Tereza Zádorová

Isotopic Tracing of Thallium Contamination in Soils Affected by Emissions from Coal-Fired Power Plants

Here, for the first time, we report the thallium (Tl) isotope record in moderately contaminated soils with contrasting land management (forest and meadow soils), which have been affected by emissions from coal-fired power plants. Our findings clearly demonstrate that Tl of anthropogenic (high-temperature) origin with light isotope composition was deposited onto the studied soils, where heavier Tl (ε(205)Tl ∼ -1) naturally occurs. The results show a positive linear relationship (R(2) = 0.71) between 1/Tl and the isotope record, as determined for all the soils and bedrocks, also indicative of binary Tl mixing between two dominant reservoirs. We also identified significant Tl isotope variations within the products from coal combustion and thermo-desorption experiments with local Tl-rich coal pyrite. Bottom ash exhibited the heaviest Tl isotope composition (ε(205)Tl ∼ 0), followed by fly ash (ε(205)Tl between -2.5 and -2.8) and volatile Tl fractions (ε(205)Tl between -6.2 and -10.3), suggesting partial Tl isotope fractionations. Despite the evident role of soil processes in the isotope redistributions, we demonstrate that Tl contamination can be traced in soils and propose that the isotope data represent a possible tool to aid our understanding of postdepositional Tl dynamics in surface environments for the future.

Phase-dependent phytoavailability of thallium – A synthetic soil experiment

The study deals with the environmental stability of Tl-modified phases (ferrihydrite, goethite, birnessite, calcite and illite) and phytoavailability of Tl in synthetically prepared soils used in a model vegetation experiment. The data presented here clearly demonstrate a strong relationship between the mineralogical position of Tl in the model soil and its uptake by the plant (Sinapis alba L.). The maximum rate of Tl uptake was observed for plants grown on soil containing Tl-modified illite. In contrast, soil enriched in Ksat-birnessite had the lowest potential for Tl release and phytoaccumulation. Root-induced dissolution of synthetic calcite and ferrihydrite in the rhizosphere followed by Tl mobilization was detected. Highly crystalline goethite was more stable in the rhizosphere, compared to ferrihydrite, leading to reduced biological uptake of Tl. Based on the results obtained, the mineralogical aspect must be taken into account prior to general environmental recommendations in areas affected by Tl.

Assessment of Soil Degradation by Erosion Based on Analysis of Soil Properties Using Aerial Hyperspectral Images and Ancillary Data, Czech Republic

The assessment of the soil redistribution and real long-term soil degradation due to erosion on agriculture land is still insufficient in spite of being essential for soil conservation policy. Imaging spectroscopy has been recognized as a suitable tool for soil erosion assessment in recent years. In our study, we bring an approach for assessment of soil degradation by erosion by means of determining soil erosion classes representing soils differently influenced by erosion impact. The adopted methods include extensive field sampling, laboratory analysis, predictive modelling of selected soil surface properties using aerial hyperspectral data and the digital elevation model and fuzzy classification. Different multivariate regression techniques (Partial Least Square, Support Vector Machine, Random forest and Artificial neural network) were applied in the predictive modelling of soil properties. The properties with satisfying performance (R2 > 0.5) were used as input data in erosion classes determination by fuzzy C-means classification method. The study was performed at four study sites about 1 km2 large representing the most extensive soil units of the agricultural land in the Czech Republic (Chernozems and Luvisols on loess and Cambisols and Stagnosols on crystalline rocks). The influence of site-specific conditions on prediction of soil properties and classification of erosion classes was assessed. The prediction accuracy (R2) of the best performing models predicting the soil properties varies in range 0.8–0.91 for soil organic carbon content, 0.21–0.67 for sand content, 0.4–0.92 for silt content, 0.38–0.89 for clay content, 0.73–089 for Feox, 0.59–0.78 for Fed and 0.82 for CaCO3. The performance and suitability of different properties for erosion classes’ classification are highly variable at the study sites. Soil organic carbon was the most frequently used as the erosion classes’ predictor, while the textural classes showed lower applicability. The presented approach was successfully applied in Chernozem and Luvisol loess regions where the erosion classes were assessed with a good overall accuracy (82% and 67%, respectively). The model performance in two Cambisol/Stagnosol regions was rather poor (51%–52%). The results showed that the presented method can be directly and with a good performance applied in pedologically and geologically homogeneous areas. The sites with heterogeneous structure of the soil cover and parent material will require more precise local-fitted models and use of further auxiliary information such as terrain or geological data. The future application of presented approach at a regional scale promises to produce valuable data on actual soil degradation by erosion usable for soil conservation policy purposes.

Thallium isotopes in metallurgical wastes/contaminated soils: a novel tool to trace metal source and behavior

Thallium (Tl) concentration and isotope data have been recorded for contaminated soils and a set of industrial wastes that were produced within different stages of Zn ore mining and metallurgical processing of Zn-rich materials. Despite large differences in Tl levels of the waste materials (1-500mgkg-1), generally small changes in ε205Tl values have been observed. However, isotopically lighter Tl was recorded in fly ash (ε205Tl∼-4.1) than in slag (ε205Tl∼-3.3), implying partial isotope fractionation during material processing. Thallium isotope compositions in the studied soils reflected the Tl contamination (ε205Tl∼-3.8), despite the fact that the major pollution period ended more than 30 years ago. Therefore, we assume that former industrial Tl inputs into soils, if significant, can potentially be traced using the isotope tracing method. We also suggest that the isotope redistributions occurred in some soil (subsurface) horizons, with Tl being isotopically heavier than the pollution source, due to specific sorption and/or precipitation processes, which complicates the discrimination of primary Tl. Thallium isotope analysis proved to be a promising tool to aid our understanding of Tl behavior within the smelting process, as well as its post-depositional dynamics in the environmental systems (soils).

Influence of Elevation Data Resolution on Spatial Prediction of Colluvial Soils in a Luvisol Region.

The development of a soil cover is a dynamic process. Soil cover can be altered within a few decades, which requires updating of the legacy soil maps. Soil erosion is one of the most important processes quickly altering soil cover on agriculture land. Colluvial soils develop in concave parts of the landscape as a consequence of sedimentation of eroded material. Colluvial soils are recognised as important soil units because they are a vast sink of soil organic carbon. Terrain derivatives became an important tool in digital soil mapping and are among the most popular auxiliary data used for quantitative spatial prediction. Prediction success rates are often directly dependent on raster resolution. In our study, we tested how raster resolution (1, 2, 3, 5, 10, 20 and 30 meters) influences spatial prediction of colluvial soils. Terrain derivatives (altitude, slope, plane curvature, topographic position index, LS factor and convergence index) were calculated for the given raster resolutions. Four models were applied (boosted tree, neural network, random forest and Classification/Regression Tree) to spatially predict the soil cover over a 77 ha large study plot. Models training and validation was based on 111 soil profiles surveyed on a regular sampling grid. Moreover, the predicted real extent and shape of the colluvial soil area was examined. In general, no clear trend in the accuracy prediction was found without the given raster resolution range. Higher maximum prediction accuracy for colluvial soil, compared to prediction accuracy of total soil cover of the study plot, can be explained by the choice of terrain derivatives that were best for Colluvial soils differentiation from other soil units. Regarding the character of the predicted Colluvial soils area, maps of 2 to 10 m resolution provided reasonable delineation of the colluvial soil as part of the cover over the study area.

Mapping soil degradation using remote sensing data and ancillary data: South-East Moravia, Czech Republic

ABSTRACT Data on the real extent of soil that is degraded by erosion represent important information for the purposes of conservation policy. However, this type of data is rarely available for large areas. A remote-sensing-based method for identifying of eroded areas at the regional scale has been tested using a combination of time series of free access Sentinel-2 image data, airborne orthoimages and ground-truth data. The unsupervised classification ISODATA of the Sentinel-2A images has been performed. The minimum distance method has been applied for the assignment of unsupervised classes to four erosion classes using the ground-truth data. The automatic classification of eroded soils achieved an overall accuracy of 55.2% for three distinguished classes. An accumulated class has been eliminated as no unsupervised classes were assigned to this erosion class. A simplified classification of two classes (strongly eroded and other soils) reached an accuracy of 80.9%. The overall accuracy of the simplified classification increased to 86.9% after the visual refinement using orthoimages. This study shows the potential of the tested approach to produce valuable data on actual soil degradation by erosion. The limitations of the method are related to the soil cover variability, masking effect of clouds, vegetation or litter and the spectral separability of individual classes.