Multivariate analysis for discriminating profiles of soil heavy metals as influenced by various contamination sources

Abstract

Discriminating between different sources of anthropogenic heavy metal contamination is a topic of scientific interest. The main objective of this study was to demonstrate the efficiency of multivariate statistical methods to provide identification and differentiation of soil heavy metals as affected by three representative contamination sources including traffic, coal-burning power plant and cement plant. A total of 76 soil samples were collected, and the concentrations of 11 heavy metals including Cr, Mn, Fe, Co, Ni, Cu, Zn, Cd, Pb, Sb and Mo were determined by inductively coupled plasma–mass spectrometry (ICP–MS). Analysis of variance (ANOVA) showed that heavy metal concentrations including Cr, Mn, Fe, Co, Ni, Cu, Sb and Mo differed across the sites (p < 0.01). Principal component analysis (PCA) was applied to reduce variables, and score plots indicated that soils from the three sites were not distributed separately fully from each other. Discriminant analysis (DA) yielded an overall classification rate of 98.7 %. Copper, Mn, Fe, Ni, Cd, Sb and Mo contributed most to the discriminant function. Our study could be used as a case to provide the usefulness of discriminant analysis in discriminating contamination sources of soil heavy metals in the present study area.