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dc.contributor.author
Bonansea, Matias
dc.contributor.author
Ledesma, Claudia
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Rodriguez, Claudia
dc.contributor.author
Pinotti, Lucio Pedro
dc.date.available
2019-02-05T14:57:17Z
dc.date.issued
2015-06
dc.identifier.citation
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
dc.identifier.issn
2224-7955
dc.identifier.uri
http://hdl.handle.net/11336/69398
dc.description.abstract
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.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Nordic Association for Hydrology
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.subject
Cluster Analysis
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Monitoring
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Multivariate Statistical Techniques
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Principal Components
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Surface Water
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Water Quality
dc.subject.classification
Meteorología y Ciencias Atmosféricas
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Ciencias de la Tierra y relacionadas con el Medio Ambiente
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CIENCIAS NATURALES Y EXACTAS
dc.title
Water quality assessment using multivariate statistical techniques in Río Tercero Reservoir, Argentina
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2019-02-04T13:11:48Z
dc.journal.volume
46
dc.journal.number
3
dc.journal.pagination
377-388
dc.journal.pais
Reino Unido
dc.description.fil
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
dc.description.fil
Fil: Ledesma, Claudia. Universidad Nacional de Río Cuarto. Facultad de Agronomía y Veterinaria; Argentina
dc.description.fil
Fil: Rodriguez, Claudia. Universidad Nacional de Río Cuarto. Facultad de Agronomía y Veterinaria; Argentina
dc.description.fil
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
dc.journal.title
Hydrology Research
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/https://dx.doi.org/10.2166/nh.2014.174
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://iwaponline.com/hr/article-abstract/46/3/377/1000/Water-quality-assessment-using-multivariate?redirectedFrom=fulltext
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