https://orcid.org/0000-0003-2793-4244
ABSTRACT
Municipal solid waste (MSW) dumping sites are only used for trash discarding. Beyond the reek that the dumpsites generate, which defiles the milieu, the primary issue is the spreading of fluid from the landfills. The thrust of this research is to utilize the contiguous data from geophysics and laboratory analysis of groundwater samples to decipher the influence of MSW on aquifer systems in Uyo and Eket Counties. Geo-electrical studies featured vertical electrical sounding (VES) and constant electrode separation techniques to generate 1D and 2D electrical resistivity models. The analyzed field data were integrated with water sample analysis to deduce the influence of leachate on groundwater. The VES curve types conformed to A (7.1%), HK (21.4%), Q (21.4%), and K (50.0%). The created 2D image maps for MSW locations lay bare the fact that leachate dispersal between source and sink obeys the inverse square rule. The heavy ions, which were all higher than the approved standards have, chromium as the highest and nickel as the least. The anionic average relative abundance has hydrogen trioxocarbonate ion concentration as the highest and fluoride ions as the least. The survey demonstrates that leachate has a considerable impact on groundwater.
HIGHLIGHTS
The manuscript employs geo-resistivity techniques.
Resistivity decreases with leachate.
The study area shows increasing conductivity with the age of dumpsite.
The study area has high indices of geochemical parameter.
The results show that the source–sink rate of leachate follows the inverse square law.
INTRODUCTION
Diagrams showing sundry waste materials at Uyo MSW site and Eket MSW site where the survey was carried out.
Diagrams showing sundry waste materials at Uyo MSW site and Eket MSW site where the survey was carried out.
Conceptual framework
According to Nabegu (2010) groundwater contamination by leachate-loaded plumes is reliant on transit levels, geology, age of the MSW site, volume of waste, season, and nature of waste. Geo-electrical techniques take advantage of the fact that surface or groundwater containing leachate has low resistance or high conductivity (Inim et al., 2020). As posted by Ogwueleka (2004) and George et al. (2014), the decrease in bulk and water resistivity and pores in geological units containing polluted fluid will have a decreased formation factor, and the image maps created from 1D and 2D bulk resistivity can show the infusion of leachate at the upstream (sink), midstream (transition), and downstream (source). Leachate–water interaction modifies surface and groundwater properties physically and chemically, and this alters water chemistry entirely. A model based on electrical resistivity and hydro-physiochemical study of WSs from compactly populated cities is proposed to guesstimate the dissolution of leachate plumes as well as recommend methods to minimize these resources. Jiménez-Madrid et al. (2010) suggested that this idea can aid in managing and monitoring of water resources around MSW sites.
MSW locations and their precincts’ geology
and 
and latitudes 
and 
, according to the schematic diagram in Figure 2. In addition, the MSW dumpsite in Eket local government areas (LGAs) is located between latitudes 
and 
and latitudes 
and 
. With elevation above mean sea level ranging from 10.0 to 50.0 m (average: 31.5 m), the locations are level and represent the site elevations where they are situated. According to Evans et al. (2010), George (2020), and Etesin et al. (2023), the contiguity of Atlantic Ocean to the study area has influenced the undulating terrain topography at the study sites, reaching up to 200 feet above sea level (Udo & Udofia, 2020). The state is naturally situated on the coast of Nigeria, and parts of it are made up of ravines, wetlands, and mild valleys. 
Schematic diagram showing map of Nigeria and Akwa Ibom State indicating the general geology, open MSW dumpsites in Uyo and Eket, control VES points and boreholes around the dumpsites, as well as the contiguous LGAs.
Schematic diagram showing map of Nigeria and Akwa Ibom State indicating the general geology, open MSW dumpsites in Uyo and Eket, control VES points and boreholes around the dumpsites, as well as the contiguous LGAs.
As posited by Ekanem et al. (2022a), the study area and the adjoining regions have a damp tropical milieu with two distinctive climates: the wet climate (April–October) and the dry climate (November–March). The milieu is calm for the residents, which consist of farmers/agriculturists, craftspeople, traders, and industrialists whose works contribute greatly to the compositions of the waste in the MSW. According to Ibanga & George (2016) and George et al. (2016b), the mapped district receives an average of 66 inches of rainfall every year, and the area investigated is part of the Southern Nigerian Benin Formation (BF), also known as Coastal Plain Sand, which is coupled with coastal brackish water swamps, dehydrated land, freshwater swamps, and beaches. George et al. (2010) and Ekanem et al. (2022b) opined that in the dominant geology of Akwa Ibom State, the BF is, in general, laden with natural deposits of marine, deltaic, estuarine, lagoonal, and fluviolacustrine elements (Udo et al., 2020; George et al., 2024). The BF is replete with fine to medium and medium to coarse/gravelly sands interlaced with slight and irregular argillites. The aquifer is made up of partially closed to open sands with variable ranks of salinity and argillites (George & Thomas, 2023). The site has continental alluvium deposits and sandy flood plains characterized by satisfactory level of permeability and porosity according to Inim et al. (2020). Under the freshwater lens, which comes from about 5 kilometers from the coast, saltwater incursion into sedimentary water reservoirs takes place (Umoh et al., 2022b). The BF with poorly arranged continental (fine-medium to coarse) units and croaky sands sandwiched with lignite streaks, argillites, and lenses to form the hydrogeological units within the area studied. The resulting geologic units and its contiguity form the many-layered aquifer systems as posited by George et al. (2016a & 2017a).
MATERIALS AND EXPERIMENTAL METHODS
Materials
The resources used to achieve this work comprised the global positioning system (GPS) radar for measurement of coordinates, WINRESIT software package for vertical electrical sounding (VES) inversion and RES2INV for 2D inversion, Golden Surfer 12 for 2D image map, bottles for WS, and a terrameter and its accessories for electrical resistivity tomography (ERT) and VES field measurements.
Methods
This research used a broad procedure that started with a detailed analysis of pertinent literature and the generation of dumpsite site maps within and MSW locations. Following the procedures, geophysics-based field measurements and analysis of WSs (in situ and laboratory) were carried out. Pilot surveys, geo-electrical sounding, hydrogeological mapping, and dataset interpretations were among the initiatives undertaken.
Hydrogeological mapping
The geological study was accomplished by creating a base map on a scale of 1:1,000 m from the Akwa Ibom State map via GPS instrument. Geo-referencing of the map was necessary to demarcate the location of the Uyo and Eket MSW dumpsites for hydrogeological mapping. The exercise allowed for the precise placing of VES, ERT, and surface and underground WSs in the survey locations. To aid in the selection of profile orientations, a compass clinometer was deployed to estimate the dip and strike directions. Again, borehole data were employed to investigate information about water tables in the control well (George et al., 2016c).
Geo-electrical sounding
At MSW locations in Uyo and Eket, five VES with Schlumberger electrode configurations and four ERT with Wenner electrode configurations were taken in each location (Figure 2). Moreover, two control VES were done at an average distance of 0.19 km (190 m) from active boreholes in each location (Figure 2). At the same time as the VES with AB/2 = 200 m, current electrode separations were needed to establish the most favorable drilling depths of boreholes inside and outside the MSW locations. Interestingly, to guesstimate the lithologies from top to bottom, the ERT with 105 m current electrode separations was needed to estimate the image of the shallow layers' subsurface conductivity signature, revealed in resistivity (George et al., 2017b).
The field investigation was performed in a southerly direction, in harmony with the regional underground water flow. The field survey involved much processing that gave rise to data quality assurance. The field requirement procedures incorporated maintaining a linear profile, ensuring cross-over distance in measurement and taking mean value readings at each point (George et al., 2022). It was also ensured that a minimum of one-third of the electrodes were driven into the ground before the circuit was completed for reading on the terrameter. After the connection, the current (I) was injected from the source through current electrodes (A and B) and the potential difference 
was measured from the potential electrodes (M and N). AB was regularly extended, and the spacing between potential electrodes (MN) was increased little by little. The resistance apparent 
was read from the terrameter display unit. For every AB/2 and MN/2, the apparent resistivity 
was obtained by multiplying the geometric factor (G) by 
(George et al., 2017b).
Data acquisition
Diagram schematically illustrating the arrangement in geo-electrical resistivity operation.
Diagram schematically illustrating the arrangement in geo-electrical resistivity operation.
in the Schlumberger array was estimated at every measurement spot by carefully varying electrode spacing to obey the criterion of AB ≫ MN in Equation (1) or (2):
and geometric factor Gs were related as in Equations (3) and (4):
where 
is the apparent resistance, 
is the Wenner apparent resistivity, and 
is the geometric factor in the Wenner electrode spacing technique (Obiora et al., 2016):
Data processing
The WINRESIST software package was used to process the VES data obtained. The software program was employed to key in the apparent resistivity estimated from Equation (2) using the AB/2 electrode spacing. The interpretation began by comparing field data with the theoretical data using the iterative least squares technique. The process, which aligned the measured datasets with the theoretical data, was terminated after iterations that ran from 1 to 28 times. The value of the root mean square error (RMSE) was utilized to establish the goodness of fit (Uwa et al., 2019). Udosen & George (2018a, 2018b) posited that better results are realized when the inversion has an RMSE <10%. This approach provided the true resistivity for layers, as well as the thickness and depth of lithologies penetrated by leachate at MSW sites (Table 1). The data gathering approach includes an easy-to-use interpretation technique. The VES approach, which has a wide dynamic range, is comparatively reliable for resolving vertical and horizontal properties in economic aquifer systems (Asfahani et al. 2023), mostly when the interpretation is constrained by ground truthing data. Based on the main thrust of this work, which is to estimate and validate the extent of leachate incursion into the geologic units of the MSW sites and their environs, 2D ERT data according to Equation (5) were gathered and processed using the tetrameter and RES2INV software program, respectively. To concretize the field data results, WSs from MSW sites and their contiguous areas were obtained near the VES stations. In situ measurements were performed on the data to get the results of some environmentally influenced parameters, while some were evaluated at the laboratory for groundwater physiochemical parameters. The VES locations 1–5 and ERT locations 1–4 were, respectively, considered at the Eket and Uyo MSW sites (Figure 1). The VES was coded as UV1–UV5 and ERT as ERT-U1–ERT-U4 at the Uyo MSW site. In the same way, the Eket MSW site VES and ERT data were coded as EV1–EV5 and ERT-E1–ERT-E4, respectively. Five WSs (WS1U–WS5U) were collected and processed at the Uyo MSW site, while two WSs (WS1E and WS2E) were obtained and processed at Eket. A WS from each borehole at each of the two control sites was also analyzed.
Summary of geo-electric results from computer modeling showing coordinates of sounding points, elevations, and primary geo-electrical indices with bold typeface showing shallower and economic aquifers
| VES no. | Longitude (deg.) | Latitude (deg.) | Elevation (m) | No. of layers | Layer resistivity (Ωm) | Layer thickness (m) | Layer depth (m) | Curve type | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ρ1 | ρ2 | ρ3 | ρ4 | h1 | h2 | h3 | D1 | D2 | D3 | ||||||
| EV 1 | 7.933472 | 4.616333 | 12.0 | 3 | 38.6 | 73.1 | 243.8 | – | 2.1 | 80.8 | – | 2.1 | 82.8 | – | A |
| EV 2 | 7.933500 | 4.616389 | 11.0 | 3 | 39.0 | 61.8 | 18.2 | – | 2.1 | 27.4 | – | 2.1 | 27.4 | – | K |
| EV 3 | 7.933528 | 4.616444 | 11.0 | 3 | 72.6 | 173.4 | 2.9 | – | 1.2 | 21.1 | – | 1.2 | 22.3 | – | K |
| EV 4 | 7.937000 | 4.616139 | 10.0 | 3 | 86.8 | 257.8 | 103.2 | – | 4.5 | 66.1 | – | 4.5 | 70.5 | – | K |
| EV 5 | 7.936972 | 4.615889 | 12.0 | 4 | 67.6 | 19.3 | 175.1 | 8.0 | 2.4 | 3.7 | 17.8 | 2.4 | 6.1 | 24.0 | HK |
| EV 6C | 7.936972 | 4.618111 | 10.0 | 3 | 424.3 | 2,029.1 | 767.8 | – | 1.8 | 77.6 | – | 1.8 | 79.4 | – | K |
| EV 7C | 7.936917 | 4.618861 | 11.0 | 3 | 137.4 | 685.4 | 285.1 | – | 4.4 | 76.3 | – | 4.4 | 80.8 | – | K |
| UV 1 | 7.936528 | 5.046611 | 49.0 | 3 | 377.3 | 80.8 | 5.0 | – | 4.1 | 31.4 | – | 4.1 | 35.5 | – | Q |
| UV 2 | 7.936389 | 5.046167 | 49.0 | 4 | 12.4 | 1.5 | 12.4 | 0.6 | 2.7 | 6.4 | 22.0 | 2.7 | 9.1 | 31.2 | HK |
| UV 3 | 7.936361 | 5.046306 | 49.0 | 3 | 20.5 | 5.4 | 2.3 | – | 1.1 | 87.1 | – | 1.1 | 88.1 | – | Q |
| UV 4 | 7.933583 | 5.046306 | 48.0 | 3 | 10.8 | 6.1 | 2.2 | – | 5.0 | 77.1 | – | 5.0 | 82.1 | – | Q |
| UV 5 | 7.936389 | 5.046222 | 49.0 | 4 | 17.2 | 3.2 | 9.4 | 4.7 | 3.3 | 17.9 | 66.7 | 3.3 | 21.2 | 87.9 | HK |
| UV 6C | 7.937278 | 5.046306 | 49.0 | 3 | 254.8 | 1,502.8 | 850.5 | – | 2.4 | 89.4 | – | 2.4 | 91.8 | – | K |
| UV 7C | 7.937417 | 5.046444 | 50.0 | 3 | 135.9 | 743.3 | 219.0 | – | 7.0 | 70.5 | – | 7.0 | 77.6 | – | K |
| Minimum | 10 | 10.8 | 1.5 | 2.2 | 0.6 | 1.1 | 3.7 | 17.8 | 1.1 | 6.1 | 24.0 | ||||
| Maximum | 50 | 424.3 | 2,029.1 | 850.5 | 8.0 | 7.0 | 89.4 | 66.7 | 7.0 | 91.8 | 87.9 | ||||
| Mean | 31.3 | 121.1 | 403.1 | 192.6 | 4.4 | 3.2 | 52.3 | 51.6 | 3.2 | 55.3 | 47.7 | ||||
| VES no. | Longitude (deg.) | Latitude (deg.) | Elevation (m) | No. of layers | Layer resistivity (Ωm) | Layer thickness (m) | Layer depth (m) | Curve type | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ρ1 | ρ2 | ρ3 | ρ4 | h1 | h2 | h3 | D1 | D2 | D3 | ||||||
| EV 1 | 7.933472 | 4.616333 | 12.0 | 3 | 38.6 | 73.1 | 243.8 | – | 2.1 | 80.8 | – | 2.1 | 82.8 | – | A |
| EV 2 | 7.933500 | 4.616389 | 11.0 | 3 | 39.0 | 61.8 | 18.2 | – | 2.1 | 27.4 | – | 2.1 | 27.4 | – | K |
| EV 3 | 7.933528 | 4.616444 | 11.0 | 3 | 72.6 | 173.4 | 2.9 | – | 1.2 | 21.1 | – | 1.2 | 22.3 | – | K |
| EV 4 | 7.937000 | 4.616139 | 10.0 | 3 | 86.8 | 257.8 | 103.2 | – | 4.5 | 66.1 | – | 4.5 | 70.5 | – | K |
| EV 5 | 7.936972 | 4.615889 | 12.0 | 4 | 67.6 | 19.3 | 175.1 | 8.0 | 2.4 | 3.7 | 17.8 | 2.4 | 6.1 | 24.0 | HK |
| EV 6C | 7.936972 | 4.618111 | 10.0 | 3 | 424.3 | 2,029.1 | 767.8 | – | 1.8 | 77.6 | – | 1.8 | 79.4 | – | K |
| EV 7C | 7.936917 | 4.618861 | 11.0 | 3 | 137.4 | 685.4 | 285.1 | – | 4.4 | 76.3 | – | 4.4 | 80.8 | – | K |
| UV 1 | 7.936528 | 5.046611 | 49.0 | 3 | 377.3 | 80.8 | 5.0 | – | 4.1 | 31.4 | – | 4.1 | 35.5 | – | Q |
| UV 2 | 7.936389 | 5.046167 | 49.0 | 4 | 12.4 | 1.5 | 12.4 | 0.6 | 2.7 | 6.4 | 22.0 | 2.7 | 9.1 | 31.2 | HK |
| UV 3 | 7.936361 | 5.046306 | 49.0 | 3 | 20.5 | 5.4 | 2.3 | – | 1.1 | 87.1 | – | 1.1 | 88.1 | – | Q |
| UV 4 | 7.933583 | 5.046306 | 48.0 | 3 | 10.8 | 6.1 | 2.2 | – | 5.0 | 77.1 | – | 5.0 | 82.1 | – | Q |
| UV 5 | 7.936389 | 5.046222 | 49.0 | 4 | 17.2 | 3.2 | 9.4 | 4.7 | 3.3 | 17.9 | 66.7 | 3.3 | 21.2 | 87.9 | HK |
| UV 6C | 7.937278 | 5.046306 | 49.0 | 3 | 254.8 | 1,502.8 | 850.5 | – | 2.4 | 89.4 | – | 2.4 | 91.8 | – | K |
| UV 7C | 7.937417 | 5.046444 | 50.0 | 3 | 135.9 | 743.3 | 219.0 | – | 7.0 | 70.5 | – | 7.0 | 77.6 | – | K |
| Minimum | 10 | 10.8 | 1.5 | 2.2 | 0.6 | 1.1 | 3.7 | 17.8 | 1.1 | 6.1 | 24.0 | ||||
| Maximum | 50 | 424.3 | 2,029.1 | 850.5 | 8.0 | 7.0 | 89.4 | 66.7 | 7.0 | 91.8 | 87.9 | ||||
| Mean | 31.3 | 121.1 | 403.1 | 192.6 | 4.4 | 3.2 | 52.3 | 51.6 | 3.2 | 55.3 | 47.7 | ||||
The physical parameters like pH, electrical conductivity (EC), alkalinity (AK), dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), and total dissolved solids (TDS) of aquifer interstitial water and surface water were estimated via analysis of groundwater samples. Also, hydro-geochemical concentrations for heavy metallic ion cations from Cr, Fe, Cd, Pb, Ni, Mn, and Cu were estimated. These metals have comparatively elevated densities and are poisonous even at lower concentrations in water where they are present. Furthermore, the cations of non-heavy ion species such as NH3, Ca, Mg, Na, and K as well as anions of 
, 
, and 
were all estimated (Table 2).
Statistics for physical and geo-hydro-physiochemical species obtained from WS analysis
| S. no. | Parameters | WS1U | WS2U | WS3U | WS4U | WS5U | WS1E | WS2E | BCU | BCE | Range | Mean | SD | CV (%) | WHO standard | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Fresh | Brackish | Saline | |||||||||||||||
| 1 | pH | 8.1 | 8.3 | 8.7 | 8.0 | 7.9 | 7.8 | 7.7 | 6.4 | 6.1 | 6.1–8.7 | 7.67 | 0.86 | 11.2 | <7.5 | 7.5–8.5 | >8.5 |
| 2 | Temperature (oC) | 30.1 | 30.3 | 30.0 | 30.4 | 30.2 | 29.8 | 29.3 | 29.5 | 28.8 | 28.8–30.4 | 29.7 | 0.54 | 1.8 | <40 | – | – |
| 3 | EC (μS/cm) | 1,400.8 | 2,063.1 | 2,009.1 | 460.5 | 2,100.1 | 1,987.9 | 900.7 | 770.0 | 760.1 | 460.5–2,100.1 | 1,383.6 | 669.2 | 48.4 | <750 | 750–1,500 | >1,500 |
| 4 | TDS (mg/L) | 980.6 | 1,444.2 | 1,406.4 | 322.4 | 1,470.1 | 1,391.5 | 630.5 | 539.0 | 532.1 | 322.4–1,470.1 | 968.5 | 468.4 | 48.4 | <500 | 500–1,000 | >1,000 |
| 5 | AK (mg/L) | 1,378.9 | 154.3 | 160.6 | 136.8 | 129.9 | 144.2 | 145.9 | 159.9 | 104.8 | 104.8–1,378.9 | 279.5 | 412.6 | 147.6 | 20–200 | Varies | Varies |
| 6 | COD (mg/l) | 406.7 | 340.7 | 406.7 | 1,226.1 | 395.7 | 28.9 | 18.6 | 12.1 | 23.7 | 12.1–1,226.1 | 317.7 | 387.4 | 121.9 | <10 | 10–15 | >15 |
| 7 | BOD (mg/l) | 111.1 | 80.3 | 111.1 | 296 | 9.8 | 4.6 | 2.7 | 2.1 | 3.6 | 2.1–296.0 | 100.8 | 77.2 | 76.6 | <2 | 2–5 | >5 |
| 8 | DO (mg/l) | 3.1 | 2.9 | 3.8 | 3.6 | 3.9 | 4.8 | 3.8 | 4.7 | 4.9 | 2.9–4.9 | 3.9 | 0.7 | 17.9 | >5 | – | – |
| 9 |  (mg/l) | 400.3 | 870.7 | 600.3 | 607.9 | 602.1 | 543.30 | 612.0 | 498.2 | 540.9 | 400.3–870.7 | 578.7 | 380.5 | 65.8 | <500 | 500–600 | >600 |
| 10 |  (mg/l) | 136.7 | 140.6 | 191.0 | 121.90 | 187.30 | 170.9 | 156.6 | 160.0 | 177.9 | 121.9–191.0 | 160.3 | 22.4 | 14.0 | <150 | 150–200 | >200 |
| 11 | Cl− (mg/l) | 120.6 | 138.3 | 200.5 | 188.6 | 139.4 | 137.5 | 169.1 | 160.8 | 212.8 | 120.6–212.8 | 123.6 | 33.0 | 26.7 | <200 | 200–250 | >250 |
| 12 | F− (mg/l) | 1.76 | 1.98 | 1.38 | 1.51 | 1.49 | 1.23 | 1.34 | 1.55 | 1.61 | 1.2–2.0 | 1.56 | 0.22 | 14.1 | <1.5 | 1.5–2.5 | >2.5 |
| 13 | K+ (mg/l) | 31.8 | 28.50 | 32.70 | 19.10 | 23.99 | 20.11 | 19.7 | 22.9 | 31.00 | 19.1–32.7 | 25.7 | 5.22 | 20.3 | <30 | 30–55 | >55 |
| 14 | Na+ (mg/l) | 152.9 | 191.5 | 140.3 | 162.8 | 100.6 | 190.8 | 164.9 | 156.0 | 198.77 | 100.6–198.8 | 160.0 | 28.89 | 18.1 | <150 | 150–200 | >200 |
| 15 | Ca2+ (mg/l) | 66.9 | 94.90 | 67.20 | 78.90 | 87.9 | 15.6 | 50.7 | 60.7 | 98.9 | 15.6–98.9 | 67.8 | 24.58 | 36.3 | <100 | 100–200 | >200 |
| 16 | Mg2+ (mg/l) | 40.9 | 38.60 | 45.9 | 32.3 | 37.0 | 34.8 | 67.0 | 78.1 | 51.0 | 32.5–78.1 | 47.3 | 14.74 | 31.2 | <50 | 50–100 | >100 |
| 17 | Fe2+ (mg/l) | 0.22 | 0.31 | 0.49 | 0.38 | 0.55 | 0.26 | 0.23 | 0.45 | 0.61 | 0.2–0.6 | 0.4 | 0.14 | 35.0 | <0.3 | 0.3–0.5 | >0.5 |
| 18 | Mn2+ (mg/l) | 0.26 | 0.65 | 0.11 | 4.12 | 0.39 | 0.01 | 0.32 | 0.400 | 0.54 | 0.01–4.12 | 0.7 | 1.21 | 172.9 | < 0.05 | 0.05–0.10 | >0.1 |
| 19 | Cu2+ (mg/l) | 0.10 | 0.20 | 0.32 | 0.15 | 0.14 | 0.30 | 0.80 | 1.21 | 2.00 | 0.1–2.0 | 0.7 | 0.72 | 102.9 | <0.05 | 0.05–2.00 | >2.00 |
| 20 | Pb2+ (mg/l) | 0.16 | 0.22 | 0.19 | 0.24 | 0.21 | 0.25 | 0.26 | 0.46 | 0.58 | 0.2–0.6 | 0.30 | 0.14 | 46.7 | <0.01 | 0.01–0.05 | >0.05 |
| 21 | Cd2+ (mg/l) | 0.004 | 0.005 | 0.007 | 0.006 | 0.14 | 0.005 | 0.006 | 0.005 | 0.130 | 0.004–0.162 | 0.05 | 0.07 | 140.0 | <0.003 | – | – |
| 22 | Cr2+ (mg/l) | 1.300 | 2.700 | 2.005 | 1.005 | 2.110 | 1.600 | 2.650 | 1.110 | 1.145 | 1.0–2.7 | 1.74 | 0.62 | 35.6 | <0.003 | – | – |
| 23 | Ni2+ (mg/l) | 0.02 | 0.01 | 0.02 | 0.01 | 0.02 | 0.01 | 0.02 | 0.01 | 0.03 | 0.01–0.03 | 0.02 | 0.01 | 50.0 | <0.02 | – | – |
| 24 |  (mg/l) | 2,279.1 | 2,410.7 | 800.9 | 887.4 | 610.7 | 480.8 | 560.4 | 500.0 | 510.8 | 480.8–2,410.7 | 1,066.3 | 802.5 | 75.3 | 0.25–32.5 | – | – |
| S. no. | Parameters | WS1U | WS2U | WS3U | WS4U | WS5U | WS1E | WS2E | BCU | BCE | Range | Mean | SD | CV (%) | WHO standard | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Fresh | Brackish | Saline | |||||||||||||||
| 1 | pH | 8.1 | 8.3 | 8.7 | 8.0 | 7.9 | 7.8 | 7.7 | 6.4 | 6.1 | 6.1–8.7 | 7.67 | 0.86 | 11.2 | <7.5 | 7.5–8.5 | >8.5 |
| 2 | Temperature (oC) | 30.1 | 30.3 | 30.0 | 30.4 | 30.2 | 29.8 | 29.3 | 29.5 | 28.8 | 28.8–30.4 | 29.7 | 0.54 | 1.8 | <40 | – | – |
| 3 | EC (μS/cm) | 1,400.8 | 2,063.1 | 2,009.1 | 460.5 | 2,100.1 | 1,987.9 | 900.7 | 770.0 | 760.1 | 460.5–2,100.1 | 1,383.6 | 669.2 | 48.4 | <750 | 750–1,500 | >1,500 |
| 4 | TDS (mg/L) | 980.6 | 1,444.2 | 1,406.4 | 322.4 | 1,470.1 | 1,391.5 | 630.5 | 539.0 | 532.1 | 322.4–1,470.1 | 968.5 | 468.4 | 48.4 | <500 | 500–1,000 | >1,000 |
| 5 | AK (mg/L) | 1,378.9 | 154.3 | 160.6 | 136.8 | 129.9 | 144.2 | 145.9 | 159.9 | 104.8 | 104.8–1,378.9 | 279.5 | 412.6 | 147.6 | 20–200 | Varies | Varies |
| 6 | COD (mg/l) | 406.7 | 340.7 | 406.7 | 1,226.1 | 395.7 | 28.9 | 18.6 | 12.1 | 23.7 | 12.1–1,226.1 | 317.7 | 387.4 | 121.9 | <10 | 10–15 | >15 |
| 7 | BOD (mg/l) | 111.1 | 80.3 | 111.1 | 296 | 9.8 | 4.6 | 2.7 | 2.1 | 3.6 | 2.1–296.0 | 100.8 | 77.2 | 76.6 | <2 | 2–5 | >5 |
| 8 | DO (mg/l) | 3.1 | 2.9 | 3.8 | 3.6 | 3.9 | 4.8 | 3.8 | 4.7 | 4.9 | 2.9–4.9 | 3.9 | 0.7 | 17.9 | >5 | – | – |
| 9 | (mg/l) | 400.3 | 870.7 | 600.3 | 607.9 | 602.1 | 543.30 | 612.0 | 498.2 | 540.9 | 400.3–870.7 | 578.7 | 380.5 | 65.8 | <500 | 500–600 | >600 |
| 10 | (mg/l) | 136.7 | 140.6 | 191.0 | 121.90 | 187.30 | 170.9 | 156.6 | 160.0 | 177.9 | 121.9–191.0 | 160.3 | 22.4 | 14.0 | <150 | 150–200 | >200 |
| 11 | Cl− (mg/l) | 120.6 | 138.3 | 200.5 | 188.6 | 139.4 | 137.5 | 169.1 | 160.8 | 212.8 | 120.6–212.8 | 123.6 | 33.0 | 26.7 | <200 | 200–250 | >250 |
| 12 | F− (mg/l) | 1.76 | 1.98 | 1.38 | 1.51 | 1.49 | 1.23 | 1.34 | 1.55 | 1.61 | 1.2–2.0 | 1.56 | 0.22 | 14.1 | <1.5 | 1.5–2.5 | >2.5 |
| 13 | K+ (mg/l) | 31.8 | 28.50 | 32.70 | 19.10 | 23.99 | 20.11 | 19.7 | 22.9 | 31.00 | 19.1–32.7 | 25.7 | 5.22 | 20.3 | <30 | 30–55 | >55 |
| 14 | Na+ (mg/l) | 152.9 | 191.5 | 140.3 | 162.8 | 100.6 | 190.8 | 164.9 | 156.0 | 198.77 | 100.6–198.8 | 160.0 | 28.89 | 18.1 | <150 | 150–200 | >200 |
| 15 | Ca2+ (mg/l) | 66.9 | 94.90 | 67.20 | 78.90 | 87.9 | 15.6 | 50.7 | 60.7 | 98.9 | 15.6–98.9 | 67.8 | 24.58 | 36.3 | <100 | 100–200 | >200 |
| 16 | Mg2+ (mg/l) | 40.9 | 38.60 | 45.9 | 32.3 | 37.0 | 34.8 | 67.0 | 78.1 | 51.0 | 32.5–78.1 | 47.3 | 14.74 | 31.2 | <50 | 50–100 | >100 |
| 17 | Fe2+ (mg/l) | 0.22 | 0.31 | 0.49 | 0.38 | 0.55 | 0.26 | 0.23 | 0.45 | 0.61 | 0.2–0.6 | 0.4 | 0.14 | 35.0 | <0.3 | 0.3–0.5 | >0.5 |
| 18 | Mn2+ (mg/l) | 0.26 | 0.65 | 0.11 | 4.12 | 0.39 | 0.01 | 0.32 | 0.400 | 0.54 | 0.01–4.12 | 0.7 | 1.21 | 172.9 | < 0.05 | 0.05–0.10 | >0.1 |
| 19 | Cu2+ (mg/l) | 0.10 | 0.20 | 0.32 | 0.15 | 0.14 | 0.30 | 0.80 | 1.21 | 2.00 | 0.1–2.0 | 0.7 | 0.72 | 102.9 | <0.05 | 0.05–2.00 | >2.00 |
| 20 | Pb2+ (mg/l) | 0.16 | 0.22 | 0.19 | 0.24 | 0.21 | 0.25 | 0.26 | 0.46 | 0.58 | 0.2–0.6 | 0.30 | 0.14 | 46.7 | <0.01 | 0.01–0.05 | >0.05 |
| 21 | Cd2+ (mg/l) | 0.004 | 0.005 | 0.007 | 0.006 | 0.14 | 0.005 | 0.006 | 0.005 | 0.130 | 0.004–0.162 | 0.05 | 0.07 | 140.0 | <0.003 | – | – |
| 22 | Cr2+ (mg/l) | 1.300 | 2.700 | 2.005 | 1.005 | 2.110 | 1.600 | 2.650 | 1.110 | 1.145 | 1.0–2.7 | 1.74 | 0.62 | 35.6 | <0.003 | – | – |
| 23 | Ni2+ (mg/l) | 0.02 | 0.01 | 0.02 | 0.01 | 0.02 | 0.01 | 0.02 | 0.01 | 0.03 | 0.01–0.03 | 0.02 | 0.01 | 50.0 | <0.02 | – | – |
| 24 | (mg/l) | 2,279.1 | 2,410.7 | 800.9 | 887.4 | 610.7 | 480.8 | 560.4 | 500.0 | 510.8 | 480.8–2,410.7 | 1,066.3 | 802.5 | 75.3 | 0.25–32.5 | – | – |
Physiochemical analytical techniques of groundwater samples
To determine the physiochemical parameters of aquifer and surface WSs from the earmarked MSW locations, five leachate-infested surface WSs were collected and labeled as WS1U–WS5U at the Uyo dumpsite, and two surface WSs were obtained and coded as WS1E and WS2E the Eket MSW site. The said samples of water were collected using 1,000-ml pre-acid-washed polyethylene bottles from the sites. They were all transported and analyzed in the analytical laboratory. Total AK, which is the capacity of water to resist changes in pH, was predetermined from each of the samples. Similarly, temperature was estimated with a Fisher Scientific mercury-filled waterproof thermometer calibrated from 0 to 100 °C. The COD and BOD were also determined prior to transportation to the laboratory. Notably, the accuracy of EC measured was up to 0.001 S/cm. The COD, which reflects the quantity of oxygen that is allowed to be taken per unit volume of the samples, was estimated spectrophotometrically in mg/l. BOD levels were found to be elevated as DO levels declined owing to the consumption of the existing oxygen in the WS by bacteria. This showed the quantity of DO needed by aerobic microbes during organic matter decomposition in the analyzed leachate water. The WSs’ BOD was evaluated using standard procedures governing the operation of the BOD bottle. To guarantee that no air bubbles were present in the WSs, the bottles were completely filled with leachate-contaminated water and the plastic caps on the bottles were covered to prevent exogenic activities such as vaporization. The appropriate protocols were followed to guarantee that there was no agitation during sample transportation to the laboratory. This common approach was followed by Rana et al. (2018) by allowing the samples to remain below the temperatures 4 °C prior to laboratory testing. The hydro-physiochemical features were evaluated in line with the American Public Health Association criteria (APHA, 2005). The measurements of major cations such as 
, 
, 
, 
, and 
were achieved through the use of an atomic absorption spectrophotometer (AAS) deploying inductively coupled plasma mass spectrometry using TECHCOMP (Version: AA6000). Also, 
, 
, 
, and 
were estimated via the titrimetric or colorimetric technique in the laboratory. All ion species in Table 2 were estimated in mg/l apart from pH and EC (S/cm). Table 2 shows the minimum, maximum, mean, and standard deviation (SD) and their accompanying coefficients of variation of the analyzed physiochemical species and statistical systematization. These seven heavy metallic ions 
were digested using the routine method aided by the pretreated samples using AASs described by Gregg (1989) and United Nations (2000).
Groundwater pollution index
To determine the spread of leachate from MSW sites, the groundwater pollution index (GPI) was calculated using the mean values of a control borehole located approximately 161 m from the dumpsite at Uyo (BCU) and the other located approximately 215 m from the Eket MSW dumpsite. As a result, the GPI assessment was performed in accordance with Subba Rao (2012) to determine the relative contributions of various physical and chemical facies. The determined species were pH, ELC, TDS, 
, 
, 
, 
, 
, 
, 
, 
, and 
. Some of these species were utilized to determine the overall quality of groundwater wells used for drinking within the MSW enclave. The GPI was estimated using the approach of Subba Rao (2012), which consisted of the following steps:
Step 1: Each species' quality was assigned a relative weight (RW) ranging from 1 to 5, due to how much impact from it is felt on the general quality of the drinking water. As posited by Kamaraj et al. (2021), the highest RW value of ‘5’ was assigned to 
, 
, and 
, which have the most outstanding impact and the lowest RW value of ‘1’ was assigned to factors 
and 
, which have the least impacts. Again, 
, pH, and TDS were assigned an RW of ‘4’ as while ‘2’ was assigned for 
, 
, and 
(Table 3).
RW, WPs, and DWQSs and estimated GIP and its pollution level
| Physiochemical species | Unit | RW | WP | DWQS | Sum of GPI | Pollution level |
|---|---|---|---|---|---|---|
| pH | – | 4 | 0.114 | 7.5 | 0.19 | Insignificant |
| TDS | mg/l | 4 | 0.114 | 500 | 0.24 | Insignificant |
 | mg/l | 2 | 0.057 | 75.0 | 0.12 | Insignificant |
 | mg/l | 2 | 0.057 | 30.0 | 0.25 | Insignificant |
 | mg/l | 4 | 0.114 | 200.0 | 0.20 | Insignificant |
 | mg/l | 1 | 0.029 | 12.0 | 0.13 | Insignificant |
 | mg/l | 1 | 0.029 | 300.0 | 0.10 | Insignificant |
 | mg/l | 5 | 0.143 | 250.0 | 0.21 | Insignificant |
 | mg/l | 5 | 0.143 | 200.0 | 0.24 | Insignificant |
 | mg/l | 5 | 0.143 | 15.0 | 0.03 | Insignificant |
 | mg/l | 2 | 0.057 | 1.5 | 38.10 | Very high |
| Total | – | 35 | 1.000 | – | – | – |
| Physiochemical species | Unit | RW | WP | DWQS | Sum of GPI | Pollution level |
|---|---|---|---|---|---|---|
| pH | – | 4 | 0.114 | 7.5 | 0.19 | Insignificant |
| TDS | mg/l | 4 | 0.114 | 500 | 0.24 | Insignificant |
| | mg/l | 2 | 0.057 | 75.0 | 0.12 | Insignificant |
| | mg/l | 2 | 0.057 | 30.0 | 0.25 | Insignificant |
| | mg/l | 4 | 0.114 | 200.0 | 0.20 | Insignificant |
| | mg/l | 1 | 0.029 | 12.0 | 0.13 | Insignificant |
| | mg/l | 1 | 0.029 | 300.0 | 0.10 | Insignificant |
| | mg/l | 5 | 0.143 | 250.0 | 0.21 | Insignificant |
| | mg/l | 5 | 0.143 | 200.0 | 0.24 | Insignificant |
| | mg/l | 5 | 0.143 | 15.0 | 0.03 | Insignificant |
| | mg/l | 2 | 0.057 | 1.5 | 38.10 | Very high |
| Total | – | 35 | 1.000 | – | – | – |
Step 2: The weight parameter (WP) was estimated.
to the totality of all RW, was computed via Equation (6):
was gauged by dividing the variable of chemical concentrations 
for each WS by the associated drinking water quality standards 
via Equation (7):
According to the propositions of Subba Rao et al. (2018) and Kamaraj et al. (2021), GPI is grouped into five levels of pollution: very high pollution level (GPI > 2.5), high pollution level (GPI: 2.0–2.5), moderate pollution level (GPI: 1.5–2.0); low pollution level (GPI: 1.0–1.5), and insignificant pollution level (GPI < 1.0).
Indices of soil water irrigation via sodium adsorption ratio and total hardness
These indices were utilized to agronomically characterize the MSW sites. This investigation was required to assess the influence of MSW on the dumpsites.
RESULTS AND DISCUSSION OF THE RESEARCH
Geo-electrical results
at EV1 and 50.0% of K 
at EV2, EV3, EV4, EVC6, EVC7, UV6C, and UV7C. Other sorts of curves discernible in the research locations were 21.4% of HK 
at EV5, UV2, and UV5 and 21.4% of Q 
at UV1, UV3, and UV4. 
Correlations of some interpreted VES curves, ERT sections, and nearby lithology logs: (a) Uyo VES 3 (UV1) near Uyo VES 3 (UV3) and nearby ERT at Uyo (ERT-Uyo A); (b) BH7 (VES5 versus ERT-B); (c) BH5 (VES8 versus ERT-C) and (d) BH8 (VES 9 versus ERT-E).
Correlations of some interpreted VES curves, ERT sections, and nearby lithology logs: (a) Uyo VES 3 (UV1) near Uyo VES 3 (UV3) and nearby ERT at Uyo (ERT-Uyo A); (b) BH7 (VES5 versus ERT-B); (c) BH5 (VES8 versus ERT-C) and (d) BH8 (VES 9 versus ERT-E).
Resistivity curve distribution frequency showing the curve types that dominate the study areas.
Resistivity curve distribution frequency showing the curve types that dominate the study areas.
(a) Image map of the supposed economic aquifer showing the drastically reduced aquifer in the Uyo MSW site. (b) Image map of the supposed economic aquifer showing the drastically reduced aquifer in the Eket MSW site.
(a) Image map of the supposed economic aquifer showing the drastically reduced aquifer in the Uyo MSW site. (b) Image map of the supposed economic aquifer showing the drastically reduced aquifer in the Eket MSW site.
Physiochemical results
To investigate geochemically coupled taints in underground water within MSW locations, light cations, anions, and heavy metallic ions and other species were considered in MSW leachate-contaminated fluid and the surrounding boreholes. The physical parameters examined in the WSs analytically assessed, as given in Table 2, were pH, with a gamut of 6.1–8.7 and an average of 7.67 (SD = 0.86); temperature, with a gamut of 28.8–30.4 °C and an average of 29.7 °C (SD = 0.54 °C); EC with a mean of 460.5–2,100.1 μS cm−1 and an average of 1,383.6 μS cm−1 (SD = 669.2 μS cm−1); TDS with a gamut of 322.4–1,470.1 mg/l and an average index of 968.5 mg/l (SD = 468.4 mg/l); and AK with a gamut of 104.8–1,378.9 mg/l and a mean of 279.5 (SD = 412.6 mg/l). On the whole, pH (coefficient of variation (CV): 11.2%), temperature (CV: 1.8%), and EC (CV: 48.4%) are transitorily variable, whereas AK (CV: 147.6%) reveals the highest variability in the WSs. Furthermore, TDS (CV: 48.4%) has a lower average variability than AK. With the exception of DO, K, Ca, Mg ions, and temperature in Table 2, all freshwater pH, EC, TDS, and AK levels exceeded WHO standards (WHO 2017). According to UNICEF (2008), colder temperatures affect MSW by suppressing the growth of some bacteria (degraders), allowing waste to remain undigested for longer periods of time. Because of the varied character of MSW in the sites investigated, the predicted pH value range and average imply that the soil and subsoil fluids (leachate) are not circumneutral but transit between acidic medium and basic medium (Thomas et al., 2020). The leachate that seeped into the surrounding surface and groundwater resources is responsible for the extraordinarily high EC values recorded in the MSW sites evaluated. This indicates that TDS in WSs are elevated (Inim et al., 2020). The high EC and TDS results most likely point to the fast solute breakdown of MSW in surface water and succeeding migration of the associated leachate to adjoining groundwater resources. According to Ekström et al. (2016), high EC and TDS values indicate delayed ion exchange involving water and geogenic minerals or the availability of fast soluble geologic rocks and minerals. The considered COD, with a high range of 12.0–1,226.1 mg/l and a mean of 317.7 mg/l, is maximally variable due to a CV of 121.9%. Moreover, the BOD, with a range of 2.1–296.0 mg/l and an average of 100.8 mg/l, is thought to be somewhat greatly capricious, as indicated by the calculated CV (76.6%). The COD–BOD ratio is a measure of the amount of organic and inorganic materials present. However, the middling estimated BOD–COD ratio in the MSW sites under research is 0.32, signifying that the organic treatment process will be slightly delayed due to the longer time required for microorganisms to acclimate to degrading activities (Chen et al., 2014). The ratio also indicates that the wastewater is not completely biodegradable because the BOD–COD ratio does not fall between 0.92 and 1.00. However, the revelation from BOD–COD ratio opines that wastewater is biologically treatable by adding seeding (microorganism sludge) to accelerate the breakdown process according to Chen et al. (2014). The DO, which ranged from 2.9 to 4.9 mg/l with an average of 3.9 mg/l, and a CV (17.9%), indicates that DO variability is not significantly different across the two dumpsites. According to Thomas et al. (2020) and George et al. (2023), the MSW sites are more anoxic (condition of insufficient oxygen) than oxic (condition of sufficient oxygen) (DO > 5 mg/l) with an average BOD of 5 mg/l and the highest elevated value not exceeding 5 mg/l. As opined by Wang et al. (2009), the average value of DO predictable in the two sites signifies that DO is used up by waste-related microbes in water, even up to the control boreholes in the Uyo and Eket sites (BCU: 4.7 mg/l and BCE: 4.9 mg/l, correspondingly). The hydro-geochemistry of surface and groundwater analyses in Table 2 puts forward a series of mean abundances of light ion species (cations) in the order 
; heavy metals as Cr2+ > Mn2+ = Cu2+ > Fe2+ > Pb2+ > Cd2+ > Ni2+. The anionic mean of comparative abundance follows the order: 
. The relative middling abundances of the anions show that, with the exception of the chloride ion, they all have elevated values, exceeding the WHO (2014) standard. As anticipated in MSW sites, the flow of leachate shown in Figure 6(a) and 6(b) has defiled the use of groundwater for drinking because there is connectivity between the waste source (the dumpsite) and the sink (nearby groundwater sources). Again, the determined light cations were within the WHO safe range, with the exception of sodium and ammonium ions, which seemed to be significantly higher (Table 2). The heavy metals usually showed higher density than water, and in this work, their ions are found in quantities greater than the WHO criteria for safe water (Table 2). The considerably elevated average indices of all heavy metals in surface and groundwater resources at the MSW sites and environs suggested health risks associated with the use of groundwater for agriculture and drinking according to WHO (2014) values. This is owing to the physiological health consequences associated with even the water sources with minor increases over WHO guidelines. The increase in resulting hydro-geochemical ion concentrations could be attributable to geological or human action (leachates) (Ibuot et al. 2017). Fascinatingly, causes from geogenic sources are unalterable. However, causes from anthropogenic sources are generated by the dumping of inorganic and organic substances, which may be the source of heavy metals diffused in leachate into surface and adjoining groundwater resources (Etesin et al., 2023).
Correlation
and negatively strong 
indices of correlation exist between two hydro-physiochemical facies. Positively strong binary correlations, for example, have been discovered between the following species: Tem and pH, TDS and EC, COD and Tem, BOD and COD, 
and 
, 
versus COD and DO, 
versus 
and DO, 
and DO, 
and 
, 
and 
,
and 
as well as 
and 
. The derived data also revealed strongly negative correlations in DO against pH across the research area. Tem, 
versus pH and Tem, 
versus pH and Tem, as well as 
and DO equally gave strong negative binary correlations. Figure 7, in addition, presents pairs of hydro-physiochemical species that show negative and positive moderate binary correlations of 
and 
, correspondingly. Moderate correlations are shown in green, whereas positively and negatively weak correlations, respectively, are indicated by the correlation indices of 
and 
, provided in light green (Figure 7). Concisely, the reasonable correlations are seen in the couples of pH versus EC and TDS, DO and Tem, 
and pH, Tem, and COD, 
and 
, 
versus EC and TDS, 
versus AK, 
, etc. The weak correlations according to the results are well-known in the pairs of COD and pH, 
and AK, 
versus Tem, EC, TDS and AK, 
versus EC and TDS, 
versus pH and DO as well as 
versus pH, Tem, 
, 
, 
, 
, and 
, etc. The significance of these links in this work is to quickly deduce the relation one hydro-physiochemical species with other. The positive and negative connections reveal information about the species' nature/origin, chemical and physical compositions, as well as potential standalone and combinational impacts on leachate and the groundwater to which the resulting plume is dissolving in (Kothari et al., 2021). A number of the binary correlations of Figure 7 have value coefficients of less than 0.5, demonstrating that their dissolution in the surface or groundwater under study is insignificant. Positive (direct), negative (inverse), infinitesimal, weak, moderate, and strong binary correlation values are all realistically prominent and should be considered when treating surface and groundwater resources for consumption purposes. Figure 7 elaborates on the utility of this work by bringing to notice the binary interplay between leachate-loaded suspended and dissolved hydro-physiochemical facies in surface and groundwater resources within MSW sites, which gives details regarding the quality of water. 
Pearson's correlation coefficients revealing the interrelationship of mean hydro-physiochemical properties in the MSW sites studied.
Pearson's correlation coefficients revealing the interrelationship of mean hydro-physiochemical properties in the MSW sites studied.
Groundwater pollution index
The various hydro-physiochemical features and concentrations were determined, and the six approaches proposed by Subba Rao (2012) in the procedure to discover GPI were utilized to give and guesstimate the various compositional indicators listed in Equations (6)–(9). Table 3 displays the assigned and calculated values for 11 hydro-physiochemical facies from 9 WSs considered for GPI analysis. The GPI values for each sample were calculated using the OGQ from Equation (9). The result obtained ranged from 0.03 to 38.10 and with an average value of 3.62 (Table 3). According to Subba Rao et al. (2018) and Kamaraj et al. (2021), the estimated range in Table 4 spans from an insignificant GPI level upstream (sink) to a very high GPI level downstream (source). The very high GPI is only attributed to ammonium ion concentration throughout the MSW sites evaluated. This research emphasizes the significance of monitoring and educating the government, nongovernmental organizations, and individuals on the dangers of environmental hazards associated with open MSW sites in Akwa Ibom State, southern Nigeria. Leachate drains horizontally and vertically within the subsurface from downstream (dumpsite locations) to upstream (dumpsite contiguous zones), unleashing major threats to presumably potable water supplies, as indicated in Figure 4 and image maps of aquifer units (Figure 6(a) and 6(b)). This revelation provokes the need for a wake-up call to environmentalists to step up their efforts to prevent new boreholes from being dug within the considered dumpsite-adjoining radii, as the water in the wells can be carcinogenic (Saxena et al. 2004).
The indices of SAR and TH in the nine WSs across the MSW sites analyzed
| Water code | SAR | TH (mEq/l) |
|---|---|---|
| WS1U | 3.6 | 337.6 |
| WS2U | 3.3 | 398.1 |
| WS3U | 3.5 | 359.3 |
| WS4U | 3.6 | 331.8 |
| WS5U | 3.4 | 373.9 |
| WS1E | 4.9 | 184.0 |
| WS2E | 3.3 | 405.9 |
| BCU | 2.6 | 646.9 |
| BCE | 2.2 | 909.0 |
| Min | 2.2 | 184.0 |
| Max | 4.9 | 909.0 |
| Mean | 3.4 | 438.5 |
| STD | 0.7 | 213.4 |
| CV (%) | 20.6 | 48.7 |
| Water code | SAR | TH (mEq/l) |
|---|---|---|
| WS1U | 3.6 | 337.6 |
| WS2U | 3.3 | 398.1 |
| WS3U | 3.5 | 359.3 |
| WS4U | 3.6 | 331.8 |
| WS5U | 3.4 | 373.9 |
| WS1E | 4.9 | 184.0 |
| WS2E | 3.3 | 405.9 |
| BCU | 2.6 | 646.9 |
| BCE | 2.2 | 909.0 |
| Min | 2.2 | 184.0 |
| Max | 4.9 | 909.0 |
| Mean | 3.4 | 438.5 |
| STD | 0.7 | 213.4 |
| CV (%) | 20.6 | 48.7 |
IRRIGATION EVALUATION INDICES
Hydro-physical and hydro-chemical-based classification indices
With reference to Table 4, the average TDS of surface and underground water resources is 968.5 mg/l. The estimated value is less than the requisite saline water (TDS > 1,000 mg/l) for irrigation (Kothari et al., 2021). Water samples from the two MSW sites were compared and only WS1E with 322.4 mg/l (11.0%) showed freshwater (TDS < 500) (see Table 4).
Surprisingly, the water sources at WS1U, WS2E, BCU, and BCE reflect TDS values from 500 to 1,000 mg/l showing that 44.5% is brackish. Surface WSs from Uyo and Eket at WS2U, WS3U, WS5U, and WS1E representing 44.5% of the samples analyzed were all saline (TDS > 1,000 mg/l). The diverse character of the biodegradable and nonbiodegradable wastes discharged at the locations is linked to the value of TDS in the MSW-loaded leachate (Ibuot et al., 2017). Since irrigation water must be brackish or saline to maximize agricultural output, the MSW locations evaluated are thought to have WSs that are convincingly suitable for irrigation in terms of TDS (Freeze & Cherrey, 1979). According to Saxena et al. (2004), the WSs were examined for irrigation use in terms of EC. As a result, the WS is satisfactory at WS4U (250–750 μs/cm), indicating 11.1%. Water samples from WS1U, WS1E, WS2E, BCU, and BCE comprise 55.6%, while WSs from WS2U, WS3U, and WS5U represent 33.3% (Table 2).
This finding shows that numerous experiments must be conducted before irrigation water is settled to establish its efficacy (Hwang et al., 2017). The observed increase in EC values can be attributed to ion exchange reactions and evaporation, and the elevated salinity risk in the majority of the WSs in Table 4 may have an impact on crop fertility and yield (Toth, 1999). According to the estimated EC, the WS is satisfactory at WS4U (250–750 μs/cm), representing 11.1% of the samples analyzed. WSs from WS1U, WS1E, WS2E, BCU, and BCE comprising 55.6% are regarded permissible with 750–2,000 μS/cm, while WSs from WS2U, WS3U, and WS5U represent 33.3% (Table 2) and are doubtful due to EC of 2,000–3,000 μS/cm.
Total hardness
The TH of the WSs calculated using Equation (11) ranged from 184.0 to 909.0 mg/l, with an average of 438.5 mg/l. TH had a CV of 48.7% and an SD of 213.4 (Table 3). The CV value suggests that the WSs examined were variable. According to Sawyer and McCarthy (1967), all of the WSs tested for hardness in Equation (11) had extremely high hardness (>300 mg/l), with the exclusion of WS1E, which had 184 mg/l. The kind of wastes disposed of at MSW sites (George et al., 2014) and the hydrogeological units that absorb the surface leachate-loaded flow are thought to have a high degree of TH observed in MSW sites and their neighboring groundwater (Kamaraj et al., 2021). The aquifer's geology consists of reasonably fine and medium-grained sands suspected of holding dissolved Mg and Ca ions, which explain the observable and astronomical increase in hardness in nearly all of the WSs analyzed. According to Ibanga & George (2016), the presence of Ca, Mg, and Fe (Table 2) in water might accelerate the formation of rust and its accumulation in metallic and plastic general-purpose tanks. As a result, Rawat et al. (2018) stated that plants have a harder time absorbing and breaking down hard water than soft water because hard water combines with soil nutrients, making it difficult for plants to absorb the water they need. Due to the increased surface and groundwater hardness at MSW sites, crop planters/growers may need to raise their irrigation rate to mitigate the negative effects of deploying surface and groundwater near MSW sites for irrigation purposes.
Sodium adsorption ratio
The SAR values in the hydrogeological samples from the MSW sites and their bordering radial zones were 2.2 and 4.9 at BCE and WS1E, respectively, according to Equation (10). The average of these results was 3.4, with an SD of 0.7 and a CV of 20.6%. The CV represents the degree of variability among the sites examined. Richards (1954) and Kamaraj et al. (2021) found that MSW sites had a low SAR (<26), indicating that the WSs tested were suitable for irrigation. With low SAR values suggesting low sodium content in surface and groundwater, there is an urgent need to lower soil AK for certain crops while increasing soil structure and texture to optimize sodium content exchange with other cations (Thomas et al., 2020). According to Biswas et al. (2002), this approach can promote the circulation of water and air into the soil, hence influencing crop growth.
CONCLUSION
To assess the impact of leachate released by open dumpsites on surface water and underground water within the dumpsites, as well as nearby groundwater resources, a significant assessment of MSW sites was carried out, which included the interpretation of geo-electrical interpretations constrained by borehole data and hydro-physiochemical analyses. According to geo-electrical data, municipal dumpsites in Uyo and Eket have an impact on surface water, underground water, and nearby groundwater resources, as demonstrated by the elevated EC values or decreased resistivity values. Because of the dissolved and suspended elements in leachate, the inferred economic and prospective aquifers between 20 and 90 m have been determined to be primarily saline and brackish. The open dumpsites have defiled the nearby boreholes used as control within the Eket and Uyo MSW sites, which are approximately 161 and 215 km apart, respectively. According to the correlation between geo-electrical results and borehole data, the study region has motley topsoil, fine grains, medium grains, and gravelly sands, which is consistent with Inim et al. (2020). Fine- and medium-grained sand makes up the economic potential aquifers in MSW sites and nearby areas (Ibuot et al., 2017). The survey region's most common VES curve types are K (50.0%), HK and Q (21.4%), and A (7.1%). The disclosures show intercalations of diverse lithological suites. Survey locations with shallow water tables (<20 m) are characterized by thick layer strata. At MSW sites, the vertical and horizontal movements of leachate within the subsurface are geologically controlled from the downstream (source) to the upstream. The generated species of hydro-geochemistry of surface and groundwater in the area demonstrated a sequence of average abundance of cations of light ion species with ammonium ion as the highest and potassium as the least. The heavy metal ions, which were all higher than the approved standards have chromium as the highest and nickel as the least. The anionic average relative abundance has hydrogen trioxocarbonate ion concentration as the highest and fluoride ions as the least. Furthermore, ammonium ions have been identified as the most severe source of water contamination, with other ions appearing to play a minor role. The CV of heavy metals is relatively high, indicating substantial variability in their compositions in WSs and the resulting carcinogenic consequences on people (Mitra et al., 2022). The projected BOD–COD ratio (0.32) suggests that the tested regions are biodegradable. As a result, organic treatment, such as introducing seeding (microorganism sludge) to accelerate the degrading process (Chen et al., 2014), is essential. The TH of the MSW sites and their surrounding areas exceeds 300 mg/l, making them unsuitable for irrigation. As a result, farmers may need to increase their irrigation rate to mitigate the detrimental effects of using surface and even groundwater near MSW sites for irrigation. MSW sites have a low SAR (<26), indicating that the WSs studied are suitable for irrigation. With these low SAR values suggesting low sodium content in surface and groundwater, there is an urgent need to lower soil AK for specific crops while also improving soil structure and texture to increase sodium content exchange with other cations (Thomas et al., 2020).
ACKNOWLEDGEMENTS
We are indebted to our colleagues and postgraduate students in the Department of Geology and as well as the Department of Physics and the Geophysics Research Group (GRG), Akwa Ibom State University, for their assistance during the field data acquisition, analysis, and editing of the manuscript.
DATA AVAILABILITY STATEMENT
All relevant data are included in the paper or its Supplementary Information.
CONFLICT OF INTEREST
The authors declare there is no conflict.
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