A comprehensive exploration of risk assessment and source quantification of potentially toxic elements in road dust: A case study from a large Cu smelter in central China

Abstract Large-scale industrial activities emit large amounts of potentially toxic elements (PTEs) in the form of particulate pollutants. Most of these pollutants settle on the road and eventually mix into the road dust. In the long-term, they will have adverse effects on ecosystems and human health. In this research, various analytical methods were used to assess the pollution characteristics of the 12 PTEs (Mn, Ni, Cu, Zn, Hg, Cd, As, Cr, Pb, Tl, Co, and Sb) in the study area, and geo-statistics served to analyze the spatial distribution characteristics of PTEs. Combining the potential ecological risk method and health risk assessment with positive matrix factorization (PMF) from different sources entailed a quantitative evaluation. Taking a large Cu smelter in central China as an example, three sources were quantitatively apportioned, these being smelting emissions and transportation activities (54.04%), daily human activities and natural existence (33.36%), as well as coal-fired activities of industrial production (12.60%) separately. The results showed that in more than 90% of the samples, Cd, Cu, Hg, Pb, As, Zn, Tl and Sb indicated high levels of contamination. In terms of spatial distribution, the high-value PTEs were mainly distributed near the smelter. Regarding ecological risk, smelting emissions and transportation activities were the greatest source, followed by coal-fired activities. As for human health risk, adults did not have significant non-carcinogenic risk, but children had obvious non-carcinogenic risk. Among different sources, the risk of smelting emissions and transportation activities were the highest. After calculating the value of carcinogenic risks, it emerged there was no obvious carcinogenic risk for children and adults in the study area. The comprehensive approach combining risk assessments with source identification is very effective in identifying prior pollutants and important sources of pollution. They can provide a good theoretical reference for effective prevention and control of pollution.