Mineralogical and Geochemical Nature of Calcareous Vineyard Soils from Alcubillas (La Mancha, Central Spain)

Insights into the Effect of Aggregate Sizes on the Soil Radiation Interaction Properties Based on X-ray Fluorescence

Soils subjected to disaggregation can break into aggregates of different sizes composed of sand, clay, and silt particles. Each aggregate contains different oxides, which can vary according to the aggregate size and influence its properties, such as the radiation interaction parameters. These parameters are relevant in the evaluation of radiation shielding and soil physical properties. Thirteen tropical/subtropical soils of contrasting textures (clayey and loamy/sandy) with two aggregate sizes (2-1 mm and <45 μm) were studied. The radiation parameters analyzed were the atomic (σ_A), electronic (σ_E), and molecular (σ_M) cross-sections; the effective atomic number (Z_eff); and the electron density (N_el). We verified that the aggregate sizes affected the major oxides (SiO₂, Al₂O₃, Fe₂O₃). In general, the attenuation coefficient and Z_eff were sensitive to the clayey soils' aggregate sizes (low photon energies). However, the loamy/sandy soils did not exhibit differences among the parameters. As the photon energy increased, only Z_eff presented differences for most soils. We also verified that σ_M, Z_eff, and N_el were the most sensitive parameters to the soil composition. Although the soil chemical composition was influenced by the studied aggregate sizes, the radiation parameters exhibited differences for only some of these parameters. This means that the aggregate size is practically irrelevant when radiation parameters are determined based on X-ray fluorescence.

Application of Geostatistical Analysis and Random Forest for Source Analysis and Human Health Risk Assessment of Potentially Toxic Elements (PTEs) in Arable Land Soil

Arable land soil is one of the most precious natural resources of Earth, it provides the fundamental material and numerous resources essential for the development of human society. To determine the pollution of potential toxic factors in the surface soil of cultivated land and its risks to human health, concentrations of five different potentially toxic elements (PTEs) were detected in 1109 soil samples collected in Xiangzhou, China, in 2019. In this study, health risk assessment was used to judge the degree of pollution in the study area, the result of Geographic Information System (GIS) was as used to research the spatial distribution characteristics of PTEs, and random forest (RF) was used to assess the natural and man-made influencing factors. We investigated the sources of PTEs through quantifying the indicators, which gave further insights. The main results are: (1) In arable land soil, the average content of PTEs is 0.14 mg/kg cadmium (Cd), 0.05 mg/kg mercury (Hg), 12.89 mg/kg arsenic (As), 29.23 mg/kg lead (Pb), and 78.58 mg/kg chromium (Cr), respectively. The content of As and Pb outpaced the background value of Hubei soil. (2) The human health risk assessment in Xiangzhou indicates that the most important exposure pathway is soil ingestion, occupied about 99% to health risks of PTEs; non-carcinogenic risk from exposure to As, Pb and Cr in soil was higher than the limit (overall potential risk index, HI > 1) for both children and adults. Moreover, carcinogenic risk postured by Cd, Cr, and As was higher than the limit (10^-4) through soil exposure for both children and adults, indicating that Cd, As, Pb and Cr in soil have significant effect on people's health through exposure. (3) We found that the increased PTEs in the arable land soil mainly originated from potential water sources, air and soil pollution sources, breeding farms, and mining areas.

Coupling Square Wave Anodic Stripping Voltammetry with Support Vector Regression to Detect the Concentration of Lead in Soil under the Interference of Copper Accurately

In this study, an effective method for accurately detecting Pb(II) concentration was developed by coupling square wave anodic stripping voltammetry (SWASV) with support vector regression (SVR) based on a bismuth-film modified electrode. The interference of different Cu²⁺ contents on the SWASV signals of Pb²⁺ was investigated, and a nonlinear relationship between Pb²⁺ concentration and the peak currents of Pb²⁺ and Cu²⁺ was determined. Thus, an SVR model with two inputs (i.e., peak currents of Pb²⁺ and Cu²⁺) and one output (i.e., Pb²⁺ concentration) was trained to quantify the above nonlinear relationship. The SWASV measurement conditions and the SVR parameters were optimized. In addition, the SVR mode, multiple linear regression model, and direct calibration mode were compared to verify the detection performance by using the determination coefficient (R²) and root-mean-square error (RMSE). Results showed that the SVR model with R² and RMSE of the test dataset of 0.9942 and 1.1204 μg/L, respectively, had better detection accuracy than other models. Lastly, real soil samples were applied to validate the practicality and accuracy of the developed method for the detection of Pb²⁺ with approximately equal detection results to the atomic absorption spectroscopy method and a satisfactory average recovery rate of 98.70%. This paper provided a new method for accurately detecting the concentration of heavy metals (HMs) under the interference of non-target HMs for environmental monitoring.