This study investigates the climatic dynamics of Odesa, Ukraine, by integrating over 200 years of archival meteorological records with recent observations from the Davis Vantage Pro2 weather station and advanced machine learning techniques. The results reveal a distinct warming trend since 1985, with average annual temperatures projected by a CNN–LSTM model to rise by more than 6–7 °C above the mid-20th-century baseline by 2029, indicating an exceptionally rapid regional climatic shift. Spatial analysis of the July 2024 heatwave demonstrated pronounced thermal gradients, with the strongest overheating observed inland and the moderating influence of the Black Sea reducing temperature extremes in coastal areas. Precipitation analysis (1985–2024) showed an overall statistically insignificant increase; however, the summer months exhibited drying tendencies, a trend reinforced by model forecasts. Solar radiation dynamics (2012–2024) highlighted significant local variability shaped primarily by atmospheric conditions rather than solar activity, with notable monthly increases in October, November, and February. The novelty of this research lies in combining long-term datasets with deep learning methods to produce localized climate scenarios for Odesa, offering new insights into the city’s transition toward extreme warming, shifting precipitation patterns, and evolving solar energy potential. The findings have direct implications for environmental modeling, energy efficiency, and the development of climate change adaptation strategies in urbanized coastal regions.

Purpose. To determine the patterns of distribution and accumulation of pollutants in the air of large urban areas of Ukraine and the impact of natural and anthropogenic factors on these processes. Methodology. The aim of the study was realised by collecting, systematising, mathematically processing and analysing the results of observation of air pollution indicators in the right-bank part of Kyiv. Findings. The regularities were identified and the influence of natural (meteorological) and anthropogenic factors on the concentrations of particulate matter, carbon monoxide and sulphur dioxide in the air of an urbanised environment was assessed. Originality. The nature of the dependence or lack of dependence of the concentration of the main pollutants in the atmospheric air of the right-bank territory of the city of Kyiv on basic meteorological indicators has been established. A negative correlation between the content of solid particles in the air and wind speed and wind gust speed has been revealed. Moreover, wind gust speed has a greater impact on dustiness than wind speed, and its direction is irrelevant for the city of Kyiv. A strong negative correlation between air dustiness and rainfall has been recorded. A correlation in particulate matter content has been established between all areas of the city, even those far apart from each other. This makes it possible to predict, with a calculable probability, the nature of changes in air dustiness in areas where monitoring is temporarily not being carried out. A clear daily cycle of carbon monoxide concentration in the atmospheric air and a correlation between different areas of the city for this indicator have been established, as well as a correlation between carbon monoxide concentration and solid particles. There is no daily cycle in the concentration of sulphur dioxide in the atmospheric air of the city of Kyiv. The correlation between CO and SO2 and precipitation is moderately negative. Practical value. The use of the obtained results makes it possible to make quick and informed decisions in the field of air quality management in large urban areas.

This study applies spectral analysis and singular spectrum analysis (SSA) to mean annual runoff of the Dniester River for 1950–2024 to identify dominant periodic components governing the hydrological regime of this transboundary basin shared by Ukraine and Moldova. The novelty lies in a basin-specific integration in the first systematic application of a combined spectral–SSA framework to the Dniester River, enabling consistent characterization of runoff variability and assessment of large-scale natural drivers. Time series from three gauging stations are analysed to develop data-driven runoff models and medium-term forecasts. Four stable groups of periodic variability are identified, with characteristic timescales of approximately 30, 11, 3–5.8, and 2 years, corresponding to major atmospheric–oceanic oscillations (AMO, NAO, PDO, ENSO, QBO) and the 11-year solar cycle. Cross-spectral and coherence analyses reveal a statistically significant relationship between solar activity and river discharge, with an estimated lag of about 2 years. SSA reconstructions explain more than 80% of discharge variance, indicating high model reliability. Forecast comparisons show that spectral methods tend to amplify long-term trends, CNN–LSTM models produce conservative trajectories, while a hybrid ensemble approach provides the most balanced and physically interpretable projections. Ensemble forecasts indicate reduced runoff during 2025–2028, followed by recovery in 2029–2034, supporting long-term water-resources planning and climate adaptation.

The object of research is methods for reducing the level of air pollution in working areas with industrial dust. The problem of reducing the impact of dustiness of production premises on the workers' health is solved by improving the aspiration air purification system. A new approach is proposed that provides the blower (fan) with an additional purification function by attaching a dust collector to its housing. By organizing the circulation movement of the dust-gas flow, part of the dust settles in the dust collector before it enters the main dust collector. Such a combined dust collector can be used as an independent device in closed aspiration systems or as part of complex dust removal systems, reducing the load on the main device. A methodology for calculating purification indicators has been developed, which is based on the discretization of the differential dust mass distribution curve by particle size. The method allows to assess the efficiency of the process, determine the dimensions of the dust collection container and the frequency of dust discharge from it. The technology was used for the air aspiration system of the working area of the building materials processing facility to reduce the risk to workers' health from air pollution. The calculations have established the conditions of acceptable non-carcinogenic risk at the level of construction dust concentration not higher than 61.42 mg/m3, and with the introduction of the proposed combined dust collector, it is possible to increase the dust level to 99.1 mg/m³ without exceeding the risk threshold. The proposed technology is cost-effective, requires minimal design changes and can be implemented at most industrial facilities, especially in conditions of high dustiness of production premises.

The article examines the current state, problems and prospects of cross-border cooperation of the border regions of Ukraine in terms of achieving the goals of sustainable development. It is shown that cross-border cooperation allows to formulate common solutions for territories that have the same economic, environmental and socio-cultural features. The main challenges to the implementation of cooperation are insufficient coordination between the authorities, lack of a common information space, insufficient funding, political and military risks. The success of cross-border projects is determined by their multisectoral nature, innovative technology, participation of NGOs, improvement of the institutional framework, development of financial instruments, and digitalisation of management processes. In terms of achieving the Sustainable Development Goals, cross-border cooperation has high potential for Ukraine, but its effectiveness depends on the coherence of actions of all participants in the process, the harmonisation of the regulatory framework, and the use of modern and effective management technologies.