Predicting nickel concentration in soil using reflectance spectroscopy associated with organic matter and clay minerals

Abstract

Heavy metal contamination in soil has become a serious environmental problem. Visible and near-infrared reflectance spectroscopy (VNIRS) is recognized as an alternative to rapidly predict heavy metal concentration in soil. The correlation between soil spectrally active constituents and heavy metals provides a mechanism for the prediction. Generally, the entire VNIR spectral region (VNIR-SR) without discrimination is used. Considering the adsorption and retention of heavy metals on soil spectrally active constituents, a method was proposed to predict heavy metal concentration in soil using VNIRS. Organic matter and clay minerals have strong sorption and retention for Ni in soil. Therefore, the spectral bands associated with organic matter and clay minerals were used to predict nickel (Ni) concentration to validate the proposed method. In this study, two sets of reflectance spectra of soil samples collected in Chenzhou and Hengyang, Hunan Province, China were used. The prediction model was calibrated with a combination of genetic algorithm and partial least squares regression (GA-PLSR). In Chenzhou, the ratio of prediction to deviation (RPD) and the coefficient of determination (R2) were improved from 1.566 and 0.577 to 2.139 and 0.773 by the prediction using the spectral bands associated with organic matter and clay minerals compared with the prediction using the entire VNIR-SR. The RPD and R2 were improved from 1.805 and 0.672 to 3.144 and 0.892 by the proposed method in Hengyang. To further validate the proposed method, the soil samples from Chenzhou were reallocated to calibration and validation sets according to Ni concentration. The RPD and R2 were improved from 1.193 and 0.267 to 2.396 and 0.818 by using the spectral bands associated with organic matter and clay minerals. The results indicate that the proposed method is effective in predicting Ni concentration and has the potential to predict other heavy metals in soil.