Source Identification of Heavy Metals in River Sediments by Using Factor Analysis in Combination with K-Means Cluster Analysis

Source identification of heavy metals in river sediments and spacial polluted sample separation are important for either river system protection or remediation. In this study, concentrations of five heavy metals (Fe, As, Cr, Cu, Pb) in the sediments from Bianhe River, northern Anhui Province, China have been measured and analysed by factor and cluster analysis for tracing their sources. The results suggest that there are three kinds of sources for these metals: natural, anthropogenic one and two. Fe is mainly contributed by natural source, 14 points are mainly polluted by As and Cr and four points are polluted by Cu and Pb. In comparison with the location of sampling, the anthropogenic As and Cr are mainly supplied by urban activities, whereas the anthropogenic Cu and Pb are mainly related to traffic, to a lesser extent, point pollution. The study demonstrated that a combination use of factor and K-means cluster analysis can provide reliable information for identifying the source of heavy metals and the location with specific pollution.