Tracing Microplastic Aging Processes Using Multimodal Deep Learning: A Predictive Model for Enhanced Traceability

Abstract

The aging process of microplastics (MPs) affects their surface physicochemical properties, thereby influencing their behaviors in releasing harmful chemicals, adsorption of organic contaminants, sinking, and more. Understanding the aging process is crucial for evaluating MPs’ environmental behaviors and risks, but tracing the aging process remains challenging. Here, we propose a multimodal deep learning model to trace typical aging factors of aged MPs based on MPs’ physicochemical characteristics. A total of 1353 surface morphology images and 1353 Fourier transform infrared spectroscopy spectra were achieved from 130 aged MPs undergoing different aging processes, demonstrating that physicochemical properties of aged MPs vary from aging processes. The multimodal deep learning model achieved an accuracy of 93% in predicting the major aging factors of aged MPs. The multimodal deep learning model improves the model’s accuracy by approximately 5–20% and reduces prediction bias compared to the single-modal model. In practice, the established model was performed to predict the major aging factors of naturally aged MPs collected from typical environment matrices. The prediction results aligned with the aging conditions of specific environments, as reported in previous studies. Our findings provide new insights into tracing and understanding the plastic aging process, contributing more accurately to the environmental risk assessment of aged MPs.