River plastic transport and storage budget.

Rivers are one of the main conduits that deliver plastic from land into the sea, and also act as reservoirs for plastic retention. Yet, our understanding of the extent of river exposure to plastic pollution remains limited. In particular, there has been no comprehensive quantification of the contributions from different river compartments, such as the water surface, water column, riverbank and floodplain to the overall river plastic transport and storage. This study aims to provide an initial quantification of these contributions. We first identified the main relevant transport processes for each river compartment considered. We then estimated the transport and storage terms, by harmonizing available observations on surface, suspended and floodplain plastic. We applied our approach to two river sections in The Netherlands, with a focus on macroplastics (≥2.5 cm). Our analysis revealed that for the studied river sections, suspended plastics account for over 96% of item transport within the river channel, while their relative contribution to mass transport is only 30%-37% (depending on the river section considered). Surface plastics predominantly consisted of heavier items (mean mass: 7.1 g/#), whereas suspended plastics were dominated by lighter fragments (mean mass: 0.1 g/#). Additionally, the majority (98%) of plastic mass was stored within the floodplains, with the river channel accounting for only 2% of the total storage. Our study developed a harmonized approach for quantifying plastic transport and storage across different river compartments, providing a replicable methodology applicable to different regions. Our findings emphasize the importance of systematic monitoring programs across river compartments for comprehensive insights into riverine plastic pollution.

Defining plastic pollution hotspots.

Plastic pollution in the natural environment poses a growing threat to ecosystems and human health, prompting urgent needs for monitoring, prevention and clean-up measures, and new policies. To effectively prioritize resource allocation and mitigation strategies, it is key to identify and define plastic hotspots. UNEP's draft global agreement on plastic pollution mandates prioritizing hotspots, suggesting a potential need for a defined term. Yet, the delineation of hotspots varies considerably across plastic pollution studies, and a definition is often lacking or inconsistent without a clear purpose and boundaries of the term. In this paper, we applied four common definitions of hotspot locations to plastic pollution datasets ranging from urban areas to a global scale. Our findings reveal that these hotspot definitions encompass between 0.8 % to 93.3 % of the total plastic pollution, covering <0.1 % to 50.3 % of the total locations. Given this wide range of results and the possibility of temporal inconsistency in hotspots, we emphasize the need for fit-for-purpose criteria and a unified approach to defining plastic hotspots. Therefore, we designed a step-wise framework to define hotspots by determining the purpose, units, spatial scale, temporal scale, and threshold values. Incorporating these steps in research and policymaking yields a harmonized definition of hotspots, facilitating the development of effective plastic pollution prevention and reduction measures.

River export of macro- and microplastics to seas by sources worldwide.

Seas are polluted with macro- (>5 mm) and microplastics (<5 mm). However, few studies account for both types when modeling water quality, thus limiting our understanding of the origin (e.g., basins) and sources of plastics. In this work, we model riverine macro- and microplastic exports to seas to identify their main sources in over ten thousand basins. We estimate that rivers export approximately 0.5 million tons of plastics per year worldwide. Microplastics are dominant in almost 40% of the basins in Europe, North America and Oceania, because of sewage effluents. Approximately 80% of the global population live in river basins where macroplastics are dominant because of mismanaged solid waste. These basins include many African and Asian rivers. In 10% of the basins, macro- and microplastics in seas (as mass) are equally important because of high sewage effluents and mismanaged solid waste production. Our results could be useful to prioritize reduction policies for plastics.

Macroplastic concentrations in the water column of the river Rhine increase with higher discharge.

Riverine macroplastic pollution (>0.5 cm) negatively impacts ecosystems and human livelihoods. Monitoring data are crucial for understanding this issue and for the design of effective interventions strategies. Macroplastic pollution floating on the river surface and plastic deposited on riverbanks are studied relatively often. Data on riverine plastics in the water column remain scarce. In this study, we utilized trawl nets at different depths to sample plastic pollution in the water column at the entry point of the river Rhine to the Netherlands. We show that plastic concentrations in the water column increased during higher discharge. Moreover, the results indicate that the vertical distribution of macroplastic pollution changes during different flow conditions. Significantly higher concentrations of macroplastic can be seen near the riverbed during low discharge conditions, while no significant differences in concentration are observed between the bottom, middle, and surface samples during high discharge conditions. Taking into account the recurrence time of low discharge conditions the transport of plastic during low discharge conditions is substantial. These findings provide first insights into the key role of hydrology in explaining macroplastic transport in the water column. These insights can be used to improve future monitoring and intervention strategies.

Deep learning for detecting macroplastic litter in water bodies: A review.

Plastic pollution in water bodies is an unresolved environmental issue that damages all aquatic environments, and causes economic and health problems. Accurate detection of macroplastic litter (plastic items >5 mm) in water is essential to estimate the quantities, compositions and sources, identify emerging trends, and design preventive measures or mitigation strategies. In recent years, researchers have demonstrated the potential of computer vision (CV) techniques based on deep learning (DL) for automated detection of macroplastic litter in water bodies. However, a systematic review to describe the state-of-the-art of the field is lacking. Here we provide such a review, and we highlight current knowledge gaps and suggest promising future research directions. The review compares 34 papers with respect to their application and modeling related criteria. The results show that the researchers have employed a variety of DL architectures implementing different CV techniques to detect macroplastic litter in various aquatic environments. However, key knowledge gaps must be addressed to overcome the lack of: (i) DL-based macroplastic litter detection models with sufficient generalization capability, (ii) DL-based quantification of macroplastic (mass) fluxes and hotspots and (iii) scalable macroplastic litter monitoring strategies based on robust DL-based quantification. We advocate for the exploration of data-centric artificial intelligence approaches and semi-supervised learning to develop models with improved generalization capabilities. These models can boost the development of new methods for the quantification of macroplastic (mass) fluxes and hotspots, and allow for structural monitoring strategies that leverage robust DL-based quantification. While the identified gaps concern all bodies of water, we recommend increased efforts with respect to riverine ecosystems, considering their major role in transport and storage of litter.

Future microplastics in the Black Sea: River exports and reduction options for zero pollution.

The Black Sea receives increasing amounts of microplastics from rivers. In this study, we explore options to reduce future river export of microplastics to the Black Sea. We develop five scenarios with different reduction options and implement them to a Model to Assess River Inputs of pollutaNts to seA (MARINA-Global) for 107 sub-basins. Today, European rivers draining into the Black Sea export over half of the total microplastics. In 2050, Asian rivers draining into the sea will be responsible for 34-46% of microplastic pollution. Implemented advanced treatment will reduce point-source pollution. Reduced consumption or more collection of plastics will reduce 40% of microplastics in the sea by 2050. In the optimistic future, sea pollution is 84% lower than today when the abovementioned reduction options are combined. Reduction options affect the share of pollution sources. Our insights could support environmental policies for a zero pollution future of the Black Sea.

Disentangling Variability in Riverbank Macrolitter Observations.

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 R2 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.