This paper reports the principles of design and the examined protective properties of liquid materials for shielding the electric, magnetic, and electromagnetic fields over a wide frequency range. The materials were made on the basis of iron ore concentrate and a pigment additive, with water-dispersed and geopolymer paints used as a matrix. The tests of protective properties for the electrical and magnetic components of the electromagnetic field of industrial frequency showed that the electric field shielding coefficients at a concentration of the screening substance of 15−60 % (by weight) equaled 1.1−8.6; magnetic field – 1.2−5.3. The shielding coefficients of the material based on a water-dispersed paint are lower than those of a geopolymer one, which can be explained by the oxidation of an iron-containing component and a decrease in electrical conductivity. The shielding coefficients of the electromagnetic field with a frequency of 2.45 GHz are 1.2−7.9. The highest coefficients are inherent in the material with filler made of iron ore concentrate and titanium-containing pigment powder in a ratio of 1:1. To design materials with the required (predictable) protective properties, the relative magnetic, dielectric permeability of materials was calculated. It is shown that the obtained data are acceptably the same as the results from direct measurements of magnetic and dielectric permeability and could be used to calculate the wave resistance of the material and the predicted reflection coefficient of electromagnetic waves. Thus, there is reason to assert the need to build a database on the frequency dependence of effective magnetic and dielectric permeability in order to automate the design processes of composite materials with predefined protective properties.

Development of the method of evaluation of the level of environmental safety of housing accommodation and its approbation

This study addresses the urgent need for advanced digitalization tools in air pollution detection, particularly within resource-constrained municipal settings like those in Ukraine, aligning with directives such as the AAQD. The forecasting information system for integrating data processing, analysis, and visualization to improve environmental monitoring practices is described in this article. The system utilizes machine learning models (ARIMA and BATS) for time series forecasting, automatically selecting the optimal model based on accuracy metrics. Spatial analysis employing inverse distance weighting (IDW) provides insights into pollutant distribution, while correlation analysis identifies relationships between pollutants. The system was tested using retrospective data from the Kremenchuk agglomeration (2007–2024), demonstrating its ability to forecast air quality parameters and identify areas exceeding maximum permissible pollutant concentrations. Results indicate that BATS often outperforms ARIMA for several key pollutants, highlighting the importance of automated model selection. The developed system offers a cost-effective solution for local municipalities, enabling data-driven decision-making, optimized monitoring network placement, and improved alignment with European Union environmental standards.

The main problem that determines the efficiency of environmental monitoring systems is the lack of validity of management decisions on correction of environmental situations. In such conditions, a formal versatile basis that describes the information-analytical system (IAS) of environmental monitoring is required. For the development and description of the IAS composition and structure, elements of the theory of fuzzy logic and fuzzy sets and methods of system analysis are used. Thus, the theoretical basis for the development of a versatile IAS structure of environmental monitoring is formed. The set-theoretical model of the information-analytical system of environmental monitoring of atmospheric air at the municipal level, which includes subsystems of the urban system parameter monitoring, decision support, the information system “parameter database - situation knowledge base” is proposed.The decision support subsystem is presented as the decision model that determines the allowable transformations of situations and a set of strategies for applying these transformations to solve the problem of eliminating an adverse situation. The adaptive fuzzy model of situation recognition in the process of environmental monitoring, which allows producing diagnostic conclusions is developed. The diagnostic process is represented by a sequence of actions, which involves three steps: determining the criticality for each situation feature; determining the degree of criticality; providing linguistic features. The advantage of the proposed IAS architecture is the possibility of fast scaling of the decision support system. This is achieved by simply expanding the feature and situation dictionary and the knowledge base, as well as the flexible configuration of the knowledge base by correction of weight ratios of elementary premises of the rules. The general description of the information technology of monitoring and support of operational decision-making on correction of environmentally hazardous situations is formed. The results of setting the fuzzy model of situation recognition by means of experimental learning of the system on the examples – specific results of observations of atmospheric air quality are obtained. The self-learning ability of the system is found, which ultimately will allow limiting the involvement of real individuals as experts in the assessment of environmental situations by automating the diagnostic process

The object of this study is a regional center with a developed industry and a significant traffic load. The study provides an assessment of the impact of the urbanization process on the conditions of development of urban edaphotopes and their role in the functioning of urban ecosystems. It was determined that the geochemistry of the soils of urban ecosystems is significantly different from natural landscapes, which is due to the symbiosis of natural and anthropogenic factors. The content of mobile forms of heavy metals in the soil of 14 localities of different functional zones within the city was investigated. A comparative assessment of the spatial heterogeneity of the content of heavy metals in the vegetative organs (root, shoot) of the diagnostic species Polygonum aviculare L. was performed. Based on the sources of emission of heavy metals and the formed geochemical anomalies, a direct dependence was established on their accumulation by the vegetative organs of Polygonum aviculare L. According to the calculated biological absorption coefficient (BAC), high bioavailability for the accumulation of man-made toxicants Cu, Zn, Pb, Cd by the phytomass of Polygonum aviculare L. was proven. It was determined that the content of heavy metals in different parts of the test object is due to their physiological ability to accumulate these toxicants in the root and above ground mass. Active translocation of toxicants from the soil to the roots is characteristic of localities with intense influence of man-made factors, for which the value of the bioavailability coefficient varies within 0.6>BAC<0.85. Correlation coefficients between the content of heavy metals in the atmosphere, soil, and vegetative organs of the plant were determined. The research allows us to evaluate the prospects for using Polygonum aviculare L. as a cumulative indicator of metal pollution in urban technogenic ecosystems and their high phytoremediation value under conditions of environmental pollution with heavy metals