Impact of Microplastics on Oil Dispersion Efficiency in the Marine Environment

Min Yang, Baiyu Zhang, Yifu Chen, Xiaying Xin, Kenneth Lee and Bing Chen
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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

Sustainability, 2021, vol. 13, issue 24, 1-13

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.

Keywords: microplastics; oil spill; dispersant; oil volume concentration; mixing energy; machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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