The aim of this work was to compare nitrogen dioxide atmspheric pollution in some parts of Cracow and to indicate the area in which ambient air concentration of N02 may be the highest. The measurements of 24- hour N02 concentrarion were made by spectrophotometric method with passive sampling in 20 sites in the period of 2 years. The results were analysed by statistical methods, such as: analysis of variance and correlation. The 24-hour NO, concentration has significant spatial variation in the area of Cracow. The highest values were measured in the street canyons, the lowest in residential areas. The highest values of N02 concentration were observed during heating season. The results of passive method are comparable with the results of authomatic method and because of this both methods may be use together in monitoring networks.

Results of testing air quality in the vicinity of Gliwice transport routes arc presented in the paper. Assessment of air contamination with nitrogen dioxide from motor transport, for typical conditions dominating in big cities of high transit movement without any ring roads was the studies objective. Presented results will be used in the future to determine the impact of opening the ring road on air quality in the city. In the studies, the passive method of sampling, with further application of spectrophotometric technique to determine nitrogen dioxide concentration, was used. Average annual nitrogen dioxide concentrations were based on average daily concentrations measured from July 2004 to June 2005 at 16 measuring points. As they meet conditions for random distribution of measuring days and cover the measuring time, they were treated as average concentrations of nitrogen dioxide in a calendar year and were compared with a permissible concentration to make an assessment of air quality..

High concentrations of nitrogen dioxide in the air, particularly in heavily urbanized areas, have an adverse eff ect on many aspects of residents’ health. A method is proposed for modelling daily average, minimal and maximal atmospheric NO ₂ concentrations in a conurbation, using two types of modelling: multiple linear regression (LR) an advanced data mining technique – Random Forest (RF). It was shown that Random Forest technique can be successfully applied to predict daily NO ₂ concentration based on data from 2015–2017 years and gives better fit than linear models. The best results were obtained for predicting daily average NO ₂ values with R ² =0.69 and RMSE=7.47 μg/m . The cost of receiving an explicit, interpretable function is a much worse fit (R ² from 0.32 to 0.57). Verification of models on independent material from the first half of 2018 showed the correctness of the models with the mean average percentage error equal to 16.5% for RF and 28% for LR modelling daily average concentration. The most important factors were wind conditions and traffic flow. In prediction of maximal daily concentration, air temperature and air humidity take on greater importance. Prevailing westerly and south-westerly winds in Wrocław effectively implement the idea of ventilating the city within the studied intersection. Summarizing: when modeling natural phenomena, a compromise should be sought between the accuracy of the model and its interpretability.

The commercially available metal-oxide TGS sensors are widely used in many applications due to the fact that they are inexpensive and considered to be reliable. However, they are partially selective and their responses are influenced by various factors, e.g. temperature or humidity level. Therefore, it is important to design a proper analysis system of the sensor responses. In this paper, the results of examinations of eight commercial TGS sensors combined in an array and measured over a period of a few months for the purpose of prediction of nitrogen dioxide concentration are presented. The measurements were performed at different relative humidity levels. PLS regression was employed as a method of quantitative analysis of the obtained sensor responses. The results of NO2 concentration prediction based on static and dynamic responses of sensors are compared. It is demonstrated that it is possible to predict the nitrogen dioxide concentration despite the influence of humidity.