Assessment of Groundwater Quality and the Influence of Land-Use Types on the Nairobi Aquifer, Kenya, Using GIS-Based Index Techniques
- 1 Department of Civil, Construction and Environmental Engineering, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
- 2 Department of Civil, Construction and Environmental Engineering, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
- 3 Department of Civil, Construction and Environmental Engineering, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Abstract
The Nairobi Aquifer is a strategic source of water for a metropolitan population exceeding four million, yet its sustainability is increasingly threatened by rapid urbanisation and intensifying land-use change. This study quantifies groundwater quality and evaluates the influence of land-use type on that quality using a Geographic Information System (GIS)-based Groundwater Quality Index (GQI). Six physico-chemical parameters: pH, electrical conductivity (EC), chloride, fluoride, nitrate and manganese, were obtained from borehole completion reports for seventeen boreholes distributed across Nairobi County and benchmarked against World Health Organization (WHO, 2017) drinking-water guidelines. A weighted-arithmetic index was computed for each borehole and interpolated across the aquifer using inverse-distance weighting (IDW). Land-use classes were derived from a 2020 land-cover map and overlaid on the GQI surface using zonal statistics. The computed GQI ranged from 13 to 492, indicating pronounced spatial heterogeneity. Built-up land exhibited the poorest mean groundwater quality (mean GQI = 150.2), classified as “unsuitable”, whereas forest cover yielded the best quality (mean GQI = 38, “good”); open space and agriculture returned intermediate values of 61.3 and 52, respectively. Elevated indices coincided with densely settled, high-imperviousness districts where contamination is attributable to on-site sanitation, solid-waste leachate and surface runoff, while the large within-class variance of built-up land (standard deviation = 132.6) reflects the heterogeneity of urban contaminant sources. The results provide a spatially explicit, decision-ready basis for prioritising groundwater protection and integrating aquifer safeguards into urban land-use planning.
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