Sorption of Nickel(II) on a Calcareous Aridisol Soil, China: Batch, XPS, and EXAFS Spectroscopic Investigations

Predicting Heavy Metal Adsorption on Soil with Machine Learning and Mapping Global Distribution of Soil Adsorption Capacities

Studying heavy metal adsorption on soil is important for understanding the fate of heavy metals and properly assessing the related environmental risks. Existing experimental methods and traditional models for quantifying adsorption, however, are time-consuming and ineffective. In this study, we developed machine learning models for the soil adsorption of six heavy metals (Cd(II), Cr(VI), Cu(II), Pb(II), Ni(II), and Zn(II)) using 4420 data points (1105 soils) extracted from 150 journal articles. After a comprehensive comparison, our results showed that the gradient boosting decision tree had the best performance for a combined model based on all the data. The Shapley additive explanation method was used to identify the feature importance and the effects of these features on the adsorption, based on which six independent models were developed for the six metals to achieve better model performance than the combined model. Using these independent models, the global distribution of heavy metal adsorption capacities on soils was predicted with known soil properties. Reversed models, including one combined model for all the six metals and six independent models, were also built using the same data sets to predict the heavy metal concentration in water when the adsorbed amount is known for a soil/sediment.

The transfer of trace metals in the soil-plant-arthropod system

Essential and non-essential trace metals are capable of causing toxicity to organisms above a threshold concentration. Extensive research has assessed the behaviour of trace metals in biological and ecological systems, but has typically focused on single organisms within a trophic level and not on multi-trophic transfer through terrestrial food chains. This reinforces the notion of metal toxicity as a closed system, failing to consider one trophic level as a pollution source to another; therefore, obscuring the full extent of ecosystem effects. Given the relatively few studies on trophic transfer of metals, this review has taken a compartment-based approach, where transfer of metals through trophic pathways is considered as a series of linked compartments (soil-plant-arthropod herbivore-arthropod predator). In particular, we consider the mechanisms by which trace metals are taken up by organisms, the forms and transformations that can occur within the organism and the consequences for trace metal availability to the next trophic level. The review focuses on four of the most prevalent metal cations in soil which are labile in terrestrial food chains: Cd, Cu, Zn and Ni. Current knowledge of the processes and mechanisms by which these metals are transformed and moved within and between trophic levels in the soil-plant-arthropod system are evaluated. We demonstrate that the key factors controlling the transfer of trace metals through the soil-plant-arthropod system are the form and location in which the metal occurs in the lower trophic level and the physiological mechanisms of each organism in regulating uptake, transformation, detoxification and transfer. The magnitude of transfer varies considerably depending on the trace metal concerned, as does its toxicity, and we conclude that biomagnification is not a general property of plant-arthropod and arthropod-arthropod systems. To deliver a more holistic assessment of ecosystem toxicity, integrated studies across ecosystem compartments are needed to identify critical pathways that can result in secondary toxicity across terrestrial food-chains.

Development of highly sensitive 1,4-dioxane sensor with semiconductor NiO-doped Nd2O3 nanostructures by electrochemical approach

In this approach, selective and sensitive 1,4-dioxane sensor probe was developed based on facile NiO@Nd₂O₃ nanocomposites embedded GCE for the safety management of environment and health care fields at a large scale.