Integration of machine learning and meta-analysis reveals the behaviors and mechanisms of antibiotic adsorption on microplastics

Microplastics (MPs) can adsorb antibiotics (ATs) to cause combined pollution in the environment. Research on this topic has been limited to specific types of MPs and ATs, resulting in inconsistent findings, particularly for the influencing factors and adsorption mechanisms. Therefore, this study combined meta-analysis and machine learning to analyze a dataset comprising 6805 records from 123 references. The results indicated that polyamide has the highest adsorption capacity for ATs, which is primarily attributed to the formation of hydrogen bonds by its N-H groups, and MPs exhibited the strongest affinity for chlortetracycline because the CO and -Cl groups in chlortetracycline form hydrogen and halogen bonds with MPs. Moreover, the particle size, MP and AT concentrations, and pH were key factors affecting the adsorption process with notable interaction effects. Hydrogen bonding and electrostatic interaction were commonly involved in the adsorption of ATs onto MPs. Finally, an interactive graphical user interface was deployed to predict the adsorption amount, affinity constant, and maximum adsorption capacity of MPs for ATs, with results aligning well with the latest published data. This study provides crucial insights into the behavior of MPs carrying ATs, thereby facilitating accurate assessment of the combined environmental risks of them.