Abstract

This study aimed to determine seasonal (summer vs. winter) and spatial distribution of the selected heavy elements (HEs) (As, Cd, Co, Cr, Cu, Ni, and Pb) in soil samples collected from a typical Central European town, Novi Sad, Serbia. The highest Pb concentrations were observed in summer because of intensive ground-flushing, whereas Cu had its highest concentration in winter, which may be attributed to traffic emissions. Source characterization and spatial distribution were carried out using cluster analysis (CA), principal component analysis (PCA), variogram calculation and theoretical model fitting, GIS-based geostatistical methods, and positive matrix factorization (PMF) data processing tools. Environmental impact of HEs found in different functional areas demonstrated that the quantified concentrations of Pb, As, Co, and Cu ranked soil as unpolluted to moderately polluted, while the presence of Co, Ni, and Cr classified urban soil as moderately polluted. Pollution load index (PLI) suggested a significant HEs enrichment while the new modified approach to Nemerow integrated risk index (NIRI) indicated high risk, being extreme for some selected locations. Machine learning classifiers were used for the first time to identify the differences between urban soil and dust samples in situations when simultaneous analysis of both matrices was carried out, as well as for temporal distribution (summer versus winter), based on the obtained concentration of HEs. Variogram calculation suggested that the pattern in spatial variability within the system emerged from the combined action of key structural factors (e.g., the parent soil material, landforms and topography, and climate) and random factors related to human activities. The estimated human health risk for two segments of the population revealed that ingestion is the primary route of exposure to HEs for children and adults.

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