• Journal of the Korean Society of Safety
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• v.35 no.6
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• pp.15-24
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• 2020

With the development of the chemical industry, the chemical accident is increasing every year, thereby increasing the risk of accidents caused by chemicals. The Ministry of Environment provides the criteria for determining shelter-in-place or outdoor evacuation by material, duration of accident, and distance from the toxic substance leak. However, it is hard to say that the criteria for determining the transition point are not clear. Transition point mean the time that evacuation method is switched from shelter-in-place to outdoor evacuation. So, the purpose of this study was to calculate appropriate transition point by comparing the cumulative toxic load. Namdong-gu in Incheon Metropolitan City was finally selected as the target area, considering the current status of the population of Incheon Metropolitan City in 2016 and the statistical survey of chemicals in 2016. The target materials were HCl, HF, and NH₃. Modeling was simulated by ALOHA and performed assuming that the entire amount would be leaked for 10 min. Residents' evacuation scenarios were assumed to be shelter-in-place, immediate outdoor evacuation, and outdoor evacuation at an appropriate time after shelter-in-place. Based on the above method, the appropriate transition point from residents located in A(800 m away), B(1,200 m away), C(1,400 m away) and D(2,200 m away) was identified. In HCl, appropriate transition point was after 15 min, after 16 min, after 17 min, after 20 min in order by A, B, C and D. In HF, appropriate transition point was before 1 min or after 16 min, before 4 min or after 19 min, before 5 min or after 20 min, before 14 min or after 26 min in order by A, B, C and D. In NH₃, appropriate transition point at A was before 4 min or after 16. Others are not in chemical cloud. This study confirmed the transition point to minimize the cumulative toxic load can be obtained by quantitative method. Through this, it might be possible to select evacuation method quantitatively that cumulative toxic load are minimal. In addition, if the shelter-in-place is maintained without transition to outdoor evacuation, the cumulative toxic load will increase more than outdoor evacuation. Therefore, it was confirmed that actions to reduce the concentration of chemicals in the room were necessary, such as conducting ventilation after the chemical cloud passed through the site.

• Journal of Korean Society on Water Environment
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• v.38 no.1
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• pp.19-30
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• 2022

Climate change has increased the average air temperature. Rising air temperature are absorbed by water bodies, leading to increasing water temperature. Increased water temperature will cause eutrophication and excess algal growth, which will reduce water quality. In this study, long-term trends of air and water temperatures in the Han-river basin over the period of 1997-2020 were discussed to assess the impacts of climate change. Future (~2100s) levels of air temperature were predicted based on the climate change scenarios (Representative concentration pathway (RCP) 2.6, 4.5, 6.0, and 8.5). The results showed that air and water temperatures rose at an average rate of 0.027℃ year^-1 and 0.038℃ year^-1 respectively, over the past 24 years (1997 to 2020). Future air temperatures under RCP 2.6, 4.5, 6.0, and 8.5 increased up to 0.32℃ 1.18℃, 2.14℃, and 3.51℃, respectively. An increasing water temperature could dissolve more minerals from the surrounding rock and will therefore have a higher electrical conductivity. It is the opposite when considering a gas, such as oxygen, dissolved in the water. Water temperature also governs the kinds of organisms that can live in rivers and lakes. Fish, insects, zooplankton, phytoplankton, and other aquatic species all have a preferred temperature range. As temperatures get too far above or below this preferred range, the number of individuals of the species decreases until finally there are none. Therefore, changes of water temperature that are induced by climate change have important implications on water supplies, water quality, and aquatic ecosystems of a watershed.

• Journal of Korean Society of Disaster and Security
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• v.14 no.4
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• pp.17-27
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• 2021

Streamflow prediction is a very vital disaster mitigation approach for effective flood management and water resources planning. Lately, torrential rainfall caused by climate change has been reported to have increased globally, thereby causing enormous infrastructural loss, properties and lives. This study evaluates the contribution of rainfall to streamflow prediction in normal and peak rainfall scenarios, typical of the recent flood at Piney Resort in Vernon, Hickman County, Tennessee, United States. Daily streamflow, water level, and rainfall data for 20 years (2000-2019) from two USGS gage stations (03602500 upstream and 03599500 downstream) of the Piney River watershed were obtained, preprocesssed and fitted with Long short term memory (LSTM) model. Tensorflow and Keras machine learning frameworks were used with Python to predict streamflow values with a sequence size of 14 days, to determine whether the model could have predicted the flooding event in August 21, 2021. Model skill analysis showed that LSTM model with full data (water level, streamflow and rainfall) performed better than the Naive Model except some rainfall models, indicating that only rainfall is insufficient for streamflow prediction. The final LSTM model recorded optimal NSE and RMSE values of 0.68 and 13.84 m³/s and predicted peak flow with the lowest prediction error of 11.6%, indicating that the final model could have predicted the flood on August 24, 2021 given a peak rainfall scenario. Adequate knowledge of rainfall patterns will guide hydrologists and disaster prevention managers in designing efficient early warning systems and policies aimed at mitigating flood risks.

• Korean Journal of Radiology
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• v.22 no.11
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• pp.1764-1776
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• 2021

Objective: This study aimed to validate a deep learning-based fully automatic calcium scoring (coronary artery calcium [CAC]_auto) system using previously published cardiac computed tomography (CT) cohort data with the manually segmented coronary calcium scoring (CAC_hand) system as the reference standard. Materials and Methods: We developed the CAC_auto system using 100 co-registered, non-enhanced and contrast-enhanced CT scans. For the validation of the CAC_auto system, three previously published CT cohorts (n = 2985) were chosen to represent different clinical scenarios (i.e., 2647 asymptomatic, 220 symptomatic, 118 valve disease) and four CT models. The performance of the CAC_auto system in detecting coronary calcium was determined. The reliability of the system in measuring the Agatston score as compared with CAC_hand was also evaluated per vessel and per patient using intraclass correlation coefficients (ICCs) and Bland-Altman analysis. The agreement between CAC_auto and CAC_hand based on the cardiovascular risk stratification categories (Agatston score: 0, 1-10, 11-100, 101-400, > 400) was evaluated. Results: In 2985 patients, 6218 coronary calcium lesions were identified using CAC_hand. The per-lesion sensitivity and false-positive rate of the CAC_auto system in detecting coronary calcium were 93.3% (5800 of 6218) and 0.11 false-positive lesions per patient, respectively. The CAC_auto system, in measuring the Agatston score, yielded ICCs of 0.99 for all the vessels (left main 0.91, left anterior descending 0.99, left circumflex 0.96, right coronary 0.99). The limits of agreement between CAC_auto and CAC_hand were 1.6 ± 52.2. The linearly weighted kappa value for the Agatston score categorization was 0.94. The main causes of false-positive results were image noise (29.1%, 97/333 lesions), aortic wall calcification (25.5%, 85/333 lesions), and pericardial calcification (24.3%, 81/333 lesions). Conclusion: The atlas-based CAC_auto empowered by deep learning provided accurate calcium score measurement as compared with manual method and risk category classification, which could potentially streamline CAC imaging workflows.

• Korean Journal of Remote Sensing
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• v.36 no.5_4
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• pp.1267-1276
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• 2020

To quantitatively predict the impacts of large-scale volcanic eruptions of Mt. Baekdu on air quality and damage around the Korean Peninsula, a three-dimensional chemistry-transport modeling system (Weather Research & Forecasting - Sparse Matrix Operation Kernel Emission - Comunity Multi-scale Air Quality) was adopted. A worst-case meteorology scenario was selected to estimate the direct impact on Korea. This study applied the typical worst-case scenarios that are likely to cause significant damage to Korea among worst-case volcanic eruptions of Mt. Baekdu in the past decade (2005~2014) and assumed a massive VEI 4 volcanic eruption on May 16, 2012, to analyze the concentration of PM_2.5 caused by the volcanic eruption. The effects of air quality in each region-cities, counties, boroughs-were estimated, and vulnerable areas were derived by conducting an exposure assessment reflecting vulnerable groups. Moreover, the effects of cities, counties, and boroughs were analyzed with a high-resolution scale (9 km × 9 km) to derive vulnerable areas within the regions. As a result of analyzing the typical worst-case volcanic eruptions of Mt. Baekdu, a discrepancy was shown in areas between high PM_2.5 concentration, high population density, and where vulnerable groups are concentrated. From the result, PM_2.5 peak concentration was about 24,547 ㎍/㎥, which is estimated to be a more serious situation than the eruption of Mt. St. Helensin 1980, which is known for 540 million tons of volcanic ash. Paju, Gimpo, Goyang, Ganghwa, Sancheong, Hadong showed to have a high PM_2.5 concentration. Paju appeared to be the most vulnerable area from the exposure assessment. While areas estimated with a high concentration of air pollutants are important, it is also necessary to develop plans and measures considering densely populated areas or areas with high concentrations of susceptible population or vulnerable groups. Also, establishing measures for each vulnerable area by selecting high concentration areas within cities, counties, and boroughs rather than establishing uniform measures for all regions is needed. This study will provide the foundation for developing the standards for disaster declaration and preemptive response systems for volcanic eruptions.

• The Korean Journal of Ecology
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• v.27 no.3
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• pp.165-171
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• 2004

Weight loss and nutrients dynamics during decomposition of oak roots (diameter classes: R₁〈0.2㎝, 0.5㎝〈R₂〈1㎝, 1㎝〈R₃〈2㎝, 2㎝.〈R₄〈4㎝) (Quercus acutissima) were studied for 33-months in Kongiu, Korea. After 33-months, decomposition rate of R₁, R₂, R₃ and R₄ was 49.6%, 47.5%, 66.4% and 66.1%, respectively. The decomposition constant(k) for R₁, R₂, R₃, and R₄ was 0.249/yr, 0.234/yr, 0.397/yr and 0.393/yr, respectively. Larger diameter class of the root lost more weight than smaller diameter class. N concentration in decomposing oak roots increased in all diameter classes. After 33-months, remaining N in R₁, R₂, R₃ and R₄ was 66.5%, 80.7%, 84.4% and 44.4%, respectively. K concentration in decomposing oak roots decreased in early part of decomposition and then increased in later stage of decomposition. After 33-months, remaining P in R₁, R₂, R₃ and R₄ was 64.7%, 62.4%, 93.1% and 30.7%, respectively. K concentration in decomposing oak roots decreased rapidly in early stage of decomposition. Remaining K in R₁, R₂, R₃ and R₄ was 11.6%, 10.6%, 5.9% and 7.7%, respectively. Ca concentration in decomposing oak roots showed different among diameter classes. After 33-months, remaining Ca in R₁, R₂, R₃ and R₄ was 66.2%, 51.0%, 39.1% and 48.3%, respectively. Initial concentration of Mg in oak root was higher in smaller diameter class. After 33-months, remaining Mg in R₁, R₂, R₃ and R₄ was 15.3%, 29.9%, 24.5% and 69.4%, respectively.

• Journal of the Korean Society of Food Science and Nutrition
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• v.41 no.8
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• pp.1094-1099
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• 2012

This study investigated the effect of a long-term high-fat diet on energy metabolic substrate utilization in resting rats in order to revalue source fat energy efficiency during a high-fat diet and its effect on energy expenditure and body fat accumulation. Sprague-Dawley male rats at 4 weeks of age were bought from Orient Bio Con. The rats were divided into a control (CON) group and a high-fat diet (HF) group. Rats ate a high-fat diet (w/w 40%, kcal/kcal 64.9%) ad libitum for 5 weeks. Food intake and body weight were measured every day at 09:00 throughout the experimental period. Energy expenditure was measured using an animal energy metabolism chamber after 4 weeks. The final body weight did not change between the CON and HF groups, but caloric intake was significantly higher in the HF group than in the CON group (p<0.05). There was no difference between the groups in oxygen uptake, however carbon dioxide production was significantly higher in the HF group. Also, the respiratory exchange ratio was higher in the HF group. Carbohydrate oxidation was lower in the HF group than in the CON group, but fat oxidation in the HF group was greater. These results mean that energy substrate oxidation at rest is affected by diet composition, especially dietary fat content. Abdominal fat fad weights were significantly higher by 33% in the HF group than in the CON group even though the calorie intake in the HF group was higher by 6%. These results suggested that the dietary fat calorie value might have a higher Atwater value of 9 kcal/g, which mean that dietary fat calorie values could be reconsidered in body weight control scenarios such as which the obese or weight class athletes.

• Journal of Korea Water Resources Association
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• v.47 no.2
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• pp.145-159
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• 2014

This study is to evaluate the future climate change impact on turbidity water and eutrophication for Chungju Lake by using CE-QUAL-W2 reservoir water quality model coupled with SWAT watershed model. The SWAT was calibrated and validated using 11 years (2000~2010) daily streamflow data at three locations and monthly stream water quality data at two locations. The CE-QUAL-W2 was calibrated and validated for 2 years (2008 and 2010) water temperature, suspended solid, total nitrogen, total phosphorus, and Chl-a. For the future assessment, the SWAT results were used as boundary conditions for CE-QUAL-W2 model run. To evaluate the future water quality variation in reservoir, the climate data predicted by MM5 RCM(Regional Climate Model) of Special Report on Emissions Scenarios (SRES) A1B for three periods (2013~2040, 2041~2070 and 2071~2100) were downscaled by Artificial Neural Networks method to consider Typhoon effect. The RCM temperature and precipitation outputs and historical records were used to generate pollutants loading from the watershed. By the future temperature increase, the lake water temperature showed $0.5^{\circ}C$ increase in shallow depth while $-0.9^{\circ}C$ in deep depth. The future annual maximum sediment concentration into the lake from the watershed showed 17% increase in wet years. The future lake residence time above 10 mg/L suspended solids (SS) showed increases of 6 and 17 days in wet and dry years respectively comparing with normal year. The SS occupying rate of the lake also showed increases of 24% and 26% in both wet and dry year respectively. In summary, the future lake turbidity showed longer lasting with high concentration comparing with present behavior. Under the future lake environment by the watershed and within lake, the future maximum Chl-a concentration showed increases of 19 % in wet year and 3% in dry year respectively.

• Journal of Environmental Impact Assessment
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• v.24 no.2
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• pp.134-153
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• 2015

When making urban planning, it is important to understand climate effect caused by urban structural changes. Seoul city applies UPIS(Urban Plan Information System) which provides information on urban planning scenario. Technology for analyzing climate effect resulted from urban planning needs to developed by linking urban planning scenario provided by UPIS and climate analysis model, CAS(Climate Analysis Seoul). CAS develops for analyzing urban climate conditions to provide realistic information considering local air temperature and wind flows. Quantitative analyses conducted by CAS for the production, transportation, and stagnation of cold air, wind flow and thermal conditions by incorporating GIS analysis on land cover and elevation and meteorological analysis from MetPhoMod(Meteorology and atmospheric Photochemistry Meso-scale model). In order to reflect land cover and elevation of the latest information, CAS used to highly accurate raster data (1m) sourced from LiDAR survey and KOMPSAT-2(KOrea Multi-Purpose SATellite) satellite image(4m). For more realistic representation of land surface characteristic, DSM(Digital Surface Model) and DTM(Digital Terrain Model) data used as an input data for CFD(Computational Fluid Dynamics) model. Eight inflow directions considered to investigate the change of flow pattern, wind speed according to reconstruction and change of thermal environment by connecting green area formation. Also, MetPhoMod in CAS data used to consider realistic weather condition. The result show that wind corridors change due to reconstruction. As a whole surface temperature around target area decreases due to connecting green area formation. CFD model coupled with CAS is possible to evaluate the wind corridor and heat environment before/after reconstruction and connecting green area formation. In This study, analysis of climate impact before and after created the green area, which is part of 'Connecting green network across the north and south in Seoul' plan, one of the '2020 Seoul master plan'.

• Asia pacific journal of information systems
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• v.24 no.4
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• pp.493-513
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• 2014

With the development of related technologies, Location-Based Services (LBS) are growing fast and being used in many ways. Past LBS studies have focused on adoption of LBS because of the fact that LBS users have privacy concerns regarding revealing their location information. Meanwhile, the number of LBS users and revenues from LBS are growing rapidly because users can get some benefits by revealing their location information. Little research has been done on how LBS affects consumers' information search behavior in product purchase. The purpose of this paper is examining the effect of LBS information filtering on buyers' uncertainty and their information search behavior. When consumers purchase a product, they try to reduce uncertainty by searching information. Generally, there are two types of uncertainties - knowledge uncertainty and choice uncertainty. Knowledge uncertainty refers to the lack of information on what kinds of alternatives are available in the market and/or their important attributes. Therefore, consumers having knowledge uncertainty will have difficulties in identifying what alternatives exist in the market to fulfil their needs. Choice uncertainty refers to the lack of information about consumers' own preferences and which alternative will fit in their needs. Therefore, consumers with choice uncertainty have difficulties selecting best product among available alternatives.. According to economics of information theory, consumers narrow the scope of information search when knowledge uncertainty is high. It is because consumers' information search cost is high when their knowledge uncertainty is high. If people do not know available alternatives and their attributes, it takes time and cognitive efforts for them to acquire information about available alternatives. Therefore, they will reduce search breadth. For people with high knowledge uncertainty, the information about products and their attributes is new and of high value for them. Therefore, they will conduct searches more in-depth because they have incentive to acquire more information. When people have high choice uncertainty, people tend to search information about more alternatives. It is because increased search breadth will improve their chances to find better alternative for them. On the other hand, since human's cognitive capacity is limited, the increased search breadth (more alternatives) will reduce the depth of information search for each alternative. Consumers with high choice uncertainty will spend less time and effort for each alternative because considering more alternatives will increase their utility. LBS provides users with the capability to screen alternatives based on the distance from them, which reduces information search costs. Therefore, it is expected that LBS will help users consider more alternatives even when they have high knowledge uncertainty. LBS provides distance information, which helps users choose alternatives appropriate for them. Therefore, users will perceive lower choice uncertainty when they use LBS. In order to test the hypotheses, we selected 80 students and assigned them to one of the two experiment groups. One group was asked to use LBS to search surrounding restaurants and the other group was asked to not use LBS to search nearby restaurants. The experimental tasks and measures items were validated in a pilot experiment. The final measurement items are shown in Appendix A. Each subject was asked to read one of the two scenarios - with or without LBS - and use a smartphone application to pick a restaurant. All behaviors on smartphone were recorded using a recording application. Search breadth was measured by the number of restaurants clicked by each subject. Search depths was measured by two metrics - the average number of sub-level pages each subject visited and the average time spent on each restaurant. The hypotheses were tested using SPSS and PLS. The results show that knowledge uncertainty reduces search breadth (H1a). However, there was no significant correlation between knowledge uncertainty and search depth (H1b). Choice uncertainty significantly reduces search depth (H2b), but no significant relationship was found between choice uncertainty and search breadth (H2a). LBS information filtering significantly reduces the buyers' choice uncertainty (H4) and reduces the negative relationship between knowledge uncertainty and search breadth (H3). This research provides some important implications for service providers. Service providers should use different strategies based on their service properties. For those service providers who are not well-known to consumers (high knowledge uncertainty) should encourage their customers to use LBS. This is because LBS would increase buyers' consideration sets when the knowledge uncertainty is high. Therefore, less known services have chances to be included in consumers' consideration sets with LBS. On the other hand, LBS information filtering decrease choice uncertainty and the near service providers are more likely to be selected than without LBS. Hence, service providers should analyze geographically approximate competitors' strength and try to reduce the gap so that they can have chances to be included in the consideration set.