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Environmental studies 2026
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Authors: Samah Dib, Arafa Mohamed, Fatima A. Al- Khayat, S. Veerasingam, V. M. Aboobacker, Jassim A. Al-Khayat & P. Vethamony This study examined microplastic (MP) accumulation in the gastrointestinal tracts of 170 fish from four commercially important species in Qatar. MP-like particles were extracted through chemical digestion and density separation, then analyzed via stereomicroscopy, revealing fibers as the dominant shape and blue as the most common color. Spectroscopy confirmed only 7 particles (4.12% of samples) as true MPs, matching >70% with polymer databases and identifying polyethylene (PE) and polypropylene (PP). The mean MP abundance was 0.070 ± 0.090 MPs/g of fish gut, relatively low compared to other Gulf and global regions.
01 - Distinguishing microplastics from microplastic-like particles in the marine fish from Qatar
02 - A machine learning-driven framework for enhancing underwater visual signal processing in marine ecosystem economic monitoring and anthropogenic impact assessment
Authors: Wang Minglong, Zhu Feng, and Jian Hu Underwater monitoring faces challenges from optical distortions, ecological variability, and dynamic behaviors, which conventional image processing methods struggle to address due to issues like spectral attenuation and scattering. Traditional terrestrial-based approaches are ineffective for tasks such as species tracking and habitat mapping. To resolve this, a machine learning framework combining physics-aware modeling and ecological adaptivity is proposed, featuring the BOANE module for correcting radiance distortions and encoding biological information, and the CAMSE module for real-time enhancement via ecological priors, feature alignment, and semantic filtering.
03 - Spatio-temporal dynamics of fish assemblages at an offshore wind farm and a comparison with mature artificial reefs
Authors: Kwang Tsao Shao, Ching Han Chang, Ching Yi Chen, Lin Tai Ho, Yi Ta Shao, Hsin-Chieh Chiang, Wei-Ning Lee, Hsien Ju Tsai, Tien-Yu Huang, and Chun-Yu Chang Offshore wind farms (OWFs) create new habitats for fish via their underwater foundations, but their ecological role compared to artificial reefs (ARs) remains uncertain. A multi-year study at Taiwan’s Formosa Wind Farm found 86 reef-associated fish species exclusively within 50 m of turbine foundations, absent in surrounding areas or prior records. Fish assemblages changed over time, and while species composition differed from established ARs in 2025, key ecological metrics (richness, diversity, trophic structure) showed no significant differences.
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04 - Innovation below the surface: development of a canine underwater search training device for submerged scent detection
Authors: Paul Bunker, and Christina Brewster This paper introduces the Canine Underwater Search Training Device (CUSTD), a novel remotely operated system that safely delivers target odors from the front of a boat, conditioning canines to search and indicate detection from that position without contaminating water sources. This innovative methodology offers broad applications in environmental monitoring, oil spill response, search and recovery, and marine biology research, providing a versatile and ethical platform for advancing both scientific study and operational canine deployment in underwater detection.
05 - A global dataset of impact forces from submarine landslides on pipelines and cables
Authors: Xiaolei Liu, Shuzhou Wei, Xiangshuai Meng, Botao Xie, Xuejian Chen & Xingsen Guo Submarine pipelines and cables, vital for offshore energy and global communications, face growing threats from submarine landslides that can damage infrastructure. Existing research on landslide-pipeline interactions is fragmented and lacks standardized data, hindering comparative analysis. To address this, the authors compiled a dataset of 864 entries from 24 studies, capturing key parameters like impact velocity, flow type, and drag/lift forces. Standardizing force definitions and categorizing conditions by Reynolds number regimes enhances comparability. This dataset serves as a critical resource for risk assessment, offshore design, and modeling pipeline-landslide interactions.
06 - Research on a Lightweight Detection Method for Underwater Diseased Corals
Authors: Mingqi Li and Ming Chen This study addresses challenges in underwater diseased coral detection, such as high variability, small pathological regions, and environmental interference. A novel lightweight method, CD-YOLO, is proposed. Key improvements include: 1) a lightweight CDShufflenet backbone to reduce model complexity while maintaining performance; 2) a SPDConv downsampling module to preserve fine-grained coral details; and 3) integrated attention mechanisms (EMA and SENetV2) to enhance pathological feature representation and suppress environmental noise.
07 - The Greater Agulhas Current System – Circulation, Variability, Long-Term Trends and Impacts on Weather, Climate and Ecosystems.
Authors: Issufo Halo et al. The Greater Agulhas Current System (GACS) is essential for the exchange between the Indian and Atlantic Oceans and influences weather, climate, and marine ecosystems in southern Africa. Since the last major publication nearly twenty years ago, advancements in technology and research have improved understanding of GACS from 2006 to 2025. Key developments include new observational tools and models, enhanced knowledge of transport and eddy dynamics, and links to Southern Hemisphere wind patterns. The review highlights knowledge gaps and suggests future research needs, focusing on continuous observations and better modeling of fine-scale processes.
08 - Using surface drifters to characterise near-surface ocean dynamics in the southern North Sea: a data-driven approach
Authors: Jimena Medina-Rubio, Madlene Nussbaum, Ton S. van den Bremer, and Erik van Sebille Traditional drifters struggle to replicate the transport of buoyant ocean-surface objects due to complex interactions with wind, currents, and waves. To address this, 12 ultra-thin drifters were deployed in the southern North Sea for 68 days, and their velocities were modeled using hydrodynamic and atmospheric data using linear and machine-learning (random forests, support vector regression) approaches. Analysis revealed tidal forcing as the dominant driver of zonal motion, while wind primarily influenced meridional movement, though its effect saturated at higher speeds.