Dynamics of Water Quality and Public Health Risks in the Case of Upper Awash River Basin, Ethiopia
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Date
2025-05
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Addis Ababa University
Abstract
Introduction: Water quality issues are a major global concern, particularly in the upper Awash subbasin, where reports highlight both water quality problems and unmet water demands. As a critical socioeconomic and political center and a key water source for rural and urban residents, the subbasin is experiencing declining water quality due to rapid urbanization, industrialization, agriculture, and population growth. Efforts should focus on mitigating water pollution risks by addressing these factors. This study aims to address water quality issues and public health risks associated with drinking water consumption while narrowing existing gaps in understanding the cumulative effects of these factors on water quality and public health. Through the lens of watershed management, this study introduces new approaches to quantify public health risks, exposed populations, and vulnerable water supply schemes. It employs technologies such as GISs, statistical models, and risk characterization tailored to the local context, offering fresh insights to improve existing practices, water quality management, advocacy, and policy formulation.
Objective: To analyze the dynamics of water quality and public health risks in the Upper Awash Sub-River Basin, including mapping water pollution risks, characterizing temporal and spatial water quality distributions, identifying pollution sources, quantifying public health risks, and delineating protection zones to ensure the safety of water supply users, with the aim of informing effective management and mitigation strategies. Methods: To achieve the objectives of this study, the DRASTIC model was employed along with the integration of the National WASH Inventory-2 (NWI-2) to identify vulnerable water schemes and conduct water source pollution risk (WSPR) mapping and water pollution indexing, including the estimation of exposed populations via the ArcGIS environment. Additionally, an artificial neural network (ANN) was utilized to predict the water quality index (WQI) from samples collected from 60 water supply schemes during dry and wet seasons, along with samples from 10 river water sampling stations across three seasons. Furthermore, Aquachem 2014.2, principal component analysis (PCA), and positive matrix factorization (PMF) models were applied to analyze 197 borehole samples, 70 surface water samples, and 60 water supply samples to identify water pollution sources. In accordance with the WHO guidelines, 120 water samples were collected from 60 drinking water supply schemes in both the dry and wet seasons, which are located in low and high water pollution risk (WPR) areas. XIV The concentrations of the target parameters were measured via instruments such as multiple meters, spectrophotometers, digital arsenators, and microbiological test kits. The assessment involved methods of hazard identification, exposure and dose‒response analysis, and risk characterization, including hazard quotient (HQ), cancer risk (CR), hazard index (HI), and probability of infection.
Results: The findings reveal that 32.96% of the groundwater in the study area has low pollution risk, while 53.56% are at a moderate risk level, and 13.5% face high groundwater risk, with a model explanation of 67.8% (R2 =0.678). In terms of surface water, 72.64% of the sites presented a low pollution risk, whereas 27.36% presented moderate to very high risks, including 4.82% at high and 3.7% at very high pollution levels, with water pollution index values exceeding 1 for all ten water quality monitoring sites during the dry season, indicating significant surface water pollution. The study estimates that 5.64%, 3.88%, and 2.30% of the population are exposed to high groundwater pollution risk (GWPR), surface water pollution risk (SWPR), and water source pollution risk (WSPR), respectively. Additionally, among the 2,864 water supply schemes analyzed, only 14.4% had a water safety plan, while 20.7% practiced water safety, and 6% reported the occurrence of waterborne diseases. Over 39.23% of the schemes were located in high vulnerability areas, with 12.32% in very high vulnerability areas and only 8% in low vulnerability areas, as validated by a model accuracy of 61.7%. Animal grazing (66.7%), agriculture (61.7%), and other human activities (40%) were identified as potential sources of water pollution in water supply systems. Na-HCO3 (65%) and Ca-HCO3 (32.5%) geogenic sources contributed to 64% of the drinking water pollutants, with 29% and 7% attributed to agricultural and anthropogenic sources, respectively. Significant variations in drinking water quality were observed between districts. The surface water parameters, such as total hardness, TDS, pH, F, Mg, chloride, and HCO3, varied significantly between the dry and wet seasons. The ANN model accurately predicted drinking WQI via five parameters (85% prediction accuracy and 94% overall accuracy) and surface water quality (95.1% accuracy) via four parameters. The Health Quotient (HQ) for nitrate exceeded unity (HQ>1) in the dry season for all groups, whereas a chromium HQ>1 was observed for women (1.1E+00) and children (1.4E+00) in the wet season in high Water Pollution Risk (WPR) areas. The risk of arsenicrelated cancer exceeded 1 in 10,000 children in the dry season across all groups and for women and children in the wet season in high WPR areas. The cancer risk associated with chromium XV exceeded 1 in 1000 people. Moreover, the Hazard Index (HI) was consistently above unity (HI>1) for most cases, and all daily and annual risks of E. coli infection were deemed unacceptable.
Conclusion and recommendations: WSPR modeling plays a crucial role in identifying vulnerabilities and pollution risks in new and existing water supply systems. Integrating various approaches and models, along with predicting populations exposed to health risks associated with water quality, emphasizes the importance of considering public health through a comprehensive approach. The demonstration of the integration of NWI-2 with vulnerability assessments along with the quantification of public health risks and identification of water pollution sources, contributes to solve water quality issues. This highlights the importance of implementing water source protection measures by all relevant authorities for the integration of WASH inventory in monitoring and evaluation systems, utilizing GIS technology, and adopting integrated watershed management practices. Specifically, recommendation for actions are 1. Comprehensive water source protection measures, including vulnerability assessments, water source pollution risk mapping, watershed management, and the application of water treatment technologies and sanitation measures at the source and point-of-use levels, should be implemented. 2. Waste disposal management should be enhanced, groundwater quality should be monitored, fertilizer use should be controlled, and the conservation of water sources via integrated watershed management should be promoted. 3. The enabling environment should be strengthened through policy formulation, regulatory frameworks, and community awareness initiatives to address pollution sources, integrate public health into water management, protect water resources, and institutionalize Water Safety Plans (WSPs). Furthermore, to fill evidence gaps, research on pollutant travel, assimilation, and land use priorities for accurate delineation should be undertaken. 4. Further research is needed on pollutant travel time, assimilation capacity, and land-use priorities to effectively delineate protection zones, along with policy analysis for identified risks and pollution sources.
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Keywords
Cancer and noncancer risks, groundwater vulnerability index, delineation of protection zone, exposed population, water quality index, source contribution