Search Results (280)

Addressing the challenges of extreme sample scarcity, complex underwater optical environments, and significant variations in lesion scales in real-world aquaculture, this paper proposes a small-sample grass carp disease recognition method, namely Swin Transformer with Supervised Contrastive Learning (ST-SCL), integrating dual-stream data augmentation and supervised contrastive learning. First, a frequency-spatial dual-stream augmentation strategy is constructed. In the frequency domain, the Amplitude-Mix technique is introduced to simulate diverse lighting and turbidity styles by mixing background amplitude spectra, thereby enhancing environmental generalization. In the spatial domain, a pathology-mask-guided instance-level Copy-Paste strategy is employed to directionally expand scarce lesion samples and address data imbalance. Second, the Swin Transformer is adopted as the backbone network, leveraging its hierarchical shifted window attention mechanism to effectively capture multi-scale features, balancing the detection of tiny parasites and extensive superficial ulcerations. Finally, supervised contrastive learning is incorporated to maximize intra-class compactness and minimize inter-class separability within the feature space, effectively reducing overfitting inherent to small-sample learning. Experimental results demonstrate that the proposed method achieves a macro-average F1-score of 95.86% across six disease categories. Compared with mainstream models such as ResNet and ConvNeXt, the ST-SCL exhibits notable performance improvements and enhanced robustness in small-sample scenarios, offering a promising technical path for precise fish disease diagnosis in complex aquatic environments. Full article

The primary productivity of phytoplankton (PP_eu) is critical to the carbon cycle in aquatic ecosystems. However, in complex lakes covered by ice, the estimation of PP_eu using remote sensing techniques is constrained. To address this limitation, this study developed an estimation model for ice-covered PP_eu by incorporating optical parameters such as the ice surface refractive index and the extinction coefficient of the ice layer into the vertical generalized production model (VGPM). This approach overcomes the challenges associated with remote sensing-based estimation of PP_eu during ice-covered periods. The results indicate that the annual carbon sequestration of the WLSHL is 1.72 × 10⁴ t C, with an average annual PP_eu of 316.96 mg C·m⁻²·d⁻¹. In addition to the indicators that are directly involved in the estimation of PP_eu, the environmental factors that affect PP_eu include water temperature (WT), ice thickness (IT), snow, water depth (D), total dissolved solids (TDSs), salinity (S), ammonia nitrogen (NH₄⁺-N), nitrate nitrogen (NO₃⁻-N), and oxidation–reduction potential (ORP). The PP_eu in the ice period is found to be only 17% lower than that in the ice-free period. However, the PP_eu during the ice period is considerably higher than that during the ice + snow period. The findings indicate that the impact of freezing on PP_eu during the winter is relatively limited, whereas the influence of snowfall is more pronounced. In order to mitigate the elevated PP_eu and the occurrence of algal blooms during the summer, the intensity of underwater radiation can be regulated on a periodic basis. To optimize the function of the carbon sink in winter lakes, the PP_eu can be enhanced through initiatives such as water replenishment prior to freezing and snow removal following freezing. Full article

The excessive accumulation of biogenic amines (BAs) in aquatic products poses serious health risks, necessitating the development of rapid and sensitive detection methods. This study reports the synthesis of a novel fluorescent nanocomposite, carbon-dot-embedded covalent organic frameworks (CDs@COFs). Comprehensive characterization (TEM, XPS, FTIR, UV–Vis, and fluorescence spectroscopy) confirmed the successful fabrication of the nanocomposites, which exhibited excellent thermal and optical stability. A significantly enhanced quantum yield of 36.22% (compared with 12.92% for pure carbon dots) was obtained. As a fluorescent probe, the composite enabled the detection of nine BAs based on a fluorescence quenching mechanism. The proposed method demonstrated good linearity (1~100 ng/mL) and low detection limits of 0.58~0.98 ng/mL. The method was successfully applied to analyze tyramine in large yellow croaker, showing accurate spike recoveries ranging from 91.93% to 101.43% and excellent reproducibility (RSD < 3%). These results highlight the great potential of the developed method as a powerful tool for the rapid screening of BAs in aquatic products. Full article

Global freshwater resources face severe water quality degradation, with chlorophyll-a (Chl-a) concentration serving as a critical eutrophication indicator. While deep learning methods enable accurate Chl-a retrieval from remote sensing reflectance (Rrs) spectra, the scarcity of paired Rrs-Chl-a samples limits model generalization and causes overfitting, particularly in optically complex inland waters. To address this data bottleneck, we propose a physics-constrained latent diffusion model for synthesizing high-fidelity paired Rrs-Chl-a data to augment limited training sets for deep learning-based water quality retrieval. Our framework integrates three key innovations: (1) a lightweight variational autoencoder achieving 8.6:1 latent space compression, reducing computational overhead while preserving spectral features; (2) band-selective attention mechanisms targeting chlorophyll-sensitive wavelengths (440, 550, 680, and 700–750 nm) based on bio-optical principles; and (3) physics-guided conditional encoding that captures concentration-dependent spectral responses across oligotrophic to eutrophic regimes. Evaluated on the GLORIA dataset, our model demonstrates superior performance in spectral similarity (0.535), sample diversity (0.072), and distribution matching (Fréchet distance 0.0008) compared to conventional generative models. When applied to data augmentation, synthetic spectra improved downstream Chl-a retrieval from R²= 0.75 to 0.91, reducing RMSE by 39%. This physics-informed generative approach addresses data scarcity in aquatic remote sensing research, supporting global needs for enhanced understanding of inland and coastal water quality dynamics in data-limited regions. Full article

Electric discharge cavitation is an effective method for water treatment that combines physical and chemical effects within a single process. It enables water disinfection, extraction acceleration, dispersion of solid particles, and enhancement of porous material permeability. Compared to conventional chemical treatment, it reduces the demand for reagents and minimizes secondary pollution. This new and developing technology significantly contributes to the preservation of natural aquatic ecosystems by providing a sustainable alternative to traditional decontamination methods, thereby reducing the overall anthropogenic pressure on the environment. This study focuses on developing a reliable method for assessing electric discharge cavitation intensity and controlling water purification processes. The proposed approach is based on the oxidation of iodide ions to molecular iodine by reactive species generated during electric discharge cavitation. The adapted iodometric method is sensitive, reproducible, and does not require complex optical or acoustic equipment. Experimental results confirmed that iodometry provides an accurate evaluation of cavitation intensity, allowing control of specific energy consumption and optimization of treatment parameters. Optimal operating conditions were established to control the water processing by electric discharge cavitation: stainless-steel electrodes, specific input energy not exceeding 280 kJ·L⁻¹, the presence of a free liquid surface in the working chamber, and a discharge pulse frequency below 10 Hz. The proposed method supports the development of energy-efficient, low-waste technologies for wastewater and natural water treatment and facilitates the integration of electric discharge systems into existing water treatment infrastructure, particularly under resource-limited conditions. Full article

The primary objective of this research is to engineer a high-performance, sustainable material for aquatic remediation by repurposing low-cost biogenic silica into a selective hydrophobic adsorbent. By integrating the natural hierarchical porosity of Diatomaceous Earth (DE) with a tailored silanization strategy, this work aims to provide a scalable and eco-friendly solution for the efficient encapsulation and mechanical recovery of hydrocarbons from contaminated water. To overcome the inherent hydrophilicity of DE, a two-step functionalization process was developed, involving alkaline activation followed by the covalent grafting of hexadecyltrimethoxysilane (C16) in different concentrations. The resulting C16@DE hybrid materials underwent a dramatic surface energy transformation, shifting from hydrophilic behavior to robust hydrophobicity, with static contact angles reaching up to 134.8°. Optical analysis revealed a unique remediation mechanism: while pristine DE disperses homogeneously in the aqueous phase, functionalized C16@DE spontaneously organizes into discrete pellets upon contact with diesel, effectively encapsulating the fuel. Quantitative UV/vis spectrophotometry confirmed that these composites sequester approximately 55–56% of the diesel phase. Together, these results demonstrate that C16@DE materials couple intrinsic biosilica porosity with tailored hydrophobicity to achieve efficient hydrocarbon capture. By combining the natural hierarchical porosity of diatoms with engineered surface selectivity, this research positions functionalized DE as a scalable, low-cost, and eco-friendly promising solution for marine oil spill recovery and industrial wastewater treatment. Full article

The eye is a sensitive target of sublethal stress in aquaculture-reared fish due to its direct exposure to the aquatic environment. This study tested a photoelectrocatalytic (PEC) water treatment system, integrated into a standard recirculating aquaculture system (RAS), to improve water quality and evaluated ocular health in Oncorhynchus mykiss (rainbow trout) reared at 30 kg/m³ for 28 days, with particular emphasis on the cornea as an indicator of fish welfare. Ocular analyses focused on the cornea and retina, two anatomically and functionally distinct structures. PEC significantly reduced ammonia levels and modulated nitrate concentrations compared to the control group (CTR), represented by a standard RAS. No differences in growth performance or body condition were observed between groups. Corneal integrity was assessed using optical coherence tomography, histology, and mucous cell staining to evaluate epithelial structure and protective responses. Corneal tissue was examined to detect local oxidative effects through morphological analysis and immunohistochemistry for 8-hydroxy-2′-deoxyguanosine (8-OHdG). Alcian Blu–Periodic Acid–Schiff (AB–PAS) staining did not reveal significant differences in mucin-producing cells among groups. CTR fish exhibited epithelial disruption and increased 8-OHdG immunoreactivity, whereas fish reared in the RAS equipped with the PEC system, ensuring improved water quality, showed preserved corneal architecture despite mild oxidative stress. Molecular analysis of ocular tissues revealed no differential expression of oxidative stress-related genes, such as GPx1, GR, or sod1, in the two groups. Overall, these findings support the use of the cornea as a sensitive indicator of sublethal environmental stress in farmed fish and suggest that PEC treatment may contribute to improved water quality management and welfare monitoring in intensive aquaculture systems. Full article

Oil spills are severe environmental disasters that cause long-lasting damage to marine ecosystems and impose significant economic costs, underscoring the urgent need for efficient detection and monitoring technologies. Conventional field-based observation methods, while valuable, are constrained by limited spatial coverage, high costs, and labor-intensive processes, making them impractical for large-scale or rapid-response applications. To overcome these challenges, satellite remote sensing has been used as an effective alternative for oil spill monitoring. In particular, the advent of Google Earth Engine (GEE), a cloud-based geospatial platform, has transformed oil spill research by enabling scalable management and analysis of large satellite remote sensing datasets. This review synthesizes studies employing GEE for oil spill detection, across marine environments and interconnected aquatic systems, focusing on methodologies based on optical imagery and synthetic aperture radar data and approaches that integrate machine learning techniques. The analysis underscores that GEE enhances oil spill monitoring by facilitating rapid data processing, supporting reproducible workflows, and expanding access to multi-source satellite data. Furthermore, this review highlights the necessity of incorporating very-high-resolution satellite data and achieving tighter integration of external deep learning framework within GEE to improve detection accuracy and the operational applicability in complex marine and coastal contexts. Full article

The increasing release of pharmaceutical pollutants, particularly antibiotics and analgesics, into aquatic environments poses a significant environmental challenge and necessitates sustainable removal strategies. In this study, lavender-derived biochar was produced by pyrolysis at 450 and 650 °C and subsequently modified with Zn²⁺ (3 and 5 mmol) via a solvothermal method. The resulting materials were evaluated as photocatalysts for the degradation of doxycycline and paracetamol in distilled water under UV-A irradiation. Structural and optical characterization (SEM–EDS, XRD, PL, FTIR) was conducted to elucidate structure–performance relationships relevant to photocatalytic activity. The sample pyrolyzed at 450 °C and modified with 5 mmol Zn²⁺ exhibited the highest photocatalytic performance, achieving degradation efficiencies of 62.78% for doxycycline (k = 0.0032 min⁻¹) and 75.19% for paracetamol (k = 0.0113 min⁻¹). The results demonstrate that controlled Zn incorporation into lavender-derived biochar enhances photocatalytic performance and highlight the role of synthesis parameters in governing catalytic behavior. This work underscores the potential of agro-waste-derived biochar as a functional matrix in sustainable photocatalytic systems. Full article

This study addresses the critical need for accurate, continuous monitoring of surface water quality parameters (SWQPs) using remote sensing, overcoming limitations in existing models that often rely on pre-trained networks ill-suited for complex aquatic environments. We present a customized convolutional neural network (CNN) architecture, implemented in the MATLAB environment, designed to simultaneously predict optically active (Total Organic Carbon, TOC) and non-optically active (Dissolved Oxygen, DO) parameters from eighteen Sentinel-2 Level-2A satellite images, acquired between 2023 and 2024. Our approach integrates spatial and spectral data through a customized CNN with three convolutional layers and two dense layers, optimized via adaptive learning strategies, data augmentation, and rigorous regularization to enhance predictive performance and prevent overfitting. The models were trained and validated on fused datasets of satellite imagery and in situ measurements, organized into comprehensive four-dimensional arrays capturing spectral, spatial, and sample dimensions. The results demonstrated high accuracy, with coefficient of determination (R²) values exceeding 0.97 and low root mean square error (RMSE) across training, validation, and testing subsets. Spatial prediction maps generated at high resolution revealed realistic ecological and hydrological patterns consistent with known regional water quality dynamics in New Brunswick. Our contribution, accessible to users with MATLAB, lies in the development of a transparent, adaptable, and reproducible CNN framework tailored for multiparameter water quality estimation, which extends beyond traditional empirical, site-specific regression models by enabling non-invasive, cost-effective, and continuous monitoring from satellite platforms over a large, heterogeneous province-scale domain. Additionally, model interpretability was enhanced through SHapley Additive exPlanations (SHAP) analysis, which identified key spectral bands influencing predictions and provided ecological insights, offering guidance for future sensor design and data reduction strategies. This study addresses a significant research gap by providing a dual-parameter focused, end-to-end deep learning solution optimized for province-scale remote sensing data, facilitating more informed environmental management. This study can support water managers and agencies by providing province-wide DO and TOC maps derived from freely available Sentinel-2 imagery, reducing reliance on sparse field sampling alone and helping to identify areas of low oxygen or high organic carbon. Future work will extend this framework temporally and spatially and explore hybrid CNN architectures incorporating temporal dependencies for improved generalization and accuracy. Full article

The selective detection of microplastics (MPs) in aquatic environments is hindered by particle size diversity and matrix-induced interferences. This study reports an excitation–emission matrix (EEM) fluorescence sensing platform using polyamide-derived carbon quantum dots (PACQDs; 0.5–2.6 nm) for the size- and concentration-resolved detection of polyethylene terephthalate MPs (PETMPs). PACQDs exhibited a pronounced fluorescence “turn-off” response upon PETMP interaction, governed by particle size (10–149 μm) and loading (4–8 g L⁻¹). Small PETMPs (10 μm) followed linear Stern–Volmer behavior, achieving a detection limit of 1.67 mg L⁻¹ in deionized water. Conversely, larger particles induced non-linear optical effects, including scattering-driven enhancement and inner-filter effects. Multivariate analysis using PCA and PARAFAC resolved three distinct components associated with surface-state quenching, scattering-mediated redistribution, and surface area-driven binding. Component-specific scores confirmed that PACQDs are most sensitive to small PETMPs, while larger particles primarily introduce optical interference. Selectivity tests showed distinct discrimination of PETMPs over polyamide and polypropylene. In tap water, significant matrix effects were corrected via matrix-matched calibration, achieving recoveries within 80–120%. This study establishes EEM-based multivariate fluorescence as a mechanism-informed strategy for PETMP sensing, highlighting the robust applicability of PACQDs for monitoring small PETMPs in real-world water matrices. Full article

Dissolved oxygen (DO) is a cornerstone of aquatic ecosystem vitality, yet conventional in situ monitoring methods, reliant on field probes, buoys, and lab analyses, struggle to capture the spatiotemporal variability of DO at regional or global scales. Satellite remote sensing has revolutionized water quality assessment by enabling systematic, high-frequency, and spatially continuous monitoring of surface waters, transcending the logistical and financial constraints of traditional approaches. This systematic review critically evaluates satellite-based methodologies for estimating DO concentrations, emphasizing their capacity to address global environmental challenges such as eutrophication, hypoxia, and climate-driven deoxygenation. Following the PRISMA 2020 guidelines, large bibliographic databases (Scopus, Web of Science, and Google Scholar) identified that studies on satellite-derived DO concentrations are focused on both spectral and thermal foundations of DO retrieval, including empirical relationships with proxy variables (e.g., Chlorophyll-a, sea surface temperature, and turbidity) as well as direct optical signatures linked to oxygen absorption in the red and near-infrared spectra. The 77 results included in this review (accessed on 27 November 2025) indicate that the reported advances in sensor technologies (e.g., Sentinel-2,3’s OLCI and MODIS) have greatly expanded the ability to monitor DO levels across different types of water bodies, and that there has been a significant paradigm shift towards more complex and sophisticated machine learning and deep learning architectures. Recent work demonstrates that advanced machine learning and deep learning models can effectively estimate DO from remote sensing proxies, achieving high predictive performance when validated against in situ observations. Overall, this review indicates that their effectiveness depends heavily on high-quality training data, rigorous validation, and careful recalibration. Global case studies illustrate applications showcasing the scalability of remote sensing solutions. An OSF project was created to enhance transparency, while the review protocol was not prospectively registered, which is consistent with the PRISMA 2020 guidelines for non-registered reviews. Full article

The efficacy of Underwater Camouflaged Object Detection (UCOD) is fundamentally constrained by severe boundary ambiguity, where biological mimicry blends targets into complex backgrounds and aquatic optical degradation erodes edge details. We propose a lightweight boundary perception detector named CAR-YOLO (Camouflage Ambiguity Resolution YOLO). Specifically, a frequency-domain dual-path mechanism (FRM-DWT/EG-IWT) leverages selective wavelet aggregation and dynamic injection to recover high-frequency edges. Subsequently, these high-frequency cues are synergized with low-frequency semantic information via the Low-level Adaptive Fusion (LAF) module. To further address noisy samples, an Uncertainty Calibration Head (UCH) refines supervision via prediction consistency. Finally, we constructed specialized datasets based on public data for training and evaluation, including UCOD10K and UWB-COT220. On UCOD10K, CAR-YOLO achieves 27.1% mAP50–95, surpassing several state-of-the-art (SOTA) methods while reducing parameters from 2.58 M to 2.43 M and GFLOPs from 6.3 to 5.9. On the challenging UWB-COT220 benchmark, the model attains 30.7% mAP50–95, marking a 7.7-point improvement over YOLOv11. Furthermore, cross-domain experiments on UODD demonstrate strong generalization. These results indicate that CAR-YOLO effectively mitigates boundary ambiguity, achieving an optimal balance between accuracy, robustness, and efficiency. Full article

Light, as one of the most crucial environmental factors, plays an essential role in the growth, physiology, and evolutionary survival of fish. To cope with diverse light conditions in aquatic environments, fish adapt through photosensory systems composed of both visual and non-visual pathways. The retina is a key component of the visual system of fish, capable of converting external optical signals into neural electrical signals, making it crucial for visual formation. During the process of visual signal transduction, opsins serve as the molecular foundation for vision formation. They can be divided into two major categories: visual opsins and non-visual opsins. Among these, melanopsin, as a member of the non-visual opsin family, acts as a key upstream factor in the circadian phototransduction pathway of fish. In this review, we review the adaptability of fish retinal structures to light reception and introduce in detail the gene diversity and relative expression levels of fish opsins. At the same time, we comprehensively describe the molecular mechanism by which fish adapt to changes in the underwater light environment. We also concluded that melanopsin, as a non-imaging photoreceptor, possesses not only core light-sensing functions but also non-imaging visual functions such as circadian rhythm regulation, body coloration changes, and hormone secretion. This review suggests that future research should not only elucidate the physiological functions of melanopsin in fish but also comprehensively reveal the mechanisms underlying the multi-adaptive nature of fish vision across varying light environments. Through these studies, researchers can have a deeper understanding of the physiological regulation mechanism of fish in complex light environments, and then formulate fish light environment management strategies, optimize aquaculture practices, improve economic returns, and promote the development of related fields. Full article

The extensive consumption of freshwater resources and the continuous discharge of pharmaceutical residues pose serious risks to aquatic ecosystems and public health. In this study, pristine ZnO, TiO₂, Zn@TiO₂, and Ti@ZnO nanocomposites were synthesized via a precipitation-assisted solid–liquid interference method and systematically evaluated for the photocatalytic degradation of the antibiotic levofloxacin under UV and visible light irradiation. The structural, optical, and surface properties of the synthesized materials were characterized using X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), UV–visible diffuse reflectance spectroscopy (UV–DRS), and X-ray photoelectron spectroscopy (XPS). XRD analysis confirmed the crystalline nature of all samples, while SEM images revealed spherical and agglomerated morphologies. Photocatalytic experiments were conducted using a 50-ppm levofloxacin solution with a catalyst dosage of 1 g L⁻¹. Pristine ZnO exhibited limited visible-light activity (33.81%) but high UV-driven degradation (92.98%), whereas TiO₂ showed comparable degradation efficiencies under UV (78.6%) and visible light (78.9%). Notably, Zn@TiO₂ nanocomposites demonstrated superior photocatalytic performance, achieving over 90% and near 70% degradation under both UV and visible light, respectively, while Ti@ZnO composites exhibited less than 60% degradation. The enhanced activity of Zn@TiO₂ is attributed to improved interfacial charge transfer, suppressed electron–hole recombination, and extended light absorption. These findings highlight Zn@TiO₂ nanocomposites as promising photocatalysts for efficient treatment of pharmaceutical wastewater under dual-light irradiation. Full article