Machine Learning-Driven Prediction of Microplastic Aging Processes and Environmental Risk Assessment Across...

Area of Science:

• Environmental Science
• Data Science
• Toxicology

Background:

• Current machine learning (ML) for microplastic (MP) studies uses limited lab data and single environmental models, failing to capture real-world complexities.
• Existing methods neglect cross-media transport and environmental interactions crucial for understanding MP lifecycles.

Purpose of the Study:

• To shift ML application from fragmented data-fitting to a holistic, privacy-preserving, physics-aware strategy for reconstructing MP aging and assessing risks.
• To develop advanced models for analyzing spatiotemporal aging trajectories and toxicological impacts of microplastics.

Main Methods:

• A novel probabilistic framework employing Bayesian inference for mechanistic fingerprinting to reconstruct the environmental history of field-sampled MPs.
• Introduction of the TRACE framework (Transport, Aging, Corona, Ecotoxicity) integrating physics-informed models and causal discovery.
• Advocacy for federated learning (FL) to enable secure, multi-institutional collaborative modeling without raw data sharing.

Main Results:

• The proposed framework enables reconstruction of MP environmental history and improves trajectory models for source attribution and risk assessment.
• The TRACE framework mechanistically links MP surface transformations to biological risks by modeling feedback loops between physicochemical evolution and eco-corona formation.
• Federated learning facilitates harmonization of heterogeneous datasets, overcoming privacy barriers in multi-institutional collaborations.

Conclusions:

• This cohesive strategy bridges laboratory-field disparities in MP research.
• It moves towards predictive, evidence-based, and targeted mitigation strategies for global plastic pollution.
• The approach enhances the potential of ML for comprehensive microplastic lifecycle assessment and risk evaluation.