ABSTRACT
Trace elements in plants primarily derive from soils, subsequently influencing human health through the food chain. Therefore, it is essential to understand the relationship of trace elements between plants and soils. Since trace elements from soils absorbed by plants is a nonlinear process, traditional multiple linear regression (MLR) models failed to provide accurate predictions. Zinc (Zn) was chosen as the objective element in this case. Using soil geochemical data, artificial neural networks (ANN) were utilized to develop predictive models that accurately estimated Zn content within wheat grains. A total of 4036 topsoil samples and 73 paired rhizosphere soil-wheat samples were collected for the simulation study. Through Pearson correlation analysis, the total content of elements (TCEs) of Fe, Mn, Zn, and P, as well as the available content of elements (ACEs) of B, Mo, N, and Fe, were significantly correlated with the Zn bioaccumulation factor (BAF). Upon comparison, ANN models outperformed MLR models in terms of prediction accuracy. Notably, the predictive performance using ACEs as input factors was better than that using TCEs. To improve the accuracy, a two-step model was established through multiple testing. Firstly, ACEs in the soil were predicted using TCEs and properties of the rhizosphere soil as input factors. Secondly, the Zn BAF in grains was predicted using ACE as input factors. Consequently, the content of Zn in wheat grains corresponding to 4036 topsoil samples was predicted. Results showed that 85.69 % of the land was suitable for cultivating Zn-rich wheat. This finding offers a more accurate method to predict the uptake of trace elements from soils to grains, which helps to warn about abnormal levels in grains and prevent potential health risks.
ABSTRACT
High loads of phthalate esters (PAEs) in background regions can be directly attributed to the local sources, and their association with soil particles may determine the environment behaviors. However, little is known about the particle-size specific distributions of PAEs in soils from point source to the surroundings. In this study, 12 PAE congeners were measured in clay (< 2 µm), silt (2-63 µm) and sand fractions (63-250 µm) from surficial soils and soil profiles (0-200 cm) around the Lhasa landfill. The total concentrations of PAEs in bulk soils varied from 0.44 to 22.3 µg/g, with a dominance of bis(2-ethylhexyl) phthalate (DEHP). The clay-sorbed PAEs exhibited a decreasing trend with the increasing distance from landfill. This distribution pattern was well described by the Gaussian air pollution model, suggesting the airborne particles/gaseous transport of clay-sorbed PAEs. The Boltzmann equation explained the spatial variation of silt-sorbed PAEs, reflecting the atmospheric dispersion of silt-sorbed PAEs. In comparison, the sand-sorbed PAEs in surrounding soils showed downslope accumulation possibly due to the aeolian transport of sand particles. Half-life of the most abundant PAE congener DEHP was assumed based on the soil inventories from observed concentration and the Level III fugacity model simulations, and the results indicated significant longer half-life of DEHP in deeper soils (~24,000 h) than in surficial soils (5500 h). This study elucidates that the distribution and fate of soil PAEs would depend on their association with particles in the source area, and the relative stability of DEHP in deeper soils would further increase PAE inventory in soil compartment.
