Major overlap in plant and soil organic carbon hotspots across Africa.

Terrestrial plant and soil organic carbon stocks are critical for regulating climate change, enhancing soil fertility, and supporting biodiversity. While a global-scale decoupling between plant and soil organic carbon has been documented, the hotspots and interconnections between these two carbon compartments across Africa, the second-largest continent on the planet, have been significantly overlooked. Here, we have compiled over 10,000 existing soil organic carbon observations to generate a high-resolution map, illustrating the distribution pattern of soil organic carbon in Africa.

Prediction of the concentration of cadmium in agricultural soil in the Czech Republic using legacy data, preferential sampling, Sentinel-2, Landsat-8, and ensemble models.

The current study assesses and predicts cadmium (Cd) concentration in agricultural soil using two Cd datasets, namely legacy data (LD) and preferential sampling-legacy data (PS-LD), along with four streams of auxiliary datasets extracted from Sentinel-2 (S2) and Landsat-8 (L8) bands. The study was divided into two contexts: Cd prediction in agricultural soil using LD, ensemble models, 10 and 20 m spatial resolution of S2 and L8 (context 1), and Cd prediction in agricultural soil using PS-LD, ensemble models and 10 and 20 m spatial resolution of S2 and L8 (context 2). In context 1, ensemble 1, L8 with PS-LD was the cumulative optimal approach that predicted Cd in agricultural soil with a higher R value of 0.

Prediction of the concentration of antimony in agricultural soil using data fusion, terrain attributes combined with regression kriging.

Potentially toxic elements in agricultural soils are primarily derived from anthropogenic and geogenic sources. This study aims to predict and map antimony (Sb) concentration in soil using multiple regression kriging in two distinct modeling approaches, namely Sb prediction using data fusion coupled with regression kriging (scenario 1) and Sb prediction using data fusion, terrain attributes, and regression kriging (scenario 2). Cubist regression kriging (cubist_RK), conditional inference forest regression kriging (CIF_RK), extreme gradient boosting regression kriging (EGB_RK) and random forest regression kriging (RF_RK) were the modeling techniques used in the estimation of Sb concentration in agricultural soil.

Optimal zinc level and uncertainty quantification in agricultural soils via visible near-infrared reflectance and soil chemical properties.

Zinc (Zn) is a vital element required by all living creatures for optimal health and ecosystem functioning. Therefore, several researchers have modeled and mapped its occurrence and distribution in soils. Nonetheless, leveraging model predictive performances while coupling information derived from visible near-infrared (Vis-NIR) and soils (i.

Combination of enrichment factor and positive matrix factorization in the estimation of potentially toxic element source distribution in agricultural soil.

The study intended to assess the level of pollution of potential toxic elements (PTEs) at different soil depths and to evaluate the source contribution in agricultural soil. One hundred and two soil samples were collected for both topsoil (51), and the subsoil (51) and the content of PTEs (Cr, Cu, Cd, Mn, Ni, Pb, As and Zn) were determined using inductively coupled plasma-optical emission spectroscopy (ICP-OES). The concentrations of Zn and Cd in both soil horizons indicated that the current study levels were higher than the upper continental crust (UCC), world average value (WAV), and European average values (EAV).

Prediction of nickel concentration in peri-urban and urban soils using hybridized empirical bayesian kriging and support vector machine regression.

Soil pollution is a big issue caused by anthropogenic activities. The spatial distribution of potentially toxic elements (PTEs) varies in most urban and peri-urban areas. As a result, spatially predicting the PTEs content in such soil is difficult.

A geostatistical approach to estimating source apportionment in urban and peri-urban soils using the Czech Republic as an example.

Unhealthy soils in peri-urban and urban areas expose individuals to potentially toxic elements (PTEs), which have a significant influence on the health of children and adults. Hundred and fifteen (n = 115) soil samples were collected from the district of Frydek Mistek at a depth of 0-20 cm and measured for PTEs content using Inductively coupled plasma-optical emission spectroscopy. The Pearson correlation matrix of the eleven relevant cross-correlations suggested that the interaction between the metal(loids) ranged from moderate (0.

An in-depth human health risk assessment of potentially toxic elements in highly polluted riverine soils, Příbram (Czech Republic).

Environmental pollution by potentially toxic element (PTE) and the associated health risks in humans are increasingly becoming a global challenge. The current study is an in-depth assessment of PTEs including the often studied lead (Pb), manganese (Mn), zinc (Zn), arsenic (As) and the less-studied titanium (Ti), rubidium (Rb), strontium (Sr), zirconium (Zr), barium (Ba) and thorium (Th) in highly polluted floodplain topsoil samples from the Litavka River, Czech Republic. Soil chemical properties including carbon (C) and reaction (pH_HO) together with iron (Fe) were assessed in the same soils.

Comparison of Cubist models for soil organic carbon prediction via portable XRF measured data.

Soil organic carbon (SOC) tends to form complexes with most metallic ions within the soil system. Relatively few studies compare SOC predictions via portable X-ray fluorescence (pXRF) measured data coupled with the Cubist algorithm. The current study applied three different Cubist models to estimate SOC while using several pXRF measured data.

Trend analysis of global usage of digital soil mapping models in the prediction of potentially toxic elements in soil/sediments: a bibliometric review.

The rising and continuous pollution of the soil from anthropogenic activities is of great concern. Owing to this concern, the advent of digital soil mapping (DSM) has been a tool that soil scientists use in this era to predict the potentially toxic element (PTE) content in the soil. The purpose of this paper was to conduct a review of articles, summarize and analyse the spatial prediction of potentially toxic elements, determine and compare the models' usage as well as their performance over time.

Source apportionment, contamination levels, and spatial prediction of potentially toxic elements in selected soils of the Czech Republic.

The sustenance of humans and livestock depends on the protection of the soil. Consequently, the pollution of the soil with potentially toxic elements (PTEs) is of great concern to humanity. The objective of this study is to investigate the source apportionment, concentration levels and spatial distribution of PTEs in selected soils in Frýdek-Místek District of the Czech Republic.

Zirconium as a suitable reference element for estimating potentially toxic element enrichment in treated wastewater discharge vicinity.

The suitability of a reference element or normalizer used in assessing soil contamination levels using enrichment factor (EF) is important for soil quality assessment and monitoring. This study evaluated the results of using three reference elements Ti, Fe, and Zr for EF determination of Rb and Sr in soils within treated wastewater discharge vicinity, Central Botswana. The upper continental crust (UCC), world average values (WAV), and the local background values (LBV) were used in EF assessment of eight pedons.