Risks for cyanobacterial harmful algal blooms due to land management and climate interactions

Influence of climate extremes on long-term changes in cyanobacterial blooms in a eutrophic and shallow lake

Climate change and human activities have crucial effects on the variations in phytoplankton blooms in lakes worldwide. A record-breaking heatwave and drought event was reported in the middle and lower reaches of the Yangtze River during the summer of 2022, but only little is known about how cyanobacterial blooms in lakes respond to such climate extremes. Here, we utilized MODIS images to generate the area, occurrence, and initial blooming date (IBD) of cyanobacterial blooms in Lake Chaohu from 2000 to 2022. We found that the area and occurrence of cyanobacterial blooms were largely reduced. At the same time, the IBD was delayed in 2022 compared with the previous 20 years. The annual occurrence and mean area of cyanobacterial blooms in 2022 were 17 % and 23.1 km2, respectively, which were the lowest reported levels since the 21st century. The IBD in 2022 was four months late compared with the IBD in 2020. The high wind speed in spring delayed the spring blooms in 2022. The record-breaking heatwaves and drought from June to August reduced the blooms by influencing the growth of cyanobacteria and reducing the flow of nutrients from the watershed into the lake. This study highlights the compound impact of heatwave and drought climate events on reducing cyanobacterial blooms in a long-term period, enhancing additional understanding of the changes in phytoplankton blooms in lakes.

Environmental drivers behind the exceptional increase in cyanobacterial blooms in Okavango Delta, Botswana

The Okavango Delta region in Botswana experienced exceptionally intense landscape-wide cyanobacterial harmful algal blooms (CyanoHABs) in 2020. In this study, the drivers behind CyanoHABs were determined from thirteen independent environmental variables, including vegetation indices, climate and meteorological parameters, and landscape variables. Annual Land Use Land Cover (LULC) maps were created from 2017 to 2020, with ∼89% accuracy to compute landscape variables such as LULC change. Generalized Additive Models (GAM) and Structural Equation Models (SEM) were used to determine the most important drivers behind the CyanoHABs. Normalized Difference Chlorophyll Index (NDCI) and Green Line Height (GLH) algorithms served as proxies for chlorophyll-a (green algae) and phycocyanin (cyanobacteria) concentrations. GAM models showed that seven out of the thirteen variables explained 89.9% of the variance for GLH. The models showcased that climate variables, including monthly precipitation (8.8%) and Palmer Severity Drought Index- PDSI (3.2%), along with landscape variables such as changes in Wetlands area (7.5%), and Normalized Difference Vegetation Index (NDVI) (5.4%) were the determining drivers behind the increased cyanobacterial activity within the Delta. Both PDSI and NDVI showed negative correlations with GLH, indicating that increased drought conditions could have led to large increases in toxic CyanoHAB activity within the region. This study provides new information about environmental drivers which can help monitor and predict regions at risk of future severe CyanoHABs outbreaks in the Okavango Delta, Botswana, and other similar data-scarce and ecologically sensitive areas in Africa. Plain Language Summary: The waters of the Okavango Delta in Northern Botswana experienced an exceptional increase in toxic cyanobacterial activity in recent years. Cyanobacterial blooms have been shown to affect local communities and wildlife in the past. To determine the drivers behind this increased bloom activity, we analyzed the effects of thirteen independent environmental variables using two different statistical models. Within this research, we focused on vegetation indices, meteorological, and landscape variables, as previous studies have shown their effect on cyanobacterial activity in other parts of the world. While driver determination for cyanobacteria has been done before, the environmental conditions most important for cyanobacterial growth can be specific to the geographic setting of a study site. The statistical analysis indicated that the increases in cyanobacterial bloom activity within the region were mainly driven by persistent drier conditions. To our knowledge, this is the first study to determine the driving factors behind cyanobacterial activity in this region of the world. Our findings will help to predict and monitor areas at risk of future severe cyanobacterial blooms in the Okavango Delta and other similar African ecosystems.

One-Week-Ahead Prediction of Cyanobacterial Harmful Algal Blooms in Iowa Lakes

Cyanobacterial harmful algal blooms (CyanoHABs) pose serious risks to inland water resources. Despite advancements in our understanding of associated environmental factors and modeling efforts, predicting CyanoHABs remains challenging. Leveraging an integrated water quality data collection effort in Iowa lakes, this study aimed to identify factors associated with hazardous microcystin levels and develop one-week-ahead predictive classification models. Using water samples from 38 Iowa lakes collected between 2018 and 2021, feature selection was conducted considering both linear and nonlinear properties. Subsequently, we developed three model types (Neural Network, XGBoost, and Logistic Regression) with different sampling strategies using the nine selected variables (mcyA_M, TKN, % hay/pasture, pH, mcyA_M:16S, % developed, DOC, dewpoint temperature, and ortho-P). Evaluation metrics demonstrated the strong performance of the Neural Network with oversampling (ROC-AUC 0.940, accuracy 0.861, sensitivity 0.857, specificity 0.857, LR+ 5.993, and 1/LR- 5.993), as well as the XGBoost with downsampling (ROC-AUC 0.944, accuracy 0.831, sensitivity 0.928, specificity 0.833, LR+ 5.557, and 1/LR- 11.569). This study exhibited the intricacies of modeling with limited data and class imbalances, underscoring the importance of continuous monitoring and data collection to improve predictive accuracy. Also, the methodologies employed can serve as meaningful references for researchers tackling similar challenges in diverse environments.

Understanding the Risks of Diffusion of Cyanobacteria Toxins in Rivers, Lakes, and Potable Water

Blue-green algae, or cyanobacteria, may be prevalent in our rivers and tap water. These minuscule bacteria can grow swiftly and form blooms in warm, nutrient-rich water. Toxins produced by cyanobacteria can pollute rivers and streams and harm the liver and nervous system in humans. This review highlights the properties of 25 toxin types produced by 12 different cyanobacteria genera. The review also covered strategies for reducing and controlling cyanobacteria issues. These include using physical or chemical treatments, cutting back on fertilizer input, algal lawn scrubbers, and antagonistic microorganisms for biocontrol. Micro-, nano- and ultrafiltration techniques could be used for the removal of internal and extracellular cyanotoxins, in addition to powdered or granular activated carbon, ozonation, sedimentation, ultraviolet radiation, potassium permanganate, free chlorine, and pre-treatment oxidation techniques. The efficiency of treatment techniques for removing intracellular and extracellular cyanotoxins is also demonstrated. These approaches aim to lessen the risks of cyanobacterial blooms and associated toxins. Effective management of cyanobacteria in water systems depends on early detection and quick action. Cyanobacteria cells and their toxins can be detected using microscopy, molecular methods, chromatography, and spectroscopy. Understanding the causes of blooms and the many ways for their detection and elimination will help the management of this crucial environmental issue.

Chemodiversity of Cyanobacterial Toxins Driven by Future Scenarios of Climate Warming and Eutrophication

Climate change and eutrophication are two environmental threats that can alter the structure of freshwater ecosystems and their service functions, but we know little about how ecosystem structure and function will evolve in future scenarios of climate warming. Therefore, we created different experimental climate scenarios, including present-day conditions, a 3.0 °C increase in mean temperature, and a "heatwaves" scenario (i.e., an increase in temperature variability) to assess the effects of climate change on phytoplankton communities under simultaneous stress from eutrophication and herbicides. We show that the effects of climate warming, particularly heatwaves, are associated with elevated cyanobacterial abundances and toxin production, driven by a change from mainly nontoxic to toxic Microcystis spp. The reason for higher cyanobacterial toxin concentrations is likely an increase in abundances because under the dual pressures of climate warming and eutrophication individual Microcystis toxin-producing ability decreased. Eutrophication and higher temperatures significantly increased the biomass of Microcystis, leading to an increase in the cyanobacterial toxin concentrations. In contrast, warming alone did not produce higher cyanobacterial abundances or cyanobacterial toxin concentrations likely due to the depletion of the available nutrient pool. Similarly, the herbicide glyphosate alone did not affect abundances of any phytoplankton taxa. In the case of nutrient enrichment, cyanobacterial toxin concentrations were much higher than under warming alone due to a strong boost in biomass of potential cyanobacterial toxin producers. From a broader perspective our study shows that in a future warmer climate, nutrient loading has to be reduced if toxic cyanobacterial dominance is to be controlled.

Evaluating Ultrasonicator Performance for Cyanobacteria Management at Freshwater Sources

Algal blooms consisting of potentially toxic cyanobacteria are a growing source water management challenge faced by water utilities globally. Commercially available sonication devices are designed to mitigate this challenge by targeting cyanobacteria-specific cellular features and aim to inhibit cyanobacterial growth within water bodies. There is limited available literature evaluating this technology; therefore, a sonication trial was conducted in a drinking water reservoir within regional Victoria, Australia across an 18-month period using one device. The trial reservoir, referred to as Reservoir C, is the final reservoir in a local network of reservoirs managed by a regional water utility. Sonicator efficacy was evaluated through qualitative and quantitative analysis of algal and cyanobacterial trends within Reservoir C and surrounding reservoirs using field data collected across three years preceding the trial and during the 18-month duration of the trial. Qualitative assessment revealed a slight increase in eukaryotic algal growth within Reservoir C following device installation, which is likely due to local environmental factors such as rainfall-driven nutrient influx. Post-sonication quantities of cyanobacteria remained relatively consistent, which may indicate that the device was able to counteract favorable phytoplankton growth conditions. Qualitative assessments also revealed minimal prevalence variations of the dominant cyanobacterial species within the reservoir following trial initiation. Since the dominant species were potential toxin producers, there is no strong evidence that sonication altered Reservoir C’s water risk profiles during this trial. Statistical analysis of samples collected within the reservoir and from the intake pipe to the associated treatment plant supported qualitative observations and revealed a significant elevation in eukaryotic algal cell counts during bloom and non-bloom periods post-installation. Corresponding cyanobacteria biovolumes and cell counts revealed that no significant changes occurred, excluding a significant decrease in bloom season cell counts measured within the treatment plant intake pipe and a significant increase in non-bloom season biovolumes and cell counts as measured within the reservoir. One technical disruption occurred during the trial; however, this had no notable impacts on cyanobacterial prevalence. Acknowledging the limitations of the experimental conditions, data and observations from this trial indicate there is no strong evidence that sonication significantly reduced cyanobacteria occurrence within Reservoir C.

Monitoring trophic status using in situ data and Sentinel-2 MSI algorithm: lesson from Lake Malombe, Malawi

With excessive nutrient enrichment exacerbated by anthropogenic drivers, many standing water bodies are changing from oligotrophic to mesotrophic, eutrophic, and finally hypertrophic-negatively affecting ecosystem functioning, biodiversity, and human populations. Efforts have been devoted to developing novel algorithms for estimating chlorophyll-a (chl-a), cyno-blooms, and floating vegetation. However, to this date, little research has focused on freshwater lakes in the data-scarce Sub-Saharan African countries such as Malawi. We, therefore, estimated the trophic status of Lake Malombe in Malawi-a lake likely to be affected by eutrophication and algal bloom-emerging threats to freshwater ecosystem functioning globally-especially with the onset of climatic and anthropogenic drivers. We integrated in situ data with high-resolution Sentinel-2 Multispectral Imagery Analysis (MSI). We independently assessed the remote sensing technique using in situ data and tested the model at multiple stages. The scatter plot showed that most points were in the 95% confidence interval. The validation results between the measured in situ chl-a concentrations and the Sentinel-2 MSI-based chl-a retrieval had a root mean square error (RMSE) of 2.88 µg/L. The chl-a concentrations retrieved from MSI images were consistent with in situ data, indicating that the normalized difference chlorophyll index (NDCI) algorithm estimated chl-a concentrations in Lake Malombe with acceptable accuracy. Dissolved oxygen (DO), sulfate (SO₄^2-), nitrite [Formula: see text], soluble reactive phosphorous [Formula: see text]), total dissolved solids (TDS), and chl-a, except for temperatures from the hot-dry-season, cold-dry-windy-season, and rainy-season, were significantly different (P < 0.05). The Sentinel-2 MSI imagery analysis also depicted similar results, with high chl-a concentration reported in March (rainy season) and October (hot-dry season) and the lowest from May to August (cold-dry-windy season). On the contrary, the ANOVA results for water quality parameters from all five points had P > 0.05. The correlation matrix showed coefficients of (0.798 < r < 0.930, n = 30, P < 0.005), suggesting that Lake Malombe is homogenous. Our results demonstrate that integrating remote sensing based on MSI imagery and in situ data to estimate chl-a can provide an effective tool for monitoring eutrophication in small, medium, and large standing waterbodies-crucial information required to respond to global ecological and climatic dynamics.

Effect of three commercial algaecides on cyanobacteria and microcystin-LR: implications for drinking water treatment using activated carbon

Toxic cyanobacterial blooms in aquatic ecosystems are associated to both public health and environmental concerns worldwide. Depending on the treatment technologies used, the removal capacity of cyanotoxins by drinking water treatment plants (DWTPs) is not sufficient to reach safe levels in drinking water. Likewise, controlling these blooms with algaecide may impair the efficiency of DWTPs due to the possible lysis of cyanobacterial cells and consequent release of cyanotoxins. We investigated the effects of three commercial algaecides (cationic polymer, copper sulfate, and hydrogen peroxide) on the growth parameters of the cyanobacterium Microcystis aeruginosa and the release of microcystin-LR (MC-LR). The potential interference of each algaecide on the MC-LR removal by adsorption on activated carbon (AC) was also tested through adsorption isotherms and kinetics experiments. Most algaecides significantly decreased the cell density and biovolume of M. aeruginosa, as well as increased the release of MC-LR. Interestingly, the presence of the algaecides in binary mixtures with MC-LR affected the adsorption of the cyanotoxin. Relevant adsorption parameters (e.g., maximum adsorption capacity, adsorption intensity, and affinity between MC-LR and AC) were altered when the algaecides were present, especially in the case of the cationic polymer. Also, the algaecides influenced the kinetics (e.g., by shifting the initial adsorption and the desorption constant), which may directly affect the design and operation of DWTPs. Our study indicated that algaecides can significantly impact the fate and the removal of MC-LR in DWTPs when the adsorption process is employed, with important implications for the management and performance of such facilities.

Effects of extracellular polymeric substances on the aggregation of Aphanizomenon flos-aquae under increasing temperature

Aphanizomenon flos-aquae (A. flos-aquae) blooms are serious environmental and ecological problems. Extracellular polymeric substances (EPSs) are among the most important indicators for the growth and aggregation of A. flos-aquae. In this study, the secretion of the EPS matrix under temperature rise (7–37°C) was investigated and the role of this matrix in A. flos-aquae aggregation was quantified. First, when the temperature increased, the aggregation ratio increased from 41.85 to 91.04%. Meanwhile, we found that when soluble EPSs (S-EPSs), loosely bound EPSs (LB-EPSs), and tightly bound EPSs (TB-EPSs) were removed successively, the aggregation ratio decreased from 69.29 to 67.45%, 61.47%, and 41.14%, respectively. Second, the content of polysaccharides in the EPS matrix was higher than the content of proteins under temperature change. The polysaccharide in TB-EPSs was closely related to the aggregation ability of A. flos-aquae (P < 0.01). Third, PARAFAC analysis detected two humic-like substances and one protein-like substance in EPSs. Furthermore, Fourier transforms infrared spectroscopy (FTIR) showed that with increasing temperature, the polysaccharide-related functional groups increased, and the absolute value of the zeta potential decreased. In conclusion, these results indicated that a large number of polysaccharides in TB-EPSs were secreted under increasing temperature, and the polysaccharide-related functional groups increased correspondingly, which reduced the electrostatic repulsion between algal cells, leading to the destruction of the stability of the dispersion system, and then the occurrence of aggregation. This helps us to understand the process of filamentous cyanobacterial aggregation in lakes.

Google Earth Engine and Artificial Intelligence (AI): A Comprehensive Review

Remote sensing (RS) plays an important role gathering data in many critical domains (e.g., global climate change, risk assessment and vulnerability reduction of natural hazards, resilience of ecosystems, and urban planning). Retrieving, managing, and analyzing large amounts of RS imagery poses substantial challenges. Google Earth Engine (GEE) provides a scalable, cloud-based, geospatial retrieval and processing platform. GEE also provides access to the vast majority of freely available, public, multi-temporal RS data and offers free cloud-based computational power for geospatial data analysis. Artificial intelligence (AI) methods are a critical enabling technology to automating the interpretation of RS imagery, particularly on object-based domains, so the integration of AI methods into GEE represents a promising path towards operationalizing automated RS-based monitoring programs. In this article, we provide a systematic review of relevant literature to identify recent research that incorporates AI methods in GEE. We then discuss some of the major challenges of integrating GEE and AI and identify several priorities for future research. We developed an interactive web application designed to allow readers to intuitively and dynamically review the publications included in this literature review.

Short-term rainfall limits cyanobacterial bloom formation in a shallow eutrophic subtropical urban reservoir in warm season

The global increase in dominance of toxic blooms of cyanobacteria has severely impacted aquatic ecosystems and threatened human health for decades. Although it has been shown that high levels of rainfall may inhibit the growth of bloom-forming cyanobacteria, it is still unclear how cyanobacteria respond to short-term rainfall events. Based on five-year (2016-2020) high-frequency (half-week) sampling data from a shallow eutrophic urban reservoir in subtropical China, we explored the short-term effects of rainfall events on cyanobacterial biomass (CBB) by constructing generalized additive models of CBB in rainy periods during warm (April to September) and cool (December and January) months, respectively. We find evidence in support of the hypotheses that short-term rainfall events significantly reduce CBB in warm months, but the opposite response was observed in the cool months. We also highlight a difference in the factors explaining CBB decreases in warm months (precipitation, air temperature, relative humidity, dissolved oxygen and total phosphorus) compared with factors explaining the response of CBB in cool months (sunshine hours, pH and total carbon). In particular, meteorological factors (precipitation, wind speed and sunlight) might drive changes in water temperature and hydro-dynamics of the reservoir, thereby causing a rapid reduction of CBB after rainfall events in warm months. This varying response of cyanobacteria to short-term rainfall events in the shallow eutrophic subtropical reservoir may also be expected in temperate or cool lakes as climate change effects become stronger.

Modeling the sensitivity of cyanobacteria blooms to plausible changes in precipitation and air temperature variability

Many recent studies have attributed the observed variability of cyanobacteria blooms to meteorological drivers and have projected blooms with worsening societal and ecological impacts under future climate scenarios. Nonetheless, few studies have jointly examined their sensitivity to projected changes in both precipitation and temperature variability. Using an Integrated Assessment Model (IAM) of Lake Champlain's eutrophic Missisquoi Bay, we demonstrate a factorial design approach for evaluating the sensitivity of concentrations of chlorophyll a (chl-a), a cyanobacteria surrogate, to global climate model-informed changes in the central tendency and variability of daily precipitation and air temperature. An Analysis of Variance (ANOVA) and multivariate contour plots highlight synergistic effects of these climatic changes on exceedances of the World Health Organization's moderate 50 μg/L concentration threshold for recreational contact. Although increased precipitation produces greater riverine total phosphorus loads, warmer and drier scenarios produce the most severe blooms due to the greater mobilization and cyanobacteria uptake of legacy phosphorus under these conditions. Increases in daily precipitation variability aggravate blooms most under warmer and wetter scenarios. Greater temperature variability raises exceedances under current air temperatures but reduces them under more severe warming when water temperatures exceed optimal values for cyanobacteria growth more often. Our experiments, controlled for wind-induced changes to lake water quality, signal the importance of larger summer runoff events for curtailing bloom growth through reductions of water temperature, sunlight penetration and stratification. Finally, the importance of sequences of wet and dry periods in generating cyanobacteria blooms motivates future research on bloom responses to changes in interannual climate persistence.

Exposure to microcystin-LR in tropical reservoirs for water supply poses high risks for children and adults

While the presence of microcystin-LR (MC-LR) in raw water from eutrophic reservoirs poses human health concerns, the risks associated with the ingestion of MC-LR in drinking water are not fully elucidated. We used a time series of MC-LR in raw water from tropical urban reservoirs in Brazil to estimate the hazard quotients (HQs) for non-carcinogenic health effects and the potential ingestion of MC-LR through drinking water. We considered scenarios of MC-LR removal in the drinking water treatment plants (DWTPs) of two supply systems (Cascata and Guarapiranga). The former uses coagulation/flocculation/sedimentation/filtration/disinfection, while the latter has an additional step of membrane ultrafiltration, with contrasting expected MC-LR removal efficiencies. We considered reference values for infants (0.30 μg L^-1), children/adults (1.60 μg L^-1), or the population in general (1.0 μg L^-1). For most scenarios for Cascata, the 95% upper confidence level of the HQ indicated high risks of exposure for the population (HQ > 1), particularly for infants (HQ = 30.910). The water treatment in Cascata was associated to the potential exposure to MC-LR due to its limited removal capacity, with up to 263 days/year with MC-LR above threshold values. The Guarapiranga system had the lowest MC-LR in the raw water as well as higher expected removal efficiencies in the DWTP, resulting in negligible risks. We reinforce the importance of integrating raw water quality characteristics and treatment technologies to reduce the risks of exposure to MC-LR, especially for vulnerable population groups. Our results can serve as a starting point for risk management strategies to minimize cases of MC-LR intoxication in Brazil and other developing countries.

The proliferation rule of Microcystis aeruginosa under different initial pH conditions and its influence on the pH value of the environment

This study investigated the characteristics of the proliferation process of Microcystis aeruginosa and its changes to environmental pH values under different initial pH values and different initial inoculation densities. The results showed that although the initial pH value or the initial inoculation density was different, the pH values of the culture systems fluctuated up and down throughout the proliferation of M. aeruginosa, both on a daily and hourly time scale, and then tended to stabilize around the same value of 10.0 at the end of proliferation. The optimal pH value for the proliferation of M. aeruginosa was 9.55. This study creatively proposes that the period when the environmental pH value starts to rise rapidly toward 9.0 could be selected as an early warning period for a cyanobacterial outbreak, and the environmental pH value could be adjusted to below 8.0 to delay the outbreak. These results provide a scientific basis for further understanding the mechanism of cyanobacterial blooms and formulating pH-based control strategies.

Continuous and Synoptic Assessment of Indian Inland Waters for Harmful Algae Blooms

Cyanobacterial Harmful Algal Blooms (CyanoHABs) are progressively becoming a major water quality, socioeconomic, and health hazard worldwide. In India, there are frequent episodes of severe CyanoHABs, which are left untreated due to a lack of awareness and monitoring infrastructure, affecting the economy of the country gravely. In this study, for the first time, we present a country-wide analysis of CyanoHABs in India by developing a novel interactive cloud-based dashboard called "CyanoKhoj" in Google Earth Engine (GEE) which uses Sentinel-3 Ocean and Land Colour Instrument (OLCI) remotely sensed datasets. The main goal of this study was to showcase the utility of CyanoKhoj for rapid monitoring and discuss the widespread CyanoHABs problems across India. We demonstrate the utility of Cyanokhoj by including select case studies of lakes and reservoirs geographically spread across five states: Bargi and Gandhisagar Dams in Madhya Pradesh, Hirakud Reservoir in Odisha, Ukai Dam in Gujarat, Linganamakki Reservoir in Karnataka, and Pulicat Lake in Tamil Nadu. These sites were studied from September to November 2018 using CyanoKhoj, which is capable of near-real-time monitoring and country-wide assessment of CyanoHABs. We used CyanoKhoj to prepare spatiotemporal maps of Chlorophyll-a (Chl-a) content and Cyanobacterial Cell Density (CCD) to study the local spread of the CyanoHABs and their phenology in these waterbodies. A first-ever all-India CCD map is also presented for the year 2018, which highlights the spatial spread of CyanoHABs throughout the country (32 large waterbodies across India with severe bloom: CCD>2,500,000). Results indicate that CyanoHABs are most prevalent in nutrient-rich waterbodies prone to industrial and other nutrient-rich discharges. A clear temporal evolution of the blooms showed that they are dominant during the post-monsoon season (September-October) when the nutrient concentrations in the waterbodies are at their peak, and they begin to decline towards winter (November-December). CyanoKhoj is an open-source tool that can have a significant broader impact in mapping CyanoHABs not only throughout cyanobacteria data-scarce India, but on a global level using archived and future Sentinel-3A/B OLCI data.

High-resolution temporal detection of cyanobacterial blooms in a deep and oligotrophic lake by high-frequency buoy data

Cyanobacterial blooms are increasing in magnitude, frequency, and duration worldwide. However, our knowledge of cyanobacterial blooms dynamics and driving mechanisms is still limited due to their high spatiotemporal variability. To determine the potential driving mechanisms of cyanobacterial blooms in oligotrophic lakes, we collected a high-frequency depth profile of chlorophyll fluorescence (ChlF) and synchronous water quality, hydrometeorological data in early spring 2016 in oligotrophic Lake Qiandaohu. The vertical distribution of ChlF exhibited two patterns, "aggregated" and "discrete", using Morisita's index, and the aggregated ChlF presented subsurface chlorophyll maxima during the thermal stratification period. The ChlF concentration was positively correlated with water temperature and negatively correlated with turbidity. Significantly linear relationships were observed between ChlF vertical structure parameters (e.g., Morisita's index, subsurface chlorophyll maxima depth and thickness) and thermal stratification parameters (e.g., mixing layer depth and relative water column stability). After rainstorm floods, the ChlF pattern suddenly change from "aggregated" to "discrete" and a ChlF concentration <1 μg/L was observed for 7-11 days with a significant increase in the mixing depth layer and turbidity. The results suggest that cyanobacterial blooms are robustly associated with thermal stratification and rainstorm floods in the deep and oligotrophic lake. Thermal stratification boosts surface phytoplankton accumulation by increasing water temperature, enhancing light availability and restricting phytoplankton vertical distribution. Rainstorm floods interrupt the accumulation by disrupting thermal stratification and decreasing the available light. Furthermore, wind speed and air temperature both regulate the phytoplankton dynamics by affecting thermal stratification. Our research quantifies the cyanobacterial bloom dynamics and their relationship between environmental factors, improving our knowledge of the driving mechanisms of cyanobacterial bloom for the protection of drinking water safety and aquatic organism health in lakes.

The Impact of Extreme Weather Events on Bacterial Communities and Opportunistic Pathogens in a Drinking Water Treatment Plant

Drinking water treatment processes are highly effective at improving water quality, but pathogens can still persist in treated water, especially after extreme weather events. To identify how extreme weather events affected bacterial populations in source and treated water, water samples were collected from the Yangtze River Delta area and a local full-scale drinking water treatment plant. Bacterial community structure and the occurrence of pathogens were investigated in samples using 16S rRNA sequencing and qPCR techniques. In this study, the results show that intense rainfall can significantly increase levels of bacteria and opportunistic pathogens in river and drinking water treatment processes (p < 0.05); in particular, the relative abundance of Cyanobacteria increased after a super typhoon event (p < 0.05). The biological activated carbon (BAC) tank was identified as a potential pathogen reservoir and was responsible for 52 ± 6% of the bacteria released downstream, according to Bayesian-based SourceTracker analysis. Our results provide an insight into the challenges faced by maintaining finished water quality under changing weather conditions.

Rapid estimation of CO2 emissions from forest fire events using cloud-based computation of google earth engine

One of the main sources of greenhouse gases is forest fire, with carbon dioxide as its main constituent. With increasing global surface temperatures, the probability of forest fire events also increases. A method that enables rapid quantification of emissions is even more necessary to estimate the environmental impact. This study introduces the application of the Google Earth Engine platform to monitor burned areas in forest fire events in Mount Arjuno, Indonesia, during the 2016-2019 period, using Landsat-8 and Sentinel-2 satellite imageries. The events particularly affected grassland and tropical forest areas, as well as a fraction of agricultural areas, with a total estimated emission of 2.5 × 10³ tCO₂/km² burned area. Higher carbon dioxide emissions were also observed, consistent with the higher local surface temperature as well as the CO total column mixing ratio average retrieved from Sentinel-5 p Tropospheric Monitoring Instrument during the period of analysis.

Monitoring cyanoHABs and water quality in Laguna Lake (Philippines) with Sentinel-2 satellites during the 2020 Pacific typhoon season

Laguna Lake, the largest freshwater lake in the Philippines, is permanently subject to nutrient-driven eutrophication and pollution and experiences harmful algal blooms (cyanoHABs) periodically with serious socio-economic implications. The aim of this study is to evaluate the suitability of the Sentinel-2 imagery of the European Commission's Copernicus Earth Observation programme for lake monitoring during the 2020 Pacific typhoon season (September-November 2020). The Case-2 Regional CoastColour processor is used to atmospherically correct Level 1 data and generate water quality parameters, such as chlorophyll-a (Chl-a) and total suspended matter (TSM) at 10 m. Results show that Super Typhoon Goni and Typhoon Vamco delivered high suspended sediment loads to the reservoir at concentrations above 170 g/m³ compared to pre-storm situations (0-35 g/m³). The typhoons also affect Chl-a, with a mean concentration of 10 mg/m³ and 30 mg/m³ for pre- and post-typhoons, respectively. In addition, the normalized difference chlorophyll index (NDCI) is used in the Google Earth Engine platform for near-real time monitoring of cyanoHABs at 20 m spatial resolution. Satellite maps are key for detecting the distribution of the blooms due to the patchiness of the green algae species, which usually form scum and elongated slicks in the lake. Maximum records of bloom detection during the study period occur in the Central Bay, one of the lake sections with major aquaculture and fisheries activities. The Sentinel-2 mission improves synoptic mapping of cyanoHABs and enables trends in their extent and severity to be documented. These available methods provide an essential tool for rapid detection after extreme events and for regular water quality monitoring, which will assist and benefit the cost-effective management of Laguna Lake.

Responses of phytoplankton community structure and association to variability in environmental drivers in a tropical coastal lagoon

Spatial and seasonal heterogeneity in phytoplankton communities are governed by many biotic and abiotic drivers. However, the identification of long-term spatial and temporal trends in abiotic drivers, and their interdependencies with the phytoplankton communities' structure is understudied in tropical brackish coastal lagoons. We examined phytoplankton communities' spatiotemporal dynamics from a 5-year dataset (n = 780) collected from 13 sampling stations in Chilika Lagoon, India, where the salinity gradient defined the spatial patterns in environmental variables. Generalized additive models showed a declining trend in phytoplankton biomass, pH, and dissolved PO₄ in the lagoon. Hierarchical modelling of species communities revealed that salinity (44.48 ± 28.19%), water temperature (4.37 ± 5.65%), and season (4.27 ± 0.96%) accounted for maximum variation in the phytoplankton composition. Bacillariophyta (Indicator Value (IV): 0.74) and Dinophyta (IV: 0.72) emerged as top indicators for polyhaline regime whereas, Cyanophyta (IV: 0.81), Euglenophyta (IV: 0.79), and Chlorophyta (IV: 0.75) were strong indicators for oligohaline regime. The responses of Dinophyta and Chrysophyta to environmental drivers were much more complex as random effects accounted for ~70-75% variation in their abundances. Prorocentrum minimum (IV: 0.52), Gonyaulax sp. (IV: 0.52), and Alexandrium sp. (IV: 0.51) were potential indicators of P-limitation. Diploneis weissflogii (IV: 0.43), a marine diatom, emerged as a potential indicator of N-limitation. Hierarchical modelling revealed the positive association between Cyanophyta, Chlorophyta, and Euglenophyta whereas, Dinophyta and Chrysophyta showed a negative association with Cyanophyta, Chlorophyta, and Euglenophyta. Landsat 8-Operational Land Imager satellite models predicted the highest and lowest Cyanophyta abundances in northern and southern sectors, respectively, which were in accordance with the near-coincident field-based measurements from the lagoon. This study highlighted the dynamics of phytoplankton communities and their relationships with environmental drivers by separating the signals of habitat filtering and biotic interactions in a monsoon-regulated tropical coastal lagoon.

AlgaeMAp: Algae Bloom Monitoring Application for Inland Waters in Latin America

Due to increasing algae bloom occurrence and water degradation on a global scale, there is a demand for water quality monitoring systems based on remote sensing imagery. This paper describes the scientific, theoretical, and methodological background for creating a cloud-computing interface on Google Earth Engine (GEE) which allows end-users to access algae bloom related products with high spatial (30 m) and temporal (~5 day) resolution. The proposed methodology uses Sentinel-2 images corrected for atmospheric and sun-glint effects to generate an image collection of the Normalized Difference Chlorophyll-a Index (NDCI) for the entire time-series. NDCI is used to estimate both Chl-a concentration, based on a non-linear fitting model, and Trophic State Index (TSI), based on a tree-decision model classification into five classes. Once the Chl-a and TSI algorithms had been calibrated and validated they were implemented in GEE as an Earth Engine App, entitled Algae Bloom Monitoring Application (AlgaeMAp). AlgaeMAp is the first online platform built within the GEE platform that offers high spatial resolution of water quality parameters. The App benefits from the huge processing capability of GEE that allows any user with internet access to easily extract detailed spatial (30 m) and long temporal Chl-a and TSI information (from August 2015 and with images every 5 days) throughout the most important reservoirs in the State of São Paulo/Brazil. The application will be adapted to extend to other relevant areas in Latin America.

Setting the Phosphorus Boundaries for Greek Natural Shallow and Deep Lakes for Water Framework Directive Compliance

Eutrophication caused by nutrient enrichment is a predominant stressor leading to lake degradation and, thus, the set-up of boundaries that support good ecological status, the Water Framework Directive’s main target, is a necessity. Greece is one of the Member States that have recorded delays in complying with the coherent management goals of European legislation. A wide range of different statistical approaches has been proposed in the Best Practice Guide for determining appropriate nutrient thresholds. To determine the nutrient thresholds supporting the good status of natural Greek lakes, the phytoplankton dataset gathered from the national monitoring programme (2015–2020) was used for shallow and deep natural lakes. The regression analyses were sufficient and robust in order to derive total phosphorus thresholds that ranged from 20 to 41 μg/L in shallow and 15–32 μg/L in deep natural lake types. Nutrient boundaries that encompass the stressors these lakes are subject to, are essential in proper lake management design.

Mapping Freshwater Chlorophyll-a Concentrations at a Regional Scale Integrating Multi-Sensor Satellite Observations with Google Earth Engine

Monitoring harmful algal blooms (HABs) in freshwater over regional scales has been implemented through mapping chlorophyll-a (Chl-a) concentrations using multi-sensor satellite remote sensing data. Cloud-free satellite measurements and a sufficient number of matched-up ground samples are critical for constructing a predictive model for Chl-a concentration. This paper presents a methodological framework for automatically pairing surface reflectance values from multi-sensor satellite observations with ground water quality samples in time and space to form match-up points, using the Google Earth Engine cloud computing platform. A support vector machine model was then trained using the match-up points, and the prediction accuracy of the model was evaluated and compared with traditional image processing results. This research demonstrates that the integration of multi-sensor satellite observations through Google Earth Engine enables accurate and fast Chl-a prediction at a large regional scale over multiple years. The challenges and limitations of using and calibrating multi-sensor satellite image data and current and potential solutions are discussed.

Spatiotemporal Variability in Phytoplankton Bloom Phenology in Eastern Canadian Lakes Related to Physiographic, Morphologic, and Climatic Drivers

Phytoplankton bloom monitoring in freshwaters is a challenging task, particularly when biomass is dominated by buoyant cyanobacterial communities that present complex spatiotemporal patterns. Increases in bloom frequency or intensity and their earlier onset in spring were shown to be linked to multiple anthropogenic disturbances, including climate change. The aim of the present study was to describe the phenology of phytoplankton blooms and its potential link with morphological, physiographic, anthropogenic, and climatic characteristics of the lakes and their watershed. The spatiotemporal dynamics of near-surface blooms were studied on 580 lakes in southern Quebec (Eastern Canada) over a 17-year period by analyzing chlorophyll-a concentrations gathered from MODIS (Moderate Resolution Imaging Spectroradiometer) satellite images. Results show a significant increase by 23% in bloom frequency across all studied lakes between 2000 and 2016. The first blooms of the year appeared increasingly early over this period but only by 3 days (median date changing from 6 June to 3 June). Results also indicate that high biomass values are often reached, but the problem is seldom extended to the entire lake surface. The canonical correlation analysis between phenological variables and environmental variables shows that higher frequency and intensity of phytoplankton blooms and earlier onset date occurred for smaller watersheds and higher degree-days, lake surface area, and proportion of urban zones. This study provides a regional picture of lake trophic state over a wide variety of lacustrine environments in Quebec, a detailed phenology allowing to go beyond local biomass assessments, and the first steps on the development of an approach exploiting regional trends for local pattern assessments.

CyanoTRACKER: A cloud-based integrated multi-platform architecture for global observation of cyanobacterial harmful algal blooms

Over the past decade, the global proliferation of cyanobacterial harmful algal blooms (CyanoHABs) have presented a major risk to the public and wildlife, and ecosystem and economic services provided by inland water resources. As a consequence, water resources, environmental, and healthcare agencies are in need of early information about the development of these blooms to mitigate or minimize their impact. Results from various components of a novel multi-cloud cyber-infrastructure referred to as "CyanoTRACKER" for initial detection and continuous monitoring of spatio-temporal growth of CyanoHABs is highlighted in this study. The novelty of the CyanoTRACKER framework is the collection and integration of combined community reports (social cloud), remote sensing data (sensor cloud) and digital image analytics (computation cloud) to detect and differentiate between regular algal blooms and CyanoHABs. Individual components of CyanoTRACKER include a reporting website, mobile application (App), remotely deployable solar powered automated hyperspectral sensor (CyanoSense), and a cloud-based satellite data processing and integration tool. All components of CyanoTRACKER provided important data related to CyanoHABs assessments for regional and global water bodies. Reports and data received via social cloud including the mobile App, Twitter, Facebook, and CyanoTRACKER website, helped in identifying the geographic locations of CyanoHABs affected water bodies. A significant increase (124.92%) in tweet numbers related to CyanoHABs was observed between 2011 (total relevant tweets = 2925) and 2015 (total relevant tweets = 6579) that reflected an increasing trend of the harmful phenomena across the globe as well as an increased awareness about CyanoHABs among Twitter users. The CyanoHABs affected water bodies extracted via the social cloud were categorized, and smaller water bodies were selected for the deployment of CyanoSense, and satellite data analysis was performed for larger water bodies. CyanoSense was able to differentiate between ordinary algae and CyanoHABs through the use of their characteristic absorption feature at 620 nm. The results and products from this infrastructure can be rapidly disseminated via the CyanoTRACKER website, social media, and direct communication with appropriate management agencies for issuing warnings and alerting lake managers, stakeholders and ordinary citizens to the dangers posed by these environmentally harmful phenomena.

Mitigating the global expansion of harmful cyanobacterial blooms: Moving targets in a human- and climatically-altered world

Cyanobacterial harmful algal blooms (CyanoHABs) are a major threat to human and environmental health. As global proliferation of CyanoHABs continues to increase in prevalence, intensity, and toxicity, it is important to identify and integrate the underlying causes and controls of blooms in order to develop effective short- and long-term mitigation strategies. Clearly, nutrient input reductions should receive high priority. Legacy effects of multi-decadal anthropogenic eutrophication have altered limnetic systems such that there has been a shift from exclusive phosphorus (P) limitation to nitrogen (N) limitation and N and P co-limitation. Additionally, climate change is driving CyanoHAB proliferation through increasing global temperatures and altered precipitation patterns, including more extreme rainfall events and protracted droughts. These scenarios have led to the "perfect storm scenario"; increases in pulsed nutrient loading events, followed by persistent low-flow, long water residence times, favoring bloom formation and proliferation. To meet the CyanoHAB mitigation challenge, we must: (1) Formulate watershed and airshed-specific N and P input reductions on a sliding scale to meet anthropogenic and climatic forcings. (2) Develop CyanoHAB management strategies that incorporate current and anticipated climatic changes and extremes. (3) Make nutrient management strategies compatible with other physical-chemical-biological mitigation approaches, such as altering freshwater flow and flushing, dredging, chemical applications, introduction of selective grazers, etc. (4) Target CyanoHAB toxin production and developing management approaches to reduce toxin production. (5) Develop broadly applicable long-term strategies that incorporate the above recommendations.