DOI QR코드

DOI QR Code

Analysis of Time Series Models for Ozone Concentration at Anyang City of Gyeonggi-Do in Korea

경기도 안양시 오존농도의 시계열모형 연구

  • Published : 2008.10.31

Abstract

The ozone concentration is one of the important environmental issue for measurement of the atmospheric condition of the country. This study focuses on applying the Autoregressive Error (ARE) model for analyzing the ozone data at middle part of the Gyeonggi-Do, Anyang monitoring site in Korea. In the ARE model, eight meteorological variables and four pollution variables are used as the explanatory variables. The eight meteorological variables are daily maximum temperature, wind speed, amount of cloud, global radiation, relative humidity, rainfall, dew point temperature, and water vapor pressure. The four air pollution variables are sulfur dioxide $(SO_2)$, nitrogen dioxide $(NO_2)$, carbon monoxide (CO), and particulate matter 10 (PM10). The result shows that ARE models both overall and monthly data are suited for describing the oBone concentration. In the ARE model for overall ozone data, ozone concentration can be explained about 71% to by the PM10, global radiation and wind speed. Also the four types of ARE models for high level of ozone data (over 80 ppb) have been analyzed. In the best ARE model for high level of ozone data, ozone can be explained about 96% by the PM10, daliy maximum temperature, and cloud amount.

Keywords

Ozone;High level of ozone;Autoregressive error (ARE) model;Anyang City;Meteorological variables;Pollution variables;Explanatory variables

References

  1. Bauer, G., M. Deistler, and W. Scherrer (2001) Time series models for short term forecasting of ozone in the eastern part of Austria, Environmetrics, 12, 117-130 https://doi.org/10.1002/1099-095X(200103)12:2<117::AID-ENV448>3.0.CO;2-N
  2. Hubbard, M. and W. Cobourn (1998) Development of a regression model to forecast ground-level ozone concentration in Louisville, KY, Atmospheric Environment, 32(14/15), 2637-2647 https://doi.org/10.1016/S1352-2310(97)00444-5
  3. Lin, Y. and W.G. Cobourn (2006) Fuzzy system models combined with nonlinear regression for daily groundlevel ozone predictions, Atmospheric Environment, 41(2007), 3502-3513 https://doi.org/10.1016/j.atmosenv.2006.11.060
  4. 전의찬, 우정헌(1999) 오존 농도에 영향을 미치는 주 기상 요소의 도출 및 예측모형 수립, 한국대기환경학회지, 15(3), 257-266
  5. 허정숙, 김동술(1993) 다변량 통계분석을 이용한 서울시 고 농도 오존의 예측에 관한 연구, 한국대기보전학회지, 9(3), 207-215
  6. Brulfert, G., O. Galvez, F. Yang, and J.J. Sloan (2007) A regional modeling study of the high ozone episode of June 2001 in southern Ontario, Atmospheric Environment, 41, 3777-3788 https://doi.org/10.1016/j.atmosenv.2007.01.030
  7. 허명회, 문승호(2006) 탐색적 자료분석, 자유아카데미
  8. Bower, J.S., K.J. Stevenson, G.F.J. Broughyon, and J.E. Lampert (1994) Assessing recent surface ozone concentrations in the U.K., Atmospheric Environment, 28D, 115-128
  9. 김진영, 김영성(2001) 상세한 기상관측 자료를 이용한 1997 년 서울 수도권 고농도 오존 사례의 모델링, 한국대기환경학회지, 17(1), 1-17
  10. Ludwig, F.L, I.J. Jiang, and J. Chen (1995) Classification of ozone and weather pattern associated with high ozone concentrations in the San Francisco and Monterey Bay Areas, Atmospheric Environment, 29, 2915-2828 https://doi.org/10.1016/1352-2310(95)00091-C
  11. 김신도(1998) 오존예보모델 및 에보시스템의 개선, 오존예 보시시템에 관한 전문가토론회, 16-23
  12. 김유근, 손건태, 문윤섭, 오인보(1999) 서울 지역의 지표오존 농도 예보를 위한 전이함수 모델 개발, 한국대기환경학회지, 15(6), 779-789
  13. Cobourn, W. and M.C. Hubbard (1999) An enhanced ozone forecasting model using air mass trajectory analysis, Atmospheric Environment, 33, 4663-4674 https://doi.org/10.1016/S1352-2310(99)00240-X
  14. Lyons, W.A., J.L. Eastman, R.A. Pielke, C.T. Tremback, P.A. Moon, and K.R. Limcoln (1991) The meteorological of ozone episodes in the lower lake Michigan air quality region, air & waste management association, For presentation at the 83th Annual Meeting & Exhibition, Vancouver, British Columbia, June 16-21
  15. 환경부(2004) 대기환경연보 2004년 6월, 60pp
  16. Pagowski, M., G.A. Grell, D. Devenyi, S.E. Peckham, S.A. McKeen, W. Gong, L. Delle Monache, J.N. Mc- Henrt, J. McQueen, and P. Lee (2006) Application of dynamic linear Regression to improve the skill of ensemble-based deterministic ozone forecasts, Atmospheric Environment, 40(2006), 3240-3250 https://doi.org/10.1016/j.atmosenv.2006.02.006
  17. Poulida, O., R.P. Dickerson, B.G. Doddridge, J.Z. Holland, R.G. Wardell, and J.G. Wartjins (1991) Trace gas concentrations and meteorology in rural Virginia: ozone and carbon monoxide, Journal of Geo-physical Research, 96, 22461-22475 https://doi.org/10.1029/91JD02353
  18. 이훈자(2007) 경기도 남부지역 지표오존농도의 시계열모형 연구, 한국대기환경학회지, 23(3), 364-372 https://doi.org/10.5572/KOSAE.2007.23.3.364
  19. Jorquera, H., R. Perez, A. Cipriano, A. Espejo, M.V. Letelier, and G. Acuna (1998) Forecasting ozone daily maximum levels at Santiago, Chile, Atmospheric Environment, 32(20), 3415-3424 https://doi.org/10.1016/S1352-2310(98)00035-1
  20. 김유근, 이소영, 임윤구, 송상근(2007) 중회귀모형을 이용한 울산지역 오존 포텐셜 모형의 설계 및 평가, 한국대기환경학회지, 23(1), 14-28 https://doi.org/10.5572/KOSAE.2007.23.1.014
  21. Byun, D.W., S. Kim, and S. Kim (2006) Evaluation of air quality models for the simulation of a high ozone episode in the Houston metropolitan area, Atmospheric Environment, 41, 837-853 https://doi.org/10.1016/j.atmosenv.2006.08.038
  22. 환경부(2003) 대기환경연보 2003, 65pp
  23. McKendry, I.G. (1993) Ground-level ozone in Montreal, Canada, Atmospheric Environment, 27B, 93-103