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

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 2014–2019. This data set is unique in the sense that data is gathered consistently over various years by many volunteers (a total of 14,000), on beaches which are quite similar in substrate (sandy). This makes the data set valuable to identify what environmental variables might play an important role in the beaching process, and to explore the variability of beach litter. We investigate this by fitting a random forest machine learning regression model to the observed litter concentrations. We find that especially tides play an important role, where an increasing tidal variability and tidal height lead 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 what processes cause the accumulation of litter on the coast, recommendations can be given for more effective removal of litter from the marine environment. We estimate that 16,000–31,400 kilograms (95 % confidence interval) of litter are located on the 365 kilometers of Dutch North Sea coastline.