Exploring relationship of soil PTE geochemical and "VIS-NIR spectroscopy" patterns near Cu-Mo mine (Armenia).

PTE contamination of soils remains one of the global environmental concerns. The ways of detecting and monitoring PTE concentrations in soils varies including traditional field sampling accompanied by sample preparation and chemical analysis and state of the art visible and near-infrared (Vis-NIR) spectroscopic approaches. Among the different Machine Learning (ML) to extract soil information from spectra and to explore the relationship between spectral reflectance data and soil PTE content PLSR method is a well-established one to construct a soil PTE estimation model. This study aimed to explore the relationship of soil PTE geochemical and VIS-NIR spectroscopy characteristics in agricultural soils near Cu-Mo mine area in Armenia. PLSR method is applied to identify the links between the spectra and agricultural soil Ti, V, Cr, Mn, Fe, Co, Ba, Pb, Zn, Cu, Sr, Zr and Mo contents to reveal the potential of VIS-NIR spectroscopy in ex-situ monitoring of Kajaran soils. The results show that different portions of VIS-NIR spectra are responsible for Ti (1100-1200 nm, 2350-2500 nm), V (350-500 nm, 700-750 nm, 1000-1100 nm, 1400-2500 nm), Cr (1300-1400 nm, 1900-2100 nm) and Ba (450-500 nm, 600-800 nm, 1050-1700 nm, 2000-2100 nm, 2350-2400 nm) estimations through PLSR correspondingly. However, among the studied PTEs Ti and V, which shows significant negative correlations in VIS-NIR spectra registered at around 400-600 nm and 850-1150 nm regions, are remarkable and promising with the PLSR estimation results using VIS-NIR spectra Ti (R = 0.74), V (R = 0.71). This study shows that VIS-NIR spectroscopy has a high potential for the estimation of at least several PTE in soils and PLSR modelis reliable for deriving information from there.