Thallium and lead variations in a contaminated peatland : a combined isotopic study from a mining/smelting area

Vertical profiles of Tl, Pb and Zn concentrations and Tl and Pb isotopic ratios in a contaminated peatland/fen (Wolbrom, Poland) were studied to address questions regarding (i) potential long-term immobility of Tl in a peat profile, and (ii) a possible link in Tl isotopic signatures between a Tl source and a peat sample. Both prerequisites are required for using peatlands as archives of atmospheric Tl deposition and Tl isotopic ratios as a source proxy. We demonstrate that Tl is an immobile element in peat with a conservative pattern synonymous to that of Pb, and in contrast to Zn. However, the peat Tl record was more affected by geogenic source(s), as inferred from the calculated element enrichments. The finding further implies that Tl was largely absent from the pre-industrial emissions (>~250 years BP). The measured variations in Tl isotopic ratios in respective peat samples suggest a consistency with anthropogenic Tl (ε205Tl between ~ -3 and 4), as well as with background Tl isotopic values in the study area (ε205Tl between ~0 and 1), in line with detected 206Pb/207Pb ratios (1.16–1.19). Therefore, we propose that peatlands can be used for monitoring trends in Tl deposition and that Tl isotopic ratios can serve to distinguish source origin(s). However, given that the studied fen has a particularly complicated geochemistry (attributed to significant environmental changes in its history), it seems that ombrotrophic peatland(s) could be better suited for this type of Tl research

Soil sequences atlas. 2

TäistekstThis is the second book in the series of Soil Sequence Atlases. The first volume was published in 2014. Main pedogeographic features are presented in the form of sequences to give a comprehensive picture of soils - their genesis and correlations with the environment in typical landscapes of Central Europe from Estonia furthest north, through Latvia, Lithuania, Poland, Germany, Czechia, Slovakia and Hungary to the southernmost Slovenia. Soils of natural landscapes - loess and sand (continental dunes) - are presented, as well as those of plains of various origin, karst lands, low mountains, and anthropically modified soils. Each chapter presents soil profiles supplemented by landscape information and basic analytical data. Then, genetic interpretations of soil properties related to soil forming agents are given as schematic catenas. When one factor changes while the others are more or less stable, the soil sequence can be recognised. Depending on the dominant soil-forming factor affecting repeated soil patterns, different types can be distinguished. Chapters are arranged roughly in accordance with the main soil-forming process in sequences, and referring to the WRB key (peat formation, vertic and gleyic process, podzolisation, humus accumulation, clay illuviation), with one small exception - the Technosols have been placed at the end of book. The main objective of this book is to present the diversity of relations between soil and landscape, climate, hydrology and human relations, and to present interpretations reflecting the World Reference Base for Soil Resources (2015) classification with comments on the choice of qualifiers. Sixteen Reference Soil Groups are featured, and represented by 67 soil profiles. The secondary objective is pedological education. One of the aims of soil science education is to explain to students the relations between landscape and soil cover. The patterns of soil units within landscapes are to some extent predictable. The collected data is intended as a useful educational tool in teaching soil science, supporting understanding of the reasons for the variability of soil cover, and also as a WRB classification guideline. The Atlas was developed as part of the EU Erasmus+ FACES project (Freely Accessible Central European Soil). Marcin Šwitoniak, Przemyslaw Charzynsk

FEATURES AND CONDITIONS OF ORIGIN OF COLLUVIAL SOILS IN SELECTED LOCALIDES

Department of Physical Geography and GeoecologyKatedra fyzické geografie a geoekologieFaculty of SciencePřírodovědecká fakult

Colluvial soils - their characteristics and spatial delineation at chosen study areas in the Czech republic

Colluvial soils, formed in areas of increased accumulation of soil material, represent an important element in landscape and soil mosaic, whose development is still in progress. Their spatial distribution and profile thickess are considered to be important indicators of processes leading to significant landscape changes. Their importance also consists in very deep humus horizon which makes them a vast storage of organic carbon. Mapping of colluvial soils represents a substantial contribution in the update process of classic soil maps. The aim of the thesis is a complex analysis of the colluvial soil unit in terms of its relation to soil properties, terrain and geological predispositions and relevance in soil mapping. The study results are based mainly on detailed terrain survey, digital terrian model analysis and modern pedometric methods application. The research was proceeded in three study areas with significant pedological and geological differences and various predisposiotion for colluviation intensity and velocity and resulting character of colluvial profiles. Diverse character of the study areas was the main factor of the spatial distribution and properties of the colluvial soils. In Chernozem region, intensive erosion resulted in formation of colluvial soils characterized by thick humus...Koluvizemě, půdy vznikající v oblastech zvýšené akumulace půdního materiálu v důsledku zrychlené půdní eroze, představují důležitý prvek ve stále se vyvíjející krajinné a půdní mozaice. Plošné rozšíření a mocnost koluvizemí je tak možno chápat jako indikátory změn krajiny, jejichž důsledky jsou patrné v široké škále přírodních procesů. Význam koluvizemí spočívá i ve značném množství organického uhlíku, který je v těchto hlubokohumózních půdách uložen. V neposlední řadě je mapování koluvizemí důležitým krokem při aktualizaci stávajících půdních map. Cílem předkládané práce bylo především zachytit půdní typ koluvizem a proces koluviace v jeho komplexitě, tedy ve vztahu k půdním vlastnostem, k terénním a geologickým predispozicím i k významu v půdním mapování. Práce prezentuje výsledky výzkumu založeného na podrobném terénním průzkumu, analýzách digitálního modelu terénu a použití moderních pedometrických metod. Studie probíhala ve třech pedologicky a geologicky odlišných oblastech s různou predispozicí ke koluviaci, její rychlosti a výsledné podobě profilů. Odlišný charakter území se ukázal být zásadním faktrorem při výsledném rozsahu rozšíření i podobě koluvizemě. V černozemní oblasti vedla výrazná eroze k vývoji koluvizemí s hlubokým humózním profilem a relativně značným plošným zastoupením. V...Department of Physical Geography and GeoecologyKatedra fyzické geografie a geoekologieFaculty of SciencePřírodovědecká fakult

Colluvial soils - their characteristics and spatial delineation at chosen study areas in the Czech republic

Colluvial soils, formed in areas of increased accumulation of soil material, represent an important element in landscape and soil mosaic, whose development is still in progress. Their spatial distribution and profile thickess are considered to be important indicators of processes leading to significant landscape changes. Their importance also consists in very deep humus horizon which makes them a vast storage of organic carbon. Mapping of colluvial soils represents a substantial contribution in the update process of classic soil maps. The aim of the thesis is a complex analysis of the colluvial soil unit in terms of its relation to soil properties, terrain and geological predispositions and relevance in soil mapping. The study results are based mainly on detailed terrain survey, digital terrian model analysis and modern pedometric methods application. The research was proceeded in three study areas with significant pedological and geological differences and various predisposiotion for colluviation intensity and velocity and resulting character of colluvial profiles. Diverse character of the study areas was the main factor of the spatial distribution and properties of the colluvial soils. In Chernozem region, intensive erosion resulted in formation of colluvial soils characterized by thick humus..

Soil Organic Carbon Mapping Using Multispectral Remote Sensing Data: Prediction Ability of Data with Different Spatial and Spectral Resolutions

The image spectral data, particularly hyperspectral data, has been proven as an efficient data source for mapping of the spatial variability of soil organic carbon (SOC). Multispectral satellite data are readily available and cost-effective sources of spectral data compared to costly and technically demanding processing of hyperspectral data. Moreover, their continuous acquisition allows to develop a composite from time-series, increasing the spatial coverage of SOC maps. In this study, an evaluation of the prediction ability of models assessing SOC using real multispectral remote sensing data from different platforms was performed. The study was conducted on a study plot (1.45 km2) in the Chernozem region of South Moravia (Czechia). The adopted methods included field sampling and predictive modeling using satellite multispectral Sentinel-2, Landsat-8, and PlanetScope data, and multispectral UAS Parrot Sequoia data. Furthermore, the performance of a soil reflectance composite image from Sentinel-2 data was analyzed. Aerial hyperspectral CASI 1500 and SASI 600 data was used as a reference. Random forest, support vector machine, and the cubist regression technique were applied in the predictive modeling. The prediction accuracy of models using multispectral data, including Sentinel-2 composite, was lower (RPD range from 1.16 to 1.65; RPIQ range from 1.53 to 2.17) compared to the reference model using hyperspectral data (RPD = 2.26; RPIQ = 3.34). The obtained results show very similar prediction accuracy for all spaceborne sensors (Sentinel-2, Landsat-8, and PlanetScope). However, the spatial correlation between the reference mapping results obtained from the hyperspectral data and other maps using multispectral data was moderately strong. UAS sensors and freely available satellite multispectral data can represent an alternative cost-effective data source for remote SOC mapping on the local scale

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

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

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