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Advances in Science and Research Contributions in Applied Meteorology and Climatology
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Volume 2, issue 1 | Copyright
Adv. Sci. Res., 2, 119-126, 2008
https://doi.org/10.5194/asr-2-119-2008
© Author(s) 2008. This work is distributed under
the Creative Commons Attribution 3.0 License.

  03 Jul 2008

03 Jul 2008

STAT-IMM, a statistical approach to determine local and background contributions to PM10 levels

W. Enke, F. Kreienkamp, and A. Spekat W. Enke et al.
  • Climate & Environment Consulting Potsdam GmbH, Potsdam, Germany

Abstract. When studying concentrations of particulate matter with a size of 10 µm or below (PM10), measured locally, it becomes evident that two main portions need to be quantified: The concentration produced by sources in the vicinity of the station and the long range transports. The traditional approaches include analyses of the components of PM10, comparisons upwind and downwind of a station, investigation of trajectories and complex chemical transport modelling. The development of an independent strategy which makes use of statistical methods, including regression and correlation analysis is a reasonable alternative. This method, presented here, does not apply the concept of PM10 sources, but, rather, analyzes the relations between times series of PM10 measurements and atmospheric properties. It is applied to identify the shares of the local portion and the large-scale background plus a stochastic portion that cannot be attributed to either of the two. Using regression analysis, a set of objectively chosen meteorological parameters is used to reconstruct the local PM10 measurement series, defining the local portion. This weather-dependent part of the series is then removed and the residuum, which contains the large-scale PM10 background and a stochastic portion is analyzed further with correlations. Results are shown for a three-year set of data which includes well over 250 PM10 stations across Germany. The data is analyzed according to different stratifications, such as the PM10 load and the wind direction as well as for the data set as a whole. In a further development of the method, a study of PM10 transports across several border sections is shown.

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