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Application of multivariate statistical techniques to evaluate organic pollution on a river in Argentina

Author(s): Soledad Oliva González | César A. Almeida | Sylvia Quintar | Miguel A. Mallea | Patricia S. González

Journal: Ambiente e Água : An Interdisciplinary Journal of Applied Science
ISSN 1980-993X

Volume: 6;
Issue: 3;
Start page: 27;
Date: 2011;
Original page

Keywords: Water quality | organic pollution | multivariate techniques | cluster analysis | principal component | discriminant analysis

The aim of this paper was the application of multivariate statistical techniques to evaluate spatial and temporal variations in the water quality of Potrero de los Funes River using physical, chemical and bacteriological parameters and select the most significant parameters of organic pollution in the river in order to implement in the future water quality monitoring. The river was monitored regularly at three sites: RP1, RP2 and RP3, over the period 2008–2009, for 16 parameters. The complex data matrix was treated with three multivariate statistical techniques: cluster analysis (CA), principal component analysis (PCA) and discriminant analysis (DA). CA generated three groups of sites, cluster 1 (RP1), cluster 2 (RP2) and cluster 3 (RP3) according to relatively low, very high and moderate pollution regions, respectively. PCA identified two components, which were responsible for the data structure explaining 73% of the total variance of the data matrix. Temporal DA (Wet season and Dry season) showed that turbidity, NO3- and COD were the discriminant variables. Spatial DA shows that there were significant differences between the three categorical classes, 1 (RP1, low pollution region), 2 (RP2, strongly polluted zone) and 3 (RP3, moderate polluted site) .The discriminating functions contained only eight parameters (EC, NO3-, turbidity, DO, BOD, COD, total coliform and fecal coliform) to discriminate between sites. The application of these techniques has achieved meaningful classification of physical, chemical and bacteriological variables and of river water samples, based on seasonal and spatial criteria. This study is essential for the future design of fast and effective monitoring programs of river water quality. That would include only parameters that are indicative of organic pollution.
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