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.

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.

This study evaluated the ability of two plants, Phragmites australis and Typha latifolia, to bioaccumulate zinc from industrial effluents in constructed wetlands using ceramsite as a support medium. Two types of experiments were conducted: one with real industrial effluent containing 9.4 mg/L of Zn and another with synthetic effluent containing Zn at concentrations ranging from 10 to 100 mg/L. Zinc uptake in plant segments, ceramsite, and its concentration in wastewater were determined using ICP-OES. Both plants removed 97–99% of zinc ions from the industrial effluent, with the highest metal uptake occurring in the roots. In the case of synthetic solutions, Typha latifolia demonstrated higher zinc removal efficiency (95–99% removal) compared to Phragmites australis (74–90%). Typha latifolia also accumulated significantly higher levels of Zn, primarily in the roots. Transfer factor values were calculated to assess zinc translocation within plant tissues. No visual signs of toxicity were observed during the experiment. This phytoremediation approach could represent a sustainable and environmentally friendly method for treating industrial effluents.

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.

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.