Abstract
In order to monitor Potentially Toxic Elements (PTEs) in anthropogenic soils on brown coal mining dumpsites, a large number of samples and cumbersome, time-consuming laboratory measurements are required. Due to its rapidity, convenience and accuracy, reflectance spectroscopy within the Visible-Near Infrared (Vis-NIR) region has been used to predict soil constituents. This study evaluated the suitability of Vis-NIR (350–2500 nm) reflectance spectroscopy for predicting PTEs concentration, using samples collected on large brown coal mining dumpsites in the Czech Republic. Partial Least Square Regression (PLSR) and Support Vector Machine Regression (SVMR) with cross-validation were used to relate PTEs data to the reflectance spectral data by applying different preprocessing strategies. According to the criteria of minimal Root Mean Square Error of Prediction of Cross Validation (RMSEPcv) and maximal coefficient of determination (R2 cv) and Residual Prediction Deviation (RPD), the SVMR models with the first derivative pretreatment provided the most accurate prediction for As (R2 cv) = 0.89, RMSEPcv = 1.89, RPD = 2.63). Less accurate, but acceptable prediction for screening purposes for Cd and Cu (0.66 ˂ R2 cv) ˂ 0.81, RMSEPcv = 0.0.8 and 4.08 respectively, 2.0 ˂ RPD ˂ 2.5) were obtained. The PLSR model for predicting Mn (R2 cv) = 0.44, RMSEPcv = 116.43, RPD = 1.45) presented an inadequate model. Overall, SVMR models for the Vis-NIR spectra could be used indirectly for an accurate assessment of PTEs’ concentrations.
Highlights
Our society and civilization rely heavily on the mining industry to sustain our way of living
This study demonstrated the application of laboratory Vis-Near Infrared (NIR) reflectance spectroscopy for the prediction of Potential Toxic Elements (PTEs), including Cu, Mn, Cd, Zn, Fe, Pb and As, using soil samples taken from six brown coal mining dumpsites of the Czech Republic
Vis-NIR calibration models were created by Partial Least Squares Regression (PLSR) and Support Vector Machine Regression (SVMR) algorithms
Summary
Our society and civilization rely heavily on the mining industry to sustain our way of living. Among the various geo-environmental impacts of mining, contamination of soil is by far the most significant. Elevated concentrations of Potential Toxic Elements (PTEs) in soils do impact the soil quality, but due to their persistent nature and long biological half-lives, can accumulate in the food chain and can eventually influence human health [3,4,5]. Toxic Elements (PTEs) concentrations in soils can be measured, but their determination depends on large-scale sampling and physical or conventional analysis techniques, which are time-consuming, inefficient, and expensive when applied on a large scale [6]. According to Xie et al [5], conventional methods for environmental soil monitoring require the collection of numerous samples, followed by laboratory analyses that involve complex processes such as separation and preconcentration. Sampling density and analytical diversity are frequently less than sufficient due to the significant costs of analyses
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