Review of existing heavy metal contamination indices and development of an entropy-based improved indexing approach

Abstract

The present study aims at providing a novel technique for assessment of heavy metal contamination in a water body employing information entropy. For this purpose, 8 different locations, each from four tributaries of Brahmaputra river, Beki, Manas, Baralia and Pagladia, were chosen. Water samples from these tributaries were collected and analyzed for iron (Fe), manganese (Mn), lead (Pb), chromium (Cr), zinc (Zn) and copper (Cu) using atomic absorption spectroscopy (AAS). Information (Shannon) entropy was employed to assign weights to each heavy metal which was then coupled with the sub-indices to evaluate the entropy weighted heavy metal contamination index (EHCI). Spatial trend of EHCI values indicated that the water quality of Beki and Baralia river was either “excellent” (EHCI 100). Similar trend was observed for Pagladia river. A comparative approach was also carried out among all the heavy metal contamination indices to decide the efficacy of EHCI. The results indicated major conflicts between the indices at various sampling locations, and thus, it was evident from the water quality dataset that EHCI provided a better insight to the classification of water quality with respect to heavy metals. The study would thus be of significant aid in scientific research of stream restoration and water treatment strategies.