A model supported by GIS for locating and quantifying PM2.5 emission originated from residential wood burning.
Identifieur interne : 000416 ( PubMed/Corpus ); précédent : 000415; suivant : 000417A model supported by GIS for locating and quantifying PM2.5 emission originated from residential wood burning.
Auteurs : De-Min Zhou ; John Radke ; Yong-Qian Tian ; Jian-Chun Xu ; Lan MuSource :
- Journal of environmental sciences (China) [ 1001-0742 ] ; 2005.
English descriptors
- KwdEn :
- MESH :
- chemical , analysis : Air Pollutants.
- geographic : California.
- statistics & numerical data : Air Pollution.
- Climate, Demography, Environment, Geographic Information Systems, Geography, Linear Models, Models, Theoretical, Wood.
Abstract
A research method was presented for spatially quantifying and allocating the potential activity of a fine particle matter emission (PM2.5), which originated from residential wood burning (RWB) in this study. Demographic, hypsographic, climatic and topographic data were compiled and processed within a geographic information system (GIS), and as independent variables put into a linear regression model for describing spatial distribution of the potential activity of residential wood burning as primary heating source. In order to improve the estimation, the classifications of urban, suburban and rural were redefined to meet the specifications of this application. Also, several definitions of forest accessibility were tested for estimation. The results suggested that the potential activity of RWB was mostly determined by elevation of a location, forest accessibility, urban/non-urban position, climatic conditions and several demographic variables. The linear regression model could explain approximately 86% of the variation of surveyed potential activity of RWB. The analysis results were validated by employing survey data collected mainly from a WebGIS based phone interview over the study area in central California. Based on lots free public GIS data, the model provided an easy and ideal tool for geographic researchers, environmental planners and administrators to understand where and how much PM2.5 emission from RWB was contributed to air quality. With this knowledge they could identify regions of concern, and better plan mitigation strategies to improve air quality. Furthermore, it allows for future adjustment on some parameters as the spatial analysis method is implemented in the different regions or various eco-social models.
PubMed: 16313020
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pubmed:16313020Le document en format XML
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Demographic, hypsographic, climatic and topographic data were compiled and processed within a geographic information system (GIS), and as independent variables put into a linear regression model for describing spatial distribution of the potential activity of residential wood burning as primary heating source. In order to improve the estimation, the classifications of urban, suburban and rural were redefined to meet the specifications of this application. Also, several definitions of forest accessibility were tested for estimation. The results suggested that the potential activity of RWB was mostly determined by elevation of a location, forest accessibility, urban/non-urban position, climatic conditions and several demographic variables. The linear regression model could explain approximately 86% of the variation of surveyed potential activity of RWB. The analysis results were validated by employing survey data collected mainly from a WebGIS based phone interview over the study area in central California. Based on lots free public GIS data, the model provided an easy and ideal tool for geographic researchers, environmental planners and administrators to understand where and how much PM2.5 emission from RWB was contributed to air quality. With this knowledge they could identify regions of concern, and better plan mitigation strategies to improve air quality. Furthermore, it allows for future adjustment on some parameters as the spatial analysis method is implemented in the different regions or various eco-social models.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">16313020</PMID><DateCreated><Year>2005</Year><Month>11</Month><Day>29</Day></DateCreated><DateCompleted><Year>2006</Year><Month>06</Month><Day>22</Day></DateCompleted><DateRevised><Year>2008</Year><Month>02</Month><Day>08</Day></DateRevised><Article PubModel="Print"><Journal><ISSN IssnType="Print">1001-0742</ISSN><JournalIssue CitedMedium="Print"><Volume>17</Volume><Issue>5</Issue><PubDate><Year>2005</Year></PubDate></JournalIssue><Title>Journal of environmental sciences (China)</Title><ISOAbbreviation>J Environ Sci (China)</ISOAbbreviation></Journal><ArticleTitle>A model supported by GIS for locating and quantifying PM2.5 emission originated from residential wood burning.</ArticleTitle><Pagination><MedlinePgn>861-5</MedlinePgn></Pagination><Abstract><AbstractText>A research method was presented for spatially quantifying and allocating the potential activity of a fine particle matter emission (PM2.5), which originated from residential wood burning (RWB) in this study. Demographic, hypsographic, climatic and topographic data were compiled and processed within a geographic information system (GIS), and as independent variables put into a linear regression model for describing spatial distribution of the potential activity of residential wood burning as primary heating source. In order to improve the estimation, the classifications of urban, suburban and rural were redefined to meet the specifications of this application. Also, several definitions of forest accessibility were tested for estimation. The results suggested that the potential activity of RWB was mostly determined by elevation of a location, forest accessibility, urban/non-urban position, climatic conditions and several demographic variables. The linear regression model could explain approximately 86% of the variation of surveyed potential activity of RWB. The analysis results were validated by employing survey data collected mainly from a WebGIS based phone interview over the study area in central California. Based on lots free public GIS data, the model provided an easy and ideal tool for geographic researchers, environmental planners and administrators to understand where and how much PM2.5 emission from RWB was contributed to air quality. With this knowledge they could identify regions of concern, and better plan mitigation strategies to improve air quality. Furthermore, it allows for future adjustment on some parameters as the spatial analysis method is implemented in the different regions or various eco-social models.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Zhou</LastName><ForeName>De-min</ForeName><Initials>DM</Initials><AffiliationInfo><Affiliation>Northeast Institute of Geography and Agricultural Ecology, Chinese Academy Sciences, Changchun 130012, China. zhoudemin@neigae.ac.cn</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Radke</LastName><ForeName>John</ForeName><Initials>J</Initials></Author><Author ValidYN="Y"><LastName>Tian</LastName><ForeName>Yong-qian</ForeName><Initials>YQ</Initials></Author><Author ValidYN="Y"><LastName>Xu</LastName><ForeName>Jian-chun</ForeName><Initials>JC</Initials></Author><Author ValidYN="Y"><LastName>Mu</LastName><ForeName>Lan</ForeName><Initials>L</Initials></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D003160">Comparative Study</PublicationType><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList></Article><MedlineJournalInfo><Country>Netherlands</Country><MedlineTA>J Environ Sci (China)</MedlineTA><NlmUniqueID>100967627</NlmUniqueID><ISSNLinking>1001-0742</ISSNLinking></MedlineJournalInfo><ChemicalList><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D000393">Air Pollutants</NameOfSubstance></Chemical></ChemicalList><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000393" MajorTopicYN="N">Air Pollutants</DescriptorName><QualifierName UI="Q000032" MajorTopicYN="Y">analysis</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D000397" MajorTopicYN="N">Air Pollution</DescriptorName><QualifierName UI="Q000706" MajorTopicYN="Y">statistics & numerical data</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D002140" MajorTopicYN="N" Type="Geographic">California</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D002980" MajorTopicYN="N">Climate</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D003710" MajorTopicYN="N">Demography</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D004777" MajorTopicYN="Y">Environment</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D040362" MajorTopicYN="Y">Geographic Information Systems</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D005843" MajorTopicYN="N">Geography</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D016014" MajorTopicYN="N">Linear Models</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008962" MajorTopicYN="Y">Models, Theoretical</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D014934" MajorTopicYN="Y">Wood</DescriptorName></MeshHeading></MeshHeadingList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="pubmed"><Year>2005</Year><Month>11</Month><Day>30</Day><Hour>9</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2006</Year><Month>6</Month><Day>23</Day><Hour>9</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2005</Year><Month>11</Month><Day>30</Day><Hour>9</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">16313020</ArticleId></ArticleIdList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
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