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A model for the size distribution of marine microplastics: A statistical mechanics approach

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  • Kunihiro Aoki
  • Ryo Furue

Abstract

The size distribution of marine microplastics provides a fundamental data source for understanding the dispersal, break down, and biotic impacts of the microplastics in the ocean. The observed size distribution at the sea surface generally shows, from large to small sizes, a gradual increase followed by a rapid decrease. This decrease has led to the hypothesis that the smallest fragments are selectively removed by sinking or biological uptake. Here we propose a new model of size distribution, focusing on the fragmentation of marine plastics. The model is inspired by ideas from statistical mechanics. In this model, the original large plastic piece is broken into smaller pieces once by the application of “energy” or work by waves or other processes, under two assumptions, one that fragmentation into smaller pieces requires larger energy and the other that the occurrence probability of the “energy” exponentially decreases toward larger energy values. Our formula well reproduces observed size distributions over wide size ranges from micro- to mesoplastics. According to this model, the smallest fragments are fewer because large “energy” required to produce such small fragments occurs more rarely.

Suggested Citation

  • Kunihiro Aoki & Ryo Furue, 2021. "A model for the size distribution of marine microplastics: A statistical mechanics approach," PLOS ONE, Public Library of Science, vol. 16(11), pages 1-19, November.
    • Handle: RePEc:plo:pone00:0259781
    DOI: 10.1371/journal.pone.0259781
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    References listed on IDEAS

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    1. Katsiaryna Pabortsava & Richard S. Lampitt, 2020. "High concentrations of plastic hidden beneath the surface of the Atlantic Ocean," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
    2. Fairley, Iain & Lewis, Matthew & Robertson, Bryson & Hemer, Mark & Masters, Ian & Horrillo-Caraballo, Jose & Karunarathna, Harshinie & Reeve, Dominic E., 2020. "A classification system for global wave energy resources based on multivariate clustering," Applied Energy, Elsevier, vol. 262(C).
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