No abstract

Anthropogenic litter in aquatic ecosystems negatively impacts ecosystems, species and economic activities. Rivers play a key role in transporting land-based waste towards the ocean. A large portion however is retained within river basins, for example in the estuary, in sediments and on the riverbanks. To effectively identify litter sources, sinks and transport mechanisms, reliable data are crucial. Furthermore, such data can support optimizing litter prevention mitigation and clean-up efforts. This paper presents the results of a 2-year monitoring campaign focused on riverbank macrolitter (>0.5 cm) in the Dutch Rhine–Meuse delta. Between 2017 and 2019, volunteers sampled 152 415 litter items at 212 unique locations. All items were categorized based on the River-OSPAR method (based on the OSPAR beach litter guidelines), which includes 110 specific item categories across ten parent categories. The median litter density was 2060 items/km, and the most observed items were foam, hard, and soft plastic fragments (55.8%). Plastic bottles, food wrappings and packaging, caps, lids and cotton swabs were the most abundant specific items. The litter density and most abundant items vary considerably between rivers, along the river, and over time. For both rivers however, the highest litter density values were found at the Belgian (Meuse) and German (Rhine) borders, and at the Biesbosch National Park, the most downstream location. With this paper, we aim to provide a first scientific overview of the abundance, top item categories, and spatiotemporal variation of anthropogenic litter on riverbanks in the Dutch Rhine–Meuse delta. In addition, we evaluate the used River-OSPAR method and provide suggestions for future implementation in (inter)national long-term monitoring strategies. The results can be used by scientists and policy-makers for future litter monitoring, prevention and clean-up strategies.

Anthropogenic macrolitter (>0.5 cm) in rivers is of increasing concern. It has been found to have an adverse effect on riverine ecosystem health, and the livelihoods of the communities depending on and living next to these ecosystems. Yet, little is known on how macrolitter reaches and propagates through these ecosystems. A better understanding of macrolitter transport dynamics is key in developing effective reduction, preventive, and cleanup measures. In this study, we analyzed a novel dataset of citizen science riverbank macrolitter observations in the Dutch Rhine–Meuse delta, spanning two years of observations on over 200 unique locations, with the litter categorized into 111 item categories according to the river-OSPAR protocol. With the use of regression models, we analyzed how much of the variation in the observations can be explained by hydrometeorology, observer bias, and location, and how much can instead be explained by temporal trends and seasonality. The results show that observation bias is very low, with only a few exceptions, in contrast with the total variance in the observations. Additionally, the models show that precipitation, wind speed, and river flow are all important explanatory variables in litter abundance variability. However, the total number of items that can significantly be explained by the regression models is 19% and only six item categories display an R 2 above 0.4. This suggests that a very substantial part of the variability in macrolitter abundance is a product of chance, caused by unaccounted (and often fundamentally unknowable) stochastic processes, rather than being driven by the deterministic processes studied in our analyses. The implications of these findings are that for modeling macrolitter movement through rivers effectively, a probabilistic approach and a strong uncertainty analysis are fundamental. In turn, point observations of macrolitter need to be planned to capture short-term variability.

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.

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.