Pollution characteristics, spatial distribution, and source identification of heavy metals in road dust in a central eastern city in China: a comprehensive survey

Road dust enriched with heavy metals (HMs) is detrimental to ecosystems and human health in urban environments. In this study, it is to explore the concentrations, spatial distribution, contaminated levels, and source identification of six HMs (lead (Pb), zinc (Zn), copper (Cu), cobalt (Co), chromium (Cr), and nickel (Ni)) based on 130 road dusts in Xinyang urban area. The results indicated that the contents of Pb, Zn, Cu, and Co were higher than the background values in more than 99% of the samples, and their average concentrations were 15.2, 9.2, 8.6, and 6.3 times the background value, respectively. The spatial distribution of high-value areas for Pb, Zn, Cu, Cr, and Ni was more similar, which was associated with traffic density near major roads and population and settlement patterns. Co was relatively different from the five elements, which was distributed in the areas of residence, commerce, and industry. Furthermore, the investigated HMs were clearly polluted, with Pb, Zn, Cu, and Co indicating high levels of contamination, while Cr and Ni were moderately polluted. The comprehensive pollution of the six HMs was mostly moderate to heavy in this study. Moreover, three sources of HMs designated by correlation analysis (CA) and principal component analysis (PCA) were mixed traffic emissions and industrial waste for Cu and Cr; automotive emissions for Pb, Ni, and Zn; and mixed domestic waste and industrial activities for Co, with contributions of 42.3%, 46.4%, and 11.3% via the principal component analysis-multiple linear regression (PCA-MLR) model. The multi-factor index for pollution assessment combined with source identification is extremely effective and practical for providing reliable data support and a theoretical reference for pollution monitoring and governance.