Assessment of heavy metal pollution in a Himalayan river using multivariate statistical tools: a case study of the Kameng River in Arunachal Pradesh, India

Kameng River is a high-altitude fluvial system that originates in the snow-clad mountain ranges of the eastern Himalayas in the state of Arunachal Pradesh of India. In the present research, the spatial and seasonal distributions of selected heavy metals (Fe, Cd, Cu, Mn, Pb, Zn, and As) were studied to assess their pollution status within the river basin. This study reports the first systematic evaluation of heavy metals in the Kameng River within the geographical boundary of Arunachal Pradesh. The Kameng River is a major source of water to the indigenous communities residing within the river basin. In recent years, some serious debates have been going on the issues surrounding hydro-energy exploration in the river basin. Construction of large- and medium-scale hydroelectric projects is likely to fragment the entire river course and alter the environmental flow of the river that would invariably affect the water quality regime. In order to predict the future changes in the water quality parameters, it is important to carry out water quality studies at present. In the present study, water samples were collected from 27 locations representing four sub-watersheds within the Kameng River basin. The study revealed that the river water was polluted by Cd, Pb, Mn, and Zn with respect to the safe limit prescribed by regulatory authorities. The multivariate statistical analysis using principal component analysis (PCA) and hierarchical cluster analysis (HCA) tools suggested that the concentration of the heavy metals was primarily controlled by geochemical factors that included the dissolution of phosphate rocks, weathering of carbonate rocks, and base metal mineralization. Further, the solubility mechanics of these metals were controlled by processes of co-precipitation, adsorption, and ion precipitation on pre-existing mineral surfaces. The study also indicated the contribution of heavy metals from anthropogenic sources that primarily included the leaching of pesticides from agricultural soils. This study illustrates the successful application of multivariate statistical tools in understanding the distribution of heavy metals in river basins.