# Analysis of Heavy Metal Sources in Xutuan Mining Area Based on APCS-MLR and PMF Model
## 1 Introduction  
Although mineral resources have greatly contributed to economic development worldwide, they have caused a series of environmental issues [1]. Mining activities are one of the main sources of heavy metal pollution in the environment [2]. In the process of mining and smelting of mineral resources, heavy metals can be transported to the surrounding environment through atmospheric sedimentation, surface water transport (including acid mine drainage), and groundwater seepage [3]. In fact, heavy metals, with different chemical states, can migrate, transform, and accumulate in environmental media (water, soils, and sediments) through coal mining activities, causing environmental contamination or pollution [4, 5]. These impacts cannot only affect ecological environments but also endanger human health through the food chain and food web [3]. Heavy metals in water bodies can also gradually migrate and accumulate in sediments through various physicochemical processes. Most of the heavy metals in water are trapped in sediment by co-precipitation with oxides, cation exchange, particle surface adsorption, and the assimilation of organic matter [6, 7]. These pollutants are difficult to biodegrade and may cause direct or indirect harm to organisms [8]. The environmental behaviors and toxicity levels of heavy metals in sediments are related mainly to their total amounts and chemical forms. In addition, the transfer, transformation, toxicity, and potential environmental hazards of heavy metals in sediments depend mainly on their forms. Specifically, changes in sediment-water interfacescan consequently release some weakly bound heavy metals from the surfaces of sediments into water bodies, leading to the occurrence of secondary pollution [9; 10]. In recent years, many scholars have focused on heavy metals in mining areas. [PERSON] et al. [11] highlighted significantly higher heavy metal pollution levels in river sediments near the Luanchuan mine than those in adjacent water bodies. [PERSON] et al. [12] showed serious soil heavy metal pollution in an abandoned coal mine area, with significantly higher Cd contents than those of other heavy metals. [PERSON] et al. [13] found moderate to severe total heavy metal amounts in farmland soils near abandoned mining areas in Guangdong Province, China. In addition, [PERSON] [14] found that farmland soils in a coal mine area in Bangladesh were contaminated by heavy metals to varying degrees, showing clear biological poisoning. However, there is still a lack of unified standards for comprehensively assessing environmental risks associated with heavy metals in water and sediments. Moreover, the different assessment methods used in previous related studies have resulted in different evaluation results. Each method has its advantages and limitations, making the application of a single evaluation method inappropriate for providing comprehensive assessments of heavy metal pollution. Hence, it is crucial to conduct comprehensive assessment studies by combining multiple appropriate methods. Numerous studies have assessed heavy metal contamination and pollution levels in China and other regions worldwide using different parametric methods, such as the geological accumulation index method [15], comprehensive pollution index method [11], risk assessment coding method [16], and potential ecological risk index method [17]. Other studies have shown that the positive matrix factorization (PMF) model [18; 19], isotopic tracer method [20], and the Unmix model [21; 22] can identify and analyze the sources of heavy metals in environmental media. The Unmix model supports uncertainty analysis but requires high sample data quantity, and high dimensional data need to be reduced. Isotope tracing can be conducted directly but is expensive. Traditional multivariate statistical methods, such as PCA and FA, can only qualitatively predict potential sources of pollution. The APCS-MLR and PMF models can realize the leap from qualitative to quantitative pollution sources.