지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2010.12
- 수록면
- 666 - 682 (17page)
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The forecasting system for Today’s and Tomorrow’s PM10 was developed based on the statistical model and the forecasting was performed at 9 AM to predict Today’s 24 hour average PM10 concentration and at 5 PM to predict Tomorrow’s 24 hour average PM10. The Today’s forecasting model was operated based on measured air quality and meteorological data while Tomorrow’s model was run by monitored data as well as the meteorological data calculated from the weather forecasting model such as MM5 (Mesoscale Meteorological Model version 5). The observed air quality data at ambient air quality monitoring stations as well as measured and forecasted meteorological data were reviewed to find the relationship with target PM10 concentrations by the regression analysis.
The PM concentration, wind speed, precipitation rate, mixing height and dew-point deficit temperature were major variables to determine the level of PM10 and the wind direction at 500 hpa height was also a good indicator to identify the influence of long-range transport from other countries. The neural network, regression model, and decision tree method were used as the forecasting models to predict the class of a comprehensive air quality index and the final forecasting index was determined by the most frequent index among the three model’s predicted indexes. The accuracy, false alarm rate, and probability of detection in Tomorrow’s model were 72.4%, 0.0%, and 42.9% while those in Today’s model were 80.8%, 12.5%, and 77.8%, respectively. The statistical model had the limitation to predict the rapid changing PM10 concentration by long-range transport from the outside of Korea and in this case the chemical transport model would be an alternative method.
The PM concentration, wind speed, precipitation rate, mixing height and dew-point deficit temperature were major variables to determine the level of PM10 and the wind direction at 500 hpa height was also a good indicator to identify the influence of long-range transport from other countries. The neural network, regression model, and decision tree method were used as the forecasting models to predict the class of a comprehensive air quality index and the final forecasting index was determined by the most frequent index among the three model’s predicted indexes. The accuracy, false alarm rate, and probability of detection in Tomorrow’s model were 72.4%, 0.0%, and 42.9% while those in Today’s model were 80.8%, 12.5%, and 77.8%, respectively. The statistical model had the limitation to predict the rapid changing PM10 concentration by long-range transport from the outside of Korea and in this case the chemical transport model would be an alternative method.
목차
Abstract
1. 서론
2. 예보시스템 구성
3. 예보 입력변수와 미세먼지 농도와의 상관관계 분석
4. 미세먼지 통계예보모형의 이론
5. 미세먼지 예보모형의 평가
6. 결론
감사의 글
참고문헌
참고문헌 (21)
참고문헌 신청
1. [학술지(정기간행물)] - Brian, E. / / A demonstration of the national air quality forecast capability / U.S. EPA’s 2009 National Air Quality Conference
2. [학술지(정기간행물)] - Colbourn, W.G. / 2010 / An enhanced PM2.5 air quality forecast model based on nonlinear regression and back-trajectory concentrations / Atmospheric Environment 44 (25) : 3015 ~ 3023
3. [학술지(정기간행물)] - Hooyberghs, J. / 2005 / A neural network forecast for daily average PM10 concentrations in elgium / Atmospheric Environment 39 (18) : 3279 ~ 3289
5. [학술지(정기간행물)] - Kim, M.W. / 2009 / Development of PM2.5 forecasting model and a web-based air quality mapping and forecasting system for Ohio / U.S.EPA’s 2009 National Air Quality Conference
6. [학술지(정기간행물)] - Kim, Y.P. / 2006 / Air pollution in Seoul caused by aerosols / J.Korean Soc. Atmos. Environ. 22 (5) : 535 ~ 553
2. [학술지(정기간행물)] - Colbourn, W.G. / 2010 / An enhanced PM2.5 air quality forecast model based on nonlinear regression and back-trajectory concentrations / Atmospheric Environment 44 (25) : 3015 ~ 3023
3. [학술지(정기간행물)] - Hooyberghs, J. / 2005 / A neural network forecast for daily average PM10 concentrations in elgium / Atmospheric Environment 39 (18) : 3279 ~ 3289
5. [학술지(정기간행물)] - Kim, M.W. / 2009 / Development of PM2.5 forecasting model and a web-based air quality mapping and forecasting system for Ohio / U.S.EPA’s 2009 National Air Quality Conference
6. [학술지(정기간행물)] - Kim, Y.P. / 2006 / Air pollution in Seoul caused by aerosols / J.Korean Soc. Atmos. Environ. 22 (5) : 535 ~ 553
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