Assessment of the multilevel correlations of the pollution indicators and lithological vulnerabilities in a passive limestone mining and cement producing environment

Abstract

This study presents the assessment of the air, soil, and water quality within the residential communities around two passive limestone mining/cement factories. The associations between the pollutants were tested across the media, within each medium, between the layers, and between two groups of the communities. The mean values for the PM_1.0, PM_2.5, and PM₁₀ were 65.8 µgm⁻³, 50.1, and 73.7, respectively, in the air; for the Mn, Fe, Cu, Zn, Cr, K-40, U-238, and Th-232 were 0.433 g/kg, 8.950, 0.005, 0.054, 0.104, 161.57 Bq. kg⁻¹, 61.10, and 15.85, respectively, in the topsoil; 0.365 g/kg, 8.259, 0.004, 0.029, 0.057, 71.84 Bq. kg⁻¹, 16.37, 4.66, respectively, in the subsoil; and for the Mn, Fe, and Zn were 0.190, 1.499, and 0.256 mg/l, respectively, in the water. The PM₁₀, Fe, and K-40 were the most abundant pollutants. The Co and Mn, Zn and Cu, Fe and Cu, the absorbed dose rate (ADR) and K-40, and ADR and U-238 correlated significantly. Though the Ibese group was more polluted than the Ewekoro group, the generally low levels of the pollutions were confirmatory of the earlier suspicions of the mining/production activities. The 2nd lithological layer at 0.5 to 1.9 m depths or the 3rd lithological layer at 1.1 to 7.69 m depths for the Ibese group and the 1st layer at the surface or the 2nd layer at 0.5 m depth for the Ewekoro group are protective layers for the groundwater that must not be exploited, given the three classes of groundwater vulnerability indices observed in the area.

Introduction

Many of the raw materials used in producing cement, such as limestone, gypsum, chalk, shale, clay, and sand, are mined from the earth (Albeanu et al., 2004), and this justifies why most cement plants are situated near a limestone deposit. Limestone quarrying requires drilling, blasting, excavation, and energy consumption, while cement production requires the raw materials to be calcinated at high temperature, thereby contributing to dust and other particulate matter (Cheung et al., 2011), volatile organic compounds (VOC), and Co₂ emissions.

It was reported that 5 to 6% of the CO₂that is generated anthropogenically comes from cement production (Mishra & Siddiqui, 2014; Potgieter, (Potgieter 2012). The exhaust gases from a cement kiln contain nitrogen oxides, carbon oxides, sulfur oxides, water, oxygen, small quantities of dust and organic compounds, chlorides, fluorides, and heavy metals which deteriorate air quality, thereby degrading human health (Sivakumar & Britto, 1995). Emissions have local and global environmental impacts, resulting in global warming and all its attendant consequences (Pariyar et al., 2013). Humans are negatively affected in a number of ways, such as itchy eyes, silicosis, chest discomfort, chronic bronchitis, asthma attacks, and cardiovascular diseases (Ponsby et al., 2000; Mehraj & Balkhi, 2012). The sulfur oxides and nitrogen oxides either accumulate in the atmosphere with other greenhouse gases or react with water and other compounds to form various acidic compounds, which when deposited to the earth’s surface impair the quality of soil and water bodies and acidify lakes and streams, thus affecting ecosystems (Gbadebo & Amos, 2010; Ogedengbe & Oke, 2011). Additionally, slags, industrial effluents, and other residues have been known to impact the concentrations of some heavy metals in soil and water. NOx and volatile organic compounds react in the atmosphere in the presence of sunlight to form ground-level ozone via photochemical reactions, thus developing into smog (Vans Oss & Padovani, 2003) or potentially contributing to the greenhouse effect and global warming (Ait-Helal et al., 2014).

The Dangote and Lafarge cement production factories, which are two of the largest in the West Africa, are currently hosted within the Ibese and Ewekoro groups of communities, respectively. In addition, the two communities are mostly agrarian, and they are situated around the limestone mining areas. The mining of limestone and production of cement had been reported to have caused deterioration of quality in the air, soil, and water environments (Afeni et al., 2012; Kittipongvises, 2017; Lamare & Singh, 2015, 2017; Mehraj & Balkhi, 2012; Moronkola et al., 2021; Rathore, 2020). Previous studies in the area had reported in the area incidences of unproductive agricultural land, contamination of the underground water, and the frequent outbreaks of diarrhea, typhoid, respiratory conditions, and eye diseases, as a result of the constant exposure to the potentially toxic elements in the respirable dust, soils, and water (Ojo & Guntimehin, 2017).

The naturally occurring radionuclides (uranium (U), thorium (Th), and potassium (K) series) have been reported in varying concentrations in the air, soil, and water (Hutchinson, 1994), and the radiations emitted by the radionuclides have been said to depend on the properties of overlying soil materials (Belivermis et al., 2009). Therefore, the naturally occurring radioactive materials (NORMS), in the environments of the study area, could have possibly been technologically enhanced (TECH-NORMS) as a result of the mining and production activities in the communities (Gessel & Prichard, 1975). The assessment of population exposures to radiation within the mining and cement production sites was therefore very necessary, and knowledge of the concentration of natural radionuclides in the environment was required due to its potentially devastating effect on human health (Trevisi et al., 2012; UNSCEAR (United Nations Scientific Committee on the Effects of Atomic Radiation), 2008).

Hence, the gaps that this study intended to bridge stemmed from the fact that (i) none of the previous data collections in the area integrated radiometry and geochemistry concurrently during survey; (ii) correlations in-between some metals, in-between some radionuclides, and even between some metals and some radionuclides, as a result of their common affinity for clay minerals, have been severally established in soils and water, but inter-layer and inter-media correlations have gained little attention; (iii) previous monitoring surveys in the study area were said to have taken place during active industrial and anthropogenic activities; and (iv) it was necessary to compare the levels of pollutions within the two groups of communities. Since previous investigators had reported air, water, and soil pollutions in the same area, and also in similar limestone mining environments, the a priori hypothesis was that the low industrial activities, occasioned by the coronavirus disease of 2019 (COVID-19), would significantly reduce the pollution levels and present a strong platform for proper source apportionment. The objectives were, therefore, to (i) obtain the relevant quality parameters in the air, soil, and water environments in the study area; (ii) identify the pollution source(s) by carrying out the survey under passive anthropogenic activities, i.e., shortly after when restriction of human movement, due to the COVID-19 pandemic, was relaxed; (iii) determine the intra-parameter and inter-parameter correlations between the Ibese and the Ewekoro groups of communities, and within each group of communities; and (iv) identify the pollution pathways and groundwater vulnerabilities using lithology mapping.

The study was expected to provide a robust reference system for future investigations on the environmental effects of limestone mining and cement production using integrated measurements of physico-chemical, geochemical, and radiometric investigations within the communities.

Geology of the study area

The first cement company is located in the Ewekoro town, Ewekoro Local Government area, Ogun State, Southwest, Nigeria. The town is located between latitude 6° 55′ 59.99″ N and longitude 3° 12′ 60.00″ E. The area is that of lowland with forest-type vegetation, draining mainly by the Ewekoro river, which forms a dense network all over the area. The Ewekoro formation comprises non-crystalline and highly non-fossiliferous limestone and thinly laminated fissile and probably non-fossiliferous shale (Fig. 1a). The second cement company is located in the Ibese town, Yewa North Local Government Area, Ogun State. It lies between latitudes 6° 58′ to 6° 60′ N and longitudes 3° 2′ to 3° 4′ E. The geology of Ibese and its environs also consists of the Ewekoro formation, which is marine and of the Paleocene age. It consists of a limestone unit, which is several meters in thickness, overlain by a shale unit almost three times as thick as the limestone. The area belongs to the eastern part of the Dahomey basin, extending from the Volta Delta to the western flank of the Niger Delta in Nigeria (Ogbe, 1972). However, the general succession of the rock units is that of the underlying rock, which comprises the Abeokuta group, followed by the Ewekoro, Akinbo, Oshosun, and Ilaro formations, and is covered by coastal plain sands (Omatsola & Adegoke, 1981).

Fig. 1.

 a Geological map of Ogun State. b Sampling points within the communities

Experimental design, materials, and methods

Sampling configuration

In situ measurements were designed for the monitoring of the air quality parameters (AQP), and the geophysical investigation, while laboratory measurements were designed for both geochemical tests on the soils and water and radiometric tests on the soils. Five equal sampling locations were selected per study area (Fig. 1b). The sampled communities in the Ewekoro group, where the Lafarge cement factory is located, included Lapeleke, Akinbo, Itori, Elebute, and Ewekoro. Afami, Ajibawo, Abule-Oke, Balogun, and Ibese were the communities investigated in the Ibese group, where the Dangote cement factory, which is the largest in West Africa, is located. The air sampling was carried out above ground level in the Ibese group (Fig. 2a) and in the Ewekoro group (Fig. 2b). A soil auger was used to collect the soil samples at 0–15 cm (topsoil) and 16–30 cm (subsoil) at each sampling point in the Ibese group (Fig. 3a) and in the Ewekoro group (Fig. 3b) for the determination of the heavy metal concentrations (HMC) and the radionuclide activity concentrations (RAC) in the soils. The water samples were collected from the rivers, surface wells, and boreholes (Table 1) which were sources for both household and agricultural uses in the Ibese group (Fig. 4a) and in the Ewekoro group (Fig. 4b).

Fig. 2.

a Air sampling points in the Ibese group. b Air sampling points in the Ewekoro group

Fig. 3.

a Soil sampling points in the Ibese group. b Soil sampling points in the Ewekoro group

Table 1.

Sources of the sampled water within the communities

S/N	Community	Source
	Ibese group
1	Afami	Borehole
2	Afami	Surface well
3	Ajibawo	Surface well
4	Ajibawo	Surface well
5	Abule oke	Borehole
6	Abule oke	Surface well
7	Balogun	Surface well
8	Ibese	River
9	Ibese	Surface well
	Ewekoro group
10	Lapaleke	Borehole
11	Lapaleke	Surface well
12	Akinbo	River
13	Akinbo	Surface well
14	Ewekoro	River
15	Ewekoro	River
16	Ewekoro	Surface well
17	Ewekoro	Surface well
18	Itori	River
19	Itori	Surface well
20	Elebute	River
21	Elebute	Surface well

Fig. 4.

a Water sampling points in the Ibese group. b Water sampling points in the Ewekoro group

The geophysical measurements were made beside the points that were previously sampled for water and soil in each of the ten communities where sufficient space was found for the cables to be drawn. The soil, water, and air sampling were carried out within the perimeters of the two traverses in each location. The electrical resistivity transverse lines are shown for the Ibese (Fig. 5a) and the Ewekoro (Fig. 5b) groups. The geoelectrical resistivity field survey technique, which comprises the Wenner and the Schlumberger arrays to measure the lateral resistivity and vertical resistivity variations with depth, was adopted to delineate the lithology of the area (Table 2). The vertical electrical sounding was achieved using the Schlumberger configuration to assess the vertical trends of the geoelectric parameters on traverses. A 2D resistivity survey was conducted using the Wenner configuration to delineate lateral and vertical changes in the apparent subsurface resistivity values of the study location.

Fig. 5.

a Electrical resistivity traverses in the Ibese group. b Electrical resistivity traverses in the Ewekoro group

Table 2.

Coordinates for the electrical resistivity traverses

Communities	Coordinates		Coordinates
Ibese group	Easting	Northing	Ewekoro group
Afami	3.069162	7.017345	Lapeleke	3.206199	6.921431
Afami	3.069329	7.016924	Lapeleke	3.206142	6.920983
Ajibawo	3.048627	7.021088	Akinbo	3.190614	6.899421
Ajibawo	3.04874	7.02065	Akinbo	3.190923	6.899092
Abule oke	3.047086	7.006169	Ewekoro	3.216788	6.939753
Abule oke	3.047199	7.005731	Ewekoro	3.216731	6.939305
Balogun	3.031781	7.019602	Itori	3.225592	6.930235
Balogun	3.031781	7.01915	Itori	3.225161	6.930375
Ibese	3.038028	6.965793	Elebute	3.199537	6.888357
Ibese	3.037971	6.966241	Elebute	3.199703	6.887936

Sampling tools and testing devices

The gaseous, liquid, and solid compositions measured were formaldehyde (HCHO), total volatile organic compounds (TVOC), and particulate matter (PM) depositions (PM_1.0, PM_2.5, and PM₁₀) using the DM106 air quality tester. The soil samples collected using core samplers were wrapped in the foil and transported to the laboratory using the polythene bags. The materials deployed for the water sampling included 2.5 l Winchester bottles, nitric acid, detergents, distilled water, and ice chests. Black nylon bags and a 76 × 76 mm NaI (TI) scintillation counter (Bircon Detector ff-669) were the materials and equipment for radiometric assessment. The concentrations of heavy metals (HM) in the soils and the water were determined using the Buck Scientific model 210VGP flame atomic absorption spectrophotometer as well as nitric acid (Mohan et al., 1996). The geoelectrical investigation was adopted to map the lithology, depth, and thickness within the aquifer using the Herojat Rhomega resistivity meter.

Methods

Measurement of the particulate matter depositions

The DM106 instrument was held at approximately 1.5 m above the ground level for the air quality sampling. After achieving stability, measured depositions were displayed which included meteorological parameters such as temperature and relative humidity as well as the overall air quality indicators (Kampa & Kastanas, 2008). Two points, within a 20 m distance, were sampled in each community with the exceptions of the Akinbo, Ewekoro, Elebute, and Itori communities, where three points were sampled due to larger population densities, making a total of 24 samples.

Measurement of the heavy metal concentrations

For the HMCs in soils, two points within a 20 m distance were sampled per community to obtain a good representation with the exception of the Elebute community where only one point was sampled. Both topsoil and subsoil samples were taken at each point, making a total of thirty-eight soil samples collected (Akinyemi et al., 2022). Samples were wrapped in foil paper and kept in polythene bags for easy carriage to the laboratory for analysis. For heavy metal concentrations in water, two samples were taken from each community (one from a river and one from a surface well or borehole, as much as possible), and a total of twenty-one water samples (Table 1) were collected (nine from the Ibese group and twelve from the Ewekoro group). Exceptions were the Balogun (one sample from the surface well), Ajibawo (two surface wells), Lapaleke (one surface well and one borehole), and Ewekoro (two rivers and two surface wells) communities. Winchester bottles used for water sampling were washed with detergent and rinsed with distilled water. The bottles were then soaked in 10% nitric acid overnight to remove any impurities and contaminants attached to the walls and rinsed again with distilled water. At the sampling site, bottles were rinsed three times with water samples. Water samples were immediately kept in the ice chest and then moved to the laboratory. For heavy metal concentrations in both soils and water, water samples were digested using 5 ml concentrated HNO₃ in a 100 ml water sample, which was heated and evaporated down to 20 ml. Then, 5 ml HNO₃ was again added upon cooling, and heating continued with more acid additions until a light-colored and clear solution was achieved. The insoluble part was filtered off to avoid clogging of the atomizer, after which the clear solution was adjusted to 100 cm³ with distilled water according to USEPA Standard Methods 200.6/200.9 (USEPA (United State Environmental Protection Agency), 2005, 2016a). Most metals may be present in water in dissolved, suspended, or acid-extractable form and can be determined by atomic absorption spectrophotometry based on the principle that free atoms in the ground state will absorb light of a certain wavelength. The metal of interest was first reduced to the elemental state, vaporized, and imposed in the beam of separation from the light source. A solution of the sample was drawn as a fine mist into a suitable flame, while absorption, which is proportional to concentration, was measured at a selected wavelength characteristic of each targeted element. Hence, concentrations of Mn, Fe, Cu, Zn, Co, Cr, Cd, Ni, and Pb were determined with the aid of an atomic absorption spectrophotometer, which provides integrated measurements in absorbance or emission intensity, as well as sample concentration in comparison to standard solution, while integrating readings over a period from 0.5 to 10 s (Klavins et al., 2000; Sani & Abdullahi, 2017).

Measurement of the radionuclide activity concentrations

For the RACs in soils, a total of thirty-eight soil samples weighing approximately 2 kg each were collected in black nylon bags from the study areas. Two soil samples were collected, each from 0 to 15 cm depth and 16 to 30 cm depth, from different points within 20 m distance in each of the ten communities with the exception of the Elebute community, where only one point was sampled. Soil samples were spread in trays and dried until there was no detectable change in the mass of the sample at 110 °C in a temperature-controlled oven. The dry samples were then crushed using a mortar and pestle; the crushed samples were sieved using a 2 mm mesh size. Samples were later sealed in a radon-tight container for approximately 4 weeks to achieve secular equilibrium between the Radon ²²⁶Ra and its daughter nuclides before radiometric counting. Radioactivity measurement was based on the principle of gamma-ray spectroscopy using a NaI (TI) scintillation counter spectrometer enclosed in a graded 10 cm thick Canberra lead shield. Standard International Atomic Energy Agency (IAEA) sources were used for calibration (Abusini, 2007). The soil samples were placed on top of the detector, and the counting period was 10 h. The net area under the corresponding γ-ray peaks in the energy spectrum was used to compute the activity concentrations in the soil samples, using Eq. (1), as

$$C_s=\frac{C_a}{P_{\gamma }{M}_s{\epsilon }_{\gamma }{t}_c}$$ 1

C_s (Bq. kg⁻¹) is the activity concentration, C_a is the net counts, ε_γ is the efficiency of the detector for a γ-ray of interest, M_s is the sample mass, t_c is total counting time, and P_γ is the probability of a γ-ray emission.

Lithology mapping

An inter-electrode spacing of 5 m was used for the Wenner array, while half spacing of the current electrode (AB/2) was adopted for the Schlumberger array, starting from 1 to 50 m in successive steps of 10, after initial spacing of 1 m, 5 m, and 10 m. This involved the spacing of four electrodes, with two current electrodes widely spaced outside and two potential electrodes closely spaced within them along the survey profile (Fig. 6). In this array, potential electrodes M and N were fixed, while current electrodes A and B were moved.

Fig. 6.

Schlumberger VES configuration

A total of 20 vertical electrical sounding (VES) points, with two VESs in each of the 10 communities, were carried out at 50 m inter-VES spacing. A total of 124 geo-electric parameters were determined, where the apparent resistivity, ρ_a, is given, according to Eq. (2), as

$${\rho }_a=\frac{{\left({}^{\mathit{AB}},{/}_2\right)}^2-{\left({}^{\mathit{MN}},{/}_2\right)}^2\mathrm{x}\pi }{2\left({}^{\mathit{MN}},{/}_2\right)}\mathrm{x}R$$ 2

The geometric factor is given, according to Eq. (3), as

$$G=\frac{{\left({}^{\mathit{AB}},{/}_2\right)}^2-{\left({}^{\mathit{MN}},{/}_2\right)}^2\mathrm{x}\pi }{2\left({}^{\mathit{MN}},{/}_2\right)}$$ 3

Hence, the apparent resistivity was determined, according to Eq. (4), as

$${\rho }_a=G\mathrm{x}R$$ 4

The apparent electrical resistivity data were processed quantitatively through two steps. The first step was performed using the generalized bi-logarithm graph method and the curve matching technique, while the second step involved the treatment of the output results using the Ipi2win geophysical package. The resistivity data acquired from the field were plotted against the corresponding AB/2 on the bi-logarithm papers to produce the VES curves. The partial curve matching and a 1D forward modeling approach using the Ipi2win application were used to generate geo-electric sections. The matching of field curves to system-defined parameters, after a preset number of iterations, was expected to yield the estimated number of layers (N), resistivity (ρ), thickness (h), layer depths (d), and root mean square (RMS) error. The RMS ranged from 0.55 to 2.6% with an average of 1.50%, and it explains the level of nonalignment between the field data curve and the computer-defined curve (Telford et al., 1990). The resistivity of the different layers and the corresponding thicknesses were produced after a number of inversions until the model geo-electric parameters of the VES curve were completely resolved with the fitting error.

Results and discussion

Trends of the quality indicators across the communities

Trends of the air quality parameters

The ranges for the PM_1.0, PM_2.5, and PM₁₀, across the 24 sampling points, were from 14 in Lapeleke to 145 in Ewekoro, from 19 in Lapeleke to 156 in Ewekoro, and from 1.8 in Ewekoro to 221 in Ewekoro, respectively. H-CHO and TVOC ranged from nil in Abule-Oke/Balogun/Ibese to 0.591 in Ajibawo, and from nil in Abule-Oke/Balogun/Ibese to 0.83 in Elebute, respectively. The overall air assessment was severe in Afami, while it ranged from mild to good in other communities. PM_1.0 within the Ibese group of communities ranged between 25 and 134 µg/m³ in Ajibawo and Afami, respectively, and within the Ewekoro group of communities ranged from 14 to 109 µg/m³ in Lapeleke and Ewekoro, respectively. The PM_2.5 within the Ibese group ranged from 31 to 188 µg/m³ in Ajibawo and Afami, respectively, and within the Ewekoro group ranged from 19 to 156 µg/m³ in Lapeleke and Ewekoro, respectively. PM₁₀ within the Ibese group ranged between 36 and 207 µg/m³ in Ajibawo and Afami, respectively, and within the Ewekoro group, it ranged between 21 and 180 µg/m³ in Lapeleke and Ewekoro, respectively. The PM₁₀ across both communities was lower than the 150 µg/m³ limit set by NESREA (2014) with the exception of Afami and Ewekoro, while TVOC and H-CHO were generally lower than the 300 mg/m³limit of NESREA (National Environmental Standards and Regulations Enforcement Agency) (2014). The ambient temperature was largely below 30 °C, which is the benchmark with the possibility of influencing the dispersion of emitted pollutants with resultant poor air quality, while the relative humidity averaged 63.3%, indicating a lower chance of dispersion of air pollutants (Ramasamy et al., 2013). The overall mean values for the PM_1.0, PM_2.5, and PM₁₀ were 50.1, 65.8, and 73.7, respectively, indicating that the PM₁₀ was the most abundant within the communities, while the mean values in the Ibese group of communities and the Ewekoro group of communities are shown in Table (3). The air condition in the Afami community was severe, while others ranged from mild to excellent. A total of 50% of the communities were good, 20% mild, 10% moderate, 10% excellent, and 10% severe.

Table 3.

Descriptive statistics of the AQP within the Ibese and Ewekoro groups

Ibese group	Afami		Ajibawo		Abule-oke		Balogun		Ibese
	Mean	SD	Mean	SD	Mean	SD	Mean	SD	Mean	SD
PM1.0	134b	± 33	25a	± 32	28a	± 25	38a	± 32	33a	± 38
PM2.5	188b	± 44	31a	± 43	36a	± 32	39a	± 30	42a	± 41
PM10	207b	± 50	36a	± 69	41a	± 36	42a	± 40	50a	± 46
TVOC	0.537b	± 0.29	0.206a	± 0.492	Nil		Nil		Nil
θ	25.9b	± 27.3	27.8b	± 26.4	20.3a	± 20.9	19.3a	± 20.4	18.3a	± 21.5
RH	65a	± 66	60a	± 66	85b	± 82	85b	± 88	83b	± 80
Remark	Severe		Good		Good		Good		Mild

Ewekoro group	Lapeleke		Akinbo		Ewekoro		Itori		Elebute
	Mean	SD	Mean	SD	Mean	SD	Mean	SD	Mean	SD
PM1.0	14a	± 15	20a	± 17	109c	± 145	38b	± 32	62b	± 20
PM2.5	19a	± 22	26a	± 23	156b	± 134	39b	± 43	82b	± 26
PM10	21a	± 25	28a	± 26	180d	± 221	44b	± 49	88c	± 28
TVOC	0.244a	± 0.32	0.232a	± 0.193	0.51b	± 0.32	0.308b	± 0.482	0.83c	± 0.21
θ	34c	± 36	34.9c	± 33.3	24.8a	± 30.4	29.4b	± 30.1	29.3b	± 29.1
RH	36a	± 40	39a	± 40	68c	± 55	55b	± 54	57b	± 56
Remark	Excellent		Good		Mild		Good		Moderate

Values are in mean ± standard deviation; values with the same superscript down the column are not significantly different at p > 0.05 statistical level

Trends of the heavy metal concentrations in the water and the soil

Across the 21 water sampled points, the ranges of concentrations of the Mn, Fe, and Zn were 0.017–0.962, 0.215–10.017, and 0.027–0.702 in which case the rivers from the Ewekoro and Ibese communities, and the surface wells from the Itori, community stood as outliers. In comparison to the WHO (2004) thresholds of 0.5, 1.0, and 3.0, respectively, only the iron concentration was severely outside the permissible limit. The copper concentration was only detected in the borehole in Abule-Oke (0.027 mg/l) and in the surface well in Ewekoro (0.027 mg/l); the nickel concentration was only detected in the surface well in Itori (0.129 mg/l), while the potentially toxic elements—lead, nickel, cadmium, and cobalt—were not detected. Ogedengbe and Oke (2011) measured concentrations of Mn, Fe, and Zn in rivers from Elebute and Itori as 7.66, 2.38, and 0.21 and 16.37, 1.61, and 0.42, respectively, as opposed to much lower values of 0.079, 1.673, and 0.282 and 0.122, 3.522, and 0.289 obtained from rivers from the same communities in this study. With respect to the average HMC for each community, the manganese concentration in water was highest in Balogun (0.054 mg/l) within the Ibese group and in Ewekoro (0.777 mg/l) within the Ewekoro group. The iron concentration in the water was highest in Afami (0.522 mg/l) within the Ibese group and in Ewekoro (8.887 mg/l) within the Ewekoro group. The Ibese and Itori communities recorded the highest concentration of Zn, 0.24 and 0.495 mg/l, within the Ibese and Ewekoro groups, respectively. Across the 38 soil sampling points, the lead concentration was below the detection limit within the Ibese group but was detected only in the topsoils of Elebute and Itori, ranging between 0.035 and 0.056 g/kg, respectively, and in the subsoil of Ewekoro (0.023 g/kg). For the other potentially toxic elements, the nickel concentration was only detected in Ibese, Lapeleke, and Ewekoro, ranging from 0.010 g/kg in Ibese topsoil to 0.022 g/kg in Ewekoro subsoil; the cadmium concentration was only detected in the topsoils of Lapeleke (0.001 g/kg) and Ewekoro (0.001 g/kg); the cobalt concentration was highest in Ibese topsoil (0.013 g/kg) and Lapeleke subsoil (0.015 g/kg). The HMC in soils was still largely within the WHO (2004) permissible levels and the national standards for drinking water quality. With respect to the mean HMC in each community, the Mn was highest in the Afami topsoil (0.708 g/kg) and Ajibawo subsoil (0.647 g/kg) within the Ibese group. The iron concentration was highest in Ajibawo topsoil (13.863 g/kg) and Ibese subsoil (13.876 g/kg). The Ajibawo community had the highest concentrations of Cu (0.011 g/kg) and Zn (0.13 g/kg) in the topsoil and the highest concentrations of Cu (0.01 g/kg) and Zn (0.112 g/kg) in the subsoil. The chromium concentration was highest in Abule-Oke topsoil (0.538 g/kg) and Ibese subsoil (0.073 g/kg). Within the Ewekoro group, the Mn concentration was highest in Ewekoro topsoil (0.634 g/kg) and Lapeleke subsoil (0.608 g/kg). The iron concentration was highest in the Ewekoro topsoil (26.932 g/kg) and the Ewekoro subsoil (23.597 g/kg), while the copper concentration was highest in the Itori topsoil (0.007 g/kg) and the Lapeleke subsoil (0.005 g/kg). The zinc concentration was highest in the Itori topsoil (0.101 g/kg) and the Ewekoro subsoil (0.026 g/kg), while the chromium concentration was highest in the Ewekoro topsoil (0.092 g/kg) and the Ewekoro subsoil (0.101 g/kg). Given the detection of the HM within the permissible limits of concentrations in the soil and water (Europian Union, 2002, 2006; Food and Agriculture Organisation, 1985; NESREA, 2014; WHO, 2004), and the near-absence of the lead, cadmium, nickel, and cobalt, it can be safe to apportion the source of the contaminations observed by previous investigators in the area to the limestone mining and cement manufacturing activities. The overall mean concentrations for the Mn, Fe, Cu, Zn, and Cr were 0.434, 8.950, 0.005, 0.054, and 0.104, respectively, in the topsoil; 0.365, 8.259, 0.004, 0.029, and 0.057, respectively, in the subsoil; while the overall mean concentrations for the Mn, Fe, and Zn were 0.190, 1.499, and 0.256, respectively, in the water, indicating that the Fe was the most abundant HM both in the soil and water. The mean concentrations in the soils and the water for the Ibese group of communities and the Ewekoro group of communities are shown in Tables 4 and 5, respectively.

Table 4.

Descriptive statistics of the HMC in the soil within the Ibese and Ewekoro groups

HMC	Ibese group	Topsoil (0–15 cm)		Subsoil (15–30 cm)		Ewekoro group	Topsoil (0–15 cm)		Subsoil (15–30 cm)
Mn	Afami	0.708c	± 0.152	0.581c	± 0.040	Lapeleke	0.483ab	± 0.348	0.608b	± 0.411
	Ajibawo	0.544c	± 0.148	0.647c	± 0.123	Akinbo	0.380ab	± 0.141	0.370ab	± 0.203
	Abule-Oke	0.531c	± 0.054	0.467bc	± 0.002	Ewekoro	0.634b	± 0.334	0.513ab	± 0.182
	Balogun	0.123a	± 0.059	0.051a	± 0.022	Itori	0.158ab	± 0.051	0.049a	± 0.028
	Ibese	0.441bc	± 0.268	0.229ab	± 0.072	Elebute	0.333ab	± 0.007	0.136ab	± 0.002
Fe	Afami	5.144a	± 0.137	6.330a	± 0.074	Lapeleke	6.662a	± 2.712	6.038a	± 1.556
	Ajibawo	13.863a	± 0.359	13.031a	5.698	Akinbo	4.644a	± 0.589	3.761a	± 0.227
	Abule-Oke	5.096a	± 0.275	4.768a	± 0.215	Ewekoro	26.932b	± 5.030	23.597b	± 2.695
	Balogun	2.136a	± 0.116	1.808a	± 0.254	Itori	9.771a	± 8.284	6.929a	± 7.459
	Ibese	9.377a	4.92	13.876a	13.353	Elebute	5.876a	± 0.024	2.450a	± 0.020
Cu	Afami	0.005a	± 0.002	0.003a	± 0.001	Lapeleke	0.003ab	± 0.001	0.003ab	± 0.000
	Ajibawo	0.011b	± 0.001	0.010b	± 0.003	Akinbo	0.003ab	± 0.001	0.001a	± 0.000
	Abule-Oke	0.004a	± 0.000	0.003a	± 0.000	Ewekoro	0.006bc	± 0.001	0.005bc	± 0.001
	Balogun	0.003a	± 0.000	0.002a	± 0.000	Itori	0.007c	± 0.003	0.003ab	± 0.001
	Ibese	0.005a	± 0.000	0.004a	± 0.001	Elebute	0.006bc	± 0.001	0.002ab	± 0.001
Zn	Afami	0.011a	± 0.008	0.005a	± 0.001	Lapeleke	0.042bc	± 0.012	0.016ab	± 0.005
	Ajibawo	0.130a	± 0.019	0.112a	± 0.084	Akinbo	0.021ab	± 0.008	0.007a	± 0.004
	Abule-Oke	0.011a	± 0.003	0.006a	± 0.000	Ewekoro	0.032ab	± 0.013	0.026ab	± 0.007
	Balogun	0.008a	± 0.003	0.004a	± 0.001	Itori	0.101d	± 0.028	0.015ab	± 0.008
	Ibese	0.113a	± 0.136	0.087a	± 0.102	Elebute	0.066c	± 0.005	0.014ab	± 0.007
Co	Afami	0.009a	± 0.003	0.008a	± 0.000	Lapeleke	0.006ab	± 0.008	0.011b	± 0.005
	Ajibawo	0.010a	± 0.000	0.012b	± 0.004	Akinbo	BDL		BDL
	Abule-Oke	0.007a	± 0.001	0.006a	± 0.001	Ewekoro	0.009b	± 0.000	0.004ab	± 0.005
	Balogun	BDL		BDL		Itori	BDL		BDL
	Ibese	0.007a	± 0.009	0.005a	± 0.008	Elebute	BDL		BDL
Cr	Afami	0.030a	± 0.002	0.032a	± 0.001	Lapeleke	0.075bcd	± 0.011	0.061abc	± 0.003
	Ajibawo	0.042ab	± 0.001	0.046ab	± 0.003	Akinbo	0.032a	± 0.001	0.048ab	± 0.005
	Abule-Oke	0.053a	± 0.004	0.070b	± 0.006	Ewekoro	0.092 cd	± 0.000	0.101d	± 0.017
	Balogun	0.033a	± 0.003	0.029a	± 0.003	Itori	0.065abcd	± 0.036	0.067abcd	± 0.027
	Ibese	0.073b	± 0.032	0.073b	± 0.025	Elebute	0.063abcd	± 0.001	0.045ab	± 0.007

Values are in mean ± standard deviation; values with the same superscript down the column are not significantly different at p > 0.05 statistical level

Table 5.

Descriptive statistics of the HMC in the water within the Ibese and Ewekoro groups

Community	Mn	SD	Fe	SD	Zn	SD	Cu	SD
Afami	0.038a	± 0.029	0.522b	± 0.206	0.029a	± 0.003	BDL
Ajibawo	0.038a	± 0.029	0.368ab	± 0.065	0.233a	± 0.077	BDL
Abule-Oke	0.043a	± 0.012	0.488ab	± 0.072	0.217a	± 0.028	0.013a	± 0.019
Balogun	0.054a	± 0.001	0.384ab	± 0.002	0.233a	± 0.007	BDL
Ibese	0.350a	± 0.268	0.227a	± 0.017	0.240a	± 0.278	BDL
Lapeleke	0.043a	± 0.037	0.564a	± 0.176	0.257a	± 0.006	BDL
Akinbo	0.021a	± 0.006	0.522a	± 0.360	0.228a	± 0.036	BDL
Ewekoro	0.777b	± 0.262	8.887b	± 1.598	0.271a	± 0.000	BDL
Itori	0.367ab	± 0.347	1.988a	± 2.169	0.495a	± 0.292	0.013a	± 0.019
Elebute	0.171a	± 0.130	1.035a	± 0.902	0.354a	± 0.103	BDL

Values are in mean ± standard deviation; values with the same superscript down the column are not significantly different at p > 0.05 statistical level

Trends of the activity concentrations of the radionuclides in the soil

Across the 38 sampled points, the ranges of RAC (Bq. kg⁻¹) for K-40, U-238, Th-232, and ADR (nGy h⁻¹) were 1.42–685, 0.3–51.9, 0.3–11.6, and 0.39–33.76, respectively, while the world average limit values (UNSCEAR, 2008) were respectively, 400, 37, 33, and 59. With respect to the average RAC in each community, sampled points from Abule-Oke topsoil and Ewekoro subsoil exceeded the world average for K-40; topsoils from the Abule-Oke, Elebute, Lapeleke, and Afami communities and subsoils from the Ibese, Abule-Oke, and Lapeleke communities exceeded the world average for U-238, and all sampled points were within limits for Th-232 and ADR. Within the Ibese group, the activity concentration of 40-K ranged from 75.24 at Ibese to 426.75 at Abule-Oke in the topsoil and from 54.81 at Balogun to 159.71 at Afami in the subsoil. The activity concentration of 238-U ranged from 6.37 at Balogun to 39.45 at Afami in the topsoil and from 12.68 at Balogun to 34.66 at Abule-Oke in the subsoil. The activity concentration of 232-Th ranged from 3.41 at Afami to 6.01 at Abule-Oke in the topsoil and from 4.17 at Ibese to 6.29 at Ajibawo in the subsoil. The ADR ranged from 12.66 at Balogun to 32.74 at Abule-Oke in the topsoil and from 11.58 at Balogun to 25.17 at Abule-Oke in the subsoil. Within the Ewekoro group, the activity concentration of 40-K ranged from 26.68 at Elebute to 402.18 at Ewekoro in the topsoil and from 9.02 at Itori to 31.61 at Elebute in the subsoil. The activity concentration of 238-U ranged from 69.81 at Akinbo to 137.15 at Elebute in the topsoil and from 3.20 at Akinbo to 11.16 at Ewekoro in the subsoil. The activity concentration of 232-Th ranged from 8.05 at Akinbo to 50.22 at Lapeleke in the topsoil and from 2.77 at Itori to 4.68 at Akinbo in the subsoil. The ADR ranged from 8.53 at Akinbo to 23.35 at Elebute in the topsoil and from 9.66 at Itori to 22.40 at Elebute in the subsoil. Gbadebo and Amos (2010) measured RAC for K-40, U-238, and Th-232 in the top and subsoil as 8.76, 7.22, 12.40 and 7.78, 8.99, 17.63 for Ewekoro, and 12.52, 16.48, 7.85 and 13.36, 17.59, 8.85 for Lapeleke as opposed to much higher values of 354.81, 17.42, 10.71 and 449.55, 14.1, 1.6 for Ewekoro and 62.53, 51.1, 5.5 and 76, 49.33, 3.55 for Lapeleke. The mean activity concentrations for the K-40, U-238, and Th-232 were 161.57, 61.10, and 15.85 Bq. kg⁻¹ and 71.84, 16.37, and 4.66 Bq. kg⁻¹, respectively, in the topsoil and the subsoil, indicating that the K-40 had the most abundant activity concentration in the soil within the communities. The mean concentrations in the Ibese group of communities and the Ewekoro group of communities are shown in Table 6.

Table 6.

Descriptive statistics of the RAC in the soil within the Ibese and Ewekoro groups

	Ibese	Top soil (0–15 cm)		Subsoil (15–30 cm)		Ewekoro	Top soil (0–15 cm)		Subsoil (15–30 cm)
40 K	Afami	94.900a	± 36.628	159.705ab	± 29.267	Lapeleke	69.265a	± 9.525	76.180a	± 12.558
	Ajibawo	107.910ab	± 32.654	145.850ab	± 16.758	Akinbo	59.820a	± 82.590	69.810a	± 4.525
	Abule-Oke	426.750b	± 365.221	155.865ab	± 42.660	Ewekoro	402.180c	± 66.991	117.420ab	± 81.204
	Balogun	148.505ab	± 138.713	54.810a	± 13.138	Itori	204.470b	± 50.813	93.870a	± 19.417
	Ibese	75.240a	± 71.757	115.985ab	± 22.606	Elebute	26.680a	± 0.085	137.150ab	± 1.909
238U	Afami	39.450a	± 17.607	26.900ab	± 6.081	Lapeleke	50.215d	± 1.252	20.865ab	± 7.870
	Ajibawo	12.360ab	± 4.610	27.950ab	± 2.051	Akinbo	9.050a	± 12.374	12.550a	± 6.435
	Abule-Oke	25.155ab	± 30.342	34.660ab	± 2.206	Ewekoro	15.760ab	± 2.348	12.155a	± 10.260
	Balogun	6.370a	± 6.689	12.675ab	± 13.513	Itori	14.170ab	± 11.696	9.020a	± 4.964
	Ibese	33.255ab	± 4.320	34.010ab	± 7.057	Elebute	44.990 cd	± 1.909	31.605bc	± 0.714
232Th	Afami	3.410a	± 0.509	5.125a	± 0.106	Lapeleke	4.525a	± 1.379	3.900a	± 3.408
	Ajibawo	5.805a	± 3.246	6.290a	± 1.344	Akinbo	3.200a	± 4.101	4.675a	± 0.813
	Abule-Oke	6.005a	± 1.266	5.595a	± 1.025	Ewekoro	11.155b	± 0.629	3.750a	± 3.182
	Balogun	5.525a	± 0.757	5.750a	± 1.344	Itori	4.240a	± 0.226	2.765a	± 1.322
	Ibese	4.520a	± 1.245	4.165a	± 0.516	Elebute	4.330a	± 0.127	4.585a	± 0.120
Absorbed dose rate (nGy/h)	Afami	23.216c	± 6.370	21.685c	± 3.773	Lapeleke	27.510 cd	± 1.056	14.767ab	± 1.641
	Ajibawo	13.712ab	± 1.192	22.331c	± 2.480	Akinbo	8.534a	± 11.520	11.441a	± 3.118
	Abule-Oke	32.737d	± 1.452	25.173c	± 0.160	Ewekoro	31.096d	± 2.224	12.655ab	± 5.702
	Balogun	12.655ab	± 3.454	11.581a	± 4.363	Itori	17.503abc	± 2.743	9.662a	± 2.199
	Ibese	20.467bc	± 4.042	22.266c	± 1.741	Elebute	23.346bcd	± 0.909	22.401bcd	± 0.467

Values are in mean ± standard deviation; values with the same superscript down the column are not significantly different at p > 0.05 statistical level

Analysis of correlations

Interdependence analysis for the intra-group and the intra-layer correlations

The Pearson’s correlation coefficient is a statistical covariance tool for measuring the relationship, or association, between two continuous variables on the same interval. An outcome of − 1 represents total negative linear correlation; 1 represents total positive correlation; and 0 represents no correlation. The soil samples averaged from 0 to 30 cm, the soil samples in the 0–15 cm (layer 1) depth, the soil samples in the 15–30 cm (layer 2) depth, and the water samples are represented by the prefixes S, S15, S30, and W, respectively. The correlations were examined between the HMC in the soils (Table 7), between the RAC in the soils (Table 8), and between the HMC in the water sources (Table 9). The within-soil category and the within-water category consisted of the inter-metal/intra-group and the inter-radionuclide/intra-group correlations. At p < 0.01 in the Ibese group, the metal pairs, {S-Co: S-Mn}, {S-Co: S-Fe}, {S-Co: S-Cu}, {S-Zn: S-Cu}, {S-Cu: S-Mn}, and {S-Cu: S-Fe}, were significantly correlated at r = 0.807, 0.732, 0.597, 0.638, 0.476, and 0.661, respectively, in the soil. At p < 0.01 in the Ewekoro group, the correlation of the metal pair, {W-Fe: W-Mn}, was significant (r = 0.780) in the water, while the metal pairs, {S-Pb: S-Zn}, {S-Cr: S-Fe}, {S-Cr: S-Cu}, {S-Co: S-Mn}, {S-Zn: S-Cu}, and {S-Cu: S-Fe}, were significantly correlated at r = 0.780, 0.853, 0.600, 0.712, 0.766, and 0.568, respectively, in the soil. At p < 0.01 in the Ibese group, the correlation of the pairs, {S-ADR: S-K-40} and {S-ADR: S-U-238}, was significantly correlated at r = 0.562 and 0.588, respectively, in the soil, while in the Ewekoro group, the correlation of the pairs, {S-ADR: S-U-238}, {S-ADR: S-Th-232}, and {S-Th-232: S-K-40}, was significantly correlated at r = 0.688, 0.625, and 0.702, respectively, in the soil.

Table 7.

Correlation matrix for the HMC in the soils within the Ibese and Ewekoro groups

Ibese	S-Mn	S-Fe	S-Cu	S-Zn	S-Co	S-Cr	S-Cd	S-Pb	S-Ni
S-Mn	1
S-Fe	0.299	1
S-Cu	0.476*	0.661**	1
S-Zn	0.086	0.34	0.638**	1
S-Co	0.807**	0.732**	0.597**	0.031	1
S-Cr	0.078	0.539*	0.001	0.033	0.372	1
S-Cd	0.306	− 0.128	− 0.066	− 0.181	0.119	− 0.271	1
S-Pb
S-Ni	− 0.162	0.667**	− 0.03	− 0.096	0.3	0.728**	− 0.107		1

Ewekoro	S-Mn	S-Fe	S-Cu	S-Zn	S-Co	S-Cr	S-Cd	S-Pb	S-Ni
S-Mn	1
S-Fe	0.404	1
S-Cu	0.166	0.568**	1
S-Zn	− 0.14	0.164	0.766**	1
S-Co	0.712**	0.39	0.156	− 0.131	1
S-Cr	0.39	0.853**	0.600**	0.268	0.372	1
S-Cd	0.204	0.473*	0.072	0.122	0.475*	0.34	1
S-Pb	− 0.182	− 0.024	0.526*	0.780**	− 0.34	0.038	− 0.176	1
S-Ni	0.503*	0.690**	0.325	− 0.144	0.722**	0.445*	0.416	− 0.27	1

**Correlation is significant at the 0.01 level (2-tailed), *Correlation is significant at the 0.05 level (2-tailed)

Table 8.

Correlation matrix for the RAC in the soil within the Ibese and Ewekoro groups

	S-K-40(Bq/kg)	S-U-238(Bq/kg)	S-Th-232(Bq/kg)	S-ADR (nGy/h)
Ibese
S-K-40(Bq/kg)	1
S-U-238(Bq/kg)	− 0.328	1
S-Th-232(Bq/kg)	0.025	− 0.286	1
S-ADR (nGy/h)	0.562**	0.588**	− 0.105	1
Ewekoro
S-K-40(Bq/kg)	1
S-U-238(Bq/kg)	− 0.221	1
S-Th-232(Bq/kg)	0.701**	0.011	1
S-ADR (nGy/h)	0.540*	0.688**	0.625**	1

^**Correlation is significant at the 0.01 level (2-tailed); *Correlation is significant at the 0.05 level (2-tailed)

Table 9.

Correlations matrix for the HMC in the water within the Ibese and the Ewekoro groups

	W-Mn	W-Fe	W-Cu	W-Zn
Ibese
W-Mn	1
W-Fe	− 0.547	1
W-Cu	− 0.117	0.103	1
W-Zn	0.595	− 0.351	0.02	1
Ewekoro
W-Mn	1
W-Fe	0.780**	1
W-Cu	− 0.099	− 0.159	1
W-Zn	0.395	− 0.184	− 0.023	1

^**Correlation is significant at the 0.01 level (2-tailed)

Interdependence analysis for the intra-group and inter-layer correlations

The intra-metal/inter-layer correlations in the Ibese group were explored using the two-tailed T-test (Table 10) based on the standard deviation (SD), the standard error mean (SEM), and the T-value. The Mn in the topsoil and the subsoil had a positive association (r = 0.887, R² = 0.7868, p = 0.045). The mean concentration was higher in the topsoil than in the subsoil by 0.074 with SD as 0.115 and T as 1.441. The Fe in the topsoil and subsoil had a significantly positive association (r = 0.912, R² = 0.8317, p = 0.031). The mean concentration was lower in the topsoil than in the subsoil by 0.839 with SD as 2.181 and T as − 0.86. The Cu and Zn individually had significantly positive association in the topsoil and subsoil, with r and p values as 0.990 and 0.001 and 0.997 and 0, respectively, and strong confidence levels in the predictive capacities from the paired differences, with SEM, T, and p as 0, 6, and 0.004 and 0.004, 2.704, and 0.054, respectively. The T-values in the range of 2 < T < − 2 are generally considered acceptable to give confidence in the coefficient as a predictor. The intra-metal/inter-layer correlations in the Ewekoro group revealed that the Cu in the topsoil and subsoil had a moderate positive association (r = 0.450, R² = 0.2025, p = 0.446). The mean concentration was higher in the topsoil than in the subsoil by 0.002 (SEM = 0.001, T = 2.75, p = 0.051). The intra-radionuclide/inter-layer correlations in the Ibese group (Table 11) revealed that the ⁴⁰ K in the topsoil and subsoil had a weak positive association (r = 0.251, R² = 0.0630). The mean value was higher in the topsoil than in the subsoil by 44.218 (p = 0.522). The ²³⁸U in the topsoil and subsoil had a strong positive association (r = 0.640, R² = 0.4096). The mean value was lower in the topsoil than in the subsoil by 3.921 (p = 0.457, T = − 0.823). The intra-radionuclide/inter-layer correlations in the Ewekoro group revealed that the ²³⁸U in the topsoil and subsoil had a weak positive association (r = 0.428, R² = 0.0543, p = 0.472). The mean value was significantly higher in the topsoil than in the subsoil by 93.396 (SD = 27.038, T = 7.724, p = 0.002), thus giving confidence in the reliability of the predictors. The ADR in the topsoil and subsoil had a weak positive association (r = 0.327, R² = 0.1069, p = 0.591). The mean value was higher in the topsoil than in the subsoil by 7.413 (T = 1.919, p = 0.127).

Table 10.

The T-test of the HMC in the topsoil and the subsoil of the Ibese group and the Ewekoro group

HMC	Mean	SD	SEM	HMC pair	Correlation	Sig	Paired difference	Mean	SD	SEM	T	Sig. (2-tailed)
Ibese
S15-Mn	0.469	0.216	0.097	S15-Mn: S30-Mn	0.887	0.045	S15-Mn − S30-Mn	0.074	0.115	0.052	1.441	0.223
S30-Mn	0.395	0.25	0.112
S15-Fe	7.123	4.566	2.042	S15-Fe: S30-Fe	0.912	0.031	S15-Fe − S30-Fe	− 0.839	2.181	0.976	− 0.86	0.438
S30-Fe	7.963	5.277	2.36
S15-Cu	0.006	0.003	0.001	S15-Cu: S30-Cu	0.99	0.001	S15-Cu − S30-Cu	0.001	0	0	6	0.004
S30-Cu	0.004	0.003	0.001
S15-Zn	0.055	0.061	0.027	S15-Zn: S30-Zn	0.997	0	S15-Zn − S30-Zn	0.012	0.01	0.004	2.704	0.054
S30-Zn	0.043	0.053	0.023
S15-Cr	0.143	0.221	0.099	S15-Cr: S30-Cr	0.603	0.282	S15-Cr − S30-Cr	0.093	0.21	0.094	0.995	0.376
S30-Cr	0.05	0.021	0.009
Ewekoro
S15-Mn	0.398	0.177	0.079	S15-Mn: S30-Mn	0.864	0.059	S15-Mn − S30-Mn	0.062	0.124	0.055	1.124	0.324
S30-Mn	0.335	0.239	0.107
S15-Fe	10.777	9.227	4.127	S15-Fe: S30-Fe	0.991	0.001	S15-Fe − S30-Fe	2.222	1.362	0.609	3.648	0.022
S30-Fe	8.555	8.595	3.844
S15-Cu	0.005	0.002	0.001	S15-Cu: S30-Cu	0.45	0.446	S15-Cu − S30-Cu	0.002	0.002	0.001	2.75	0.051
S30-Cu	0.003	0.001	0.001
S15-Zn	0.052	0.032	0.014	S15-Zn: S30-Zn	0.003	0.996	S15-Zn − S30-Zn	0.037	0.033	0.015	2.528	0.065
S30-Zn	0.016	0.007	0.003
S15-Cr	0.065	0.022	0.01	S15-Cr: S30-Cr	0.782	0.118	S15-Cr − S30-Cr	0.001	0.015	0.007	0.153	0.886
S30-Cr	0.064	0.022	0.01

Table 11.

The T-test of the RAC in the topsoil and the subsoil of the Ibese group and the Ewekoro group

RAC	Mean	SD	SEM	RAC pair	Correlation	Sig	Paired difference	Mean	SD	SEM	T	Sig. (2-tailed)
Ibese
S15-K-40	170.661	145.649	65.136	S15-K-40: S30-K-40	0.251	0.683	S15-K-40 − S30-K-40	44.218	141.139	63.119	0.701	0.522
S30-K-40	126.443	43.558	19.48
S15-U-238	23.318	13.871	6.204	S15-U-238: S30-U-238	0.64	0.245	S15-U-238 − S30-U-238	− 3.921	10.658	4.766	− 0.823	0.457
S30-U-238	27.239	8.855	3.96
S15-Th-232	5.053	1.081	0.484	S15-Th-232: S30-Th-232	0.616	0.268	S15-Th-232 − S30-Th-232	− 0.332	0.862	0.385	− 0.862	0.438
S30-Th-232	5.385	0.799	0.357
S15-ADR	20.557	8.135	3.638	S15-ADR: S30-ADR	0.693	0.194	S15-Th-232 − S30-Th-232	− 0.05	5.878	2.629	− 0.019	0.986
S30-ADR	20.607	5.225	2.337
Ewekoro
S15-K-40	152.483	155.233	69.422	S15-K-40: S30-K-40	-0.593	0.292	S15-K-40 − S30-K-40	135.244	160.831	71.926	1.88	0.133
S30-K-40	17.239	9.15	4.092
S15-U-238	98.886	28.257	12.637	S15-U-238: S30-U-238	0.428	0.472	S15-U-238 − S30-U-238	93.396	27.038	12.092	7.724	0.002
S30-U-238	5.49	3.208	1.435
S15-Th-232	26.637	19.442	8.695	S15-Th-232 & S30-Th-232	0.233	0.706	S15-Th-232 − S30-Th-232	22.702	19.277	8.621	2.633	0.058
S30-Th-232	3.935	0.77	0.345
S15-ADR	21.598	8.883	3.973	S15-ADR: S30-ADR	0.327	0.591	S15-Th-232 − S30-Th-232	7.413	8.639	3.864	1.919	0.127
S30-ADR	14.185	4.954	2.216

Interdependence analysis for the inter-group correlations

The mean PM_2.5, PM_1.0 and PM₁₀, TVOC, θ, and RH were T-tested, between the Ibese (i) and Ewekoro (e) groups to investigate the intra-aqp/inter-group associations (Table 12) and revealed that the PM_2.5, PM_1.0, and PM₁₀ in the Ibese group had moderately negative associations with their corresponding parameters in the Ewekoro group. The TVOC was lower by 0.276 in the Ibese group than the Ewekoro group (T = − 1.427, p = 0.227). The T-values in the range of 2 < T < − 2 are generally accepted to give confidence in the reliability of the coefficient as a predictor. The corresponding association of the TVOC between the Ibese group and the Ewekoro group was moderately negative (r = − 0.577, R² = 0.3329, p = 0.308). The θ and RH were lower in the Ibese group than the Ewekoro group, and additionally, the θ had a very strong corresponding association (r = 0.810, R² = 0.6561). The corresponding association of the RH was strong (r = 0.917) and significantly (p = 0.028) correlated. The paired test indicated that the θ was significantly lower by 8.160 in the Ibese group than the Ewekoro group (p = 0.002 < 0.05, T = − 7.082 < − 2), while the RH in the Ibese group was significantly (p = 0.000, T = 10.340 > 2) higher than the Ewekoro group by 24.60. Hence, the air in the Ibese group had higher depositions of the PM_2.5, PM_1.0, and PM₁₀; lower values of the TVOC and θ; and higher percentage of the RH than the air in the Ewekoro group.

Table 12.

The T-test of the air and meteorological parameters within the Ibese group and the Ewekoro group

Paired samples statistics
		Mean		Std. deviation	Std. error Mean
Pair 1	iPM1.0	51.600		46.328	20.719
	ePM1.0	48.600		38.585	17.256
Pair 2	iPM2.5	67.200		67.651	30.255
	ePM2.5	64.400		56.748	25.378
Pair 3	iPM10	75.200		73.849	33.026
	ePM10	72.200		65.652	29.361
Pair 4	iTVOC	0.149		0.235	0.105
	eTVOC	0.425		0.252	0.113
Pair 5	iθ	22.320		4.249	1.900
	eθ	30.480		4.085	1.827
Pair 6	iRH	75.600		12.116	5.418
	eRH	51.000		13.323	5.958
Paired samples correlations
			Correlation	Sig
Pair 1	iPM1.0: ePM1.0		− 0.490	0.403
Pair 2	iPM2.5: ePM2.5		− 0.443	0.455
Pair 3	iPM10: ePM10		− 0.439	0.459
Pair 4	iTVOC: eTVOC		− 0.577	0.308
Pair 5	iθ: eθ		0.810	0.097
Pair 6	iRH: eRH		0.917	0.028
Paired samples test
		Paired differences
					95% confidence interval of the difference
		Mean	Std. deviation	Std. error mean	Lower	Upper	T	Sig. (2-tailed)
Pair 1	iPM1.0 − ePM1.0	3.000	73.386	32.819	− 88.121	94.121	0.091	0.932
Pair 2	iPM2.5 − ePM2.5	2.800	105.836	47.331	− 128.612	134.212	0.059	0.956
Pair 3	iPM10 − ePM10	3.000	118.415	52.957	− 144.031	150.031	0.057	0.958
Pair 4	iTVOC − eTVOC	− 0.276	0.433	0.194	− 0.813	0.261	− 1.427	0.227
Pair 5	iθ − eθ	− 8.160	2.576	1.152	− 11.359	− 4.961	− 7.082	0.002
Pair 6	iRH − eRH	24.600	5.320	2.379	17.995	31.205	10.340	0.000

The intra-metal mean concentrations of the Mn, Fe, and Zn in the water were T-tested between the Ibese and the Ewekoro groups to investigate the inter-group associations (Table 13). The Mn in both groups had a weakly negative association (r = − 0.168, R² = 0.0282, p = 0.788). The mean Mn in the Ibese group was lower by 0.171 than in the Ewekoro group (T = − 1.059 > − 2, p = 0.349). The Fe concentrations in both groups had a very weak association (r = 0.394, p = 0.511). The mean concentration of the Fe concentration in the water of the Ibese group was lower than its corresponding concentration in the water of the Ewekoro group by 2.201 (T = − 1.398 > − 2, p = 0.235). The Zn concentrations in both groups had a weak association (r = 0.360, R² = 0.1296, p = 0.552). The mean concentration of the Zn in the water of the Ibese group was lower than its corresponding concentration in the water of the Ewekoro group by 0.131 (T = − 2.579 < − 2, p = 0.06). Hence, the water in the Ewekoro group had higher concentrations of the Mn, Fe, and Zn than the water in the Ibese group. Similar analysis for the HMC in the soils revealed that the topsoil in the Ibese group had higher concentrations of the Mn, Cu, Zn, Cr, and K-40 and lower concentrations of the Fe, U-238, and Th-232 than the topsoil in the Ewekoro group, while the subsoil of the Ibese group had higher concentrations of the Mn, Cu, Zn, K-40, U-238, Th-232, and ADR and lower concentrations of the Fe and Cr than the subsoil of the Ewekoro group.

Table 13.

The T-test of the HMC in the water of the Ibese and Ewekoro groups

Paired samples statistics
		Mean		Std. deviation	Std. error mean
Pair 1	iW-Mn	0.105		0.137	0.061
	eW-Mn	0.276		0.312	0.140
Pair 2	iW-Fe	0.398		0.116	0.052
	eW-Fe	2.599		3.564	1.594
Pair 3	iW-Zn	0.190		0.091	0.041
	eW-Zn	0.321		0.108	0.048
Paired samples correlations
			Correlation	Sig
Pair 1	iW-Mn: eW-Mn		− 0.168	0.788
Pair 2	iW-Fe: eW-Fe		0.394	0.511
Pair 3	iW-Zn: eW-Zn		0.360	0.552
Paired samples test
		Paired differences
					95% confidence interval of the difference
		Mean	Std. deviation	Std. error mean	Lower	Upper	T	Sig. (2-tailed)
Pair 1	iW-Mn − eW-Mn	− 0.171	0.361	0.162	− 0.620	0.278	− 1.059	0.349
Pair 2	iW-Fe − eW-Fe	− 2.201	3.520	1.574	− 6.572	2.169	− 1.398	0.235
Pair 3	iW-Zn − eW-Zn	− 0.131	0.113	0.051	− 0.271	0.010	− 2.579	0.061

Spatial analysis of the RAC within the groups

The ArcMap and the ArcScene tools of the ArcGIS 10.4.1 package were used for the interpolation of the measured data in the 2D and the visualization of the interpolated data in the 3D, respectively. The inverse distance weighting interpolation component, in the ArcMap tool, was used for the interpolation of the 15 cm and the 30 cm depths, respectively. The boundary shapefile layer of the study area was used to generate the output which formed a new raster layer for each of the parameters that was interpolated. The interpolated parameter layers were imported to the ArcScene to render depths for the ease of visualization. The spread of the radionuclide activity concentrations for the K-40 (Fig. 7a, b), the U-238 (Fig. 8a, b), and the Th-232 (Fig. 9a, b) revealed the spatial patterns in the Ibese and the Ewekoro groups, respectively. The K-40 was spatially low in concentration in the soils of the Ibese group and the Ewekoro group with the exceptions of some spikes at the topsoil of the Abule-Oke community and some spikes at both the topsoil and the subsoil of the Ewekoro community. The U-238 reduced westward in both the topsoil and the subsoil of the Ibese community. The Ewekoro group increased westward at the topsoil and was spatially low in concentration in the subsoil, with the exception of a high concentration only at the center. The Th-232 increased northward in both the topsoil and the subsoil of the Ibese community and increased westward in both the topsoil and the subsoil of the Ewekoro community.

Fig. 7.

a The K-40 in the soil of the Ibese group. b The K-40 in the soil of the Ewekoro group

Fig. 8.

a The U-238 in the soil of the Ibese group. b The U-238 in the soil of the Ewekoro group

Fig. 9.

a The Th-232 in the soil of the Ibese group. b The Th-232 in the soil of the Ewekoro group

The subsurface characterization

The subsurface lithology was investigated using both the qualitative and the quantitative interpretations of the apparent electrical resistivity data obtained from the geophysical measurements. The purpose was to delineate the possible pollution pathways and investigate for the presence of the protective layers against the contamination of the aquifers (Anudu et al., 2008; Osele et al., 2016) through lithological characterization.

The VES data interpretations

The VES curves generated the geo-electric properties, such as the number of layers, apparent resistivity, thickness, depth to layer, and inferred lithology, for the Ibese group (Table 14) and the Ewekoro group (Table 15). Three to four geo-electric layers were present within the Ibese group of communities and characterized by the H, Q, K, HK, KH, and QH curve types. The Afami topsoil comprised clay to clayey sand, with a resistivity range of 28 to 61 Ωm and thickness ranging from 0.5 to 1.4 m, underlain by clayey sand to clay with a resistivity range of 21 to 254 Ωm and thickness ranging from 0.9 to 2.6 m. This was followed by an infinitely thick conductive (resistivity of 21.4 Ωm) layer inferred to be clay. In the Ajibawo community, the resistive (363 to 916 Ωm) topsoil comprised a lateritic layer, with thickness ranging from 0.5 to 1.7 m, underlain by the sandy clay to sandy layer with resistivity ranging from 93 to 394 Ωm and thicknesses ranging from 1.4 to 4.9 m. Beneath this was an infinitely continuous layer with resistivity ranging from 70 to 76 Ωm inferred to be clay. In the Abule-Oke community, the first layer (topsoil) comprised clay to clayey sand, with a resistivity range of 91 to 100 Ωm and thickness ranging from 0.5 to 1.9 m. This layer was underlain by the clayey to sandy layer with a resistivity range of 75 to 102 Ωm and thickness ranging from 0.6 to 5.28 m, followed by an infinitely thick and resistive (above 200 Ωm) layer inferred to be sand/limestone. The Balogun topsoil comprised the clayey to sandy clay layer, with resistivity and thickness ranging from 41 to 52 Ωm and 0.5 to 0.8 m, respectively, underlain by the clay layer with average resistivity and thickness of 33.5 Ωm and 1.49 m, respectively. This was followed by the resistive layer inferred to be sand/limestone with resistivity and thickness ranging from 400 to 1400 Ωm and 8.1 to 25.4 m, respectively. Beneath was the infinitely continuous layer with an average resistivity value ranging from 11 to 33 Ωm, inferred to be clay. Ibese comprised sandy clay at the topmost part, with resistivity and thickness ranges of 50 to 81 Ωm and 1.7 to 1.9 m, respectively. This layer was underlain by a sequence of conductive layers inferred to be shale with resistivity ranging from 6 to 12 Ωm and thickness ranging from 4 to 6 m, followed by an infinitely thick conductive (7 to 21 Ωm) layer inferred to be limestone. The geo-electric sounding curves produced showed three to four geo-electrically equivalent layers within the Ewekoro Group, characterized by the HK, KH, A, and Q sounding curve types. The topsoil around the Lapeleke community comprised sand, with a resistivity range of 167 to 170 Ωm and a thickness of 0.5 m. The layer was underlain by a clay layer with an average resistivity of 29 Ωm and a thickness ranging from 2.25 to 2.7 m. This layer was followed by a slightly resistive layer with a resistivity ranging from 100 to 134 Ωm and a thickness ranging from 5 to 7 m. Beneath this layer was an infinitely thick conductive layer with an average resistivity of 32 Ωm inferred to be a fractured layer. In the Akinbo community, the resistive topsoil (46.4 to 47.5 Ωm) comprised a clayey layer, with a thickness of 0.5 m, underlain by a clayey layer with a resistivity range of 37.6 Ωm to 38.5 Ωm and a thickness ranging from 2.77 to 2.89 m. Underneath was an infinitely continuous layer with resistivity averaging 13.7 Ωm inferred to be shale.

Table 14.

Geo-electric parameters for all the VESs in the Ibese group

Community	VES no	Layer no	Resistivity (Ωm)	Thickness (m)	Depth to layer (m)	Curve type	Lithology
Afami	1	3	28.3	1.41	0	K	Clayey Topsoil
			254	2.55	1.41		Sandy clay
			21.5	–	3.96		Clay
	2	4	60.9	0.5	0	HK	Clayey sand topsoil
			21.3	0.9	0.5		Clay
			259	2.55	1.4		Sandy clay
			21.3	–	3.95		Clay
Ajibawo	1	3	916	0.5	0	Q	Lateritic topsoil
			394	1.39	1.39		Sand
			70.1	–	1.89		Clay
	2	3	363	1.67	0	Q	Lateritic topsoil
			93.1	4.86	1.67		Sandy clay
			76.4	–	6.53		Clay
Abule-Oke	1	4	90.7	0.5	0	KH	Clayey topsoil
			102	0.67	0.5		Sand
			46.9	3.2	1.1		Clay
			250	–	4.3		Sand/limestone
	2	3	100	1.9	0	H	Clayey sand topsoil
			74.5	5.28	1.9		Clay
			200	–	7.18		Sand/limestone
Balogun	1	4	40.8	0.8	0	HK	Clayey topsoil
			33.2	1.59	0.8		Clay
			1400	8.14	2.23		Limestone/sand
			10.6	–	10		Clay
	2	4	51.8	0.5	0	HK	Sand clay topsoil
			33.9	1.39	0.5		Clay
			400	25.4	1.89		Limestone/sand
			32.9	–	26		Clay
Ibese	1	4	49.5	1.89	0	QH	Sandy clay topsoil
			6.52	5.8	1.89		Shale
			3.66	20	7.69		Shale
			20.9	–	27.69		Limestone
	2	4	81	1.65	0	QH	Sandy clay topsoil
			11.6	3.56	1.65		Shale
			4.01	5.27	5.21		Shale
			7.43	–	10.48		Clay

Table 15.

Geo-electric parameters for all the VESs in the Ewekoro group

Community	VES no	Layer no	Resistivity (Ωm)	Thickness (m)	Depth to layer (m)	Curve type	Lithology
Lapeleke	1	4	170	0.5	0	HK	Sandy topsoil
			28.4	2.25	0.5		Clay
			99.9	7	2.75		Limestone
			32	–	9.75		Saturated limestone
	2	4	167	0.5	0	HK	Sandy topsoil
			29.4	2.7	0.5		Clay
			134	5.24	3.2		Limestone
			31.1	–	8.44		Saturated limestone
Akinbo	1	3	47.5	0.5	0	Q	Clayey topsoil
			37.6	2.89	0.5		Clay
			13.6	–	3.39		Shale
	2	3	46.4	0.5	0	Q	Clayey topsoil
			38.5	2.77	0.5		Clay
			13.8	–	3.27		Shale
Ewekoro	1	4	25	1.44	0	KH	Clayey topsoil
			89.1	2.69	1.44		Clayey sand
			16	7.72	4.13		Saturated limestone
			84.2	–	11.85		Limestone
	2	4	32.7	1.44	0	KH	Clayey topsoil
			60.6	2.69	1.44		Clayey sand
			29.6	30.2	4.13		Saturated limestone
			3742	–	34.33		Limestone
Itori	1	3	15	2.37	0	A	Clayey topsoil
			70.2	9.7	2.37		Limestone
			25.8	–-	12.07		Saturated limestone
	2	3	13.4	2.67	0	A	Clayey topsoil
			112	4.4	2.67		Limestone
			36.3	–	7.07		Saturated limestone
Elebute	1	4	103	0.5	0	HK	Clayey sand topsoil
			28.4	2.15	0.5		Clay
			98	7.16	2.65		Limestone
			32.1	–	9.81		Saturated limestone
	2	4	114	0.5	0	HK	Clayey sand topsoil
			30.4	2.9	0.5		Clay
			143	5.59	3.4		Limestone
			27.7	–	8.99		Saturated limestone

The Ewekoro topsoil was clay, with resistivity ranging from 25 to 33 Ωm and a thickness of 1.44 m. The layer was underlain by a clayey sand layer with resistivity ranging from 61 to 89 Ωm and a thickness of 2.69 m. Beneath was a slightly conductive layer inferred to be saturated limestone with resistivity ranging from 16 to 30 Ωm and thickness ranging from 7.72 to 30.2 m, respectively. The layer was followed by an infinitely thick and resistive (84 to 3742 Ωm) layer inferred to be limestone. The Itori topsoil comprised clayey materials, with resistivity and thickness ranging from 13.4 to 15 Ωm and 2.37 to 2.67 m, respectively, underlain by a resistive layer inferred to be limestone, with resistivity and thickness ranges of 70 to 112 Ωm and 4.4 to 9.7 m, respectively. Beneath was an infinitely continuous layer with resistivity ranging from 26 to 36 Ωm and inferred to be fractured limestone. The Elebute topsoil was clayey sand, 0.5 m thick, with a resistivity range of 103 to 114 Ωm. This layer was underlain by a conductive layer inferred to be clay with resistivity and thickness ranging from 28 to 30 Ωm and 2.2 to 2.9 m, respectively, and a slightly resistive layer conductive layer inferred to be limestone with resistivity and thickness ranging from 98 to 143 Ωm and 5.6 to 7.2 m, respectively. Underneath was an infinitely continuous layer with resistivity ranging from 28 to 32 Ωm and inferred to be a saturated limestone.

Since samples were obtained from layers 1 and 2 within the 10 communities, it was necessary to pay closer attention to the lithology of the first 15 cm (layer 1) and the second 15 cm (layer 2). Hence, this information was extracted from the general lithology inferred from geo-electric sections. The Afami layer 1 was sand overlain by a thin skin of clay, while layer 2 was largely sandy; the Ajibawo layers 1 and 2 were lateritic; the Abule-Oke layers 1 and 2 were both clayey in the eastern part, while layer 1 was clayey in the western part, and layer 2 was partly clayey but underlain by a highly resistive thin spread of sand; the Balogun layers 1 and 2 were both clayey, and the Ibese layers 1 and 2 were both clayey but overlain by a thin spread of shale. The Lapeleke layer 1 was a highly resistive sandy zone, while layer 2 was partly sandy but underlain by a thin spread of clay; the Akinbo layer 1 was sandy, while layer 2 was partly sandy but underlain by a thin spread of clay; the Itori layers 1 and 2 were clayey; the Elebute (Agberi) layers 1 and 2 were both sandy zones.

Pathway identification for the pollutants

To further track the pathway for the potential migration of the pollutants, it was necessary to identify the patterns of spread for clay and/or shale lithological sheets in the vadoze zone for the purposes of quantifying the vulnerability of the groundwater resources to contaminants. Clay is very plastic and cohesive when moist and therefore possesses high retention capacities for pollutants, thereby serving as a protective layer, unlike sands, which drain very easily because of the large openings and large particle sizes and allow easy passage of pollutants. Weathered shale is often transformed into clay-rich soils, which usually have very low shear strength. The protective layer is porous but less permeable, thus protecting the aquifer unit against contaminants that can infiltrate the groundwater. The protective layer was either found in the 2nd lithological layer in the Ibese group at depths of 0.5 to 1.9 m or the 3rd lithological layer at depths of 1.1 to 7.69 m, while for the Ewekoro group, it was either at the 1st lithological layer at the surface or at the 2nd lithological layer at 0.5 m depth. Aquifer protective layer parameters are shown for the Ibese group and the Ewekoro group of communities (Table 16).

Table 16.

Protective layer descriptions in the Ibese group

Community	VES number	Protective layer	Resistivity (Ωm)	Layer position	Layer thickness (m)	Depth to layer (m)
Ibese group
Afami	VES1	Clay	28.3, 21.5	1st, 3rd	1.41, infinity	1.41, 3.96
	VES2	Clay	60.9, 21.3	2nd, 4th	0.9, infinity	0.5, 3.95
Ajibawo	VES1	Clay	70.1	3rd	Infinity	1.89
	VES2	Clay	76.4	3rd	Infinity	6.53
Abule oke	VES1	Clay	90.7, 46.9	1st, 3rd	0.5, 3.2	0, 1.1
	VES2	Clay	74.5	2nd	5.28	1.9
Balogun	VES1	Clay	40.8, 33.2, 10.6	1st, 2nd, 4th	0.8, 1.59, infinity	0, 0.8, 10
	VES2	Clay	33.9, 32.9	2nd, 4th	1.39, infinity	0.5, 26
Ibese	VES1	Shale	6.52, 3.66	2nd, 3rd	5.8, 20	1.89, 7.69
	VES2	Shale	11.6, 4.01, 7.43	2nd, 3rd, 4th	3.56, 5.27, infinity	1.65, 5.21, 10.48
Ewekoro group
Lapeleke	VES1	Clay	28.4	2nd	2.25	0.5
	VES2	Clay	29.4	2nd	2.7	0.5
Akinbo	VES1	Clay/shale	47.5, 37.6, 13.6	1st, 2nd, 3rd	0.5, 2.89, infinity	0, 0.5, 3.39
	VES2	Clay/shale	46.4, 38.5, 13.8	1st, 2nd, 3rd	0.5, 2.77, infinity	0, 0.5, 3.27
Ewekoro	VES1	Clay	25	1st	1.44	0
	VES2	Clay	32.7	1st	1.44	0
Itori	VES1	Clay	15	1st	2.37	0
	VES2	Clay	13.4	1st	2.67	0
Elebute	VES1	Clay	28.4	2nd	2.15	0.5
	VES2	Clay	30.4	2nd	2.9	0.5

The Ibese and Ewekoro groups consisted of strata of less permeable materials, intercalated with sand/limestone in some parts, with resistivity, depth to top, and thickness of the layer ranging from 3.66 to 90.7 Ωm, 0 to 26 m, and 0.5 m to infinity and 13.4 to 47.5 Ωm, 0 to 0.5 m, and 0.5 m to infinity, respectively. The groundwater vulnerability was estimated using the modified overlay–index method based on the assumptions and nature of the investigated site (Aller et al., 1987; The National Research Council, 1993; Wang & Yang, 2008; Worrall & Besien, 2005; Worrall & Kolpin, 2004). Owing to the size of the investigated sites, in addition to physical observations, net recharge, aquifer media, topography, impact of vadoze zone media, and hydraulic conductivity of the aquifer will largely remain the same around the two groups of communities. The thickness of clay was used to substitute for the soil media, while the depth to infinity top was used to substitute for the depth to the water table. Hence, a modified drastic index was determined, according to Eq. (5), as:

$$mD{R}_i=D_w{D}_r+T_w{T}_r$$ 5

where mDR_i = modified drastic index; D = depth to infinity top; T = clay aggregate thickness; w = weight of the respective DRASTIC factor for D (5), T (3); r = rate of the respective DRASTIC factor for D and T: 3 (1 to 10), 2 (11 to 21), and 1 (> 21). Hence, the mDR_i, after averaging, was computed and categorized into classes A (1 to 11), B (12 to 20), and C (21 to 30). The Balogun community was categorized as class A vulnerability index; the Afami, Ajibawo, Ibese, Akinbo, and Ewekoro communities as class B; and the Abule-Oke, Lapeleke, Itori, and Elebute communities as class C, where C > B > A with regard to vulnerability concerns. The estimated mDR values were generally low, but given that only two out of the seven DRASTIC components were factored while others were assumed to be uniform, classes B and C, which have higher pollution indices, tend to pose more vulnerability concerns, and this will provide useful information towards environmental management and mitigation purposes.

Conclusion

This study presents the assessment of the particulate matter depositions and the total volatile organic compounds in the air, the activity concentrations of the naturally occurring radioactive materials in the soils, and the heavy metal concentrations in the soils and water, in a limestone mining and cement producing environment. Correlations were tested for the inter-metal, inter-radionuclide, intra-metal/inter-layer, intra-radionuclide/inter-layer associations in the intra-group/within-soil category; intra-metal, intra-aqp associations in the inter-group/within-soil category; and the inter-metal association in the within-water category. Overall, the ranges of concentrations in water for the Mn, Fe, and Zn were 0.017–0.962, 0.215–10.017, and 0.027–0.702, respectively, across the 21 sampling points within the 10 communities. The ranges of the RAC (Bq. kg⁻¹) for K-40, U-238, Th-232, and ADR (nGy h⁻¹) were 1.42–685, 0.3–51.9, 0.3–11.6, and 0.39–33.76, respectively, across the 38 sampling points. The Abule-Oke topsoil and the Ewekoro subsoil exceeded the world average for K-40; the Abule-Oke, Elebute, Lapeleke, and Afami topsoils and the Ibese, Abule-Oke, and Lapeleke subsoils exceeded the world average for the U-238; and all sampling points were within limits for Th-232 and the ADR. The metal pairs, Co and Mn, Zn and Cu, and Fe and Cu, correlated significantly both in the Ibese and the Ewekoro groups, while the ADR correlated significantly with the K-40 and the U-238, both in the Ibese and the Ewekoro groups. The Fe, Cu, Zn, and Mn concentrations decreased vertically downwards from soil layer 1 to layer 2, while the Cr concentration remained largely unchanged. The Ibese group was polluted, more in the air, more in the topsoil with the HMC and less with the RAC, more in the subsoil with the HMC and the RAC, and less in the water with the HMC, than the Ewekoro group. The protective layer was either found in the 2nd lithological layer in the Ibese group at the depths of 0.5 to 1.9 m or the 3rd lithological layer at depths of 1.1 to 7.69 m, while for the Ewekoro group, it was either at the 1st lithological layer at the surface or at the 2nd lithological layer at 0.5 m depth. The Ibese and Ewekoro groups consisted of strata of less permeable materials which are intercalated with sand/limestone in some parts, with resistivity, depth to top, and thickness of the layer ranging from 3.66 to 90.7 Ωm, 0 to 26 m, and 0.5 m to infinity and 13.4 to 47.5 Ωm, 0 to 0.5 m, and 0.5 m to infinity, respectively. The Balogun community was categorized as class A vulnerability index; the Afami, Ajibawo, Ibese, Akinbo, and Ewekoro communities as class B; and the Abule-Oke, Lapeleke, Itori, and Elebute communities as class C, where C > B > A with regard to vulnerability concerns. The estimated values were generally low, but given that only two out of the seven DRASTIC components were factored, classes B and C with higher pollution indices tend to pose more vulnerability concerns.

Author contribution

Akinyemi, O. D.: conceptualization, methodology, supervision, review, editing and final writing. Kazeem, S.: data acquisition, data processing, and initial writing. Alatise, O.: supervision, review and editing. Bada, B.: supervision, review and editing. Alayaki, F.: supervision, review and editing.

Funding

This work was supported by the Tertiary Education Trust Fund (TETF/DR&D/CE/NRF/UNI/FUNAAB/STI/61), Nigeria.

Data availability

The datasets generated during and analyzed during the current study are available in the Mendeley Data repository, https://data.mendeley.com/datasets/8kfxcrpw9f.

Declarations

This data article is the authors’ original work, which has not been previously published elsewhere.

Ethical approval

All authors have read, understood, and have complied as applicable with the statement on “Ethical responsibilities of authors” as found in the Instructions for Authors and are aware that with minor exceptions, no changes can be made to authorship once the paper is submitted.

Competing interests

The authors declare competing interests.