A Statistical Model of Background Air Pollution Frequency Distributions

Antonovsky, M.Y., Buchstaber, V.M., & Zaleniuk, E.A. (1988). A Statistical Model of Background Air Pollution Frequency Distributions. IIASA Working Paper. IIASA, Laxenburg, Austria: WP-88-102

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Abstract

The authors of this paper describe an approach for identifying statistically stable central tendencies in the frequency distributions of time series of observations of background atmospheric pollutants. The data were collected as daily mean values of concentrations of sulfur dioxide and suspended particulate matter at five monitoring stations -- three in the USSR, one in Norway, and one in Sweden.

In their approach, the authors use well-developed statistical techniques and the usual method of constructing multimodal distributions. The problem is subdivided into two parts: first, a decomposition of the observations in order to obtain a description of each season separately and second, an investigation of this description in order to derive statistically stable characteristics of the entire data set. The main hypothesis of the investigation is that dispersion processes interact in such a way that in the zone of influence of one process (near its mode) the "tails" of the other process are not observed. This permits illumination of interrelations between the physics and the chemistry of the atmosphere.

During the last 15-20 years, a wide range of monitoring programs has been initiated at national and international levels including, for example, the European Monitoring and Evaluation Program (EMEP) under the auspices of the ECE, and the Background Air Pollution Monitoring Network (BAPMoN) under the auspices of the WMO.

The flow of data from the system of monitoring stations has led to national and international projects for the development of extensive environmental data bases such as NOAANET (NDAA), GRID/GEMS/UNEP/NASA, etc. The degree of information obtained should be sufficient for the goals of the analysis but often there is an overabundance of such data. The methods discussed in this paper therefore help in air pollution assessments, particularly with respect to distinguishing the baseline components, and their trends over decades.

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