An improved estimation model for soil heavy metal(loid) concentration retrieval in mining areas using reflectance spectroscopy

Purpose: An advanced estimation model CARS-PLS (competitive adaptive reweighted sampling-partial least squares) has been developed for feature extraction and soil contamination analysis. However, this method works well for a single mining area and has limited capacity in coping with the variations from multiple sites. In this paper, we present an improved estimation model, CARS-PLS-SVM, to cope with the nonlinear problem in multiple sites with SVM (support vector machines). Materials and methods: We selected two study areas located in metal mining and coal mining areas of north China. A total of 65 soil samples were collected. The heavy metal(loid) concentrations (Cr and As) were determined by inductively coupled plasma-mass spectrometry (ICP-MS) and atomic fluorescence spectrometry (AFS), and the visible and near-infrared spectra of the soil samples were measured with an ASD (Analytical Spectral Devices) field spectrometer (350–2500 nm). Samples were divided into calibration set (n = 41) and validation set (m = 24) according to heavy metal(loid) concentration. After different pretreatment methods, the new features extracted from CARS-PLS are used as the input to model the data further with SVMs. Results and discussion: The performance of CARS-PLS-SVM was investigated under seven different pretreatment methods. Results showed that using combination of Savitzky-Golay and standard normal transformation pretreatment method achieved the best accuracy for Cr estimation (coefficient of determination for prediction, R_p ² = 0.9705, root-mean-square error for prediction, RMSEP = 5.0253, residual prediction deviation, RPD = 5.9001, ratio of prediction performance to interquartile range, RPIQ = 11.4043) and for As prediction (R_p ² = 0.9483; RMSEP = 1.2024; RPD = 3.0689; RPIQ = 5.3643). Besides, CARS-PLS-SVM is better than partial least squares (PLS) regression under the same pretreatment methods. Compared with three current state-of-the-art models: wavelet transform PLS (WT-PLS), synergy interval PLS (siPLS), and the CARS-PLS model, CARS-PLS-SVM was found to be superior to the other methods for Cr prediction (R_p ² = 0.9705; RMSEP = 5.0253; RPD = 5.9001; RPIQ = 11.4043) and As prediction (R_p ² = 0.9483; RMSEP = 1.2024; RPD = 3.0689; RPIQ = 11.5.3643). Conclusions: The results demonstrate that compared with other linear models, the nonlinear model CARS-PLS-SVM has the highest precision in soil heavy metal(loid) estimation modeling of multiple mining areas by the use of proper spectral feature extraction from the pretreated spectra.