Journal of Atmospheric Chemistry

, Volume 57, Issue 1, pp 19–40 | Cite as

Application of statistical method in pollution episode analyses



Observed pollutants are of both primary and secondary origins, influenced by local topography and meteorology. Identifying sources and relative contribution often require vast detailed data collection and complicated models. This study applied a statistical time series analysis to two selected pollution events, spring and fall, at two sites in northern Taiwan. Vector moving average representations were used to quantitatively examine relationships among chemical pollutants and estimate their lifetimes. Results from impulse responses show that wind direction change alters the characteristic of pollution observed in opposite sites of Taipei City, from chemical dominant system to transport dominant one and vice versa. Chemicals are clearly separated into photochemical pollutants and primary pollutants. Results pointed out that Taipei City is the major source of photochemical smog, but not these primary pollutants. Derived chemical lifetimes at same location vary from 20% to four times under different meteorological condition. Estimated concentrations of hydroxyl radical range between 2 to 8 × 107 cm−3. Photochemical pollutants are responsible for parts of PM10 collected in both station observed. Oxidation of SO2 is only important in PM10 observed at one station. This study provides a simpler tool to derive information usually from complex models, therefore, is suitable as complement in decision-making process.

Key words

air pollution lifetime estimation statistical analysis variance decomposition 



This study was supported by grant from Environmental Protection Bureau, Taipei County, and National Science Council of Taiwan.


  1. DeMore, William B., Sander, S.P., Golden, D.M., Hampson, R.F., Kurylo, M.J., Howard, C.J., Ravishankara, A.R., Kolb, C.E., Molina, M.J.: Chemical Kinetics and Photochemical Data for use in Stratospheric Modeling. JPL publication 97-4, Pasadena, California (1997)Google Scholar
  2. EPA: Annual Report on Environment. Environmental Protection Administration, Taiwan (2000)Google Scholar
  3. Hsu, K.-J.: Time series analysis of the interdependence among air pollutants. Atmos. Environ. 26B, 491–503 (1992)Google Scholar
  4. Hsu, K.-J.: Application of vector autoregressive time series analysis to aerosol studies. Tellus 49B, 327–342 (1997)Google Scholar
  5. Jacobson, M.Z.: Atmospheric Pollution: History, Science, and Regulation. Cambridge University Press, Cambridge, United Kingdom (2002)Google Scholar
  6. Liao, H., Seinfeld, J.H., Adams, P.J., Mickley, L.J.: Global radiative forcing of coupled tropospheric ozone and aerosols in a unified general circulation model. J. Geophys. Res. 109, D16207, (2004)
  7. Luhar, A.K., Hurley, P.J.: Evaluation of TAPM, a prognostic meteorological and air pollution model, using urban and rural point-source data. Atmos. Environ. 37, 2795–2810 (2003)CrossRefGoogle Scholar
  8. Lutkepohl, H.: Introduction to Multiple Time Series Analysis. Springer, Berlin Heidelberg New York (1991)Google Scholar
  9. Mayer, M., Wang, C., Webster, M., Prinn, R.G.: Linking local air pollution to global chemistry and climate. J. Geophys. Res. 105, 22, 869–22,896 (2000)CrossRefGoogle Scholar
  10. Priestley, M.B.: Spectral Analysis and Time Series. Academic, London, United Kingdom (1981)Google Scholar
  11. Seinfeld, John H.: Atmospheric Chemistry and Physics of Air Pollution. Wiley, New York (1986)Google Scholar

Copyright information

© Springer Science+Business Media, B.V. 2007

Authors and Affiliations

  1. 1.Department of Atmospheric SciencesNational Taiwan UniversityTaipeiTaiwan

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