Machine Learning Approaches for Microplastic Pollution Analysis in Mytilus galloprovincialis in the Western Black Sea

Maria Emanuela Mihailov, Alecsandru Vladimir Chiroșca (), Elena Daniela Pantea () and Gianina Chiroșca
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Maria Emanuela Mihailov: Research-Development and Innovation Center, Maritime Hydrographic Directorate “Comandor Alexandru Catuneanu”, Fulgerului Street No. 1, 900218 Constanta, Romania
Alecsandru Vladimir Chiroșca: Faculty of Physics, University of Bucharest, Atomiștilor 405, 077125 Magurele, Romania
Elena Daniela Pantea: Ecology and Marine Biology Department, National Institute for Marine Research and Development (NIMRD) “Grigore Antipa”, 300 Mamaia Blvd., 900581 Constanta, Romania
Gianina Chiroșca: National R&D Institute for Optoelectronics “INOE 2000”, Atomistilor 409, 077125 Magurele, Romania

Sustainability, 2025, vol. 17, issue 12, 1-22

Abstract: Microplastic pollution presents a significant and rising risk to both ecological integrity and the long-term viability of economic activities reliant on marine ecosystems. The Black Sea, a region sustaining economic sectors such as fisheries, tourism, and maritime transport, is increasingly vulnerable to this form of contamination. Mytilus galloprovincialis , a well-established bioindicator, accumulates microplastics, providing a direct measure of environmental pollution and indicating potential economic consequences deriving from degraded ecosystem services. While previous studies have documented microplastic pollution in the Black Sea, our paper specifically quantified microplastic contamination in M. galloprovincialis collected from four sites along the western Black Sea coast, each characterised by distinct levels of anthropogenic influence: Midia Port, Constanta Port, Mangalia Port, and 2 Mai. We used statistical analysis to quantify site-specific microplastic contamination in M. galloprovincialis and employed machine learning to develop models predicting accumulation patterns based on environmental variables. Our findings demonstrate the efficacy of mussels as bioindicators of marine plastic pollution and highlight the utility of machine learning in developing effective predictive tools for monitoring and managing marine litter contamination in marine environments, thereby contributing to sustainable economic practices.

Keywords: Mytilus galloprovincialis; marine litter; sustainable economic practices; Black Sea; machine learning; microplastic accumulation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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