• Economic and Environmental Geology
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• v.54 no.4
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• pp.409-425
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• 2021

Carbon dioxide has been known to be a typical greenhouse gas causing global warming, and a number of efforts have been proposed to reduce its concentration in the atmosphere. Among them, carbon dioxide capture and storage (CCS) has been taken into great account to accomplish the target reduction of carbon dioxide. In order to commercialize the CCS, its safety should be secured. In particular, if the stored carbon dioxide is leaked in the arable land, serious problems could come up in terms of crop growth. This study was conducted to investigate the effect of carbon dioxide leaked from storage sites on soil fertility. The leakage of carbon dioxide was simulated using the facility of its artificial injection into soils in the laboratory. Several soil chemical properties, such as pH, cation exchange capacity, electrical conductivity, the concentrations of exchangeable cations, nitrogen (N) (total-N, nitrate-N, and ammonia-N), phosphorus (P) (total-P and available-P), sulfur (S) (total-S and available-S), available-boron (B), and the contents of soil organic matter, were monitored as indicators of soil fertility during the period of artificial injection of carbon dioxide. Two kinds of soils, such as non-cultivated and cultivated soils, were compared in the artificial injection tests, and the latter included maize- and soybean-cultivated soils. The non-cultivated soil (NCS) was sandy soil of 42.6% porosity, the maize-cultivated soil (MCS) and soybean-cultivated soil (SCS) were loamy sand having 46.8% and 48.0% of porosities, respectively. The artificial injection facility had six columns: one was for the control without carbon dioxide injection, and the other five columns were used for the injections tests. Total injection periods for NCS and MCS/SCS were 60 and 70 days, respectively, and artificial rainfall events were simulated using one pore volume after the 12-day injection for the NCS and the 14-day injection for the MCS/SCS. After each rainfall event, the soil fertility indicators were measured for soil and leachate solution, and they were compared before and after the injection of carbon dioxide. The results indicate that the residual concentrations of exchangeable cations, total-N, total-P, the content of soil organic matter, and electrical conductivity were not likely to be affected by the injection of carbon dioxide. However, the residual concentrations of nitrate-N, ammonia-N, available-P, available-S, and available-B tended to decrease after the carbon dioxide injection, indicating that soil fertility might be reduced. Meanwhile, soil pH did not seem to be influenced due to the buffering capacity of soils, but it is speculated that a long-term leakage of carbon dioxide might bring about soil acidification.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.66 no.4
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• pp.307-317
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• 2021

In recent years, global warming has led to frequent climate change-related problems, and elevated temperatures, among adverse climatic factors, represent a critical problem negatively affecting crop growth and yield. In this context, the present study examined the physiological traits of wheat plants grown under high temperatures. Specifically, the effects of elevated temperatures on seed development after heading were evaluated, and the vegetation indices of different organs were assessed using hyperspectral analysis. Among physiological traits, leaf greenness and OJIP parameters were higher in the high-temperature treatment than in the control treatment. Similarly, the leaf photosynthetic rate during seed development was higher in the high-temperature treatment than in the control treatment. Moreover, temperature by organ was higher in the high-temperature treatment than in the control treatment; consequently, the leaf transpiration rate and stomatal conductance were higher in the control treatment than in the high-temperature treatment. On all measuring dates, the weight of spikes and seeds corresponding to the sink organs was greater in the high-temperature treatment than in the control treatment. Additionally, the seed growth rate was higher in the high-temperature treatment than in the control treatment 14 days after heading, which may be attributed to the higher redistribution of photosynthates at the early stage of seed development in the former. In hyperspectral analysis, the vegetation indices related to leaf chlorophyll content and nitrogen state were higher in the high-temperature treatment than in the control treatment after heading. Our results suggest that elevated temperatures after the booting stage positively affect wheat growth and yield.

• The Korean Journal of Nuclear Medicine Technology
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• v.25 no.2
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• pp.48-54
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• 2021

Purpose In ¹⁸F-FDG PET/CT, the absorption of ¹⁸F-FDG due to the activation of Brown Adipose Tissue (BAT) greatly interferes with the discrimination of lymph node malignant metastasis. Warming the patient's body temperature before and after injection of ¹⁸F-FDG to prevent FDG absorption by BAT is a safe and non-pharmacological approach. The purpose of this study was to identify and select patients with a high potential for BAT activation in advance, and to investigate whether BAT can inhibit FDG absorption when the body temperature is raised for a short time by directly applying heat to the target patient. Materials and Methods Among the patients who underwent ¹⁸F-FDG PET/CT at the National Cancer Center from January 2020 to December 2020, 825 female patients (415 in the thermal group, 410 in the non-thermal group) under 50 years old were included. The thermal group was administered heat for 10 minutes before injection of ¹⁸F-FDG. For statistical analysis, the Z test comparing the ratios between the two groups was used, and logistic regression analysis was performed to correct for important variables (BMI, outdoor temperature, blood sugar) according to the results of the previous retrospective study. Results Among 825 patients, 19 patients with BAT activated (Thermal group: 5(1.2%), Non-thermal group: 14(3.41%)) accounted for 2.3% of the total. As a result of performing the Z test to compare the ratios between the two groups, the activation of BAT in the thermal group was significantly decreased (P=0.034). In the univariate logistic regression analysis, the activation of BAT was also decreased in the thermal group (OR: 0.34, P<0.05). In the multivariate results, BAT activation increased in patients younger than 45 years old (OR: 4.46, P<0.05) and outdoor temperature less than 13.2 degrees (OR: 9.97, P<0.05). BAT activation tended to decrease in the thermal group, but there was no significant difference (OR: 0.37, P=0.066). Conclusion We confirmed that the activation of BAT tends to decrease by 62.5% in the group subjected to the thermal method, and it will be of great help in preventing FDG absorption of BAT more effectively in the future.

• Journal of the Korean earth science society
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• v.42 no.6
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• pp.632-645
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• 2021

Satellite sea surface temperature (SST) composites provide important data for numerical forecasting models and for research on global warming and climate change. In this study, six types of representative SST composite database were collected from 2007 to 2018 and the characteristics of spatial structures of SSTs were analyzed in seas around the Korean Peninsula. The SST composite data were compared with time series of in-situ measurements from ocean meteorological buoys of the Korea Meteorological Administration by analyzing the maximum value of the errors and its occurrence time at each buoy station. High differences between the SST data and in-situ measurements were detected in the western coastal stations, in particular Deokjeokdo and Chilbaldo, with a dominant annual or semi-annual cycle. In Pohang buoy, a high SST difference was observed in the summer of 2013, when cold water appeared in the surface layer due to strong upwelling. As a result of spectrum analysis of the time series SST data, daily satellite SSTs showed similar spectral energy from in-situ measurements at periods longer than one month approximately. On the other hand, the difference of spectral energy between the satellite SSTs and in-situ temperature tended to magnify as the temporal frequency increased. This suggests a possibility that satellite SST composite data may not adequately express the temporal variability of SST in the near-coastal area. The fronts from satellite SST images revealed the differences among the SST databases in terms of spatial structure and magnitude of the oceanic fronts. The spatial scale expressed by the SST composite field was investigated through spatial spectral analysis. As a result, the high-resolution SST composite images expressed the spatial structures of mesoscale ocean phenomena better than other low-resolution SST images. Therefore, in order to express the actual mesoscale ocean phenomenon in more detail, it is necessary to develop more advanced techniques for producing the SST composites.

• Korean Journal of Remote Sensing
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• v.38 no.3
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• pp.265-282
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• 2022

Sea surface temperature (SST) is a factor that greatly influences ocean circulation and ecosystems in the Earth system. As global warming causes changes in the SST near the Korean Peninsula, abnormal water temperature phenomena (high water temperature, low water temperature) occurs, causing continuous damage to the marine ecosystem and the fishery industry. Therefore, this study proposes a methodology to predict the SST near the Korean Peninsula and prevent damage by predicting abnormal water temperature phenomena. The study area was set near the Korean Peninsula, and ERA5 data from the European Center for Medium-Range Weather Forecasts (ECMWF) was used to utilize SST data at the same time period. As a research method, Long Short-Term Memory (LSTM) algorithm specialized for time series data prediction among deep learning models was used in consideration of the time series characteristics of SST data. The prediction model predicts the SST near the Korean Peninsula after 1- to 7-days and predicts the high water temperature or low water temperature phenomenon. To evaluate the accuracy of SST prediction, Coefficient of determination (R²), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) indicators were used. The summer (JAS) 1-day prediction result of the prediction model, R²=0.996, RMSE=0.119℃, MAPE=0.352% and the winter (JFM) 1-day prediction result is R²=0.999, RMSE=0.063℃, MAPE=0.646%. Using the predicted SST, the accuracy of abnormal sea surface temperature prediction was evaluated with an F1 Score (F1 Score=0.98 for high water temperature prediction in summer (2021/08/05), F1 Score=1.0 for low water temperature prediction in winter (2021/02/19)). As the prediction period increased, the prediction model showed a tendency to underestimate the SST, which also reduced the accuracy of the abnormal water temperature prediction. Therefore, it is judged that it is necessary to analyze the cause of underestimation of the predictive model in the future and study to improve the prediction accuracy.

• Korean Journal of Remote Sensing
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• v.38 no.2
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• pp.179-188
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• 2022

Global warming causes climate change and increases extreme weather events worldwide, and the occurrence of heatwaves and droughts is also increasing in Korea. For the monitoring of extreme weather, various satellite data such as LST (Land Surface Temperature), TCI (Temperature Condition Index), NDVI (Normalized Difference Vegetation Index), VCI (Vegetation Condition Index), and VHI (Vegetation Health Index) have been used. VHI, the combination of TCI and VCI, represents the vegetation stress affected by meteorological factors like precipitation and temperature and is frequently used to assess droughts under climate change. TCI and VCI require historical reference values for the LST and NDVI for each date and location. So, it is complicated to produce the VHI from the recent satellite GK2A (Geostationary Korea Multi-Purpose Satellite-2A). This study examined the retrieval of VHI using GK2A AMI (Advanced Meteorological Imager) by referencing the historical data from VIIRS (Visible Infrared Imaging Radiometer Suite) NDVI and LST as a proxy data. We found a close relationship between GK2A and VIIRS data needed for the retrieval of VHI. We produced the TCI, VCI, and VHI for GK2A during 2020-2021 at intervals of 8 days and carried out the interpretations of recent extreme weather events in Korea. GK2A VHI could express the changes in vegetation stress in 2020 due to various extreme weather events such as heatwaves (in March and June) and low temperatures (in April and July), and heavy rainfall (in August), while NOAA (National Oceanic and Atmospheric Administration) VHI could not well represent such characteristics. The GK2A VHI presented in this study can be utilized to monitor the vegetation stress due to heatwaves and droughts if the historical reference values of LST and NDVI can be adjusted in a more statistically significant way in the future work.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1107-1118
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• 2021

Climate change brought on by global warming increased the frequency of flood and drought on the Korean Peninsula, along with the casualties and physical damage resulting therefrom. Preparation and response to these water disasters requires national-level planning for water resource management. In addition, watershed-level management of water resources requires flow duration curves (FDC) derived from continuous data based on long-term observations. Traditionally, in water resource studies, physical rainfall-runoff models are widely used to generate duration curves. However, a number of recent studies explored the use of data-based deep learning techniques for runoff prediction. Physical models produce hydraulically and hydrologically reliable results. However, these models require a high level of understanding and may also take longer to operate. On the other hand, data-based deep-learning techniques offer the benefit if less input data requirement and shorter operation time. However, the relationship between input and output data is processed in a black box, making it impossible to consider hydraulic and hydrological characteristics. This study chose one from each category. For the physical model, this study calculated long-term data without missing data using parameter calibration of the Soil Water Assessment Tool (SWAT), a physical model tested for its applicability in Korea and other countries. The data was used as training data for the Long Short-Term Memory (LSTM) data-based deep learning technique. An anlysis of the time-series data fond that, during the calibration period (2017-18), the Nash-Sutcliffe Efficiency (NSE) and the determinanation coefficient for fit comparison were high at 0.04 and 0.03, respectively, indicating that the SWAT results are superior to the LSTM results. In addition, the annual time-series data from the models were sorted in the descending order, and the resulting flow duration curves were compared with the duration curves based on the observed flow, and the NSE for the SWAT and the LSTM models were 0.95 and 0.91, respectively, and the determination coefficients were 0.96 and 0.92, respectively. The findings indicate that both models yield good performance. Even though the LSTM requires improved simulation accuracy in the low flow sections, the LSTM appears to be widely applicable to calculating flow duration curves for large basins that require longer time for model development and operation due to vast data input, and non-measured basins with insufficient input data.

• 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.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.3
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• pp.145-154
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• 2022

Due to abnormal weather conditions caused by climate change, natural disasters and damages are gradually increasing around the world. Global climate change as accompanied by warming is projected to exert adverse impact on production of potato, which is known as cool season crop. Even though, role of potato as a food security crop is expected to increase in the future, the climate change impacts on potato and adaption strategies are not sufficiently established. Therefore, this study was conducted to analyze the damage pattern of potatoes due to high temperature treatment and to evaluate the response of cultivars. T he high temperature treatment (35~38℃) induced heat stress by sealing the plastic house in midsummer (July), and the quantity and quality characteristics of potatoes were compared with the control group. T otal yield, marketable yield (>80 g) and the number of tubers per plants decreased when heat treatment was performed, and statistical significance was evident. In the heat treatment, 'Jayoung' cultivar suffered a high heat damage with an 84% reduction in yield of >80 g compared to the control group. However, in Jopung cultivar, the decrease was relatively small at 26%. Tuber physiological disturbances (Secondary growth, Tuber cracking, Malformation) tended to increase in the heat stress. Under heat conditions, the tubers were elongated overall, which means that the marketability of potatoes was lowered. T he tuber firmness and dry matter content tended to decrease significantly in the heat-treated group. T herefore, the yield and quality of tubers were damaged when growing potatoes in heat conditions. T he cultivar with high heat adaptability was 'Jopung'. T his result can be used as basic data for potato growers and breeding of heat-resistant cultivars.

• Journal of Appropriate Technology
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• v.7 no.2
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• pp.127-135
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• 2021

To respond to the threat of global warming, countries around the world are promoting the spread of renewable energy and reduction of carbon emissions. In accordance with the United Nation's Sustainable Development Goal to combat climate change and its impacts, global automakers are pushing for a full transition to electric vehicles within the next 10 years. Electric vehicles can be a useful means for reducing carbon emissions, but in order to reduce carbon generated in the stage of producing electricity for charging, a power generation system using eco-friendly renewable energy is required. In this study, we propose a smart electric mobility operating system integrated with off-grid solar power plants established in Tanzania, Africa. By applying smart monitoring and communication functions based on Arduino-based computing devices, information such as remaining battery capacity, battery status, location, speed, altitude, and road conditions of an electric vehicle or electric motorcycle is monitored. In addition, we present a scenario that communicates with the surrounding independent solar power plant infrastructure to predict the drivable distance and optimize the charging schedule and route to the destination. The feasibility of the proposed system was verified through test runs of electric motorcycles. In considering local environmental characteristics in Tanzania for the operation of the electric mobility system, factors such as eco-friendliness, economic feasibility, ease of operation, and compatibility should be weighed. The smart electric mobility operating system proposed in this study can be an important basis for implementing the SDGs' climate change response.