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.

Pollution Characteristics and Risk Assessment of Potential Toxic Elements in a Tin-polymetallic Mine Area Southwest China: Environmental Implications by Multi-Medium Analysis.

A multi-medium system, involving tailing area (tailings, surrounding soils and water) and downstream agricultural area (river water, sediments and farmland soils), was conceived to evaluate the pollution status of potential toxic elements (PTEs, including Fe, Mn, Ni, Cu, Zn, As, Sn, Pb, Cr and Cd) and environmental risks in a tin-polymetallic mine area southwest China. The results indicated that tailings exhibited representative enrichment and combination characteristics of Sn, Cu, Ni, Fe, As, Pb and Cr compared to surrounding soils. Acid mine drainage (AMD) from tailings and other mining-related sources greatly affected river water and farmland soils, resulting in soil acidification and accumulation of Sn, As, Cu and Pb in paddy soils. Overall, potential ecological risks posed by tailings and river sediments, and pollution risks from Cu, As and Pb in farmland should be concerned. Therefore, effective measures should be urgently taken to prevent PTEs and AMD into surrounding environmental media.

Estimation of multi-media metal(loid)s around abandoned mineral processing plants using hyperspectral technology and extreme learning machine.

Hyperspectral techniques are promising alternatives to traditional methods of investigating potentially toxic metal(loid) contamination. In this study, hyperspectral technology combined with partial least squares regression (PLSR) and extreme learning machine (ELM) established estimation models to predict the contents of copper (Cu), zinc (Zn), arsenic (As), cadmium (Cd), lead (Pb) and tin (Sn) in multi-media environments (mine tailings, soils and sediments) surrounding abandoned mineral processing plants in a typical tin-polymetallic mineral agglomeration in Guangxi Autonomous Region. Four spectral preprocessing methods, Savitzky-Golay (SG) smoothing, continuum removal (CR), first derivative (FD) and continuous wavelet transform (CWT), were used to eliminate noise and highlight spectral features. The optimum combinations of spectral preprocessing and machine learning algorithms were explored, then the estimation models with best accuracy were obtained. CWT and CR were excellent spectral pretreatments for the hyperspectral data regardless of the applied algorithms. The coefficients of determination (R2) of estimation models for the best accuracy of various metals (loid) are as follows: Cu (CWT-ELM:0.85), Zn (CR-PLSR:0.93), As (CWT-ELM: 0.86), Cd (CR-PLSR: 0.89), Pb (CWT-PLSR: 0.75) and Sn (CR-ELM: 0.81). In contrast, ELM models had higher accuracy with R2 > 0.80 (except Cd and Pb). In conclusion, ELM-based spectral estimation models are able to predict metal (loid) concentrations with high accuracy and efficiency, providing a potential new combinatorial approach for estimating toxic metal contamination in multi-media environments.