The index was applied to the data set on water quality of the Potrero de los Funes River (San Luis, Argentina), generated during 2 years (2009-2010). Following the RWQI values classification, most of the Potrero de los Funes water samples fell in the good quality range during the study period.

Water pollution caused by organic matter is a major global problem which requires continuous evaluation. Multivariate statistical analysis was applied to assess spatial and temporal changes caused by natural and anthropogenic phenomena along Potrero de los Funes River. Cluster analysis (CA), principal component analysis (PCA) and analysis of variance (ANOVA) were applied to a data set collected throughout a period of 3 years (2010-2012), which monitored 22 physical, chemical and biological parameters. Content of dissolved oxygen in water and biochemical oxygen demand in a watercourse are indicators of pollution caused by organic matter. For this reason, the Streeter-Phelps model was used to evaluate the water self-purification capacity. Hierarchical cluster analysis grouped the sampling sites based on the similarity of water quality characteristics. PCA resulted in two latent factors explaining 75.2 and 17.6 % of the total variance in water quality data sets. Multidimensional ANOVA suggested that organic pollution is mainly due to domestic wastewater run-offs and anthropogenic influence as a consequence of increasing urbanization and tourist influx over the last years. Besides, Streeter-Phelps parameters showed a low reaeration capacity before dam with low concentration of dissolved oxygen. Furthermore, self-purification capacity loss was correlated with the decrease of the Benthic Index. This measurement suggested that biological samplings complement the physical-chemical analysis of water quality.

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