The constant increase in the amount of food waste accumulating in landfills and discharged into the water reservoirs causes environment pollution and threatens human health. Solid and liquid food wastes include fruit, vegetable, and meat residues, alcohol bard, and sewage from various food enterprises. These products contain high concentrations of biodegradable organic compounds and represent an inexpensive and renewable substrate for the hydrogen fermentation. The goal of the work was to study the efficiency of hydrogen obtaining and decomposition of solid and liquid food waste via fermentation by granular microbial preparation (GMP). The application of GMP improved the efficiency of the dark fermentation of food waste. Hydrogen yields reached 102 L/kg of solid waste and 2.3 L/L of liquid waste. The fermentation resulted in the 91-fold reduction in the weight of the solid waste, while the concentration of organics in the liquid waste decreased 3-fold. Our results demonstrated the potential of granular microbial preparations in the production of hydrogen via dark fermentation. Further development of this technology may help to clean up the environment and reduce the reliance on fossil fuels by generating green hydrogen via recycling of household and industrial organic wastes.

The study assessed the radiological risks associated with the presence of natural and artificial radionuclides in the Bory Stobrawskie forests (PL). Using the conversion factors given by UNSCEAR and the measurements results of ²³²Th series, ²³⁸U series, ⁴⁰K, and ¹³⁷Cs specific activities in the 10-cm soil layer the values of absorbed dose (D) and the annual effective dose equivalent derived from terrestrial gamma radiation (E) were calculated. The calculated dose was compared with doses directly measured on the area studied.

This paper presents the results of studies aiming at the assessment and classification of wastewater using an electronic nose. During the experiment, an attempt was made to classify the medium based on an analysis of signals from a gas sensor array, the intensity of which depended on the levels of volatile compounds in the headspace gas mixture above the wastewater table. The research involved samples collected from the mechanical and biological treatment devices of a full-scale wastewater treatment plant (WWTP), as well as wastewater analysis. The measurements were carried out with a metal-oxide-semiconductor (MOS) gas sensor array, when coupled with a computing unit (e.g., a computer with suitable software for the analysis of signals and their interpretation), it formed an e-nose—that is, an imitation of the mammalian olfactory sense. While conducting the research it was observed that the intensity of signals sent by sensors changed with drops in the level of wastewater pollution; thus, the samples could be classified in terms of their similarity and the analyzed gas-fingerprint could be related to the pollution level expressed by physical and biochemical indicators. Principal component analysis was employed for dimensionality reduction, and cluster analysis for grouping observation purposes. Supervised learning techniques confirmed that the obtained data were applicable for the classification of wastewater at different stages of the purification process.

The aquatic plant Pistia stratiotes L. is environmentally hazardous and requires effective methods for its utilization. The harmfulness of these plants is determined by their excessive growth in water bodies and degradation of local aquatic ecosystems. Mechanical removal of these plants is widespread but requires fairly resource-intensive technology. However, these aquatic plants are polymer-containing substrates and have a great potential for conversion into bioenergy. The aim of the work was to determine the main patterns of Pistia stratiotes L. degradation via granular microbial preparation (GMP) to obtain biomethane gas while simultaneously detoxifying toxic copper compounds. The composition of the gas phase was determined via gas chromatography. The pH and redox potential parameters were determined potentiometrically, and Cu(II) concentration photocolorimetrically. Applying the preparation, high efficiency of biomethane fermentation of aquatic plants and Cu(II) detoxification were achieved. Biomethane yield reached 68.0 ± 11.1 L/kg VS of Pistia stratiotes L. biomass. The plants’ weight was decreased by 9 times. The Cu(II) was completely removed after 3 and 10 days of fermentation from initial concentrations of 100 ppm and 200 ppm, respectively. The result confirms the possibility of using the GMP to obtain biomethane from environmentally hazardous substrates and detoxify copper-contaminated fluids.

In the context of the STEM approach to education, motivating pupils through tailored research and leveraging IT in the classroom is relevant. The justification of these approaches hasn't received much examination, though. The purpose of the study is to support the decision to use an AR-plant recognition application to give tailored instruction throughout both extracurricular activities and the school day. Every phase of an app's interaction with a user was examined and used to categorize every app. Also described were the social settings of the applications and how they were used for extracurricular activities. There has been discussion on the didactics of using AR recognition apps in biology classes. A survey of experts in digital education regarding the ease of installation, the friendliness of the interface, and the accuracy of image processing was conducted to give usability analysis. Applications were examined for their ability to accurately identify plants on the "Dneprovskiy district of Kiev" list in order to assess the rationale of usage. It has been established that Google Lens is the best option. As an alternative to Seek or Flora Incognita, according to the analysis's findings, these apps were less accurate.