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.

No abstract

Vegetation influences water flow and solute transport in a soil not only because of its transpiration and root uptake of solutes, but also because of its impact on precipitation redistribution on the soil surface. The aim of this study was to assess the impact of the precipitation redistribution (stemflow and throughfall) under a beech tree (Fagus sylvatica L.) on the water regime and Al transport within the one‐ and two‐dimensional soil profiles using the HYDRUS‐1D and HYDRUS 2D/3D simulation models. The field study was performed at the Smědava Mountain in the Jizera Mountains in the Czech Republic. Simulations were performed for one selected soil (Haplic Podzol) on a relatively steep hillside. The simulation results for the 1D and 2D scenarios showed that the spatially redistributed precipitation under the beech tree caused funneled water flow and solute transport near the stem base. For the 1D scenarios, slightly higher weighted average water and solute discharge at the bottom were calculated as compared to those for the uniformly distributed precipitation. This spatial difference in discharge was simulated although the weighted average infiltration under the beech tree was lower than that for the uniformly distributed precipitation because runoff during stemflow around the tree bases was also obtained. For the 2D scenarios, all the fluxes simulated by assuming a spatially redistributed precipitation under the trees were higher than the fluxes obtained with the uniformly distributed precipitation because of the higher applied potential infiltration rates along the top boundary for that case. The funneled water and solute flow near the stem base caused the intensive simulated Al leakage around the tree stem. The Al content increased in the subsurface layer at the lower part of the simulated vertical transect as compared to that for the uniformly distributed precipitation. The results suggest that spatially redistributed precipitation should not be neglected when predicting potential leaching of toxic substances toward ground and surface water bodies.

No abstract