Identification of heavy metal pollution sources and its associated risk assessment in an industrial town using the K-means clustering technique

Abstract

This study intends to (i) identify the potential sources of heavy metal (HM) pollution in an industrial town situated in the northwestern part of Iran and (ii) and assess whether the pollution levels discharged by HMs are significant. A suite of geochemical samples collected from the topsoil horizon of the study area were analyzed for HMs, namely Cr, Ni, Co, Cu, V, Zn, Mn, and Pb, to attain the objectives mentioned above. Compositional data analysis (CODA) techniques were employed for pinpointing the potential source of HM pollution. Initially, the centered log-ratio (clr) transformation was applied to the dataset containing raw values of chemically analyzed soil samples to address the data closure problem. Multifractal moving average interpolation was then applied to the clr-transformed data to model the spatial distribution of HMs in the study area. Maps of the spatial distribution of HMs in the study area were further classified by the concentration-area (C-A) fractal technique for identifying the potential hotspots of HM pollution in the area. It was followed by using the K-means clustering technique for classifying samples into distinct classes. This method was selected for geochemical pattern recognition owing to its simplicity and robustness in identifying subtle patterns embedded in geochemical data. The above hybrid methodology points to three anthropogenic sources discharging HM pollution into the area: leather factories, brickworks, and highways. Finally, pollution indices, namely the geo-accumulation index, pollution load index, and potential ecological risk, revealed that the elevated levels of Ni, Cr, and Co can pose serious impediments to the soil quality of the study area.