Highly time-resolved analysis of seasonal water dynamics and algal kinetics based on in-situ multi-sensor-system monitoring data in Lake Taihu, China

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.

Machine learning for cyanobacteria inversion via remote sensing and AlgaeTorch in the Třeboň fishponds, Czech Republic

Cyanobacteria blooms in fishponds, driven by climate change and anthropogenic activities, have become a critical concern for aquatic ecosystems worldwide. The diversity in fishpond sizes and fish densities further complicates their monitoring. This study addresses the challenge of accurately predicting cyanobacteria concentrations in turbid waters via remote sensing, hindered by optical complexities and diminished light signals. A comprehensive dataset of 740 sampling points was compiled, encompassing water quality metrics (cyanobacteria levels, total chlorophyll, turbidity, total cell count) and spectral data obtained through AlgaeTorch, alongside Sentinel-2 reflectance data from three Třeboň fishponds (UNESCO Man and Biosphere Reserve) in the Czech Republic over 2022-2023. Partial Least Squares Regression (PLSR) and three machine learning algorithms, Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost), were developed based on seasonal and annual data volumes. The SVM algorithm demonstrated commendable performance on the one-year data validation dataset from the Svět fishpond for the prediction of cyanobacteria, reflected by the key performance indicators: R2 = 0.88, RMSE = 15.07 μg Chl-a/L, and RPD = 2.82. Meanwhile, SVM displayed steady results in the unified one-year validation dataset from Naděje, Svět, and Vizír fishponds, with metrics showing R2 = 0.56, RMSE = 39.03 μg Chl-a/L, RPD = 1.50. Thus, Sentinel data proved viable for seasonal cyanobacteria monitoring across different fishponds. Overall, this study presents a novel approach for enhancing the precision of cyanobacteria predictions and long-term ecological monitoring in fishponds, contributing significantly to the water quality management strategies in the Třeboň region.

Improving algal bloom detection using spectroscopic analysis and machine learning: A case study in a large artificial reservoir, South Korea

The prediction of algal blooms using traditional water quality indicators is expensive, labor-intensive, and time-consuming, making it challenging to meet the critical requirement of timely monitoring for prompt management. Using optical measures for forecasting algal blooms is a feasible and useful method to overcome these problems. This study explores the potential application of optical measures to enhance algal bloom prediction in terms of prediction accuracy and workload reduction, aided by machine learning (ML) models. Compared to absorption-derived parameters, commonly used fluorescence indices such as the fluorescence index (FI), humification index (HIX), biological index (BIX), and protein-like component improved the prediction accuracy. However, the prediction accuracy was decreased when all optical indices were considered for computation due to increased noise and uncertainty in the models. With the exception of chemical oxygen demand (COD), this study successfully replaced biochemical oxygen demand (BOD), dissolved organic carbon (DOC), and nutrients with selected fluorescence indices, demonstrating relatively analogous performance in either training or testing data, with consistent and good coefficient of determination (R2) values of approximately 0.85 and 0.74, respectively. Among all models considered, ensemble learning models consistently outperformed conventional regression models and artificial neural networks (ANNs). However, there was a trade-off between accuracy and computation efficiency among the ensemble learning models (i.e., Stacking and XGBoost) for algal bloom prediction. Our study offers a glimpse of the potential application of spectroscopic measures to improve accuracy and efficiency in algal bloom prediction, but further work should be carried out in other water bodies to further validate our proposed hypothesis.

A Digital Twin Lake Framework for Monitoring and Management of Harmful Algal Blooms

Harmful algal blooms (HABs) caused by lake eutrophication and climate change have become one of the most serious problems for the global water environment. Timely and comprehensive data on HABs are essential for their scientific management, a need unmet by traditional methods. This study constructed a novel digital twin lake framework (DTLF) aiming to integrate, represent and analyze multi-source monitoring data on HABs and water quality, so as to support the prevention and control of HABs. In this framework, different from traditional research, browser-based front ends were used to execute the video-based HAB monitoring process, and real-time monitoring in the real sense was realized. On this basis, multi-source monitored results of HABs and water quality were integrated and displayed in the constructed DTLF, and information on HABs and water quality can be grasped comprehensively, visualized realistically and analyzed precisely. Experimental results demonstrate the satisfying frequency of video-based HAB monitoring (once per second) and the valuable results of multi-source data integration and analysis for HAB management. This study demonstrated the high value of the constructed DTLF in accurate monitoring and scientific management of HABs in lakes.

Cyanobacterial Harmful Algal Bloom Toxin Microcystin and Increased Vibrio Occurrence as Climate-Change-Induced Biological Co-Stressors: Exposure and Disease Outcomes via Their Interaction with Gut-Liver-Brain Axis

The effects of global warming are not limited to rising global temperatures and have set in motion a complex chain of events contributing to climate change. A consequence of global warming and the resultant climate change is the rise in cyanobacterial harmful algal blooms (cyano-HABs) across the world, which pose a threat to public health, aquatic biodiversity, and the livelihood of communities that depend on these water systems, such as farmers and fishers. An increase in cyano-HABs and their intensity is associated with an increase in the leakage of cyanotoxins. Microcystins (MCs) are hepatotoxins produced by some cyanobacterial species, and their organ toxicology has been extensively studied. Recent mouse studies suggest that MCs can induce gut resistome changes. Opportunistic pathogens such as Vibrios are abundantly found in the same habitat as phytoplankton, such as cyanobacteria. Further, MCs can complicate human disorders such as heat stress, cardiovascular diseases, type II diabetes, and non-alcoholic fatty liver disease. Firstly, this review describes how climate change mediates the rise in cyanobacterial harmful algal blooms in freshwater, causing increased levels of MCs. In the later sections, we aim to untangle the ways in which MCs can impact various public health concerns, either solely or in combination with other factors resulting from climate change. In conclusion, this review helps researchers understand the multiple challenges brought forth by a changing climate and the complex relationships between microcystin, Vibrios, and various environmental factors and their effect on human health and disease.

From collapse to the health of the aquatic ecosystem in Dasha River (2006-2021): a case study of Shenzhen city in the Guangdong-Hong Kong-Macao Greater Bay Area, China

Compared with the aquatic ecosystem destruction caused by rapid urban development, substantial ecological restoration usually requires long periods and is a challenging process. Although river ecological restoration has been successful in different regions, the relationship between biodiversity, water quality, and effective measures applicable to developing countries remains poorly understood. This study was conducted in the Dasha River in Shenzhen city, one of the fastest-growing cities in China. The rehabilitation measures were sorted out in four phases to study the impact on water quality and biodiversity. In response, three campaigns were carried out to take phytoplankton, zooplankton, and benthos samples within the last three engineering stages, in 2007, 2012, and 2021. Synchronized investigations of water quality were conducted monthly from 2006 to 2021. Our analysis showed that the biodiversity of benthos has improved in recent years, which marks a turnaround for the aquatic ecological environment. According to the Hilsenhoff family biotic index (FBI), the water quality level in the 2021 campaign was promoted to "Good" in the downstream and "Fair" in the upper and middle streams. By analyzing Pearson's correlations between response ratios of water quality parameters and the Shannon-Wiener index of phytoplankton, zooplankton, and benthos, we concluded that biodiversity is significantly related to water quality. Specifically, the biodiversity of zooplankton is associated with ammonia nitrogen (NH₃-N) (R² =  - 0.77, P < 0.05), and benthos diversity is strongly negatively correlated with NH₃-N, total nitrogen, chemical oxygen demand, and biochemical oxygen demand (R² ≥ -0.82, P < 0.01). Despite the temporary negative impact of along-river interception on aquatic organisms in the campaign of 2012, the measures quickly and effectively improved water quality, which is the foundation for biodiversity improvement in 2021. This study provides insights into relationships among biodiversity, water quality, and regulation projects and can offer a reference for selecting aquatic ecosystem restoration measures in developing areas.

Time-Resolved Kinetic Measurement of Microalgae Agglomeration for Screening of Polysaccharides-Based Coagulants/Flocculants

Time-resolved monitoring of microalgae agglomeration facilitates screening of coagulants/flocculants (CFs) from numerous biopolymer candidates. Herein, a filtering-flowing analysis (FFA) apparatus was developed in which dispersed microalgal cells were separated from coagulates and flocs formed by CFs and pumped into spectrophotometer for real-time quantification. Polysaccharides-based CFs for Microcystis aeruginosa and several other microalgae were tested. Cationic hydroxyethyl cellulose (CHEC), chitosan quaternary ammonium (CQA) and cationic guar gum (CGG) all triggered coagulation obeying a pseudo-second-order model. Maximal coagulation efficiencies were achieved at their respective critical dosages, i.e., 0.086 g/g_M.a. CHEC, 0.022 g/g_M.a. CQA, and 0.216 g/g_M.a. CGG. Although not active independently, bacterial exopolysaccharides (BEPS) aided coagulation of M. aeruginosa and allowed near 100% flocculation efficiency when 0.115 g/g_M.a. CQA and 1.44 g/g_M.a. xanthan were applied simultaneously. The apparatus is applicable to other microalgae species including Spirulina platensis, S. maxima, Chlorella vulgaris and Isochrysis galbana. Bio-based CFs sorted out using this apparatus could help develop cleaner processes for both remediation of harmful cyanobacterial blooms and microalgae-based biorefineries.

Chlorophyll soft-sensor based on machine learning models for algal bloom predictions

Harmful algal blooms (HABs) are a growing concern to public health and aquatic ecosystems. Long-term water monitoring conducted by hand poses several limitations to the proper implementation of water safety plans. This work combines automatic high-frequency monitoring (AFHM) systems with machine learning (ML) techniques to build a data-driven chlorophyll-a (Chl-a) soft-sensor. Massive data for water temperature, pH, electrical conductivity (EC) and system battery were taken for three years at intervals of 15 min from two different areas of As Conchas freshwater reservoir (NW Spain). We designed a set of soft-sensors based on compact and energy efficient ML algorithms to infer Chl-a fluorescence by using low-cost input variables and to be deployed on buoys with limited battery and hardware resources. Input and output aggregations were applied in ML models to increase their inference performance. A component capable of triggering a 10 [Formula: see text]g/L Chl-a alert was also developed. The results showed that Chl-a soft-sensors could be a rapid and inexpensive tool to support manual sampling in water bodies at risk.

Monitoring and control methods of harmful algal blooms in Chinese freshwater system: a review

Harmful algal blooms (HABs) are a worldwide problem with substantial adverse effects on the aquatic environment as well as human health, which have prompted researchers to study measures to stem and control them. Meanwhile, it is key to research and develop monitoring methods to establish early warning HABs. However, both the current monitoring methods and control methods have some shortcomings, making the field application limited. Thus, we need to improve current approaches for monitoring and controlling HABs efficiently. Based on the freshwater system features in China, we review various monitoring and control methods of HABs, summarize and discuss the problems with these methods, and propose the future development direction of monitoring and control HABs. Finally, we envision that it can combine physical, chemical, and biological methods to inhibit HAB expansion in the future, complementing each other with advantages. Further, we promise to establish a long-term strategy of controlling HABs with various algicidal bacteria co-cultivate for field applications in China. Efforts in studying algicidal bacteria must be increased to better control HABs and mitigate the risks of aquatic ecosystems and human health in China.

A Review of Remote Sensing for Water Quality Retrieval: Progress and Challenges

Water pollution has become one of the most serious issues threatening water environments, water as a resource and human health. The most urgent and effective measures rely on dynamic and accurate water quality monitoring on a large scale. Due to their temporal and spatial advantages, remote sensing technologies have been widely used to retrieve water quality data. With the development of hyper-spectral sensors, unmanned aerial vehicles (UAV) and artificial intelligence, there has been significant advancement in remotely sensed water quality retrieval owing to various data availabilities and retrieval methodologies. This article presents the application of remote sensing for water quality retrieval, and mainly discusses the research progress in terms of data sources and retrieval modes. In particular, we summarize some retrieval algorithms for several specific water quality variables, including total suspended matter (TSM), chlorophyll-a (Chl–a), colored dissolved organic matter (CDOM), chemical oxygen demand (COD), total nitrogen (TN) and total phosphorus (TP). We also discuss the significant challenges to atmospheric correction, remotely sensed data resolution, and retrieval model applicability in the domains of spatial, temporal and water complexity. Finally, we propose possible solutions to these challenges. The review can provide detailed references for future development and research in water quality retrieval.

Associations between climate variables and water quality in low- and middle-income countries: A scoping review

Understanding how climate change will affect water quality and therefore, health, is critical for building resilient water services in low- and middle-income countries (LMICs) where the effect of climate change will be felt most acutely. Evidence of the effect of climate variables such as temperate and rainfall on water quality can generate insights into the likely impact of future climate change. While the seasonal effects on water quality are known, and there is strong qualitative evidence that climate change will impact water quality, there are no reviews that synthesise quantitative evidence from LMICs on links between climate variables and water quality. We mapped the available evidence on a range of climate exposures and water quality outcomes and identified 98 peer-reviewed studies. This included observational studies on the impact of temperature and rainfall events (which may cause short-term changes in contaminant concentrations), and modelling studies on the long-term impacts of sea level rise. Evidence on links between antecedent rainfall and microbiological contamination of water supplies is strong and relatively evenly distributed geographically, but largely focused on faecal indicator bacteria and on untreated shallow groundwater sources of drinking water. The literature on climate effects on geogenic contaminants was sparse. There is substantial research on the links between water temperature and cyanobacteria blooms in surface waters, although most studies were from two countries and did not examine potential effects on water treatment. Similarly, studies modelling the impact of sea level rise on groundwater salinity, mostly from south-Asia and the Middle East, did not discuss challenges for drinking water supplies. We identified key future research priorities based on this review. These include: more studies on specific pathogens (including opportunistic pathogens) in water supplies and their relationships with climate variables; more studies that assess likely relationships between climate variables and water treatment processes; studies into the relationships between climate variables and geogenic contaminants, including risks from heavy metals released as glacier retreat; and, research into the impacts of wildfires on water quality in LMICs given the current dearth of studies but recognised importance.

Application of Machine Learning for eutrophication analysis and algal bloom prediction in an urban river: A 10-year study of the Han River, South Korea

The increasing release of nutrients to aquatic environments has led to great concern regarding eutrophication and the risk of unwanted algal blooms. Based on observational data of 20 water quality parameters measured on a monthly basis at 40 stations from 2011 to 2020, this study applied different Machine Learning (ML) algorithms to suggest the best option for algal bloom prediction in the Han River, a large river in South Korea. Eight different ML algorithms were categorized into several groups of statistical learning, regression family, and deep learning, and were then compared for their suitability to predict the chlorophyll-derived trophic index (TSI-Chla). ML algorithms helped identify the most important water quality parameters contributing to algal bloom prediction. The ML results confirmed that eutrophication and algal proliferation were governed by the complex interplay between nutrients (nitrogen and phosphorus), organic contaminants, and environmental factors. Of the models tested, the adaptive neuro-fuzzy inference system (ANFIS) exhibited the best performance owing to its consistent and outperforming prediction both quantitatively (i.e., via regression) and qualitatively (i.e., via classification), which was evidenced by the lowest value of mean absolute error (MAE) of 0.09, and the highest F1-score, Recall and Precision of 0.97, 0.98 and 0.96, respectively. In a further step, a representative web application was constructed to assist common users to predict the trophic status of the Han River. This study demonstrated that ML techniques are not only promising for highly accurate water quality modeling of urban rivers, but also reduce time and labor intensity for experiments, which decreases the number of monitored water quality parameters, providing further insights into the driving factors of water quality deterioration. They ultimately help devise proactive strategies for sustainable water management.

Motility changes rather than EPS production shape aggregation of Chlamydomonas microsphaera in aquatic environment

Microalgal aggregation is a key for both microalgae harvesting and water purification, where changes in extracellular polymeric substance (EPS) secretion and cell motility changes are of core importance. In this study, we investigated the aggregation process of Chlamydomonas microsphaera confronting resource limitation and chlorine disinfection, and tried to compare changes in the magnitude of EPS secretion and cell motility. Results show that the presence of mild chlorine solution (0.20%) dose stimulated microalgal aggregation (with an aggregated to planktonic cells ratio of 3.2), with extracellular protein concentration and mean cell velocity reaching a maximum of 43.43 ± 0.01 mg/L and 201 ± 35 µm/s, respectively. These values are 71% and 191% higher than those of the control. Comparably, nutrient availability had only a limited impact on microalgal aggregation and was associated with mild EPS secretion and cell motility. Correlation analysis revealed a strong positive impact of cell motility (mean velocity) on microalgae aggregation, with little effect on EPS excretion. Together, these quantitative estimations may shed light on understanding the mechanisms of microalgae aggregation in aqueous systems, which could help future design and practical operation of source water pretreatment or microalgae harvesting.

Sensor-based detection of algal blooms for public health advisories and long-term monitoring

Throughout the United States, many eutrophic freshwater bodies experience seasonal blooms of toxic cyanobacteria. These blooms limit recreational uses and pose a threat to both human and ecological health. Traditional bi-weekly chlorophyll-based sampling programs designed to assess overall algal biomass fail to capture important bloom parameters such as bloom timing, duration, and peak intensity. In-situ optical and fluorometric measurements have the potential to fill this gap. However, relating in-situ measurements to relevant water quality measures (e.g. cyanobacterial cell density or chlorophyll concentration) is a challenge that limits the implementation of probe-based monitoring strategies. This study, of Aphanizomenon dominated blooms in Boston's Charles River, combines five years of cyanobacterial cell counts with high resolution insitu sensor measurements to relate turbidity and fluorometric readings to cyanobacterial cell density. Our work compares probe and lab-based estimates of summer-mean chlorophyll concentration and highlights the challenges of working with raw fluorescence in cyanobacteria dominated waterbodies. A strong correlation between turbidity and cyanobacterial cell density (R 2 = 0.84) is used to construct a simple cell-density-estimation-model suitable for triggering rapid bloom-responsesampling and classifying bloom events with a true positive rate of 95%. The approach described in this study is potentially applicable to many cyanobacteria dominated freshwater bodies.

Simulating chlorophyll-a fluorescence changing rate and phycocyanin fluorescence by using a multi-sensor system in Lake Taihu, China

Algal pollution in water sources has posed a serious problem. Estimating algal concentration in advance saves time for drinking water plants to take measures and helps us to understand causal chains of algal dynamics. This paper explores the possibility of building a short-term algal early warning model with online monitoring systems. In this study, we collected high-frequency data for water quality and weather conditions in shallow and eutrophic Lake Taihu by an in situ multi-sensor system (BIOLIFT) combined with a weather station. Extracted chlorophyll-a from water samples and chlorophyll-a fluorescence differentiated according to different algal classeses verified that chlorophyll-a fluorescence continuously measured by BIOLIFT only represent chlorophyll-a of green algae and diatoms. Stepwise linear regression was used to simulate the chlorophyll-a fluorescence changing rate of green algae and diatoms together (ΔChl_a-f%) and phycocyanin fluorescence concentration (blue-green algae) on the water surface layer (CyanoS). The results show that nutrients (total N, NO₃-N, NH₄-N, total P) were not necessary parameters for short-term algal models. ΔChl_a-f % is greatly influenced by the seasons, so seasonal partition of data before modeling is highly recommended. CyanoS_max and ΔChl_a-f% were simulated by only using multi-sensor and meteorological data (R² = 0.73; 0.75). All the independent variables (wave, water temperature, relative humidity, depth, cloud cover) used in the model were measured online and predictable. Wave height is the most important independent variable in the shallow lake. This paper offers a new approach to simulate and predict the algal dynamics, which also can be applied in other surface water.

Identifying spatio-temporal dynamics of trace metals in shallow eutrophic lakes on the basis of a case study in Lake Taihu, China

In shallow eutrophic lakes, metal remobilization is closely related to the resuspension and eutrophication. An improved understanding of metal dynamics by biogeochemical processes is essential for effective management strategies. We measured concentrations of nine metals (Cr, Cu, Zn, Ni, Pb, Fe, Al, Mg, and Mn) in water and sediments during seven periods from 2014 to 2018 in northern Lake Taihu, to investigate the metal pollution status, spatial distributions, mineral constituents, and their interactions with P. Moreover, an automatic weather station and online multi-sensor systems were used to measure meteorological and physicochemical parameters. Combining these measurements, we analyzed the controlling factors of metal dynamics. Shallow and eutrophic northern Lake Taihu presents more serious metal pollution in sediments than the average of lakes in Jiangsu Province. We found chronic and acute toxicity levels of dissolved Pb and Zn (respectively), compared with US-EPA "National Recommended Water Quality Criteria". Suspended particles and sediment have been polluted in different degrees from uncontaminated to extremely contaminated according to German pollution grade by LAWA (Bund/Länder-Arbeitsgemeinschaft Wasser). Polluted particles might pose a risk due to high resuspension rate and intense algal activity in shallow eutrophic lakes. Suspended particles have similar mineral constituents to sediments and increased with increasing wind velocity. Al, Fe, Mg, and Mn in the sediment were rarely affected by anthropogenic pollution according to the geoaccumulation index. Among them, Mn dynamics is very likely associated with algae. Micronutrient uptake by algal will affect the migration of metals and intensifies their remobilization. Intensive pollution of most particulate metals were in the industrialized and down-wind area, where algae form mats and decompose. Moreover, algal decomposition induced low-oxygen might stimulate the release of metals from sediment. Improving the eutrophication status, dredging sediment, and salvaging cyanobacteria biomass are possible ways to remove or reduce metal contaminations.

Effect of Physical Factors on the Growth of Chlorella Vulgaris on Enriched Media Using the Methods of Orthogonal Analysis and Response Surface Methodology

In addition to chemical factors, physical conditions also play a key role in the growth of microalgae. In this study, solid sediment in rivers was simulated by pure quartz sand with different particle sizes and the physical effects of disturbance rate, solid–liquid ratio and particle size on the growth of Chlorella vulgaris (C. vulgaris) were investigated through orthogonal analysis and response surface methodology (RSM) during co-cultivation of C. vulgaris and sediment. The result of ANOVA in orthogonal analysis showed that the effect ability of a single factor on biomass can be ranked as disturbance rate > particle size > solid–liquid ratio, 100 r/min disturbance rate and 30–40 M particle size are the most significant at the 0.05 level. Furthermore, the specific growth rate can reach 0.25/d and 0.27/d, respectively. With the growth of C. vulgaris, the pH of the solution reached a maximum of 10.7 in a week. The results from the RSM showed that strong interactions are reflected in the combinations of disturbance rate and solid–liquid ratio, and disturbance rate and particle size. Ramp desirability of the biomass indicates that the optimum levels of the three variables are 105 r/min disturbance rate, 0.117 g/mL solid–liquid ratio and 30–40 M particle size. In this case, the biomass can grow seven times in a week with 0.27/d specific growth rate and a pH value of 7–10.4. This study shows that the growth of C. vulgaris can be regulated by changing physical conditions simultaneously, and the optimization of physical conditions can be applied to biomass production, algae prediction and acid water treatment in rivers, lakes and reservoirs.