Multi-Scale Geostatistical Modelling of Soil Heavy Metal Contamination in the Zamfara Lead-Gold Mining Belt: Implications for Remediation Targeting and Public Health

The Zamfara lead poisoning crisis represents one of the most severe acute environmental health disasters in African history, yet spatially rigorous characterisation of the multi-scale distribution and persistence of heavy metal contamination in soil across the mining-affected landscape has not been achieved, limiting the precision and effectiveness of remediation targeting. This dissertation conducts original multi-scale geostatistical modelling of soil lead, arsenic, and mercury contamination across an 8,000-square-kilometre study area in Zamfara State's Anka and Bukkuyum mining belt. A stratified random sampling design generates 1,240 geo-referenced soil samples analysed by ICP-MS for twelve heavy metals. Variogram modelling and multi-scale kriging are applied to characterise spatial correlation structures at household, community, and landscape scales, revealing distinct contamination spatial signature patterns for smelting sites, mine pits, water channel routes, and residential settlement dust pathways. A novel Multi-Source Contamination Pathway Model is developed to attribute spatial contamination patterns to specific mining process inputs, integrating geostatistical interpolation with stable lead isotope ratio mapping. Bayesian maximum entropy spatial estimation is applied to produce probabilistic contamination maps with explicit uncertainty quantification relevant for remediation prioritisation decisions. Health risk surface modelling integrates soil contamination with bioaccessibility experimental data and child population density to map spatial paediatric blood lead elevation risk. The dissertation delivers an original methodological framework for precision contamination mapping and provides the most spatially comprehensive heavy metal contamination atlas of the Zamfara mining belt to date. Keywords: geostatistical modelling, heavy metal contamination, Zamfara, mining, public health.