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).

The PM₁₀ concentrations in the studied region (Ostravsko-karvinská agglomeration, Czech Republic) exceed air pollution limit values in the long-term and pose a significant problem for human health, quality of life and the environment. In order to characterize the pollution in the region and identify the pollution origin, Instrumental Neutron Activation Analysis (INAA) was employed for determination of 34 elements in PM₁₀ samples collected at a height of 90 m above ground level. From April 2018 to March 2019, 111 PM₁₀ samples from eight basic wind directions and calm and two smog situations were sampled. The elemental composition significantly varied depending on season and sampling conditions. The contribution of three important industrial sources (iron and steelworks, cement works) was identified, and the long-range cross-border transport representing the pollution from the Polish domestic boilers confirmed the most important pollution inflow during the winter season.

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).

The presented article presents a hydrological analysis of the extreme flood in September 2024 in the experimental catchments of the Slučí, Sokolí and Suchý brook, which are right-hand tributaries of the Černá Opava River. The interruption of monitoring and morphological changes in the riverbeds in these catchments due to the destruction of water-gauging stations forced the reconstruction of the flood using mathematical models, which is generally an obvious fact for the Jeseníky region with the greater or lesser meaning. The rainfall-runoff models HYDROG, HEC-HMS, MIKE SHE, SIMWE and the hydraulic models HEC-RAS and DHI MIKE were used for this analysis. The Mnichov hydrological station on the Černá Opava was chosen as the calibration profile. The causal precipitation belongs among the absolute measured records in the stations in several locations in the Jeseníky Mountains, and the propagation of the flood further down the Opava catchment represented an absolute maximum in the measured water level and evaluated flow for some stations. The situation was not different in the catchments of the Slučí, Sokolí and Suchý brook. The analyses also included a comparison of the results of individual rainfall-runoff models and the methods they implement, e.g. SCS-CN, Horton or Green-Ampt. The simulated hydrographs show good correlation between the measured and evaluated data, with the Nash-Sutcliffe coefficient not falling below 0.7, mostly due to the declining part of the flood hydrograph, when standard rainfall-runoff models are unable to simulate the slowdown of runoff due to water accumulation in inundation areas. For this and other reasons, it is appropriate to use a combination of rainfall-runoff and hydraulic models.

Bryophytes disperse efficiently via wind-borne spores, yet long-distance transport alone does not guarantee successful establishment at the deposition site. Species-rich hotspots are expected to arise where a sustained regional diaspore supply coincides with a high diversity of suitable substrates, creating both safe sites for colonization and stepping-stone nodes for further dispersal. Here, we use the Ostrava Zoo, located in the core of a heavily industrialized city in the Moravian-Silesian region (Czech Republic), as a case study to examine how these processes shape bryophyte diversity in an urban landscape. Between 2021 and 2024, we recorded 129 bryophyte taxa (18 liverworts, 111 mosses) within the Zoo, a species richness comparable to that of nearby protected areas in the Moravian-Silesian Beskids. The assemblage includes several regionally rare taxa and a pronounced epiphytic component (38 epiphytic species), indicating both improved air quality and the importance of old, healthy tree stands. Analyses of species occurrence across habitat categories demonstrate that the exceptional diversity of the Zoo is underpinned by high substrate and microhabitat heterogeneity, spanning remnants of beech forest, wetlands, disturbed soils in animal enclosures and a wide range of artificial structures. Back-trajectory modelling of air masses during the main vegetation season further shows that prevailing winds frequently connect the Zoo with bryophyte-rich reserves in the Beskids, supporting the hypothesis that the Zoo functions as a recipient and secondary source of diaspores in a fragmented urban matrix. Our results highlight that large urban green areas such as zoological gardens can act as bryophyte refugia and stepping-stone sites of conservation importance within industrial regions.