• Journal of Korea Water Resources Association
• /
• v.51 no.spc
• /
• pp.1091-1103
• /
• 2018

Having knowledge regarding to which region is prone to drought or flood is a crucial issue in water resources planning and management. This could be more challenging when the occurrence of these hazards affected by climate change. In this study the future streamflow during the wet season (July to September) and dry season (October to March) for the twenty first century of South Korea was investigated. This study used the statistics of precipitation, maximum and minimum temperature of one global climate model (i.e., INMCM4) with 2 RCPs (RCP4.5 and RCP8.5) scenarios as inputs for The Precipitation-Runoff Modelling System (PRMS) model. The PRMS model was tested for the historical periods (1966-2016) and then the parameters of model were used to project the future changes of 5 large River basins in Korea for three future periods (2025s, 2055s, and 2085s) compared to the reference period (1976-2005). Then, the different responses in climate and streamflow projection during these two seasons (wet and dry) was investigated. The results showed that under INMCM4 scenario, the occurrence of drought in dry season is projected to be stronger in 2025s than 2055s from decreasing -7.23% (-7.06%) in 2025s to -3.81% (-0.71%) in 2055s for RCP4.5 (RCP8.5). Regarding to the far future (2085s), for RCP 4.5 is projected to increase streamflow in the northern part, and decrease streamflow in the southern part (-3.24%), however under RCP8.5 almost all basins are vulnerable to drought, especially in the southern part (-16.51%). Also, during the wet season both increasing (Almost in northern and western part) and decreasing (almost in the southern part) in streamflow relative to the reference period are projected for all periods and RCPs under INMCM4 scenario.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.24 no.2
• /
• pp.12-28
• /
• 2021

Based on the global smart city promotion trend, in 2018, the "Fourth Industrial Revolution Committee" selected "sustainability" and "people-centered" as keywords in relation to the direction of domestic smart city policy. Accordingly, the Living Lab program, which is an active citizen-centered innovation methodology, is applied to each stage of the domestic smart city construction project. Through the Living Lab program, and in collaboration with the public and experts, the smart city discovers local issues as it focuses on citizens, devises solutions to sustainable urban problems, and formulates a regional development plan that reflects the needs of citizens. However, compared to citizen participation in urban regeneration projects that have been operated for a relatively long time, participation in smart city projects was found to significantly differ in level and sustainability. Therefore, this study conducted a comparative analysis of the characteristics of citizen participation at each stage of an urban regeneration project and, based on Arnstein's "Participation Ladder" model, examined the level of citizen participation activities in the Living Lab program carried out in a smart city commercial area from 2018 to 2019. The results indicated that citizen participation activities in the Living Lab conducted in the smart city project had a great influence on selecting smart city services, which fit the needs of local residents, and on determining the technological level of services appropriate to the region based on a relatively high level of authority, such as selection of smart city services or composition of solutions. However, most of the citizen participation activities were halted after the project's completion due to the one-off recruitment of citizen participation groups for the smart city construction project only. On the other hand, citizens' participation activities in the field of urban regeneration were focused on local communities, and continuous operation and management measures were being drawn from the project planning stage to the operation stage after the project was completed. This study presented a plan to revitalize citizen participation for the realization of a more sustainable smart city through a comparison of the characteristics and an examination of the level of citizen participation in such urban regeneration and smart city projects.

• Journal of Wetlands Research
• /
• v.24 no.2
• /
• pp.83-92
• /
• 2022

The amount of the plastic waste has been increasing according to global demand for plastic. Microplastics are the most hazardous among all plastic pollutants due to their toxicity and unknown physicochemical properties. This study investigates the optimal methodology that can be applied to sewage samples for detecting microplastics before discussing reducing microplastics in MWTPs. In this study, the effect of different pretreatment methods while detecting microplastic analysis of MWTP influent samples was investigated; the samples were collected from the J sewage treatment plant. There are many pretreatment methods but two of them are widely used: Fenton digestion and hydrogen peroxide oxidation. Although there are many pretreatment methods that can be applied to investigate microplastics, the most widely used methods for sewage treatment plant samples are Fenton digestion and H₂O₂ oxidation. For each pretreatment method, there were factors that could cause an error in the measurement. To overcome this, in the case of the Fenton digestion pretreatment, it is recommended to proceed with the analysis by filtration instead of the density separation method. In the case of the H₂O₂ oxidation method, the process of washing with distilled water after the reaction is recommended. As a result of the analysis, the concentration of microplastics was measured to be 2.75ea/L for the sample using the H₂O₂ oxidation method and 3.2ea/L for the sample using the Fenton oxidation method, and most of them were present in the form of fibers. In addition, it is difficult to guarantee the reliability of measurement results from quantitative analysis performed via microscope with eyes. A calibration curve was created for prove the reliability. A total of three calibration curves were drawn, and as a result of analysis of the calibration curves, all R² values were more than 0.9. This ensures high reliability for quantitative analysis. The qualitative analysis could determine the series of microplastics flowing into the MWTP, but could not confirm the chemical composition of each microplastic. This study can be used to confirm the chemical composition of microplastics introduced into MWTP in the future research.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.28 no.5
• /
• pp.746-752
• /
• 2022

Kiribati, a South Pacific island, and its surrounding countries are gradually submerging to rising sea levels. The sea level continues to change according to the degree of thermal expansion of glaciers and seawater that decreases with increase in temperature. Global warming affects both the amount and volume of seawater, thus increasing sea level. Tidal phenomena occur twice a day to the attraction of celestial bodies such as the moon and the sun. The moon changes the angle of orbiting surface with the Earth equator every 18.6 years, and the magnitude of the tidal force changes depending on the distance between the Earth equator and the moon orbital surface. The University of Hawaii Sea Level Center selected Tarawa, Christmas, Kanton of Kiribati,, Lautoka, Suva of Fiji,Funafuti of Tuvalu, Nuk1u'alofa of Tonga, and Port Vila of Vanuatu. When comparing tide levels for each year for 19 years, the focus was on checking the change in sleep to Tide levels, and rising sea levels was the effect of Tide levels. The highest astronomical tides (HAT) and lowest astronomical tides (LAT) were identified as Tarawa 297.0, 50.8 cm, Christmas 123.8, 19.9 cm, Kanton 173.7, 39.9 cm, Lautoka 240.7, 11.3 cm, Funafuti 328.6, 98.4 cm, Nuk1u'alofa 188.8, 15.5 cm, Port Vila 161.5, -0.5cm, respectively. The Sea level rising speed was Tarawa 3.1 mm/year, Christmas -1.0 mm/year, Kanton 1.6 mm/year, Lautoka 3.1 mm/year, Suva 7.4 mm/year, Funafuti 1.4 mm/year, Nuk1u'alofa 4.2 mm/year, and Port Vila -1.2 mm/year, respectively

• Journal of Korea Water Resources Association
• /
• v.56 no.9
• /
• pp.543-551
• /
• 2023

Snow is an essential climate factor that affects the climate system and surface energy balance, and it also has a crucial role in water balance by providing solid water stored during the winter for spring runoff and groundwater recharge. In this study, statistical analysis of Local Data Assimilation and Prediction System (LDAPS), Modern.-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), and ERA5-Land snow depth data were used to evaluate the applicability in South Korea. The statistical analysis between the Automated Synoptic Observing System (ASOS) ground observation data provided by the Korea Meteorological Administration (KMA) and the reanalysis data showed that LDAPS and ERA5-Land were highly correlated with a correlation coefficient of more than 0.69, but LDAPS showed a large error with an RMSE of 0.79 m. In the case of MERRA-2, the correlation coefficient was lower at 0.17 because the constant value was estimated continuously for some periods, which did not adequately simulate the increase and decrease trend between data. The statistical analysis of LDAPS and ASOS showed high and low performance in the nearby Gangwon Province, where the average snowfall is relatively high, and in the southern region, where the average snowfall is low, respectively. Finally, the error variance between the four independent snow depth data used in this study was calculated through triple collocation (TC), and a merged snow depth data was produced through weighting factors. The reanalyzed data showed the highest error variance in the order of LDAPS, MERRA-2, and ERA5-Land, and LDAPS was given a lower weighting factor due to its higher error variance. In addition, the spatial distribution of ERA5-Land snow depth data showed less variability, so the TC-merged snow depth data showed a similar spatial distribution to MERRA-2, which has a low spatial resolution. Considering the correlation, error, and uncertainty of the data, the ERA5-Land data is suitable for snow-related analysis in South Korea. In addition, it is expected that LDAPS data, which is highly correlated with other data but tends to be overestimated, can be actively utilized for high-resolution representation of regional and climatic diversity if appropriate corrections are performed.

• Journal of Environmental Impact Assessment
• /
• v.32 no.6
• /
• pp.377-388
• /
• 2023

Carbon management is emerging as an important factor for global warming control, and land use change is considered one of the causes. To quantify the changes in carbon stocks due to development, this study attempted to calculate carbon storage by borrowing the formula of the InVEST Carbon Storage and Sequestration Model (InVEST Model). Before analyzing carbon stocks, a carbon pool was compiled based on previous studies in Korea. Then, we estimated the change in carbon stocks according to the development of Osong National Industrial Park (ONIP) and the application of alternatives. The analysis shows that 16,789.5 MgC will be emitted under Alternative 1 and 16,305.3 MgC under Alternative 2. These emissions account for 44.4% and 43.1% of the pre-project carbon stock, respectively, and shows that choosing Alternative 2 is advantageous for reducing carbon emissions. The difference is likely due to the difference in grassland area between Alternatives 1 and 2. Even if Alternative 2 is selected, efforts are needed to increase the carbon storage effect by managing the appropriate level of green cover in the grassland, creating multi-layered vegetation, and installing low-energy facilities. In addition, it is suggested to conserve wetlands that can be lost during the stream improvement process or to create artificial wetlands to increase carbon storage. The assessment of carbon storage using carbon pools by land cover can improve the objectivity of comparison and evaluation analysis results for land use plans in Environmental Impact Assessment and Strategic Environmental Impact Assessment. In addition, the carbon pool generated in this study is expected to be used as a basis for improving the accuracy of such analyses.

• Journal of the Korean Institute of Gas
• /
• v.27 no.3
• /
• pp.52-58
• /
• 2023

With the increasing awareness of the importance of carbon neutrality in response to global climate change, the utilization of hydrogen as a carbon-free fuel source is also growing. Hydrogen is commonly used in fuel cells (FC), but it can also be utilized in internal combustion engines (ICE) that are based on combustion. Particularly, ICEs that already have established infrastructure for production and supply can greatly contribute to the expansion of hydrogen energy utilization when it becomes difficult to rely solely on fuel cells or expand their infrastructure. However, a disadvantage of utilizing hydrogen through combustion is the potential generation of nitrogen oxides (NOx), which are harmful emissions formed when nitrogen in the air reacts with oxygen at high temperatures. In particular, for the EURO-7 exhaust regulation, which includes cold start operation, efforts to reduce exhaust emissions during the warm-up process are required. Therefore, in this study, the characteristics of nitrogen oxides and fuel consumption were investigated during the warm-up process of cooling water from room temperature to 88℃ using a 2-liter direct injection spark ignition (SI) engine fueled with hydrogen. One advantage of hydrogen, compared to conventional fuels like gasoline, natural gas, and liquefied petroleum gas (LPG), is its wide flammable range, which allows for sparser control of the excessive air ratio. In this study, the excessive air ratio was varied as 1.6/1.8/2.0 during the warm-up process, and the results were analyzed. The experimental results show that as the excessive air ratio becomes sparser during warm-up, the emission of nitrogen oxides per unit time decreases, and the thermal efficiency relatively increases. However, as the time required to reach the final temperature becomes longer, the cumulative emissions and fuel consumption may worsen.

• Korean Journal of Soil Science and Fertilizer
• /
• v.45 no.5
• /
• pp.853-860
• /
• 2012

To estimate potential use of fly ash in reducing $CH_4$ and $CO_2$ emission from soil, $CH_4$ and $CO_2$ fluxes from a paddy soil mixed with fly ash at different rate (w/w; 0, 5, and 10%) in the presence and absence of fertilizer N ($(NH_4)_2SO_4$) addition were investigated in a laboratory incubation for 60 days under changing water regime from wetting to drying via transition. The mean $CH_4$ flux during the entire incubation period ranged from 0.59 to $1.68mg\;CH_4\;m^{-2}day^{-1}$ with a lower rate in the soil treated with N fertilizer due to suppression of $CH_4$ production by $SO_4^{2-}$ that acts as an electron acceptor, leading to decreases in electron availability for methanogen. Fly ash application reduced $CH_4$ flux by 37.5 and 33.0% in soils without and with N addition, respectively, probably due to retardation of $CH_4$ diffusion through soil pores by addition of fine-textured fly ash. In addition, as fly ash has a potential for $CO_2$ removal via carbonation (formation of carbonate precipitates) that decreases $CO_2$ availability that is a substrate for $CO_2$ reduction reaction (one of $CH_4$ generation pathways) is likely to be another mechanisms of $CH_4$ flux reduction by fly ash. Meanwhile, the mean $CO_2$ flux during the entire incubation period was between 0.64 and $0.90g\;CO_2\;m^{-2}day^{-1}$, and that of N treated soil was lower than that without N addition. Because N addition is likely to increase soil respiration, it is not straightforward to explain the results. However, it may be possible that our experiment did not account for the substantial amount of $CO_2$ produced by heterotrophs that were activated by N addition in earlier period than the measurement was initiated. Fly ash application also lowered $CO_2$ flux by up to 20% in the soil mixed with fly ash at 10% through $CO_2$ removal by the carbonation. At the whole picture, fly ash application at 10% decreased global warming potential of emitted $CH_4$ and $CO_2$ by about 20%. Therefore, our results suggest that fly ash application can be a soil management practice to reduce green house gas emission from paddy soils. Further studies under field conditions with rice cultivation are necessary to verify our findings.

• Journal of Intelligence and Information Systems
• /
• v.25 no.2
• /
• pp.123-139
• /
• 2019

The Korean NPL market was formed by the government and foreign capital shortly after the 1997 IMF crisis. However, this market is short-lived, as the bad debt has started to increase after the global financial crisis in 2009 due to the real economic recession. NPL has become a major investment in the market in recent years when the domestic capital market's investment capital began to enter the NPL market in earnest. Although the domestic NPL market has received considerable attention due to the overheating of the NPL market in recent years, research on the NPL market has been abrupt since the history of capital market investment in the domestic NPL market is short. In addition, decision-making through more scientific and systematic analysis is required due to the decline in profitability and the price fluctuation due to the fluctuation of the real estate business. In this study, we propose a prediction model that can determine the achievement of the benchmark yield by using the NPL market related data in accordance with the market demand. In order to build the model, we used Korean NPL data from December 2013 to December 2017 for about 4 years. The total number of things data was 2291. As independent variables, only the variables related to the dependent variable were selected for the 11 variables that indicate the characteristics of the real estate. In order to select the variables, one to one t-test and logistic regression stepwise and decision tree were performed. Seven independent variables (purchase year, SPC (Special Purpose Company), municipality, appraisal value, purchase cost, OPB (Outstanding Principle Balance), HP (Holding Period)). The dependent variable is a bivariate variable that indicates whether the benchmark rate is reached. This is because the accuracy of the model predicting the binomial variables is higher than the model predicting the continuous variables, and the accuracy of these models is directly related to the effectiveness of the model. In addition, in the case of a special purpose company, whether or not to purchase the property is the main concern. Therefore, whether or not to achieve a certain level of return is enough to make a decision. For the dependent variable, we constructed and compared the predictive model by calculating the dependent variable by adjusting the numerical value to ascertain whether 12%, which is the standard rate of return used in the industry, is a meaningful reference value. As a result, it was found that the hit ratio average of the predictive model constructed using the dependent variable calculated by the 12% standard rate of return was the best at 64.60%. In order to propose an optimal prediction model based on the determined dependent variables and 7 independent variables, we construct a prediction model by applying the five methodologies of discriminant analysis, logistic regression analysis, decision tree, artificial neural network, and genetic algorithm linear model we tried to compare them. To do this, 10 sets of training data and testing data were extracted using 10 fold validation method. After building the model using this data, the hit ratio of each set was averaged and the performance was compared. As a result, the hit ratio average of prediction models constructed by using discriminant analysis, logistic regression model, decision tree, artificial neural network, and genetic algorithm linear model were 64.40%, 65.12%, 63.54%, 67.40%, and 60.51%, respectively. It was confirmed that the model using the artificial neural network is the best. Through this study, it is proved that it is effective to utilize 7 independent variables and artificial neural network prediction model in the future NPL market. The proposed model predicts that the 12% return of new things will be achieved beforehand, which will help the special purpose companies make investment decisions. Furthermore, we anticipate that the NPL market will be liquidated as the transaction proceeds at an appropriate price.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.33 no.6
• /
• pp.2255-2265
• /
• 2013

This study presents the width function-based Clark model. To this end, rescaled width function with distinction between hillslope and channel velocity is used as time-area curve and then it is routed through linear storage within the framework of not finite difference scheme used in original Clark model but analytical expression of linear storage routing. There are three parameters focused in this study: storage coefficient, hillslope velocity and channel velocity. SCE-UA, one of the popular global optimization methods, is applied to estimate them. The shapes of resulting IUHs from this study are evaluated in terms of the three statistical moments of hydrologic response functions: mean, variance and the third moment about the center of IUH. The correlation coefficients to the three statistical moments simulated in this study against these of observed hydrographs were estimated at 0.995 for the mean, 0.993 for the variance and 0.983 for the third moment about the center of IUH. The shape of resulting IUHs from this study give rise to satisfactory simulation results in terms of the mean and variance. But the third moment about the center of IUH tend to be overestimated. Clark model proposed in this study is superior to the one only taking into account mean and variance of IUH with respect to skewness, peak discharge and peak time of runoff hydrograph. From this result it is confirmed that the method suggested in this study is useful tool to reflect the heterogeneity of drainage path and hydrodynamic parameters. The variation of statistical moments of IUH are mainly influenced by storage coefficient and in turn the effect of channel velocity is greater than the one of hillslope velocity. Therefore storage coefficient and channel velocity are the crucial factors in shaping the form of IUH and should be considered carefully to apply Clark model proposed in this study.