A mechanism study of reflectance spectroscopy for investigating heavy metals in soils

Conventional methods for investigating heavy metal contamination in soil are time consuming and expensive. In this study, we (i) explored reflectance spectroscopy as an alternative method for assessing heavy metals, and (ii) further explored the physicochemical mechanism that allows estimation of heavy metals with the reflectance spectroscopy method. We first investigated the spectral response of changing concentrations of heavy metals in soils. The results indicated that only at very high concentration can transition elements exhibit their inherent absorption features. In spite of this observation, we successfully predicted low levels of heavy metals in agricultural soils. The best prediction accuracies were obtained for the siderophile elements Ni, Cr, and Co. The poorest prediction was for Cd. The order of prediction accuracy for metals was approximately the same as the order of their correlation coefficients with Fe. Complementary to some previous studies that found that the intercorrelation between heavy metals and active soil components (such as Fe oxides, organic matter, and clay) is the major predictive mechanism, in the present study we concluded that the correlation with total Fe (including active and residual Fe) is the major mechanism. This conclusion was further supported by both correlation analysis and chemical sequential extraction. Correlation analysis showed that all metals are negatively correlated with reflectance while positively correlated with the absorption depth at about 500 nm, a feature resulting from goethite. The chemical forms of heavy metals, which showed that besides the crystalline Fe oxide and organic matter fractions, heavy metals have significant amounts in the residual fraction, also strengthened the conclusion. Abbreviations: CSE, chemical sequential extraction; ICP-AES, inductively coupled plasma-atomic emission spectroscopy; ICP-MS, inductively coupled plasma-mass spectroscopy; PI, pollution index; PLSR, partial least-squares regression; RMSEP, root-mean-square error of prediction; RPD, ratio of the standard deviation of the population to the root-mean-square error of prediction; VNIRS, visible and near-infrared reflectance spectroscopy.