The adsorption of Fe(III) onto the surface of microplastics (MPs) enhances their toxicity and mobility in aquatic environments, posing a serious threat to human health and ecosystem balance. This study investigated the adsorption mechanism and influencing factors of Fe(III) on three types of MPs with varying particle sizes and aging degrees using batch experiments in freshwater and saltwater. Machine learning (ML) techniques were employed to predict the adsorption capacity and conduct attribution analysis. The results showed that Fe(III) adsorption in both freshwater and saltwater followed Pseudo-First-Order kinetics and Langmuir isotherms, indicating a monolayer homogeneous physical reaction driven by oxygen-containing functional groups, hydrogen bonds and aromatic rings on the MP surface. The adsorption capacity of MPs for Fe(III) was higher in freshwater than in saltwater, and was positively correlated with the aging degree and pH value, but negatively correlated with the particle size. Among the tested ML models, the Random Forest and Gaussian Process Regression models with Bayesian Optimization performed well in predicting adsorption capacity, with pH value and aging degree identified as key factors based on SHAP analysis. This study conducted a comprehensive investigation of the adsorption behavior between MPs and Fe(III) in water, providing valuable insights for the risk assessment and prevention of MP and Fe(III) pollution in aquatic environments.

Coral sand dissolution may weaken particle strength and compromise the foundation stability of reclaimed reef islands. However, its dissolution mechanisms and associated effects under saline–freshwater conditions remain poorly quantified. This study combined dissolution experiments, inverse hydrogeochemical modeling, statistical analysis, machine learning, and multiscale characterization to identify dominant mechanisms, quantify their contributions, determine key factors, and evaluate alterations in reef island groundwater. Results demonstrated that the dissolution capacity of coral sand (q) ranged from 0.04 to 0.24 mg, increasing with salinity but decreasing with pH and particle size. Coral sand dissolution was mainly controlled by carbonic-acid-mediated dissolution and Ca-Na cation exchange. The cation exchange contribution (p) reached 63–95% under alkaline conditions and increased with pH, salinity, and particle size. Random Forest accurately predicted q and p, with R² values of 0.875 and 0.980, respectively. SHAP analysis identified salinity and pH as the dominant predictors of q and p, respectively. With increasing q, the relative aragonite content decreased, whereas calcite content increased; particle surfaces became rougher, BET specific surface area and porosity increased by 5–28% and 2–10.5%, respectively, and single-particle compressive strength decreased by 70–87%. These findings provide a theoretical basis for assessing stability and reinforcing coral sand foundations on artificial islands.