Water quality degradation due to heavy metal contamination poses serious threats to both human health and aquatic ecosystems. The rise in the concentration of heavy metals in aquatic environments is largely attributable to anthropogenic activities. These metals accumulate over time in water bodies, necessitating rigorous monitoring to accurately assess pollution levels. The present study is concerned with the assessment of heavy metal pollution in the Styr River (Ukraine) before and after the discharge of water from a nuclear power plant. The assessment is based on three indices: the Heavy Metal Pollution Index, the Heavy Metal Evaluation Index, and the Degree of Contamination. Therefore, heavy metals, including zinc (Zn), cadmium (Cd), lead (Pb), copper (Cu), nickel (Ni), manganese (Mn), arsenic (As) and chromium (Cr), were analyzed in this study. Water samples were collected at two locations on a monthly basis over the course of five years (2018–2022) and subsequently analysed using inductively coupled plasma optical emission spectroscopy. The results indicates a low contamination level at both sampling sites, indicating stable and uniform concentrations of metals across the study area. Moreover, statistical analysis highlights significant associations between certain metals and pollution indices, supporting the indices’ utility in tracking pollution trends and assessing environmental impacts. This dataset underscores the importance of ongoing monitoring for effective water quality management.

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.

The discharge of cooling water into surface waters containing residual heat energy is a pressing issue in the environmental impact of nuclear power. Cooling water flow can cause negative changes in the water and ecological processes of natural waters, in particular an imbalance of organic matter, in combination with a complex of natural factors. The purpose of the article was to perform a statistical analysis of the dynamics of organic matter content in the water of the Styr River in the Rivne Nuclear Power Plant (Rivne NPP) impact zone to assess the manifestation of the environmental impact of the discharge of cooling water with residual heat. The monitoring data series consisted of the results of monthly monitoring of the rate of chemical oxygen demand, the rate of biochemical oxygen demand in 5 days, and total organic carbon in the Styr River sections before and after the discharge of Rivne NPP cooling water in 2018–2022.