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Impact of Microplastics on Oil Dispersion Efficiency in the Marine Environment

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  • Min Yang

    (Department of Civil Engineering, Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John’s, NL A1B 3X5, Canada)

  • Baiyu Zhang

    (Department of Civil Engineering, Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John’s, NL A1B 3X5, Canada)

  • Yifu Chen

    (Department of Civil Engineering, Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John’s, NL A1B 3X5, Canada)

  • Xiaying Xin

    (State Key Laboratory of Marine Pollution (SKLMP), School of Energy and Environment, City University of Hong Kong, Hong Kong SAR, China)

  • Kenneth Lee

    (Fisheries and Oceans Canada, Ecosystem Science, Ottawa, ON K1A 0E6, Canada)

  • Bing Chen

    (Department of Civil Engineering, Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John’s, NL A1B 3X5, Canada)

Abstract

Oil spill and microplastics (MPs) pollution has raised global concerns, due to the negative impacts on ocean sustainability. Chemical dispersants were widely adopted as oil-spill-treating agents. When MPs exist during oil dispersion, MP/oil-dispersant agglomerates (MODAs) are observed. This study explored how MPs affect oil-dispersion efficiency in oceans. Results showed that, under dispersant-to-oil volumetric ratio (DOR) 1:10 and mixing energy of 200 rpm, the addition of MPs increased the oil droplet size, total oil volume concentration, and oil-dispersion efficiency. Under DOR 1:25 and mixing energy of 120 rpm, the addition of MPs increased the oil droplet size but resulted in a decrease of total oil volume concentration and dispersion efficiency. Compared with the oil volume concentration, the oil droplet size may no longer be an efficient parameter for evaluating oil-dispersion efficiency with the existence of MODAs. A machine learning (ML)-based XGBRegressor model was further constructed to predict how MPs affected oil volume concentration and oil-dispersion efficiency in oceans. The research outputs would facilitate decision-making during oil-spill responses and build a foundation for the risk assessment of oil and MP co-contaminants that is essential for maintaining ocean sustainability.

Suggested Citation

  • Min Yang & Baiyu Zhang & Yifu Chen & Xiaying Xin & Kenneth Lee & Bing Chen, 2021. "Impact of Microplastics on Oil Dispersion Efficiency in the Marine Environment," Sustainability, MDPI, vol. 13(24), pages 1-13, December.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:24:p:13752-:d:701562
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    References listed on IDEAS

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    1. Andrius Vabalas & Emma Gowen & Ellen Poliakoff & Alexander J Casson, 2019. "Machine learning algorithm validation with a limited sample size," PLOS ONE, Public Library of Science, vol. 14(11), pages 1-20, November.
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