• Journal of Wetlands Research
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• v.26 no.3
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• pp.245-253
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• 2024

Blue-green algal bloom, or harmful algal bloom has a negative impact on the aquatic ecosystem and purified water supply system due to oxygen depletion in the water body, odor, and secretion of toxic substances in the freshwater ecosystem. This Blue-green algal bloom is expected to increase in intensity and frequency due to the increase in algae's residence time in the water body after the construction of the Nakdong River weir, as well as the increase in surface temperature due to climate change. In this study, in order to respond to the expected increase in green algae phenomenon, an algal bloom forecast system based on hydro-meteorological factors was presented for preemptive response before issuing a algal bloom warning. Through polyserial correlation analysis, the preceding influence periods of temperature and discharge according to the algal bloom forecast level were derived. Using the decision tree classification, a machine learning technique, Classification models for the algal bloom forecast levels based on temperature and discharge of the preceding period were derived. And a algal bloom forecast system based on hydro-meteorological factors was derived based on the results of the decision tree classification models. The proposed algae forecast system based on hydro-meteorological factors can be used as basic research for preemptive response before blue-green algal blooms.

• Korean Journal of Environmental Biology
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• v.37 no.3
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• pp.433-445
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• 2019

This study was designed to develop simple tools to easily and efficiently predict the occurrence of algal bloom in agricultural lakes. Physicochemical water quality parameters were examined to reflect the phytoplankton productivity in 182 samples collected from 15 agricultural lakes from April to October 2018. Total phytoplankton abundance was significantly correlated with chlorophyll-a (Chl-a) (r=0.666) and Secchi depth (SD) (r= -0.351). The abundances of cyanobacteria and harmful cyanobacteria were also correlated with Chl-a (r=0.664, r=0.353) and SD (r= -0.340, r= -0.338), respectively, but not with total nitrogen (TN) and total phosphorus (TP). The Chl-a concentration was correlated with SD (r= -0.434), showing a higher similarity than phytoplankton abundance. Therefore, Chl-a and SD were selected as diagnostic factors for algal bloom prediction, instead of analyzing the standing crop of harmful cyanobacteria used in algae alarm systems. Specifically, accurate diagnoses were made using realtime SD measurements. The algal bloom diagnostic tool is an inverse cone-shaped container with an algal bloom diagnosis chart that modified SD and turbidity measurement methods. Lake water was collected to observe the number of rings visible in the container or the number indicated in each ring, depending on the degree of algal bloom,and to determine the final stage of algal blooming by comparison to the colorimetric level on the diagnosis chart. For an accurate diagnosis, we presented 4-step diagnostic criteria based on the concentration of Chl-a and the number of rings and a fan-shaped algal bloom diagnosis chart with Hexa code names. This tool eliminated the variables and errors of previous methods and the results were easily interpreted. This study is expected to facilitate the diagnosis of algal bloom in agricultural lakes and the establishment of an efficient algal bloom management plan.

• Environmental Engineering Research
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• v.24 no.3
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• pp.397-403
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• 2019

Microcystis sp. is one of the most common harmful cyanobacteria that release toxic substances. Counting algal cells is often used for effective control of harmful algal blooms. However, Microcystis sp. is commonly observed as a colony, so counting individual cells is challenging, as it requires significant time and labor. It is urgent to develop an accurate, simple, and rapid method for counting algal cells for regulatory purposes, estimating the status of blooms, and practicing proper management of water resources. The flow cytometer and microscope (FlowCAM), which is a dynamic imaging particle analyzer, can provide a promising alternative for rapid and simple cell counting. However, there is no accurate method for counting individual cells within a Microcystis colony. Furthermore, cell counting based on two-dimensional images may yield inaccurate results and underestimate the number of algal cells in a colony. In this study, a three-dimensional cell counting approach using a novel model algorithm was developed for counting individual cells in a Microcystis colony using a FlowCAM. The developed model algorithm showed satisfactory performance for Microcystis sp. cell counting in water samples collected from two rivers, and can be used for algal management in fresh water systems.

• Fisheries and Aquatic Sciences
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• v.3 no.2
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• pp.111-117
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• 2000

To clarify the ichthyotoxic mechanisms of a harmful dinoflagellate Cochlodinium polykrikoides, hematological responses of the flounder Paralichthys olivaceus and red sea bream Pagrus major exposed to this algal bloom were investigated. The mortality of red sea bream was considerably larger than that of flounder, and the threshold lethal density of C. polykrikoides to the test fish was approximately 3,000 cells/ml. Blood $PO_2$declined in proportion to the increasing density of algal cells. The blood $PO_2$ of moribund fish was about $40-60\% of control test fish. Particularly, the fishes began to be killed when the blood $PO_2$ fell below 30-40 mmHg. However, the blood pH dropped almost 1.0 unit just before fish kill. Hemoglobin and hematocrit levels of fish exposed to C. polykrikoides of 5,000 cells/ml for 24 h and of moribund fish did not show great difference. The concentrations of plasma $Na^+$, $K^+$ and $Cl^-$ were slightly elevated to different magnitudes except $Ca^{2+}$ and plasma osmolality was also increased in Cochlodinium-exposed fish. In the plasma cortisol level, these values of moribund flounder and red sea bream were 4- 5 times higher than those of control fish. These results suggest that the drop of blood $PO_2$ was may be one of the principal causes of fish kill by C. polykrikoides, and the changes of other hematological parameters were secondary responses elicited by the decrease in blood $PO_2$.

• Fisheries and Aquatic Sciences
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• v.7 no.3
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• pp.122-129
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• 2004

Harmful Cochlodinium polykrikoides is a notorious harmful algal bloom (HAB) species that is causing mass mortality of farmed fish along the Korean coast with increasing frequency. We analyzed the sequence of the large subunit (LSD) rDNA D1-D3 region of C. polykrikoides and conducted phylogenetic analyses using Bayesian inference of phylogeny and the maximum likelihood method. The molecular phylogeny showed that C. polykrikoides had the genetic relationship to Amphidinium and Gymnodinium species supported only by the relatively high posterior probabilities of Bayesian inference. Based on the LSU rDNA sequence data of diverse dinoflagellate taxa, we designed the C. polykrikoides-specific PCR primer set, CPOLY01 and CPOLY02 and developed PCR detection assays for its sensitive, accurate HAB monitoring. CPOLY01 and CPOLY02 specifically amplified C. polykrikoides and did not cross-react with any dinoflagellates tested in this study or environmental water samples. The effective annealing temperature $(T_{p})$ of CPOLY01 and CPOLY02 was $67^{\circ}C$. At this temperature, the conventional and nested PCR assays were sensitive over a wide range of C. polykrikoides cell numbers with detection limits of 0.05 and 0.0001 cells/reaction, respectively.

• Fisheries and Aquatic Sciences
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• v.2 no.1
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• pp.58-65
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• 1999

Fibrocapsa japonica (Raphidophyceae) is regarded as a harmful algal bloom organism in Japanese waters, where it has been linked to fish kills. Fibrocapsa is a common species in New Zealand coastal waters, particularly in the Hauraki Gulf, where it has regularly bloomed in the spring under E1 Nino climate conditions for the past six years. The New Zealand isolate had 1.4 times more total polyunsaturated acids than the Japanese isolate under the same growth conditions, suggesting that eicosapentaenoic acid in particular coold be used as a discriminating chemotaxonomic marker. The molecular probes tested showed no differential binding of the raphidophytes to lectins, but oligonucleotide probes targeted F. japonica ribosomal RNA bound specifically to both isolates. Neither strain was toxic in mouse or neuroblastoma bioassays. There is no evidence that the New Zealand F. japonica isolates investigated to date produce ichthyotoxins.

• Journal of Microbiology and Biotechnology
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• v.14 no.5
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• pp.959-966
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• 2004

Twenty-three cultures of harmful algal bloom (HAB)-(causing dinoflagellates were isolated from the coastal waters of Korea. For each of the 14 morphospecies, the nuclearencoded small subunit (SSU) rDNA was analyzed to determine the phylogenetic relatedness of the species. Despite temporal and spatial isolation, 3-4 clonal cultures of Alexandrium catenella, Cochlodinium polykrikoides, and Gymnodinium catenatum had 100% identical SSU rDNA sequences. In contrast, heterogeneities in the SSU rDNA sequences were observed in Akashiwo sanguinea and Lingulodinium polyedrum strains. Extreme sequence polymorphism was shown within the SSU rRNA genes of an Al. tamarense clonal culture. A homology search in GenBank revealed that 11 dinoflagellate species were located in clusters corresponding to their morphological classification. The SSU rDNA sequences of C. polykrikoides, Gyrodinium instriatum, and Pheopolykrikos hartmannii, which were determined for the first time in this study, showed the following phylogenetic relationships: C. polykrikoides formed an independent branch separated from other dinoflagellates; Gyr. instriatum was placed in a monophyletic group with Gyr. dorsum and Gyr. uncatenum; and Ph. hartmanii, which forms a distinct two-celled pseudocolony, belonged to Gymnodinium sensu Hansen and Moestrup.

• Korean Journal of Environmental Biology
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• v.38 no.3
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• pp.424-432
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• 2020

The present study aimed to analyze the metaproteome of the microbial community comprising harmful algal bloom (HAB) in the Daechung reservoir, Korea. HAB samples located at GPS coordinates of 36°29'N latitude and 127°28'E longitude were harvested in October 2013. Microscopic observation of the HAB samples revealed red signals that were presumably caused by the autofluorescence of chlorophyll and phycocyanin in viable cyanobacteria. Metaproteomic analysis was performed by a gelbased shotgun proteomic method. Protein identification was conducted through a two-step analysis including a forward search strategy (FSS) (random search with the National Center for Biotechnology Information (NCBI), Cyanobase, and Phytozome), and a subsequent reverse search strategy (RSS) (additional Cyanobase search with a decoy database). The total number of proteins identified by the two-step analysis (FSS and RSS) was 1.8-fold higher than that by one-step analysis (FSS only). A total of 194 proteins were assigned to 12 cyanobacterial species (99 mol%) and one green algae species (1 mol%). Among the species identified, the toxic microcystin-producing Microcystis aeruginosa NIES-843 (62.3%) species was the most dominant. The largest functional category was proteins belonging to the energy category (39%), followed by metabolism (15%), and translation (12%). This study will be a good reference for monitoring ecological variations at the meta-protein level of aquatic microalgae for understanding HAB.

• Korean Journal of Ecology and Environment
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• v.37 no.1 s.106
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• pp.64-69
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• 2004

Growth inhibition of Microcystis aeruginosa was examined with single-or mixed treatment of algicidal bacterium Streptomyces neyagawensis and heterotrich ciliate Stentor roeseli, which isolated from natural freshwater. The harmful Cyanobac-terium, Microcystis aeruginosa density was effectively suppressed by the algicidal bacterium Streptomyces neyagawensis, and the bacterial biomass was few changed. The heterotrich ciliate S, roegeji isolated from the eutrophic Pal'tang riverine, Korea suppressed the algal biomass effectively. But mixed-treatment of both bio-agents was less effective, leading to an increase in algal density.

• Journal of Korean Society on Water Environment
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• v.29 no.1
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• pp.19-28
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• 2013

Artificial neural networks were used for time series modelling of algal dynamics of whole year and by season at the Paldang dam station (confluence area). The modelling was based on comprehensive weekly water quality data from 1997 to 2004 at the Paldang dam station. The results of validation of seasonal models showed that the timing and magnitude of the observed chlorophyll a concentration was predicted better, compared with the ANN model for whole year. Internal weightings of the inputs in trained neural networks were obtained by sensitivity analysis for identification of the primary driving mechanisms in the system dynamics. pH, COD, TP determined most the dynamics of chlorophyll a, although these inputs were not the real driving variable for algal growth. Short-term prediction models that perform one or two weeks ahead predictions of chlorophyll a concentration were designed for the application of Harmful Algal Alert System in Lake Paldang. Short-term-ahead ANN models showed the possibilities of application of Harmful Algal Alert System after increasing ANN model's performance.