Issue 12, 2023

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

Abstract

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.

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

Supplementary files

Article information

Article type
Paper
Submitted
28 Apr. 2023
Accepted
17 Okt. 2023
First published
27 Okt. 2023
This article is Open Access
Creative Commons BY license

Environ. Sci.: Adv., 2023,2, 1629-1640

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

H. D. M. Back, D. Pottmaier, C. Kneubl Andreusi and O. E. Alarcon, Environ. Sci.: Adv., 2023, 2, 1629 DOI: 10.1039/D3VA00106G

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

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