# 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
#### 3.4.2 APCS-MLR-Based Source Analysis  
SPSS was used to conduct a principal component analysis of the standardized sediment heavy metal data. KMO and Bartlett tests show that the KMO value is 0.791, and the significance level of the Bartlett test is 0.01. Based on the principle that the eigenvalue is greater than 1, two principal components were extracted by the principal component analysis, and the cumulative variance contribution rate was 83.376%.  
As can be seen from Table 5, PC1 explains the variance of 70.247%, in which Cu(0.942), Cr(0.926), Zn(0.909), and As(0.855) have higher positive loads. The results of the correlation analysis showed that Cu, Cr, Zn, and As may come from the same source, which is consistent with the results of the principal component analysis. According to the Igeo results, the Igeo values of Cu, Cr, and Zn elements are less than 0, which is at the clean level, which indicates that they are not significantly polluted. In summary, we believe that Cu, Cr, As, and Zn are less affected by human activities, so Factor 1 is judged to be a natural source. PC2 explains 13.129% of the variance, and Pb(0.405) and Cd(0.663) have higher loads. Cu, Cr, Zn, and As elements have lower loads. The PCA analysis results are basically consistent with those of [PERSON] et al. [47]. Cd in the study area has strong variability and a significant difference in spatial distribution, indicating that it is seriously disturbed by human activities. Studies have shown [48] that the external sources of Cd are mainly industrial sources and fossil fuel combustion, automobile exhaust and traffic dust, and pesticides and fertilizers. The main sources of Pb are gas oil burning, engines, and tire friction. So, Factor 2 is judged to be an industrial source. Based on the APCS-MLR model, the two principal component factor scores of the principal component analysis were converted into absolute principal component factor scores, and multiple linear regression was performed with the contents of six the heavy metals. The correlation coefficient (R\({}^{2}\)) is used to measure the correlation between the model and the actual observed value. The closer the value is to 1, the higher the linear fit and the better the simulation result. The correlation coefficients (R\({}^{2}\)) of Cu, Cr, Cd, Zn, As, and Pb were 0.825, 0.729, 0.627, 0.496, 0.689, and 0.476, respectively. Except for Zn and Pb, the elements fit well. According to the common formula in the APCS-MLR model, the tribute rates of the two pollution sources were calculated, and the results are shown in Figure 5. Natural sources have a large contribution rate to Cu, Cr, Zn, Pb, and As, which are 80%, 84%, 87%, 68%, and 75%, respectively. The contribution rate of industrial sources to Cd is 59%. APCS-MLR simplified the complexities of the six heavy metals into two dominant sources, efficiently underscoring natural inputs (72.5%). Compared with [PERSON] et al.'s analysis of heavy metal pollution sources based on the traditional PCA method, the APCS-MLR model quantified the contribution rate of pollution sources [47].  
Figure 5: Contribution rate of heavy metal pollution sources.