Bayesian approach quantifies heavy metal risks in Ghana children’s water
Quantifying the Threat: A Bayesian Probabilistic Framework for Heavy Metal Health Risks among Children in the Densu Basin, Ghana
Artificial IntelligenceMachine Learning
Summary
Groundwater in parts of Ghana is used for drinking but contains harmful metals that can threaten children’s health. This study uses a statistical method called Bayesian modeling to better understand the risks, including uncertainty and how multiple metals together affect safety. The researchers found that lead, arsenic, and cadmium are the biggest contributors and that risk varies widely across locations. Their approach gives clearer maps of risk that can help guide water safety monitoring and health efforts in places with limited data.
What this means in practice
- •For environmental health teams: Use uncertainty-aware maps of heavy metal risk in groundwater to guide monitoring and prioritize safety interventions in contaminated areas.
- •For public health planners: Incorporate spatially explicit probabilistic hazard assessments to better allocate resources and design mitigation strategies targeting children’s health in water-risk zones.
Authors
T. Ansah-Narh, G. Y. Afrifa, J. B. Tandoh, E. N. N. Nortey, D. Adjei, R. Opoku-Sarkordie, R. Davis
Abstract
Groundwater in Ghana's Densu River Basin remains a key domestic water source, but multi-metal contamination threatens children's health. Previous deterministic hazard index assessments provide limited treatment of uncertainty, co-exposure, and spatial variability. This study applies a Bayesian probabilistic framework for non-carcinogenic risk assessment using 94 groundwater samples and measurements of Fe, Mn, Ni, Pb, Cd, and As. A Bayesian regression model propagates parameter uncertainty and accounts for contaminant co-occurrence. Adaptive Hamiltonian Monte Carlo is used for posterior inference, while inverse distance weighting generates spatially continuous risk surfaces. Pb, As, and Cd dominate cumulative risk. Posterior predictive results indicate that approximately 31% of sampling sites have a probability greater than 0.5 of exceeding a hazard index (HI) of 1. The risk surface is highly heterogeneous, with a posterior mean of 0.372 and a standard deviation of 0.416. High-risk zones often reflect moderate co-exposure rather than extreme contamination by a single metal. The framework provides uncertainty-aware, spatially explicit information to support groundwater monitoring, risk mitigation, and public health planning in data-limited regions.