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

Abstract

Abstract. 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.

Knowns and unknowns of plastic waste flows in the Netherlands

“…The second method has been designed via a combination of previous data sets and models, hereafter called the mismanagement model. It is based on as much observational plastic litter data as possible that are applicable to our analysis (namely litter on Dutch beaches and riverbanks, following Kaandorp et al, 2022 ; van Emmerik et al, 2020 , respectively). The ‘destinations’ are therefore measured Dutch littered plastics and inadequately managed plastics abroad ( Figure 1 ).…”

“…The volunteers weighed the total litter collected and identified the items (wherever possible) and found that the overall plastic fraction of the litter in terms of numbers is 80–90% ( Kaandorp et al, 2022 ). This matches recent findings from Scottish beached OSPAR data ( Smith and Turrell, 2021 ) (88.3% in terms of numbers, 86.6% in terms of weight).…”

“…Beached estimates (L2). Kaandorp et al (2022) developed a model to estimate beached litter quantities, based on data collected along the Dutch North Sea coastline by volunteers from the North Sea Foundation during the month of August for six consecutive years (2014)(2015)(2016)(2017)(2018)(2019). As the beach litter was collected only once per year per location as opposed to twice a year for the riverbanks, beached litter fluxes could be underestimated compared to the riverbank litter fluxes since a higher litter collection frequency results in higher flux estimates (Ryan et al, 2014).…”

Influence of Particle Size and Fragmentation on Large-Scale Microplastic Transport in the Mediterranean Sea

“…The beaching and resuspension of particles on coastlines is modeled following Onink et al Concentrations of beached microplastics are influenced by a wide range of processes, such as winds, coastal morphology, tides, and human usage of the beach. ,,,,,, However, such processes typically act on smaller spatial and temporal scales than those resolved in ocean reanalysis products. As such, particle beaching is implemented as a stochastic process where a particle’s beaching probability p B for time step dt is p B = true{ lefttrue if d ≤ D , p B = 1 − exp false( prefix− italicdt / λ B false) if d > D , p B = 0 where d is the distance from the particle to the nearest model land cell, D sets the outer limit of the beaching zone within which beaching is possible, and λ B is the beaching time scale in days.…”

“…The beaching and resuspension of particles on coastlines is modeled following Onink et al 62 Concentrations of beached microplastics are influenced by a wide range of processes, such as winds, coastal morphology, tides, and human usage of the beach. 9,10,20,32,40,68,72…”