• Journal of Korean Society on Water Environment
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• v.33 no.2
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• pp.130-139
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• 2017

With the need to evaluate accuracy of real-time measurement of cyanobacterial fluorescence to determine cyanobacterial blooms, this research examined 357 paired data (2013-2016) comprising both microscopic toxic cyanobacterial cell counts and concurrent real-time cyanobacterial concentrations at 2 sites (YS1 and YS2) in Yeongsan river. The increase in real-time cyanobacterial concentration was closely associated with the exceedance of 5,000 cyanobacterial cells/ml (odds ratio [OR] 1.07, 95% confidence interval [CI] 1.03-1.12) and 10,000 cells/ml (OR 1.08, 95% CI 1.04-1.12) at YS2 site. The area under the receiver operating characteristic (ROC) curve for the real-time cyanobacterial measurement at the YS2 site was 0.93, which indicates the measurement provides a high accurate detection of cyanobacterial blooms. On the ROC curve, the early alert levels of real-time cyanobacteria ranging $16-23{\mu}g$ chl-a/L would produce acceptable sensitivity of 79% and specificities greater than 90%. The real-time fluorescence measurement was found to be an accurate indicator of cyanobacteria and can serve as a tool for detecting toxic cyanobacterial bloom events in Youngsan river.

• Korean Journal of Environmental Biology
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• v.31 no.4
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• pp.233-242
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• 2013

Cyanobacteria (blue-green algae) are not only the first oxygenic organisms on earth but also the foremost primary producers in aquatic environment. Massive growth of cyanobacteria, in eutrophic waters, usually changes the water colour to green and is called as algal (cyanobacterial) bloom or green tide. Cyanobacterial blooms are a result of high levels of primary production by certain species such as Microcystis sp., Anabaena sp., Oscillatoria sp., Aphanizomenon sp. and Phormidium sp. These cyanobacterial species can produce hepatotoxins or neurotoxins as well as malodorous compounds like geosmin and 2-methylisoborneol (MIB). In order to solve the nationwide problem of hazardous cyanobacterial blooms in Korea, the following technically and strategically sound approaches need to be developed. 1) As a long-term strategy, reduction of the nutrients such as phosphorus and nitrogen in our water bodies to below permitted levels. 2) As a short term strategy, field application of combination of already established bloom remediation techniques. 3) Development of emerging convergence technologies based on information and communication technology (ICT), environmental technology (ET) and biotechnology (BT). 4) Finally, strengthening education and creating awareness among students, public and industry for effective reduction of pollution discharge. Considering their ecological roles, a complete elimination of cyanobacteria is not desirable. Hence a holistic approach mentioned above in combination to addressing the issue from a social perspective with cooperation from public, government, industry, academic and research institutions is more pragmatic and desirable management strategy.

• Proceedings of the Microbiological Society of Korea Conference
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• 2005.05a
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• pp.33-35
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• 2005

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.168-168
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• 2023

The frequent occurrence of cyanobacterial harmful algal blooms (CHABs) in inland waters under climate change seriously damages the ecosystem and human health and is becoming a big problem in South Korea. Satellite remote sensing is suggested for effective monitoring CHABs at a larger scale of water bodies since the traditional method based on sparse in-situ networks is limited in space. However, utilizing a standalone variable of satellite reflectances in common CHABs dual-models, which relies on both chlorophyll-a (Chl-a) and phycocyanin or cyanobacteria cells (Cyano-cell), is not fully beneficial because their seasonal variation is highly impacted by surrounding meteorological and bio-environmental factors. Along with the development of Artificial Intelligence (AI), monitoring CHABs from space with analyzing the effects of environmental factors is accessible. This study aimed to investigate the potential application of AI in the dual-model strategy (Chl-a and Cyano-cell are output parameters) for monitoring seasonal dynamics of CHABs from satellites over Korean inland waters. The Sentinel-3 satellite was selected in this study due to the variety of spectral bands and its unique band (620 nm), which is sensitive to cyanobacteria. Via the AI-based feature selection, we analyzed the relationships between two output parameters and major parameters (satellite water-leaving reflectances at different spectral bands), together with auxiliary (meteorological and bio-environmental) parameters, to select the most important ones. Several AI models were then employed for modelling Chl-a and Cyano-cell concentration from those selected important parameters. Performance evaluation of the AI models and their comparison to traditional semi-analytical models were conducted to demonstrate whether AI models (using water-leaving reflectances and environmental variables) outperform traditional models (using water-leaving reflectances only) and which AI models are superior for monitoring CHABs from Sentinel-3 satellite over a Korean inland water body.

• Korean Journal of Ecology and Environment
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• v.56 no.3
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• pp.268-279
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• 2023

Nowadays, artificial intelligence model approaches such as machine and deep learning have been widely used to predict variations of water quality in various freshwater bodies. In particular, many researchers have tried to predict the occurrence of cyanobacterial blooms in inland water, which pose a threat to human health and aquatic ecosystems. Therefore, the objective of this study were to: 1) review studies on the application of machine learning models for predicting the occurrence of cyanobacterial blooms and its metabolites and 2) prospect for future study on the prediction of cyanobacteria by machine learning models including deep learning. In this study, a systematic literature search and review were conducted using SCOPUS, which is Elsevier's abstract and citation database. The key results showed that deep learning models were usually used to predict cyanobacterial cells, while machine learning models focused on predicting cyanobacterial metabolites such as concentrations of microcystin, geosmin, and 2-methylisoborneol (2-MIB) in reservoirs. There was a distinct difference in the use of input variables to predict cyanobacterial cells and metabolites. The application of deep learning models through the construction of big data may be encouraged to build accurate models to predict cyanobacterial metabolites.

• Journal of Environmental Health Sciences
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• v.31 no.3
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• pp.192-198
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• 2005

The occurrence of toxic cyanobacterial blooms has been reported worldwide and poses a threat to human health through drinking water exposure. The toxins they produce are highly water soluble and can leach into the water body. To eliminate any risk of drinking water exposure, removal of these toxins is essential before the water is consumed. Conventional water treatment techniques such as chlorination, if managed well, can be effectively used to remove some of these toxins, however, saxitoxin and its derivatives pose a problem. Little toxicological data are available to evaluate the real threat of these toxins.

• Journal of Microbiology and Biotechnology
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• v.16 no.2
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• pp.212-218
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• 2006

Three out of fourteen Microcystis-dominant cyanobacterial blooms in Central India were found to be toxic to mice ($LD_{50}$ ranging from 35-450 mg bloom dry mass/kg body weight). The liver architecture of the treated mice showed characteristic symptoms of hepatotoxicity relative to the untreated controls, with increased enzyme activities of serum lactate dehydrogenase (LDH), serum glutamate oxaloacetate transaminase (SGOT), alkaline phosphatase (ALP), and serum glutamate pyruvate transaminase (SGPT). RP-HPLC revealed the presence of microcystin-LR, microcystin-RR, and desmethyl microcystin-RR in the given region to maximum amounts of 390, 1,030, and $860{\mu}g/g$ bloom dry weight, respectively, corresponding to a maximum of 2.8 mg/l microcystin-LR in the lake water. Further confirmation of the microcystin variants was conducted using a MALDI-TOF MS analysis.

• Korean Journal of Environmental Biology
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• v.33 no.1
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• pp.1-6
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• 2015

Increased harmful algal blooms by cyanobacteria are threatening public health and limiting human activities related with freshwater ecosystems. Phosphorus (P) has long been suggested as a critical nutrient for cyanobacterial bloom through field research in Canada during the 1970s, proposing a P-based freshwater management guideline. However, recently, nitrogen (N) has also been highlighted as an impacting nutrient on cyanobacterial harmful algal blooms (CyanoHABs). Due to the intensive and frequent observation of Microcystis, this kind of paradigm shift from P limitation to season-dependent N or P limitation has an important implication for a dual nutrient management strategy in eutrophic freshwaters. Through recent international researches, general strategies to control CyanoHABs in lakes and reservoirs are as follows: a dual nutrient (N & P) reduction, wastewater collection and treatment, pre-treatment of influent water in buffer zones, dredging of sediment, reduction of residence time, algal collection, and precipitation and flocculation of cyanobacteria. In addition, sustainable and integrative freshwater algae management should be carried out, based on the ecological aspect, because cyanobacteria are not the target organism to be eradicated, but an essential microbial member in the freshwater ecosystem.

• Ecology and Resilient Infrastructure
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• v.4 no.3
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• pp.130-141
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• 2017

We evaluated the field application feasibility that biologically derived substances (Naphthoquinone derivate: NQ 2-0) can be used for the eco-friendly mitigation of natural harmful cyanobacterial blooms in freshwater. We conducted a 30 ton scale mesocosm experiment to investigate the effects of NQ 2-0 on biotic and abiotic factors in water collected from Gi-heung reservoir. In the mesocosm experiments, the abundance of Microcystis sp. was continuously increased in the control. However, the Microcystis sp. cell density was sharply decreased on the $10^{th}$ day. In the treatment, NQ 2-0 showed the strong and selective algicidal activity toward the target cyanobacteria (Microcystis sp.). Accordingly, the algicidal activity of NQ 2-0 compound increased gradually until $10^{th}$, $15^{th}$ days and algal biomass was decreased to 99.4 and 100 %, respectively. NQ 2-0 compound was not only selective algicidal activity but also the growth of other phytoplankton and increased the Shannon-Wiener diversity index of phytoplankton. In the mesocosm experiments, the dynamics of biotic (bacteria, heterotrophic nanoflagellate, ciliates, zooplankton) and abiotic (water temperature, dissolved oxygen, pH, conductivity, nutrients) factors remained unaffected. These results suggest that NQ 2-0 could be a selective and ecologically safe algicide to mitigate harmful cyanobacterial blooms. In addition, it is believed that NQ 2-0 will play a major role in forming a healthy aquatic ecosystem by facilitating habitat and food supply of aquatic organisms.

• Journal of Korean Society on Water Environment
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• v.40 no.3
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• pp.121-129
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• 2024

Harmful cyanobacterial blooms (HCBs) are caused by the rapid proliferation of cyanobacteria and are believed to be exacerbated by climate change. However, the extent to which HCBs will be stimulated in the future due to increased temperature remains uncertain. This study aims to predict the future occurrence of cyanobacteria in the Nakdong River, which has the highest incidence of HCBs in South Korea, based on temperature rise scenarios. Representative Concentration Pathways (RCPs) were used as the basis for these scenarios. Data-driven model simulations were conducted, and out of the four machine learning techniques tested (multiple linear regression, support vector regressor, decision tree, and random forest), the random forest model was selected for its relatively high prediction accuracy. The random forest model was used to predict the occurrence of cyanobacteria. The results of boxplot and time-series analyses showed that under the worst-case scenario (RCP8.5 (2100)), where temperature increases significantly, cyanobacterial abundance across all study areas was greatly stimulated. The study also found that the frequencies of HCB occurrences exceeding certain thresholds (100,000 and 1,000,000 cells/mL) increased under both the best-case scenario (RCP2.6 (2050)) and worst-case scenario (RCP8.5 (2100)). These findings suggest that the frequency of HCB occurrences surpassing a certain threshold level can serve as a useful diagnostic indicator of vulnerability to temperature increases caused by climate change. Additionally, this study highlights that water bodies currently susceptible to HCBs are likely to become even more vulnerable with climate change compared to those that are currently less susceptible.