There are growing worries regarding sufficient supplies of safe water in Obot Akara and Ikot Ekpene Local Government Areas of Akwa Ibom State. Thus, this study aims to investigate aquifer storage properties and contamination risk potential using electrical resistivity techniques in the two counties. The counties are shown to comprise 3–4 lithological successions of sandy and gravelly layers with slight clay intercalations. Groundwater abstraction takes place in the third and fourth layers in the area at depths between 5.4 and 121.4 m. The results demonstrate that the aquifer's prolificacy requires a specific yield larger than 0.15 or a specific retention less than 0.15. 94% of the study region is revealed to have good groundwater potential for the sustainability of water boreholes. Furthermore, the results show that 87.5% of the study region has weak/poor protection while 12.5% has moderate protection against infiltrating contaminants. In terms of susceptibility potential rating, 21.9% of the area has a moderate rating while the remaining 78.1% has a low rating. The identified areas with weak/poor protection and moderate susceptibility are adjudged to have moderate/high groundwater contamination risk potential. These findings provide valuable guidelines for formulating sustainable groundwater utilization and management strategies in the area.

  • Investigation of aquifer storage properties and groundwater contamination risk potential in parts of Akwa Ibom State.

  • Finding groundwater potential in terms of specific yield and specific retention.

  • Groundwater protectivity and contamination risk potential map created for the study area.

  • Guidelines for sustainable groundwater utilization and management strategy in parts of Akwa Ibom State.

a

pore geometrical factor

AB

current electrode distance

APC

aquifer protection capacity

dw

water density

F

formation factor

g

acceleration due to gravity

GLSI

Geoelectric Layer Susceptibility Index

h

thickness

Kh

hydraulic conductivity

Kp

permeability

LC

longitudinal conductance

LGA

local government area

LGAs

local government areas

m

cementation exponent

MCDA

Multi-Criteria Decision Analysis

MN

potential electrode distance

n

number of layers

OBAKIKE

Obot Akara and Ikot Ekpene

RA

apparent resistance

RMSE

root mean square error

S

saturation at specific retention

Sr

aquifer-specific retention

Sy

aquifer-specific yield

VES

vertical electrical sounding

λ

the aquifer-specific yield – specific retention ratio

ρ

resistivity

ρA

apparent resistivity

ρb

aquifer bulk resistivity

ρsat

resistivity of the saturated portion of the aquifer

ρunsat

resistivity of the unsaturated geological unit overlying the topmost aquifer

ρw

water resistivity

σw

conductivity

aquifer porosity

μd

viscosity of water

The necessity of water for life has sparked a global surge in groundwater studies. Groundwater is stored in underground rocks called aquifers. The rock must be both permeable and porous in order to function as a prolific aquifer. The ability of an aquifer to release water from storage in response to a decrease in hydraulic head or to increase heads in response to an accumulation of water is defined by its storage characteristics. Aquifer storage capacity is greatly influenced by aquifer effective porosity, which is the fractional volume of interconnected empty spaces in the aquifer material. The amount of water that an aquifer can store depends on its effective porosity. The capacity of the aquifer to store water increases with porosity and vice versa. Nevertheless, not all the stored water in the aquifer is discharged during pumping into wells or boreholes. For unconfined aquifers, the fraction of the volume of water that the aquifer discharges by gravity to the overall volume of the aquifer is referred to as aquifer-specific yield or storativity Sy (Lohman 1972; Fetter 1994; Schwartz & Zhang 2003). Aquifer-specific retention Sr, on the other hand, is the fraction of the volume of water reserved or retained by the aquifer to the overall aquifer volume (Healy & Cook 2002). Aquifer effective porosity is the sum of specific retention and specific yield (Frohlich & Kelly 1988; Tizro et al. 2012). Factors affecting specific yield include grain size, shape and distribution of pores in the rock material. Adequate understanding of aquifer storage properties especially specific yield and retention of unconfined aquifers is important in the evaluation of groundwater potential of a given area (Lin et al. 2023). In this context, groundwater potential pertains to the capacity of an aquifer to produce water in sufficient quantity given certain hydrogeological circumstances. Groundwater potential investigation entails the identification of regions with prolific aquifers for optimum location of water wells (Ekanem et al. 2022a; Ekanem & Udosen 2023a). When the specific yield is higher than the specific retention, the aquifer releases more water than it keeps back and therefore has a high potential to supply water to a well or borehole during pumping. The reverse is also true and corresponds to low groundwater potentiality.

Undoubtedly, groundwater potentiality is high in most sedimentary basins (Ekanem & Udosen 2023a, 2023b; George et al. 2024). However, groundwater contamination by anthropogenic and natural sources poses a serious risk to the quality of groundwater (Thomas et al. 2020; Ekanem 2022a; Ekanem et al. 2022b; Ikpe et al. 2022; Ekanem & Udosen 2023b). This could be the result of human-induced processes or natural processes, which include agricultural practices, erosion, salt intrusion, poor sewage system, poor waste disposal, mining activities, landfill leachates and poor channelization of surface run-off (Uddin et al. 2021; Ekanem et al. 2022a). Wash-off from leachates and other chemical substances emanating from the breakdown of these poorly managed waste materials can drip down into the subsurface to cause groundwater contamination. Depending on the nature of the geomaterials of the vadose zone, the underlying aquifers may or may not have some degree of protection from surface or near-surface contaminants (Ekanem 2020; Ekanem et al. 2021; George 2021; Ikpe et al. 2022). This is referred to as aquifer protectivity. Aquifer susceptibility potential is the likelihood of the aquifer being contaminated by surface or near-surface contaminants (Awawdeh & Jaradat 2010; Ekanem et al. 2022a; Ekanem & Udosen 2023b). Investigation of groundwater contamination risk potential involves identifying areas that are prone to contamination arising from either anthropogenic or natural processes. This investigation is indeed necessary for the sustainable exploitation and management of groundwater georesource to meet the growing demand for potable water.

As a result of significant human population growth and other business operations in Obot Akara and Ikot Ekpene local government areas (LGAs) of Akwa Ibom State, there are growing worries regarding sustainable and sufficient supplies of clean water in the area. Studies by Ekanem et al. (2021) on the assessment of aquifer protectivity and corrosivity of shallow layers in some communities in the Obot Akara LGA have shown that most of the aquifer units in the area lack adequate protection against surface or near-surface contaminants. In the same manner, studies by Ikpe et al. (2022) on the assessment of the protectivity of hydrogeological units in some communities in Ikot Ekpene LGA reveal that a greater proportion of the aquifers in the area are characterized by weak/poor protection. Their findings demonstrate that the aquifer overburden layers are made up of previous geomaterials, which enhance easy percolation of any contaminated fluid into the aquifer system to cause groundwater contamination. George (2021) evaluated the vulnerability potential of the hinterland aquifers in some communities in the Niger Delta region where this research was carried out and found that a greater proportion of the aquifers have moderate/high vulnerability potential. The results of Ekanem (2022a) and Ekanem et al. (2022a) still in parts of the Niger Delta region equally demonstrate that most of the aquifer units in the area are characterized by moderate/high vulnerability potential. These results, they attributed to the low slope terrain in the area which enhances rapid infiltration of any leachates and other chemical substances originating from the decomposition of poorly disposed solid waste materials littering some streets in the study area during rainfall. All these studies however fail to evaluate the aquifer storage properties (specific yield and retention), which may have a link with the groundwater potential for the sustainability of water wells. Equally, it is paramount to have a fuller understanding of the groundwater contamination risk potential in the area to guide the development, exploitation and utilization of groundwater in the area. To date, the area has no groundwater contamination potential map. Consequently, the main aim of this study is to investigate aquifer storage properties and groundwater contamination risk potential in Obot Akara and Ikot Ekpene (OBAKIKE) LGAs of Akwa Ibom State in the Niger Delta region of southern Nigeria via the deployment of the surface resistivity technique. Ikot Ekpene municipality, being a major commercial nerve centre in the state continues to experience population increase over the years. Public water supply facilities in OBAKIKE LGAs are far too insufficient to cater for the water requirements of the inhabitants. Thus, the local population massively depends on groundwater abstracted from boreholes or hand-dug wells in the area, most of which is accomplished through a wild-cat drilling approach (Ekanem et al. 2021; Ikpe et al. 2022; Ekanem & Udosen 2023a, 2023b). The productivity and sustainability of such boreholes and water wells depend to a large extent on the hydraulic and storage characteristics of the aquifer system in the area. Furthermore, of major concern is the status of the groundwater quality from such boreholes and wells, which depends on the susceptibility potential of the aquifer to contaminants infiltration. To this end, an investigation of the storage properties and contamination risk potential of the aquifer units in the OBAKIKE LGAs is essential to map out areas for optimum borehole sustainability and low susceptibility potential to contamination. Groundwater contamination risk potential can be effectively controlled and reduced by detecting and monitoring contaminated areas.

The resistivity method is a low-cost technique for quickly determining the resistivity distribution of the subsurface. In this method, a DC current or low-frequency AC current is injected into the earth via two electrodes on the surface and the resultant potential difference produced is detected by another pair of inner electrodes also planted on the Earth's surface (George et al. 2016, 2017, 2020, 2021; Ekanem et al. 2020). Ohm's law provides the basis for getting the apparent resistance of the earth layers penetrated by the current. This technique has been extensively used by many scholars for groundwater studies globally (e.g. Evans et al. 2015; George et al. 2018, 2022a, 2022b; Umoh et al. 2022; Inyang et al. 2023, 2024 etc.). The storage properties considered in this study are aquifer-specific yield and retention, which have a link with groundwater potential. Groundwater contamination risk potential was evaluated via the use of Longitudinal Conductance (LC) and Geoelectric Layer Susceptibility Index (GLSI) parameters. This project was designed especially to achieve the following objectives: evaluation of aquifer-specific yield and retention, mapping of locations with high groundwater capability to sustain water wells, assessing the susceptibility of the hydrological units to contaminations that might emanate from or near the surface, generation of the groundwater and groundwater contamination potential maps in the study area. The results of this study provide valuable guidelines to the local government authorities and other policymakers in formulating sustainable groundwater exploration and management strategies in the area.

This study was carried out in two LGAs –OBAKIKE LGAs of Akwa Ibom State in the Niger Delta region of southern Nigeria. OBAKIKE lies generally between latitudes 5°8′ and 5°20′ North and longitudes 7°32′ and 7°46′ East as depicted in Figure 1. Climatically, the area is characterized by two seasons, which are the rainy seasons and the dry seasons, respectively. The rainy season sets in from around late February to early November whereas the rest of the months of the year constitute the dry season (George et al. 2016; Thomas et al. 2020; Ekanem et al. 2021; Ikpe et al. 2022). In any case, slight fluctuations are noticeable in the upper and lower limits of the two seasons by reason of global changes in climatic conditions (George et al. 2017; Ekanem 2021; Ikpe et al. 2022). The area is generally accessible by a network of roads and has a relatively flat topography. Annual rainfall in OBAKIKE LGAs ranges from 2,008 to 2,289 mm while annual temperature is between 20 and 35 °C correspondingly in the rainy and dry seasons (George et al. 2016, 2018; Ekanem 2020, 2022a).
Figure 1

Location map and broad geology of the study area: (a) Nigeria; (b) Akwa Ibom State; and (c) OBAKIKE.

Figure 1

Location map and broad geology of the study area: (a) Nigeria; (b) Akwa Ibom State; and (c) OBAKIKE.

Close modal

Geologically, the overall chronology of the Niger Delta region comprises the Benin, the Agbada and the Akata Formations in that sequence (Obaje 2009; Udo & Udofia 2020; Udo et al. 2020; Udofia & Udo 2021). The Benin Formation is the youngest unit in the Niger Delta and is composed of sands of unequal grain sizes ranging from fine sands to coarse sands and gravelly sands with diverse thicknesses, intermingled with thin clay beddings, lenses, and lignite streaks in some places (Mbipom et al. 1996; Udo & Mode 2013a, 2013b; Udo et al. 2023a, 2023b, 2023c; Allen et al. 2024). The local population in OBAKIKE obtain their groundwater from the Benin Formation, which constitutes the main hydrostratigraphic unit in the area. Where they occur, the sand–clay interbeddings cause the establishment of multi-aquifer systems (Esu et al. 1999).

The electrical resistivity method involving vertical electrical sounding (VES) was employed in this study to examine the subsurface resistivity distribution. Data obtained were analyzed by the use of Microsoft Excel and interpreted by the use of WINRESIST computer software. Aquifer storage properties were estimated by the use of empirical relations between the aquifer geoelectric properties derived from VES data interpretation while aquifer contamination risk potential was evaluated by using LC of the aquifer overlying layers and the GLSI approach. The image maps were generated by the use of SURFER 10 software via the deployment of the kriging method. The kriging method is more versatile and efficient in creating attractive maps for the majority of data sets. The kriging model employed was the linear variogram model.

Field data acquisition

To achieve the aim of this study, resistivity data were acquired at 32 locations in the study area using the ABEM terrameter (SAS 1000 model) and its accessories. Vertical electrical soundings (VESs) were made via the deployment of the Schlumberger electrode layout. The theory of VES is well documented in the literature (e.g. Keary et al. 2002; Lowrie 2007; Reynolds 2011 etc.) and the Schlumberger electrode has been effectively implemented in many parts of the world in groundwater and other related studies (Awawdeh & Jaradat 2010; Tizro et al. 2012; Evans et al. 2015; George et al. 2018, 2021; Umoh et al. 2022; Lin et al. 2023; Ekanem et al. 2024). The distribution of the VES stations is shown in Figure 2. The outer electrode distance AB and inner electrode distance MN were systematically increased about the sounding centre at each VES location so as to suit the potential gradient assumption of AB greater or equal to 5 MN (George et al. 2017; Ekanem 2021). This was especially necessary to ensure deeper current injection into the ground for the detection of deeper layers (George et al. 2022a, 2022b; Ekanem & Udosen 2023a, 2023b; Inyang et al. 2023). On the whole, the distances AB/2 and MN/2 in this study ranged between 1 and 300 m and 0.25 and 10 m, respectively. Some soundings were made near already drilled boreholes with available lithological data in the area as displayed in Figure 2.
Figure 2

Distribution of VES stations with available boreholes in the study area.

Figure 2

Distribution of VES stations with available boreholes in the study area.

Close modal

Field data interpretation

Interpretation of the acquired resistivity data was done in two phases. The first phase involved the calculation of apparent resistivity values for each of the sounding locations, plotting the apparent resistivity data on a log–log scale and smoothening of the plotted sounding curves to reveal the resistivity variation pattern. The second phase involved computer modelling, which was achieved in this study by the deployment of the WINRESIST computer software program. Accordingly, the measured apparent resistance RA at each sounding station was converted into the corresponding apparent resistivity ρA through the use of Equation (1):
(1)
The plotted sounding curves were manually smoothened by deleting extremely anomalous ρA values regarded as outliers and finding the average of the two ρA values at the crossover points while preserving the prevalent curve trend in the data (Evans et al. 2015; Thomas et al. 2020; Umoh et al. 2022; Ekanem & Udosen 2023a, 2023b; Inyang et al. 2023). This in essence is necessary to reduce the root mean square errors (RMSE) during the computer modelling stage of the interpretation (Evans et al. 2015; Thomas et al. 2020; Umoh et al. 2022; Ekanem & Udosen 2023a, 2023b; Inyang et al. 2024). In the final stage of data interpretation, the smoothened curves were inputted into the WINRESIST software, which also required initial inputs of the layer parameters (i.e. number of layers, layer thickness or depth and resistivity). The software employs the input data to compute a hypothetical model and then matches it with the smoothened field data to generate the eventual model parameters when a match is established. The RMSE value provides a measure of the goodness of the match and in this study was generally less than 5%. The lithological data from the already drilled boreholes in the area were very helpful in constraining the computer-aided interpretation of the field data and the eventual layer thickness, depths and resistivities generated were taken as the true first-order geoelectric parameters of the penetrated layers. Figure 3 depicts some of the eventual model curves correlated with available drilling data in the study area.
Figure 3

Sample eventual VES curves and their correlation with available borehole lithological data in the study area.

Figure 3

Sample eventual VES curves and their correlation with available borehole lithological data in the study area.

Close modal

Estimation of aquifer storage properties

Aquifer storage properties typify the capacity of an aquifer to discharge water from storage in reaction to a reduction in hydraulic head or raise heads in reaction to the accumulation of water. The aquifer storage properties investigated in this study are the aquifer-specific yield and specific retention. These properties are greatly influenced by aquifer porosity , which is the amount of void space in the aquifer material. The porosity of an aquifer controls the amount of water it can store. The aquifer's capacity to store water increases with porosity and vice versa. However, not all the stored water in the aquifer is emptied during pumping into wells or boreholes. Aquifer-specific yield or storativity Sy is a storage term for unconfined aquifers and is defined as the proportion of the volume of water that the aquifer releases by gravity to the overall volume of the aquifer (Lohman 1972; Fetter 1994; Schwartz & Zhang 2003). Conversely, aquifer-specific retention Sr is the proportion of the volume of water retained by the aquifer to the overall aquifer volume (Healy & Cook 2002). Mathematically, aquifer porosity, specific yield and retention are related by Equation (2):
(2)
Aquifer effective porosity is gettable from measured aquifer bulk resistivity ρb and water resistivity ρw by the use of Equation (3):
(3)
where m and a are the cementation exponent and pore geometrical factor, respectively. Water resistivity is the inverse of conductivity σw (), a parameter that can be measured directly from water samples collected from boreholes using the conductivity meter. The formation factor F is the inferred ratio of the aquifer bulk and water resistivities in Equation (3). It is a hydrodynamic property which depends on how porous, tortuous, and constricted the pore space is that allows diffusion into the rock configuration. Assuming 100% water saturation of the aquifer, the specific yield is given as (Frohlich & Kelly 1988):
(4)
where S is saturation at specific retention. From Archie's law:
(5)
(6)
where ρsat is the resistivity of the saturated portion of the aquifer obtained from the difference between the bulk resistivity of the aquifer and the water resistivity and ρunsat is the resistivity of the unsaturated geological unit overlying the topmost aquifer in the unconfined aquifer system. Combining Equations (5) and (6) in Equation (4) gives:
(7)

Equation (7) forms the basis of using electrical resistivity data to calculate aquifer-specific yield (Frohlich & Kelly 1988).

Longitudinal conductance

LC, according to Henriet (1976), Oladapo & Akintorinwa (2007) and Abiola et al. (2009) is a valuable secondary geoelectric parameter that can be utilized to evaluate the level of protectivity of hydrogeological units. Protectivity here simply refers to the capability of the layers above the aquifer layer to filter and reduce the infiltrating rate of surface fluid getting to the underground water system (Olorunfemi et al. 1999; Ekanem et al. 2021). This ability is largely dependent on a couple of factors, which include the thickness, permeability, soil particle size, and porosity of the overlying layers (Adeniji et al. 2014; Ekanem 2020). LC for a stack of layers each of thickness h and resistivity ρ is obtained from the combination of the thickness and resistivity according to Equation (8):
(8)
where i is the number of layers from 1 to n. Values of S greater than unity are associated with good/excellent protectivity whereas values less than unity are associated with poor or weak protectivity as summarized in Table 1. From Equation (8), S is directly proportional to aquifer thickness but inversely proportional to resistivity. By implication, a layer with high thickness and low resistivity will retard vertical infiltration of contaminants to the underground water system and thus offer some degree of protection to an underlying aquifer (Ekanem et al. 2021; George 2021).
Table 1

LC and aquifer protective capacity rating (after Henriet 1976; Oladapo & Akintorinwa 2007)

S (mhos)APC rating
>10.00 Excellent 
5.00–10.00 Very good 
0.70–4.49 Good 
0.20–0.69 Moderate 
<0.10–0.19 Poor–weak 
S (mhos)APC rating
>10.00 Excellent 
5.00–10.00 Very good 
0.70–4.49 Good 
0.20–0.69 Moderate 
<0.10–0.19 Poor–weak 

GLSI method

The GLSI method is a hydrogeological technique used for the assessment of aquifer susceptibility to surface or near-surface contaminants (Oni et al. 2017). As opposed to LC, the GLSI technique involves assigning ratings to the thickness and resistivity values of the aquifer overlying layers derived from electrical resistivity data interpretation, respectively, according to the data in Table 2. This technique is very effective and is a useful addition to other vulnerability assessment techniques. The GLSI is computed from the rated layer parameters by using Equation (9) (Oni et al. 2017):
(9)
where h1r, h2r, and hnr, are the first, second and n layer thickness index ratings and ρ1r, ρ2r, and ρnr are the first, second and n layer resistivity index ratings, respectively. N is the total number of layers considered. The GLSI technique makes use of the multi-criteria decision analysis (MCDA) approach for the rated geoelectric indices. Consequently, normalization of the index ratings of the geoelectric parameters is accomplished by dividing by the number of identified geoelectric layers above the aquifer system (Equation (9)). Evaluation of aquifer susceptibility potential to surface or near-surface contamination is done by the use of the information in Table 3.
Table 2

The GLSI rating for thickness and resistivity parameters (Oni et al. 2017; George 2021)

Thickness parameter ratings
Resistivity parameter ratings
Thickness (m)Index ratingResistivity range (Ωm)LithologySusceptibility index rating
<2 <20 Clay/silt 
2–5 20–50 Sandy clay 
5–20 51–100 Clayey sand 
>20 101–150 Sand 
  151–400 Lateritic sand 
  >400 Laterite 
Thickness parameter ratings
Resistivity parameter ratings
Thickness (m)Index ratingResistivity range (Ωm)LithologySusceptibility index rating
<2 <20 Clay/silt 
2–5 20–50 Sandy clay 
5–20 51–100 Clayey sand 
>20 101–150 Sand 
  151–400 Lateritic sand 
  >400 Laterite 
Table 3

GLSI parametric rating (Oni et al. 2017; George 2021)

IndexVulnerability rating
1.00–1.99 Low 
2.00–2.99 Moderate 
3.00–3.99 High 
4.00 Extreme 
IndexVulnerability rating
1.00–1.99 Low 
2.00–2.99 Moderate 
3.00–3.99 High 
4.00 Extreme 

VES interpretation results

The eventual VES model curves obtained from the VES interpretation reveal five classes of curve types with the HK curve type dominating (43.8%) as shown in the bar chart of Figure 4. This might suggest a transition from unsaturated to saturated strata, indicating a strong likelihood of groundwater occurrence in the area (George et al. 2020; Ekanem et al. 2024). Three to four geoelectric strata are identified from the model curves as presented in Table 4. As listed in Table 4, each stratum is characterized by distinctive resistivity, thickness and depth values (otherwise known as first-order geoelectric properties). The first layer broadly interpreted as the motley topsoil has resistivity values of 79.2–1,190.2 Ωm and is 0.4–33.2 m thick. The whole study area is covered by this layer, often regarded as being fictitious due to human activities like agricultural practices, building and road construction notwithstanding other bioturbating activities continuously taking place in the layer (Ekanem 2020; George et al. 2021; Ekanem 2022a, 2022b). This also offers a possible explanation of the wide resistivity variations observed for the layer. The second and third identified layers are characterized by resistivity values of 59.1–2,648.1 Ωm and 374.0–2,839.0 Ωm and thicknesses of 7.6–88.2 m and 37.6–82.4 m, respectively, while the fourth (last identified) layer has resistivity values of 216.1–2,658.3 Ωm. The thickness of the fourth layer was not ascertained within the limits of the maximum AB employed in the field survey. These three layers were accordingly construed as sandy layers (fine/coarse/gravelly) with minor clay intercalations at some communities as constrained by the available borehole lithological data. The huge variations in the resistivity values of these layers may be due to the uneven grain sizes of the geomaterials of the layer, which obviously typify the Coastal Plain Sands of the study area (Mbipom et al. 1996). The resistivity variation patterns of the four geoelectric layers are illustrated in the image maps of Figure 5. The standard errors of the gridding interpolation method used for generating these maps for the four layers are, respectively, 1.88, 4.24, 5.06 and 4.02. These errors are relatively small, implying higher precision in the interpolation. The central part of the Ikot Ekpene LGA and the north-western part of the Obot Akara LGA are characterized by resistivity values of less than 400 Ωm for layer one. Relatively high resistivity values are observed in the south-western and south-eastern parts, respectively, for the two LGAs. For layer two, the northern part and parts of the southern region of the study area are typified with resistivity values of below 800 Ωm. Lower resistivity values of less than 1,000 Ωm are noticed in only a few communities as Figure 5 reveals while the fourth layer resistivity values are roughly less than 1,400 Ωm except at the northern part of the study area. The high variability of the resistivity values of the identified layers may be attributed to such factors like porosity, pore space connectivity, and water saturation aside from the variability in grain sizes of the layers (Ekanem et al. 2020, 2021, 2022a, 2022b).
Table 4

VES interpretation result summary in the study area

VES no.LocationLongitude (°)Latitude (°)Bulk resistivity (Ωm)
Thickness (m)
Depth (m)
Curve type
ρ1ρ2ρ3ρ4h1h2h3d1d2d3
Abiakpo Edem Idim 7.7002 5.1471 865.7 327.2 2,041.3 637.8 0.8 9.7 64.9 0.8 10.5 75.4 HK 
Abiakpo Ibo 7.6712 5.3019 694.0 338.8 2,492.6  11.5 81.3  11.5 92.8  
Abiakpo Ikot Ukam 7.6052 5.2876 353.8 112.6 2,716.7 1,475.7 3.2 8.6 68.9 3.2 11.8 80.7 HK 
Ibong Ikot Akan 7.6780 5.1710 228.5 2,111.6 434.5  6.1 49.3  6.1 55.4  
Ikono Road 7.7117 5.1981 207.4 2,648.1 1,506.3  4.5 80.9  4.5 85.4  
Ikot Abia Idem 7.6992 5.2024 224.4 59.1 1,264.5 324.9 2.1 8.4 59.9 2.1 10.5 70.4 HK 
Ikot Ideh 7.5669 5.2311 326.2 599.1 2,225.2 1,273.4 0.4 24.9 71.5 0.8 25.3 96.8 AK 
Ikot Idem Udo 7.5989 5.2532 443.1 1,343.9 628.1  73.5  82.5  
Ikot Udom 7.6314 5.2811 204.9 2,481.4 849.8  33.2 88.2  33.2 121.4  
10 Ikot Utu 7.6325 5.2623 795.3 444.3 2,436 801.5 3.6 20.8 61.9 3.6 24.4 86.3 HK 
11 Ikpon Road 7.6698 5.1667 590.7 149.2 2,478.6 827.9 1.3 11.1 61.4 1.3 12.4 73.8 HK 
12 Ikwen 1 7.6369 5.2867 125 612.8 1,904.1 401.9 2.7 25.6 60 2.7 28.3 88.3 AK 
13 Ikwen 2 7.6222 5.2953 79.2 2,080 692.6 1,139.2 3.3 40.1 37.6 3.3 43.4 81 KH 
14 Mbat Esifon 7.6398 5.2417 705.0 2,355.5 1,336.2  21.7 66  21.7 87.7  
15 Mbiaso 7.6780 5.2360 903.5 601.3 2,073.6 1,421.8 1.8 14.6 80.2 1.8 16.4 96.6 HK 
16 Nto Edino 1 7.5812 5.2831 400.3 1,738.5 524.6  6.6 86.3  6.6 92.9  
17 Nto Esu 7.6244 5.2772 608.2 133.3 1,706.6 414.6 4.4 20.3 82.4 4.4 24.7 107.1 HK 
18 Nto Eton 1 7.5949 5.2026 847.5 239.1 994.8 332.6 1.7 12.6 73 1.7 14.3 87.3 HK 
19 Nto Eton 2 7.5790 5.1800 694.6 1,573.9 374  61  68  
20 Nto Ndang 1 7.6620 5.2760 205.3 2,083.6 904  10.5 81.7  10.8 92.2  
21 Nto Ndang 2 7.6436 5.3023 196.8 758.8 1,500.4 670 7.9 17.6 60 7.9 25.5 85.5 AK 
22 Okpo Eto 7.5984 5.2733 724.4 190.6 1,063.6 717.3 0.8 16.3 71.9 0.8 17.1 89 HK 
23 Progress Road 7.7069 5.1794 157.3 66.8 1,196.1 379.0 1.4 7.6 75.7  9.0 84.7 HK 
24 Ubon Ukwa 7.5588 5.1918 1,172.1 863.6 1,471.2 455.7 1.7 27.7 65.7 1.7 29.4 95.1 HK 
25 Uruk Uso 7.7292 5.1767 630.4 148.9 2,472.8 690.4 1.3 11.1 68.1 1.3 12.4 80.5 HK 
26 Usaka Annang 7.5660 5.2950 474.9 1,063.6 489 2,658.3 0.9 29.3 55.3 0.9 30.2 85.5 KH 
27 Utu Ikot Ekpenyong 7.7308 5.1637 1,145.9 2,005.2 762.0  2.3 82.1  2.1 84.4  
28 Utu Ikot Inyang 7.6275 5.3119 556.6 234.9 2,711.9 216.1 5.4 13.8 51.7 5.4 19.2 70.9 HK 
29 Utu Ikpe Near Palace 7.7220 5.1525 418.4 1,062.4 2,839.0 865.4 0.9 23.8 45.6 0.9 24.7 70.3 AK 
30 Utu Ikpe Near Prison 7.7053 5.1631 430.7 2,283.3 851.2  1.1 60.4 – 1.1 61.5  
31 Utu Ikpe Near Dumpsite 7.6958 5.1702 1,190.2 248.6 1,750.8 418.7 2.6 8.5 49.2 2.6 11.1 60.3 HK 
32 Utu Uyo Road 7.7442 5.1568 1,131.8 2,004.7 1,097.0  2.1 61.0 – 2.1 63.1  
 Minimum   79.2 59.1 374.0 216.1 0.4 7.6 37.6 0.8 9.0 60.3  
 Maximum   1,190.2 2,648.1 2,839.0 2,658.3 33.2 88.2 82.4 33.2 121.4 107.1  
 Mean   554.1 1,030.1 1,493.4 806.1 5.1 38.3 63.2 5.3 43.4 83.3  
VES no.LocationLongitude (°)Latitude (°)Bulk resistivity (Ωm)
Thickness (m)
Depth (m)
Curve type
ρ1ρ2ρ3ρ4h1h2h3d1d2d3
Abiakpo Edem Idim 7.7002 5.1471 865.7 327.2 2,041.3 637.8 0.8 9.7 64.9 0.8 10.5 75.4 HK 
Abiakpo Ibo 7.6712 5.3019 694.0 338.8 2,492.6  11.5 81.3  11.5 92.8  
Abiakpo Ikot Ukam 7.6052 5.2876 353.8 112.6 2,716.7 1,475.7 3.2 8.6 68.9 3.2 11.8 80.7 HK 
Ibong Ikot Akan 7.6780 5.1710 228.5 2,111.6 434.5  6.1 49.3  6.1 55.4  
Ikono Road 7.7117 5.1981 207.4 2,648.1 1,506.3  4.5 80.9  4.5 85.4  
Ikot Abia Idem 7.6992 5.2024 224.4 59.1 1,264.5 324.9 2.1 8.4 59.9 2.1 10.5 70.4 HK 
Ikot Ideh 7.5669 5.2311 326.2 599.1 2,225.2 1,273.4 0.4 24.9 71.5 0.8 25.3 96.8 AK 
Ikot Idem Udo 7.5989 5.2532 443.1 1,343.9 628.1  73.5  82.5  
Ikot Udom 7.6314 5.2811 204.9 2,481.4 849.8  33.2 88.2  33.2 121.4  
10 Ikot Utu 7.6325 5.2623 795.3 444.3 2,436 801.5 3.6 20.8 61.9 3.6 24.4 86.3 HK 
11 Ikpon Road 7.6698 5.1667 590.7 149.2 2,478.6 827.9 1.3 11.1 61.4 1.3 12.4 73.8 HK 
12 Ikwen 1 7.6369 5.2867 125 612.8 1,904.1 401.9 2.7 25.6 60 2.7 28.3 88.3 AK 
13 Ikwen 2 7.6222 5.2953 79.2 2,080 692.6 1,139.2 3.3 40.1 37.6 3.3 43.4 81 KH 
14 Mbat Esifon 7.6398 5.2417 705.0 2,355.5 1,336.2  21.7 66  21.7 87.7  
15 Mbiaso 7.6780 5.2360 903.5 601.3 2,073.6 1,421.8 1.8 14.6 80.2 1.8 16.4 96.6 HK 
16 Nto Edino 1 7.5812 5.2831 400.3 1,738.5 524.6  6.6 86.3  6.6 92.9  
17 Nto Esu 7.6244 5.2772 608.2 133.3 1,706.6 414.6 4.4 20.3 82.4 4.4 24.7 107.1 HK 
18 Nto Eton 1 7.5949 5.2026 847.5 239.1 994.8 332.6 1.7 12.6 73 1.7 14.3 87.3 HK 
19 Nto Eton 2 7.5790 5.1800 694.6 1,573.9 374  61  68  
20 Nto Ndang 1 7.6620 5.2760 205.3 2,083.6 904  10.5 81.7  10.8 92.2  
21 Nto Ndang 2 7.6436 5.3023 196.8 758.8 1,500.4 670 7.9 17.6 60 7.9 25.5 85.5 AK 
22 Okpo Eto 7.5984 5.2733 724.4 190.6 1,063.6 717.3 0.8 16.3 71.9 0.8 17.1 89 HK 
23 Progress Road 7.7069 5.1794 157.3 66.8 1,196.1 379.0 1.4 7.6 75.7  9.0 84.7 HK 
24 Ubon Ukwa 7.5588 5.1918 1,172.1 863.6 1,471.2 455.7 1.7 27.7 65.7 1.7 29.4 95.1 HK 
25 Uruk Uso 7.7292 5.1767 630.4 148.9 2,472.8 690.4 1.3 11.1 68.1 1.3 12.4 80.5 HK 
26 Usaka Annang 7.5660 5.2950 474.9 1,063.6 489 2,658.3 0.9 29.3 55.3 0.9 30.2 85.5 KH 
27 Utu Ikot Ekpenyong 7.7308 5.1637 1,145.9 2,005.2 762.0  2.3 82.1  2.1 84.4  
28 Utu Ikot Inyang 7.6275 5.3119 556.6 234.9 2,711.9 216.1 5.4 13.8 51.7 5.4 19.2 70.9 HK 
29 Utu Ikpe Near Palace 7.7220 5.1525 418.4 1,062.4 2,839.0 865.4 0.9 23.8 45.6 0.9 24.7 70.3 AK 
30 Utu Ikpe Near Prison 7.7053 5.1631 430.7 2,283.3 851.2  1.1 60.4 – 1.1 61.5  
31 Utu Ikpe Near Dumpsite 7.6958 5.1702 1,190.2 248.6 1,750.8 418.7 2.6 8.5 49.2 2.6 11.1 60.3 HK 
32 Utu Uyo Road 7.7442 5.1568 1,131.8 2,004.7 1,097.0  2.1 61.0 – 2.1 63.1  
 Minimum   79.2 59.1 374.0 216.1 0.4 7.6 37.6 0.8 9.0 60.3  
 Maximum   1,190.2 2,648.1 2,839.0 2,658.3 33.2 88.2 82.4 33.2 121.4 107.1  
 Mean   554.1 1,030.1 1,493.4 806.1 5.1 38.3 63.2 5.3 43.4 83.3  
Figure 4

Distribution of curve types in the study area.

Figure 4

Distribution of curve types in the study area.

Close modal
Figure 5

Resistivity variation patterns of the four identified layers in the study area: (a) Layer 1 resistivity (ρ1); (b) Layer 2 resistivity (ρ2); (c) Layer 3 resistivity (ρ3); and (4) Layer 4 resistivity (ρ4).

Figure 5

Resistivity variation patterns of the four identified layers in the study area: (a) Layer 1 resistivity (ρ1); (b) Layer 2 resistivity (ρ2); (c) Layer 3 resistivity (ρ3); and (4) Layer 4 resistivity (ρ4).

Close modal

Aquifer storage properties and groundwater potential

The local population of OBAKIKE LGAs draw their groundwater from the third and fourth layers, respectively, depending on where they live. The depth to the top of these hydrostratigraphic units varies between 5.4 and 121.4 m at Utu Ikot Inyang and Ikot Udom communities correspondingly, while the resistivity lies between 234.9 and 1,506.3 Ωm. The aquifer thickness varies from 37.6 to 82.4 m at VESs 13 and 17, respectively. Water resistivity ρw was gauged from the aquifer bulk resistivity ρb in this study by the use of Equation (10) (George & Thomas 2023) while aquifer hydraulic conductivity Kh and permeability Kp were gauged from Equations (11) (Ekanem et al. 2020) and 12 correspondingly:
(10)
(11)
(12)
μd in Equation (12) is the viscosity of water and was taken as 0.0014 kg/ms for unconsolidated fine/coarse/gravelly sands (Fetter 1994) while dw is water density (taken as 1,000 kg/m3). The ρw values obtained range from 83.2 to 398.5 Ωm. Aquifer porosity ranges from 0.28 to 30 while hydraulic conductivity and permeability, respectively, vary between 0.68 and 2.61 m/day and 1,132.33 and 4,380.18 mD. The estimated aquifer-specific yield values obtained lie between 0.12 and 0.29 while the specific retention lies between 0.02 and 0.16. The aquifer-specific yield – specific retention ratio (λ) computed varies from 0.79 to 17.15. All the estimated aquifer parameters are summarized in Table 5. The distribution of the aquifer bulk resistivity, water resistivity, thickness and depth are shown in the image maps of Figure 6. The standard errors of the gridding interpolation method deployed in creating these maps for the four parameters are 2.81, 0.70, 0.21 and 0.07, respectively. The estimated water resistivity correlates with the aquifer bulk resistivity as areas of high water resistivity correspond to areas of high aquifer bulk resistivity and vice versa. Figure 7 shows the distribution of the aquifer specific yield and specific retention in the study area. Again, the standard errors of the gridding interpolation method employed in creating these maps for the two parameters are 4.1 × 10−4 and 3.5 × 10−4, respectively. Accordingly, the figure shows that where the specific yield is high, the specific retention is low. The relationship between aquifer-specific yield, aquifer-specific retention and specific yield – aquifer-specific retention ratio (λ) is given in Figure 8. When the specific yield is equal to the specific retention, the aquifer retains the same quantity of water as it releases to the well during pumping and λ is one (1). This occurs at a specific yield/retention value of around 0.15 as Figure 8 reveals. When the specific yield is greater than the specific retention, the aquifer releases more water to the well during pumping than it retains and λ is greater than unity. On the contrary, when the specific yield is less than the specific retention, the aquifer retains more water than it releases to the well during pumping and λ is less than unity. Thus, the aquifer has high groundwater potential when λ is greater than 1 and low groundwater potential when λ is less than unity. On this basis, 94% of the study area is adjudged to have good groundwater potential. Figure 9 shows the distribution of aquifer permeability and specific yield – specific retention ratio in the study area. Once more, the standard errors of the gridding interpolation method employed in creating the maps in Figure 9 are 5.58 and 0.03, respectively. About 94% of the study area is typified with specific yield – specific retention ratio (λ) greater than unity, implying that the area has high groundwater potential to sustain water wells.
Table 5

Summary of estimated aquifer properties in the study area

VES no.Locationρb (Ωm)Depth (m)Thickness (m)ρw (Ωm)Fφρsat (Ωm)ρunsat (Ωm)SySrSy/SrKh (m/day)Kp (mD)
Abiakpo Edem Idim 637.8 75.4 64.9 183.2 3.48 0.29 454.6 2,041.3 0.24 0.06 4.06 1.26 2,116.82 
Abiakpo Ibo 338.8 11.5 81.3 109.0 3.11 0.32 229.8 694.0 0.20 0.11 1.82 2.00 3,355.02 
Abiakpo Ikot Ukam 1,475.7 80.7 68.9 391.0 3.77 0.28 1,084.7 2,716.7 0.16 0.12 1.27 0.69 1,149.38 
Ibong Ikot Akan 434.5 55.4  132.7 3.27 0.31 301.8 2,111.6 0.29 0.02 17.15 1.67 2,799.23 
Ikono Road 1,506.3 85.4  398.5 3.78 0.28 1,107.8 2,648.1 0.15 0.13 1.16 0.68 1,132.33 
Ikot Abia Idem 324.9 70.4 59.9 105.6 3.08 0.32 219.3 1,264.5 0.28 0.04 7.81 2.06 3,458.92 
Ikot Ideh 1,273.4 96.8 71.5 340.8 3.74 0.28 932.6 2,225.2 0.15 0.13 1.15 0.76 1,279.62 
Ikot Idem Udo 628.1 82.5  180.7 3.47 0.29 447.4 1,343.9 0.19 0.10 1.80 1.28 2,140.57 
Ikot Udom 849.8 121.4  235.7 3.60 0.29 614.1 2,481.4 0.22 0.07 3.23 1.03 1,717.72 
10 Ikot Utu 801.5 86.3 61.9 223.8 3.58 0.29 577.7 2,436.0 0.22 0.06 3.53 1.07 1,792.47 
11 Ikpon Road 827.9 73.8 61.4 230.3 3.59 0.29 597.6 2,478.6 0.22 0.07 3.41 1.04 1,750.68 
12 Ikwen 1 401.9 88.3 60 124.7 3.22 0.31 277.2 1,904.1 0.29 0.02 15.53 1.77 2,962.76 
13 Ikwen 2 692.6 43.4 37.6 196.7 3.52 0.29 495.9 2,080.0 0.23 0.06 3.49 1.19 1,993.53 
14 Mbat Esifon 1,336.2 87.7  356.4 3.75 0.28 979.8 2,355.5 0.15 0.13 1.17 0.74 1,235.55 
15 Mbiaso 1,421.8 96.6 80.2 377.6 3.77 0.28 1,044.2 2,073.6 0.12 0.16 0.79 0.70 1,180.94 
16 Nto Edino 1 524.6 92.9  155.1 3.38 0.30 369.5 1,738.5 0.24 0.05 4.52 1.46 2,440.39 
17 Nto Esu 414.6 107.1 82.4 127.8 3.24 0.31 286.8 1,706.6 0.28 0.03 8.65 1.73 2,896.41 
18 Nto Eton 1 332.6 87.3 73 107.5 3.09 0.32 225.1 994.8 0.25 0.06 3.91 2.03 3,400.44 
19 Nto Eton 2 374 68  117.7 3.18 0.31 256.3 1,573.9 0.28 0.03 9.66 1.86 3,122.08 
20 Nto Ndang 1 904 92.2  249.2 3.63 0.29 654.8 2,083.6 0.19 0.09 2.01 0.98 1,642.12 
21 Nto Ndang 2 670 85.5 60 191.1 3.51 0.29 478.9 1,500.4 0.19 0.10 1.95 1.22 2,042.26 
22 Okpo Eto 717.3 89 71.9 202.9 3.54 0.29 514.4 1,063.6 0.13 0.16 0.86 1.16 1,943.32 
23 Progress Road 379.0 84.7 75.7 119.0 3.19 0.31 260.0 1,196.1 0.25 0.06 4.29 1.85 3,092.04 
24 Ubon Ukwa 455.7 95.1 65.7 138.0 3.30 0.30 317.7 1,471.2 0.25 0.06 4.35 1.61 2,703.81 
25 Uruk Uso 690.4 80.5 68.1 196.2 3.52 0.29 494.2 2,472.8 0.24 0.05 5.25 1.19 1,998.15 
26 Usaka Annang 489 30.2 55.3 146.3 3.34 0.30 342.7 1,063.6 0.20 0.10 1.91 1.53 2,568.49 
27 Utu Ikot Ekpenyong 762.0 84.4  214.0 3.56 0.29 548.0 2,005.2 0.21 0.08 2.64 1.11 1,859.65 
28 Utu Ikot Inyang 234.9 5.4 51.7 83.2 2.82 0.34 151.7 556.6 0.24 0.09 2.65 2.61 4,380.18 
29 Utu Ikpe Near Palace 865.4 70.3 45.6 239.6 3.61 0.29 625.8 2,839.0 0.23 0.06 4.15 1.01 1,695.12 
30 Utu Ikpe Near Prison 851.2 61.5 – 236.1 3.61 0.29 615.1 2,283.3 0.21 0.08 2.71 1.02 1,715.66 
31 Utu Ikpe Near Dumpsite 418.7 60.3 49.2 128.8 3.25 0.31 289.9 1,750.8 0.28 0.03 9.10 1.72 2,875.74 
32 Utu Uyo Road 1,097.0 63.1 – 297.0 3.69 0.28 800.0 2,004.7 0.16 0.12 1.27 0.85 1,426.34 
 Minimum 234.9 5.4 37.6 83.2 2.82 0.28 151.7 556.6 0.12 0.02 0.79 0.68 1,132.33 
 Maximum 1,506.3 121.4 82.4 398.5 3.78 0.34 1,107.8 2,839.0 0.29 0.16 17.15 2.61 4,380.18 
 Mean 722.9 75.4 63.2 204.2 3.44 0.30 518.6 1,848.7 0.22 0.08 4.29 1.34 2,245.87 
VES no.Locationρb (Ωm)Depth (m)Thickness (m)ρw (Ωm)Fφρsat (Ωm)ρunsat (Ωm)SySrSy/SrKh (m/day)Kp (mD)
Abiakpo Edem Idim 637.8 75.4 64.9 183.2 3.48 0.29 454.6 2,041.3 0.24 0.06 4.06 1.26 2,116.82 
Abiakpo Ibo 338.8 11.5 81.3 109.0 3.11 0.32 229.8 694.0 0.20 0.11 1.82 2.00 3,355.02 
Abiakpo Ikot Ukam 1,475.7 80.7 68.9 391.0 3.77 0.28 1,084.7 2,716.7 0.16 0.12 1.27 0.69 1,149.38 
Ibong Ikot Akan 434.5 55.4  132.7 3.27 0.31 301.8 2,111.6 0.29 0.02 17.15 1.67 2,799.23 
Ikono Road 1,506.3 85.4  398.5 3.78 0.28 1,107.8 2,648.1 0.15 0.13 1.16 0.68 1,132.33 
Ikot Abia Idem 324.9 70.4 59.9 105.6 3.08 0.32 219.3 1,264.5 0.28 0.04 7.81 2.06 3,458.92 
Ikot Ideh 1,273.4 96.8 71.5 340.8 3.74 0.28 932.6 2,225.2 0.15 0.13 1.15 0.76 1,279.62 
Ikot Idem Udo 628.1 82.5  180.7 3.47 0.29 447.4 1,343.9 0.19 0.10 1.80 1.28 2,140.57 
Ikot Udom 849.8 121.4  235.7 3.60 0.29 614.1 2,481.4 0.22 0.07 3.23 1.03 1,717.72 
10 Ikot Utu 801.5 86.3 61.9 223.8 3.58 0.29 577.7 2,436.0 0.22 0.06 3.53 1.07 1,792.47 
11 Ikpon Road 827.9 73.8 61.4 230.3 3.59 0.29 597.6 2,478.6 0.22 0.07 3.41 1.04 1,750.68 
12 Ikwen 1 401.9 88.3 60 124.7 3.22 0.31 277.2 1,904.1 0.29 0.02 15.53 1.77 2,962.76 
13 Ikwen 2 692.6 43.4 37.6 196.7 3.52 0.29 495.9 2,080.0 0.23 0.06 3.49 1.19 1,993.53 
14 Mbat Esifon 1,336.2 87.7  356.4 3.75 0.28 979.8 2,355.5 0.15 0.13 1.17 0.74 1,235.55 
15 Mbiaso 1,421.8 96.6 80.2 377.6 3.77 0.28 1,044.2 2,073.6 0.12 0.16 0.79 0.70 1,180.94 
16 Nto Edino 1 524.6 92.9  155.1 3.38 0.30 369.5 1,738.5 0.24 0.05 4.52 1.46 2,440.39 
17 Nto Esu 414.6 107.1 82.4 127.8 3.24 0.31 286.8 1,706.6 0.28 0.03 8.65 1.73 2,896.41 
18 Nto Eton 1 332.6 87.3 73 107.5 3.09 0.32 225.1 994.8 0.25 0.06 3.91 2.03 3,400.44 
19 Nto Eton 2 374 68  117.7 3.18 0.31 256.3 1,573.9 0.28 0.03 9.66 1.86 3,122.08 
20 Nto Ndang 1 904 92.2  249.2 3.63 0.29 654.8 2,083.6 0.19 0.09 2.01 0.98 1,642.12 
21 Nto Ndang 2 670 85.5 60 191.1 3.51 0.29 478.9 1,500.4 0.19 0.10 1.95 1.22 2,042.26 
22 Okpo Eto 717.3 89 71.9 202.9 3.54 0.29 514.4 1,063.6 0.13 0.16 0.86 1.16 1,943.32 
23 Progress Road 379.0 84.7 75.7 119.0 3.19 0.31 260.0 1,196.1 0.25 0.06 4.29 1.85 3,092.04 
24 Ubon Ukwa 455.7 95.1 65.7 138.0 3.30 0.30 317.7 1,471.2 0.25 0.06 4.35 1.61 2,703.81 
25 Uruk Uso 690.4 80.5 68.1 196.2 3.52 0.29 494.2 2,472.8 0.24 0.05 5.25 1.19 1,998.15 
26 Usaka Annang 489 30.2 55.3 146.3 3.34 0.30 342.7 1,063.6 0.20 0.10 1.91 1.53 2,568.49 
27 Utu Ikot Ekpenyong 762.0 84.4  214.0 3.56 0.29 548.0 2,005.2 0.21 0.08 2.64 1.11 1,859.65 
28 Utu Ikot Inyang 234.9 5.4 51.7 83.2 2.82 0.34 151.7 556.6 0.24 0.09 2.65 2.61 4,380.18 
29 Utu Ikpe Near Palace 865.4 70.3 45.6 239.6 3.61 0.29 625.8 2,839.0 0.23 0.06 4.15 1.01 1,695.12 
30 Utu Ikpe Near Prison 851.2 61.5 – 236.1 3.61 0.29 615.1 2,283.3 0.21 0.08 2.71 1.02 1,715.66 
31 Utu Ikpe Near Dumpsite 418.7 60.3 49.2 128.8 3.25 0.31 289.9 1,750.8 0.28 0.03 9.10 1.72 2,875.74 
32 Utu Uyo Road 1,097.0 63.1 – 297.0 3.69 0.28 800.0 2,004.7 0.16 0.12 1.27 0.85 1,426.34 
 Minimum 234.9 5.4 37.6 83.2 2.82 0.28 151.7 556.6 0.12 0.02 0.79 0.68 1,132.33 
 Maximum 1,506.3 121.4 82.4 398.5 3.78 0.34 1,107.8 2,839.0 0.29 0.16 17.15 2.61 4,380.18 
 Mean 722.9 75.4 63.2 204.2 3.44 0.30 518.6 1,848.7 0.22 0.08 4.29 1.34 2,245.87 
Figure 6

Distribution of aquifer properties in the study area: (a) bulk resistivity (ρb); (b) water resistivity (ρw); (c) depth to the top d; and (d) thickness (h).

Figure 6

Distribution of aquifer properties in the study area: (a) bulk resistivity (ρb); (b) water resistivity (ρw); (c) depth to the top d; and (d) thickness (h).

Close modal
Figure 7

Distribution of aquifer storage properties in the study area: (a) specific yield (Sy) and (b) specific retention (Sr).

Figure 7

Distribution of aquifer storage properties in the study area: (a) specific yield (Sy) and (b) specific retention (Sr).

Close modal
Figure 8

Plot of aquifer-specific yield and retention against aquifer-specific yield – retention ratio in the study area.

Figure 8

Plot of aquifer-specific yield and retention against aquifer-specific yield – retention ratio in the study area.

Close modal
Figure 9

Distribution of aquifer parameters in the study area: (a) permeability (Kp) and (b) specific yield – specific retention ratio (Sy/Sr).

Figure 9

Distribution of aquifer parameters in the study area: (a) permeability (Kp) and (b) specific yield – specific retention ratio (Sy/Sr).

Close modal

Groundwater contamination risk potential

Groundwater contamination risk potential depends on aquifer protection capacity (APC) and aquifer susceptibility potential. The results of the LC and GLSI analysis are summarized in Table 6. The values of LC vary from 0.01 to 0.26 S while that of GLSI lies between 1.00 and 2.75. Based on the LC values, 87.5% of the study area has weak – poor protection while 12.5% has moderate protection against infiltrating surface or near-surface contaminants. On the basis of the GLSI values, 21.9% of the study area has a moderate susceptibility rating while 78.1% has a low susceptibility potential rating. Areas with weak/poor protection will have a moderate/high susceptibility rating while those with moderate protection will also have a moderate susceptibility rating. The protectivity and susceptibility maps shown in Figure 10 seem to corroborate this assertion. The high percentage of weak/poor protection rating is possibly due to the permeable nature of the sandy layers overlying the aquifers in the area. Consequently, the primary route of contaminants transport and dissemination into the aquifer from the earth's surface as a result of human-induced and natural processes is the intergranular pore spaces in the aquifer overburden layers.
Table 6

Summary of groundwater contamination risk potential

VES no.LocationLongitudinal conductanceProtectivity ratingGLSIVulnerability rating
Abiakpo Edem Idim 0.06 Poor 1.83 Low 
Abiakpo Ibo 0.26 Moderate 1.50 Low 
Abiakpo Ikot Ukam 0.11 Weak 2.00 Moderate 
Ibong Ikot Akan 0.05 Poor 1.50 Low 
Ikono Road 0.05 Poor 1.75 Low 
Ikot Abia Idem 0.20 Moderate 2.00 Moderate 
Ikot Ideh 0.07 Poor 1.67 Low 
Ikot Idem Udo 0.08 Poor 1.25 Low 
Ikot Udom 0.20 Moderate 1.50 Low 
10 Ikot Utu 0.08 Poor 1.33 Low 
11 Ikpon Road 0.10 Weak 2.17 Moderate 
12 Ikwen 1 0.09 Poor 1.83 Low 
13 Ikwen 2 0.06 Poor 2.75 Moderate 
14 Mbat Esifon 0.06 Poor 1.00 Low 
15 Mbiaso 0.06 Poor 1.67 Low 
16 Nto Edino 1 0.07 Poor 1.25 Low 
17 Nto Esu 0.21 Moderate 1.83 Low 
18 Nto Eton 1 0.13 Weak 1.83 Low 
19 Nto Eton 2 0.05 Poor 1.25 Low 
20 Nto Ndang 1 0.09 Poor 1.50 Low 
21 Nto Ndang 2 0.10 Weak 1.50 Low 
22 Okpo Eto 0.15 Weak 1.83 Low 
23 Progress Road 0.19 Weak 2.17 Moderate 
24 Ubon Ukwa 0.08 Poor 1.50 Low 
25 Uruk Uso 0.10 Weak 2.17 Moderate 
26 Usaka Annang 0.03 Poor 1.75 Low 
27 Utu Ikot Ekpenyong 0.04 Poor 1.50 Low 
28 Utu Ikot Inyang 0.01 Poor 2.00 Moderate 
29 Utu Ikpe near palace 0.04 Poor 1.50 Low 
30 Utu Ikpe near prison 0.03 Poor 1.75 Low 
31 Utu Ikpe near dumpsite 0.06 Poor 1.67 Low 
32 Utu Uyo Road 0.03 Poor 1.50 Low 
VES no.LocationLongitudinal conductanceProtectivity ratingGLSIVulnerability rating
Abiakpo Edem Idim 0.06 Poor 1.83 Low 
Abiakpo Ibo 0.26 Moderate 1.50 Low 
Abiakpo Ikot Ukam 0.11 Weak 2.00 Moderate 
Ibong Ikot Akan 0.05 Poor 1.50 Low 
Ikono Road 0.05 Poor 1.75 Low 
Ikot Abia Idem 0.20 Moderate 2.00 Moderate 
Ikot Ideh 0.07 Poor 1.67 Low 
Ikot Idem Udo 0.08 Poor 1.25 Low 
Ikot Udom 0.20 Moderate 1.50 Low 
10 Ikot Utu 0.08 Poor 1.33 Low 
11 Ikpon Road 0.10 Weak 2.17 Moderate 
12 Ikwen 1 0.09 Poor 1.83 Low 
13 Ikwen 2 0.06 Poor 2.75 Moderate 
14 Mbat Esifon 0.06 Poor 1.00 Low 
15 Mbiaso 0.06 Poor 1.67 Low 
16 Nto Edino 1 0.07 Poor 1.25 Low 
17 Nto Esu 0.21 Moderate 1.83 Low 
18 Nto Eton 1 0.13 Weak 1.83 Low 
19 Nto Eton 2 0.05 Poor 1.25 Low 
20 Nto Ndang 1 0.09 Poor 1.50 Low 
21 Nto Ndang 2 0.10 Weak 1.50 Low 
22 Okpo Eto 0.15 Weak 1.83 Low 
23 Progress Road 0.19 Weak 2.17 Moderate 
24 Ubon Ukwa 0.08 Poor 1.50 Low 
25 Uruk Uso 0.10 Weak 2.17 Moderate 
26 Usaka Annang 0.03 Poor 1.75 Low 
27 Utu Ikot Ekpenyong 0.04 Poor 1.50 Low 
28 Utu Ikot Inyang 0.01 Poor 2.00 Moderate 
29 Utu Ikpe near palace 0.04 Poor 1.50 Low 
30 Utu Ikpe near prison 0.03 Poor 1.75 Low 
31 Utu Ikpe near dumpsite 0.06 Poor 1.67 Low 
32 Utu Uyo Road 0.03 Poor 1.50 Low 
Figure 10

Aquifer protectivity and susceptibility maps of the study area: (a) distribution of LC and (b) distribution of the GLSI.

Figure 10

Aquifer protectivity and susceptibility maps of the study area: (a) distribution of LC and (b) distribution of the GLSI.

Close modal

The findings of this study agree with previous results of George et al. (2021, 2024), Ekanem et al. (2021, 2022a, 2022b), Ikpe et al. (2022), George (2021) and Ekanem (2022a). Although the results demonstrate that the study area has high groundwater potential for the sustainability of water wells, the aquifer units in the area are mostly unconfined. Most aquifers by and large lack impermeable overburden layers, which can retard the percolation of contaminated fluids or filter contaminated fluids before reaching the aquifer system except at a few locations in the northeastern and southeastern parts of the study area as illustrated in Figure 10. Since rainfall is the major source of recharge in the area, by implication the aquifer system in these locations can easily be contaminated by surface run-off, carrying debris and other harmful dissolved substances infiltrating into the aquifer system. Consequently, town planners and other government agencies in the area need to ensure proper channelization of run-off in the area aside from making stringent policies to regulate indiscriminate dumping of wastes on the streets of the study area. Water from boreholes already drilled in the contamination-prone localities in the area needs to be treated before consumption and the locations of new ones need to be checked to forestall the outbreak of epidemics in the area.

This study involves the investigation of aquifer storage properties and contamination risk potential using an electrical resistivity technique in Ikot Ekpene and Obot Akara LGAs in Akwa Ibom State. The two LGAs are shown to comprise three to four lithological successions of sandy and gravelly layers with slight clay intercalations. Groundwater abstraction in the layer is shown to take place in the third layer at some locations and the fourth layer at other locations. The depth to the top of this hydrostratigraphic layer varies from 5.4 m at Utu Ikot Inyang to 121.4 m at Ikot Udom communities, respectively, while the resistivity lies between 234.9 and 1,506.3 Ωm. Also, the aquifer thickness varies from 37.6 to 82.4 m at VESs 13 and 17, respectively. Aquifer porosity ranges from 0.28 to 30 while hydraulic conductivity and permeability, respectively, vary between 0.68 and 2.61 m/day and 1,132.33 and 4,380.18 mD. The estimated aquifer-specific yield values obtained lie between 0.12 and 0.29 while the specific retention lies between 0.02 and 0.16. The aquifer-specific yield – specific retention ratio computed varies from 0.79 to 17.15. The results show that an inverse relation exists between the aquifer-specific yield and specific retention. At a value of 0.15 for specific yield and specific retention corresponding to a unit value of specific yield – specific retention ratio (λ), the aquifer releases the same amount of water as it retains during pumping. However, at values of specific yield greater than 0.15 and specific retention less than 0.15, more water is released by the aquifer during pumping and λ is greater than one. The reverse is the case when the specific yield is less than 0.15 and the specific retention is greater than 0.15. On this basis, about 94% of the study area is shown to have good groundwater potential for the sustainability of water boreholes in the area.

Groundwater contamination risk potential was assessed via the use of LC and GLSI. Accordingly, the values of LC vary from 0.01 to 0.26 S while that of GLSI lies between 1.00 and 2.75. 87.5% of the study area is shown to have weak/poor protection while 12.5% has moderate protection against infiltrating surface or near-surface contaminants based on the LC results. On the basis of the GLSI values, 21.9% of the study is shown to have a moderate susceptibility rating while the remaining 78.1% has low susceptibility potential rating. The identified areas with weak/poor protection and moderate susceptibility are adjudged to have high risk to groundwater contamination. Although the area is characterized by high groundwater potential adjudged from the computed aquifer storage properties, the majority of the aquifer units lack impervious confining layers and hence have poor protection against any surface or near-surface percolating contaminated fluid. Surface wash-off of leachates and other hazardous chemical substances from the breakdown of poorly disposed wastes in the area can thus infiltrate easily to contaminate the groundwater system. This therefore calls for a need to formulate stringent waste disposal schemes and proper channelization of run-off in the area by town planners and other local government agencies. There is also a need for water treatment for the boreholes already drilled in the contamination-prone localities while there should be effective regulations on the locations of new ones to forestall the outbreak of epidemics in the area. The findings of this study ultimately will aid in formulating efficient groundwater exploration and management strategies in the area such as demarcation of sites for drilling new water boreholes, proper monitoring of waste disposal in the area by residents to forestall leachate percolation to contaminate groundwater, construction of sufficient gutters for run-off on the major streets and removal of blockages in the existing ones where necessary.

The authors are grateful to the postgraduate students and members of the GRG of the Department of Physics, Akwa Ibom State University for their supports especially during the data acquisition stage of this study.

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

The authors declare there is no conflict.

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