Mapping marine litter on coastal dunes with unmanned aerial systems: A showcase on the Atlantic Coast

Macrolitter contamination in beach, dune, and mangrove ecosystems on a Caribbean island: A comparative analysis

Litter pollution is a global issue that threatens biodiversity and ecosystem services, especially on vulnerable islands. This study assesses and compares the abundance and composition of macrolitter, including plastic bottles, and identifies its sources across beach, dune, and mangrove ecosystems in Jamaica. The highest macrolitter abundance occurred on the beach (12 items m-2), followed by dune (4.3 items m-2) and mangrove (1.8 items m-2) ecosystems. Plastics dominated (83 %-99 %), with sizes ranging from 2.5 to 25 cm (91 %-99 % of the items), and most had lifetimes shorter than one year (60 %-82 %). Lightweight items easily transported by wind accumulated in the dune, while floating items carried by tides prevailed in beach and mangrove ecosystems, with similar plastic bottle abundances (0.3 items m-2). Result showed that the primary causes of macrolitter contamination in all three ecosystems are poor waste management and recreational activities. Whereas most (54 %-84 %) of the analyzed plastic bottles were produced in Jamaica, there were also bottles made in neighboring countries, such as the Dominican Republic, Haiti, and Trinidad and Tobago, which are not sold locally. These foreign bottles were likely carried by currents from these countries or improperly disposed of by ships.

Cigarette butts contamination influence by peak season along an urban Brazilian beach

Coastal areas globally face mounting challenges due to increased human activities, resulting in substantial environmental impacts on beaches and marine ecosystems. This study delves into the influence of peak tourist seasons on cigarette butts (CBs) contamination along an urban beach, specifically Mar Grosso beach boardwalk in Laguna, Brazil. The study period spanned peak tourist seasons (PS) and off-seasons (OS), with data collected on CBs density and degradation levels across different areas of the boardwalk. A total of 6621 CBs were collected from the beach boardwalk along Mar Grosso beach, resulting in a density of 0.50 CBs/m2. It is worth noting that higher CBs densities were observed during the PS (0.38 CBs/m2), particularly in areas with urban structures that attract human presence, despite the presence of waste collection bins. Stages II and III of CBs degradation were the most abundant along the Mar Grosso beach boardwalk. Moreover, our research indicates a severe pollution index for the Mar Grosso Beach boardwalk during the PS season in all analyzed sectors (18.25-31.60). The study underscores the urgent need for improved waste management practices, environmental education, and policy interventions to curb CBs contamination and its adverse impacts on coastal ecosystems and public health.

Monitoring macroplastics in aquatic and terrestrial ecosystems: Expert survey reveals visual and drone-based census as most effective techniques

Anthropogenic litter, such as plastic, is investigated by the global scientific community from various fields employing diverse techniques. The goal is to assess and finally mitigate the pollutants' impacts on the natural environment. Plastic litter can accumulate in different matrices of aquatic and terrestrial ecosystems, impacting both biota and ecosystem functioning. Detection and quantification of macroplastics, and other litter, can be realized by jointly using visual census and remote sensing techniques. The primary objective of this research was to identify the most effective approach for monitoring macroplastic litter in riverine and marine environments through a comprehensive survey based on the experiences of the scientific community. Researchers involved in plastic pollution evaluated four litter occurrence and flux investigation methods (visual census, drone-based surveys, satellite imagery, and GPS/GNSS trackers) through a questionnaire. Traditional visual census and drone deployment were deemed as the most popular approaches among the 46 surveyed researchers, while satellite imagery and GPS/GNSS trackers received lower scores due to limited field validation and short performance ranges, respectively. On a scale from 0 to 5, visual census and drone-based surveys obtained 3.5 and 2.0, respectively, whereas satellite imagery and alternative solutions received scores lower than 1.2. Visual and drone censuses were used in high, medium and low-income countries, while satellite census and GPS/GNSS trackers were mostly used in high-income countries. This work provides an overview of the advantages and drawbacks of litter investigation techniques, contributing i) to the global harmonization of macroplastic litter monitoring and ii) providing a starting point for researchers and water managers approaching this topic. This work supports the selection and design of reliable and cost-effective monitoring approaches to mitigate the ambiguity in macroplastic data collection, contributing to the global harmonization of macroplastic litter monitoring protocols.

How much does marine litter weigh? A literature review to improve monitoring, support modelling and optimize clean-up activities

The weight of marine litter has been marginally considered in comparison to counting and categorizing items. However, weight determines litter dynamics on water and coasts, and it is an essential parameter for planning and optimizing clean-up activities. This work reviewed 80 publications that reported both the number and weight of beached macro-litter worldwide. On average, a litter item weighed 19.5 ± 20.3 g, with a median weight of 13.4 g. Plastics composed 80% by number and 51% by weight of the global litter bulk. A plastic item weighed 12.9 ± 13.8 g on average, with a median weight of 9 g. The analysis based on continents and on water bodies returned similar values, which can be used to estimate litter weight on beaches from past and future visual census surveys, and from remote sensing imagery. Overall, this work can improve litter monitoring reports and support dynamics modelling, thereby contributing for environmental protection and mitigation efforts.

Experimental detection of marine plastic litter in surface waters by 405 nm LD-based fluorescence lidar

Plastic pollution has become a global challenge, affecting water quality and health. Plastics including polystyrene (PS), polyvinyl chloride (PVC), polypropylene (PP), polyethylene terephthalate (PET), and high-density polyethylene (HDPE), are significant contributors to environmental pollution. With the growing need for investigation and detection of plastics found in natural waters, we propose the use of a portable laser diode (LD)-based fluorescence lidar system for in-situ detection of plastic litters in surface waters based on excitation-emission fluorescence spectroscopic data. The experiments were carried out in a controlled environment using a fluorescence lidar system with 405 nm excitation wavelength to determine the fluorescence signals of several plastics at 470 nm emission wavelength. Simultaneous detection of PET plastic and Chlorella vulgaris were also observed to determine the fluorescence influence of chlorophyll in surface waters. Attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy was employed to study the chemical composition of the plastics used before and after being submerged in the water. Scanning electron microscopy (SEM) and high-resolution camera microscopy were used to analyze the morphology of the submerged PET samples. This study provides a basis for a new in-situ technique using a fluorescence lidar system for submerged or transparent plastics in surface waters.

Quantification of litter in cities using a smartphone application and citizen science in conjunction with deep learning-based image processing

Cities are a major source of litter pollution. Determination of the abundance and composition of plastic litter in cities is imperative for effective pollution management, environmental protection, and sustainable urban development. Therefore, here, a multidisciplinary approach to quantify and classify the abundance of litter in urban environments is proposed. In the present study, litter data collection was integrated via the Pirika smartphone application and conducted image analysis based on deep learning. Pirika was launched in May 2018 and, to date, has collected approximately one million images. Visual classification revealed that the most common types of litter were cans, plastic bags, plastic bottles, cigarette butts, cigarette boxes, and sanitary masks, in that order. The top six categories accounted for approximately 80 % of the total, whereas the top three categories accounted for more than 60 % of the total imaged litter. A deep-learning image processing algorithm was developed to automatically identify the top six litter categories. Both precision and recall derived from the model were higher than 75 %, enabling proper litter categorization. The quantity of litter derived from automated image processing was also plotted on a map using location data acquired concurrently with the images by the smartphone application. Conclusively, this study demonstrates that citizen science supported by smartphone applications and deep learning-based image processing can enable the visualization, quantification, and characterization of street litter in cities.

From riverbank to the sea: An initial assessment of plastic pollution along the Ciliwung River, Indonesia

This study presents the first comprehensive analysis of anthropogenic debris on the riverbanks of the Ciliwung River, covering upstream to downstream areas. The mean of debris found in each measurement was 32.79 ± 15.38 items/m2 with a weight of 106.00 ± 50.23 g/m2. Plastic debris accounted for over 50 % of all litter items identified and represents 55 % by weight, signifying a significantly high prevalence compared to global studies examining litter along riverbanks. The majority of the plastics found originated from Single-use applications and were predominantly made from Styrofoam. This investigation demonstrated the importance of actions to reduce single use applications and to improve waste management strategies. This can be achieved through proactive initiatives coupled with adaptable approaches, such as implementing effective urban planning and enhancing waste collection capacity.

Application of improved machine learning in large-scale investigation of plastic waste distribution in tourism Intensive artificial coastlines

Oceans are ultimately a sink of plastic waste. Complex artificial coastlines pose remarkable challenges for coastal plastic waste monitoring. With the development of machine learning methods, high detection accuracy can be achieved; however, many false positives have been noted in various network models used for plastic waste investigation. In this study, extensive surveys of artificial coastlines were conducted using drones along the Dongjiang Port artificial coastline in the Binhai District, Tianjin, China. The deep learning model YOLOv8 was enhanced by integrating the InceptionNeXt and LSK modules into the network to improve its detection accuracy for plastic waste and reduce instances of tourists being misidentified as plastic. In total, 553 high-resolution coastline images with 3488 items of detected plastic waste were compared using the original and improved YOLOv8 models. The improved YOLOv8s-IL model achieved a detection rate of 64.9%, a notable increase of 11.5% compared with that of the original model. The number of false positives in the improved YOLOv8s-IL model was reduced to 32.3%, the multi-class F-score reached 76.5%, and the average detection time per image was only 2.7 s. The findings of this study provide technical support for future large-scale monitoring of plastic waste on artificial coastlines.

Marine litter weight estimation from UAV imagery: Three potential methodologies to advance macrolitter reports

In the context of marine litter monitoring, reporting the weight of beached litter can contribute to a better understanding of pollution sources and support clean-up activities. However, the litter scaling task requires considerable effort and specific equipment. This experimental study proposes and evaluates three methods to estimate beached litter weight from aerial images, employing different levels of litter categorization. The most promising approach (accuracy of 80 %) combined the outcomes of manual image screening with a generalized litter mean weight (14 g) derived from studies in the literature. Although the other two methods returned values of the same magnitude as the ground-truth, they were found less feasible for the aim. This study represents the first attempt to assess marine litter weight using remote sensing technology. Considering the exploratory nature of this study, further research is needed to enhance the reliability and robustness of the methods.

Assessment of beach macrolitter using unmanned aerial systems: A study along the Bulgarian Black Sea Coast

Over the years, the Black Sea has been impacted by the issue of marine litter, which poses ecological and health threats. A mid-term monitoring program initiated in 2018 assessed the abundance, density, and composition of beach litter (BL) on 40 frequently visited beaches. From 2018 to 2022, there was a significant increase in average abundance, rising by 261 %. Artificial polymer materials accounted for the majority (84 %) of the litter. Land-based sources dominated 77 % of the litter. The Clean Coast Index (CCI) categorized the beaches as "moderate" with an average value of 8.9 for the period between 2018 and 2022. However, the years 2021 and 2022, during the COVID-19 epidemic, were identified as the "dirtiest period" with 11 beaches classified as "extremely dirty" due to high domestic tourist pressure. The study demonstrates a successful combination of standard in situ visual assessment supported by unmanned aerial systems for beach litter surveys.

Drones for litter monitoring on coasts and rivers: suitable flight altitude and image resolution

Multirotor drones can be efficiently used to monitor macro-litter in coastal and riverine environments. Litter on beaches, dunes and riverbanks, along with floating litter on coastal and river waters, can be spotted and mapped from aerial drone images. Items detection and classification are prone to image resolution, which is expressed in terms of Ground Sampling Distance (GSD). The GSD is determined by drone flight altitude and camera properties. This paper investigates what is a suitable GSD value for litter survey. Drone flight altitude and camera setup should be chosen to obtain a GSD between 0.5 cm/px and 1.25 cm/px. Within this range, the lowest GSD allows litter categorization and classification, whereas the highest value should be adopted for a coarser litter census. In the vision of drawing up a global protocol for drone-based litter surveys, this work sets the ground for homogenizing data collection and litter assessments.

Anthropogenic debris pollution in peri-urban mangroves of South China: Spatial, seasonal, and environmental drivers in Hong Kong

Excessive mismanaged debris along tropical coasts pose a threat to vulnerable mangrove ecosystems. Here, we examined the spatial, seasonal and environmental drivers of anthropogenic debris abundance and its potential ecological impact in peri-urban mangroves across Hong Kong. Seasonal surveys were conducted in both landward and seaward zones, with identification, along belt transects, of macrodebris (>5 mm) based on material type and use. Our results indicate spatial variability in debris abundance and distribution, with plastic being the predominant material type identified. Both plastic and non-plastic domestic items covered the most surface area. Debris aggregation was highest at the landward zones, consistent with the literature. In the dry season, more debris accumulated and covered greater surface area in both seaward and landward zones. These results confirm that land-derived debris from mismanaged waste, rather than debris coming from the Pearl River, is the primary source of anthropogenic debris pollution threatening Hong Kong's mangroves.

The octopus pot on the North Atlantic Iberian coast: A plague of plastic on beaches and dunes

This baseline focuses on the octopus pot, a litter item found on the North Atlantic Iberian coast. Octopus pots are deployed from vessels in ropes, with several hundred units, and placed on the seabed, to capture mostly Octopus Vulgaris. The loss of gears due to extreme seas state, bad weather and/or fishing-related unforeseen circumstances, cause the octopus pots contaminating beaches and dunes, where they are transported by sea current, waves and wind actions. This work i) gives an overview of the use of octopus pot on fisheries, ii) analyses the spatial distribution of this item on the coast, and iii) discusses the potential measures for tackling the octopus pot plague on the North Atlantic Iberian coast. Overall, it is urgent to promote conducive policies and strategies for a sustainable waste management of octopus pots, based on Reduce, Reuse and Recycle hierarchical framework.

Difference between invasive alien and native vegetation in trapping beach litter: A focus on a typical sandy beach of W-Mediterranean Basin

Beach litter is one of the most pervasive pollution issues in coastal environments worldwide. In this study, we aim to assess the amount and distribution of beach litter on Porto Paglia beach, its entrapment across psammophilous habitats, and whether the invasive Carpobrotus acinaciformis (L.) L.Bolus plays a different role in trapping litter than native vegetation. To this end, two seasonal samplings (in spring and autumn) were conducted using a paired sampling method that considers plots in all coastal habitats with and without C. acinaciformis. Our results confirm that the main beach litter category is plastic, and that its distribution varies across habitats: the white dune seems to play a greater role in trapping and filtering beach litter, reducing its amount in the backdune. A correlation was found between the Naturalness index (N) and the beach litter amount, supporting the finding that invaded habitats trap beach litter better than native ones.

Impacts of a massive beach music festival on a coastal ecosystem - A showcase in Portugal

Beach music festivals are numerous and popular worldwide. The concerns about the environmental sustainability of these events have been increasing among scientists, coastal managers and local communities. Nevertheless, the negative effects of beach music festivals on the coastal environment have been poorly studied. This work identified, analysed and discussed the eco-geomorphological impacts of a massive beach music festival held on a Portuguese beach-dune system over three days in July 2022. Drone-based orthophotos and pictures collected in the field were analysed to evaluate the impact of pre- and post-festival works, which turned the beach into a construction site over about twenty days. Digital Surface Model (DSM) analysis showed that beach configuration was approximately restored to the pre-festival configuration after the event. In contrast, the comparison of Normalized Difference Vegetation Index (NDVI) maps revealed that 18,500 m² of embryonic dune vegetation, which represented 35 % of the existing plant community, was removed by works on the beach and by trampling of festival attendees. To authors' knowledge, this is the first work that evaluates the eco-geomorphological impact of a massive beach music festival on the delicate coastal ecosystem. Overall, it contributes in raising awareness for making these events more respectful of the coastal environment.

Dunal plants intercepting macrolitter: Implications for beach clean-ups

Coastal vegetation intercepts macroplastics and, consequently, it may represent a reservoir of anthropogenic litter and organic wrack. We aimed at investigating (i) the abundance variation of macrolitter from the beach to foredune and backdune (three cross-shore plots over 20 long-shore sectors) and (ii) the role of the halo-psammophilous plants and Phragmites australis reedbed in intercepting the macrolitter, respectively, in the foredunes and backdunes. The vegetation in the foredunes (mainly halo-psammophilous species) acted as a first interception belt for macrolitter, while the bigger litter reached the backdunes. Our results might be of great concern with implications for beach clean-ups - which must also be mainly focused in foredunes and backdunes, however warning operators in advance that they could damage the vegetation by trampling on.

Understanding through drone image analysis the interactions between geomorphology, vegetation and marine debris along a sandy spit

Marine litter (ML) is recognized as one of the main socio-economic and environmental concerns and monitoring operations have been realized worldwide in order to collect information on the types, quantities and distribution of marine debris. In this study, we used Unmanned Aerial Vehicle (UAV) images to map the presence of ML on a coastal spit in relation to geomorphological aspects and vegetation. Our results show that ML is present everywhere, but concentrates in the beach wrack, dunes, and saltmarshes, highlighting the role of the vegetation in trapping ML. Moreover, ML will most probably remain trapped by the saltmarsh vegetation, since they are not visible and easily accessible to allow cleaning operations. On the contrary, cleaning operations may remove the ML present in the beach wrack. Finally, our results provide useful information to support decision-makers for improving beach cleaning activities in the Po river Delta areas (Italy).

The halophyte Cakile maritima Scop. 1772 as a trap of plastic litter on the Moroccan coast

Some plant communities of coastal dunes may affect the magnitude and distribution of litter on the ecosystem. In this study, the aim is to assess the aptitude of the halophyte Cakile maritima Scop. 1772 to be a trap and sink of plastic litter on the Moroccan Atlantic coast. Overall, a significant difference was noted between plastic litter trapped in C. maritima patches (1173 items) and control plots (502 items). Food containers and ropes were the most common trapped items. Shoreline and recreational activities, followed by dumping and ocean/waterway activities are the main sources of the trapped plastic items. The findings suggest the expansion of the cleaning operations to include coastal dunes, the need to change behavior among beachgoers in regard to food plastics disposal, as well the control of C. maritima distribution in the study area, and similar plant species in other regions.

Plastic litter in coastal sand dunes: Degradation behavior and impact on native and non-native invasive plants

Pollution associated to marine plastic litter is raising increasing concerns due to its potential harmful effects on human health, biota, and coastal ecosystems. However, limited information is available on the degradation behavior of plastics, especially biodegradable ones, in dune habitats. Moreover, the effects of plastics on dune plant growth and ability to withstand environmental stresses and invasion by non-native plants have been largely neglected. This is a particularly relevant issue since biological invasions are major threats to dune ecosystems. In this 18-month study, we examined the degradation behavior of two plastic bags, non-biodegradable (NBP) or biodegradable/compostable (BP), in the dune environment by visual observations and analytical techniques. Concomitantly, we investigated the individual and combined effects of bag type and sand burial (no burial vs. partial burial) on the performance of a native dune plant (Thinopyrum junceum) and an invasive plant (Carpobrotus sp.) and on their interaction. NBP did not show relevant degradation signs over the experimental period as expected. BP exhibited gradual surface modifications and changes in chemical functionality and were almost disintegrated after 18 months. Bags and burial reduced independently T. junceum survival and growth, and most plants died within 8 months of plastic exposure. Bags and burial did not affect Carpobrotus survival. However, burial decreased Carpobrotus growth while NBP increased it. Both plastics increased Carpobrotus competitive ability, and no T. junceum plants survived to co-occurrent Carpobrotus, BP, and burial. These findings indicate that removing all littered plastics from beach-dune systems not only is critical to reduce plastic pollution but also to prevent further spread of invasive species in coastal dunes.

Beach litter survey by drones: Mini-review and discussion of a potential standardization

The abundance of beach litter has been increasing globally during the last decades, and it is an issue of global concern. A new survey strategy, based on uncrewed aerial vehicles (UAV, aka drones), has been recently adopted to improve the monitoring of beach macro-litter items abundance and distribution. This work identified and analysed the 15 studies that used drone for beach litter surveys on an operational basis. The analysis of technical parameters for drone flight deployment revealed that flight altitude varied between 5 and 40 m. The analysis of final assessments showed that, through manual and/or automated items detection on images, most of studies provided litter bulk characteristics (type, material and size), along with litter distribution maps. The potential standardization of drone-based litter survey would allow a comparison among surveys, however it seems difficult to propose a standard set of flight parameters, given the wide variety of coastal environments, the different devices available, and the diverse objectives of drone-based litter surveys. On the other hand, in our view, a set of common outcomes can be proposed, based on the grid mapping process, which can be easily generated following the procedure indicated in the paper. This work sets the ground for the development of a standardized protocol for drone litter data collection, analysis and assessments. This would allow the provision of broad scale comparative studies to support coastal management at both national and international scales.

UAV remote sensing applications in marine monitoring: Knowledge visualization and review

With the booming development of information technology and the growing demand for remote sensing data, unmanned aerial vehicle (UAV) remote sensing technology has emerged. In recent years, UAV remote sensing technology has developed rapidly and has been widely used in the fields of military defense, agricultural monitoring, surveying and mapping management, and disaster and emergency response and management. Currently, increasingly serious marine biological and environmental problems are raising the need for effective and timely monitoring. Compared with traditional marine monitoring technologies, UAV remote sensing is becoming an important means for marine monitoring thanks to its flexibility, efficiency and low cost, while still producing systematic data with high spatial and temporal resolutions. This study visualizes the knowledge domain of the application and research advances of UAV remote sensing in marine monitoring by analyzing 1130 articles (from 1993 to early 2022) using a bibliometric approach and provides a review of the application of UAVs in marine management mapping, marine disaster and environmental monitoring, and marine wildlife monitoring. It aims to promote the extensive application of UAV remote sensing in the field of marine research.

Close-range remote sensing-based detection and identification of macroplastics on water assisted by artificial intelligence: A review

Detection and identification of macroplastic debris in aquatic environments is crucial to understand and counter the growing emergence and current developments in distribution and deposition of macroplastics. In this context, close-range remote sensing approaches revealing spatial and spectral properties of macroplastics are very beneficial. To date, field surveys and visual census approaches are broadly acknowledged methods to acquire information, but since 2018 techniques based on remote sensing and artificial intelligence are advancing. Despite their proven efficiency, speed and wide applicability, there are still obstacles to overcome, especially when looking at the availability and accessibility of data. Thus, our review summarizes state-of-the-art research about the visual recognition and identification of different sorts of macroplastics. The focus is on both data acquisition techniques and evaluation methods, including Machine Learning and Deep Learning, but resulting products and published data will also be taken into account. Our aim is to provide a critical overview and outlook in a time where this research direction is thriving fast. This study shows that most Machine Learning and Deep Learning approaches are still in an infancy state regarding accuracy and detail when compared to visual monitoring, even though their results look very promising.

Detection of River Plastic Using UAV Sensor Data and Deep Learning

Plastic pollution is a critical global issue. Increases in plastic consumption have triggered increased production, which in turn has led to increased plastic disposal. In situ observation of plastic litter is tedious and cumbersome, especially in rural areas and around transboundary rivers. We therefore propose automatic mapping of plastic in rivers using unmanned aerial vehicles (UAVs) and deep learning (DL) models that require modest compute resources. We evaluate the method at two different sites: the Houay Mak Hiao River, a tributary of the Mekong River in Vientiane, Laos, and Khlong Nueng canal in Talad Thai, Khlong Luang, Pathum Thani, Thailand. Detection models in the You Only Look Once (YOLO) family are evaluated in terms of runtime resources and mean average Precision (mAP) at an Intersection over Union (IoU) threshold of 0.5. YOLOv5s is found to be the most effective model, with low computational cost and a very high mAP of 0.81 without transfer learning for the Houay Mak Hiao dataset. The performance of all models is improved by transfer learning from Talad Thai to Houay Mak Hiao. Pre-trained YOLOv4 with transfer learning obtains the overall highest accuracy, with a 3.0% increase in mAP to 0.83, compared to the marginal increase of 2% in mAP for pre-trained YOLOv5s. YOLOv3, when trained from scratch, shows the greatest benefit from transfer learning, with an increase in mAP from 0.59 to 0.81 after transfer learning from Talad Thai to Houay Mak Hiao. The pre-trained YOLOv5s model using the Houay Mak Hiao dataset is found to provide the best tradeoff between accuracy and computational complexity, requiring model resources yet providing reliable plastic detection with or without transfer learning. Various stakeholders in the effort to monitor and reduce plastic waste in our waterways can utilize the resulting deep learning approach irrespective of location.

Monitoring Light Pollution with an Unmanned Aerial Vehicle: A Case Study Comparing RGB Images and Night Ground Brightness

There are several tools and methods to quantify light pollution due to direct or reflected light emitted towards the sky. Unmanned aerial vehicles (UAV) are still rarely used in light pollution studies. In this study, a digital camera and a sky quality meter mounted on a UAV have been used to study the relationship between indices computed on night images and night ground brightness (NGB) measured by an optical device pointed downward towards the ground. Both measurements were taken simultaneously during flights at an altitude of 70 and 100 m, and with varying exposure time. NGB correlated significantly both with the brightness index (−0.49 ÷ −0.56) and with red (−0.52 ÷ −0.58) and green band indices (−0.42 ÷ −0.58). A linear regression model based on the luminous intensity index was able to estimate observed NGB with an RMSE varying between 0.21 and 0.46 mpsas. Multispectral analysis applied to images taken at 70 m showed that increasing exposure time might cause a saturation of the colors of the image, especially in the red band, that worsens the correlation between image indices and NGB. Our study suggests that the combined use of low cost devices such as UAV and a sky quality meter can be used for assessing hotspot areas of light pollution originating from the surface.

Where does marine litter hide? The Providencia and Santa Catalina Island problem, SEAFLOWER Reserve (Colombia)

The SEAFLOWER Biosphere Reserve (SBR) is the largest Marine Protected Area in the Caribbean Sea and the second largest in Latin America. Marine protected areas are under pressure from various stressors, one of the most important issues being pollution by marine litter, especially plastic. In this study our aim is to establish the distribution pattern and potential sources of solid waste in the different marine/coastal ecosystems of the islands of Providencia and Santa Catalina (SBR), as well as assess any interconnections between these ecosystems. At the same time, the distribution characteristics of marine litter in the different compartments facilitated a more dynamic understanding of the load of marine litter supplied by the islands, both locally and externally. We observed that certain ecosystems, principally back-beach vegetation and mangroves, act as crucial marine litter accumulation zones. Mangroves are important hotspots for plastic accumulation, with densities above eight items/m² (minimum 8.38 and maximum 10.38 items/m²), while back-beach vegetation (minimum 1.43 and maximum 7.03 items/m²) also removes and stores a portion of the marine litter that arrives on the beaches. Tourist beaches for recreational activities have a low density of marine litter (minimum 0.01 and maximum 0.72 items/m²) due to regular clean-ups, whereas around non-tourist beaches, there is a greater variety of sources and accumulation (minimum 0.31 and maximum 5.41 items/m²). The low density of marine litter found on corals around the island (0-0.02 items/m²) indicates that there is still no significant marine litter stream to the coral reefs. Identifying contamination levels in terms of marine litter and possible flows between ecosystems is critical for adopting management and reduction strategies for such residues.

Beached and Floating Litter Surveys by Unmanned Aerial Vehicles: Operational Analogies and Differences

The abundance of litter pollution in the marine environment has been increasing globally. Remote sensing techniques are valuable tools to advance knowledge on litter abundance, distribution and dynamics. Images collected by Unmanned Aerial Vehicles (UAV, aka drones) are highly efficient to map and monitor local beached (BL) and floating (FL) marine litter items. In this work, the operational insights to carry out both BL and FL surveys using UAVs are detailly described. In particular, flight planning and deployment, along with image products processing and analysis, are reported and compared. Furthermore, analogies and differences between UAV-based BL and FL mapping are discussed, with focus on the challenges related to BL and FL item detection and recognition. Given the efficiency of UAV to map BL and FL, this remote sensing technique can replace traditional methods for litter monitoring, further improving the knowledge of marine litter dynamics in the marine environment. This communication aims at helping researchers in planning and performing optimized drone-based BL and FL surveys.

Operational use of multispectral images for macro-litter mapping and categorization by Unmanned Aerial Vehicle

The use of Unmanned Aerial Systems (UAS, aka drones) has shown to be feasible to perform marine litter surveys. We operationally tested the use of multispectral images (5 bands) to classify litter type and material on a beach-dune system. For litter categorization by their multispectral characteristics, the Spectral Angle Mapping (SAM) technique was adopted. The SAM-based categorization of litter agreed with the visual classification, thus multispectral images can be used to fasten and/or making more robust the manual RGB image screening. Fully automated detection returned an F-score of 0.64, and a reasonable categorization of litter. Overall, the image-based litter density maps were in line with the manual detection. Assessments were promising given the complexity of the study area, where different dunes plants and partially-buried items challenged the UAS-based litter detection. The method can be easily implemented for both floating and beached litter, to advance litter survey in the environment.

In an octopus's garden in the shade: Underwater image analysis of litter use by benthic octopuses

Benthic octopuses have been widely documented in artificial shelters for decades, and this use is apparently increasing. Despite any possible positive effects, the use of litter as shelter could have negative implications. In this work, we aimed to elucidate the interactions of octopuses with marine litter, identifying types of interactions and affected species and regions. To achieve this, we obtained 261 underwater images from 'citizen science' records, and identified 8 genera and 24 species of benthic octopuses interacting with litter. Glass objects were present in 41.6% of interactions, and plastic in 24.7%. Asia presented the highest number of images, and most records were from 2018 to 2021. Citizen science provided important evidence on octopus/marine litter interactions, highlighting its value and the need for more investigations on the subject. This information is fundamental to help prevent and mitigate the impacts of litter on octopuses, and identify knowledge gaps that require attention.

Marine litter on sandy beaches with different human uses and waste management along the Gulf of Nicoya, Costa Rica

This study was a baseline with quantitative data of marine litter along the Gulf of Nicoya, Costa Rica. The objective of the study was to quantify marine litter and its association with human activities in this estuarine gulf. A total of fourteen sandy beaches were cataloged by the degree of urbanization, tourism intensity, beach cleaning programs, and tributary rivers as possible drivers of marine litter presence. The items of the marine litter were separated and weighted by type. Analysis by the clean coastal index (CCI) and multivariate statistics were applied to find spatial patterns in marine litter in the gulf. On beaches with the highest touristic activity, cigarette butts and straws were the main components. Locations with river plume influence, less frequent cleanup, or waste cans showed more bottles, plastic parts, and sanitary waste than beaches in other conditions. A beach in a fisherman town had recently utilized plastic bags, household goods, and boat parts in the marine litter. A wildlife refuge beach showed only small plastic and coffee foam cup fragments that came with currents from other points in the estuary. River basin management, solid waste disposal programs, and environmental education to avoid single-use items combined with correct waste disposal are needed to reduce marine litter in tropical countries focused on ecological tourism.

Pixel-level image classification for detecting beach litter using a deep learning approach

Mitigating and preventing beach litter from entering the ocean is urgently required. Monitoring beach litter solely through human effort is cumbersome, with respect to both time and cost. To address this problem, an artificial intelligence technique that can automatically identify different-sized beach litter is proposed. The technique was established by training a deep learning model that enables pixel-wise classification (semantic segmentation) using beach images taken by an observer on the beach. Eight segmentation classes that include two beach litter classes were defined, and the results were qualitatively and quantitatively verified. Segmentation performance was adequately high based on three metrics: Intersection over Union (IoU), precision, and recall, although there is room for further improvement. The potency of the method was demonstrated when it was applied to images taken in different places from training data images, and the coverage of artificial litter calculated and discussed using drone images provided ground truth.

Is coastal erosion a source of marine litter pollution? Evidence of coastal dunes being a reservoir of plastics

This baseline reports scientific evidence of marine litter items embedded in the dune volume at two study sites on the North Atlantic Portuguese coast. We described how stranded litter participate in the sand dune growth/erosion processes on a natural beach-dune system. From the storm-eroded foredunes on the urbanized beach, we documented exhumed plastics with age up to 38 years. Whether litter burial was due to beach-dune morphodynamic processes, or to irresponsible and/or illegal dumping in the past, this work emphasises the need of improving buried litter census and monitoring on coastal dunes. Coastal erosion processes may further exhume litter buried in dune volumes and on other coastal environments over short- and long-term, re-exposing items into the marine environment. Thus, coastal erosion can be accounted as a secondary diffuse source of littering pollution, beside the multiple sources already identified in the environment.

Coastal dunes as a sink and secondary source of marine plastics: A study at Perran Beach, southwest England

Plastic food packaging and containers (n = 263) have been retrieved from the scarped foredunes at Perran Beach, SW England, following a storm surge. Samples displayed evidence of cracking, scratching, discolouration, staining and hydroxyl and carbonyl stretching, but legible text indicating their origin, dates of manufacture/expiration, packaging codes and logos, coupled with online product searches, allowed 25 food packets and 87 containers to be aged. Estimates of food packaging age spanned a 43-year period (1975-2018), with a median age of 25 years, while estimates for containers spanned 57 years (1962-2019), with a median age of 19 years. Plastic derived from local littering and offshore sources appears to be trapped within the foredunes for years to decades and subsequently released as "fresh" beach litter following surges sufficient to effect scarping. Dunal systems may act as significant reservoirs of historical plastics and play a critical role in their recycling and retention in the coastal zone.

Carpobrotus spp. patches as trap for litter: Evidence from a Mediterranean beach

Dunal plants may affect the patterns of deposition of beach litter. In this study, we aimed at evaluating if Carpobrotus spp. patches may act as a litter trap in coastal dune systems. To do so, we counted the number of macrolitter occurring in both Carpobrotus and control (embryo dune vegetation) patches classifying each item into categories according to the Marine Strategy. Totally, we observed a significant difference between litter trapped in Carpobrotus (331 items, representing 62.4% of the total beach litter) and control (199, 37.6%). Plastic fragments were the most trapped items by both Carpobrotus (46.2%) and control patches (47.2%). We also calculated the item co-occurrence, obtaining a random aggregated 'litter community'. The main emerging output is that Carpobrotus patches act as filter in respect to different anthropogenic materials (overall plastics), suggesting that alien plant management actions may contribute to solve beach litter issues as well.

Citizen Science for Marine Litter Detection and Classification on Unmanned Aerial Vehicle Images

Unmanned aerial vehicles (UAV, aka drones) are being used for mapping macro-litter in the environment. As drone images require a manual processing task for detecting marine litter, it is of interest to evaluate the accuracy of non-expert citizen science operators (CSO) in performing this task. Students from Italian secondary schools (in this work, the CSO) were invited to identify, mark, and classify stranded litter items on a UAV orthophoto collected on an Italian beach. A specific training program and working tools were developed for the aim. The comparison with the standard in situ visual census survey returned a general underestimation (50%) of items. However, marine litter bulk categorisation was fairly in agreement with the in situ survey, especially for sources classification. The concordance level among CSO ranged between 60% and 91%, depending on the item properties considered (type, material, and colour). As the assessment accuracy was in line with previous works developed by experts, remote detection of marine litter on UAV images can be improved through citizen science programs, upon an appropriate training plan and provision of specific tools.

A seasonal comparison of litter impacts along the Viña del Mar - Concón coastal strip, Valparaiso region of Chile

The Vina del Mar - Concón Coastal strip is well known for its urban beaches, which play an essential role in the national economy. With extreme urban development, these beaches have become more polluted by litter. This paper evaluates the abundance, spatio-temporal distribution, typology, and sources of beach litter in 14 sectors located inside this coastal strip. A total of 19,886 litter items were collected and grouped into 50 different categories (11 litter typologies). Overall average litter abundance was 0.21 items/m² while during the fall and winter averages were 0.25 items/m² and 0.17 items/m². Values changed along the area and between seasons. Plastics, cigarette butts and paper-cardboard typologies dominated the samples with 42% (0.088 items/m²), 25.4% (0.053 items/m²) and 20.8% (0.043 items/m²).

The effectiveness of legislative and voluntary strategies to prevent ocean plastic pollution: Lessons from the UK and South Pacific

The islands of the South Pacific contribute a fraction of the mis-managed plastics in the world's ocean, yet the region is one of the main recipients of its impacts. Based on expert interviews and a review of current strategies to prevent marine plastic pollution in six countries (Australia, New Zealand, Fiji, Tonga, Vanuatu, United Kingdom), this paper identifies several interventions - legislative, financial, voluntary - which governments, organisations and individuals can learn from. Both voluntary and statutory consumer-based behaviour change campaigns are well developed and somewhat successful in several countries. While sub-national policies do not inhibit progress, they are not optimal. Harmonisation across the territories of federal and devolved systems is beneficial, such as container return schemes, levies, and bans. Vanuatu has displayed high ambition, and the challenges in achieving this serve as a case study. A coordinated global strategy with associated legislation aimed at tackling plastic pollution is critical.

Integrating Drone Technology into an Innovative Agrometeorological Methodology for the Precise and Real-Time Estimation of Crop Water Requirements

Precision agriculture has been at the cutting edge of research during the recent decade, aiming to reduce water consumption and ensure sustainability in agriculture. The proposed methodology was based on the crop water stress index (CWSI) and was applied in Greece within the ongoing research project GreenWaterDrone. The innovative approach combines real spatial data, such as infrared canopy temperature, air temperature, air relative humidity, and thermal infrared image data, taken above the crop field using an aerial micrometeorological station (AMMS) and a thermal (IR) camera installed on an unmanned aerial vehicle (UAV). Following an initial calibration phase, where the ground micrometeorological station (GMMS) was installed in the crop, no equipment needed to be maintained in the field. Aerial and ground measurements were transferred in real time to sophisticated databases and applications over existing mobile networks for further processing and estimation of the actual water requirements of a specific crop at the field level, dynamically alerting/informing local farmers/agronomists of the irrigation necessity and additionally for potential risks concerning their fields. The supported services address farmers’, agricultural scientists’, and local stakeholders’ needs to conform to regional water management and sustainable agriculture policies. As preliminary results of this study, we present indicative original illustrations and data from applying the methodology to assess UAV functionality while aiming to evaluate and standardize all system processes.

Morphological and ecological responses of a managed coastal sand dune to experimental notches

In northern Europe, coastal dune remobilization by restoring natural processes is considered by some to maintain the coastal dune in chronically eroding sectors by migrating landward and to restore dune ecology. In wet climatic contexts, this nature-based solution has been shown to induce an increase in both sand bare areas and vegetation diversity. However, it has never been tested in the coastal dunes of southern Europe with a drier climate and, thus, more stressful conditions, where disturbance may inversely decrease vegetation diversity. An original experiment was set up in 2018 on a 4-km stretch of coastal dune in southwest France where Experimental Notches (EN) were excavated in the incipient foredune, referred to as West Experimental Notch (WEN), and in the established foredune, referred as to East Experimental Notch (EEN). Morphological and ecological responses were monitored using UAV photogrammetry and vegetation sampling along transects during two years with contrasted winter storm conditions. During the first winter characterized by calm wind conditions, a rapid filling of the WENs and the initiation of deposition lobes landward of the EENs were observed. Stronger winds during the second winter led to the development of deposition lobes of the EENs, increasing both their volume, up to 6 times, and their cross-shore elongation. The increase in disturbance induced by the notches had a significant impact on vegetation. New sandy bares were colonized by pioneer species leading to an increase in species richness and rejuvenation, in particular landward of the EENs. Although longer-term monitoring is required to draw conclusions, these results suggest that the excavation of foredune notches are able to re-establish an ecomorphological dynamic in the dunes of southwest France on the time scales of years, promoting landward sand transport and, thus, the foredune landward translation, while not threatening diversity. Such approach may become a relevant adaptation strategy to sea level rise and increased erosion in this region of the world.

Microbial communities of polyhydroxyalkanoate (PHA)-based biodegradable composites plastisphere and of surrounding environmental matrix: a comparison between marine (seabed) and coastal sediments (dune sand) over a long-time scale

Most researches on the plastisphere in coastal environments deal with plastics floating in seawater. Comparatively smaller attention has been devoted to the plastisphere of plastics buried in marine sediments, and very little is known on that of plastics on coastal sand dunes. Yet, limited information is available on the impact of plastics, especially biodegradable plastics, on microbial organisms in their surroundings. Nevertheless, a large amount of plastics sink on the seabed or is deposited on beach-dune systems. We investigated the succession of plastisphere microbial community on two biodegradable composites based on poly(hydroxybutyrate-co-hydroxyvalerate) (PHBV) and seagrass fibres (PHBV/PO), buried in seabed and dune sediments over a 27 months period in mesocosm. PHBV is regarded as a valuable alternative to conventional plastics and PHBV/PO has recently been designed for applications in coastal habitat restoration. We also examined the degradation rate and impact of these plastics on the microbial communities of surrounding sediments. Microbial communities of the surface of PHBV and PHBV/PO in seabed and dune sand differ from those of surrounding sediments, displaying a lower richness. Plastics colonization occurs largely from bacteria present in surrounding sediments, although the contribution from the water column bacterial pool could be not negligible for plastics in the seabed. No significant differences were detected between the communities of the two plastics and no significant impact of plastics on microbial community of the surrounding sediments was detected. The exceptional long duration of this study allowed to gain information on the succession of a plastisphere community over a previously unexplored time scale. Succession appears highly dynamic in dune sand even after two years, while the community structure in seabed seems to reach stability after one year. These findings highlight the importance of performing long-term studies when trying to characterize composition and dynamics of plastisphere bacterial communities.

A Citizen Science Unmanned Aerial System Data Acquisition Protocol and Deep Learning Techniques for the Automatic Detection and Mapping of Marine Litter Concentrations in the Coastal Zone

Marine litter (ML) accumulation in the coastal zone has been recognized as a major problem in our time, as it can dramatically affect the environment, marine ecosystems, and coastal communities. Existing monitoring methods fail to respond to the spatiotemporal changes and dynamics of ML concentrations. Recent works showed that unmanned aerial systems (UAS), along with computer vision methods, provide a feasible alternative for ML monitoring. In this context, we proposed a citizen science UAS data acquisition and annotation protocol combined with deep learning techniques for the automatic detection and mapping of ML concentrations in the coastal zone. Five convolutional neural networks (CNNs) were trained to classify UAS image tiles into two classes: (a) litter and (b) no litter. Testing the CCNs’ generalization ability to an unseen dataset, we found that the VVG19 CNN returned an overall accuracy of 77.6% and an f-score of 77.42%. ML density maps were created using the automated classification results. They were compared with those produced by a manual screening classification proving our approach’s geographical transferability to new and unknown beaches. Although ML recognition is still a challenging task, this study provides evidence about the feasibility of using a citizen science UAS-based monitoring method in combination with deep learning techniques for the quantification of the ML load in the coastal zone using density maps.

Plastic pollution on the Colombian central Caribbean beaches

Along 24 beaches of the Central Caribbean Coast of Colombia, plastic items were collected and grouped into 43 different typologies. The average plastic abundance was 4.54 items/m² being eight typologies responsible for 82% of all plastic collected. The application of the Clean Coast Index (CCI) and the Plastic Abundance Index (PAI) allows categorization of the study area as "Extremely Dirty" coastal strip with a "Very High Abundance" of plastics. Beaches were statistically grouped into three specific types: moderate, bad and extremely bad environmental conditions. The typology and magnitudes of plastics found in the study area suggest a combination of sources that primarily include dumping and direct activities on the beach. Plastic medical and sanitary waste, ocean/waterway items, and plastic items related to smoking-related activities also were observed.

Beach-dune morphodynamics and marine macro-litter abundance: An integrated approach with Unmanned Aerial System

This work shows an integrated approach for coastal environmental monitoring, which aimed to understand the relation between beach-dune morphodynamics, marine litter abundance and environmental forcing. Three unmanned aerial system (UAS) flights were deployed on a beach-dune system at the Atlantic Portuguese coast to assess two main goals: (i) quantifying the morphological changes that occurred among flights, with focus on dune erosion, and (ii) mapping the changes of marine macro-litter abundance on the shore. Two most vulnerable-to-erosion sectors of the beach were identified. In the northern sector, the groin affected the downdrift shoreline, with dune erosion of about 1 m. In the central part of the beach, the dunes recessed about 4 m during the winter, being more exposed to environmental forcing due to the absence of dune vegetation. Marine litter occupation area on the beach decreased from 25% to 20% over the winter, with octopus pots (13%) and fragments (69%) being the most abundant items on average. Litter distribution varied in relation to swash elevation, wind speed and direction. With low swash elevation, the wind played a predominant role in moving the stranded items northwards, whereas high swash elevation concentrated the items at the dune foot. This study emphasizes the potential of UAS in allowing an integrated approach for coastal erosion monitoring and marine litter mapping, and set the ground for marine litter dynamic modelling on the shore.

Combined effect of plastic litter and increased atmospheric nitrogen deposition on vegetative propagules of dune plants: A further threat to coastal ecosystems

Large amounts of non-biodegradable plastics are currently deposited on beach-dune systems, and biodegradable plastics could enter these already declining habitats in coming years. Yet, the impacts of plastics on vegetative recruitment, a plant strategy playing a key role in dune stabilization, are unknown. Whether these pollutants interact with increased atmospheric nitrogen (N) deposition, a major global driver of plant biodiversity loss, in affecting plant communities of such nutrient-poor habitats, and how plant-plant interactions mediate their effects need to be explored. In a one-year field experiments, we examined individual and combined effects of plastic (non-biodegradable, biodegradable), N deposition (ambient, elevated) and biotic condition (no interaction, interaction with a conspecific or with a hetero-specific) on the colonization success and growth of vegetative propagules of dune plants. Thinopyrum junceum and Sporobolus pumilus were chosen as models because they co-occur along Mediterranean dunes and differ in ecological role (dune- vs. non dune-building) and photosynthetic pathway (C3 vs. C4). For both species, survival probability was reduced by non-biodegradable plastic and elevated N by up to 100%. Thinopyrum junceum survival was also reduced by S. pumilus presence. Elevated N and biodegradable plastic reduced T. junceum shoot biomass when grown alone and with a conspecific, respectively; these factors in combination mitigated their negative individual effects on root biomass. Biodegradable plastic increased S. pumilus shoot and root biomass, and in combination with elevated N caused a greater biomass investment in belowground (root plus rhizome) than aboveground organs. Non-biodegradable plastic may be a further threat to dune habitats by reducing plant colonization. Biodegradable plastic and increased N deposition could favour the generalist S. pumilus and hinder the dune-building T. junceum. These findings highlight the urgency of implementing measures for preventing plastic deposition on beaches and reducing N input.

Floating marine macro-litter in the North Western Mediterranean Sea: Results from a combined monitoring approach

The aim of the present study was twofold: (i) to validate the drone methodology for floating marine macro-litter (FMML) monitoring, by comparing the results obtained through concurrent drone surveys and visual observations from vessels, and (ii) to assess FMML densities along the North Western Mediterranean Sea using the validated drone surveys. The comparison between monitoring techniques was performed based on 18 concurrent drone/vessel transects. Similar densities of FMML were detected through the two methods (16 items km^-2 from the drone method vs 19 items km^-2 from the vessel-based visual method). The assessment of FMML densities was done using 40 additional drone transects performed over the waters off the Catalan coast. The densities of FMML observed ranged 0-200 items km^-2. These results provide a validation of the use of drones to monitor FMML and contribute to increasing the knowledge about the density of FMML in the North Western Mediterranean Sea.

Quantifying Marine Macro Litter Abundance on a Sandy Beach Using Unmanned Aerial Systems and Object-Oriented Machine Learning Methods

Unmanned aerial systems (UASs) have recently been proven to be valuable remote sensing tools for detecting marine macro litter (MML), with the potential of supporting pollution monitoring programs on coasts. Very low altitude images, acquired with a low-cost RGB camera onboard a UAS on a sandy beach, were used to characterize the abundance of stranded macro litter. We developed an object-oriented classification strategy for automatically identifying the marine macro litter items on a UAS-based orthomosaic. A comparison is presented among three automated object-oriented machine learning (OOML) techniques, namely random forest (RF), support vector machine (SVM), and k-nearest neighbor (KNN). Overall, the detection was satisfactory for the three techniques, with mean F-scores of 65% for KNN, 68% for SVM, and 72% for RF. A comparison with manual detection showed that the RF technique was the most accurate OOML macro litter detector, as it returned the best overall detection quality (F-score) with the lowest number of false positives. Because the number of tuning parameters varied among the three automated machine learning techniques and considering that the three generated abundance maps correlated similarly with the abundance map produced manually, the simplest KNN classifier was preferred to the more complex RF. This work contributes to advances in remote sensing marine litter surveys on coasts, optimizing the automated detection on UAS-derived orthomosaics. MML abundance maps, produced by UAS surveys, assist coastal managers and authorities through environmental pollution monitoring programs. In addition, they contribute to search and evaluation of the mitigation measures and improve clean-up operations on coastal environments.

Macroplastic Storage and Remobilization in Rivers

The paper presents a conceptual model of the route of macroplastic debris (>5 mm) through a fluvial system, which can support future works on the overlooked processes of macroplastic storage and remobilization in rivers. We divided the macroplastic route into (1) input, (2) transport, (3) storage, (4) remobilization and (5) output phases. Phase 1 is mainly controlled by humans, phases 2–4 by fluvial processes, and phase 5 by both types of controls. We hypothesize that the natural characteristics of fluvial systems and their modification by dam reservoirs and flood embankments construction are key controls on macroplastic storage and remobilization in rivers. The zone of macroplastic storage can be defined as a river floodplain inundated since the beginning of widespread disposal of plastic waste to the environment in the 1960s and the remobilization zone as a part of the storage zone influenced by floodwaters and bank erosion. The amount of macroplastic in both zones can be estimated using data on the abundance of surface- and subsurface-stored macroplastic and the lateral and vertical extent of the zones. Our model creates the framework for estimation of how much plastic has accumulated in rivers and will be present in future riverscapes.