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

• Main Authors: A. Spekat, F. Kreienkamp, W. Enke
• Format: Article
• Language: English
• Published: Copernicus Publications 2008-07-01
• Series: Advances in Science and Research
• Online Access: http://www.adv-sci-res.net/2/119/2008/asr-2-119-2008.pdf

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.