1,721,024 research outputs found
Human health risk assessment from potentially toxic elements in the soils of Sudan: A meta-analysis
No full texthttp://dx.doi.org/10.13039/100005156 Alexander von Humboldt Foundationhttp://dx.doi.org/10.13039/501100003385 University of Göttinge
Quantitative soil mapping in Sudan−a systematic review
No full texthttp://dx.doi.org/10.13039/100005156 Alexander von Humboldt-Stiftun
Factors determining carbonate content in a lithosequence in a Mediterranean environment, Syria
No full texthttp://dx.doi.org/10.13039/100005156 Alexander von Humboldt-Stiftun
Pollution and ecological risk assessment of heavy metals in anthropogenically-affected soils of Sudan: A systematic review and meta-analysis
No full textOpen-Access-Publikationsfonds 202
A novel method to determine background concentrations and spatial distributions of heavy metals in soil at large scale using machine learning coupled with remote sensing-terrain attributes
No full textSoil heavy metals are among the most hazardous materials in the environment. Their harmful effects can extend to surrounding systems (air, plants, water), and given the appropriate conditions may ultimately have negative effects on human health. Thus, preventing pollution and protecting pristine soils and preindustrial areas from human activities that lead to the concentration of heavy metals (HMs) is a priority. Here, a novel methodology was proposed to establish background concentrations of eight soil HMs, cobalt (Co), chromium (Cr), copper (Cu), iron (Fe), manganese (Mn), nickel (Ni), lead (Pb), and zinc (Zn), and digitally map their spatial distributions in an area (i.e., harrats region) that has not yet been impacted by industrial activity. The proposed methodology combined measurements of the target HMs and fifty-two environmental covariates (ECOVs) derived from 2017 to 2021 Landsat 8/9 OLI and Shuttle Radar Topography Mission (SRTM)-derived terrain attributes. Random forest and stepwise multiple linear regression models were further used to digitally map the studied HMs. The methodology is important for any future environmental pollution/monitoring studies in the area and can be applied in other similar environments. Machine learning algorithms show great ability to use available environmental variables and investigate the relationships between the factors influencing HMs accumulation under a given soil environment. The proposed methodology was effective for describing HMs spatial variability in the environments investigated. • The proposed method is a novel way to predict soil HMs and their spatial distribution over large areas. • Remote sensing/digital elevation models (DEMs)-derived ECOVs are useful for predicting and digitally mapping soil HMs, thus important for future environmental monitoring studies. • Explainable algorithms (i.e., RF and SMLR) are able to utilize ECOVs for HMs prediction and to establish background concentrations over large areas.Therefore, the combination of machine learning and RS/DEMs-based ECOVs is crucial to overcome the disadvantages of HMs determination via conventional methods
Integrating machine learning and environmental-soil variables for estimating soluble and exchangeable potassium in dryland regions: agronomical implications
No full textMapping soil vulnerability to floods under varying land use and climate
No full textFloods may only last few hours and can cause considerable damage and a possible threat to life. Flood prediction requires quantitative knowledge about infiltration and runoff dynamics, which is generally gained at the local scale. When scaling up local measurements to the catchment scale, account needs to be taken off the catchment’s organization (connectivity and patchiness). For this purpose, we developed a new method to map soil vulnerability to floods based on two steps: (1) identification of the flow processes at the plot scale, and (2) up-scaling this knowledge to the catchment scale. Excess surface runoff was scaled up by means of terrain analysis using digital elevation models (TauDEMs) calibrated with in situ sprinkling experiments of three rainfall-simulation intensities carried out on 57 plots under grassland and forest that dominate in the investigated area. The marked differences in textural and structural porosities between forest and grassland plots appear to control runoff processes. On the one hand, forest soil has a higher storage capacity than grassland soil, probably caused by a high unsaturated hydraulic conductivity and root water uptake, and resulting in lower surface runoff. On the other hand, fine material in the topmost 10 cm of grassland soil helps to build a structure that impedes vertical downward percolation and thus enhances surface runoff. However, within each soil category, slope plays an important role in generating surface runoff. In addition, raising the rainfall-simulation intensity from 24 to 48 mm h−1 increases the risk of predisposition to surface runoff from middle to high in major parts of the catchment under grassland, whereas forest soils showed vertical percolation in all cases except on slopes steeper than 31.3 degrees. Scaling up runoff processes using TauDEM based on sprinkling experiments provided new quantitative insights into flow processes and enabled us to trace the hydrological connectivity between zones of various predispositions to excess surface runoff under different land uses. These promising results indicate that the approach is suited for mapping soil vulnerability to floods under varying land use and climate at any scale. Our study showed that the peak discharge can be significantly reduced if the succession and connectivity of land use are well planned
Straw uses trade-off only after soil organic carbon steady-state
Full text linkSoil organic matter (SOM) is the key for a healthy soil and a relevant property to achieve the sustainability on soil management. However, soils are still net exporters of organic matter. One example is the use of wheat straw residue for industrial and energy applications, which has gained attention in the last years. The off-farm use of this abundant and low cost resource should follow sustainability criteria to avoid soil degradation and SOM losses. Straw residue incorporation is recognized as a recommended management practice to control erosion and mitigate CO2 emissions by increasing SOM. The goal of this work was: i) to evaluate the steady-state carbon (C) level in relation to C input and estimate the minimum residue input needed to maintain this SOC level in a durum wheat-based cropping system in long-term experiment; and ii) estimate the potential availability of durum wheat straws for alternative use. Results showed that a C steady-state can be achieved after 3.4 years with an annual organic C input of 4.5 Mgha(-1). Only after reaching a steady-state, straws can be used for trade-off, leaving 1.03 Mgha(-1)y(-1) of C input remain in the soil
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
- …
