• Journal of Korean Society of Environmental Engineers
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• v.28 no.5
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• pp.549-557
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• 2006

The characteristics of supercritical water oxidation have been studied to decompose the waste anionic exchange resins which were produced from a power plant. The waste resins from a power plant were mixture of anionic and cationic exchange resins. The waste anionic exchange resins had been separated from the waste resins using a solid-liquid fluidized bed. It was confirmed that the cationic exchange resins were not included in the separated anionic exchange resins by the elemental and thermogravimetric analysis. A slurry of anionic exchange resins which could be fed continuously to a supercritical water oxidation apparatus by a high pressure pump was prepared using a wet ball mill. Although the COD of liquid effluent had been reduced more than 99.9% at 25.0 MPa and $500^{\circ}C$ within 2 min, the total nitrogen content was reduced only 41%. The addition of nitric acid to the slurry could reduce the total nitrogen content in treated water. The central composite design as a statistical desist of experiments had been applied to optimize the conditions of decomposing anionic resin slurry by means of the COD and total nitrogen contents in treated waters as the key process output variables. The COD values of treated waters had been reduced sufficiently to $99.9{\sim}100%$ af the reaction conditions of $500{\sim}540^{\circ}C$, 25.0 MPa within 2 min. The effects of temperature and nitric acid concentration on COD were not significant. However, the effect of nitric acid concentration on the total nitrogen was found to be significant. The regression equation for the total nitrogen had been obtained with nitric acid concentration and the coefficient of determination($r^2$) was 95.8%.

• Journal of agricultural medicine and community health
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• v.34 no.2
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• pp.175-187
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• 2009

Objectives: This study was conducted to investigate whether joint effects between family allergy history and environmental tobacco smoke(ETS) by parents were associated with pediatric asthma and wheezing. Methods: The study objects of this study were 2301 element school students and their parents in an urban-rural areas of Gyeonggi-do. Pediatric asthma and wheezing were identified by measures of International Study of Asthma and Allergies in Childhood (ISAAC) questionnaires. We investigated history of parental allergy, ETS, and other socioeconomic status of both parent. Data were analyzed using logistic regression methods. Results: After adjusting other variables, children with maternal asthma history were more likely to be reported life time wheezing (OR: 3.79 95%CI:2.43-5.90), recent wheezing (OR:4.09 95%CI:2.28-7.38), and diagnostic asthma (OR:2.61 95%CI: 1.44-4.75). Paternal asthma history increasing risk of life time wheezing (OR 2.01 95%CI:1.19-3.38) and recent wheezing (OR:2.38 95%CI:1.24-4.56). Joint effect between parental allergy history and ETS significantly effected on child's life time wheezing and recent wheezing. The risks of life time wheezing (OR:2.47 95%CI:1.64-3.717) and recent wheezing (OR: 2.51 95%CI:1.34-4.69) were significantly higher than others without both factors. The risk of recent wheezing of children with maternal recent smoking and parental allergy history (OR:4.83 95%CI:1.89-12.33) was higher than their counterpart. Conclusions: The result of this study implies that children with family allergy history and passive smoking are more likely to be get asthma and wheezing than children with family allergy history and non-passive smoking. This study provide the object information to increase the efficiency of non-smoking campaign and education for decreasing pediatric asthma risk.

• Journal of Environmental Impact Assessment
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• v.28 no.6
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• pp.568-579
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• 2019

The present study aims to identify the effect of land cover categories on particulate matter (PM) concentrations by analyzing the correlation between monthly PM concentrations in Seoul's air quality monitoring network and the percentages of land cover categories by buffers around air quality monitoring stations. According to a monthly correlation analysis between land cover categories and PM concentrations, in the buffer 3km, PM₁₀ showed a better correlation than PM_2.5, there was a clear negative correlation with the forest area, the grassland and the urbanized area had some positive correlation with PM₁₀, and the barren land and the urbanized area had some positive correlation with PM_2.5. According to a monthly correlation analysis of dominant land cover sub-categories and sub-sub-categories within the buffer 3km, PM₁₀ showed a clear negative correlation with the broad-leaved forest, and some positive correlation with the road was dominant. PM_2.5 showed partly negative correlation with the broad-leaved forest and partly positive correlation with the commercial area. There was a very low or no correlation with other grassland and bare land subcategories. A monthly stepwise regression analysis on noticeable land cover sub-categories and sub-sub-categories with positive or negative correlations revealed that an increasing percentage of the broad-leaved forest had a clear effect on reducing PM₁₀ concentrations, and the road was excluded from the selected variables. Although an increasing percentage of the commercial area had some effect on increasing monthly PM_2.5 concentrations and an increasing percentage of the broad-leaved forest had an effect on decreasing the PM_2.5 concentrations, their effect size was smaller than that on PM₁₀. The forest area around the city center had the largest and clearest effect on reducing PM concentrations. The urbanized area's sub-categories and sub-sub-categories were also confirmed to have some effect on increasing PM concentrations.

• Journal of Korea Water Resources Association
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• v.50 no.11
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• pp.715-723
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• 2017

Extracellular Polymeric Substances (EPS) in the water environment assemble fine, colloidal particles, such as clays, microorganisms and biomass, in large flocs, which are eventually subject to sedimentation and deposition and determine water/sediment quality and quantity. This study hence aimed to investigate the way that water and colloidal chemistry affects EPS-mediated flocculation of colloidal particles, using a jar-test experiment. Especially, ionic strength, divalent cation and humic substances concentrations were selected as experimental variables in the jar-test experiments, to elucidate their effects on EPS-mediated flocculation. A higher ionic strength increased flocculation capability, reducing electrostatic repulsion between EPS-attached colloidal particles and enhancing particle aggregation. 0.1 M NaCl ionic strength had higher flocculation capability, with 3 times larger floc size and 2.5 times lower suspended solid concentration, than 0.001 M NaCl. Divalent cations, such as $Ca^{2+}$, built divalent cationic bridges between colloidal particles and EPS (i.e., $colloid-Ca^{2+}-EPS$ or $EPS-Ca^{2+}-EPS$) and hence made colloidal particles to build into large, settelable flocs. A small $Ca^{2+}$ concentration enhanced flocculation capability, reducing suspended solid concentration 20 times lower than the initial dosed concentration. However, humic substances, adsorbed on colloidal particles, reduced flocculation, because they blocked EPS adsorption on colloidal particles and increased negative charges and electrostatic repulsion of colloidal particles. Suspended solid concentration in the tests with humic substances remained as high as the initial dosed concentration, indicating stabilization rather than flocculation. Findings about EPS-mediated flocculation in this research will be used for better understanding the fate and transport of colloidal particles in the water environment and for developing the best management practices for water/sediment quality.

• Economic and Environmental Geology
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• v.38 no.4 s.173
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• pp.435-450
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• 2005

The purpose of the study is to analyze the hydrogeochemical characteristics by multivariate statistical method, to interpret the hydrogeochemical processes for the new variables calculated from principal components analysis (PCA), and to infer the groundwater flow and circulation mechanism by applying the geostatistical methods for each element and principal component. Chloride and nitrate are the most influencing components for groundwater quality, and the contents of $NO_3$ increased by the input of agricultural activities show the largest variation. The results of PCA, a multivariate statistical method, show that the first three principal components explain $73.9\%$ of the total variance. PC1 indicates the increase of dissolved ions, PC2 is related with the dissolution of carbonate minerals and nitrate contamination, and PC3 shows the effect of cation exchange process and silicate mineral dissolution. From the results of experimental semivariogram, the components of groundwater are divided into two groups: one group includes electrical conductivity (EC), Cl, Na, and $NO_3$, and the other includes $HCO_3,\;SiO_2,$ Ca, and Sr. The results for spatial distribution of groundwater components showed that EC, Cl, and Na increased with approaching the coastal line and nitrate has close relationship with the presence of agricultural land. These components are also correlated with the topographic features reflecting the groundwater recharge effect. The kriging analysis by using principal components shows that PC 1 has the different spatial distribution of Cl, Na, and EC, possibly due to the influence of pH, Ca, Sr, and $HCO_3$ for PC1. It was considered that the linear anomaly zone of PC2 in western area was caused by the dissolution of carbonate mineral. Consequently, the application of multivariate and geostatistical methods for groundwater in the study area is very useful for determining the quantitative analysis of water quality data and the characteristics of spatial distribution.

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.33-56
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• 2016

Many researchers have focused on developing bankruptcy prediction models using modeling techniques, such as statistical methods including multiple discriminant analysis (MDA) and logit analysis or artificial intelligence techniques containing artificial neural networks (ANN), decision trees, and support vector machines (SVM), to secure enhanced performance. Most of the bankruptcy prediction models in academic studies have used financial ratios as main input variables. The bankruptcy of firms is associated with firm's financial states and the external economic situation. However, the inclusion of qualitative information, such as the economic atmosphere, has not been actively discussed despite the fact that exploiting only financial ratios has some drawbacks. Accounting information, such as financial ratios, is based on past data, and it is usually determined one year before bankruptcy. Thus, a time lag exists between the point of closing financial statements and the point of credit evaluation. In addition, financial ratios do not contain environmental factors, such as external economic situations. Therefore, using only financial ratios may be insufficient in constructing a bankruptcy prediction model, because they essentially reflect past corporate internal accounting information while neglecting recent information. Thus, qualitative information must be added to the conventional bankruptcy prediction model to supplement accounting information. Due to the lack of an analytic mechanism for obtaining and processing qualitative information from various information sources, previous studies have only used qualitative information. However, recently, big data analytics, such as text mining techniques, have been drawing much attention in academia and industry, with an increasing amount of unstructured text data available on the web. A few previous studies have sought to adopt big data analytics in business prediction modeling. Nevertheless, the use of qualitative information on the web for business prediction modeling is still deemed to be in the primary stage, restricted to limited applications, such as stock prediction and movie revenue prediction applications. Thus, it is necessary to apply big data analytics techniques, such as text mining, to various business prediction problems, including credit risk evaluation. Analytic methods are required for processing qualitative information represented in unstructured text form due to the complexity of managing and processing unstructured text data. This study proposes a bankruptcy prediction model for Korean small- and medium-sized construction firms using both quantitative information, such as financial ratios, and qualitative information acquired from economic news articles. The performance of the proposed method depends on how well information types are transformed from qualitative into quantitative information that is suitable for incorporating into the bankruptcy prediction model. We employ big data analytics techniques, especially text mining, as a mechanism for processing qualitative information. The sentiment index is provided at the industry level by extracting from a large amount of text data to quantify the external economic atmosphere represented in the media. The proposed method involves keyword-based sentiment analysis using a domain-specific sentiment lexicon to extract sentiment from economic news articles. The generated sentiment lexicon is designed to represent sentiment for the construction business by considering the relationship between the occurring term and the actual situation with respect to the economic condition of the industry rather than the inherent semantics of the term. The experimental results proved that incorporating qualitative information based on big data analytics into the traditional bankruptcy prediction model based on accounting information is effective for enhancing the predictive performance. The sentiment variable extracted from economic news articles had an impact on corporate bankruptcy. In particular, a negative sentiment variable improved the accuracy of corporate bankruptcy prediction because the corporate bankruptcy of construction firms is sensitive to poor economic conditions. The bankruptcy prediction model using qualitative information based on big data analytics contributes to the field, in that it reflects not only relatively recent information but also environmental factors, such as external economic conditions.

• Journal of Cadastre & Land InformatiX
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• v.48 no.2
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• pp.79-98
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• 2018

The purpose of this study is to suggest a model analysing spatio-temporal characteristics of the civil complaints for the officially assessed land price based on big data mining. Specifically, in this study, the underlying reasons for the civil complaints were found from the spatio-temporal perspectives, rather than the institutional factors, and a model was suggested monitoring a trend of the occurrence of such complaints. The official documents of 6,481 civil complaints for the officially assessed land price in the district of Jung-gu of Incheon Metropolitan City over the period from 2006 to 2015 along with their temporal and spatial poperties were collected and used for the analysis. Frequencies of major key words were examined by using a text mining method. Correlations among mafor key words were studied through the social network analysis. By calculating term frequency(TF) and term frequency-inverse document frequency(TF-IDF), which correspond to the weighted value of key words, I identified the major key words for the occurrence of the civil complaint for the officially assessed land price. Then the spatio-temporal characteristics of the civil complaints were examined by analysing hot spot based on the statistics of Getis-Ord $Gi^*$. It was found that the characteristic of civil complaints for the officially assessed land price were changing, forming a cluster that is linked spatio-temporally. Using text mining and social network analysis method, we could find out that the occurrence reason of civil complaints for the officially assessed land price could be identified quantitatively based on natural language. TF and TF-IDF, the weighted averages of key words, can be used as main explanatory variables to analyze spatio-temporal characteristics of civil complaints for the officially assessed land price since these statistics are different over time across different regions.

• Journal of Wetlands Research
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• v.21 no.2
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• pp.102-113
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• 2019

Slack-tide sampling was carried out at 6 stations at high and low tide for a tidal cycle during spring tide of the early summer (June) and summer (July, August) of 2016 to determine the difference of water quality according to tide in Masan Bay, Korea. The mixing regime of all the water quality components investigated was well explained through the correlation with SAL. In the early summer and summer, TURB, DSi and NNN which mainly flow into the bay from the streams and SS, COD, AMN and $H_2S$ which mainly indicate the internal sink and source materials have a property of conservative mixing and non-conservative mixing, respectively. The conservative mixing showed a good linear relationship of the water quality between high and low tide, and the non-conservative mixing showed a variation of different pattern each other. Factor analysis performed on the concentration difference data sets between high and low tide helped in identifying the principal latent variables for them. In early summer, multiple effects (tidal action, natural influx and internal sinks and sources etc.) acted in combination for the differences to be distributed evenly in four factors (VF1~4), since there were few allochthonous inputs as a low-water season. On the contrary, in summer, the parameters showing large concentration difference at ST-1 affected by stream water were concentrated in one factor (VF1) and clearly distinguished from the parameters affected by the internal sinks and sources. In fact, there is no estuary (bay) that always maintains steady state flow conditions. The mixing regime of an estuary might be changed at any time due to the change of flushing time, and furthermore the change of end-member conditions due to the internal sinks and sources makes the occurrence of concentration difference inevitable. Therefore, when investigating the water quality of the estuary, it is necessary to take a sampling method considering the tide to obtain average water quality data.

• 한국노년학
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• v.36 no.3
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• pp.673-691
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• 2016

As the provision of long-term care policy takes root and with a gradual increase in elderly population, the use of elderly care service has become a growing norm. More than ever, there exists an urgent need for a paradigm shift in the building of an institutional basis for the improvement of care service, from the prevalent practice of 'need based service' toward the concept of 'human rights based service'. A great focus is being shed on care-workers, at the 'front line' of advocating human rights, as their human rights advocacy behaviour is seen as a key variable in providing high quality care service for elders. This study aims to examine how care-workers' individual human rights awareness levels, and the influence of their respective organizations, as an environmental factor, affect their human rights advocacy behaviour. The study includes a comprehensive analysis of the interactions between the regulatory effect of environmental factors (service orientation?) on an organizational level, human rights awareness (individual level) and the service environment (organizational). The analysis sample consisted of 782 registered non-profit corporation of long-term care facilities all over the country in 2014. The findings of the thesis suggest that human rights awareness at individual levels has a significant influence on human rights advocacy behavior. The interaction of human resources management in service orientations was also found to influence human rights advocacy on a significant level. Both human rights awareness at individual level and service orientations at organizational level were thus determined as key variables for improving the human rights awareness of care worker in long-term care facilities in Korea.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.4
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• pp.218-233
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• 2022

The long-term (1986~2020) predictability of the number of days of heat and cold damages for each growth stage of soybean is evaluated using the daily maximum and minimum temperature (Tmax and Tmin) data produced by Pusan National University Coupled General Circulation Model (PNU CGCM)-Weather Research and Forecasting (WRF). The Predictability evaluation methods for the number of days of damages are Normalized Standard Deviations (NSD), Root Mean Square Error (RMSE), Hit Rate (HR), and Heidke Skill Score (HSS). First, we verified the simulation performance of the Tmax and Tmin, which are the variables that define the heat and cold damages of soybean. As a result, although there are some differences depending on the month starting with initial conditions from January (01RUN) to May (05RUN), the result after a systematic bias correction by the Variance Scaling method is similar to the observation compared to the bias-uncorrected one. The simulation performance for correction Tmax and Tmin from March to October is overall high in the results (ENS) averaged by applying the Simple Composite Method (SCM) from 01RUN to 05RUN. In addition, the model well simulates the regional patterns and characteristics of the number of days of heat and cold damages by according to the growth stages of soybean, compared with observations. In ENS, HR and HSS for heat damage (cold damage) of soybean have ranged from 0.45~0.75, 0.02~0.10 (0.49~0.76, -0.04~0.11) during each growth stage. In conclusion, 01RUN~05RUN and ENS of PNU CGCM-WRF Chain have the reasonable performance to predict heat and cold damages for each growth stage of soybean in South Korea.