The study aimed to address the complex and critical issue of surface water quality monitoring by proposing a diversified approach that incorporates a range of chemical indicators. (1) Background: the purpose of the study was to address the problem of surface water quality monitoring in relation to the toxic effects of ammonium on aquatic ecosystems by developing predictive models using fuzzy logic and artificial neural networks. (2) Water samples from the Styr River, influenced by the Rivne Nuclear Power Plant, were analyzed using certified standard methods and measured parameters, while fuzzy logic and artificial neural network models, including Mamdani’s algorithm and various configurations of activation functions and optimization algorithms, were employed to assess water quality and predict ammonium toxicity. (3) A fuzzy logic system was developed to classify water quality based on ammonia content and other parameters, and six Artificial Neural Network (ANN) models were tested, with the ANN#2 model (using ReLU activation and ADAM optimizer) showing the best performance. (4) This study emphasizes the critical need for precise monitoring and modeling of total ammonium in surface water, considering its variable toxicity and interactions with environmental factors, to effectively protect aquatic ecosystems, namely ichthyofauna.

Among a number of climate-related factors, moisture has the greatest impact on crop productivity. In recent years, certain changes have been observed under conditions of the Forest-Steppe of Ukraine with regard to precipitation – from low to in some cases – abnormally high, which requires the...

This study examines the environmental impact of a nuclear power plant’s (NPP) open-cycle cooling systems (CCSs), focusing on the Rivne NPP in Ukraine. Regulatory standards from the Water Code of Ukraine and the EU’s Water Framework Directive guide the analysis. Over four years (2019–2022), water quality indicators were monitored. Results show compliance with regulatory standards for makeup and cooling water, with pH levels within the range of 6.5–8.5. Total dissolved solids (TDS) remained below 500 mg/dm³, while total hardness (TH) did not exceed 200 mg/dm³ as CaCO₃. Salinity components, including Cl⁻, SO₄²⁻, Na⁺, and K⁺, were within acceptable limits. Biogenic compounds, such as PO₄³⁻ and N-NH₃, occasionally exceeded maximum permissible concentrations in the receiving Styr River. Chemical oxygen demand levels were consistently below 30 mg/dm³. Pearson’s correlation coefficient and principal component analysis revealed strong relationships between water quality indicators. TH and SO₄²⁻ were identified as dominant components in CCS water discharge, remaining below prescribed maximum permissible discharge limits. Overall, this study indicates that there is no negative non-radiological impact of water discharge of polluting chemicals with the effluent of the Rivne NPP CCS on the surface waters of the Styr River.

For the first time, an analysis of structural damage of the peripheral blood erythrocyte nucleus in Rutilus rutilus (Linnaeus, 1758) was carried out for the rivers of the north-western region of Ukraine. The effect of the hydrochemical regime on the formation of cytogenetic disturbances of the representatives of this species was also clarified. It was observed that during 2013–2017, the content of phosphates, suspended solids, fluorides, iron, copper, zinc, manganese, nitrite nitrogen and nitrate nitrogen in the surface waters of rivers most significantly exceeded the current quality standards. The average frequency of nuclear disorders of peripheral blood erythrocytes R. rutilus from the studied hydrosystems is 5.25 ± 0.29 ‰ (p < 0.01), with a noticeable excess of the level of spontaneous mutations in the older age groups of fish. In all analyzed samples of R. rutilus, among the total number of identified structural disorders of the nucleus, the largest share is made up of red blood cells from the micronucleus (from 40.0 to 62.0 %), which is evidence of cytogenetic disturbances in the body of fish. At the same time, disturbances of erythrocyte blood of roach from small rivers are on average 1.3 times (or 21.9 %) higher compared with individuals from medium-sized rivers. The formation of cytogenetic disturbances in R. rutilus under the conditions of the hydrochemical regime of rivers is described by a close (r = 0.99 at p < 0.003) multifactorial regression dependence. More important regressive coefficients of the dependency are acquired by such biogenes (NH⁴⁺, NO³⁻, PO⁴⁻) and toxicants (Cu²⁺, Zu²⁺, Mn²⁺, F₂) in the background of oxygen regime peculiarities (COD, BOD₅, O₂).

Green structures, buildings incorporating living plants, offer numerous benefits, including carbon sequestration and improved indoor air quality. (1) However, the effective design and analysis of these structures are hindered by a lack of standardised methodologies. This work proposes a novel four-zone method for instantaneous gas exchange testing in green structures. (2) The method involves estimating gas exchange by measuring gas content at the plant zone’s entrance and exit, with individual CO₂ content measurements within the zone to account for inertia and stratification effects exacerbated by mixing. (3) This method has been adopted into a national standard draft. The dependency of CO₂ absorption/release is obtained for Chlorophytum comosum. The method is also successfully tested on Ligustrum vulgare. (4) The developed method provides formulas and data on CO₂ absorption/release for use in designing green structures and indoor greening systems. Results show that Chlorophytum comosum can consume over 23 μg/s of CO₂ per square meter of foliage, with a light saturation threshold for CO₂ absorption between 8500 and 20,000 phlx, depending on CO₂ levels.

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.