Towards A universal settling model for microplastics with diverse shapes: Machine learning breaking morphological...

Accurately predicting the settling velocity of microplastics in aquatic environments is a prerequisite for reliably modeling their transport processes. An increasing number of settling models have been proposed for microplastics with fragmented, filmed, and fibrous morphologies, respectively. However, none of the existing models demonstrates universal applicability across all three morphologies. Scientists now have to rely on the predominate microplastic morphology extracted from filed samples to determine the appropriate settling model used for transport modeling. Given the spatiotemporal variability in morphologies and the coexistence of diverse morphologies of microplastics in natural aquatic environments, the extracted morphological information poses significant challenges in reliably determining the appropriate model. Evidently, to reliably model the transport of microplastics in aquatic environments, a universal settling model for microplastics with diverse shapes is warranted. To develop such a universal model, a unique shape factor, which can explicitly distinguish between the distinct morphologies of microplastics, was first proposed in this study by using a specifically-modified machine learning method. Using this newly-proposed shape factor, a universal model for predicting the settling velocity of microplastics with distinct morphologies was developed by using a physics-informed machine learning algorithm and then systematically evaluated against independent data sets. The newly-developed model enables reasonable predictions of the settling velocity of microplastic fragments, films, and fibers. In contrast to purely data-driven models, the newly-developed model is characterized by its transparent formulaic structure and physical interpretability, which is conducive to further expansion and improvement. This study can serve as a paradigm for future studies, inspiring the adoption of machine learning techniques in the development of physically-based models to investigate the transport of microplastics in aquatic environments.