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

Chlorophyll-a determinations in mesocosms under varying nutrient and temperature treatments: in-situ fluorescence sensors versus in-vitro measurements

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

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

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

Assessment of in-situ monitoring and tracking the vertical migration of cyanobacterial blooms using LISST-HAB

Cyanobacterial harmful algal blooms (cyanoHABs) are becoming increasingly common in aquatic ecosystems worldwide. However, their heterogeneous distributions make it difficult to accurately estimate the total algae biomass and forecast the occurrence of surface cyanoHABs by using traditional monitoring methods. Although various optical instruments and remote sensing methods have been employed to monitor the dynamics of cyanoHABs at the water surface (i.e., bloom area, chlorophyll a), there is no effective in-situ methodology to monitor the dynamic change of cell density and integrated biovolume of algae throughout the water column. In this study, we propose a quantitative protocol for simultaneously measurements of multiple indicators (i.e., biovolume concentration, size distribution, cell density, and column-integrated biovolume) of cyanoHABs in water bodies by using the laser in-situ scattering and transmissometry (LISST) instrument. The accuracy of measurements of the biovolume and colony size of algae was evaluated and exceeded 95% when the water bloom was dominated by cyanobacteria. Furthermore, the cell density of cyanobacteria was well estimated based on total biovolume and mean cell volume measured by the instrument. Therefore, this methodology has the potential to be used for broader applications, not only to monitor the spatial and temporal distribution of algal biovolume concentration but also monitor the vertical distribution of cell density, biomass and their relationship with size distribution patterns. This provides new technical means for the monitoring and analysis of algae migration and early warning of the formation of cyanoHABs in lakes and reservoirs.

Ecological degradation of a fragile semi-arid wetland and the implications in its microbial community: The case study of Las Tablas de Daimiel National Park (Spain)

Las Tablas de Daimiel National Park (TDNP, Iberian Peninsula) is a semi-arid wetland of international significance for waterfowl and serves as a migratory route for various bird species. However, TDNP presents strong anthropization and fluctuating water levels, making it a highly fragile ecosystem. Water physico-chemical parameters and microbial diversity of the three domains (Bacteria-Archaea- Eukarya) were analysed in Zone A and Zone B of the wetland (a total of eight stations) during spring and summer, aiming to determine how seasonal changes influence the water quality, trophic status and ultimately, the microbial community composition. Additionally, Photosynthetically Active Radiation (PAR) was used to determine the trophic status instead of transparency using Secchi disk, setting the threshold to 20-40 μmol/sm2 for benthic vegetation growth. In spring, both zones of the wetland were considered eutrophic, and physico-chemical parameters as well as microbial diversity were similar to other wetlands, with most abundant bacteria affiliated to Actinobacteriota, Cyanobacteria, Bacteroidota, Gammaproteobacteria and Verrumicrobiota. Methane-related taxa like Methanosarcinales and photosynthetic Chlorophyta were respectively the most representative archaeal and eukaryotic groups. In summer, phytoplankton bloom led by an unclassified Cyanobacteria and mainly alga Hydrodictyon was observed in Zone A, resulting in an increase of turbidity, pH, phosphorus, nitrogen, chlorophyll-a and phycocyanin indicating the change to hypertrophic state. Microbial community composition was geographical and seasonal shaped within the wetland as response to changes in trophic status. Archaeal diversity decreases and methane-related species increase due to sediment disturbance driven by fish activity, wind, and substantial water depth reduction. Zone B in summer suffers less seasonal changes, maintaining the eutrophic state and still detecting macrophyte growth in some stations. This study provides a new understanding of the interdomain microbial adaptation following the ecological evolution of the wetland, which is crucial to knowing these systems that are ecological niches with high environmental value.

In Situ Water Quality Monitoring Using an Optical Multiparameter Sensor Probe

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

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.

Spatiotemporal analysis of lake chlorophyll-a with combined in situ and satellite data

We estimated chlorophyll-a (Chl-a) concentration using various combinations of routine sampling, automatic station measurements, and MERIS satellite images. Our study site was the northern part of the large, shallow, mesotrophic Lake Pyhäjärvi located in southwestern Finland. Various combinations of measurements were interpolated spatiotemporally using a data fusion system (DFS) based on an ensemble Kalman filter and smoother algorithms. The estimated concentrations together with corresponding 68% confidence intervals are presented as time series at routine sampling and automated stations, as maps and as mean values over the EU Water Framework Directive monitoring period, to evaluate the efficiency of various monitoring methods. The mean Chl-a calculated with DFS in June-September was 6.5-7.5 µg/l, depending on the observations used as input. At the routine monitoring station where grab samples were used, the average uncertainty (standard deviation, SD) decreased from 2.7 to 1.6 µg/l when EO data were also included in the estimation. At the automatic station, located 0.9 km from the routine monitoring site, the SD was 0.7 µg/l. The SD of spatial mean concentration decreased from 6.7 to 2.9 µg/l when satellite observations were included in June-September, in addition to in situ monitoring data. This demonstrates the high value of the information derived from satellite observations. The conclusion is that the confidence of Chl-a monitoring could be increased by deploying spatially extensive measurements in the form of satellite imaging or transects conducted with flow-through sensors installed on a boat and spatiotemporal interpolation of the multisource data.

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.

Near-term phytoplankton forecasts reveal the effects of model time step and forecast horizon on predictability

As climate and land use increase the variability of many ecosystems, forecasts of ecological variables are needed to inform management and use of ecosystem services. In particular, forecasts of phytoplankton would be especially useful for drinking water management, as phytoplankton populations are exhibiting greater fluctuations due to human activities. While phytoplankton forecasts are increasing in number, many questions remain regarding the optimal model time step (the temporal frequency of the forecast model output), time horizon (the length of time into the future a prediction is made) for maximizing forecast performance, as well as what factors contribute to uncertainty in forecasts and their scalability among sites. To answer these questions, we developed near-term, iterative forecasts of phytoplankton 1-14 days into the future using forecast models with three different time steps (daily, weekly, fortnightly), that included a full uncertainty partitioning analysis at two drinking water reservoirs. We found that forecast accuracy varies with model time step and forecast horizon, and that forecast models can outperform null estimates under most conditions. Weekly and fortnightly forecasts consistently outperformed daily forecasts at 7-day and 14-day horizons, a trend that increased up to the 14-day forecast horizon. Importantly, our work suggests that forecast accuracy can be increased by matching the forecast model time step to the forecast horizon for which predictions are needed. We found that model process uncertainty was the primary source of uncertainty in our phytoplankton forecasts over the forecast period, but parameter uncertainty increased during phytoplankton blooms and when scaling the forecast model to a new site. Overall, our scalability analysis shows promising results that simple models can be transferred to produce forecasts at additional sites. Altogether, our study advances our understanding of how forecast model time step and forecast horizon influence the forecastability of phytoplankton dynamics in aquatic systems and adds to the growing body of work regarding the predictability of ecological systems broadly.

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.

Hydrodynamic Drivers of Nutrient and Phytoplankton Dynamics in a Subtropical Reservoir

Chlorophyll-a (chla) is an important parameter to assess water quality in lakes and reservoirs, since it is a proxy for phytoplankton biomass and primary production. The increasing availability of data with high spatial and temporal resolution allows assessing short-term dynamics and small-scale variations of chla within larger water bodies. In freshwater reservoirs, the nutrient concentrations and the physical conditions that control phytoplankton growth vary along their longitudinal extend. Here, we analyze how the flow paths of the inflowing river water into density-stratified reservoirs affect the vertical and longitudinal distribution of nutrients and chla. We combine spatially resolved and high-frequency measurements of chla from satellite remote sensing and in-situ sensors, with numerical simulations using a three-dimensional hydrodynamic model to assess the influence of density currents on chla dynamics along a subtropical drinking water reservoir in the south of Brazil. Chla did not have pronounced seasonal dynamics (4.9 ± 1.2 µg L−1, at the continuous measurement station); instead, spatial variability along the reservoir was more pronounced (4.4 ± 2.1 µg L−1, all monitored points within the euphotic zone). Most of the nutrients from the inflowing river were consumed in the upstream region, and phytoplankton in the lacustrine zone depended on internal loading. Temporal variability was observed only in the upstream region, and phytoplankton produced in that area was transported downstream by density currents, resulting in large concentrations of chla below the euphotic zone. The results of a hydrodynamic model that simulates the present state are in good agreement with the observations. Two simulated scenarios, where the density current patterns were altered, resulted only in slight variations in density currents, indicating that the influence of the main inflow was of minor relevance in chla concentrations in downstream regions of the reservoir. Our results highlight the importance of two-dimensional hydrodynamic processes in regulating phytoplankton dynamics in reservoirs.

Rapid in measurements of brown tide algae cell concentrations using fluorescence spectrometry and generalized regression neural network

The frequent occurrence of brown tide pollution in recent years has brought great losses to the economy of coastal areas. Therefore, accurate and efficient detection of the brown tide algae cell concentration is of great significance to the prevention of marine environmental pollution. In this paper, a combination of three-dimensional fluorescence spectroscopy and generalized regression neural network is used to detect the concentration of Aureococcus anophagefferens (A. anophagefferens). Firstly, the fluorescence spectrometer was used to collect spectra of A. anophagefferens with different growth cycles and different concentrations. In order to reduce the complexity of fluorescence spectral data and improve the efficiency of model calculation, the gradient boosting decision tree (GBDT) algorithm is used to rank the importance of spectral features, and select spectral features with great importance as input and concentration of algal cells as output. In view of the nonlinear relationship between input and output, a generalized regression neural network model optimized by the improved sparrow search algorithm (FASSA-GRNN) was established to predict the concentration of algae cells, The model results show that MSE is 0.0046, MAE is 0.0569, and R² is 0.9955. In addition, the FASSA-GRNN model is compared with the prediction results of the SSA-GRNN, GWO-GRNN, and GRNN models. The results show that the prediction accuracy of FASSA-GRNN is better than other models, and the improved sparrow search algorithm (FASSA) can reach the global optimum faster during the training process. This research provides a new method for predicting the concentration of algae cells.

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.

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.