Abstract
Weathering and oxidation of sulphide minerals in mine wastes release toxic elements in surrounding environments. As an alternative to traditional sampling and chemical analysis methods, the capability of proximal and remote sensing techniques was investigated in this study to predict Chromium (Cr) concentration in 120 soil samples collected from a dumpsite in Sarcheshmeh copper mine, Iran. The samples’ mineralogy and Cr concentration were determined and were then subjected to laboratory reflectance spectroscopy in the range of Visible–Near Infrared–Shortwave Infrared (VNIR–SWIR: 350–2500 nm). The raw spectra were pre-processed using Savitzky-Golay First-Derivative (SG-FD) and Savitzky-Golay Second-Derivative (SG-SD) algorithms. The important wavelengths were determined using Partial Least Squares Regression (PLSR) coefficients and Genetic Algorithm (GA). Artificial Neural Networks (ANN), Stepwise Multiple Linear Regression (SMLR) and PLSR data mining methods were applied to the selected spectral variables to assess Cr concentration. The developed models were then applied to the selected bands of Aster, Hyperion, Sentinel-2A, and Landsat 8-OLI satellite images of the area. Afterwards, rasters obtained from the best prediction model were segmented using a binary fitness function. According to the outputs of the laboratory reflectance spectroscopy, the highest prediction accuracy was obtained using ANN applied to the SD pre-processed spectra with R2 = 0.91, RMSE = 8.73 mg/kg and RPD = 2.76. SD-ANN also showed an acceptable performance on mapping the spatial distribution of Cr using the ordinary kriging technique. Using satellite images, SD-SMLR provided the best prediction models with R2 values of 0.61 and 0.53 for Hyperion and Sentinel-2A, respectively. This led to the higher visual similarity of the segmented Hyperion and Sentinel-2A images with the Cr distribution map. This study’s findings indicated that applying the best prediction models obtained by spectroscopy to the selected wavebands of Hyperion and Sentinel-2A satellite imagery could be considered a promising technique for rapid, cost-effective and eco-friendly assessment of Cr concentration in highly heterogeneous mining areas.
Highlights
Mining and related activities are among the industries heavily releasing toxic elements into the environment
The dumpsite no. 31, located in the northeastern part of the mine’s main pit, was chosen as the study area (Figure 1). This dump site was selected according to the following criteria: (i) waste dump inactivity, so that no change occurs in the time gap between sampling and the image acquisition date, (ii) dump surface be accessible via road network, (iii) being highly contaminated and has severe acid generation risk; no vegetation can grow on its surface due to the extreme condition of the dump environment, and (iv) has a flat surface that avoids spectral diffraction
According to the 2D plots obtained from the first two PCs of raw and pre-processed spectral data, five similar samples were outside the 95% confidence level ellipse, which were considered as outliers and excluded from the data set (Figure 3)
Summary
Mining and related activities are among the industries heavily releasing toxic elements into the environment. Accumulation of these elements in the upper soil horizons and their transfer into the food chain have adverse effects on crop yield, food quality and soil microbial groups [1]. Chromium (Cr), as one of the most harmful toxic elements, causes severe health implications such as skin and mucous membrane ulceration, allergic. 2021, 13, 1277 and eczematous skin reactions, allergic asthmatic reactions, perforation of the nasal septum and bronchial carcinomas [2]. Continuous environmental monitoring should be conducted to identify and track toxic elements, including Cr, at their early release stages. A fast, low-cost and environmentalfriendly method is desired to evaluate and monitor changes in soils of contaminated areas
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