# Analysis of Heavy Metal Sources in Xutuan Mining Area Based on APCS-MLR and PMF Model
## 3 Results and Discussion
### Analysis of the Potential Sources of the Sediment Heavy Metal Elements
#### 3.4.3 PMF-Based Source Analysis  
In this study, a PMF quantitative source analysis was further performed to understand the sources of the heavy metals in the mining areas using EPA PMF5.0 software. The numbers of operations and source factors were set to 20 and within the 3-5 range, respectively. The four-factor solution was determined from the minimum stable Q value, with most residuals between \(-\)3 and 3. The signal-to-noise ratio (S/N) \(>\) 8 was considered strong, indicating the rationality of the model [49]. The coefficient of determination (R\({}^{2}\)) values of all the heavy metal elements were greater than 0.9, except for Zn (R\({}^{2}\) = 0.4). The contribution rates of Factor 1 to Cr, Zn, Pb, and Cu were relatively high, reaching 31.75, 28.88, 28.57, and 28.27%, respectively (Figure 6). The average sediment values in the Cr, Zn, Pb, and Cu contents were 51.89, 37.29, 16.42, and 16.44 mg/kg, which are lower than the corresponding local background values of 60.6, 47.3, 24.6, and 22.6 mg/kg, respectively. The Pearson correlation analysis results indicated that Cu, Zn, and Cr might come from the same pollution source. In addition, the correlations between the heavy metal elements also suggested the impacts of soil parent materials [50]. Therefore, Factor 1 was related mainly to the natural sources of the heavy metal elements. The contribution rate of Factor 2 to Cd was 57.92%. The results indicated higher Cd contents than the corresponding local background value (Table 2). Moreover, the I\({}_{\text{geo}}\) and RAC results suggested a strong pollution level and a moderate risk associated with this heavy metal, respectively (Figure 4). The study area was located in coal mining subsidence zones. During the mining process, a large amount of Cd-containing wastewater can flow into water bodies and accumulate in sediments [51; 52]. Therefore, it can be concluded that Factor 2 was related to the anthropogenic sources in the study area, particularly coal mining activities. The contribution rate of Factor 3 to As was 36.13%, which is in line with the relatively high sediment contents of this heavy metal element. The I\({}_{\text{geo}}\) and RAC results indicated a moderate pollution level and a low risk associated with As in the study area. Numerous studies have highlighted the abundance of As in pesticide and fertilizer products, with volatilization and migration characteristics [53; 54]. Indeed, the long-term applications of As-containing nitrogen (N) fertilizers, phosphate fertilizers, insecticides, herbicides, and other pesticide products may result in the gradual accumulation of As in soils. The study area is, in fact, surrounded by multiple agricultural fields. The accumulated As in agricultural soils can subsequently migrate into water bodies and sediments through surface runoff. Therefore, Factor 3 can be related to agricultural pollution sources. The contribution rate of Factor 4 to Pb was 36.83%. The main sources of Pb pollution in the study area are oil leakage from machinery and equipment through coal mining activities, as well as automobile exhaust emissions and tire wear [55; 56; 57]. The study area is characterized by the presence of a surrounding coal mine haulage road, with heavy and frequent traffic flows, promoting the accumulation of Pb in the research area. Therefore, it can be concluded that Factor 4 is related to heavy metal pollution caused by traffic. According to the PMF modeling results, the contribution rates of the natural sources, coal mining activities, agricultural activities, and traffic sources to the sediment heavy metal contents in the study area were 25.1, 26.4, 21.44, and 27.05%, respectively. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Components** & **Cu** & **Cr** & **Zn** & **As** & **Pb** & **Cd** & **Contributing Percentage \%** \\ \hline
1 & 0.942 & 0.926 & 0.909 & 0.855 & 0.728 & 0.617 & 70.247\% \\
2 & \(-\)0.217 & \(-\)0.184 & \(-\)0.066 & \(-\)0.315 & 0.405 & 0.663 & 83.376\% \\ \hline \end{tabular}
\end{table}