Experimental study of buoyant macro- and mesoplastic trapping by Spartina maritima
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Nuñez Perez, Paula; Pérez García, Laura; Rezaee, Seyed Meysam; Bárcena Gómez, Javier Francisco; García Gómez, AndresFecha
2025Derechos
© 2025 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Publicado en
Marine Pollution Bulletin, 2025, 216, 118005
Editorial
Elsevier Ltd
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Palabras clave
Macro− /mesoplastics
Plastic debris trapping
Spartina maritima
Estuaries
Salt marshes
Laboratory experiments
Resumen/Abstract
A set of laboratory experiments was conducted to assess the trapping efficiency of buoyant plastic debris by the estuarine vegetation Spartina maritima. Different hydrodynamic conditions typical of salt marshes were simulated in a hydraulic flume. These conditions included varying water levels between 40% and 90% of stem height, wind speeds up to 2 m/s, and unidirectional current velocities between 0.1 and 0.3 m/s. Moreover, three vegetation densities (small/medium/high) and nine plastic debris types varying in shape (elongated/two-dimensional/three-dimensional) and size (macro/meso) were tested. The results indicate that Spartina maritima functions as a natural trap. Specifically, the study highlights that lower surface velocities, higher stem densities, greater emergent heights, and larger debris sizes significantly enhance trapping efficiency. It was also inferred that for a Spartina maritima density comparable to that observed in marshes, the dominance of surface velocity or debris size effects on trapping efficiency is primarily dependent on debris shape. Consequently, surface velocity has a greater impact on two-dimensional elements, whereas debris size is more significant for three-dimensional elements. Finally, a preliminary trapping model was developed to integrate all the aforementioned variables. This model has the potential to enhance the accuracy of numerical predictions regarding the transport and fate of plastic debris using Lagrangian modeling, and can be further refined by incorporating additional data.
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