Search Results (80)

Fibrocapsa japonica (Raphidophyceae) is a cosmopolitan species frequently associated with harmful algal blooms (HABs) and fish mortality events, representing a potential threat to aquaculture and coastal ecosystems. This study provides the first comprehensive morphological, phylogenetic, pigmentary, and toxicological characterization of F. japonica strains isolated from Argentina. Light and transmission electron microscopy confirmed key diagnostic features of the species, including anterior flagella and the conspicuous group of mucocyst in the posterior region. Phylogenetic analysis based on the LSU rDNA D1–D2 region revealed monophyletic relationships with strains from geographically distant regions. Pigment analysis by HPLC identified chlorophyll-a (62.3 pg cell⁻¹) and fucoxanthin (38.4 pg cell⁻¹) as the main dominant pigments. Cytotoxicity assays using RTgill-W1 cells exposed for 2 h to culture supernatants and intracellular extracts showed strain-specific effects. The most toxic strain (LPCc049) reduced gill cell viability down to 53% in the supernatant exposure, while LC₅₀ values ranged from 1.6 × 10⁴ to 4.7 × 10⁵ cells mL⁻¹, depending directly on the strain and treatment type. No brevetoxins (PbTx-1, -2, -3, -6, -7, -8, -9, -10, BTX-B1 and BTX-B2) were detected by LC–MS/MS, suggesting that the cytotoxicity may be linked to the production of reactive oxygen species (ROS), polyunsaturated fatty acids (PUFAs), or hemolytic compounds, as previously hypothesized in the literature. These findings offer novel insights into the toxic potential of F. japonica in South America and underscore the need for further research to elucidate the mechanisms underlying its ichthyotoxic effect. Full article

The western region of Lake Erie has been experiencing severe water-quality issues, mainly through the infestation of algal blooms, highlighting the urgent need for action. Understanding the drivers and the intricacies associated with algal bloom phenomena is important to develop effective water-quality remediation strategies. In this study, the influences of multiple bloom drivers were explored, together with Harmonized Landsat Sentinel-2 (HLS) images, using the datasets collected in Western Lake Erie from 2013 to 2022. Bloom drivers included a group of physicochemical and meteorological variables, and Chlorophyll-a (Chl-a) served as a proxy for algal blooms. Various combinations of these datasets were used as predictor variables for three machine learning models, including Support Vector Regression (SVR), Extreme Gradient Boosting (XGB), and Random Forest (RF). Each model is complemented with the SHapley Additive exPlanations (SHAP) model to understand the role of predictor variables in Chl-a estimation. A combination of physicochemical variables and optical spectral bands yielded the highest model performance (R² up to 0.76, RMSE as low as 8.04 µg/L). The models using only meteorological data and spectral bands performed poorly (R² < 0.40), indicating the limited standalone predictive power of meteorological variables. While satellite-only models achieved moderate performance (R² up to 0.48), they could still be useful for preliminary monitoring where field data are unavailable. Furthermore, all 20 variables did not substantially improve model performance over models with only spectral and physicochemical inputs. While SVR achieved the highest R² in individual runs, XGB provided the most stable and consistently strong performance across input configurations, which could be an important consideration for operational use. These findings are highly relevant for harmful algal bloom (HAB) monitoring, where Chl-a serves as a critical proxy. By clarifying the contribution of diverse variables to Chl-a prediction and identifying robust modeling approaches, this study provides actionable insights to support data-driven management decisions aimed at mitigating HAB impacts in freshwater systems. Full article

Numerous remediation strategies exist for cyanobacterial harmful algal blooms (cyanoHABs); however, most are limited by challenges of scalability and adverse off-target effects on the surrounding ecosystem. Germicidal ultraviolet light (UV-C) has emerged as a promising method for suppressing cyanoHABs in a sustainable, chemical-free manner that is both scalable and results in limited off-target ecological effects in the surrounding area. In this study, the US Army Engineer Research and Development Center’s (ERDC)’s CyanoSTUN^TM (Cyanobacterial Suppression Through Ultraviolet-Light-C Neutralization) vessel was deployed to a cyanoHAB as part of a field trial to determine whether UV-C could effectively suppress cellular growth, degrade associated cyanotoxins, and inhibit harmful phytoplankton species more readily than beneficial species without the addition of chemicals. The cyanoHAB exhibited an average cyanobacteria abundance of 3.75 × 10⁵ cells/mL (n = 5, SD = 6.76 × 10⁴ cells/mL) and average total microcystin concentration of 3.5 µg/L (n = 5; SD = 0.24 µg/L). Pre- and post-treatment samples were collected and re-grown for 9 days in the laboratory to observe differences in microcystin, chlorophyll a, and phycocyanin concentrations, optical density, cell density, and community composition. The results of the field trial showed that the CyanoSTUN UV-C treatment effectively suppressed the growth of the cyanobacteria community for approximately two days at the three tested UV-C doses. The CyanoSTUN UV-C treatment also demonstrated a sustained, dose-dependent effect on microcystin concentration; the average reduction in microcystin concentration for 15, 30, and 45 mJ/cm² treatment doses was 31.6% (n = 10, SD = 20.1%; 1.3 µg/L reduced), 45.7% (n = 10, SD = 10.8%; 1.9 µg/L reduced), and 49.9% (n = 10, SD = 8.2%; 1.7 µg/L reduced), respectively, over the 9-day regrowth period. Non-cyanobacteria were too scarce in this CyanoHAB to conclude whether the CyanoSTUN UV-C inhibits harmful phytoplankton species more readily than beneficial species. Further field studies with the CyanoSTUN^TM are required to validate performance under more severe cyanoHAB conditions, however the results reported herein from the first field trial with the CyanoSTUN^TM suggest that this treatment method may offer water managers confronted with a CyanoHAB the ability to rapidly and safely pause a bloom for multiple days and reduce the risks posed by its associated cyanotoxins without adding chemicals. Full article

Allelochemicals are recognized as promising algaecides due to their environmental safety. Para-tert-butylcatechol (TBC) and L-lysine exhibit significant potential in suppressing harmful algal blooms (HABs); however, their combined effects and algae inhibition mechanisms remain unelucidated. Therefore, this study systematically investigated the growth inhibition of Microcystis aeruginosa by TBC and L-lysine individually and in combination, while simultaneously examining their combined effects on algal growth, cell membrane integrity, photosynthetic activity, antioxidant responses, and microcystin production. The results revealed a significant interactive effect between TBC (0.04 mg/L) and L-lysine (1 mg/L), achieving over 90% growth inhibition within 96 h. The combined treatment significantly inhibited M. aeruginosa growth through impaired photosynthetic efficiency and elevated oxidative stress. Compared to the control group, the treatment group exhibited a continuous decline in chlorophyll-a content, phycobiliprotein levels, F_v/F_m, YII, α, and rETR_max, while phosphoenolpyruvate carboxylase (PEPC) activity decreased by 96.48% by day 8. And antioxidant enzymes, including superoxide dismutase (SOD) and reduced glutathione (GSH), showed a progressive increase in activity. In addition, the structure and integrity of the cell membrane of M. aeruginosa were damaged after treatment, and the conductivity of the treatment groups increased continuously from 2.32 to 4.63 μs/cm. In addition, under combined treatment, intra- and extracellular microcystin levels initially increased (peaking at day 2) but sharply declined thereafter, becoming significantly lower than controls by day 8. These findings highlight the potential of combining TBC and L-lysine as an eco-friendly and cost-effective strategy for mitigating M. aeruginosa-dominated harmful algal blooms. Full article

Harmful algal blooms (HABs), driven by environmental pollution, pose significant threats to water quality, public health, and aquatic ecosystems. This study enhances the prediction of HABs in Lake Erie, part of the Great Lakes system, by utilizing ensemble machine learning (ML) models coupled with explainable artificial intelligence (XAI) for interpretability. Using water quality data from 2013 to 2020, various physical, chemical, and biological parameters were analyzed to predict chlorophyll-a (Chl-a) concentrations, which are a commonly used indicator of phytoplankton biomass and a proxy for algal blooms. This study employed multiple ensemble ML models, including random forest (RF), deep forest (DF), gradient boosting (GB), and XGBoost, and compared their performance against individual models, such as support vector machine (SVM), decision tree (DT), and multi-layer perceptron (MLP). The findings revealed that the ensemble models, particularly XGBoost and deep forest (DF), achieved superior predictive accuracy, with R² values of 0.8517 and 0.8544, respectively. The application of SHapley Additive exPlanations (SHAPs) provided insights into the relative importance of the input features, identifying the particulate organic nitrogen (PON), particulate organic carbon (POC), and total phosphorus (TP) as the critical factors influencing the Chl-a concentrations. This research demonstrates the effectiveness of ensemble ML models for achieving high predictive accuracy, while the integration of XAI enhances model interpretability. The results support the development of proactive water quality management strategies and highlight the potential of advanced ML techniques for environmental monitoring. Full article

Reliable forecasts of large-scale chlorophyll-a (Chl-a) levels one week ahead in the Murray–Darling Basin are essential for water resources management, as increasing Chl-a levels in water bodies indicate possible harmful algal blooms, a serious threat for freshwater security. A lack of high-resolution data in space and time is a major constraint for delivering early warnings. To address data scarcity, we developed a forecasting model integrating remote sensing data and time-series modelling. Using in situ Chl-a measurements from Murray–Darling Basin water bodies, we locally recalibrated a two-band ratio algorithm, namely the Normalized Difference Chlorophyll Index (NDCI), from Sentinel-2 data to derive Chl-a levels. The recalibrated model significantly improved the accuracy of high Chl-a estimates in our dataset after mitigating data heteroscedasticity. Building on these improved satellite-derived Chl-a estimates, we developed a time-series model for forecasting weekly Chl-a levels including quantification of forecast uncertainty through prediction intervals. The developed model, validated at eight sites for 2021–2022 data, performed well at shorter lead times, showing R² = 0.41 and RMSE = 8.1 μg/L for overall performance at a one-week lead time. The prediction intervals generally aligned well with nominal levels, demonstrating their reliability. This study provides a valuable tool for the water managers/decision-makers to issue early warnings of algal blooms in the Murray–Darling Basin. Full article

Harmful Algal Blooms (HABs), predominantly driven by cyanobacteria, pose significant risks to water quality, public health, and aquatic ecosystems. Lake Erie, particularly its western basin, has been severely impacted by HABs, largely due to nutrient pollution and climatic changes. This study aims to identify key physical, chemical, and biological drivers influencing HABs using a multivariate regression analysis. Water quality data, collected from multiple monitoring stations in Lake Erie from 2013 to 2020, were analyzed to develop predictive models for chlorophyll-a (Chl-a) and total suspended solids (TSS). The correlation analysis revealed that particulate organic nitrogen, turbidity, and particulate organic carbon were the most influential variables for predicting Chl-a and TSS concentrations. Two regression models were developed, achieving high accuracy with R² values of 0.973 for Chl-a and 0.958 for TSS. This study demonstrates the robustness of multivariate regression techniques in identifying significant HAB drivers, providing a framework applicable to other aquatic systems. These findings will contribute to better HAB prediction and management strategies, ultimately helping to protect water resources and public health. Full article

Cyanobacteria harmful blooms (Cyano-HABs) have become a globally critical environmental issue, threatening freshwater ecosystems by degrading water quality and posing risks to human and aquatic life. Chlorophyll-a (Chl-a), a key biomarker of bloom intensity, offers crucial insights into algal bloom dynamics. However, predicting Chl-a concentrations remains challenging due to the complex interactions between various environmental factors. This study utilizes machine learning (ML) models to predict Chl-a concentrations, focusing on Lake Taihu in China, a large eutrophic lake that serves as an example of numerous freshwater lakes suffering from Cyano-HABs. The research leverages nine critical water quality parameters—water temperature, pH, dissolved oxygen, turbidity, electrical conductivity permanganate index, ammonia nitrogen, total phosphorus, and total nitrogen—to develop an ensemble ML model using XGBoost, known for its ability to handle nonlinear relationships and integrate multiple variables. The XGBoost model achieved superior predictive accuracy with an R² value of 0.78 and RMSE of 8.97 mg/m³ on the test set, outperforming traditional models like linear regression, decision trees, multi-layer perceptrons, support vector regression, and random forests. Feature importance analysis identified electrical conductivity, turbidity, and water temperature as the most significant predictors of Chl-a levels. This study further enhances model interpretability through Pearson correlation analysis, which quantifies the relationships between Chl-a concentrations and other water quality factors. Additionally, we employed principal component analysis (PCA), mutual information, Spearman rank correlation coefficients, and SHAP models to analyze feature importance and model interpretability in ML. The model’s robustness was tested across multiple monitoring sites in Lake Taihu, demonstrating its potential for broader application in other eutrophic lakes facing similar environmental challenges. By providing a reliable tool for forecasting Chl-a concentrations, this research contributes to the development of early warning systems that can help mitigate the impacts of Cyano-HABs, aiding in more effective water resource management. Full article

Lake Taihu has highly turbid inland waters with complex optical properties. Due to the bottom effect of submerged aquatic plants in optically shallow waters, currently available phytoplankton chlorophyll-a retrieval algorithms tend to overestimate chlorophyll-a concentrations in the eastern part of Lake Taihu. This overestimation can distort the eutrophication evaluation of the entire lake. This paper identifies submerged and emergent plants, determines the retrieval models for the upwelling (K_u) and downwelling (K_d) irradiance attenuation coefficients, and proposes a phytoplankton chlorophyll-a retrieval model using a water depth optimization-based method to remove the bottom effect. The results show the following: (1) The normalized difference vegetation index (NDVI) method can distinguish the bottom mud (NDVI < −0.46) and submerged aquatic plants (−0.46 ≤ NDVI < 0.52) from the emergent plants (NDVI ≥ 0.52) with 90% accuracy. (2) The downwelling and upwelling irradiance attenuation coefficients are highly correlated with the suspended sediments, and retrieval models for these coefficients in three visible bands with high accuracy are presented. (3) Compared to traditional algorithms without bottom effect removal, the proposed chlorophyll-a concentration estimation algorithm based on the water depth-optimized bottom effect removal method efficiently reduces the bottom effect of the submerged aquatic plants. The root mean square error (RMSE) for the obtained chlorophyll-a concentrations decreases from 45.61 ${\mathsf{\mu }\mathrm{g}·\mathrm{L}}^{-1}$ to 8.69 ${\mathsf{\mu }\mathrm{g}·\mathrm{L}}^{-1}$, and the mean absolute percentage error (MAPE) is reduced from 245.12% to 19.58%. In the validation step, the obtained RMSE of 10.89 ${\mathsf{\mu }\mathrm{g}·\mathrm{L}}^{-1}$ and MAPE of 17.52% are consistent with the proposed algorithm. This research provides a good reference for the determination of chlorophyll-a concentrations in phytoplankton in complex inland water bodies. The findings are potentially useful for the operational monitoring of harmful algal blooms in the future. Full article

Synthetic non-metallic nanoparticles (NMNPs) such as carbon nanotubes (CNTs), carbon nanodots (CNDs), and cell-penetrating peptides (CPPs) have been explored to treat harmful algal blooms. However, their strain-specific algicidal activities have been rarely investigated. Here we determined their acute toxicity to nine freshwater cyanobacterial strains belonging to seven genera, including Microcystis aeruginosa UTEX 2386, M. aeruginosa UTEX 2385, M. aeruginosa LE3, Anabaena cylindrica PCC 7122, Aphanizomenon sp. NZ, Planktothrix agardhii SB 1810, Synechocystis sp. PCC 6803, Lyngbya sp. CCAP 1446/10, and Microcoleus autumnale CAWBG635 ATX. We prepared in-house three batches of CNDs using glucose (CND-G) or chloroform and methanol (CND-C/M) as the substrate and one batch of single-walled CNTs (SWCNTs). We also ordered a commercially synthesized CPP called γ-Zein-CADY. The axenic laboratory culture of each cyanobacterial strain was exposed to an NMNP at two dosage levels (high and low, with high = 2 × low) for 48 h, followed by measurement of five endpoints. The endpoints were optical density (OD) at 680 nm (OD₆₈₀) for chlorophyll-a estimation, OD at 750 nm (OD₇₅₀) for cell density, instantaneous pigment fluorescence emission (FE) after being excited with 450 nm blue light (FE₄₅₀) for chlorophyll-a or 620 nm red light (FE₆₂₀) for phycocyanin, and quantum yield (QY) for photosynthesis efficiency of photosystem II. The results indicate that the acute toxicity was strain-, NMNP type-, dosage-, and endpoint-dependent. The two benthic strains Microcoleus autumnale and Lyngbya sp. were more resistant to NMNP treatment than the other seven free-floating strains. SWCNTs and fraction A14 of CND-G were more toxic than CND-G and CND-C/M. The CPP was the least toxic. The high dose generally caused more severe impairment than the low dose. OD₇₅₀ and OD₆₈₀ were more sensitive than FE₄₅₀ and FE₆₂₀. QY was the least sensitive endpoint. The strain dependence of toxicity suggested the potential application of these NMNPs as a target-specific tool for mitigating harmful cyanobacterial blooms. Full article

Remote sensing (RS) has been widely used to monitor cyanobacterial blooms in inland water bodies. However, the accuracy of RS-based monitoring varies significantly depending on factors such as waterbody type, sensor characteristics, and analytical methods. This study comprehensively evaluates the current capabilities and challenges of RS for cyanobacterial bloom monitoring, with a focus on achievable accuracy. We find that chlorophyll-a (Chl-a) and phycocyanin (PC) are the primary indicators used, with PC demonstrating greater accuracy and stability than Chl-a. Sentinel and Landsat satellites are the most frequently used RS data sources, while hyperspectral images, particularly from unmanned aerial vehicles (UAVs), have shown high accuracy in recent years. In contrast, the Medium-Resolution Imaging Spectrometer (MERIS) and Moderate-Resolution Imaging Spectroradiometer (MODIS) have exhibited lower performance. The choice of analytical methods is also essential for monitoring accuracy, with regression and machine learning models generally outperforming other approaches. Temporal analysis indicates a notable improvement in monitoring accuracy from 2021 to 2023, reflecting advances in RS technology and analytical techniques. Additionally, the findings suggest that a combined approach using Chl-a for large-scale preliminary screening, followed by PC for more precise detection, can enhance monitoring effectiveness. This integrated strategy, along with the careful selection of RS data sources and analytical models, is crucial for improving the accuracy and reliability of cyanobacterial bloom monitoring, ultimately contributing to better water management and public health protection. Full article

Harmful algal blooms (HABs) have emerged as a significant environmental challenge, impacting aquatic ecosystems, drinking water supply systems, and human health due to the combined effects of human activities and climate change. This study investigates the performance of deep learning models, particularly the Transformer model, as there are limited studies exploring its effectiveness in HAB prediction. The chlorophyll-a (Chl-a) concentration, a commonly used indicator of phytoplankton biomass and a proxy for HAB occurrences, is used as the target variable. We consider multiple influencing parameters—including physical, chemical, and biological water quality monitoring data from multiple stations located west of Lake Erie—and employ SHapley Additive exPlanations (SHAP) values as an explainable artificial intelligence (XAI) tool to identify key input features affecting HABs. Our findings highlight the superiority of deep learning models, especially the Transformer, in capturing the complex dynamics of water quality parameters and providing actionable insights for ecological management. The SHAP analysis identifies Particulate Organic Carbon, Particulate Organic Nitrogen, and total phosphorus as critical factors influencing HAB predictions. This study contributes to the development of advanced predictive models for HABs, aiding in early detection and proactive management strategies. Full article

In this study, we build an empirical model to estimate pigments in the South American Lake Villarrica. We use data from Dirección General de Aguas de Chile during the period of 1989–2024 to analyze the behavior of limnological parameters and trophic condition in the lake. Four seasonal linear regression models were developed by us, using a set of water quality variables that explain the values of phycocyanin pigment in Lake Villarrica. In the first case, we related chlorophyll-a (Chl-a) to phycocyanin, expecting to find a direct relationship between both variables, but this was not fulfilled for all seasons of the year. In the second case, in addition to Chl-a, we included water temperature, since this parameter has a great influence on the algal photosynthesis process, and we obtained better results. We discovered a typical seasonal variability given by temperature fluctuations in Lake Villarrica, where in the spring, summer, and autumn seasons, conditions are favorable for algal blooms, while in winter, the natural seasonal conditions do not allow increases in algal productivity. For a third case, we included the turbidity variable along with the variables mentioned above and the statistical performance metrics of the models improved significantly, obtaining R² values of up to 0.90 in the case of the model for the fall season and a mean squared error (MSE) of 0.04 µg/L. In the last case used, we added the variable dissolved organic matter (MOD), and the models showed a slight improvement in their performance. These models may be applicable to other lakes with harmful algal blooms in order to alert the community to the potential toxicity of these events. Full article

Regenerative agriculture increasingly relies on organic soil amendments to improve soil fertility and crop productivity. This study evaluates the effects of dried algae (DA), vermicompost (VC), liquid hydrolyzed fish and seaweed fertilizer (LA), and a control (S0, untreated soil without amendments) on the soil fertility, growth, nutrient uptake, and physiology of sunn hemp (Crotalaria juncea L.), a key cover crop for soil improvement. Treatments were applied at 1 ton/ha (DA), 3 ton/ha (VC), and 8 mL/L (LA). Plants were grown for 10 weeks, during which plant growth, chlorophyll content, and biomass were measured. Soil and plant samples were analyzed for macro- and micronutrients. S0 and DA treatments produced the highest biomass, with S0 showing the highest total carbon and organic matter content. LA-treated soils exhibited elevated phosphorus, potassium, and sodium levels, while DA and S0 shoots had significantly higher sulfur and zinc concentrations. LA treatment notably increased chlorophyll content by the study’s end. Overall, DA demonstrated strong potential as a nutrient-rich organic amendment, while S0 provided a robust baseline for biomass production. VC enriched phosphorus and potassium but resulted in the lowest total biomass. LA promoted shoot growth and chlorophyll content but required root development and sodium management optimization. These findings highlight the need to align the amendment choice with soil characteristics and environmental conditions to optimize crop productivity and soil health in sustainable farming systems. Full article

Cyanobacterial harmful algal blooms (cHABs) are increasing due to eutrophication and climate change, as is irrigation of crops with freshwater contaminated with cHAB toxins. A few studies, mostly in aquatic protists and plants, have investigated the effects of cHAB toxins or cell extracts on various aspects of photosynthesis, with variable effects reported (negative to neutral to positive). We examined the effects of cyanobacterial live cultures and cell extracts (Microcystis aeruginosa or Anabaena flos-aquae) and individual cHAB toxins (anatoxin-a, ANA; beta-methyl-amino-L-alanine, BMAA; lipopolysaccharide, LPS; microcystin-LR, MC-LR) on photosynthesis in intact plants and leaf pieces in corn (Zea mays) and lettuce (Lactuca sativa). In intact plants grown in soil or hydroponically, overall net photosynthesis (P_n), but not Photosystem-II (PSII) electron-transport yield (Φ_PSII), decreased when roots were exposed to cyanobacterial culture (whether with intact cells, cells removed, or cells lysed and removed) or individual toxins in solution (especially ANA, which also decreased rubisco activity); cyanobacterial culture also decreased leaf chlorophyll concentration. In contrast, Φ_PSII decreased in leaf tissue vacuum-infiltrated with cyanobacterial culture or the individual toxins, LPS and MC-LR, though only in illuminated (vs. dark-adapted) leaves, and none of the toxins caused significant decreases in in vitro photosynthesis in thylakoids. Principal component analysis indicated unique overall effects of cyanobacterial culture and each toxin on photosynthesis. Hence, while cHAB toxins consistently impacted plant photosynthesis at ecologically relevant concentrations, the effects varied depending on the toxins and the mode of exposure. Full article