Application of multivariate statistical techniques in hydrogeochemical studies--a case study: Brahmani-Koel River (India).

This study presents the usefulness of multivariate statistical techniques, such as correlation matrix, cluster analysis, and factor analysis, for the evaluation and interpretation of complex water quality data sets of Brahmani-Koel river along the Rourkela Industrial Complex, India, and the apportionment of pollution sources/factors. The correlation study suggests that dissolved heavy metals, biochemical oxygen demand (BOD), and chemical oxygen demand (COD) are contributed by anthropogenic sources. The results of R-mode factor analyses revealed that anthropogenic contributions are responsible for increase in metals of the river water, which is mainly responsible for contamination of the river. It also reflected that the level of pollution in the river was very high. The factor score plot and loading plot have been drawn, which indicate that the polluted stations are identified by the heavy metals. The relationships among the stations are highlighted by cluster analysis, represented in dendograms to categorize different levels of contamination. An attempt has been made to study the degree of contamination of the river waters by using a tool like enrichment ratio (ER). The ER for heavy metal concentrations concluded that metals like Ni, Co, Cr, and Fe are showing high enrichment with respect to global background and metal ions like Fe, Mn, Cu, and Zn show high enrichment with respect to local background.