期刊名称：Journal of Geoscience and Environment Protection
印刷版ISSN：2327-4336
电子版ISSN：2327-4344
出版年度：2022
卷号：10
期号：4
页码：202-226
DOI：10.4236/gep.2022.104013
语种：English
出版社：Scientific Research Pub
摘要：This paper developed an optimization technique for groundwater vulnerability in Kano Metropolis, North-Western Nigeria. A combination of DRASTIC is taken from initial letters of seven parameters namely depth to water table (D), net recharge (R), aquifer media (A), soil media (S), topography (T), impact of vadose zone (V) and hydraulic conductivity (C), while GOD also represents groundwater confinement (G), overlaying strata (O), depth of water (D) and multi-criteria evaluation (MCE) techniques were used in the optimization method by integrating other important and sensitive parameters for groundwater pollution, principally the anthropogenic point source pollution parameters (dump site, petroleum stations, automobile shops and under storage tanks). Geographic Information System was used to perform the sensitivity analysis (SA) using the single parameter and map removal sensitivity methods. Result of sensitivity optimization revealed the depth to groundwater (D), net recharge (N), impact of vadose zone (V) from DRASTIC model, and groundwater conferment (G) from GOD model having significant impact on the groundwater vulnerability, respectively. A combination of these four parameters was used to generate DNVG groundwater vulnerability for the area. This suggests that an integration of other point source pollution parameters can enhance the influence of DRASTIC and GOD model parameters on groundwater vulnerability condition. The paper recommends for the application of the optimization method used in this study in another area with similar geological and anthropogenic point source of pollution with a view to validating or improving on it. In this study, several input data, such as anthropogenic point sources of contamination, are added to the existing DRASTIC and GOD model parameters as part of a sensitivity analysis aiming to optimise the performance of the resultant models.
