Estimation of Heavy Metals in Tailings and Soils Using Hyperspectral Technology: A Case Study in a Tin-Polymetallic Mining Area.

Rapid assessment of heavy metal (HM) pollution in mining areas is urgently required for further remediation. Here, hyperspectral technology was used to predict HM contents of multi-media environments (tailings, surrounding soils and agricultural soils) in a mining area. The correlation between hyperspectral data and HMs was explored, then the prediction models were established by partial least squares regression (PLSR) and back propagation neural networks (BPNN). The determination coefficients (R2), root mean squared error and ratios of performance to interquartile range (RPIQ) were used to evaluate the performance of the models. Results show that: (1) both PLSR and BPNN had good prediction ability, and (2) BPNN had better generalization ability (Cu (R2 = 0.89, RPIQ = 3.05), Sn (R2 = 0.86, RPIQ = 4.91), Zn (R2 = 0.74, RPIQ = 1.44) and Pb (R2 = 0.70, RPIQ = 2.10)). In summary, this study indicates that hyperspectral technology has potential application in HM estimation and soil pollution investigation in polymetallic mining areas.