Evaluation of groundwater vulnerability using GIS-based DRASTIC model in Greater Monrovia, Montserrado County, Liberia
Author | |
Abstract |
To ensure that groundwater resources are effectively protected and to improve the quality of life, it is vital to take into consideration all polluting activities that could pose a potential risk to the resource. Groundwater potential research conducted only covers 5% of Montserrado County excluding Greater Monrovia in Liberia. Although this little percentage of groundwater potential research is well known, studies on the vulnerability of the aquifer to pollution are non-existent. Therefore, this study aims at assessing groundwater vulnerability in Greater Monrovia, Montserrado County, Liberia, which will help in optimizing water well drilling activities and protecting the resource. A groundwater vulnerability map for the study area using the Geographic Information System (GIS) based DRASTIC Model was developed and the results suggest that 73% of the study area is very sensitive to pollution, whereas 15% and 11% are moderately and weakly sensitive to pollution, respectively. The key pollution areas identified within the study area were communities of intensive anthropogenic activities and associated geological contamination. The effectiveness of the GIS-based DRASTIC Model in groundwater vulnerability assessment was validated and nearly 60% of the wells contained fluoride concentrations that exceeded the Liberia Water Quality Standard (LWQS) permissible limit. The findings suggest that even though the water table is relatively shallow, future projects in the high and moderate sensitivity zones should be handled carefully. Planners, groundwater managers, and decision-makers may utilize the maps created by this study as a general point of reference for vulnerability when making attempts to safeguard this delicate resource. |
Year of Publication |
2023
|
Journal |
Urban Climate
|
Volume |
48
|
Number of Pages |
101427
|
Date Published |
03/2023
|
ISSN Number |
2212-0955
|
URL |
https://www.sciencedirect.com/science/article/pii/S2212095523000214
|
DOI |
https://doi.org/10.1016/j.uclim.2023.101427
|