Monitoring water quality of the Sergipe River basin: an evaluation using multivariate data analysis
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | RBRH (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2318-03312018000100223 |
Resumo: | ABSTRACT This study evaluated the efficiency of the water quality monitoring network of the Sergipe river basin, using multivariate data analysis, such as principal component analysis (PCA) and hierarchical cluster analysis (HCA). The PCA was applied to a data matrix consisting of 12 sampling stations and mean concentrations of 23 water quality parameters, obtained in four sampling campaigns from June/2013 to November/2015. All 12 sampling stations were considered as main (weight>0.7) and therefore should remain in the monitoring program. The PCA pointed out that of the 23 measured parameters, only 16 are essential for water quality assessment, in the dry period and 17 in the rainy season. The HCA separated the stations of the monitoring network in 4 groups according to the water quality characteristics, considering the natural and anthropogenic impacts. The main impacts were originated from natural sources (mineral constituents) and the anthropogenic contributions were associated with urban input, sewage, industrial dumps and surface runoff from agricultural areas. |
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Monitoring water quality of the Sergipe River basin: an evaluation using multivariate data analysisMonitoringWater qualityPrincipal component analysisCluster analysisSergipe RiverABSTRACT This study evaluated the efficiency of the water quality monitoring network of the Sergipe river basin, using multivariate data analysis, such as principal component analysis (PCA) and hierarchical cluster analysis (HCA). The PCA was applied to a data matrix consisting of 12 sampling stations and mean concentrations of 23 water quality parameters, obtained in four sampling campaigns from June/2013 to November/2015. All 12 sampling stations were considered as main (weight>0.7) and therefore should remain in the monitoring program. The PCA pointed out that of the 23 measured parameters, only 16 are essential for water quality assessment, in the dry period and 17 in the rainy season. The HCA separated the stations of the monitoring network in 4 groups according to the water quality characteristics, considering the natural and anthropogenic impacts. The main impacts were originated from natural sources (mineral constituents) and the anthropogenic contributions were associated with urban input, sewage, industrial dumps and surface runoff from agricultural areas.Associação Brasileira de Recursos Hídricos2018-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S2318-03312018000100223RBRH v.23 2018reponame:RBRH (Online)instname:Associação Brasileira de Recursos Hídricos (ABRH)instacron:ABRH10.1590/2318-0331.231820170124info:eu-repo/semantics/openAccessAlves,José do Patrocinio HoraFonseca,Lucas CruzChielle,Raisa de Siqueira AlvesMacedo,Lúcia Calumby Barretoeng2018-07-04T00:00:00Zoai:scielo:S2318-03312018000100223Revistahttps://www.scielo.br/j/rbrh/https://old.scielo.br/oai/scielo-oai.php||rbrh@abrh.org.br2318-03311414-381Xopendoar:2018-07-04T00:00RBRH (Online) - Associação Brasileira de Recursos Hídricos (ABRH)false |
dc.title.none.fl_str_mv |
Monitoring water quality of the Sergipe River basin: an evaluation using multivariate data analysis |
title |
Monitoring water quality of the Sergipe River basin: an evaluation using multivariate data analysis |
spellingShingle |
Monitoring water quality of the Sergipe River basin: an evaluation using multivariate data analysis Alves,José do Patrocinio Hora Monitoring Water quality Principal component analysis Cluster analysis Sergipe River |
title_short |
Monitoring water quality of the Sergipe River basin: an evaluation using multivariate data analysis |
title_full |
Monitoring water quality of the Sergipe River basin: an evaluation using multivariate data analysis |
title_fullStr |
Monitoring water quality of the Sergipe River basin: an evaluation using multivariate data analysis |
title_full_unstemmed |
Monitoring water quality of the Sergipe River basin: an evaluation using multivariate data analysis |
title_sort |
Monitoring water quality of the Sergipe River basin: an evaluation using multivariate data analysis |
author |
Alves,José do Patrocinio Hora |
author_facet |
Alves,José do Patrocinio Hora Fonseca,Lucas Cruz Chielle,Raisa de Siqueira Alves Macedo,Lúcia Calumby Barreto |
author_role |
author |
author2 |
Fonseca,Lucas Cruz Chielle,Raisa de Siqueira Alves Macedo,Lúcia Calumby Barreto |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Alves,José do Patrocinio Hora Fonseca,Lucas Cruz Chielle,Raisa de Siqueira Alves Macedo,Lúcia Calumby Barreto |
dc.subject.por.fl_str_mv |
Monitoring Water quality Principal component analysis Cluster analysis Sergipe River |
topic |
Monitoring Water quality Principal component analysis Cluster analysis Sergipe River |
description |
ABSTRACT This study evaluated the efficiency of the water quality monitoring network of the Sergipe river basin, using multivariate data analysis, such as principal component analysis (PCA) and hierarchical cluster analysis (HCA). The PCA was applied to a data matrix consisting of 12 sampling stations and mean concentrations of 23 water quality parameters, obtained in four sampling campaigns from June/2013 to November/2015. All 12 sampling stations were considered as main (weight>0.7) and therefore should remain in the monitoring program. The PCA pointed out that of the 23 measured parameters, only 16 are essential for water quality assessment, in the dry period and 17 in the rainy season. The HCA separated the stations of the monitoring network in 4 groups according to the water quality characteristics, considering the natural and anthropogenic impacts. The main impacts were originated from natural sources (mineral constituents) and the anthropogenic contributions were associated with urban input, sewage, industrial dumps and surface runoff from agricultural areas. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-01-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2318-03312018000100223 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2318-03312018000100223 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/2318-0331.231820170124 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Associação Brasileira de Recursos Hídricos |
publisher.none.fl_str_mv |
Associação Brasileira de Recursos Hídricos |
dc.source.none.fl_str_mv |
RBRH v.23 2018 reponame:RBRH (Online) instname:Associação Brasileira de Recursos Hídricos (ABRH) instacron:ABRH |
instname_str |
Associação Brasileira de Recursos Hídricos (ABRH) |
instacron_str |
ABRH |
institution |
ABRH |
reponame_str |
RBRH (Online) |
collection |
RBRH (Online) |
repository.name.fl_str_mv |
RBRH (Online) - Associação Brasileira de Recursos Hídricos (ABRH) |
repository.mail.fl_str_mv |
||rbrh@abrh.org.br |
_version_ |
1754734701539491840 |