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Groundwater contamination is of global concern. The study area (Ikot Ekpene–Obot Akara Local Government Areas) continues to experience a swift increase in human population and associated economic activities, leading to the generation of more waste. The fundamental goal of this work is therefore to weigh up the groundwater standard through hydrogeochemical investigation of groundwater samples and the susceptibility potential of the economically exploited aquifer units in the area. The results of the electrical geo-sounding data acquired at 28 locations in the area reveal three to four lithological successions comprising fine/coarse sands and gravels amid patches of thin clay interbeddings at several places. The primary aquifer is the third layer, which is between 10.5 and 101.5 m deep with resistivity values between 359.4 and 2,472.8 Ωm. The hydrogeochemical evaluation of groundwater samples in the area shows that the measured physicochemical parameters are well within the World Health Organization's acceptable limits except for lead and nickel ions. The groundwater quality and susceptibility potential maps generated seem to correlate well and clearly demarcate the poor groundwater quality/high susceptibility potential zones. These maps are useful tools that could aid policymakers in successful groundwater management in the area to meet the needs of the populace.

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  • Investigations of groundwater quality and susceptibility were carried out.

  • Three to four earth layers are identified in the study area.

  • Third layer is the economically exploitable aquifer.

  • Areas of moderate/high susceptibility ratings and poor, good and excellent groundwater quality are well-demarcated.

  • Results could be helpful in successful groundwater management in the area.

aquifer, contamination, DRASTIC, groundwater quality index (GWQI), permeability, susceptibility potential
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One of the problems of the 21st century is the inadequacy of sufficient quantities of fresh water to satisfy human domestic and industrial needs. With the world's burgeoning population, groundwater is becoming a more sought-after source of fresh water due to its accessibility and generally higher quality relative to surface sources of water like rivers, streams and lakes (George et al. 2017a, 2022a, 2022b; Abu-Bakr 2020; Thomas et al. 2020; Umoh et al. 2022). Groundwater exists in underground rock units known as aquifers. Like surface water, the quality of groundwater can be negatively impacted by made-made or natural activities, which include saltwater intrusion, improper disposal of waste, erosion, continuous agriculture, inefficient sewage systems, industrialization, mining operations, landfill leachates, effluent from wastewater treatment plant and urbanization (Uddin et al. 2021; Ikpe et al. 2022). Actually, as humankind continues to strongly rely on it to meet human domestic, industrial and agricultural demands, groundwater contamination is of global concern due to its severe effects on both human wellbeing as well as environmental services (Kumar & Krishna 2020; Ekanem 2022a, 2022b; Ikpe et al. 2022). Hence, in an attempt to create efficient groundwater management and groundwater protection programmes, groundwater susceptibility assessment has developed into a useful technique for identifying areas that are susceptible to contamination (Amiri et al. 2020; Ekanem 2022b; Ekanem et al. 2022a). Water is a good solvent and can easily collect and dissolve impurities. The implication of this is that groundwater may become contaminated by many soluble chemicals (Thomas et al. 2020) and thus become unfit for human use. The actual quality of groundwater can be investigated via physicochemical/geochemical analysis of water samples for physicochemical and biological parameters. These parameters should adhere to established standards and guidelines. When they exceed the allowable limits, it may be harmful to human health.

The length of time it takes contaminants that are surface or near surface based to permeate into the groundwater is controlled by the characteristics of the geomaterials above the water table (Ekanem 2020; George 2021). The possibility of the aquifer becoming contaminated or polluted by surface or near surface contaminated fluids is known as groundwater susceptibility potential (GSP; Awawdeh & Jaradat 2010; Ekanem et al. 2022a). In this context, susceptibility refers to how easily contaminants that are surface or near surface based could percolate into the underlying hydrogeological strata and contaminate groundwater. The contaminated fluid infiltrating time to the aquifer system is largely determined by a couple of factors, which include the net recharge, characteristics of the unsaturated layers above the aquifer units, groundwater system depth and of course the contaminated fluids' geochemical properties (Maxe & Johansson 1998; Edet 2013; Shirazi et al. 2013; Kumar & Krishna 2020; Thomas et al. 2020; Ekanem 2022a). By implication, aquifers that are closer to the Earth's surface have more susceptibility potential than those that are deeper, depending upon the composition of the aquifer overlying earth layers.

The surface resistivity geo-sounding technique provides an easy, swift and cheap means of investigating underground aquifers and has been used successfully in many groundwater studies (Chakravarthi et al. 2007; Udoh et al. 2015; Shamsuddin et al. 2018; Ekanem 2021; George et al. 2021; Ekanem et al. 2022b; Ikpe et al. 2022). Through this technique, aquifer properties such as depth, thickness, resistivity, hydraulic conductivity, transmissivity, porosity and permeability can be determined with borehole lithological data as controls in delineating the lithological succession. These parameters are then utilized in the susceptibility assessment of the hydrogeological units. Several techniques are available for conducting groundwater susceptibility assessment. The DRASTIC (an acronym formed from depth (D), net recharge (R), aquifer media (A), soil media (S), topography (T), impact of vadose zone (I) and hydraulic conductivity (C) of groundwater), GOD (an acronym from the parameters: groundwater occurrence (G), overlying lithology of the aquifer (O) and depth to groundwater (D)), susceptibility indexing (SI) and aquifer vulnerability index (AVI) methods are a few of these techniques. As a result of the ease of usage and getting the needed data, the DRASTIC method is most often adopted notwithstanding its ability to give an explicit explanation of groundwater susceptibility to contaminants (Barbulescu 2020; Ekanem et al. 2022a). The DRASTIC method requires seven input parameters, which makes it very efficient in the assessment of GSP by minimizing the effects of individual parameter errors on the final results. As a matter of fact, the DRASTIC method has proven very successful in assessing aquifer susceptibility to contaminants that are surface or near surface based globally (Awawdeh et al. 2015; Amiri et al. 2020; Barbulescu 2020; George 2021; Ekanem et al. 2022a) and was adopted in this study. One of the techniques available for the evaluation of water standards is the water quality index (WQI) technique introduced by Horton (1965). This method, which has been modified by many scholars involves the combination of a number of parameters related to water standards in a mathematical equation to rate the acceptability of this important georesource for human usage (Ram et al. 2021) and was employed in this work to investigate the actual quality of groundwater.

The dwellers of Ikot Ekpene and Obot Akara Local Government Areas (LGAs) in southern Nigeria depend on groundwater to meet their water needs, in part as a result of insufficient surface water sources in the LGAs and because of the contamination of the few available ones. This is made through an increasing number of water boreholes drilled in the area by private individuals as well as government agencies such as the Millennium Water Project (MDG). In recent times, the study area has witnessed a swift population increase occasioned by the creation of small-scale industries (e.g. wood industries, palm fruit processing mills, hospitality industries, banks, construction companies, transport companies and other small-scale merchandizing businesses) in the area coupled with other commercial activities. Consequently, increasing solid wastes (vegetable wastes, waste papers, scrap metals, cans containing different chemicals, plastic containers, old rags, vehicle tyres, scalpels and human wastes) could be seen littering in some streets in the area (Umoh & Etim 2013; Ekanem et al. 2022a). Leachates produced by the breakdown of these wastes, particularly rainwater (the area's primary source of replenishment of groundwater), have the potential to contaminate the aquifer units (George et al. 2014; Ikpe et al. 2022). A lot of research on aquifer vulnerability assessment has been carried out in recent times as groundwater has become a dependable source of potable water for human use. For instance, Adeyemo et al. (2016) carried out an assessment of aquifer vulnerability at Ipinnsa-Okeodu, Akure, Nigeria using geoelectrically derived GODT (acronym from type of aquifer (G), overburden lithology (O), depth of aquifer (D) and topography (T)) and found that the area is characterized by four vulnerability zones, namely: very low, low, moderate and high vulnerable zones, respectively. George et al. (2017b) similarly investigated the vulnerability of surficial aquifers in the oil-producing localities in the Niger Delta province of southern Nigeria. Their results established that the shallow earth layers in the study area are highly vulnerable to contamination. However, very few geophysical studies have been performed in Ikot Ekpene and Obot Akara LGAs to ascertain the susceptibility and protectivity potentials of the economically exploitable aquifer units. The results of the geophysical investigations of the aquifer protectivity potential carried out by George (2021), Ekanem et al. (2021) and Ikpe et al. (2022) in parts of these LGAs show that a greater percentage of aquifers in the respective areas have poor/weak protection against surface or near surface contaminants. George (2021) utilized the DRASTIC method to evaluate the vulnerability potential of hinterland aquifers in Obot Akara and Nsit Atai LGAs of Akwa Ibom State and established that a greater percentage of the aquifers have moderate/high vulnerability potential. Ekanem et al. (2022a) and Ekanem (2022a) conducted an integrated study involving the use of surface resistivity techniques, the DRASTIC, GOD and AVI methods to appraise the susceptibility of the groundwater system to contaminants in parts of Ikot Ekpene and Obot Akara LGAs and found out that majority of the aquifer units have moderate/high vulnerability to contaminants. They attributed their results to the generally lower slope terrain in the area coupled with the absence of impermeable protecting layers above the main aquifer system. Apart from not covering the whole of the LGAs, no hydrogeochemical analyses of borehole water samples in the study area have been conducted in all these studies to ascertain the groundwater contamination level. Thus, the major thrust of this research work is to utilize the surface electrical resistivity sounding method with borehole lithological data and hydrogeochemical analysis of water samples to appraise the groundwater susceptibility to surface/near surface contaminants and groundwater quality in the entire area to delineate zones that may be prone to contamination. This is especially necessary for the formulation of efficient groundwater development, exploration and waste disposal schemes by the policymakers in the research area.

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This research was performed in Ikot Ekpene and Obot Akara LGAs in the northern segment of Akwa Ibom State, Nigeria. This region, as displayed in Figure 1, is located in southern Nigeria's oil-rich Niger Delta province, roughly between latitudes 5°8′ and 5°20′ north and 7°32′ and 7°46′ east, respectively. The area, which has an equatorial climate, has two distinct seasons. These are the dry months (starting from November till around late February) and the rainy seasons (from March till around late October) (George et al. 2016a, 2016b, 2017a). Nonetheless, localized modest adjustments in the lower and upper bounds of these seasons have been seen due to global changes in climate (George et al. 2021, 2022a, 2022b). As stated by Isaiah et al. (2021) and Ekanem (2022a), the LGAs experience an annual rainfall, which varies roughly from 2,008 to 2,289 mm with an average annual temperature of between 20 and 35 °C, respectively, in the monsoon and dry seasons (George et al. 2018; Ekanem 2020). Groundwater in the study area is primarily recharged by rainfall. Topographically, the elevation above mean sea level in the study area varies between 54 m in the south-eastern parts and 102 m in the northern parts. Rainwater thus flows generally towards the south-eastern part, where there is a ravine (black ellipse in Figure 1). This must have informed the decision of the local government authority to abandon the old dumpsite (red circle) to the new one sited in the ravine (black circle) as illustrated in Figure 1. The run-off carries debris and other dissolved chemicals/contaminated fluid into the ravine area.
Figure 1
Schematic map of the study location. (a) Nigeria displaying Akwa Ibom State in the southern part, (b) Akwa Ibom State displaying the study area (Ikot Ekpene–Obot Akara LGAs) and (c) the study area displaying its geology, sounding stations and borehole locations.
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Schematic map of the study location. (a) Nigeria displaying Akwa Ibom State in the southern part, (b) Akwa Ibom State displaying the study area (Ikot Ekpene–Obot Akara LGAs) and (c) the study area displaying its geology, sounding stations and borehole locations.

Figure 1
Schematic map of the study location. (a) Nigeria displaying Akwa Ibom State in the southern part, (b) Akwa Ibom State displaying the study area (Ikot Ekpene–Obot Akara LGAs) and (c) the study area displaying its geology, sounding stations and borehole locations.
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Schematic map of the study location. (a) Nigeria displaying Akwa Ibom State in the southern part, (b) Akwa Ibom State displaying the study area (Ikot Ekpene–Obot Akara LGAs) and (c) the study area displaying its geology, sounding stations and borehole locations.

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The Benin Formation, commonly recognized as Coastal Plain Sands (CPS), is where the study region is geologically located. The Tertiary-Quaternary Benin Formation is noted for its alluvial settings (Mbipom et al. 1996). The Agbada Formation underlies the CPS (Short & Stauble 1967; Stacher 1995). The CPS encompasses over 80% of the study area and ranges in size from fine/coarse sands to gravel with patches of clay interbeddings in several places (Reijers & Petters 1987; Mbipom et al. 1996). The CPS makes up the key aquifer units in the study area, from which the inhabitants extract groundwater. In some places in the region under investigation, the sand–clay interbedding sequences create a multi-aquifer network (Edet & Okereke 2002). Underlying the Agbada Formation is the Akata Formation (Short & Stauble 1967; Stacher 1995). Figure 2 illustrates the broad stratigraphy of the Niger Delta province, where the study area is situated.
Figure 2
An illustration of the broad stratigraphy of the Niger Delta province, where the study area is located (modified from Obaje (2009)).
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An illustration of the broad stratigraphy of the Niger Delta province, where the study area is located (modified from Obaje (2009)).

Figure 2
An illustration of the broad stratigraphy of the Niger Delta province, where the study area is located (modified from Obaje (2009)).
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An illustration of the broad stratigraphy of the Niger Delta province, where the study area is located (modified from Obaje (2009)).

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The electrical subsurface resistivity variation pattern of the study area was investigated by making use of the vertical electrical sounding (VES) technique. Three key geoelectric parameters (thickness, resistivity as well as depth) of the layers identified in the area were derived from the VES data interpretation. These parameters were utilized to determine the different lithological and aquifer units and their properties in the survey region, in tandem with the available drilling data in the region. Topography data of the survey region was drawn from the digital elevation model (DEM). All these data were integrated into the DRASTIC model to appraise GSP using ArcGIS 10.5 in the study area. Borehole water samples were obtained from 12 locations in the neighbourhood of the VES stations and dispatched out to the laboratory for geochemical analysis.

Acquisition and interpretation of VES data

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The IGIS resistivity meter (model SSP-MP-ATS) was used in this study for resistivity geo-sounding measurements utilizing the VES technique. The soundings were done through the application of the Schlumberger electrode array at 28 different locations in the research area distributed as indicated in Figure 1. Four electrodes were inserted into the ground in a straight line at each sounding position; two electrodes, A and B, served as current sources and sinks, respectively, while M and N electrodes were employed to pick up the produced difference in potential (pd). The technique works on the basis of Ohm's law, which can be stated mathematically as in Equation (1).
(1)
where V is potential difference, I is the current and RA is apparent resistance of the earth strata through which current has passed. All four electrodes were joined to the appropriate terminals of the measuring instrument, which displayed RA values (V/1) of the penetrated earth layers on its LED output unit. The distance MN ranged from 0.5 to 25 m while that of AB varied between 1 and 1,000 m. At each sounding location, the distance AB was steadily increased about the sounding centre while MN was also occasionally expanded about the centre of the sounding. In all cases, the separations respectively satisfied the potential gradient assumption of AB ≥ 5 MN (Dobrin & Savit 1988; Ekanem et al. 2021). As depicted in Figure 1, a number of soundings were conducted in the environs of drilled water boreholes. This especially was important for ease of demarcation of the identified lithological sequence using the drilling data to constrain the interpretation.
The interpretation of the VES apparent resistance data was done in two phases. The first phase entailed the calculation of the apparent resistivity values of the penetrated earth layers for each sounding station, plotting the apparent resistivity on a log-log graph on the vertical axis against AB/2 on the horizontal axis and manual smoothening of the plotted data. Apparent resistivity ρA was computed using Equation (2):
(2)
Manual smoothening is a noise removal operation (Thomas et al. 2020) and was done in this work by taking the average of the two values of ρA at the point of cross-over and deleting any outliers as required while maintaining the dominant curve trend. This operation reduces the root mean square errors, which would have occurred in the quantitative computer modelling phase of interpreting the VES data (Ikpe et al. 2022). The general trends in the smoothened curves after the noise filtering process are then considered to be solely due to the vertical resistivity variation pattern of the penetrated earth layers. The smoothened VES curves were then partially curve-matched (Zohdy et al. 1974) to create the starting layer first-order geoelectric indices. The second phase of data interpretation was the quantitative interpretation implemented by utilizing the WINRESIST software program, which makes use of the least-square iteration algorithm (Vander Velpen & Sporry 1993). The preliminary layer parameters (thickness and resistivity) obtained in the first phase served as input parameters in the computer-assisted interpretation phase. The WINRESIST software program utilizes the input starting layer parameters to compute a hypothetical model and fits the computed model with the VES field data to create the ultimate resistivity models of the subsurface layers. The borehole information was used as controls in this interpretation stage and the resistivities and thicknesses obtained were taken as the true parameters of the penetrated earth layers. Samples of these ultimate resistivity model curves and how they correlate with the borehole lithological data are depicted in Figure 3.
Figure 3
Sample model VES curves at (a) Ikwen – VES 5, (b) Okpo Eto – VES 12, (c) Utu Ikot Ekpenyong – VES 19 and (d) Ikot Abia Idem – VES 24. Correlations between borehole lithological logs and the VES results are displayed in the inset legends.
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Sample model VES curves at (a) Ikwen – VES 5, (b) Okpo Eto – VES 12, (c) Utu Ikot Ekpenyong – VES 19 and (d) Ikot Abia Idem – VES 24. Correlations between borehole lithological logs and the VES results are displayed in the inset legends.

Figure 3
Sample model VES curves at (a) Ikwen – VES 5, (b) Okpo Eto – VES 12, (c) Utu Ikot Ekpenyong – VES 19 and (d) Ikot Abia Idem – VES 24. Correlations between borehole lithological logs and the VES results are displayed in the inset legends.
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Sample model VES curves at (a) Ikwen – VES 5, (b) Okpo Eto – VES 12, (c) Utu Ikot Ekpenyong – VES 19 and (d) Ikot Abia Idem – VES 24. Correlations between borehole lithological logs and the VES results are displayed in the inset legends.

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DRASTIC method of GSP investigation

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The DRASTIC method of investigating GSP to surface contaminants introduced by the United States Environmental Protection Agency (Aller et al. 1987; USEPA 1994) involves the combination of seven environmental variables. These seven variables are (i) aquifer depth (D), (ii) net recharge (R), (iii) aquifer media (A), (iv) soil media (S), (v) topography (T), (vi) impact of vadose zone (I) and (vii) aquifer hydraulic conductivity (C), thus forming the acronym ‘DRASTIC’. The depth variable has a direct relationship with the percolation time of contaminants in the groundwater system. Shallow water table implies more groundwater susceptibility to contaminants at the surface or close to it conversely (Maxe & Johansson 1998; Amiri et al. 2020; Kumar & Krishna 2020). Net recharge is the entire volume of water that can infiltrate into the hydrogeological units from precipitation and other man-made sources. It is a crucial means of contamination of groundwater by surface contaminants and thus greatly affects GSP (Shirazi et al. 2013; Ekanem et al. 2022a). The amount of water from rainfall that seeps down into the groundwater is greatly influenced by the characteristics of the geomaterials of the strata above the aquifer. Aquifer media, which refer to the geomaterials of which the hydrogeological units are made, control the flow of contaminants in the aquifer system. These media can attenuate contaminants depending on the permeability of the constituent geomaterials (Amiri et al. 2020; Ekanem 2020), which is determined by their grain sizes (Ekanem et al. 2021). Geomaterials of lower permeability will result in higher attenuation of percolating contaminated fluids and hence lower rating of susceptibility potential to contaminants (Neh et al. 2015; Venkatesan et al. 2019; Ekanem 2020; George 2021). Another factor affecting the aquifer recharge from rainfall is the topography. Regions with lower slopes will cause a slower flow rate of run-off water and thus enhance more percolation of any surface contaminants into the groundwater system, depending on the compositions of the overlying layers. The flow rate of groundwater and certainly, contaminants within the subsurface hydrogeological system is regulated by the aquifer hydraulic conductivity.

According to how much it affects groundwater susceptibility to contaminants, each of the seven DRASTIC model factors outlined above is given a weight (W), which varies between 1 and 5 as stated by Aller et al. (1987). The factors with the greatest severity are given a weighting value of 5 while the less severe ones are given a weighting value of 1 as indicated (Table 1). Additionally, every one of the seven DRASTIC factors is broken down into groups and categorized as ratings. A rating (R) is given to each of the ranges from 1 to 10 as shown in Table 1. The parameter with the least impact is given the rating value of 1 while the parameter with the highest impact is assigned a rating of 10. As stated by Knox et al. (1993), the DRASTIC index can be computed from the combination of these weightings and ratings from Equation (3).
(3)
where Dr, Rr, Ar, Sr, Tr, Ir and Cr are the depth, net recharge, aquifer media, soil media, topography, the impact of vadose zone and hydraulic conductivity ratings, respectively. On the other hand, Dw, Rw, Aw, Sw, Tw, Iw and Cw are the corresponding depth, net recharge, aquifer media, soil media, topography, impact of vadose zone and hydraulic conductivity weightings, respectively.
Table 1

Ranges, ratings and weight of DRASTIC factors (Aller et al. 1987; Barres-Lallemend 1994)

Depth of water (m)
Aquifer media
Soil media
Topography
Impact of vadose zone
Hydraulic conductivity (m/s)
IntervalRWIntervalRWIntervalRWIntervalRWIntervalRWIntervalRW
<20 10 5 Massive shale 2 3 Thin or absent 10 2 0–5 10 1 Thin or absent 10  5 >9.4 × 10−4 10 3 
20–40 9  Metamorphic/Igneous 3  Gravel 10      Gravel 10  4.7 × 10−4 to 9.4 × 10−4 8  
40–60 7  Weathered 4  Sand 9  5–15 8   Sand 9  32.9 × 10−5 to 4.7 × 10−4 6  
60–80 5  Glacial till 5  Laterite/peat 8      Laterite/peat 8  14.7 × 10−4 to 32.9 × 10−5 4  
80–100 3  Bedded sandstones 6  Shrinking and/aggregated clay 7  15–25 6   Shrinking and/aggregated clay 7  4.7 × 10−5 to 14.7 × 10−5 2  
100–120 2  Limestone and shale   Sandy loam 6      Sandy loam 6     
>120 1  Sequences 6  Loam 5  25–35 4   Loam 5  4.7 × 10−7 to 4.7 × 10−5 1  
   Massive sandstone 6  Silty loam 4      Silty loam 4     
   Massive limestone 6  Clay loam 3  >35 1   Clay loam 3     
   Sand and gravel 8  Muck 2      Muck 2     
   Basalt 9  Nonshrinky and nonaggregated clay 1      Nonshrinky and nonaggregated clay  1     
   Karst limestone 10              
Depth of water (m)
Aquifer media
Soil media
Topography
Impact of vadose zone
Hydraulic conductivity (m/s)
IntervalRWIntervalRWIntervalRWIntervalRWIntervalRWIntervalRW
<20 10 5 Massive shale 2 3 Thin or absent 10 2 0–5 10 1 Thin or absent 10  5 >9.4 × 10−4 10 3 
20–40 9  Metamorphic/Igneous 3  Gravel 10      Gravel 10  4.7 × 10−4 to 9.4 × 10−4 8  
40–60 7  Weathered 4  Sand 9  5–15 8   Sand 9  32.9 × 10−5 to 4.7 × 10−4 6  
60–80 5  Glacial till 5  Laterite/peat 8      Laterite/peat 8  14.7 × 10−4 to 32.9 × 10−5 4  
80–100 3  Bedded sandstones 6  Shrinking and/aggregated clay 7  15–25 6   Shrinking and/aggregated clay 7  4.7 × 10−5 to 14.7 × 10−5 2  
100–120 2  Limestone and shale   Sandy loam 6      Sandy loam 6     
>120 1  Sequences 6  Loam 5  25–35 4   Loam 5  4.7 × 10−7 to 4.7 × 10−5 1  
   Massive sandstone 6  Silty loam 4      Silty loam 4     
   Massive limestone 6  Clay loam 3  >35 1   Clay loam 3     
   Sand and gravel 8  Muck 2      Muck 2     
   Basalt 9  Nonshrinky and nonaggregated clay 1      Nonshrinky and nonaggregated clay  1     
   Karst limestone 10              
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DRASTIC index (DI) of between 1 and 100 corresponds to low groundwater susceptibility, 101 and 140 corresponds to moderate susceptibility, 140 and 200 corresponds to high susceptibility while values greater than 200 correspond to very high susceptibility, respectively (Aller et al. 1987; Amiri et al. 2020; Ekanem et al. 2022a). The above classifications constitute the basis of GSP rating using the DRASTIC method, which was adopted in this research.

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Collection of water samples and geochemical analyses

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Twelve borehole water samples were obtained from existing water boreholes in the research area in two 75 ml plastic bottles near the VES stations, respectively. One plastic bottle was used for cations while a different bottle was used for anions in each location. To ensure that the water sample was not contaminated, the bottles were prewashed with 0.05 M HCl and thereafter rinsed with distilled water. The water samples collected were then immediately dispatched to the laboratory for chemical analysis for major cations (sodium (Na), copper (Cu), iron (Fe), manganese (Mn), calcium (Ca), potassium (K), cadmium (Cd), nickel (Ni), chromium (Cr), lead (Pb) and magnesium (Mg)) and anions (bicarbonates, sulphates, chloride and sulphides).

Measurement of physical properties of the collected water samples such as dissolved oxygen (DO), pH, total dissolved solids (TDS) and temperature were made on-site during the field work whereas that of the biological oxygen and chemical oxygen demands (BOD and COD), respectively, were obtained from the water samples that were delivered to Quality Control and Testing Laboratories, Trans Amadi industrial layout, Port Harcourt, Nigeria, where the geochemical analyses were done. Measurement of TDS, pH and temperature were made on-site by the use of a waterproof pH/TDS/EC/temperature meter while that of DO was made also on-site by the use of a Jenway Model 1970 water proof DO meter. COD, which is a measure of the amount of oxygen needed for the breakdown of organic substances that constitute contaminants in water, was obtained by the use of the titration method. BOD concentration was determined by taking the difference in DO before and after incubating the water sample at 20 °C for 5 days. BOD is a measure of the amount of oxygen that microorganisms in water samples use when breaking down organic materials. It measures the level of water contamination by organic materials. The American Public Health Association (APHA) (2005) standard procedures were complied with in the geochemical examination of the anions and cations concentrations. Measurement of the concentrations of the anions was achieved using an Atomic Absorption Spectrophotometer (AAS) (Varian spectra 100 model) while that of the cations was made via titrimetric analyses in the laboratory. A summary of the determined water sample properties is given in Table 2.

Table 2

Summary of measured parameters from water samples

S/NBoreholeBH1BH2BH3BH4BH5BH6BH7BH8BH9BH10BH11BH12
Lat. (Deg.) 5.251 5.2867 5.2733 5.2532 5.2335 5.2024 5.1981 5.2110 5.1568 5.1832 5.1680 5.1760 
Long. (Deg.) 7.703 7.6369 7.5984 7.5989 7.6235 7.6992 7.7117 7.6820 7.7442 7.6914 7.6690 7.7111 
LocationIkot AtasungIkwenOkpo EtoIkot Idem UdoIkot UkpongIkot Abia IdemIkono RoadUruk UsoUtu Edem UsungIfuhoIkot OsuruaLibrary Avenue
1 pH 6.00 6.20 5.90 5.90 5.80 9.80 6.60 6.40 6.00 5.60 7.50 7.50 
 2 Temp. (°C) 28.20 28.40 27.30 27.10 27.20 27.60 27.30 27.10 27.30 27.20 28.10 28.30 
3 DO (mg/L) 5.80 5.20 5.00 4.50 4.70 5.30 5.20 5.10 5.80 5.60 5.60 4.80 
4 TDS (ppm) 1.00 3.00 1.00 1.00 1.00 4.50 2.00 1.00 2.00 4.00 56.00 18.90 
5 COD (ppm) 1.10 0.81 1.10 1.10 1.00 1.30 0.48 0.63 1.20 1.40 3.31 6.60 
6 BOD (mg/L) 2.30 2.70 2.80 2.10 2.10 2.60 2.50 2.40 3.30 2.90 5.30 4.85 
7 Cl− (mg/L) 1.80 1.70 2.20 3.00 3.00 3.31 2.20 1.03 1.10 1.30 12.20 8.10 
8  (mg/L) 1.00 1.00 2.30 1.40 1.20 5.77 1.00 1.00 1.50 1.70 2.20 2.70 
9  (mg/L) 1.40 1.20 1.55 2.00 2.50 4.10 2.20 1.30 2.20 1.20 16.00 18.60 
10  (mg/L) 0.10 0.03 0.03 0.02 0.04 0.13 0.02 0.01 0.10 0.10 0.20 0.15 
11 Na+ (mg/L) 1.00 1.40 2.70 3.40 5.50 9.10 1.05 1.10 2.30 2.70 1.20 4.20 
12 K+ (mg/L) 0.20 0.20 0.30 1.20 0.50 1.60 0.11 0.11 0.20 0.20 0.40 0.80 
13 Mg2+ (mg/L) 1.47 1.10 1.30 1.70 1.60 2.20 1.70 1.20 2.20 2.20 2.00 2.70 
14 Ca2+ (mg/L) 0.06 0.05 0.07 0.04 0.07 1.70 0.05 0.05 0.07 0.05 0.08 0.08 
15 Fe2+ (mg/L) 0.0053 0.0043 0.0048 0.0553 0.0050 0.0825 0.0550 0.0543 0.0875 0.0925 0.1000 0.1000 
16 Cu2+ (mg/L) 0.0650 0.0703 0.1015 0.0568 0.0665 0.0825 0.0513 0.0840 0.0548 0.0488 0.0800 0.0800 
17 Pb2+ (mg/L) 0.0003 0.0005 0.0003 0.0005 0.0005 0.0005 0.0005 0.0003 0.0005 0.0005 0.0010 0.0004 
18 Cd2+ (mg/L) 0.0055 0.0050 0.0028 0.0310 0.0028 0.0013 0.0045 0.0045 0.0050 0.0030 0.0800 0.0500 
19 Cr2+ (mg/L) 0.0028 0.0053 0.0035 0.0250 0.0048 0.0018 0.0288 0.0510 0.0028 0.0055 0.1200 0.1800 
20 Mn2+ (mg/L) 0.0165 0.0100 0.0055 0.0260 0.0063 0.0060 0.0103 0.0305 0.0170 0.0333 0.0600 0.0600 
21 Ni2+ (mg/L) 0.0235 0.0115 0.0158 0.0290 0.0188 0.0115 0.0158 0.0290 0.0188 0.0290 0.0200 0.0200 
22 Zn2+ (mg/L) 0.0470 0.0355 0.0483 0.0503 0.0483 0.0503 0.0553 0.0415 0.0713 0.0510 0.1300 0.1000 
S/NBoreholeBH1BH2BH3BH4BH5BH6BH7BH8BH9BH10BH11BH12
Lat. (Deg.) 5.251 5.2867 5.2733 5.2532 5.2335 5.2024 5.1981 5.2110 5.1568 5.1832 5.1680 5.1760 
Long. (Deg.) 7.703 7.6369 7.5984 7.5989 7.6235 7.6992 7.7117 7.6820 7.7442 7.6914 7.6690 7.7111 
LocationIkot AtasungIkwenOkpo EtoIkot Idem UdoIkot UkpongIkot Abia IdemIkono RoadUruk UsoUtu Edem UsungIfuhoIkot OsuruaLibrary Avenue
1 pH 6.00 6.20 5.90 5.90 5.80 9.80 6.60 6.40 6.00 5.60 7.50 7.50 
 2 Temp. (°C) 28.20 28.40 27.30 27.10 27.20 27.60 27.30 27.10 27.30 27.20 28.10 28.30 
3 DO (mg/L) 5.80 5.20 5.00 4.50 4.70 5.30 5.20 5.10 5.80 5.60 5.60 4.80 
4 TDS (ppm) 1.00 3.00 1.00 1.00 1.00 4.50 2.00 1.00 2.00 4.00 56.00 18.90 
5 COD (ppm) 1.10 0.81 1.10 1.10 1.00 1.30 0.48 0.63 1.20 1.40 3.31 6.60 
6 BOD (mg/L) 2.30 2.70 2.80 2.10 2.10 2.60 2.50 2.40 3.30 2.90 5.30 4.85 
7 Cl− (mg/L) 1.80 1.70 2.20 3.00 3.00 3.31 2.20 1.03 1.10 1.30 12.20 8.10 
8  (mg/L) 1.00 1.00 2.30 1.40 1.20 5.77 1.00 1.00 1.50 1.70 2.20 2.70 
9  (mg/L) 1.40 1.20 1.55 2.00 2.50 4.10 2.20 1.30 2.20 1.20 16.00 18.60 
10  (mg/L) 0.10 0.03 0.03 0.02 0.04 0.13 0.02 0.01 0.10 0.10 0.20 0.15 
11 Na+ (mg/L) 1.00 1.40 2.70 3.40 5.50 9.10 1.05 1.10 2.30 2.70 1.20 4.20 
12 K+ (mg/L) 0.20 0.20 0.30 1.20 0.50 1.60 0.11 0.11 0.20 0.20 0.40 0.80 
13 Mg2+ (mg/L) 1.47 1.10 1.30 1.70 1.60 2.20 1.70 1.20 2.20 2.20 2.00 2.70 
14 Ca2+ (mg/L) 0.06 0.05 0.07 0.04 0.07 1.70 0.05 0.05 0.07 0.05 0.08 0.08 
15 Fe2+ (mg/L) 0.0053 0.0043 0.0048 0.0553 0.0050 0.0825 0.0550 0.0543 0.0875 0.0925 0.1000 0.1000 
16 Cu2+ (mg/L) 0.0650 0.0703 0.1015 0.0568 0.0665 0.0825 0.0513 0.0840 0.0548 0.0488 0.0800 0.0800 
17 Pb2+ (mg/L) 0.0003 0.0005 0.0003 0.0005 0.0005 0.0005 0.0005 0.0003 0.0005 0.0005 0.0010 0.0004 
18 Cd2+ (mg/L) 0.0055 0.0050 0.0028 0.0310 0.0028 0.0013 0.0045 0.0045 0.0050 0.0030 0.0800 0.0500 
19 Cr2+ (mg/L) 0.0028 0.0053 0.0035 0.0250 0.0048 0.0018 0.0288 0.0510 0.0028 0.0055 0.1200 0.1800 
20 Mn2+ (mg/L) 0.0165 0.0100 0.0055 0.0260 0.0063 0.0060 0.0103 0.0305 0.0170 0.0333 0.0600 0.0600 
21 Ni2+ (mg/L) 0.0235 0.0115 0.0158 0.0290 0.0188 0.0115 0.0158 0.0290 0.0188 0.0290 0.0200 0.0200 
22 Zn2+ (mg/L) 0.0470 0.0355 0.0483 0.0503 0.0483 0.0503 0.0553 0.0415 0.0713 0.0510 0.1300 0.1000 
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Groundwater quality evaluation

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Evaluation of the groundwater standard was achieved in this work by the use of the groundwater quality index (GWQI) parameter based on the World Health Organization's recommended standards (WHO 2017). This was achieved by the use of Horton's weighted arithmetic technique introduced in 1965 and further refined by Brown et al. (1972). Mathematically, GWQI can be computed from Equation (4):
(4)
where n is the parameters' number, i denotes an index that runs from 1 to n, Wi is the unit weight and Qi denotes the quality rating value of the ith parameter, respectively. Wi is defined by Equation (5):
(5)
where C is a proportionality constant defined by Equation (6) and Sv is the ith parameter's permissible value.
(6)
The quality rating Qi can be computed by the use of Equation (7):
(7)
where Ov and Iv are the observed and ideal values of the respective parameters at the sampling site. The ideal values are considered as 0 for potable water apart from values of DO and pH (Tripaty & Sahu 2005; Ram et al. 2021). The ideal values of DO and pH are respectively 14.6 mg/L and 7.0 (Ram et al. 2021). In a situation where Qi = 0, there is a complete absence of contaminants. However, for Qi between 0 and 100, the contaminants are within the permissible limits. Qi values above 100 imply the contamination levels are above the acceptable limits. The groundwater quality categorization based on the weighted arithmetic GWQI method is summarized in Table 3.
Table 3

Groundwater quality rating based on the weighted arithmetic GWQI method (adapted from Brown et al. (1972), Chatterjee & Raziuddin (2002), Shankar & Kawo (2019) and Ram et al. (2021))

GWQI valuesQuality rating
0–25 Excellent 
26–50 Good 
51–75 Poor 
76–100 Very poor 
>100 Unsuitable 
GWQI valuesQuality rating
0–25 Excellent 
26–50 Good 
51–75 Poor 
76–100 Very poor 
>100 Unsuitable 
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VES data interpretation findings

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Table 4 shows the rundown of the findings of the VES resistivity data interpretation. Three to four earth strata have been identified in the research area with their corresponding primary parameters (thicknesses, depths and resistivities). The lithological entities that were identified based on their patterns of resistivity variations were further interpreted using the geological drilling data from the survey region. The first stratum, which was generally construed as the motley topsoil, is 0.6 and 24.2 m thick with resistivity values between 64.6 and 1,131.8 Ωm. The characteristic resistivity distribution pattern of this motley topsoil stratum may be caused by persistent bioturbating and human activities taking place in the stratum (George et al. 2016a, 2016b, 2017a, 2017b; Ekanem et al. 2020, 2021). The second layer detected in the research area and interpreted as fine sand in some places and sandy clay in other places, is 8.4–83.9 m thick with resistivity values of between 40.6 and 2,648.1 Ωm. Once more, the uneven grain sizes of the material composition of this stratum, which are peculiar to the Niger Delta's Benin Formation (Stacher 1995; Mbipom et al. 1996), may be responsible for the resistivity variation pattern seen in the layer. The third stratum, detected at a depth of 10.5–101.5 m with resistivity values of between 354.2 and 2,478.6 Ωm was construed as fine sands or coarse sands and sandy clay in a number of communities and sands with gravel in others. Groundwater extraction is carried out mainly in this identified third layer, which lacks impervious confining layers at most locations occupied except VES 2, 6 12, 13, 20, 23 and 24. The high aquifer resistivity values obtained at some locations as indicated in Table 4 are due to the aquifer layer composed of gravelly sands as revealed by the ground truth data obtained from local boreholes in the vicinity of the VES stations. Similar aquifer resistivity values were obtained by Ekanem (2021, 2022a, 2022b), Ikpe et al. (2022) and George et al. (2022a, 2022b) in areas with similar geology for gravelly sand aquifers. The resistivity of rocks is generally controlled by pore water conditions and therefore exhibits a wide range of variations. The fourth and last stratum detected in the study area has resistivity values ranging between 75.6 and 2,658.3 Ωm and was categorized as sandy clay in some places and fine/gravelly sands in others. The depth and thickness of this last layer are undefined since the injected current could not get down to its bottom with the maximum current electrode spacings employed in this study. The resistivity variation patterns for the four layers identified are illustrated in Figure 4. The red colour zones in Figure 4(a) and 4(b) indicate that the aquifer layer (third layer) has some level of protection by the impervious low resistivity layers above it. The resistivity values ranging from 1,417 to 2,472 Ωm indicated by the green colour in Figure 4(c) suggest that the subsurface water-bearing units in these segments of the survey region may have high-quality groundwater.
Table 4

Summary of VES data interpretation results

VES No.LocationLongitude (Degrees)Latitude (Degrees)No. of layersResistivity (Ωm)Thickness (m)Depth (m)Lithology
1 Ubon Ukwa 7.5588 5.1918 4 1,172.1 1.7 1.7 Top soil 
863.6 27.7 29.4 Lateritic sand 
1,471.2 65.7 95.1 Coarse sand 
455.7   Fine sand 
2 Nto Eton 1 7.5949 5.2026 4 847.5 1.7 1.7 Top soil 
239.1 12.6 14.3 Sandy clay 
994.8 73.0 87.3 Coarse sand 
332.6   Sandy clay 
3 Ikot Idem Udo 7.5989 5.2532 3 443.1 9.0 9.0 Top soil 
1,343.9 73.5 82.5 Coarse sand 
628.1   Fine sand 
4 Mbiaso 7.6780 5.2360 4 903.5 1.8 21.7 Top soil 
601.3 14.6 16.4 Fine sand 
2,073.6 80.2 96.6 Gravelly sand 
1,421.8   Coarse sand 
5 Ikwen 7.6369 5.2867 4 125.0 2.7 2.7 Top soil 
612.8 19.8 22.5 Fine sand 
1,904.1 68.9 91.4 Gravelly sand 
401.9   Sandy clay 
6 Nto Esu 7.6244 5.2772 4 608.2 4.4 4.4 Top soil 
133.3 20.3 24.7 Sandy clay 
1,706.6 82.4 107.1 Gravelly sand 
414.6   Sandy clay 
7 Ikot Okim 7.6102 5.2892 3 200.5 19.5 19.0 Top soil 
1,397.2 30.2 49.7 Coarse sand 
2,065.0   Gravelly sand 
8 Nto Ndang 1 7.6620 5.2760 3 205.3 10.5 7.9 Top soil 
2,083.6 81.7 92.2 Gravelly sand 
904.0   Coarse sand 
9 Nto Ndang -Eriam Road 7.6629 5.307 3 379.7 22.7 17.2 Top soil 
861.4 75.3 98 Fine sand 
1,834.9   Gravelly sand 
10 Ikot Atasung 7.703 5.251 3 65.3 4.5 4.5 Top soil 
1,079.8 38.6 43.1 Coarse sand 
1,625.6   Gravelly sand 
11 Oku Obom 7.6345 5.2618 3 231.1 11.2 11.6 Top soil 
716 70.2 81.4 Fine sand 
1,748.9   Gravelly sand 
12 Okpo Eto 7.5984 5.2733 4 724.4 0.8 3.3 Top soil 
190.6 16.3 17.1 Sandy clay 
1,063.6 71.9 89 Coarse sand 
717.3   Fine sand 
13 Ikot Essien 7.5645 5.2482 4 399.2 3.8 3.8 Top soil 
97.4 8.9 12.7 Clay 
359.4 40.1 52.9 Sand clay 
789.3   Fine sand 
14 Ikot Ukpong /Ntong Uno 7.6235 5.2335 3 263.5 12.5 12.6 Top soil 
495.1 89 101.5 Fine sand 
1,427.9   Coarse sand 
15 Nto Eton 2 7.579 5.18 3 694.6 7 8.5 Top soil 
1,573.9 61 68 Gravelly sand 
374   Sandy clay 
16 Imama 7.6259 5.195 3 214.1 14.4 15.5 Top soil 
517.8 83.3 97.7 Fine sand 
1,101.5   Coarse sand 
17 Nto Edino 1 7.5812 5.2831 3 400.3 6.6 6.6 Top soil 
1,738.5 86.3 92.9 Gravelly sand 
524.6   Fine sand 
18 Abiakpo Edem Idim 7.7090 5.1610 4 865.7 0.8 0.8 Top soil 
327.2 9.7 10.5 Sandy clay 
2,041.3 64.9 75.4 Gravelly sand 
637.8   Fine sand 
19 Utu Ikot Ekpenyong 7.7442 5.1568 3 1,145.9 2.3 2.3 Top soil 
2,004.2 82.1 84.4 Gravelly sand 
762.0   Fine sand 
20 Uruk Uso 7.7292 5.1767 4 630.4 1.3 1.3 Top soil 
148.9 11.1 12.4 Sandy clay 
2,472.8 68.1 80.5 Gravelly sand 
690.4   Fine sand 
21 Ikot Ekpene Housing, Ifuho 7.6914 5.1832 3 213.0 2.3 2.3 Top soil 
970.8 70.0 72.3 Coarse sand 
1,493.7   Gravelly sand 
22 Ibong Ikot Akan 7.6780 5.1710 3 228.5 6.1 6.1 Top soil 
2,111.6 49.3 55.4 Gravelly sand 
434.5   Sandy clay 
23 Ibong Road 7.6820 5.2110 4 431.9 1.4 1.4 Top soil 
40.6 14.6 16.0 Clay 
375.5 47.5 63.5 Sandy clay 
75.6   Clay 
24 Ikot Abia Idem 7.6992 5.2024 4 224.4 2.1 2.1 Top soil 
59.1 8.4 10.5 Clay 
1,264.5 59.9 70.4 Coarse sand 
324.9   Sandy clay 
25 Ikono Road 7.7117 5.1981 3 207.4 4.5 4.5 Sandy clay 
2,648.1 80.9 85.4 Gravel 
1,506.3   Coarse sand 
26 Ikot Ideh 7.56694 5.231111 4 326.2 0.4 0.4 Top soil 
599.1 24.9 25.3 Fine sand 
2,225.2 71.5 96.8 Gravelly sand 
1,273.4   Coarse sand 
27 Nto Edino 2 7.602222 5.27444 3 185.8 1.6 1.6 Top soil 
1,245.8 76.2 77.8 Coarse sand 
2,106.5   Gravelly sand 
28 Usaka Annang 7.550278 5.290278 4 474.9 0.9 0.9 Top soil 
1,063.6 29.3 30.2 Coarse sand 
489 55.3 85.4 Fine sand 
2,658.3   Gravelly sand 
VES No.LocationLongitude (Degrees)Latitude (Degrees)No. of layersResistivity (Ωm)Thickness (m)Depth (m)Lithology
1 Ubon Ukwa 7.5588 5.1918 4 1,172.1 1.7 1.7 Top soil 
863.6 27.7 29.4 Lateritic sand 
1,471.2 65.7 95.1 Coarse sand 
455.7   Fine sand 
2 Nto Eton 1 7.5949 5.2026 4 847.5 1.7 1.7 Top soil 
239.1 12.6 14.3 Sandy clay 
994.8 73.0 87.3 Coarse sand 
332.6   Sandy clay 
3 Ikot Idem Udo 7.5989 5.2532 3 443.1 9.0 9.0 Top soil 
1,343.9 73.5 82.5 Coarse sand 
628.1   Fine sand 
4 Mbiaso 7.6780 5.2360 4 903.5 1.8 21.7 Top soil 
601.3 14.6 16.4 Fine sand 
2,073.6 80.2 96.6 Gravelly sand 
1,421.8   Coarse sand 
5 Ikwen 7.6369 5.2867 4 125.0 2.7 2.7 Top soil 
612.8 19.8 22.5 Fine sand 
1,904.1 68.9 91.4 Gravelly sand 
401.9   Sandy clay 
6 Nto Esu 7.6244 5.2772 4 608.2 4.4 4.4 Top soil 
133.3 20.3 24.7 Sandy clay 
1,706.6 82.4 107.1 Gravelly sand 
414.6   Sandy clay 
7 Ikot Okim 7.6102 5.2892 3 200.5 19.5 19.0 Top soil 
1,397.2 30.2 49.7 Coarse sand 
2,065.0   Gravelly sand 
8 Nto Ndang 1 7.6620 5.2760 3 205.3 10.5 7.9 Top soil 
2,083.6 81.7 92.2 Gravelly sand 
904.0   Coarse sand 
9 Nto Ndang -Eriam Road 7.6629 5.307 3 379.7 22.7 17.2 Top soil 
861.4 75.3 98 Fine sand 
1,834.9   Gravelly sand 
10 Ikot Atasung 7.703 5.251 3 65.3 4.5 4.5 Top soil 
1,079.8 38.6 43.1 Coarse sand 
1,625.6   Gravelly sand 
11 Oku Obom 7.6345 5.2618 3 231.1 11.2 11.6 Top soil 
716 70.2 81.4 Fine sand 
1,748.9   Gravelly sand 
12 Okpo Eto 7.5984 5.2733 4 724.4 0.8 3.3 Top soil 
190.6 16.3 17.1 Sandy clay 
1,063.6 71.9 89 Coarse sand 
717.3   Fine sand 
13 Ikot Essien 7.5645 5.2482 4 399.2 3.8 3.8 Top soil 
97.4 8.9 12.7 Clay 
359.4 40.1 52.9 Sand clay 
789.3   Fine sand 
14 Ikot Ukpong /Ntong Uno 7.6235 5.2335 3 263.5 12.5 12.6 Top soil 
495.1 89 101.5 Fine sand 
1,427.9   Coarse sand 
15 Nto Eton 2 7.579 5.18 3 694.6 7 8.5 Top soil 
1,573.9 61 68 Gravelly sand 
374   Sandy clay 
16 Imama 7.6259 5.195 3 214.1 14.4 15.5 Top soil 
517.8 83.3 97.7 Fine sand 
1,101.5   Coarse sand 
17 Nto Edino 1 7.5812 5.2831 3 400.3 6.6 6.6 Top soil 
1,738.5 86.3 92.9 Gravelly sand 
524.6   Fine sand 
18 Abiakpo Edem Idim 7.7090 5.1610 4 865.7 0.8 0.8 Top soil 
327.2 9.7 10.5 Sandy clay 
2,041.3 64.9 75.4 Gravelly sand 
637.8   Fine sand 
19 Utu Ikot Ekpenyong 7.7442 5.1568 3 1,145.9 2.3 2.3 Top soil 
2,004.2 82.1 84.4 Gravelly sand 
762.0   Fine sand 
20 Uruk Uso 7.7292 5.1767 4 630.4 1.3 1.3 Top soil 
148.9 11.1 12.4 Sandy clay 
2,472.8 68.1 80.5 Gravelly sand 
690.4   Fine sand 
21 Ikot Ekpene Housing, Ifuho 7.6914 5.1832 3 213.0 2.3 2.3 Top soil 
970.8 70.0 72.3 Coarse sand 
1,493.7   Gravelly sand 
22 Ibong Ikot Akan 7.6780 5.1710 3 228.5 6.1 6.1 Top soil 
2,111.6 49.3 55.4 Gravelly sand 
434.5   Sandy clay 
23 Ibong Road 7.6820 5.2110 4 431.9 1.4 1.4 Top soil 
40.6 14.6 16.0 Clay 
375.5 47.5 63.5 Sandy clay 
75.6   Clay 
24 Ikot Abia Idem 7.6992 5.2024 4 224.4 2.1 2.1 Top soil 
59.1 8.4 10.5 Clay 
1,264.5 59.9 70.4 Coarse sand 
324.9   Sandy clay 
25 Ikono Road 7.7117 5.1981 3 207.4 4.5 4.5 Sandy clay 
2,648.1 80.9 85.4 Gravel 
1,506.3   Coarse sand 
26 Ikot Ideh 7.56694 5.231111 4 326.2 0.4 0.4 Top soil 
599.1 24.9 25.3 Fine sand 
2,225.2 71.5 96.8 Gravelly sand 
1,273.4   Coarse sand 
27 Nto Edino 2 7.602222 5.27444 3 185.8 1.6 1.6 Top soil 
1,245.8 76.2 77.8 Coarse sand 
2,106.5   Gravelly sand 
28 Usaka Annang 7.550278 5.290278 4 474.9 0.9 0.9 Top soil 
1,063.6 29.3 30.2 Coarse sand 
489 55.3 85.4 Fine sand 
2,658.3   Gravelly sand 
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Figure 4
Resistivity variation patterns of the interpreted geoelectric layers. (a) Layer 1, (b) Layer 2, (c) Layer 3 – aquifer layer and (d) Layer 4. The inset legend shows the resistivity values in Ohm-metre (Ωm).
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Resistivity variation patterns of the interpreted geoelectric layers. (a) Layer 1, (b) Layer 2, (c) Layer 3 – aquifer layer and (d) Layer 4. The inset legend shows the resistivity values in Ohm-metre (Ωm).

Figure 4
Resistivity variation patterns of the interpreted geoelectric layers. (a) Layer 1, (b) Layer 2, (c) Layer 3 – aquifer layer and (d) Layer 4. The inset legend shows the resistivity values in Ohm-metre (Ωm).
View largeDownload slide

Resistivity variation patterns of the interpreted geoelectric layers. (a) Layer 1, (b) Layer 2, (c) Layer 3 – aquifer layer and (d) Layer 4. The inset legend shows the resistivity values in Ohm-metre (Ωm).

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DRASTIC model results

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Water table depth (D) parameter

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The estimated values of D from the VES data interpretation results vary from 10.5 m in Abiakpo Edem Idim to 101.5 m in Ikot Ukpong communities, respectively. The data in Table 1 was utilized to rate the depth ranges from 2 to 10 and the corresponding depth index ranges from 10 to 50 as displayed in Figure 5(a). Over 60% of the communities in the study area have a depth index value of more than 25 as Figure 5(a) reveals. These communities include Usaka, Ikwen, Ubon Ukwa, Mbiaso, Ibong and Utu Ikpe. Inferentially, the aforementioned communities may be highly susceptible to groundwater contamination by contaminants at the earth's surface or near the earth's surface.
Figure 5
Depth and topography of the study area. (a) Depth index, (b) ASTER digital elevation model (DEM), (c) slope (%) and (d) recharge index.
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Depth and topography of the study area. (a) Depth index, (b) ASTER digital elevation model (DEM), (c) slope (%) and (d) recharge index.

Figure 5
Depth and topography of the study area. (a) Depth index, (b) ASTER digital elevation model (DEM), (c) slope (%) and (d) recharge index.
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Depth and topography of the study area. (a) Depth index, (b) ASTER digital elevation model (DEM), (c) slope (%) and (d) recharge index.

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Net recharge (R) parameter

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The key source of groundwater replenishment in the study area is rainwater. As a consequence of the unavailability of data on net groundwater replenishment in the survey region, Piscopo's procedure put forward in 2001 was adopted to estimate the net recharge value through Equation (8):
(8)
where Fs and Fr are the slope and rainfall factors and Fsp represents the permeability of the soil factor. Around 2,008.0 mm of rain falls on the average per year in the study area (Isaiah et al. 2021; Ekanem et al. 2022a). The study area slope (in %) was derived from the ASTER DEM on the platform of ArcGIS. Figure 5(b) and 5(c), respectively, shows the DEM and slopes that were obtained. The hydraulic conductivity Kh of the soil was obtained from the experiential relation established by Ekanem et al. (2020) for the region. The relation is given in Equation (9):
(9)
where ρr is the bulk resistivity. Using Equation (10), permeability Kp was calculated for each of the sounding locations:
(10)
where μd is water dynamic viscosity (taken as 1.4 × 10−3 kg/ms (Fetter 1994)), g = 9.8 m/s2 (i.e. gravitational acceleration and δw = 1,000 kg/m3 (i.e. water density). The values of Kp obtained vary from 543.7 to 4,449.5 mD. Considering the data in Table 5, ratings for the slope, mean annual precipitation and soil permeability parameters were assigned. The slope and soil permeability characteristics both received ratings of 1–3 whereas the precipitation parameter received a fixed rating of 4. These ratings were put together in accordance with Equation (8) to get the values of net recharge for each of the VES positions. These values fall between 7 and 11 and are similar to those obtained by George (2021) in parts of the study area in the Niger Delta province. The information in Table 5 was further used to reclassify the net recharge values to give ratings of 3–8, respectively, as in George (2021). The respective ratings for each sounding location were multiplied by 4 (the net recharge parameter's weight) to give the values of the net recharge index, which range from 12 to 32 as shown in Figure 5(d). The least value of the computed index occurs around the Ikwen community in the northern segment of the survey region while higher values of the index occur around the Mbiaso and Nto Edino communities, respectively.
Table 5

Net recharge ratings for the study (Piscopo 2001; Al-Adamat et al. 2003)

Slope (%)RatingRainfall (mm)RatingSoil permeabilityRatingNet recharge (weight W = 4)Rating
< 2 4 < 500 1 Very slow 1 11–13 10 
2–10 3 500–700 2 Slow 2 9–11 8 
10–33 2 700–850 3 Moderate 3 7–9 5 
> 33 1 > 850 4 Moderate–high 4 5–7 3 
        High 5 3–5 1 
Slope (%)RatingRainfall (mm)RatingSoil permeabilityRatingNet recharge (weight W = 4)Rating
< 2 4 < 500 1 Very slow 1 11–13 10 
2–10 3 500–700 2 Slow 2 9–11 8 
10–33 2 700–850 3 Moderate 3 7–9 5 
> 33 1 > 850 4 Moderate–high 4 5–7 3 
        High 5 3–5 1 
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Aquifer media parameter

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The aquifer media as summarized in Table 4 in the study area are composed of gravelly sands in some communities and fine/coarse sands/sandy clay in other communities, as determined by the VES interpretation results using geological drilling data as controls. Aquifer media have a weighting of 3 as pointed out in Table 1. The sandy clay and fine/coarse/gravelly sand aquifer media were given ratings of 6 and 8, respectively (Table 1). The aquifer media index was obtained from the product of the ratings and the aquifer media weight and its distribution are given in Figure 6(a). The index lies between 18 and 48. Figure 6(a) reveals that the survey region is typified by aquifer media index values that are bigger than 20 except for VES 13, 15, 22 and 23, where sandy clay constitutes the aquifer media. This suggests that aquifer media at these locations may have relatively lower susceptibility potential.
Figure 6
Distribution of the DRASTIC parameters in the study area. (a) Aquifer media index, (b) Soil media index, (c) topography index and (d) vadose zone index.
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Distribution of the DRASTIC parameters in the study area. (a) Aquifer media index, (b) Soil media index, (c) topography index and (d) vadose zone index.

Figure 6
Distribution of the DRASTIC parameters in the study area. (a) Aquifer media index, (b) Soil media index, (c) topography index and (d) vadose zone index.
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Distribution of the DRASTIC parameters in the study area. (a) Aquifer media index, (b) Soil media index, (c) topography index and (d) vadose zone index.

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Soil media parameter

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Soil media in this study were also inferred from the analysis of the VES data interpretation results. Unconsolidated sandy loam soil makes up the majority of the soil media in the study area, with patches of clayey loam soil in some communities. Clayey loam soil media was given a rating of 3 while the sandy loam soil media was given a rating of 6 (Table 1). The soil media index was mathematically obtained from the product of the respective ratings and a fixed weight factor of 2 (Table 1). The resulting values vary from 6 to 12 as displayed in Figure 6(b). A low soil media index of less than 10 is observed in around 64% of the study area.

Topography parameter

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Topography describes the earth's surface slope. In places of low slope, run-off water (rainwater) will linger or gush very slowly thereby enhancing easy permeation of any contaminating fluids to the groundwater. Accordingly, depending on the kind of soil media, locations with lower slopes tend to have higher susceptibility potential than locations with higher slopes. The slope values range from 2.5 to 47% and were given ratings from 1 to 10 (Table 1). The slope value ratings were multiplied by the constant weight of 1 for the topography factor to have the topography index, which also varies between 1 and 10 as depicted in Figure 6(c). Most places in the study area have relatively high topography index ranging between 6 and 10 with the north-eastern part and a spot in the western part having low index of between 1 and 4. This has the consequence of a slow run-off flow rate in most of the study area, which will make the aquifer easily contaminated by any contaminants on the earth's surface or close to it, especially in the absence of adequate protection of the aquifer by the layers above it.

Impact of vadose zone parameter

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The outcomes of the VES data analysis demonstrate that the vadose zone is primarily made of sands (lateritic, fine and coarse sands in addition to sands with gravel) with minor argil intercalation at some locations. Sandy clay was assigned a rating of 3, lateritic, fine/coarse sand a rating of 9 while gravelly sand received a rating of 10 (Table 1). The corresponding vadose zone index ranges between 15 and 50 and is spread as illustrated in Figure 6(d). This spread gives a suggestion that the study area may be highly vulnerable to contaminants at the earth's surface. A large number of the villages in the research area have indices of greater than 30, with pockets having low indices of less than 30 as seen in Figure 6(d). By implication, the majority of the communities have pervious geomaterials in the layer above the aquifer and this may result in easy infiltration of any contaminated fluid into the aquifer.

Aquifer hydraulic conductivity parameter

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The estimated aquifer hydraulic conductivity values range between 5.5 × 10−6 and 2.2 × 10−5 m/s. These values are similar to those reported by Shamsuddin et al. (2018), George et al. (2021), Ekanem et al. (2020,,2022b) and George (2021) for aquifers composed of fine to gravelly sands. The hydraulic conductivity values received a fixed rating of 1 from the information in Table 1 and the corresponding index of 3 was obtained.

Groundwater susceptibility potential (GSP) and DRASTIC index (DI)

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The seven DRASTIC indices for each of the geo-sounding stations were combined together via Equation (3) to get the corresponding DI depicted in Table 6. The computed DI values range between 111 and 173 and their spread is given in Figure 7(a). These values were employed to grade the aquifer susceptibility potential in the study region into two classes. These classes are moderate (DI = 111–134) and high (DI = 142–173) as illustrated Figure 7(b). Analysis of the susceptibility ratings reveals that 75% of the study area has a moderate rating while the remaining 25% has a high rating. This result may be caused by the relatively flat topography in the research area together with the enormous pervious geomaterials of the strata above the aquifer system, which aid swift infiltration of any contaminants to the water table.
Table 6

Computed DRASTIC index (DI) and groundwater susceptibility potential rating (GSPR) in the study area

VES No.ParameterD
R
A
S
T
IC
DIGSPR
Weight5
4
3
2
1
5
3
LocationDrDrDWRrRrRWArArAWSrSrSWTrTrTWIrIrIWCrCrCW
1 Ubon Ukwa 9 45 5 20 8 24 6 12 8 8 9 45 1 3 157 High 
2 Nto Eton 1 10 50 5 20 8 24 6 12 6 6 3 15 1 3 130 Moderate 
3 Ikot Idem Udo 3 15 5 20 8 24 3 6 6 6 9 45 1 3 119 Moderate 
4 Mbiaso 10 50 5 20 8 24 6 12 8 8 9 45 1 3 162 High 
5 Ikwen 9 45 5 20 8 24 3 6 6 6 9 45 1 3 149 High 
6 Nto Esu 9 45 5 20 8 24 6 12 6 6 3 15 1 3 125 Moderate 
7 Ikot Okim 7 35 5 20 8 24 3 6 6 6 9 45 1 3 139 Moderate 
8 Nto Ndang 1 3 15 3 12 8 24 3 6 1 1 10 50 1 3 111 Moderate 
9 Nto Ndang -Eriam Road 3 15 5 20 8 24 3 6 4 4 9 45 1 3 117 Moderate 
10 Ikot Atasung 7 35 8 32 8 24 3 6 4 4 9 45 1 3 149 High 
11 Oku Obom 3 15 5 20 8 24 3 6 8 8 9 45 1 3 121 Moderate 
12 Okpo Eto 10 50 5 20 8 24 6 12 6 6 3 15 1 3 130 Moderate 
13 Ikot Essien 10 50 5 20 6 18 3 6 6 6 3 15 1 3 118 Moderate 
14 Ikot Ukpong 2 10 5 20 8 24 3 6 6 6 9 45 1 3 114 Moderate 
15 Nto Eton 2 5 25 5 20 6 18 6 12 6 6 10 50 1 3 134 Moderate 
16 Imama 3 15 5 20 8 24 3 6 6 6 9 45 1 3 119 Moderate 
17 Nto Edino 1 3 15 5 20 8 24 3 6 6 6 10 50 1 3 124 Moderate 
18 Abiakpo Edem Idim 10 50 5 20 8 24 6 12 6 6 3 15 1 3 130 Moderate 
19 Utu Ikot Ekpenyong 3 15 5 20 8 24 6 12 6 6 10 50 1 3 130 Moderate 
20 Uruk Uso 10 50 5 20 8 24 6 12 6 6 3 15 1 3 130 Moderate 
21 Ifuho 5 25 5 20 8 24 6 12 8 8 10 50 1 3 142 High 
22 Ibong Ikot Akan 7 35 5 20 6 18 3 6 8 8 10 50 1 3 140 Moderate 
23 Ibong Road 10 50 5 20 6 18 3 6 8 8 3 15 1 3 120 Moderate 
24 Ikot Abia Idem 10 50 5 20 8 24 3 6 8 8 3 15 1 3 126 Moderate 
25 Ikono Road 3 15 5 20 8 24 3 6 6 6 10 50 1 3 124 Moderate 
26 Ikot Ideh 9 45 5 20 8 32 3 6 4 4 9 45 1 3 155 High 
27 Nto Edino 2 5 25 8 32 8 40 3 6 10 10 9 45 1 3 161 High 
28 Usaka Annang 9 45 5 20 8 48 3 6 6 6 9 45 1 3 173 High 
 Minimum 2 10 3 12 6 18 3 6 1 1 3 15 1 3 111  
 Maximum 10 50 8 32 8 48 6 12 10 10 10 50 1 3 173  
VES No.ParameterD
R
A
S
T
IC
DIGSPR
Weight5
4
3
2
1
5
3
LocationDrDrDWRrRrRWArArAWSrSrSWTrTrTWIrIrIWCrCrCW
1 Ubon Ukwa 9 45 5 20 8 24 6 12 8 8 9 45 1 3 157 High 
2 Nto Eton 1 10 50 5 20 8 24 6 12 6 6 3 15 1 3 130 Moderate 
3 Ikot Idem Udo 3 15 5 20 8 24 3 6 6 6 9 45 1 3 119 Moderate 
4 Mbiaso 10 50 5 20 8 24 6 12 8 8 9 45 1 3 162 High 
5 Ikwen 9 45 5 20 8 24 3 6 6 6 9 45 1 3 149 High 
6 Nto Esu 9 45 5 20 8 24 6 12 6 6 3 15 1 3 125 Moderate 
7 Ikot Okim 7 35 5 20 8 24 3 6 6 6 9 45 1 3 139 Moderate 
8 Nto Ndang 1 3 15 3 12 8 24 3 6 1 1 10 50 1 3 111 Moderate 
9 Nto Ndang -Eriam Road 3 15 5 20 8 24 3 6 4 4 9 45 1 3 117 Moderate 
10 Ikot Atasung 7 35 8 32 8 24 3 6 4 4 9 45 1 3 149 High 
11 Oku Obom 3 15 5 20 8 24 3 6 8 8 9 45 1 3 121 Moderate 
12 Okpo Eto 10 50 5 20 8 24 6 12 6 6 3 15 1 3 130 Moderate 
13 Ikot Essien 10 50 5 20 6 18 3 6 6 6 3 15 1 3 118 Moderate 
14 Ikot Ukpong 2 10 5 20 8 24 3 6 6 6 9 45 1 3 114 Moderate 
15 Nto Eton 2 5 25 5 20 6 18 6 12 6 6 10 50 1 3 134 Moderate 
16 Imama 3 15 5 20 8 24 3 6 6 6 9 45 1 3 119 Moderate 
17 Nto Edino 1 3 15 5 20 8 24 3 6 6 6 10 50 1 3 124 Moderate 
18 Abiakpo Edem Idim 10 50 5 20 8 24 6 12 6 6 3 15 1 3 130 Moderate 
19 Utu Ikot Ekpenyong 3 15 5 20 8 24 6 12 6 6 10 50 1 3 130 Moderate 
20 Uruk Uso 10 50 5 20 8 24 6 12 6 6 3 15 1 3 130 Moderate 
21 Ifuho 5 25 5 20 8 24 6 12 8 8 10 50 1 3 142 High 
22 Ibong Ikot Akan 7 35 5 20 6 18 3 6 8 8 10 50 1 3 140 Moderate 
23 Ibong Road 10 50 5 20 6 18 3 6 8 8 3 15 1 3 120 Moderate 
24 Ikot Abia Idem 10 50 5 20 8 24 3 6 8 8 3 15 1 3 126 Moderate 
25 Ikono Road 3 15 5 20 8 24 3 6 6 6 10 50 1 3 124 Moderate 
26 Ikot Ideh 9 45 5 20 8 32 3 6 4 4 9 45 1 3 155 High 
27 Nto Edino 2 5 25 8 32 8 40 3 6 10 10 9 45 1 3 161 High 
28 Usaka Annang 9 45 5 20 8 48 3 6 6 6 9 45 1 3 173 High 
 Minimum 2 10 3 12 6 18 3 6 1 1 3 15 1 3 111  
 Maximum 10 50 8 32 8 48 6 12 10 10 10 50 1 3 173  
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Figure 7
Groundwater susceptibility potential assessment result in the study area. (a) Distribution of DRASTIC index and (b) susceptibility potential rating map showing two classes of ratings.
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Groundwater susceptibility potential assessment result in the study area. (a) Distribution of DRASTIC index and (b) susceptibility potential rating map showing two classes of ratings.

Figure 7
Groundwater susceptibility potential assessment result in the study area. (a) Distribution of DRASTIC index and (b) susceptibility potential rating map showing two classes of ratings.
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Groundwater susceptibility potential assessment result in the study area. (a) Distribution of DRASTIC index and (b) susceptibility potential rating map showing two classes of ratings.

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Water sample geochemical analyses results

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The outcomes of the borehole water sample geochemical analyses for the various parameters measured and the standards provided by WHO (2017) are presented in Tables 2 and 7. The measured parameter values were compared to the WHO standard to find out if groundwater in the area is suitable for human usage. The measured values of a greater number of the parameters are well below the acceptable WHO standards except parameters like pH at some locations (boreholes 1, 2, 3, 4, 5, 6, 8, 9 and 10), BOD at nearly all the borehole locations, chromium ions (boreholes 11 and 12) and nickel ions (boreholes 1, 8, 10, 11 and 12). Details of the distribution of the parameters are discussed below.

Table 7

Statistical analysis of the observed groundwater quality parameters and WHO standards

S/NParametersWHO Standard (2017) WiMeasured in this study
Minimum valueMaximum valueMean valueStandard deviation
1 pH 6.50–8.50 6.0 × 10−04 5.60 9.80 6.60 1.18 
2 DO (mg/L) 6.50–8.00 3.4 × 10−04 4.50 5.80 5.22 0.43 
3 TDS (ppm) 500.00 1.0 × 10−05 1.00 56.00 7.95 15.93 
4 COD (ppm) 120.00 4.3 × 10−05 0.48 6.60 1.67 1.71 
5 BOD (mg/L) 2.00 2.6 × 10−03 2.10 5.30 2.99 1.04 
6 Cl− (mg/L) 250.00 2.1 × 10−05 1.03 12.20 3.41 3.35 
7  (mg/L) 250.00 2.1 × 10−05 1.00 5.77 1.90 1.35 
8  (mg/L) 250.00 2.1 × 10−05 1.20 18.60 4.52 6.05 
9  (mg/L) 250.00 2.1 × 10−05 0.01 0.20 0.08 0.06 
10 Na+ (mg/L) 200.00 2.6 × 10−05 1.00 9.10 2.97 2.39 
11 K+ (mg/L) 10.00 5.1 × 10−04 0.11 1.60 0.49 0.48 
12 Mg2+ (mg/L) 50.00 1.0 × 10−04 1.10 2.70 1.78 0.49 
13 Ca2+ (mg/L) 75.00 6.8 × 10−05 0.04 1.70 0.20 0.47 
14 Fe2+ (mg/L) 0.30 1.7 × 10−02 0.0043 0.1000 0.0539 0.04 
15 Cu2+ (mg/L) 2.00 2.6 × 10−03 0.0488 0.1015 0.0701 0.02 
16 Pb2+ (mg/L) 0.01 5.1 × 10−01 0.0003 0.0010 0.0005 0.00 
17 Cd2+ (mg/L) 0.003 5.1 × 10−02 0.0013 0.0800 0.0163 0.02 
18 Cr2+ (mg/L) 0.05 1.0 × 10−01 0.0018 0.1800 0.0359 0.06 
19 Mn2+ (mg/L) 0.10 5.1 × 10−02 0.0055 0.0600 0.0234 0.02 
20 Ni2+ (mg/L) 0.02 2.6 × 10−01 0.0115 0.0290 0.0202 0.01 
21 Zn2+ (mg/L) 3.00–5.00 1.0 × 10−03 0.0355 0.1300 0.0607 0.03 
        
S/NParametersWHO Standard (2017) WiMeasured in this study
Minimum valueMaximum valueMean valueStandard deviation
1 pH 6.50–8.50 6.0 × 10−04 5.60 9.80 6.60 1.18 
2 DO (mg/L) 6.50–8.00 3.4 × 10−04 4.50 5.80 5.22 0.43 
3 TDS (ppm) 500.00 1.0 × 10−05 1.00 56.00 7.95 15.93 
4 COD (ppm) 120.00 4.3 × 10−05 0.48 6.60 1.67 1.71 
5 BOD (mg/L) 2.00 2.6 × 10−03 2.10 5.30 2.99 1.04 
6 Cl− (mg/L) 250.00 2.1 × 10−05 1.03 12.20 3.41 3.35 
7  (mg/L) 250.00 2.1 × 10−05 1.00 5.77 1.90 1.35 
8  (mg/L) 250.00 2.1 × 10−05 1.20 18.60 4.52 6.05 
9  (mg/L) 250.00 2.1 × 10−05 0.01 0.20 0.08 0.06 
10 Na+ (mg/L) 200.00 2.6 × 10−05 1.00 9.10 2.97 2.39 
11 K+ (mg/L) 10.00 5.1 × 10−04 0.11 1.60 0.49 0.48 
12 Mg2+ (mg/L) 50.00 1.0 × 10−04 1.10 2.70 1.78 0.49 
13 Ca2+ (mg/L) 75.00 6.8 × 10−05 0.04 1.70 0.20 0.47 
14 Fe2+ (mg/L) 0.30 1.7 × 10−02 0.0043 0.1000 0.0539 0.04 
15 Cu2+ (mg/L) 2.00 2.6 × 10−03 0.0488 0.1015 0.0701 0.02 
16 Pb2+ (mg/L) 0.01 5.1 × 10−01 0.0003 0.0010 0.0005 0.00 
17 Cd2+ (mg/L) 0.003 5.1 × 10−02 0.0013 0.0800 0.0163 0.02 
18 Cr2+ (mg/L) 0.05 1.0 × 10−01 0.0018 0.1800 0.0359 0.06 
19 Mn2+ (mg/L) 0.10 5.1 × 10−02 0.0055 0.0600 0.0234 0.02 
20 Ni2+ (mg/L) 0.02 2.6 × 10−01 0.0115 0.0290 0.0202 0.01 
21 Zn2+ (mg/L) 3.00–5.00 1.0 × 10−03 0.0355 0.1300 0.0607 0.03 
        
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The pH values measured vary between 5.6 and 9.8 with 6.6 mean and 1.18 standard deviation values (Table 7). pH is an important parameter that determines the alkalinity or acidity and corrosivity of groundwater, mobility and solubility of dissolved metals and reveals the types of gases and minerals that groundwater has reacted with during recharging. Analysis of the data in Tables 2 and 7 reveals that 67% of the water samples are acidic, 8% is alkaline while the remaining 25% is within WHO's limits of 6.5–8.5. The lower pH values may be attributed to underground geological activities. Underground water temperature ranges between 27.1 and 28.4 °C. Water temperature is especially vital because it affects biochemical reactions in aquatic life. DO concentration (in mg/L) varies between 4.5 and 5.8 with 5.22 average 0.43 standard deviation values, respectively (Table 7). These values are much below the acceptable limit of 6.5–8.0 mg/L recommended by the World Health Organization. TDS and COD both in parts per million (ppm) range from 1.0 to 56 and 0.48 to 6.67 with mean values of 7.95 and 1.67, respectively. These ranges are well under the respective limits of 500 and 120 given by the World Health Organization. The biochemical oxygen demand (BOD) in mg/L varies between 2.1 and 5.3 with a 2.99 mean value. These ranges are all above the allowable limit of 2 provided by WHO (2017). The concentrations of all the anions (Cl−, ⁠, and ⁠) in mg/L measured from the water samples are all well below the allowable limits of WHO (2017) as summarized in Table 7. Similarly, as shown in Table 7, the concentrations of the cations (Na+, K+, Mg2+, Ca2+, Fe2+, Cu2+, Pb2+, Cd2+, Mn2+ and Zn2+) in mg/L are all well below the allowable limits of WHO (2017) except for Cr2+ (boreholes 11 and 12) and Ni2+ (boreholes 1, 8, 10, 11 and 12). By implication, these variables do not pose significant contamination risk except for Cr2+ and Ni2+ at the borehole locations indicated above. Table 8 displays the correlation matrix of the measured physical properties from the borehole water samples. The correlation is split into four groups, namely: very strong, strong, moderate and weak correlations as summarized in Table 8. The degree of correlations between the respective variables is an indication of the kind of relationship that exists between them. Very strong and strong correlations imply that a strong linear relationship exists between the respective variables. An increase in one variable will therefore result in a corresponding increase in the other variable. This will eventually lead to increased contamination and consequently high vulnerability potential. The reverse is true for weak correlations between the variables. The majority of the measured parameters exhibit very strong correlations with each other in this study. This is an indication that increasing concentrations of the respective parameters will pose more contamination risk and hence elevate the aquifer vulnerability potential.

Table 8

Correlation matrix of measured parameters from borehole water sample

 
 
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Groundwater quality investigation results

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Groundwater quality investigation was done via an estimate of the GWQI. The investigation result is given in Table 9. The value of the GWQI varies between 18.2 at Ikot Abia Idem community and 70.7 at Library Avenue, which is close to the location of the old and abandoned dumpsite in the study area. Using these values and the information detailed in Table 3, the quality of groundwater was categorized into three ratings. These ratings are accordingly excellent (25%) with GWQI values of 18.2–22.2, good (50%) with GWQI values of 27.5–47.5 and poor (25%) with GWQI values of 50.5–70.7. The distribution of the values of GWQI and the corresponding groundwater quality rating map of the study area generated by the use of ArcGIS 10.5 is given in Figure 8. The map indicates clearly that a greater part of the survey region belongs to the good/excellent groundwater standard zone. The southern section of the survey region, including the neighbourhoods of Utu Ikpe, Library Avenue and Ikot Osurua, has poor groundwater quality. The DEM map of Figure 5(b) reveals that these communities are located around the ravine area. Leachates produced by the breakdown of the heaps of solid wastes littering the streets of the study area and other debris/dissolved hazardous chemicals are carried by rainwater to the ravine. This has the potential of contaminating the aquifer units in these locations. Besides, a lot of contaminants may have emanated from the old dumpsite on Library Avenue, which was only recently abandoned to the new site at Utu Ikpe community as displayed in Figure 1. This therefore provides a reasonable explanation for the poor water quality rating of the communities in this part of the study area. The relevance of the investigation findings is that water boreholes may be sited in the northern division of the survey region with good to excellent quality rating of groundwater. Particularly, water from boreholes in the vicinity of the demarcated poor groundwater quality zone needs treatment before consumption to forestall any harmful effects.
Table 9

Groundwater quality investigation results in the study area

Borehole numberLocationGWQIWater quality rating
1 Ikot Atasung 32.7 Good 
2 Ikwen 18.7 Excellent 
3 Okpo Eto 22.2 Excellent 
4 Ikot Idem Udo 47.5 Good 
5 Ikot Ukpong 27.5 Good 
6 Ikot Abia Idem 18.2 Excellent 
7 Ikono Road 29.3 Good 
8 Uruk Uso 50.5 Poor 
9 Utu Edem Usung 28.5 Good 
10 Ifuho 42.8 Good 
11 Ikot Osurua 63.1 Poor 
12 Library Avenue 70.7 Poor 
Borehole numberLocationGWQIWater quality rating
1 Ikot Atasung 32.7 Good 
2 Ikwen 18.7 Excellent 
3 Okpo Eto 22.2 Excellent 
4 Ikot Idem Udo 47.5 Good 
5 Ikot Ukpong 27.5 Good 
6 Ikot Abia Idem 18.2 Excellent 
7 Ikono Road 29.3 Good 
8 Uruk Uso 50.5 Poor 
9 Utu Edem Usung 28.5 Good 
10 Ifuho 42.8 Good 
11 Ikot Osurua 63.1 Poor 
12 Library Avenue 70.7 Poor 
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Figure 8
Groundwater quality assessment result in the study area. (a) Distribution of groundwater quality index (GWQI) and (b) Groundwater quality rating (GWQR) map showing three classes of ratings.
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Groundwater quality assessment result in the study area. (a) Distribution of groundwater quality index (GWQI) and (b) Groundwater quality rating (GWQR) map showing three classes of ratings.

Figure 8
Groundwater quality assessment result in the study area. (a) Distribution of groundwater quality index (GWQI) and (b) Groundwater quality rating (GWQR) map showing three classes of ratings.
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Groundwater quality assessment result in the study area. (a) Distribution of groundwater quality index (GWQI) and (b) Groundwater quality rating (GWQR) map showing three classes of ratings.

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Sensitivity analysis

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Sensitivity analysis was performed on both the DRASTIC and GWQI schemes to examine the impact of each of the input variables on the overall results. The analysis mentioned above was essential, especially in the case of the DRASTIC scheme owing to the bias in giving weightings and ratings to the input parameters (Gogu & Dassargues 2000; Ekanem et al. 2022b). For the DRASTIC model, the single parameter and the map removal techniques were adopted while only the single parameter removal technique was adopted in the case of the GWQI model. The single-variable elimination technique investigates the effect of each input variable on the overall results. In contrast, the map elimination sensitivity analysis examines the effects of eliminating each variable or a set of variables on the overall computed index (Ekanem et al. 2022b).

In the single-variable elimination technique, the effectual weights Weff (in %) of each model input parameter are calculated by employing Equation (11) (Napolitano & Fabbri 1996):
(11)
where Vu is the unperturbed index, Pth is the theoretically assigned weight and Prt is the rating value. Using Equation (12), the variation index VI in percentage was calculated (Napolitano & Fabbri 1996). VI provides a measure of the variation resulting from the elimination of one or more parameters.
(12)

where Vp is the perturbed index after the removal of one or more parameters, respectively.

The outcome of the single-variable elimination sensitivity analysis is shown in Table 10 for the DRASTIC scheme. The values of the variation index of all the factors except soil media, topography and aquifer hydraulic conductivity are greater than 1, which implies that the respective removal of the parameters leads to a decrease in the computed susceptibility index.

Table 10

Results of the single parameter removal sensitivity analysis

Parameters removedMean variation index (%)
D (Depth) 24.7 
R (Recharge 15.4 
A (Aquifer media) 18.5 
S (Soil media) 6.1 
T (Topography) 4.7 
I (Impact of vadose zone) 28.2 
C (Aquifer hydraulic conductivity) 2.3 
Parameters removedMean variation index (%)
D (Depth) 24.7 
R (Recharge 15.4 
A (Aquifer media) 18.5 
S (Soil media) 6.1 
T (Topography) 4.7 
I (Impact of vadose zone) 28.2 
C (Aquifer hydraulic conductivity) 2.3 
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In this instance, the removed parameter has a significant impact on the overall calculated susceptibility index value. Table 10 reveals that the elimination of the vadose zone impact variable causes the maximum average disparity (mean variation of 28.2%) followed by the water table depth parameter (24.7% mean variation). This may be because of the high hypothetical weighting of 5 for these two parameters. The elimination of the aquifer media and net recharge variables also influences the disparity of the computed susceptibility index as Table 10 also reveals. The least variation is caused by the elimination of the aquifer hydraulic conductivity parameter (average variation index of 2.3). This is probably due to the constant rating of 1 and weighting of 3 given to the aquifer hydraulic conductivity variable. The result of the map elimination sensitivity analysis is depicted in Table 11 for the DRASTIC model. The taking away of the parameters was achieved following the order of increasing impact on the overall susceptibility index as summarized in Table 10. The summarized result of the map elimination sensitivity analysis (Table 11) correlates very well with that of Table 10 with the vadose zone, depth, aquifer media and net recharge variables having greater impacts on the final computed index in that order. Soil media, topography and aquifer hydraulic conductivity have a low influence on the overall index in that order. The mean values of the variation index in Table 11 indicate that the susceptibility index increases with the removal of more parameters. This, according to area Khakhar et al. (2017) and Ekanem et al. (2022b), may be due to the hypothetical weights given to the diverse variables, changes in the individual parameters within the study area and poorer depiction of the strata relative to the actual location characteristics. Similar to the findings of Khakhar et al. (2017), the DRASTIC model's usage of fewer parameters will lead to more fluctuations in the susceptibility index's final value. This shows that each DRASTIC parameter is key in the susceptibility index calculation. For the GWQI model, the result of the single-variable elimination sensitivity examination is given in Table 12. The values of the variation index of all the measured parameters are nearly zero except for lead and nickel ions, respectively. For lead, the variation index is −90.1%, which implies that the removal of the concentration of this metal leads to an increase in the final quality index. Conversely, the removal of the measured concentration of nickel ion gives a variation index of 66.4%, which means that the removal of this metal concentration leads to a decrease in the computed GWQI values. Thus, out of all the borehole water sample parameters measured, the single-variable sensitivity analysis result showed that lead and nickel constituted the most important parameters that influence the groundwater standard in the study area. The distribution of the concentrations of these two metals is shown in Figure 9. The nickel and lead ions' concentrations are relatively higher in mostly the south-eastern portion of the study area, spreading towards the south-eastern region.
Table 11

Results of the map removal sensitivity analysis

Parameters usedMean variation index (%)
DRASTI 2.3 
DRASI 6.9 
DRAI 13.1 
DAI 28.5 
D1 47.0 
I 71.7 
Parameters usedMean variation index (%)
DRASTI 2.3 
DRASI 6.9 
DRAI 13.1 
DAI 28.5 
D1 47.0 
I 71.7 
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Table 12

Sensitivity analysis on the GWQI model results

Removed parameterVariation index (%)
Average variation index (%)
BH1BH2BH3BH4BH5BH6BH7BH8BH9BH10BH11BH12
pH −0.16 −0.23 −0.24 −0.14 −0.24 0.55 −0.12 −0.11 −0.21 −0.19 −0.03 −0.03 −0.10 
DO −2.32 −4.33 −3.72 −1.84 −3.12 −4.42 −2.79 −1.65 −2.69 −1.83 −1.25 −1.22 −2.60 
TDS 0.01 0.01 0.02 0.01 0.00 −0.01 0.00 0.00 −0.01 0.00 0.00 0.00 0.00 
COD 0.01 −0.01 0.01 0.01 −0.01 −0.01 −0.01 −0.01 −0.01 0.00 0.00 0.00 0.00 
BOD 0.66 1.60 1.39 0.32 0.72 1.57 0.84 0.35 1.23 0.62 0.83 0.62 0.90 
Cl− 0.01 −0.01 0.02 0.01 −0.01 −0.01 −0.01 −0.01 −0.01 0.00 0.00 0.00 0.00 
 0.01 −0.01 0.02 0.01 −0.01 −0.01 −0.01 −0.01 −0.01 0.00 0.00 0.00 0.00 
 0.01 −0.01 0.02 0.01 −0.01 −0.01 −0.01 −0.01 −0.01 0.00 0.00 0.00 0.00 
 0.01 −0.01 0.02 0.01 −0.01 −0.01 −0.01 −0.01 −0.01 0.00 0.00 0.00 0.00 
Na+ 0.01 0.01 0.02 0.01 −0.01 −0.01 −0.01 −0.01 −0.01 0.00 0.00 0.00 0.00 
K+ −0.03 −0.04 −0.03 −0.03 −0.05 −0.01 −0.05 −0.05 −0.05 −0.05 −0.05 −0.05 −0.04 
Mg2+ 0.05 −0.01 0.01 0.00 −0.01 −0.02 −0.01 −0.01 −0.01 −0.01 −0.01 −0.01 0.00 
Ca2+ 0.01 0.00 0.01 0.00 −0.01 −0.01 −0.01 −0.01 −0.01 0.00 0.00 −0.01 0.00 
Fe2+ −1.63 −1.61 −1.60 −1.06 −1.64 0.88 −0.65 −1.12 0.04 −0.48 −0.82 −0.92 −0.88 
Cu2+ −0.22 −0.21 −0.18 −0.23 −0.23 −0.21 −0.24 −0.24 −0.24 −0.24 −0.24 −0.24 −0.23 
Pb2+ −97.30 −77.41 −93.47 −94.28 −86.22 −76.44 −87.38 −100.00 −86.88 −93.09 −88.69 −99.43 −90.07 
Cd2+ −4.50 −3.97 −4.72 −1.87 −4.87 −5.05 −4.58 −4.93 −4.46 −5.03 1.45 −1.58 −3.68 
Cr2+ −9.50 −5.03 −7.81 0.61 −7.48 −9.24 11.04 11.64 −9.23 −8.50 32.05 46.80 3.78 
Mn2+ −2.66 −2.52 −4.05 −2.44 −4.18 −3.63 −3.52 −2.15 −2.18 −1.21 −0.26 −0.82 −2.47 
Ni2+ 89.60 71.54 88.10 70.81 83.26 74.57 58.40 64.50 79.20 82.30 20.17 14.31 66.40 
Zn2+ −0.09 −0.10 −0.08 −0.09 −0.10 −0.10 −0.10 −0.10 −0.48 −0.10 −0.10 −0.10 −0.13 
Removed parameterVariation index (%)
Average variation index (%)
BH1BH2BH3BH4BH5BH6BH7BH8BH9BH10BH11BH12
pH −0.16 −0.23 −0.24 −0.14 −0.24 0.55 −0.12 −0.11 −0.21 −0.19 −0.03 −0.03 −0.10 
DO −2.32 −4.33 −3.72 −1.84 −3.12 −4.42 −2.79 −1.65 −2.69 −1.83 −1.25 −1.22 −2.60 
TDS 0.01 0.01 0.02 0.01 0.00 −0.01 0.00 0.00 −0.01 0.00 0.00 0.00 0.00 
COD 0.01 −0.01 0.01 0.01 −0.01 −0.01 −0.01 −0.01 −0.01 0.00 0.00 0.00 0.00 
BOD 0.66 1.60 1.39 0.32 0.72 1.57 0.84 0.35 1.23 0.62 0.83 0.62 0.90 
Cl− 0.01 −0.01 0.02 0.01 −0.01 −0.01 −0.01 −0.01 −0.01 0.00 0.00 0.00 0.00 
 0.01 −0.01 0.02 0.01 −0.01 −0.01 −0.01 −0.01 −0.01 0.00 0.00 0.00 0.00 
 0.01 −0.01 0.02 0.01 −0.01 −0.01 −0.01 −0.01 −0.01 0.00 0.00 0.00 0.00 
 0.01 −0.01 0.02 0.01 −0.01 −0.01 −0.01 −0.01 −0.01 0.00 0.00 0.00 0.00 
Na+ 0.01 0.01 0.02 0.01 −0.01 −0.01 −0.01 −0.01 −0.01 0.00 0.00 0.00 0.00 
K+ −0.03 −0.04 −0.03 −0.03 −0.05 −0.01 −0.05 −0.05 −0.05 −0.05 −0.05 −0.05 −0.04 
Mg2+ 0.05 −0.01 0.01 0.00 −0.01 −0.02 −0.01 −0.01 −0.01 −0.01 −0.01 −0.01 0.00 
Ca2+ 0.01 0.00 0.01 0.00 −0.01 −0.01 −0.01 −0.01 −0.01 0.00 0.00 −0.01 0.00 
Fe2+ −1.63 −1.61 −1.60 −1.06 −1.64 0.88 −0.65 −1.12 0.04 −0.48 −0.82 −0.92 −0.88 
Cu2+ −0.22 −0.21 −0.18 −0.23 −0.23 −0.21 −0.24 −0.24 −0.24 −0.24 −0.24 −0.24 −0.23 
Pb2+ −97.30 −77.41 −93.47 −94.28 −86.22 −76.44 −87.38 −100.00 −86.88 −93.09 −88.69 −99.43 −90.07 
Cd2+ −4.50 −3.97 −4.72 −1.87 −4.87 −5.05 −4.58 −4.93 −4.46 −5.03 1.45 −1.58 −3.68 
Cr2+ −9.50 −5.03 −7.81 0.61 −7.48 −9.24 11.04 11.64 −9.23 −8.50 32.05 46.80 3.78 
Mn2+ −2.66 −2.52 −4.05 −2.44 −4.18 −3.63 −3.52 −2.15 −2.18 −1.21 −0.26 −0.82 −2.47 
Ni2+ 89.60 71.54 88.10 70.81 83.26 74.57 58.40 64.50 79.20 82.30 20.17 14.31 66.40 
Zn2+ −0.09 −0.10 −0.08 −0.09 −0.10 −0.10 −0.10 −0.10 −0.48 −0.10 −0.10 −0.10 −0.13 
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Figure 9
Major water quality parameters in the study area. (a) Nickel ion concentration in mg/L and (b) lead ion concentration in mg/L.
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Major water quality parameters in the study area. (a) Nickel ion concentration in mg/L and (b) lead ion concentration in mg/L.

Figure 9
Major water quality parameters in the study area. (a) Nickel ion concentration in mg/L and (b) lead ion concentration in mg/L.
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Major water quality parameters in the study area. (a) Nickel ion concentration in mg/L and (b) lead ion concentration in mg/L.

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Comparison of GSP and quality maps

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The groundwater quality and susceptibility potential maps were placed side by side as shown in Figure 10 for easy comparison. The susceptibility map has two ratings, moderate and high ratings while the groundwater quality map, however, has three ratings: poor, good and excellent ratings, respectively. Most of the communities in the study area belong to the moderate susceptibility potential rating zone except a small number of villages in the north-western, south-western and south-eastern segments of the survey region with a high rating as distributed in Figure 10(a). Most of the communities in the northern segment and the Ifuho community in the south-eastern part belong to the region with excellent groundwater quality while very few of the communities in the south-eastern part of the study area are graded as having bad or poor groundwater standards. From the findings of the VES data interpretation, most of the aquifers in the northern portion of the survey region have some degree of protection by the overlying clay/sandy clay impervious layers (Table 4). In comparison, Ikwen, Usaka and Ubon Ukwa communities belong to the high susceptibility potential rating region (Figure 10(a)) while on the groundwater quality map (Figure 10(b)), these communities fall under the good/excellent groundwater quality rating zone. This disparity is possible as a result of no significant contaminant loading in these communities. These communities are characterized by low concentrations of nickel and lead ions (Figure 9), which are the parameters that have the greatest influence on the GWQI as the single parameter sensitivity analysis reveals (Table 12). The high susceptibility potential rating of these communities may have arisen from the fact that the aquifer overlying layers are made up of unconsolidated permeable sands, which are adjudged to facilitate easy infiltration of surface contaminated fluids to the water table. These findings are consistent with the reports of Rahman (2008) and Phok et al. (2021). The ratings of the remaining communities on the two maps correlate fairly well as illustrated in Figure 10. The poor groundwater rating of the communities in the south-eastern division of the research area despite the moderate rating on the susceptibility potential rating map may be due to the proximity of these communities to the old and abandoned dumpsite (red circle) and new dumpsite (blue circle) in Figure 10(b), which are situated in the ravine portion of the survey region. The distribution of the key factors influencing the GWQI (Nickel and lead ion concentrations) is relatively higher in these communities as shown in Figure 9. Any borehole drilled in these communities thus needs water treatment before consumption.
Figure 10
Comparison of the groundwater susceptibility and quality rating maps. (a) Groundwater susceptibility potential map and (b) groundwater quality rating map.
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Comparison of the groundwater susceptibility and quality rating maps. (a) Groundwater susceptibility potential map and (b) groundwater quality rating map.

Figure 10
Comparison of the groundwater susceptibility and quality rating maps. (a) Groundwater susceptibility potential map and (b) groundwater quality rating map.
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Comparison of the groundwater susceptibility and quality rating maps. (a) Groundwater susceptibility potential map and (b) groundwater quality rating map.

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In this work, GSP and quality have been investigated using integrated hydrogeochemical and geophysical techniques. The research area is established to be made up of three to four strata with the third stratum constituting the economically exploited aquifer. The aquifer depth varies between 10.5 and 101.5 m. These results show consistency with previous reports of George et al. (2014), George (2021), Ekanem et al. (2021, 2022a) and Ikpe et al. (2022). GSP was investigated by the use of the DRASTIC model whose index varies from 111 to 173. This parameter has been used to classify the study area into two classes: moderate (75%) and high (25%) susceptibility potential ratings, respectively. The quality of groundwater was examined through geochemical analysis of borehole water samples in the region. The results show that the physicochemical parameters are below the allowable standards provided by the World Health Organization except for parameters like pH at some locations (boreholes 1, 2, 3, 4, 5, 6, 8, 9 and 10), BOD at nearly all the borehole locations, chromium ions (boreholes 11 and 12) and nickel ions (boreholes 1, 8, 10, 11 and 12). The actual groundwater quality was assessed via the use of the GWQI, which varies from 18.2 to 70.7. Based on these GWQI values, three classes of ratings have been established for the study area: poor (25%), good (50%) and excellent (25%). Most of the exploitable aquifers in the area belong to the region with good to excellent water quality ratings. The findings of the sensitivity analyses of the DRASTIC scheme indicate that the vadose zone, depth of water table, aquifer media and net recharge are the most significant variables affecting the overall DI results in that order. It is demonstrated that the aquifer units' hydraulic conductivity make up the factor with the least influence on the overall DI values. In the same way, the sensitivity analysis of the GWQI results demonstrates that nickel and lead ions are the most consequential variables affecting the standard of groundwater in the study area. Zones of high GSP and poor groundwater quality have been clearly demarcated by the generated GSP and water quality maps, respectively. The implication of these findings is that any boreholes drilled in these demarcated zones may not provide good-quality groundwater, which puts the local population's health and ecological services in serious danger. The maps thus, should be used as guides in selecting areas where water boreholes can be sited in the area. Boreholes should be drilled to at least 10.5 m depending on location as distributed in the depth index map. No borehole should be sited close to the ravine area, where the dumpsites (both abandoned and new) are located.

The GSP and water quality maps seem to correlate well and therefore constitute effective tools that could be employed by the government and other policymakers for efficient planning and exploitation of groundwater in the research region. The local government authorities should put in place appropriate measures to check the indiscriminate dumping of waste in the area. Particularly, an effective waste disposal scheme needs to be instituted in the area, where designated points are provided for dumping of wastes. These wastes should then be collected regularly by officials of the Ministry of Environment and Sanitation or other government agencies and disposed of appropriately at the new dumpsite, where there are no residents. Also, proper channelization of run-off needs to be ensured by the state/local government authorities in the area. This is especially necessary to ensure that all debris and dissolved chemicals/surface contaminated fluids carried by run-off are disposed of into the ravine area, where the dumpsite is situated. Additionally, point-of-use water treatment, proper placement of on-site sanitation systems and routine groundwater quality monitoring are also possible management strategies. Government authorities should put in place a monitoring scheme to check and ensure that no boreholes are drilled near the dumpsites. Even though the results of this work are very promising and valuable in groundwater management and exploitation in the study area, it is recommended that further studies involving microbiological analyses of groundwater samples from water boreholes in the area be carried out to firm up the findings of this study. Such studies can include analysis of parameters such as NO3, PO4, NH4 and E. coli to provide more information about groundwater susceptibility to domestic wastewater pollution as well.

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The authors are thankful to the Tertiary Education Trust Fund (TETFund), Nigeria for providing financial support for this research and permitting the authors to publish the work. The authors are also appreciative of the support from all the GRG members of the Physics Department at Akwa Ibom State University.

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

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The authors declare there is no conflict.

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