Prediction of 3D spatial distribution, driving factors, and risk for heavy metal(loid)s in sediment of Dongdagou River based on ensemble learning.

Sediments contaminated with heavy metal(loid)s can cause harm to the environment. This study proposed the use of machine learning methods to predict, classify, and identify the three-dimensional distribution of heavy metal(loid)s in sediment. Based on the 1423 sampling data from Dongdagou, a total of 8 metal(loid)s and 18 covariates were used to train and test the model.

Ensemble learning-assisted quantitative identifying influencing factors of cadmium and arsenic concentration in rice grain based multiplexed data.

Rapid and accurate prediction of rice Cd (rCd) and rice As (rAs) bioaccumulation are important for assessing the safe utilization of rice. Currently, there is lack of comprehensive and systematic exploration of the factors of rCd and rAs. Herein, ensemble learning (EL) was first used to analysis the 23 factors in 8 categories (heavy metal pollution characteristics, soil properties, geographical characteristics, meteorological factors, socio-economic factors, environmental factors, rice type, and nutrient element) in typical regions of China based on the results of 193 research papers from 2000 to 2024 in Web of Science database.

Simulation, prediction and optimization for synthesis and heavy metals adsorption of schwertmannite by machine learning.

Due to its sea urchin-like structure, Schwertmannite is commonly applied for heavy metals (HMs) pollutant adsorption. The adsorption influence parameters of Schwertmannite are numerous, the traditional experimental enumeration is powerless. In recent years, machine learning (ML) has been gradually employed for adsorbent materials, but there is no comprehensive research on the Schwertmannite adsorbent.

A review of hydroxyapatite synthesis for heavy metal adsorption assisted by machine learning.

Heavy metals (HMs) represent a persistent and significant threat to aquatic ecosystems. Hydroxyapatite (HAp) has emerged as a utilized material in the remediation of environmental HMs, owing to its exceptionally high porosity, expansive surface area, and the presence of three-dimensional ordered channels. An in-depth study of the synthesis strategy of HAp and its adsorption properties can help reduce the cost of remediating HMs in aquatic environments and alleviate the water shortage.

Available heavy metals concentrations in agricultural soils: Relationship with soil properties and total heavy metals concentrations in different industries.

Heavy metal (HM) pollution in agricultural soils has arisen sharply in recent years. However, the impact of main factors on available HMs concentrations in agricultural soils of the three main industries (smelting, chemical and mining industry) is unclear. Herein, soil properties (pH, cation exchange capacity (CEC) and texture (sand, slit, clay)), total and available concentrations were concluded based on the results of 165 research papers from 2000 to 2023 in Web of Science database.