In cities with an extensive air quality monitoring (AQM) system, the results of pollutant concentration measurements obtained in this system can be used not only for current assessments of air pollution, but also for analyzes aimed at better identification of factors influencing the air quality and for tracking trends in changes taking place in this regard. This can be achieved with the use of statistical methods that allow for the assessment of the variability of measurement data observed at stations of various types and for the determination of possible interdependencies between these data. In this article, an analysis of this type was carried out for traffic, urban background and industrial AQM stations in Krakow (Southern Poland) operating in the years 2017–2018 with the use of, i.a., cluster analyzes, as well as dependent samples t-test and Wilcoxon signed-rank test, taking into account the concentrations of air pollutants such as fine particulate matter (PM₁₀), nitrogen dioxide (NO₂), benzene (C₆H₆) and sulfur dioxide (SO₂). On the basis of the conducted analyzes, similarities and differences were shown between the data observed at individual types of stations, and the possibilities of using them to identify the causes of the observed changes and the effects of remedial actions to improve air quality undertaken recently and planned in the future were indicated. It was found that the air concentrations of some substances measured at these stations can be used to assess the emission abatement effects in road transport (NO₂, PM₁₀ or C₆H₆), residential heating (PM₁₀ or SO₂), and selective industrial plants (SO₂, NO₂ or C₆H₆).

One of the elements of strategy aimed at minimizing the impact of road transport on air quality is the introduction of its reorganization resulting in decreased pollutant emissions to the air. The aim of the study was to determine the optimal strategy of corrective actions in terms of the air pollutant emissions from road transport. The study presents the assessment results of the emission reduction degree of selected pollutants (PM₁₀, PM_2.5, and NO_x) as well as the impact evaluation of this reduction on their concentrations in the air for adopted scenarios of the road management changes for one of the street canyons in Krakow (Southern Poland). Three scenarios under consideration of the city authorities were assessed: narrowing the cross-section of the street by eliminating one lane in both directions, limiting the maximum speed from 70 km/h to 50 km/h, and allowing only passenger and light commercial vehicles on the streets that meet the Euro 4 standard or higher. The best effects were obtained for the variant assuming banning of vehicles failing to meet the specified Euro standard. It would result in a decrease of the yearly averaged PM₁₀ and PM_2.5 concentrations by about 8–9% and for NO_x by almost 30%.

The exceedance of air quality standards in urban agglomerations leads local communities to take actions that aim to improve aerosanitary conditions. For these actions to be efficient, it is essential to regularly collect accurate quantitative data that is able to characterize the degree of ambient air pollution. In order to achieve this objective, air quality monitoring systems are constantly being extended.In this paper, the usefulness of newly established air quality measuring stations in Krakow was examined. The assessment was carried out using statistical me-thods on the basis of the spatial and temporal variability of particulate matter (PM10) air concentrations over the period of 2016–2017. In the analysis, meteorological data (wind directions) were applied. The main part of this assessment was a pairwise comparison of the PM10 concentrations measured at particular stations. The differences between the average values and the Pearson correlation coefficient were considered. In order to verify the statistical significance of the obtained results, the t-Student test was conducted. The greatest absolute differences between the measured values occurred during the autumn-winter period (heating season). Notwithstanding the foregoing, a high variability was also observed among the traffic stations.

The episodes of elevated concentrations of different gaseous pollutants and particulate matter (PM) are of major concern worldwide, especially in city agglomerations. Krakow is an example of an urban–industrial agglomeration with constantly occurring PM₁₀ air limit value exceedances. In recent years, a number of legislative actions have been undertaken to improve air quality in this area. The multitude of factors affecting the emergence of cases of very high air pollutant concentrations makes it difficult to analyze them using simple statistical methods. Machine learning (ML) methods can be an adequate option, especially when proper amounts of credible data are available. The main aim of this paper was to examine the influence of various factors (including main gaseous pollutant concentrations and some meteorological factors) on the effect of high PM₁₀ concentration episodes in the ambient air in Krakow (Poland) using the random forest algorithm. The original methodology based on the PM₁₀ limit and binary classification of cases with and without the occurrence of high concentration episodes was developed. The data used were derived from routine public air quality monitoring and a local meteorological station. A range of random forest classification models with various predictor sets and for different subsets of the observations coupled with variable importance analysis were performed. The performance of the algorithm was assessed using confusion matrices. The variable importance rankings revealed, among other things, the dominant impact of the mixing layer height on elevated PM₁₀ concentration episode formation. This research work showed the usefulness of the random forest algorithm in identifying factors contributing to poor air quality, even in the absence of reliable emission data.

Photovoltaic (PV) modules undergo comprehensive testing to validate their electrical and thermal properties prior to market entry. These evaluations consist of durability and efficiency tests performed under realistic outdoor conditions with natural climatic influences, as well as in controlled laboratory settings. The overall performance of PV cells is affected by several factors, including solar irradiance, operating temperature, installation site parameters, prevailing weather, and shading effects. In the presented study, three distinct PV modules were analyzed using a sophisticated large-scale steady-state solar simulator. The current–voltage (I-V) characteristics of each module were precisely measured and subsequently scrutinized. To augment the analysis, a three-layer artificial neural network, specifically the multilayer perceptron (MLP), was developed. The experimental measurements, along with the outputs derived from the MLP model, served as the foundation for a comprehensive global sensitivity analysis (GSA). The experimental results revealed variances between the manufacturer’s declared values and those recorded during testing. The first module achieved a maximum power point that exceeded the manufacturer’s specification. Conversely, the second and third modules delivered power values corresponding to only 85–87% and 95–98% of their stated capacities, respectively. The global sensitivity analysis further indicated that while certain parameters, such as efficiency and the ratio of V_oc/V, played a dominant role in influencing the power-voltage relationship, another parameter, U, exhibited a comparatively minor effect. These results highlight the significant potential of integrating machine learning techniques into the performance evaluation and predictive analysis of photovoltaic modules.

In this paper, we explore the deflagration combustion of methane–air mixtures through both experimental and numerical analyses. The key parameters defining deflagration combustion dynamics include maximum explosion pressure (P_max), maximum rate of explosion pressure rise (dP/dt)_max, deflagration index (K_G), and laminar burning velocity (S_U). Understanding these parameters enhances the process of safety design across the energy sector, where light-emissive fuels play a crucial role in energy transformation. However, most knowledge on these parameters comes from experiments under standard conditions (P = 1 bar, T = 293.15 K), with limited data on light hydrocarbon fuels at elevated temperatures. Our study provides new insights into methane–air mixture deflagration dynamics at temperatures ranging from 293 to 348 K, addressing a gap in the current process industry knowledge, especially in gas and chemical engineering. We also conduct a comparative analysis of predictive models for the laminar burning velocity of methane mixtures in air, including the Manton, Lewis, and von Elbe, Bradley and Mitcheson, and Dahoe models, alongside various chemical kinetic mechanisms based on experimental findings. Notably, despite their simplicity, the Bradley and Dahoe models exhibit a satisfactory predictive accuracy when compared with numerical simulations from three chemical kinetic models using Cantera v. 3.0.0 code. The findings of this study enrich the fundamental combustion data for methane mixtures at elevated temperatures, vital for advancing research on natural gas as an efficient “bridge fuel” in energy transition.