Field and laboratory studies of fluorescence-based technologies for real-time tracking of cyanobacterial cell lysis and potential microcystins release.

Elevated levels of dissolved microcystins (MCs) in source water due to rapid cell lysis of harmful cyanobacterial blooms may pose serious challenges for drinking water treatment. Catastrophic cell lysis can result from outbreaks of naturally-occurring cyanophages - as documented in Lake Erie during the Toledo water crisis of 2014 and in 2019, or through the application of algaecides or water treatment chemicals. Real-time detection of cyanobacterial cell lysis in source water would provide a valuable tool for drinking water plant and reservoir managers. In this study we explored two real-time fluorescence-based devices, PhycoSens and PhycoLA, that can detect unbound phycocyanin (uPC) as a potential indication of cell lysis and MCs release. The PhycoSens was deployed at the Low Service pump station of the City of Toledo Lake Erie drinking water treatment plant from July 15 to October 19, 2022 during the annual cyanobacteria bloom season. It measured major algal groups and uPC in incoming lake water at 15-min intervals during cyanobacteria dominant and senescence periods. Intermittent uPC detections from the PhycoSens over a three-month period coincided with periods of increasing proportions of extracellular MCs relative to total (intracellular and extracellular) MCs, indicating potential for uPC use as an indicator of cyanobacterial cell integrity. Following exposures of laboratory-cultured MCs-producing Microcystis aeruginosa NIES-298 (120 µg chlorophyll/L) to cyanophage Ma-LMM01, copper sulfate (0.5 and 1 mg Cu/L), sodium carbonate peroxyhydrate (PAK® 27, 6.7 and 10 mg H2O2/L), and potassium permanganate (2.5 and 4 mg/L), appearance of uPC coincided with elevated fractions of extracellular MCs. The PhycoLA was used to monitor batch samples collected daily from Lake Erie water exposed to algaecides in the laboratory. Concurrence of uPC signal and surge of dissolved MCs was observed following 24-h exposures to copper sulfate and PAK 27. Overall results indicate the appearance of uPC is a useful indicator of the onset of cyanobacterial cell lysis and the release of MCs when MCs are present.

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

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

Hyperspectral Data and Machine Learning for Estimating CDOM, Chlorophyll a, Diatoms, Green Algae and Turbidity.

Inland waters are of great importance for scientists as well as authorities since they are essential ecosystems and well known for their biodiversity. When monitoring their respective water quality, in situ measurements of water quality parameters are spatially limited, costly and time-consuming. In this paper, we propose a combination of hyperspectral data and machine learning methods to estimate and therefore to monitor different parameters for water quality. In contrast to commonly-applied techniques such as band ratios, this approach is data-driven and does not rely on any domain knowledge. We focus on CDOM, chlorophyll a and turbidity as well as the concentrations of the two algae types, diatoms and green algae. In order to investigate the potential of our proposal, we rely on measured data, which we sampled with three different sensors on the river Elbe in Germany from 24 June⁻12 July 2017. The measurement setup with two probe sensors and a hyperspectral sensor is described in detail. To estimate the five mentioned variables, we present an appropriate regression framework involving ten machine learning models and two preprocessing methods. This allows the regression performance of each model and variable to be evaluated. The best performing model for each variable results in a coefficient of determination R 2 in the range of 89.9% to 94.6%. That clearly reveals the potential of the machine learning approaches with hyperspectral data. In further investigations, we focus on the generalization of the regression framework to prepare its application to different types of inland waters.

Since 2015 the SinoGerman research project SIGN supports water quality improvement in the Taihu region, China.

The Taihu (Tai lake) region is one of the most economically prospering areas of China. Due to its location within this district of high anthropogenic activities, Taihu represents a drastic example of water pollution with nutrients (nitrogen, phosphate), organic contaminants and heavy metals. High nutrient levels combined with very shallow water create large eutrophication problems, threatening the drinking water supply of the surrounding cities. Within the international research project SIGN (SinoGerman Water Supply Network, www.water-sign.de), funded by the German Federal Ministry of Education and Research (BMBF), a powerful consortium of fifteen German partners is working on the overall aim of assuring good water quality from the source to the tap by taking the whole water cycle into account: The diverse research topics range from future proof strategies for urban catchment, innovative monitoring and early warning approaches for lake and drinking water, control and use of biological degradation processes, efficient water treatment technologies, adapted water distribution up to promoting sector policy by good governance. The implementation in China is warranted, since the leading Chinese research institutes as well as the most important local stakeholders, e.g. water suppliers, are involved.

Use of cyanopigment determination as an indicator of cyanotoxins in drinking water.

The indicator function of the fluorescence signals of the cyanopigments phycocyanin and phycoerythrin as early warning parameters against the microcystins in drinking water was investigated by lab- and pilot-scale studies. The early warning function of the fluorescence signals was examined with regard to the signals' real-time character, their sensitivity and the behaviour of the cyanopigments in different treatment stages in comparison to microcystins. Fluorescence measurements confirmed the real-time character, since they can be carried out on-site without the pre-concentration of pigments. The limit of detection of phycoerythrin is determined at 0.7 microg/L and of phycocyanin at 5.3 microg/L respectively. If the pigment/microcystin ratio is known and calculated to be higher than 1, very low microcystin concentrations can be estimated by the fluorescence signals. The compared behaviour of both pigments and selected microcystins (MC-LR and MC-RR) during water treatment shows that pigments have an early warning function against microcystins in conventional treatment stages using pre-oxidation with permanganate, powdered-activated carbon and chlorination. In contrast, cyanopigments do not have an early warning function if chlorine dioxide is used as a pre-oxidant or final disinfection agent. In order to use pigment control measurements in drinking water treatment the initial pigment/toxin ratio of the raw water must be known.

A model for correcting the fluorescence signal from a free-falling depth profiler.

Measuring chlorophyll fluorescence at five different wavelengths provides the discrimination of four phytoplankton groups. Here the problems associated with a free-falling depth profiler for phytoplankton discrimination are considered. When F0, F, and Fm are determined sequentially in the same measuring cell, then the algae inside the cell have a different light history. It depends on their different locations in the cell as caused by the induction curve of chlorophyll fluorescence. Mathematical algorithms are developed which enable the calculation of the concentrations of individual phytoplankton groups from the integral fluorescence signal (averaged for 1s) for different velocities of the falling probe. The theory requires the knowledge of the fluorescence behaviour of phytoplankton in stationary suspensions. The predictions of the model are compared with measurements in flowing suspensions containing chlorophyta, cyanobacteria, cryptophyta and diatoms. The comparison shows the reliability of the algorithms. The application of the algorithms is indispensable for dark-adapted cells and is less important for light-adapted cells.