source="NLM">PMC4626962</OtherID><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">BP-ANN model</Keyword><Keyword MajorTopicYN="N">PM2.5</Keyword><Keyword MajorTopicYN="N">geographical information system</Keyword><Keyword MajorTopicYN="N">optimization algorithms</Keyword><Keyword MajorTopicYN="N">population exposure risk</Keyword><Keyword MajorTopicYN="N">simulation and prediction</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2015</Year><Month>06</Month><Day>30</Day></PubMedPubDate><PubMedPubDate PubStatus="revised"><Year>2015</Year><Month>09</Month><Day>21</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2015</Year><Month>09</Month><Day>23</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2015</Year><Month>10</Month><Day>2</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2015</Year><Month>10</Month><Day>2</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2016</Year><Month>5</Month><Day>28</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>epublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">26426030</ArticleId><ArticleId IdType="pii">ijerph121012171</ArticleId><ArticleId IdType="doi">10.3390/ijerph121012171</ArticleId><ArticleId IdType="pmc">PMC4626962</ArticleId></ArticleIdList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
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|texte= Temporal and Spatial Simulation of Atmospheric Pollutant PM2.5 Changes and Risk Assessment of Population Exposure to Pollution Using Optimization Algorithms of the Back Propagation-Artificial Neural Network Model and GIS.
}}
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| HfdSelect -Kh $EXPLOR_AREA/Data/PubMed/Curation/biblio.hfd \
| NlmPubMed2Wicri -a SisAgriV1
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