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

A systematic monitoring approach to assess floating marine macro litter in Italian waters: Baseline, thresholds, good environmental status, and mitigation priorities under the EU MSFD

The EU Marine Strategy Framework Directive (MSFD, 2008/56/EC) requires Member States to establish monitoring programs for Descriptor 10-Marine Litter, to track progress towards achieving a marine Good Environmental Status (GES). Italy conducted systematic monitoring of Floating Marine Macro Litter (FMML) in three Marine Reporting Units: Western, Central Mediterranean, and Adriatic (2018-2022, 534 surveys, 2719 km2 across all seasons). This study assessed baseline values for FMML amount and composition, giving indication for tracking GES progress. Following the beach litter approach and considering the differences between coastal and offshore environment, two threshold values were identified: 14.4 items/km2 (objects >2.5 cm) in coastal environments; 0.6 items/km2 (objects>20 cm) in offshore. Based on mean density values (95.8 ± 6.4 items >2.5 cm/km2 coastal; 20.7 ± 2 items >20 cm/km2 coastal; 2.7 ± 0.18 items >20 cm/km2 offshore), all sub-regions were found to be in non-GES status. Priority for mitigation measures and for the replication of the approach are given.

Identifying macrofloating debris hotspots in the Mediterranean Sea applying multiplatform methodologies

The Mediterranean Sea is considered the world's sixth greatest hotspot for marine litter; however, quantifying the extent of marine debris in the oceans is a challenge, especially due to variations in survey methodologies. This study aims to assess the spatial abundance and distribution of macrofloating debris (> 2.5 cm) in the Mediterranean basin through visual surveys carried out by methods (research vessel, sailing vessel, and aerial) and approaches (science and citizen science). Aerial and research vessel surveys estimated litter at 1.88 ± 2.3 items·km-2 and 0.89 ± 1.61 items·km-2 respectively for the whole Mediterranean; moreover both methods agreed that the main macrofloating debris hotspots were in the east of Algeria, Tyrrhenian, Adriatic and Alboran Seas. Likewise, for the common blocks analysed aerial surveys estimated greater amounts of macrofloating debris than research vessels (mean 1.92 ± 2.61 items·km-2 vs. 0.94 ± 1.69 items·km-2) highlighting the different detection capacities of the two methods. In the Spanish Mediterranean continental shelf, results obtained from research vessels showed mean values of 8.6 ± 7.8 items·km-2 for 2021 and 3.86 ± 3.96 items·km-2 for 2022. Sailing vessels along the Spanish coastline registered up to 70.87 ± 257.23 items·km-2 in waters of the Cabrera Island, which is a Marine Protected Area. No significant differences between citizen science and scientific methods were found, which suggests that the implementation of this tool could be very useful in obtaining greater datasets. Results on the abundance of macrofloating debris could be attributed to various factors, including the influence of mighty rivers, a dense population in these areas, especially during some seasons like summer, and the effect of some currents and eddies, such as the Algerian and the northern currents, which also influence the transboundary plastics.

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.

Microplastic characterization in the stomachs of swordfish (Xiphias gladius) from the western Mediterranean Sea

In this study, we aimed to quantify the presence of microplastics (MPs) in the stomachs of large pelagic fish (swordfish, Xiphias gladius, Linnaeus, 1758) sampled in the western Mediterranean Sea, and assess temporal trends (2011-2012 vs. 2017-2019) in MP ingestion. MPs were extracted from stomachs and characterized by μ-Fourier transform infrared spectroscopy. Results highlighted the ingestion of MP in 39 out of 49 stomachs analysed. Ingested MPs consisted mostly of small (<1 mm) fibers (88.6 %, mean ± standard deviation = 2.5 ± 6.1 particles per stomach), with a greater frequency of occurrence (FO) in the second period (FO = 90 %, 3.3 ± 8.0 particles per stomach). The predominant colours were purple, black and blue, and polyethylene terephthalate was the most frequently detected polymer. These results are crucial for the development of management actions aimed at the conservation of swordfish in the Mediterranean Sea and the prevention of health risks to humans.

Unveiling reflectance spectral characteristics of floating plastics across varying coverages: insights and retrieval model

Marine floating debris, particularly chemically stable plastics, poses a significant global environmental concern. These materials, due to their prevalence and durability, linger on the ocean surface for extended durations, inflicting considerable harm on marine ecosystems, life, and the food chain. The traditional methodology for investigating marine floating debris mainly uses field observations, which are time-consuming, laborious, and constrained in observational scope. Consequently, there is an urgent need for more effective methodologies, such as remote sensing, to monitor marine floating debris, which will be of great significance for enhancing the management of their pollution. In this study, we employ controlled experiments and theoretical model simulations to investigate the spectral characteristics of remote sensing reflectance (Rrs(λ)) of two common types of floating plastic debris, specifically polyvinyl chloride (PVC) buoys and polypropylene (PP) bottles. Our analysis reveals distinct Rrs(λ) spectral characteristics for each type of plastic debris, differing significantly from that of the background water. Furthermore, both PVC buoys and PP bottles exhibit a similar absorption valley in the short-wave infrared region, with its depth increasing alongside the plastic coverage. Based on these findings, we develop a novel floating plastic index (FPI) and a corresponding retrieval model for estimating the coverage of floating plastic debris. Validation with simulated data and measurements from control experiments shows good performance of the retrieval model with high inversion accuracy, demonstrated by the values of the coefficient of determination, mean percentage error, mean absolute percentage error, and root mean square error of 0.97, -0.3%, 17.5%, and 3.98%, respectively, for the experimentally measured dataset. Our research provides a theoretical and methodological foundation for remote sensing retrieval of the coverages of floating PVC and PP plastics, as well as offers valuable insights for the analysis of other floating debris types in future studies.

Model-based estimation of seasonal transport of macro-plastics in a marine protected area

Management of plastic litter in Marine Protected Areas (MPAs) is expensive but crucial to avoid harms to critical environments. In the present work, an open-source numerical modelling chain is proposed to estimate the seasonal pathways and fates of macro-plastics, and hence support the effective planning and implementation of sea and beach cleaning operations. The proposed approach is applied to the nearshore region that includes the MPA of Capo Milazzo (Italy). A sensitivity analysis on the influence of tides, wind, waves and river floods over the year indicates that seasonality only slightly affects the location and extension of the macro-plastic accumulation zones, and that beach cleaning operations should be performed in autumn. Instead, the influence of rivers on plastic litter distribution is crucial for the optimal planning of cleaning interventions in the coastal area.

Marine plastic pollution detection and identification by using remote sensing-meta analysis

The persistent plastic litter, originating from different sources and transported from rivers to oceans, has posed serious biological, ecological, and chemical effects on the marine ecosystem, and is considered a global issue. In the past decade, many studies have identified, monitored, and tracked marine plastic debris in coastal and open ocean areas using remote sensing technologies. Compared to traditional surveying methods, high-resolution (spatial and temporal) multispectral or hyperspectral remote sensing data have been substantially used to monitor floating marine macro litter (FMML). In this systematic review, we present an overview of remote sensing data and techniques for detecting FMML, as well as their challenges and opportunities. We reviewed the studies based on different sensors and platforms, spatial and spectral resolution, ground sampling data, plastic detection methods, and accuracy obtained in detecting marine litter. In addition, this study elaborates the usefulness of high-resolution remote sensing data in Visible (VIS), Near-infrared (NIR), and Short-Wave InfraRed (SWIR) range, along with spectral signatures of plastic, in-situ samples, and spectral indices for automatic detection of FMML. Moreover, the Thermal Infrared (TIR), Synthetic aperture radar (SAR), and Light Detection and Ranging (LiDAR) data were introduced and these were demonstrated that could be used as a supplement dataset for the identification and quantification of FMML.

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.

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.

'Eye in the sky': Off-the-shelf unmanned aerial vehicle (UAV) highlights exposure of marine turtles to floating litter (FML) in nearshore waters of Mayo Bay, Philippines

Litter is a serious threat to the marine environment, with detrimental effects on wildlife and marine biodiversity. Limited data as a result of funding and logistical challenges in developing countries hamper our understanding of the problem. Here, we employed commercial unmanned aerial vehicle (UAV) as a cost-effective tool to study the exposure of marine turtles to floating marine litter (FML) in waters of Mayo Bay, Philippines. A quadcopter UAV was flown autonomously with on-board camera capturing videos during the flight. Still frames were extracted when either turtle or litter were detected in post-flight processing. The extracted frames were georeferenced and mapped using QGIS software. Results showed that turtles are highly exposed to FML in nearshore waters. Moreover, spatial dependence between FML and turtles was also observed. The study highlights the effectiveness of UAVs in marine litter research and underscores the threat of FML to turtles in nearshore waters.

Aerial and underwater drones for marine litter monitoring in shallow coastal waters: factors influencing item detection and cost-efficiency

Although marine litter monitoring has increased over the years, the pollution of coastal waters is still understudied and there is a need for spatial and temporal data. Aerial (UAV) and underwater (ROV) drones have demonstrated their potential as monitoring tools at coastal sites; however, suitable conditions for use and cost-efficiency of the methods still need attention. This study tested UAVs and ROVs for the monitoring of floating, submerged, and seafloor items using artificial plastic plates and assessed the influence of water conditions (water transparency, color, depth, bottom substrate), item characteristics (color and size), and method settings (flight/dive height) on detection accuracy. A cost-efficiency analysis suggests that both UAV and ROV methods lie within the same cost and efficiency category as current on-boat observation and scuba diving methods and shall be considered for further testing in real scenarios for official marine litter monitoring methods.

Floating marine macro litter in the Black Sea: Toward baselines for large scale assessment

The Black Sea is a semi-enclosed basin subject to major anthropogenic pressures, including marine litter and plastic pollution. Due to numerous large rivers draining into the basin and a population settled along the coast, the region could accumulate significant amounts of floating litter over time. Until now, only limited field data were available, and litter quantities and distribution remained unknown. In this study, floating marine macro litter (FMML) was assessed at the regional Black Sea scale for the first time, showing relatively high litter densities across the basin that reached a weighted mean of 81.5 items/km². Monitoring data revealed an accumulation of floating items offshore in the eastern part of the basin, resembling on a small scale a 'garbage patch', where litter items were trapped, showing elevated densities in comparison to their surrounding areas. Most of these items were made of plastic materials (ca. 96%) and included large numbers of plastic and polystyrene fragments of small size ranges (2.5-10 cm). Harmonised field data collection through consistent and regular monitoring programmes across the region is essential to establish baselines and thresholds for large scale assessment at international level.

Intrapopulation and temporal differences of phthalate concentrations in North Atlantic fin whales (Balaenoptera physalus)

The fin whale (Balaenoptera physalus) is a migratory filter-feeding species that is susceptible to ingest plastics while lunge feeding across the oceans. Plastic additives, such as phthalates, are compounds that are added to plastics to give them specific characteristics, such as flexibility. These so-called plasticizers are currently raising major concern because of their potential adverse effects on marine fauna. However, little is known about phthalate concentrations in tissues of baleen whales as well as their potential relation with biological variables (i.e., sex, body length and age) and their trends with time. In this study, we assessed the concentration of 13 phthalates in the muscle of 31 fin whales sampled in the feeding grounds off western Iceland between 1986 and 2015. We detected 5 of the 13 phthalates investigated, with di-n-butylphthalate (DBP), diethylphthalate (DEP) and bis(2-ethylhexyl) phthalate (DEHP) being the most abundant. None of the biological variables examined showed a statistically significant relationship with phthalate concentrations. Also, phthalate concentrations did not significantly vary over the 29-year period studied, a surprising result given the global scenario of increasing plastic pollution in the seas. The lack of time trends in phthalate concentration may be due in part to the fact that phthalates also originate from other sources. Although no adverse effects of phthalates on fin whales have been detected to date, further monitoring of these pollutants is required to identify potential toxic effects in the future.

Detection and Classification of Floating Plastic Litter Using a Vessel-Mounted Video Camera and Deep Learning

Marine plastic pollution is a major environmental concern, with significant ecological, economic, public health and aesthetic consequences. Despite this, the quantity and distribution of marine plastics is poorly understood. Better understanding of the global abundance and distribution of marine plastic debris is vital for global mitigation and policy. Remote sensing methods could provide substantial data to overcome this issue. However, developments have been hampered by the limited availability of in situ data, which are necessary for development and validation of remote sensing methods. Current in situ methods of floating macroplastics (size greater than 1 cm) are usually conducted through human visual surveys, often being costly, time-intensive and limited in coverage. To overcome this issue, we present a novel approach to collecting in situ data using a trained object-detection algorithm to detect and quantify marine macroplastics from video footage taken from vessel-mounted general consumer cameras. Our model was able to successfully detect the presence or absence of plastics from real-world footage with an accuracy of 95.2% without the need to pre-screen the images for horizon or other landscape features, making it highly portable to other environmental conditions. Additionally, the model was able to differentiate between plastic object types with a Mean Average Precision of 68% and an F1-Score of 0.64. Further analysis suggests that a way to improve the separation among object types using only object detection might be through increasing the proportion of the image area covered by the plastic object. Overall, these results demonstrate how low-cost vessel-mounted cameras combined with machine learning have the potential to provide substantial harmonised in situ data of global macroplastic abundance and distribution.

Assessment of Marine Debris on Hard-to-Reach Places Using Unmanned Aerial Vehicles and Segmentation Models Based on a Deep Learning Approach

It is difficult to assess the characteristics of marine debris, especially on hard-to-reach places such as uninhabited islands, rocky coasts, and seashore cliffs. In this study, to overcome the difficulties, we developed a method for marine debris assessment using a segmentation model and images obtained by UAVs. The method was tested and verified on an uninhabited island in Korea with a rocky coast and a seashore cliff. Most of the debris was stacked on beaches with low slopes and/or concave shapes. The number of debris items on the whole coast estimated by the mapping was 1295, which was considered to be the actual number of coastal debris items. However, the number of coastal debris items estimated by conventional monitoring method-based statistical estimation was 6741 (±1960.0), which was severely overestimated compared with the mapping method. The segmentation model shows a relatively high F1-score of ~0.74 when estimating a covered area of ~177.4 m2. The developed method could provide reliable estimates of the class of debris density and the covered area, which is crucial information for coastal pollution assessment and management on hard-to-reach places in Korea.

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.

Organophosphate ester plasticizers in edible fish from the Mediterranean Sea: Marine pollution and human exposure

Concentrations of organophosphate esters (OPEs) plasticizers were analysed in the present study. Fifty-five fish samples belonging to three highly commercial species, European sardine (Sardina pilchardus), European anchovy (Engraulis encrasicolus), and European hake (Merluccius merluccius), were taken from the Western Mediterranean Sea. OPEs were detected in all individuals, except for two hake samples, with concentrations between 0.38 and 73.4 ng/g wet weight (ww). Sardines presented the highest mean value with 20.5 ± 20.1 ng/g ww, followed by anchovies with 14.1 ± 8.91 ng/g ww and hake with 2.48 ± 1.76 ng/g ww. The lowest OPE concentrations found in hake, which is a partial predator of anchovy and sardine, and the higher δ¹⁵N values (as a proxy of trophic position), may indicate the absence of OPEs biomagnification. Eleven out of thirteen tested OPEs compounds were detected, being diphenyl cresyl phosphate (DCP) one of the most frequently detected in all the species. The highest concentration values were obtained for tris(1,3-dichloro-2-propyl) phosphate (TDClPP), trihexyl phosphate (THP), and tris(2-butoxyethyl) phosphate (TBOEP), for sardines, anchovies, and hakes, respectively. The human health risk associated with the consumption of these fish species showing that their individual consumption would not pose a considerable threat to public health regarding OPE intake.

Buoyancy affects stranding rate and dispersal distance of floating litter entering the sea from river mouths

Rivers are a major source of litter entering the sea but our understanding of the transport and fate of plastics in estuarine environments is poor. Marked blocks of varying buoyancy were released at three river mouths in South Africa. Of the 1400 blocks released, 80% were recovered on nearby beaches, with a higher recovery rate for more buoyant blocks. Dispersal distances increased with decreasing buoyancy at all sites; median dispersal distance of stranded items ranged from 20 to 90 m for expanded polystyrene (EPS) to 70-90 m for wood and 60-1042 m for high density polyethylene (HDPE) blocks. Floating litter in estuaries is subject to bidirectional flow and export is largely controlled by hydrodynamic conditions such as tides, winds, and wave action, as well as coastal structure and vegetation. Cleaning beaches around river mouths will help to reduce leakage of plastic and other litter into the sea.

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.

Drones for litter mapping: An inter-operator concordance test in marking beached items on aerial images

Unmanned aerial systems (UAS, aka drones) are being used to map macro-litter on the environment. Sixteen qualified researchers (operators), with different expertise and nationalities, were invited to identify, mark and categorize the litter items (manual image screening, MS) on three UAS images collected at two beaches. The coefficient of concordance (W) among operators varied between 0.5 and 0.7, depending on the litter parameter (type, material and colour) considered. Highest agreement was obtained for the type of items marked on the highest resolution image, among experts in litter surveys (W = 0.86), and within territorial subgroups (W = 0.85). Therefore, for a detailed categorization of litter on the environment, the MS should be performed by experienced and local operators, familiar with the most common type of litter present in the target area. This work provides insights for future operational improvements and optimizations of UAS-based images analysis to survey environmental pollution.

Spatial and size distribution of macro-litter on coastal dunes from drone images: A case study on the Atlantic coast

This work analyses the cross-shore (80 m) and long-shore (200 m) spatial and size distribution of macro-litter on coastal dunes, employing a mapping framework based on an Unmanned Aerial System (UAS, aka drone) and a GIS mobile application. Over the cross-shore, plastic percentage increased from 60% to 90% landwards. The largest items (processed wood) were found on the embryo dune. Plastic bottles and paper napkins were trapped by the foredune grass, while the largest fishing-related items were intercepted by the low scrub plant community on the backdune. Over the long-shore, plastic percentage and items size increased from the urbanized area towards the natural dunes. This work assessed the abundance of marine litter on coastal dune sectors, underlining the role of distinct vegetation types in trapping items of different size. The mapping framework can promote further marine litter monitoring programs and support specific strategies for protecting the dune ecosystems.

Detecting stranded macro-litter categories on drone orthophoto by a multi-class Neural Network

The use of Unmanned Aerial Systems (UAS, aka drones) images for mapping macro-litter in the environment have been exponentially increasing in the recent years. In this work, we developed a multi-class Neural Network (NN) to automatically identify stranded plastic litter categories on an UAS-derived orthophoto. The best results were assessed for items that did not have substantial intra-class colour variability, such as octopus pots and fishing ropes (F-score = 61%, on average). Instead, performance was poor (37%) for plastic bottles and fragments, due to their changing intra-class colours. On average, the performance improved 24% when the binary detection (litter/non-litter, F-Score = 73%) was considered, however this approach did not discriminate the litter categories. This work gives a new perspective for the automated litter detection on drone images, suggesting that colour-based approach can be used to improve the categorization of stranded litter on UAS orthophoto.

Extended study on floating litter in Malta's coastal waters (Central Mediterranean)

This study aims to determine the level of floating marine litter in coastal and inshore areas around Malta and Gozo (Central Mediterranean) and to investigate factors which influence litter distribution. Observations of macro-litter and mega-litter were conducted through seasonal boat surveys around Malta and Gozo during 2018-2019. Visual observations were conducted along line transects whilst maintaining a 6 m observation width. For coastal areas, of up to -1 km away from the shoreline, the total density of litter ranged between 27 and 2428 items/km², with a mean of 292 ± 85 items/km². Within inshore areas the density varied between 180 and 46,289 items/km², with a mean of 3242 ± 1880 items/km². The highest density was present in winter, this being 2.5 higher than the lowest density season. 91% of the litter was plastic. The manner in which anthropogenic factors and natural events influenced litter accumulations and distributions, are described.

Automatic detection and quantification of floating marine macro-litter in aerial images: Introducing a novel deep learning approach connected to a web application in R

The threats posed by floating marine macro-litter (FMML) of anthropogenic origin to the marine fauna, and marine ecosystems in general, are universally recognized. Dedicated monitoring programmes and mitigation measures are in place to address this issue worldwide, with the increasing support of new technologies and the automation of analytical processes. In the current study, we developed algorithms capable of detecting and quantifying FMML in aerial images, and a web-oriented application that allows users to identify FMML within images of the sea surface. The proposed algorithm is based on a deep learning approach that uses convolutional neural networks (CNNs) capable of learning from unstructured or unlabelled data. The CNN-based deep learning model was trained and tested using 3723 aerial images (50% containing FMML, 50% without FMML) taken by drones and aircraft over the waters of the NW Mediterranean Sea. The accuracies of image classification (performed using all the images for training and testing the model) and cross-validation (performed using 90% of images for training and 10% for testing) were 0.85 and 0.81, respectively. The Shiny package of R was then used to develop a user-friendly application to identify and quantify FMML within the aerial images. The implementation of this, and similar algorithms, allows streamlining substantially the detection and quantification of FMML, providing support to the monitoring and assessment of this environmental threat. However, the automated monitoring of FMML in the open sea still represents a technological challenge, and further research is needed to improve the accuracy of current algorithms.

Floating marine macro litter: Density reference values and monitoring protocol settings from coast to offshore. Results from the MEDSEALITTER project

Monitoring Floating Marine Macro Litter (FMML) is a global priority, stressed within international programs, and regulated for the European Seas by the Marine Strategy Framework Directive. Although some well-defined common protocols exist for the assessment of beach litter and ingested litter, methodologies for FMML monitoring still vary, leading to some inconsistent results and hampering the global assessment of this threat. Within the MEDSEALITTER project (2016-2019), field experiments were implemented to define optimal monitoring parameters for FMML visual monitoring at different spatial scales, by assessing the influence of platform speed, strip width, observers experience, weather conditions, and litter size on its detectability. Along with the results of these experiments, we present the FMML density ranges detected across the over 20,000 km surveyed, highlighting a decreasing gradient from river mouths to coastal areas and the open sea, and providing a valuable contribution to the assessment of FMML in the Mediterranean Sea.