한국환경보건학회지 (Journal of Environmental Health Sciences)

  • 제44권6호
  • /
  • Pages.539-547
  • /
  • 2018
  • /
  • 1738-4087(pISSN)
  • /
  • 2233-8616(eISSN)
한국환경보건학회 (Korean Society of Environmental Health)
권호 표지
DOI QR코드

DOI QR Code

Simple Forecasting of Surface Ozone through a Statistical Approach

  • Ma, Chang-Jin (Department of Environmental Science, Fukuoka Women's University) ;
  • Kang, Gong-Unn (Department of Medical Administration, Wonkwang Health Science University)
  • 투고 : 2018.10.12
  • 심사 : 2018.11.22
  • 발행 : 2018.11.28
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Objectives: Ozone ($O_3$) advisories are issued by provincial/prefectural and city governments in Korea and Japan when oxidant concentrations exceed the criteria of the related country. Advisories issued only after exposure to high $O_3$ concentrations cannot be considered ideal measures. Forecasts of $O_3$ would be more beneficial to citizens' health and daily life than real-time advisories. The present study was undertaken to present a simplified forecasting model that can predict surface $O_3$ concentrations for the afternoon of the day of the forecast. Methods: For the construction of a simple and practical model, a multivariate regression model was applied. The monitored data on gases and climate variables from Japan's air quality networks that were recorded over nearly one year starting from April 2016 were applied as the subject for our model. Results: A well-known inverse correlation between $NO_2$ and $O_3$ was confirmed by the monitored data for Iksan, Korea and Fukuoka, Japan. Typical time fluctuations for $O_3$ and $NO_x$ were also found. Our model suggests that insolation is the most influential factor in determining the concentration of $O_3$. $CH_4$ also plays a major role in our model. It was possible to visually check for the fit of a theoretical distribution to the observed data by examining the probability-probability (P-P) scatter plot. The goodness of fit of the model in this study was also successfully validated through a comparison (r=0.8, p<0.05) of the measured and predicted $O_3$ concentrations. Conclusions: The advantage of our model is that it is capable of immediate forecasting of surface $O_3$ for the afternoon of the day from the routinely measured values of the precursor and meteorological parameters. Although a comparison to other approaches for $O_3$ forecasting was not carried out, the model suggested in this study would be very helpful for the citizens of Korea and Japan, especially during the $O_3$ season from May to June.

키워드

참고문헌

  1. Lee JH, Oh IB., Kin MH, Bang JH, Park SJ, Yoon SH, Kim YH., Change in the prevalence of allergic diseases and its association with air pollution in major cities of Korea - Population under 19 years old in different land-use areas -. Korean Journal of Environmental Health. 2017; 43(6): 478-490.
  2. Bae HJ, Ha JS, Lee AK, Park JI. Age dependencies in air pollution-associated asthma hospitalization. Korean Journal of Environmental Health. 2008; 34(2): 124-130. https://doi.org/10.5668/JEHS.2008.34.2.124
  3. Sicard P, De Marco A, Troussier F, Renou C, Vas N Paoletti E. Decrease in ozone mean concentrations at Mediterranean remote sites and increase in the cities. Atmospheric Environment. 2013; 79: 705-715. https://doi.org/10.1016/j.atmosenv.2013.07.042
  4. Contran N, Paoletti E. Visible foliar injury and physiological responses to ozone in Italian provenances of Fraxinus excelsior and Fornus. The Scientific World Journal. 2007; 7: 90-97. https://doi.org/10.1100/tsw.2007.10
  5. Paoletti E. Impacts of ozone on Mediterranean forests: A review. Environmental Pollution. 2006; 144: 463-474. https://doi.org/10.1016/j.envpol.2005.12.051
  6. Mauzerall DL., Jacob DJ, Fan SM, Bradshaw JD, Gregory GL, Sachse GW, Blake DR. Origin of tropospheric ozone at remote high northern latitudes in summer. Journal of Geophysical Research. 1996; 101: 4175-4188. https://doi.org/10.1029/95JD03224
  7. Jeonbuk Daily. 2017. http://www.jjan.kr/news/articleView.html?idxno=1139914
  8. Wang E, Sakurai T, Ueda H. Assessment of ozone variability in East Asia during recent years. Acid Deposition Monitoring Network in East Asia (EANET) Science Bulletin. 2008; 1: pp. 3-20.
  9. Hara Y, Uno I, Shimizu A, Sugimoto N, Matsui I, Yumimoto K, Kurokawa J, Ohara T, Liu Z. Seasonal characteristics of spherical aerosol distribution in eastern Asia: Integrated analysis using ground/space-based lidars and a chemical transport model. SOLA. 2011; 7: 121-124. doi: 10.2151/sola.2011-031
  10. Iwamoto S, Oishi O, Tagami S, Chikara H, Yamamoto S. Classify causes for high concentration of photochemical oxidant in Fukuoka prefecture. Japan Society for Atmospheric Environment. 2008; 43: 173-179.
  11. Box GEP, Jenkins GM. Time series analysis forecasting and control; Holden day: San Francisco, CA.1976.
  12. Kumar K, Yadav AK, Singh MP, Hassan H, Jain VK. Forecasting daily maximum surface ozone concentrations in Brunei Darussalam-An ARIMA modeling approach. Journal of the Air & Waste Management Association. 2004; 54(7): 809-814, DOI: 10.1080/10473289.2004.
  13. Ortiz-garcia EG, Salcedo-Sanz S, Perez-Bellido AM, Portilla-Figueras JA, Prieto L. Prediction of hourly $O_3$ concentrations using support vector regression algorithms, Atmospheric Environment. 2010; 44: 4481-4488. https://doi.org/10.1016/j.atmosenv.2010.07.024
  14. Sousa SIV, Martins FG, Alvim-Ferraz MCM, Pereira MC. Multiple linear regression and artificial neural networks based on principal components to predict ozone concentrations. Environmental Modelling Software. 2007; 22: 97-103. https://doi.org/10.1016/j.envsoft.2005.12.002
  15. Wang W, Lu W, Wang X, Leung AYT. Prediction of maximum daily ozone level sing combined neural network and statistical characteristics. Environment International. 2003; 29: 555-562. https://doi.org/10.1016/S0160-4120(03)00013-8
  16. Robeson SM, Steyn DG. Evaluation and comparison of statistical forecast models for daily maximum ozone concentrations. Atmospheric Environment. 1990; 24B: 303-312.
  17. Argiriou AA. Use of neural networks for tropospheric ozone time series approximation and forecasting. Atmospheric Chemistry and Physics Discussions. 2007; 7: 5739-5767. https://doi.org/10.5194/acpd-7-5739-2007
  18. Seo J, Youn D, Kim JY, Lee H. Extensive spatiotemporal analyses of surface ozone and related meteorological variables in South Korea for the period 1999-2010. Atmospheric Chemistry and Physics. 2014; 14: 6395-6415 https://doi.org/10.5194/acp-14-6395-2014
  19. Tu J, Xia ZG, Wang H, Li W. Temporal variations in surface ozone and its precursors and meteorological effects at an urban site in China. Atmospheric Research. 2007; 85(3-4): 310-337. https://doi.org/10.1016/j.atmosres.2007.02.003
  20. Ma CJ, Kim KH. Artificial and biological particles in the springtime atmosphere. Asian Journal of Atmospheric Environment. 2013; 7: 209-216. https://doi.org/10.5572/ajae.2013.7.4.209
  21. Milionis AE, Davies TD. Regression and stochastic models for air pollution-I. Review, comments and suggestions. Atmospheric Environment. 1994; 28(17): 2801-2810. https://doi.org/10.1016/1352-2310(94)90083-3
  22. Ulke AG, Mazzeo NA. Climatological aspects of the daytime mixing height in Buenos Aires City, Argentina. Atmospheric Environment. 1998; 32: 1615-1622. https://doi.org/10.1016/S1352-2310(97)00396-8
  23. Han S, Bian H, Feng Y, Liu A, Li X, Zeng F, Zhang X. Analysis of the Relationship between $O_3$, NO and $NO_2$ in Tianjin, China. Aerosol and Air Quality Research. 2011; 11: 128-139. https://doi.org/10.4209/aaqr.2010.07.0055
  24. Leighton PA. Photochemistry of air pollution. Academic Press, New York. 1961; pp.300.
  25. Chatterjee S, Hadi AS. Regression analysis by example. Wiley & Sons, New York. 2006.
  26. Hossain MG, Sabiruzzaman M, Islam S, Ohtsuki F, Lestrel PE. Effect of craniofacial measures on the cephalic index of Japanese adult female students. Anthropological Science. 2010; 118(2): 117-121. https://doi.org/10.1537/ase.091022