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

Unveiling the impact of additives on structural integrity, thermal and color stability of C-phycocyanin - Agar hydrocolloid

C-Phycocyanin and sugar (C-PC/S) blended agar hydrocolloid was prepared and its rheological, thermo-functional and morphological properties were examined based on the fluorescence excitation-emission matrix profile. Sucrose (40%, w/v) determined as a superior preservative, maintaining the native conformation of C-PC effectively. C-PC/S exhibited enhanced structural integrity with high storage modulus (G') and 86.4% swelling index. FT-IR demonstrated strong intramolecular bonding. TGA revealed that the presence of sucrose prolonged the devolatilization peak up to 325 °C, with a degradation rate of -2.273 mg/min, it the thermal stability. C-PC/S fortified hydrocolloid in ice cream (5.0% w/w), reduced melting rate up to five times. In conclusion, sucrose as a promising enhancer of color stability and structural integrity for C-PC, and this combination effectively improves the functional and rheological properties. Further, the findings exposed the agar hydrocolloid as a potential enhancer of color retention and improved performance for various food and cosmetic products.

Exploring Spatiotemporal Patterns of Algal Cell Density in Lake Dianchi with Explainable Machine Learning

The escalating global occurrence of algal blooms poses a growing threat to ecosystem services. In this study, the spatiotemporal heterogeneity of water quality parameters was leveraged to partition Lake Dianchi into three clusters. Considering water quality parameters and both the delayed and instantaneous effects of meteorological factors, ensemble learning, and quasi-Monte Carlo methods were employed to predict daily algal cell density (AD) between January 2021 and January 2024. Consistently, superior predictive accuracy across all three clusters was exhibited by the Stacking-Elastic-Net regularization model. Furthermore, the minimum combination of drivers that achieved near-optimal accuracy for each cluster was identified, striking a balance between accuracy and cost. The ranking of the effect of drivers on AD varied by cluster, while the delayed effect of meteorological factors on AD generally outweighed their instantaneous effect for all clusters. Additionally, the heterogeneous or homogeneous thresholds and responses between drivers and AD were explored. These findings could serve as a scientific and cost-effective basis for government agencies to develop regional sustainable strategies for managing water quality.

Unveiling the trifecta of cyanobacterial quorum sensing: LuxI, LuxR and LuxS as the intricate machinery for harmful algal bloom formation in freshwater ecosystems

Harmful algal blooms (HABs) are causing significant disruptions in freshwater ecosystems, primarily due to the proliferation of cyanobacteria. These blooms have a widespread impact on various lakes globally, leading to profound environmental and health consequences. Cyanobacteria, with their ability to produce diverse toxins, pose a particular concern as they negatively affect the well-being of humans and animals, exacerbating the situation. Notably, cyanobacteria utilize quorum sensing (QS) as a complex communication mechanism that facilitates coordinated growth and toxin production. QS plays a critical role in regulating the dynamics of HABs. However, recent advances in control and mitigation strategies have shown promising results in effectively managing and reducing the occurrence of HABs. This comprehensive review explores the intricate aspects of cyanobacteria development in freshwater ecosystems, explicitly focusing on deciphering the signaling molecules associated with QS and their corresponding genes. Furthermore, a concise overview of diverse measures implemented to efficiently control and mitigate the spread of these bacteria will be provided, shedding light on the ongoing global efforts to address this urgent environmental issue. By deepening our understanding of the mechanisms driving cyanobacteria growth and developing targeted control strategies, we hope to safeguard freshwater ecosystems and protect the health of humans and animals from the detrimental impacts of HABs.

Smarter eco-cities and their leading-edge artificial intelligence of things solutions for environmental sustainability: A comprehensive systematic review

The recent advancements made in the realms of Artificial Intelligence (AI) and Artificial Intelligence of Things (AIoT) have unveiled transformative prospects and opportunities to enhance and optimize the environmental performance and efficiency of smart cities. These strides have, in turn, impacted smart eco-cities, catalyzing ongoing improvements and driving solutions to address complex environmental challenges. This aligns with the visionary concept of smarter eco-cities, an emerging paradigm of urbanism characterized by the seamless integration of advanced technologies and environmental strategies. However, there remains a significant gap in thoroughly understanding this new paradigm and the intricate spectrum of its multifaceted underlying dimensions. To bridge this gap, this study provides a comprehensive systematic review of the burgeoning landscape of smarter eco-cities and their leading-edge AI and AIoT solutions for environmental sustainability. To ensure thoroughness, the study employs a unified evidence synthesis framework integrating aggregative, configurative, and narrative synthesis approaches. At the core of this study lie these subsequent research inquiries: What are the foundational underpinnings of emerging smarter eco-cities, and how do they intricately interrelate, particularly urbanism paradigms, environmental solutions, and data-driven technologies? What are the key drivers and enablers propelling the materialization of smarter eco-cities? What are the primary AI and AIoT solutions that can be harnessed in the development of smarter eco-cities? In what ways do AI and AIoT technologies contribute to fostering environmental sustainability practices, and what potential benefits and opportunities do they offer for smarter eco-cities? What challenges and barriers arise in the implementation of AI and AIoT solutions for the development of smarter eco-cities? The findings significantly deepen and broaden our understanding of both the significant potential of AI and AIoT technologies to enhance sustainable urban development practices, as well as the formidable nature of the challenges they pose. Beyond theoretical enrichment, these findings offer invaluable insights and new perspectives poised to empower policymakers, practitioners, and researchers to advance the integration of eco-urbanism and AI- and AIoT-driven urbanism. Through an insightful exploration of the contemporary urban landscape and the identification of successfully applied AI and AIoT solutions, stakeholders gain the necessary groundwork for making well-informed decisions, implementing effective strategies, and designing policies that prioritize environmental well-being.

Employing hybrid deep learning for near-real-time forecasts of sensor-based algal parameters in a Microcystis bloom-dominated lake

Harmful cyanobacterial blooms (CyanoHABs) are increasingly impacting the ecosystem of lakes, reservoirs and estuaries globally. The integration of real-time monitoring and deep learning technology has opened up new horizons for early warnings of CyanoHABs. However, unlike traditional methods such as pigment quantification or microscopy counting, the high-frequency data from in-situ fluorometric sensors display unpredictable fluctuations and variability, posing a challenge for predictive models to discern underlying trends within the time-series sequence. This study introduces a hybrid framework for near-real-time CyanoHABs predictions in a cyanobacterium Microcystis-dominated lake - Lake Dianchi, China. The proposed model was validated using hourly Chlorophyll-a (Chl a) concentrations and algal cell densities. Our results demonstrate that applying decomposition-based singular spectrum analysis (SSA) significantly enhances the prediction accuracy of subsequent CyanoHABs models, particularly in the case of temporal convolutional network (TCN). Comparative experiments revealed that the SSA-TCN model outperforms other SSA-based deep learning models for predicting Chl a (R2 = 0.45-0.93, RMSE = 2.29-5.89 μg/L) and algal cell density (R2 = 0.63-0.89, RMSE = 9489.39-16,015.37 cells/mL) at one to four steps ahead predictions. The forecast of bloom intensities achieved a remarkable accuracy of 98.56 % and an average precision rate of 94.04 % ± 0.05 %. In addition, scenarios involving various input combinations of environmental factors demonstrated that water temperature emerged as the most effective driver for CyanoHABs predictions, with a mean RMSE of 2.94 ± 0.12 μg/L, MAE of 1.55 ± 0.09 μg/L, and R2 of 0.83 ± 0.01. Overall, the newly developed approach underscores the potential of a well-designed hybrid deep-learning framework for accurately predicting sensor-based algal parameters. It offers novel perspectives for managing CyanoHABs through online monitoring and artificial intelligence in aquatic ecosystems.

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

In recent years, frequent occurrences of algal blooms due to environmental changes have posed significant threats to the environment and human health. This paper analyzes the reasons of algal bloom from the perspective of environmental factors such as nutrients, temperature, light, hydrodynamics factors and others. Various commonly used algal bloom monitoring methods are discussed, including traditional field monitoring methods, remote sensing techniques, molecular biology-based monitoring techniques, and sensor-based real-time monitoring techniques. The advantages and limitations of each method are summarized. Existing algal bloom prediction models, including traditional models and machine learning (ML) models, are introduced. Support Vector Machine (SVM), deep learning (DL), and other ML models are discussed in detail, along with their strengths and weaknesses. Finally, this paper provides an outlook on the future development of algal bloom warning techniques, proposing to combine various monitoring methods and prediction models to establish a multi-level and multi-perspective algal bloom monitoring system, further improving the accuracy and timeliness of early warning, and providing more effective safeguards for environmental protection and human health.

Quantitative relationships among high-throughput sequencing, cyanobacteria toxigenic genotype abundance and microcystin occurrence in bathing waters

Toxin-producing cyanobacteria pose significant threats to human and animal health if exposed during recreational activities in bathing waters. To better safeguard public health and reduce health risks during the bathing season, an effective monitoring and management strategy is required. Molecular tools used to monitor toxigenic cyanobacteria have been evaluated on the basis of the efficiency and applicability of the method used to (i) establish an early-warning monitoring strategy for EU bathing water sites using both targeted quantitative polymerase chain reaction (qPCR) and non-targeted high-throughput sequencing (HTS) genotype analysis and (ii) to compare the toxigenic potential of cyanobacteria with actual microcystin (MC) occurrence and concentrations. For this purpose, 16 bathing water sites were monitored according to the bathing water directive (BWD) of the European Union (EU) during the bathing season of the summer of 2020 in eastern Austria. The cyanobacterial community composition was analyzed through HTS and qPCR by targeting the microcystin synthetase B gene (mcyB), which indicates MC synthesis within the genera Microcystis and Planktothrix. Within the genus Microcystis, which was identified as the primary MC producer, the mcyB genotypes formed stable subpopulations that increased linearly in correlation with the total Microcystis population. Notably, the HTS cell equivalents assigned to Microcystis and Planktothrix correlated with the corresponding qPCR estimates of genotype abundance, which serves as a confirmation of the suitability of (semi)-quantitative sequencing through HTS. In addition to the elevated trophic state, reduced transparency, increasing water temperatures, as well as cyanobacterial HTS read numbers and Microcystis cell number equivalents per mL estimated through qPCR, were associated with positive MC samples. Therefore, in combination with the monitoring of standard environmental parameters, the use of HTS and qPCR techniques is considered highly useful to ensure the timely identification of health risks to recreational users, as mandated by the BWD.

A novel low-cost plug-and-play multi-spectral LED based fluorometer, with application to chlorophyll detection

In this paper a novel low-cost multi-spectral optical fluorometer is presented and evaluated. The device uses a range of LEDs in the blue and violet regions of the electromagnetic spectrum and a mini-spectrometer to detect the emitted fluorescence in the UV to IR spectrum region. Custom built electronics and software were designed to control the system and the components were housed in bespoke 3D printed parts. A number of known fluorophores were tested to determine the capabilities of the fluorometer. Application of the device is demonstrated for the detection of chlorophyll a (Chl a) from laboratory grown algae and from environmental samples while analytical performance is established using both in vivo and extracted Chl a fluorescence and by comparison with a benchtop fluorometer.

Integrated portable food safety testing pipette based on a color-switchable fluorescence probe for rapid visual discrimination of mild food deterioration

A sensitive, portable, easy-to-operate, directly-readable food freshness monitoring device has been developed for rapid visual identification of mild food spoilage.

Potential of Mie-Fluorescence-Raman Lidar to Profile Chlorophyll a Concentration in Inland Waters

Vertical distribution of phytoplankton is crucial for assessing the trophic status and primary production in inland waters. However, there is sparse information about phytoplankton vertical distribution due to the lack of sufficient measurements. Here, we report, to the best of our knowledge, the first Mie-fluorescence-Raman lidar (MFRL) measurements of continuous chlorophyll a (Chl-a) profiles as well as their parametrization in inland water. The lidar-measured Chl-a during several experiments showed good agreement with the in situ data. A case study verified that MFRL had the potential to profile the Chl-a concentration. The results revealed that the maintenance of subsurface chlorophyll maxima (SCM) was influenced by light and nutrient inputs. Furthermore, inspired by the observations from MFRL, an SCM model built upon surface Chl-a concentration and euphotic layer depth was proposed with root mean square relative difference of 16.5% compared to MFRL observations, providing the possibility to map 3D Chl-a distribution in aquatic ecosystems by integrated active-passive remote sensing technology. Profiling and modeling Chl-a concentration with MFRL are expected to be of paramount importance for monitoring inland water ecosystems and environments.

Monitoring of cyanobacterial breakthrough and accumulation by in situ phycocyanin probe system within full-scale treatment plants

Worldwide, there has been an increase in the presence of potentially toxic cyanobacterial blooms in drinking water sources and within drinking water treatment plants (DWTPs). The objective of this study is to validate the use of in situ probes for the detection and management of cyanobacterial breakthrough in high and low-risk DWTPs. In situ phycocyanin YSI EXO2 probes were devised for remote control and data logging to monitor the cyanobacteria in raw, clarified, filtered, and treated water in three full-scale DWTPs. An additional probe was installed inside the sludge holding tank to measure the water quality of the surface of the sludge storage tank in a high-risk DWTP. Simultaneous grab samplings were carried out for taxonomic cell counts and toxin analysis. A total of 23, 9, and 4 field visits were conducted at the three DWTPs. Phycocyanin readings showed a 93-fold fluctuation within 24 h in the raw water of the high cyanobacterial risk plant, with higher phycocyanin levels during the afternoon period. These data provide new information on the limitations of weekly or daily grab sampling. Also, different moving averages for the phycocyanin probe readings can be used to improve the interpretation of phycocyanin signal trends. The in situ probe successfully detected high cyanobacterial biovolumes entering the clarification process in the high-risk plant. Grab sampling results revealed high cyanobacterial biovolumes in the sludge for both high and low-risk plants.

Cyanobacterial bloom monitoring and assessment in Latin America

Cyanobacterial blooms have serious adverse effects on human and environmental health. In Latin America, one of the main world's freshwater reserves, information on this phenomenon remains sparse. To assess the current situation, we gathered reports of cyanobacterial blooms and associated cyanotoxins in freshwater bodies from South America and the Caribbean (Latitude 22° N to 45° S) and compiled the regulation and monitoring procedures implemented in each country. As the operational definition of what is a cyanobacterial bloom remains controversial, we also analyzed the criteria used to determine the phenomena in the region. From 2000 to 2019, blooms were reported in 295 water bodies distributed in 14 countries, including shallow and deep lakes, reservoirs, and rivers. Cyanotoxins were found in nine countries and high concentrations of microcystins were reported in all types of water bodies. Blooms were defined according to different, and sometimes arbitrary criteria including qualitative (changes in water color, scum presence), quantitative (abundance), or both. We found 13 different cell abundance thresholds defining bloom events, from 2 × 10³ to 1 × 10⁷ cells mL^-1. The use of different criteria hampers the estimation of bloom occurrence, and consequently the associated risks and economic impacts. The large differences between countries in terms of number of studies, monitoring efforts, public access to the data and regulations regarding cyanobacteria and cyanotoxins highlights the need to rethink cyanobacterial bloom monitoring, seeking common criteria. General policies leading to solid frameworks based on defined criteria are needed to improve the assessment of cyanobacterial blooms in Latin America. This review represents a starting point toward common approaches for cyanobacterial monitoring and risk assessment, needed to improve regional environmental policies.

Growth conditions impact particulate absorption and pigment concentrations in two common bloom forming cyanobacterial species

Remote sensing using satellite imagery has been promoted as a method to broaden the scale and frequency of cyanobacterial monitoring. This relies on the ability to establish relationships between the reflectance spectra of water bodies and the abundance of cyanobacteria. A challenge to achieving this comes from a limited understanding of the extent to which the optical properties of cyanobacteria vary according to their physiological state and growth environment. The aim of the present study was to determine how growth stage, nutrient status and irradiance affect pigment concentrations and absorption spectra in two common bloom forming cyanobacterial taxa: Dolichospermum lemmermannii and Microcystis aeruginosa. Each species was grown in laboratory batch culture under a full factorial design of low or high light intensity and low, medium, or high nitrate concentrations. Absorption spectra, pigment concentrations and cell density were measured throughout the growth phases. The absorption spectra were all highly distinguishable from each other, with greater interspecific than intraspecific differences, indicating that both D. lemmermannii and M. aeruginosa can be readily differentiated using hyperspectral absorption spectra. Despite this, each species exhibited different responses in the per-cell pigment concentrations with varying light intensity and nitrate exposure. Variability among treatments was considerably higher in D. lemmermannii than in M. aeruginosa, which exhibited smaller changes in pigment concentrations among the treatments. These results highlight the need to understand the physiology of the cyanobacteria and to take caution when estimating biovolumes from reflectance spectra when species composition and growth stage are unknown.

A soft sensor for simulating algal cell density based on dynamic response to environmental changes in a eutrophic shallow lake

There is a great need for timely monitoring and rapid water quality assessment to control the algal blooms that often occur in eutrophic lakes. While algal cell density (ACD) is a critical indicator of algal growth, field monitoring is laborious and time-consuming, and rapid assessment of algal blooms based on ACD is often not possible. To address the limitations of conventional ACD detection, we proposed a soft sensor approach that uses surrogate indicators to simulate ACD in machine learning models. We conducted a case study using monitoring data from Chaohu Lake collected between 2016 and 2019. We found that ensemble learning models, especially extreme gradient boosting (XGBoost), outperformed traditional machine learning algorithms by comparing various machine learning algorithms. Also, considering the influence of input variable selection on model performance, we combined the results of different filter methods in the multi-stage variable selection process. Finally, we screened out seven key variables out of the 43 initial candidate variables, including dissolved oxygen (DO), chlorophyll-a (Chl-a), Secchi disk depth (SD), pH, permanganate index (COD_Mn), week of the year (WOY), and wind velocity (WV). Their inclusion substantially improved data accessibility and supported the development of a rapid simulation model. The final model was capable of reliable spatiotemporal generalization, with an overall R² value of 0.761. On the theoretical side, our study makes a new attempt to simulate ACD values in a eutrophic lake. For practical purposes, the soft sensor can facilitate the rapid assessment of bloom conditions, which helps the local administration with emergency prevention and control.

Remote sensing monitoring of mining tailings in the fluvial-estuarine-coastal ocean continuum of the Lower Doce River Valley (Brazil)

Water clarity is a key parameter of aquatic ecosystems impacted by mining tailings. Tracking down tailings dispersion along the river basin requires a regional monitoring approach. The longitudinal fluvial connectivity, river-estuary-coastal ocean, and the lateral connectivity, river-floodplain-alluvial lakes are interconnected by hydrological flows, particularly during high fluvial discharge. The present study aims to track the dispersal of iron ore tailing spill, from the collapse of the Fundão dam (Mariana, MG, Brazil), on November 5, 2015, in the Lower Doce River Valley. A semi-empirical model of turbidity data, as a water clarity proxy, and multispectral remote sensing data (MSI Sentinel-2), based on different hydrological conditions and well-differentiated water types, yielded an accuracy of 92%. Five floods (> 3187m³ s^-1) and five droughts (< 231m³ s^-1) events occurred from 2013 to 2020. The flood of January 2016 occurred one month after the mining slurries reached the coast, intruding tailings on some alluvial and coastal plain lakes with highly turbid waters (> 400 NTU). A fluvial plume is formed in the inner shelf adjoining the river mouth on high flow. The dispersion of river plume was categorized as plume core (turbidity > 200 NTU), plume core and inner shelf waters (100-199 NTU), other shelf water (50-99 NTU), and offshore waters (< 50 NTU). Fluvial discharge and local winds are the main drivers for river plume dispersion and transport of terrigenous material along the coast. This work provides elements for evaluating the impact of mining tailings and an approach for remote sensing regional monitoring of surface water quality.

Biosynthesis of 2-methylisoborneol is regulated by chromatic acclimation of Pseudanabaena

Cyanobacteria can sense different light color by adjusting the components of photosynthetic pigments including chlorophyll a (Chl a), phycoerythrin (PE), and phycocyanin (PC), etc. Filamentous cyanobacteria are the main producer of 2-methylisoborneol (MIB) and many can increase their PE levels so that they are more competitive in subsurface layer where green light is more abundant, and have caused extensive odor problems in drinking water reservoirs. Here, we identified the potential correlation between MIB biosynthesis and ambient light color induced chromatic acclimation (CA) of a MIB-producing Pseudanabaena strain. The results suggest Pseudanabaena regulates the pigment proportion through Type III CA (CA3), by increasing PE abundance and decreasing PC in green light. The biosynthesis of MIB and Chl a share the common precursor, and are positively correlated with statistical significance regardless of light color (R²=0.68; p<0.001). Besides, the PE abundance is also positively correlated with Chl a in green light (R²=0.57; p=0.019) since PE is the antenna that can only transfer the energy to PC and Chl a. In addition, significantly higher MIB production was observed in green light since more Chl a was synthesized.

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.

Simultaneous detection of lysosomal SO2 and viscosity using a hemicyanine-based fluorescent probe

The changes in sulfur dioxide and viscosity of lysosomes are significant indicators in physiological processes and the cell microenvironment. This study aimed to synthesize a hemicyanine-based probe for simultaneous detection of SO₂ and viscosity. The probe could not only rationally detect sulfur dioxide in a semi-aqueous solution with high sensitivity (limit of detection = 0.78 μM) and fast response (within 30 s) but also monitor viscosity via fluorescence emission enhancement at 580 nm. Further, the dual-response probe was successfully used to image SO₂ and viscosity in the lysosomes of living cells.

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.

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

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

Remote sensing of the cyanobacteria life cycle: A mesocosm temporal assessment of a Microcystis sp. bloom using coincident unmanned aircraft system (UAS) hyperspectral imagery and ground sampling efforts

Remote sensing technologies offer a consistent, spatiotemporal approach to assess water quality, which includes the detection, monitoring, and forecasting of cyanobacteria harmful algal blooms. In this study, a series of ex-situ mesoscale experiments were conducted to first develop and then monitor a Microcystis sp. bloom using a hyperspectral sensor mounted on an unmanned aircraft system (UAS) along with coincident ground sampling efforts including laboratory analyses and in-situ field probes. This approach allowed for the simultaneous evaluation of both bloom physiology (algal growth stages/life cycle) and data collection method on the performance of a suite of 41 spectrally-derived water quality algorithms across three water quality indicators (chlorophyll a, phycocyanin and turbidity) in a controlled environment. Results indicated a strong agreement between Lab and Field-based methods for all water quality indicators independent of growth phase, with regression R²-values above 0.73 for mean absolute percentage error (MAPE) and 0.87 for algorithm R² values. Three of the 41 algorithms evaluated met predetermined performance criteria (MAPE and algorithm R² values); however, in general, algal growth phase had a substantial impact on algorithm performance, especially those with blue and violet wave bands. This study highlights the importance of co-validating sensor technologies with appropriate ground monitoring methods to gain foundational knowledge before deploying new technologies in large-scale field efforts.

A quinoline-based probe for the ratiometric fluorescent detection of sulfite in lysosomes of living cells

A lysosome-targeting ratiometric fluorescent probe was synthesized for detecting sulfite based on sulfite-triggered nucleophilic addition reaction. Due to the specific reaction, the fluorescence intensity ratio (I₅₃₀/I₃₉₀) of the probe in an almost aqueous solution (0.5% DMSO) changed significantly after the addition of HSO₃^-, corresponding to the change in the fluorescence color of the solution from green to blue. The recognition was conducted using high-resolution mass spectrometry, proton nuclear magnetic resonance, and density functional theory calculations. The fluorescent probe could be utilized to quantitatively monitor HSO₃^- in lysosomes of living C6 glioma cells and real-water samples.

Classic indicators and diel dissolved oxygen versus trend analysis in assessing eutrophication of potable-water reservoirs

Potable source-water reservoirs are the main water supplies in many urbanizing regions, yet their long-term responses to cultural eutrophication are poorly documented in comparison with natural lakes, creating major management uncertainties. Here, long-term discrete data (June 2006-June 2018) for classical eutrophication water quality indicators, continuous depth-profile data for dissolved oxygen (DO), and an enhanced hybrid statistical trend analysis model were used to evaluate the eutrophication status of a potable source-water reservoir. Based on classical indicators (nitrogen, N and phosphorus, P concentrations and ratios; phytoplankton biomass as chlorophyll a, chl a; and trophic state indices), the reservoir was eutrophic to hypereutrophic and stoichiometrically imbalanced. Anoxia/hypoxia occurred for 7-8 months annually systemwide, even throughout the water column for days to weeks in some years; and elevated total ammonia (up to ~900 μg tNH₃ L^-1 ) in surface waters from late summer/fall through late winter/early spring suggested substantial internal legacy nutrient loading. These surprising DO and tNH₃ phenomena may characterize many reservoirs in urbanizing areas, and the associated cascade of negative impacts may increasingly affect them under global warming. Total organic carbon (TOC), seasonally influenced by phytoplankton biomass, commonly exceeded 6 mg L^-1 , which is problematic for potable-water treatment, and significantly trended up over time. Wet-year inflow dilution influenced an apparent decreasing trend in nutrients within the hypereutrophic upper reservoir, which receives most tributary inputs. Nevertheless, significant reservoirwide trends (increasing total phosphorus [TP], phytoplankton chl a, TOC) and mid- and/or lower region trends (increasing total nitrogen [TN], tNH₃ , decreasing TN:TP ratios) suggest that water quality degradation from eutrophication has worsened over time. These findings support broadly applicable recommendations to strengthen protection of potable source-water reservoirs in urbanizing watersheds: (1) protective numeric water quality criteria are needed for TOC as well as TN, TP, and chl a; (2) continuous diel data capture more realistic DO conditions than traditional sampling, and can provide important insights for water treatment managers; and (3) assessment of reservoir eutrophication status to track management progress over time should emphasize classic indicators equally as statistical trends, which are highly sensitive to short-term meteorological forcing.

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.

Reusable and pH-Stable Luminescent Sensors for Highly Selective Detection of Phosphate

Phosphate sensors have been actively studied owing to their importance in water environment monitoring because phosphate is one of the nutrients that result in algal blooms. As with other nutrients, seamless monitoring of phosphate is important for understanding and evaluating eutrophication. However, field-deployable phosphate sensors have not been well developed yet due to the chemical characteristics of phosphate. In this paper, we report on a luminescent coordination polymer particle (CPP) that can respond selectively and sensitively to a phosphate ion against other ions in an aquatic ecosystem. The CPPs with an average size of 88.1 ± 12.2 nm are embedded into membranes for reusable purpose. Due to the specific binding of phosphates to europium ions, the luminescence quenching behavior of CPPs embedded into membranes shows a linear relationship with phosphate concentrations (3-500 μM) and detection limit of 1.52 μM. Consistent luminescence signals were also observed during repeated measurements in the pH range of 3-10. Moreover, the practical application was confirmed by sensing phosphate in actual environmental samples such as tap water and lake water.

Individual-based modelling of cyanobacteria blooms: Physical and physiological processes

Lakes and reservoirs throughout the world are increasingly adversely affected by cyanobacterial harmful algal blooms (CyanoHABs). The development and spatiotemporal distributions of blooms are governed by complex physical mixing and transport processes that interact with physiological processes affecting the growth and loss of bloom-forming species. Individual-based models (IBMs) can provide a valuable tool for exploring and integrating some of these processes. Here we contend that the advantages of IBMs have not been fully exploited. The main reasons for the lack of progress in mainstreaming IBMs in numerical modelling are their complexity and high computational demand. In this review, we identify gaps and challenges in the use of IBMs for modelling CyanoHABs and provide an overview of the processes that should be considered for simulating the spatial and temporal distributions of cyanobacteria. Notably, important processes affecting cyanobacteria distributions, in particular their vertical passive movement, have not been considered in many existing lake ecosystem models. We identify the following research gaps that should be addressed in future studies that use IBMs: 1) effects of vertical movement and physiological processes relevant to cyanobacteria growth and accumulations, 2) effects and feedbacks of CyanoHABs on their environment; 3) inter and intra-specific competition of cyanobacteria species for nutrients and light; 4) use of high resolved temporal-spatial data for calibration and verification targets for IBMs; and 5) climate change impacts on the frequency, intensity and duration of CyanoHABs. IBMs are well adapted to incorporate these processes and should be considered as the next generation of models for simulating CyanoHABs.

Influence of Artificial Intelligence in Civil Engineering toward Sustainable Development—A Systematic Literature Review

The widespread use of artificial intelligence (AI) in civil engineering has provided civil engineers with various benefits and opportunities, including a rich data collection, sustainable assessment, and productivity. The trend of construction is diverted toward sustainability with the aid of digital technologies. In this regard, this paper presents a systematic literature review (SLR) in order to explore the influence of AI in civil engineering toward sustainable development. In addition, SLR was carried out by using academic publications from Scopus (i.e., 3478 publications). Furthermore, screening is carried out, and eventually, 105 research publications in the field of AI were selected. Keywords were searched through Boolean operation “Artificial Intelligence” OR “Machine intelligence” OR “Machine Learning” OR “Computational intelligence” OR “Computer vision” OR “Expert systems” OR “Neural networks” AND “Civil Engineering” OR “Construction Engineering” OR “Sustainable Development” OR “Sustainability”. According to the findings, it was revealed that the trend of publications received its high intention of researchers in 2020, the most important contribution of publications on AI toward sustainability by the Automation in Construction, the United States has the major influence among all the other countries, the main features of civil engineering toward sustainability are interconnectivity, functionality, unpredictability, and individuality. This research adds to the body of knowledge in civil engineering by visualizing and comprehending trends and patterns, as well as defining major research goals, journals, and countries. In addition, a theoretical framework has been proposed in light of the results for prospective researchers and scholars.

A cationic fluorescent probe for highly selective detection of sodium dodecyl sulfate (SDS) by electrostatic and hydrophobic self-assembly

Sodium dodecyl sulfate (SDS) has a wide range of applications in the chemical industry due to its excellent characteristics including good emulsification, foaming, water solubility and stability, easy synthesis and low price. However, it is a kind of anionic surfactant which is slightly toxic to the human body, and use of a large amount will cause potential pollution of the environment. Therefore, the development of a simple method to realize the monitoring of SDS in the environment is of great significance. Herein, a cationic fluorescent probe was prepared by the condensation reaction between 4-di-p-tolylamino-benzaldehyde and 3-ethylbenzothiazolium iodide. It can be used for the quantitative determination of SDS in the range of 5-50 μM showing red fluorescence and high selectivity by forming banded assemblies. This work provides an effective tool based on a new strategy for the detection of SDS.

Cyanobacterial Harmful Algal Blooms in Aquatic Ecosystems: A Comprehensive Outlook on Current and Emerging Mitigation and Control Approaches

An intensification of toxic cyanobacteria blooms has occurred over the last three decades, severely affecting coastal and lake water quality in many parts of the world. Extensive research is being conducted in an attempt to gain a better understanding of the driving forces that alter the ecological balance in water bodies and of the biological role of the secondary metabolites, toxins included, produced by the cyanobacteria. In the long-term, such knowledge may help to develop the needed procedures to restore the phytoplankton community to the pre-toxic blooms era. In the short-term, the mission of the scientific community is to develop novel approaches to mitigate the blooms and thereby restore the ability of affected communities to enjoy coastal and lake waters. Here, we critically review some of the recently proposed, currently leading, and potentially emerging mitigation approaches in-lake novel methodologies and applications relevant to drinking-water treatment.

A 2-Hydroxy-1-naphthaldehyde Schiff Base for Turn-on Fluorescence Detection of Zn2+ Based on PET Mechanism

Zinc ion is closely related to human health. Its content in human body is small, while the effect is large. However, it is not the more the better, must be in a scientific balance. Therefore, it is significant to the rapid detection of Zn2+ in the environment and organism. Herein, a fluorescent probe based on 2-hydroxy-1-naphthalene formaldehyde and furan-2-carbohydrazide was conveniently synthesized via Schiff base reaction. And this probe has been successfully applied to the accurate and quantitative detection of Zn2+ in real samples, showing turn on fluorescence, good selectivity, very low detection limit, real time response and reusability. In addition, this probe has the potential application to trace Zn2+ in living cells with low cytotoxicity.

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.

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.

Monitoring Approaches for Faecal Indicator Bacteria in Water: Visioning a Remote Real-Time Sensor for E. coli and Enterococci

A comprehensive review was conducted to assess the current state of monitoring approaches for primary faecal indicator bacteria (FIB) E. coli and enterococci. Approaches were identified and examined in relation to their accuracy, ability to provide continuous data and instantaneous detection results, cost, environmental awareness regarding necessary reagent release or other pollution sources, in situ monitoring capability, and portability. Findings showed that several methods are precise and sophisticated but cannot be performed in real-time or remotely. This is mainly due to their laboratory testing requirements, such as lengthy sample preparations, the requirement for expensive reagents, and fluorescent tags. This study determined that portable fluorescence sensing, combined with advanced modelling methods to compensate readings for environmental interferences and false positives, can lay the foundations for a hybrid FIB sensing approach, allowing remote field deployment of a fleet of networked FIB sensors that can collect high-frequency data in near real-time. Such sensors will support proactive responses to sudden harmful faecal contamination events. A method is proposed to enable the development of the visioned FIB monitoring tool.

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.

Probing the Cyanobacterial Microcystis Gas Vesicles after Static Pressure Treatment: A Potential In Situ Rapid Method

The vertical migration trend of cyanobacterial cells with gas vesicles in water ecosystems can reflect the changes in the natural environment, such as temperature, nutrients, light conditions, etc. The static pressure treatment is one of the most important approaches to study the properties of the cyanobacterial cell and its gas vesicles. In this paper, a polarized light scattering method is used to probe the collapse and regeneration of the cyanobacterial gas vesicles exposed to different static pressures. During the course, both the axenic and wild type strain of cyanobacterial Microcystis were first treated with different static pressures and then recovered on the normal light conditions. Combining the observation of transmission electron microscopy and floating-sinking photos, the results showed that the collapse and regeneration of the cyanobacterial gas vesicles exposed to different static pressures can be characterized by the polarization parameters. The turbidity as a traditional indicator of gas vesicles but subjected to the concentration of the sample was also measured and found to be correlated with the polarization parameters. More analysis indicated that the polarization parameters are more sensitive and characteristic. The polarized light scattering method can be used to probe the cyanobacterial gas vesicles exposed to different static pressures, which has the potential to provide an in situ rapid and damage-free monitoring tool for observing the vertical migration of cyanobacterial cells and forecasting cyanobacterial blooms.

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.

Impact of bloom events on dissolved organic matter fluorophore signatures in Ohio waters

Due to the increase in severe cyanobacterial blooms in drinking water sources and recreational waters across the globe, inexpensive and reliable methods of detecting oncoming blooms are needed. Cyanobacterial blooms can contribute substantially to the bulk chromophoric dissolved organic matter pool. Thus, the fluorescence signature of organic matter derived from these blooms may be an indicator of upcoming blooms. Water samples from five sites around Ohio were collected regularly between February and October 2017. A PARAFAC model was developed to determine if these protein-like fluorophores could be linked to bloom biomass and whether they were absent in dissolved organic matter from oligotrophic waters. One reference site at an oligotrophic reservoir was included to confirm the lack of protein-like fluorophores in the absence of a bloom event. We found that an increase in tryptophan-like and tyrosine-like fluorophores occurs before the increase in chlorophyll a levels, associated with bloom biomass, in some Ohio waters affected by cyanobacterial blooms; however, protein-like fluorophores are not correlated with levels of the cyanotoxin, microcystin. The excitation and emission wavelengths of these fluorophores (tryptophan-like: 239/341 nm, tyrosine-like: 248/306 nm) may be used to monitor impending blooms in waters heavily impacted by cyanobacteria but may not be applicable to waters receiving treated wastewater discharges.

Perspective: Advancing the research agenda for improving understanding of cyanobacteria in a future of global change

Harmful cyanobacterial blooms (=cyanoHABs) are an increasing feature of many waterbodies throughout the world. Many bloom-forming species produce toxins, making them of particular concern for drinking water supplies, recreation and fisheries in waterbodies along the freshwater to marine continuum. Global changes resulting from human impacts, such as climate change, over-enrichment and hydrological alterations of waterways, are major drivers of cyanoHAB proliferation and persistence. This review advocates that to better predict and manage cyanoHABs in a changing world, researchers need to leverage studies undertaken to date, but adopt a more complex and definitive suite of experiments, observations, and models which can effectively capture the temporal scales of processes driven by eutrophication and a changing climate. Better integration of laboratory culture and field experiments, as well as whole system and multiple-system studies are needed to improve confidence in models predicting impacts of climate change and anthropogenic over-enrichment and hydrological modifications. Recent studies examining adaptation of species and strains to long-term perturbations, e.g. temperature and carbon dioxide (CO₂) levels, as well as incorporating multi-species and multi-stressor approaches emphasize the limitations of approaches focused on single stressors and individual species. There are also emerging species of concern, such as toxic benthic cyanobacteria, for which the effects of global change are less well understood, and require more detailed study. This review provides approaches and examples of studies tackling the challenging issue of understanding how global changes will affect cyanoHABs, and identifies critical information needs for effective prediction and management.

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.

Early warning method for cyanobacteria toxin, taste and odor problems by the evaluation of fluorescence signals

Permanganate and ozone are often used in drinking water treatment plants for the oxidation of taste and odor compounds, toxins, and algae as well as the reduction of mussel activity. The disadvantage of an overuse of such oxidants is the potential lysis of cyanobacterial cells. Cell lysis causes taste and odor components as well as toxins to be released into the water, which results in the need for even more treatment to remove these compounds completely. Our research in the CLIENT-SIGN project investigated an innovative method to monitor the lysis of cyanobacteria cells: increases in a specific fluorescence emission spectrum of the cyanobacteria pigment phycocyanin were used as a proxy for cell lysis and other compounds (taste/odor, toxins) leaving the cells. We call this form of phycocyanin "free phycocyanin" or "unbound phycocyanin". By monitoring free phycocyanin via a relatively fast and inexpensive measurement, water utilities will be better able to optimize the dosage of pre-oxidation compounds to remove extracellular compounds while preventing the lysing of cells. Laboratory studies and a case study at Yangcheng Lake (adjacent to Lake Taihu, Yangcheng Lake Water Treatment Plant, Suzhou Industrial Park, China) are presented herein. An online surveillance system that monitors incoming raw water and the water after pre-oxidation is proposed to better cope with changing water conditions.

Fluorescence Spectra Predict Microcystin-LR and Disinfection Byproduct Formation Potential in Lake Water

Disinfection byproducts (DBPs) and algal toxins can be expensive to monitor and represent significant potential risks to human health. DBPs, including haloacetic acids and trihalomethanes, are possible or probable human carcinogens. Microcystin-LR-produced by cyanobacteria-is linked with various adverse health effects. Here we show that fluorescence spectra predict both microcystin-LR occurrence and DBP formation potential (DBPfp) in lake water. We compared models with either fluorescence spectra or a suite of water quality predictors as inputs. A regularized logistic regression model with fluorescence spectral inputs correctly classified 94% of test data with respect to microcystin-LR occurrence, with a 96% probability of correctly ranking a detect/nondetect pair. Regularized linear regression predicted DBPfp based on fluorescence inputs with a combined R² of 0.83 on test data. A gradient-boosted classifier with seven water quality inputs was comparable in detecting microcystin-LR (91% correct), as was UV₂₅₄ in predicting DBPfp (combined test R² = 0.84), but no single parameter matched fluorescence spectra over both predictive tasks. Results highlight the potential for multiparameter monitoring via fluorescence spectroscopy, extending previous work on predicting DBPs alone. As a high-frequency monitoring tool, this approach could supplement mass spectrometric methods that may only be applicable at low frequency due to resource limitations.

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.