• Proceedings of the Korean Society of Fisheries Technology Conference
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• 2001.10a
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• pp.204-205
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• 2001

Manful algae, Cochlodinium polykrikoides has damaged to fisheries organisms by making massive blooms mainly in the South Sea during the higher water temperature season since 1995 in Korea. Ecological and hydrodynamic studies of the species offer useful information in understanding its bloom mechanism giving promising data for the modeling and prediction of the blooms. (omitted)

• The KIPS Transactions:PartB
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• v.18B no.5
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• pp.291-294
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• 2011

Red tide is a temporary natural phenomenon to change sea color by harmful algal blooms, which finfish and shellfish die en masse. There have been many studies on red tide due to increasing of harmful algae damage of fisheries in Korea. Particularly, red tide damage can be minimized by means of prediction of red tide blooms. However, the most of red tide research in Korea has been focused only classification of red tide which it is not enough for predicting red tide blooms. In this paper, we proposed the red tide blooms prediction method using fuzzy reasoning.

• ALGAE
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• v.29 no.4
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• pp.311-319
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• 2014

The biocide sodium hypochlorite (NaOCl) is widely used for controlling algal growth, and this application can be extended to marine environments as well. This study evaluates the biocidal efficiency and cellular toxicity of NaOCl on the harmful dinoflagellate Cochlodinium polykrikoides, with emphasis on pigment production and antioxidant enzyme activity. The test organism showed dose-dependent decrease in growth rate on exposure to NaOCl, and the 72 h $EC_{50}$ was measured to be $0.584mg\;L^{-1}$. NaOCl significantly decreased pigment levels and chlorophyll autofluorescence intensity, indicating possible detrimental effects on the photosystem of C. polykrikoides. Moreover, it significantly increased the activities of antioxidant enzymes, suggesting the production of reactive oxygen species in the cells. These data indicate that NaOCl exerted deleterious effects on the photosynthetic machinery and induced oxidative damage in the dinoflagellate and this biocide could be effectively used for the control of algal blooms.

• International Journal of Advanced Culture Technology
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• v.5 no.1
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• pp.64-69
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• 2017

The rapid growth of algae occurs can induce the algae bloom when nutrients are supplied from anthropogenic sources such as fertilizer, animal waste or sewage in runoff the water currents or upwelling naturally. The algae blooms creates the human health problem in the environment as well as in the water resource managers including hypoxic dead zones and harmful toxins and pose challenges to water treatment systems. The algal blooms in the source water in water treatment systems affects the drinking water taste & odor while clogging or damaging filtration systems and putting a strain on the systems designed to remove algal toxins from the source water. This paper propose the emerging In-Situ self-diagnosable smart algae sensing device with wireless connectivity for smart remote monitoring and control. In this research, we developed the In-Site Algae diagnosable sensing device with wireless sensor network (WSN) connectivity with Optical Biological Sensor and environmental sensor to monitor the water treatment systems. The proposed system emulated in real-time on the water treatment plant and functional evaluation parameters are presented as part of the conceptual proof to the proposed research.

• ALGAE
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• v.21 no.3
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• pp.283-286
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• 2006

This study carried out phylogenetic analysis of dinoflagellate Gonyaulax polygramma which was responsible for a harmful algal bloom episode in Korea in 2004. Molecular phylogenetic tree inferred from the partial LSU rDNA data showed that G. polygramma came up among the monophyletic Gonyaulax clade, but did not have apparent genetic affiliation to other Gonyaulax species. This result appears to be consistent with characteristic morphological features of G. polygramma such as epitheca sharply tapering to the apex and thecal plates ornamented with numerous longitudinal striations.

• ALGAE
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• v.26 no.2
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• pp.181-192
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• 2011

Ballasting and deballasting of shipping vessels in foreign ports have been reported worldwide as a vector of introduction of non-native aquatic plants and animals. Recently, attention has turned to ballast water as a factor in the global increase of harmful algal blooms (HABs). Many species of microalgae, including harmful dinoflagellate species, can remain viable for months in dormant benthic stages (cysts) in ballast sediments. Over a period of four years, we surveyed ballast water and sediment of ships docked in two ports of Tampa Bay, Florida, USA. Sampling conditions encountered while sampling ballast water and sediments were vastly different between vessels. Since no single sample collection protocol could be applied, existing methods for sampling ballast were modified and new methods created to reduce time and labor necessary for the collection of high-quality, qualitative samples. Five methods were refined or developed, including one that allowed for a directed intake of water and sediments. From 63 samples, 1,633 dinoflagellate cysts and cyst-like cells were recovered. A native, cyst-forming, harmful dinoflagellate, Alexandrium balechii (Steidinger) F. J. R. Taylor, was collected, isolated, and cultured from the same vessel six months apart, indicating that ships exchanging ballast water in Tampa Bay have the potential to transport HAB species to other ports with similar ecologies, exposing them to non-native, potentially toxic blooms.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.9
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• pp.1881-1888
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• 2011

Red tide is a natural phenomenon to bloom harmful algal, which fish and shellfish die en masse. Red tide damage with respect to sea farming has been occurred each year. Red tide damage can be minimized by means of prediction of red tide blooms. Red tide prediction using naive bayes classifier can be achieve good prediction results. The result of naive bayes method only determine red tide blooms, whereas the method can not know how increasing of red tide algae density. In this paper, we proposed the red tide blooms prediction method using fuzzy reasoning and naive bayes classifier. The proposed method can enhance the precision of red tide prediction and forecast the increasing density of red tide algae.

• Journal of Korean Society on Water Environment
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• v.39 no.3
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• pp.223-230
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• 2023

Harmful algae blooms have become a serious environmental problem in major river basins in Korea. They are known to produce various algal organic matters (AOMs) including intracellular organic matters (IOMs) and extracellular organic matters (EOMs). Generally AOMs cannot be easily removed by coagulation/flocculation process in conventional drinking water plants. AOMs produced by blue-green algae also include various toxins such as Microcystins, Anatoxin-a, and Saxitoxin known to have harmful effects on living organisms in aquatic environment. In this study, toxic effects of EOMs produced by three different algae species (Microcystis sp., Anabaena sp., and Oscillatoria sp.) on zebrafish were investigated using electroencephalography (EEG) recording method, a technology for recording brain activity. Electroencephalographic changes in zebrafish revealed that a low EOM had a negative effect on zebrafish compared to both Anabaena sp. and Oscillatoria sp. at 30 ppm EOM exposures. This result might be due to Microcystins present in EOMs produced by Microcystis sp. As a result of power spectrum density anallysis, exposure to EOMs produced by Microcystis sp. caused a state of vigilance in zebrafish. This EEG based toxicity test can be used to examine effects of harmful materials at low levels on living organisms in an aquatic system.

• Journal of the korean society of oceanography
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• v.34 no.3
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• pp.167-171
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• 1999

Since toxic Alexandrium catenella and non-toxic A. fraterculus are morphologically similar, they are difficult to discriminate under the light microscope. However, a novel technology, such as fluorescein isothiocyanate (FITC)-conjugated lectin probes enables easy and rapid differentiation. Toxic A. catenella bound seven different lectins, whereas the non-toxic A. fratercuzus did not bind Arachis hypogaea (PNA) lectin. In addition, Pseudo-nitrschia species in this study were also difficult to identify to species level with light microscope techniques, but it was possible to classify them using fluorescent lectins. Pseudo-nitzschia multistriata, P. subfraudulenta and P. pungens bound Canavalia ensiformis (ConA), whereas P. subpaclfica did not, and P. pungens also bound Ricinus communis (RCA). These results imply that lectin could be used as a critical tool in the differentiation of P. multistriata, P. subfraudulenta and P. pungens. However, P. subpacifica was not differentiated by the lectins tested. Therefore, it isconcluded that lectin probes are useful for discriminating toxic A. catenella from non-toxic A. fraterculus, and for the identification of some Pseudo-nitzschia species. In addition, this method has a great potential to speed and detection between non-toxic and toxic harmful algal blooms (HABs) in Korean biotoxin monitoring systems.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1167-1181
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• 2021

In relation to the algae bloom, four types of blue-green algae that emit toxic substances are designated and managed as harmful Cyanobacteria, and prediction information using a physical model is being also published. However, as algae are living organisms, it is difficult to predict according to physical dynamics, and not easy to consider the effects of numerous factors such as weather, hydraulic, hydrology, and water quality. Therefore, a lot of researches on algal bloom prediction using machine learning have been recently conducted. In this study, the characteristic importance of water quality factors affecting the occurrence of Cyanobacteria harmful algal blooms (CyanoHABs) were analyzed using the random forest (RF) model for Bohyeonsan Dam and Yeongcheon Dam located in Yeongcheon-si, Gyeongsangbuk-do and also predicted the occurrence of harmful blue-green algae using the machine learning and deep learning models and evaluated their accuracy. The water temperature and total nitrogen (T-N) were found to be high in common, and the occurrence prediction of CyanoHABs using artificial neural network (ANN) also predicted the actual values closely, confirming that it can be used for the reservoirs that require the prediction of harmful cyanobacteria for algal management in the future.