Current status and prospects of algal bloom early warning technologies: A Review

Early warning on the potential harmful algal bloom species in Beibu Gulf of South China Sea under the background of climate change and human activity

Human activity and global climate change increasingly affect marine environments, leading to increases in harmful algal blooms (HABs) caused by phytoplankton. These blooms pose significant threats to public health, tourism, fisheries, and ecosystems. As an important fishing ground and tourist destination, the Beibu Gulf faces growing environmental pressure. This study sought to assess the phytoplankton community structure and status of HABs, with a focus on potential HAB species. Using environmental DNA (eDNA) metabarcoding, summer and winter surveys at both coastal and offshore waters revealed 66 potential HAB species, 23 of which were newly recorded in the Beibu Gulf. The potential HAB species exhibited greater richness and relative abundance in summer than in winter. Offshore areas showed greater diversity, whereas coastal areas showed greater relative abundance. Temperature emerged as the most influential factor shaping phytoplankton composition, and pH was found to play an important role in coastal areas. Nutrients such as silicate and ammonium are critical for the distribution of potential HAB species. Among the potential HAB species, Cyclotella cryptica predominated in coastal areas during winter, whereas Chaetoceros tenuissimus predominated in summer. Some species that caused severe HAB events in other oceanic regions were first detected in this study, including Margalefidinium polykrikoides, Karlodinium veneficum, and Prorocentrum concavum. This study revealed the diversity and complexity of the phytoplankton community in the Beibu Gulf, emphasizing the critical importance of monitoring and early warning of potential HAB species, particularly those driven by human activities and climate change.

Cascade-amplification-based electrochemical detection of Akashiwo sanguinea at pre-outbreak stage

Red tide events caused by Akashiwo sanguinea (A. sanguinea) pose a significant threat to ecosystems. However, studies that offer promising approaches for portable and onsite detection with precise identification of A. sanguinea remain insufficient. In this study, we developed an electrochemical biosensor (E-biosensor) for detecting A. sanguinea combined with cascade amplification strategies, termed TDW-E-biosensor. A predictive relationship was also established to predict algal cell density based on electrochemical signals. The experiment results showed that the TDW-E-biosensor was successfully applied for detecting A. sanguinea at the pre-outbreak stage and demonstrated excellent analytical performance, showing a low limit of detection (LOD) of 0.0676 fM and quantitation (LOQ) of 0.102 fM for the three-electrode system, and a low LOD of 6.873 fg μL-1 and LOQ of 20.460 fg μL-1 for the portable system. The accuracy of the TDW-E-biosensor was validated through comparison with droplet digital PCR (ddPCR) and Bland-Altman analysis, demonstrating a high level of agreement (a mean difference of 0.132 and a standard deviation of 0.184). The reliability of the predictive relationship was evidenced by controlled laboratory experiments and Bland-Altman analysis. The developed TDW-E-biosensor provides an innovative and promising tool for early warning efforts regarding harmful algae.

Algal bloom forecasting leveraging signal processing: A novel perspective from ensemble learning

Accurate forecasting of algal blooms is essential for implementing timely control measures. However, given their inherent complex time-frequency characteristics, capturing the dynamics of algal blooms remains an ongoing challenge in standalone models. Targeting this challenge, this study demonstrates an ensemble framework that combines signal processing with machine learning (ML) techniques to collectively forecast algal dynamics. This method utilizes an efficient signal processing algorithm, namely the compete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), to decompose the highly non-stationary patterns of algal dynamics, while leveraging the complementary strengths of four distinct ML models to optimize the learning of the decomposed components. Our results demonstrated that CEEMDAN can largely improve the forecasting performance of standalone ML models (e.g., long short-term memory), achieving an average increase in validation R2 by 63 %. Moreover, by incorporating the ensemble effects that leverage model-specific strengths, this performance gain was further amplified, resulting in an average increase of 75 % in validation R2 compared to standalone ML models. The developed method, termed CEEMDAN-Hybrid-Ensemble (CHES) model, consistently delivered accurate forecasting of algal dynamics across multiple time resolutions (hourly, daily, and biweekly) in both rivers (River Enborne and The Cut) and lakes (Blelham Tarn and Lake Lillinonah), as suggested by high validation R2 values of 0.955, 0.878, 0.824, and 0.957, respectively. In addition, the CHES model achieved stable multi-step forecasting of algal dynamics with gaps ranging from 1 to 7 steps, as indicated by an average validation R2 of 0.72 ± 0.17 (S.D.) and an average validation root-mean-square-error (RMSE) of 0.32 ± 0.11 RFU. This study highlighted the ensemble effect achieved by integrating signal processing and ML techniques, presenting a novel perspective that enhances forecasting robustness to support the early warning of algal blooms.

U-shaped deep learning networks for algal bloom detection using Sentinel-2 imagery: Exploring model performance and transferability

Inland water sources, such as lakes, support diverse ecosystems and provide essential services to human societies. However, these valuable resources are under increasing pressure from rapid climate changes and pollution resulting from human activities. Combining remote sensing technologies with advanced artificial intelligence algorithms enables frequent monitoring of these ecosystems, timely detection of potential threats, and effective conservation measures. This study evaluated U-shaped deep learning (DL) networks, including U-Net, Residual U-Net (RU-Net), Attention U-Net, Attention Residual U-Net (ARU-Net), and SegNet, for detecting and mapping algal blooms using Sentinel-2 satellite imagery. Multitemporal Sentinel-2 imagery spanning different dates was used to construct robust DL models, with ground truth datasets representing both high- and low-density algae formations. The study emphasized the importance of diverse datasets in addressing the limitations of previous models, particularly in detecting low-density blooms and generalizing across temporal and geographical contexts. The models' transferability was assessed using imagery from different dates and geographical locations, including Lake Burdur, Lake Chaohu, and Lake Turawskie. RU-Net and ARU-Net consistently outperformed other models, achieving exceptional F-scores, such as 99.80 % for Lake Burdur, 97.23 % for Lake Chaohu, and 99.61 % for Lake Turawskie. ARU-Net demonstrated superior generalization capabilities, effectively detecting low-density algae, which is critical for comprehensive environmental assessments. These findings underscored the efficacy and transferability of U-shaped DL networks in accurately detecting algal blooms, offering valuable insights for environmental monitoring and management applications.

Climate Change Drives Long-Term Spatiotemporal Shifts in Red Noctiluca scintillans Blooms Along China's Coast

Climate change has significantly altered the spatiotemporal distribution and phenology of marine organisms, yet the long-term trends and mechanisms driving these changes remain insufficiently understood. In this study, we analysed historical Noctiluca scintillans bloom data from coastal China (1933, 1952, 1981-2023), sea surface temperature (SST) records from the past 40 years, and 509 field samples using Single Molecule Real-Time (SMRT) sequencing (2019-2024). Our results indicate that SST is the primary driver of N. scintillans blooms, exhibiting a nonlinear unimodal correlation. Long-term SST warming has caused a northward shift in bloom locations, aligning with the 21.9°C-22.7°C isotherms, as reflected by the increasing average latitudes of bloom occurrences. Over the past 4 decades, bloom frequency and duration have followed an overall increasing trend, displaying an approximate 10-year cyclical pattern. Ocean warming has also contributed to earlier bloom initiation, extended peak bloom periods and delayed bloom termination, shaping the long-term dynamics of N. scintillans blooms. SMRT sequencing confirmed that local N. scintillans populations persist year-round, serving as latent seed sources that can rapidly bloom when environmental conditions become favourable. These findings provide critical insights into the dynamics of harmful algal blooms in the context of climate change and lay a foundation for future ecological and environmental research.

Eco-Friendly Algicidal Potential of Zanthoxylum bungeanum Leaf Extracts: Extraction Optimization and Impact on Algal Growth

Zanthoxylum bungeanum leaves were regarded as a waste byproduct for a long period of time, yet their functional components presented potential as novel antimicrobial agents. However, their effectiveness in controlling algal blooms remains unexplored. In this study, the inhibition effect of Z. bungeanum leaf extracts on algal blooms was firstly demonstrated, and the flavonoid profiles of the leaf extract were identified using non-targeted metabolomics analysis. Then, response surface methodology was performed for extraction to further evaluate the feasibility of industrial application. Specifically, the effects of extracts on the cell density, photosynthetic efficiency, and antioxidant activity of Tetrodesmus obliquus was investigated. The results showed that the extraction yield of flavonoids from Z. bungeanum leaves reached 6.85% under the optimized conditions of an ultrasonic power of 600 W, an LSR of 20:1 mL/g, an ethanol concentration of 77.5%, an ultrasonic duration of 18 min, and an ultrasonic temperature of 80 °C, which significantly decreased the Fv/Fm and PIabs values by 54.60% and 98.22%, respectively, after exposure of T. obliquus to 40.0 mg/L Z. bungeanum leaf extract for 66 h. Meanwhile, treatment with Z. bungeanum leaf extract at a dose of 40.0 mg/L generated T-AOC values that were 4.0 times higher than the control without the addition of Z. bungeanum leaf extracts. These results suggest that Z. bungeanum leaf extracts could be used in the development of potentially effective biological algicides. Our study provides data to support the development of algicides and realizes the resource application of Z. bungeanum leaf waste, achieving a synergistic outcome of both economic and ecological benefits.

Integration of Fluorescence Spectroscopy into a Photobioreactor for the Monitoring of Cyanobacteria

Phytoplankton are essential to aquatic ecosystems but can cause harmful algal blooms (HABs) that threaten water quality, aquatic life, and human health. Developing new devices based on spectroscopic techniques offers a promising alternative for rapid and accurate monitoring of aquatic environments. However, phytoplankton undergo various physiological changes throughout their life cycle, leading to alterations in their optical properties, such as autofluorescence. In this study, we present a modification of a low-cost photobioreactor designed to implement fluorescence spectroscopy to analyze the evolution of spectral signals during phytoplankton growth cycles. This device primarily facilitates the characterization of changes in autofluorescence, providing valuable information for the development of future spectroscopic techniques for detecting and monitoring phytoplankton. Additionally, real-time testing was performed on cyanobacterial cultures, where changes in autofluorescence were observed under different conditions. This work demonstrates a cost-effective implementation of spectroscopic techniques within a photobioreactor, offering a preliminary analysis for the future development of functional field devices for monitoring aquatic ecosystems.

Electrochemical biosensor based on strand displacement reaction for on-site detection of Skeletonema costatum

An electrochemical biosensor was constructed for quantitatively detecting Skeletonema costatum (S. costatum) by combining the electrode modification material (NC-Au) with a strand displacement reaction (SDR). The SDR process addresses the issues of steric hindrance and electrostatic repulsion resulting from the large size of genomic DNA. It enhances the efficiency of the interfacial hybridization reaction and endows the biosensor with remarkable sensitivity. The limit of detection (LOD) and limit of quantification (LOQ) were 33.43 fg/μL (831 cells/L) and 87.21 fg/μL (2112 cells/L), respectively. Additionally, the biosensor has demonstrated excellent accuracy compared to methods such as microscopy and ddPCR (P > 0.05). Subsequent assessment of the adjacent waters of the Beibu Gulf using biosensors indicated a low risk of S. costatum red tide outbreaks in the region, which is consistent with the findings of the ecological survey. Therefore, we believe that this biosensor can provide a completely new idea for the dynamic monitoring and early warning of S. costatum red tide.

Regionally differentiated responses of chlorophyll-a concentrations to reduced human activity during COVID-19 lockdown in the San Francisco Bay area

The COVID-19 lockdown created a unique opportunity to study the impact of reduced human activities on water quality. This study aimed to explore how changes in human activities, specifically reduced traffic emissions, influenced water quality in the San Francisco Bay Area from 2019 to 2021. Using chlorophyll-a (Chl-a) concentration as an indicator of water quality and NO₂ concentration as a proxy for traffic emissions, we analyzed the effects of reduced emissions on water quality across different regions of the Bay. This study combined traffic flow, satellite remote sensing, and meteorological data. Pearson correlation analysis was used to assess the time-lag effects between NO₂ and Chl-a concentrations. Additionally, a random forest regression model was applied to analyze the drivers of Chl-a concentrations across different regions. The findings revealed that NO₂'s impact on Chl-a exhibited significant spatial heterogeneity: positive correlations increased in the Upper Bay region, a general positive correlation was observed in the Central Bay region, the Coastal Zone shifted from positive to negative correlations, and there was no significant correlation in the Lower South Bay region. The random forest regression analysis demonstrated that the main drivers of Chl-a varied by region. In Upper Bay and Central Bay, NO₂ concentration was the most significant driver of Chl-a, whereas temperature was the primary influence in the Coastal Zone and Lower South Bay. These findings offer insights into the spatially variable impacts of traffic emissions on water quality and suggest strategies for targeted management of water quality in urban estuarine environments.

Are more data always better? - Machine learning forecasting of algae based on long-term observations

Bloom-forming algae present a unique challenge to water managers as they can significantly impair provision of important ecosystem services and cause health risks to humans and animals. Consequently, effective short-term algae forecasts are important as they provide early warnings and enable implementation of mitigation strategies. In this context, machine learning (ML) emerges as a promising forecasting tool. However, the performance of ML models is heavily dependent on the availability of appropriate training data. Consequently, it is essential to determine the volume of data necessary to develop reliable ML forecasts. Understanding this will guide future monitoring strategies, optimize resource allocation, and set realistic expectations for management outcomes. In this study, we used 30 years of fortnightly measurements of 13 different parameters from a lake in the English Lake District (UK) to examine the impact of training data duration on the performance of ML models for forecasting chlorophyll-a two weeks in advance. Once training data availability exceeded four years, a Random Forest model was found to consistently outperform naive benchmarks (mean absolute percentage error 16.4 % lower than the best-performing benchmark). With more than 5 years of training data, model performance generally continued to improve, but with diminishing returns. Furthermore, it was found that equivalent and, in some cases, better performance could be achieved by only using a subset of the most important input features. Additionally, it was found that reducing the sampling frequency had negative impacts on performance, both due to the reduced number of training observations available, and increased forecast horizon. Our findings demonstrate that for lakes ecologically similar to the study site, a consistent and regular sampling programme focused on monitoring a limited number of key parameters can provide sufficient observations for generating short-term algae forecasts after approximately five years of data collection. Importantly, this result provides justification for the initiation of new monitoring programmes for sites where algal blooms are a concern, and suggests that there are likely many pre-existing monitoring datasets which would be suitable for training algae forecast models.

A comprehensive review of various environmental factors' roles in remote sensing techniques for assessing surface water quality

The aim of this research was to evaluate the existing remote sensing (RS) products, various tools and techniques, and their limitations in retrieving the optically active (OA) Chlorophyll-a (CHL) concentration from transitional, coastal and inland waters. In recent decades, satellite RS technique has emerged as a vital tool for assessing surface water quality (WQ) in a cost-effective and timely manner. Initially used in the 1970s to study ocean color (OC), RS techniques have advanced significantly, enabling the retrieval of key WQ indicators like CHL, colored dissolved organic matter (CDOM), total suspended matter (TSM), turbidity (TURB), and more from satellite images. Among these indicators, CHL is particularly important as it directly signifies eutrophication. While RS technique has been reliable in estimating CHL concentrations in open waterbodies (case1 water) such as oceans, it's application in shallow, turbid waters (case2 water) like transitional, coastal and inland areas faces challenges. Interference from other OA-WQ indicators like CDOM and TSM, coupled with environmental factors such as atmospheric components, sun-glint, and adjacency effects (AE), complicate the accurate CHL estimation. To address these challenges, researchers have developed four categories of CHL retrieval algorithms: empirical, semi-empirical, hybrid and data-driven models. Empirical and data-driven methods are straightforward but require regional calibration for accuracy, whereas semi-empirical approaches, rooted in solid theoretical foundations, demand extensive ancillary optical measurements. To harness the potential of RS in WQ assessment fully, it is essential to optimize these algorithms regionally, tailoring them to the specific optical characteristics of diverse waterbodies. This optimization process is vital for integrating RS technique as a complementary data source alongside traditional monitoring approach. By addressing the impact of environmental factors and fine-tuning of CHL retrieval methods according to regional nuances, satellite RS technique can significantly enhance the reliability and effectiveness of surface WQ evaluation, thereby contributing to more informed and efficient water resource management strategies. This review emphasizes the impact of these factors, categorizes CHL retrieval algorithms into empirical, semi-empirical, hybrid and data-driven methods and applicability in terms of tools/models' reliability and challenges for the further advancement of this approaches for monitoring transitional, coastal and inland waters. To optimize the reliability of remotely sensed CHL data, regional configuration(s) of retrieving algorithms is vital. By addressing these challenges and tailoring methods to specific regions, integrating satellite RS into traditional monitoring approaches can significantly enhance surface WQ assessment.

Development of a deep learning-based feature stream network for forecasting riverine harmful algal blooms from a network perspective

Global increases in the occurrence of harmful algal blooms (HABs) are of major concern in water quality and resource management. A predictive model capable of quantifying the spatiotemporal associations between HABs and their influencing factors is required for effective preventive management. In this study, a feature stream network (FSN) model is proposed to provide daily forecasts of cyanobacteria abundance at multiple monitoring sites simultaneously in a river network. The spatial connectivity between monitoring sites was expressed as a directed acyclic graph comprising edges and nodes representing flows and monitoring sites, respectively. Furthermore, a segment-wise node connection structure was developed to extract the latent features of a river segment comprising individual nodes and sequentially transfer them to the downstream segment(s). In addition, a feature engineering-attention hybrid mechanism was employed to address temporal mismatches among different monitoring schemes while adding explainability to the model. Consequently, the FSN showed improved predictive performance, temporal resolution, and explainability for multi-site forecasts of HAB in a single model framework. The developed model was applied to a bloom-prone middle course of the Nakdong River, South Korea. Various hydrological, environmental, and biological factors were utilized for forecasting the cyanobacteria abundance. The FSN exhibited a high degree of accuracy across the sites for the test data with a coefficient of determination in the range of 0.64-0.71 and root mean square error in the range of 2.06-2.26 cells/mL on natural log scales. Although the relative importance of input features varied across the sites, the features extracted from nearby nodes consistently exhibited high importance in forecasting the cyanobacteria abundance. These explanations indicate that the proposed model can successfully characterize the spatial hierarchy of a river network. A scenario analysis suggested that reduced total nitrogen loads in the effluents from the wastewater treatment plant and the combined operations of upstream and downstream weirs were effective in managing HABs.

Utilization strategy for algal bloom waste through co-digestion with kitchen waste: Comprehensive kinetic and metagenomic analysis

Anaerobic co-digestion (AcoD) with kitchen waste (KW) is an alternative utilization strategy for algal bloom waste (AW). However, the kinetic characteristic and metabolic pathway during this process need to be explored further. This study conducted a comprehensive kinetic and metagenomic analysis for AcoD of AW and KW. A maximum co-digestion performance index (CPI) of 1.13 was achieved under the 12% AW addition. Co-digestion improved the total volatile fatty acids generation and the organic matter transformation efficiency. Kinetic analysis showed that the Superimposed model fit optimally (R2Adj = 0.9988-0.9995). The improvement of the kinetic process by co-digestion was mainly reflected in the increase of the methane production from slowly biodegradable components. Co-digestion enriched the cellulolytic bacterium Clostridium and the hydrogenotrophic methanogenic archaea Methanobacterium. Furthermore, for metagenome analysis, the abundance of key genes concerned in cellulose and lipid hydrolysis, pyruvate and methane metabolism were both increased in co-digestion process. This study provided a feasible process for the utilization of AW produced seasonally and a deeper understanding of the AcoD synergistic mechanism from kinetic and metagenomic perspectives.

Principles and research progress of physical prevention and control technologies for algae in eutrophic water

The abnormal reproduction of algae in water worldwide is prominent in the context of human interference and global climate change. This study first thoroughly analyzed the effects of physical factors, such as light, temperature, hydrodynamics, and operational strategies, on algal growth and their mechanisms. Physical control techniques are safe and have great potential for preventing abnormal algal blooms in the absence of chemical reagents. The focus was on the principles and possible engineering applications of physical shading, ultrasound, micro-current, and ultraviolet (UV) technologies, in controlling abnormal algal reproduction. Physical shading can inhibit or weaken photosynthesis in algae, thereby inhibiting their growth. Ultrasound mainly affects the physiological and biochemical activities of cells by destroying the cell walls, air cells, and active enzymes. Micro-currents destroy the algal cell structure through direct and indirect oxidation, leading to algal cell death. UV irradiation can damage DNA, causing organisms to be unable to reproduce or algal cells to die directly. This article comprehensively summarizes and analyzes the advantages of physical prevention and control technologies for the abnormal reproduction of algae, providing a scientific basis for future research. In the future, attempts will be made toward appropriately and comprehensively utilizing various physical technologies to control algal blooms. The establishment of an intelligent, comprehensive physical prevention and control system to achieve environmentally friendly, economical, and effective physical prevention and control of algae, such as the South-to-North Water Diversion Project in China, is of great importance for specific waters.

Effects of the catastrophic 2020 Yangtze River seasonal floods on microcystins and environmental conditions in Three Gorges Reservoir Area, China

Introduction

During July and August 2020, Three Gorges Reservoir Area (TGRA) suffered from catastrophic seasonal floods. Floods changed environmental conditions and caused increase in concentration of microcystins (MCs) which is a common and potent cyanotoxin. However, the effects and seasonal variations of MCs, cyanobacteria, and environmental conditions in TGRA after the 2020 Yangtze River extreme seasonal floods remain largely unclear, and relevant studies are lacking in the literature.

Methods

A total of 12 representative sampling sites were selected to perform concentration measurement of relevant water quality objectives and MCs in the representative area of the TGRA. The sampling period was from July 2020 to October 2021, which included the flood period. Organic membrane filters were used to perform the DNA extraction and analyses of the 16S rRNA microbiome sequencing data.

Results

Results showed the seasonal floods result in significant increases in the mean values of microcystin-RR (MCRR), microcystin-YR (MCYR), and microcystin-LR (MCLR) concentration and some water quality objectives (i.e., turbidity) in the hinterland of TGRA compared with that in non-flood periods (p < 0.05). The mean values of some water quality objectives (i.e., total nitrogen (TN), total phosphorus (TP), total dissolved phosphorus (TDP), and turbidity), MC concentration (i.e., MCRR, MCYR, and MCLR), and cyanobacteria abundance (i.e., Cyanobium_PCC-6307 and Planktothrix_NIVA-CYA_15) displayed clear tendency of increasing in summer and autumn and decreasing in winter and spring in the hinterland of TGRA.

Discussions

The results suggest that seasonal floods lead to changes in MC concentration and environmental conditions in the hinterland of TGRA. Moreover, the increase in temperature leads to changes in water quality objectives, which may cause water eutrophication. In turn, water eutrophication results in the increase in cyanobacteria abundance and MC concentration. In particular, the increased MC concentration may further contribute to adverse effects on human health.