A combined model to quantitatively assess human health risk from different sources of heavy metals in soils around coal waste pile

Abstract

The heavy metals of soils around coal waste pile at Taoyuan coal mine (in Anhui province, China) has exceeded provincial’s background values, posing a serious threat to residents in the mine area. Quantitative evaluation on human health risks (HHR) from different sources of heavy metals can determine priority sources and help to reduce risks. Statistics, spatial distribution, and positive matrix factorization (PMF) were applied to identify contribution of sources, and HHR from different sources were quantitatively calculated by combining the HHR with PMF model. In this study, 37 samples were collected from topsoil (0–20 cm) around coal waste pile. Four sources were apportioned as follow: industrial activities (48.37%), transportation (22.44%), natural source (15.44%), and leaching from coal waste pile (13.70%). As for carcinogenic risk, industrial activities was the priority source. Moreover, the noncarcinogenic risk and carcinogenic risk for children were obviously higher than adults, and the main exposure pathway for adults and children was similar, mainly from oral ingestion. The combined model was effective to assess HHR quantitatively from different sources, thereby the outcomes of the study provide a better understanding and suggestions for pollution control and human health from sources.

Keywords:

• Heavy metals
• coal waste pile
• quantitative evaluation
• priority pollution sources
• PMF-HHR model

Disclosure statement

No potential conflict of interest was reported by the authors.

Authors’ contributions

J.X. conceived and wrote this paper; H.G. revised the manuscript and collected samples; J.C. and C.L. provided technical assistance of graphs; C.Z., Y.L., J.C., and C.L. sampled and performed the experiments.

Additional information

Funding

This research was funded by the Key natural science research projects of Suzhou University [grant numbers: 2020yzd03 and 2020yzd07], National Natural Science Foundation of China [grant number: 41773100] and Funding projects for research activities of academic and technological leaders of Anhui Province [grant number: 2020D239].