Predicting Heavy Metals Adsorption on Microplastics and Unraveling the Adsorption Mechanism with Machine Learning Methods

Machine Learning Approaches for Predicting Microplastic Removal

AI summary · Read the abstract

Researchers applied Bayesian optimization combined with machine learning models — including boosted regression trees, neural networks, and support vector regression — to predict microplastic removal during coagulation, finding the BOA-BRT hybrid outperformed conventional methods by up to 71%, with microplastic particle size identified as the most influential variable.

Heavy metal sorption in marine microplastic over time

AI summary

This meta-analysis investigated pathogen associations in marine plastisphere communities, synthesizing metagenomics and culture-based data on pathogenic microorganisms colonizing plastic surfaces in ocean environments. The analysis demonstrates that plastic marine debris enriches for potential pathogens at higher rates than surrounding seawater.

Revealing the key factors of synergistic transport of microplastics and heavy metals in groundwater: Batch experiments, density functional theory calculations, molecular dynamics simulations, and machine learning modeling

Machine Learning to Predict the Adsorption Capacity of Microplastics

AI summary · Read the abstract

Researchers developed machine learning models to predict the adsorption capacity of microplastics for chemical pollutants, providing a computational tool to better understand how microplastics act as vectors for contaminant dispersal in aquatic environments.

A Novel Approach for Fast Microplastic Quantification in Sediments Using Machine Learning—Spectrometer Combinations

AI summary · Read the abstract

Researchers combined visible near-infrared and FTIR spectroscopy with machine learning models (SVR, PLSR, BPNN) to rapidly estimate concentrations of PE, PP, PS, and PVC microplastics in river and loess sediments without extraction or manual counting. Faster, non-destructive quantification methods are crucial for scaling up microplastic monitoring across diverse environmental matrices and enabling consistent cross-study comparisons of pollution levels.