Tesfa Aklilu, Geremew Sahilu, Argaw Ambelu, Abdilbasit Hamid and Balew Ybel
Groundwater and surface water pollution risk assessment involves evaluating the potential effects of pollutants on water quality, human health, and the environment. This study focuses on water pollution risks from the intrinsic vulnerability and from population growth, urbanization, industrialization, poor soil and water conservation, climate change impacts, and inadequate governance of Upper Awash Sub-river Basin in Ethiopia. It used Groundwater Pollution Risk Index (GWPRI), Surface Water Pollution Risk Index (SWPRI), integrated Water Source Pollution Risk (WSPR) mapping, water pollution index as well as estimation of exposed population for the identified risks through ArcGIS environment. Linear regression analyzes concentrations of nitrate from 851 boreholes and product map raster values were applied for model validation, achieving a 67.8% explanation (R2=0.678). In addition, Nemerow pollution index was also applied based on samples from ten monitoring sites. As the result, findings reveal 32.96% with low groundwater pollution risk, 53.56% at a moderate risk level, and 13.5% facing high groundwater risk. For surface water, 72.64% has low pollution risk, while 27.36% experiences more than moderate risks, including 4.82% high and 3.7% very high pollution risks. The combined risks shown that 68.1% low, 27.5% moderate, and 4.4% high risks for water source pollution in the sub-basin. The study estimates that 82.52% of the population resides in low WSPR areas, with over 17.47% in moderately risked areas. Furthermore, 5.64%, 3.88%, and 2.30% of the population are exposed to high GWPR, SWPR, and WSPR, respectively. The computed Water Pollution Index values for the dry season exceeded one for all ten water quality monitoring sites, indicating pollution of surface water. In conclusion, WSPR modeling is crucial for identifying vulnerabilities and pollution risks in both new and existing water supply systems. Integrating various approaches and models, coupled with predicting exposed populations for health risks associated with water quality, highlights the importance of considering public and ecosystem health issues in the comprehensive approach of source control, treatment technologies, and regulatory measures