Modeling relationships between catchment attributes and river water quality in southern catchments of the Caspian Sea.
Identifieur interne : 000094 ( PubMed/Corpus ); précédent : 000093; suivant : 000095Modeling relationships between catchment attributes and river water quality in southern catchments of the Caspian Sea.
Auteurs : Mohammad Hasani Sangani ; Bahman Jabbarian Amiri ; Afshin Alizadeh Shabani ; Yousef Sakieh ; Sohrab AshrafiSource :
- Environmental science and pollution research international [ 1614-7499 ] ; 2015.
English descriptors
- KwdEn :
- MESH :
- chemical : Soil.
- methods : Environmental Monitoring.
- Agriculture, Geographic Information Systems, Humans, Hydrology, Models, Theoretical, Oceans and Seas, Rivers, Water Movements, Water Quality.
Abstract
Increasing land utilization through diverse forms of human activities, such as agriculture, forestry, urban growth, and industrial development, has led to negative impacts on the water quality of rivers. To find out how catchment attributes, such as land use, hydrologic soil groups, and lithology, can affect water quality variables (Ca(2+), Mg(2+), Na(+), Cl(-), HCO 3 (-) , pH, TDS, EC, SAR), a spatio-statistical approach was applied to 23 catchments in southern basins of the Caspian Sea. All input data layers (digital maps of land use, soil, and lithology) were prepared using geographic information system (GIS) and spatial analysis. Relationships between water quality variables and catchment attributes were then examined by Spearman rank correlation tests and multiple linear regression. Stepwise approach-based multiple linear regressions were developed to examine the relationship between catchment attributes and water quality variables. The areas (%) of marl, tuff, or diorite, as well as those of good-quality rangeland and bare land had negative effects on all water quality variables, while those of basalt, forest land cover were found to contribute to improved river water quality. Moreover, lithological variables showed the greatest most potential for predicting the mean concentration values of water quality variables, and noting that measure of EC and TDS have inversely associated with area (%) of urban land use.
DOI: 10.1007/s11356-014-3727-5
PubMed: 25395322
Links to Exploration step
pubmed:25395322Le document en format XML
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To find out how catchment attributes, such as land use, hydrologic soil groups, and lithology, can affect water quality variables (Ca(2+), Mg(2+), Na(+), Cl(-), HCO 3 (-) , pH, TDS, EC, SAR), a spatio-statistical approach was applied to 23 catchments in southern basins of the Caspian Sea. All input data layers (digital maps of land use, soil, and lithology) were prepared using geographic information system (GIS) and spatial analysis. Relationships between water quality variables and catchment attributes were then examined by Spearman rank correlation tests and multiple linear regression. Stepwise approach-based multiple linear regressions were developed to examine the relationship between catchment attributes and water quality variables. The areas (%) of marl, tuff, or diorite, as well as those of good-quality rangeland and bare land had negative effects on all water quality variables, while those of basalt, forest land cover were found to contribute to improved river water quality. Moreover, lithological variables showed the greatest most potential for predicting the mean concentration values of water quality variables, and noting that measure of EC and TDS have inversely associated with area (%) of urban land use.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">25395322</PMID><DateCreated><Year>2015</Year><Month>03</Month><Day>20</Day></DateCreated><DateCompleted><Year>2015</Year><Month>07</Month><Day>27</Day></DateCompleted><DateRevised><Year>2015</Year><Month>03</Month><Day>20</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1614-7499</ISSN><JournalIssue CitedMedium="Internet"><Volume>22</Volume><Issue>7</Issue><PubDate><Year>2015</Year><Month>Apr</Month></PubDate></JournalIssue><Title>Environmental science and pollution research international</Title><ISOAbbreviation>Environ Sci Pollut Res Int</ISOAbbreviation></Journal><ArticleTitle>Modeling relationships between catchment attributes and river water quality in southern catchments of the Caspian Sea.</ArticleTitle><Pagination><MedlinePgn>4985-5002</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1007/s11356-014-3727-5</ELocationID><Abstract><AbstractText>Increasing land utilization through diverse forms of human activities, such as agriculture, forestry, urban growth, and industrial development, has led to negative impacts on the water quality of rivers. To find out how catchment attributes, such as land use, hydrologic soil groups, and lithology, can affect water quality variables (Ca(2+), Mg(2+), Na(+), Cl(-), HCO 3 (-) , pH, TDS, EC, SAR), a spatio-statistical approach was applied to 23 catchments in southern basins of the Caspian Sea. All input data layers (digital maps of land use, soil, and lithology) were prepared using geographic information system (GIS) and spatial analysis. Relationships between water quality variables and catchment attributes were then examined by Spearman rank correlation tests and multiple linear regression. Stepwise approach-based multiple linear regressions were developed to examine the relationship between catchment attributes and water quality variables. The areas (%) of marl, tuff, or diorite, as well as those of good-quality rangeland and bare land had negative effects on all water quality variables, while those of basalt, forest land cover were found to contribute to improved river water quality. Moreover, lithological variables showed the greatest most potential for predicting the mean concentration values of water quality variables, and noting that measure of EC and TDS have inversely associated with area (%) of urban land use.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Hasani Sangani</LastName><ForeName>Mohammad</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>Department of Environmental Science, Faculty of Natural Resources, University of Tehran, Tehran, 31587-77878, Iran.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Jabbarian Amiri</LastName><ForeName>Bahman</ForeName><Initials>B</Initials></Author><Author ValidYN="Y"><LastName>Alizadeh Shabani</LastName><ForeName>Afshin</ForeName><Initials>A</Initials></Author><Author ValidYN="Y"><LastName>Sakieh</LastName><ForeName>Yousef</ForeName><Initials>Y</Initials></Author><Author ValidYN="Y"><LastName>Ashrafi</LastName><ForeName>Sohrab</ForeName><Initials>S</Initials></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2014</Year><Month>11</Month><Day>15</Day></ArticleDate></Article><MedlineJournalInfo><Country>Germany</Country><MedlineTA>Environ Sci Pollut Res Int</MedlineTA><NlmUniqueID>9441769</NlmUniqueID><ISSNLinking>0944-1344</ISSNLinking></MedlineJournalInfo><ChemicalList><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D012987">Soil</NameOfSubstance></Chemical></ChemicalList><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000383" MajorTopicYN="N">Agriculture</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D004784" MajorTopicYN="N">Environmental Monitoring</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D040362" MajorTopicYN="N">Geographic Information Systems</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D062070" MajorTopicYN="N">Hydrology</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008962" MajorTopicYN="Y">Models, Theoretical</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D009792" MajorTopicYN="N">Oceans and Seas</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D045483" MajorTopicYN="Y">Rivers</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D012987" MajorTopicYN="N">Soil</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D014872" MajorTopicYN="Y">Water Movements</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D060753" MajorTopicYN="Y">Water Quality</DescriptorName></MeshHeading></MeshHeadingList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2014</Year><Month>02</Month><Day>24</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2014</Year><Month>10</Month><Day>14</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2014</Year><Month>11</Month><Day>15</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2014</Year><Month>11</Month><Day>15</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2015</Year><Month>7</Month><Day>28</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">25395322</ArticleId><ArticleId IdType="doi">10.1007/s11356-014-3727-5</ArticleId></ArticleIdList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
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