• Analytical Science and Technology
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• v.26 no.5
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• pp.326-332
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• 2013

Geosmin and 2-methylisoborneol (2-MIB) are volatile organic compounds responsible for the majority of unpleasant taste and odor events in drinking water. Geosmin and 2-MIB are byproducts of blue-green algae (cyanobacteria) with musty and earthy odors. These compounds have odor threshold concentration at ng/L levels. It is needed to develop a sensitive method for determination of geosmin and 2-MIB to control the quality of drinking water. In this study, geosmin and 2-MIB in water samples were determined by gas chromatography-mass spectrometry (GC-MS) with headspace-solid phase microextraction (HS-SMPE). The detection limits of this method were 1.072 ng/L and 1.021 ng/L for geosmin and 2-MIB, respectively. Good accuracy and precision was also obtained by this method. Concentrations of the two compounds were measured in raw waters from Nakdong River in the cyanobacterial blooming season. Water bloom formed by cyanobacteria has been occurred currently in Nakdong River. It is needed to investigate the concentrations of geosmin and 2-MIB to control the quality of drinking water from Nakdong River. Both geosmin and 2-MIB were detected in raw waters from Nakdong River at concentrations ranging from 4 to 24 ng/L and 6 to 16 ng/L, respectively.

• Journal of Korean Society of Water and Wastewater
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• v.25 no.4
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• pp.597-604
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• 2011

This study was performed to compare finished water quality among three different processes. A detailed assessment of performance was carried out during the five months of operation. Finished water quality was evaluated on the basis of parameters such as Dissolved organic carbon (DOC), $UV_{254}$ absorbance, haloacetic acid formation potential (HAAFP), geosmin, 2-methylisoborneol (2-MIB), heterotrophic bacteria and total coliform bacteria. The treatment processes were Process 1 (coagulation-flocculation-sedimentation-sand filtration-ozone-GAC), Process 2 (coagulation-flocculation-sedimentation -microfiltration-ozone-GAC), and Process 3 (coagulation-flocculation-sedimentation- sand filtration-GAC), compared side by side in the pilot testing. Process 2 was found to have better removal efficiency of DOC, $UV_{254}$ absorbance, HAAFP and heterotrophic bacteria in comparison with process 1 and process 3 under identical conditions. Geosmin, 2-MIB and total coliform bacteria were not detected in finished water from each process.

• Journal of Korean Society of Environmental Engineers
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• v.27 no.10
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• pp.1123-1128
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• 2005

NIB(methylisoborneol) is an earthy/musty odor compound produced as a second metabolite by cyanobacteria and actinomycetes. MIB is not removed by conventional water treatment(coagulation, sedimentation, filtration) and its presence in tap water, even at low ng/L levels, can result in consumer complaints. PAC(powdered activated carbon) can effectively remove MIB when the correct dose is applied. But, since most operators in water treatment plants apply a PAC dose and then adjust that dose depending on direct observation (odor detection) after treatment, the result is often under-dose or eve,-dose. In this study, kinetic and isotherm tests using $^{14}C$-radiolabeled MIB were performed to determine coefficients for the HSDM(homogeneous surface diffusion model), including liquid film mass transfer coefficient($K_f$) and surface diffusion coefficient ($D_s$). The HSDM gave a reasonable fit and allowed prediction with the experimental data. Base on the HSDM, the authors developed an optimum PAC dose prediction program using the Excel spreadsheet. When the developed program was applied at two water treatment plants, the PAC dose based on the experience of operators in the water treatment plant was significantly different from that recommended by the newly developed program. If operators are willing to use the optimum PAC dose prediction program, it should solve dosing problems.

• Journal of Korean Society on Water Environment
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• v.37 no.5
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• pp.381-397
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• 2021

Off-flavor materials (geosmin and 2-methylisoborneol (2-MIB)) produced by microorganisms, such as, cyanobacteria and actinomycetes, cause freshwater use problems worldwide. Due to unpleasant taste and odor, these microorganisms have raised issues especially in drinking water resources. Recently, there has been increasing concern about 2-MIB and causal cyanobacteria, namely, Pseudanabaena, in Korea. However, material production and ecological dynamics remain largely unexplored. This study reviewed the distribution of Pseudanabaena, its species diversity, and the research trend of molecular ecology related to 2-MIB production in Korea. Based on published literature, we found that seven species of Pseudanabaena which include P. mucicola, P. limnetica, P. redekei, P. catenata, P. galeata, P. yagii, and P. cinerea appeared to occur in a variety of Korean water systems. All of these Pseudanabaena species were found in the North-Han River system (Lakes Soyang, Chuncheon, Uiam, and Paldang). Some of these species were also detected in other watersheds, but the precise species diversity was not identified. Species belonging to the Pseudanabaena genus are hard to classify through general microscopic alpha taxonomy, due to their very small cell size and similar morphological characters. Moreover, the potential of 2-MIB production cannot be detected by microscopic observation. Combining molecular ecological techniques, such as, environmental genomic materials (eDNA, eRNA) analyses to conventional methods could be useful to better understand the off-flavor material production and dynamics, thereby providing more efficient management strategies of freshwater systems.

• 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.

• 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.