Evaluation of agricultural nonpoint source pollution potential risk over China with a Transformed-Agricultural Nonpoint Pollution Potential Index method.
Identifieur interne : 000156 ( PubMed/Corpus ); précédent : 000155; suivant : 000157Evaluation of agricultural nonpoint source pollution potential risk over China with a Transformed-Agricultural Nonpoint Pollution Potential Index method.
Auteurs : Fei Yang ; Zhencheng Xu ; Yunqiang Zhu ; Chansheng He ; Genyi Wu ; Jin Rong Qiu ; Qiang Fu ; Qingsong LiuSource :
- Environmental technology [ 0959-3330 ]
English descriptors
- KwdEn :
- Agriculture (statistics & numerical data), China, Data Interpretation, Statistical, Environment, Environmental Monitoring (methods), Geographic Information Systems, Remote Sensing Technology (methods), Risk Assessment (methods), Spatio-Temporal Analysis, Water Pollution (analysis), Water Pollution (statistics & numerical data), Water Quality.
- MESH :
- geographic : China.
- analysis : Water Pollution.
- methods : Environmental Monitoring, Remote Sensing Technology, Risk Assessment.
- statistics & numerical data : Agriculture, Water Pollution.
- Data Interpretation, Statistical, Environment, Geographic Information Systems, Spatio-Temporal Analysis, Water Quality.
Abstract
Agricultural nonpoint source (NPS) pollution has been the most important threat to water environment quality. Understanding the spatial distribution of NPS pollution potential risk is important for taking effective measures to control and reduce NPS pollution. A Transformed-Agricultural Nonpoint Pollution Potential Index (T-APPI) model was constructed for evaluating the national NPS pollution potential risk in this study; it was also combined with remote sensing and geographic information system techniques for evaluation on the large scale and at 1 km2 spatial resolution. This model considers many factors contributing to the NPS pollution as the original APPI model, summarized as four indicators of the runoff, sediment production, chemical use and the people and animal load. These four indicators were analysed in detail at 1 km2 spatial resolution throughout China. The T-APPI model distinguished the four indicators into pollution source factors and transport process factors; it also took their relationship into consideration. The studied results showed that T-APPI is a credible and convenient method for NPS pollution potential risk evaluation. The results also indicated that the highest NPS pollution potential risk is distributed in the middle-southern Jiangsu province. Several other regions, including the North China Plain, Chengdu Basin Plain, Jianghan Plain, cultivated lands in Guangdong and Guangxi provinces, also showed serious NPS pollution potential. This study can provide a scientific reference for predicting the future NPS pollution risk throughout China and may be helpful for taking reasonable and effective measures for preventing and controlling NPS pollution.
DOI: 10.1080/09593330.2013.796008
PubMed: 24617054
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pubmed:24617054Le document en format XML
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Understanding the spatial distribution of NPS pollution potential risk is important for taking effective measures to control and reduce NPS pollution. A Transformed-Agricultural Nonpoint Pollution Potential Index (T-APPI) model was constructed for evaluating the national NPS pollution potential risk in this study; it was also combined with remote sensing and geographic information system techniques for evaluation on the large scale and at 1 km2 spatial resolution. This model considers many factors contributing to the NPS pollution as the original APPI model, summarized as four indicators of the runoff, sediment production, chemical use and the people and animal load. These four indicators were analysed in detail at 1 km2 spatial resolution throughout China. The T-APPI model distinguished the four indicators into pollution source factors and transport process factors; it also took their relationship into consideration. The studied results showed that T-APPI is a credible and convenient method for NPS pollution potential risk evaluation. The results also indicated that the highest NPS pollution potential risk is distributed in the middle-southern Jiangsu province. Several other regions, including the North China Plain, Chengdu Basin Plain, Jianghan Plain, cultivated lands in Guangdong and Guangxi provinces, also showed serious NPS pollution potential. This study can provide a scientific reference for predicting the future NPS pollution risk throughout China and may be helpful for taking reasonable and effective measures for preventing and controlling NPS pollution.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">24617054</PMID><DateCreated><Year>2014</Year><Month>03</Month><Day>12</Day></DateCreated><DateCompleted><Year>2014</Year><Month>04</Month><Day>03</Day></DateCompleted><DateRevised><Year>2014</Year><Month>03</Month><Day>12</Day></DateRevised><Article PubModel="Print"><Journal><ISSN IssnType="Print">0959-3330</ISSN><JournalIssue CitedMedium="Print"><Volume>34</Volume><Issue>21-24</Issue><PubDate><MedlineDate>2013 Nov-Dec</MedlineDate></PubDate></JournalIssue><Title>Environmental technology</Title><ISOAbbreviation>Environ Technol</ISOAbbreviation></Journal><ArticleTitle>Evaluation of agricultural nonpoint source pollution potential risk over China with a Transformed-Agricultural Nonpoint Pollution Potential Index method.</ArticleTitle><Pagination><MedlinePgn>2951-63</MedlinePgn></Pagination><Abstract><AbstractText>Agricultural nonpoint source (NPS) pollution has been the most important threat to water environment quality. Understanding the spatial distribution of NPS pollution potential risk is important for taking effective measures to control and reduce NPS pollution. A Transformed-Agricultural Nonpoint Pollution Potential Index (T-APPI) model was constructed for evaluating the national NPS pollution potential risk in this study; it was also combined with remote sensing and geographic information system techniques for evaluation on the large scale and at 1 km2 spatial resolution. This model considers many factors contributing to the NPS pollution as the original APPI model, summarized as four indicators of the runoff, sediment production, chemical use and the people and animal load. These four indicators were analysed in detail at 1 km2 spatial resolution throughout China. The T-APPI model distinguished the four indicators into pollution source factors and transport process factors; it also took their relationship into consideration. The studied results showed that T-APPI is a credible and convenient method for NPS pollution potential risk evaluation. The results also indicated that the highest NPS pollution potential risk is distributed in the middle-southern Jiangsu province. Several other regions, including the North China Plain, Chengdu Basin Plain, Jianghan Plain, cultivated lands in Guangdong and Guangxi provinces, also showed serious NPS pollution potential. This study can provide a scientific reference for predicting the future NPS pollution risk throughout China and may be helpful for taking reasonable and effective measures for preventing and controlling NPS pollution.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Yang</LastName><ForeName>Fei</ForeName><Initials>F</Initials></Author><Author ValidYN="Y"><LastName>Xu</LastName><ForeName>Zhencheng</ForeName><Initials>Z</Initials></Author><Author ValidYN="Y"><LastName>Zhu</LastName><ForeName>Yunqiang</ForeName><Initials>Y</Initials></Author><Author ValidYN="Y"><LastName>He</LastName><ForeName>Chansheng</ForeName><Initials>C</Initials></Author><Author ValidYN="Y"><LastName>Wu</LastName><ForeName>Genyi</ForeName><Initials>G</Initials></Author><Author ValidYN="Y"><LastName>Qiu</LastName><ForeName>Jin Rong</ForeName><Initials>JR</Initials></Author><Author ValidYN="Y"><LastName>Fu</LastName><ForeName>Qiang</ForeName><Initials>Q</Initials></Author><Author ValidYN="Y"><LastName>Liu</LastName><ForeName>Qingsong</ForeName><Initials>Q</Initials></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>Environ Technol</MedlineTA><NlmUniqueID>9884939</NlmUniqueID><ISSNLinking>0959-3330</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000383" MajorTopicYN="N">Agriculture</DescriptorName><QualifierName UI="Q000706" MajorTopicYN="Y">statistics & numerical data</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D002681" MajorTopicYN="N" Type="Geographic">China</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D003627" MajorTopicYN="N">Data Interpretation, Statistical</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D004777" MajorTopicYN="N">Environment</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D004784" MajorTopicYN="N">Environmental Monitoring</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D040362" MajorTopicYN="Y">Geographic Information Systems</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D058998" MajorTopicYN="N">Remote Sensing Technology</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D018570" MajorTopicYN="N">Risk Assessment</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D062211" MajorTopicYN="N">Spatio-Temporal Analysis</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D014876" MajorTopicYN="N">Water Pollution</DescriptorName><QualifierName UI="Q000032" MajorTopicYN="N">analysis</QualifierName><QualifierName UI="Q000706" MajorTopicYN="Y">statistics & numerical data</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D060753" MajorTopicYN="Y">Water Quality</DescriptorName></MeshHeading></MeshHeadingList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="entrez"><Year>2014</Year><Month>3</Month><Day>13</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2014</Year><Month>3</Month><Day>13</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2014</Year><Month>4</Month><Day>4</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">24617054</ArticleId><ArticleId IdType="doi">10.1080/09593330.2013.796008</ArticleId></ArticleIdList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
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