Intersections between materials science and marine plastics to address environmental degradation drivers: a machine learning approach

Plastics are an integral part of the material structure in modern societies. However, their widespread contamination in the environment raises concerns regarding the sustainable use of these materials. Plastic pollution research has accelerated rapidly in the past 20 years and developed as a broad and multidisciplinary field, due to its complex nature. Research on the fate of plastics in the environment, specifically their dispersal and degradation, is one of the pillars in the field. In this study, we used machine learning and text mining tools to bridge the marine plastics community to the existing knowledge from polymer science regarding degradation drivers. Topic modelling enabled visualising hot topic trends in marine plastics research and filtering for relevant publications with minimal expert intervention. The recurrence of drivers was verified in the literature, indicating particular areas of focus on the marine degradation of plastics. The results show weathering conditions to be more recurrent than polymer properties and the latter to be rarely discussed in depth. Also, biodegradation is found to be a hot topic in the field, while degradation caused by abiotic factors is less addressed. This may be necessary as polymer engineering is traditionally less concerned about the end-of-life of plastic products. Finally, we argue that not only a deep understanding of plastics from polymer science should aid fundamental degradation studies by the marine plastics community, but also that the latter has the opportunity to largely contribute to the former by filling the gaps it has left.