Geo-electro stratigraphic assessments of aquifer potentiality, protectivity, and pliable level of vulnerability within a coastal milieu were undertaken with geo-electrical technology. The vertical electrical soundings were undertaken at 20 locations and the 2D electrical resistivity tomography surveys were undertaken at five locations within the study area. Results obtained from these geo-electrical surveys coupled with hydro-geophysical investigations within the area indicated the presence of four geo-electric layers: motley topsoil, sandy clay, fine sand, and coarse sand. The geo-stratigraphic data assessed groundwater potentiality, protectivity, and vulnerability to contamination with measures of transverse resistance, hydraulic conductivity, transmissivity, hydraulic diffusivity, aquifer storativity, and longitudinal conductance. Geo-hydraulic characterization indicated mean aquifer resistivity of 554.6 Ωm, mean aquifer conductivity of 0.0004 S/m, mean longitudinal conductance of 0.67 Ω−1, mean transverse resistance of 5601.7 Ωm2, mean hydraulic conductivity of 3.5 m/day, mean transmissivity of 283 m2/day, mean storativity of 0.0002, and mean hydraulic diffusivity of 1 × 105 m2/day. The results indicated that the region's groundwater potential ranged from medium to high. Longitudinal conductance values indicated that the aquifer protective capacity ranged from moderate to poor. Geo-electrical technology was therefore found to be an effective methodology for delineating aquifer potentiality, protectivity, and vulnerability within the vulnerable coastal aquifer system.

  • Geo-electrical technology was employed to determine transverse resistance, hydraulic conductivity, transmissivity, hydraulic diffusivity, aquifer storativity, and longitudinal conductance.

  • Results indicated that groundwater potential ranged from medium to high.

  • Aquifer protective capacity ranged from moderate to poor.

  • The aquifer has a high vulnerability to contamination.

  • This aquifer system has been characterized.

The exponential growth in the human population and the environmentally hazardous anthropogenic activities carried out in the quest for sustenance have increased the susceptibility of groundwater resources to contamination. Consequently, groundwater potentiality and aquifer protectivity studies are a crucial part of conserving the hydrological system (Edet 2004; Eke et al. 2015; Oke et al. 2018). Regular monitoring of vulnerable aquifer systems is necessary to ensure optimal groundwater abstraction procedures that will reduce the rate of contaminant percolation into the aquifer. The need to monitor an aquifer's potentiality and susceptibility to contamination is critical, given that contaminated groundwater resources are more expensive and difficult to restore compared to surface water resources like streams and lakes.

Multiple approaches have been developed for analyzing groundwater susceptibility to contamination (Foster et al. 2002). These approaches have drawbacks and benefits; hence no single method can be considered optimal for all kinds of geological situations. An example of an approach developed to evaluate groundwater potentiality and susceptibility to contamination is the use of the Dar-Zarrouk parameters which employ quantitative geophysical data to generate measures of longitudinal conductance and transverse resistance. Another approach to evaluate groundwater susceptibility is the Aquifer Vulnerability Index (AVI) methodology. Yet another technique to evaluate aquifer susceptibility is the GOD methodology which employs groundwater occurrence (G), overlying lithology (O) and depth to aquifer (D) in its evaluation, hence the acronym GOD. The GOD method uses parameters obtained from hydro-geophysical information in the determination of aquifer vulnerability to contaminants. There is also the DRASTIC methodology which uses the following parameters in the determination of aquifer vulnerability: depth to groundwater (D), net groundwater recharge (R), aquifer media (A), soil media (S), topography (T), influence of vadose zone (I), and hydraulic conductivity (C), hence the acronym DRASTIC. These aquifer vulnerability methods (AVI, GOD, and DRASTIC), though efficient, are slightly subjective. The Dar-Zarrouk parameters (longitudinal conductance and transverse resistance), on the other hand, are quantitative measures obtained from vertical electrical sounding (VES) data and are not subjective (Oladapo et al. 2004; Huan et al. 2012; Saha & Alam 2014; Udosen 2022; Udosen et al. 2023, 2024c).

The study area, located in Southern Nigeria, is experiencing extremely fast rates of industrialization and urbanization due to the siting of key industries and major Federal and State educational institutions in the region. This rapid rate of development has led to overexploitation of available surface and groundwater resources leading to contamination. Despite a good groundwater rate in the region resulting from annual average rainfall of approximately 1,500–2,400 mm, most boreholes sited in the region are unproductive or contaminated. This is a result of inadequate planning and a dearth of expert geophysical investigations to sort through the region's complex geology. Contamination sources within the region include leached septic wastes, open waste disposal systems, oil spills, and domestic/industrial waste inappropriately disposed on the earth's surface. This challenge of contaminants infiltrating potable groundwater resources has given rise to apprehensions about the consequential health impact on humans and animals ingesting the contaminated water.

Taking the aforementioned into consideration, a comprehensive geophysical evaluation of the coastal environment was necessary to enhance comprehension of the geo-hydraulic characteristics of the aquifer and its vulnerability (Ibuot & Obiora 2021). This was done in an effort to lessen the likelihood of groundwater exploration/conservation-related problems in the region (Okoroh & Ibuot 2022). Aquifer vulnerability problems pose a far greater threat to this area as residents employ unconsolidated tube wells to abstract groundwater from unconsolidated geo-materials. An effective method for assessing groundwater potentiality and aquifer protectivity/vulnerability is electrical resistivity surveying (George et al. 2024). This technique has been applied in a wide range of hydro-geophysical investigations (Dassargues 1997; Yadav & Abolfazli 1998; Lashkaripour 2003; Singh 2005; Batte et al. 2010; Majumdar & Das 2011; Sikandar & Christen 2012; Ojuri & Bankole 2013; Rai et al. 2013; Obiora et al. 2016; Oloruntola et al. 2017; Udosen & Potthast 2018; Udosen & George 2018a, 2018b; Ibuot et al. 2021; Asfahani 2023; Asfahani et al. 2023; Opara et al. 2023). The use of geo-electrical methods, especially in conjunction with borehole lithological information, would help determine optimal locations for water projects (George et al. 2022; Udosen et al. 2024a, 2024b).

The region is an important coastal area, housing key industrial complexes and major State and Federal institutions. Groundwater is the major water resource within the region; hence it was necessary to undertake a comprehensive assessment of groundwater potentiality and its susceptibility to contamination. Incomplete knowledge of exploited aquifers and their geo-hydraulic boundaries and composition underscored the necessity of conducting a scientific mapping of the exploitable aquifer resources in the region. The need to undertake well-thought-out vulnerability mappings of the aquifer other than wildcat drilling would avert the problems of abandoned wells and reduce health risks associated with ingestion of contaminated water (Inim et al. 2020; Joshua et al. 2023; Udosen et al. 2024d). Groundwater has a high vulnerability to subsurface contamination and is more expensive to clean up compared to surface water resources like streams and lakes, hence it was important to ascertain the region's aquifer's potentiality and protectivity.

The aim of this research therefore was to investigate the hydro-geophysical properties of the region's aquifer system, assess the potential of groundwater resources within the study area, and evaluate the aquifer's susceptibility to pollution. To carry out these aims, geo-electrostratigraphic assessments employing vertical electrical soundings and 2D electrical resistivity tomography surveys were undertaken. The geophysical surveys would delineate the different lithological layers, determine conditions of groundwater, locate the aquifer zone, and evaluate the geo-hydraulic characteristics of the region's aquifer system. Results generated will aid in ascertaining aquifer properties precluding the need for new testing boreholes.

The study area, situated in Mkpat Enin, Akwa Ibom State, southern Nigeria (Figure 1), is situated between latitudes 4.614° and 4.628° N and longitudes 7.600° and 7.783° E. The region covers an area of roughly 322.352 km2. The region empties into the Kwa Iboe River, the Cross River, and the Imo River. The region's topography is undulating with elevations ranging from 15 to 186 m. The annual temperature in the region ranges from 25 to 31 °C and the annual precipitation ranges from 1,500 to 2,400 mm. The region has two major climatic seasons which are the wet season (March–October), and the dry season (November–February). During the dry season, temperatures can rise as high as 35 °C.
Figure 1

Geologic map of the study area.

Figure 1

Geologic map of the study area.

Close modal

The region under investigation is situated within the Benin Formation (Coastal Plain Sands) deposited between the Tertiary and Quaternary periods. Underlying these Coastal Plain Sands is the oil-bearing formation in the Niger Delta (Agbada Formation), and the Akata Formation. The Benin Formation comprises various fluvial and lacustrine deposits, rocks and sediments containing clay, and rock matrixes consisting of sand materials, all interwoven with each other (George et al. 2017). Alluvial ecosystems are another feature of the region. The alluvial units in the region comprise tidal and lagoonal sedimentary sands. These Coastal Plain Sands make up a majority of the region's aquiferous units which comprise coarse, medium, and finely sorted continental and gravelly sands that occasionally intercalate with thin horizons of argillaceous materials and lignite streaks. The shallow shale and argillaceous layers in the region create multi-aquifer systems. The region's vegetation comprises green foliage with short trees and patches of abandoned forest, along with secondary forests made up of shrubs and large trees. The region can be reached by a network of crisscrossing roads as well as a few other smaller road systems. Groundwater potential is moderate to good in the majority of regions where sandy formations are present. Some parts of the region area provide a difficult hydrogeological environment due to the intercalations of clay embedded within the sandy rock formations, leading to low groundwater potentials within such zones. In geologic formations with broken shale zones, groundwater is mostly accessible.

Twenty VES profiles were surveyed with an ABEM SAS 1,000 terrameter and its accessories. The Schlumberger array was employed, and the array employed a maximal current electrode spacing of 400 m. To geo-reference the coordinates of the profile lines, a hand-held GPS system was used. Schlumberger array was employed in VES data acquisition due to its heightened sensitivity to vertical subsurface layers and its wide current penetration depth. The terrain of the study area is relatively flat, and the Schlumberger array is generally effectual in locations with flat terrain. Further, the potential electrodes employed in the Schlumberger array are seldom moved, reducing the influence of near-surface lateral variations on the acquired data. The Schlumberger array employed four electrodes: a pair of current electrodes to inject subsurface current into the ground, and a second pair to measure the potential difference between the current electrodes. The spacing between the current electrodes was increased symmetrically about the array center to enable deeper penetration of current into the subsurface. The current electrode spacing (AB) ranged from 2 to 400 m while the potential electrode spacing (MN) ranged from 0.5 to 20 m. The gradual increment of potential electrode spacing was carried out to enable the generation of functional potential difference values. This is because the potential difference values tended to decrease with increments in current electrode spacing. By gradually increasing the potential electrode spacing, recognizable voltages could be generated.

The terrameter generated values of apparent resistance via Ohm's law,
formula
(1)
where V indicates potential difference measured between the current electrode pair, and I indicates current injected into the ground. To generate apparent resistivity values , the apparent resistance values were multiplied with the geometric factor of the array used, in this case, the Schlumberger array such that
formula
(2)
where indicates apparent resistivity, G indicates geometric factor of the array configuration used, and indicates apparent resistance. The geometric factor G of the Schlumberger array was given as
formula
(3)
where AB indicates current electrode spacing, and MN indicates potential electrode spacing. The apparent resistivity of the acquired field measurements was therefore derived via
formula
(4)

These measures of apparent resistivity were then evaluated using manual curve matching methods, the aim being to generate initial starting parameters that would later be input into the computerized inversion schemes (Choudhury et al. 2001; Ekanem & Udosen 2023a, 2023b). The manual curves were first plotted on a bi-logarithmic graph with apparent resistivity as ordinate against half the current electrode spacing (AB/2) as abscissa. Smoothing was undertaken on the matching curves, the aim being to remove outliers and generate trendlines that would indicate how subsurface resistivity varied with depth. The presence of outliers would have resulted in high root mean square errors during computer inversion of the data due to a poor fit between the acquired field data and calculated values generated by the inversion software. Hence it was necessary that outliers be removed. Data smoothening was undertaken by removing anomalous data points that did not fit the general trend of the curves and also by taking the mean of measurements obtained at cross-over points. Data generated from these smoothed curves were later input into the least-squares iterative software WINRESIST. The inversion software used the depths and resistivity values generated from the master curves as initial inputs during reconstruction (Joshua et al. 2011; Udosen & Potthast 2019). The reconstruction inversion models generated from WINRESIST provided information on the number of subsurface geo-layers, the thickness of each geo-layer, their depths and their mean resistivity values.

To generate information on subsurface heterogeneity in both lateral and vertical directions, the Wenner array configuration was also employed to complement information obtained from the Schlumberger array. Two-dimensional electrical resistivity tomography (ERT) surveys were undertaken in the study area with the use of the Wenner array configuration having a profile length of 105 m. The aim of this survey was to generate tomograms to delineate how resistivity varied with depth laterally within the study area. Two electrodes were employed to inject current into the subsurface, and another pair of electrodes was employed to measure the potential difference between the current electrodes. The Wenner array employs equal spacing between the electrodes, unlike the Schlumberger array, and was therefore suitable for delineating lateral variations in resistivity within the subsurface. Resistance data (derived from Ohm's law) were indicated on the terrameter. To convert the field resistance data to resistivity values implied multiplying the field resistance data by the geometric factor G of the Wenner array which is given by
formula
(5)
where a indicates electrode spacing. The resistivity data were then obtained via the formulation:
formula
(6)
where indicates apparent resistivity and indicates apparent resistance.

The generated resistivity data were modeled with RES2DINV software by GEOTOMO. The first step prior to inversion was the extermination of anomalous data points, the goal being to generate 2D tomograms with reduced root mean square errors. The RES2DINV program which employed an iterative least-squares technique, aimed at reducing the objective function (Udosen et al. 2011, 2013a, 2013b; Udosen & Potthast 2019), as it tried to match the acquired field data with its theoretical model. A resistivity model which took into account the heterogeneous nature of the earth's surface was finally generated to delineate the lateral resistivity distribution with depth along the profile line. Results obtained from electrical resistivity tomography would be integrated with results obtained from vertical electrical soundings.

Aquifer flow and potentiality were determined via the use of geo-electrical sounding data. These soundings were used to generate measures of geo-hydraulic parameters such as formation factor, porosity, hydraulic conductivity, transmissivity, and permeability. The formation factor F was computed using Archie's equation
formula
(7)
where indicates bulk resistivity of the aquifer, indicates resistivity of water, indicates porosity, a indicates pore geometrical factor (0.5245 m), and m indicates cementation factor (1.5432).
Subsequent to the determination of formation factor and porosity from Equation (7), the Kozeny-Carmen-Bear's model equation was employed to compute hydraulic conductivity such that
formula
(8)
where indicates density of water, indicates dynamic viscosity of water, indicates mean grain size, g indicates acceleration due to gravity, and indicates porosity. Measures of these parameters were mean grain size = 0.000348 m, the dynamic viscosity of water = 0.0014 kg/ms, g = 9.8 m/s2, and density of water = 1,000 kg/m3. The sum of fluid properties and inherent permeability yielded hydraulic conductivity. Regions with high hydraulic conductivity are typically linked to fine to coarse sand, sandstone, or gravel (Shevnin et al. 2006).
From the measures of hydraulic conductivity K, transmissivity T was determined via
formula
(9)
where K indicates hydraulic conductivity (m/day) and h indicates aquifer thickness (m). The permeability of the aquifer system was determined via
formula
(10)
where indicates permeability, K indicates hydraulic conductivity, indicates dynamic viscosity of water, indicates density of water, and g indicates acceleration due to gravity. Aquifer tortuosity was evaluated using
formula
(11)
where F indicates the formation factor, and φ indicates porosity.
Longitudinal conductance SL is a measure of conductance when current flows parallel to the boundaries of the geo-electrical layers while transverse resistance TR measures resistance during current flow perpendicular to the geo-layers. Given an aquifer with cross-sectional area and thickness, measures of geo-layer resistivity, thickness and depth could be used to generate values of longitudinal conductance and transverse resistance (otherwise termed the Dar-Zarrouk parameters) (Ekwe & Opara 2012). The longitudinal conductance was therefore given as
formula
(12)
where h indicates geo-layer thickness, indicates geo-layer resistivity, and indicates geo-layer conductivity. Transverse resistance TR, on the other hand, was given as
formula
(13)
where h indicates geo-layer thickness and indicates geo-layer resistivity.

From Equation (12), it was seen that an increment in the values of longitudinal conductance from one profile line to the next would mean an increment in geo-layer thickness or a decrement in mean resistivity, or both. An increment in transverse resistance (from Equation (13)), on the other hand, would imply an increment in geo-layer thickness or geo-layer resistivity, or both. Hence, reduced values of transverse resistance were related to low-resistivity formations (e.g., shale, clay, and clayey sand) and shallow basements. Larger values of transverse resistance, on the other hand, were related to resistive subsurface formations (Ejiogu et al. 2019; Nwachukwu et al. 2019). There was also a significantly positive correlation between transverse resistance and aquifer transmissivity. High transverse resistance values were linked with regions having elevated aquifer transmissivity values (Ekwe et al. 2020; Eyankware et al. 2020; Eyankware & Aleke 2021), and vice versa. High aquifer potentiality was therefore indicated by high transverse resistance values.

An earth medium's ability to slow down the percolation of fluids through it is a measure of its protectivity. Aquifer protectivity increased with the thickness of overburden formations (Oli et al. 2021). This implied that very low longitudinal conductance values may indicate that the aquifer's overlying layers were not thick enough to keep contaminant fluids from seeping into the aquifer. Furthermore, strata comprising clay, compact sandstone, and shale increased aquifer protectivity (Oloruntola et al. 2017). To assess aquifer protective capacity within the region, resultant longitudinal conductance was computed by expanding Equation (12) to obtain
formula
(14)
where indicates longitudinal conductance, h indicates geo-layer thickness, and indicates geo-layer resistivity.
Transverse resistance measures were then used to indicate aquiferous zones. Transverse resistance over a given VES profile line was given by
formula
(15)
where indicates transverse resistance, h indicates geo-layer thickness, and indicates geo-layer resistivity (Suneetha & Gupta 2018). Transverse resistance, transmissivity, and longitudinal conductance are related via Equations (9), (12) and (13) such that
formula
(16)
where T indicates transmissivity, K indicates hydraulic conductivity, h indicates geo-layer thickness, indicates transverse resistance, indicates geo-layer resistivity, σ indicates electrical conductivity and indicates longitudinal conductance. In locations with similar geology, the product K*σ would be approximately constant (Onuoha & Mbazi 1988), making it easy to determine the resistivity of a region and delineate its distribution even in locations where lithological logs are not readily accessible. So long as K values existed for a previously drilled borehole, one could easily determine the conductivity σ from the geo-electrical data generated from the electrical soundings.
Aquifer storativity S (also termed the storage coefficient) was computed via
formula
(17)
where h indicates aquifer thickness. Aquifer storativity measured the water volume stored or released by an aquifer. Hydraulic diffusivity D gave information as to how fast the stored fluids within the aquifer were transmitted during changes in hydraulic conditions. Hydraulic diffusivity employed measures of aquifer transmissivity T and storativity S and was computed as
formula
(18)
where D indicates hydraulic diffusivity, T indicates aquifer transmissivity, and S indicates aquifer storativity (Hiscock 2005).
The area's hydro-geophysical characteristics generated from the acquired vertical electrical soundings and electrical resistivity tomography coupled with well-log information (Ekanem 2020) indicated the presence of four geo-electric layers. The aquifer layer was delineated as the third geo-layer. Figures 25 indicate inversion models generated from WINRESIST software delineating the resistivity, depth, and thickness of some representative profile lines. Table 1 provides a summary of the results obtained from interpreting inversion curves for each of the profile stations investigated in the study area. The first layer (motley topsoil) had a mean resistivity of 168.2 Ωm with a mean thickness of 2.3 m. The second layer (sandy clay) had a mean resistivity of 228.5 Ωm with a mean thickness of 14.3 m. Within this layer, locations that had reduced resistivity with increased depth indicated the presence of clay and shale. The aquifer layer (fine sand) had a mean resistivity of 554.6 Ωm and a mean thickness of 79.1 m. The fourth layer (coarse sand) had a mean resistivity of 357.1 Ωm. Different curve types were identified from the interpretation of the geo-electric curves. The variability of the research area's geology was related to the variation in curve type.
Table 1

Summary of results obtained from interpretation of inversion curves for the 20 VES profile stations investigated in the study area

VES NO:Co-ordinate
Bulk resistivity (Ωm)
Thickness (m)
Depth (m)
Elevation (m)
Latitude (°)Longitude (°)ρ1ρ2ρ3ρ4h1h2h3d1d2d3
4°37′9.4″ 7°4.6′43.56″ 425.7 103.6 1,908.2 1,190.1 12.6 92.8 13.6 106.4 24 
4°37′6.0″ 7°4.6′43.6″ 212.8 741.5 232 214.7 0.7 3.2 115.5 0.7 3.9 119.4 20 
4°37′15.5″ 7°4.6′39.97″ 222 91.6 487.9 161.1 1.1 4.2 23.7 1.1 5.3 29 15 
4°37′13.7″ 7°4.6′42.3″ 361.1 225.5 2,574 1,405.6 3.6 27.6 91.1 3.6 31.2 122.3 14.5 
4°37′24.2″ 7°4.6′32.1″ 225.5 1,122.1 186.4 206.7 2.5 28.3 87.1 2.5 30.8 117.9 20 
4°37′16.6″ 7°4.6′18.9″ 329.6 32.4 260 109.2 1.6 9.6 86.4 1.6 11.1 97.6 18 
4°37′17.4″ 7°46′11.4″ 664.2 257.9 242.4 221.6 1.3 26.7 109.6 1.3 28 147.6 22 
4°37′12.3″ 7°46′29.9″ 157.1 1,203.8 167.8 262.6 0.6 18.1 97.8 0.6 18.7 116.5 35 
4°37′5.9″ 7°46′35.5″ 185.5 60.9 394.2 511.1 2.1 12.6 67.5 2.1 14.7 82.2 22 
10 4°37′15.3″ 7°46′39.7″ 12.7 31.8 258.1 72.8 4.3 1.8 49 4.3 6.1 55.1 13 
11 4°37′15.8″ 7°46′41.4″ 51.6 495.9 160.1 463.8 2.1 15.1 72.6 2.1 17.2 89.7 15 
12 4°37′15.5″ 7°46′40.9″ 21 89.5 231.1 441.3 3.3 12.6 88.3 3.3 15.6 164.2 14 
13 4°37′15.3″ 7°46′39.7″ 12.7 24.8 376.7 26.1 4.1 1.6 22.7 4.1 5.7 28.4 13 
14 4°37′14.7″ 7°46′37.29″ 39.8 307.6 82.9 62.9 1.5 42.7 74.3 1.5 44.2 118.6 15 
15 4°37′13.8″ 7°46′35.9″ 55.2 442.5 54.3 79.4 17.8 82.2 20.8 103 26 
16 4°37′13.3″ 7°46′35.2″ 113.8 251.7 514.5 60.3 5.3 14.9 45.8 5.3 20.2 66 14 
17 4°37′17.5″ 7°46′27.7″ 13.4 43.1 1,584 384.9 3.5 2.4 118.4 3.5 5.9 124.3 17 
18 4°37′16.5″ 7°46′22.5″ 219.1 14 636.1 148 13.4 133.5 14.4 147.9 17 
19 4°37′16.5″ 7°46′19.2″ 19.1 2.3 314.5 993.3 1.5 4.3 23.4 1.5 5.8 29.2 24 
20 4°37′15.3″ 7°46′44.1″ 21.4 28 427 127.2 1.6 16.6 99.8 1.6 18.2 118.0 15 
VES NO:Co-ordinate
Bulk resistivity (Ωm)
Thickness (m)
Depth (m)
Elevation (m)
Latitude (°)Longitude (°)ρ1ρ2ρ3ρ4h1h2h3d1d2d3
4°37′9.4″ 7°4.6′43.56″ 425.7 103.6 1,908.2 1,190.1 12.6 92.8 13.6 106.4 24 
4°37′6.0″ 7°4.6′43.6″ 212.8 741.5 232 214.7 0.7 3.2 115.5 0.7 3.9 119.4 20 
4°37′15.5″ 7°4.6′39.97″ 222 91.6 487.9 161.1 1.1 4.2 23.7 1.1 5.3 29 15 
4°37′13.7″ 7°4.6′42.3″ 361.1 225.5 2,574 1,405.6 3.6 27.6 91.1 3.6 31.2 122.3 14.5 
4°37′24.2″ 7°4.6′32.1″ 225.5 1,122.1 186.4 206.7 2.5 28.3 87.1 2.5 30.8 117.9 20 
4°37′16.6″ 7°4.6′18.9″ 329.6 32.4 260 109.2 1.6 9.6 86.4 1.6 11.1 97.6 18 
4°37′17.4″ 7°46′11.4″ 664.2 257.9 242.4 221.6 1.3 26.7 109.6 1.3 28 147.6 22 
4°37′12.3″ 7°46′29.9″ 157.1 1,203.8 167.8 262.6 0.6 18.1 97.8 0.6 18.7 116.5 35 
4°37′5.9″ 7°46′35.5″ 185.5 60.9 394.2 511.1 2.1 12.6 67.5 2.1 14.7 82.2 22 
10 4°37′15.3″ 7°46′39.7″ 12.7 31.8 258.1 72.8 4.3 1.8 49 4.3 6.1 55.1 13 
11 4°37′15.8″ 7°46′41.4″ 51.6 495.9 160.1 463.8 2.1 15.1 72.6 2.1 17.2 89.7 15 
12 4°37′15.5″ 7°46′40.9″ 21 89.5 231.1 441.3 3.3 12.6 88.3 3.3 15.6 164.2 14 
13 4°37′15.3″ 7°46′39.7″ 12.7 24.8 376.7 26.1 4.1 1.6 22.7 4.1 5.7 28.4 13 
14 4°37′14.7″ 7°46′37.29″ 39.8 307.6 82.9 62.9 1.5 42.7 74.3 1.5 44.2 118.6 15 
15 4°37′13.8″ 7°46′35.9″ 55.2 442.5 54.3 79.4 17.8 82.2 20.8 103 26 
16 4°37′13.3″ 7°46′35.2″ 113.8 251.7 514.5 60.3 5.3 14.9 45.8 5.3 20.2 66 14 
17 4°37′17.5″ 7°46′27.7″ 13.4 43.1 1,584 384.9 3.5 2.4 118.4 3.5 5.9 124.3 17 
18 4°37′16.5″ 7°46′22.5″ 219.1 14 636.1 148 13.4 133.5 14.4 147.9 17 
19 4°37′16.5″ 7°46′19.2″ 19.1 2.3 314.5 993.3 1.5 4.3 23.4 1.5 5.8 29.2 24 
20 4°37′15.3″ 7°46′44.1″ 21.4 28 427 127.2 1.6 16.6 99.8 1.6 18.2 118.0 15 
Figure 2

Inversion curve generated from WINRESIST software delineating the resistivity, depth, and thickness of VES profile line 3.

Figure 2

Inversion curve generated from WINRESIST software delineating the resistivity, depth, and thickness of VES profile line 3.

Close modal
Figure 3

Inversion curve generated from WINRESIST software delineating the resistivity, depth, and thickness of VES profile line 6.

Figure 3

Inversion curve generated from WINRESIST software delineating the resistivity, depth, and thickness of VES profile line 6.

Close modal
Figure 4

Inversion curve generated from WINRESIST software delineating the resistivity, depth, and thickness of VES profile line 16.

Figure 4

Inversion curve generated from WINRESIST software delineating the resistivity, depth, and thickness of VES profile line 16.

Close modal
Figure 5

Inversion curve generated from WINRESIST delineating the resistivity, depth, and thickness of VES profile line 20.

Figure 5

Inversion curve generated from WINRESIST delineating the resistivity, depth, and thickness of VES profile line 20.

Close modal
The electrical resistivity tomography technique was used to generate inversion models to complement information obtained from vertical electrical soundings. The generated tomograms (Figures 610) indicated that the region had an approximately horizontal stratification within the first geo-layer (motley topsoil) with a mean depth range of 0.6–3.75 m. This first layer was underlain by fine sand, which had a higher resistivity and a depth range of approximately 16 m. The aquifer layer whose mean thickness was 77.9 m was not delineated in the tomograms. This is because the Wenner array was employed to give high-resolution tomograms that showed how the electrical resistivity distribution varied laterally with depth within the near-surface. Locations of low-resistivity anomalies (high conductivity zones) on the tomograms indicated clay sequences.
Figure 6

Inversion tomogram generated from RES2DINV delineating the resistivity, depth, and thickness of electrical resistivity tomography profile line 1.

Figure 6

Inversion tomogram generated from RES2DINV delineating the resistivity, depth, and thickness of electrical resistivity tomography profile line 1.

Close modal
Figure 7

Inversion tomogram generated from RES2DINV delineating the resistivity, depth, and thickness of electrical resistivity tomography profile line 2.

Figure 7

Inversion tomogram generated from RES2DINV delineating the resistivity, depth, and thickness of electrical resistivity tomography profile line 2.

Close modal
Figure 8

Inversion tomogram generated from RES2DINV delineating the resistivity, depth, and thickness of electrical resistivity tomography profile line 3.

Figure 8

Inversion tomogram generated from RES2DINV delineating the resistivity, depth, and thickness of electrical resistivity tomography profile line 3.

Close modal
Figure 9

Inversion tomogram generated from RES2DINV delineating the resistivity, depth, and thickness of electrical resistivity tomography profile line 4.

Figure 9

Inversion tomogram generated from RES2DINV delineating the resistivity, depth, and thickness of electrical resistivity tomography profile line 4.

Close modal
Figure 10

Inversion tomogram generated from RES2DINV delineating the resistivity, depth, and thickness of electrical resistivity tomography profile line 5.

Figure 10

Inversion tomogram generated from RES2DINV delineating the resistivity, depth, and thickness of electrical resistivity tomography profile line 5.

Close modal
Table 2 gives a summary of geo-hydraulic parameters obtained for this aquifer system at the different VES stations. These parameters included aquifer resistivity, aquifer conductivity, water resistivity, formation factor, porosity, hydraulic conductivity and transmissivity. The aquifer resistivity iso-parametric map sections (Figure 11) indicated high values of aquifer resistivity in the north-central and southeastern sections. Aquifer resistivity values varied from 54.3Ωm at VES 15 to 1,908.2Ωm at VES 1 with an average value of 554.6 Ωm (Table 2). The iso-parametric map indicating a spatial distribution of aquifer conductivity is illustrated in Figure 12. The range of values for aquifer conductivity ranged from 0.00052–0.018 S/m, with an average of 0.0043 S/m. Figure 12 indicated that regions with low aquifer conductivity values were associated with high resistivity geo-material.
Table 2

Summary of geo-hydraulic parameters obtained for this aquifer system at the different VES stations: (i) aquifer resistivity; (ii) aquifer thickness; (iii) aquifer conductivity; (iv) water resistivity; (v) formation factor; (vi) porosity; (vii) hydraulic conductivity; and (viii) transmissivity

VES No.Latitude (°)Longitude (°)Aquifer resistivity (Ωm)Aquifer thickness (m)Aquifer conductivity (S/m)Water resistivity (Ω m)Formation factorPorosityHydraulic conductivity (m/day)Transmissivity (m2/day)
4°37′9.4″ 7°4.6′43.56″ 1,908.2 92.8 0.00052 123.88 15.4037 0.11186 0.72209 67.0104 
4°37′6.0″ 7°4.6′43.6″ 232 115.5 0.00431 27.0456 8.57809 0.16347 2.54034 293.41 
4°37′15.5″ 7°4.6′39.97″ 487.9 23.7 0.00205 41.829 11.6642 0.13395 1.30401 30.9051 
4°37′13.7″ 7°4.6′42.3″ 2,574 91.1 0.00039 162.343 15.8553 0.10979 0.67946 61.8987 
4°37′24.2″ 7°4.6′32.1″ 186.4 87.1 0.00536 24.4113 7.6358 0.17628 3.28509 286.131 
4°37′16.6″ 7°4.6′18.9″ 260 86.4 0.00385 28.6632 9.07086 0.15766 2.24758 194.191 
4°37′17.4″ 7°46′11.4″ 242.4 109.6 0.00413 27.6464 8.76785 0.16117 2.4212 265.363 
4°37′12.3″ 7°46′29.9″ 167.8 97.8 0.00596 23.3368 7.19036 0.18328 3.75591 367.328 
4°37′5.9″ 7°46′35.5″ 394.2 67.5 0.00254 36.4159 10.8249 0.1406 1.53115 103.353 
10 4°37′15.3″ 7°46′39.7″ 258.1 49 0.00387 28.5534 9.03919 0.15802 2.26484 110.977 
11 4°37′15.8″ 7°46′41.4″ 160.1 72.6 0.00625 22.892 6.99372 0.18661 3.99642 290.14 
12 4°37′15.5″ 7°46′40.9″ 231.1 88.3 0.00433 26.9936 8.56127 0.16368 2.55132 225.282 
13 4°37′15.3″ 7°46′39.7″ 376.7 22.7 0.00265 35.405 10.6398 0.14218 1.58923 36.0755 
14 4°37′14.7″ 7°46′37.29″ 82.9 74.3 0.01206 18.4321 4.49758 0.24842 11.0431 820.504 
15 4°37′13.8″ 7°46′35.9″ 54.3 82.2 0.01842 16.7799 3.23601 0.3075 24.67 2,027.87 
16 4°37′13.3″ 7°46′35.2″ 514.5 45.8 0.00194 43.3657 11.8642 0.13249 1.25733 57.5858 
17 4°37′17.5″ 7°46′27.7″ 1,584 118.4 0.00063 105.151 15.0641 0.11349 0.75685 89.6109 
18 4°37′16.5″ 7°46′22.5″ 636.1 133.5 0.00157 50.3905 12.6234 0.12727 1.1012 147.01 
19 4°37′16.5″ 7°46′19.2″ 314.5 23.4 0.00318 31.8117 9.88631 0.14911 1.86316 43.5979 
20 4°37′15.3″ 7°46′44.1″ 427 99.8 0.00234 38.3108 11.1457 0.13796 1.43781 143.493 
VES No.Latitude (°)Longitude (°)Aquifer resistivity (Ωm)Aquifer thickness (m)Aquifer conductivity (S/m)Water resistivity (Ω m)Formation factorPorosityHydraulic conductivity (m/day)Transmissivity (m2/day)
4°37′9.4″ 7°4.6′43.56″ 1,908.2 92.8 0.00052 123.88 15.4037 0.11186 0.72209 67.0104 
4°37′6.0″ 7°4.6′43.6″ 232 115.5 0.00431 27.0456 8.57809 0.16347 2.54034 293.41 
4°37′15.5″ 7°4.6′39.97″ 487.9 23.7 0.00205 41.829 11.6642 0.13395 1.30401 30.9051 
4°37′13.7″ 7°4.6′42.3″ 2,574 91.1 0.00039 162.343 15.8553 0.10979 0.67946 61.8987 
4°37′24.2″ 7°4.6′32.1″ 186.4 87.1 0.00536 24.4113 7.6358 0.17628 3.28509 286.131 
4°37′16.6″ 7°4.6′18.9″ 260 86.4 0.00385 28.6632 9.07086 0.15766 2.24758 194.191 
4°37′17.4″ 7°46′11.4″ 242.4 109.6 0.00413 27.6464 8.76785 0.16117 2.4212 265.363 
4°37′12.3″ 7°46′29.9″ 167.8 97.8 0.00596 23.3368 7.19036 0.18328 3.75591 367.328 
4°37′5.9″ 7°46′35.5″ 394.2 67.5 0.00254 36.4159 10.8249 0.1406 1.53115 103.353 
10 4°37′15.3″ 7°46′39.7″ 258.1 49 0.00387 28.5534 9.03919 0.15802 2.26484 110.977 
11 4°37′15.8″ 7°46′41.4″ 160.1 72.6 0.00625 22.892 6.99372 0.18661 3.99642 290.14 
12 4°37′15.5″ 7°46′40.9″ 231.1 88.3 0.00433 26.9936 8.56127 0.16368 2.55132 225.282 
13 4°37′15.3″ 7°46′39.7″ 376.7 22.7 0.00265 35.405 10.6398 0.14218 1.58923 36.0755 
14 4°37′14.7″ 7°46′37.29″ 82.9 74.3 0.01206 18.4321 4.49758 0.24842 11.0431 820.504 
15 4°37′13.8″ 7°46′35.9″ 54.3 82.2 0.01842 16.7799 3.23601 0.3075 24.67 2,027.87 
16 4°37′13.3″ 7°46′35.2″ 514.5 45.8 0.00194 43.3657 11.8642 0.13249 1.25733 57.5858 
17 4°37′17.5″ 7°46′27.7″ 1,584 118.4 0.00063 105.151 15.0641 0.11349 0.75685 89.6109 
18 4°37′16.5″ 7°46′22.5″ 636.1 133.5 0.00157 50.3905 12.6234 0.12727 1.1012 147.01 
19 4°37′16.5″ 7°46′19.2″ 314.5 23.4 0.00318 31.8117 9.88631 0.14911 1.86316 43.5979 
20 4°37′15.3″ 7°46′44.1″ 427 99.8 0.00234 38.3108 11.1457 0.13796 1.43781 143.493 
Figure 11

Iso-parametric map indicating spatial distribution of aquifer resistivity (Ωm) across the study area.

Figure 11

Iso-parametric map indicating spatial distribution of aquifer resistivity (Ωm) across the study area.

Close modal
Figure 12

Iso-parametric map indicating spatial distribution of aquifer conductivity (S/m) across the study area.

Figure 12

Iso-parametric map indicating spatial distribution of aquifer conductivity (S/m) across the study area.

Close modal
The map delineating the spatial distribution of aquifer thickness within the study area is shown in Figure 13. The thickness of the aquifer formation varied from 22.7 m at VES 13 to 133.5 m at VES 18 with an average value of 79.1 m. Groundwater potential and protectivity could be approximated from aquifer thickness and depth. Locations with large aquifer thickness had greater restorative ability compared to locations with smaller aquifer thickness. The greater storativity implied higher groundwater potentiality. The depth of an aquifer from the near-surface also determined how vulnerable the aquifer was to subsurface contamination. Locations with diminutive aquifer depths were more vulnerable to contamination (Kalinski et al. 1993) compared to locations with large aquifer depths.
Figure 13

Iso-parametric map indicating spatial distribution of aquifer thickness (m) across the study area.

Figure 13

Iso-parametric map indicating spatial distribution of aquifer thickness (m) across the study area.

Close modal
Figure 14

Iso-parametric map indicating spatial distribution of aquifer hydraulic conductivity (m/day) across the study area.

Figure 14

Iso-parametric map indicating spatial distribution of aquifer hydraulic conductivity (m/day) across the study area.

Close modal

Hydraulic conductivity values ranged from 0.679 m/day at VES 4 to 24.67 m/day at VES 14 (Table 2). The iso-parametric map indicating the spatial distribution of hydraulic conductivity (m/day) over the study area indicated that the northeastern sections had high hydraulic conductivity values, implying the ability of groundwater to move easily through the pore spaces within the rock matrix in those locations (Figure 14). High groundwater potential is typically indicated by locations with high aquifer hydraulic conductivity, which is linked to greater levels of hydraulic flow and groundwater recharge.

The research area's aquifers were classified using aquifer transmissivity ratings by Krásný (1993), whose results showed that aquifer transmissivity values of 1–10 m2/day indicated low aquifer potentiality, transmissivity values of 10–100 m2/day indicated medium aquifer potentiality, and transmissivity values of 100–1,000 m2/day indicated high aquifer potentiality. Aquifer transmissivity within the study area ranged from 30.90 m2/day at VES 3 to 820.5 m2/day at VES 15 with an average value of 283 m2/day, indicating medium to high aquifer potentiality. Large values of aquifer transmissivity were observed in the northeastern regions (Figure 15) and such regions were considered excellent for groundwater abstraction. Locations with high values of transmissivity were indicative of lithological units that had high permeability and porosity, implying excellent fluid flow. However, this ease of fluid flow had the disadvantage of allowing rapid percolation of contaminants into the aquifer. Very low values of transmissivity, on the other hand, could be indicative of the presence of impervious clay which reduced fluid flow within the water-bearing units. Hence, regions with high potentiality of groundwater resources with regards to the permeability and porosity of the constituent geo-layers were indicative of locations with high aquifer vulnerability to contamination.
Figure 15

Iso-parametric map indicating spatial distribution of aquifer transmissivity (m2/day) across the study area.

Figure 15

Iso-parametric map indicating spatial distribution of aquifer transmissivity (m2/day) across the study area.

Close modal
Table 3 gives a summary of more geo-hydraulic parameters obtained for this aquifer system at the different VES stations. This included permeability, tortuosity, longitudinal conductance, transverse resistance, storativity, and hydraulic diffusivity. The table showed that permeability ranged from 1,138.4 to 41,331 mD with a mean of 5,949.1 mD, tortuosity ranged from 0.9 to 1.3 with a mean of 1.2, and measures of longitudinal conductance SL varied from 0.0076 Ω−1 at VES 2 to 1.948 Ω−1 at VES 19 with an average value of 0.3 Ω−1. Using the aquifer protectivity scale (Oladapo & Akintorinwa 2007) illustrated in Table 4, the values of longitudinal conductance were categorized. Profile lines 18 and 19 were categorized as having good protectivity, profile lines 6, 9, 10, 12, 13, 17 and 20, were categorized as having moderate protectivity, profile lines 1, 4, 7, 14, and 16 were categorized as having weak protectivity, and profile lines 2, 3,5 8, 11, and 15 were categorized as extremely vulnerable to surface contamination, i.e., they had poor protectivity to surface pollution. The map indicating the spatial distribution of longitudinal conductance over the study area is shown in Figure 16.
Table 3

Summary of more geo-hydraulic parameters obtained for this aquifer system at different VES stations: (i) permeability; (ii) tortuosity; (iii) longitudinal conductance; (iv) transverse resistance; (v) storativity; and (vi) hydraulic diffusivity

VES No.Latitude (°)Longitude (°)Permeability (mD)TortuosityLongitudinal conductance (Ω−1)Transverse resistance (Ωm2)StorativityHydraulic diffusivity (m2/day)
4°37′9.4″ 7°4.6′43.56″ 1,209.79 1.31267 0.123971 1,731.06 0.000278 240,698.3 
4°37′6.0″ 7°4.6′43.6″ 4,256.06 1.18419 0.007605 2,521.76 0.000347 846,781.3 
4°37′15.5″ 7°4.6′39.97″ 2,184.72 1.24998 0.050806 628.92 7.11E-05 434,670.6 
4°37′13.7″ 7°4.6′42.3″ 1,138.36 1.31936 0.132364 7,523.76 0.000273 226,486.5 
4°37′24.2″ 7°4.6′32.1″ 5,503.79 1.16019 0.036307 32,319.18 0.000261 1,095,029 
4°37′16.6″ 7°4.6′18.9″ 3,765.56 1.19588 0.301151 838.4 0.000259 749,192.8 
4°37′17.4″ 7°46′11.4″ 4,056.44 1.18876 0.105486 7,749.39 0.000329 807,066 
4°37′12.3″ 7°46′29.9″ 6,292.6 1.14798 0.018855 21,883.04 0.000293 1,251,971 
4°37′5.9″ 7°46′35.5″ 2,565.27 1.23367 0.218217 1,156.89 0.000203 510,383.3 
10 4°37′15.3″ 7°46′39.7″ 3,794.48 1.19515 0.395186 111.85 0.000147 754,946.3 
11 4°37′15.8″ 7°46′41.4″ 6,695.55 1.1424 0.071147 7,596.45 0.000218 1,332,140 
12 4°37′15.5″ 7°46′40.9″ 4,274.46 1.18378 0.297925 1,197 0.000265 850,441.6 
13 4°37′15.3″ 7°46′39.7″ 2,662.57 1.22993 0.387351 91.75 6.81E-05 529,743.3 
14 4°37′14.7″ 7°46′37.29″ 18,501.5 1.05701 0.176505 13,194.22 0.000223 3,681,042 
15 4°37′13.8″ 7°46′35.9″ 41,331.8 0.99753 0.094574 8,042.1 0.000247 8,223,333 
16 4°37′13.3″ 7°46′35.2″ 2,106.52 1.25373 0.10577 4,353.47 0.000137 419,110.8 
17 4°37′17.5″ 7°46′27.7″ 1,268.01 1.30753 0.316878 150.34 0.000355 252,283 
18 4°37′16.5″ 7°46′22.5″ 1,844.93 1.26749 0.961707 406.7 0.000401 367,066.4 
19 4°37′16.5″ 7°46′19.2″ 3,121.51 1.21414 1.948099 38.54 7.02E-05 621,053.4 
20 4°37′15.3″ 7°46′44.1″ 2,408.89 1.24002 0.667623 499.04 0.000299 479,270 
VES No.Latitude (°)Longitude (°)Permeability (mD)TortuosityLongitudinal conductance (Ω−1)Transverse resistance (Ωm2)StorativityHydraulic diffusivity (m2/day)
4°37′9.4″ 7°4.6′43.56″ 1,209.79 1.31267 0.123971 1,731.06 0.000278 240,698.3 
4°37′6.0″ 7°4.6′43.6″ 4,256.06 1.18419 0.007605 2,521.76 0.000347 846,781.3 
4°37′15.5″ 7°4.6′39.97″ 2,184.72 1.24998 0.050806 628.92 7.11E-05 434,670.6 
4°37′13.7″ 7°4.6′42.3″ 1,138.36 1.31936 0.132364 7,523.76 0.000273 226,486.5 
4°37′24.2″ 7°4.6′32.1″ 5,503.79 1.16019 0.036307 32,319.18 0.000261 1,095,029 
4°37′16.6″ 7°4.6′18.9″ 3,765.56 1.19588 0.301151 838.4 0.000259 749,192.8 
4°37′17.4″ 7°46′11.4″ 4,056.44 1.18876 0.105486 7,749.39 0.000329 807,066 
4°37′12.3″ 7°46′29.9″ 6,292.6 1.14798 0.018855 21,883.04 0.000293 1,251,971 
4°37′5.9″ 7°46′35.5″ 2,565.27 1.23367 0.218217 1,156.89 0.000203 510,383.3 
10 4°37′15.3″ 7°46′39.7″ 3,794.48 1.19515 0.395186 111.85 0.000147 754,946.3 
11 4°37′15.8″ 7°46′41.4″ 6,695.55 1.1424 0.071147 7,596.45 0.000218 1,332,140 
12 4°37′15.5″ 7°46′40.9″ 4,274.46 1.18378 0.297925 1,197 0.000265 850,441.6 
13 4°37′15.3″ 7°46′39.7″ 2,662.57 1.22993 0.387351 91.75 6.81E-05 529,743.3 
14 4°37′14.7″ 7°46′37.29″ 18,501.5 1.05701 0.176505 13,194.22 0.000223 3,681,042 
15 4°37′13.8″ 7°46′35.9″ 41,331.8 0.99753 0.094574 8,042.1 0.000247 8,223,333 
16 4°37′13.3″ 7°46′35.2″ 2,106.52 1.25373 0.10577 4,353.47 0.000137 419,110.8 
17 4°37′17.5″ 7°46′27.7″ 1,268.01 1.30753 0.316878 150.34 0.000355 252,283 
18 4°37′16.5″ 7°46′22.5″ 1,844.93 1.26749 0.961707 406.7 0.000401 367,066.4 
19 4°37′16.5″ 7°46′19.2″ 3,121.51 1.21414 1.948099 38.54 7.02E-05 621,053.4 
20 4°37′15.3″ 7°46′44.1″ 2,408.89 1.24002 0.667623 499.04 0.000299 479,270 
Table 4

Relationship between values of longitudinal conductance and protective capacity rating of an aquifer (Oladapo & Akintorinwa 2007)

Longitudinal conductance (Ω−1)Aquifer protective capacity rating
>10 Excellent 
5–10 Very good 
0.7–4.9 Good 
0.2–0.69 Moderate 
0.1–0.19 Weak 
<0.1 Poor 
Longitudinal conductance (Ω−1)Aquifer protective capacity rating
>10 Excellent 
5–10 Very good 
0.7–4.9 Good 
0.2–0.69 Moderate 
0.1–0.19 Weak 
<0.1 Poor 
Figure 16

Iso-parametric map indicating spatial distribution of longitudinal conductance (Ω−1) across the study area.

Figure 16

Iso-parametric map indicating spatial distribution of longitudinal conductance (Ω−1) across the study area.

Close modal
Transverse resistance (TR) had a mean value of 5,601.7 Ωm2 (Table 3) and its iso-parametric map was illustrated in Figure 17. This map of aquifer transverse resistance, similar to that of aquifer transmissivity, indicated that the northeast had high aquifer potentiality.
Figure 17

Iso-parametric map indicating spatial distribution of transverse resistance (Ωm2) across the study area.

Figure 17

Iso-parametric map indicating spatial distribution of transverse resistance (Ωm2) across the study area.

Close modal

Aquifer storativity ranged from 7 × 10−5 to 0.0004, with a mean storativity of 0.0002, suggestive of a productive aquifer (Ugada et al. 2014). Locations with extremely low storativity values implied low groundwater potentiality, and vice versa. High values of hydraulic diffusivity would indicate formations with fast rates of transmitting stored fluids during changes in hydraulic conditions within the aquifer. Table 3 indicated high values of hydraulic diffusivity in the region with a mean of 1 × 105 m2/day, indicating the presence of a productive aquifer.

This work was undertaken to assess aquifer potentiality, protectivity, and pliable level of vulnerability to contamination within a major coastal milieu in Southern Nigeria using geo-electrical technology. Four geo-electric strata were delineated: motley topsoil, sandy clay, fine sand, and coarse sand. The regions with the largest potential for groundwater were sandy geo-materials with aquifer thickness >30 m. Areas with aquifer thickness ranging from 15–30 m were classified as having an average groundwater potential, while areas with aquifer thickness that were <15 m were classified as having low groundwater potential. The overburden's composition, marked by medium to low longitudinal conductance values, provided the underlying aquifer with little protection.

Characterization of aquifer geo-hydraulic parameters yielded the following values for those parameters: mean aquifer resistivity of 554.6 Ωm, mean aquifer conductivity of 0.0004 S/m, mean longitudinal conductance of 0.67 Ω−1, mean transverse resistance of 5,601.7 Ωm2, mean hydraulic conductivity of 3.5 m/day, mean transmissivity of 283 m2/day, mean storativity of 0.0002, and mean hydraulic diffusivity of 1 × 105 m2/day. The research region had a range of groundwater potentials, ranging from medium to high. The research findings showed that the northeastern parts of the area had greater chances for groundwater potential resulting from its higher values of transmissivity, transverse resistance, and thickness. Locations with sandy lithologies underlying them were noticeably more prolific than locations with argillaceous formations.

These results have contributed to the knowledge of the region's groundwater potentiality and protectivity. It has also indicated how vulnerable the aquifer system is to contamination. The results have also shown via computation of geo-hydraulic characteristics, that the aquifer potentiality is higher in the northeast. This work will aid in the enactment of policies on groundwater management/conservation in the region. It will also aid in the development of sustainable developmental goals within the region to safeguard declining groundwater resources.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

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