Analysis of the literature shows a remarkably consistent relationship between size and abundance of microplastics across different environmental matrices. (15th February 2023)

• Record Type: Journal Article
• Authors: Leusch, Frederic DL.; Lu, Hsuan-Cheng; Perera, Kushani; Neale, Peta A.; Ziajahromi, Shima
• Is Part Of: Environmental pollution. Volume 319(2023)
• Publication Date: 2023-02-15
• Subjects: Microplastics -- Distribution -- Occurrence -- Soil -- Water -- Air; Pollution -- Periodicals; Pollution -- Environmental aspects -- Periodicals; Environmental Pollution -- Periodicals; Pollution -- Périodiques; Pollution -- Aspect de l'environnement -- Périodiques; Pollution -- Effets physiologiques -- Périodiques; Pollution; Pollution -- Environmental aspects; Periodicals; Electronic journals; 363.73
• DOI: 10.1016/j.envpol.2022.120984
• Languages: English
• ISSNs: 0269-7491

Abstract

Abstract: Microplastics come in a variety of shapes, polymer types and sizes. Due to the lack of a harmonised approach to analyse and quantify microplastics, there are huge disparities in size detection limits and size classifications used in the literature. This has caused large variations in reported microplastic data and has made comparing microplastic abundance between studies extremely challenging. Herein, we applied a simple mathematical approach that allows for a meaningful comparison between size and abundance (number of particles) of microplastics irrespective of the size classifications used. This method was validated using two separate datasets (microplastics in air and sediment) and applied to re-analyse 127 publications reporting microplastics in various environmental matrices. We demonstrate a strong negative linear relationship between microplastic concentrations and their sizes with comparable slopes across all matrices. Using this method, it is possible to compare the concentration of microplastics of various sizes between studies. It also allows estimation of the abundance of microplastics of a specific size where data are not available. This enables researchers to predict environmentally relevant concentrations of microplastics (particularly for smaller microplastics) and provide realistic exposure scenarios in future toxicity studies, which will greatly improve our understanding of the risks that microplastics pose to living organisms. Graphical abstract: Image 1 Highlights: New mathematical model developed to standardise microplastic (MP) concentration. Occurrence of MPs follows a remarkably similar distribution, irrespective of matrix. The model allows predictions of abundance of MPs for sizes with no or limited data. This enables better estimation of MP concentrations for realistic exposure scenarios.