Establishment of an Alert Level Framework for cyanobacteria in drinking water resources by using the Algae Online Analyser for monitoring cyanobacterial chlorophyll a

Deep-learning and data-resampling: A novel approach to predict cyanobacterial alert levels in a reservoir

The proliferation of harmful algal blooms results in adverse impacts on aquatic ecosystems and public health. Early warning system monitors algal bloom occurrences and provides management strategies for promptly addressing high-concentration algal blooms following their occurrence. In this study, we aimed to develop a proactive prediction model for cyanobacterial alert levels to enable efficient decision-making in management practices. We utilized 11 years of water quality, hydrodynamic, and meteorological data from a reservoir that experiences frequent harmful cyanobacterial blooms in summer. We used these data to construct a deep-learning model, specifically a 1D convolution neural network (1D-CNN) model, to predict cyanobacterial alert levels one week in advance. However, the collected distribution of algal alert levels was imbalanced, leading to the biased training of data-driven models and performance degradation in model predictions. Therefore, an adaptive synthetic sampling method was applied to address the imbalance in the minority class data and improve the predictive performance of the 1D-CNN. The adaptive synthetic sampling method resolved the imbalance in the data during the training phase by incorporating an additional 156 and 196 data points for the caution and warning levels, respectively. The selected optimal 1D-CNN model with a filter size of 5 and comprising 16 filters achieved training and testing prediction accuracies of 97.3% and 85.0%, respectively. During the test phase, the prediction accuracies for each algal alert level (L-0, L-1, and L-2) were 89.9%, 79.2%, and 71.4%, respectively, indicating reasonably consistent predictive results for all three alert levels. Therefore, the use of synthetic data addressed data imbalances and enhanced the predictive performance of the data-driven model. The reliable forecasts produced by the improved model can support the development of management strategies to mitigate harmful algal blooms in reservoirs and can aid in building an early warning system to facilitate effective responses.

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.

Pulse Feature-Enhanced Classification of Microalgae and Cyanobacteria Using Polarized Light Scattering and Fluorescence Signals

Harmful algal blooms (HABs) pose a global threat to the biodiversity and stability of local aquatic ecosystems. Rapid and accurate classification of microalgae and cyanobacteria in water is increasingly desired for monitoring complex water environments. In this paper, we propose a pulse feature-enhanced classification (PFEC) method as a potential solution. Equipped with a rapid measurement prototype that simultaneously detects polarized light scattering and fluorescence signals of individual particles, PFEC allows for the extraction of 38 pulse features to improve the classification accuracy of microalgae, cyanobacteria, and other suspended particulate matter (SPM) to 89.03%. Compared with microscopic observation, PFEC reveals three phyla proportions in aquaculture samples with an average error of less than 14%. In this paper, PFEC is found to be more accurate than the pulse-average classification method, which is interpreted as pulse features carrying more detailed information about particles. The high consistency of the dominant and common species between PFEC and microscopy in all field samples also demonstrates the flexibility and robustness of the former. Moreover, the high Pearson correlation coefficient accounting for 0.958 between the cyanobacterial proportion obtained by PFEC and the cyanobacterial density given by microscopy implies that PFEC serves as a promising early warning tool for cyanobacterial blooms. The results of this work suggest that PFEC holds great potential for the rapid and accurate classification of microalgae and cyanobacteria in aquatic environment monitoring.

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.

Robust imputation method with context-aware voting ensemble model for management of water-quality data

Water-quality monitoring and management are crucial for ensuring the safety and sustainability of water resources. However, missing data is a frequent problem in water-quality datasets, which can result in biased results in hydrological modeling and data analysis. While classic statistical methods and emerging machine/deep learning methods have been applied for imputing missing values, most existing studies perform well in specific missing scenarios, but not in universal scenarios. Therefore, existing imputation methods often fail to robustly impute missing values across various scenarios. To address the problem, we propose an imputation method that uses a context-aware voting-ensemble model to dynamically select optimal weights to integrate various imputation models across different missingness scenarios. For first identify the attributes of missingness scenarios that influence imputation accuracy. Then after introducing missing values in collected data according to the missingness scenarios, we measure the accuracy of various imputation models across the missingness scenarios. Weights of imputation models are optimized by estimating non-linear functions with regression model that can capture relationships between missingness scenarios and imputation accuracies of models. The final imputed value of the ensemble model for a missing scenario can be determined by multiplying each imputation model's weight by its imputed value, then summing the products. The method inherits the advantages of state-of-art imputation models, including the ability to learn long-term dependencies in time series, as well as the flexibility of using a dynamic weighting strategy to process various missingness scenarios. To validate the superiority of our method, we evaluate on real-world water-quality data from a river in South Korea. The proposed method achieves higher accuracy and lower variation of imputed values than baseline models across various missingness scenarios. Furthermore, we showed the applicability of our method to various hydrological environment by validating our method on industrial water quality dataset. This study highlights the potential value of the ensemble model with dynamic weighting in robust imputation of water-quality data.

Rapid in situ assessment of high-resolution spatial and temporal distribution of cyanobacterial blooms using fishery echosounder

Cyanobacterial blooms are increasing in frequency, magnitude, and duration globally because of enhanced eutrophication and climate change. Thus, comprehensive investigation and systematic monitoring of the spatial and temporal distribution of cyanobacteria in aquatic environments are urgently needed to better understand bloom development and complex interactions within a dynamic environment. Various methods have been used to investigate the distribution of cyanobacteria, however, none of them can provide high-resolution data for the three-dimensional spatial structure of the bloom and its dynamics in real time. In the present study, we investigated the applicability of a high-frequency (200 kHz) fishery echosounder, a type widely used in fisheries acoustics, to detect and estimate the cyanobacterial genus Microcystis bloom distribution and biomass in a shallow lake (Sulejów Reservoir, Poland). Verification of the usefulness of in situ acoustic quantification of bloom-forming cyanobacteria was based on a comparison of acoustic estimates of cyanobacterial biomass with the ground truth-that is, fluorometric measurements and chlorophyll a concentrations. We compared the acoustic estimates with other methods for continuous measurements along 10 predetermined parallel transects and point samples at 14 stations situated on the transects. In vertical hydroacoustic measurements at night, we observed that cyanobacterial biomass was highest in the uppermost layer and diminished continuously with depth. For both horizontal and vertical continuous measurements, we found significant positive correlations between acoustic and fluorometric estimates of cyanobacterial biomass. The traditional point samples measurements, however, did not agree equally well with the acoustic estimates, especially for vertical beam. We argue that the point measurements have more stochastic character and less adequately describe dynamic changes in the cyanobacteria distribution than continuous acoustic estimates. More studies are required to explore the cyanobacteria distribution patterns under different biological, physical, and meteorological conditions.

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.

Advances in Technological Research for Online and In Situ Water Quality Monitoring—A Review

Monitoring water quality is an essential tool for the control of pollutants and pathogens that can cause damage to the environment and human health. However, water quality analysis is usually performed in laboratory environments, often with the use of high-cost equipment and qualified professionals. With the progress of nanotechnology and the advance in engineering materials, several studies have shown, in recent years, the development of technologies aimed at monitoring water quality, with the ability to reduce the costs of analysis and accelerate the achievement of results for management and decision-making. In this work, a review was carried out on several low-cost developed technologies and applied in situ for water quality monitoring. Thus, new alternative technologies for the main physical (color, temperature, and turbidity), chemical (chlorine, fluorine, phosphorus, metals, nitrogen, dissolved oxygen, pH, and oxidation–reduction potential), and biological (total coliforms, Escherichia coli, algae, and cyanobacteria) water quality parameters were described. It was observed that there has been an increase in the number of publications related to the topic in recent years, mainly since 2012, with 641 studies being published in 2021. The main new technologies developed are based on optical or electrochemical sensors, however, due to the recent development of these technologies, more robust analyses and evaluations in real conditions are essential to guarantee the precision and repeatability of the methods, especially when it is desirable to compare the values with government regulatory standards.

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.

Chlorophyll a estimation in lakes using multi-parameter sonde data

Algae blooms are of considerable concern in freshwater lakes and reservoirs worldwide. In-situ Chlorophyll a (Chl-a) fluorometers are widely used for rapid assessments of algae biomass. However, accurately converting Chl-a fluorescence to an equivalent concentration is challenging due to natural variations in the relationship as well as nonphotochemical quenching (NPQ) which occurs commonly in surface waters during daytime. This study is based on water quality data from a freshwater lake from October 2018 to December 2020. Initial analysis of sonde Chl-a fluorescence and laboratory extracted Chl-a concentrations shows that the two data sets exhibit a nonlinear relationship with positive correlation and significant errors. A bias correction method was next developed based on (1) concurrent sonde measurements of other water quality parameters (to account for nonlinearities) and (2) a bias correction approach for nonphotochemical quenching effects in surface waters. The new Chl-a model exhibits much improved accuracy, with a root mean square error (RMSE) less than 0.95 µg/L. The new method facilitates accurate Chl-a characterization in freshwater lakes and reservoirs based on readily obtainable in-situ fluorescence sonde measurements.

Prediction of harmful algal blooms in large water bodies using the combined EFDC and LSTM models

Harmful algal blooms (HABs) is a worldwide water environmental problem. HABs usually happens in short time and is difficult to be controlled. Early warning of HABs using data-driven models is prospective in making time for taking precaution against HABs. High-frequency water quality monitoring data are necessary to improve the reliability of the model, but it is expensive. This research used environmental fluid dynamics code (EFDC) to extend one-point data obtained by only one instrument to the whole 249 ha water area instead of multi-instruments monitoring, followed by Long short-term memory (LSTM) to predict the HABs in the whole water body. Correlation analysis and principal component analysis were used to reduce the data dimension and improve model accuracy. Finally, the LSTM model was calibrated to predict chlorophyll-a (Chl-a) for the next 1 to 3 time steps. The Nash-Sutcliffe efficiency coefficient (NSE) and mean absolute percentage error (MAPE) of EFDC-LSTM were 0.797-0.991 and 2.74-13.16%, respectively, suggesting the promising utilization of this model in early warning systems for HABs. EFDC-LSTM achieves high-precision HABs forecasting in a cost-effective manner, providing a reliable way to detect HABs in advance.

A machine learning approach for early warning of cyanobacterial bloom outbreaks in a freshwater reservoir

Understanding the dynamics of harmful algal blooms is important to protect the aquatic ecosystem in regulated rivers and secure human health. In this study, artificial neural network (ANN) and support vector machine (SVM) models were used to predict algae alert levels for the early warning of blooms in a freshwater reservoir. Intensive water-quality, hydrodynamic, and meteorological data were used to train and validate both ANN and SVM models. The Latin-hypercube one-factor-at-a-time (LH-OAT) method and a pattern search algorithm were applied to perform sensitivity analyses for the input variables and to optimize the parameters of the models, respectively. The results indicated that the two models well reproduced the algae alert level based on the time-lag input and output data. In particular, the ANN model showed a better performance than the SVM model, displaying a higher performance value in both training and validation steps. Furthermore, a sampling frequency of 6- and 7-day were determined as efficient early-warning intervals for the freshwater reservoir. Therefore, this study presents an effective early-warning prediction method for algae alert level, which can improve the eutrophication management schemes for freshwater reservoirs.

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.

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.

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.

Use statistical machine learning to detect nutrient thresholds in Microcystis blooms and microcystin management

The frequency of toxin-producing cyanobacterial blooms has increased in recent decades due to nutrient enrichment and climate change. Because Microcystis blooms are related to different environmental conditions, identifying potential nutrient control targets can facilitate water quality managers to reduce the likelihood of microcystins (MCs) risk. However, complex biotic interactions and field data limitations have constrained our understanding of the nutrient-microcystin relationship. This study develops a Bayesian modelling framework with intracellular and extracellular MCs that characterize the relationships between different environmental and biological factors. This model was fit to the across-lake dataset including three bloom-plagued lakes in China and estimated the putative thresholds of total nitrogen (TN) and total phosphorus (TP). The lake-specific nutrient thresholds were estimated using Bayesian updating process. Our results suggested dual N and P reduction in controlling cyanotoxin risks. The total Microcystis biomass can be substantially suppressed by achieving the putative thresholds of TP (0.10 mg/L) in Lakes Taihu and Chaohu, but a stricter TP target (0.05 mg/L) in Dianchi Lake. To maintain MCs concentrations below 1.0 μg/L, the estimated TN threshold in three lakes was 1.8 mg/L, but the effect can be counteracted by the increase of temperature. Overall, the present approach provides an efficient way to integrate empirical knowledge into the data-driven model and is helpful for the management of water resources.

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.

Remote sensing of cyanobacterial blooms in inland waters: present knowledge and future challenges

Timely monitoring, detection and quantification of cyanobacterial blooms are especially important for controlling public health risks and understanding aquatic ecosystem dynamics. Due to the advantages of simultaneous data acquisition over large geographical areas and high temporal coverage, remote sensing strongly facilitates cyanobacterial bloom monitoring in inland waters. We provide a comprehensive review regarding cyanobacterial bloom remote sensing in inland waters including cyanobacterial optical characteristics, operational remote sensing algorithms of chlorophyll, phycocyanin and cyanobacterial bloom areas, and satellite imaging applications. We conclude that there have many significant progresses in the remote sensing algorithm of cyanobacterial pigments over the past 30 years. The band ratio algorithms in the red and near-infrared (NIR) spectral regions have great potential for the remote estimation of chlorophyll a in eutrophic and hypereutrophic inland waters, and the floating algae index (FAI) is the most widely used spectral index for detecting dense cyanobacterial blooms. Landsat, MODIS (Moderate Resolution Imaging Spectroradiometer) and MERIS (MEdium Resolution Imaging Spectrometer) are the most widely used products for monitoring the spatial and temporal dynamics of cyanobacteria in inland waters due to the appropriate temporal, spatial and spectral resolutions. Future work should primarily focus on the development of universal algorithms, remote retrievals of cyanobacterial blooms in oligotrophic waters, and the algorithm applicability to mapping phycocyanin at a large spatial-temporal scale. The applications of satellite images will greatly improve our understanding of the driving mechanism of cyanobacterial blooms by combining numerical and ecosystem dynamics models.

Application of Bayesian network including Microcystis morphospecies for microcystin risk assessment in three cyanobacterial bloom-plagued lakes, China

Microcystis spp., which occur as colonies of different sizes under natural conditions, have expanded in temperate and tropical freshwater ecosystems and caused seriously environmental and ecological problems. In the current study, a Bayesian network (BN) framework was developed to access the probability of microcystins (MCs) risk in large shallow eutrophic lakes in China, namely, Taihu Lake, Chaohu Lake, and Dianchi Lake. By means of a knowledge-supported way, physicochemical factors, Microcystis morphospecies, and MCs were integrated into different network structures. The sensitive analysis illustrated that Microcystis aeruginosa biomass was overall the best predictor of MCs risk, and its high biomass relied on the combined condition that water temperature exceeded 24 °C and total phosphorus was above 0.2 mg/L. Simulated scenarios suggested that the probability of hazardous MCs (≥1.0 μg/L) was higher under interactive effect of temperature increase and nutrients (nitrogen and phosphorus) imbalance than that of warming alone. Likewise, data-driven model development using a naïve Bayes classifier and equal frequency discretization resulted in a substantial technical performance (CCI = 0.83, K = 0.60), but the performance significantly decreased when model excluded species-specific biomasses from input variables (CCI = 0.76, K = 0.40). The BN framework provided a useful screening tool to evaluate cyanotoxin in three studied lakes in China, and it can also be used in other lakes suffering from cyanobacterial blooms dominated by Microcystis.

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.

Dominance and Growth Factors of Pseudanabaena sp. in Drinking Water Source Reservoirs, Southern China

Pseudanabaena sp. is a common and harmful species in freshwater cyanobacteria blooms. There are very few studies on its distribution characteristics and growth influencing factors. In the current study, it was found to be dominant in three cascading reservoirs in Southern China. Field observations and laboratory experiments were integrated to investigate the dominance and growth factors of Pseudanabaena sp. The effects of temperature, light intensity, nutrients, chemical oxygen demand (COD), pH, and disturbance on Pseudanabaena sp. growth were evaluated. The results indicated that Pseudanabaena sp. had significant positive correlations with water temperature, pH, and COD (p < 0.01) and a positive correlation with NH3-N (p < 0.05). The optimum growth temperature range for Pseudanabaena sp. was from 20 to 30 °C; hence, it usually has outbreaks in May and August. The optimum light intensity and pH for Pseudanabaena sp. were 27 μmol photons m−2s−1 and from 7 to 9, respectively. The superior tolerance for low light, disturbance, and phosphorus deficiency of Pseudanabaena sp. may be the main factors affecting its dominance in reservoirs. Controlling nitrogen was more effective than controlling phosphorus to avoid the risk that was brought by Pseudanabaena sp. This study contributed to the theoretical knowledge for the prediction and control of the growth of Pseudanabaena sp.

Using generalized additive models to investigate factors influencing cyanobacterial abundance through phycocyanin fluorescence in East Lake, China

East Lake is a shallow lake (in Wuhan, China) where cyanobacteria blooms occurred frequently from 1970 to 1985. During the study period, all Carlson trophic state index values were > 50, indicating that East Lake is in a eutrophic state. In this study, phycocyanin concentrations were measured through phycocyanin fluorometry for rapid assessment of cyanobacterial abundance. The smoothing splines of the optimal generalized additive model (GAM) indicated that Secchi depth (SD), total phosphorus (TP) and dissolved oxygen (DO) concentrations, electrical conductivity (EC), chemical oxygen demand (COD), and ratios of total nitrogen (TN) to TP (TN:TP) were the main environmental factors in a moderate nonlinear relationship with cyanobacterial phycocyanin concentrations in East Lake. The shape of the GAM smoother can be used to quantify the relationship between a response variable and an explanatory variable in the scatterplot. Phycocyanin concentrations were sharply and negatively related to both SD and EC when the SD was 20-80 cm and EC was > 270 mg/L. Phycocyanin concentrations increased with concentrations of TP, DO, and COD. Phycocyanin concentrations increased sharply with TP concentrations when TP concentrations were > 0.10 mg/L and approached to a constant when DO concentrations were > 8.20 mg/L. Approximately, 85% of the phycocyanin concentrations were negatively correlated with TN:TP of < 26. In summary, organic compounds and TP were inferred to the key factors limiting the potential growth of cyanobacteria in East Lake. These change points/thresholds of smoothing splines of aforementioned variables may serve as a framework for managing the cyanobacterial growth.

Potent removal of cyanobacteria with controlled release of toxic secondary metabolites by a titanium xerogel coagulant

Cyanobacteria blooming is a serious environmental issue throughout the world. Removal of cyanobacterial cells from surface water with controlled release of cyanobacterial organic matter (COM), especially toxic microcystins (MCs), would potentially reduce the processing burden in follow-up water treatment. Coagulation is a key technique in water treatment. Herein, the potential application of a novel titanium xerogel coagulant (TXC) was evaluated for the treatment of cyanobacteria-laden water in terms of cyanobacteria removal efficiency, variation of cell viability, the release and evolution of COM in the floc accumulation and storage process. Under acidic to neutral conditions, TXC showed a higher removal efficiency of approximately 99% for cyanobacteria and a lower residual Ti concentration than the widely-used commercial polyferric sulfate (PFS) and polyaluminum chloride (PAC). Another advantage of TXC was the reduced MCs concentration caused by the released acetylacetone (AcAc) from the hydrolysis of TXC. Under solar irradiation, AcAc degraded the extracellular MCs from an initial concentration of 40 μg/L to a residual concentration of 7 μg/L during a 16-day floc storage process. The low residual Ti concentration (< 0.04 mg/L) and the efficient removal of COM/MCs following TXC coagulation reduced the toxicity to photobacteria. The results demonstrate that TXC is a promising dual-effect coagulant for treatment of cyanobacteria-laden water.

Stepwise strategy for monitoring toxic cyanobacterial blooms in lentic water bodies

Climate change has been causing the increase in frequency, severity, and duration of harmful algal blooms, which makes the establishment of water management strategies indispensable. For cyanobacteria, several methods are currently used in monitoring programs. However, these methods are time-consuming and require specialists, and results are usually not provided within an adequate timeframe for taking timely mitigation actions. This work proposes a strategy for a faster, easier, and more cost-effective monitoring of cyanobacterial blooms, using a stepwise approach based on fluorometric determination of phycocyanin at an early stage. Complementary parameters (chlorophyll a, enumeration of dominant cyanobacterial species and cyanotoxin potential and quantification) are determined when necessary, thus progressively allocating human and financial resources within the monitoring program. This strategy was applied and validated using nine lentic eutrophic freshwater bodies prone to the occurrence of cyanobacterial blooms. Samples were sequentially evaluated, and the study ended up with two samples that showed high health risks. However, according to WHO guidelines, eight of the nine samples would be classified as having "moderate risk of adverse health effects" and could lead to preventive measures that would have an important regional economic impact. Therefore, the present approach proved to be a promising alternative to increase the effectiveness and accuracy of the risk assessment process in water bodies where cyanobacterial blooms occur.

Assessment of in situ fluorometry to measure cyanobacterial presence in water bodies with diverse cyanobacterial populations

A YSI EXO2 water quality sonde fitted with fluorometric sensors for chlorophyll-a (Chl-a) and phycocyanin (CPC) was used to determine its applicability in cyanobacterial quantification in three small urban ponds in Sydney, Australia displaying considerable variations in cyanobacterial community composition and abundance, as well as eukaryotic algae, turbidity and chromophoric dissolved organic matter. CPC and Chl-a measured in situ with the instrument was compared against laboratory measures of cyanobacterial biovolume over two summer sampling periods. A good correlation was found between CPC and total cyanobacterial biovolume in two of the three ponds. The poor correlation in the third was due to the frequent dominance of picoplanktonic sized cyanobacteria. CPC did not correlate well with cell counts, and Chl-a was a poor measure of cyanobacterial presence. The relationship between CPC measured by fluorometry varied according to the dominant cyanobacterial taxa present in the ponds at any one time. Fluorometry has good potential for use in environmental monitoring of cyanobacterial biovolume, but may need to be based on predetermined relations applicable to local water bodies. Management guidelines based on CPC concentrations would also enhance the usefulness of in situ CPC measurements.

Breakthrough of cyanobacteria in bank filtration

The removal of cyanobacteria cells in well water following bank filtration was investigated from a source water consisting of two artificial lakes (A and B). Phycocyanin probes used to monitor cyanobacteria in the source and in filtered well water showed an increase of fluorescence values demonstrating a progressive seasonal growth of cyanobacteria in the source water that were correlated with cyanobacterial biovolumes from taxonomic counts (r = 0.59, p < 0.00001). A strong correlation was observed between the cyanobacterial concentrations in the lake water and in the well water as measured by the phycocyanin probe (p < 0.001, 0.73 ≤ r(2) ≤ 0.94). Log removals from bank filtration estimated from taxonomic counts ranged from 0.96 ± (0.5) and varied according to the species of cyanobacteria. Of cyanobacteria that passed through bank filtration, smaller cells were significantly more frequent in well water samples (p < 0.05) than larger cells. Travel times from the lakes to the wells were estimated as 2 days for Lake B and 10 days for Lake A. Cyanobacterial species in the wells were most closely related to species found in Lake B. Thus, a travel time of less than 1 week permitted the breakthrough of cyanobacteria to wells. Winter samples demonstrated that cyanobacteria accumulate within bank filters, leading to continued passage of cells beyond the bloom season. Although no concentrations of total microcystin-LR were above detection limits in filtered well water, there is concern that cyanobacterial cells that reach the wells have the potential to contain intracellular toxins.

Development of Toxicological Risk Assessment Models for Acute and Chronic Exposure to Pollutants

Alert level frameworks advise agencies on a sequence of monitoring and management actions, and are implemented so as to reduce the risk of the public coming into contact with hazardous substances. Their effectiveness relies on the detection of the hazard, but with many systems not receiving any regular monitoring, pollution events often go undetected. We developed toxicological risk assessment models for acute and chronic exposure to pollutants that incorporate the probabilities that the public will come into contact with undetected pollution events, to identify the level of risk a system poses in regards to the pollutant. As a proof of concept, we successfully demonstrated that the models could be applied to determine probabilities of acute and chronic illness types related to recreational activities in waterbodies containing cyanotoxins. Using the acute model, we identified lakes that present a ‘high’ risk to develop Day Away From Work illness, and lakes that present a ‘low’ or ‘medium’ risk to develop First Aid Cases when used for swimming. The developed risk models succeeded in categorising lakes according to their risk level to the public in an objective way. Modelling by how much the probability of public exposure has to decrease to lower the risks to acceptable levels will enable authorities to identify suitable control measures and monitoring strategies. We suggest broadening the application of these models to other contaminants.

Extracellular microcystin prediction based on toxigenic Microcystis detection in a eutrophic lake

Existing models for predicting microcystin concentration in water body generally use chlorophyll or cyanobacteria concentration as input variables, although microcystins only originate from toxigenic strains of a few species. Moreover, the nonconcurrency between harmful algal growth and toxin release has yet to be quantified. Therefore, this study explored a new prediction method that considers these toxin production mechanisms for the eutrophic Yangcheng Lake, a large-scale drinking water source in China. The Lake was monitored weekly at six sampling sites from July to October in 2012, including the detection of toxigenic Microcystis (expressed as mcyA copy number) by qPCR. Compared with chlorophyll a, cyanobacteria, and total Microcystis abundance, toxigenic Microcystis concentration was more significant in predicting extracellular microcystin. Site-specific nonlinear regression models that link mcyA to microcystins were established. Parameters for toxin release delay (i.e., one or two weeks) were embedded in these models. Further analysis ascribed the different release timescale to NH₃-N:TN and TN:TP ratios of approximately 0.015 and 9.2, respectively, which may decrease the delay in microcystin release. Model applications in determining mcyA monitoring frequency and its warning thresholds were discussed.

Integrative Indicator for Assessing the Alert Levels of Algal Bloom in Lakes: Lake Taihu as a Case Study

Algal blooms have recently become one of the most serious environmental problems in eutrophic freshwater ecosystems worldwide. Although many observation and simulation approaches have been applied to predict algal blooms, few studies have addressed the alert levels of algal blooms using integrative indicators in a large lake with multiple service function and significant horizontal heterogeneity. This study developed an integrative indicator assessment system (IIAS) to rank the alert level of algal blooms. In the IIAS, algal biomass, area percentage, distance from drinking water intake points, distance from scenic zones and duration of algal bloom were used as indicators to calculate a comprehensive alert level, which was classified into five grades (Vigilance, Low, Moderate, High, and Severe). Lake Taihu was taken as a case study to assess the comprehensive alert level of algal blooms in 2007 and 2010. The comprehensive alert level showed obvious spatial-temporal patterns, with an acceptable accuracy in Lake Taihu. The comprehensive alert levels were relatively higher in typical phytoplankton subzones than typical hydrophytes subzones and are more sensitive to weight factor in the northern and western subzones where high biomass usually occurs. Case study showed a very good application of the proposed comprehensive alert level assessment methodology, which can be adjusted to predict the degree of hazard of algal blooms in multi-service function large lakes to help the government and decision makers to act to prevent the disaster from algal bloom spreading.

Use of log-transformed absorbance spectra for online monitoring of the reactivity of natural organic matter

This study examined the significance of water quality monitoring parameters obtained via logarithmic transformation of the absorbance spectra of raw and treated drinking water. The data were generated using samples of the influent, settled and filtered water acquired weekly over a six months period at two full scale treatment plants. Examination of the weekly plant samples combined with the data of laboratory fractionation and chlorination experiments showed that the slopes of the log-transformed spectra are correlated with typically reported water quality parameters (e.g., its specific absorbance at 254 nm, SUVA254); yet the determination of spectral slopes is considerably simpler and potentially information-rich. The spectral slopes determined for the range of wavelength 280-350 nm were shown to be correlated with the yields of trihalomethanes (THMs) and haloacetic acids (HAAs). These results support the notion that multi-wavelength monitoring of the absorbance spectra of drinking water and their interpretation via logarithmic transformation constitutes a promising practically implementable approach for online water quality monitoring.

Cyanobacterial bloom management through integrated monitoring and forecasting in large shallow eutrophic Lake Taihu (China)

The large shallow eutrophic Lake Taihu in China has long suffered from eutrophication and toxic cyanobacterial blooms. Despite considerable efforts to divert effluents from the watershed, the cyanobacterial blooms still reoccur and persist throughout summer. To mitigate cyanobacterial bloom pollution risk, a large scale integrated monitoring and forecasting system was developed, and a series of emergency response measures were instigated based on early warning. This system has been in place for 2009-2012. With this integrated monitoring system, it was found that the detectable maximum and average cyanobacterial bloom area were similar to that before drinking water crisis, indicating that poor eutrophic status and cyanobacterial bloom had persisted without significant alleviation. It also revealed that cyanobacterial bloom would occur after the intense storm, which may be associated with the increase in buoyance of cyanobacterial colonies. Although the cyanobacterial blooms had persisted during the monitoring period, there had been a reduction in frequency and intensity of the cyanobacterial bloom induced black water agglomerates (a phenomenon of algal bloom death decay to release a large amount black dissolved organic matter), and there have been no further drinking water crises. This monitoring and response strategy can reduce the cyanobacterial bloom pollution risk, but cannot reduce eutrophication and cyanobacterial blooms, problems which will take decades to resolve.

The establishment of preliminary safety threshold values for cyanobacteria based on periodic variations in different microcystin congeners in Lake Chaohu, China

As harmful cyanobacterial proliferation threatens the safety of drinking water supplies worldwide, it is essential to establish a safety threshold (ST) for cyanobacteria to control cyanobacterial density effectively in water sources. For this purpose, cyanobacterial abundance, microcystin (MC) production, and environmental parameters were monitored monthly from September 2011 to August 2012 in one drinking water source of Lake Chaohu. The cyanobacterial density ranged from 1400 to 220 000 cells per mL with the succession of two dominant species Microcystis and Dolichospermum, which was determined by water temperature and nutrient loading. The MC concentrations were correlated significantly with the cyanobacterial density and they varied between 0.28 and 8.86 μg L(-1). Therefore, the characteristics of MC cell quotas were classified according to four stages of the development of cyanobacteria, namely: recruitment, multiplication, decline and dormancy. The ST for cyanobacteria was established for different periods based on the MC cell quota and its guideline wherein three commonly monitored MC congeners (MC-LR, -RR and -YR) were considered in the present study. Its reliability was verified in the water source using the data collected between June 2013 and May 2014. The results highlighted the necessity to classify the ST-values in different periods referring to the main MC congeners rather than MC-LR, which will facilitate the management and control of toxic cyanobacterial proliferation in drinking water sources.

Impact of green algae on the measurement of Microcystis aeruginosa populations in lagoon-treated wastewater with an algae online analyser

Tests on the algae online analyser (AOA) showed that there was a strong direct linear correlation between cell density and in vivo Chl-a concentration for M. aeruginosa over the range of interest for a biologically treated effluent at a wastewater treatment plant (25,000-65,000 cells mL(-1), equivalent to a biovolume of 2-6 mm3 L(-1)). However, the AOA can provide an overestimate or underestimate of M. aeruginosa populations when green algae are present in the effluent, depending on their species and relative numbers. The results from this study demonstrated that the green algae (e.g., Euglena gracilis, Chlorella sp.) in the field phytoplankton population should be considered during calibration. In summary, the AOA has potential for use as an alert system for the presence of M. aeruginosa, and thus potentially of cyanobacterial blooms, in wastewater stabilization ponds.

Evaluating fluorescence spectroscopy as a tool to characterize cyanobacteria intracellular organic matter upon simulated release and oxidation in natural water

Intracellular organic matter (IOM) from cyanobacteria may be released into natural waters following cell death in aquatic ecosystems and during oxidation processes in drinking water treatment plants. Fluorescence spectroscopy was evaluated to identify the presence of IOM from three cyanobacteria species during simulated release into natural water and following oxidation processes (i.e. ozone, free chlorine, chloramine, chlorine dioxide). Peak picking and the fluorescence index (FI) were explored to determine which IOM components (e.g., pigments) provide unique and persistent fluorescence signatures with minimal interferences from the background dissolved organic matter (DOM) found in Colorado River water (CRW). When IOM was added to ultrapure water, the fluorescence signature of the three cyanobacteria species showed similarities to each other. Each IOM exhibited a strong protein-like fluorescence and fluorescence at Ex 370 nm and Em 460 nm (FDOM), where commercial fluorescence sensors monitor. All species also had strong phycobiliprotein fluorescence (i.e. phycocyanin or phycoerythrin) in the higher excitation range (500-650 nm). All three IOM isolates had FI values greater than 2. When IOM was added to CRW, phycobiliprotein fluorescence was quenched through interactions between IOM and CRW-DOM. Mixing IOM and CRW demonstrated that protein-like and FDOM intensity responses were not a simple superposition of the starting material intensities, indicating that interactions between IOM and CRW-DOM fluorescing moieties were important. Fluorescence intensity in all regions decreased with exposure to ozone, free chlorine, and chlorine dioxide, but the FI still indicated compositional differences compared to CRW-DOM. The phycobiliproteins in IOM are not promising as a surrogate for IOM release, because their fluorescence intensity is quenched by interactions with DOM and decreased during oxidation processes. Increases in both FDOM intensity and FI are viable qualitative indicators of IOM release in natural waters and following oxidation and may provide a more robust real-time indication of the presence of IOM than conventional dissolved organic carbon or UV absorbance measurements.

Response of Daphnia's antioxidant system to spatial heterogeneity in Cyanobacteria concentrations in a lowland reservoir

Many species and clones of Daphnia inhabit ecosystems with permanent algal blooms, and they can develop tolerance to cyanobacterial toxins. In the current study, we examined the spatial differences in the response of Daphnia longispina to the toxic Microcystis aeruginosa in a lowland eutrophic dam reservoir between June (before blooms) and September (during blooms). The reservoir showed a distinct spatial pattern in cyanobacteria abundance resulting from the wind direction: the station closest to the dam was characterised by persistently high Microcystis biomass, whereas the upstream stations had a significantly lower biomass of Microcystis. Microcystin concentrations were closely correlated with the cyanobacteria abundance (r = 0.93). The density of daphniids did not differ among the stations. The main objective of this study was to investigate how the distribution of toxic Microcystis blooms affects the antioxidant system of Daphnia. We examined catalase (CAT) activity, the level of the low molecular weight antioxidant glutathione (GSH), glutathione S-transferase (GST) activity and oxidative stress parameters, such as lipid peroxidation (LPO). We found that the higher the abundance (and toxicity) of the cyanobacteria, the lower the values of the antioxidant parameters. The CAT activity and LPO level were always significantly lower at the station with the highest M. aeruginosa biomass, which indicated the low oxidative stress of D. longispina at the site with the potentially high toxic thread. However, the low concentration of GSH and the highest activity of GST indicated the occurrence of detoxification processes at this site. These results demonstrate that daphniids that have coexisted with a high biomass of toxic cyanobacteria have effective mechanisms that protect them against the toxic effects of microcystins. We also conclude that Daphnia's resistance capacity to Microcystis toxins may differ within an ecosystem, depending on the bloom's spatial distribution.

Determining ecoregional numeric nutrient criteria by stressor-response models in Yungui ecoregion lakes, China

The importance of developing numeric nutrient criteria has been recognized to protect the designated uses of water bodies from nutrient enrichment that is associated with broadly occurring levels of nitrogen/phosphorus pollution. The identification and estimation of stressor-response models in aquatic ecosystems has been shown to be useful in the determination of nutrient criteria. In this study, three methods based on stressor-response relationships were applied to determine nutrient criteria for Yungui ecoregion lakes with respect to total phosphorus (TP), total nitrogen (TN), and planktonic chlorophyll a (Chl a). Simple linear regression (SLR) models were established to provide an estimate of the relationship between a response variable and a stressor. Multiple linear regressions were used to simultaneously estimate the effect of TP and TN on Chl a. A morphoedaphic index (MEI) was applied to derive nutrient criteria using data from Yungui ecoregion lakes, which were considered as areas with less anthropogenic influences. Nutrient criteria, as determined by these three methods, showed broad agreement for all parameters. The ranges of numeric nutrient criteria for Yungui ecoregion lakes were determined as follows: TP 0.008-0.010 mg/L and TN 0.140-0.178 mg/L. The stressor-response analysis described will be of benefit to support countries in their numeric criteria development programs and to further the goal of reducing nitrogen/phosphorus pollution in China.

Application of in vivo measurements for the management of cyanobacteria breakthrough into drinking water treatment plants

The increasing presence of potentially toxic cyanobacterial blooms in drinking water sources and within drinking water treatment plants (DWTPs) has been reported worldwide. The objectives of this study are to validate the application of in vivo probes for the detection and management of cyanobacteria breakthrough inside DWTPs, and to verify the possibility of treatment adjustment based on intensive real-time monitoring. In vivo phycocyanin YSI probes were used to monitor the fate of cyanobacteria in raw water, clarified water, filtered water, and chlorinated water in a full scale DWTP. Simultaneous samples were also taken for microscopic enumeration. The in vivo probe was successfully used to detect the incoming densities of high cyanobacterial cell number into the clarification process and their breakthrough into the filtered water. In vivo probes were used to trace the increase in floating cells over the clarifier, a robust sign of malfunction of the coagulation-sedimentation process. Pre-emptive treatment adjustments, based on in vivo probe monitoring, resulted in successful removal of cyanobacterial cells. The field results on validation of the probes with cyanobacterial bloom samples showed that the probe responses are highly linear and can be used to trigger alerts to take action.

Monitoring approaches for a toxic cyanobacterial bloom

Cyanobacterial blooms, dominated by Microcystis sp. and associated microcystin variants, have been implicated in illnesses of humans and animals. Little is known regarding the formation of blooms and the presence of cyanotoxin variants in water bodies. Furthermore, the role played by ecological parameters, in regulating Microcystis blooms is complicate and diverse. Local authorities responsible for water management are often faced with the challenging task of dealing with cyanobacterial blooms. Therefore, the development of suitable monitoring approaches to characterize cyanobacterial blooms is an important goal. Currently, various biological, biochemical and physicochemical methods/approaches are being used to monitor cyanobacterial blooms and detect microcystins in freshwater bodies. Because these methods can vary as to the information they provide, no single approach seemed to be sufficient to accurately monitor blooms. For example, immunosensors are more suited for monitoring the presence of toxins in clear water bodies while molecular methods are more suited to detect potentially toxic strains. Thus, monitoring approaches should be tailored for specific water bodies using methods based on economic feasibility, speed, sensitivity and field applicability. This review critically evaluates monitoring approaches that are applicable to cyanobacterial blooms, especially those that focus on the presence of Microcystis, in freshwater bodies. Further, they were characterized and ranked according to their cost, speed, sensitivity and selectivity. Suggested improvements were offered as well as future research endeavors to accommodate anticipated environmental changes.

An early warning and control system for urban, drinking water quality protection: China's experience

An event-driven, urban, drinking water quality early warning and control system (DEWS) is proposed to cope with China's urgent need for protecting its urban drinking water. The DEWS has a web service structure and provides users with water quality monitoring functions, water quality early warning functions, and water quality accident decision-making functions. The DEWS functionality is guided by the principles of control theory and risk assessment as applied to the feedback control of urban water supply systems. The DEWS has been deployed in several large Chinese cities and found to perform well insofar as water quality early warning and emergency decision-making is concerned. This paper describes a DEWS for urban water quality protection that has been developed in China.

The influence of hard substratum reflection and calibration profiles on in situ fluorescence measurements of benthic microalgal biomass

Measuring chlorophyll-a fluorescence is a commonly used method to determine microphytobenthic biomass expressed as chlorophyll-a per square centimetre. However, this in situ method is affected by reflection from the substratum which triggers an additional fluorescence signal within the microphytobenthic biofilm. Depending on the colour and texture of the natural substratum, this effect can lead to a considerable overestimation of microphytobenthic biomass. The results cannot be corrected for this effect by performing an auto-zero measurement, since the overestimation is not caused by an offset of the fluorometer. This article describes a substratum-specific correction procedure using a 700 nm signal to eliminate this effect by quantifying the fluorescence signal as a result of the reflection. An empirical relationship between the 700 nm signal and the additional fluorescence is used to calculate a correction factor for the reflective properties of the substratum. The factor is determined and applied during each biomass measurement, thereby making an additional calibration step for each individual type of substratum superfluous. This new method improves the reliability of the results significantly without increasing the time necessary to perform the measurements and without complicating the measurement procedure.

Estimation of chromophoric dissolved organic matter (CDOM) and photosynthetic activity of estuarine phytoplankton using a multiple-fixed-wavelength spectral fluorometer

The utility of a multiple-fixed-wavelength spectral fluorometer, the Algae Online Analyser (AOA), as a means of quantifying chromophoric dissolved organic matter (CDOM) and phytoplankton photosynthetic activity was tested using algal cultures and natural communities from North Inlet estuary, South Carolina. Comparisons of AOA measurements of CDOM to those by spectrophotometry showed a significant linear relationship, but increasing amounts of background CDOM resulted in progressively higher over-estimates of chromophyte contributions to a simulated mixed algal community. Estimates of photosynthetic activity by the AOA at low irradiance (≈ 80 μmol quanta m(-2) s(-1)) agreed well with analogous values from the literature for the chlorophyte, Dunaliella tertiolecta, but were substantially lower than previous measurements of the maximum quantum efficiency of photosystem II (F(v)/F(m)) in Thalassiosira weissflogii (a diatom) and Rhodomonas salina (a cryptophyte). When cells were exposed to high irradiance (1500 μmol quanta m(-2) s(-1)), declines in photosynthetic activity with time measured by the AOA mirrored estimates of cellular fluorescence capacity using the herbicide 3'-(3, 4-dichlorophenyl)-1',1'-dimethyl urea (DCMU). The AOA shows promise as a tool for the continuous monitoring of phytoplankton community composition, CDOM, and the group-specific photosynthetic activity of aquatic ecosystems.

Measurement of cyanobacteria using in-vivo fluoroscopy -- effect of cyanobacterial species, pigments, and colonies

The effect of instrument calibration range, algal growth phase, chlorophyll-a and turbidity interference and colony size, on the measurement of phycocyanin by in-vivo fluoroscopy (IVF) was investigated. The cyanobacterial species Microcystis aeruginosa PCC 7820, Anabaena circinalis and Planktothricoides raciborskii were used to investigate variation in phycocyanin content in the different cyanobacteria and growth phases. The green alga, Chodatella sp., and Kaolin particles were used as the sources of chlorophyll-a and turbidity respectively to determine how these factors can impact on phycocyanin measurements. Another cyanobacterium, M. aeruginosa PCC 7005, which forms large colonies, was used to investigate the relationships between colony size and phycocyanin concentration measured using IVF. Results showed that chlorophyll-a, turbidity, and the colonial status of the cyanobacteria significantly interfered with the measurement of phycocyanin fluorescence. Models were developed to compensate for the effect of chlorophyll-a, turbidity and colony size on the measurement. The models were successfully used to correct phycocyanin probe data collected from several reservoirs in Taiwan to establish good correlation between measurements made using the phycocyanin probe and microscopic cell counts.

A multi-technique approach for the quantification of Microcystis aeruginosa FACHB-905 biomass during high algae-laden periods

A pronounced dominance of toxic cyanobacteria has been found in eutrophic water bodies, with Microcystis being a common species. Although toxic cyanobacteria are commonly described worldwide, few recent papers on the sensitive and effective quantification of cyanobacteria have been published. In this paper, a multi-technique approach was applied by the use of cell density counting, cell viability testing, chlorophyll a determination, microcystin monitoring and gene extraction techniques to quantitatively analyse the cyanobacterial biomass of Microcystis aeruginosa FACHB-905. The entire dataset was used to examine the relationships between these indices. Results showed that, for 10(7) viable cells in the experimental conditions, the contents of chlorophyll a, microcystin-LR and total genes (16S rDNA) averaged 2.65 microg, 0.61 microg and 0.79 microg, respectively. For different cell viability proportions in the same particular phase of growth, it is easy to obtain the respective amount of viable cells and inactive cells and their measurable indices when any one of the three indices, chlorophyll a, DNA or microcystin-LR, is measured. This study provides a new perspective and method for determining multiple indices of toxic cyanobacteria during the same conditions and phases.