Simulating chlorophyll-a fluorescence changing rate and phycocyanin fluorescence by using a multi-sensor system in Lake Taihu, China. (February 2021)

• Record Type: Journal Article
• Authors: Yang, Jingwei; Holbach, Andreas; Stewardson, Michael J.; Wilhelms, Andre; Qin, Yanwen; Zheng, Binghui; Zou, Hua; Qin, Boqiang; Zhu, Guangwei; Moldaenke, Christian; Norra, Stefan
• Is Part Of: Chemosphere. Volume 264(2021)Part 2
• Publication Date: 2021-02
• Subjects: Eutrophic shallow lakes -- Online monitoring -- Algal classes -- Stepwise multiple regression -- Short-term simulation; Pollution -- Periodicals; Pollution -- Physiological effect -- Periodicals; Environmental sciences -- Periodicals; Atmospheric chemistry -- Periodicals; 551.511
• DOI: 10.1016/j.chemosphere.2020.128482
• Languages: English
• ISSNs: 0045-6535

Abstract

Abstract: 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 (ΔChl a-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. ΔChl a-f % is greatly influenced by the seasons, so seasonal partition of data before modeling is highly recommended. CyanoSmax and ΔChl a-f % were simulated by only using multi-sensor and meteorological data ( R 2 = 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. Graphical abstract: Image 1 Highlights: Chlorophyll-a and phycocyanin fluorescence can be predicted using online sensor data. Wave is the leading factor for the short-term changes of algae in shallow lakes. Nutrients are not important factors in short-term algal simulation in eutrophic lakes.