Artículo
Water quality assessment using multivariate statistical techniques in Río Tercero Reservoir, Argentina
Fecha de publicación:
06/2015
Editorial:
Nordic Association for Hydrology
Revista:
Hydrology Research
ISSN:
2224-7955
Idioma:
Inglés
Tipo de recurso:
Artículo publicado
Clasificación temática:
Resumen
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.
Archivos asociados
Licencia
Identificadores
Colecciones
Articulos(CCT - CORDOBA)
Articulos de CTRO.CIENTIFICO TECNOL.CONICET - CORDOBA
Articulos de CTRO.CIENTIFICO TECNOL.CONICET - CORDOBA
Citación
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
Compartir
Altmétricas