Assessment of groundwater quantity, quality, and associated health risk of the Tano river basin, Ghana

Acta Geochimica(2024)

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摘要
In the Tano River Basin, groundwater serves as a crucial resource; however, its quantity and quality with regard to trace elements and microbiological loadings remain poorly understood due to the lack of groundwater logs and limited water research. This study presents a comprehensive analysis of the Tano River Basin, focusing on three key objectives. First, it investigated the aquifer hydraulic parameters and the results showed significant spatial variations in borehole depths, yields, transmissivity, hydraulic conductivity, and specific capacity. Deeper boreholes were concentrated in the northeastern and southeastern zones, while geological formations, particularly the Apollonian Formation, exhibit a strong influence on borehole yields. The study identified areas with high transmissivity and hydraulic conductivity in the southern and eastern regions, suggesting good groundwater availability and suitability for sustainable water supply. Secondly, the research investigated the groundwater quality and observed that the majority of borehole samples fall within WHO (Guidelines for Drinking-water Quality, Environmental Health Criteria, Geneva, 2011, 2017. http://www.who.int ) limit. However, some samples have pH levels below the standards, although the groundwater generally qualifies as freshwater. The study further explores hydrochemical facies and health risk assessment, highlighting the dominance of Ca–HCO 3 water type. Trace element analysis reveals minimal health risks from most elements, with chromium (Cr) as the primary contributor to chronic health risk. Overall, this study has provided a key insights into the Tano River Basin’s hydrogeology and associated health risks. The outcome of this research has contributed to the broader understanding of hydrogeological dynamics and the importance of managing groundwater resources sustainably in complex geological environments.
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关键词
Groundwater,Unsupervised machine learning technique,Hydrochemistry,Aquifer hydraulic parameter,Health risk
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