Search Results (1,270)

Egypt’s largest coastal lagoon, Manzala Lagoon, has undergone severe degradation due to sediment infilling, aquatic vegetation proliferation, and untreated wastewater. It has shrunk from 805 km² in 1985 to 525 km² by 2017, with poor water quality and heavy metal accumulation. The 2017–2022 restoration project deepened the lagoon to 3–4 m, restoring 750 km² of open water and temporarily improving water quality. However, the reuse of dredged sediments to construct 13 elongated sand barriers and man-made islands inadvertently created semi-isolated sub-basins, disrupting east–west circulation, fostering localized stagnation, and coinciding with vegetation resurgence and seasonal algal blooms. This study employs coupled CMS-Flow and CMS-Wave modeling to evaluate hydrodynamic conditions and test innovative restoration strategies. Four scenarios were analyzed: pre-purification (2017), post-intervention project (2025), and two proposed interventions aimed at restoring connectivity, either through complete barrier removal or selective channel excavation, to enhance east–west water circulation and reduce stagnation. This study demonstrates that targeted, data-driven interventions can rapidly restore water circulation, revive ecological function, and optimize management strategies, providing a conceptually transferable framework for hydrodynamic assessment and sustainable management of coastal lagoons subject to similar anthropogenic pressures. Full article

Phytoplankton are the primary producers in freshwater lake ecosystems and play a fundamental role in maintaining the structure and function of lacustrine food webs. Baiyangdian Lake, located at the core of Xiong’an New Area, is vital for regional aquatic ecological security. However, systematic data on phytoplankton community dynamics throughout the phytoplankton critical growing season are scarce. In this study, we conducted a monthly investigation of phytoplankton communities in Baiyangdian Lake from April to October 2025, analyzing community composition, abundance, and diversity patterns. A total of 152 phytoplankton taxa across 8 major algal groups were identified, with Chlorophyta, Bacillariophyta, and Cyanobacteria being the dominant groups. Phytoplankton abundance exhibited distinct seasonal variation, peaking in August and reaching its lowest in October. The Shannon–Wiener diversity index (H′) and Pielou evenness index (J′) were generally at favorable levels, indicating a relatively stable community structure. The mean phytoplankton density across all sampling sites during the growing season was 8.70 × 10⁶ cells/L, categorizing the lake as having “no obvious bloom” according to standard bloom severity classifications. The overall trophic state of Baiyangdian Lake during the study period was mesotrophic. These findings provide fundamental baseline data and scientific support for the management of algal bloom risks and the long-term conservation of the lake’s aquatic ecosystem. Full article

Dissolved organic matter (DOM) plays a central role in estuarine carbon cycling and exhibits dynamically coupled interactions with chlorophyll-a (Chl-a). Under increasing nutrient loads, elevated Chl-a concentrations and shifts in DOM composition serve as key indicators of eutrophication in estuarine aquatic ecosystems. Previous studies have mainly focused on the composition and fluorescence properties of DOM in rivers and lakes. Here, 84 water samples were collected from the Ganjiang River Estuary of Lake Poyang during wet, normal, and dry seasons across the mainstream, middle, and south branches. The average Chl-a concentration showed wet season (6.61 μg·L⁻¹) > normal season (4.54 μg·L⁻¹) > dry season (2.01 μg·L⁻¹). By employing EEM-PARAFAC, five fluorescent components were identified, including C1, C2, C3, C4, and C5. Notably, microbial humic-like substances remained consistently high during the wet season. Two-dimensional correlation spectroscopy was further employed to evaluate sequential changes in DOM components, while a moving window was used to identify temporal variation characteristics. Based on Noda’s rules, the DOM response sequence was identified as C3→C2→C1→C4→C5. Kernel PCA showed that the variable cluster represented by PC1, which consisted of organic pollutants and nutrients, co-varied negatively with Chl-a, whereas the PC2 cluster, representing biogenic organic matter, co-varied positively with Chl-a. Moreover, partial least squares path modeling showed that humic-like and tryptophan-like substances were positively correlated with Chl-a, with the path coefficients of 0.47 and 0.19, respectively. These findings revealed the interaction patterns between DOM components and Chl-a at the river-lake confluence zone, thereby enhancing our understanding of the factors influencing the spatio-temporal variations in Chl-a concentration, and further providing a guide for the control of algal blooms. Full article

Algae rarely occur as solitary phototrophs in nature or engineering; instead, they are embedded in complex bacterial consortia that control their physiology, productivity and ecological performance. The phycosphere, a microscale niche rich in algal exudates, promotes extensive metabolic exchange and chemical signaling, defining these associations. Bacteria capitalize on the dissolved organic carbon released by algae, providing growth supporting molecules such as vitamins, trace metals, and siderophores, as well as regenerated inorganic nutrients. Bidirectional beneficial interactions range from obligate mutualism to facultative commensalism and antagonism, depending on environmental context and community membership. Bacterial partners can stimulate algal growth, morphogenesis, and stress tolerance, as well as modulating defense and programmed cell death during the decline and bloom succession of algae resulting from algicidal taxa. Metabolic cooperation, QS signaling, extracellular enzyme activity, and chemically induced gene expression produce the exometabolome in the phycosphere, which in turn reprograms gene expression in all partners. Recent advances in multi-omics toolboxes, single-cell isotopic analyses, and microfluidics have greatly enhanced our understanding of the functional and spatiotemporal orientation of algal microbiomes. Ecologically, algal–bacterial interactions manage the phytoplankton community structure, control HABs, and modulate carbon and nutrient fluxes in both marine and freshwater realms. Biotechnologically, engineered algal–bacterial consortia are a promising tool for enhancing biomass production, stabilizing large-scale cultivation, improving wastewater treatment, and upgrading biofuels and fine chemicals. Despite these notable research advances, the context- and species-dependent complexity of multispecies interactions remains a major obstacle to their practical modeling and scalable implementation. Integrative research frameworks that combine molecular, ecological, and bioengineering approaches are urgently needed to unlock the full potential of sustainable applications in the future. Full article

Aquaculture is the fastest-growing food production sector, supplying more than half of the world’s seafood and projected to expand further to meet rising global protein demands. Among the various pressures confronting this industry, harmful algal blooms (HABs) rank among the most alarming. Ichthyotoxic flagellates are microalgae known for producing toxins or inducing gill damage that leads to widespread fish mortality. Their increasing frequency poses a critical threat to aquaculture, emphasizing the urgent need for effective and environmentally sustainable strategies to regulate and mitigate these harmful episodes. This study investigated the responses of three ichthyotoxic flagellates renowned for impacting aquaculture operations (Prymnesium parvum, Heterosigma akashiwo, and Fibrocapsa japonica) and tested their susceptibility to two routinely applied chemical agents, hydrogen peroxide (H₂O₂) and copper sulfate (CuSO₄). Mortality, viability, and motility were assessed alongside trajectory-based metrics, including mean squared displacement (MSD) and probability density function (PDF). The results revealed species-specific sensitivities: P. parvum was highly susceptible to H₂O₂, while H. akashiwo and F. japonica were more susceptible to copper toxicity. Ichthyotoxic flagellates exhibited differential sensitivities to chemical treatments, with copper sulfate generally achieving lower EC₅₀ thresholds and greater inhibition of motility than hydrogen peroxide, except in P. parvum. The rapid attenuation of motility at sublethal concentrations highlights swimming behavior as a functional vulnerability, reinforcing the potential for behavior-based mitigation strategies that minimize chemical loading and reduce unintended impacts on cultured fish and surrounding ecosystems. Full article

Algal blooms in water sources, exacerbated by global climate change and water eutrophication, pose a significant threat to water quality, ecological safety, and human health. Seasonal algae blooms in drinking water sources, particularly those in colloidal form, present substantial challenges to the safe and stable operation of drinking water treatment plants. To address these challenges and gain a better understanding, this study reviews current issues and perspectives on algae in the drinking water supply system (DWSS). Algal contaminations are more frequent and severe in the tropics and subtropics spatially, while temporally, they pose greater concern during summer and autumn. Moreover, various detection methods, including conventional and advanced techniques, are discussed based on the advantages and disadvantages in species identification, cell quantity, and morphology observation. Additionally, treatment processes in DWSS, particularly pre-oxidation and coagulation, are effective in removing most algae. Furthermore, judging from the characteristics, microalgae and Microcystis aeruginosa exist as colloidal algae among the whole process of DWSS, yet the physical states of algae have been largely overlooked in previous research. This study fills this gap by introducing colloidal algae as a distinct form and analyzing its detection and removal from a colloid science perspective. This review thus provides a new reference for targeted algae control in DWSS. Full article

Predicting algal proliferation in freshwater systems is crucial for effective water quality management and ecological sustainability. This study proposes a novel data-driven framework that integrates correlation-based feature ranking with a concatenation-enhanced artificial neural network (ANN) architecture to improve algae prediction accuracy. The analysis was conducted through a systematic evaluation of parameter relationships, employing Pearson’s correlation coefficient and standardized coefficients (Beta) to determine feature importance. Based on the magnitude of these coefficients, the input variables were progressively grouped into six feature sets, enabling a comparative assessment of predictive performance. The ANN models were trained and validated using root mean squared error (RMSE), mean absolute error (MAE) and Normalized Nash–Sutcliffe Efficiency (NNSE) as evaluation metrics. The results demonstrate that the fourth feature set, including chlorophyll-a, temperature, dissolved oxygen, total dissolved solids, and ammonia (NH₃), identified through combined Pearson and Beta analysis, achieved the lowest prediction errors and superior generalization performance. These findings highlight the effectiveness of feature selection guided by correlation and standardized coefficients in enhancing ANN performance for algae prediction. The proposed framework offers valuable insights for improving the predictive modeling of algal dynamics, thereby supporting proactive water quality monitoring and the sustainable management of aquatic ecosystems. Full article

Rapid algal proliferation in human-impacted freshwater ecosystems necessitates advanced predictive tools for effective management. This study aims to capture the stochastic dynamics of algal blooms in the Fuxi River, China, using high-frequency monitoring and interpretable machine learning. A 2 h interval dataset was utilized to construct Random Forest models in Python for predicting Chlorophyll-a (Chl-a) and algal density, both measured via in situ multi-wavelength fluorescence. Model interpretability was achieved through SHAP (SHapley Additive exPlanations) analysis to identify non-linear environmental drivers and ecological thresholds. The models demonstrated high predictive accuracy. SHAP analysis revealed that dissolved oxygen (>10 mg/L) is the primary diagnostic indicator for peak Chl-a, with an optimal thermal window of 15–20 °C identified for proliferation. For algal density, chemical oxygen demand (COD_Cr > 25 mg/L) and conductivity (>1000 μS/cm) were identified as critical tipping points, showing pronounced synergistic effects between organic enrichment and nutrient levels. This study underscores that managing organic loading and monitoring specific thermal–hydrochemical windows are vital for mitigating extreme algal events, providing a robust, interpretable framework for real-time water quality early warning. Full article

The Yangtze River Economic Belt supports over 400 million people and contributes nearly half of China’s GDP, yet decades of industrialization, urbanization, and agricultural intensification have resulted in severe contamination and pressing environmental challenges. This systematic review synthesizes three decades of peer-reviewed and governmental data to examine the spatiotemporal distribution, sources, and ecological and human health risks of major pollutants, including heavy metals, microplastics, persistent organic pollutants, and excess nutrients. While point-source emission of heavy metals such as cadmium, lead, and mercury have decreased by 35–42% since 2013 following policy interventions like the 10-Point Water Plan and the Yangtze River Protection Law, legacy contaminants in sediments and diffuse agricultural inputs continue to pose significant risks. Cadmium levels in rice still exceed food safety standards, arsenic in groundwater surpasses health guidelines, and microplastic flux into the East China Sea has reached 8.3 × 10¹² particles per year. Nutrient surpluses also drive extensive algal blooms, causing substantial economic losses. This review evaluates remediation strategies such as dredging, phytoremediation, wetland restoration, and AI-enhanced monitoring, which show removal efficiencies of 60–90% at reduced costs. However, critical gaps remain in understanding chronic mixture toxicity, the long-term fate of emerging contaminants, and pollutant–climate interactions. We propose an integrated basin-wide roadmap combining zero-liquid-discharge mandates, green infrastructure, and adaptive, performance-based governance to secure the Yangtze’s ecological and economic sustainability. This framework offers a transferable model for large-scale watershed management worldwide. Full article

Cyanotoxins were evaluated in seven water bodies in central Chile (Avendaño, Lo Galindo, Grande de San Pedro, Lanalhue, Vichuquén, Torca, and Llico) from October 2022 to May 2023. Microcystins (MC-RR, MC-YR, MC-LR, MC-LA) and nodularin were quantified by HPLC-DAD, and their relationships with environmental variables and cyanobacterial abundance were assessed using Spearman correlation and principal component analysis (PCA). Cyanotoxins were detected in six systems, with MC-LR as the dominant congener. The highest concentration (407.5 µg/L) occurred in the mesotrophic Laguna Grande de San Pedro. Correlation analysis showed nodularin positively associated with conductivity (ρ = 0.40, p < 0.05), while microcystins were negatively correlated with temperature (ρ to −0.60, p < 0.05). PCA explained 57.7% of variance, distinguishing toxin patterns along gradients of temperature, pH, conductivity, and N:P ratio. Cyanotoxin occurrence was weakly related to cyanobacterial abundance but consistently associated with low N:P ratios. These findings confirm the presence of cyanotoxin-producing strains in the studied water bodies and highlight the need to integrate nutrient dynamics, cyanobacterial community structure, and multi-congener toxin analysis into monitoring programs. Furthermore, the results demonstrate that mesotrophic systems could represent emerging sources of cyanotoxin production, underscoring the need to improve risk assessment and management strategies. Full article

Under the combined impacts of climate change and intensified human activities, harmful algal blooms (HABs) have occurred with increasing frequency in China’s coastal waters, posing growing risks to marine ecosystems and regional sustainability. Chlorophyll-a concentration (Chl-a), a key indicator of phytoplankton biomass, plays a crucial role in HAB monitoring and early warning. This study integrates satellite remote sensing data from 2000 to 2004, 2011 to 2013, and 2023 to 2024 with in situ measurements and environmental variables (e.g., dissolved oxygen) to investigate Chl-a dynamics in the East China Sea. The results indicate pronounced spatiotemporal heterogeneity across the region. Spectral features were represented using band-ratio methods and the BRG model, followed by variable selection based on the Bayesian Information Criterion (BIC) to determine the optimal band combinations for model training. Six mainstream machine learning models were evaluated, and the Backpropagation Neural Network (BP) was selected as the baseline model due to its superior performance. To further improve model robustness and global optimization capability, the Artificial Lemming Algorithm (ALA) was employed to optimize the BP network, resulting in the ALA-BP inversion model. The optimized model achieved correlation coefficients of 0.933 on the test set and 0.940 on the independent validation set, outperforming the other models. The proposed model was further applied to the 2024 algal bloom event in the East China Sea, successfully capturing the spatiotemporal variations of Chl-a. This study provides an effective retrieval framework for Chl-a in optically complex coastal waters and demonstrates its applicability in HAB monitoring. Full article

Saxitoxin (STX), produced by certain harmful algal bloom (HAB) species, bioaccumulates through the food chain and can cause paralytic toxicity in humans, potentially resulting in fatal outcomes. To date, STX detection has primarily been conducted under laboratory-controlled conditions, and the availability of a gold-standard method for the proactive monitoring and prevention of HAB-induced secondary damage remains limited. Therefore, the present study introduces an electrochemical-based biosensor that is capable of early monitoring of STX in HAB-occurred environments. The conserved region of sxtA4, a nucleic acid precursor that is essential for STX biosynthesis, is immobilized on the sensing membrane surface in a sandwich structure. In this process, target detection is recognized as an electrochemical signal by a methylene blue-labeled detection probe, and the reliability of biosensing is supplemented by an electrochemical trend that is opposite to DNA binding. The application of an alternating current electrochemical flow technique achieves more sensitive detection at attomolar levels and rapid measurement within 10 min than a conventional DNA biosensor based on hybridization. In addition, the designed biosensing structure selectively detects STX-synthesizing and non-synthesizing dinoflagellates significantly. The proposed platform can utilize the identification of STX-induced secondary damage of HAB and provide insight into a field-ready biosensor based on its characterization and detection performance. Full article

In view of the limitations of existing studies, in which remote sensing extraction of algal blooms is easily affected by cloud interference, and mechanistic models are constrained by excessive parameters and inadequate representation of nonlinear relationships, resulting in limited timeliness and accuracy, this study took Taihu Lake as the study area and constructed a research framework of bloom extraction-scale matching-spatial prediction-scenario response based on Landsat imagery and gridded meteorological data, constructing the relationship between meteorological factors and algal blooms using machine learning methods. First, the Tasseled Cap transformation (TCap) and Floating Algae Index (FAI) were combined to extract the spatial distribution and area of algal blooms, while cloud interference was addressed to improve recognition stability under complex background conditions. Next, the spatial scales of bloom rasters and meteorological factors were unified to build a matched bloom-meteorological dataset. On this basis, a U-Net model driven by multiple meteorological factors was developed to predict remote-sensing-based bloom distribution/extent patterns under three warming scenarios. The results showed that: (1) the combination of TCap and FAI improved the accuracy and efficiency of bloom extraction; FAI was more sensitive but tended to overestimate bloom area, whereas TCap was more stable under cloud interference; (2) the U-Net model achieved an overall accuracy of 95% and a prediction accuracy of 88%; and (3) bloom area increased under all three warming scenarios, and the extent of expansion generally became more pronounced with increasing warming magnitude, although the response was not strictly monotonic across all cases. Based on the seasonal mean bloom-area increase relative to the baseline condition (S0), the warming response was strongest in spring, followed by summer and autumn, and weakest in winter. This study can provide a reference for cyanobacterial bloom early warning and water environment management in Lake Taihu. Full article

Algal proliferation in subtropical drinking water reservoirs has become increasingly severe, and developing a reliable prediction for algal abundance through high-frequency in situ data is essential for early risk warning and effective management. This study analyzed the interannual variations in algal abundance in the Shanmei (SM) Reservoir, located in Quanzhou City, Fujian Province, China, based on the high-frequency data between 2020 and 2025, and forecasted algal abundance 24 h ahead via the optimized Transformer model. Results revealed that the SM reservoir exhibited seasonal variability in environmental factors, with persistently elevated pH during spring and summer, ranging from 7.12 to 9.66, and relatively high total nitrogen concentrations, ranging from 1.17 to 2.28 mg/L. Overall, algal abundance increased throughout the study period, and the annual average algal abundance in 2025 was 8.18 × 10⁶ cells/L, which was twice that in 2021. Model comparisons revealed that the optimized Transformer model exhibited the highest performance in terms of R² = 0.88 when predicting the next hour using 12 days of data. Feature importance analysis based on SHapley Additive exPlanations (SHAPs) revealed that the predictions of algal dynamics were primarily influenced by previous-hours algal abundance, permanganate index, dissolved oxygen, air temperature, wind speed, and pH. This study revealed that the optimized independent learning model with integrated multi-scale features can significantly enhance the predictive performance of algal dynamics, offering a technical basis for early warning of algal blooms and refined reservoir management. Full article

The availability of high-resolution oceanographic data is critical for evidence-based coastal environmental management and climate resilience planning, yet it remains constrained by observational gaps and the prohibitive computational cost of fine-scale hydrodynamic modeling. While downscaling techniques provide a viable pathway, current data-driven approaches often lack statistical physical associations, overlook multivariate environmental interactions, and struggle to represent complex coastal topography. To address these limitations, we present MEOFGAN—an environmentally informed downscaling framework that integrates multivariate empirical orthogonal function (MEOF) decomposition with a generative adversarial network (GAN). The model extracts physically interpretable spatial modes of coupled ocean variables, learns their cross-scale transitions through adversarial training, and systematically incorporates high-resolution bathymetry as a static environmental constraint to enhance spatial fidelity. When applied to the Bohai Sea, MEOFGAN successfully downscales sea surface temperature (SST) and sea surface height (SSH) from 1/4° to 1/12°, achieving error reductions of 30–68% compared to benchmark methods while preserving ecologically relevant structural patterns (SSIM > 0.92). The framework demonstrates strong generalization by reconstructing 500 m resolution distributions of chlorophyll-a (Chl-a), dissolved oxygen (DO), and salinity in Bohai Bay, capturing fine-scale environmental gradients during a documented algal bloom event. This work establishes a methodological framework that can be transferred as a paradigm for generating high-resolution coastal datasets. Rather than serving as a universally transferable pre-trained model, the framework requires region-specific training and application. Data generated in this manner can directly support water quality monitoring, eutrophication assessment, habitat mapping, and regionally tailored climate adaptation strategies. Full article