Adsorption behavior and mechanism of heavy metals onto microplastics: A meta-analysis assisted by machine learning.

Microplastics (MPs) have the potential to adsorb heavy metals (HMs), resulting in a combined pollution threat in aquatic and terrestrial environments. However, due to the complexity of MP/HM properties and experimental conditions, research on the adsorption of HMs onto MPs often yields inconsistent findings. To address this issue, we conducted a comprehensive meta-analysis assisted with machine learning by analyzing a dataset comprising 3340 records from 134 references. The results indicated that polyamide (PA) (ES = -1.26) exhibited the highest adsorption capacity for commonly studied HMs (such as Pb, Cd, Cu, and Cr), which can be primarily attributed to the presence of C=O and N-H groups. In contrast, polyvinyl chloride (PVC) demonstrated a lower adsorption capacity, but the strongest adsorption strength resulting from the halogen atom on its surface. In terms of HMs, metal cations were more readily adsorbed by MPs compared with metalloids and metal oxyanions, with Pb (ES = -0.78) exhibiting the most significant adsorption. As the pH and temperature increased, the adsorption of HMs initially increased and subsequently decreased. Using a random forest model, we accurately predicted the adsorption capacity of MPs based on MP/HM properties and experimental conditions. The main factors affecting HM adsorption onto MPs were HM and MP concentrations, specific surface area of MP, and pH. Additionally, surface complexation and electrostatic interaction were the predominant mechanisms in the adsorption of Pb and Cd, with surface functional groups being the primary factors affecting the mechanism of MPs. These findings provide a quantitative summary of the interactions between MPs and HMs, contributing to our understanding of the environmental behavior and ecological risks associated with their correlation.