In this paper a method of analyzing air pollution data in an optional automatic measuring station, allowing for identification of the directions of the pollution inflow has been presented. The method is based on four parameters provided by the measuring station: pollution concentration, wind direction, wind speed and fluctuation of the wind directions. For the description of the wind direction fluctuation in 30-minutes' periods a coefficient of relative turbulent diffusion rr(3, 30) was used, which is defined as a deviation of 3-minutes' wind vectors from the 30-minutes' vector. The presented method was applied for identification of the inflow directions of SO2 and NO2 using the measuring data from a telemetric system OPSIS at the Institute for Ecology of Industrial Areas in Katowice-Załęże.

The vapour pressure of most explosives is very low. Therefore, the explosive trace detection is very difficult. To overcome the problem, concentration units can be applied. At the Institute of Optoelectronics MUT, an explosive vapour concentration and decomposition unit to operate with an optoelectronic sensor of nitrogen dioxide has been developed. This unit provides an adsorption of explosive vapours from the analysed air and then their thermal decomposition. The thermal decomposition is mainly a chemical reaction, which consists in breaking up compounds into two or more simple compounds or elements. During the heating process most explosive particles, based on nitro aromatics and alkyl nitrate, release NO2 molecules and other products of pyrolysis. In this paper, the most common methods for the NO2 detection were presented. Also, an application of the concentration and decomposition unit in the NO2 optoelectronic sensor has been discussed.

Data sets gathered continuously in air monitoring systems are never entirely complete. The problem of missing data in monitoring measure series often has to be solved by modeling. A new method of air monitoring data modelling was tested in the paper. Regional diurnal concentration courses (RDCCs) were used as the main source of knowledge of predicted time series during specified days. The paper presents a comparison of predicted and measured diurnal concentration patterns of two frequently used parameters in air monitoring (PM10 and NO2). The analysis was based on hourly time series of these air pollutants collected in a 3-year period at nine monitoring stations in the Lodz Region. It was shown that well determined regional diurnal concentration patterns could be useful to missing data modelling at the specified monitoring site. Improvement of modelling accuracy is possible after modification of modelling results by adding local difference vectors (LDVs), describing the specificity of the monitoring station.