• Journal of Environmental Science International
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• v.14 no.11
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• pp.1049-1056
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• 2005

[ $NO_2$ ] concentration characteristics of Busan metropolitan city was analysed by statistical method using hourly $NO_2$ concentration data$(1998\~2000)$ collected from air quality monitoring sites of the metropolitan city. 4 representative regions were selected among air quality monitoring sites of Ministry of environment. Concentration data of $NO_2$, 5 air pollutants, and data collected at AWS was used. Both Stepwise Multiple Regression model and ARIMA model for prediction of $NO_2$ concentrations were adopted, and then their results were compared with observed concentration. While ARIMA model was useful for the prediction of daily variation of the concentration, it was not satisfactory for the prediction of both rapid variation and seasonal variation of the concentration. Multiple Regression model was better estimated than ARIMA model for prediction of $NO_2$ concentration.

• Asian Journal of Atmospheric Environment
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• v.7 no.2
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• pp.85-94
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• 2013

Methane concentrations have been monitored at the Anmyeon-do Observatory, Korea, since 1999. In recent years, the methane concentration has increased, but the sources of this increase have yet to be identified. This study was designed to identify the major source contributing to the increase by using World Data Centre for Greenhouse Gases (WDCGG) data and the Greenhouse Gases Emission Presumption (GEP) method. The data were collected at Anmyeon-do between 2003 and 2009 (except 2008), and the analyses showed that the increase in methane concentration originated mainly in rice paddies around the observation point. The annual average methane concentration at Anmyeon-do was 1894 ppb, of which 100-103 ppb (5.3-5.4%) was shown to originate mainly from rice paddies. The seasonal fluctuation in methane concentration from May to October estimated by the GEP method was compared with experimental data of previous research conducted on rice paddies. The close match obtained through this comparison shows that the GEP method is effective. The difference in methane concentration was also analyzed in terms of land use and land cover. It was shown that although rice paddies account for only 14.7% of the area surveyed, they accounted for between 69 and 90% of the total increase in methane concentration. These results confirm that rice paddies are the main source of the increase in methane concentration observed at Anmyeon-do.

• Atmosphere
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• v.22 no.1
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• pp.13-28
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• 2012

The $PM_{10}$ concentration data is useful for indentifying intensity and a transport way of Asian dust. However, it is difficult to identify them properly due to the limited spatial resolution and coverage. Therefore, a methodology to estimate $PM_{10}$ concentration using visibility data obtained from synoptic observation was developed. To derive the converting function, correlation between visibility and $PM_{10}$ concentration is investigated using visibility and $PM_{10}$ concentration data observed at 20 stations in Korea from 2005 to 2009. To minimize bias due to atmospheric moisture, data with higher relative humidity over a critical value were eliminated while deriving $PM_{10}$-visibility relationship. As a result, an exponentially decreasing function of visibility is obtained under the condition that relative humidity is less than 82%. Verification of the visibility converting function to $PM_{10}$ concentration was carried out for the dust cases in 2010. It was found that spatial distributions of $PM_{10}$ calculated by visibility are in good agreement with the observed $PM_{10}$ distribution, especially for the strong dust cases in 2010. And correlation between the derived and observed $PM_{10}$ concentration was 0.63. We applied the function to obtain distributions of $PM_{10}$ concentration over North Korea, in which concentration data are not available, and compared them with satellite derived dust index, IODI distributions for dust cases in 2010. It is shown that the visibility function estimates quite similar patterns of dust concentration with IODI image, which suggests that it can contribute for prediction by indentifying transport route of Asian dust.

• Journal of the Korean housing association
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• v.26 no.4
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• pp.55-62
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• 2015

The international concern on the inhalable fine dust is continuing to increase. In addition to the toxic properties of the fine dust itself, it can be more dangerous than other environmental factors since the dust pollution is hard to be detected by human sense. Although the information on outdoor air condition can be acquired easily, the indoor dust concentration is another problem because the indoor air condition is influenced by the architectural environment and human activity. It means occupants may be exposed to indoor dust pollution over a long period without being aware. Therefore the indoor dust concentration should be measured separately and visualized as an intuitive information. By visualizing, the indoor dust concentration in each space can be recognized practically in compare with the degree of pollution in adjacent spaces. Besides the visualization outcome can be used as base data for related research such as an analysis of the relation between indoor dust concentration and architectural environment. Meanwhile, with the development of network and micro sensing devices, it became possible to collect wide range of indoor environment data. In this regards, this paper suggests a system for visualization of indoor dust concentration and demonstrates it on an actual space.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.3
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• pp.90-96
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• 2018

This paper proposed data driven techniques to forecast the time point of water management of the water reservoir without measuring manganese concentration with the empirical data as Juam Dam of years of 2015 and 2016. When the manganese concentration near the surface of water goes over the criteria of 0.3mg/l, the water management should be taken. But, it is economically inefficient to measure manganese concentration frequently and regularly. The water turnover by the difference of water temperature make manganese on the floor of water reservoir rise up to surface and increase the manganese concentration near the surface. Manganese concentration and water temperature from the surface to depth of 20m by 5m have been time plotted and exploratory analyzed to show that the water turnover could be used instead of measuring manganese concentration to know the time point of water management. Two models for forecasting the time point of water turnover were proposed and compared as follow: The regression model of CR20, the consistency ratio of water temperature, between the surface and the depth of 20m on the lagged variables of CR20 and the first lag variable of max temperature. And, the Box-Jenkins model of CR20 as ARIMA (2, 1, 2).

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.368-368
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• 2000

In this paper, we present forecasting ozone concentration with decision support system. Since the mechanism of ozone concentration is highly complex, nonlinear, and nonstationary, modeling of ozone prediction system has many problems and results of prediction are not good performance so far. Forecasting ozone concentration with decision support system is acquired to information from human knowledge and experiment data. Fuzzy clustering method uses the acquisition and dynamic polynomial neural network gives us a good performance for ozone prediction with ability of superior data approximation and self-organization.

• Journal of Wellbeing Management and Applied Psychology
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• v.6 no.2
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• pp.1-7
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• 2023

Purpose: The purpose of this study is to investigate the characteristics of airborne radon concentration by season in public-use facilities in South Korea. Research design, data and methodology: The data is provided by the public data portal, and public-use facilities nationwide where radon in the air is measured are specialized sanatorium for senior citizens, libraries, childcare facilities, postpartum care centers, medical institutions, funeral halls, underground shopping malls, and underground subway stations. Results: The facility with the highest radon concentration in public-use facilities was childcare facilities with an average of 50.2 ± 21.7 Bq/m³, while the average of medical institutions was the lowest at 24.8 ± 5.7 Bq/m³. The season with the largest difference in average radon concentration between childcare facilities and medical institutions was in the order of fall (28.6 Bq/m³), followed by winter (28.1 Bq/m³), spring (23.0 Bq/m³), and summer (22.0 Bq/m³). Conclusions: The main concentration levels of each public-use facility shown in this study are all below domestic and international standards, but there is a significant concentration difference between facilities. By season, winter showed the highest average concentration (40.6 ± 21.3 Bq/m³) and summer showed the lowest average concentration (23.8 ± 14.0 Bq/m³).

• Journal of Information Technology Services
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• v.18 no.1
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• pp.173-186
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• 2019

Recently, as the amount of fine dust has risen rapidly, our interest is increasing day by day. It is virtually impossible to remove fine dust. However, it is best to predict the concentration of fine dust and minimize exposure to it. In this study, we developed a mathematical model that can predict the concentration of fine dust using various information related to the weather and air quality, which is provided in real time in 'Air Korea (http://www.airkorea.or.kr/)' and 'Weather Data Open Portal (https://data.kma.go.kr/).' In the mathematical model, various domestic seasonal variables and atmospheric state variables are extracted by multiple regression analysis. The parameters that have significant influence on the fine dust concentration are extracted, and using ANN (Artificial Neural Network) and SVM (Support Vector Machine), which are machine learning techniques, we proposed a prediction model. The proposed model can verify its effectiveness by using past dust and weather big data.

• Water Engineering Research
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• v.1 no.2
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• pp.95-105
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• 2000

A method is presented which enables easily the computation of the suspended sediment discharge as the mean sediment concentration and mean flow velocity. This method has significant advantages over the traditional method, which principally depend on a set of measured concentration data. The method is based on both a new sediment concentration and mean sediment concentration equations which have been derived from the entropy concept used in statistical mechanics and information theory: (1) The sediment concentration distribution equations derived, are capable of describing the variation of the concentration in the vertical direction. (2) The mean concentration equation derived, is capable of calculating easily the mean concentration by using only one measured concentration in open channel. The present study mainly addresses the following two subjects : (1) new sediment concentration and mean sediment concentration equations are derived from the entropy concept : (2) An efficient and useful method of suspended sediment-discharge measurements is developed which can facilitate the estimation of suspended sediment-discharge in open channel. Flume and laboratory data are used to carry out the research task outlined above. An efficient method for determining the suspended sediment-discharge in the open channel has been developed. The method presented also is efficient and applicable in estimating the sediment transport in rivers and the sediment deposit in the reservoirs, and can drastically reduce the time and cost of sediment measurements.

• Journal of Environmental Science International
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• v.8 no.4
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• pp.465-471
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• 1999

The concentration of air pollution in a large city such as Pusan has been increased every years due to the increase on fuel consumption at factories and by vehicles as well as the gravitation of the population. In this study, we have analyzed $NO_2$ concentration data and various data of meteorological factors during 1994-1997 to investigate the characteristics of $NO_2$ concentration and how the high $NO_2$ concentration is generated under the meterological condition. According to the study, $NO_2$ peak concentration at most sites occured about 1h later after the rush hour. In the characteristics of emissions in sites, sinpyeong-dong was highly contributed to point source while the other sites were highly contributed to line source. The high $NO_2$ concentration had high generation probability when temperature contained typical seasonal characteristics and wind speed was low. Using the relationship between meteorological factors and the daily average $NO_2$ concentration, correlation analysis was practiced. the seasonal variation of the daily average $NO_2$ concentration was correlated with air temperature, solar radiation and wind speed, but the correlation coefficient between meteorological factors and the daily average $NO_2$ concentration was not so much high. Thus we have known that the daily average $NO_2$ concentration is partially explained by meteorological factors.