• Proceedings of the Korea Air Pollution Research Association Conference
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• 2009.10a
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• pp.314-315
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• 2009

• Proceedings of the Korean Environmental Sciences Society Conference
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• 2007.05a
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• pp.162-164
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• 2007

• Journal of the Korean Society for Railway
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• v.11 no.4
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• pp.378-383
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• 2008

In December 2006, the Ministry of Environment Republic of Korea established the guideline which is "Indoor Air Quality Management Guidelines in Public Transportation." as control items, $CO_2$ (carbon dioxide) and PM10 (particle matter) are classified two categories, that is, Level 1 (non-rush hour), Level 2 (rush hour). Therefore, the quality of air in train and subway should be controlled in accordance with the guideline. We took a measure the air freshness inside train twice at Line 4 (Tangogae-Oido), in Sep. 2007 and at Line 1 (Dongincheon-Yongsan) in Nov. 2007, respectively and, also expected the emitted $CO_2$ concentration by using a property of matter such as EMU (Electric Multiple Unit) design reviewing specification and air. According to the measured values, the concentration of PM10 was 44, 57, 45% and the concentration of $CO_2$ was 39, 36, 44% respectively, all measured values are within the guideline and also, as a result we found the expected value and measured value are similar.

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2008.04a
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• pp.521-523
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• 2008

• Journal of Korean Society of Environmental Engineers
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• v.30 no.11
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• pp.1111-1115
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• 2008

The characterization of PM$_{2.5}$ and PM$_{10}$ concentration is considered by analysis of ionic and heavy metal component to measured suspended particle at atmosphere in Hoseo university of Asan area. The variation of concentration is studied at the periods of asian dust occured. In asian dust, the PM$_{2.5}$ ratio is decreased from 79.7% to 40.1%, whereas the size-classified mean concentration of suspended particle is increased largely. It is found that the PM$_{2.5}$ ratio is decreased relatively because the coarse particle is increased largely according to the analysis of the mass concentration to divide the fine and coarse particle on 2.1 $\mu$m basis. It is observed that the Ca$^{2+}$ion is about 40 magnifications and Na$^+$, SO$_4{^{2-}}$ ion is increased in sequence in coarse particle, whereas the variation of ionic concentration is slightly increased in the fine particle. Furthermore, Mn, Fe, Zn, and Al are increased in sequence as the result of heavy metal component analysis, and Al is shown the most increased as mass concentration.

• Journal of Wetlands Research
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• v.11 no.2
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• pp.47-54
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• 2009

This study was conducted to consider the characteristics of PM10(particulate matter with aerodynamic diameters less than 10 ${\mu}m$) concentration according to land-use in Busan coastal area. Fine particle is affected by emissions, geographical conditions and meteorological factors. In case industrial area, Gamjeondong(inland) PM10 concentration was higher than Noksandong(seashore) at all season except for Summer. Primary peak at Gamjeondong cleared than Noksandong in Fall and Winter. In case green area, Daejeodong(inland) PM10 concentration was higher than Dongsamdong(seashore) at all seasons. In case commercial area, primary peak occurrence time at Jeonpodong lagged one hour according to season and diurnal change of PM10 concentration at Gwangbokdong was higher than Jeonpodong in Spring. In case residential area, high PM10 concentration(80~90 ${\mu}g/m^3$) lasted for six hours during the daytime in Spring at Deogcheondong and Yongsuri(inland).

• Journal of Environmental Impact Assessment
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• v.19 no.1
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• pp.59-74
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• 2010

The trend of the PM10 concentrations in the Seoul Metropolitan Area (SMA) is reviewed and relative contributions of major contributors (paved road emissions and long-range transport from outside the SMA) are discussed. It was shown that the PM10 concentrations in the SMA have generally decreased except Incheon between 1999 and 2005. Further, it was identified that the difference of the PM10 mass concentration between the roadside stations and urban ambient stations has decreased between 2004 and 2008. Based on the emission estimates, it was suggested that the reduction of resuspension of aerosols on the road is the major reason for that. Based on the modeling results, it was identified that outside effects be about 30% of the ambient PM10 concentration in the SMA. Further research and policy issues to identify major sources of PM10 in the SMA are discussed.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.280-282
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• 2021

A particular matter prediction model was designed using a deep learning algorithm to solve the problem of particular matter forecast with subjective judgment applied. RNN and LSTM were used among deep learning algorithms, and it was designed by applying optimal parameters by proceeding with hyperparametric navigation. The predicted performance of the two models was evaluated through RMSE and predicted accuracy. The performance assessment confirmed that there was no significant difference between the RMSE and accuracy, but there was a difference in the detailed forecast accuracy.

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2004.11a
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• pp.208-209
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• 2004

• KIPS Transactions on Software and Data Engineering
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• v.9 no.4
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• pp.129-136
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• 2020

PM2.5 which is a very tiny air particulate matter even smaller than PM10 has been issued in the environmental problem. Since PM2.5 can cause eye diseases or respiratory problems and infiltrate even deep blood vessels in the brain, it is important to predict PM2.5. However, it is difficult to predict PM2.5 because there is no clear explanation yet regarding the creation and the movement of PM2.5. Thus, prediction methods which not only predict PM2.5 accurately but also have the interpretability of the result are needed. To predict hourly PM2.5 of Seoul city, we propose a method using random forest with the adjusted bootstrap number from the time series ground data preprocessed on different sources. With this method, the prediction model can be trained uniformly on hourly information and the result has the interpretability. To evaluate the prediction performance, we conducted comparative experiments. As a result, the performance of the proposed method was superior against other models in all labels. Also, the proposed method showed the importance of the variables regarding the creation of PM2.5 and the effect of China.