Identifying Marine Sources of Beached Plastics through a Bayesian Framework: Application to Southwest Netherlands

Bram Duinen; Mikael L. A. Kaandorp; Erik van Sebille

doi:10.1029/2021GL097214

Identifying Marine Sources of Beached Plastics through a Bayesian Framework: Application to Southwest Netherlands

Bram Duinen, Mikael L. A. Kaandorp, Erik van Sebille

Research output: Contribution to journal › Article › Academic › peer-review

Abstract

Beaches are thought to be a large reservoir for marine plastics. To protect vulnerable beaches, it is advantageous to have information on the sources of this plastic. Here, we develop a universally applicable Bayesian framework to map sources of plastic arriving on a specific beach. In this framework, we combine Lagrangian backtracking simulations of drifting particles with estimates of plastic input from coastlines, rivers and fisheries. The advantage over traditional Lagrangian simulations is that the Bayesian framework can consider information on known sources, and thus facilitates spatiotemporal source attribution for plastic arriving at the specified beach. We show that the main sources for our target beach in southwest Netherlands are the east coast of the UK, the Dutch coast, the English Channel (fisheries) and the Thames, Seine, Rhine and Trieux (rivers). We also show that floating time is a major uncertainty in source attribution using backtracking.

Original language	English
Article number	e2021GL097214
Pages (from-to)	1-9
Journal	Geophysical Research Letters
Volume	49
Issue number	4
Early online date	4 Feb 2022
DOIs	https://doi.org/10.1029/2021GL097214
Publication status	Published - 28 Feb 2022

Bibliographical note

Funding Information:
This study has been conducted using E.U. Copernicus Marine Service Information (E.U. Copernicus Marine Service Information,?2020; E.U. Copernicus Marine Service Information,?2021). FES2014 was produced by Noveltis, Legos and CLS and distributed by Aviso+, with support from Cnes (https://www.aviso.altimetry.fr). MLAK and EvS are part of the ?Tracking Of Plastic In Our Seas? (TOPIOS) project, supported through funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (Grant Agreement No. 715386).

Funding Information:
This study has been conducted using E.U. Copernicus Marine Service Information (E.U. Copernicus Marine Service Information, 2020 ; E.U. Copernicus Marine Service Information, 2021 ). FES2014 was produced by Noveltis, Legos and CLS and distributed by Aviso+, with support from Cnes ( https://www.aviso.altimetry.fr ). MLAK and EvS are part of the “Tracking Of Plastic In Our Seas” (TOPIOS) project, supported through funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (Grant Agreement No. 715386).

Publisher Copyright:
© 2022 The Authors.

Keywords

Bayes theorem
Lagrangian modeling
backtracking
marine pollution
plastic pollution

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1029/2021GL097214Licence: CC BY-NC

Geophysical Research Letters - 2022 - Duinen - Identifying Marine Sources of Beached Plastics Through a Bayesian Framework Final published version, 1.98 MBLicence: CC BY-NC

Cite this

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abstract = "Beaches are thought to be a large reservoir for marine plastics. To protect vulnerable beaches, it is advantageous to have information on the sources of this plastic. Here, we develop a universally applicable Bayesian framework to map sources of plastic arriving on a specific beach. In this framework, we combine Lagrangian backtracking simulations of drifting particles with estimates of plastic input from coastlines, rivers and fisheries. The advantage over traditional Lagrangian simulations is that the Bayesian framework can consider information on known sources, and thus facilitates spatiotemporal source attribution for plastic arriving at the specified beach. We show that the main sources for our target beach in southwest Netherlands are the east coast of the UK, the Dutch coast, the English Channel (fisheries) and the Thames, Seine, Rhine and Trieux (rivers). We also show that floating time is a major uncertainty in source attribution using backtracking.",

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Identifying Marine Sources of Beached Plastics through a Bayesian Framework: Application to Southwest Netherlands

Abstract

Bibliographical note

Keywords

UN SDGs

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