Trace metal content prediction along an AMD (acid mine drainage)-contaminated stream draining a coal mine using VNIR–SWIR spectroscopy

Abstract

AbstractThe current study investigated the use of VNIR–SWIR (visible/near infrared to short-wavelength infrared: 400–2500 nm) spectroscopy for predicting trace metals in overbank sediments collected in the study site. Here, we (i) derived spectral absorption feature parameters (SAFPs) from measured ground spectra for correlation with trace metal (Pb, Cd, As, and Cu) contents in overbank sediments, (ii) built univariate regression models to predict trace metal concentrations using the SAFPs, and (iii) evaluated the predictive capacities of the regression models. The derived SAFPs associated with goethite in overbank sediments were Depth433b, Asym433b, and Width433b, and those associated with kaolinite in overbank sediments were Depth1366b, Asym1366b, Width1366b, Depth2208b, Asym2208b, and Width2208b. Cadmium in the overbank sediments showed the strongest correlations with the goethite-related SAFPs, whereas Pb, As, and Cu showed strong correlations with goethite- and kaolinite-related SAFPs. The best predictive models were obtained for Cu (R2 = 0.73, SEE = 0.15) and Pb (R2 = 0.73, SEE = 0.21), while weaker models were obtained for As (R2 = 0.46, SEE = 0.31) and Cd (R2 = 0.17, SEE = 0.81). The results suggest that trace metals can be predicted indirectly using the SAFPs associated with goethite and kaolinite. This is an important benefit of VNIR–SWIR spectroscopy considering the difficulty in analyzing “trace” metal concentrations, on large scales, using conventional geochemical methods.