A Novel and Convenient Method for Early Warning of Algal Cell Density by Chlorophyll Fluorescence Parameters and Its Application in a Highland Lake

Abstract

The occurrence of algal blooms in drinking water sources and recreational water bodies have been increasing and causing severe environmental problems worldwide, particularly when blooms dominated by Microcystis spp. Bloom prediction and early warning mechanisms are becoming increasingly important for preventing harmful algal blooms in freshwater ecosystems. Chlorophyll fluorescence parameters (CFpars) have been widely used to evaluate growth scope and photosynthetic efficiency of phytoplankton. According to our 2-year monthly monitor datasets in Lake Erhai, a simple but convenient method was established to predict Microcystis blooms and algal cell densities based on a CFpar representing maximal photochemical quantum yield of Photosystems II (PSII) of algae. Generalized linear mixed models, used to identify the key factors related to the phytoplankton biomass in Lake Erhai, showed significant correlations between Chl a concentration and both the light attenuation coefficient and water temperature. We fitted seasonal trends of CFpars (Fv/Fm and ΔF/Fm′) and algal cell densities into the trigonometric regression to predict their seasonal variations and the autocorrelation function was applied to calculate the time lag between them. We found that the time lag only existed between Fv/Fm from blue channel and algal cell densities even both Fv/Fm and ΔF/Fm′ show the significant non-linear dynamics relationships with algal cell densities. The peak values of total algal cell density, cyanobacteria density and Microcystis density followed the foregoing peak value of Fv/Fm from blue channel with a time lagged around 40 days. Therefore, we could predict the possibilities of Microcystis bloom and estimate the algal cell densities in Lake Erhai ahead of 40 days based on the trends of Fv/Fm values from blue channel. The results from our study implies that the corresponding critical thresholds between Fv/Fm value and bloom occurrence, which might give new insight into prediction of cyanobacteria blooms and provide a convenient and efficient way for establishment of early warning of cyanobacteria bloom in eutrophic aquatic ecosystems.

The Observation and Characterisation of Fluorescent Bioaerosols Using Real-Time UV-LIF Spectrometry in Hong Kong from June to November 2018

“…For example, Woo et al [10] found many marine-derived phylotypes during summer, with Chlorophyta dominating their samples. These are a taxon of green algae, for which previous literature suggests Chlorophyll-∝ as the dominant fluorophore [34]. Neither the PLAIR Rapid-E nor the WIBS-NEO are suited to algal identification because Chlorophyll-∝ has a much higher excitation wavelength of 400-485 nm [35].…”

A Complete Proposed Framework for Coastal Water Quality Monitoring System With Algae Predictive Model

“…Besides, several kinds of deep learning models are usually utilised in time-series forecasting, for instance, RNN and its variant LSTM. RNN has been suggested to elucidate the dynamics [64], [65]. It is a network with feedback connections from the hidden and output layers to the preceding ones, through which the dynamics of sequential data can be recorded, and the memories of the prior patterns are kept via cycles in the network.…”

Light, but Not Nutrients, Drives Seasonal Congruence of Taxonomic and Functional Diversity of Phytoplankton in a Eutrophic Highland Lake in China

“…Mean and maximum depth are 10.5 and 20.5 m, respectively. Lake Erhai, is currently in the early stages of eutrophication (Lin et al, 2016;Wang et al, 2018), with concentration of total nitrogen (TN) of 0.7 mg/L (Zhu et al, 2018), total phosphorus (TP) of 0.03 mg/L (Zhu et al, 2018) and chlorophyll a (Chl a) of 13.33 µg/L during June 2013 to May 2015, with a peak value exceeding 30 µg/L (Wang et al, 2018), all of which exceed the threshold value of the eutrophication categories (TP > 0.03 mg/L, TN > 0.65 mg/L, and Chl a > 9 µg/L, Nürnberg, 1996). Samples were collected monthly, from 15 locations, between January 2012 and December 2014 ( Figure 1).…”