On-road mobile sources of emissions make important contributions to particulate matter pollution (PM_2.5–PM₁₀) in cities. The quantification of such pollution is, however, highly challenging due to the number of interacting factors that affect emissions such as vehicle category, emission standard, vehicle speed and weather conditions. The proper identification of individual sources of emission is particularly necessary for air quality management areas. In this study, we estimated exhaust and non-exhaust traffic-related PM_2.5 and PM₁₀ contributions to total ambient pollution in Banská Bystrica (Slovak republic) by simulation based on the AERMOD dispersion model. Emission rates of particular vehicle categories were obtained through vehicle population statistics, traffic data survey and emission factors from the EMEP/EEA air pollutant emission inventory guidebook. Continuous PM₁₀ and PM_2.5 data from air quality monitoring stations were analysed for the years 2019–2020 and compared with modelled concentrations. The annual concentration values of PM_2.5 and PM₁₀ in the study area reached 16.71 μg/m³ and 15.57 μg/m³, respectively. We found that modelled PM_2.5 peak concentration values exceeded the WHO air quality guideline annual mean limit. Traffic-related PM_2.5 and PM₁₀ contributions to ambient pollution at the reference point located nearby to a busy traffic route were approximately 25% and 17%, respectively. The reference point located outside the main transport corridors showed an approximately 11% contribution, both for PM_2.5 and PM₁₀ concentrations. The simulations showed that PM pollution is greatly contributed to by on-road mobile sources of emissions in the study area, and especially non-exhaust emissions, which require serious attention in association with their health impacts and the selection of Banská Bystrica as an air quality management area.

Workers in primary aluminum smelter are exposed to fluoride from cryolite (Na₃AlF₆) used in the electrolysis process. Post-shift urinary fluoride is considered as an appropriate index for examination of fluoride exposure. The objective of the study was to investigate the exposure to fluoride in primary aluminum smelter in Žiar nad Hronom (Slovakia) during three consecutive two-year periods between 2012 and 2018. The relationship between fluoride exposure in the occupational environment, tobacco smoking, and pre- and post-shift urinary fluoride concentration was investigated in 76 male workers in the ages from 21 to 60 years. Workers were monitored by personal fluoride sampling equipment. Their urinary samples were collected prior to the start and at the end of an eight-hour shift. Fluoride content in urine samples was analyzed by potentiometric ion-selective electrode and expressed as weight ratio of fluoride content to creatinine. The Mean ± SD particulate fluoride concentration in occupational air was 0.966 ± 1.658 mg/m³ and gas-phase fluoride concentration was 0.327 ± 0.809 mg/m³. Mean urinary fluoride concentration of all workers was significantly higher (p < 0.001) after the eight-hour shift. Smokers tended to have a higher post-shift mean urinary fluoride concentration than non-smokers, but this difference was not statistically significant (p = 0.11). The difference between these two groups of workers was also not statistically significant (p = 0.62) before the shift. Therefore, according to results, smoking caused no statistically significant difference in urinary fluoride levels between the group of smokers and group of non-smokers in primary aluminum workers.

In order to keep the home and occupational environment clean and non-infectious, the consumption of cleaners and disinfectants, including cosmetics, is increasing. Excessive use of these products results in their accumulation in the aquatic environment. Conventional wastewater treatment plants are unable to effectively remove the emergent pollutants, including personal care products. This article is focused on the monitoring of the presence of personal care products in surface waters in two river basins in the Slovak Republic, in terms of the surfactant content. Ecotoxicological evaluation of the selected samples from the monitored river basins was performed by an acute toxicity test using the test organism Daphnia magna. The monitoring results indicate the presence of personal care products in the aquatic environment which poses an ecological and environmental risk. Monitoring in the Hron and Nitra river basins confirmed contamination with the surfactants, to which the measures related to the COVID-19 pandemic contributed. The content of the surfactants in personal care products is significant, and their impact on the aquatic environment is not sufficiently monitored.

Chainsaw operation is associated with several hazards, including exposure to wood dust, a risk factor which requires serious attention due to health impacts such as respiratory disease. This study aims to investigate the effect of saw chain type and wood species on the mass concentration of airborne wood dust during chainsaw cross-cutting operations. Real-time inhalable and respirable dust measurements in the breathing zone of the chainsaw operator were carried out using a desktop aerosol monitor. Three types of cutter shape (semi chisel, full chisel, and chamfer chisel) and four types of wood species (oak, beech, spruce, and fir) were used in the experiments. The split-plot ANOVA results showed that both respirable and inhalable mass concentrations of wood dust were affected exclusively by wood species. No statistically significant differences were found among particular levels of cutter shape. The highest average mass concentrations of airborne dust were generated by the combination of oak wood and a full chisel cutter.

The use of paper sludge as a waste stream from industrial facilities represents a significant environmental challenge due to its quantity and heterogeneous composition. The aim of the study was to evaluate the adsorption characteristics of paper sludge in neutral mine effluents and aquatic solutions of metal ions: Cu(II), Zn(II), Cd(II), and Mn(II). The main novelty of the research is a comparison of the adsorption process in synthetically prepared aquatic solutions and neutral mine drainage from field sampling. The adsorption process of the monitored metals was evaluated in terms of adsorption capacity, parameters of the Freundlich and Langmuir adsorption isotherm, and the separation factor. The adsorption capacity of paper sludge of all metals is significantly lower in neutral mine drainage (NMD) compared to adsorption in aquatic solution. The adsorption capacity of Zn(II) in aqueous solution reaches equilibrium over time, similarly to Cu(II), with values ranging from 0.2 to 1.6 mg/g. For Cd(II), a slight increasing trend in the adsorption capacity of paper sludge is observed at higher initial concentrations (3–5 mg/L) over a contact time of 90–120 min. In general, aqueous solutions of metal ions exhibited higher adsorption capacities compared to NMD, with the highest value recorded for Cu(II) at 4.742 mg/g. As the concentration values in the original solution increased, a decline in K_R (from 268% to 137% at a C0 range of 4–20 mg/L) was observed. In the mine drainage with the addition of Zn(II), K_R values were also lower compared to those in aquatic solutions. The reduction in K_R became more pronounced with increasing initial concentration, showing a decrease of 29.9% to 38.9% at C0 levels ranging from 2 to 10 mg/L. The separation factors for Cu(II), Zn(II), and Cd(II) were lower in NMD, indicating better metal separation from real mine waters. The results confirm the potential of paper sludge as a low-cost adsorbent for the treatment of heavy metal contaminated waters.

The integration of acoustic emission (AE) signals into adaptive control systems for CNC wood milling represents a promising advancement in intelligent manufacturing. This study investigated the feasibility of using AE signals for the real-time monitoring and control of CNC milling processes, focusing on medium-density fiberboard (MDF) as the workpiece material. AE signals were captured using dual-channel sensors during side milling on a five-axis CNC machine, and their characteristics were analyzed across varying spindle speeds and feed rates. The results showed that AE signals were sensitive to changes in machining parameters, with higher spindle speeds and feed rates producing increased signal amplitudes and distinct frequency peaks, indicating enhanced cutting efficiency. The statistical analysis confirmed a significant relationship between AE signal magnitude and cutting conditions. However, limitations related to material variability, sensor configuration, and the narrow range of process parameters restrict the broader applicability of the findings. Despite these constraints, the results support the use of AE signals for adaptive control in wood milling, offering potential benefits such as improved machining efficiency, extended tool life, and predictive maintenance capabilities. Future research should address signal variability, tool wear, and sensor integration to enhance the reliability of AE-based control systems in industrial applications.