Heavy Metal Contamination of Surface Sediments-Soil Adjoining the Largest Copper Mine Waste Dump in Central India Using Multivariate Pattern Recognition Techniques and Geo-Statistical Mapping

This detailed study assessed heavy metal contamination of sediments/soil near central India’s largest copper mining area using 38 sampling sites within 10 km of the mine using atomic absorption spectroscopy. This study utilized multivariate pattern recognition methods, namely hierarchical clustering analysis (HCA) and principal component analysis (PCA), for source identification. Twelve parameters, i.e., copper (Cu), manganese (Mn), cobalt (Co), zinc (Zn), nickel (Ni), lead (Pb), organic matter (OM), cation exchange capacity (CEC), soil pH, distance (D), and elevation (E) were analyzed. The hierarchical cluster analysis (HCA) was used to analyze the sample sites with similar metal contamination and principal component analysis (PCA) was used to analyze the relationship between the parameters as well as to identify sources of heavy metal pollution. Three major pollution hotspots were detected by AHC and were classified as unpolluted/low pollution sites (UPS: mean concentration factor of 1.35 for Cu), highly polluted sites (HPS: mean concentration factor of 22 for Cu), and extremely polluted sites (EPS: mean concentration factor of 74 for Cu). PCA revealed three hidden factors/components, namely PC1 (explaining 38% of the variability), PC2 (18% of the variability), and PC3 (14% of the variability). Metals showed strong positive loading in PC1, explaining the highest variability. The mean content of Cu in soil/sediment samples was 502.526 mg/kg. The mean copper content was 10 times higher than the natural crustal value of 45mg/kg, indicating severe pollution in several sites around the study area. Mapping of copper contamination was conducted to reveal the spatial distribution of copper contamination using QGIS. This study exposes the heavy metal contamination level in surface sediments/soil and the effectiveness of pattern recognition techniques for the assessment of multivariate datasets in discerning spatial disparities and identifying the contamination causes.