• Journal of Korea Water Resources Association
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• v.54 no.9
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• pp.719-730
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• 2021

The objective of this study is to develop the water quality and aquatic ecosystem model for Andong lake using SWAT-WET (Soil and Water Assessment Tool-Water Ecosystem Tool) and to evaluate the applicability of WET. To quantify the pollutants load flowing into Andong lake, a watershed model of SWAT was constructed for Andong Dam basin (1,584 km²). The calibration results for Dam inflow and water quality loads (SS, T-N, T-P) were analyzed that average R² was more than 0.76, 0.69, 0.84, and 0.60 respectively. The calibrated SWAT results of streamflow and nutrients concentration was used into WET input data. WET was calibrated and validated for water temperature, dissolved oxygen, and water quality concentration (T-N, T-P) of Andong lake. The WET calibrated results was analyzed that PBIAS was +19%, -13%, +4%, and +26.5% respectively and showed that it was simulated to a significant level compared with the observation data. The observed dry weight (gDW/m²) of zoobenthos was less than 0.5, but the average value of simulation was analyzed to be 0.8, which is because the WET model considers zoobenthos with a broader concept. Although accurate calibration is difficult due to the lack of observed data, SWAT-WET can analyze the effects of environmental change in the upstream watershed on the lake based on long-term simulation based on watershed model. Therefore, the results of this study can be used as basic data for managing the aquatic environment of Andong lake.

• 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 the Korean earth science society
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• v.43 no.1
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• pp.91-109
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• 2022

The ocean is a significant sink for atmospheric anthropogenic CO₂, absorbing one-third of the total CO₂ emitted by human activities. In return, oceans have experienced significant declines in seawater pH and the aragonite saturation state also called ocean acidification. This study evaluates the distribution of aragonite saturation state, an indicator to assess the potential threat from ocean acidification, by combining newly obtained data from the west coast of South Korea with previous datasets covering the Yellow Sea, East Sea, northern South China Sea, and southeast coast of South Korea. In general, offshore waters absorb atmospheric CO₂; however, most of the collected water samples show aragonite oversaturation. On the southeast coast, the aragonite saturation state was significantly affected by river discharge and associated variables, such as freshwater input with nutrients, seasonal stratification, biological carbon fixation, and bacterial remineralization. In summer, hypoxia and mixing with relatively acidic freshwater made the Jinhae and Gwangyang Bays undersaturated with respect to aragonite, possibly threatening marine organisms with CaCO₃ shells. However, widespread aragonite undersaturation was not observed on the west coast, which receives considerable river water discharge. In addition, occasional upwelling events may have worsened the ocean acidification in the southwestern part of the East Sea. These results highlight the importance of investigating site-specific ocean acidification processes in coastal waters. Along with the above-mentioned seasonal factors, the dissolution of atmospheric CO₂ and the deposition of atmospheric acidic substances will continue to reduce the aragonite saturation state in Korean waters. To protect marine ecosystems and resources, an ocean acidification monitoring program should be established for Korean waters.

• Journal of IKEEE
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• v.27 no.1
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• pp.116-121
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• 2023

In this paper, we develop a deep learning structure for a complex microbial incubator that applies deep learning prediction result information. The proposed complex microbial incubator consists of pre-processing of complex microbial data, conversion of complex microbial data structure, design of deep learning network, learning of the designed deep learning network, and GUI development applied to the prototype. In the complex microbial data preprocessing, one-hot encoding is performed on the amount of molasses, nutrients, plant extract, salt, etc. required for microbial culture, and the maximum-minimum normalization method for the pH concentration measured as a result of the culture and the number of microbial cells to preprocess the data. In the complex microbial data structure conversion, the preprocessed data is converted into a graph structure by connecting the water temperature and the number of microbial cells, and then expressed as an adjacency matrix and attribute information to be used as input data for a deep learning network. In deep learning network design, complex microbial data is learned by designing a graph convolutional network specialized for graph structures. The designed deep learning network uses a cosine loss function to proceed with learning in the direction of minimizing the error that occurs during learning. GUI development applied to the prototype shows the target pH concentration (3.8 or less) and the number of cells (10⁸ or more) of complex microorganisms in an order suitable for culturing according to the water temperature selected by the user. In order to evaluate the performance of the proposed microbial incubator, the results of experiments conducted by authorized testing institutes showed that the average pH was 3.7 and the number of cells of complex microorganisms was 1.7 × 10⁸. Therefore, the effectiveness of the deep learning structure for the complex microbial incubator applying the deep learning prediction result information proposed in this paper was proven.

• Journal of Bio-Environment Control
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• v.32 no.2
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• pp.97-105
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• 2023

Concentration of nitrogen, one of the major elements, and ratio of two nitrogen forms (NH₄⁺ and NO₃^-) in the nutrient solution affect the quality and food safety of fresh vegetable produce. This study was conducted to find an appropriate strength and NH₄⁺:NO₃^- ratio of a nutrient solution for growth and development of a Romaine lettuce (Lactuca sativa L. var. longiflora) 'Caesar Green', a representative leafy vegetable, grown in a home hydroponic system. In the first experiment, plants were grown using three types of nutrient solution: A commercial nutrient solution (Peters) and two strengths (GNU1 and GNU2) of a multipurpose nutrient solution (GNU solution) developed in a Gyeongsang National University lab. Plants grown with the GNU1 and GNU2 had greater shoot length, leaf length and width, and biomass yield than Peters. On the other hand, the root hairs of plants grown with Peters were short and dark in color. Tissue NH₄⁺ content in the Peters was higher than that of the GNU1 and GNU2. The higher contents of NH₄⁺ in this solution may have caused ammonium toxicity. In the second experiment, eight treatment solutions, combining GNU1 and GNU2 solutions with four ratios of NO₃^- :NH₄⁺ named as 1, 2, 3 and 4 were used. Both experiments showed more growth in the GNU2 group, which had a relatively low ionic strength of the nutrient solution. The growth of Romaine lettuce showed the greatest fresh weight along with low tissue NO₃^- content in the GNU2-2. This was more advantageous in terms of food safety in that it suppressed the accumulation of surplus NO₃^- in tissues due to the low ionic trength of the GNU2 subgroup. In addition, this is preferable in that it can reduce the absolute amount of the input of inorganic nutrients to the nutrient solution.