Application of multivariate statistical approach to identify heavy metal sources in bottom soil of the Seyhan River (Adana), Turkey

In this study, freshly deposited soils were sampled from the Seyhan River (Turkey) from the exit of the Seyhan Dam to the Adana exit. Heavy metal contents were measured with X-ray fluorescence spectrometer. Multivariate statistical approach is used to identify the sources of heavy metals and other elements in soil samples. Considering the size of anomalies, metals are ranked as Co>Pb>Cr>Zn>Al. Based on the hierarchical cluster analysis results, three clusters were observed. P, Mg, Ti, Fe, Ca, Na, K, Al, Si, and Nb form the first cluster, Zn, Sr, Pb, and Cr associated as the second cluster, and Ba and Co form the third cluster. Three factors computed from principal component analysis are explained with a cumulative variance of 95%. The first factor is defined with “high background lithogenic factor” Co, the second factor with “local industrial factor” Pb, Cr, Ba, and Mg, and the third factor with “natural factor” Cr and Pb.