Water quality assessment using multivariate statistical techniques in Río Tercero Reservoir, Argentina

Autores
Bonansea, Matias; Ledesma, Claudia; Rodriguez, Claudia; Pinotti, Lucio Pedro
Año de publicación
2015
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Water quality monitoring programs generate complex multidimensional data sets. In this study, multivariate statistical techniques were employed as an effective tool for the analysis and interpretation of these water quality data sets. Principal component analysis (PCA) and cluster analysis (CA) were applied to evaluate spatial and temporal variation of water quality in Río Tercero Reservoir (Argentina). Six sampling sites were surveyed each climatic season for 21 parameters during 2003-2010. The results revealed that PCA showed the existence of four significant principal components (PCs) which account for 96.7% of the total variance of the data set. The first PC was assigned to mineralization whereas the other PCs were built from variables indicative of pollution. Hierarchical CA grouped the six monitoring sites into three clusters and classified the different climatic seasons into two clusters based on similarities in water quality characteristics.
Fil: Bonansea, Matias. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina. Universidad Nacional de Río Cuarto. Facultad de Agronomía y Veterinaria; Argentina
Fil: Ledesma, Claudia. Universidad Nacional de Río Cuarto. Facultad de Agronomía y Veterinaria; Argentina
Fil: Rodriguez, Claudia. Universidad Nacional de Río Cuarto. Facultad de Agronomía y Veterinaria; Argentina
Fil: Pinotti, Lucio Pedro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina. Universidad Nacional de Río Cuarto. Facultad de Ciencias Exactas, Fisicoquímicas y Naturales. Departamento de Geología; Argentina
Materia
Cluster Analysis
Monitoring
Multivariate Statistical Techniques
Principal Components
Surface Water
Water Quality
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/69398
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/69398
network_acronym_str CONICETDig
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network_name_str CONICET Digital (CONICET)
spelling Water quality assessment using multivariate statistical techniques in Río Tercero Reservoir, ArgentinaBonansea, MatiasLedesma, ClaudiaRodriguez, ClaudiaPinotti, Lucio PedroCluster AnalysisMonitoringMultivariate Statistical TechniquesPrincipal ComponentsSurface WaterWater Qualityhttps://purl.org/becyt/ford/1.5https://purl.org/becyt/ford/1Water quality monitoring programs generate complex multidimensional data sets. In this study, multivariate statistical techniques were employed as an effective tool for the analysis and interpretation of these water quality data sets. Principal component analysis (PCA) and cluster analysis (CA) were applied to evaluate spatial and temporal variation of water quality in Río Tercero Reservoir (Argentina). Six sampling sites were surveyed each climatic season for 21 parameters during 2003-2010. The results revealed that PCA showed the existence of four significant principal components (PCs) which account for 96.7% of the total variance of the data set. The first PC was assigned to mineralization whereas the other PCs were built from variables indicative of pollution. Hierarchical CA grouped the six monitoring sites into three clusters and classified the different climatic seasons into two clusters based on similarities in water quality characteristics.Fil: Bonansea, Matias. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina. Universidad Nacional de Río Cuarto. Facultad de Agronomía y Veterinaria; ArgentinaFil: Ledesma, Claudia. Universidad Nacional de Río Cuarto. Facultad de Agronomía y Veterinaria; ArgentinaFil: Rodriguez, Claudia. Universidad Nacional de Río Cuarto. Facultad de Agronomía y Veterinaria; ArgentinaFil: Pinotti, Lucio Pedro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina. Universidad Nacional de Río Cuarto. Facultad de Ciencias Exactas, Fisicoquímicas y Naturales. Departamento de Geología; ArgentinaNordic Association for Hydrology2015-06info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/69398Bonansea, Matias; Ledesma, Claudia; Rodriguez, Claudia; Pinotti, Lucio Pedro; Water quality assessment using multivariate statistical techniques in Río Tercero Reservoir, Argentina; Nordic Association for Hydrology; Hydrology Research; 46; 3; 6-2015; 377-3882224-7955CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.2166/nh.2014.174info:eu-repo/semantics/altIdentifier/url/https://iwaponline.com/hr/article-abstract/46/3/377/1000/Water-quality-assessment-using-multivariate?redirectedFrom=fulltextinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:32:20Zoai:ri.conicet.gov.ar:11336/69398instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:32:20.525CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Water quality assessment using multivariate statistical techniques in Río Tercero Reservoir, Argentina
title Water quality assessment using multivariate statistical techniques in Río Tercero Reservoir, Argentina
spellingShingle Water quality assessment using multivariate statistical techniques in Río Tercero Reservoir, Argentina
Bonansea, Matias
Cluster Analysis
Monitoring
Multivariate Statistical Techniques
Principal Components
Surface Water
Water Quality
title_short Water quality assessment using multivariate statistical techniques in Río Tercero Reservoir, Argentina
title_full Water quality assessment using multivariate statistical techniques in Río Tercero Reservoir, Argentina
title_fullStr Water quality assessment using multivariate statistical techniques in Río Tercero Reservoir, Argentina
title_full_unstemmed Water quality assessment using multivariate statistical techniques in Río Tercero Reservoir, Argentina
title_sort Water quality assessment using multivariate statistical techniques in Río Tercero Reservoir, Argentina
dc.creator.none.fl_str_mv Bonansea, Matias
Ledesma, Claudia
Rodriguez, Claudia
Pinotti, Lucio Pedro
author Bonansea, Matias
author_facet Bonansea, Matias
Ledesma, Claudia
Rodriguez, Claudia
Pinotti, Lucio Pedro
author_role author
author2 Ledesma, Claudia
Rodriguez, Claudia
Pinotti, Lucio Pedro
author2_role author
author
author
dc.subject.none.fl_str_mv Cluster Analysis
Monitoring
Multivariate Statistical Techniques
Principal Components
Surface Water
Water Quality
topic Cluster Analysis
Monitoring
Multivariate Statistical Techniques
Principal Components
Surface Water
Water Quality
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.5
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Water quality monitoring programs generate complex multidimensional data sets. In this study, multivariate statistical techniques were employed as an effective tool for the analysis and interpretation of these water quality data sets. Principal component analysis (PCA) and cluster analysis (CA) were applied to evaluate spatial and temporal variation of water quality in Río Tercero Reservoir (Argentina). Six sampling sites were surveyed each climatic season for 21 parameters during 2003-2010. The results revealed that PCA showed the existence of four significant principal components (PCs) which account for 96.7% of the total variance of the data set. The first PC was assigned to mineralization whereas the other PCs were built from variables indicative of pollution. Hierarchical CA grouped the six monitoring sites into three clusters and classified the different climatic seasons into two clusters based on similarities in water quality characteristics.
Fil: Bonansea, Matias. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina. Universidad Nacional de Río Cuarto. Facultad de Agronomía y Veterinaria; Argentina
Fil: Ledesma, Claudia. Universidad Nacional de Río Cuarto. Facultad de Agronomía y Veterinaria; Argentina
Fil: Rodriguez, Claudia. Universidad Nacional de Río Cuarto. Facultad de Agronomía y Veterinaria; Argentina
Fil: Pinotti, Lucio Pedro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina. Universidad Nacional de Río Cuarto. Facultad de Ciencias Exactas, Fisicoquímicas y Naturales. Departamento de Geología; Argentina
description Water quality monitoring programs generate complex multidimensional data sets. In this study, multivariate statistical techniques were employed as an effective tool for the analysis and interpretation of these water quality data sets. Principal component analysis (PCA) and cluster analysis (CA) were applied to evaluate spatial and temporal variation of water quality in Río Tercero Reservoir (Argentina). Six sampling sites were surveyed each climatic season for 21 parameters during 2003-2010. The results revealed that PCA showed the existence of four significant principal components (PCs) which account for 96.7% of the total variance of the data set. The first PC was assigned to mineralization whereas the other PCs were built from variables indicative of pollution. Hierarchical CA grouped the six monitoring sites into three clusters and classified the different climatic seasons into two clusters based on similarities in water quality characteristics.
publishDate 2015
dc.date.none.fl_str_mv 2015-06
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/69398
Bonansea, Matias; Ledesma, Claudia; Rodriguez, Claudia; Pinotti, Lucio Pedro; Water quality assessment using multivariate statistical techniques in Río Tercero Reservoir, Argentina; Nordic Association for Hydrology; Hydrology Research; 46; 3; 6-2015; 377-388
2224-7955
CONICET Digital
CONICET
url http://hdl.handle.net/11336/69398
identifier_str_mv Bonansea, Matias; Ledesma, Claudia; Rodriguez, Claudia; Pinotti, Lucio Pedro; Water quality assessment using multivariate statistical techniques in Río Tercero Reservoir, Argentina; Nordic Association for Hydrology; Hydrology Research; 46; 3; 6-2015; 377-388
2224-7955
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.2166/nh.2014.174
info:eu-repo/semantics/altIdentifier/url/https://iwaponline.com/hr/article-abstract/46/3/377/1000/Water-quality-assessment-using-multivariate?redirectedFrom=fulltext
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Nordic Association for Hydrology
publisher.none.fl_str_mv Nordic Association for Hydrology
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1844612985602965504
score 13.070432

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