• Journal of Korean Society on Water Environment
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• v.27 no.1
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• pp.120-128
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• 2011

Korea Groundwater Quality Monitoring Network is a database of annual groundwater quality survey results to prevent groundwater pollution. We estimated contamination index (CI) values for each type of land use, and analyzed temporal trends of pollutant concentration data in the Groundwater Quality Monitoring Network from 2001 to 2009. Among the pollutants considered in the database, the concentrations of nitrate and chloride were higher than their standards. In the case of nitrate, recreation parks, golf courses and general waste dumping regions showed increasing trends according to linear regression analysis, whereas industrial complexes and residential regions of urgan and recreation parks showed increasing trends in the chloride concentration data. According to multiple variable linear regression analysis, EC, pH and topography were major factors influencing CI values. These results suggest that groundwater with a high CI value and increasing trend is vulnerable for potential contamination, which requires more careful groundwater pollution control.

• Journal of Korea Artificial Intelligence Association
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• v.1 no.2
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• pp.15-20
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• 2023

This study aims to enhance the accuracy of fine dust predictions by analyzing various factors within the local environment, in addition to atmospheric conditions. In the atmospheric environment, meteorological and air pollution data were utilized, and additional factors contributing to fine dust generation within the region, such as traffic volume and electricity transaction data, were sequentially incorporated for analysis. XGBoost, Random Forest, and ANN (Artificial Neural Network) were employed for the analysis. As variables were added, all algorithms demonstrated improved performance. Particularly noteworthy was the Artificial Neural Network, which, when using atmospheric conditions as a variable, resulted in an MAE of 6.25. Upon the addition of traffic volume, the MAE decreased to 5.49, and further inclusion of power transaction data led to a notable improvement, resulting in an MAE of 4.61. This research provides valuable insights for proactive measures against air pollution by predicting future fine dust levels.

• Journal of Korean Society for Atmospheric Environment
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• v.11 no.E
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• pp.45-54
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• 1995

Indoor air quality models are useful to predict indoor air pollutant concentrations as a function of several indoor factors. Indoor air quality model was developed to evaluate the pollutant removal efficiency of variable-air-volume/bypass filtration system (VAV/BPFS) compared with the conventional variable-air-volume (VAV) system. This model provides relative pollutant removal effectiveness of VAV/BPFS by concentration ratio between the conventional VAV system and VAV/BPFS. The predictions agree closely, from 5 to 10 percent, with the measured values for each energy load. As a results, we recommend the VAV/BPFS is a promising alternative to conventional VAV system because it is capable of reducing indoor air pollutant concentration and maintaining good indoor air quality.

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2010.04a
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• pp.646-648
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• 2010

• Proceedings of the KIPE Conference
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• 1999.07a
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• pp.659-662
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• 1999

As utility pollution take a growing interest, ac/dc converter optimizing utility condition has been vigorously studied in decades. In this paper three phase DCM converter is analyzed and equations for average input currents are presented. Also relationships of voltage gain & duty according to angular velocity are presented and variable frequency controller is implemented using reset integrator which is designed in detail. In result power factor and THD characteristics of 3-stage and 4-stage operation ae compared respectively.

• Journal of Korean Society of Environmental Engineers
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• v.33 no.8
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• pp.572-577
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• 2011

The unit load has simply been used to estimate total pollutant loading from non-point sources, however, it does not count on the variable pollutant loading according to land use characteristics and rainfall intensity. Since pollutant emission from the watershed is strongly dependent on the rainfall intensity, it is necessary to find out the relationship between pollutant loading and rainfall intensity. The objective of this study is to develop simple and easy method to compute non-point source pollution loads with consideration of rainfall intensity. Two non-point source removal facility at Gyeongan-dong (Gwangju-si) and Mohyeon-myeon (Yongin-si), Gyeonggi-do was selected to monitor total rainfall, rainfall intensity, runoff characteristics and water quality from June to November, 2010. Most of Event Mean Concentrations (EMC) of measured water quality data were higher in Gyeongan which has urban land use than in Mohyeon which has rural land use. For the case of TP (Total Phosphorus), Mohyeon has higher values by the influence of larger chemical uses such as fertilizer. The relationship between non-point source pollution load and rainfall intensity is perfectly well explained by cubic regression with 0.33~0.81 coefficients of determination($R^2$). It is expected that the pollution loading function based on the long-term monitoring would be very useful with good accuracy in computing non-point source pollution load, where a rainfall intensity is highly variable.

• Journal of the Korean Data and Information Science Society
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• v.26 no.6
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• pp.1409-1416
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• 2015

The change of temperature influences on the various aspect, especially human health, plant and animal's growth, economics, industry, and culture of the country. In this article, the autoregressive error (ARE) model has been considered for analyzing the monthly temperature data at the Suwon monitoring site in Korea. In the ARE model, five meteorological variables, four greenhouse gas variables and five pollution variables are used as the explanatory variables for the temperature data set. The five meteorological variables are wind speed, rainfall, radiation, amount of cloud, and relative humidity. The four greenhouse gas variables are carbon dioxide ($CO_2$), methane ($CH_4$), nitrous oxide ($N_2O$), and chlorofluorocarbon ($CFC_{11}$). And the five air pollution explanatory variables are particulate matter ($PM_{10}$), sulfur dioxide ($SO_2$), nitrogen dioxide ($NO_2$), ozone ($O_3$), and carbon monoxide (CO). Among five meteorological variables, radiation, amount of cloud, and wind speed are more influence on the temperature. The radiation influences during spring, summer and fall, whereas wind speed influences for the winter time. Also, among four greenhouse gas variables and five pollution variables, chlorofluorocarbon, methane, and ozone are more influence on the temperature. The monthly ARE model explained about 43-69% for describing the temperature.

• Journal of Climate Change Research
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• v.3 no.2
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• pp.153-159
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• 2012

CFC-12 used in mobile air conditioning(MAC) system has been replaced by R-134a, a type of HFC refrigerant, from 1991 to 1994. R-134a has since been widely used as a refrigerant of a mobile air conditioner. However, it is one of the six main green house gases listed in Kyoto Protocol, which makes it imperative to regulate its emission and develop alternative refrigerants. In this study, the concentration of leaked R-134a was measured using VT(Variable Temperature) shed and Running loss test shed to analyze the level of air conditioner refrigerant leaked in a vehicle. According to the analysis of the concentration of R-134a leaked from a vehicle parked, annual leakage amount of R-134a was in the range of 6.46~13.28 g/yr. The figure was similar with the leakage from the mobile air conditioning system currently used. In a study using the same vehicle model, a vehicle equipped with dual evaporation system had a higher leakage rate of refrigerant than a vehicle with a single evaporation system. It appears that the added fittings and joints of the dual evaporator system led to higher leakage rate. Besides, the analysis of the change in R-134a concentration under various car speed found that more refrigerant leaked under high speed(100km/hr) and but the volume of the wind did not affect to the variation of refrigerant leakage.

• Journal of Families and Better Life
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• v.22 no.1
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• pp.55-64
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• 2004

The major purpose of this study was to investigate purchasing behavior of environmental products by Korean consumers, which will ultimately help foster sustainable consumption. Environmental consciousness, environmental behaviors, level of awareness of environmental products, and purchasing of environmental products were examined. Mean differences between purchasers and non-purchasers of environmental products were compared in terms of environmental consciousness and behaviors, and the level of awareness of environmental products. A survey was conducted on 310 consumers in the greater Seoul metropolitan area. The data were analyzed by frequencies, percentages, logistic regression, and t-tests using a variable for interval scale and a variable for nominal scale. There were significant mean differences between purchasers and non-purchasers of environmental products on three variables of environmental consciousness and behaviors. Those who were educated on environmental issues showed a higher preference in purchasing environmental products. Among socio-demographic variables, the income level was the only variable that showed a significant mean difference between the two groups. Also, there was a remarkable difference in purchasing behavior between the two groups. For the purchasers of environmental products, the purchasing decisions took into account environment-friendliness of products. Non-purchasers of environmental products put more emphasis on price or quality of products. The results of the logistic regression analysis indicated that those who had higher education, who viewed environmental pollution as a serious problem, and who are more cognizant of the environmental labeling tend to purchase more environmental products.

• Economic and Environmental Geology
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• v.55 no.6
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• pp.617-632
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• 2022

In recent years, environmental pollution and determining the main factors causing this pollution have become an important issue. This study investigates the relationship between the agricultural sector and environmental pollution in Azerbaijan for 1992-2018. The dependent variable in the study is the agricultural greenhouse gas emissions (CO₂ equivalent). Eight variables were selected as explanatory variables: four agricultural inputs and four agricultural macro indicators. Unit root tests, ARDL boundary test, FMOLS, DOLS and CCR long-term estimators, Granger causality analysis, and variance decomposition analyses were used to investigate the effect of these variables on agricultural emissions. The results show that chemical fertilizer consumption, livestock number, and pesticide use positively and statistically significantly affect agricultural emissions from agricultural input variables. In contrast, agricultural energy consumption has a negative and significant effect. From agricultural macro indicator variables, it was found that the crop and animal production index had a positive and significant effect on agricultural emissions. According to the Granger causality test results, it was concluded that there are a causality relationship from chemical fertilizer consumption, livestock number, crop and livestock production index variables towards agricultural emissions. Considering all the results obtained, it is seen that the variables that have the most effect on the increase in agricultural emissions in Azerbaijan are the number of livestock, the consumption of chemical fertilizers, and the use of pesticides, respectively. The results from the research will contribute to the information on agricultural greenhouse gas emissions and will play an enlightening role for policymakers and the general public.