## 3 Results and Discussion
### Receptor Prediction Model  
[PERSON] et al. (1985) first proposed that, after the normalization of original data, the absolute real score of a factor could be obtained using factor analysis ([PERSON] et al., 1985), and then combined with a multivariate linear regression model to calculate a common factor contribution rate on water targets. PC\({}_{\text{S}}\)-SMLR can quantitatively depict the main factor's contribution to the receptor of each index, such as the air and water pollution source analysis used in studies ([PERSON] et al., 1985; [PERSON] et al., 1985). This methodology has rarely been used in the soil environment. Because it only focuses on linear regression to determine the relationships between multiple influencing factors on metal(loid)s in crops ([PERSON] et al., 1985), it cannot eliminate the collinearity between elements, leading to inaccurate prediction results. PC\({}_{\text{S}}\)-SMLR is a method that eliminates the collinearity of soil factors ([PERSON] et al., 1985), reduces the complexity of factors, and provides more accurate results. The results of a Kaiser-Meyer-Olkin (KMO) test (0.804) and [PERSON]'s sphericity test (\(P<0.001\)) indicated that the dataset was acceptable for use in a PCA. The variance of PC2-PC14 from 7.58%  
Figure 6: Contribution of metal(loid)s in three mediums (soil, wheat, and corn) to the total health risk for adults and children. Figure 7: The distribution map of TTHQ for adults and children in the study area. to 3.59%, except for PC1 (21.67%), and 14 components out of 16 were extracted with the component loadings rotated with maximum variance. The total cumulative contribution rate was 98.98%. Fe\({}_{2}\)O\({}_{3}\), Mn, clay, and CeC possessed the most significant loadings in PC1 (principal component 1), where P and N were significant loadings in PC2. Similarly, significant loadings were found for available Fe in PC3, and available Mn in PC4. Additionally, B, CaO, pH, Mo, TOC, MgO, K, F, clay, and N had relatively higher loadings in PC5 to PC14 (load value \(>\) 0.6; Table S1, Figure 8), respectively. The scores obtained from PCA were then used as independent variables in the SMLR analysis to determine the most significant PCs for soil properties and BCF. According to the health risk assessment, Cu, Zn, and As in wheat and corn are relatively harmful to human health (HQ \(>\) 0.1). To determine how to reduce the harm to human health, PC\({}_{5}\)-SMLR was used to establish the prediction model of soil physical and chemical indexes and the enrichment capacity of Cu, Zn, and As in crops. The prediction model R of BCF values for wheat Cu is 0.76 (\(P<0.000\)) (Table 3). According to the prediction, wheat Cu is related to components PC\({}_{51}\), PC\({}_{59}\), and PC\({}_{55}\). The prediction model R of BCF values for wheat Zn is 0.62 (\(P<0.000\)), where wheat Zn is related to components PC\({}_{51}\) and PC\({}_{514}\). The predictive model R of BCF values for wheat As is 0.56 (\(P<0.000\)), where wheat As is related to components PC\({}_{55}\), PC\({}_{511}\), and PC\({}_{514}\). The predictive model R of BCF values for corn Cu is 0.64 (\(P<0.000\)), where corn Cu is related to the components PC\({}_{51}\), PC\({}_{510}\), and PC\({}_{513}\). The predictive model R of BCF values for corn Zn is 0.66 (\(P<0.000\)), where corn Zn is related to the components PC\({}_{510}\) and PC\({}_{56}\). The predictive model R of BCF values for corn As is 0.58 (\(P<0.000\)), where corn As is related to the components PC\({}_{57}\) and PC\({}_{54}\)  
Figure 8: The factor load value of 16 components by Varimax with Kaiser normalization. (Fourteen components were extracted; the red line shows the load value \(>\) 0.6).