한국대기환경학회지 (Journal of Korean Society for Atmospheric Environment)

  • 제23권4호
  • /
  • Pages.449-456
  • /
  • 2007
  • /
  • 1598-7132(pISSN)
  • /
  • 2383-5346(eISSN)
한국대기환경학회 (Korean Society for Atmospheric Environment)
권호 표지
DOI QR코드

DOI QR Code

분산계수의 전처리에 의한 대기분산모델 성능의 개선

Improvement of Atmospheric Dispersion Model Performance by Pretreatment of Dispersion Coefficients

  • Park, Ok-Hyun (Department of Environmental Engineering, Pusun National University) ;
  • Kim, Gyung-Soo (Department of Environmental Engineering, Pusun National University)
  • 발행 : 2007.08.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Dispersion coefficient preprocessing schemes have been examined to improve plume dispersion model performance in complex coastal areas. The performances of various schemes for constructing the sigma correction order were evaluated through estimations of statistical measures, such as bias, gross error, R, FB, NMSE, within FAC2, MG, VG, IOA, UAPC and MRE. This was undertaken for the results of dispersion modeling, which applied each scheme. Environmental factors such as sampling time, surface roughness, plume rising, plume height and terrain rolling were considered in this study. Gaussian plume dispersion model was used to calculate 1 hr $SO_2$ concentration 4 km downwind from a power plant in Boryeung coastal area. Here, measured data for January to December of 2002 were obtained so that modelling results could be compared. To compare the performances between various schemes, integrated scores of statistical measures were obtained by giving weights for each measure and then summing each score. This was done because each statistical measure has its own function and criteria; as a result, no measure can be taken as a sole index indicative of the performance level for each modeling scheme. The best preprocessing scheme was discerned using the step-wise method. The most significant factor influencing the magnitude of real dispersion coefficients appeared to be sampling time. A second significant factor appeared to be surface roughness, with the rolling terrain being the least significant for elevated sources in a gently rolling terrain. The best sequence of correcting the sigma from P-G scheme was found to be the combination of (1) sampling time, (2) surface roughness, (3) plume rising, (4) plume height, and (5) terrain rolling.

키워드

참고문헌

  1. Angell, J.K. and D.J. Pack (1965) Atmospheric lateral diffusion estimates from tetroons, Journal of Applied Meteorology, 4, 418-425 https://doi.org/10.1175/1520-0450(1965)004<0418:ALDEFT>2.0.CO;2
  2. Briggs, G.A. (1970) Some recent analysis of plume rise observations, Contribution 38, NOAA Research Laboratories, Oka Ridge, Tennesses
  3. Chang, J.C. and S.R. Hanna (2004) Air quality model performance evaluation, Meteorology and Atmospheric Physics, 87, 167-196
  4. Engel, P.L., T.O. Williams, and T. Muirhead (1997) Utilzing ISCST to model composting facility odors, 90th Annual Air and Waste Management Association Meeting and Exhibition, Toronto, Ont., Canada, 8-13, June
  5. Hanna, S.R., G.A. Briggs, J. Deardoff, B.A. Egan, F.A. Gifford, and F. Pasquill (1977) Meeting review-AMS workshop on stability classification scheme and sigma curve-summary of recommendations, Bulletin American Meteorological Society, 1305-1309
  6. Hanna, S.R., G.A. Briggs, and R.P. Hosker (1982) Handbook on Atmospheric Diffusion, U.S. DOE, Technical Information Center of DOE
  7. Hanna, S.R., J.C. Chang, and D.G. Strimaitis (1993) Hazardous gas model evaluation with field observations, Atmospheric Environment, 27A, 2265-2285
  8. Mason, P.J. and D.J. Thompson (1987) Large eddy simulations of the neutral-static-stability planetary boundary layer, Meteorological Society, 113, 413-443 https://doi.org/10.1256/smsqj.47601
  9. Okamoto, H., R. Ohba, and S. Kinoshita (1986) Prediction of diffusion taking account of topographical effects, Proc. 7th World Clean Air Congress, Sydney, Australia, 34-41
  10. Park, O.H., S.J. Seo, and S.H. Lee (1999) An experimenta study on the vertical dispersion of plume in convective boundary layer using a composite turbulence water tank, Journal of Korean Society for Atmospheric Environment, 15, 639-647
  11. Park, O.H. and C.O. Yoon (2000) An experimental study on the variation of vertical dispersion within boundary layer with surface roughness, Journal of Korean Society for Atmospheric Environment, 16, 237-246
  12. Sarma, P.B.S., J.W. Delleur, and A.R. Rao (1973) Comparison of rainfall-runoff models for urban areas, Hydrology, 18, 329-347 https://doi.org/10.1016/0022-1694(73)90056-5
  13. Scire, J.S., D.G. Strimaitis, and R.J. Yamatino (2000) A User's Guide for the CALPUFF Dispersion Model (version 5). Earth Tech. Inc., Concord, MA
  14. Wieringa. J. (1992) Updating the Davenport roughness classifications, Journal of Wind Engineering and Industrial Aerodynamics, 41, 357-368 https://doi.org/10.1016/0167-6105(92)90434-C
  15. Wilczak, J.M. and M.S. Phillips (1986) An indirect estimation of convection boundary layer structure for use in pollution dispersion models, Climate and Applied Meteorology, 25, 1609-1624 https://doi.org/10.1175/1520-0450(1986)025<1609:AIEOCB>2.0.CO;2
  16. Zannetti, P. (1990) Air Pollution Modeling, Van Nostrand Reinhold, New York, 56-60
  17. Zawar-Reza, P., S. Kingham, and J. Pearce (2005) Evaluation of a year-long dispersion modeling of PM10 using the mesoscale model TAPM for Christchurch, New Zealand, Science of the Total Environment, 349, 249-259 https://doi.org/10.1016/j.scitotenv.2005.01.037
  18. Ziomas, I.C., P. Tzoumaka, and D. Balis (1998) Ozone episodes in Athens, Greece. A modelling approach using data from the medcaphot-trace, Atmospheric Environment, 32, 2313-2321 https://doi.org/10.1016/S1352-2310(97)00414-7