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

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.

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

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

Effects of Floods on Zooplankton Community Structure in the Huayanghe Lake

Floods can change the physicochemical factors of the water body and the zooplankton community. In the summer of 2020, Huayanghe Lake experienced floods. Here, eight cruises were conducted in Huayanghe Lake from 2020 to 2022 to study the response of environmental factors and the zooplankton community to the floods. The results demonstrated that floods increased the concentrations of total nitrogen, total phosphorus and chlorophyll a. In addition, during the floods, the number of rotifer species increased, while the number of cladoceran and copepod species decreased. Floods also reduced the average density and biomass of zooplankton. The results of Pearson correlation analysis and redundancy analysis showed that environmental factors, such as water depth, water temperature, transparency, nitrogen and phosphorus concentration, conductivity, coverage of aquatic vegetation and chlorophyll a, were closely related to the seasonal dynamics of zooplankton in Huayanghe Lake. Our research emphasizes that zooplankton can quickly respond to floods, providing data support for the ecological relationship between flooding and the zooplankton community, which is crucial for the preservation and restoration of the lake water ecosystem.

Hierarchical deep learning model to simulate phytoplankton at phylum/class and genus levels and zooplankton at the genus level

Harmful algal blooms (HABs) have become a global issue, affecting public health and water industries in numerous countries. Because funds for monitoring HABs are limited, model development may be an alternative approach for understanding and managing HABs. Continuous monitoring based on grab sampling is time-consuming, costly, and labor-intensive. However, improving simulation performance remains a major challenge in modeling, and current methods are limited to simulating phytoplankton (e.g., Microcystis sp., Anabaena sp., Aulacoseira sp., Cyclotella sp., Pediastrum sp., and Eudorina sp.) and zooplankton (e.g., Cyclotella sp., Pediastrum sp., and Eudorina sp.) at the genus level. The traditional modeling approach is limited for evaluating the interactions between phytoplankton and zooplankton. Recently, deep learning (DL) models have been proposed for solving modeling problems because of their large data handling capabilities and model structure flexibilities. In this study, we evaluated the applicability of DL for simulating phytoplankton at the phylum/class and genus levels and zooplankton at the genus level. Our work was an explicit representation of the taxonomic and ecological hierarchy of the DL model structure. The prerequisite for this model design was the data collection at two taxonomic and hierarchical levels. Our model consisted of hierarchical DL with classification transformer (TF) and regression TF models. These DL models were hierarchically connected; the output of the phylum/class level model was transferred to the genus level simulation model, and the output of the genus level model was fed into the zooplankton simulation model. The classification TF model determined the phytoplankton occurrence initiation date, whereas the regression TF model quantified the cell concentration of plankton. The hierarchical DL showed potential to simulate phytoplankton at the phylum/class and genus levels by producing average R^2, and root mean standard error values of 0.42 and 0.83 [log(cells mL^-1)], respectively. All simulated plankton results closely matched the measured concentrations. Particularly, the simulated cyanobacteria showed good agreement with the measured cell concentration, with an R² value of 0.72. In addition, our simulated result showed good agreement in peak concentration compared to observations. However, a limitation remained in following the temporal variation of Tintinnopsis sp. and Bosmia sp. Using an importance map from the TF model, water temperature, total phosphorus, and total nitrogen were identified as significant variables influencing phytoplankton and zooplankton blooms. Overall, our study demonstrated that DL can be used for modeling HABs at the phylum/class and genus levels.