• Economic and Environmental Geology
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• v.35 no.5
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• pp.395-406
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• 2002

The sixty natural springs and community wells used as a drinking water in the Daejeon area are mainly located at the parks and the natural green districts. The purpose of this study is to investigate the characteristics of water quality and the contamination of the springs and the wells, and to suggest the management strategy for the springs and wells. For this study, we undertook water quality data from Daejeon City. According to the statistic analysis of water quality data, unacceptable rate as a drinking water was about 28 percent in 1999 and 24.5 percent in 2000, respectively. Major unacceptable factor is coliform, and others are bacteria, yersinia, color, turbidity, Fe and F. The unacceptable rate shows a roughly positive relationship with precipitation, that is, it shows highest rate during a rainy season between June and September. The major contamination source is likely to be the excrement of wild animals around natural springs and wells. Most of springs are vulnerable to the contamination of coliform and bacteria because of short residence time and shallow circulation in subsurface environment. The water samples collected from 31 springs or wells show weak acidic pHs, the electrical conductivity ranging from 63 to 357 $\mu\textrm{S}$/cm, and the hydrochemical types of Na(Ca)-HC0$_3$ and Ca-HC0$_3$. The groundwater samples of low total dissolved solid(TDS) belong to Na(Ca)-HC0$_3$. type, and the groundwater of high total dissolved solid is shifted towards Ca-HC0$_3$ type in the chemical composition. These hydrochemical characteristics indicate that most natural springs is in the early stage of geochemical evolution. The natural springs should be closed during a rainy season, which shows a high contamination rate. We suggest that a protection barrier around the springs should be built to keep wild animals away from the springs.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.37 no.2
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• pp.178-184
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• 1992

The changes in contents of chlorophyll and free proline in the seedling leaves of ten rice cultivars as affected by salt stress were checked in order to obtain the basic information on the judgement of the degrees of salt injury. The difference in salt injury among the cultivars was clearly observed about 25 days after 6% salt treatment. Chlorophyll content was decreased in both Gayabyeo and Taebaegbyeo for 14 days after different salt treatment as salt concentration was increased and the decreased tendency was much higher in Taebaegbyeo than in Gayabyeo over 0.4% salt concentration. Chlorophyll content in Gayabyeo after 0.6% salt treatment was decreased slowly, while in Taebaegbyeo, deminished very rapidly as time progressed, therefore it decreased by about 16% in Gayabyeo and 67% in Taebaebyeo compared to the control at 20 days, respectively. The relationship between chlorophyll content and the degrees of salt injury in ten rice cultivars showed significant negative correlation at 10 day after 0.6% salt treatment. Free proline content in Gayabyeo was increased gradually for 14 days after different salt treatment as salt became higher, while in Taebaebyeo, it was increased rapidly under 0.6% but rather decreased under 0.8% salt concentration. Particularly, it was much higher Taebaegbyeo than in Gayabyeo under salt concentration from 0.4 to 0.6%. Free proline content in Gayabyeo after 0.6% salt treatment was increased from 15 days, on the other hand in Taebaegbyeo, it was increased from 5 days, but rather decreased from 20 days, and it was 6 times higher in Taebaegbyeo than in Gayabyeo at 10 days. There was significant positive correlation between free proline content and the degrees of salt injury in ten rice cultivars at 10 days after 0.6% salt treatment. From the above results, chlorophyll and free proline content may be used as an indicative character of intensity of salt stress as well as varietal difference in resistance to salt stress in the seedling stage.

• Tuberculosis and Respiratory Diseases
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• v.44 no.6
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• pp.1245-1255
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• 1997

Background : Rifampicin(RFP) is a key component of the antituberculous short-course chemotherapy and the RFP resistance is a marker of multi-drug resistant(MDR) tuberculosis. RPoB gene encodes the $\beta$-subunit of RNA polymerase of M. tuberculosis which is the target of RFP. And rpoB gene mutations are the cause of RFP resistance of M. tuberculosis. Although several reports showed that PCR-SSCP would be a rapid diagnostic method for identifying the RFP resistance, there were few reports Performed using direct, clinical specimens. So we Performed PCR-SSCP analysis of rpoB gene of M. tuberculosis in direct, clinical specimens. Methods : 75 clinical specimens were collected from patients at Asan Medical Center from June to August 1996. After PCR of IS 6110 fragments, 43 both AFB smear-positive and IS6110 fragment PCR-positive specimens were evaluated. The RFP susceptibility test was referred to the referral laboratory of the Korean Tuberculosis Institute. DNA was extracted by bead beater method. And heminested PCR was done using 0.1ul(1uCi) [$\alpha-^{32}P$]-dCTP. SSCP analysis was done using non-denaturating MDE gel electrophoresis. Results : The results of PCR of IS6110 fragments of M. tuberculosis were positive in 55(73%) cases of 75 AFB smear-positive clinical specimens. Of the 55 specimens, RFP susceptibility was confirmed in only 43 specimens. Of the 43 AFB smear-positive and IS6110 fragment-positive specimens, 29 were RFP susceptible and 14 were RFP resistant. All the RFP susceptible 29 strains showed the same mobility compared with that of RFP sensitive H37Rv in SSCP analysis of ropB gene. And all the other RFP resistant 13 strains showed the different mobility. In other words they showed 100% identical results between PCR-SSCP analysis and traditional susceptibility test. Conclusion : The PCR-sseP analysis of rpoB gene in direct clinical specimens could be used as a rapid diagnostic method for detecting RFP resistant M. tuberculosis.

• Tuberculosis and Respiratory Diseases
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• v.49 no.6
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• pp.676-683
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• 2000

Background : Surgery may have a role when medical treatment alone is not successful in patients with multidrug resistant (MDR) pulmonary tuberculosis (PTB). To document the role of resection in MDR PTB, we analyzed 4 years of our experience. Methods : A retrospective review was performed on thirteen patients that underwent pulmonary resection for MDR PTB between May 1996 and February 2000. All patients had organisms resistant to many of the first-line drugs including isoniazid (INH) and rifampicin (RFP). Results : The thirteen patients were $37.5{\pm}12.4$ years old (mean${\pm}$S.D.)(M : F=5:8), and their sputum was culture positive even with adequate medication for prolonged periods ($109.7{\pm}132.0$ months), resistant to 2-8 drugs including isoniazid and rifampin. All patients had localized lesion(s) and most (92.3%) had cavities. At least 3 sensitive anti-TB medications were started before surgery in all patients according to the drug sensitivity test. The preoperative $FEV_1$ was $2.37{\pm}0.83$ L. Lobectomy was performed in 11 patients and pleuropneumonectomy in two. Postoperative mortality did not occur, but pneumonia occurred as a complication in one (7.7%). After $41.5{\pm}58.9$ days (range 1~150 days) follow up, negative conversion of sputum culture was achieved in all patients within 5 months. Only one patient (7.7%) recurred 32 months after lung resection. Conclusion : When medical treatment alone is not successful, surgical resection can be a good treatment option in patients with localized MDR PTB.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.1
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• pp.29-41
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• 2019

Terrestrial Gross Primary Production (GPP) is the largest global carbon flux, and forest ecosystems are important because of the ability to store much more significant amounts of carbon than other terrestrial ecosystems. There have been several attempts to estimate GPP using mechanism-based models. However, mechanism-based models including biological, chemical, and physical processes are limited due to a lack of flexibility in predicting non-stationary ecological processes, which are caused by a local and global change. Instead mechanism-free methods are strongly recommended to estimate nonlinear dynamics that occur in nature like GPP. Therefore, we used the mechanism-free machine learning techniques to estimate the daily GPP. In this study, support vector machine (SVM), random forest (RF) and artificial neural network (ANN) were used and compared with the traditional multiple linear regression model (LM). MODIS products and meteorological parameters from eddy covariance data were employed to train the machine learning and LM models from 2006 to 2013. GPP prediction models were compared with daily GPP from eddy covariance measurement in a deciduous forest in South Korea in 2014 and 2015. Statistical analysis including correlation coefficient (R), root mean square error (RMSE) and mean squared error (MSE) were used to evaluate the performance of models. In general, the models from machine-learning algorithms (R = 0.85 - 0.93, MSE = 1.00 - 2.05, p < 0.001) showed better performance than linear regression model (R = 0.82 - 0.92, MSE = 1.24 - 2.45, p < 0.001). These results provide insight into high predictability and the possibility of expansion through the use of the mechanism-free machine-learning models and remote sensing for predicting non-stationary ecological processes such as seasonal GPP.

• Journal of Korea Water Resources Association
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• v.55 no.10
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• pp.761-774
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• 2022

Accurate hydrologic prediction is essential to analyze the effects of drought, flood, and climate change on flow rates, water quality, and ecosystems. Disentangling the uncertainty of the hydrological model is one of the important issues in hydrology and water resources research. Hydrologic data assimilation (DA), a technique that updates the status or parameters of a hydrological model to produce the most likely estimates of the initial conditions of the model, is one of the ways to minimize uncertainty in hydrological simulations and improve predictive accuracy. In this study, the two ensemble-based sequential DA techniques, ensemble Kalman filter, and particle filter are comparatively analyzed for the daily discharge simulation at the Yongdam catchment using airGRdatassim. The results showed that the values of Kling-Gupta efficiency (KGE) were improved from 0.799 in the open loop simulation to 0.826 in the ensemble Kalman filter and to 0.933 in the particle filter. In addition, we analyzed the effects of hyper-parameters related to the data assimilation methods such as precipitation and potential evaporation forcing error parameters and selection of perturbed and updated states. For the case of forcing error conditions, the particle filter was superior to the ensemble in terms of the KGE index. The size of the optimal forcing noise was relatively smaller in the particle filter compared to the ensemble Kalman filter. In addition, with more state variables included in the updating step, performance of data assimilation improved, implicating that adequate selection of updating states can be considered as a hyper-parameter. The simulation experiments in this study implied that DA hyper-parameters needed to be carefully optimized to exploit the potential of DA methods.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.627-646
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• 2022

Recently, the seriousness of climate change-related problems caused by global warming is growing, and the average temperature is also rising. As a result, it is affecting the environment in which various temperature-sensitive creatures and creatures live, and changes in the ecosystem are also being detected. Seasons are one of the important factors influencing the types, distribution, and growth characteristics of creatures living in the area. Among the most popular and easily recognized plant seasonal phenomena among the indicators of the climate change impact evaluation, the blooming day of flower and the peak day of autumn leaves were modeled. The types of plants used in the modeling were forsythia and cherry trees, which can be seen as representative plants of spring, and maple and ginkgo, which can be seen as representative plants of autumn. Weather data used to perform modeling were temperature, precipitation, and solar radiation observed through the ASOS Observatory of the Korea Meteorological Administration. As satellite data, MODIS NDVI was used for modeling, and it has a correlation coefficient of about -0.2 for the flowering date and 0.3 for the autumn leaves peak date. As the model used, the model was established using multiple regression models, which are linear models, and Random Forest, which are nonlinear models. In addition, the predicted values estimated by each model were expressed as isopleth maps using spatial interpolation techniques to express the trend of plant seasonal changes from 2003 to 2020. It is believed that using NDVI with high spatio-temporal resolution in the future will increase the accuracy of plant phenology modeling.

• The Korean Journal of Applied Statistics
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• v.36 no.1
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• pp.49-62
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• 2023

Solar energy, which is rapidly increasing in proportion, is being continuously developed and invested. As the installation of new and renewable energy policy green new deal and home solar panels increases, the supply of solar energy in Korea is gradually expanding, and research on accurate demand prediction of power generation is actively underway. In addition, the importance of solar radiation prediction was identified in that solar radiation prediction is acting as a factor that most influences power generation demand prediction. In addition, this study can confirm the biggest difference in that it attempted to predict solar radiation using medium-term forecast weather data not used in previous studies. In this paper, we combined the multi-linear regression model, KNN, random fores, and SVR model and the clustering technique, K-means, to predict solar radiation by hour, by calculating the probability density function for each cluster. Before using medium-term forecast data, mean absolute error (MAE) and root mean squared error (RMSE) were used as indicators to compare model prediction results. The data were converted into daily data according to the medium-term forecast data format from March 1, 2017 to February 28, 2022. As a result of comparing the predictive performance of the model, the method showed the best performance by predicting daily solar radiation with random forest, classifying dates with similar climate factors, and calculating the probability density function of solar radiation by cluster. In addition, when the prediction results were checked after fitting the model to the medium-term forecast data using this methodology, it was confirmed that the prediction error increased by date. This seems to be due to a prediction error in the mid-term forecast weather data. In future studies, among the weather factors that can be used in the mid-term forecast data, studies that add exogenous variables such as precipitation or apply time series clustering techniques should be conducted.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.17 no.6
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• pp.511-522
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• 1984

The Nagdong is one of the biggest rivers in Korea, which is very important water source not only for tap water of Pusan city but also for the industrial water. Therefore, authors tried to check the water quality year by year. In this experiment one hundred and twenty water samples collected from August 1983 to July 1984 were analyzed bacteriologically and physiologically. Fifteen sampling stations were established between near Samrangjin and estuary of the river. To evaluate the water quality, temperature, pH, chloride ion, salinity, chemical oxygen demand (COD), electrical conductivity, nutrients, total coliform, fecal coliform, fecal streptococcus, viable cell count and bacterial flora were observed. The variation of water temperature was ranged $-1.5{\sim}29.0^{\circ}C$ (Mean value $13.9{\sim}16.5^{\circ}C$), it in spring was higher as $10{\sim}15^{\circ}C$ about $10^{\circ}C$ than in winter and it in autumm was very stabilized as about $20^{\circ}C$ at each station. The pH variation of the samples was ranged $6.68{\sim}9.15$. The range of concentration of chloride ion and salinity varied $7.4{\sim}l,020.5$ mg/l and $1.05{\sim}33.0\%0$, respectively. Especially, salinity of the 3rd water war was the higher than others as $25.76{\sim}31.58\%0$. COD was ranged $1.45{\sim}14.94$ mg/l and the lower part of the Nagdong River was heavily contaminated by domesitc sewage and waste water from the adjacent factor area. The range of electrical conductivity was $1.360{\times}10^2{\sim}5.650{\times}10^4{\mu}{\mho}/cm$ and that was by far higher the estuary than the upper. Concentration of nutrients were $0.008{\sim}0.040$ mg/l (Mean value $0.019{\sim}0.068$ mg/l) for $NO_2-N,\;0.038{\sim}5.253$ mg/l ($0.351{\sim}2.347$ mg/l) for $NO_3-N,\;0.100{\sim}2.685$ mg/l($0.117{\sim}1.380$ mg/l) for $NH_4-N,\;0.003{\sim}0.084$ mg/l($0.014{\sim}0.065$ mg/l) for $PO_4-P$ and $0.154{\sim}6.123$ mg/l ($1.165{\sim}3.972$ mg/l) for $SiO_2-Si$, respectively. Usually nutrients contents of the water in the upper part(included station 1 to 5) were higher than those of the estuarine area. The bacterial density of the samples ranged 7.3 to 460,000/100 ml for total coliforms, 3.6 to 460,000/100 ml for fecal coliform, $0{\sim}46,000/100ml$ for fecal streptococcus and $<30{\sim}1.2{\times}10^5/ml$ for viable cell count. Composition of coliform was $28\%$ Escherichia coli group, $18\%$ Citrobacter freundii group, $31\%$ Enterobacter aerogenes group and $22\%$ others. Predominant species among the 659 strains isolated from the samples were Pseudomonas spp. ($42\%$), Flavobacterium spp. ($20\%$) and Moraxella spp. ($12\%$).