Most marine litter pollution is assumed to originate from land-based sources, entering the marine environment through rivers. To better understand and quantify the risk that plastic pollution poses on aquatic ecosystems, and to develop effective prevention and mitigation methods, a better understanding of riverine plastic transport is needed. To achieve this, quantification of riverine plastic transport is crucial. Here, we demonstrate how established methods can be combined to provide a rapid and cost-effective characterization and quantification of floating macroplastic transport in the River Rhine. We combine visual observations with passive sampling to arrive at a first-order estimate of macroplastic transport, both in number (10-75 items per hour) and mass per unit of time (1.3-9.7 kg per day). Additionally, our assessment gives insight in the most abundant macroplastic polymer types the downstream reach of the River Rhine. Furthermore, we explore the spatial and temporal variation of plastic transport within the river, and discuss the benefits and drawbacks of current sampling methods. Finally, we present an outlook for future monitoring of major rivers, including several suggestions on how to expand the rapid assessment presented in this paper.

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.

Plastic pollution in rivers negatively impacts human livelihood and aquatic ecosystems. Monitoring data are crucial for a better understanding of sources, sinks and transport mechanisms of riverine macroplastics. In turn, such understanding is key to develop effective plastic pollution prevention, mitigation, and removal strategies. Riverine plastic is mostly studied through the monitoring of floating plastic and through the quantification of plastic deposited on riverbanks. Existing riverbank plastic measurement methods vary greatly, which complicates direct comparison of data collected with different monitoring strategies. We present a framework to better compare and to aid the design of riverbank plastic monitoring methods, which is based on four common elements distilled from riverbank (plastic) litter monitoring methods currently in use. This framework can be used by scientists and practitioners to find the right trade-offs between the data required to answer specific research questions, and the available resources. Subsequently, we use the framework to suggest effective monitoring strategies for four frequently asked research questions. With this paper, we aim to provide a first step toward harmonization of riverbank plastic litter monitoring 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 1‐year observational data set. 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 world'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.

Several studies have suggested Indonesia to be among the top plastic polluting countries globally. Data on the presence and amounts of plastic pollution are required to help design effective plastic reduction and mitigation strategies. Research quantifying plastic pollution in Indonesia has picked up in recent years. However, a lack of central coordination in this research has led to research output with different goals, methods, and data formats. In this study we present a meta-analysis of studies published on plastic pollution in Indonesia to uncover gaps and biases in current research, and to use these insights to suggest ways to improve future research to fill these gaps. Research gaps and biases identified include a clear preference for marine research, and a bias toward certain environmental compartments within the marine, riverine, and terrestrial systems that have easy to apply methods. Units of measurement used to express results vary greatly between studies, making it difficult to compare data effectively. Nevertheless, we identify polypropylene (PP) and polyethylene variants (PE, HDPE, LDPE) to be among the most frequently found polymers in both macro- and microplastic pollution in Indonesia, though polymer identification is lacking in a large part of the studies. Plastic research is mostly done on Java (59% of the studies). We recommend research methods used to quantify plastic pollution to be harmonized. Moreover, we recommend a shift in focus of research toward the riverine and terrestrial environments and a shift of focus of environmental compartments analyzed within these systems, an increase in spatial coverage of research across Indonesia, and lastly, a larger focus on polymer characterization. With these changes we envision future research which can aid with the design of more effective and targeted reduction and mitigation 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.

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