Submit your papersSubmit Now
For Enquiries: [email protected]
IIARD LogoIIARD

GIS-Based Groundwater Vulnerability Assessment and Hadrochemical Validation of Borehole Exposure to Waste Contamination Risks in Kaduna Polytechnic, Nigeria

A.D. Umar, A.Abdulsalam, M. Aliyu

Abstract

Groundwater is the primary source of potable water for many tertiary institutions in sub- Saharan Africa, yet increasing anthropogenic pressures from inadequate waste management threaten groundwater quality and long-term water security. This study assessed groundwater vulnerability to waste contamination within Kaduna Polytechnic, Nigeria, using an integrated Geographic Information System and hydro chemical validation framework. Geographic coordinates of 58 boreholes and 61 waste disposal points were extracted from geotagged field photographs using Python-based metadata processing and incorporated into a GIS database. Spatial analyses, including nearest-hub assessment, proximity modelling, and vulnerability classification, were conducted in QGIS. Boreholes were classified into critical (≤15 m), high (>15–30 m), moderate (>30–100 m), and low (>100 m) vulnerability zones based on their proximity to waste disposal points. Twenty-five boreholes located within critical and high-risk zones were identified for investigation, of which fifteen operational boreholes were subjected to hydro chemical analysis. Parameters analyzed included pH, electrical conductivity (EC), total dissolved solids , turbidity, nitrate, sulphate, chemical oxygen demand , and total organic carbon . A Groundwater Exposure Index was developed by integrating spatial and hydro chemical indicators. Results revealed a mean borehole–waste point distance of 49.89 m, while correlation analysis showed moderate inverse relationships between distance and EC (r = −0.431) and TDS (r = −0.424). The GEI identified BH42, BH27, BH04, and BH02 as the most vulnerable boreholes. The study demonstrates that integrating GIS-based vulnerability assessment with hydro chemical validation provides an effective decision-support framework for groundwater protection in institutional environments.

Keywords

Groundwater vulnerability; GIS; Python; hydro chemical validation; Groundwater Exposure Index ; institutional water security.

References

Bakhtiarizadeh, A., Najafzadeh, M., & Mohamadi, S. (2024). Enhancement of groundwater quality prediction using machine learning and improved DRASTIC methods. Scientific Reports. Barbulescu, A. (2020). Assessing groundwater vulnerability: DRASTIC and DRASTIC-like methods: A review. Water. Biswas, T., et al. (2026). Prediction of groundwater vulnerability in the Ganges Delta using integrated environmental indicators. International Journal of River Basin Management. El Alaoui, A., et al. (2025). A GIS-Based Hydrochemical and Pollution Risk Assessment. Sustainability, 17(12), 5392. Escalonilla, G., & Santos, M. (2024). A machine learning approach to map groundwater vulnerability to agricultural contamination. Hydrology. Fannakh, A., et al. (2025). Assessment and validation of shallow groundwater vulnerability using nitrate and electrical conductivity indicators. Water, 17(5), 739. Goyal, D., Haritash, A., & Singh, S. (2021). A comprehensive review of groundwater vulnerability assessment using index-based, modelling and coupling methods. Journal of Environmental Management. Hamed, M. H., Dara, R. N., & Kirlas, M. C. (2024). Groundwater vulnerability assessment using a GIS-based DRASTIC method in the Erbil Dumpsite area. Journal of Groundwater Science and Engineering. Hyeroba, E. N., & Kalin, R. M. (2024). Assessment of groundwater quality and vulnerability in the Nakivale Sub-Catchment of the Transboundary Lake Victoria Basin, Uganda. Water, 16(23), 3386. Kaliraj, S., Firose, S., Abishek, S. R., Srinivas, R., Palanivel, K., & Karuppannan, S. (2026). Groundwater vulnerability and pollution risk assessment in the Achankovil River Basin, Western Ghats, Southern India: Integration of GIS-based DRASTIC-LC model, hydrochemical indicators and multivariate statistical analysis. Environmental Sciences Europe. Konaté, A. A., et al. (2025). Groundwater vulnerability to artisanal gold mining pollution using GIS-based assessment. Geoenvironmental Disasters. United Nations. (2023). Clean Water and Sanitation Progress Report. Wang, Y., et al. (2026). A novel hybrid model for groundwater vulnerability assessment integrating spatial and hydrochemical indicators. Sustainability, 18(2), 674. World Health Organization. (2022). Guidelines for drinking-water quality. Zeynab, M., Reza, D., & Hossein, M. (2023). Integration of GIS-based DRASTIC model and machine learning approach for groundwater vulnerability assessment. Groundwater for Sustainable Development.

More Articles from INTERNATIONAL JOURNAL OF ENGINEERING AND MODERN TECHNOLOGY