A high resolution map of soil types and physical properties for Cyprus : a digital soil mapping optimization

Fine-resolution soil maps constitute important data for many different environmental studies. Digital soil mapping techniques represent a cost-effective method to obtain detailed information about soil types and soil properties over large areas. The main objective of the study was to extend predictions from 1:25,000 legacy soil surveys (including WRB soil groups, soil depth and soil texture classes) to the larger area of Cyprus. A multiple-trees classification technique, namely Random Forest (RF), was applied. Specific objectives were: (i) to analyze the role and importance of a large data set of environmental predictors, (ii) to investigate the effect of the number of training points, forest size (ntree), the numbers of predictors sampled per node (mtry) and tree size (nodesize) in RF; (iii) to compare RF-derived maps with maps derived with a multinomial logistic regression model, in terms of validation error (test set and independent profiles) and map uncertainty, using the confusion index and a newly developed reliability index. The optimized RF model was run using half of the input points available (over a million) and with ntree equal to 350. The mtry parameter was set to 5 (close to half the number of the environmental variables used) for both soil series and soil properties. The nodesize calibration showed no relevant performance increase and was kept at its default value (1). In terms of environmental variables, the model used 10 predictors, covering all the soil formation factors considered in the scorpan formula, to derive the three maps. Soil properties, derived from geochemistry data, showed a high importance in deriving soil groups, depths and texture. Random Forest constructed a better predictive model than multinomial logistic regression, showing comparable predictive uncertainty but much lower validation error. The RF-derived maps show very low out of bag (OOB) errors (around 10% for both soil groups and soil properties) but relatively high validation error from independent profiles (45% for soil depth, 51% for soil texture). The resulting reliability index was low in the main mountainous area of Cyprus, where predictions were extrapolations as indicated by the multivariate environmental similarity surface, but medium to high in the main agricultural areas of the country

Geochemical fingerprinting and source discrimination of agricultural soils at continental scale

2108 agricultural soil samples (Ap-horizon, 0-20cm) were collected in Europe (33 countries, area 5.6 million km2) as part of the recently completed GEMAS (GEochemical Mapping of Agricultural and grazing land Soil) soil mapping project. GEMAS soil data have been used to provide a general view of element origin and mobility with a main focus on source parent material (and source rocks) at the continental scale, either by reference to average crustal abundances or to normalized patterns of element mobility during weathering processes. The survey area covers a large territory with diverse types of soil parent materials, with distinct geological history and a wide range of climate zones, and landscapes.To normalize the chemical composition of European agricultural soil, mean values and standard deviation of the selected elements have been compared to model compositions of the upper continental crust (UCC) and mean European river suspended sediment. Some elements are enriched relative to the UCC (Al, P, Pb, Zr,) whereas others, such as Mg, Na and Sr are depleted. The concept of the UCC extended normalization pattern has been applied to selected elements. The mean values of Rb, K, Y, Ti, Al, Si, Zr, Ce and Fe are very similar to the values from the UCC model, even when standard deviations indicate slight enrichment or depletion. Zirconium has the best fit to the UCC model using both mean value and standard deviation. Lead and Cr are enriched in European soil when compared to the UCC model, but their standard deviation values span a large, particularly towards very low values, which can be interpreted as a lithological effect.GEMAS soil data have been normalized to Al and Na, taking into account the main lithologies of the UCC, in order to discriminate provenance sources. Additionally, sodium normalization highlights variations related to the soluble and insoluble behavior of some elements (e.g., K, Rb versus Ti, Al, Si, V, Y, Zr, Ba, and La, respectively), their reactivity (e.g, Fe, Mn, Zn) and association with carbonates (e.g., Ca and Sr). Maps of Europe showing the spatial distribution of normalized compositions and element ratios reveal difficulties with the use of classical element ratios because of the large lithological differences in compositions of soil parent material. The ratio maps and color composite images extracted from the GEMAS data can help to discriminate the main lithologies in Europe at the regional scale but need to be used with caution due to the complexity of superimposed processes responsible for the soil chemical composition

Mercury in European agricultural and grazing land soils

Agricultural (Ap, Ap-horizon, 0–20 cm) and grazing land soil samples (Gr, 0–10 cm) were collected from a large part of Europe (33 countries, 5.6 million km2) at an average density of 1 sample site/2500 km2. The resulting more than 2 x 2000 soil samples were air dried, sieved to <2 mm and analysed for their Hg concentrations following an aqua regia extraction. Median concentrations for Hg are 0.030 mg/kg (range: <0.003–1.56 mg/kg) for the Ap samples and 0.035 mg/kg (range: <0.003–3.12 mg/kg) for the Gr samples. Only 5 Ap and 10 Gr samples returned Hg concentrations above 1 mg/kg. In the geochemical maps the continental-scale distribution of the element is clearly dominated by geology. Climate exerts an important influence. Mercury accumulates in those areas of northern Europe where a wet and cold climate favours the build-up of soil organic material. Typical anthropogenic sources like coal-fired power plants, waste incinerators, chlor-alkali plants, metal smelters and urban agglomerations are hardly visible at continental scales but can have a major impact at the local-scale

GEMAS: Geochemical background and mineral potential of emerging tech-critical elements in Europe revealed from low-sampling density geochemical mapping

The demand for ‘high-tech’ element resources (e.g., rare earth elements, lithium, platinum group elements) has increased with their continued consumption in developed countries and the emergence of developing economies. To provide a sound knowledge base for future generations, it is necessary to identify the spatial distribution of critical elements at a broad-scale, and to delineate areas for follow-up surveys. Subsequently, this knowledge can be used to study possible environmental consequences of the increased use of these resources. In this paper, three critical industrial elements (Sb, W, Li) from low-sampling density geochemical mapping at the continental-scale are presented. The geochemical distribution and spatial patterns have been obtained from agricultural soil samples (Ap-horizon, 0–20 cm; N = 2108 samples) collected at a density of 1 site per 2500 km2 and analysed by ICP-MS after a hot aqua regia digestion as part of the GEMAS (GEochemical Mapping of Agricultural and grazing land Soil) soil-mapping project in 33 European countries. Most of the geochemical maps show exclusively natural background element concentrations with minor, or without, anthropogenic influence. The maximum extent of the last glaciation is marked as a discrete element concentration break, and a distinct difference occurs in element concentration levels between the soil of northern and southern Europe, most likely an effect of soil genesis, age and weathering. The Sb, W and Li concentrations in soil provide a general overview of element spatial distribution in relation to complexity of the underlying bedrock and element mobility in the surface environment at the continental-scale. The chemical composition of agricultural soil represents largely the primary mineralogy of the source bedrock, the effects of pre- and post-depositional chemical weathering, formation of secondary products, such as clays, and element mobility, either by leaching or mineral sorting. Observed geochemical patterns of Li, W and Sb can be often linked with known mineralisation as recorded in the ProMine Mineral Database, where elements in question occur either as main or secondary resources. Anthropogenic impact has only been identified locally, predominantly in the vicinity of large urban agglomerations. Unexplained high element concentrations may potentially indicate new sources for high-tech elements and should be investigated at a more detailed scale