Anthropogenic litter is omnipresent in terrestrial and freshwater systems, and can have major economic and ecological impacts. Monitoring and modelling of anthropogenic litter comes with large uncertainties due to the wide variety of litter characteristics, including size, mass, and item type. It is unclear as to what the effect of sample set size is on the reliability and representativeness of litter item statistics. Reliable item statistics are needed to (1) improve monitoring strategies, (2) parameterize litter in transport models, and (3) convert litter counts to mass for stock and flux calculations. In this paper we quantify sample set size requirement for riverbank litter characterization, using a database of more than 14,000 macrolitter items (>0.5 cm), sampled for one year at eight riverbank locations along the Dutch Rhine, IJssel and Meuse rivers. We use this database to perform a Monte Carlo based bootstrap analysis on the item statistics, to determine the relation between sample size and variability in the mean and median values. Based on this, we present sample set size requirements, corresponding to selected uncertainty and confidence levels. Optima between sampling effort and information gain is suggested (depending on the acceptable uncertainty level), which is a function of litter type heterogeneity. We found that the heterogeneity of the characteristics of litter items varies between different litter categories, and demonstrate that the minimum required sample set size depends on the heterogeneity of the litter category. More items of heterogeneous litter categories need to be sampled than of heterogeneous item categories to reach the same uncertainty level in item statistics. For example, to describe the mean mass the heterogeneous category soft fragments (>2.5cm) with 90% confidence, 990 items were needed, while only 39 items were needed for the uniform category metal bottle caps. Finally, we use the heterogeneity within litter categories to assess the sample size requirements for each river system. All data collected for this study are freely available, and may form the basis of an open access global database which can be used by scientists, practitioners, and policymakers to improve future monitoring strategies and modelling efforts.

Plastic pollution in aquatic ecosystems is a growing threat to ecosystem health and human livelihood. Recent studies show that the majority of environmental plastics accumulate within river systems for years, decades and potentially even longer. Long-term and system-scale observations are key to improve the understanding of transport and retention dynamics, to identify sources and sinks, and to assess potential risks. The goal of this study was to quantify and explain the variation in floating plastic transport in the Rhine-Meuse delta, using a novel one-year observational dataset. We found a strong positive correlations between floating plastic transport and discharge. During peak discharge events, plastic transport was found up to six times higher than under normal conditions. Plastic transport varied up to a factor four along the Rhine and Meuse rivers, which is hypothesized to be related to the complex river network, locations of urban areas, and tidal dynamics. Altogether, our findings demonstrate the important role of hydrology as driving force of plastic transport dynamics. Our study emphasizes the need for exploring other factors that may explain the spatiotemporal variation in floating plastic transport. The worldâ\euro™s most polluted rivers are connected to the ocean through complex deltas. Providing reliable observations and data-driven insights in the transport and dynamics are key to optimize plastic pollution prevention and reduction strategies. With our paper we aim to contribute to both advancing the fundamental understanding of plastic transport dynamics, and the establishment of long-term and harmonized data collection at the river basin scale.