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PhD: Decision support framework for plastic clean-up technologies in rivers and estuaries: minimizing unintentional bycatch while maintaining efficient plastic removal under realistic environmental conditions

Principal funding codes: 7003 - FWO fellowships
Period: November 2021 till October 2025
Status: Completed

Thesaurus terms (Micro)plastics; Estuaries; Marine pollution; PhD project
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Institutes (3)  Top | Publications | Datasets 

Abstract

Plastic pollution is ubiquitous throughout the environment, and its potentially hazardous effects have become of great concern in past years. More than 80% of the plastics enter the marine environment  from land-based sources via rivers. To prevent plastic litter from reaching the marine environment, where it spreads rapidly and it is difficult to extract a posteriori, more than thirty plastic clean-up technologies are commercially available to date to remove plastic from inland waters. The focus of these technologies is to collect plastic most efficiently, but less attention is given to their potential bycatch, such as living organisms and organic debris. The organisms and organic debris accidentally and unintentionally bycaught often 
could provide essential functions in the rivers and estuaries’ ecosystems where clean-up technologies are deployed. River estuaries are regarded as regions of high biodiversity, providing feeding grounds, shelter, and nurseries for some threatened and commercially important species. To date, an objective tool to quantify and assess the bycatch of plastic clean-up technologies is lacking. 

This study aims to resolve this knowledge gap by creating, with the aid of a probabilistic model, a decision framework for the plastic clean-up technologies. Such a decision framework will guide policymakers and river managers to wisely select the best-suited technology to remove plastics from rivers, while mitigating potential collateral damage.


Datasets (3)  Top | Institutes | Publications 
  • Data
    • Leone, G.; Catarino, A.; De Keukelaere, L.; Bossaer, M.; Knaeps, E.; Everaert, G.; Flanders Marine Institute (VLIZ); Flemish Institute for Technological Research (VITO): Belgium; (2025): Hyperspectral reflectance dataset for dry, wet and submerged plastics in clear and turbid water. Marine Data Archive., more
    • Leone, G.; Catarino, A.I.; Pauwels, I.; Bossaer, M.; Conda Oco, R.; Chu, C.Y.; Troch, P.; Goethals, P.; Everaert, G.; Flanders Marine Institute (VLIZ); Ghent University (UGent); Research Institute for Nature and Forest; Oceans & Lakes: Belgium; (2024): Experimental data on the proportion of biota and plastic caught by two plastic clean-up mechanisms. Marine Data Archive., more
    • Leone, G.; Moulaert, I.; Devriese, L.I.; Sandra, M.; Pauwels, I.; Goethals, P.L.M.; Everaert, G.; Catarino, A.I.; Research Group Aquatic Ecology: Ghent University; Flanders Marine Institute (VLIZ); Aquatic Management: Research Institute for Nature and Forest: Belgium; (2023): Plastic clean-up and prevention overview. Marine Data Archive., more

Publications (5)  Top | Institutes | Datasets 
  • Leone, G.; Moulaert, I.; Devriese, L.I.; Sandra, M.; Pauwels, I.; Goethals, P.L.M.; Everaert, G.; Catarino, A.I. (2023). A comprehensive assessment of plastic remediation technologies. Environ. Int. 173: 107854. https://dx.doi.org/10.1016/j.envint.2023.107854, more
  • Falk-Andersson, J.; Rognerud, I.; De Frond, H.; Leone, G.; Karasik, R.; Diana, Z.; Dijkstra, H.; Ammendolia, J.; Eriksen, M.; Utz, R.; Walker, T.R.; Fürst, K. (2023). Cleaning up without messing up: Maximizing the benefits of plastic clean-up technologies through new regulatory approaches. Environ. Sci. Technol. 57(36): 13304-13312. https://dx.doi.org/10.1021/acs.est.3c01885, more
  • Leone, G.; Catarino, A.I.; De Keukelaere, L.; Bossaer, M.; Knaeps, E.; Everaert, G. (2023). Hyperspectral reflectance dataset of pristine, weathered, and biofouled plastics. ESSD 15(2): 745-752. https://dx.doi.org/10.5194/essd-15-745-2023, more
  • Leone, G.; Catarino, A.I.; Pauwels, I.; Mani, T.; Tishler, M.; Egger, M.; Forio, M.A.E.; Goethals, P.L.M.; Everaert, G. (2022). Integrating Bayesian Belief Networks in a toolbox for decision support on plastic clean-up technologies in rivers and estuaries. Environ. Pollut. 296: 118721. https://dx.doi.org/10.1016/j.envpol.2021.118721, more
  • Leone, G.; Catarino, A.; Pauwels, I.; Bossaer, M.; Conda Oco, R.; Chu, C.-Y.; Troch, P.; Goethals, P.L.M.; Everaert, G. (2026). Plastic clean-up mechanisms: Experimental insights on their bycatch. Mar. Pollut. Bull. 226(Spec. Issue): 119326. https://dx.doi.org/10.1016/j.marpolbul.2026.119326, more

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