With an ever-increasing population and growing consumption, plastic waste management has become one of the most challenging global problems. Both mismanagement and illegal dumping pose significant environmental and public health risks, leading to severe issues such as the release of harmful chemicals and heavy metals into the air through burning, and significant ocean pollution from riverine plastic discharge. Indonesia is estimated to be one of the top emitters of riverine plastics and a significant portion of the country’s municipal solid waste is either burned or uncollected. Despite the recognized importance of tackling mismanaged plastic waste, comprehensive data on plastic waste flow remain largely unavailable. This study presents a plastic Material Flow Analysis (MFA) in Bandung, Indonesia, using a bottom-up, geo-referenced approach to tackle the absence of data.

Our methodology involves quantifying the volume of uncollected waste and identifying its specific locations through georeferenced mapping and spatial analysis. The findings reveal that household plastic waste consumption ranges from 14 to 20 kg per capita per year. On average, over 50% of plastic waste is sent to landfills, 20-25% is source-separated and recycled, 12% remains uncollected, and 1-2% is burned. Limited infrastructure and collection capacity result in higher rates of uncollected waste and burning. These mismanaged waste hotspots are often located near riverbanks or open spaces adjacent to households.

Accessibility analysis indicates that areas with higher uncollected waste are farther from waste collection points and lack adequate infrastructure, including roads and transport systems, increasing reliance on informal disposal methods such as burning and dumping. This suggests that mismanaged waste is not only an environmental issue but also a predictor of social inequalities within cities, as affected communities often face poor living conditions and inadequate access to basic services such as clean water. By providing data-driven insights and actionable recommendations, this research contributes to the development of sustainable and equitable waste management strategies in Indonesia. Furthermore, this study tests the utility of applying a bottom-up georeferenced Material Flow Analysis to measure plastic waste flows, contributing to the growing body of research in this field.

How to cite: Frigo, G., Binder, C., Giuliani, G., and Zurbrügg, C.: Uncollected Urban Plastic Waste in Bandung: A Geo-Referenced Material Flow Analysis Revealing Spatial Inequalities and Management Challenges, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7033, https://doi.org/10.5194/egusphere-egu25-7033, 2025.

Ocean currents originating in the south of Europe have been proposed to function as major transport routes of microplastics from the more densely populated southern areas in Europe to the Arctic. However, given the limited empirical data and lack of harmonized methodologies for sample collection, little is known about the role urban sites play as emission sources. Here we present the outcomes of a study applying passive and active air-samplers for wet and dry deposition on two remote monitoring stations, Ny Ålesund (Svalbard) in the High Norwegian Arctic, and at Birkenes in mainland Norway in 2022, 2023 and 2024. We complement the results with samples collected in three Norwegian cities (Tromsø, Trondheim and Oslo). Bi-weekly samples were collected during the period of June-December in 2022 and 2023 for the Norwegian onshore samples and during June 2021 and 2023 for the arctic offshore samples. In 2024 we sampled from January to December with the same approach. We used full metal bulk precipitation samplers and suspended air samplers (Innovation NILU’s Atmospheric Microplastic Collector).

All samples were handled under strict QA/QC requirements, with all sample treatment occurring in controlled conditions of clean rooms and laminar flow cabinets. After filtration on a GF/F filter, polymer determination was performed by pyr-GC/MS (Frontier lab multi shot pyrolizer EGA/PY 3030D connected to a Frontier lab AS 1020E Auto shot sampler connected to a ThermoScience TSQ9000 GC/MS/MS). All samples were accompanied with field and procedural blanks. Results were further analysed with respect to their spatial origin and long-range transport using the Lagrangian particle dispersion model FLEXPART.

MP concentrations in deposition samples were more than 10000-times higher than in active samples, and Arctic samples were in general lower than samples from the Norwegian mainland.

Rubber from car tires and Nylon dominated most samples, followed by PMMA and PVC. While tire wear particles (TWP) and Nylon dominate in the Norwegian mainland samples, contribute almost every of the measured polymers to the samples from Zeppelin station, Svalbard. MP concentrations in deposition samples were more than 10-times higher than in active samples, and remote samples were lower than samples from the urban sites. The prevalence of TWP in most samples, and especially in urban samples, indicates the important role TWP play in the overall inventory of atmospheric microplastic. Seasonal variations could be observed at all sites as well, with increasing microplastic concentrations found in the fall. Results were further analysed with respect to their spatial origin and long-range transport using the Lagrangian particle dispersion model FLEXPART. Seaspray, roaddust and agricultural sources were among the main sources identified by the model.

How to cite: Herzke, D., Schmidt, N., Schulze, D., Eckhardt, S., and Evangeliou, N.: Comparison of Atmospheric Microplastic in remote and urban locations in Norway; occurrence, composition and sources, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20432, https://doi.org/10.5194/egusphere-egu25-20432, 2025.

Agricultural plastic mulch films are widely used in vegetable production to optimise soil temperature, moisture retention and weed control. However, they are also an important pathway for plastics to enter the soil, where they degrade over time into microplastics (MPs). The fate of these MPs in soil is still uncertain, however it is assumed that embedment in soil aggregates will protect MPs from further degradation.

The aim of this study was to investigate i) how much of the MPs from biodegradable and conventional films in European topsoils are occluded within soil aggregates, ii) if soil properties control this occlusion, and iii) whether certain sizes and shapes of MPs are favoured for the embedment.

To answer these questions, we analysed samples from field plot trials in Finland, Spain and Germany where MPs (< 1 mm) derived from recycled low-density polyethylene and starch - polybutylene adipate terephthalate films were incorporated into topsoil (0-10 cm) at a concentration of 0.05%. Barley was grown there in two consecutive years and soil samples were taken immediately after harvest.

Free MPs and MPs embedded in soil aggregates were separated using a combination of plastic extraction (density separation and organic matter digestion) and aggregate separation techniques (ultrasonication and shaking). The size and shape of MPs were analysed using a UNet model applied to digital microscopic images.

Our results showed that up to 80% of MPs are embedded in soil aggregates, with the highest proportions found in Spain, followed by Germany and Finland. Significant differences in the distribution of MPs inside and outside aggregates were observed in both Spain and Finland. The clay content had a significant effect on the occlusion of the MP in the aggregates. MPs embedded in aggregates were on average 2.5 times smaller than those outside, with most of them being smaller than 100 µm. We conclude that large portions of MPs are embedded in soil aggregates, how this affect their fate must now be analysed (see Groß et al., (EGU 2025): Microplastic degradation in agricultural soils across Europe: Comparative study of MPs inside and outside soil aggregates over two years).

How to cite: Braun, M., Gross, M., Bogner, C., Hennig, L., Heyse, R., Hurley, R., Leonhardt, J., Martínez-Hernández, V., Nizzetto, L., Roscher, R., Redondo-Hasselerharm, P. E., Schlierenkamp, V., Selonen, S., Soinne, H., and Amelung, W.: Microplastic incorporation into soil aggregates: Insights from two-year field experiments in European agricultural topsoils, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16100, https://doi.org/10.5194/egusphere-egu25-16100, 2025.

Plastic pollution has become an escalating global issue, with large quantities of plastics being produced and taking a long time to degrade in the environment. Once in the environment, plastics break down into microplastics (<5 mm), which have been detected in various environmental compartments worldwide. Microplastics contribute to pollution in water, air, and soil, with consequences for the normal functioning of the ecosystems, and have been linked to human health concerns. The growing urban population has exacerbated pollution, particularly in cities. Urban areas are significant pollution sources, with roads, industrial activities, wastewater and landfills serving as key hotspots. Pollutants like microplastics are transported from these sources through pathways such as wind and rain, making it difficult to quantify, manage, and remediate them – an ongoing challenge recognized by the European Commission.
Experts emphasize that green urban areas can act as natural filters for pollutants, including microplastics, by capturing them in vegetation. These areas can help control the transport of pollutants. While much is known about microplastic contamination, further investigation is needed into their presence in soils, their transport mechanisms, and the role of vegetation in filtering microplastics, particularly in urban environments.
This study focuses on (1) the spatial distribution of microplastics in urban soils across different land uses, and in runoff and streams waters, (2) their transport via atmospheric deposition and wind erosion, and (3) their deposition in vegetation, including grass and tree leaves. Coimbra, a medium-sized city in central Portugal, serves as the case study. Soil, sediment, water, and vegetation samples were collected from Coimbra and analyzed at Wageningen University & Research labs. Microplastics were extracted using density separation with Sodium Phosphate solution (~1.4 g cm−3) and filtration methods, then visualized under a stereo microscope and identified using u-FTIR.

How to cite: Leitão, I., van Schaik, L., Ferreira, A., and Geissen, V.: The urban microplastic footprint: investigating the distribution and transport, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21807, https://doi.org/10.5194/egusphere-egu25-21807, 2025.

Sewage systems may be the preferred pathways for plastic debris from urban areas to the natural environment during wet periods. Some French local authorities are trying to prevent this leakage into the environment by equipping combined sewer (mixed of stormwater and wastewater) or stormwater outfalls (separate sewer systems) with nets. More than a curative solution, these devices represent a unique opportunity to monitoring urban litter, including plastic debris, as close as possible to their source of emission, i.e., urban areas. Since 2020, nets are being (or have been) in used in French cities. In several cities, anthropogenic litter from the nets was collected, washed, air dried and sorted according to the J-list classification (Fleet et al., 2021), which is the updated European classification first developed for marine and riverine litter (MSFD Technical Subgroup on Marine Litter, 2013). Results show that urban waters are a major source of macroplastics for rivers, with mass flows per capita within the orders of magnitude of those estimated in French rivers (1-10 g/cap/yr). In addition, mass flows and items categories differ relative to the type of sewage systems, land use and local specificities. In combined sewer, wipes are by far the main waste found in nets often followed by tobacco-related products and sweet wrappers from roadways. In stormwater run-off, tobacco-related products and sweet wrappers are the main categories by numb, but bottles (in metal, glass and plastic) rank TOP 5 by mass. Acquiring those data is a very harsh task and a dedicated technical platform is under development to extend monitoring at the national level (or beyond) over the long term.

How to cite: Tramoy, R., Tassin, B., Ledieu, L., Dris, R., and Gasperi, J.: Monitoring plastic debris in urban stormwater: fluxes and management issues, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4400, https://doi.org/10.5194/egusphere-egu25-4400, 2025.

Globally, plastic pollution in aquatic environments has been considered one of the major contemporary environmental challenges. Even though environmental effects associated with plastic pollution have been largely known, research on plastic concentrations mainly focuses on the marine environment. In recent years, an increasing number of studies reported environmental consequences and concentrations of plastic particles in freshwater systems comparable to those found in marine ecosystems. The observed abundance of plastic particles in ecosystems may be influenced not only by their actual presence in the aquatic environment but also by factors such as sampling methods and identification processes. Facing that, in this study, we assessed the variation in macro- and mesoplastics abundance and composition in the river Rhine collected using a larvae net, a trawl net, and a stow net. Additionally, we highlighted the strengths, weaknesses, opportunities, and threats through a SWOT analysis of the used methods for plastic monitoring. During trawl net and stow net monitoring, more unique macro- and mesoplastics categories were found in comparison with simultaneous larvae net monitoring. However, the main categories follow the same patterns among methods, and the relative abundance per category per method slightly differs. Overall, the SWOT analysis pointed towards a better performance of the trawl net for plastic monitoring in the river Rhine. The outcome of the current study can be used to support policymakers, industry, and the scientific community to devise a successful monitoring strategy for macro- and mesoplastics pollution in rivers that best aligns with the specific monitoring goals and the environmental conditions of the target area.

How to cite: Oswald, S. B., Vriend, P., Ragas, A. M. J., Schoor, M. M., and Collas, F. P. L.: Methodological Assessment of Macro- and Mesoplastics Pollution in Rivers, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8527, https://doi.org/10.5194/egusphere-egu25-8527, 2025.

Given the exponential rise in global plastic production and its significant ecological and socio-economic impacts, monitoring macroplastics in rivers has become a central focus of water management efforts. However, standardized monitoring methodologies have not kept pace with the increasing volume of plastic waste entering aquatic systems worldwide. This resulted in a critical shortage of spatially and temporally refined data on macroplastic pollution circulating in inland waters. Recent advancements in remote sensing technologies such as satellites, unmanned aerial systems (UASs) and camera systems coupled with crowd-sourced data and automated detection using machine and deep learning, offer promising opportunities for versatile monitoring solutions. Towards improving monitoring practices, we reviewed emerging remote sensing methods and tools to tackle macroplastic identification in riverine environments. Our investigation highlights that overcoming the challenges of remote sensing-based river macroplastics monitoring requires further efforts to integrate multiple platforms and prioritize long-term monitoring strategies. The RiverWatch project exemplifies these advancements by developing an innovative infrastructure for detecting buoyant plastics in rivers. Utilizing fixed cameras along river networks and mobile cameras, including those operated by citizens via smartphones, RiverWatch employs advanced computer vision algorithms to analyse collected data. Focused on the Sarno River, among the most polluted rivers in Italy, this project harnesses low-cost, adaptable technologies and empowers citizen science through the RiverWatch mobile app, enhancing both spatial and temporal monitoring resolution. The project aligns with the broader goals of offering scalable and harmonized monitoring solutions. Furthermore, it serves as an example of integrating emerging technologies into standardized methodologies, bridging the gap between research advancements and practical applications for global riverine systems.

How to cite: Marye, A. T., Caramiello, C., De Nardi, D., Miglino, D., Proietti, G., Saddi, K. C., Biscarini, C., Manfreda, S., Poggi, M., and Tauro, F.: Remote Sensing for Monitoring Macroplastics in Rivers: The Case of The Sarno River, Italy, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8418, https://doi.org/10.5194/egusphere-egu25-8418, 2025.

Microplastics (MPs) are an emerging contaminant that is found throughout the environment. In this study we sought to quantify and characterize MPs along the Truckee River, located in the western United States. The Truckee River begins in the Sierra Nevada, flows to Lake Tahoe, a lake known for its clarity and pristine water quality and continues to Pyramid Lake. The Truckee River basin is utilized for its drinking water and all-season recreation throughout the watershed. Additionally, the Truckee River system is an important aquatic habitat for endangered and endemic species. For these reasons, assessing the MPs present in this system is essential for determining risks to human and aquatic health.

Samples were taken along the Truckee River starting downstream of Lake Tahoe’s outlet sampling above and below major areas of land use change: urban population centers, wastewater treatment facilities, confluences, and agricultural areas at a total of 6 sampling sites in the fall of 2022 and 8 sampling sites during the spring of 2023. Two seasons were analyzed to capture the low flow (fall) and high flow (spring) discharge periods along the Truckee River. MP results were compared to a variety of spatial data to understand the concentration of MPs in the Truckee River, potential sources of MPs to the river from land use, and whether MP concentrations vary with seasonal flow changes. We show that MP concentrations vary with discharge and number of stormwater drainages. We also show the plastic types reflect commonly used single-use plastics.

How to cite: Arienzo, M., Lukasik, H., Kozloski, R., Wright, M., and Kruger, B.: Microplastic Concentration in the Truckee River, United States, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7294, https://doi.org/10.5194/egusphere-egu25-7294, 2025.

Globally, rivers have been found to contain high concentrations of microplastics and are also the major conveyors of microplastic pollution to the ocean. This has engendered an increased focus on microplastic sources, transport, and fate in riverine systems. But how should we design microplastic monitoring plans for rivers if our goal is to quantify concentration, character, and flux? Here we present the results of microplastics monitoring campaigns conducted on several riverine systems draining coastal watersheds in Southern California and discuss lessons learned as well as future directions to support flux-based monitoring of microplastics. Key topics include consideration of microplastic distribution across the water column, sampler performance, concentration and character dependency on discharge/time, and by extension – effective discharge.

How to cite: Gray, A., Murphy-Hagan, C., Singh, S., Cowger, W., and Hapich, H.: Riverine Microplastic Fluxes, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13279, https://doi.org/10.5194/egusphere-egu25-13279, 2025.

Recent advances in hydrological monitoring using different camera systems provide a huge potential in long-term monitoring of plastic transport, which is necessary to find the plastic sources and to monitor any progress in efforts to reduce riverine plastic transport. The high interest in using machine learning in different environmental monitoring applications allowed the fast development of models aimed to translate manual visual to computer vision monitoring. However, there is still a lack of robust plastic image datasets that could support machine learning models to detect different plastic classes (i.e., plastic bag, plastic bottle, plastic straw, etc.) that are found in the environment. 

In this study, we aimed to identify which data features could be useful to enhance the capabilities of the YOLO series of models (i.e., YOLO World, YOLO NAS, YOLOv8, YOLOv10, YOLOv11) initially trained using a merged dataset (999 images, 15,212 annotations, and 13 plastic classes) taken from different countries (Indonesia, The Netherlands and Vietnam). In addition, we used crowd-sourced images data of river plastics collected with the CrowdWater app (https://crowdwater.ch/), a citizen science app that allows users to report plastic pollution in water bodies. The data was fed to the models for detection 0 (first plastic detection which generates initial labels for iterative training later), in which those learned are considered redundant and unlearned essential–auto image curation. These labels were validated through manual label curation and adjustment. The essential data was added to the existing dataset to fine tune the set of models and the auto image curation will be run again for at least 10 iterations. The performance of these models has been compared for the base dataset (existing and all crowd data) and the optimized dataset (existing and curated crowd data). 

This work leverages the value of utilising crowd-sourced diverse data, without the need for a big dataset or a complex algorithm architecture, to implement river plastic detection from local to global scale in the future.

Keywords: river plastic monitoring, crowdwater, image-based object detection

How to cite: Saddi, K. C., Miglino, D., Moe, A. C., Caramiello, C., Poggi, M., van Meerveld, I., van Emmerik, T. H. M., and Manfreda, S.: The value of Crowd-sourced data in Image-based River Plastic Detection, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18045, https://doi.org/10.5194/egusphere-egu25-18045, 2025.

It has been two decades since scientists started reporting microplastic data in the marine environment. During that time, research on plastic pollution in aquatic systems has evolved rapidly and expanded from the ocean to upstream sources in the river basins. Despite the progress made in acquiring new data and knowledge, the issue of harmonizing methodologies for monitoring, analysis and reporting plastic pollution remains open, hindering data comparison. In the case of microplastic studies, intrinsic questions persist nowadays, e.g., representativeness of samples, minimum and maximum size of items, item size distributions, contamination of samples, meaningful polymer analyses, etc., although these issues were identified a decade ago [1] . In this work, we assessed current issues related to monitoring, analysis and reporting plastic pollution, based on a global literature review (ca. 600 studies) via the Riverine Litter Database (RLDB) implemented under the Horizon Europe Project INSPIRE, and propose a ‘requirement list’ on how to process field data to improve reporting for comparability of results.

We identified that, during monitoring, sampling size was frequently not adapted to answer the scientific question in place, meaning the samples were too small to cover in a representative way the selected size ranges (micro-, meso-, and macroplastic), hindering assessment of both spatial and temporal variability. Analyses were often incomplete, lacking essential information such as particle size distribution and polymer identification based on statistical requirements. As a general overview, we highlight that, besides the quality of the monitoring and analysis methodologies, data reporting was missing important metadata and data in many studies. Some of that missing information would imply elementary data, like GPS location, date, sample size and number of particles identified per sample. Furthermore, a large part of our ‘requirement list’ for data reporting was mostly not accessible or had not been considered during the sample analyses, which would include reporting on particle size and mass distributions, concentrations per size bins (beyond distinguishing only among micro-, meso- and macroplastics concentrations), or making accessible raw data at particle level for microplastics or harmonised item classification for macroplastics. Such details would facilitate framing the significance of the results of each study and improve comparability. In INSPIRE, we implement a data processing framework following a common guideline with elementary and advance requirements for data harmonization to improve reporting of results for extended comparability, making existing data more accessible and reusable.

How to cite: González-Fernández, D., Sánchez-Guerrero-Hernández, M. J., Vélez-Nicolás, M., Quintana, R., Manzano, S., Stibora, M., Catarino, A. I., Miranda, M. N., and Everaert, G.: Improving monitoring, analysis and reporting to assess plastic pollution: a matter of comparability, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19847, https://doi.org/10.5194/egusphere-egu25-19847, 2025.

Quantifying plastic pollution is a key activity to unlock: understanding of plastic transport processes; verification of modelling efforts; baseline estimations at river basin level; performance measurement of cleanup efforts; and others. Visual counting and visual classification are cornerstone methodologies to quantify macroplastic fluxes in rivers, providing comparable datasets and replicable methodologies. This study compares monitoring conducted in 50 locations over the past 10 years; it includes some datasets already published while others are novel. This is a growing dataset, part of ongoing monitoring efforts. Most data collection was done so far in Southeast Asia (>50% of surveys), while efforts in Central America (appr. 16%) were done mostly within the same river basin in Guatemala. The dataset covers 37 rivers and also include a few surveys in North America, Europe and Africa (appr. 7%). Most of the surveys were conducted in natural waterways, with widths varying between 6 and 550 meters, while at least 40% were up to 100 meters in width. In this study, we compare these datasets in terms of fluxes and composition and assess what they can tell about plastic pollution and its correlation with the environment (e.g. precipitation, flow regime, tides). We also discuss opportunities and shortcomings in the methodology and its applicability in such diverse contexts. The main outlook is that these findings reflect the diversity of fluxes and composition across different river systems. These methodologies can be a cost-effective tool to bridge the gap in quantifying plastic pollution across the globe, whilst other techniques (e.g. camera-driven, GPS drifters), can cover its limitations or complement the efforts.

How to cite: Assumpção, T. H., Higgins, D., Correia, R., and Pinson, S.: Exploring visual counting and visual classification as monitoring tools to quantify macroplastic emissions: findings from 50 campaigns across the globe, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17725, https://doi.org/10.5194/egusphere-egu25-17725, 2025.

How to cite: Finch, K., Dura, T., Gray, A., DePaolis, J., Allard, A., Docev, T., Montgomery, A., Roy, P., Williams, M., Hatcher, B., and Corbett, R.: Characterizing the temporal trends in the concentration and composition of microplastics over the 20th century to present in the Chesapeake Bay region, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11487, https://doi.org/10.5194/egusphere-egu25-11487, 2025.

Marine litter is a growing ecologic, economic, and societal concern that must be addressed at a global scale. Floating material aggregates under the effect of oceanic processes to form so-called “windrows”, used as proxies for marine litter. Windrows reach sizes that make them visible for high-resolution optical satellites. Most recently, the availability of labeled datasets of Sentinel-2 images (MARIDA, FloatingObjects) has enabled the use of deep learning for large-scale marine litter monitoring: a segmentation model can be trained in a supervised manner to predict the presence of floating objects. 

However, the temporal resolution of Sentinel-2 (up to 6 days between consecutive acquisitions) limits the operational impact of such tools. Within this context, PlanetScope images can be leveraged to fill the temporal gaps of Sentinel-2 even at a higher spatial resolution: PlanetScope images have a higher spatial resolution than Sentinel-2 (3m vs. 10m) and are acquired daily. Nevertheless, there is a lack of labeled PlanetScope images for the specific purpose of marine debris detection.

To address this gap, we propose a cross-sensor training strategy that allows a model to transfer knowledge from Sentinel-2 to PlanetScope without extra supervision. In particular, we leverage self-supervised learning to pre-train a model that learns a common latent space between the two sensors. Sensor-specific embedding layers project their features into a common U-Net model, itself trained to remove noise from the input images as a self-supervised learning task. Thanks to this self-supervised task, the model learns the semantics of the data without requiring any labels. Next, the model is fine-tuned on labeled Sentinel-2 images, as in most recent deep learning solutions. Since self-supervised cross-sensor pre-training has forced the model to learn a common representation between the two satellite sources, while learning to identify marine litter on Sentinel-2 images, the model co-learns to segment PlanetScope data. Thus, at prediction time, the model can be directly applied to PlanetScope images with excellent results.

We evaluate the performances of the developed model on a manually annotated validation set of PlanetScope images: both visual inspection and quantitative assessment highlight the significant improvement of the proposed model, compared against a fully supervised model trained on Sentinel-2 only. This demonstrates the effectiveness of the proposed pre-training strategy as a promising solution to enable continuous large-scale mapping of marine litter on optical satellites.

How to cite: Dalsasso, E., Russwurm, M., Donner, C., de Vries, R., Volpi, M., and Tuia, D.: A cross-sensor approach for marine litter detection with self-supervised learning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8279, https://doi.org/10.5194/egusphere-egu25-8279, 2025.

The Mediterranean Sea region's coastal zones are densely populated, with 427 million inhabitants, and attract a significant number of tourists. This high level of human activity, combined with the region's topography and inadequate waste management in many countries, has led to the accumulation of plastic debris in the Mediterranean Sea and its connected rivers. Plastic litter is prevalent in the rivers, on beaches, and in the sea, where it accumulates due to the limited flow to the Atlantic Ocean.

This study aims to address the issue of plastic pollution in the Mediterranean Sea by implementing novel approaches for monitoring and detecting marine litter. The primary objective is to report on the monitoring activities of beach macro litter (>2.5 cm) on six beaches in six Mediterranean countries (Italy, Slovenia, Albania, Greece, Morocco, France) during 2024. Seasonal monitoring was conducted together with volunteers four times per year using the REMEDIES mobile app, in accordance with the Marine Strategy Framework Directive (MSFD). This app facilitates the collection of data on the localization, types, quantities, materials, and sources of macro litter on beaches, thereby contributing to efforts to mitigate plastic pollution, protect marine life, and preserve the ecological balance in the Mediterranean region.

This comprehensive approach aims to provide a clearer understanding of the extent and sources of plastic pollution, enabling more effective strategies for its reduction and management. By leveraging technology and international collaboration, this study seeks to make a significant impact on the health of the Mediterranean marine environment.

 Acknowledgements

The authors acknowledge financial support from the European Union’s HORIZON EUROPE innovation program for the project REMEDIES awarded under Grant Agreement No. 101093964.

How to cite: Velimirović, M., Puhar, J., Vujanović, A., Struga, M., Çela, K., Barkaoui, A., Eleftheriou, A., Camedda, A., Petit, S., Petelin, M., Poletto, D., Bizjak, T., and Palatinus, A.: Monitoring Beach Litter in the Mediterranean Sea Using the REMEDIES Mobile App, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8260, https://doi.org/10.5194/egusphere-egu25-8260, 2025.

The integration of participatory science (PS) data into official monitoring frameworks offers a promising pathway to enhance the spatial and temporal coverage of environmental assessments. Significant efforts have been made within the framework of the Spanish National Marine Strategy, which transposes the Marine Strategy Framework Directive (56/2008/EC), to integrate citizen science data, particularly regarding the impacts of macroplastics. In this study, we analyze the methodological challenges and potential efficiencies of integrating official monitoring programme data on marine litter on beaches with participatory science data in Spain using Bayesian machine learning.

Leveraging a flexible Gaussian Process Regression framework, we model the spatial distribution of beach litter pollution along the Spanish coastline, accounting for the differing uncertainties inherent to the two data sources. This data-driven approach enables us to produce robust estimations of macroplastic pollution levels with associated uncertainty maps and identify locations where PS contributions significantly reduce the uncertainty of official monitoring efforts. Preliminary results include spatial predictions of marine beach litter density, uncertainty quantification along Spanish coastlines, and insights into the added value of PS data for underrepresented regions.

Beyond providing actionable insights for Spain, this study presents a globally adaptable blueprint for the assimilation of participatory science data into official environmental monitoring programmes. The present study demonstrates the potential of combining machine learning, official monitoring programmes and participatory science to achieve actionable science, with the aim of strengthening policy, optimising resource allocation and enhancing coastal management practices on a global scale.

How to cite: Rieger, N., Olmedo, E., Sánchez Fernández, B., Zorzo, P., López-Samaniego, E., Salvo, V.-S., Corredor, L., and Piera, J.: Integrating participatory science with official programmes using Bayesian machine learning to estimate beach macroplastic pollution in Spain, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15691, https://doi.org/10.5194/egusphere-egu25-15691, 2025.

The North Pacific Garbage Patch (NPGP) is known for accumulating floating plastic debris, but little is known on the dominating mechanisms that form its spatial heterogeneity in concentration. Submesoscale processes are likely to be the main drivers of such heterogeneity, especially if their effect on transport is object-specific. Dynamics at these spatial scales remain largely unresolved to date in ocean circulation models, therefore, current studies have to rely on in-situ measurements. The authors present a new method that measures floating plastic debris’ horizontal transport over small scales along vessels’ trajectories. The method applies particle tracking velocimetry on objects detected by an optical artificial intelligence algorithm during The Ocean Cleanup’s campaigns. Given the method’s sensitivity to the vessel’s movement, a Monte Carlo simulation is conducted to estimate object position errors with and without the presence of waves. The same method is applied to overlapping samples of drone-based optical data and the results are compared across measuring devices. Measurement accuracy depends on factors such as sea state, object distance from the vessel, and tracking duration. A first application on a subset of manually classified objects is presented. The ability to estimate floating plastic debris’ transport from in-situ measurements, combined with the collection of meteorological and oceanographic data, will likely gather insightful information on object-specific small scale dynamics in the region of interest. This is not only valuable for research purposes, but essential to assess and improve clean-up efforts.

How to cite: Romero, M., Pham, Y., Gómez Navarro, L., de Vries, R., and Sainte-Rose, B.: Measuring the Transport of Floating Plastic Debris Using Vessel-Based Optical Data and Artificial Intelligence, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4267, https://doi.org/10.5194/egusphere-egu25-4267, 2025.

Plastic debris in coastal environments usually undergo weathering due to various environmental conditions. However, the weathering effects on exposed and shaded sides of the same plastics are underexplored. In this study, 1573 plastic fragments were collected from 15 coastal sites worldwide between December 2021 and December 2022, and weathering experiments were conducted outdoors. The field investigation showed significant two-sided weathering differences of plastic fragments. The weathering morphology included biota, cracks, delamination, discoloration, etc. The weathering degree was assessed with three metrics, i.e., line density (0–58 mm/mm²), surface loss (0–92 %), and texture index (0−2). The 3D magnitudes of these three metrics revealed the two-sided weathering differences of plastic fragments. Specifically, 43 % of the samples had magnitudes > 5, indicating significant differences. Outdoor simulations suggested that sun-exposed sides developed more cracks, pores, and bubbles, while shaded sides remained smoother. After 12 months, the line density increased from 2.85 to 9.23 mm/mm² for polyethylene (PE) and 4.16–8.47 mm/mm² for polypropylene (PP) (p < 0.05). The carbonyl index increased from 0.50 to 1.70 (PE), from 0.18 to 1.10 (PP), and from 0.45 to 1.57 (polyvinyl chloride). This increase indicated oxidative degradation on sun-exposed sides. Our results highlighted the uneven degree of weathering on both sides of the same plastic fragment due to different environmental factors. The study provided critical insights for creating more accurate models to predict plastic degradation, which will help inform global strategies to reduce plastic pollution.

How to cite: Hu, B., Shi, H., Jong, M.-C., Frias, J., and Su, L.: Two sides of the same coin: Weathering differences of plastic fragments in coastal environments around the globe, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2452, https://doi.org/10.5194/egusphere-egu25-2452, 2025.

Microplastics (MPs) pose a growing concern in the marine environment, but their global prevalence remains largely unknown due to the absence of precise and standardized detection methods. This is because current techniques used to quantify MPs in marine field studies can feature methodological inaccuracies or limitations, which collectively prevent a global and reliable MP pollution status for being drawn. These inaccuracies are related, for example, to the exclusion of particle sizes within the broad range of MP size intervals or to the level of identification of polymer types by using spectroscopic analysis or specific extraction methods. Once these inaccuracies have been considered and addressed, the reported MP abundances can be recalculated. This resulted in a significant underestimation of the global pollution levels regarding MPs in the 10–5000 µm size range. MP abundances are then shown to be up to 15 times higher than in the data presented in the public domain in marine waters and up to 11 times higher within marine sediments. This study emphasizes the critical need for global and integrated MP studies and encourages current and future MP researchers to adopt standardized protocols for MP analysis to avoid misleading outcomes.

How to cite: Reineccius, J., Ivar do Sul, J. A., and Waniek, J. J.: Critical reassessment of microplastic detection methodologies and abundances in the marine environment, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-461, https://doi.org/10.5194/egusphere-egu25-461, 2025.

Plastic pollution is a growing global concern, with significant implications for marine ecosystems. While microplastics (<5 mm) are abundant in shallow marine environments, their transport pathways and fluxes to the deep sea remain poorly understood. Submarine canyons, such as the Congo Canyon off West Africa, act as major conduits for sediment and associated pollutants, including plastics, to the deep-sea environment. These canyons are frequently flushed by fast gravity-driven sediment flows called turbidity currents capable of transporting vast quantities of material over distances of >1,000 km. These are the longest sediment flows yet measured in action on Earth, and they eroded and carried a mass of terrestrial organic carbon similar to that buried each year in the global oceans. However, despite their significance in natural particle transport, it remains unclear how efficiently they carry anthropogenic particles, such as microplastics, to the deep sea.

This study presents the first dataset that directly measures microplastics transported by turbidity currents. A sediment trap moored 156 km offshore in the Congo Canyon, at a water depth of 2,172 m, captured sediments from eight (0.5-1 m/s) turbidity current events occurring between September and December 2019. Microplastics were extracted and analyzed for their number, size, shape, and polymer composition using Laser Direct Infrared (LDIR) imaging. Microplastic flux estimates were calculated to quantify the transport capability of these flows.

The results demonstrate that turbidity currents are highly efficient in transporting microplastics, with concentrations reaching up to 13,266 particles per kg of sediment. PET (polyethylene terephthalate) and rubber were the most abundant polymer types, likely due to their higher density and resistance to degradation. Variability in microplastic abundance across different flow events appears to be influenced by differences in sediment sources and flow dynamics. Annual fluxes of microplastics transported through the Congo Canyon are estimated to be approximately 50,000 kg, underscoring the significant role of turbidity currents in redistributing microplastics on the deep seafloor. These microplastics may accumulate in canyon floors and distal lobes, forming potential sinks.

This research provides critical insights into the mechanisms governing the deep-sea transport of microplastics and highlights the importance of submarine canyons in global plastic pollution dynamics.

How to cite: Pohl, F., Hildebrandt, L., Baker, M. L., Talling, P. J., Eggenhuisen, J. T., Hage, S., Ruffell, S. C., Proefrock, D., Silva Jacinto, R., Heijnen, M. S., Simmons, S. M., and Hasenhündl, M.: Transport and Fluxes of Microplastics to Deep-Sea Sediments via Turbidity Currents through the Congo Canyon, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6652, https://doi.org/10.5194/egusphere-egu25-6652, 2025.