aceDetermining groundwater quality and its associated human health risk using hydrochemical signatures and some machine learning techniques
Groundwater is a major source of domestic water in coastal Ghana, but its quality is increasingly threatened by salinisation, nutrient enrichment, geogenic mineralisation, and localised anthropogenic contamination. In addition to hydrochemical indices and multivariate statistics that have been used over the years to assess coastal water quality, this study has incorporated nonlinear machine learning and probabilistic risk assessment to enhance source discrimination and uncertainty-based health risk characterisat…
Probabilistic assessment of the sampled wells indicated high non-carcinogenic risk for children and infants, with 32.11% and 36.49% exceedance and fluoride identified as the dominant driver, alongside nitrate and fluoride above WHO guidelines at some sites.
Findings are bounded to the central coastal zone of Ghana and reflect high spatial variability in salinity and mineralisation, limiting generalisation to other aquifers or seasons.
Evidence
- Peer-reviewedEnvironmental Geochemistry and Health2026-08-05
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Truvace Impact Record TRV-2026-0668, v1: “Determining groundwater quality and its associated human health risk using hydrochemical signatures and some machine learning techniques.” Truvace, 2026-08-06. /record/TRV-2026-0668 (accessed at citation time). sha256 072eaf0c1ab68c69…
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