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Necessity calls for the environmental aspects of groundwater to be evaluated and properly managed based on the observed spatial distribution with respect to quality, as it contributes to a significant portion of average water usage globally. Variations in groundwater quality in the Ibadan Metropolis might be a result of physical and chemical trends in the region leading to a decline in quality. The study was geared towards the spatial evaluation of groundwater quality using factor analysis and the Kriging algorithm. The parameters examined include pH, electrical conductivity, total dissolved solids, carbonates, chloride, nitrate, sulphate, calcium, sodium, magnesium, and potassium, which were sampled and analysed from the existing municipal deep wells in the Ibadan Metropolitan area; and distribution maps of each parameter were created using a geostatistical approach. Factor analysis examined the relationship between human activities and concentration levels. Semi-variograms were tested to ascertain the best-fitted model accuracy measures, average standard error, root mean square error, and root mean square error standardised. The groundwater index was calculated to ascertain the drinkability of the water in the study area. Overall, the result shows that the groundwater in the study area is suitable for consumption; drinking, and other uses. Kriging is a suitable assessment tool for modelling environmental parameters.

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  • Geo-statistics was adopted to model the spatial distribution of concentration levels of parameters.

  • Factor analysis was employed to elucidate the predominant lithological effect.

  • The GWQI assessed groundwater suitability in the study area.

  • Importance of geostatistical techniques in water quality modelling was presented.

  • Importance of research to individuals and relevant agencies was presented.

Graphical Abstract

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Graphical Abstract
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Graphical Abstract
Graphical Abstract
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factor analysis, geo-statistics, groundwater, Kriging, water quality, water quality index, digitalwatercollection, digitalwatertechniques
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Globally, the consumption of groundwater is to a large extent by a substantial portion of the world population, qualifying it as the most significant natural resource (Belkhiri et al. 2020). Groundwater contributes to roughly 95% of accessible freshwater globally and 31.5% of average water usage (Murphy et al. 2017). Groundwater is an important commodity, particularly in semi-arid and arid regions that constitute around 15% of the land surface of Earth, and is the sole resource available for people living in many arid and semi-arid regions (Díaz-Alcaide & Martínez-Santos 2019; Elubid et al. 2019). Groundwater has become an indispensable source of drinking water worldwide and especially in developing countries, becoming a primary water resource whose quality support is the prerequisite of groundwater usage, and is crucial to human health and social development (Xiao et al. 2018). The interaction between groundwater and the mineral content of the aquifer components through which it passes is largely responsible for variation in groundwater chemistry (Bouteraa et al. 2019). Variations in groundwater quality might be a result of physical and chemical trends in a region determined by anthropogenic activities, leading to a decline in quality (Elubid et al. 2019; Ali & Ahmad 2020).

In comparison to surface water, groundwater has unrivalled benefits in terms of spatiotemporal availability, high stability, simple accessibility, good quality, and contamination resilience (Murphy et al. 2017; Gu et al. 2018). Even though groundwater is not instantly tainted, it is difficult to eradicate pollutants once they have been introduced (Jiang et al. 2019; Wang et al. 2021). Geo-environmental concerns arise as a result of the continued extraction of groundwater resources and the socioeconomic disparities of urbanisation (He & Wu 2019). For sustainable development and successful groundwater management, it is vital to determine the mechanisms responsible for groundwater chemistry (Pazand et al. 2018; Aragaw & Gnanachandrasamy 2021). Rising population and rapid agricultural growth are the leading causes of aquifer overexploitation, which results in the degradation of groundwater quality (Aragaw & Gnanachandrasamy 2021), constant salinisation of topsoil, and decreased crop production (Boudibi et al. 2019).

Groundwater is branded by appallingly low-slung movement gradation, with an average domicile timeline of about 1,500 years in the aquifers (Saito et al. 2020). Throughout the extensive residence stretch, it consumes a reasonable time-spell to interconnect with the adjacent media of the aquifers (Thomas 2021), and damaging elements such as fluoride, arsenic, and additional lethal elements can turn out to be dissolved (Wang et al. 2018; Adimalla et al. 2019; Marghade et al. 2019). Additionally, numerous isolated constituents in groundwater have been portrayed to be higher in recent decades in different areas all over the world (Dar et al. 2017). For example, nitrogen (nitrates, nitrites, and ammonia) in aquifers have been revealed to be foremost in both metropolitan and rural districts. The origins of these multiplexes instigate divergences from inherent grounds, like outflows and septic pools, to agrarian actions (Busico et al. 2020). The gradation of the toxic component in groundwater has congruently been recognised in speedy upsurges in many locations, for instance, landfill spots, effluent/domesticated water irrigation lands, mining zones, and industrial situates (Ahmadi et al. 2018). The decline of water quality has been described in several aquifers globally (Jia et al. 2018; Dube et al. 2020).

Over the years, the concept of geostatistical interpolation and spatial correlation and respective applications have been reported by diverse array of researchers globally (Fallah et al. 2019). A number of researchers have applied the techniques of geospatial statistics in the examination of groundwater quality variation (Gharbia et al. 2016; Johnson et al. 2018; Belkhiri et al. 2020). Geo-statistics is a spatial statistical technique that can be used to assess and represent the distribution of concentration spatially and temporally (Narany et al. 2014). It predicts the estimated values based on the relationship between the sample points and estimates the uncertainty of that prediction. Kriging is a linear interpolation procedure that is used to create probabilistic models of uncertainty relating to the values of the attributes. Hence, when spatial information is mapped together, it creates a powerful means for monitoring and management (Ali & Ahmad 2020).

Recent advances in the use of the geographic information system (GIS) have expanded its capabilities for spatiotemporal data to establish the distribution pattern of water quality variables (Bouteraa et al. 2019), and to map groundwater quality evaluation using geo-statistics (Nas & Berktay 2010; Selmane et al. 2022). To map the spatial variability, geo-statistics uses Kriging, the best linear unbiased estimator for predicting missing data at unknown places, which is the most widely used approach for environmental studies, particularly in ecological and water quality investigations. Recent advances in the use of the GIS have expanded its capabilities for spatiotemporal data to establish the geographic range of groundwater quality parameters and to map groundwater quality evaluation using geo-statistics (Venkatramanan et al. 2016).

Groundwater quality science has advanced rapidly during the last three decades, and significant progress has been made (Li et al. 2019). Kriging is a well-known geostatistical interpolation approach that is based on the spatial connections between the various measures surrounding the forecast site (Obaid & Mohammed 2020). The approach is an estimating procedure that determines unknown values using known values and a variogram (Selmane et al. 2022). When estimating values at unknown positions, it considers both the distance and the degree of variance between known data positions (Rata et al. 2018).

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Study area description

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Ibadan (the capital of Oyo State, Nigeria) is located in the heart of Southwest Nigeria, ranks third concerning the population after Lagos and Kano states, and with respect to the geographical area, it is the largest city in Nigeria (Figure 1). Ibadan has an alternating wet, of up to 8 months, and dry, of about 4 months, seasons with relatively constant atmospheric temperature per annum. The mean maximum temperature of Ibadan is about 26.46 °C, the mean minimum temperature of 21.42 °C, and relative humidity of 74.55% (Amanambu 2015). The month of June has the highest record of mean monthly rainfall of approximately 125 mm, with January having the lowest of approximately 18 mm. The mean annual rainfall is about 1,205 mm, which falls for about 109 days, having two peaks in June and September (Egbinola & Amanambu 2014).
Figure 1
Map showing the study area.
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Map showing the study area.

Figure 1
Map showing the study area.
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Map showing the study area.

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The study site is underlain by a basement complex, characterised by igneous and metamorphic rocks of the Precambrian era. Granite quartzite and migmatite are the major rock types (Egbinola & Amanambu 2014). Usually, the rock types found within this area are regarded as poor aquifers, given their low permeability and porosity (Egbinola & Amanambu 2014; Amanambu 2015). Though, some levels of porosity and permeability are developed through fractures and weathering, which in turn depends on the parent material. Therefore, the accessibility of groundwater depends on the weathered material's level and the extent to which joints and fractures are present (Egbinola & Amanambu 2014).

Methodology

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To assess the level of groundwater contamination, sampling of groundwater is done from hand-dug well located in the study area's residential and agricultural areas. Good quality narrow mouth screw-capped polypropylene bottles of 2-l capacity were used to collect the sample. Bottles were first washed with dilute nitric acid, and then rinsed thrice with DM (demineralised) water. The groundwater samples retrieved in prewashed polyethylene bottles were analysed for the following parameters: pH, E.C., and TDS, and were taken onsite with the aid of a multi-parameter water meter. The concentrations of calcium (Ca+) and magnesium (Mg+) were measured by the volumetric method in the presence of an aqueous ethylenediamine tetraacetic acid (EDTA) solution; this method was also used for titration of carbonates (HCO3). Chloride (Cl−) was determined in the neutral medium by a titrated solution of silver nitrate in the presence of potassium chromate. The measurement of nitrates (⁠⁠) and sulphate (⁠⁠) was carried out by a spectrophotometric method (Bashir et al. 2020), and potassium (K+) and sodium (Na+) measurements were determined by a flame photometer (Bouteraa et al. 2019).

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Groundwater hydrochemistry of the study area

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A descriptive analysis was carried out on the data, as well as a test for normality with respect to the distribution of the data. The result of the analysis is given in Table 1; the mean pH value is 6.48 while the pH of the whole dataset ranges between 4.40 (minimum) and 7.10 (maximum), this shows that the water samples obtained lie within the permissible limit for natural and potable water, respectively. The range of electrical conductivity (E.C.) lies within 270 μS/cm (minimum) and 1,870 μS/cm (maximum), while the average E.C. is 893.38 μS/cm. The total dissolved solids (TDS) ranges between a minimum of 142 mg/l and a maximum of 1,720 mg/l, with a mean TDS of 667.88 mg/l. It is obvious that there is a strong affinity between the presence of high TDS and high values of E.C. The result of the data (Table 2) skewness analysis revealed that majority of the data sets of the parameters under consideration were positively skewed, however, some are more positively skewed than others. Noteworthy is the skewness of the parameters, ‘pH, calcium, and sulphate’ which are the negatively skewed parameters under consideration. In conclusion the dataset does not entirely appear to be normally distributed, however, these were normalised by employing the log-normal distribution during analysis.

Table 1

Summary statistics of parameters

ParameterNMinimumMaximumMeanStd. deviationSkewness
pH 60 4.40 7.10 6.4880 0.49104 −2.133 0.309 
EC (μS/cm) 60 270.00 1,870.00 893.5585 326.55731 0.650 0.309 
TDS (mg/l) 60 142.00 1,720.00 536.8767 290.22752 2.322 0.309 
Sodium (mg/l) 60 5.10 59.52 31.9623 13.94075 0.151 0.309 
Magnesium (mg/l) 60 3.73 9.50 6.0187 1.25199 0.496 0.309 
Calcium (mg/l) 60 1.30 7.37 4.7102 1.33943 −0.618 0.309 
Chloride (mg/l) 60 57.60 1,476.00 445.0062 304.69540 0.899 0.309 
Potassium (mg/l) 60 0.00 6.10 2.2550 1.30636 0.763 0.309 
Carbonate (mg/l) 60 100.50 609.00 253.0008 111.75046 1.116 0.309 
Sulphates (mg/l) 60 0.22 35.13 19.6747 9.78644 −0.039 0.309 
Nitrates (mg/l) 60 0.63 39.06 9.6590 7.43786 2.008 0.309 
ParameterNMinimumMaximumMeanStd. deviationSkewness
pH 60 4.40 7.10 6.4880 0.49104 −2.133 0.309 
EC (μS/cm) 60 270.00 1,870.00 893.5585 326.55731 0.650 0.309 
TDS (mg/l) 60 142.00 1,720.00 536.8767 290.22752 2.322 0.309 
Sodium (mg/l) 60 5.10 59.52 31.9623 13.94075 0.151 0.309 
Magnesium (mg/l) 60 3.73 9.50 6.0187 1.25199 0.496 0.309 
Calcium (mg/l) 60 1.30 7.37 4.7102 1.33943 −0.618 0.309 
Chloride (mg/l) 60 57.60 1,476.00 445.0062 304.69540 0.899 0.309 
Potassium (mg/l) 60 0.00 6.10 2.2550 1.30636 0.763 0.309 
Carbonate (mg/l) 60 100.50 609.00 253.0008 111.75046 1.116 0.309 
Sulphates (mg/l) 60 0.22 35.13 19.6747 9.78644 −0.039 0.309 
Nitrates (mg/l) 60 0.63 39.06 9.6590 7.43786 2.008 0.309 
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Table 2

Concentration level of parameters

SSpHEC (μS/cm)TDS (mg/l)K+ (mg/l)Na+ (mg/l)Mg2+ (mg/l)Ca+ (mg/l)Cl− (mg/l) (mg/l) (mg/l) (mg/l)
1 6.80 548.00 471.90 92.32 40.50 4.94 7.37 57.60 156.04 33.34 39.06 
2 6.80 432.00 658.60 42.58 40.38 6.13 5.35 57.60 151.05 29.42 20.10 
3 6.80 1,020.00 625.60 66.25 40.31 5.72 2.85 86.40 156.05 26.31 6.40 
4 6.90 1,010.00 330.50 96.03 40.58 7.15 5.13 151.20 156.03 31.28 3.05 
5 6.80 990.00 831.60 99.05 40.37 5.54 5.57 115.20 100.50 21.79 2.97 
6 6.90 779.00 422.40 73.28 40.54 5.54 5.28 64.80 244.04 29.11 5.38 
7 6.85 1,870.00 508.80 40.23 45.85 5.87 5.47 108.00 250.05 32.65 4.70 
8 7.01 1,115.00 557.70 47.32 45.53 5.47 3.48 208.30 156.05 17.69 8.78 
9 7.00 1,294.00 924.00 97.04 40.56 3.73 5.54 525.60 200.04 10.26 3.76 
10 6.81 1,470.00 324.70 45.89 42.51 5.38 4.07 180.00 200.20 11.26 8.22 
11 6.80 1,420.00 271.90 31.25 46.56 5.37 5.29 237.60 290.06 19.66 10.75 
12 7.00 1,470.00 452.70 61.85 52.19 5.37 5.40 302.60 154.04 11.56 13.29 
13 6.80 270.00 361.60 59.75 40.36 5.06 5.52 187.20 144.50 9.35 14.27 
14 6.80 1,710.00 289.00 64.55 59.52 5.04 5.82 208.30 250.04 27.33 12.71 
15 6.81 715.00 673.20 66.62 57.05 5.29 5.58 288.00 156.05 33.59 14.76 
16 6.70 998.00 666.50 55.40 50.06 5.06 5.85 259.20 156.05 10.79 18.68 
17 6.80 948.00 653.40 47.40 50.02 5.54 5.30 170.30 156.05 22.53 10.72 
18 7.00 480.50 950.40 55.39 45.08 5.84 5.07 107.00 156.03 13.78 5.98 
19 7.00 1,260.01 564.50 46.89 47.65 5.03 3.36 109.80 156.03 23.86 20.12 
20 6.70 640.00 1,470.00 60.00 45.52 5.47 3.14 187.50 156.04 24.21 9.99 
21 6.80 771.00 1,470.00 50.63 50.52 5.45 2.54 152.00 200.20 28.15 2.79 
22 6.80 845.00 142.00 60.54 56.81 5.06 5.33 206.00 244.04 31.82 3.34 
23 6.70 1,400.00 1,720.00 62.35 45.04 5.46 5.14 409.57 290.85 9.67 6.79 
24 6.80 492.00 603.50 56.37 48.58 5.17 5.39 242.00 156.05 12.39 8.21 
25 6.80 412.00 752.60 59.01 50.65 5.64 5.67 460.50 144.50 11.68 1.83 
26 6.30 553.00 353.90 40.40 35.40 5.90 7.30 360.00 609.00 18.97 2.53 
27 6.80 602.00 350.00 62.40 22.40 4.60 2.30 602.00 276.60 15.78 9.37 
28 6.20 860.00 540.00 56.50 20.70 7.60 4.90 429.00 219.44 25.23 11.15 
29 6.60 884.00 390.00 64.00 20.70 6.30 5.40 724.60 284.72 26.54 9.87 
30 6.60 924.00 366.70 64.00 19.60 8.70 3.00 360.00 203.00 21.65 11.56 
31 6.20 818.00 500.00 57.80 20.70 7.50 4.30 439.50 224.13 2.16 37.56 
32 6.30 944.00 480.00 53.40 20.50 7.70 5.70 408.00 210.07 13.79 18.60 
33 6.20 403.00 257.90 24.40 8.60 6.30 4.60 144.00 460.00 15.73 5.20 
34 6.20 753.00 350.00 58.10 22.90 6.30 1.30 753.00 225.40 9.70 1.85 
35 4.40 820.00 420.00 75.30 27.20 5.90 5.60 820.00 353.00 6.47 1.77 
36 6.30 513.00 328.50 49.30 15.30 6.80 6.10 252.00 446.00 13.36 4.88 
37 6.70 915.00 420.00 42.50 25.60 4.40 6.40 915.00 187.48 15.09 3.00 
38 6.50 753.00 481.90 77.70 23.40 9.50 6.60 558.00 469.00 11.64 7.08 
39 6.00 1,044.00 600.00 58.70 28.70 7.30 4.90 930.00 283.00 9.05 2.26 
40 6.70 1,283.00 821.10 98.80 26.80 5.60 5.60 1,476.00 353.00 10.13 6.92 
41 6.40 957.00 390.00 43.90 24.00 5.60 3.20 957.00 353.00 32.33 9.45 
42 6.50 596.00 400.00 28.80 24.50 4.60 4.70 596.00 276.60 32.97 11.99 
43 6.60 823.00 420.00 29.10 25.10 4.50 4.60 823.00 225.40 35.13 12.97 
44 6.80 706.00 400.00 41.20 26.10 4.20 5.80 706.00 161.29 31.47 11.41 
45 4.70 770.00 430.00 41.80 26.70 4.00 6.20 770.00 283.00 28.66 13.46 
46 6.30 883.00 309.10 26.20 9.40 7.10 2.90 306.00 340.00 33.62 17.38 
47 6.30 1,083.00 693.10 91.20 26.10 7.50 2.50 1,062.00 426.00 20.69 11.82 
48 6.20 9,64.00 379.50 29.30 10.80 7.90 5.10 486.00 113.00 27.80 7.08 
49 6.10 1,004.00 328.30 41.90 13.30 6.80 3.40 600.00 147.00 35.13 21.22 
50 7.10 813.00 520.30 64.10 17.80 4.30 4.90 828.00 283.00 26.94 11.09 
51 6.30 611.00 320.00 56.10 23.40 4.30 2.10 611.00 283.00 5.39 1.69 
52 5.90 1,084.00 450.00 50.70 25.70 7.40 2.30 910.00 353.00 0.22 3.82 
53 6.30 902.00 550.00 56.10 20.60 7.60 2.30 418.50 214.76 5.17 5.29 
54 5.80 1,123.00 400.00 55.20 24.80 7.50 5.70 712.00 163.51 29.96 10.11 
55 6.00 513.00 328.30 9.80 5.10 6.40 4.80 558.00 423.00 15.52 3.73 
56 6.10 673.00 430.70 63.90 18.40 5.80 4.40 576.00 501.00 4.96 .63 
57 6.00 1,183.00 757.10 99.60 38.00 7.30 4.90 540.00 370.00 26.32 7.47 
58 6.10 483.00 309.10 51.10 15.80 6.90 5.10 103.00 473.00 17.53 9.25 
59 6.40 986.00 440.00 57.40 20.50 7.80 4.80 397.50 205.38 9.56 8.67 
60 6.40 1,028.00 600.00 59.70 20.40 7.90 4.40 487.00 200.69 12.34 10.76 
SSpHEC (μS/cm)TDS (mg/l)K+ (mg/l)Na+ (mg/l)Mg2+ (mg/l)Ca+ (mg/l)Cl− (mg/l) (mg/l) (mg/l) (mg/l)
1 6.80 548.00 471.90 92.32 40.50 4.94 7.37 57.60 156.04 33.34 39.06 
2 6.80 432.00 658.60 42.58 40.38 6.13 5.35 57.60 151.05 29.42 20.10 
3 6.80 1,020.00 625.60 66.25 40.31 5.72 2.85 86.40 156.05 26.31 6.40 
4 6.90 1,010.00 330.50 96.03 40.58 7.15 5.13 151.20 156.03 31.28 3.05 
5 6.80 990.00 831.60 99.05 40.37 5.54 5.57 115.20 100.50 21.79 2.97 
6 6.90 779.00 422.40 73.28 40.54 5.54 5.28 64.80 244.04 29.11 5.38 
7 6.85 1,870.00 508.80 40.23 45.85 5.87 5.47 108.00 250.05 32.65 4.70 
8 7.01 1,115.00 557.70 47.32 45.53 5.47 3.48 208.30 156.05 17.69 8.78 
9 7.00 1,294.00 924.00 97.04 40.56 3.73 5.54 525.60 200.04 10.26 3.76 
10 6.81 1,470.00 324.70 45.89 42.51 5.38 4.07 180.00 200.20 11.26 8.22 
11 6.80 1,420.00 271.90 31.25 46.56 5.37 5.29 237.60 290.06 19.66 10.75 
12 7.00 1,470.00 452.70 61.85 52.19 5.37 5.40 302.60 154.04 11.56 13.29 
13 6.80 270.00 361.60 59.75 40.36 5.06 5.52 187.20 144.50 9.35 14.27 
14 6.80 1,710.00 289.00 64.55 59.52 5.04 5.82 208.30 250.04 27.33 12.71 
15 6.81 715.00 673.20 66.62 57.05 5.29 5.58 288.00 156.05 33.59 14.76 
16 6.70 998.00 666.50 55.40 50.06 5.06 5.85 259.20 156.05 10.79 18.68 
17 6.80 948.00 653.40 47.40 50.02 5.54 5.30 170.30 156.05 22.53 10.72 
18 7.00 480.50 950.40 55.39 45.08 5.84 5.07 107.00 156.03 13.78 5.98 
19 7.00 1,260.01 564.50 46.89 47.65 5.03 3.36 109.80 156.03 23.86 20.12 
20 6.70 640.00 1,470.00 60.00 45.52 5.47 3.14 187.50 156.04 24.21 9.99 
21 6.80 771.00 1,470.00 50.63 50.52 5.45 2.54 152.00 200.20 28.15 2.79 
22 6.80 845.00 142.00 60.54 56.81 5.06 5.33 206.00 244.04 31.82 3.34 
23 6.70 1,400.00 1,720.00 62.35 45.04 5.46 5.14 409.57 290.85 9.67 6.79 
24 6.80 492.00 603.50 56.37 48.58 5.17 5.39 242.00 156.05 12.39 8.21 
25 6.80 412.00 752.60 59.01 50.65 5.64 5.67 460.50 144.50 11.68 1.83 
26 6.30 553.00 353.90 40.40 35.40 5.90 7.30 360.00 609.00 18.97 2.53 
27 6.80 602.00 350.00 62.40 22.40 4.60 2.30 602.00 276.60 15.78 9.37 
28 6.20 860.00 540.00 56.50 20.70 7.60 4.90 429.00 219.44 25.23 11.15 
29 6.60 884.00 390.00 64.00 20.70 6.30 5.40 724.60 284.72 26.54 9.87 
30 6.60 924.00 366.70 64.00 19.60 8.70 3.00 360.00 203.00 21.65 11.56 
31 6.20 818.00 500.00 57.80 20.70 7.50 4.30 439.50 224.13 2.16 37.56 
32 6.30 944.00 480.00 53.40 20.50 7.70 5.70 408.00 210.07 13.79 18.60 
33 6.20 403.00 257.90 24.40 8.60 6.30 4.60 144.00 460.00 15.73 5.20 
34 6.20 753.00 350.00 58.10 22.90 6.30 1.30 753.00 225.40 9.70 1.85 
35 4.40 820.00 420.00 75.30 27.20 5.90 5.60 820.00 353.00 6.47 1.77 
36 6.30 513.00 328.50 49.30 15.30 6.80 6.10 252.00 446.00 13.36 4.88 
37 6.70 915.00 420.00 42.50 25.60 4.40 6.40 915.00 187.48 15.09 3.00 
38 6.50 753.00 481.90 77.70 23.40 9.50 6.60 558.00 469.00 11.64 7.08 
39 6.00 1,044.00 600.00 58.70 28.70 7.30 4.90 930.00 283.00 9.05 2.26 
40 6.70 1,283.00 821.10 98.80 26.80 5.60 5.60 1,476.00 353.00 10.13 6.92 
41 6.40 957.00 390.00 43.90 24.00 5.60 3.20 957.00 353.00 32.33 9.45 
42 6.50 596.00 400.00 28.80 24.50 4.60 4.70 596.00 276.60 32.97 11.99 
43 6.60 823.00 420.00 29.10 25.10 4.50 4.60 823.00 225.40 35.13 12.97 
44 6.80 706.00 400.00 41.20 26.10 4.20 5.80 706.00 161.29 31.47 11.41 
45 4.70 770.00 430.00 41.80 26.70 4.00 6.20 770.00 283.00 28.66 13.46 
46 6.30 883.00 309.10 26.20 9.40 7.10 2.90 306.00 340.00 33.62 17.38 
47 6.30 1,083.00 693.10 91.20 26.10 7.50 2.50 1,062.00 426.00 20.69 11.82 
48 6.20 9,64.00 379.50 29.30 10.80 7.90 5.10 486.00 113.00 27.80 7.08 
49 6.10 1,004.00 328.30 41.90 13.30 6.80 3.40 600.00 147.00 35.13 21.22 
50 7.10 813.00 520.30 64.10 17.80 4.30 4.90 828.00 283.00 26.94 11.09 
51 6.30 611.00 320.00 56.10 23.40 4.30 2.10 611.00 283.00 5.39 1.69 
52 5.90 1,084.00 450.00 50.70 25.70 7.40 2.30 910.00 353.00 0.22 3.82 
53 6.30 902.00 550.00 56.10 20.60 7.60 2.30 418.50 214.76 5.17 5.29 
54 5.80 1,123.00 400.00 55.20 24.80 7.50 5.70 712.00 163.51 29.96 10.11 
55 6.00 513.00 328.30 9.80 5.10 6.40 4.80 558.00 423.00 15.52 3.73 
56 6.10 673.00 430.70 63.90 18.40 5.80 4.40 576.00 501.00 4.96 .63 
57 6.00 1,183.00 757.10 99.60 38.00 7.30 4.90 540.00 370.00 26.32 7.47 
58 6.10 483.00 309.10 51.10 15.80 6.90 5.10 103.00 473.00 17.53 9.25 
59 6.40 986.00 440.00 57.40 20.50 7.80 4.80 397.50 205.38 9.56 8.67 
60 6.40 1,028.00 600.00 59.70 20.40 7.90 4.40 487.00 200.69 12.34 10.76 
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Hydrochemistry and anthropogenic activities

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The analysed groundwater samples were subjected to factor analysis in order to acquire an overall idea about assembling the samples in a multidimensional space specified by the assessed parameters. Factor analysis is a valuable approach for gaining a better understanding of the connection between variables and identifying groupings (or clusters) that are mutually associated with a data body. The anthropogenic land use activities identified in the study area include educational, residential, commercial, waste disposal, and industrial activities, respectively. These land use activities gave rise to the prevalent factors identified affecting the quality of groundwater in the study area. These include use of pesticides, fertilisers, effluents and industrial wastes, landfills and waste dumpsites, and septic discharge. These are associated with the dominant land use around each sample location. All samples were subjected to factor analysis, which revealed a 0.587 value for the Kaiser–Maiyer–Olkin (KMO) and a value of 241.159 (p 0.000) for Bartlett's sphericity, indicating that FA was effective in giving significant reductions in dimensionality. From the data, four factors (Table 3), explaining 98.35% of the total variance, were estimated on the basis of the Kaiser criterion (Kaiser 1960) of the eigenvalues greater than or equal to 1.

Table 3

Rotated factor loading matrix showing eigen values of parameters

ParametersFactor
1234
pH .024 −0.996 −0.017 −0.018 
EC .993 0.007 −0.051 −0.079 
TDS .749 −0.159 −0.609 0.163 
Sodium .848 0.148 −0.243 0.430 
Magnesium .798 −0.512 .314 0.020 
Calcium .117 0.810 .527 0.133 
Chloride .542 0.443 −0.108 −0.695 
Potassium −0.961 −0.058 0.242 0.053 
Carbonates −0.734 0.215 −0.469 0.440 
Sulphates .408 0.879 0.004 0.209 
Nitrates .570 −0.302 0.607 0.397 
Total eigen value 5.167 3.067 1.469 1.117 
% of variance 46.969 27.883 13.352 10.151 
Cumulative % 46.969 74.852 88.205 98.356 
ParametersFactor
1234
pH .024 −0.996 −0.017 −0.018 
EC .993 0.007 −0.051 −0.079 
TDS .749 −0.159 −0.609 0.163 
Sodium .848 0.148 −0.243 0.430 
Magnesium .798 −0.512 .314 0.020 
Calcium .117 0.810 .527 0.133 
Chloride .542 0.443 −0.108 −0.695 
Potassium −0.961 −0.058 0.242 0.053 
Carbonates −0.734 0.215 −0.469 0.440 
Sulphates .408 0.879 0.004 0.209 
Nitrates .570 −0.302 0.607 0.397 
Total eigen value 5.167 3.067 1.469 1.117 
% of variance 46.969 27.883 13.352 10.151 
Cumulative % 46.969 74.852 88.205 98.356 
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A scree plot is a basic linear graph that illustrates the proportion of total variation explained or represented by each element in the data. The factors are arranged, and therefore given a number label, in decreasing order of contribution to the total variance. A scree plot is a graph of eigenvalues in ascending order of magnitude. It demonstrates a clear distinction between the slope angle of the strong eigenvalues and the progressive falling off of the remaining components. The four extracted components (eigenvalues > 1) appropriately represented the aggregate dimensions of the data set and compensated for 98.356% of the total variance in the current research, whereas the other six factors (eigenvalues 1) accounted for just 1.644% of the total variance (Figure 2).
Figure 2
Scree plot of factor loadings.
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Scree plot of factor loadings.

Figure 2
Scree plot of factor loadings.
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Scree plot of factor loadings.

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E.C., sodium, magnesium, and TDS marked factor 1, which explained 46.969% of the total variance. Factor 1 had a high positive loading for E.C. (0.993), sodium (0.848), magnesium (0.798), and TDS (0.749), respectively. On the other hand, Factor 1 had high negative loading on sulphur and carbonate: −0.961 and −0.734, respectively. High positive loadings indicated a strong linear correlation between the factor and parameters. Usage of fertilisers pesticides and herbicides can be marked as factor 1, obviously of anthropogenic origins from the agricultural land use. Factor 2, with higher positive loading of sulphates and calcium, explained 27.883% of the variance with a loading of 0.879 and 0.810. Factor 2 had a negative loading on pH; −0.996. Factor 3 accounted for 13.352% of the total variance and best represented by nitrates and partly calcium, 0.607 and 0.527, respectively. Groundwater of high TDS value encourages the mobilisation of compound contaminants such as carbonates, and nitrates. This single fact confirms the relationship between TDS and other compound contaminants. Leaching through downward washing of fertilisers through the overlying lateritic sand can increase the potassium of the groundwater and the process is evidenced in higher TDS values. Dissolved solids can produce hard water, which leaves deposits and films on fixtures, and on the insides of hot water pipes and boilers. Soaps and detergents do not produce as much lather with hard water as with soft water. As well as this, high amounts of dissolved solids can stain household fixtures, corrode pipes, and have a metallic taste. Factor 4 was responsible for 10.151% of total variance and was best represented by carbonates (0.440) and sodium (0.430). Groundwater of high sodium value is probably indicative of natural occurrence. This is an indication that groundwater with high levels of dissolved inorganic salts must have originated from water that has flowed through a region where the rocks have a high salt content (Amanambu 2015). Groundwater of high calcium value encourages the higher dissolution of solids leading to hardness of water. The human body needs calcium for strong teeth and bones.

Spatial interpolation and groundwater modelling

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Spatial modelling accuracy

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Kriging is a geostatistical approach for interpolating a surface from a distributed collection of known points in order to forecast a continuous surface of values between specific places. Kriging weights are controlled using the Variogram model. The mathematical definition of a variogram is a measure of semi-variance as a function of distance.
(1)
where γ(h) is the semi-variance; N(h) the number of pairs separated by distance or lag h; z(xi) the measured sample at point xi; and z (xi + h) the measured sample at point (xi + h). Fitting a mathematical model to the experimental data determines the spatial organisation of the data. The mathematical models depict the structure of the spatial heterogeneity as well as the Kriging input parameters. The model was fit to the water quality parameters, which revealed spatial autocorrelation in their functional limits.

The experimental semi-variogram of data pairs was fitted using five models (Table 4). Nugget and sill parameters were computed by the Geostatistical Analyst tool for each model, and there was no basis to alter them (Setianto & Triandini 2013). The nugget is essentially the Y-intercept of the semi-variogram model, while sill is the semi-variogram value at which the model smoothens out (Munyati & Sinthumule 2021). However, the spherical model appeared to be the most consistent having yielded both the nugget and sill ratio, within the confines of this study.

Table 4

Comparative error of the semi-variogram models and parameters tested

ParameterModelNNuggetSill
pH Spherical 60 0.868 0.132 
Circular 60 0.879 0.102 
Stable 60 0.899 0.101 
Gaussian 60 0.923 0.077 
Exponential 60 0.864 0.136 
E.C. Spherical 60 0.969 0.052 
Circular 60 0.997 0.024 
Stable 60 1.007 0.015 
Gaussian 60 1.028 0.022 
Exponential 60 1.038 0.0 
TDS Spherical 60 0.965 0.052 
Circular 60 1.016 0 
Stable 60 1.001 0 
Gaussian 60 1.016 0 
Exponential 60 1.016 0 
Ca Spherical 60 0.550 0.465 
Circular 60 0.766 0.394 
Stable 60 0.704 0.312 
Gaussian 60 0.702 0.313 
Exponential 60 0.457 0.557 
Mg Spherical 60 0.957 0.065 
Circular 60 1.016 0.011 
Stable 60 1.010 0.012 
Gaussian 60 1.017 0.009 
Exponential 60 0.995 0 
Na Spherical 60 0.488 0.222 
Circular 60 0.494 0.223 
Stable 60 0.533 0.189 
Gaussian 60 0.551 0.189 
Exponential 60 0.447 0.278 
Cl Spherical 60 0.498 0.905 
Circular 60 0.554 0.872 
Stable 60 0.454 0.991 
Gaussian 60 0.608 0.759 
Exponential 60 0.166 1.275 
HCO3 Spherical 60 0.528 0.176 
Circular 60 0.549 0.154 
Stable 60 0.579 0.133 
Gaussian 60 0.559 0.153 
Exponential 60 0.476 0.230 
K Spherical 60 0.755 0.462 
Circular 60 0.777 0.466 
Stable 60 0.847 0.402 
Gaussian 60 0.840 0.410 
Exponential 60 0.625 0.598 
SO4 Spherical 60 0.797 0.527 
Circular 60 0.847 0.484 
Stable 60 0.943 0.392 
Gaussian 60 0.925 0.412 
Exponential 60 1.002 0.327 
NO3 Spherical 60 0.841 0.175 
Circular 60 1.016 0 
Stable 60 1.016 0 
Gaussian 60 1.016 0 
Exponential 60 1.016 0 
ParameterModelNNuggetSill
pH Spherical 60 0.868 0.132 
Circular 60 0.879 0.102 
Stable 60 0.899 0.101 
Gaussian 60 0.923 0.077 
Exponential 60 0.864 0.136 
E.C. Spherical 60 0.969 0.052 
Circular 60 0.997 0.024 
Stable 60 1.007 0.015 
Gaussian 60 1.028 0.022 
Exponential 60 1.038 0.0 
TDS Spherical 60 0.965 0.052 
Circular 60 1.016 0 
Stable 60 1.001 0 
Gaussian 60 1.016 0 
Exponential 60 1.016 0 
Ca Spherical 60 0.550 0.465 
Circular 60 0.766 0.394 
Stable 60 0.704 0.312 
Gaussian 60 0.702 0.313 
Exponential 60 0.457 0.557 
Mg Spherical 60 0.957 0.065 
Circular 60 1.016 0.011 
Stable 60 1.010 0.012 
Gaussian 60 1.017 0.009 
Exponential 60 0.995 0 
Na Spherical 60 0.488 0.222 
Circular 60 0.494 0.223 
Stable 60 0.533 0.189 
Gaussian 60 0.551 0.189 
Exponential 60 0.447 0.278 
Cl Spherical 60 0.498 0.905 
Circular 60 0.554 0.872 
Stable 60 0.454 0.991 
Gaussian 60 0.608 0.759 
Exponential 60 0.166 1.275 
HCO3 Spherical 60 0.528 0.176 
Circular 60 0.549 0.154 
Stable 60 0.579 0.133 
Gaussian 60 0.559 0.153 
Exponential 60 0.476 0.230 
K Spherical 60 0.755 0.462 
Circular 60 0.777 0.466 
Stable 60 0.847 0.402 
Gaussian 60 0.840 0.410 
Exponential 60 0.625 0.598 
SO4 Spherical 60 0.797 0.527 
Circular 60 0.847 0.484 
Stable 60 0.943 0.392 
Gaussian 60 0.925 0.412 
Exponential 60 1.002 0.327 
NO3 Spherical 60 0.841 0.175 
Circular 60 1.016 0 
Stable 60 1.016 0 
Gaussian 60 1.016 0 
Exponential 60 1.016 0 
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The mean error (ME) indicates the probability of the predictions made by the Kriging method of being biased based on the possibility of an average as too high or too low. It reflects the average difference between the measured and the predicted values. It is expressed as
(2)
The root mean square error (RMSE) indicates how closely a model predicts the measured values and it is expressed mathematically as:
(3)
The average standard error (ASE) presents the mean of prediction errors
(4)
The root mean square error standardised (RMSES), indicates over or under estimation of the model predictions.
(5)
where n is the sample size (number of data points), z(Si) is the measured value (number of individual parameters) at location Si, z(Si) is the estimated value (predicted value of individual parameters) at location Si; and σ2(Si) is the Kriging variance for the ith data point (Munyati & Sinthumule 2021). If indication of the comparative suitability of a semi-variogram model could not be derived based on the combination of all three cross-validation statistics, ME and RMSES were prioritised because they indicated the extent to which the model interpolation predictions deviated or agreed from the measured values (Johnson et al. 2018; Elubid et al. 2019). The values of these four cross-validation techniques help to adequately understand and better explain the accuracy of the predicting model. The model posits that the closer the values of the ME and RMSES are to null (zero) and unity (one), respectively, the more accurate the model is and vice versa.

The cross-validation statistics criteria used ensured that the predictions are unbiased, this is indicated by: (1) an ME close to null (zero) (Gharbia et al. 2016), (2) estimates do not diverge significantly from the field observed values, indicated by a difference between the RMSE and ASE that is as small as possible (Nas & Berktay 2010), and (3) standard errors are precise, indicated by an RMSES prediction error close to unity (one) (Munyati & Sinthumule 2021).

Based on the cross-validation criteria to ensure unbiased predictions, the best model for the prediction of parameter concentration level was developed using a stable semi-variogram and true anisotropy. Table 5 presents the model accuracy measures as a result of the cross-validation matrix. The cross-validation matrix produced ME values that are close to zero ranging between −1.735 and 0.004; RMSES values that ranged between 0.821 and 1.134 (these values are close to 1, without approximation); and the least difference in the values of ASE and RMSE criss-crossing the entire data considered ranging between 0.058 and 33.078, to represent each parameter within the study. Co-Kriging surfaces were also constructed utilising covariate pairings and a stable, anisotropic semi-variogram. Cross-validation tests for parameter concentration found that the accuracy metrics (ME, RMSES, ASE, and RMSE) gave similar values across the sample points for each of the parameters.

Table 5

Model accuracy metrics

VariablesMERMSESASERMSE(ASE – RMSE)
pH 0.004 1.134 0.429 0.487 −0.058 
E.C. −0.797 0.907 326.67 323.96 2.71 
TDS −0.685 1.081 266.22 287.87 −21.65 
HCO3 −0.507 0.829 101.55 84.432 17.118 
Ca −0.246 0.821 25.579 27.577 −1.998 
Mg 0.025 0.996 1.246 1.242 0.004 
Na −0.006 0.535 20.163 21.998 −1.835 
NO3 −0.209 0.899 6.836 7.378 0.821 
Cl− −1.735 0.877 346.51 279.71 66.8 
K 0.011 1.009 1.198 1.183 0.015 
SO4 −0.133 0.882 9.271 9.207 1.204 
VariablesMERMSESASERMSE(ASE – RMSE)
pH 0.004 1.134 0.429 0.487 −0.058 
E.C. −0.797 0.907 326.67 323.96 2.71 
TDS −0.685 1.081 266.22 287.87 −21.65 
HCO3 −0.507 0.829 101.55 84.432 17.118 
Ca −0.246 0.821 25.579 27.577 −1.998 
Mg 0.025 0.996 1.246 1.242 0.004 
Na −0.006 0.535 20.163 21.998 −1.835 
NO3 −0.209 0.899 6.836 7.378 0.821 
Cl− −1.735 0.877 346.51 279.71 66.8 
K 0.011 1.009 1.198 1.183 0.015 
SO4 −0.133 0.882 9.271 9.207 1.204 
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Furthermore, spatial correlations were quantified with the aid of the semi-variogram. In sum, the application of Kriging with semi-variogram models is proportional to the expected squared differences between the paired variables, i.e., the data values (x) and the distance lag, which separates different locations (h) (Gharbia et al. 2016). The spherical semi-variogram model being the most consistent was used for each water quality parameter. The Predictive performance of the fitted model had previously been evaluated using the cross-validation tests (Table 5). After the cross-validation process, maps of spatial estimates of parameters were generated to render a visual interpretation of the distribution of water quality parameters. As well as the best-fitted semi-variograms for the water quality parameters for the study area.

Geostatistical modelling

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The normalcy of the investigated water quality parameters (pH, E.C., TDS, HCO3, Ca, Mg, Na, NO3, Cl−, S, SO4) was taken into account for the optimal performance of Kriging techniques. The ASE, RMSE and RMSES values were used to choose the best-fitting semi-variogram models (Table 4). Each of the Kriging algorithms, in addition to providing projections, provides the Kriging variations, which measure the variability of the forecasts from the measured value. When RMSES is near unity (1), the model is deemed effective and produces the best accurate estimates. The Semi-variance analysis is a technique to assess the geographical dependency of the groundwater quality index (GWQI), which revealed that the estimated index was modelled using multiple semi-variogram models. The results of the selecting of the best-fitted variogram model show that spherical model was found as the most accurate model for water quality parameters. For all water quality parameter values, the exponential semi-variogram model is suited best (Figure 3(a)–3(k)).
Figure 3
(a)–(k) Best-fitted semi-variogram models for water quality parameters. (a) pH, (b) E.C., (c) TDS, (d) calcium, (e) magnesium, (f) sodium, (g) carbonate, (h) nitrates, (i) chloride, (j) potassium, and (k) sulphates.
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(a)–(k) Best-fitted semi-variogram models for water quality parameters. (a) pH, (b) E.C., (c) TDS, (d) calcium, (e) magnesium, (f) sodium, (g) carbonate, (h) nitrates, (i) chloride, (j) potassium, and (k) sulphates.

Figure 3
(a)–(k) Best-fitted semi-variogram models for water quality parameters. (a) pH, (b) E.C., (c) TDS, (d) calcium, (e) magnesium, (f) sodium, (g) carbonate, (h) nitrates, (i) chloride, (j) potassium, and (k) sulphates.
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(a)–(k) Best-fitted semi-variogram models for water quality parameters. (a) pH, (b) E.C., (c) TDS, (d) calcium, (e) magnesium, (f) sodium, (g) carbonate, (h) nitrates, (i) chloride, (j) potassium, and (k) sulphates.

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Spatial prediction of concentration levels

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A prediction/spatial variation map was generated to show the variation of pH over the study area (Figure 4(a)–4(k)). The coordinates of the sample points were used to aid in the depiction of the parameter over space. The map revealed that all the simulated pH levels were within the range +6.1 to +6.8 which were all in the slightly acidic range, and fall within permissible limits. TDS being a measure of materials dissolved in water, it is a measure of the combined content of all inorganic and organic substances contained in a liquid in molecular, ionised or micro-granular suspended form (Thomas 2021). The simulated TDS values as obtained from laboratory analysis ranged between 402.79 and 715.44 mg/l. The map revealed that lower levels of TDS are dispersed across the study area stretching from the centre region of the study area while higher values of TDS was predicted at upper central region (point 5), and the south-western area of the entire study area (points 58 and 59). The simulated E.C. values as obtained from laboratory analysis ranged between 726.95 and 1,046.57 μS/cm. The map revealed that mid-range values and lower levels of E.C. are dispersed across the study area stretching from the centre region of the study area while higher values of E.C. was predicted at upper central region (points 45 and 49), and the north-western area of the entire study area (points 22, 44, and 46). Calcium (Ca+) was simulated following the values obtained from laboratory analysis and it ranged between 13.14 and 57.81 mg/l. The map revealed that higher concentration levels of calcium (Ca+) are clustered towards the central region (points 25, 31, 34), while lower concentration values of calcium (Ca+) was observed to be spread across the eastern stretch of the study area. Mid-range values were observed to be dispersed across the study area. Sodium (Na+) values ranged between 1.21 and 37.47 mg/l. The map revealed that higher concentration levels of sodium (Na+) can be found towards the northern end of the study area (points 29, 45, 49, 54, and 56) and the southwestern end of the study area (points 3, 52, 57, 58, and 59). Magnesium (Mg2+) values ranged between 5.49 and 6.77 mg/l. Higher concentration levels of magnesium (Mg2+) are clustered towards north-east of the study area (points 6, 12, and 30) while lower concentration and mid-range values were observed to be dispersed across the study area. Carbonates (⁠⁠) values ranged between 21.13 and 179.13 mg/l. Very high concentration levels of carbonates (⁠⁠) are clustered towards south-eastern end of the study area (points 58 and 59), as well as at the north-central end (points 45, 49, 54, and 56) with high concentration values. Lower concentration and mid-range values were observed to be discrete across the study area. Potassium (K+) ranged between 1.62 and 3.11. Very high concentration levels of potassium (K+) are clustered towards north-east of the study area (points 6, 13, 18, 25, 27, and 30), as well as at the north-west end (points 1, 4, 22, 24, 32, 44, and 46) with high concentration values as well. Mid-range values and lower concentration were observed to be spread out across the study area. Sulphate (⁠⁠) values ranged between 10.04 and 28.9 mg/l. The map revealed that higher concentration levels of sulphate (⁠⁠) was recorded at sample points 2, 4, 41 and 42, and 15 towards the western end of the study area; and at sample points 45 and 29 in the north end of the study area. While lower concentration values of sulphate (⁠⁠) was scattered around the central west and southern section of the study area. Mid-range values were observed to be dispersed across the study area. The nitrates (⁠⁠) values ranged between 1.0 and 38.22 mg/l. Higher concentration levels of nitrates (⁠⁠) was recorded at sample points 1 and 31 only. Mid-range concentration values and lower concentration values of nitrates (⁠⁠) are dispersed across the study area. The chloride (Cl−) values ranged between 173.43 and 699.43 mg/l. Higher concentration levels of chloride (Cl−) was recorded at sample points 21, 25 and 31 only. Mid-range concentration values and Lower concentration values of chloride (Cl−) are dispersed across the study area.
Figure 4
(a)–(k) Optimal surface prediction for water quality parameters using Kriging showing local governments. (a) pH, (b) TDS, (c) E.C., (d) calcium, (e) sodium, (f) magnesium, (g) HCO3, (h) potassium, (i) SO4, (j) NO3, and (k) Cl−.
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(a)–(k) Optimal surface prediction for water quality parameters using Kriging showing local governments. (a) pH, (b) TDS, (c) E.C., (d) calcium, (e) sodium, (f) magnesium, (g) HCO3, (h) potassium, (i) SO4, (j) NO3, and (k) Cl−.

Figure 4
(a)–(k) Optimal surface prediction for water quality parameters using Kriging showing local governments. (a) pH, (b) TDS, (c) E.C., (d) calcium, (e) sodium, (f) magnesium, (g) HCO3, (h) potassium, (i) SO4, (j) NO3, and (k) Cl−.
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(a)–(k) Optimal surface prediction for water quality parameters using Kriging showing local governments. (a) pH, (b) TDS, (c) E.C., (d) calcium, (e) sodium, (f) magnesium, (g) HCO3, (h) potassium, (i) SO4, (j) NO3, and (k) Cl−.

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Groundwater suitability

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The GWQI was used to estimate the suitability of the water for consumption. This index provides an unbiased measure of water quality considering all the measured parameters for each water sample. The GWQI is a recently adopted method for revealing the quality of groundwater at a glance. The Water Quality Index (WQI) computations for each sampling location in the current study involved three successive steps (Li et al. 2011). The first step was ‘assigning of weight’. Each of the parameters except were assigned weights (Wi), according to their relative importance in the overall drinking water quality. The most significant parameters were given a weight of 5 and the least significant a weight of 1 (Mahmud et al. 2020).

The second step will be the calculation of relative weights for each water quality feature. The relative weight (Wi) was computed using Equation (5) as pointed out by Mahmud et al. (2020).
(6)

where Wi is the relative weight, wi is the weight of each parameter, and n is the number of parameters.

The third step was the computation of the quality rating scale. The quality rating scale (qi) for each parameter was calculated using Equation (6) (Iwar et al. 2021):

(7)
where qi is the quality rating, Ci is the concentration of each chemical parameter in each water sample in mg/l, except pH, and Si is the World Health Organisation (WHO) standard for each chemical parameter.
Finally, the Wi and qi were used to compute the SIi for each chemical parameter (Equation (7)), and then the WQI was calculated using Equation (8).
(8)
(9)
where SIi is the sub-index of each parameter, qi is the rating based on the concentration of each parameter, and n is the number of parameters’. The computed GWQI values for each location were categorised, and detailed computations for the GWQI for each location and parameter was provided (Table 6) (Al-Omran et al. 2015; Bashir et al. 2020).
Table 6

Relative weights of parameters in the study area

ParametersWHO standardUnit weightWiqiWiqi
pH 7.5 0.1333 0.456360525 86.66666667 39.55125 
EC 400 0.0025 0.00855676 223.389625 1.911491 
TDS 500 0.0020 0.006845408 107.37534 0.735028 
Sodium 200 0.0050 0.01711352 15.98115 0.273494 
Magnesium 50 0.0200 0.068454079 12.0374 0.824009 
Calcium 100 0.0100 0.034227039 4.7102 0.161216 
Chloride 250 0.0040 0.013690816 178.00248 2.436999 
Potassium 12 0.0833 0.285225328 18.79166667 5.359859 
Carbonates 125 0.0080 0.027381631 202.40064 5.54206 
Sulphates 250 0.0040 0.013690816 7.86988 0.107745 
Nitrates 50 0.0200 0.068454079 19.318 1.322396 
Sum   1  58.22554 
ParametersWHO standardUnit weightWiqiWiqi
pH 7.5 0.1333 0.456360525 86.66666667 39.55125 
EC 400 0.0025 0.00855676 223.389625 1.911491 
TDS 500 0.0020 0.006845408 107.37534 0.735028 
Sodium 200 0.0050 0.01711352 15.98115 0.273494 
Magnesium 50 0.0200 0.068454079 12.0374 0.824009 
Calcium 100 0.0100 0.034227039 4.7102 0.161216 
Chloride 250 0.0040 0.013690816 178.00248 2.436999 
Potassium 12 0.0833 0.285225328 18.79166667 5.359859 
Carbonates 125 0.0080 0.027381631 202.40064 5.54206 
Sulphates 250 0.0040 0.013690816 7.86988 0.107745 
Nitrates 50 0.0200 0.068454079 19.318 1.322396 
Sum   1  58.22554 
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The overall weight of the GWQI shows that the water in the study area falls within the good water range (Table 7), of the groundwater classification according to Sahu & Sikdar (2008) and Belkhiri et al. (2020).

Table 7

Classification of groundwater based on the GWQI

RangeType of water
<50 Excellent 
50–100.1 Good water 
100–200.1 Poor water 
200–300.1 Very poor water 
>300 Water unsuitable for drinking purposes 
RangeType of water
<50 Excellent 
50–100.1 Good water 
100–200.1 Poor water 
200–300.1 Very poor water 
>300 Water unsuitable for drinking purposes 
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Geostatistical analysis techniques, such as Kriging, are considered to be useful techniques for the monitoring, evaluation and management of groundwater resources. This study uses Kriging geostatistical technique and the GWQI to map the spatial variability of groundwater quality parameters. The groundwater quality analyses were done for Ibadan Metropolitan area using a GIS-based geostatistical algorithm. Factor analysis was employed to understand the influence of prevalent human activities on each parameter.

This study found a significant correlation between anthropogenic factors and concentration levels of water quality parameters in groundwater wells. This study on the overall provided a local government level concentration prediction mapping in the Ibadan metropolis. The study generated prediction models that estimated the presence of different parameters in groundwater where direct measurements were not possible. The results of this study will be useful for residents or housing developers installing new water wells to avoid areas that are known, or predicted, to contain concentrations of parameters greater than the established WHO and USEPA secondary water quality criteria.

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Data cannot be made publicly available; readers should contact the corresponding author for details.

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The authors declare there is no conflict.

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