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.

Aiming to create more sustainable cities it is necessary to understand and manage different ecological factors which influence human health. One of such factors is black carbon (BC) in atmosphere, which currently is not commonly monitored by environmental monitoring systems. The aim of this research was to estimate by indirect approach the relation between eBC (equivalent of black carbon) concentration and other air pollutants in order to define approximate level of eBC in more efficient approach. The study was conducted in Wrocław (Poland) in October 2021, and combined data on eBC concentration (measured by microaethalometer), air quality (from national environmental monitoring system) and traffic (from municipal traffic management system). Quantile regression was used to assess the relationship between the concentrations of pollutants. The obtained results show that for rise 1 mg∙m^–3 of carbon monoxide, eBC concentration rise between 4.2 and 8.0 μg∙m^–3, depending on the period of a day. Precision of eBC concentration evaluation is influenced by sun light which results in higher precision of defining a scaling factor for night hours. Outcomes of this study constitute an added value to understanding of interconnections between different factors describing environmental conditions in cities and might be helpful for more effective environmental assessment of human habitats.