Quantitative Source Apportionment and Uncertainty Analysis of Heavy Metal(loid)s in the Topsoil of the Nansi Lake Nature Reserve

Abstract

There are numerous coal mines around the Nansi Lake Provincial Nature Reserve, and the mineral resources are extremely rich. Therefore, it is necessary to effectively assess the impact of mining activities on the topsoil. Based on a focused investigation of the contamination status and ecological risks of the Nansi Lake Nature Reserve assisted by GIS, principal component analysis was combined with positive matrix factorization to quantitatively identify the sources and contributions of heavy metal(loid)s in the topsoil before conducting uncertainty analysis. The results showed that coal mining caused higher Cu, Zn, and As contamination levels, while Hg and Cd had higher eco-toxicity and biological sensitivity. Meanwhile, principal component analysis (PCA) and positive matrix factorization (PMF) modeling displayed that Hg (59.2%) was primarily generated by industrial sources (fossil fuel combustion and mercury-containing wastewater); As (70.2%), Ni (65.6%), Cr (63.5%), Pb (61.3%), Cu (60.3%), and Zn (55.8%) were generated mainly from coal mining and processing, coal fossil fuel combustion, and coal gangue dumps; Cd (79.8%) came mainly from agricultural sources. Through uncertainty analysis, the contribution of contamination sources to the heavy metal(loid)s in the topsoil, as estimated by the PMF model, was shown to be quite different. Moreover, heavy metal(loid)s with lower contributions had errors in source identification and factor quantification. This study innovatively warned management to control the hazards of heavy metal(loid)s caused by mining engineering to protect the environment of the Nansi Lake Nature Reserve and revealed the potential harmful pathways of heavy metal(loid)s.

Analysis of the Source Apportionment and Pathways of Heavy Metals in Soil in a Coal Mining Area Based on Machine Learning and an APCS-MLR Model

“…In addition, there is a provincial road in the study area with frequent traffic activities. Based on actual investigations and data collection around the study area, the unknown source might be related to coal combustion [43][44][45], automobile exhaust emissions [46][47][48], tire wear [49], and household waste (such as batteries) [50], which are potential sources of HMs according to previous studies.…”