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Abstract

This paper presents an identification method of pollution inflow directions based on the circular graphs of percentiles of pollution concentrations and the possibilities of applying these graphs to estimation of pollutant emission. Based on these graphs of particulate matter concentration recorded in winter and summer seasons, the inflows of dust from the direction of fly ash landfill have been compared for summer and winter periods. This analysis enables to assess the relation of concentrations of pollution from secondary particulate matter suspension to concentrations of pollution generated by other sources. This paper also argues that the 24-hours concentrations, though commonly used, may prove to be an indicator of minor importance to assess air pollution status. On the one hand, excessive 24-hours concentrations impede the identification of the polluter, on the other hand, concentrations generally within permissible limits may occasionally peak dangerously at some sources. As an example, an existing case and a numerical experiment have been presented.
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Authors and Affiliations

Czesław Kliś
Marek Matejczyk
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Abstract

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.
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Authors and Affiliations

Czesław Kliś
Mieczysław Żeglin
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Abstract


Analysis of lead and cadmium concentrations in the air comparing concentration values difference between heating and summer seasons was carried out in the paper. Relevant procedure was adopted to find out if the concentration values in these two seasons differed in kind. The concentration seasonal difference was not found in case of cadmium but it was found for lead. It was proved in further part of the paper that the analysed mean 24-hour Pb concentrations for heating season could be presented as a sum of the mean annual background concentration and the concentration values resulted from Pb emission from sources active only in the heating season. In the area where the measurements were carried out residential furnaces were this kind of sources. The cumulative distribution function of the mean 24-hour lead concentration resulted from Pb emissions in the heating season was determined using two-layer neural network. It was found according to this approach that Pb concentration as the result of Pb emissions from residential furnaces, for 145 days, i.e. 80% of the heating season period, were at least two-fold lower than the lead concentration values as the result of Pb emission from the all year active sources. Only for 14 days emission sources active in the heating season produced Pb concentrations higher than Pb mean annual background concentration.
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Authors and Affiliations

Czesław Kliś
Stanisław Hławiczka
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Abstract

The paper presents a mature concept of an intelligent monitoring system of air pollution inflow and its realization in the form of a SINZaP system lunched at Institute for Ecology of Industrial Areas (]ETU) in 2006. SINZaP is a real time operating system resembling a neural network. It is designed for modeling of pollutant emissions and air pollutants concentrations, addressed to specialists or decision makers responsible for air quality management. For modeling of emission and air pollutants concentrations in SIZNaP system, a back trajectory model -BackTrack has been used, which is based on YLSTRACK model. The essential feature ofthe BackTrack model is the application of back trajectories in the selection of emission sources influencing a given receptor. For modeling of trajectories BackTrack uses three-dimensional wind fields, friction velocity, MoninObukhov length and mixing layer height. SINZaP consists of four main modules: (I) data module including data scanner for reading public data accessible in the Internet, (2) module for preparation of meteorological data, (3) BackTrack module for simulations of pollutants emissions and simulations of air pollutants concentrations, and (4) Trainer module, the task ofwhich is correction of input parameters for adjusting modeling and observed data.
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Authors and Affiliations

Czeslaw Kliś
Joachim Bronder

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