Articles | Volume 18, issue 1
https://doi.org/10.5194/os-18-269-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-18-269-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| Highlight paper
| 
03 Mar 2022
Research article | Highlight paper |
| 03 Mar 2022
| 03 Mar 2022
Using machine learning and beach cleanup data to explain litter quantities along the Dutch North Sea coast
Mikael L. A. Kaandorp
CORRESPONDING AUTHOR
Institute for Marine and Atmospheric Research Utrecht, Department of Physics, Utrecht University, Utrecht 3584 CS, the Netherlands
Stefanie L. Ypma
Institute for Marine and Atmospheric Research Utrecht, Department of Physics, Utrecht University, Utrecht 3584 CS, the Netherlands
Marijke Boonstra
Stichting De Noordzee, Arthur van Schendelstraat 600, Utrecht 3511 MJ, the Netherlands
Henk A. Dijkstra
Institute for Marine and Atmospheric Research Utrecht, Department of Physics, Utrecht University, Utrecht 3584 CS, the Netherlands
Erik van Sebille
Institute for Marine and Atmospheric Research Utrecht, Department of Physics, Utrecht University, Utrecht 3584 CS, the Netherlands
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Cited
17 citations as recorded by crossref.
- Knowns and unknowns of plastic waste flows in the Netherlands D. Lobelle et al. https://doi.org/10.1177/0734242X231180863
- A Critical Review on Artificial Intelligence—Based Microplastics Imaging Technology: Recent Advances, Hot-Spots and Challenges Y. Zhang et al. https://doi.org/10.3390/ijerph20021150
- LOCATE v1.0: numerical modelling of floating marine debris dispersion in coastal regions using Parcels v2.4.2 I. Hernandez et al. https://doi.org/10.5194/gmd-17-2221-2024
- Evaluating the performance of machine learning models for prediction of marine litter beaching: using autonomous video camera and reanalysis data S. Fetisov & I. Chubarenko https://doi.org/10.59887/2073-6673.2026.19(2)-9
- Detecting the most effective cleanup locations using network theory to reduce marine plastic debris: a case study in the Galapagos Marine Reserve S. Ypma et al. https://doi.org/10.5194/os-18-1477-2022
- Advancing numerical models for land-to-ocean litter transport: A review of hydrodynamic processes and inland contributions N. Oruc Baci et al. https://doi.org/10.1016/j.scitotenv.2025.179013
- Using artificial intelligence to support marine macrolitter research: A content analysis and an online database D. Politikos et al. https://doi.org/10.1016/j.ocecoaman.2022.106466
- Uncovering regional differences in plastic pollution distribution along the Norwegian coast A. Brilke et al. https://doi.org/10.1007/s13762-026-07430-8
- Laboratory experiments related to marine plastic pollution: a review of past work and future directions M. Poulain-Zarcos et al. https://doi.org/10.5802/crphys.217
- The use of artificial intelligence algorithms to detect macroplastics in aquatic environments: A critical review M. Astorayme et al. https://doi.org/10.1016/j.scitotenv.2024.173843
- Integrating molecular approaches for standardised monitoring of North Sea biodiversity and ecosystem dynamics S. Valk et al. https://doi.org/10.1186/s44479-026-00009-w
- Global mass of buoyant marine plastics dominated by large long-lived debris M. Kaandorp et al. https://doi.org/10.1038/s41561-023-01216-0
- Using surface drifters to characterise near-surface ocean dynamics in the southern North Sea: a data-driven approach J. Medina-Rubio et al. https://doi.org/10.5194/os-22-49-2026
- Influence of Particle Size and Fragmentation on Large-Scale Microplastic Transport in the Mediterranean Sea V. Onink et al. https://doi.org/10.1021/acs.est.2c03363
- The Role of Artificial Intelligence in Sustainable Ocean Waste Tracking and Management: A Bibliometric Analysis M. Adeoba et al. https://doi.org/10.3390/su17093912
- Tracing marine litter sources along the Barcelona coastline: Insights from observations and numerical modelling I. Hernandez et al. https://doi.org/10.1016/j.marpolbul.2025.118876
- Microplastic Deposit Predictions on Sandy Beaches by Geotechnologies and Machine Learning Models A. da Silva Ferreira et al. https://doi.org/10.3390/coasts5010004
17 citations as recorded by crossref.
- Knowns and unknowns of plastic waste flows in the Netherlands D. Lobelle et al. https://doi.org/10.1177/0734242X231180863
- A Critical Review on Artificial Intelligence—Based Microplastics Imaging Technology: Recent Advances, Hot-Spots and Challenges Y. Zhang et al. https://doi.org/10.3390/ijerph20021150
- LOCATE v1.0: numerical modelling of floating marine debris dispersion in coastal regions using Parcels v2.4.2 I. Hernandez et al. https://doi.org/10.5194/gmd-17-2221-2024
- Evaluating the performance of machine learning models for prediction of marine litter beaching: using autonomous video camera and reanalysis data S. Fetisov & I. Chubarenko https://doi.org/10.59887/2073-6673.2026.19(2)-9
- Detecting the most effective cleanup locations using network theory to reduce marine plastic debris: a case study in the Galapagos Marine Reserve S. Ypma et al. https://doi.org/10.5194/os-18-1477-2022
- Advancing numerical models for land-to-ocean litter transport: A review of hydrodynamic processes and inland contributions N. Oruc Baci et al. https://doi.org/10.1016/j.scitotenv.2025.179013
- Using artificial intelligence to support marine macrolitter research: A content analysis and an online database D. Politikos et al. https://doi.org/10.1016/j.ocecoaman.2022.106466
- Uncovering regional differences in plastic pollution distribution along the Norwegian coast A. Brilke et al. https://doi.org/10.1007/s13762-026-07430-8
- Laboratory experiments related to marine plastic pollution: a review of past work and future directions M. Poulain-Zarcos et al. https://doi.org/10.5802/crphys.217
- The use of artificial intelligence algorithms to detect macroplastics in aquatic environments: A critical review M. Astorayme et al. https://doi.org/10.1016/j.scitotenv.2024.173843
- Integrating molecular approaches for standardised monitoring of North Sea biodiversity and ecosystem dynamics S. Valk et al. https://doi.org/10.1186/s44479-026-00009-w
- Global mass of buoyant marine plastics dominated by large long-lived debris M. Kaandorp et al. https://doi.org/10.1038/s41561-023-01216-0
- Using surface drifters to characterise near-surface ocean dynamics in the southern North Sea: a data-driven approach J. Medina-Rubio et al. https://doi.org/10.5194/os-22-49-2026
- Influence of Particle Size and Fragmentation on Large-Scale Microplastic Transport in the Mediterranean Sea V. Onink et al. https://doi.org/10.1021/acs.est.2c03363
- The Role of Artificial Intelligence in Sustainable Ocean Waste Tracking and Management: A Bibliometric Analysis M. Adeoba et al. https://doi.org/10.3390/su17093912
- Tracing marine litter sources along the Barcelona coastline: Insights from observations and numerical modelling I. Hernandez et al. https://doi.org/10.1016/j.marpolbul.2025.118876
- Microplastic Deposit Predictions on Sandy Beaches by Geotechnologies and Machine Learning Models A. da Silva Ferreira et al. https://doi.org/10.3390/coasts5010004
Saved (final revised paper)
Latest update: 21 Oct 2026
Short summary
A large amount of marine litter, such as plastics, is located on or around beaches. Both the total amount of this litter and its transport are poorly understood. We investigate this by training a machine learning model with data of cleanup efforts on Dutch beaches between 2014 and 2019, obtained by about 14 000 volunteers. We find that Dutch beaches contain up to 30 000 kg of litter, largely depending on tides, oceanic transport, and how exposed the beaches are.
A large amount of marine litter, such as plastics, is located on or around beaches. Both the...
