Litter Monitoring Research Articles

Single-use take-away cups of paper are as toxic to aquatic midge larvae as plastic cups

Single-use plastics and food packaging are the most common items polluting the environment, commonly identified in surveys and litter monitoring campaigns. There are pushes to ban these products from production and use in different regions, and to replace them with other materials viewed as “safer” or “more sustainable”. Here, we address the potential environmental impacts of take-away cups and lids used for hot and cold beverages, consisting of plastic or paper. We produced leachates from plastic cups (polypropylene), lids (polystyrene), and paper cups (lined with polylactic acid), under conditions representative of plastic leaching in the environment. The packaging items were placed and left to leach in sediment and freshwater for up to four weeks, and we tested the toxicity of contaminated water and sediment separately. We used the model aquatic invertebrate Chironomus riparius and assessed multiple endpoints both on larval stages and on emergence to the adult phase. We observed a significant growth inhibition with all the materials tested when the larvae were exposed in contaminated sediment. Developmental delays were also observed for all materials, both in contaminated water and sediment. We investigated teratogenic effects via the analysis of mouthpart deformities in chironomid larvae, and observed significant effects on larvae exposed to polystyrene lid leachates (in sediment). Finally, a significant delay in time to emergence was observed for females exposed to paper cups leachates (in sediment). Overall, our results indicate that all the tested food packaging materials can have adverse effects on chironomids. These effects can be observed from one week of material leaching in environmental conditions, and tend to increase with increasing leaching time. Moreover, more effects were observed in contaminated sediment, indicating that benthic organisms might be especially at risk. This study highlights the risk posed by take-away packaging and their associated chemicals, once discarded into the environment.

Beach litter in three South American countries: A baseline for restarting monitoring and cleaning after COVID-19 closure

The COVID-19 pandemic caused that most countries established the closure of many beaches, affecting the scientific monitoring of thousands of coastal sectors. This article shows the status of beach litter in South America before and after COVID-19 closure. The data were obtained during the years 2019, 2020 and 2022 on 25 beaches using a technique BLAT-QQ. The results show that cigarette butts were the most frequent type of litter, meanwhile Brazil should improve cleanliness of general gross litter and gross polystyrene. Colombia gross vegetation litter and small vegetation litter, and Ecuador organic litter from animals. The results shown in qualitative and quantitative manner facilitate their understanding for managers, scholars and activists interested on beach litter monitoring. This baseline is useful to analyse regional and worldwide marine litter trends with the purpose to start or restart monitoring of tourist beaches from a science-based method.

Artisanal trawl fisheries as a sentinel of marine litter pollution

Systematic seafloor surveys are a highly desirable method of marine litter monitoring, but the high costs involved in seafloor sampling are not a trivial handicap. In the present work, we explore the opportunity provided by the artisanal trawling fisheries to obtain systematic data on marine litter in the Gulf of Cadiz between 2019 and 2021. We find that plastic was the most frequent material, with a prevalence of single-use and fishing-related items. Litter densities decreased with increasing distance to shore with a seasonal migration of the main litter hotspots. During pre-lockdown and post-lockdown stages derived from COVID-19, marine litter density decreased by 65 %, likely related to the decline in tourism and outdoor recreational activities. A continuous collaboration of 33 % of the local fleet would imply a removal of hundreds of thousands of items each year. The artisanal trawl fishing sector can play a unique role of monitoring marine litter on the seabed.

Heavy rains control the floating macroplastic inputs into the sea from coastal Mediterranean rivers: A case study on the Têt River (NW Mediterranean Sea)

This study focuses on the relevance of small watersheds in the macroplastic pollution of coastal environments. It aims to identify and quantify in terms of composition, number and mass, current riverine flows of floating macroplastics (>2.5 cm). Estimates are based on 66 visual monitoring of total litter over a 4-year-period (2016–2019) in a small coastal Mediterranean river, the Têt River (NW Mediterranean Sea). The plastic fraction represented 97 % of the observed litter, mainly cigarette butts (20.5 %), polystyrene fragments (18.8 %) and light packaging (16.3 %). The Tet River is characterized by frequent flash-flood events caused by heavy rain, that can induce a sudden rise of the water discharge. Such hydroclimatic forcing greatly influence macroplastic flows, both in terms of their average compositions and loads. We have estimated that 354,000 macroplastic items, corresponding to 0.65 tons, are discharged annually from the Tet River into the sea, and that 73 % of them are released during rain events (∼6 % of the year). The short observation distance from the water surface allowed to exhibit the great abundance of small litter (80 % of them were < 10 cm) and to evaluate to 1.8 g the average mass of floating plastics. Our results suggest that remediation actions must be taken on rainy days and target small litter in order to significantly limit macroplastic inputs from rivers to the sea. Moreover, the large share of cigarette butts in macrolitter inputs demonstrates that reducing ocean pollution cannot be achieved solely by improving waste management, but that changes in social behavior are also needed to stem waste production at the source.

The Travel Time of Floatable Litter of Different Densities Influenced by River Flow Velocity

Rivers are a source of life. However, these past years, the sustainability of rivers around the world has been threatened by urbanization, industrialization, and rapid development. These activities put pressure on waterway systems and rivers, leading to the emergence of riverine litter. The lack of understanding of the migration of litter in rivers can affect the decision-making efficiency of policymakers when it comes to riverine litter management. Therefore, the primary objective of this study is to evaluate the travel time for different densities of Floatable Litter (FL) in rivers influenced by river flow velocity. The density of the selected FL was manually determined by assessing litter characteristics of mass, volume, and size. The Sg. Berkelah river in Pahang, Malaysia was used as the simulation medium for the sampling of FL travel time utilizing the extrapolation of the Time of Travel (ToT) approach. The ToT technique utilizes a 30 m distance. The sampling was done under three different river flow velocity conditions. In this study, a correlation analysis between these two variables was carried out and evaluated. The results revealed that magazine papers possess the fastest FL travel time, with T = 43.93 s when v = 0.230 m/s, while cloth possesses the slowest FL travel time, with T = 204.90 s when v = 0.167 m/s. The travel time of FL is not influenced by low-class density, p &lt; 1000 kg/m3, but by other factors that have a stronger influence on the travel time of horizontally migrated litter. This study provides a basic understanding and overview of FL migrating characteristics in rivers for further reference by local authorities for litter monitoring and also future riverine litter studies.

Unfolding the science behind policy initiatives targeting plastic pollution

The intensive global plastic production, use and associated plastic pollution have caused concern for the potential risks to human health and the environment. This has led to the adoption of numerous regulatory initiatives aiming to combat plastic pollution. Despite the considerable regulatory activity in the field of plastic, it appears that there is still debate about the actual risks of plastic to humans and the environment. This raises the question of to what extent the current plastic regulation is evidence-based, a declared ambition in the European Union. Therefore, the aim of this study was to investigate to what extent key policy initiatives targeting plastic pollution are based on scientific evidence. Selection of initiatives was based on expert elicitation accounting for the opinions of persons involved in the development of the policy initiatives, and a thorough assessment of the historical development of plastic pollution regulation, with focus on their importance both with respect to regulation of plastics as well as their historical importance as drivers for societal actions on plastic pollution. We find that scientific evidence appears to be generally present in the scientific foundation for the policy initiatives analysed in this study. All the initiatives are supported by scientific articles and reports about among others plastic sources, ecological impacts of plastic production and consumption patterns. Marine litter monitoring data was found to contribute to the evidence base for 4 out of the 6 policy initiatives and thereby appears to be one of the central scientific drivers behind the societal actions on plastic pollution. Other scientific tools applied when shaping the policy initiatives include risk assessment, impact assessment and life cycle assessment. Despite the prevalent consideration and application of scientific evidence, there seems to be a broad recognition in the preparatory work of the initiatives that there is still a lot of uncertainty related to determining the harm of plastic pollution. In these cases, taking precautionary actions seems however to be justified, recalling not least the precautionary principle. As the issue of plastic pollution is complex and still subject to uncertainty, it seems important both that policy initiatives allow for flexibility and continuing adjustment to the on-going knowledge generation and that the scientific community provides the needed research to continue the science-informed policy development.

Use of UAVs and Deep Learning for Beach Litter Monitoring

Stranded beach litter is a ubiquitous issue. Manual monitoring and retrieval can be cost and labour intensive. Therefore, automatic litter monitoring and retrieval is an essential mitigation strategy. In this paper, we present important foundational blocks that can be expanded into an autonomous monitoring-and-retrieval pipeline based on drone surveys and object detection using deep learning. Drone footage collected on the islands of Malta and Gozo in Sicily (Italy) and the Red Sea coast was combined with publicly available litter datasets and used to train an object detection algorithm (YOLOv5) to detect litter objects in footage recorded during drone surveys. Across all classes of litter objects, the 50%–95% mean average precision (mAP50-95) was 0.252, with the performance on single well-represented classes reaching up to 0.674. We also present an approach to geolocate objects detected by the algorithm, assigning latitude and longitude coordinates to each detection. In combination with beach morphology information derived from digital elevation models (DEMs) for path finding and identifying inaccessible areas for an autonomous litter retrieval robot, this research provides important building blocks for an automated monitoring-and-retrieval pipeline.

Automatic Detection of Marine Litter: A General Framework to Leverage Synthetic Data

The spatial and temporal coverage of spaceborne optical imaging systems are well suited for automated marine litter monitoring. However, developing machine learning-based detection and identification algorithms requires large amounts of data. Indeed, when it comes to marine debris, ground validated data is scarce. In this study, we propose a general methodology that leverages synthetic data in order to avoid overfitting and generalizes well. The idea is to utilize realistic models of spaceborne optical image acquisition and marine litter to generate large amounts of data to train the machine learning algorithms. These can then be used to detect marine pollution automatically on real satellite images. The main contribution of our study is showing that algorithms trained on simulated data can be successfully transferred to real-life situations. We present the general components of our framework, our modeling of satellites and marine debris and a proof of concept implementation for macro-plastic detection with Sentinel-2 images. In this case study, we generated a large dataset (more than 16,000 pixels of marine debris) composed of seawater, plastic, and wood and trained a Random Forest classifier on it. This classifier, when tested on real satellite images, successfully discriminates marine litter from seawater, thus proving the effectiveness of our approach and paving the way for machine learning-based marine litter detection with even more representative simulation models.

From city to sea: Spatiotemporal dynamics of floating macrolitter in the Tiber River

Rivers are undoubtedly the main pathway of waste dispersed in the environment that from land reaches oceans and seas increasing the amount of marine litter. Major cities are a great source of riverine litter as large urbanization can originate pressure on the integrated waste management resulting in litter entering the rivers. Within this study, we aim to investigate the dynamic of floating riverine macrolitter (items >2.5 cm) in the city of Rome before it reaches the sea by assessing the composition, amount, and seasonal trends of litter transported from the urban centre to the main river mouth of Tiber River. Visual surveys for a whole year (March 2021–February 2022) were conducted from two bridges, Scienza Bridge (in the city) and Scafa Bridge (at the main river mouth) and followed JRC/RIMMEL protocol for riverine litter monitoring. Overall, similar litter composition was observed from the city centre to the mouth with a prevalence of plastic material, mainly related to fragmentation process (i.e. plastic pieces) and single use items, mainly in food and beverage sectors. An extrapolated annual loading of 4 × 105 items/year was estimated at the main mouth of Tiber River. The litter flux seems to be influenced by the seasonal variability and hydrometeorological parameters. The frequency of size classes decreases with increasing size in both sites, and more than half of the recorded items were below 10 cm. Specific categories belonging to “other plastics” have been reported related to anti-Covid-19 behaviour such as face masks and beverage sector, e.g. bottle lids and rings. The main colour of plastics was white, suggesting weathering process of floating riverine litter. This study contributes to increasing knowledge of the origin, composition and spatiotemporal dynamics of riverine floating litter from the city and entering the sea.

A novel beach litter analysis system based on UAV images and Convolutional Neural Networks

Beach litter monitoring programs play a key role in establishing effective management measures to preserve the ecological, scenic, and economic value of the coastal areas. In this study, an innovative analysis system is proposed for the automatic identification of beach debris (>2.5 cm) on aerial-photogrammetric images acquired by unmanned aerial vehicles (UAV) at different elevations. The workflow is based on a Convolutional Neural Network model designed for object segmentation and object recognition, and here used for instance segmentation tasks. Test cases were conducted along the Adriatic sector of the Apulia region (Italy), where the beaches have a remarkable economic importance, attracting national and international tourists, and ecological values, hosting species of high ecological value and protected areas. The results of the tests carried out in this study allowed defining 10 m as the desirable drone flight above ground. In addition, encouraging results have been obtained on the instance segmentation step, experimenting on real, synthetic and mixed data, produced by using a high-resolution blending technique. A beach litter density of 0.38 items m−2 (on 0.66 items m−2), an F-score of 0.96 and a mAP of 0.67 has been achieved on real data. A novel metric for comparing works at the state-of-the-art (SOTA) in beach litter monitoring is also introduced, named “density-normalized F-score”. The proposed methodology represents a benchmark for the definition of a standardize procedure for the indirect evaluation and monitoring of the coastal environmental status. Besides allowing the investigation of large areas with limited human effort, the proposed system enables the evaluation of the beach litter spatial distribution and magnitude, providing useful information for the assessment of tailored beach quality indices.

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.

Strengthening citizen science partnerships with frontline sanitation personnel to study and tackle plastic pollution

The COVID-19 outbreak has boosted demand for and use of personal protective equipment (PPE) and other single-use plastics, adding to the environment's already high levels of plastic pollution and endangering biota. Estimating the relative abundance of PPE wastes that end up in the environment is crucial and has remained a challenge for COVID-19 researchers. Citizen science has been utilized in recent studies to monitor and collect data using volunteers, and it has proven to be a valuable approach even in difficult situations. The expansion of citizen scientific participation groups is important in light of the growing anthropogenic impacts of plastic pollution. To date, frontline sanitary personnel are often overlooked and underutilized in a citizen science perspective, yet they serve critical roles in maintaining cleanliness in key environmental settings (e.g., beaches and streets) both during and beyond the pandemic. This paper explores and emphasizes the advantages and need of including frontline sanitary personnel into citizen science for the benefit of both researchers and communities, as well as to encourage long-term goals in global plastic litter monitoring, thereby exemplifying citizen science opportunities. Recommendations are made to design in order to improve the future status of citizen science development.

Increasing the Sentinel-2 potential for marine plastic litter monitoring through image fusion techniques

Sentinel-2 (S2) images have been used in several projects to detect large accumulations of marine litter and plastic targets. Their limited spatial resolution though hinders the detection of relatively small floating accumulations of marine debris. Thus, this study aims at overcoming this limit through the exploration of fusion with very high-resolution WorldView-2/3 (WV-2/3) images. Various state-of-the-art approaches (component substitution, spectral unmixing, deep learning) were applied on data collected in synchronized acquisitions of plastic targets of various sizes and materials in seawater. The fused images were evaluated for spectral and spatial distortions, as well as their ability to spectrally discriminate plastics from water. Several WV-2/3 band combinations were investigated and five litter indexes were applied. Results showed that: a) the VNIR combination is the optimal one, b) the smallest observable plastic target is 0.6 × 0.6 m2 and c) SWIR bands are important for marine litter detection.

A Combination of Machine Learning Algorithms for Marine Plastic Litter Detection Exploiting Hyperspectral PRISMA Data

A significant amount of the produced solid waste reaching the oceans is made of plastics. The amount of plastic debris in the ocean and coastal areas is steadily increasing and is now a major global environmental issue. The monitoring of marine plastic litter, ground-based monitoring systems and/or field campaigns are time-consuming, expensive, require great organisational efforts, and provide very limited information in terms of the spatial and temporal dynamics of marine debris. Earth Observation (EO) by satellite can contribute significantly to marine plastic litter detection. In 2019, a new hyperspectral satellite, called PRISMA, was launched by the Italian Space Agency. The high spectral resolution of PRISMA may allow for better detection of floating plastic materials. At the same time, Machine Learning (ML) algorithms have the potential to find hidden patterns and identify complex relations among data and are increasingly employed in EO. This paper presents the development of a new method of identifying floating plastic objects in coastal areas by exploiting pan-sharpened hyperspectral PRISMA data, based on the combination of unsupervised and supervised ML algorithms. The study consisted of a configuration phase, during which the algorithms were trained in a fully controlled test, and a validation phase, in which the pre-trained algorithms were applied to satellite data collected at different sites and in different periods of the year. Despite the limited input data, results suggest that the tested ML approach, applied to pan-sharpened PRISMA data, can effectively recognise floating objects and plastic targets. The study indicates that increasing input datasets can help achieve higher-quality results.

Future monitoring of litter and microplastics in the Arctic—challenges, opportunities, and strategies

The Arctic Monitoring and Assessment Programme has published a plan and guidelines for the monitoring of litter and microplastics (MP) in the Arctic. Here, we look beyond suggestions for immediate monitoring and discuss challenges, opportunities, and future strategies in the long-term monitoring of litter and MP in the Arctic. Challenges are related to environmental conditions, lack of harmonization and standardization of measurements, and long-term coordinated and harmonized data storage. Furthermore, major knowledge gaps exist with regard to benchmark levels, transport, sources, and effects, which should be considered in future monitoring strategies. Their development could build on the existing infrastructure and networks established in other monitoring initiatives in the Arctic, while taking into account specific requirements for litter and MP monitoring. Knowledge existing in northern and Indigenous communities, as well as their research priorities, should be integrated into collaborative approaches. The monitoring plan for litter and MP in the Arctic allows for an ecosystem-based approach, which will improve the understanding of linkages between environmental media of the Arctic, as well as links to the global problem of litter and MP pollution.

Toward City-Scale Litter Monitoring Using Autonomous Ground Vehicles

Littering is a significant challenge for environmental sustainability and a major burden for cities and densely populated areas. Current solutions for litter monitoring, such as litter watch campaigns and city-operated litter collection, are costly and challenging to conduct at a large scale. This article presents a vision for using autonomous ground vehicles (AGVs) for litter monitoring and removal and introduces a mechanism for AGVs that uses thermal dissipation resulting from sunlight to identify and remove litter objects. We identify and highlight key challenges for deploying the envisioned solution on a city scale, and demonstrate the feasibility of the solution through extensive experiments.

2020~2021년 동안의 울릉도 북서쪽 해안의 해양쓰레기 분포 특성

Ulleungdo, located in the middle of the East Sea, has an excellent marine ecosystem as the first marine protected area on the East Coast. However, as marine litter of various origins is deposited by wind and currents, scientific management and monitoring of marine debris is required. From 2019, national beach litter monitoring was conducted on Ulleungdo, and this study was conducted as part of the national beach litter monitoring. During a total of 6 surveys, 35% of the total marine debris was collected in April, during spring, out of a total of 1,172 marine debris, and by type, plastics accounted for 62% and wood accounted for 36%. Foreign origins with confirmed nationality accounted for 8.6% of the total marine litter, and marine litter of Chinese origin accounted for 85%. In Ulleungdo coast, the proportion of hard-type garbage such as drinking water bottles among plastic marine garbage was high compared to the distribution characteristics of coastal garbage nationwide. This paper is meaningful as the first report on Ulleungdo coastal debris monitoring, and in order to understand the exact distribution characteristics of Ulleungdo coastal garbage in the future, it is necessary to expand the monitoring station through resident participation.

First report of multidrug-resistant Salmonella Infantis in broiler litter in Tolima, Colombia.

Background and Aim:Salmonella has been identified as one of the most widely distributed zoonotic pathogens in broiler litter. Multidrug-resistant strains have been isolated from salmonellosis outbreaks, compromising the success of their treatment. This study aimed to isolate and identify Salmonella spp. serovars in healthy broiler litter in Tolima (Colombia), determine their resistance to different antimicrobials, and detect genes associated with b-lactam resistance that could be useful to control Salmonella spp. in poultry.Materials and Methods:In total, 45 broiler litter samples were collected. Salmonella spp. was isolated and identified using selective and differential culture media and biochemical tests. Molecular confirmation of the pathogen was performed with the invA gene and serotyping by Kauffman–White scheme. Antimicrobial susceptibility to 15 antibiotics was determined by Kirby–Bauer method. In cefotaxime-resistant strains, blaCTX-M-F, blaCTX-M-1, blaCMY, and blaTEM genes were evaluated by polymerase chain reaction (PCR).Results:In total, 817 presumptive strains were obtained from xylose lysine deoxycholate and Salmonella Shigella agars and subcultured on xylose-lysine-tergitol 4 and MacConkey agars, from which 150 strains were isolated; 29 of these strains were presumptive for Salmonella spp. after performing biochemical tests and 16 were confirmed by PCR as Salmonella Infantis (15) and Gallinarum (1). All strains were found to be multiresistant to antibiotics, showing three different profiles and isolates resistant to cefotaxime, and the blaCTX-M gene was detected.Conclusion:This is the first study to isolate S. Infantis from broiler litter in Colombia. All isolates exhibited resistance to the evaluated antimicrobials, suggesting the misuse of antimicrobials in small- and medium-sized poultry farms. The presence of Salmonella enterica serovar Infantis is a public health problem. Thus, regular monitoring of poultry litter is recommended, as these bacteria can be transmitted to humans through animal products or contaminated environments.

Baseline Marine Litter Surveys along Vietnam Coasts Using Citizen Science Approach

Marine litter is a significant threat to the marine environment, human health, and the economy. In this study, beach litter surveys along Vietnamese coasts were conducted in a local context to quantify and characterize marine litter using the modified GESAMP marine litter monitoring guideline. A total of 21,754 items weighing 136,820.2 g was recorded across 14 surveys from September 2020 to January 2021. Plastic was the most abundant type of litter by both quantity (20,744 items) and weight (100,371.2 g). Fishing gear 1 (fishing plastic rope, net pieces, fishing lures and lines, hard plastic floats) and soft plastic fragments were the most frequently observed items (17.65% and 17.24%, respectively). This study not only demonstrates the abundance and composition of marine litter in Vietnam, it also provides valuable information for the implementation of appropriate preventive measures, such as the redesign of collection, reuse, and recycling programs, and informs policy and priorities, with a focus on action and investment in Vietnam. Moreover, insights from this study indicate that citizen science is a useful approach for collecting data on marine litter in Vietnam.

An ecosystem-scale litter and microplastics monitoring plan under the Arctic Monitoring and Assessment Programme (AMAP)

Lack of knowledge on levels and trends of litter and microplastics in the Arctic, is limiting our understanding of the sources, transport, fate, and effects is hampering global activities aimed at reducing litter and microplastics in the environment. To obtain a holistic view to managing litter and microplastics in the Arctic, we considered the current state of knowledge and methods for litter and microplastics monitoring in eleven environmental compartments representing the marine, freshwater, terrestrial, and atmospheric environments. Based on available harmonized methods, and existing data in the Arctic, we recommend prioritization of implementing litter and microplastics monitoring in the Arctic in four Priority 1 compartments—water, aquatic sediments, shorelines, and seabirds. One or several of these compartments should be monitored to provide benchmark data for litter and microplastics in the Arctic and, in the future, data on spatial and temporal trends. For the other environmental compartments, methods should be refined for future sources and surveillance monitoring, as well as monitoring of effects. Implementation of the monitoring activities should include community-based local components where possible. While organized as national and regional programs, monitoring of litter and microplastics in the Arctic should be coordinated, with a view to future pan-Arctic assessments.