The purpose of the study was the analysis of spatial data gained by biomonitoring with the use of mosses. A partial goal was set to characterize the regional atmospheric deposition of pollutants in the air based on the results of the analyses and simultaneously verify the suitability of using mosses as an alternative for monitoring air quality in smaller industrial areas. In total, 93 samples of moss were collected and examined from the area of the Moravian–Silesian Region in the Czech Republic and the area of the Silesian Voivodship in Poland. The samples were analyzed using instrumental neutron activation analysis. Based on the analyses performed, 38 elements, which had been evaluated using principal component analysis, hierarchical clustering on principal components, factor analysis, correlation analysis, contamination factor, geoaccumulation index, enrichment factor, and pollution load index, were determined. The analyses resulted in a division of elements into a group with its concentrations close to the level of the values of the natural background and the second group of elements identified as emission likely originating from anthropogenic activity (Sm, W, U, Tb, and Th). The likely dominant source of emissions for the studied area was identified. Simultaneously, the results pointed to sources of local importance. The area of interest was divided into clusters according to the prevailing type of pollution and long-distance transmission of pollutants was confirmed.

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.