Seasonal and spatial dynamics of entropy-weighted water quality assessment in surface waters of Ukraine
Abstract
Introduction. Ensuring the ecological safety of river basins is one of the most urgent environmental challenges in the context of achieving the United Nations Sustainable Development Goals (SDGs), particularly SDG 6 (Clean Water and Sanitation) and SDG 12 (Responsible Consumption and Production). Surface water quality is a critical component of regional environmental stability and sustainable development. However, increasing anthropogenic pressure and climate change are destabilizing natural aquatic ecosystems and complicating the functioning of water supply systems. According to international data, over 40% of the global population faces water scarcity.
This study aims to assess the seasonal and spatial variability in the quality of surface waters in Ukraine using an entropy-weighted water quality index (EWQI). The object of the research is the system of surface water bodies of Ukraine, while the subject is the seasonal and spatial variation in their ecological status based on physical and chemical indicators.
Methods. The study utilized open-access data from Ukraine’s state environmental monitoring system, covering over 540 monitoring points across major river basins: the Dnipro, Dniester, Danube, Don, Vistula, Southern Bug, Azov Sea rivers, and the Black Sea coastal basins. Water quality data were analyzed for five seasonal periods: winter, spring, low-flow, shallow-water, and autumn. Ten key hydrochemical parameters were selected for analysis, including dissolved oxygen, biological oxygen demand, chemical oxygen demand, ammonium nitrogen, nitrates, phosphates, total hardness, and total dissolved solids.
The EWQI was calculated by normalizing each parameter and assigning it a weight based on its Shannon entropy. The greater the variability of a parameter, the higher its informational contribution. The final index was classified according to a seven-class scale, from "very clean" to "extremely polluted". Spatial analysis and visualizations were carried out using QGIS.
Results. The entropy-weighted assessment revealed clear seasonal and regional trends in surface water quality. The best water quality was recorded during the winter and spring periods, while the highest levels of pollution occurred in shallow-water and autumn seasons. This dynamic is attributed to temperature fluctuations, reduced dilution capacity during low flows, and agricultural runoff during warm periods. Spatially, the most polluted regions were identified in the basins of the Southern Bug, Azov Sea rivers, and the Black Sea littoral, where anthropogenic pressures are particularly high. EWQI values also indicated that certain tributaries and local watercourses demonstrated extreme sensitivity to seasonal factors. The integration of entropy-based weights enhanced the sensitivity of the water quality index to both spatial variability and seasonal trends, providing a more differentiated ecological picture than conventional methods.
Conclusions. The entropy-weighted water quality index provides a robust, objective, and adaptable tool for assessing the ecological status of surface waters. The method successfully captures seasonal and spatial variability, highlighting critical regions and periods that require intensified environmental monitoring and remediation measures. The research findings can serve as a scientific basis for updating national water monitoring programs and aligning with international environmental standards.
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