Nanoparticles containing metals in their structure constitute an increasingly large group of substances present in the air.

Sewage sludge from sewage treatment plants has soil-forming and fertilising properties. However, sewage sludge cannot always be used in nature, including agriculture. One of the main reasons is the concentration of heavy metals. Sludge from wastewater treatment plants operating in MBR (membrane biological reactor) and SBR (sequential batch reactor) systems was analysed. Studies comparing the risk analysis of the natural use of sludge from MBR and SBR treatment plants were performed for the first time, due to the fact that more and more MBR plants, which are a BAT technology, are being developed in Poland, displacing the classical SBR plants. MBR technology uses a combination of activated sludge and filtration with microfiltration membranes. Wastewater treated in these reactors meets the highest quality standards, both in terms of physicochemical and microbiological aspects. This paper presents studies on the mobility of heavy metals in sewage sludge carried out using the BCR sequential extraction method. Geo-accumulation index (GAI), potential environmental risk index (ER), risk assessment code (RAC), and environmental risk determinant (ERD) were calculated. Heavy metals dominated the stable fractions in all cases. Furthermore, an increased content of copper and cadmium was observed in the MBR sludge. This fact is favourable in view of the efforts to eliminate heavy metals in the environment.

Electrical cables are a potential source of ignition and fire hazards in various types of buildings and industrial installations, as well as in all modes of transportation, including aircraft. Fires in buildings pose the greatest threat to human life and health. The composition of thermal degradation products depends mainly on the type of combustible materials and the type of combustion process—flaming or smoldering. The purpose of this paper was to determine, based on experimental studies, the effects of flaming and smoldering combustion on the response times of fire smoke detectors. In addition, the concentrations of fire gases formed in the process of duct combustion, including CO, SO₂, NO₂, NO, HCN, HCl, HBr and HF, were measured using an FTIR spectrometer. The results presented confirm the significant effect of the way the cable samples burned on detector tripping time. The highest concentration of smoke (Y) in the test chamber was obtained during flame combustion. It was further found that the characteristics of the cable insulation material used, such as the type of PVC, had a significant effect on the toxicity of the emitted gases. The largest amounts of toxic gases were emitted during the smoldering combustion of a cable with a plasticized PVC sheath.

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.