Analyses of factors that affect PM10 level of Seoul focusing on meteorological factors and long range transferred carbon monooxide

서울시 미세먼지 농도에 영향을 미치는 요인 분석 : 기상 요인 및 장거리 이동 물질 중 일산화탄소를 중심으로

  • Park, A.K. (Departments of Biomedical Sciences, Seoul National University College of Medicine) ;
  • Heo, J.B. (Civil and Environmental Engineering Department, University of Wisconsin-Madison) ;
  • Kim, H. (Department of Epidemiology and Biostatistics, School of Public Health, Seoul National University)
  • 박애경 (서울대학교 의과대학 의과학과) ;
  • 허종배 (위스콘신주립대학교-메디슨 환경공학과) ;
  • 김호 (서울대학교 보건대학원 보건학과)
  • Received : 2011.05.26
  • Accepted : 2011.06.22
  • Published : 2011.06.30

Abstract

The objective of the study was to investigate the main factors that contribute the variation of $PM_{10}$ concentration of Seoul and to quantify their effects using generalized additive model (GAM). The analysis was performed with 3 year air pollution data (2004~2006) measured at 27 urban sites and 7 roadside sites in Seoul, a background site in Gangwha and a rural site in Pocheon. The diurnal variation of urban $PM_{10}$ concentrations of Seoul showed a typical bimodal pattern with the same peak times as that of roadside, and the maximum difference of $PM_{10}$ level between urban and roadside was about $14{\mu}g/m^{3}$ at 10 in the morning. The wind direction was found to be a major factor that affects $PM_{10}$ level in all investigated areas. The overall $PM_{10}$ level was reduced when air came from east, but background $PM_{10}$ level in Gangwha was rather higher than the urban $PM_{10}$ level in Seoul, indicating that the $PM_{10}$ level in Gangwha is considerably influenced by that in Seoul metropolitan area. When hourly variations of $PM_{10}$ were analyzed using GAM, wind direction and speed explained about 34% of the variance in the model where the variables were added as a 2-dimensional smoothing function. In addition, other variables, such as diurnal variation, difference of concentrations between roadside and urban area, precipitation, month, and the regression slope of a plot of carbon monooxide versus $PM_{10}$, were found to be major explanatory variables, explaining about 64% of total variance of hourly variations of $PM_{10}$ in Seoul.

References

  1. 김용표 (2006) 서울의 미세먼지에 의한 대기오염, 한국대기환경학회지, 22(5), 535-553.
  2. 이형민, 김용표 (2007) 자동차 부제에 의한 서울 대기오염 저감 효과 분석, 한국대기환경학회지, 23(4), 498-506.
  3. Guerra, S.A., D.D. Lane, G.A. Marotz, R.E. Carter, C.M. Hohl, and R.W. Baldauf (2006) Effects of wind direction on coarse and fine particulate matter concentrations in southeast Kansas, J Air Waste Manag Assoc. 56, 1525-1531. https://doi.org/10.1080/10473289.2006.10464559
  4. Hastie, T.J., and R.J. Tibshirani (1990) Generalized Additive Models. Chapman and Hall.
  5. Kerschbaumer, A and M. Lutz (2008) Origin and influence of PM10 in urban and in rural environments, Adv. Sci. Res., 2, 53-55.
  6. Sanchex-Reyna, G, K.Y.Wang, J.C.Gallardo, and D.E.Shallcross (2006) Association between PM10 mass concentration and wind direction in London. Atmos.Sci. Let, 6, 204-210.
  7. Turalioglu, S., A. Nuhoglu, and H. Bayraktar (2005) Impacts of some meteorological parameters on SO2 and TSP concentrations in Erzurum, Turkey. Chemosphere, 59, 1633-1642. https://doi.org/10.1016/j.chemosphere.2005.02.003
  8. Wood, S.N. (2003) Thin plate regression splines, J. R. Statist. Soc. 65, 95-114. https://doi.org/10.1111/1467-9868.00374
  9. Wood, S.N. (2006) Low rank scale invariant tensor product smooths for generalized additive mixed models, Biometrics, 62, 1025-1036. https://doi.org/10.1111/j.1541-0420.2006.00574.x
  10. Wood, S.N., and N.H. Augustin (2002) GAMs with integrated model selection using penalized regression splines and applications to environmental modelling, Ecological Modelling, 157, 157-177. https://doi.org/10.1016/S0304-3800(02)00193-X
  11. Yu, T.Y., and I.C. Chang (2006) Spatiotemporal features of severe air pollution in northern Taiwan. Environ. Sci. Pollut. Res. Int, 13, 268-275. https://doi.org/10.1065/espr2005.12.288