• Journal of Korean Society for Geospatial Information Science
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• v.4 no.2 s.8
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• pp.23-33
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• 1996

The conventional monitoring of water quality constrained by time and equipment produce neither complete nor synoptic geographic coverage of pollutant distribution, transport, and water quality. To circumvent these limitations in temporal and spatial measurements, the use of remote sensing is increasingly being involved in the lacustrine environmental research. Consequently, satellite remote sensing, with its synoptic coverage, is used to obtain the eutrophication-related water quality parameters in Daecheong reservoir in this study. The approach involved acquisition of water quality samples from boats of 15 sites on 20 June 1995 and 30 sites on 18 March 1996, simultaneous with Landsat-5 satellite overpass. Regression models have been developed between the water quality parameters and Landsat Thematic Mapper(TM) digital data. The best regression model was determined based on the correlation coefficient which was higher than 0.6. As a result, satellite remote sensing can provide meaningful information on water quality parameters. The regression models developed in this study show good relationship for transparency, turbidity, SS, and chlorophyll, but not for TN and TP because their spectral characteristics are not well defined.

• Journal of Wetlands Research
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• v.20 no.4
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• pp.330-337
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• 2018

The Korean Eutrophication Index($TSI_{ko}$) was estimated using water quality monitoring data of eight main sites in the SoOoak River watershed. The environmental characteristics of rivers were classified and evaluated using the $TSI_{ko}$ for each factor calculated by COD, T-P, and Chl-a. There is a good condition for the algae to grow due to shallow water depth, inflow of non-point source pollution during rainfall, influx of sewage treatment effluent and increase of residence time. It shows trophic state more than mesotrophication year round. Especially, in case of Chuso point, which is the inflow point of Daecheong Lake, the water quality deteriorated due to hydraulic characteristics and showed the eutrophic state. Therefore, it is necessary to establish the measures to improve the water quality through the precise monitoring of SoOak River.

• Journal of Applied Biological Chemistry
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• v.52 no.4
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• pp.157-162
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• 2009

An agar-liquefying bacteria (SC-22), which produces a diffusible agarase that caused agar softening around the colony was isolated from Daecheong lake in Korea. Chemotaxanomic and phylogenetic analyses based on 16S rRNA gene sequences revealed the strain was classified as Cellvibrio mixtus SC-22. The isolate SC-22 showed maximal extracellular agarase activity with 58.5 U/mL after 48 h cultivation in the presence of 0.2% agar. It was observed that the isolate produced two kinds of extracellular and three kinds of intracellular isoenzymes. The major agarase was purified from the culture filtrate of agarolytic bacteria by ammonium sulfate precipitation, anion exchange and gel filtration column chromatographic methods. The molecular mass of the purified enzyme was estimated to be 25 kDa by SDS-PAGE. The optimum pH and temperature of the purified enzyme were pH 7.0 and $50^{\circ}C$, respectively. The agarase activity was activated by $Fe^{2+}$, $Na^+$ and $Ca^{2+}$ ions while it was inhibited by $Hg^{2+}$, $Mn^{2+}$ and $Cu^{2+}$ at 1 mM concentration. The predominant hydrolysis product of agarose by the enzyme was galactose and disaccharide on TLC, indicating the cleavage of $\beta$-1,4 linkage in a random manner. The enzyme showed high substrate specificity for only agar and agarose among various polysaccharides.

• Journal of Korean Society of Water and Wastewater
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• v.35 no.6
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• pp.417-424
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• 2021

Algal bloom is an ongoing issue in the management of freshwater systems for drinking water supply, and the chlorophyll-a concentration is commonly used to represent the status of algal bloom. Thus, the prediction of chlorophyll-a concentration is essential for the proper management of water quality. However, the chlorophyll-a concentration is affected by various water quality and environmental factors, so the prediction of its concentration is not an easy task. In recent years, many advanced machine learning algorithms have increasingly been used for the development of surrogate models to prediction the chlorophyll-a concentration in freshwater systems such as rivers or reservoirs. This study used a light gradient boosting machine(LightGBM), a gradient boosting decision tree algorithm, to develop an ensemble machine learning model to predict chlorophyll-a concentration. The field water quality data observed at Daecheong Lake, obtained from the real-time water information system in Korea, were used for the development of the model. The data include temperature, pH, electric conductivity, dissolved oxygen, total organic carbon, total nitrogen, total phosphorus, and chlorophyll-a. First, a LightGBM model was developed to predict the chlorophyll-a concentration by using the other seven items as independent input variables. Second, the time-lagged values of all the input variables were added as input variables to understand the effect of time lag of input variables on model performance. The time lag (i) ranges from 1 to 50 days. The model performance was evaluated using three indices, root mean squared error-observation standard deviation ration (RSR), Nash-Sutcliffe coefficient of efficiency (NSE) and mean absolute error (MAE). The model showed the best performance by adding a dataset with a one-day time lag (i=1) where RSR, NSE, and MAE were 0.359, 0.871 and 1.510, respectively. The improvement of model performance was observed when a dataset with a time lag up of about 15 days (i=15) was added.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.402-402
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• 2020

대청호는 1998년 조류경보제 도입 이후 1999년과 2014년을 제외하고 매년 조류 경보가 발령되었으며 2001년 조류 경보 '대발생'이 발령된 후 2017년 가장 높은 조류 발생 수치를 기록하였다. 상시 조류 발생 지역인 대청호 내 녹조를 제어하기 위해 비점오염원 발생원을 기준으로 우선관리지역을 선정하고, 각 지역의 특성을 반영한 관리대책을 제안하였다. 우선관리지역 선정을 위해 대청호 유역 내 오염총량 소유역을 기준으로 각 소유역의 농업 비점오염원의 발생부하량을 산정하고 유출을 고려한 가중치를 추가하였다. 본 연구에서는 농업 비점오염원을 크게 토지계 비점오염원과 축산계 비점오염원으로 분류하였으며, 토지계 농업비점오염원은 논, 밭, 과수원 지역으로 정의하였다. 발생부하량의 산정은 오염총량관리 기술지침(2019, 국립환경과학원)을 기준으로 하였으며, 토지계 발생부하량 산정을 위한 토지계 정보원으로 환경부에서 제작 및 배포하는 세분류 토지피복도를 축산계 발생부하량 산정을 위한 축산 두수는 2017년 기준 전국오염원조사 내 축산두수를 이용하였다. 토지계 비점오염원의 하천까지 유출을 반영하기 위해 각 소유역별 평균 경사도를 가중치로 이용하였으며, 축산계 비점오염원은 오염물질이 발생한 후 하천까지의 평균 유출 정도를 확인하여 가중치로 반영하였다. 실제 하천에 미치는 영향이 높은 지역에 대한 우선적인 관리를 위해 하천수 수질측정망에서 측정한 수질 데이터와의 비교를 통하여 최종 우선관리지역을 보청A03, 보청A05, 금본F14, 금본F22 소유역으로 선정하였다. 각 소유역에 대한 수질 관리목표를 확인하였으며, 지역의 특성을 분석하여 토지계 및 축산계 비점오염원에 대한 적절한 관리대책을 제안하였다. 본 연구에서 사용한 수질 측정망 데이터가 각 소유역보다 적게 분포하여 소유역에서 발생한 비점오염물질이 하천에 미치는 영향을 직접 파악하는데 한계가 있었다. 또한, 축산계의 경우 발생한 비점오염물질이 모두 하천으로 유입되지 않으며, 축산계 비점오염원의 배출경로를 파악하는데 어려움이 있다는 한계를 가진다. 본 연구의 한계를 바탕으로 농림축산식품부 및 축협 등에서 구축하는 사육두수의 데이터를 이용하는 방법론 등의 추가적인 연구가 필요할 것으로 보인다.