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

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

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초록

The objective of the study was to investigate the main factors that contribute the variation of PM10 concentration of Seoul and to quantify their effects using generalized additive model (GAM). The analysis was performed with 3year 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 PM10 concentrations of Seoul showed a typical bimodal pattern with the same peak times as that of roadside, and the maximum difference of PM10 level between urban and roadside was about 14㎍/㎥ at 10 in the morning. The wind direction was found to be a major factor that affects PM10 level in all investigated areas. The overall PM10 level was reduced when air came from east, but background PM10 level in Gangwha was rather higher than the urban PM10 level in Seoul, indicating that the PM10level in Gangwha is considerably influenced by that in Seoul metropolitan area. When hourly variations of PM10 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 PM10, were found to be major explanatory variables, explaining about 64% of total variance of hourly variations of PM10 in Seoul.

Abstract

The objective of the study was to investigate the main factors that contribute the variation of PM10 concentration of Seoul and to quantify their effects using generalized additive model (GAM). The analysis was performed with 3year 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 PM10 concentrations of Seoul showed a typical bimodal pattern with the same peak times as that of roadside, and the maximum difference of PM10 level between urban and roadside was about 14㎍/㎥ at 10 in the morning. The wind direction was found to be a major factor that affects PM10 level in all investigated areas. The overall PM10 level was reduced when air came from east, but background PM10 level in Gangwha was rather higher than the urban PM10 level in Seoul, indicating that the PM10level in Gangwha is considerably influenced by that in Seoul metropolitan area. When hourly variations of PM10 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 PM10, were found to be major explanatory variables, explaining about 64% of total variance of hourly variations of PM10 in Seoul.

참고문헌 (11)

  1. 김용표, "서울의 미세먼지에 의한 대기오염", 한국대기환경학회지, 22(5), pp.535~553 (2006)
  2. 이형민, "자동차 부제에 의한 서울 대기오염 저감 효과 분석", 한국대기환경학회지, 23(4), pp.498~506 (2007), 한국대기환경학회
  3. Guerra, S.A, "Effects of wind direction on coarse and fine particulate matter concentrations in southeast Kansas", J Air Waste Manag Assoc, 56, pp.1525~1531 (2006)
  4. Hastie, T.J, "Generalized Additive Models" (1990), Chapman and Hall
  5. Kerschbaumer, A, "Origin and influence of PM10 in urban and in rural environments", Adv. Sci. Res, 2, pp.53~55 (2008)
  6. Sanchex-Reyna, G, "Association between PM10 mass concentration and wind direction in London", Atmos.Sci. Let, 6, pp.204~210 (2006)
  7. Turalioğlu, S, "Impacts of some meteorological parameters on SO2 and TSP concentrations in Erzurum, Turkey", Chemosphere, 59, pp.1633~1642 (2005)
  8. Wood, S.N, "Thin plate regression splines", J. R. Statist. Soc, 65, pp.95~114 (2003)
  9. Wood, S.N, "Low rank scale invariant tensor product smooths for generalized additive mixed models", Biometrics, 62, pp.1025~1036 (2006)
  10. Wood, S.N, "GAMs with integrated model selection using penalized regression splines and applications to environmental modelling", Ecological Modelling, 157, pp.157~177 (2002)
  11. Yu, T.Y, "Spatiotemporal features of severe air pollution in northern Taiwan", Environ. Sci. Pollut. Res. Int, 13, pp.268~275 (2006)

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