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Using machine learning and beach cleanup data to explain litter quantities along the Dutch North Sea coast

Ocean science, 2022-03, Vol.18 (1), p.269-293 [Peer Reviewed Journal]

COPYRIGHT 2022 Copernicus GmbH ;2022. This work is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1812-0792 ;ISSN: 1812-0784 ;EISSN: 1812-0792 ;DOI: 10.5194/os-18-269-2022

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  • Title:
    Using machine learning and beach cleanup data to explain litter quantities along the Dutch North Sea coast
  • Author: Kaandorp, Mikael L. A ; Ypma, Stefanie L ; Boonstra, Marijke ; Dijkstra, Henk A ; van Sebille, Erik
  • Subjects: Analysis ; Beaches ; Cleanup operations ; Coasts ; Confidence intervals ; Datasets ; Environmental conditions ; Harbors ; Learning algorithms ; Litter ; Low tide ; Machine learning ; Marine environment ; Marine pollution ; Ocean currents ; Oceans ; Plastic debris ; Plastic pollution ; Regression models ; Regressions ; Statistical analysis ; Substrates ; Teams ; Tides ; Variability ; Water pollution effects ; Wind
  • Is Part Of: Ocean science, 2022-03, Vol.18 (1), p.269-293
  • Description: Coastlines potentially harbor a large part of litter entering the oceans, such as plastic waste. The relative importance of the physical processes that influence the beaching of litter is still relatively unknown. Here, we investigate the beaching of litter by analyzing a data set of litter gathered along the Dutch North Sea coast during extensive beach cleanup efforts between the years 2014 and 2019. This data set is unique in the sense that data are gathered consistently over various years by many volunteers (a total of 14 000) on beaches that are quite similar in substrate (sandy). This makes the data set valuable to identify which environmental variables play an important role in the beaching process and to explore the variability of beach litter concentrations. We investigate this by fitting a random forest machine learning regression model to the observed litter concentrations. We find that tides play an especially important role, where an increasing tidal variability and tidal height leads to less litter found on beaches. Relatively straight and exposed coastlines appear to accumulate more litter. The regression model indicates that transport of litter through the marine environment is also important in explaining beach litter variability. By understanding which processes cause the accumulation of litter on the coast, recommendations can be given for more effective removal of litter from the marine environment, such as organizing beach cleanups during low tides at exposed coastlines. We estimate that 16 500–31 200 kg (95 % confidence interval) of litter is located along the 365 km of Dutch North Sea coastline.
  • Publisher: Katlenburg-Lindau: Copernicus GmbH
  • Language: English
  • Identifier: ISSN: 1812-0792
    ISSN: 1812-0784
    EISSN: 1812-0792
    DOI: 10.5194/os-18-269-2022
  • Source: AUTh Library subscriptions: ProQuest Central
    DOAJ Directory of Open Access Journals
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