Search Results (218)

Taganrog Bay is part of the Azov Sea, which has significant environmental value. However, in recent years, anthropogenic activity and climate change have increasingly impacted this coastal system. These factors have led to increased sea salinity. These factors also contribute to abundant blooms of potentially toxic cyanobacteria. One additional method for preventing the abundant growth of cyanobacteria may be the introduction of green algae into the bay. The aim of this study was to conduct a computational experiment on the biological rehabilitation of Taganrog Bay using mathematical modeling methods. For this purpose, the authors developed and analyzed a mathematical model of phytoplankton populations. A software model was developed based on modern mathematical modeling methods. The input data for the software module included grid points for advective transport velocities, salinity, and temperature, as well as phytoplankton population and nutrient concentrations. The software module outputs three-dimensional distributions of green algae and cyanobacteria concentrations. A computational experiment on biological rehabilitation of the Taganrog Bay by introducing a suspension of green algae was conducted. Green algae and cyanobacteria concentrations were obtained over 15 and 30-day time intervals. The concentration and volume of introduced suspension were empirically determined to prevent harmful cyanobacteria growth without leading to eutrophication of the bay by green algae. Full article

Harmful Algal Blooms (HABs) in large river systems present significant challenges for water quality monitoring, with traditional in-situ sampling methods limited by spatial and temporal coverage. This study evaluates the effectiveness of machine learning techniques applied to Landsat spectral data for detecting and quantifying HABs in the Ohio River system, with particular focus on the unprecedented 2015 bloom event. Our methodology combines Google Earth Engine (GEE) for satellite data processing with an ensemble machine learning approach incorporating Support Vector Regression (SVR), Neural Networks (NN), and Extreme Gradient Boosting (XGB). Analysis of Landsat 7 and 8 data revealed that the 2015 HAB event had both broader spatial extent (636.5 river miles) and earlier onset (5–7 days) than detected through conventional monitoring. The ensemble model achieved a correlation coefficient of 0.85 with ground-truth measurements and demonstrated robust performance in detecting varying bloom intensities (R² = 0.82). Field validation using ORSANCO monitoring stations confirmed the model’s reliability (Nash-Sutcliffe Efficiency = 0.82). The integration of multispectral indices, particularly the Floating Algae Index (FAI) and Normalized Difference Chlorophyll Index (NDCI), enhanced detection accuracy by 23% compared to single-index approaches. The GEE-based framework enables near real-time processing and automated alert generation, making it suitable for operational deployment in water management systems. These findings demonstrate the potential for satellite-based HAB monitoring to complement existing ground-based systems and establish a foundation for improved early warning capabilities in large river systems through the integration of remote sensing and machine learning techniques. Full article

Harmful algal blooms, particularly those caused by cyanobacteria (blue-green algae) and green algae, pose an increasing risk to aquatic ecosystems and public health. This risk is intensified by climate change and nutrient pollution. This study presents a methodology for in situ monitoring and assessment of algal contamination in surface waters, combining UV/Vis and fluorescence spectroscopy with a fuzzy pattern classifier for consideration of uncertainties. The system incorporates detailed data pre-processing to minimise measurement uncertainty and uses full-spectrum feature extraction to enhance classification accuracy. To assess the methodology under both controlled and real-world conditions, a mobile submersible probe was tested alongside a laboratory setup. The results demonstrate a high degree of agreement between the two systems, showing particular sensitivity to biological signals, such as the presence of algae. The assessment method successfully identified cyanobacterial and green algal contamination, and its predictions aligned with external observations, such as official warnings and environmental changes. By explicitly accounting for measurement uncertainty and employing a comprehensive spectral analysis approach, the system offers robust and adaptable monitoring capabilities. These findings highlight the potential for scalable, field-deployable solutions for the early detection of harmful algal blooms. Full article

Microcystis aeruginosa, a kind of cyanobacterium, can lead to water blooms under specific conditions and it is harmful to human and ecological security due to the toxins produced by certain strains. Artemisinin, which is derived from Artemisia annua Linn, has a strong allelopathic effect on algae. Artesunate is a water-soluble derivative of artemisinin. We investigated the effect of artesunate on M. aeruginosa, including growth and key photosynthetic parameters (Fv/Fm, φPSII). Our findings demonstrate that artesunate inhibits the growth of M. aeruginosa by damaging the photosynthetic center of photosystem II (PS II), and this inhibitory effect is enhanced with increasing concentration. At the concentration of 200 mol/L, the maximum inhibition rate was 41.62% for FACHB-315 and 43.19% for FACHB-927 after 96 h. After 24 h of exposure, the φPSII of the two strains decreased significantly (p < 0.01). These results could inform further studies on the use of artesunate to control cyanobacterial growth in water bodies and provide theoretical support for the application of artemisinin derivatives in treating water blooms. Full article

Harmful algal blooms (HABs) caused by toxic species such as Pseudo-nitzschia hasleana pose significant risks to marine ecosystems and human health. This study investigates the effects of heating rate on the fluorescence temperature curves (FTCs) of P. hasleana and compares them with non-toxic species (Phaeodactylum tricornutum and Picochlorum maculatum) to design a reliable detection method. An increasing heating rate leads to a change in the temperature spectrum of the fluorescence of the studied algae and to increasing differences between them. During the study, the FTCs were measured in the temperature range of 20–80 °C and at heating rates of 1, 2, 3, and 6°/min. The results showed that P. hasleana exhibited a distinct local fluorescence maximum at 45–55 °C when heated at a rate of 3 °C/min or more, which was absent in non-toxic species. Additionally, rapid heating (6 °C/min) preserved fluorescent pigment–protein complexes, yielding four-fold higher fluorescence intensity at 70–80 °C compared to slower rates. There were no such changes for the microalgae P. maculatum and P. tricornutum. The results of this study make it possible to increase the efficiency of detecting hazardous microalgae using non-invasive optical monitoring methods. These findings demonstrate that controlled heating protocols can enhance the species-specific identification of toxic microalgae, offering a practical tool for early HAB detection. Full article

HCBs (Harmful Cyanobacterial Blooms) are increasing in freshwaters across the globe, particularly at lower latitudes. In Southern Europe, a decrease in annual precipitation and an increase in drought periods have enhanced the occurrence of HCBs, impacting both freshwater ecosystems and human health. This review gathers information on isolated cyanobacterial strains with the potential to form cyanobacterial blooms or to be toxic that have been reported over the past half-century in Portugal. Strains of Microcystis aeruginosa are the most represented ones, many of them microcystin producers. Toxic M. aeruginosa strains have been isolated from lakes (Mira, Barrinha de Mira, and Blue), river sections (Tâmega and Guadiana), and reservoirs (Torrão, Vilar, Montargil, Patudos, Caia, Monte da Barca, Corgas, and Magos). Many other strains from potentially toxic species are listed, namely from Aphanizomenon gracile, Aphanizomenon flos-aquae, Sphaerospermopsis aphanizomenoides, Cuspidothrix issatschenkoi, Dolichospermum flos-aquae, Dolichospermum circinalis, Chrysosporum bergii, Raphidiopsis raciborskii or Planktothrix agardhii. Many of the isolated strains were able to produce cyanotoxins such as microcystins, saxitoxins, cylindrospermopsin, or anatoxin. Most isolates belong to the Portuguese culture collections ESSACC (Estela Sousa e Silva Algal Culture Collection); LEGE-CC (Blue Biotechnology and Ecotoxicology Culture Collection); and ACOI (Coimbra Collection of Algae). Despite many strains already having associated molecular data corroborating a correct identification, a large number of strains are still lacking DNA-based information for phylogenetic affiliation. The present checklist is intended to facilitate access to information regarding strains of potentially toxic cyanobacterial species from Portugal in order to contribute to a better understanding of species-specific HCBs at both regional and global scales. Full article

A novel Gram-negative bacterium, designated strain SN16^T, was isolated from a harmful algal bloom (HAB). Strain SN16^T exhibited potent, broad-spectrum algicidal activity against the colony-forming alga Phaeocystis globosa and eight other HAB-causing species, highlighting its potential as a promising candidate for the biological control of HABs. A phylogenetic analysis of 16S rRNA gene sequences placed strain SN16^T within the genus Flagellimonas. The average nucleotide identity (ANI) and digital DNA–DNA hybridization (dDDH) values between strain SN16^T and its relatives were 75.4–91.4% and 19.3–44.0%, respectively. These values fall below the established thresholds for species delineation, confirming that SN16^T represents a novel species. A chemotaxonomic analysis revealed its dominant cellular fatty acids to be iso-C_15:0 and iso-C_15:1 G. The major polar lipid was phosphatidylethanolamine, and the primary respiratory quinone was menaquinone-6. Genome mining identified 11 biosynthetic gene clusters (BGCs), including those encoding for terpenes, ribosomal peptide synthetases, and non-ribosomal peptide synthetases. By integrating BGC analysis with the observed algicidal activities, we predicted that pentalenolactone and xiamycin analogues are the likely causative compounds. Based on this polyphasic evidence, strain SN16^T is proposed as a novel species of the genus Flagellimonas, named Flagellimonas algicida sp. nov. This is the first report of Flagellimonas species exhibiting broad-spectrum algicidal activity, including activity against the colonial form of P. globosa—a key ecological challenge in HAB mitigation. Full article

Harmful cyanobacterial blooms (CyanoHABs) threaten freshwater ecosystems and human health. Inhibiting cyanobacteria through plant allelopathy is an effective and environmentally friendly approach for CyanoHAB control. In this study, we evaluated the inhibitory activities of several organic solvent extracts from Artemisia argyi against the common bloom-forming cyanobacterium Microcystis aeruginosa, explored the anti-algal mechanism of the active fraction, analyzed its secondary metabolites using liquid chromatography–high-resolution mass spectrometry (LC-HRMS), and screened the potential allelochemicals. The results showed that the crude extract of A. argyi leaves (CE) exhibited significant inhibitory effects on M. aeruginosa. Among several solvent fractions of CE, the dichloromethane extract (DE) demonstrated the strongest inhibitory effect, with a 7-day IC₅₀ of 70.43 mg/L. After treatment with DE, the contents of chlorophyll a (Chl a), carotenoids, and phycobiliproteins (PBPs) in M. aeruginosa were significantly reduced. Meanwhile, an excessive accumulation of reactive oxygen species (ROS), reduction of catalase (CAT) activity, increase in malondialdehyde (MDA) content, and shrinkage of the membrane were found in M. aeruginosa cells under DE treatments. There were 81 secondary metabolites annotated in DE by LC-HRMS. Among them, hispidulin, jaceosidin, 5,7,3′-trihydroxy-6,4′,5′-trimethoxyflavone, and eupatilin possessed strong inhibitory activities, with 7-day IC₅₀ values of 26.23, 27.62, 32.02, and 34.98 mg/L, respectively. These results indicated that the A. argyi extracts possess significant allelopathic activities on M. aeruginosa, and DE was identified as the primary active fraction. It inhibits algae growth by suppressing photosynthesis and inducing peroxidation, ultimately leading to cell death. Flavonoids in DE were the main allelochemicals responsible for the inhibition on algae of A. argyi extracts. Full article

Harmful algae are transported in various compartments of maritime vessels, making ports with heavy maritime traffic potential hotspots for their introduction and spread. In this study, we investigate the port of Papeete (Tahiti, French Polynesia), a key hub for numerous South Pacific shipping routes. Using metabarcoding on DNA extracted from water samples (environmental DNA, eDNA) we identified 21 species of harmful algae comprising to Bacillariophyceae (4), Dinophyceae (14), and Haptophyta (3 species). Three of those species are directly associated with fish mortality events without recognized toxigenic capacity. The remaining harmful algae species are known to produce a wide range of toxins, like the ciguatoxin produced by endemic Gambierdiscus sp., domoic acid, haemolysins, yessotoxins, and others. Health risks such as ciguatera and paralytic shellfish poisoning were identified. An increase in Gambierdiscus frequency in Papeete port waters was parallel to an increase in ciguatera fish poisoning events in Tahiti, which suggests the value of eDNA analysis for early warning of harmful algae presence. Management measures, including banning fishing near the ports, could prevent public health risks associated with harmful algae blooms. Full article

Harmful cyanobacterial blooms pose significant threats to lake ecosystems, and the stocking of filter-feeding fish has often been used for their control. However, filter-feeding fish like silver carp excrete feces that not only retain viable cyanobacterial cells but also increase nutrient loading to the sediment. Furthermore, the quantity and frequency of fecal input vary depending on the biomass of algae and fish and the stocking strategy. In this study, a two-by-two factorial microcosm experiment was carried out to investigate the effects of silver carp feces on P release in shallow lakes. Results showed that fecal input quantity was the key determinant of P release. The peak flux reached 8.82 mg m⁻² d⁻¹ in high input treatments, compared to 1.01 mg m⁻² d⁻¹ in low input treatments. Phased-input exacerbated these effects compared to single-input. The dominant mechanisms of sediment P release varied with input levels. Microbial reduction was strongly associated with P release at low fecal input, while high-input scenarios showed concurrent hypoxia, an increase in sediment pH (from 7.28 to 7.46), and competition for adsorption sites by dissolved organic matter (DOM up to 38.57 mg L⁻¹). These results indicate that stocking of filter-feeding fish for cyanobacterial bloom control substantially altered P flux dynamics, with high input treatments exhibiting fluxes from −6.02 to 8.82 mg m⁻² d⁻¹ compared to −0.007 to 0.33 mg m⁻² d⁻¹ in controls, depending on the patterns of fecal input. For the prevention and control of cyanobacterial blooms and to ensure the sustainability of lakes, the stocking of filter-feeding fish should be carried out before the outbreak of blooms to avoid the impact of large amounts of fish feces input on P release and water quality during the blooms. Full article

This review systematically summarizes recent advances in ultrasound–chemical catalytic synergistic technology for controlling harmful algae blooms, focusing on the multi-mechanism cooperation of catalysts, oxidants, and nanomaterials within sonocavitation systems. The technology enhances coupling efficiency between cavitation effects and radical oxidation while leveraging interfacial regulation capabilities of catalysts (e.g., charge adsorption, carrier migration) to selectively disrupt algae cell structures and efficiently degrade extracellular organic matter. Three key innovations are highlighted: (1) development of a multi-mechanism synergistic system that overcomes traditional technical limitations through moderate pre-oxidation strategies for precise algae control; (2) first systematic elucidation of the bridging role of sonoporation in ultrasound–chemical synergy; (3) decipherment of interface-targeted regulation mechanisms that enhance oxidation efficiency. Collectively, these advances establish an engineerable new paradigm characterized by high efficiency, operational stability, and minimized ecological risks. Full article

Kleptoplastidy is a nutrition mode in which cells of protists and some multicellular organisms acquire, maintain, and exploit chloroplasts of prey algae cells as photosynthesis reactors. It is an important aspect of the mixotrophic feeding strategy, which plays a role in the formation of harmful algae blooms (HABs). We developed a new mathematical model, in which kleptoplastidy is regarded as a mechanism of enhancing mixotrophy of protists. The model is constructed using three thought (theoretical) experiments and the concept of biological time. We propose to measure the contribution of kleptoplastidy to mixotrophy using a new ecological indicator: the kleptoplastidy index. This index is a function of two dimensionless variables, one representing the ratio of photosynthetic production of acquired chloroplasts versus native chloroplasts, and the other representing the balance between autotrophic and heterotrophic feeding modes. The index is tested by data for the globally distributed, bloom-forming potentially toxic mixotrophic dinoflagellates Prorocentrum cordatum. The model supports our hypothesis that kleptoplastidy can increase the division rate of algae significantly (by 40%), thus boosting their population growth and promoting blooms. The proposed model can contribute to advancements in ecological modeling aimed at forecasting and management of HABs that deteriorate marine coastal environments worldwide. Full article

Algal and cyanobacterial blooms are anticipated to increase in frequency, duration, and geographic extent as a result of environmental changes, including climate warming, elevated nutrient concentrations, and increased runoff in both marine and freshwater ecosystems. The eutrophication of aquatic environments represents a substantial threat to human health. As eutrophication progresses, airborne algae and cyanobacteria, particularly harmful genera originating from aquatic environments, are released into the atmosphere and may pose potential risks to human health. Furthermore, respiratory distress has been documented in individuals exposed to aerosols containing harmful algal bloom (HAB) toxins. This review investigates the generation of aerosolised harmful algal blooms, their responses to environmental factors, and their associated health risks. Evidence suggests that airborne algae, cyanobacteria, and their toxins are widespread. When these are aerosolised into micrometre-sized particles, they become susceptible to atmospheric processing, which may degrade the HAB toxins and produce byproducts with differing potencies compared to the parent compounds. Inhalation of aerosolised HAB toxins, especially when combined with co-morbid factors such as exposure to air pollutants, could present a significant health risk to a considerable proportion of the global population. A more comprehensive understanding of the chemical transformations of these toxins and the composition of harmful algal and cyanobacterial communities can improve public safety. Full article

The Arabian Gulf, bordered by major energy-producing nations, harbors diverse microalgal communities with strong potential for the bioremediation of environmental pollutants, particularly petroleum hydrocarbons. This review evaluates two key microalgal groups—micro-green algae and dinoflagellates—highlighting their distinct physiological traits and ecological roles in pollution mitigation. Dinoflagellates, including Prorocentrum and Protoperidinium, have demonstrated hydrocarbon-degrading abilities but are frequently linked to harmful algal blooms (HABs), marine toxins, and bioluminescence, posing ecological and health risks. The toxins produced by these algae can be hemolytic or neurotoxic and include compounds such as azaspiracids, brevetoxins, ciguatoxins, okadaic acid, saxitoxins, and yessotoxins. In contrast, micro-green algae such as Oedogonium and Pandorina are generally non-toxic, seldom associated with HABs, and typically found in clean freshwater and brackish environments. Some species, like Chlorogonium, indicate pollution tolerance, while Dunaliella has shown promise in remediating contaminated seawater. Both groups exhibit unique enzymatic pathways and metabolic mechanisms for degrading hydrocarbons and remediating heavy metals. Due to their respective phycoremediation capacities and environmental adaptability, these algae offer sustainable, nature-based solutions for pollution control in coastal, estuarine, and inland freshwater systems, particularly in mainland Qatar. This review compares their remediation efficacy, ecological impacts, and practical limitations to support the selection of effective algal candidates for eco-friendly strategies targeting petroleum-contaminated marine environments. Full article

Harmful algal blooms (HABs) are one of the major environmental concerns, as they have various negative effects on public and environmental health, recreational services, and economics. HAB modeling is challenging due to inconsistent and insufficient data, as well as the nonlinear nature of algae formation data. However, it is crucial for attaining sustainable development goals related to clean water and sanitation. From this point of view, we employed the sparse identification nonlinear dynamics (SINDy) technique to model microcystin, an algal toxin, utilizing dissolved oxygen as a water quality metric and evaporation as a meteorological parameter. SINDy is a novel approach that combines a sparse regression and machine learning method to reconstruct the analytical representation of a dynamical system. The model results indicate that MAPE values of approximately 2% were achieved in three out of four lakes, while the MAPE value of the remaining lake is 11%. Moreover, a model-driven and web-based interactive tool was created to develop environmental education, raise public awareness on HAB events, and produce more effective solutions to HAB problems through what-if scenarios. This interactive and user-friendly web platform allows tracking the status of HABs in lakes and observing the impact of specific parameters on harmful algae formation. Full article