Multivariate statistical analysis of heavy metals in soils of a Pb–Zn mining area, India

Abstract

Surface soil samples collected from a Pb and Zn mining area in India were subjected to multi-elemental analysis by using inductively coupled plasma-atomic emission spectrometry. Multivariate statistical methods such as principal component analysis and cluster analysis, coupled with correlation coefficient analysis, were used to analyze the data and to apportion the possible sources of elements in soils of a metal mining area. Soils in this area have elevated heavy metal concentrations especially Pb, Zn, Mn, Cu, As, and Tl. Using principal component (PC) analysis, six components were extracted, out of which two PCs explaining 50.12% of total variance are more important. The first principal component with a high contribution of Ag, As, Be, Cd, Co, Cu, Mg, Mn, Ni, Pb, and Zn was deemed to be technogenic/anthropogenic component, and the second principal component, with high loadings for the five discerning variables (Al, Be, Cr, K, Li), was considered as lithogenic component. The third component having strong loadings of Ba, Ca, K, and Na is supposed to have a mixed origin (lithogenic as well as technogenic). Electrical conductivity and total organic matter were not correlated with any element and also have a strong loading in the fifth component which is probably the biomass and ions present in these soils. The findings of the principal component analysis were also substantiated by the cluster analysis. The present study would not only enhance our knowledge regarding the soil pollution status in the study area but would also provide us information to manage the sources of these elements in the study area.

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