The Spatial Distribution and Prediction of Soil Heavy Metals Based on Measured Samples and Multi-Spectral Images in Tai Lake of China

Soil is an important natural resource. The excessive amount of heavy metals in soil can harm and threaten human health. Therefore, monitoring of soil heavy metal content is urgent. Monitoring soil heavy metals by traditional methods requires many human and material resources. Remote sensing has shown advantages in the field of monitoring heavy metals. Based on 971 heavy metal samples and Sentinel-2 multi-spectral images in Tai Lake, China, we analyzed the correlation between six heavy metals (Cd, Hg, As, Pb, Cu, Zn) and spectral factors, and selected As and Hg as the input factors of inversion model. The correlation coefficient of the best model of As was 0.53 (p < 0.01), and of Hg was 0.318 (p < 0.01). We used the methods of partial least squares regression (PLSR) and back propagation neural network (BPNN) to establish inversion models with different combinations of spectral factors by using 649 measured samples. In addition, 322 measured samples were used for accuracy evaluation. Compared with the PLSR model, the BP neural network builds the model with higher accuracy, and B1-B4 combined with LnB1-LnB4 builds the model with the highest accuracy. The accuracy of the best model was verified, with an average error of 19% for As and 45% for Hg. Analyzing the spatial distribution of heavy metals by using the interpolation method of Kriging and IDW. The overall distribution trend of the two interpolations is similar. The concentration of As elements tends to increase from north to south, and the relatively high value of Hg elements is distributed in the east and west of the study area. The factories in the study area are distributed along rivers and lakes, which is consistent with the spatial distribution of heavy metal enrichment areas. The relatively high-value areas of heavy metal elements are related to the distribution of metal products factories, refractory porcelain factories, tile factories, factories and mining enterprises, etc., indicating that factory pollution is the main reason for the enrichment of heavy metals.

Identifying Influencing Factors of Agricultural Soil Heavy Metals Using a Geographical Detector: A Case Study in Shunyi District, China

Identifying influencing factors of heavy metals is essential for soil evaluation and protection. This study investigates the use of a geographical detector to identify influencing factors of agricultural soil heavy metals from natural and anthropogenic aspects. We focused on six variables of soil heavy metals, i.e., As, Cd, Hg, Cu, Pb, Zn, and four influencing factors, i.e., soil properties (soil type and soil texture), digital elevation model (DEM), land use, and annual deposition fluxes. Experiments were conducted in Shunyi District, China. We studied the spatial correlations between variables of soil heavy metals and influencing factors at both single-object and multi-object levels. A geographical detector was directly used at the single-object level, while principal component analysis (PCA) and geographical detector were sequentially integrated at the multi-object level to identify influencing factors of heavy metals. Results showed that the concentrations of Cd, Cu, and Zn were mainly influenced by DEM (p = 0.008) and land use (p = 0.033) factors, while annual deposition fluxes were the main factors of the concentrations of Hg, Cd, and Pb (p = 0.000). Moreover, the concentration of As was primarily influenced by soil properties (p = 0.026), DEM (p = 0.000), and annual deposition flux (p = 0.000). The multi-object identification results between heavy metals and influencing factors included single object identification in this study. Compared with the results using the PCA and correlation analysis (CA) methods, the identification method developed at different levels can identify much more influencing factors of heavy metals. Due to its promising performance, identification at different levels can be widely employed for soil protection and pollution restoration.