An integrated approach for quantifying source apportionment and source-oriented health risk of heavy metals in soils near an old industrial area

Abstract

Soil contamination of heavy metals (HMs) caused by the long-term industrial activities has become a major environmental issue due to its adverse effects on human health and ecosystem. In this paper, 50 soil samples were analyzed to evaluate the contamination characteristics, source apportionment and source-oriented health risk of HMs in soils near an old industrial area in NE China by applying an integrated approach of Pearson correlation analysis, Positive matrix factorization (PMF) model and Monte Carlo simulation. The results showed that the mean concentrations of all HMs greatly exceeded the soil background values (SBV), and the surface soils in the study area were heavily polluted with HMs, displaying a very high ecological risk. The toxic HMs emitted from the bullet production were identified as the primary source of HMs contamination in soils, with a contribution rate of 33.3%. The human health risk assessment (HHRA) suggested that the Hazard quotient (HQ) values of all HMs for children and adults are within the acceptable risk level (HQ < 1). The carcinogenic risk (CR) values of HMs for children and adults significantly exceeded the acceptable threshold of 1E-6 with a basic trend: As > Pb > Cr > Co > Ni, indicating a high cancer risk. For source-oriented health risk, the CR of four pollution sources for children and adults shows a same trend: Factor 4 > Factor 3 > Factor 2 > Factor 1. Among those, the source of HMs pollution from bullet production is the largest contributor to cancer risk, and As and Pb are the most important HMs pollutants that cause cancer risk to humans. The present study sheds some light on the contamination characteristics, source apportionment and source-health risk assessment of HMs in industrially contaminated soils, which helps improve the management of environmental risk control, prevention and remediation.

Keywords: Heavy metals; Monte Carlo simulation; Positive matrix factorization model; Probabilistic health risk assessment; Soil contamination; Source apportionment.