Journal of the Korean Data and Information Science Society

The Korean Data and Information Science Society (한국데이터정보과학회)
권호 표지

Analysis of statistical models for ozone concentrations at the Paju city in Korea

경기도 파주시 오존농도의 통계모형 연구

  • Lee, Hoon-Ja (Department of Information Statistics, Pyeongtaek University)
  • 이훈자 (평택대학교 디지털응용정보학과)
  • Published : 2009.11.30
Download PDF
⟨ Previous Next ⟩

Abstract

The ozone data is one of the important environmental data for measurement of the atmospheric condition of the country. In this article, the Autoregressive Error (ARE) model and Neural Networks (NN) model have been considered for analyzing the ozone data at the northern part of the Gyeonggi-Do, Paju monitoring site in Korea. In the both ARE model and NN model, seven meteorological variables and four pollution variables are used as the explanatory variables for the ozone data set. The seven meteorological variables are daily maximum temperature, wind speed, relative humidity, rainfall, dew point temperature, steam pressure, and amount of cloud. The four air pollution explanatory variables are Sulfur dioxide ($SO_2$), Nitrogen dioxide ($NO_2$), Cobalt (CO), and Promethium 10 (PM10). The result showed that the NN model is generally better suited for describing the ozone concentration than the ARE model. However, the ARE model will be expected also good when we add the explanatory variables in the model.

지표오존 농도는 국가의 중요한 환경 척도 중의 하나이다. 본 연구에서는 경기도 파주시 오존농도를 자기회귀오차모형과 신경망모형으로 분석하였다. 오존 분석을 위한 설명변수로는 이산화황, 이산화질소, 일산화탄소, 프로메툼10 등의 대기자료와 일 최고온도, 풍속, 상대습도, 강수량, 이슬점온도, 운량, 수증기압 등의 기상자료를 사용하였다. 분석 결과 전반적으로 신경망모형이 좋은 모형으로 나타났고, 자기회귀오차모형도 오존에 영향을 주는 설명변수를 첨가하면 좋은 모형이 될 것으로 생각된다.

Keywords

References

  1. 김신도 (1998). <오존예보모델 및 예보시스템의 개선>, 오존예보시스템에 관한 전문가 토론회, 16-23.
  2. 김용국 (1994). 하계의 일 최고 오존농도 예측을 위한 신경망모델의 개발. <한국대기보존학회지>, 10, 224-232.
  3. 김유근, 손건태, 문윤섭, 오인보 (1999). 서울지역의 지표오존농도 예보를 위한 전이함수모델 개발. <한국대기환경학회지>, 15, 779-789.
  4. 조신섭, 이정형 (1007). , 자유아카데미, 경기.
  5. Bauer, G., Deistler, M. and Scherrer, W. (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
  6. Camalier, L., Cox, W. and Dolwick, P. (2007). The effects of meteorology on ozone in urban areas and their use in assessing ozone trends. Atmospheric Environment, 41, 1-11.
  7. Feister, U. and Balzer, K. (1991). Surface ozone and meteorological preddictors on a subregional scale. Atmospheric Environment , 25, 1781-1790. https://doi.org/10.1016/0960-1686(91)90262-6
  8. Guttorp, P., Meiring, W. and Samson, D. P. (1994). A space-time analysis of ground-level ozone data. Environmetrics, 5, 241-254. https://doi.org/10.1002/env.3170050305
  9. Hubbard, M. and Cobourn, W. (1998). Development of a regression model to forecast ground-level ozone concentration in Louisville. KY, Atmospheric Environment , 32, 2637-2647. https://doi.org/10.1016/S1352-2310(97)00444-5
  10. Jorquera, H., Perez, R., Cipriano, A., Espejo, A., Letelier, M. V. and Acuna, G. (1998). Forecasting ozone daily maximum levels at Santiago, Chile. Atmospheric Environment, 32, 3415-3424. https://doi.org/10.1016/S1352-2310(98)00035-1
  11. Lee, H. (2008). Analysis of time series models for ozone concentrations at the Uijeongbu city in Korea. Journal of the Korean Data & Information Science Society, 19, 1153-1164.
  12. Park, C. and Kim, H. (2009). Prediction ozone warning days based on optimal time series model. Journal of the Korean Data & Information Science Society, 20, 293-299.
  13. Smith, L. S. and Huang, L. S. (1993). Modeling the threshold exceedence of urban ozone, Technical Report No.6, National Institute for Statistical Science, Research Triangle Park, NC.