Introduction

One of the most essential aspects of water research is water quality assessment (Eyankware, Obasi, et al., 2021). According to Arif et al. (2016) and Eyankware, Igwe, et al. (2021), almost 1.1 billion people worldwide lack access to safe drinking water, and water-related diseases claim the lives of millions of people every year. Globally, over 80% of wastewater is discarded without being treated or reused, according to the United Nations (Alvim et al., 2020; Connor, 2015; Mustafa, 2020; Usman et al., 2020). In some nations around the world, this percentage might reach as high as 95% (Lyu et al., 2016). Every year, approximately one billion people, mostly from low-income groups, become ill as a result of contaminated water (Bartram et al., 2005; Gleick, 2002). In 2015, 1.8 million people died all around the world as a result of it (Mayo & Hanai, 2017). According to Eyankware and Ephraim (2021), water may be abundant, but if it is contaminated, it is deemed unsafe for consumption. Hence, it is preferable for water resources to be rarely found and free of contaminants. Ulakpa and Eyankware (2021) were of the view that the major sources of water for domestic and mining purposes around most mining hubs in Ebonyi state are stream, a few manual, and motorized boreholes. Most of the time, when most other water sources (manual and motorized boreholes) have dried up, the residents in the study area turn to these abandoned pits for domestic water (Eyankware et al., 2018).

Lately, groundwater is one of the most water resources faced with various forms of contamination as a result of geogenic and anthropogenic activities. Contamination of groundwater is a global issue that affects all living things and the ecology (Chaudhry and Malik, 2017; Schwarzenbach et al., 2010). Contamination of groundwater within Ebonyi state, southern Nigeria, is mostly attributed to mining/quarrying (Eyankware et al., 2022; Obasi et al., 2021). In the sedimentary basin of southern Nigeria, the southern Benue Trough hosts a variety of ore deposits, such as sulphide ore, lead, or limestone, among others (Fatoye & Gideon, 2013; Eyankware et al., 2018; Ike et al., 2021; Eyankware et al., 202b), with history of quarrying/mining dated over 60 decades as reported by Nnabo (2016). The presence of these mineral deposits has attracted both local and foreign miners. The most frequent quarrying/mining method in the world and the study area is surface mining, open-pit mining accounts for the majority of surface mining in southeastern Nigeria (Eyankware et al., Eyankware, Obasi, et al., 2020, Eyankware, Obasi, et al., 2020, 2022). Marzouk (2018) on the other hand, claims that the methods and equipment employed in solid mineral mining/quarrying are dependent on the purpose for which the mineral is mine/quarried.

The Nkalagu limestone quarry was mostly used for cement manufacture in the 1990s and early 2000s. This quarrying activity has left a lot of abandoned quarry pits in the study area, which is thought to be a threat to water quality within the study. The vast limestone deposit within the study area, was discovered within the Turonian Eze-Aku Formation and looks to be one of the country's largest and most economically viable limestone resource bases, is well-known in the research region (Eyankware, Igwe, et al., 2021; Odukwe, 1980). Because of the presence of this mineral deposit, quarrying activity has been drawn to the area. The Nigerian Cement Corporation (NIGERCEM) is a notable enterprise that utilizes this resource for cement manufacture in Nkalagu, Nigeria. It is Nigeria's first indigenous cement manufacturing company (Fatoye & Gideon, 2013). Mining is the most frequent method of mineral extraction, and it has resulted in the formation of numerous pits, most of which have been abandoned without being adequately closed or reclaimed (Eyankware, Obasi, et al., 2020). Because they are filled with rainwater during the rainy season yet function as water supplies for the local community throughout the dry season (Igwe et al., 2021; Moses & Ruth, 2015), these pits pose significant environmental risks (Igwe et al., 2021; Moses & Ruth, 2015).

Quarrying/mining activities across Ebonyi state have been reported to be a major source of heavy metal in the water resources around most active and abandoned quarry/mine sites across southern Nigeria (Eyankware, Obasi, et al., 2020; Eyankware, Nnabo, et al., 2020; Kalu & Ogbonna, 2018; Eyankware et al., Eyankware, Obasi, et al., 2020; Ozoko, 2013; Obasi, & Akudinobi, 2020; Okolo et al., 2018; Obasi et al., 2021; Igwe et al., 2020). They believed that mine tailings and waste from quarrying/mining activities interact with surface water, after which percolates into the ground thereby contaminating groundwater. A study conducted by Nnabo, (2015) revealed the presence of heavy metals in water resources around the mining district of Enyigba Pb–Zn mine showed the presence of heavy metals in order Cd >  >  > As >  > Pb > Ni > Zn > Cu. As earlier pointed out, decline in water resource quality has worsened throughout multiple mines and quarries in Ebonyi State, Nigeria, over the last few decades, and potable drinking water is scarce (Eyankware et al., Eyankware, Obasi, et al., 2020; Obasi et al., 2021). Globally, the variables that contribute to the deterioration of water resource quality differ from one location to the next, depending on the geogenic or anthropogenic activities that take place in the area (Eyankware, Obasi, et al., 2021; Ulakpa & Eyankware, 2021). Infiltrated water, precipitation, surface water, and subsurface geochemical processes, according to Igwe et al. (2020), all affect water quality, particularly groundwater quality. Similarly, water discharge from human activities has an impact on the quality of groundwater (Obasi et al., 2021). Groundwater pollution is a serious concern, since harmful or heavy metals are permanent, and the majority of them have toxic effects on living species when they exceed the permissible limit (Sirajudeen et al., 2014; Chakraborty et al., 2009). According to Eyankware, Nnabo, et al. (2020), 72% of water resources and soil quality are declining across Ebonyi state in southeastern Nigeria, particularly near quarrying/mining activity. Eyankware and Obasi (2021); Eyankware, Nnabo, et al. (2020) further pointed out that mining/quarrying activities introduce heavy metals into water thereby leading to contamination. As a result, there is a pressing need to assess the impact of quarrying activities on the study area's groundwater resource, with a focus on cations, anions, and heavy metals. Three key goals guided this research: (i) compile a list of abandoned limestone quarry discharge sites and collect groundwater samples for analysis; (ii) describe the chemical composition of water within the study area using hydrogeochemical facies; (iii) evaluate water using statistical approach (PCA); (4) evaluate the presence of heavy metal in water using different indices and determine its suitability for domestic use.

The area of research

The location of the study is in the Nkalagu district of Ebonyi State, Nigeria's southeastern region. On the Enugu–Abakaliki expressway, approximately 50 km northeast of Enugu. It's between 6° 10′ and 6° 40′ north latitude, and 7° 35′ and 7° 50′ east longitude. A network of major highways and pathways connects the area.

Topography and physiography

According to Igbozuruike (1975) the study location is located within Nigeria's rainforest zone. Orchard bush with small trees and varied densities of prominent elephant grasses characterize the vegetation. There are two distinct seasons in this area: wet and dry. The rainy season starts in March and ends in December, the wet season lasts from November to October, whereas the dry season lasts from November to February. The Dry Harmattan Wind, a North–East Trade Wind from the Sahara Desert, which dominates during the dry season, and the Marine Wind, SW Trade Wind from the Atlantic Ocean, which ushers in the rainy season, are the two prevailing winds that blow across the country at different times of the year. The temperatures in the dry season range from 20 to 38 °C, whereas temperatures in the wet season range from 16 to 28 °C. In January, the monthly rainfall averages 3.1 mm, while in July, it averages 270 mm (Eyankware et al., 2018). The yearly rainfall ranges from 1750 to 2250 mm. The climate in the area surely aids dispersal.

Geology

The Benue Trough is a three-part NE–SW trending trough divided into Northern, Central, and Southern, as well as Lower parts (Petters, 1982, 1991). In the Abakaliki Basin of the southern Benue Trough, sedimentation began with the Upper Albian Asu River Group, which disconformably overlies the Precambrian Basement Complex rocks. The Asu River Group is made up of the Abakaliki Shale, which includes volcanoclastics, sandstone, and sandy limestone lenses (Benkheil, 1989). On top of the Asu River Group is the Turonian Eze-Aku Formation (Table 1). This formation (Reyment, 1965) is made up of flaky, grey, or black shales with sandstones and subordinate limestone (Fig. 1). Thick limestone or sandstone strata alternate with calcareous shales in areas, where the Eze-Aku Formation exists (Ikhane et al., 2009). The Nkalagu Formation, described by Petters (1991) and Umeji (2000) in the Nigerian Cement Company (NIGERCEM) quarries (up to 6 m thick), is an example of the aforementioned description.

Table 1 Stratigraphic column of the study area. After, Obasi et al. (2020)
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Fig. 1
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Groundwater sampling spots are shown on a geology map of the study area

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Hydrogeology

The Nkalagu area's hydrogeology is characterized by poor aquifer conditions (Eyankware et al., 2018). The reason for this is that the area's principal shale units (Aquiclude) are neither porous nor permeable, and hence do not transmit water to surrounding wells. As a result, finding groundwater on a regional scale has become impossible. For example, groundwater could be present in weathered/fractured zones or at sandstone intercalation points.

Materials and methods

Sampling laboratory analysis

Boreholes and hand-dug wells were used to collect water samples around the abandoned quarry site. Groundwater was collected and stored in plastic beakers for analysis once it was collected. Before the field sampling activity, the sample beakers/containers were meticulously cleaned and soaked in distilled water acidified with 1.0 ml HNO3 for 3 days. To gather samples that accurately reflect the water resource, 15 groundwater samples were collected in a methodically manner from various water sources (Fig. 1). Potential surface films in open hand-dug wells and surface water sources were removed before sampling, and samples from boreholes were acquired after 5–10 min of pumping. Each sample was filtered into a sample bottle using disposable filters with a diameter of 0.45 m after washing the bottle with the aliquot to ensure full removal of suspended particles. The samples were acidified in the field with 1.0 ml of conc. HNO3 to prevent heavy metal precipitation, and three drops of HNO3 were added with new syringes to prevent sorption. The samples were stored in hermetically sealed ice-packed beakers at 4 °C. To avoid evaporation, the temperature was maintained during the journey to the analytical laboratory (Sehgal et al., 2012; Singh et al., 2005). Heavy metals like Fe, Ag, Cu, Co, Cd, Mn, Zn, and Pb were measured with a fast sequential (FS) (Varian 240 AA) atomic absorption spectrophotometer (AAS), whereas HCO3¯ and SO42¯ were measured with a Hach DR/2010 Spectrophotometer. The pH values were obtained using a portable pH meter (WGS 84) with temperature electrode accessories, even though the temperature of each sample was taken with a potable thermometer. All data collection and analysis followed the APHA guidelines ().

Analyzing data

Using Microsoft Excel and Surfer 11, the spread of contaminants in the area was visualized. The data were compared to the World Health Organization's (WHO) water quality criteria in terms of the related health risk (Table 2).

Table 2 Results of physicochemical properties of water
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Analyses statistics

Principal component analysis (PCA), Pearson correlation analysis, contamination, and metal pollution indices were all used to assess the researched parameters.

In the PCA, component loading entailed reducing a large data set of many variables into a smaller number of linear combinations in the component that accounted for an appropriate fraction of the total data variance and easily associated the variables to the sources or processes via Eq. (1):

$$\sigma^{2} = \frac{1}{N} \mathop \sum \limits_{i = 1}^{N} (X_{N - } N)^{2}$$
(1)

when Xi represents the statistical mean, I represents the intervals between variables, and N represents the number of outcomes.

The Metal Pollution Index (MPI), which estimated the aggregate impact of individual heavy metals on water quality (Horton, 1965; Mohan et al., 1996), was evaluated using Caeiro et al. (2015), Eq. 2:

$${\text{MPI}} = \sqrt[n]{{M_{1} \times M_{2} \times M_{3} \cdots \cdots }}{\text{Mn}}$$
(2)

where Mn = concentration of the metal.

Microsoft Excel and Surfer 12 software were used to model the concentration configuration and dispersion trend in the area. The findings were matched to WHO guidelines. Pearson's correlation coefficient was calculated using the Statistical Package for Social Science (SPSS) version 23.

Contamination factor (CF)

The CF was calculated using the Hakanson (1980) formula:

$${\text{CF}} = \frac{{C_{n} }}{{B_{n} }}$$
(3)

where Cn is the metal concentration, Bn is the background/target value (Akakuru et al., 2021b; Yahaya et al., 2021).

Pollution Load Index (PLI)

Hakanson's (1980) formula was used to calculate the PLI:

$${\text{PLI}} = \sqrt[n]{CF1 \times CF2 \times }CF3 \times \ldots \times CFn$$
(4)

where CF stands for contamination factor and n stands for element number.

Pollution index of groundwater (PIG)

The PIG is a tool for evaluating the quality of groundwater for drinking purposes (Subba Rao et al., 2018). When using the PIG, there are five steps to consider. Step I: Determine the relative weight (Rw) of each factor in the assessment of water quality as it relates to human health (on a scale of 1–5). (Table 8). Step II is the determination of the weight parameter (Wp) for each of the groundwater quality factors, intending to determine the relative contribution of each to the overall groundwater quality status (Eq. 5). Step III entails calculating the concentration status (Sc), which was calculated by dividing each sample's water variable content (C) by its associated quality standard limit (Ds) (Eq. 6). In this study, the PIG was assessed using the WHO (2011) criteria. Step IV entails calculating the overall quality of groundwater (Ow) by multiplying the weight parameter (Wp) by the concentration status (Sc) (Eq. 7). Step V entails adding up all of the Ow values per sample (Eq. 8):

$$W_{p} = \frac{{R_{w} }}{{\sum R_{w} }}$$
(5)
$$S_{c} = \frac{C}{{D_{c} }}$$
(6)
$$O_{w} = W_{p} \times S_{i}$$
(7)
$${\text{PIG}} = \sum O_{w} .$$
(8)

Health risk assessment

The non-carcinogenic risk associated with drinking contaminated river water was calculated using the criteria of the US Environmental Protection Agency (USEPA, 1989). In Eq. 9, the risk was calculated using the following function for both children and adults:

$${\text{CDI}} = \frac{{C_{w} \times {\text{IRW}} \times {\text{EF}} \times {\text{ED}}}}{{{\text{BW}} \times {\text{AT}}}},$$
(9)

where CDI denotes Chronic Daily Intake or exposure dose (mg/kg/day), Cw denotes contaminant concentration in water (mg/L), IRW denotes ingestion rate (for adults, IRW is 2 L/day, while for children, IRW is 1 L/day), EF denotes Exposure Frequency (equivalent to 365 days/year), ED denotes exposure duration (for adults and children, ED is 70 and 6 (Bortey-Sam et al., 2015; Duggal et al., 2017; Barzegar et al., 2018). The non-carcinogenic risk posed by various elements in Eq. 10 was evaluated using the hazard quotient (HQ), as defined by Li et al. (2016) and Zhang et al. (2018):

$${\text{HQ}} = \frac{{{\text{CDI}}}}{{{\text{RfD}}}}$$
(10)

RfD stands for reference dose (mg/kg/day) for a given element. RfD for various elements is similar to 0.7 (Fe), 0.3 (Zn), 0.0001 (Hg), 0.03 (Co), and 0.0035 (Co), according to Duggal et al. (2017) and Barzegar et al. (2018). (Pb). Finally, using Eq. 6, the total HQ values for the elements is computed to determine the hazard index (HI) of the water samples:

$${\text{HI}} = \sum {\text{HQ}}$$
(11)

HI and HQ values larger than 1 indicate that the given element's noncarcinogenic risk exceeds the limit of acceptance (HI = 1), while values less than 1 show that the noncarcinogenic risk is within acceptable limits (USEPA, 1989; Su et al., 2017). As reported by Bortey-Sam et al. (2015), Barzegar et al. (2018), Table 3 illustrates the USEPA (1989) classification of non-carcinogenic risk.

Table 3 United States Environmental Protection Agency (USEPA) classified non-carcinogenic risk in 1989
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Results and discussion

Principal component analysis (PCA)

PCA is a pattern identification technique that aims to explain the variation of a large number of interrelated variables (2021b; Akakuru et al., 2021a). It shows how variables are related, lowering the data set's complexity. From the covariance matrix of original data, PCA extracts eigen values and eigenvectors. The uncorrelated (orthogonal) variables obtained by multiplying the original correlated variables with the eigenvectors are known as principal components (PCs) (loadings).

The PCs' associated variance is measured by their eigen values, the original variables' participation in the PCs is determined by the loadings, and the transformed observations are referred to as scores (Helena et al., 2000; Wunderlin et al., 2001; Singh et al., 2005). From Table 4 it shows four components, PC1 shows loadings in EC, TDS, pH, Mg, Ca, Cl, Mn, Zn, Cu, Co, and Ni; PC2 shows loadings in temperature, Na, Ca, SO4, As, Pb, K, and HCO3; PC3 indicates loadings in temperature, Mg, Ca, CO3, and Cd; PC4 shows loadings in EC, CO3, Fe, As, Ni and HCO3. Strong correlations observed in the majority of the parameters imply that the groundwater mixed with saline water could be a result of saltwater intrusion. This further means that saltwater was the major recharge source for the groundwater in the review region (2021b; Akakuru et al., 2021a; Yahaya et al., 2021).

Table 4 PCA and communalities
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Pearson’s correlation matrix

In the examination of groundwater quality, Pearson's Correlation matrix analysis is a valid technique for determining the relationship and origin of hydrogeochemical indicators (Akakuru et al., 2021a). Correlation coefficients more than 0.7 indicate a strong link between the two limits; correlation coefficients between 0.5 and 0.7 indicate a shaky link, implying a moderate correlation (Akakuru et al., 2021b; Shyu et al., 2011; Qian et al., 2016) Table 5 shows a relationship between pH and EC, TDS and EC; Mg and Temp; Na and pH, Temp; Cl and Temp., Ca; SO4 and Ca; CO3 and Ca, Cl; Cd and Mg, CO3; Fe and Ca, Cl; As and Na; Pb and Temp, Ca, SO4; Mn and Mg, SO4; Zn and EC,TDS, pH; Cu and Temp., SO4, Mn. Because the majority of the data have weak relationships, it is likely that the groundwater did not interact with saline water, and hence there was no saltwater intrusion. This also implies that freshwater was the primary source of groundwater recharge in the study area (Egbueri et al., 2019; Yahaya et al., 2021; Akakuru et al., 2022)

Table 5 Correlation matrix
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Pollution Indices

To analyze heavy metal contamination, this research used pollution indices grouped into three types as described by Yang et al. (2011): Pollution Index of Groundwater (PIG), Contamination Factor (CF), Pollution Load Index (PLI), Metal Pollution Index (MPI), and Ecological Risk Index (ERI).

Pollution Index of groundwater (PIG)

PIG is a useful instrument for measuring the quality of water (surface and groundwater), and it has been used by many researchers (Subba Rao, 2012; Subba Rao and Chaudhary, 2019). Table 6 lists the components required to calculate the PIG, whereas Table 6 lists the PIG results for 15 groundwater samples collected around the study area, as well as a PIG distribution map (Fig. 0.3). PIG values vary from 0.395 to 2.511(Table 7). According to PIG's classification (Table 8), 60% of collected groundwater samples are unpolluted, while 13.3%, 20%, and 6.7 percent of the samples are in low, moderate, and extremely high pollution zones, respectively. The regional distribution of PIG in the research area is depicted in Fig. 2.

Table 6 Components used in the PIG assessment
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Table 7 Overall quality (Ow) of the groundwater samples
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Table 8 Classification of PIG
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Fig. 2
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PIG spatial distribution in the research area

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Ecological risk assessment (ERI)

Water quantity risk (physical risk), water quality risk (chemical risk), and aqua-ecosystem risk (biological risk) are the three main components of watershed ecological risk, according to Kun et al. (2017) and Akakuru et al. (2015). The overall amount and speciation of heavy metals in water sediments are linked to their potential environmental harm (Jain et al., 2005; Nayek et al., 2013). ERI values for Cd range from 0.075 to 2.325, 0.85–3.975 for Fe, 0–0.02 for As, 0–0.002 for Pb, 0.00008–0.00063 for Mn, 0.00005–0.00128 for Zn, 0.005–0.023 for Cu, 0.009–0.03 for Co, 0.001–0.0082 for Ni. Table 9 shows that the ecological hazards associated with Cd, Fe, As, Mn, Cu, Co, Ni, Zn, and Pb are generally low (less than 110) based on the ERI results. Table 10 shows the ERI categorization.

Table 9 Ecological risk index
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Table 10 ERI classification
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Contamination factor

In groundwater studies, the CF has been used to calculate the concentration ratio of heavy metals to background values.

The values of the contamination factor are described using the following criteria: CF < 1 Low contamination 1 ≤ CF ≥ 3 (CF < 1), moderate contamination 3 ≤ CF ≥ 6, significant contamination CF > 6, and very high contamination. (Akakuru et al., 2017a, 2017b; Bhutian et al., 2017). The CF of this investigation, as shown in Table 11, shows that all parameters had low values of < 1 across the study area, except for HCO3, which showed a concentration above 1 (moderate contamination), and Fe, which ranged from moderate to severe contamination. This finding indicates that geogenic processes are the primary cause of pollution. The findings of Bhutian et al. (2017) in India and Nigeria are similar (Yayaha et al., ; Akakuru et al., 2021a).

Table 11 Pollution load index and contamination factor in the study area
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Pollution Load Index (PLI)

PLI is a useful tool for determining heavy metal toxicity in representative samples (Akakuru et al., 2021b; Yang et al., 2011). No pollution (PLI1), moderate pollution (PLI > 2), heavy pollution (2PLI > 3), or extremely heavy pollution (3 > PLI) are the most common PLI classifications. The study area's groundwater had a concentration value of less than one, according to the results (Table 11). This implies that no pollution exists. This work contradicts the study undertaken in India (Bhutian et al., 2017; Gopinath et al., 2019), but it is consistent with the work done in Nigeria by Yahaya et al. (2021).

Metal pollution index (MPI)

MPI has proven to be a valuable tool in the classification of groundwater. Water with an MPI of 0.3 or less is classified as Very Pure, whereas water with an MPI of 0.3–1.0 is classified as Pure. Those with an MPI of 1.0–2.0 are classified as minimally afflicted in Class III, while those in Class IV are classified as severely affected. With an MPI of 2.0–4.0, moderately affected. Water samples with MPI between 4.0 and 6.0 are classified as significantly affected in Class V, while those with MPI > 6.0 are classified as seriously affected in Class VI (Table 12). According to the MPI results in Table 13, groundwater sample in the research region has a value of less than 0.3, indicating that it is in Class I and thus very pure (Akakuru and Akudinobi, 2018; Eyankware et al., 2016; Eyankware et al., 2017).

Table 12 MPI classification in the study area
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Table 13 Metal pollution index
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Health risk assessment

There are numerous health concerns associated with drinking heavy metal-contaminated water. Because most metals have a high contamination factor, residents are more susceptible to health concerns from their consumption. Heavy metals in water can create an unpleasant taste in beverages, stains on products and laundry, sticky coatings, and depositions in water distribution pipes, in addition to drinking (WHO, 2011; Mgbenu & Egbueri, 2019). Despite the fact that there is no health-based temperature guideline, it has been found that high water temperature promotes the growth of bacteria that may be hazardous to human systems, as well as problems with taste, odor, color, and corrosion (WHO, 2011; Mgbenu & Egbueri, 2019). Furthermore, the hazard quotients (HQ) (for heavy metals) reported in Table 14 were used to determine the non-carcinogenic risk posed by drinking water containing trace metals for both children and adults in the research area. Cd > Ni > Co > Cu > Mn > Zn > Fe is the HQ order for both children and adults, indicating that Cd and Ni are the most polluting elements in water supplies. The findings suggest that injecting heavy metal poses a substantial health risk to both adults and children. Mgbenu and Egbueri (2019) found similar results somewhere in Nigeria.

Table 14 Heavy metals' non-carcinogenic risk for children and adults as measured by the hazard quotient (HQ) and hazard index (HI)
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Hydrogeochemical facies

The hydrochemical evolution, grouping, and areal distribution of groundwater's major dissolved constituents (major cations and major anions) can be seen graphically (Akakuru et al., 2015, Akakuru et al., 2017a, b; Eyankware, Igwe, et al., 2021). Piper trilinear diagrams, Scholler plots, and Durov plots were used to assess hydrochemical facies variation in this work.

Piper trilinear and Durov plots

The Piper Trilinear plot (Piper, 1944) is one of the most effective graphical representations in groundwater quality studies; it aids in understanding the geochemistry of shallow groundwater and brings out chemical interactions in more precise terms than other plotting approaches (Akakuru et al., 2013; Eyankware, Nnabo, et al., 2020; Eyankware, Obasi, et al., 2020). The Piper trilinear diagram for the study region shows that the anion area's main ionic species are HCO3 + CO3, whereas the cation area's leading ionic species are Na + K. The graphic also shows that 80% of the research area's groundwater resources are in the geochemical zone 3 (weak acid exceeds strong acid), whereas 20% are in the geochemical zone 4. (strong acid exceed weak acid). Cation exchange has long been blamed for the formation of sodium–bicarbonate groundwater in aquifers.

Microbial oxidation of organic carbon produces carbonic acid, which dissolves calcite and yields dissolved calcium and bicarbonate, according to this concept. Calcium is exchanged for sodium adsorbed on clays, and sodium–bicarbonate groundwater is the consequence (Saha et al., 2019). This result is similar to Akakuru et al. (2021b), in Nigeria and Xu et al. (2018) in China, but not to Umar et al. (2019) in Nigeria. Figure 3 depicts a Piper diagram of the samples in the research area, whereas Fig. 4 depicts a Durov plot of the samples in the region.

Fig. 3
figure 3

Piper diagram showing dominant ionic specie

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Fig. 4
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Durov plot showing ionic dominance

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Scholler Plot

Schoeller Semi-logarithm plot that makes use of logarithmic scales in representing water quality status. The chart proposed by (Schoeller, 1967) portrays a gathering of investigations on equidistant verticals, the quantity of which relies upon the number of constituents being communicated. This diagram is particularly valuable for looking at waters of low focus and waters that don't vary incredibly in fixation (Sakram et al., 2013; Saha et al., 2019). The Schoeller diagram (Fig. 5) of the groundwater samples also reveals a hydrogeochemical trend of HCO3¯ + CO3 > SO4 > Cl +  > Na+ + K+ > Mg+ > Ca+. The Piper plot is confirmed by the Schoeller semi-logarithmic plots, which reveal a dissimilarity in ionic mobility, showing that the water is not from the same source (Akakuru et al., 2017a&b; Eyankware, Akakuru, et al., 2021).

Fig. 5
figure 5

Schoeller diagram showing the hydrogeochemical trend

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Conclusions

Heavy metal contamination in the research region was successfully assessed utilizing statistical index analysis employing contamination factor, metal pollution index, enrichment factor, and ecological risk. These results were gotten: Four components were generated from the PCA analysis, Strong correlations observed in the majority of the parameters imply that the groundwater mixed with saline water could be a result of saltwater intrusion. The Pearson's correlation matrix reveals Weak correlations in the majority of the parameters which implies that the groundwater did not mix with saline water, hence there was no saltwater intrusion. PIG ranges from 0.395 to 2.511 points. 60% of collected groundwater samples are polluted insignificantly, while 13.3%, 20%, and 6.7 percent of the samples are polluted in low, moderate, and very high pollution zones, respectively. The Ecological Risk Index values range from are generally modest, according to the Ecological Risk Index results (less than 110). The CF of this study reveals that the entire parameters had a low concentration < 1 in the entire study area except for HCO3 which had a concentration above 1(moderate contamination), and Fe which ranged from moderate contamination to considerable contamination. PLI values of the study area had a concentration value of less than one, according to the results. This implies that no pollution exists. MPI results in the entire groundwater sample in the study area is below 0.3, which signifies that they are in Class I and are said to be very pure. Mean values show that the Hazard Quotient order confirms that Cd and Ni have the highest pollution factor in the water resources. Piper trilinear diagram for the study area shows that the anion area HCO3 + CO3 are the dominant ionic species, while Na + K is the dominant ionic species for the cation area. The diagram further reveals that 80% of the groundwater resources in the study area are within the geochemical zone of 3 (weak acid exceeds strong acid), while 20% fall within the geochemical zone of 4 (strong acid exceeds weak acid). The Schoeller diagram of the groundwater samples, shows a hydrogeochemical trend of HCO3− + CO3 > SO4 > Cl+ > Na+ + K+ > Mg+ > Ca+. Based on these results, pre-use treatment of the water resources before use is highly recommended.