• Korean Journal of Ecology and Environment
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• v.45 no.4
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• pp.378-391
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• 2012

The objective of this study was to determine temporal patterns and longitudinal gradients of water chemistry at eight artificial reservoirs and ten streams within the Han-River watershed along the main axis of the headwaters to the downstreams during 2009~2010. Also, we evaluated chemical relations and their variations among major trophic variables such as total nitrogen (TN), total phosphorus (TP), and chlorophyll-a (CHL-a) and determined intense summer monsoon and annual precipitation effects on algal growth using empirical regression model. Stream water quality of TN, TP, and other parameters degradated toward the downstreams, and especially was largely impacted by point-sources of wastewater disposal plants near Jungrang Stream. In contrast, summer river runoff and rainwater improved the stream water quality of TP, TN, and ionic contents, measured as conductivity (EC) in the downstream reach. Empirical linear regression models of log-transformed CHL-a against log-transformed TN, TP, and TN : TP mass ratios in five reservoirs indicated that the variation of TP accounted 33.8% ($R^2$=0.338, p<0.001, slope=0.710) in the variation of CHL and the variation of TN accounted only 21.4% ($R^2$=0.214, p<0.001) in the CHL-a. Overall, our study suggests that, primary productions, estimated as CHL-a, were more determined by ambient phosphorus loading rather than nitrogen in the lentic systems of artificial reservoirs, and the stream water quality as lotic ecosystems were more influenced by a point-source locations of tributary streams and intense seasonal rainfall rather than a presence of artificial dam reservoirs along the main axis of the watershed.

• Korean Journal of Ecology and Environment
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• v.34 no.4 s.96
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• pp.327-336
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• 2001

Physical, chemical, and biological parameters were measured during the period from July 1993 to August 1994 near the Munui intake tower of Taechung Reservoir to evaluate effects of nutrients and suspended solids on algal chlorophyll-a and water clarity. Large amounts of precipitation during summer 1993 produced minimum conductivity ($88\;{\mu}S/cm$), minimum TN : TP (<40), and maximum total phosphorus (TP;$59\;{\mu}g/L$) and resulted in a chlorophyll-a peak ($79\;{\mu}g/L$) and minimum transparency (<1.5 m) among the seasons. At the same time, ratios of volatile suspended solids (VSS): non-volatile suspended solids (NVSS) were maximum (13.0),indicating that the reduced transparency was mainly attributed to biogenic turbidity in relation to phytoplankton growth. In contrast, severe drought in summer 1994 resulted in greater conductivity (>$120\;{\mu}S/cm$), water clarity (%gt;2 m), and lower TP and chlorophyll- a (<$10\;{\mu}g/L$) relative to those of summer 1993. Total phosphorus ($R^2=0.46$, n=59) accounted more variations of chlorophyll- a compared to total nitrogen ($R^2=0,29$, n=59). The mass ratios of TN : TP ranged from 39 to 222 and were strongly correlated with TP (r = -0.80) but not with concentrations of TN (r = 0.05). Ambient nutrient concentrations and TN : TP mass ratios indicated that seasonality of chlorophyll- a was likely determined by concentrations of phosphorus reflected by the distribution of rainfall. It was concluded that reductions of phosphorus during heavy rainfall may provide better water quality for the drinking water in the intake tower.

• 한국해양학회지
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• v.24 no.3
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• pp.157-164
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• 1989

Quantitative species distribution and primary productivity of phytoplankton were studied monthly from August, 1987 to July, 1988 along with the quantitative distribution of total heterotrophic bacterioplankton and three groups of physiologically chracteristic bacterioplankton in the intertidal and subtidal waters off Kum River Estuary, Yellow Sea. A total of 121 phytoplankton taxa including 102 diatoms occurred, and cell concentration ranged from 15 to 5451 (cells/ml). The great spatio-temporal variations of the number of phytoplankton species and cell concentration well reflected the environmental differences between the intertidal and subtidal waters. Primary productivity (in Piopt, mgC/$m^3$/hr) ranged from 0.6 to 27.3. Just after the phytoplankton bloom (March) Piopt was very low in April at station 1, where amylolytic bacterioplankton also showed quite low population density. The peaks of primary productivity were not always coincided with those of phytoplankton standing crop. The ratio of Piopt's between samples well indicated the environmental differences between the intertidal and subtidal waters. Little characteristic trend was found in the scatter diagrams of phytoplankton standing crop along the population densities of total heterotrophic bacterioplankton and the three groups of physiologically characteristic bacterioplankton. In summer the phytoplankton standing crop was minimum in contrast with the high population density of bacterioplankton, which implies the influx of much allochthonous orgainc matter from Kum River. The scatter diagrams of Piopt along bacterioplankton population density revealed some phenomena there. Piopt had highly positive correlation with the population density of amylolytie bacterioplankton($R^2$=0.84) and that of lipolytic bacterioplankton($R^2$=0.70) while total heterotrophic bacterioplankton and proteolytic bacterioplankton had lesser correlations with Piopt. From the regression lines the increase of unit Piopt (mgC/$m^3$/hr) in the study area was calculated to mean the increase of $9.0{\times}10$ cells/ml and $8.0{\times}10$ cells/ml of amylolytic bacterioplankton and lipolytic bacterioplankton, respectively.

• Korean Journal of Ecology and Environment
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• v.42 no.1
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• pp.9-18
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• 2009

The objectives of this study were to evaluate seasonal and interannual variations of water quality and nutrient input (N, P) in Junam Reservoir, a nesting waterbody of migratory birds, over 10 years during 1998$\sim$2007 along with dynamic relations of trophic parameters using empirical models. Concentrations of COD averaged 7.8 mg $L^{-1}$ during the study, while TN and TP were $1.4\;mg\;L^{-1}$ and $83{\mu}g\;L^{-1}$, respectively, indicating an eutrophic-hypereutrophic state. Values of monthly COD had strong positive relations (r=0.669, p<0.001) with conductivity, indicating that summer rainfall resulted in an ionic dilution of the reservoir water by rainwater and contributed better water quality. One-way ANOVA tests showed significant differences (F=$5.2{\sim}12.9$, p<0.05) in TN and TP between the before and after the bird migration. In other words, nutrient levels were greater in the absence of migratory birds than in the presence of the migratory birds, suggesting a no-effect on nutrient inputs by the birds. Also, one-way ANOVA indicated no significant differences (F=$0.37{\sim}0.48$, p>0.05) in $NO_{3^-}N$ and $NH_{3^-}N$ between the before and after the birds migration. Linear empirical models using trophic parameters showed that algal biomass as CHL, had significant low correlations with TN ($R^2$=0.143, p<0.001, n=119) and TP ($R^2$=0.192, p<0.001, n=119). These results suggest that influences of nutrients on the CHL were evident, but the effect was weak. This fact was supported by analysis of Trophic State Index Deviation (TSID). Over 70% in the observed values of "TSI (CHL)-TSI (SD)" and "TSI (CHL)-TSI (TP)" were less than zero, suggesting a light limitation on the CHL by inorganic suspended solids.

• Journal of Environmental Impact Assessment
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• v.30 no.5
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• pp.271-296
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• 2021

Since the thermal stratification in a reservoir inhibits the vertical mixing of the upper and lower layers and causes the formation of a hypoxia layer and the enhancement of nutrients release from the sediment, changes in the stratification structure of the reservoir according to future climate change are very important in terms of water quality and aquatic ecology management. This study was aimed to develop a data-driven inflow water temperature prediction model for Daecheong Reservoir (DR), and to predict future inflow water temperature and the stratification structure of DR considering future climate scenarios of Representative Concentration Pathways (RCP). The random forest (RF)regression model (NSE 0.97, RMSE 1.86℃, MAPE 9.45%) developed to predict the inflow temperature of DR adequately reproduced the statistics and variability of the observed water temperature. Future meteorological data for each RCP scenario predicted by the regional climate model (HadGEM3-RA) was input into RF model to predict the inflow water temperature, and a three-dimensional hydrodynamic model (AEM3D) was used to predict the change in the future (2018~2037, 2038~2057, 2058~2077, 2078~2097) stratification structure of DR due to climate change. As a result, the rates of increase in air temperature and inflow water temperature was 0.14~0.48℃/10year and 0.21~0.41℃/10year,respectively. As a result of seasonal analysis, in all scenarios except spring and winter in the RCP 2.6, the increase in inflow water temperature was statistically significant, and the increase rate was higher as the carbon reduction effort was weaker. The increase rate of the surface water temperature of the reservoir was in the range of 0.04~0.38℃/10year, and the stratification period was gradually increased in all scenarios. In particular, when the RCP 8.5 scenario is applied, the number of stratification days is expected to increase by about 24 days. These results were consistent with the results of previous studies that climate change strengthens the stratification intensity of lakes and reservoirs and prolonged the stratification period, and suggested that prolonged water temperature stratification could cause changes in the aquatic ecosystem, such as spatial expansion of the low-oxygen layer, an increase in sediment nutrient release, and changed in the dominant species of algae in the water body.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.137-154
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• 2018

Animal infectious diseases, such as avian influenza and foot and mouth disease, occur almost every year and cause huge economic and social damage to the country. In order to prevent this, the anti-quarantine authorities have tried various human and material endeavors, but the infectious diseases have continued to occur. Avian influenza is known to be developed in 1878 and it rose as a national issue due to its high lethality. Food and mouth disease is considered as most critical animal infectious disease internationally. In a nation where this disease has not been spread, food and mouth disease is recognized as economic disease or political disease because it restricts international trade by making it complex to import processed and non-processed live stock, and also quarantine is costly. In a society where whole nation is connected by zone of life, there is no way to prevent the spread of infectious disease fully. Hence, there is a need to be aware of occurrence of the disease and to take action before it is distributed. Epidemiological investigation on definite diagnosis target is implemented and measures are taken to prevent the spread of disease according to the investigation results, simultaneously with the confirmation of both human infectious disease and animal infectious disease. The foundation of epidemiological investigation is figuring out to where one has been, and whom he or she has met. In a data perspective, this can be defined as an action taken to predict the cause of disease outbreak, outbreak location, and future infection, by collecting and analyzing geographic data and relation data. Recently, an attempt has been made to develop a prediction model of infectious disease by using Big Data and deep learning technology, but there is no active research on model building studies and case reports. KT and the Ministry of Science and ICT have been carrying out big data projects since 2014 as part of national R &D projects to analyze and predict the route of livestock related vehicles. To prevent animal infectious diseases, the researchers first developed a prediction model based on a regression analysis using vehicle movement data. After that, more accurate prediction model was constructed using machine learning algorithms such as Logistic Regression, Lasso, Support Vector Machine and Random Forest. In particular, the prediction model for 2017 added the risk of diffusion to the facilities, and the performance of the model was improved by considering the hyper-parameters of the modeling in various ways. Confusion Matrix and ROC Curve show that the model constructed in 2017 is superior to the machine learning model. The difference between the2016 model and the 2017 model is that visiting information on facilities such as feed factory and slaughter house, and information on bird livestock, which was limited to chicken and duck but now expanded to goose and quail, has been used for analysis in the later model. In addition, an explanation of the results was added to help the authorities in making decisions and to establish a basis for persuading stakeholders in 2017. This study reports an animal infectious disease prevention system which is constructed on the basis of hazardous vehicle movement, farm and environment Big Data. The significance of this study is that it describes the evolution process of the prediction model using Big Data which is used in the field and the model is expected to be more complete if the form of viruses is put into consideration. This will contribute to data utilization and analysis model development in related field. In addition, we expect that the system constructed in this study will provide more preventive and effective prevention.