## 3 Results and Discussion
### Receptor Prediction Model
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Crop** & **Prediction Models by PC\({}_{5}\)-SMLR for Cu, Zn, and As** & **R** & **P** \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Wheat \\ \end{tabular} } & Cu\({}_{\text{BCF}}\) = 1.268 *** - 0.034 PC\({}_{51}\) *** - 0.022 PC\({}_{59}\) *** + 0.015 PC\({}_{55}\)* & 0.76 & \(<\)000 \\  & Zn\({}_{\text{BCF}}\) = 1.627 *** - 0.027 PC\({}_{51}\) *** - 0.016 PC\({}_{514}\) *** & 0.62 & \(<\)000 \\  & As\({}_{\text{BCF}}\) = -0.611 *** - 0.085 PC\({}_{55}\) *** - 0.066 PC\({}_{51}\) *** + 0.045 PC\({}_{514}\) *** & 0.56 & \(<\)000 \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Corn \\ \end{tabular} } & Cu\({}_{\text{BCF}}\) = 0.736 *** - 0.06 PC\({}_{51}\) *** + 0.055 PC\({}_{513}\) ** - 0.031 PC\({}_{510}\)** & 0.64 & \(<\)000 \\  & Zn\({}_{\text{BCF}}\) = 1.457 *** - 0.063 PC\({}_{510}\) *** + 0.033 PC\({}_{56}\) *** & 0.66 & \(<\)000 \\  & As\({}_{\text{BCF}}\) = -0.955 *** - 0.172 PC\({}_{57}\) *** - 0.087 PC\({}_{54}\) *** & 0.58 & \(<\)000 \\ \hline \hline \end{tabular}  
* Correlation is significant at the \(P<0.05\) level; *** Correlation is significant at the \(P<0.01\) level; *** Correlation is significant at the \(P<0.001\) level.  
\end{table} 
Table 3: The prediction and analysis results of BCF of Cu, Zn, and As in wheat and corn, plus soil properties by PC\({}_{5}\)-SMLR. According to the results of the PC\({}_{5}\)-SMLR (Table 3) and the correlation (Table 4), we know that PC\({}_{51}\) (Fe\({}_{2}\)O\({}_{3}\), Mn, CeC, and clay) and PC\({}_{59}\) (TOC) have an impact on the BCF values for Cu in wheat; PC\({}_{51}\) (Fe\({}_{2}\)O\({}_{3}\), Mn, CeC, and clay) and PC\({}_{514}\) (N) have a negative impact on the BCF values for Zn in wheat; PC\({}_{58}\)(B),PC\({}_{511}\) (K\({}_{2}\)O), and PC\({}_{514}\) (N) have a negative impact on the BCF values for As in wheat. PC\({}_{51}\) prevented the migration of Cu and Zn from soil to wheat. [PERSON] et al. (2013) showed that Fe had significant influences on Cu and Zn accumulation in wheat [64]. PC\({}_{514}\) was considered a component of a nitrogenous fertilizer group, where the application of the fertilizer should promote the BCF of wheat Zn. [PERSON] et al. (2018) also reported that nitrogenous fertilizers have a positive influence on BCF and should be controlled to an appropriate level [9]. However, as there are differences between other heavy metals and As in the soil, it is important to explain that N competes with As in the soil solid phase of the study area. On the other hand, BCF values for corn As increased with a decrease in pH (Tables 3 and 4). The prediction model also indicated that the As\({}_{\text{BCF}}\) of corn and PCs7 (pH) have a negative relationship. The pH is high in the study area, where there is a higher solubility of As(V). The dissolved As(V) easily leaches out and then migrates to the crops [75], leading to As accumulating in crop grains. In addition, a significant negative correlation was found for wheat and corn metal(loid) BCF values and CeC and clay content (Table 4, Tables S2 and S3). This may be because CeC and clay absorb the metal(loid) ions and prevent the metal(loid)s' migration from soil to crops, thereby decreasing the BCF. [PERSON] et al. (2007) and [PERSON] et al. (2017) also found that CeC and clay fixed metal(loid)s in soils, making it difficult for the metal(loid)s to migrate to crops [76; 77]. However, the correlation results showed that the BCF values for corn Cu and Zn were negatively affected by CeC, TOC, and clay. The BCF value for As was also negatively affected by CeC and clay (Table 4). Overall, the BCF is very complicated and is related to the type of crops [70] plus the soil physical and chemical properties [78; 79; 80]. [PERSON] et al. (2017) also reported that the concentration of metal(loid)s in crops was related to other metal(loid)s [33], and some of them played a role in promoting the enrichment of other metals. [PERSON] et al. (2017) and [PERSON] et al.