Search Results (194)

Mesoscale coastal eddies are key components of ocean circulation, mediating the transport of heat, nutrients, and marine debris. The Surface Water and Ocean Topography (SWOT) mission provides high-resolution sea surface height data, offering a novel opportunity to improve the observation and characterization of these features, especially in coastal regions where conventional altimetry is limited. In this study, we investigate a mesoscale anticyclonic coastal eddy observed southwest of Mallorca Island, in the Balearic Sea, to assess the impact of SWOT-enhanced altimetry in resolving its structure and dynamics. Initial eddy identification is performed using satellite ocean color imagery, followed by a qualitative and quantitative comparison of multiple altimetric datasets, ranging from conventional nadir altimetry to wide-swath products derived from SWOT. We analyze multiple altimetric variables—Sea Level Anomaly, Absolute Dynamic Topography, Velocity Magnitude, Eddy Kinetic Energy, and Relative Vorticity—highlighting substantial differences in spatial detail and intensity. Our results show that SWOT-enhanced observations significantly improve the spatial characterization and dynamical depiction of the eddy. Furthermore, Lagrangian transport simulations reveal how altimetric resolution influences modeled transport pathways and retention patterns. These findings underline the critical role of SWOT in advancing the monitoring of coastal mesoscale processes and improving our ability to model oceanic transport mechanisms. Full article

Rapid rises in water levels due to heavy rainfall can lead to the accumulation of floating debris, posing significant challenges for both water quality and resource management. However, real-time monitoring of floating debris remains difficult due to the discrepancy between meteorological conditions and the timing of debris accumulation. To address this limitation, this study proposes an amplitude change detection (ACD) model based on time-series synthetic aperture radar (SAR) imagery, which is less affected by weather conditions. The model statistically distinguishes floating debris from open water based on their differing scattering characteristics. The ACD approach was applied to 18 pairs of Sentinel-1 SAR images acquired over Daecheong Reservoir from June to September 2024. A stringent type I error threshold (α < 1 × 10⁻⁸) was employed to ensure reliable detection. The results revealed a distinct cumulative effect, whereby the detected debris area increased immediately following rainfall events. A positive correlation was observed between 10-day cumulative precipitation and the debris-covered area. For instance, on 12 July, a floating debris area of 0.3828 km² was detected, which subsequently expanded to 0.4504 km² by 24 July. In contrast, on 22 August, when rainfall was negligible, no debris was detected (0 km²), indicating that precipitation was a key factor influencing the detection sensitivity. Comparative analysis with optical imagery further confirmed that floating debris tended to accumulate near artificial barriers and narrow channel regions. Overall, this study demonstrates that this spatial pattern suggests the potential to use detection results to estimate debris transport pathways and inform retrieval strategies. Full article

Rivers are recognized as significant pathways and transportation for microplastics (MPs), an emerging contaminant, to aquatic environments. However, there is limited evidence on how riverine reservoirs influence MPs transport. To fill this gap and provide baseline empirical data and insights to South African context, the current study assessed the seasonal variation in MP densities from sediments collected upstream, within the reservoir, and downstream of the Vondo Reservoir along the Mutshindudi River. We hypothesised that MP densities would be highest within the reservoir, due to the lack of constant flow that would otherwise transport accumulated particles downriver. Additionally, we expected the cool–dry season to be associated with the highest MP densities. As expected, high MP densities were observed within the reservoir (117.38–277.46 particles kg⁻¹ dwt) when compared to the downstream (72.63–141.50 particles kg⁻¹ dwt) and upstream (28.81–91.63 particles kg⁻¹ dwt) sites of the reservoir. The cool–dry season (91.63–277.46 particles kg⁻¹ dwt) exhibited the highest MP densities compared to the hot–wet season (28.81–141.50 particles kg⁻¹ dwt). However, MP densities downstream the reservoir were higher during the hot–wet season (141.50 ± 24.34 particles kg⁻¹ dwt) compared to the cool–dry season (72.63 ± 48.85 particles kg⁻¹ dwt). The most dominant MP particles identified were white, transparent, and black fibres/filaments composed primarily of polypropylene (PP) and polyethylene (PE). This suggests diverse sources of MP particles. No significant correlations were found between water parameters and MP densities across sampling sites and seasons, indicating a widespread and context-independent presence of MPs. These findings contribute to MP studies in freshwater environments and further reinforce the role of sediments as sink for MPs and suggest that riverine reservoirs similar to dams can trap MPs, which may then be remobilized downstream during high-flow periods. Importantly, the results of this study can support local municipalities in implementing targeted plastic pollution mitigation strategies and public awareness campaigns, particularly because the Vondo Reservoir serves as a critical water resource for surrounding communities. Full article

Anthropogenic debris in urban floodplains poses significant environmental and ecological risks, with an estimated 4 to 12 million metric tons entering oceans annually via riverine transport. While remote sensing and artificial intelligence (AI) offer promising tools for automated debris detection, most existing datasets focus on marine environments with homogeneous backgrounds, leaving a critical gap for complex terrestrial floodplains. This study introduces the San Diego River Debris Dataset, a multi-resolution UAV imagery collection with ground reference designed to support automated detection of anthropogenic debris in urban floodplains. The dataset includes manually annotated debris objects captured under diverse environmental conditions using two UAV platforms (DJI Matrice 300 and DJI Mini 2) across spatial resolutions ranging from 0.4 to 4.4 cm. We benchmarked five deep learning architectures (RetinaNet, SSD, Faster R-CNN, DetReg, Cascade R-CNN) to assess detection accuracy across varying image resolutions and environmental settings. Cascade R-CNN achieved the highest accuracy (0.93) at 0.4 cm resolution, with accuracy declining rapidly at resolutions above 1 cm and 3.3 cm. Spatial analysis revealed that 51% of debris was concentrated within unsheltered encampments, which occupied only 2.6% of the study area. Validation confirmed a strong correlation between predicted debris extent and field measurements, supporting the dataset’s operational reliability. This openly available dataset fills a gap in environmental monitoring resources and provides guides for future research and deployment of UAV-based debris detection systems in urban floodplain areas. Full article

The vast majority of ocean plastics originate from land and are transported over long distances to their final sink. Yet, our current understanding of transfer mechanisms through rivers and estuaries remains poor due to a lack of consistent methods for assessing and monitoring plastic waste. In this study, we quantify and characterize the abundance of plastics in the Tijuana River estuary, located along the U.S.–Mexico border. We found a total of 2804 plastic debris items, of which 79.3% were sampled during heavy rainfalls and 20.7% during the dry period. Overall, most plastics were attributed to five economic sectors: packaging, food, construction, fishing, and tourism, highlighting losses during the use and waste management phases of the plastic’s value chain. Based on the results of the analysis, consistent monitoring of plastic pollution is recommended for managing variable plastic loads. Full article

The dispersion of microplastics in the sea is an emerging and crucial environmental problem. In this preliminary study, the hydrodynamics of microplastics transferred from flooded agricultural areas to the sea was assessed. The Daniel storm in 2023 in region of Thessaly, Greece, initiated the transfer of plastic debris via the Pinios River, which subsequently discharged to the coastal basin at the south area of Thermaikos Gulf (NW Aegean Sea). Field sampling and laboratory measurements of microplastics collected at the mouth of the Pinios were conducted. The dispersion of microplastics discharged by the Pinios River is subject to the dominant wind conditions over the area, which in turn determines the water circulation in the NW Aegean Sea. Thus, a hydrodynamic model was initially applied, followed by a transport model for the study of the dispersion of the microplastics. The models were applied for SW and NE winds and indicated that the majority of microplastics with a settling velocity 0.1 m/s accumulate in areas close to the river’s mouth or lateral coastal zones; however, under the influence of SW winds, minor quantities tend to reach the east coasts of the Thermaikos Gulf, while massive quantities are transported away from the river’s mouth in case of microplastics floating on the sea’s surface. Full article

Micromotors play a crucial role in microsystems technology, with applications in nanoparticle propulsion, targeted drug delivery, and biosensing. Optical field propulsion, particularly optical tweezers (OTs), enables precise, noncontact control but traditionally relies on Gaussian traps, which require preprogramming and offer limited rotational control. Here, we introduce a micromotor driven by optical vortex beams, utilizing phase gradients to generate optical torque. This eliminates preprogramming and enables real-time control over rotation and positioning. Using this method, we design red blood cell (RBC)-based micromotors for targeted cellular debris collection in liquid environments. Our findings provide a versatile strategy for micro-/nano-object manipulation with potential applications in biomedicine and precision transport. Full article

The northwestern slope of the Dongdao Platform in the Xisha Sea exhibits a complex geomorphological structure. Utilizing high-resolution multibeam bathymetric data and 2D seismic profiles, this study systematically reconstructs the slope morphology and its evolutionary processes. The study area displays a distinct threefold zonation: the upper slope (160–700 m water depth) has a steep gradient of 15°–25°, characterized by deeply incised V-shaped channels and slump deposits, primarily shaped by gravity-driven erosion; the middle slope (700–1200 m water depth) features a gentler gradient of 10°–15°, where channels stabilize, adopting U-shaped cross-sections with the development of lateral accretion deposits; the lower slope (1200–1500 m water depth) exhibits a milder gradient of 5°–10°, dominated by a mixture of fine-grained carbonate sediments and hemipelagic mud–marine sediments originating partly from the open ocean and partly from the nearby continental margin. The slope extends from 160 m to 1500 m water depth, hosting the C1–C4 channel system. Seismic facies analysis reveals mass-transport deposits, channel-fill facies, and facies modified by bottom currents—currents near the seafloor that redistribute sediments laterally—highlighting the interplay between fluid activity and gravity-driven processes. The slope evolution follows a four-stage model: (1) the pockmark formation stage, where overpressured gas migrates vertically through chimneys, inducing localized sediment instability and forming discrete pockmarks; (2) the initial channel development stage, during which gravity flows exploit the pockmark chains as preferential erosional pathways, establishing nascent incised channels; (3) the channel expansion and maturation stage, marked by intensified erosion from high-density debris flows, resulting in a stepped longitudinal profile, while bottom-current reworking enhances lateral sediment differentiation; (4) the stable transport stage, wherein the channels fully integrate with the Sansha Canyon, forming a well-connected “platform-to-canyon” sediment transport system. Full article

The risk of human exposure to micro- and nanoplastics (MNPs) has received increasing attention in recent years. Consumption of drinking water is a significant route of exposure to MNPs. While previous studies focus on MNPs in treated wastewater or final effluent, research addressing drinking water networks (DWNs) as potential secondary sources of MNPs remains sparse. However, how DWN, a critical component transporting water from treatment plants to consumers, contributes to secondary contamination remains underexplored in existing studies. We extracted keywords from reviewed literature using bibliometric methods and conducted correlation analyses, revealing four research clusters: baseline detection, health assessments, nanoplastic, and treatment. The abundance of MNPs in DWN ranged from 0.01 to 1.4 items/L. The abundance varied between 679.5 and 4.5 × 10⁷ items/kg when calculated based on sample mass (in scales or debris). Based on the shape and polymer composition of MNPs, the DWN is strongly suspected to contribute to the secondary contamination of MNPs in tap water. We also reviewed the main mechanisms for the formation and release of MNPs in pipelines, including mechanical forces, water hammer effects, and chemical aging. Our review highlighted the current gaps in the research on potential MNP contamination in the DWN. It will contribute to understanding the contribution of the DWN to MNP contamination and provide a framework for future monitoring and research efforts. Full article

Ligia feed on seashore algae and remove organic debris from the coastal zone, thereby playing an important role in the intertidal ecosystem. Nevertheless, the specific roles of distinct gut segments in the gut transit remain unclear. We collected and identified Ligia exotica specimens in the coast of Aoshanwei, Qingdao, Shandong Province, and analyzed their foreguts and hindguts for 16S rRNA, metagenomics, metabolomics, and proteomics. The concentrations of common metabolites, NO₃⁻-N and NH₄⁺-N, and the contents of C and N were measured. The gut transit decreased the abundances of the dominant phyla Cyanobacteria but increased Proteobacteria, Firmicutes, and Actinobacteria, and Planctomycetes and Bacteroidetes remained relatively constant. The foregut gut microbiota is involved in the carbohydrates and amino acids metabolism, as well as the decomposition of polysaccharides. The hindgut gut microbiota performs a variety of functions, including carbohydrate and amino acid metabolism, fermentation, cell motility, intracellular transport, secretion, and vesicular translocation, and the decomposition of polysaccharides, disaccharides, and oligosaccharides. The results of omics analyses and molecular experiments demonstrated that the metabolic processes involving amino acids and carbohydrates are more active in the foregut, whereas the fermentation, absorption, and assimilation processes are more active in the hindgut. Taken together, the differences in microbial community structure determine the functional specialization of different gut segments, i.e., the foregut appears to be the primary site for digesting food, while the hindgut further processes and absorbs nutrients and then excretes them. Full article

Rivers act as natural conduits for the transport of plastic debris from terrestrial sources to marine environments. Accurately quantifying plastic debris in surface waters is essential for comprehensive environmental impact assessments. However, research on the detection of plastic debris in surface waters remains limited, particularly regarding real-time monitoring in natural environments following heavy rainfall events. This study aims to develop a real-time visual recognition model for floating plastic debris detection using deep learning with multi-class classification. A YOLOv8 algorithm was trained using field video data to automatically detect and count four types of floating plastic debris such as common plastics, plastic bottles, plastic film and vinyl, and fragmented plastics. Among the various YOLOv8 algorithms, YOLOv8-nano was selected to evaluate its practical applicability in real-time detection and portability. The results showed that the trained YOLOv8 model achieved an overall F1-score of 0.982 in the validation step and 0.980 in the testing step. Detection performance yielded mAP scores of 0.992 (IoU = 0.5) and 0.714 (IoU = 0.5:0.05:0.95). These findings demonstrate the model’s robust classification and detection capabilities, underscoring its potential for assessing plastic debris discharge and informing effective management strategies. Tracking and counting performance in an unknown video was limited, with only 6 of 32 observed debris items detected at the counting line. Improving tracking labels and refining data collection are recommended to enhance precision for applications in freshwater pollution monitoring. Full article

This study investigated the complex interactions among tsunamis, debris, and coastal building structures under extreme hydrodynamic conditions. We performed numerical simulations to explore the influence of varying wave conditions, debris, and building designs to identify the most vulnerable parts of a building structure. The three-dimensional coupled fluid–structure–sediment–seabed interaction model (FS3M) was employed to simulate these interactions and validated against physical experimental data to ensure accuracy. The results revealed that debris significantly altered the wave impact dynamics, increasing the force exerted on buildings regardless of their structural features. This study provides relevant insights into the effectiveness of different building layouts in mitigating damage, highlighting the critical role of buildings with internal walls perpendicular to the wave direction, which significantly mitigated the tsunami’s impact at specific regions. Full article

Sparse road networks in high-risk geological disaster areas, characterized by long segments, few nodes, and limited alternative routes, face significant vulnerabilities to geological hazards such as landslides, rockfalls, and collapses. These disruptions hinder emergency response and resource delivery, highlighting the need for enhanced resilience strategies. This study develops a dynamic resilience assessment framework using a two-layer topological model to analyze and optimize the resilience of such networks. The model incorporates trunk and local layers to capture dynamic changes during disasters, and it is validated using the road network in Tibet. The findings demonstrate that critical nodes, including tunnels, bridges, and interchanges, play a decisive role in maintaining network performance. Resilience is influenced by disaster type, duration, and traffic capacity, with collapse events showing moderate resilience and debris flows exhibiting rapid recovery but low survivability. Notably, half-width traffic interruptions achieve the highest overall resilience (0.7294), emphasizing the importance of partial traffic restoration. This study concludes that protecting critical nodes, optimizing resource allocation, and implementing adaptive management strategies are essential for mitigating disaster impacts and enhancing recovery. The proposed framework offers a practical tool for decision-makers to improve transportation resilience in high-risk geological disaster areas. Full article

The increasing frequency of extreme weather events such as typhoons and heavy rains, driven by climate change, has intensified debris flow risks during Korea’s monsoon season, causing considerable human and economic losses. In South Korea, where mountainous terrain covers 64% of the country, localized downpours exacerbate the risk of debris flows, endangering communities and critical infrastructure. To enhance resilience and ensure sustainable risk management, the Korea Expressway Corporation developed a quantitative debris flow risk assessment system based on sensitivity and vulnerability indicators. An early warning system utilizing rainfall thresholds was subsequently introduced. However, discrepancies between rainfall data from local AWS stations and actual site conditions compromised its predictive accuracy. This study addresses those limitations by integrating the Parameter-elevation Regressions on Independent Slopes Model (PRISM) into the early warning system to enhance prediction accuracy at debris flow occurrence and non-occurrence points. Comparative analysis revealed that the PRISM-enhanced system significantly improved predictive performance. Furthermore, cumulative rainfall data from five highway sites validated the system’s reliability in short-term prediction while offering a sustainable, data-driven framework for long-term debris flow risk management. This approach strengthens adaptive infrastructure strategies, promoting more resilient transportation networks and improving public safety while minimizing environmental impacts. Full article

Organic matter (OM) is the primary carrier for the generation and occurrence of shale oil and gas. The combination of sequence stratigraphy and elemental geochemistry plays a crucial role in the study of organic matter enrichment mechanisms in marine shale, but it is rarely applied to terrestrial lacustrine basins. As a product of the last large-scale lake transgression in the Sichuan Basin, the Early Jurassic Lianggaoshan Formation (LGS Fm.) developed multiple organic-rich shale intervals, which is a good example for studying the OM enrichment in lacustrine basins. Based on a high-resolution sequence stratigraphic framework, the evolutionary process of terrestrial debris input, redox conditions, and paleo-productivity during the sedimentary period of the Lianggaoshan Formation lacustrine shale at different stages of lake-level variations has been revealed. The main controlling factors for OM enrichment and the establishment of their enrichment patterns have been determined. Sequence stratigraphy studies have shown that there are three third-order lake transgression-lake regression (T-R) cycles in the LGS Formation. The total organic carbon content (TOC) is higher in the TST cycle, especially in the T-R3 cycle, and lower in the RST cycle. There are differences in the redox conditions, paleo-productivity, terrestrial detrital transport, and OM accumulation under the influence of lacustrine shale deposition in different system tracts. The results indicate that changes in lake level have a significant impact on the reducibility of bottom water and paleo-productivity of surface seawater, but have a relatively small impact on the input of terrestrial debris. In the TST cycle, the reducibility of bottom water gradually increases, and the paleo-productivity gradually increases, while in the RST cycle, the opposite is true. Within the TST cycle, the OM accumulation is mainly influenced by paleo-productivity and redox condition of bottom water, with moderate input of terrestrial debris playing a positive role. In the RST cycle, the redox condition of bottom water is the main inducing factor for OM enrichment, followed by paleo-productivity, while terrestrial input flux plays a diluting role, which is generally not conducive to OM accumulation. Full article