A biomonitoring study using terrestrial mosses was performed in the vicinity of an Integrated Iron and Steel plant near the Czech–Polish border. Moss samples were collected in two seasons (June, October) in order to embrace the effect of the heating season on the pollution levels. The contents of metals (Al, V, Cr, Mn, Fe, Ni, Cu, Zn, Cd, Pb, As, Sb and Hg) were determined using the Inductively Coupled Plasma-Atomic Emission Spectroscopy (ICP-AES), Atomic Absorption Spectroscopy (AAS) and contents of N, C, H via elemental analysis. The influence of the proximity of the factory, the heating season and modelled concentrations of particulate matter <10 µm (PM₁₀) on determined concentrations of elements were studied via multivariate statistical methods using clr-transformed data. This approach led to the first-time demonstration that not only the distance from the industrial source but also the sampling season and PM₁₀ concentrations significantly affect the elemental content in mosses; the association of the emissions from the source and the determined concentrations of elements in moss samples were more evident outside the heating season (October). The analyses of transformed data revealed the association of Fe, Cr, V, As and Al with the coarse particles and their dominant spatial distribution depending on the prevailing wind directions. The spatial distribution of Mn, Zn and Cd, which are carried by fine particles, appears to depend more on atmospheric dispersion and long-range transport, and, thus, these metals should be considered weak markers of the pollution load in the close surroundings of an industrial source.

Air pollution dispersion modelling via spatial analyses (Land Use Regression—LUR) is an alternative approach to the standard air pollution dispersion modelling techniques in air quality assessment. Its advantages are mainly a much simpler mathematical apparatus, quicker and simpler calculations and a possibility to incorporate more factors affecting pollutant’s concentration than standard dispersion models. The goal of the study was to model the PM₁₀ particles dispersion via spatial analyses in the Czech–Polish border area of the Upper Silesian industrial agglomeration and compare the results with the results of the standard Gaussian dispersion model SYMOS’97. The results show that standard Gaussian model with the same data as the LUR model gives better results (determination coefficient 71% for Gaussian model to 48% for LUR model). When factors of the land cover were included in the LUR model, the LUR model results improved significantly (65% determination coefficient) to a level comparable with the Gaussian model. A hybrid approach of combining the Gaussian model with the LUR gives superior quality of results (86% determination coefficient).

Knowing the relationship between pollution sources and air pollution concentrations is crucial. Mathematical modeling is a suitable method for the assessment of this relationship. The aim of this research was to compare the results of the Analytical Dispersion Modelling Supercomputer System (ADMOSS), which is used for air pollution modeling in large areas, with the results of moss biomonitoring. For comparison purposes, air pollution mathematical modeling and the collection of moss samples for biomonitoring in the Czech–Polish–Slovak border area in the European Grouping of Territorial Cooperation (EGTC) Tritia were carried out. Moss samples were analyzed by multi-element instrumental neutron activation analysis (INAA). The INAA results were statistically processed using the correlation-matrix-based hierarchical clustering and correlation analysis of the biomonitoring results and ADMOSS results. Biomonitoring using bryophytes proved to be suitable for the verification of mathematical models of air pollution due to the ability of bryophytes to capture the long-term deposition of pollutants and the resulting possibility of finding the real distribution of pollutants in the area, as well as identify the specific chemical elements, the distribution of which coincides with the mathematical model.

A proper estimation of anti-epidemic measures related to the influence of the COVID-19 outbreak on air quality has to deal with filtering out the weather influence on pollution concentrations. The goal of this study was to estimate the effect of anti-epidemic measures at three pollution monitoring stations in the Ostrava region. Meteorological data were clustered into groups with a similar weather pattern, and pollution data were divided into subsets according to weather patterns. Then each subset was evaluated separately. Our estimates showed a 4.1–5.7% decrease in NO_x concentrations attributed to lower traffic intensity during the lockdown. The decrease of PM_2.5 varied more significantly between monitoring stations. The highest decrease (4.7%) was detected at the traffic monitoring station, while there was no decrease detected at the rural monitoring station, which focuses mainly on domestic heating pollution. The key result of the study was the development of an analytical method that is able to take into account the effect of meteorological conditions. The method is much simpler and easy to replicate as an alternative to other published methods.

The air pollution dispersion modelling via spatial analyses (Land Use Regression – LUR) is an alternative approach to the air quality assessment to the standard air pollution dispersion modelling techniques. Its advantages are mainly much simpler mathematical apparatus, quicker and simpler calculations and a possibility to incorporate other factors affecting pollutant’s concentration. The goal of the study was to model the PM10 particles dispersion modelling via spatial analyses v in Czech-Polish border area of Upper Silesian industrial agglomeration and compare results with results of the standard Gaussian dispersion model SYMOS’97. Results show that standard Gaussian model with the same data as the LUR model gives better results (determination coefficient 71% for Gaussian model to 48% for LUR model). When factors of the land cover and were included into the LUR model, the LUR model results were significantly improved (65% determination coefficient) to the level comparable with Gaussian model. The hybrid approach combining the Gaussian model with the LUR gives superior quality of results (65% determination coefficient).

Land Use Regression (LUR) is one of the air quality assessment modelling techniques. Its advantages lie mainly in a much simpler mathematical apparatus, quicker and simpler calculations, and a possibility to incorporate more factors affecting pollutant concentration than standard dispersion models. The goal of the study was to perform the LUR model in the Polish-Czech-Slovakian Tritia region, to test two sets of pollution data input factors, i.e., factors based on emission data and pollution dispersion model results, to test regression via neural networks and compare it with standard linear regression. Both input datasets, emission data and pollution dispersion model results, provided a similar quality of results in the case when standard linear regression was used, the R² of the models was 0.639 and 0.652. Neural network regression provided a significantly higher quality of the models, their R² was 0.937 and 0.938 for the factors based on emission data and pollution dispersion model results respectively.