A review of monitoring technologies for real-time management of cyanobacteria: Recent advances and future direction

An investigation of cyanobacteria, cyanotoxins and environmental variables in selected drinking water treatment plants in New Jersey

Harmful Algal Blooms (HAB) have the potential to impact human health primarily through their possible cyanotoxins production. While conventional water treatments can result in the removal of unlysed cyanobacterial cells and low levels of cyanotoxins, during severe HAB events, cyanotoxins can break through and can be present in the treated water due to a lack of adequate toxin treatment. The objectives of this study were to assess the HAB conditions in drinking water sources in New Jersey and investigate relationships between environmental variables and cyanobacterial communities in these drinking water sources. Source water samples were collected monthly from May to October 2019 and analyzed for phytoplankton and cyanobacterial cell densities, microcystins, cylindrospermopsin, Microcystis 16S rRNA gene, microcystin-producing mcyB gene, Raphidiopsis raciborskii-specific rpoC1 gene, and cylindrospermopsin-producing pks gene. Water quality parameters included water temperature, pH, dissolved oxygen, specific conductance, fluorescence of phycocyanin and chlorophyll, chlorophyll-a, total suspended solids, total dissolved solids, dissolved organic carbon, total nitrogen, ammonia, and total phosphorus. In addition to source waters, microcystins and cylindrospermopsin were analyzed for treated waters. The results showed all five selected New Jersey source waters had high total phosphorus concentrations that exceeded the established New Jersey Surface Water Quality Standards for lakes and rivers. Commonly found cyanobacteria were identified, such as Microcystis and Dolichospermum. Site E was the site most susceptible to HABs with significantly greater HAB variables, such as extracted phycocyanin, fluorescence of phycocyanin, cyanobacterial cell density, microcystins, and Microcystis 16S rRNA gene. All treated waters were undetected with microcystins, indicating treatment processes were effective at removing toxins from source waters. Results also showed that phycocyanin values had a significantly positive relationship with microcystin concentration, copies of Microcystis 16S rRNA and microcystin-producing mcyB genes, suggesting these values can be used as a proxy for HAB monitoring. This study suggests that drinking water sources in New Jersey are vulnerable to forthcoming HAB. Monitoring and management of source waters is crucial to help safeguard public health.

High resolution chlorophyll- a in-situ fluorescence sensors versus in-vitro chlorophyll- a measurements in mesocosms with contrasting nutrient and temperature treatments

Harmful algal blooms (HABs) are a significant threat to freshwater ecosystems, and monitoring for changes in biomass is therefore important. Fluorescence in-situ sensors enable rapid and high frequency real-time data collection and have been widely used to determine chlorophyll- a (Chla) concentrations that are used as an indicator of the total algal biomass. However, conversion of fluorescence to equivalent Chla concentrations is often complicated due to biofouling, phytoplankton composition and the type of equipment used. Here, we validated measurements from 24 Chla and 12 phycocyanin (cyanobacteria indicator) fluorescence in-situ sensors (Cyclops-7F, Turner Designs) against spectrophotometrically (in-vitro) determined Chla and tested a data-cleaning procedure for eliminating data errors and impacts of non-photochemical quenching. The test was done across a range of freshwater plankton communities in 24 mesocosms (i.e. experimental tanks) with a 2x3 (high and low nutrient x ambient, IPCC-A2 and IPCC-A2+50% temperature scenarios) factorial design. For most mesocosms (tanks), we found accurate (r 2 ≥ 0.7) calibration of in-situ Chla fluorescence data using simple linear regression. An exception was tanks with high in-situ phycocyanin fluorescence, for which multiple regressions were employed, which increased the explained variance by >16%. Another exception was the low Chla concentration tanks (r 2 < 0.3). Our results also show that the high frequency in-situ fluorescence data recorded the timing of sudden Chla variations, while less frequent in-vitro sampling sometimes missed these or, when recorded, the duration of changes was inaccurately determined. Fluorescence in-situ sensors are particularly useful to detect and quantify sudden phytoplankton biomass variations through high frequency measurements, especially when using appropriate data-cleaning methods and accounting for factors that can impact the fluorescence readings.

A paleoecological investigation of recent cyanobacterial blooms and their drivers in two contrasting lakes

Cyanobacterial blooms are one of the most significant threats to global water security and freshwater biodiversity. Interactions among multiple stressors, including habitat degradation, species invasions, increased nutrient runoff, and climate change, are key drivers. However, assessing the role of anthropogenic activity on the onset of cyanobacterial blooms and exploring response variation amongst lakes of varying size and depth is usually limited by lack of historical records. In the present study we applied molecular, paleolimnological (trace metal, Itrax-µ-XRF and hyperspectral scanning, chronology), paleobotanical (pollen) and historical data to reconstruct cyanobacterial abundance and community composition and anthropogenic impacts in two dune lakes over a period of up to 1200 years. Metabarcoding and droplet digital PCR results showed very low levels of picocyanobacteria present in the lakes prior to about CE 1854 (1839-1870 CE) in the smaller shallow Lake Alice and CE 1970 (1963-1875 CE) in the larger deeper Lake Wiritoa. Hereafter bloom-forming cyanobacteria were detected and increased notably in abundance post CE 1984 (1982-1985 CE) in Lake Alice and CE 1997 (1990-2007 CE) in Lake Wiritoa. Currently, the magnitude of blooms is more pronounced in Lake Wiritoa, potentially attributable to hypoxia-induced release of phosphorus from sediment, introducing an additional source of nutrients. Generalized linear modelling was used to investigate the contribution of nutrients (proxy = bacterial functions), temperature, redox conditions (Mn:Fe), and erosion (Ti:Inc) in driving the abundance of cyanobacteria (ddPCR). In Lake Alice nutrients and erosion had a statistically significant effect, while in Lake Wiritoa nutrients and redox conditions were significant.

In Situ Water Quality Monitoring Using an Optical Multiparameter Sensor Probe

Optical methods such as ultraviolet/visible (UV/Vis) and fluorescence spectroscopy are well-established analytical techniques for in situ water quality monitoring. A broad range of bio-logical and chemical contaminants in different concentration ranges can be detected using these methods. The availability of results in real time allows a quick response to water quality changes. The measuring devices are configured as portable multi-parameter probes. However, their specification and data processing typically cannot be changed by users, or only with difficulties. Therefore, we developed a submersible sensor probe, which combines UV/Vis and fluorescence spectroscopy together with a flexible data processing platform. Due to its modular design in the hardware and software, the sensing system can be modified to the specific application. The dimension of the waterproof enclosure with a diameter of 100 mm permits also its application in groundwater monitoring wells. As a light source for fluorescence spectroscopy, we constructed an LED array that can be equipped with four different LEDs. A miniaturized deuterium-tungsten light source (200-1100 nm) was used for UV/Vis spectroscopy. A miniaturized spectrometer with a spectral range between 225 and 1000 nm permits the detection of complete spectra for both methods.

Machine Learning-Based Early Warning Level Prediction for Cyanobacterial Blooms Using Environmental Variable Selection and Data Resampling

Many countries have attempted to mitigate and manage issues related to harmful algal blooms (HABs) by monitoring and predicting their occurrence. The infrequency and duration of HABs occurrence pose the challenge of data imbalance when constructing machine learning models for their prediction. Furthermore, the appropriate selection of input variables is a significant issue because of the complexities between the input and output variables. Therefore, the objective of this study was to improve the predictive performance of HABs using feature selection and data resampling. Data resampling was used to address the imbalance in the minority class data. Two machine learning models were constructed to predict algal alert levels using 10 years of meteorological, hydrodynamic, and water quality data. The improvement in model accuracy due to changes in resampling methods was more noticeable than the improvement in model accuracy due to changes in feature selection methods. Models constructed using combinations of original and synthetic data across all resampling methods demonstrated higher prediction performance for the caution level (L-1) and warning level (L-2) than models constructed using the original data. In particular, the optimal artificial neural network and random forest models constructed using combinations of original and synthetic data showed significantly improved prediction accuracy for L-1 and L-2, representing the transition from normal to bloom formation states in the training and testing steps. The test results of the optimal RF model using the original data indicated prediction accuracies of 98.8% for L0, 50.0% for L1, and 50.0% for L2. In contrast, the optimal random forest model using the Synthetic Minority Oversampling Technique-Edited Nearest Neighbor (ENN) sampling method achieved accuracies of 85.0% for L0, 85.7% for L1, and 100% for L2. Therefore, applying synthetic data can address the imbalance in the observed data and improve the detection performance of machine learning models. Reliable predictions using improved models can support the design of management practices to mitigate HABs in reservoirs and ultimately ensure safe and clean water resources.

Membrane inlet—ion mobility spectrometry with automatic spectra evaluation as online monitoring tool for the process control of microalgae cultivation

The cultivation of algae either in open raceway ponds or in closed bioreactors could allow the renewable production of biomass for food, pharmaceutical, cosmetic, or chemical industries. Optimal cultivation conditions are however required to ensure that the production of these compounds is both efficient and economical. Therefore, high-frequency analytical measurements are required to allow timely process control and to detect possible disturbances during algae growth. Such analytical methods are only available to a limited extent. Therefore, we introduced a method for monitoring algae release volatile organic compounds (VOCs) in the headspace above a bioreactor in real time. This method is based on ion mobility spectrometry (IMS) in combination with a membrane inlet (MI). The unique feature of IMS is that complete spectra are detected in real time instead of sum signals. These spectral patterns produced in the ion mobility spectrum were evaluated automatically via principal component analysis (PCA). The detected peak patterns are characteristic for the respective algae culture; allow the assignment of the individual growth phases and reflect the influence of experimental parameters. These results allow for the first time a continuous monitoring of the algae cultivation and thus an early detection of possible disturbances in the biotechnological process.

Detection and identification of a mixed cyanobacteria and microalgae culture using derivative spectrophotometry

Early detection and monitoring of algal blooms and potentially toxic cyanobacteria in source waters are becoming increasingly important with rising climate change and industrialization. There is a growing need to measure the mixed microalgae cultures sensitively and accurately, as multiple algae species are present in natural source waters. This study investigated the detection of an equal concentration, mixed-culture of cyanobacteria (Microcystis aeruginosa) and a common green algae (Chlorella vulgaris) in water using UV-Vis spectrophotometry while employing longer pathlengths and derivative spectrophotometry to improve the detection limit. A strong linear relationship (R² > 0.99) was found between the concentration and absorbance of the mixed-culture at 682 nm using 50 and 100 mm pathlengths. This study showed that the cyanobacterial (phycocyanin) peak could be separately identified in mixed-culture setting, while the chlorophyll peaks of both algae overlapped each other. The lowest detection limit of the mixed algal culture using traditional spectrophotometry and derivative spectrophotometry was calculated to be 25,997 cells/mL and 5505 cells/mL using a 100 mm cuvette pathlength. Lastly, the performance of mixed-culture and individual algal cultures were compared, and analyses were carried out to evaluate differences in slopes which can be used for quantification purposes. The results indicate that derivative spectrophotometry significantly improved the detection limit making the method potentially viable for the early detection of mixed algal cultures.

Management of biogenic taste and odour: From source water, through treatment processes and distribution systems, to consumers

Biogenic taste and odour (T&O) have become a global concern for water utilities, due to the increasing frequency of algal blooms and other microbial events arising from the combined effects of climate change and eutrophication. Microbially-produced T&O compounds impact source waters, drinking water treatment plants, and drinking water distribution systems. It is important to manage across the entire biogenic T&O pathway to identify key risk factors and devise strategies that will safeguard the quality of drinking water in a changing world, since the presence of T&O impacts consumer confidence in drinking water safety. This study provides a critical review of current knowledge on T&O-causing microbes and compounds for proactive management, including the identification of abiotic risk factors in source waters, a discussion on the effectiveness of existing T&O barriers in drinking water treatment plants, an analysis of risk factors for biofilm growth in water distribution systems, and an assessment of the impacts of T&O on consumers. The fate of biogenic T&O in drinking water systems is tracked from microbial production pathways, through the release of intracellular T&O by cell lysis, to the treatment of microbial cells and dissolved T&O. Based on current knowledge, five impactful research and management directions across the T&O pathway are recommended.

Impact of Stagnation on the Diversity of Cyanobacteria in Drinking Water Treatment Plant Sludge

Health-related concerns about cyanobacteria-laden sludge of drinking water treatment plants (DWTPs) have been raised in the past few years. Microscopic taxonomy, shotgun metagenomic sequencing, and microcystin (MC) measurement were applied to study the fate of cyanobacteria and cyanotoxins after controlled sludge storage (stagnation) in the dark in a full-scale drinking water treatment plant within 7 to 38 days. For four out of eight dates, cyanobacterial cell growth was observed by total taxonomic cell counts during sludge stagnation. The highest observed cell growth was 96% after 16 days of stagnation. Cell growth was dominated by potential MC producers such as Microcystis, Aphanocapsa, Chroococcus, and Dolichospermum. Shotgun metagenomic sequencing unveiled that stagnation stress shifts the cyanobacterial communities from the stress-sensitive Nostocales (e.g., Dolichospermum) order towards less compromised orders and potential MC producers such as Chroococcales (e.g., Microcystis) and Synechococcales (e.g., Synechococcus). The relative increase of cyanotoxin producers presents a health challenge when the supernatant of the stored sludge is recycled to the head of the DWTP or discharged into the source. These findings emphasize the importance of a strategy to manage cyanobacteria-laden sludge and suggest practical approaches should be adopted to control health/environmental impacts of cyanobacteria and cyanotoxins in sludge.

Improvement of In Vivo Fluorescence Tools for Fast Monitoring of Freshwater Phytoplankton and Potentially Harmful Cyanobacteria

The use of multi-wavelength spectrofluorometers for the fast detection of algal taxa, based on chlorophyll a (Chl-a) emission spectra, has become a common practice in freshwater water management, although concerns about their accuracy have been raised. Here, inter-laboratory comparisons using monoalgal cultures have been performed to assess the reliability of different spectrofluorometer models, alongside Chl-a extraction methods. Higher Chl-a concentrations were obtained when using the spectrofluorometers than extraction methods, likely due to the poor extraction efficiencies of solvents, highlighting that traditional extraction methods could underestimate algal or cyanobacterial biomass. Spectrofluorometers correctly assigned species to the respective taxonomic group, with low and constant percent attribution errors (Chlorophyta and Euglenophyceae 6-8%, Cyanobacteria 0-3%, and Bacillariophyta 10-16%), suggesting that functioning limitations can be overcome by spectrofluorometer re-calibration with fresh cultures. The monitoring of a natural phytoplankton assemblage dominated by Chlorophyta and Cyanobacteria gave consistent results among spectrofluorometers and with microscopic observations, especially when cell biovolume rather than cell density was considered. In conclusion, multi-wavelength spectrofluorometers were confirmed as valid tools for freshwater monitoring, whereas a major focus on intercalibration procedures is encouraged to improve their reliability and broaden their use as fast monitoring tools to prevent environmental and public health issues related to the presence of harmful cyanobacteria.

Early and late cyanobacterial bloomers in a shallow, eutrophic lake

Cyanobacterial blooms present challenges for water treatment, especially in regions like the Canadian prairies where poor water quality intensifies water treatment issues. Buoyant cyanobacteria that resist sedimentation present a challenge as water treatment operators attempt to balance pre-treatment and toxic disinfection by-products. Here, we used microscopy to identify and describe the succession of cyanobacterial species in Buffalo Pound Lake, a key drinking water supply. We used indicator species analysis to identify temporal grouping structures throughout two sampling seasons from May to October 2018 and 2019. Our findings highlight two key cyanobacterial bloom phases - a mid-summer diazotrophic bloom of Dolichospermum spp. and an autumn Planktothrix agardhii bloom. Dolichospermum crassa and Woronichinia compacta served as indicators of the mid-summer and autumn bloom phases, respectively. Different cyanobacterial metabolites were associated with the distinct bloom phases in both years: toxic microcystins were associated with the mid-summer Dolichospermum bloom and some newly monitored cyanopeptides (anabaenopeptin A and B) with the autumn Planktothrix bloom. Despite forming a significant proportion of the autumn phytoplankton biomass (>60%), the Planktothrix bloom had previously not been detected by sensor or laboratory-derived chlorophyll-a. Our results demonstrate the power of targeted taxonomic identification of key species as a tool for managers of bloom-prone systems. Moreover, we describe an autumn Planktothrix agardhii bloom that has the potential to disrupt water treatment due to its evasion of detection. Our findings highlight the importance of identifying this autumn bloom given the expectation that warmer temperatures and a longer ice-free season will become the norm.

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.

Evidence-Based Framework to Manage Cyanobacteria and Cyanotoxins in Water and Sludge from Drinking Water Treatment Plants

Freshwater bodies and, consequently, drinking water treatment plants (DWTPs) sources are increasingly facing toxic cyanobacterial blooms. Even though conventional treatment processes including coagulation, flocculation, sedimentation, and filtration can control cyanobacteria and cell-bound cyanotoxins, these processes may encounter challenges such as inefficient removal of dissolved metabolites and cyanobacterial cell breakthrough. Furthermore, conventional treatment processes may lead to the accumulation of cyanobacteria cells and cyanotoxins in sludge. Pre-oxidation can enhance coagulation efficiency as it provides the first barrier against cyanobacteria and cyanotoxins and it decreases cell accumulation in DWTP sludge. This critical review aims to: (i) evaluate the state of the science of cyanobacteria and cyanotoxin management throughout DWTPs, as well as their associated sludge, and (ii) develop a decision framework to manage cyanobacteria and cyanotoxins in DWTPs and sludge. The review identified that lab-cultured-based pre-oxidation studies may not represent the real bloom pre-oxidation efficacy. Moreover, the application of a common exposure unit CT (residual concentration × contact time) provides a proper understanding of cyanobacteria pre-oxidation efficiency. Recently, reported challenges on cyanobacterial survival and growth in sludge alongside the cell lysis and cyanotoxin release raised health and technical concerns with regards to sludge storage and sludge supernatant recycling to the head of DWTPs. According to the review, oxidation has not been identified as a feasible option to handle cyanobacterial-laden sludge due to low cell and cyanotoxin removal efficacy. Based on the reviewed literature, a decision framework is proposed to manage cyanobacteria and cyanotoxins and their associated sludge in DWTPs.

Determination of optical markers of cyanobacterial physiology from fluorescence kinetics

Compared to other methods to monitor and detect cyanobacteria in phytoplankton populations, fluorometry gives rapid, robust and reproducible results and can be used in situ. Fluorometers capable of providing biomass estimates and physiological information are not commonly optimized to target cyanobacteria. This study provides a detailed overview of the fluorescence kinetics of algal and cyanobacterial cultures to determine optimal optical configurations to target fluorescence mechanisms that are either common to all phytoplankton or diagnostic to cyanobacteria. We confirm that fluorescence excitation channels targeting both phycocyanin and chlorophyll a associated to the Photosystem II are required to induce the fluorescence responses of cyanobacteria. In addition, emission channels centered at 660, 685 and 730 nm allow better differentiation of the fluorescence response between algal and cyanobacterial cultures. Blue-green actinic light does not yield a robust fluorescence response in the cyanobacterial cultures and broadband actinic light should be preferred to assess the relation between ambient light and photosynthesis. Significant variability was observed in the fluorescence response from cyanobacteria to the intensity and duration of actinic light exposure, which needs to be taken into consideration in field measurements.

Chlorophyll and phycocyanin in-situ fluorescence in mixed cyanobacterial species assemblages: Effects of morphology, cell size and growth phase

Cyanobacteria harmful blooms can represent a major risk for public health due to potential release of toxins and other noxious compounds in the water. A continuous and high-resolution monitoring of the cyanobacteria population is required due to their rapid dynamics, which has been increasingly done using in-situ fluorescence of phycocyanin (f-PC) and chlorophyll a (f-Chl a). Appropriate in-situ fluorometers calibration is essential because f-PC and f-Chl a are affected by biotic and abiotic factors, including species composition. Measurement of f-PC and f-Chl a in mixed species assemblages during different growth phases - representative of most field conditions - has received little attention. We hypothesized that f-PC and f-Chl a of mixed assemblages of cyanobacteria may be accurately estimated if taxa composition and fluorescence characteristics are known. We also hypothesized that species with different morphologies would have different fluorescence per unit cell and biomass. We tested these hypotheses in a controlled culture experiment in which photosynthetic pigment fluorescence, chemical pigment extraction, optical density and microscopic enumeration of four common cyanobacteria species (Aphanocapsa sp, Microcystis aeruginosa, Dolichospermum circinale and Raphidiopsis raciborskii) were quantified. Both monocultures and mixed cultures were monitored from exponential to late stationary growth phases. The sum of fluorescence of individual species calculated for mixed samples was not significantly different than measured fluorescence of mixed cultures. Estimated and measured f-PC and f-Chl a of mixed cultures had higher correlations and smaller absolute median errors when estimations were based on fluorescence per biomass instead of fluorescence per cell. Largest errors were overestimations of measured fluorescence for species with different morphologies. Fluorescence per cell was significantly different among most species, while fluorescence per unit biomass was not, indicating that conversion of fluorescence to biomass reduces species-specific bias. This study presents new information on the effect of species composition on cyanobacteria fluorescence. Best practices of deployment and operation of fluorometers, and data-driven models supporting in-situ fluorometers calibration are discussed as suitable solutions to minimize taxa-specific bias in fluorescence estimates.

A review of microscopic cell imaging and neural network recognition for synergistic cyanobacteria identification and enumeration

Real-time cyanobacteria/algal monitoring is a valuable tool for early detection of harmful algal blooms, water treatment efficacy evaluation, and assists tailored water quality risk assessments by considering taxonomy and cell counts. This review evaluates and proposes a synergistic approach using neural network image recognition and microscopic imaging devices by first evaluating published literature for both imaging microscopes and image recognition. Quantitative phase imaging was considered the most promising of the investigated imaging techniques due to the provision of enhanced information relative to alternatives. This information provides significant value to image recognition neural networks, such as the convolutional neural networks discussed within this review. Considering published literature, a cyanobacteria monitoring system and corresponding image processing workflow using in situ sample collection buoys and on-shore sample processing was proposed. This system can be implemented using commercially available equipment to facilitate accurate, real-time water quality monitoring.

Graphical abstract

Improving the performance of machine learning models for early warning of harmful algal blooms using an adaptive synthetic sampling method

Many countries have attempted to monitor and predict harmful algal blooms to mitigate related problems and establish management practices. The current alert system-based sampling of cell density is used to intimate the bloom status and to inform rapid and adequate response from water-associated organizations. The objective of this study was to develop an early warning system for cyanobacterial blooms to allow for efficient decision making prior to the occurrence of algal blooms and to guide preemptive actions regarding management practices. In this study, two machine learning models: artificial neural network (ANN) and support vector machine (SVM), were constructed for the timely prediction of alert levels of algal bloom using eight years' worth of meteorological, hydrodynamic, and water quality data in a reservoir where harmful cyanobacterial blooms frequently occur during summer. However, the proportion imbalance on all alert level data as the output variable leads to biased training of the data-driven model and degradation of model prediction performance. Therefore, the synthetic data generated by an adaptive synthetic (ADASYN) sampling method were used to resolve the imbalance of minority class data in the original data and to improve the prediction performance of the models. The results showed that the overall prediction performance yielded by the caution level (L1) and warning level (L2) in the models constructed using a combination of original and synthetic data was higher than the models constructed using original data only. In particular, the optimal ANN and SVM constructed using a combination of original and synthetic data during both training (including validation) and test generated distinctively improved recall and precision values of L1, which is a very critical alert level as it indicates a transition status from normalcy to bloom formation. In addition, both optimal models constructed using synthetic-added data exhibited improvement in recall and precision by more than 33.7% while predicting L-1 and L-2 during the test. Therefore, the application of synthetic data can improve detection performance of machine learning models by solving the imbalance of observed data. Reliable prediction by the improved models can be used to aid the design of management practices to mitigate algal blooms within a reservoir.

A critical review on operation and performance of source water control strategies for cyanobacterial blooms: Part I-chemical control methods

Cyanobacterial blooms produce nuisance metabolites (e.g., cyanotoxins and T&O compounds) thereby posing water quality management issues for aquatic sources used for potable water production, aquaculture, and recreation. A variety of in-lake/reservoir control measures are implemented to reduce the abundance of nuisance cyanobacteria biomass or decrease the amount of available phosphorous (P). This paper critically reviews the chemical control strategies implemented for in-lake/reservoir management of cyanobacterial blooms, i.e., algaecides and nutrient sequestering coagulants/flocculants, by highlighting (i) their mode of action, (ii) cases of successful and unsuccessful treatment, (iii) and factors influencing performance (e.g., water quality, process control techniques, source water characteristics, etc.). Algaecides generally result in immediate improvements in water quality and offer selective cyanobacterial control when peroxide-based alagecides are used. However, they have a range of limitations: causing cell lysis and release of cyanotoxins, posing negative impacts on aquatic plants and animals, leaving behind environmentally relevant treatment residuals (e.g., Cu in water and sediments), and offering only short-term bloom control characterized by cyanobacterial rebound. Coagulants/flocculants (alum, iron, calcium, and lanthanum bentonite) offer long-term internal nutrient control when external nutrient loading is controlled. Treatment performance is often influenced by background water quality conditions, and source water characteristics (e.g., surface area, depth, mixing regimes, and residence time). The reviewed case studies highlight that external nutrient load reduction is the most fundamental aspect of cyanobacterial control. None of the reviewed control strategies provide a comprehensive solution to cyanobacterial blooms.

The Fusion of Microfluidics and Optics for On-Chip Detection and Characterization of Microalgae

It has been demonstrated that microalgae play an important role in the food, agriculture and medicine industries. Additionally, the identification and counting of the microalgae are also a critical step in evaluating water quality, and some lipid-rich microalgae species even have the potential to be an alternative to fossil fuels. However, current technologies for the detection and analysis of microalgae are costly, labor-intensive, time-consuming and throughput limited. In the past few years, microfluidic chips integrating optical components have emerged as powerful tools that can be used for the analysis of microalgae with high specificity, sensitivity and throughput. In this paper, we review recent optofluidic lab-on-chip systems and techniques used for microalgal detection and characterization. We introduce three optofluidic technologies that are based on fluorescence, Raman spectroscopy and imaging-based flow cytometry, each of which can achieve the determination of cell viability, lipid content, metabolic heterogeneity and counting. We analyze and summarize the merits and drawbacks of these micro-systems and conclude the direction of the future development of the optofluidic platforms applied in microalgal research.

Use of Bioluminescence for Monitoring Brown Coal Mine Waters from Deep and Surface Drainage

Open-pit mines can cause environmental changes, such as alterations of landscape structure, hydrology, air quality, and river sediments; they can also generate cones of depression. We propose a new method for surveys of mine waters using the example of an open-pit mine in central Poland. This study examines the correlations between bioluminescence and the color of brown coal mine waters and tests whether values of the three-color coordinate system reflected the physicochemical quality of mine waters measured in real-time and in the field. Our results show that alkalinity, pH reaction, and conductivity are higher in surface drainage, while values of trophic parameters (soluble reactive phosphates, total phosphorus, nitrates) are greater in samples representing subsurface drainage. Correlation analysis of bioluminescence with mine water quality parameters showed that only water color had a strong association with bioluminescence. This correlation is stronger for surface drainage, than for mine waters from subsurface drainage. Direct measurement of bioluminescence, resulting from adenosine 5`-triphosphate (ATP) using a luminometer, is a fast and reliable method for evaluation of the characteristics of mine waters in real-time.

Light-induced fluorescence quenching leads to errors in sensor measurements of phytoplankton chlorophyll and phycocyanin

Optical sensors for fluorescence of chlorophyll a (f-Chl a) and phycocyanin (f-PC) are increasingly used as a proxy for biomass of algae and cyanobacteria, respectively. They provide measurements at high-frequency and modest cost. These sensors require site-specific calibration due to a range of interferences. Light intensity affects the fluorescence yield of cyanobacteria and algae through light harvesting regulation mechanisms, but is often neglected as a potential source of error for in-situ f-Chl a and f-PC measurements. We hypothesised that diel light variations would induce significant f-Chl a and f-PC suppression when compared to dark periods. We tested this hypothesis in a controlled experiment using three commercial fluorescence probes which continuously measured f-Chl a and f-PC from a culture of the cyanobacterium Dolichospermum variabilis as well as f-Chl a from a culture of the green alga Ankistrodesmus gracilis in a simulated natural light regime. Under light, all devices showed a significant (p<0.01) suppression of f-Chl a and f-PC compared to measurements in the dark. f-Chl a decreased by up to 79% and f-PC by up to 59% at maximum irradiance compared to dark-adapted periods. Suppression levels were higher during the second phase of the diel cycle (declining light), indicating that quenching is dependent on previous light exposure. Diel variations in light intensity must be considered as a significant source of bias for fluorescence probes used for algal monitoring. This is of high relevance as most monitoring activities take place during daytime and hence f-Chl a and f-PC are likely to be systematically underestimated under bright conditions. Compensation models, design modifications to fluorometers and sampling design are discussed as suitable alternatives to overcome light-induced fluorescence quenching.

Machine learning for anomaly detection in cyanobacterial fluorescence signals

Many drinking water utilities drawing from waters susceptible to harmful algal blooms (HABs) are implementing monitoring tools that can alert them to the onset of blooms. Some have invested in fluorescence-based online monitoring probes to measure phycocyanin, a pigment found in cyanobacteria, but it is not clear how to best use the data generated. Previous studies have focused on correlating phycocyanin fluorescence and cyanobacteria cell counts. However, not all utilities collect cell count data, making this method impossible to apply in some cases. Instead, this paper proposes a novel approach to determine when a utility needs to respond to a HAB based on machine learning by identifying anomalies in phycocyanin fluorescence data without the need for corresponding cell counts or biovolume. Four widespread and open source algorithms are evaluated on data collected at four buoys in Lake Erie from 2014 to 2019: local outlier factor (LOF), One-Class Support Vector Machine (SVM), elliptic envelope, and Isolation Forest (iForest). When trained on standardized historical data from 2014 to 2018 and tested on labelled 2019 data collected at each buoy, the One-Class SVM and elliptic envelope models both achieve a maximum average F1 score of 0.86 among the four datasets. Therefore, One-Class SVM and elliptic envelope are promising algorithms for detecting potential HABs using fluorescence data only.

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.

Fully automatic detection and classification of phytoplankton specimens in digital microscopy images

BACKGROUND AND OBJECTIVE

The proliferation of toxin-producing phytoplankton species can compromise the quality of the water sources. This contamination is difficult to detect, and consequently to be neutralised, since normal water purification techniques are ineffective. Currently, the water analyses about phytoplankton are commonly performed by the specialists with manual routine analyses, which represents a major limitation. The adequate identification and classification of phytoplankton specimens requires intensive training and expertise. Additionally, the performed analysis involves a lengthy process that exhibits serious problems of reliability and repeatability as inter-expert agreement is not always reached. Considering all those factors, the automatization of these analyses is, therefore, highly desirable to reduce the workload of the specialists and facilitate the process.

METHODS

This manuscript proposes a novel fully automatic methodology to perform phytoplankton analyses in digital microscopy images of water samples taken with a regular light microscope. In particular, we propose a method capable of analysing multi-specimen images acquired using a simplified systematic protocol. In contrast with prior approaches, this enables its use without the necessity of an expert taxonomist operating the microscope. The system is able to detect and segment the different existing phytoplankton specimens, with high variability in terms of visual appearances, and to merge them into colonies and sparse specimens when necessary. Moreover, the system is capable of differentiating them from other similar objects like zooplankton, detritus or mineral particles, among others, and then classify the specimens into defined target species of interest using a machine learning-based approach.

RESULTS

The proposed system provided satisfactory and accurate results in every step. The detection step provided a FNR of 0.4%. Phytoplankton detection, that is, differentiating true phytoplankton from similar objects (zooplankton, minerals, etc.), provided a result of 84.07% of precision at 90% of recall. The target species classification, reported an overall accuracy of 87.50%. The recall levels for each species are, 81.82% for W. naegeliana, 57.15% for A. spiroides, 85.71% for D. sociale and 95% for the "Other" group, a set of relevant toxic and interesting species widely spread over the samples.

CONCLUSIONS

The proposed methodology provided accurate results in all the designed steps given the complexity of the problem, particularly in terms of specimen identification, phytoplankton differentiation as well as the classification of the defined target species. Therefore, this fully automatic system represents a robust and consistent tool to aid the specialists in the analysis of the quality of the water sources and potability.

Integrated Photonic System for Early Warning of Cyanobacterial Blooms in Aquaponics

Cyanobacterial blooms produce hazardous toxins, deplete oxygen, and secrete compounds that confer undesirable organoleptic properties to water. To prevent bloom appearance, the World Health Organization has established an alert level between 500 and 2000 cells·mL^-1, beyond the capabilities of most optical sensors detecting the cyanobacteria fluorescent pigments. Flow cytometry, cell culturing, and microscopy may reach these detection limits, but they involve both bulky and expensive laboratory equipment or long and tedious protocols. Thus, no current technology allows fast, sensitive, and in situ detection of cyanobacteria. Here, we present a simple, user-friendly, low-cost, and portable photonic system for in situ detection of low cyanobacterial concentrations in water samples. The system integrates high-performance preconcentration elements and optical components for fluorescence measurement of specific cyanobacterial pigments, that is, phycocyanin. Phycocyanin has demonstrated to be more selective to cyanobacteria than other pigments, such as chlorophyll-a, and to present an excellent linear correlation with bacterial concentration from 10² to 10⁴ cell·mL^-1 (R² = 0.99). Additionally, the high performance of the preconcentration system leads to detection limits below 435 cells·mL^-1 after 10 min in aquaponic water samples. Due to its simplicity, compactness, and sensitivity, we envision the current technology as a powerful tool for early warning and detection of low pathogen concentrations in water samples.

Optimizing short time-step monitoring and management strategies using environmental tracers at flood-affected bank filtration sites

Bank filtration is a popular pre-treatment method to produce drinking water as it benefits from the natural capacity of the sediments to attenuate contaminants. Under flood conditions, bank filtration systems are known to be vulnerable to contamination, partly because flow patterns may evolve at short timescales and result in a rapid evolution of the origin and travel times of surface water in the aquifer. However, high frequency monitoring for water quality is not common practice yet, and water quality management decisions for the operation of bank filtration systems are typically based on weekly to monthly assays. The aim of this study is to illustrate how monitoring strategies of environmental tracers at flood-affected sites can be optimized and to demonstrate how tracer-based evidence can help to define adequate pumping strategies. Data acquisition spanned two intense flood events at a two-lake bank filtration site. Based on bacteriological indicators, the bank filtration system was shown to be resilient to the yearly recurring flood events but more vulnerable to contamination during the intense flood events. The origin of the bank filtrate gradually evolved from a mixture between the two lakes towards a contribution of floodwater and one lake only. Automatized measurements of temperature and electrical conductivity at observation wells allowed to detect changes in the groundwater flow patterns at a daily timescale, while the regulatory monthly monitoring for indicator bacteria did not fully capture the potential short timescale variability of the water quality. The recovery to pre-flood conditions was shown to be accelerated for the wells operating at high rates (i.e., ≥1000 m³/day), partly because of floodwater storage in the vicinity of the less active wells. These results establish new perspectives to anticipate water quality changes through selected pumping schemes, which depend on and must be adapted to site-specific water quality issues.

Seasonal variations in arsenic mobility and bacterial diversity: The case study of Huangshui Creek, Shimen Realgar Mine, Hunan Province, China

Rivers throughout the world have been contaminated by arsenic dispersed from mining activities. The biogeochemical cycling of this arsenic has been shown to be due to factors such as pH, Eh, ionic strength and microbial activity, but few studies have examined the effects of both seasonal changes and microbial community structure on arsenic speciation and flux in mining-affected river systems. To address this research gap, a study was carried out in Huangshui Creek, Hunan province, China, which has been severely impacted by long-term historic realgar (α-As₄S₄) mining. Water and sediment sampling, and batch experiments at different temperatures using creek sediment, were used to determine the form, source and mobility of arsenic. Pentavalent (AsO₄³) and trivalent arsenic (AsO₃^3-) were the dominant aqueous species (70-89% and 30-11%, respectively) in the creek, and the maximum concentration of inorganic arsenic in surface water was 10,400 μg/L. Dry season aqueous arsenic concentrations were lower than those in the wet season samples. The sediments contained both arsenate and arsenite, and relative proportions of these varied with season. 8.3 tons arsenic per annum were estimated to be exported from Huangshui Creek. Arsenic release from sediment increased by 3 to 5 times in high water temperature batch experiments (25 and 37 °C) compared to those carried out at low temperature (8 °C). Our data suggest that the arsenic-containing sediments were the main source of arsenic contamination in Huangshui Creek. Microbial community structured varied at the different sample sites along the creek. Redundancy analysis (RDA) showed that both temperature and arsenic concentrations were the main controlling factors on the structure of the microbial community. Protecbacteria, Bacteroidetes, Cyanobacteria, Firmicutes, Verrucomicrobia, and Planctomycetes were the stable dominant phyla in both dry and wet seasons. The genera Flavobacterium, Hydrogenophaga and Sphingomonas occurred in the most highly arsenic contaminated sites, which removed arsenic by related metabolism.Our findings indicate that seasonal variations profoundly control arsenic flux and species, microbial community structure and ultimately, the biogeochemical fate of arsenic.

Intraseasonal variation of phycocyanin concentrations and environmental covariates in two agricultural irrigation ponds in Maryland, USA

Recently, cyanobacteria blooms have become a concern for agricultural irrigation water quality. Numerous studies have shown that cyanotoxins from these harmful algal blooms (HABs) can be transported to and assimilated into crops when present in irrigation waters. Phycocyanin is a pigment known only to occur in cyanobacteria and is often used to indicate cyanobacteria presence in waters. The objective of this work was to identify the most influential environmental covariates affecting the phycocyanin concentrations in agricultural irrigation ponds that experience cyanobacteria blooms of the potentially toxigenic species Microcystis and Aphanizomenon using machine learning methodology. The study was performed at two agricultural irrigation ponds over a 5-month period in the summer of 2018. Phycocyanin concentrations, along with sensor-based and fluorometer-based water quality parameters including turbidity (NTU), pH, dissolved oxygen (DO), fluorescent dissolved organic matter (fDOM), conductivity, chlorophyll, color dissolved organic matter (CDOM), and extracted chlorophyll were measured. Regression tree analyses were used to determine the most influential water quality parameters on phycocyanin concentrations. Nearshore sampling locations had higher phycocyanin concentrations than interior sampling locations and "zones" of consistently higher concentrations of phycocyanin were found in both ponds. The regression tree analyses indicated extracted chlorophyll, CDOM, and NTU were the three most influential parameters on phycocyanin concentrations. This study indicates that sensor-based and fluorometer-based water quality parameters could be useful to identify spatial patterns of phycocyanin concentrations and therefore, cyanobacteria blooms, in agricultural irrigation ponds and potentially other water bodies.

A systematic literature review of forecasting and predictive models for cyanobacteria blooms in freshwater lakes

Cyanobacteria harmful blooms (CyanoHABs) in lakes and reservoirs represent a major risk for water authorities globally due to their toxicity and economic impacts. Anticipating bloom occurrence and understanding the main drivers of CyanoHABs are needed to optimize water resources management. An extensive review of the application of CyanoHABs forecasting and predictive models was performed, and a summary of the current state of knowledge, limitations and research opportunities on this topic is provided through analysis of case studies. Two modelling approaches were used to achieve CyanoHABs anticipation; process-based (PB) and data-driven (DD) models. The objective of the model was a determining factor for the choice of modelling approach. PB models were more frequently used to predict future scenarios whereas DD models were employed for short-term forecasts. Each modelling approach presented multiple variations that may be applied for more specific, targeted purposes. Most models reviewed were site-specific. The monitoring methodologies, including data frequency, uncertainty and precision, were identified as a major limitation to improve model performance. A lack of standardization of both model output and performance metrics was observed. CyanoHAB modelling is an interdisciplinary topic and communication between disciplines should be improved to facilitate model comparisons. These shortcomings can hinder the adoption of modelling tools by practitioners. We suggest that water managers should focus on generalising models for lakes with similar characteristics and where possible use high frequency monitoring for model development and validation.

Predicting cyanobacterial biovolumes from phycocyanin fluorescence using a handheld fluorometer in the field

Toxic cyanobacterial blooms are becoming more prevalent in freshwater systems, increasing the need for monitoring to protect human health. Phycocyanin fluorescence sensors have been developed as tools for providing fast and cost-effective proxy measurements for cyanobacterial biomass. However, poor precision and low sensitivity in many of the probe sensors assessed to-date has restricted their potential for practical application in cyanobacterial monitoring programmes. In the present study, the sensitivity and accuracy of a handheld fluorometer, the CyanoFluor, was assessed using 12 cyanobacterial strains and samples from four different lakes collected weekly for 12 weeks. After the initial measurements, the samples were lysed by sonication, which we hypothesised would reduce inter and intra-specific differences. The CyanoFluor displayed high sensitivity (limit of quantification = 3.5 µg L^-1 of phycocyanin) and was able to detect cyanobacterial biovolumes to levels much lower than the threshold levels in current recreational guidelines worldwide. There were strong and significant phycocyanin to biovolume relationships (r² ≥ 0.88, P < 0.05) for all 12 cyanobacterial cultures. Collectively, strong relationships between phycocyanin fluorescence and cyanobacterial biovolumes were also identified in environmental samples (r² ≥ 0.78, P < 0.001), although weaker relationships were identified when lakes were analysed separately (r² = 0.06 - 0.90). There were differences in phycocyanin per biovolume between both cultured strains and lakes, highlighting innate interspecific differences that exist between cyanobacterial species. Lysis of samples consistently reduced variability between technical replicates, in cyanobacteria cultures (up to 87% reduction in sample variability) and environmental samples (71 - 93% reduction), indicating that it would be a useful methodological step to improve the repeatability of results. When guideline thresholds (aligned with currently enforced risk assessment categories) were modelled based on the most successful linear regression model, 74% of samples were assigned to the correct risk category. The sensitivity of the CyanoFluor and accuracy of the phycocyanin threshold models, indicates high potential for this method to be integrated into cyanobacterial monitoring programmes.

Delayed Release of Intracellular Microcystin Following Partial Oxidation of Cultured and Naturally Occurring Cyanobacteria

Oxidation processes can provide an effective barrier to eliminate cyanotoxins by damaging cyanobacteria cell membranes, releasing intracellular cyanotoxins, and subsequently oxidizing these toxins (now in extracellular form) based on published reaction kinetics. In this work, cyanobacteria cells from two natural blooms (from the United States and Canada) and a laboratory-cultured Microcystis aeruginosa strain were treated with chlorine, monochloramine, chlorine dioxide, ozone, and potassium permanganate. The release of microcystin was measured immediately after oxidation (t ≤ 20 min), and following oxidant residual quenching (stagnation times = 96 or 168 h). Oxidant exposures (CT) were determined resulting in complete release of intracellular microcystin following chlorine (21 mg-min/L), chloramine (72 mg-min/L), chlorine dioxide (58 mg-min/L), ozone (4.1 mg-min/L), and permanganate (391 mg-min/L). Required oxidant exposures using indigenous cells were greater than lab-cultured Microcystis. Following partial oxidation of cells (oxidant exposures ≤ CT values cited above), additional intracellular microcystin and dissolved organic carbon (DOC) were released while the samples remained stagnant in the absence of an oxidant (>96 h after quenching). The delayed release of microcystin from partially oxidized cells has implications for drinking water treatment as these cells may be retained on a filter surface or in solids and continue to slowly release cyanotoxins and other metabolites into the finished water.

Factors influencing distribution patterns of cyanobacteria in an upland lake of the Kumaun Himalayas, India

We study the dynamics of bloom-forming cyanobacteria and determined the major driving forces in subtropical lake of the Kumaun Himalayas, India. Water and plankton samples from different sites and depths of the lake were analyzed. Multivariate analyses were used to evaluate the factors controlling the distribution pattern of cyanobacteria. M. aeruginosa was the most abundant species with an average density of 10.39 × 10⁶ individual/m³ and distributed throughout water depths. The geomorphic threshold modulates soil erosion processes resulting in lower transparency in the Himalayan lake; thereby limiting the growth of Chlorophycea. Effective persistence for M. aeroginosa into different depths is augmented by fluxes nutrient coupling with pigments. The ratio of nitrogen/phosphorus (N/P) had a significant negative correlation (F =-0.884; p = 0.0001) with densities. Expansion of M. aeruginosa in deep de-stratified lake is episodic and their proliferation can restrict by lowering phosphorus.

Monitoring of Cyanobacteria in Water Using Spectrophotometry and First Derivative of Absorbance

Management of cyanobacteria blooms and their negative impact on human and ecosystem health requires effective tools for monitoring their concentration in water bodies. This research investigated the potential of derivative spectrophotometry in detection and monitoring of cyanobacteria using toxigenic and non-toxigenic strains of Microcystis aeruginosa. Microcystis aeruginosa was quantified in deionized water and surface water using traditional spectrophotometry and the first derivative of absorbance. The first derivative of absorbance was effective in improving the signal of traditional spectrophotometry; however, it was not adequate in differentiating between signal and noise at low concentrations. Savitzky-Golay coefficients for first derivative were used to smooth the derivative spectra and improve the correlation between concentration and noise at low concentrations. Derivative spectrophotometry improved the detection limit as much as eight times in deionized water and as much as four times in surface water. The lowest detection limit measured in surface water with traditional spectrophotometry was 392,982 cells/mL, and the Savitzky-Golay first derivative of absorbance was 90,231 cells/mL. The method provided herein provides a promising tool in real-time monitoring of cyanobacteria concentrations and spectrophotometry offers the ability to measure water quality parameters together with cyanobacteria concentrations.

Chlorophyll-a, dissolved organic carbon, turbidity and other variables of ecological importance in river basins in southern Ontario and British Columbia, Canada

Optical sensing of chlorophyll-a (chl-a), turbidity, and fluorescent dissolved organic matter (fDOM) is often used to characterize the quality of water. There are many site-specific factors and environmental conditions that can affect optically sensed readings; notwithstanding the comparative implication of different procedures used to measure these properties in the laboratory. In this study, we measured these water quality properties using standard laboratory methods, and in the field using optical sensors (sonde-based) at water quality monitoring sites located in four watersheds in Canada. The overall objective of this work was to explore the relationships among sonde-based and standard laboratory measurements of the aforementioned water properties, and evaluate associations among these eco-hydrological properties and land use, environmental, and ancillary water quality variables such as dissolved organic carbon (DOC) and total suspended solids (TSS). Differences among sonde versus laboratory relationships for chl-a suggest such relationships are impacted by laboratory methods and/or site specific conditions. Data mining analysis indicated that interactive site-specific factors predominately impacting chl-a values across sites were specific conductivity and turbidity (variables with positive global associations with chl-a). The overall linear regression predicting DOC from fDOM was relatively strong (R² = 0.77). However, slope differences in the watershed-specific models suggest laboratory DOC versus fDOM relationships could be impacted by unknown localized water quality properties affecting fDOM readings, and/or the different standard laboratory methods used to estimate DOC. Artificial neural network analyses (ANN) indicated that higher relative chl-a concentrations were associated with low to no tree cover around sample sites and higher daily rainfall in the watersheds examined. Response surfaces derived from ANN indicated that chl-a concentrations were higher where combined agricultural and urban land uses were relatively higher.

Retrospective analysis of Chlorophyll-a and its correlation with climate and hydrological variations in Mindu Dam, Morogoro, Tanzania

The measurement of Chlorophyll-a in aquatic systems has usually correlated to harmful algae in water bodies. Harmful algal blooms (HABs) are as a result of massive proliferation of blue-green algae (Cyanobacteria). Harmful algal blooms (HABs) pose threats to both the environment as well as human health, and despite this well-known fact, their monitoring and management are still challenging. Climate change, extreme weather events, and hydrological changes are the main drivers and predicted to benefits HABs dynamics in most parts of the world. In Tanzania, studies of HABs proliferation and their possible correlation with variability in climate and hydrology still lag behind despite high demand for developing predicting tools and prevention of HABs proliferation. The present study reports on the retrospective analysis of HABs variation in Mindu Dam located in Morogoro, Tanzania using remote sensing techniques. In the present study comparison between in situ measurement and ocean color (OC2) Chlorophyll-a with the surface reflectance's (band and band combinations) of Landsat 7 and Landsat 8 Operational Land Imager (OLI), was performed. Another approach involved searching for patterns and trends, and teleconnection between Chlorophyll-a index (best band ration) and the climate and hydrological variations in the catchment. The findings demonstrated that minimum and maximum temperatures, solar radiation, Chlorophyll-a concentration registered significant increasing trends. Wind speed and directions, water levels for Mindu Dam showed a significant decreasing trend. On the other hand, rainfall showed no trend. The patterns suggest that there are link and causality between the HABs variations and meteorological parameters such as temperatures, solar radiations, and water levels. The study, therefore, contributes to the application of recent advances in remote sensing and retrospectively analysis of bloom dynamics and search for their link with climate and hydrological changes.

A molecular-based method to estimate the risk associated with cyanotoxins and odor compounds in drinking water sources

A biomolecular-based monitoring approach for the assessment of water quality hazards and risks associated with cyanobacteria was developed and validated in drinking reservoirs in Taiwan and the Philippines. The approach was based upon the measurement of gene abundances of toxigenic Microcystis and Cylindrospermopsis; for cyanotoxins; and for aesthetically offensive earthy-musty odor compounds. This was compared to conventional monitoring approaches, which included cell enumeration by microscopy, and toxin and odor compound analysis by instrumental analytical methods and immunoassays as appropriate for the metabolites. The validation involved samples from ten major reservoirs on Taiwan's main island, nineteen reservoirs on the offshore islands, and Laguna de Bay in the Philippines. The gene-based approach was successfully validated statistically and compared to conventional widely utilized risk assessment schemes which have employed 'Alert Levels' for toxic cyanobacteria. In this case a new integrated scheme of 'Response Levels' is proposed which incorporates odor metabolite hazards in addition to cyanotoxins and is based upon gene copy numbers to derive quantitative triggers. The comprehensive scheme evaluated from these locations is considered to be more precise and efficient for both monitoring and as a risk assessment diagnostic tool, given that it offers the capacity for analysis of the abundance of genes for cyanobacterial metabolites in large numbers of natural water samples in a significantly reduced period of time compared to the approaches of cell enumeration by microscopy or metabolite analytical techniques. This approach is the first time both the hazard and risk for both odors and cyanotoxins from cyanobacteria have been considered together in a monitoring scheme and offers an improved means for determining the Response Levels in the risk assessment process for cyanobacteria and their metabolites in drinking water sources.

In-situ fluorescence monitoring of cyanobacteria: Laboratory-based quantification of species-specific measurement accuracy

In recent years, in-situ fluorometers have been extensively deployed to monitor cyanobacteria in near real-time. Acceptable accuracy can be achieved between measured pigments and cyanobacteria biovolume provided the cyanobacteria species are known. However, cellular photosynthetic pigment content and measurement interferences are site and species specific and can dramatically affect sensor reliability. We quantified the accuracy of an in-situ fluorometer compared with traditional methods using mono- and mixed cultures of four different cyanobacterial species. We found: (1) lower pigment content in cultures in stationary phase, (2) higher precision with the sensor compared to traditional pigment quantification methods of measuring phycocyanin and chlorophyll a, (3) species-specific relationships between sensor readings and measurements related to biovolume, (4) overestimation of pigments in mixed compared with mono cultures, (5) dissolved organic matter causing a loss in signal proportional to its degree of aromaticity, and (6) potential to quantify the degree of cell lysis with a fluorescent dissolved organic matter sensor. This study has provided important new information on the strengths and limitations of fluorescence sensors. The sensor readings can provide accurate biovolume quantification and species determination for a number of bloom-forming species when sensors are properly compensated and calibrated.

Algal Morphological Identification in Watersheds for Drinking Water Supply Using Neural Architecture Search for Convolutional Neural Network

An excessive increase in algae often has various undesirable effects on drinking water supply systems, thus proper management is necessary. Algal monitoring and classification is one of the fundamental steps in the management of algal blooms. Conventional microscopic methods have been most widely used for algal classification, but such approaches are time-consuming and labor-intensive. Thus, the development of alternative methods for rapid, but reliable algal classification is essential where an advanced machine learning technique, known as deep learning, is considered to provide a possible approach for rapid algal classification. In recent years, one of the deep learning techniques, namely the convolutional neural network (CNN), has been increasingly used for image classification in various fields, including algal classification. However, previous studies on algal classification have used CNNs that were arbitrarily chosen, and did not explore possible CNNs fitting algal image data. In this paper, neural architecture search (NAS), an automatic approach for the design of artificial neural networks (ANN), is used to find a best CNN model for the classification of eight algal genera in watersheds experiencing algal blooms, including three cyanobacteria (Microcystis sp., Oscillatoria sp., and Anabaena sp.), three diatoms (Fragilaria sp., Synedra sp., and two green algae (Staurastrum sp. and Pediastrum sp.). The developed CNN model effectively classified the algal genus with an F1-score of 0.95 for the eight genera. The results indicate that the CNN models developed from NAS can outperform conventional CNN development approaches, and would be an effective tool for rapid operational responses to algal bloom events. In addition, we introduce a generic framework that provides a guideline for the development of the machine learning models for algal image analysis. Finally, we present the experimental results from the real-world environments using the framework and NAS.

Ultrasensitive Detection of Hepatotoxic Microcystin Production from Cyanobacteria Using Surface-Enhanced Raman Scattering Immunosensor

Microcystin-LR (MC-LR) is considered the most common hazardous toxin produced during harmful algal blooms. In addition to potential risk of long-term exposure to low concentrations in drinking water, acute toxicity due to MC-LR resulting from algal blooms could result in fatalities in rare cases. Although several methods are currently available to detect MC-LR, development of a low-cost, ultrasensitive measurement method would help limit exposure by enabling early detection and continuous monitoring of MC-LR. Here, we develop a surface-enhanced Raman scattering (SERS) spectroscopic immunosensor for detection and quantification of the hepatotoxic MC-LR toxin in aquatic settings with excellent robustness, selectivity, and sensitivity. We demonstrate that the developed SERS sensor can reach a limit of detection (0.014 μg/L) at least 1 order of magnitude lower and display a linear dynamic detection range (0.01 μg/L to 100 μg/L) 2 orders of magnitude wider in comparison to the commercial enzyme-linked immunosorbent assay test. The superior analytical performance of this SERS immunosensor enables monitoring of the dynamic production of MC-LR from a Microcystis aeruginosa culture. We believe that the present method could serve as a useful tool for detection of hepatotoxic microcystin toxins in various aquatic settings such as drinking water, lakes, and reservoirs. Further development of this technique could result in single-cell microcystin resolution or real-time monitoring to mitigate the associated toxicity and economic loss.

Using rapid quantification of adenosine triphosphate (ATP) as an indicator for early detection and treatment of cyanobacterial blooms

Early detection of harmful cyanobacterial blooms allows identification of potential risk and appropriate selection of treatment techniques to prevent exposure in recreational water bodies and drinking water supplies. Here, luminescence-based adenosine triphosphate (ATP) analysis was applied to monitor and treat cultured and naturally occurring cyanobacteria cells. When evaluating lab-cultured Microcystis aeruginosa, ATP concentrations (≤252,000 pg/mL) had improved sensitivity and correlated well (R² = 0.969) with optical density measurements at 730 nm (OD730; ≤0.297 cm^-1). Following one year of monitoring of a surface water supply, ATP concentrations (≤2000 pg/mL) correlated (R² = 0.791) with chlorophyll-a concentrations (≤50 μg/L). A preliminary early warning threshold of 175 pg ATP/mL corresponded with 5 μg/L chlorophyll-a to initiate increased monitoring (e.g., of cyanotoxins). Following oxidation processes (i.e., chlorine, chloramine, ozone, permanganate), ATP was demonstrated as an indicator of cell lysis and a threshold value of <100 pg/mL was recommended for complete release of intracellular cyanotoxins. ATP was also used to assess efficacy of copper (Cu(II)) treatment on cyanobacteria-laden surface water. While 24-h exposure to 2.5 mg Cu(II)/L did not impact chlorophyll-a, ATP decreased from 13,500 to 128 pg/mL indicating metabolic activity was minimized. Ultimately, ATP analysis holds promise for early detection and mitigation of potentially harmful algal blooms based on superior sensitivity, independence from cell morphology artifacts, rapid time for analysis (<10 min), and ease of deployment in the field compared to conventional methods.

Evaluation of FluoroProbe® performance for the phytoplankton-based assessment of the ecological status of Mediterranean coastal lagoons

The European Water Framework Directive and several other legislations worldwide have selected phytoplankton for monitoring the ecological status of surface waters. This assessment is a complicated task in coastal lagoons due to their intrinsic variability, prompting moves to use real-time measurements. Here, we tested the ability of the submersible spectrofluorometer FluoroProbe® to accurately estimate the phytoplankton biomass and to efficiently discriminate spectral groups in Mediterranean coastal lagoons, by using sub-surface water samples (n = 107) collected at Biguglia lagoon (Corsica) in different environmental situations (salinity and trophic state) from March 2012 to December 2014. We compared the estimates of biomass and phytoplankton group composition obtained with the FluoroProbe® (in situ and lab measurements) with the spectrofluorimetrically measured biomass and HPLC-derived quantifications of pigment concentrations. FluoroProbe® provided good estimates of the total phytoplankton biomass (particularly, the lab measurements). The FluoroProbe® data were significantly correlated with the HPLC results, except for the in situ measurements of very weak concentrations of blue-green and red algae. Our findings indicate that factory-calibrated FluoroProbe® is an efficient and easy-to-use real-time phytoplankton monitoring tool in coastal lagoons, especially as an early warning system for the detection of potentially harmful algal blooms. Practical instructions dedicated to non-specialist field operators are provided. A simple and efficient method for discarding in situ measurement outliers is also proposed.

Enhanced real-time cyanobacterial fluorescence monitoring through chlorophyll-a interference compensation corrections

In situ fluorometers can be used as a real-time cyanobacteria detection tool to maintain safe drinking and recreational water standards. However, previous studies into fluorometers have established issues arising mainly from measurement inaccuracies due to green algae interference. Therefore, this study focusses on developing correction factors from a systematic study on the impact of green algae as an interference source. This study brings a novel technique where the chlorophyll-a (Chl-a) and phycocyanin measurements are used to correct the fluorometer output for interference bias; four fluorometers were tested against three key cyanobacterial species and the relationship between phycocyanin output, green algae and cyanobacteria concentrations were investigated. Good correlation (R² > 0.9, p-value < 0.05) was found between the fluorometer phycocyanin output and increasing green algae concentration. The optimal correction method was selected for each of the fluorometer and cyanobacteria species pairs by validating against data from the investigation of green algae as an interference source. The correction factors determined in this study reduced the measurement error for almost all the fluorometers and species tested by 21%-99% depending on the species and fluorometer, compared to previous published correction factors in which the measurement error was reduced by approximately 11%-81%. Field validation of the correction factors showed reduction in fluorometer measurement error at sites in which cyanobacterial blooms were dominated by a single species.

Cyanotoxins and Cyanobacteria Cell Accumulations in Drinking Water Treatment Plants with a Low Risk of Bloom Formation at the Source

Toxic cyanobacteria have been shown to accumulate in drinking water treatment plants that are susceptible to algal blooms. However, the risk for plants that do not experience algal blooms, but that receive a low influx of cells, is not well known. This study determined the extent of cell accumulation and presence of cyanotoxins across the treatment trains of four plants in the Great Lakes region. Samples were collected for microscopic enumeration and enzyme-linked immunosorbent assay (ELISA) measurements for microcystins, anatoxin-a, saxitoxin, cylindrospermopsin, and β-methylamino-L-alanine (BMAA). Low cell influxes (under 1000 cells/mL) resulted in significant cell accumulations (over 1 × 10⁵ cells/mL) in clarifier sludge and filter backwash samples. Microcystins peaked at 7.2 µg/L in one clarifier sludge sample, exceeding the raw water concentration by a factor of 12. Anatoxin-a was detected in the finished drinking water of one plant at 0.6 µg/L. BMAA may have been detected in three finished water samples, though inconsistencies among the BMAA ELISAs call these results into question. In summary, the results show that plants receiving a low influx of cells can be at risk of toxic cyanobacterial accumulation, and therefore, the absence of a bloom at the source does not indicate the absence of risk.

Fluorescence probes for real-time remote cyanobacteria monitoring: A review of challenges and opportunities

In recent years, there has been a widespread deployment of submersible fluorescence sensors by water utilities. They are used to measure diagnostic pigments and estimate algae and cyanobacteria abundance in near real-time. Despite being useful and promising tools, operators and decision-makers often rely on the data provided by these probes without a full understanding of their limitations. As a result, this may lead to wrong and misleading estimations which, in turn, means that researchers and technicians distrust these sensors. In this review paper, we list and discuss the main limitations of such probes, as well as identifying the effect of environmental factors on pigment production, and in turn, the conversion to cyanobacteria abundance estimation. We argue that a comprehensive calibration approach to obtain reliable readings goes well beyond manufacturers' recommendations, and should involve several context-specific experiments. We also believe that if such a comprehensive set of experiments is conducted, the data collected from fluorescence sensors could be used in artificial intelligence modelling approaches to reliably predict, in near real-time, the presence and abundance of different cyanobacteria species. This would have significant benefits for both drinking and recreational water management, given that cyanobacterial toxicity, and taste and odour compounds production, are species-dependent.

Accuracy of data buoys for measurement of cyanobacteria, chlorophyll, and turbidity in a large lake (Lake Erie, North America): implications for estimation of cyanobacterial bloom parameters from water quality sonde measurements

Microcystin (MCY)-producing harmful cyanobacterial blooms (cHABs) are an annual occurrence in Lake Erie, and buoys equipped with water quality sondes have been deployed to help researchers and resource managers track cHABs. The objective of this study was to determine how well water quality sondes attached to buoys measure total algae and cyanobacterial biomass and water turbidity. Water samples were collected next to two data buoys in western Lake Erie (near Gibraltar Island and in the Sandusky subbasin) throughout summers 2015, 2016, and 2017 to determine correlations between buoy sonde data and water sample data. MCY and nutrient concentrations were also measured. Significant (P < 0.001) linear relationships (R² > 0.75) occurred between cyanobacteria buoy and water sample data at the Gibraltar buoy, but not at the Sandusky buoy; however, the coefficients at the Gibraltar buoy differed significantly across years. There was a significant correlation between buoy and water sample total chlorophyll data at both buoys, but the coefficient varied considerably between buoys and among years. Total MCY concentrations at the Gibraltar buoy followed similar temporal patterns as buoy and water sample cyanobacterial biomass data, and the ratio of MCY to cyanobacteria-chlorophyll decreased with decreased ambient nitrate concentrations. These results suggest that buoy data are difficult to compare across time and space. Additionally, the inclusion of nitrate concentration data can lead to more robust predictions on the relative toxicity of blooms. Overall, deployed buoys with sondes that are routinely cleaned and calibrated can track relative cyanobacteria abundance and be used as an early warning system for potentially toxic blooms.

Rapid detection and quantification of the potentially toxic cyanobacterium Planktothrix rubescens by in-vivo fluorometry and flow cytometry

We combined profiling of the bloom-forming and potentially toxigenic cyanobacterium Planktothrix rubescens using a multiparameter probe equipped with a phycoerythrin sensor (in vivo fluorometry, IVL) in Lake Mondsee, Austria, with flow cytometric live analyses of discrete samples taken from several depths in the upper 20 m of the water column. Results obtained by IVL and acoustic flow cytometry (AFC) were compared to microscopic analyses of integrated (0-21 m) water samples using fixed material. This comparison was made because the integrated samples are used for the Austrian monitoring programme according to the EU Water Framework Directive. We demonstrate that AFC provides quantitative analyses of the filaments of P. rubescens that are significantly correlated to IVF and microscopic analyses, allowing rapid (within hours) and more precise calculation of P. rubescens biomass than estimates derived from IVL. Our analysis shows that vertically integrated water samples provide unreliable information on the concentration of P. rubescens in the upper surface waters and on the peak concentration of P. rubescens within the water column. We conclude that the protocol that we developed is superior to the current monitoring practice.

Sensor manufacturer, temperature, and cyanobacteria morphology affect phycocyanin fluorescence measurements

Sensors to measure phycocyanin fluorescence in situ are becoming widely used as they may provide useful proxies for cyanobacterial biomass. In this study, we assessed five phycocyanin sensors from three different manufacturers. A combination of culture-based experiments and a 30-sample field study was used to examine the effect of temperature and cyanobacteria morphology on phycocyanin fluorescence. Phycocyanin fluorescence increased with decrease in temperature, although this varied with manufacturer and cyanobacterial density. Phycocyanin fluorescence and cyanobacterial biovolume were strongly correlated (R ² > 0.83, P < 0.05) for single-celled and filamentous species. The relationship was generally weak for a colonial strain of Microcystis aeruginosa. The colonial culture was divided into different colony size classes and phycocyanin measured before and after manual disaggregation. No differences were measured, and the observation that fluorescence spiked when large colonial aggregates drifted past the light source suggests that sample heterogeneity, rather than lack of light penetration into the colonies, was the main cause of the poor relationship. Analysis of field samples showed a strong relationship between in situ phycocyanin fluorescence and spectrophotometrically measured phycocyanin (R ² > 0.7, P < 0.001). However, there was only a weak relationship between phycocyanin fluorescence and cyanobacterial biovolume for two sensors (R ² = 0.22-0.29, P < 0.001) and a non-significant relationship for the third sensor (R ² = 0.29, P > 0.4). The five sensors tested in our study differed in their output of phycocyanin fluorescence, upper working limits (1200 to > 12,000 μg/L), and responses to temperature, highlighting the need for comprehensive sensor calibration and knowledge on the limitations of specific sensors prior to deployment.

An evaluation of a handheld spectroradiometer for the near real-time measurement of cyanobacteria for bloom management purposes

A commercially available handheld spectroradiometer, the WISP-3, was assessed as a tool for monitoring freshwater cyanobacterial blooms for management purposes. Three small eutrophic urban ponds which displayed considerable within-pond and between-pond variability in water quality and cyanobacterial community composition were used as trial sites. On-board algorithms provide field measurements of phycocyanin (CPC) and chlorophyll-a (Chl-a) from surface reflectance spectra measured by the instrument. These were compared with laboratory measurements. Although significant but weak relationships were found between WISP-3 measured CPC and cyanobacterial biovolume measurements and WISP-3 and laboratory Chl-a measurements, there was considerable scatter in the data due likely to error in both WISP-3 and laboratory measurements. The relationships generally differed only slightly between ponds, indicating that different cyanobacterial communities had little effect on the pigment retrievals of the WISP-3. The on-board algorithms need appropriate modification for local conditions, posing a problem if it is to be used extensively across water bodies with differing optical properties. Although suffering a range of other limitations, the WISP-3 has a potential as a rapid screening tool for preliminary risk assessment of cyanobacterial blooms. However, such field assessment would still require adequate support by sampling and laboratory-based analysis.