Multivariable integrated risk assessment for cyanobacterial blooms in eutrophic lakes and its spatiotemporal characteristics

Dynamics of Phytoplankton Communities and Their Characteristics of Realized Niches in a Drinking Reservoir

Realized niches are crucial in defining their optimal conditions and serve as valuable tools for predicting the phytoplankton dynamics in relation to eutrophication, climate change, and harmful phytoplankton blooms. However, previous studies have largely focused on marine ecosystems, leaving freshwater systems less studied. In this study, we elucidate the patterns of phytoplankton community succession based on their niche characteristics in the Shanmei (SM) Reservoir, a drinking water source in Quanzhou, Fujian Province. Additionally, variations in phytoplankton were mainly explained by their realized niche. In the SM Reservoir, total chlorophyll a concentrations ranged from 252 to 24,008 ng/L. The phytoplankton community was dominated by Chlorophyta and Cyanophyta, which consisted mostly of Pseudanabaena and Microcystis, especially in summer. This dominance was attributed to their wide niche breadth and high mean niche for temperature, nitrogen, and dissolved reactive phosphorus. On the other hand, Cryptophyta and Bacillariophyta reached higher concentrations in autumn and winter, linked to their low mean temperature niches. Under the multiple pressures of climate change and anthropogenic activities, Chlorophyta and Cyanophyta are likely to thrive in environments characterized by rising water temperatures and elevated nutrient concentrations. This is particularly true for buoyant cyanobacteria such as Pseudanabaena, which are well-suited to the stratified water layers induced by higher water temperatures. Therefore, incorporating niche characteristics of harmful bloom-forming species would contribute to the prevention and management of harmful phytoplankton blooms, ultimately improving the safety of drinking water.

Succession of Particle-Attached and Free-Living Microbial Communities in Response to the Degradation of Algal Organic Matter in Lake Taihu, China

Decomposition of Cyanobacterial blooms frequently occurs in Lake Taihu, releasing various fractions of algal organic matter into the water through cell lysis. These fractions influence the production and consumption of dissolved organic matter, nutrient dynamics, and bacterial succession in the lake. However, the interactions between free-living and particle-attached bacterial communities with different algal organic matter fractions remain poorly understood. Herein, we investigated the effects of two distinct algal organic matter fractions, obtained through a fractionation procedure simulating cyanobacterial bloom collapse, on freshwater bacterial communities. The degradation of both fractions resulted in stage-specific changes in the chemical properties of lake water, which were divided into two distinct stages (labeled Stage I and Stage II). Flavobacteriaceae was dominant in Stage I, whereas Methylophilaceae dominated Stage II. Long-term ecological observations indicated that particle-attached bacteria responded more sensitively to different algal organic matter fractions than free-living bacteria. Compared to the degradation of algal-derived filtrate, the breakdown of algal residual exudative organic matter led to a more complex free-living bacterial community network. These findings provide new insights into the capacity of free-living and particle-attached bacterial communities to utilize different algal organic matter fractions and highlight their roles in aquatic ecosystems during the post-bloom stage.

Spatiotemporal Variation Assessment and Improved Prediction Of Cyanobacteria Blooms in Lakes Using Improved Machine Learning Model Based on Multivariate Data

Cyanobacterial blooms in shallow lakes pose a significant threat to aquatic ecosystems and public health worldwide, highlighting the urgent need for advanced predictive methodologies. As impounded lakes along the Eastern Route of the South-to-North Water Diversion Project, Lakes Hongze and Luoma play a key role in water resource management, making the prediction of cyanobacterial blooms in these lakes particularly important. To address this, satellite remote sensing data were utilized to analyze the spatiotemporal dynamics of cyanobacterial blooms in these lakes. Subsequently, a precise machine learning model, integrating the Projection Pursuit Model and Random Forest (PP-RF) algorithms, was developed to predict the extent of cyanobacterial blooms, considering a range of influencing factors, including physical, chemical, climatic, and hydrologic variables. The findings indicated pronounced seasonal fluctuations in cyanobacterial blooms, with higher levels in summer than in other seasons. Key determinants for cyanobacterial blooms prediction included solar radiation, temperature and total nitrogen for Lake Hongze, while for Lake Luoma, significant predictors were identified as temperature, water temperature, and solar radiation. Compared with traditional data preprocessing methods, PP-RF model has advantages in addressing multicollinearity. This study provides a feasible method for predicting cyanobacterial blooms in impounded lakes within inter-basin water transfer projects. By inputting region-specific data, this model could be applied broadly, contributing to against the adverse effects of cyanobacterial blooms and provide scientific guidance for the protection and management of aquatic ecosystems.

From winter dormancy to spring bloom: Regulatory mechanisms in Microcystis aeruginosa post-overwintering recovery

Cyanobacterial blooms pose a significant environmental threat in freshwater ecosystems. These cyanobacteria exhibit resilience to cold and dark conditions during winter and flourish as temperature rise in warmer seasons. However, there is a limited understanding of the dynamic growth recovery process and regulatory signaling mechanisms in cyanobacteria after overwintering. In this study, we employed Microcystis aeruginosa (M. aeruginosa) as a model to simulate its growth recovery when subjected to increasing temperature after overwintering under low temperature (4 °C) and dark conditions. We investigated changes in cell growth, microcystin levels, and signaling pathways throughout this recovery phase. Our results indicated that compared to the non-overwintering treatment (T1), the overwintered treatment (T2) experienced a 55.6 % decrease in algae density and a significant reduction in microcystin-LR (MC-LR) levels within the 15-20 °C temperature range (p < 0.05). Overwintering suppressed photosynthetic efficiency during the recovery phase of M. aeruginosa, activated the antioxidant system, and impaired cellular ultrastructure, making algal cells more vulnerable to death. At the transcriptional level, overwintering down-regulated pathways such as photosynthesis, ribosome, the Calvin cycle, and oxidative phosphorylation, hindering the growth and metabolic capacity of M. aeruginosa. In conclusion, this study highlights the inhibitory impacts of overwintering on growth and metabolism of cyanobacteria during the recovery process. It provides insights into the mechanistic foundations of seasonal cyanobacterial blooms and the crucial role of signaling regulation in these processes.

Enhanced pollution control using sediment microbial fuel cells for ecological remediation

Sediment Microbial Fuel Cell (SMFC) technology is an innovative approach to facilitate the degradation of sedimentary organic matter by electroactive microorganisms, transforming chemical energy into electrical energy and modulating the redox potential at the sediment-water interface, consequently controlling the release of endogenous pollutants. The synergistic effects of various environmental factors and intrinsic conditions can significantly impact SMFC performance. This review provides a comprehensive overview of SMFC development in research and application for water environment treatment and ecological remediation, a perspective rarely explored in previous reviews. It discusses optimization strategies for SMFC implementation, emphasizing advancements in novel or cost-effective electrode materials, the dynamics of microbial communities, and the control of typical pollutants. The review suggests a virtuous cycle path for SMFC development, highlighting future research needs, including integrating cross-disciplinary approaches like artificial intelligence, genomics, and mathematical modeling, to enhance the deployment of SMFC in real-world environmental remediation.

Monitoring, simulation and early warning of cyanobacterial harmful algal blooms: An upgraded framework for eutrophic lakes

Cyanobacterial Harmful Algal Bloom (CyanoHAB) is a global aquatic environmental issue, posing considerable eco-environmental challenges in freshwater lakes. Comprehensive monitoring and accurate prediction of CyanoHABs are essential for their scientific management. Nevertheless, traditional satellite-based monitoring and process-oriented prediction methods of CyanoHABs failed to satisfy this demand due to the limited spatiotemporal resolutions of both monitoring data and prediction results. To address this issue, this paper proposes an upgraded framework for comprehensive monitoring and accurate prediction of CyanoHABs. A collaborative CyanoHAB monitoring network was firstly constructed by integrating space, aerial, and ground-based monitoring means. As a result, CyanoHAB conditions were assessed frequently covering the entire lake, its key areas, and core positions. Furthermore, by overcoming technical limitations associated with high-precision simulation of the growth-drift-accumulation process of CyanoHABs, such as the unclear drifting process of CyanoHABs and the mechanism of its coastal accumulation, the multi-scale CyanoHAB prediction was realized interconnecting the entire lake and its nearshore areas. The implemented framework has been applied in Lake Chaohu for over three years. It provided high-frequency and high-spatial-resolution CyanoHAB monitoring, as well as its multi-scale and accurate simulation. The application of this framework in Lake Chaohu had significantly improved the accuracies of CyanoHAB monitoring, simulation, and early warning. This advancement holds significant scientific value and offers potential for CyanoHAB prevention and control in eutrophic lakes.

Potential health risk assessment of cyanobacteria across global lakes

Cyanobacterial blooms pose environmental and health risks due to their production of toxic secondary metabolites. While current methods for assessing these risks have focused primarily on bloom frequency and intensity, the lack of comprehensive and comparable data on cyanotoxins makes it challenging to rigorously evaluate these health risks. In this study, we examined 750 metagenomic data sets collected from 103 lakes worldwide. Our analysis unveiled the diverse distributions of cyanobacterial communities and the genes responsible for cyanotoxin production across the globe. Our approach involved the integration of cyanobacterial biomass, the biosynthetic potential of cyanotoxin, and the potential effects of these toxins to establish potential cyanobacterial health risks. Our findings revealed that nearly half of the lakes assessed posed medium to high health risks associated with cyanobacteria. The regions of greatest concern were East Asia and South Asia, particularly in developing countries experiencing rapid industrialization and urbanization. Using machine learning techniques, we mapped potential cyanobacterial health risks in lakes worldwide. The model results revealed a positive correlation between potential cyanobacterial health risks and factors such as temperature, N2O emissions, and the human influence index. These findings underscore the influence of these variables on the proliferation of cyanobacterial blooms and associated risks. By introducing a novel quantitative method for monitoring potential cyanobacterial health risks on a global scale, our study contributes to the assessment and management of one of the most pressing threats to both aquatic ecosystems and human health.

IMPORTANCE

Our research introduces a novel and comprehensive approach to potential cyanobacterial health risk assessment, offering insights into risk from a toxicity perspective. The distinct geographical variations in cyanobacterial communities coupled with the intricate interplay of environmental factors underscore the complexity of managing cyanobacterial blooms at a global scale. Our systematic and targeted cyanobacterial surveillance enables a worldwide assessment of cyanobacteria-based potential health risks, providing an early warning system.

Cyanobacterial blooms management: A simulation-based optimization method

Controlling cyanobacterial blooms is not only an engineering and technical issue but also an optimization problem in environment management. Under budget constraints, a novel simulation-based optimization model for cyanobacterial control is constructed in this study. The simulation model is used for simulating cyanobacteria growth and diffusion processes. The optimization model is utilized to determine the optimal search and treatment path. Through the interactive coupling of simulation modeling and resource allocation optimization, this research provides decision-makers with new operational guidelines for cyanobacterial control. Our test results demonstrate that the initial invasion frequency has a greater economic impact than invasion abundance. The nearby cells to the initial invasion are affected first, and then the influence radiates outward in a diffusion pattern. Using a slow search speed and a treatment frequency of every 10 days can achieve the lowest possible economic losses in most test scenarios. Moreover, we also find that the optimal search and treatment paths revolve around the initial invasion location. This study is a typical interdisciplinary research, which can assist water resource managers in making more accurate decisions regarding cyanobacteria removal paths, removal frequencies, and treatment speeds.

Microplastics enhanced the allelopathy of pyrogallol on toxic Microcystis with additional risks: Microcystins release and greenhouse gases emissions

Cyanobacteria blooms (CBs) caused by eutrophication pose a global concern, especially Microcystis aeruginosa (M. aeruginosa), which could release harmful microcystins (MCs). The impact of microplastics (MPs) on allelopathy in freshwater environments is not well understood. This study examined the joint effect of adding polystyrene (PS-MPs) as representative MPs and two concentrations (2 and 8 mg/L) of pyrogallol (PYR) on the allelopathy of M. aeruginosa. The results showed that the addition of PS-MPs intensified the inhibitory effect of 8 mg/L PYR on the growth and photosynthesis of M. aeruginosa. After a 7-day incubation period, the cell density decreased to 69.7 %, and the chl-a content decreased to 48 % compared to the condition without PS-MPs (p < 0.05). Although the growth and photosynthesis of toxic Microcystis decreased with the addition of PS-MPs, the addition of PS-MPs significantly resulted in a 3.49-fold increase in intracellular MCs and a 1.10-fold increase in extracellular MCs (p < 0.05). Additionally, the emission rates of greenhouse gases (GHGs) (carbon dioxide, nitrous oxide and methane) increased by 2.66, 2.23 and 2.17-fold, respectively (p < 0.05). In addition, transcriptomic analysis showed that the addition of PS-MPs led to the dysregulation of gene expression related to DNA synthesis, membrane function, enzyme activity, stimulus detection, MCs release and GHGs emissions in M. aeruginosa. PYR and PS-MPs triggered ROS-induced membrane damage and disrupted photosynthesis in algae, leading to increased MCs and GHG emissions. PS-MPs accumulation exacerbated this issue by impeding light absorption and membrane function, further heightening the release of MCs and GHGs emissions. Therefore, PS-MPs exhibited a synergistic effect with PYR in inhibiting the growth and photosynthesis of M. aeruginosa, resulting in additional risks such as MCs release and GHGs emissions. These results provide valuable insights for the ecological risk assessment and control of algae bloom in freshwater ecosystems.

Functional trait-based phytoplankton biomass and assemblage analyses in the pre-growing season for comprehensive algal bloom risk assessment

Algal bloom (AB) risk assessment is critical for maintaining ecosystem health and human sustainability. Previous AB risk assessments have focused on the potential occurrence of ABs and related factors in the growing season, whereas their hazards, especially in the pre-growing season, have attracted less attention. Here, we performed a comprehensive AB risk assessment, including water trophic levels, phytoplankton biomass, functional trait-based assemblages, and related environmental factors, in the pre-growing season in Dongting Lake, China. Although mesotrophic water and low phytoplankton biomass suggested low AB potential, toxic taxa, which constituted 13.28% of the phytoplankton biomass, indicated non-negligible AB hazards. NH4+ and water temperature were key factors affecting phytoplankton motility and toxicity. Our study establishes a new paradigm for quantitative AB risk assessment, including both potential AB occurrence and hazards. We emphasize the importance of phytoplankton functional traits for early AB warning and NH4+ reduction for AB control in the pre-growing season.

Integrated physio-biochemical and transcriptomic analysis reveals the joint toxicity mechanisms of two typical antidepressants fluoxetine and sertraline on Microcystis aeruginosa

Selective serotonin reuptake inhibitor (SSRI) antidepressants are of increasing concern worldwide due to their ubiquitous occurrence and detrimental effects on aquatic organisms. However, little is known regarding their effects on the dominant bloom-forming cyanobacterium, Microcystis aeruginosa. Here, we investigated the individual and joint effects of two typical SSRIs fluoxetine (FLX) and sertraline (SER) on M. aeruginosa at physio-biochemical and molecular levels. Results showed that FLX and SER had strong growth inhibitory effects on M. aeruginosa with the 96-h median effect concentrations (EC50s) of 362 and 225 μg/L, respectively. Besides, the mixtures showed an additive effect on microalgal growth. Meanwhile, both individual SSRIs and their mixtures can inhibit photosynthetic pigment synthesis, cause oxidative damage, destroy cell membrane, and promote microcystin-leucine-arginine (MC-LR) synthesis and release. Moreover, the mixtures enhanced the damage to photosynthesis, antioxidant system, and cell membrane and facilitated MC-LR synthesis and release compared to individuals. Furthermore, transcriptomic analysis revealed that the dysregulation of the key genes related to transport, photosystem, protein synthesis, and non-ribosomal peptide structures was the fundamental molecular mechanism underlying the physio-biochemical responses of M. aeruginosa. These findings provide a better understanding of the toxicity mechanisms of SSRIs to microalgae and their risks to aquatic ecosystems.

Prediction of hydrological and water quality data based on granular-ball rough set and k-nearest neighbor analysis

Hydrological and water quality datasets usually encompass a large number of characteristic variables, but not all of these significantly influence analytical outcomes. Therefore, by wisely selecting feature variables with rich information content and removing redundant features, it not only can the analysis efficiency be improved, but the model complexity can also be simplified. This paper considers introducing the granular-ball rough set algorithm for feature variable selection and combining it with the k-nearest neighbor method and back propagation network to analyze hydrological and water quality data, thus promoting overall and fused inspection. The results of hydrological water quality data analysis show that the proposed method produces better results compared to using a standalone k-nearest neighbor regressor.

The effects of wind-wave disturbances on sediment resuspension and phosphate release in Lake Chao

As a typical shallow lake with a wind-generated flow, the resuspension state of sediment and phosphorus release under wind field disturbance plays an important role in controlling lake eutrophication in Lake Chao. In this study, we proposed a combination of experimental analysis of dynamic disturbances, wind-wave disturbance shear stress calculation, and model simulation (experimental-calculative-modeling) to quantitatively investigate the effects of wind-wave disturbances on the resuspension state of Lake Chao bottom sediment and phosphorus release and distribution. The results showed that the release rate of phosphorus from the Lake Chao bottom sediment was affected by the wind field and bottom sediment content, which varied significantly spatially and showed some difference between different seasons. Under the condition of sufficient water body disturbance, the substrate in the Western Lake area of Lake Chao mainly adsorbed phosphate from the water body, while the substrate in the Central Lake area and the Eastern Lake area adsorbed phosphate along with the release. The magnitude of the phosphorus release rate due to sediment resuspension was mainly affected by wind speed, and the distribution of phosphorus content was influenced by the circulation generated by different dominant wind directions. The wind-wave disturbances have a significant effect on the spatial and temporal distribution of phosphorus in Lake Chao, and the proposed experimental-calculative-modeling ensemble can provide relevant technical support for the study of water pollution control strategies and comprehensive remediation and management of Lake Chao.

Temporal characteristics and trends of nitrogen loadings in lake Taihu, China and its influencing mechanism at multiple timescales

Climate warming impact on excessive nitrogen (N) load in sediment favours cyanobacterial blooms in eutrophic waters. The nitrate (NO3--N) and ammonium (NH4+-N) are two forms of N loads that contribute to algae blooms. However, little attention is paid to the impact of environmental factors on N loads variations at different time scales. This paper used a well-calibrated and validated EFDC model to investigate the temporal patterns and trends of ammonium and nitrate from June 2016 to June 2017. This paper presented the relationship and effects between these variations and environmental factors using data from satellite and reanalysis-based observations obtained for six meteorological parameters. The relationship and effects between these variations and environmental factors were also examined at different timescales (i.e., daily, monthly and seasonal scales). Model calibration results indicated that measured values reasonably matched simulated values. The validation results revealed that relative error (RE) values were within an acceptable range. The REs of ammonium at East Taihu (S12) and Xu Lake (S23) sampling sites were 55.83% and 57.61%, while that of nitrate was 24.37% (S12) and 41.08%, respectively. The daily analysis of NH4+-N and NO3--N variations was 7.318 ± 3.876 (g/m2/day) and 0.0275 ± 0.222 (g/m2/day), respectively. The monthly analysis showed NH4+-N and NO3-N range from 2.04 to 12.04 (g/m2/day) and 0.0008 to 0.064 (g/m2/day), respectively. The magnitude NH4+-N and NO3--N varied and showed distinct inter-monthly variations. , The relationship between sediment fluxes and meteorological parameters showed the magnitude of correlation coefficient (r) and strength of correlation varied significantly. At daily scales, the relationship of NH4+-N and NO3--N had a significant positive correlation with all meteorological parameters. At monthly, the correlation coefficient (r) of NH4+-N and NO3-N were heterogenous. At daily and monthly scales, air temperature and wind speed are the main drivers affecting sediment N loads' dynamics; however, the influence of relative humidity, precipitation, and evaporation on N loads are smaller. The study demonstrates the contribution of meteorological conditions to the magnitude and timing of N loadings variability in water bodies. The findings provide more insight into lake ecosystem protection and environmental remediation.

Solar/periodate-triggered rapid inactivation of Microcystis aeruginosa by interrupting the Calvin-Benson cycle

Frequent outbreak of cyanobacteria is a serious problem for drinking water treatment. The microcystins released from Microcystis aeruginosa (M. aeruginosa) could cause irreversible damage to human health. Catalyst-free solar/periodate (PI) system has recently presented great potential for bacterial inactivation, whereas the application potential and underlying mechanisms of the effective M. aeruginosa control remain unclear. Our work delineated the key role of ROS that inactivating/harmless disposing M. aeruginosa in the simulated sunlight (SSL)/PI system. Singlet oxygen may specifically cause DNA damage but maintain membrane integrity, preventing the risk of microcystins leakage. The SSL/PI 300 μM system could also effectively inhibit M. aeruginosa recovery for >7 days and completely degrade microcystin-LR (50.0 μg/L) within 30 min. Non-targeted metabolomic analysis suggested that the SSL/PI system inactivated M. aeruginosa mainly by interrupting the Calvin-Benson cycle, which damaged the metabolic flux of glycolysis and its downstream pathways such as the oxidative PPP pathway and glutathione metabolism. Furthermore, the activated PI system exhibited an even better algal inhibition under natural sunlight irradiation, evidenced by the seriously damaged cell membrane of M. aeruginosa. Overall, this study reported the comprehensive mechanisms of algal control and application potentials of solar/PI systems. The findings facilitated the development of emerging algicidal technology and its application in controlling environmental harmful algae.

Improving lake chlorophyll-a interpreting accuracy by combining spectral and texture features of remote sensing

Cyanobacterial blooms in lakes fueled by increasing eutrophication have garnered global attention, and high-precision remote sensing retrieval of chlorophyll-a (Chla) is essential for monitoring eutrophication. Previous studies have focused on the spectral features extracted from remote sensing images and their relationship with chlorophyll-a concentrations in water bodies, ignoring the texture features in remote sensing images which is beneficial to improve interpreting accuracy. This study explores the texture features in remote-sensing images. It proposes a retrieval method for estimating lake Chla concentration by combining spectral and texture features of remote sensing images. Remote sensing images from Landsat 5 TM and 8 OLI were used to extract spectral bands combination. The gray-level co-occurrence matrix (GLCM) of remote sensing images was used to obtain a total of 8 texture features; then, three texture indices were calculated using texture features. Finally, a random forest regression was used to establish a retrieval model of in situ Chla concentration from texture and spectral index. Results showed that texture features are significantly correlated with lake Chla concentration, and they can reflect the temporal and spatial distribution change of Chla. The retrieval model combining spectral and texture indices performs better (MAE = 15.22 μg·L-1, bias = 9.69%, MAPE = 47.09%) than the model without texture features (MAE = 15.76 μg·L-1, bias = 13.58%, MAPE = 49.44%). The proposed model performance varies in different Chla concentration ranges and is excellent in predicting higher concentrations. This study evaluates the potential of incorporating texture features of remote sensing images in lake water quality estimation and provides a novel remote sensing method to better estimate lake Chla concentration.