Application of multivariate geostatistics in the investigation of heavy metal contamination of roadside dusts from selected highways of the Greater Toronto Area, Canada

A good understanding of roadside soil contamination and the location of pollution sources is important for addressing many environmental problems. The results are reported here of an analysis of the content of metals in roadside dust samples of four major highways in the Greater Toronto area (GTA) in Ontario, Canada. The metals analyzed are Pb, Zn, Cd, Ni, Cr, Cu, Mn, and Fe. Multivariate geostatistical analysis correlation analysis (CA), principal component analysis (PCA), and hierarchical cluster analysis (HCA)] were used to estimate soil chemical content variability. The correlation coefficient shows a positive correlation between Cr–Cd, Mn–Fe, and Fe–Cu, while negatively between Zn–Cd, Mn–Cd, Zn-Cr, Pb–Zn, and Ni–Zn. PCA shows that the three eigenvalues are less than one, and suggests that the contamination sources are processing industries and traffic. HCA classifies heavy metals in two major groups. The cluster has two larger subgroups: the first contains only the variables Fe, Mn, Cu, Cr, Ni, and Pb, and the second includes Cd and Zn. The geostatistical analysis allows geological and anthropogenic causes of variations in the contents of roadside dust heavy metals to be separated and common pollution sources to be identified. The study shows that the high concentration of traffic flows, the parent material mineralogical and chemical composition, and land use are the main sources for the heavy metal concentration in the analyzed samples.