Geochemical modeling and hydrochemical analysis for water quality determination around mine drainage areas

Water sources in mining areas do not retain their natural quality due to the influence of mine drainage. Water quality test was through hydrochemical analysis, speciation modeling, and saturation indices. Water samples were analyzed for pH, conductivity, nitrate, phosphate, sulfate, chlorite, sodium, magnesium, calcium, turbidity, total hardness, lead, zinc, iron, copper, cadmium, manganese, nickel, and chromium. Mean values of turbidity (0.13 mg/L), lead (0.01 mg/L), and cadmium (6.40 mg/L) exceeded their permissible values for potable water. Multivariate statistical analysis shows geogenic and anthropogenic sources of chemical species. Chemical speciation shows that the cations exist mostly in their soluble and mobile forms as free ions. Water quality index of 35–45.5 shows good water for drinking, irrigation, and industrial uses. The values of 63.8–68.8 and 103–121 reflect suitable water for industrial and irrigation uses. The research is integrated and credible in predicting groundwater pollutants to solve water pollution problems.

exist mostly in their soluble and mobile forms as free ions.Water quality index of 35-45.5 shows good water for drinking, irrigation, and industrial uses.The values of 63.8-68.8 and 103-121 reflect suitable water for industrial and irrigation uses.The research is integrated and credible in predicting groundwater pollutants to solve water pollution problems.

Practitioner Points
• The mean value of turbidity, Pb, and Cd exceeded the WHO/NSDWQ standards for potable water.
• Correlation and principal component analyses show that the chemical species are from both geogenic and anthropogenic sources.
• Chemical speciation shows that the cations exist in their soluble and mobile forms as free ions except Cr.
• Water quality index shows that the water is more suitable for irrigation than drinking and industrial uses.

K E Y W O R D S
anthropogenic; chemical speciation; heavy metals; mine drainage; multivariate statistical analysis, geogenic; physical properties; water quality index

INTRODUCTION
Water is an indispensable resource that is used for irrigation, drinking, washing, and a source of fish for consumption (Badamasi et al., 2021;Connor, 2015;Manoj et al., 2017;Opoke & Osayanda, 2018).Water is a universal solvent and a life miraculous substance that can sustain human life, but polluted water causes diseases (Boobalan et al., 2015;Chandra Skhar et al., 2014).Sufficient and clean quantity of water is condition precedent for household, industrial and agricultural uses (Belkhiri et al., 2010).Water quality is controlled by properties like acidity (pH), temperature, total dissolved solids (TDS), nitrate (NO 3 À ), sulfate (PO 4 3À ), phosphate (PO 4 3À ), and major ions.Heavy metals also constitute a class of water contaminants that pose a threat to aquatic species and humans even in minute concentrations (Tay et al., 2019;Vergas et al., 2001).Hydrocarbon exploration and extraction, mining and processing of ores, quarrying, and construction activities contribute to water pollution (Ackah et al., 2011;Algbedan & Iyayi, 2007).This is through the release of harmful heavy metals into the environment (Benibo et al., 2020;Osuacha et al., 2015).Ingestion of heavy metals by humans above their thresholds in water is capable of causing cancer.Aquifer mineralogy, groundwater interaction, and geologic processes (Boobalan et al., 2015;Manoj et al., 2017) govern the chemical composition of groundwater.The study of groundwater chemistry using hydrochemical species provides information on water types for different purposes (Gurunadha Rao et al., 2013;Sarwade et al., 2007).The physicochemical characteristics of water determine its suitability for domestic, industrial, municipal, and agricultural applications (Anonymous, n.d.; Ravikumar & Somashekar, 2012).In Pakistan, Ud Din et al. (2023) and Haq and Muhammad (2022) analyzed physicochemical properties of water and its suitability for drinking and irrigation using water quality index (WQI).Muhammad and Usman (2019), Muhammad (2023), and Amin et al. (2021) determined water quality by risk analysis and comparison of parameters with the World Health Organization (WHO) water quality guidelines.The studies established different categories of water suitability for drinking and irrigation uses and hazards associated with water contamination with heavy metals and physicochemical parameters.Ma et al. (2020) and Ogbodo et al. (2020) evaluated suitability of water for drinking and irrigation uses.Some of the studies compared concentrations of physicochemical and heavy metals levels with the WHO, Bureau of Indian Standards (BIS), Nigerian Standard for Drinking Water Quality (NSDWQ), and Indian Council for Medical Research (ICMR).The result showed safe pipe-borne and borehole water but unsafe stream water sources for drinking and domestic uses.The studies contended that WQI is a suitable method for investigating water quality from different water sources.Rocha et al. (2015) assessed WQI from physicochemical and biological properties in northeast of Brazil using principal component analysis (PCA) and explained 85% of the variance.Water entering the reservoir obtained the lowest WQI while the best WQI was at the exit point.Boobalan et al. (2015) in Tamil Nadu, India, identified calcium bicarbonate (CaHCO 3 ), mixed with calcium magnesium chlorite (CaMgCl), and sodium chloride (NaCl) using Piper plot.In Cochin Area, Kerala, Piper plot and multivariate analysis showed that the water has Ca-NaMg-HCO 3 -Cl and Ca-Na-HCO 3 -Cl facies.Ramesh and Elango (2014) compared physicochemical properties with BIS in an industrial area.The study recognized calcium ion (Ca 2+ ), bicarbonate (HCO 3 ), type, Ca 2+ , and magnesium ion (Mg 2+ ) water type based on its hydrogeochemical properties.The presence of high salinity, hardness, and chloride content resulted from industrial discharges, agricultural activities, and seawater intrusion.In Cairo, Egypt, Salem et al. (2000) from heavy metals analysis showed that lead (Pb) and cadmium (Cd) in contaminated drinking water cause renal failure.Also, copper (Cu) and molybdenum (Mo) cause liver cirrhosis, nickel (Ni) and chromium (Cr) cause hair loss, and Cu and Cd cause chronic anemia.
Groundwater is mostly contaminated by salinity content caused by high TDS, Ca 2+ , Na 2+ , Mg 2+ , and SO 4 2+ (Gurunadha Rao et al., 2013;Morel et al., 1996;Nadler et al., 1981).Etim et al. (2013), Adelagun et al. (2021), andOkey-Wokeh et al. (2021) assessed WQI in stream, borehole, and pipe-borne water sources using physicochemical parameters.They established some values beyond acceptable limits for potable water.Vodela et al. (1997) and Pawer and Nkhumbh (1999) evaluated heavy metals contamination, because their contamination nature has been of global interest.In the Eyingba and Ihetu in Ishiagu Pb-Zn mining areas, Opoke and Osayanda (2018), Nnabo (2015a, 2015b), and Benibo et al. (2020) analyzed for physicochemical properties and heavy metals in water.High concentrations of Fe, Zn, Cu, Pb, and Cr in water pointed to Pb-Zn mining pollution.Obasi et al. (2023) evaluated water quality around mine drainage areas and established that concentrations of Cl À , SO 4 2À , As, Mn, Pb, Cd, Fe, and Hg in some sites exceeded the WHO standard for potable water.Mine drainage and rainfall enrich groundwater with heavy metals, which are harmful to human health (Wang et al., 2022).Mining of lead-zinc (Pb-Zn) sulfide ore deposit coupled with farming are the major occupations of the inhabitants of Ishiagu, Eyingba, and Abakaliki in Ebonyi State.This has compounded the problem of potable water supply in the area, because mining sites are sources of heavy metals pollution (Lombi et al., 2001).Waste rocks from mine sites and surface runoff from farmlands with fertilizers consist of Pb, Zn, Cu, Ni, and arsenic (As) that are water pollutants (Quereshimatva & Solanke, 2015).Through the oxidation of pyrite (FeS) in mine wastes, acidification of water sources is possible through acid mine drainage.Heavy metals are mobilized in an acidic environment, which through surface runoffs can compromise surface and groundwater quality.These activities happening around the area require water quality test to determine its suitability for washing, drinking, and irrigation uses.Water pollution is the principal cause of water-borne diseases such as diarrhea, dysentery, and renal dysfunction.This study conducted extensive and detailed groundwater quality research using physicochemical and heavy metals analysis.The water analysis was to evaluate hydrochemical processes, combined with WQI to produce a credible means of assessing water pollution.There is no previous study in the area on the effect of water quality on human health.This study is to bridge that gap.The result will be invaluable to relevant government agencies in formulating policies on public health in relation to mining and its impact on the environment.Available studies elsewhere assessed water quality using hydrochemical processes or WQI only.This study interpreted a wide range of groundwater quality approaches in contrast to related previous studies.This study is more reliable, extensive, and capable of providing a credible report on water quality analysis to meet the basic water requirement.The ultimate goal of this study is to evaluate the suitability of water for drinking, washing, and irrigation in a mining area.The aim can be realized through specific objectives such as determination of the hydrogeochemical composition of water using stiff diagram and stiff map.Through the evaluation of water quality using WQI.Model the speciation of ionic species and assessing the degree of saturation of chemical species that control water quality.

Study area description
The area is in Abakaliki Mine District situated in the eastern area of Ebonyi State and located between latitude of 6 09 0 39.5 00 to 6 08 0 50.3 00 N and longitude of 8 08 0 37.95 00 to 8 08 0 39.33 00 E (Figure 1).The study area is bounded by Abakaliki Municipal in the north, Cross River in the south, and Ezza South in the east.It falls within the Abakaliki Pb-Zn mining district comprising Enyigba, Ameka, and Ameri mines.
The area has an undulating topography alternating with ridges and hills.The planes are underlain by shale and intercalations.There are undulating ranges of shale outcrops in Enyigba, Ameri, and Ameka, which are host for Pb-Zn mining deposit (Nnabo, 2016).The highest elevation is 84 m and the lowest elevation is 58 m.The area is within the savannah belt in southeastern Nigeria.There is cultivation of rice farms, cassava, maize, and cocoyam in the area.Palm trees and thick forest trees bound the area.The trees are deforested by farming activities, and there is a regrowth of the broadleaf and scrubs.The climate is semi-tropical and the vegetation is luxuriant in the rainy season.The climate is characterized by raining and dry seasons.The dry season lasts from November to March while the raining season starts in April and end in October.The annual rainfall is estimated at 1230 ± 28 mm.The mean temperature is between 24 C and 29 C. The relative humidity is between 68.7% and 92%.The drainage pattern is dendritic and controlled by shale, clay, and sandstone lithology.The area is drained by Ebonyi River and its tributaries.

Geology and hydrogeology
The area is in the Asu River Group of the Lower Cretaceous (Figure 2) consisting of Eze Aku Shale Formation and Nkporo Formation constituting of shale, sandstone, and siltstone of the Abakaliki Anticlinorium and related Afikpo Synclinorium.The structural features are caused by folding of the sediments.Ironstones occur as interbeds within the shale and vein fillings.The vein mineralization is hosted within the dark shale (Nnabo, 2011).The geology and mineral deposits control heavy metals occurrence in the area.The sulfide minerals have elevated concentration of heavy metals.The shale host rock can retain heavy metals (Nnabo, 2011).The sedimentary succession in the Lower Benue Trough is predominantly pre-Santonian in age and has been established as Asu River Group in Abakaliki and Awi Formation, Mfamosing, Limestone, Ekenkpon Shale, and New Netim Marl in the Calabar Flank.
The study area is part of the Abakaliki Anticlinorium and underlain by the Abakaliki shale of the Asu River Group with an expression of the Abakaliki Lower Cretaceous age (Opoke & Osayanda, 2018).The black calcareous shale intercalated with siltstone dominates the sedimentary rocks.The Asu River Group consists of alternating sequence of shale, mudstone, and siltstone with some occurrence of sandstone and limestone lenses in some locations with an approximate thickness of 1500 m (Agumanu, 1989;Farrington, 1952).Kogbe (1989) described the sediments as poorly bedded sandy limestone lenses.The black shale is weathered, ferruginous, and bleached to pale grayish color with mottles of red, pink, yellow, and blue (Ukpong & Olade, 1979).The Pb-Zn ore is found in the Albian carbonaceous shale of the Asu River Group.Fissures, fault zones, and gently dipping veins control the mineralization.The predominant ores in the area are sphalerite (ZnS) and galena (PbS) associated with minor amounts of chalcopyrite.In Ameri, Ameka, and Enyigba, there was post-mineralization when lodes were developed during the end of Santonian folding episode (Nwachukwu, 1972;Wright, 1968).Pb-Zn ore mine of Abakaliki District has been linked with diverse health implication (Adaikpo et al., 2005;Onyeobi & Imeokpara, 2011).
The hydrogeology is controlled by the geological structures, and the geology determines the type of aquifer while the climate determines the quantity of groundwater recharge (Davies & De West, 1966;Todd, 1980).Surface water is limited in the area due to the nature of the underlying rocks.The drainage is irregular mostly of seasonal streams and rivers.The streams flow in the northeast direction.The water is hard and not potable due to the presence of salts in the waters.Minor joints, cracks, and fractures in the rocks help to furnish the aquifers with sufficient water supply.
F I G U R E 2 Geological map of the study area.

Sample collection, preparation, and laboratory analysis
A base map was used in the field to locate sample points and geologic features.Coordinates of sample locations were obtained using global positioning system (GPS) device.Water samples were collected from streams, boreholes, and mine pond (lake) around the mining area.Eight water samples were collected for analysis: three stream water samples, four borehole water samples, and one pond water sample.Water samples were collected into prewashed 1-L polyethylene bottles.The sample bottles were prewashed with nitric acid and then rinsed with deionized water.In each location, the bottle was rinsed twice with the water being sampled to condition the bottle before finally collecting the sample.The samples were collected with reference to mining activities.The streams are drained from these mines and likely to be contaminated by acid mine drainage from these mines.Acid mine drainage occurs when pyrite or any other sulfide mineral contained in mine wastes is oxidized to sulfuric acid in the presence of water.This was expressed (Sikakwe et al., 2016;Zielinski et al., 2001) as In Equation (2), Fe is oxidized under acidic environment to yield Fe 3+ and water.In Equation (3), Fe 3+ is hydrolyzed to produce ferric hydroxide (Fe(OH) 3 ), a reddish and toxic chemical pollutant, and mine sites.Acid water generated by this harmful chemical can dissolve toxic heavy metals capable of polluting water sources.
In each location, two water samples were collected: one bottle for analysis of anions and the other bottle for analysis of cations and heavy metals.The samples for analysis of anions were unacidified while the samples for analysis of cations and heavy metals were acidified to a pH of 2 with concentrated nitric acid (HNO 3 ) to prevent loss of ions before laboratory analysis (Nwankwoala & Udom, 2011).Physical parameters (pH, temperature, electrical conductivity [EC], and TDS) were measured in situ using pH meter, mercury in glass thermometer, conductivity meter, and spectrophotometer, respectively.The bottles were washed with distilled deionized water before sample collection.A membrane filtration of 450 nm was applied in filtering the water samples.The water samples were acidified in the field to a pH of <2 with ultra-pure HNO 3 with a molar concentration of 15.8 m of HNO 3 and 70% w/w of 63 m to avoid loss of cations.The samples were stored in a refrigerator and then later taken for laboratory analysis within a space of 48 h.
The samples were digested to destroy the matrix, which could interfere during atomization and capable of converting all forms of metals into a single oxidation state.The water samples were mixed thoroughly by shaking.During digestion, 50 mL of each sample was transferred into a glass beaker and 20 mL of a mixture of nitric and perchloric acid was added.The beaker with the content was placed on an electric hot plate and evaporated down to about 20 mL.On cooling, the samples were filtered through Whatman No. 42 filter paper to remove some insoluble materials that could clog the atomizer.The volume was adjusted to 50 mL using metal-free distilled water.The water samples were stored in a reagent bottle for elemental analysis using Varian 55B AAS air acetylene flame.
The anions were analyzed using the method described in American Public Health Association (APHA, 1995) and AOAC (1999).Atomic absorption spectrophotometer (AAS) uses the absorption spectrometry to assay the metal concentrations of the samples.A standard solution was used to establish the relationship between the measured absorbance and the analyte concentration, which depends on the Beer-Lambert law.It is mainly used to evaluate the concentration of a particular metal element in a sample.In their elemental form, metals will absorb ultraviolet light and get excited.Each metal has a characteristic wavelength that will be absorbed.The AAS instrument detects a particular metal by focussing a beam of UV light at a specific wavelength through a flame and into a detector, and the sample of interest is aspirated into a flame.If the metal is available in the sample, it will absorb some of the light, thus reducing its intensity.The device measures a change in intensity.A computer data system converts the change in intensity into absorbance.

Quality control
All samples were analyzed in triplicate.The standard solution of all heavy metals was prepared by successive dilution of certified standards (1000 mg/dm 3 ), and the calibration curve of each metal was constructed.Blank determinations were performed to rectify any background contamination from reagents, filter papers, or other systemic errors (Badamasi et al., 2021).

Precision and accuracy
Precision refers to the degree of how consistent the obtained results are during frequent use of a specific method of sample analysis.It was estimated by evaluating repeatedly the relative standard deviation (RSD) of the recovery percentage for each spike level.On the other hand, accuracy was assessed by recovery of sample spikes through the preparation of triplicate samples and recording triplicate readings (Talema et al., 2019).

Statistical treatment of data
The chemical data were analyzed for descriptive statistics, Pearson's correlation analysis (CA), and PCA using SPSS software Version 20 (SPSS Inc., Chicago, IL, USA).IBM software Version 20 was used in descriptive statistics of physicochemical parameters and heavy metals.All the graphs were plotted using RockWorks software Version 16.

Water quality assessment
Water quality index WQI reflects the aggregate control of different quality parameters on the water quality all encompassing (Priya & Vidya, 2019).For the computation of water quality, 13 index parameters were chosen.The WQI was calculated using limits of drinking water quality proposed by the WHO, NSDWQ, Canadian Council of Ministers of Environment (CCME), and ICMR.The water quality parameters were selected based on how it directly affects water quality for human uses (Priya & Vidya, 2019).The concentrations of these parameters will correspondingly increase WQI value.
In WQI calculation assuming 'n' is water quality parameter and quality rating (Qn) is the number that reflects the comparative value of n-th water quality parameters in polluted water with respect to its standard permissible value.
The water quality rating Q n is derived from the water quality data then multiplied a weighting factor that is relative to the significance of the test to water quality (Adelagum et al. 2021).Qn is given by the expression: Where Qn represents quality rating scale.Ci stands for concentration of I parameter.Sn denotes world standard value of I parameter.World standards used in this study are ICMR (Indian Council for medical research), WHO, CCME, US EPA and NSDWQ.
Relative weight (W) is given by The standard value of the n-th parameter is inversely proportional to the relative weight.The relative weight (Wn) is computing by Lastly, the overall WQI is obtained by the expression Ogbodo et al. ( 2020), and Priya and Vidya (2019) presented WQI ranges, corresponding status and the possible water uses as contained in Table 5.
Stiff map and trilinear plots were used to identify processes and hydrochemical composition of water sources in the area, which is an aspect of water quality study.Piper diagram and stiff map were constructed using RockWorks software Version 16.
Geochemical modeling of chemical data were done to predict ionic speciation and saturation indices (SI) of mineral phases in water.This aspect of water quality study was done using Visual MINTEQ software Version 3.1.

Concentrations of physicochemical parameters and heavy metals in water samples
The physical parameters (temperature, pH, EC, TDS, total suspended solids [TSS], and total solids [TS]) obtained varying concentrations below various permissible limits (Table 1).Only turbidity possesses a mean value of 6.40 mg/L, which is above the permissible limit of 5 mg/L stipulated by the WHO, CCME, and US EPA.The standard deviations of EC, TDS, TS, and total hardness (TH) (Table 1) were very high compared with other physicochemical parameters.EC, Mg 2+ , and Ca 2+ possess negative skewness while temperature, EC, TSS, TS, turbidity, TH, SO 4 2À , Cl À , PO 4 3À , Mg 2+ , Ca 2+ , and K + exhibited negative kurtosis.The mean values of heavy metals were below permissible standards except Pb and Cd that possess mean values of 0.135 and 0.015 mg/L, respectively.These values are above drinking water permissible values of 0.01 and 0.003 mg/L proposed for Pb and Cd, respectively, by the WHO, Standard Organization of Nigeria (SON), and US EPA.The elements Pb and Zn exhibited negative kurtosis and skewness.Heavy metals levels decreased in the order Fe > Zn > Pb > Cu > Cd > Mn > Cr > Ni.The spatial variation of heavy metals shows higher concentrations of Zn, Fe, Pb, and Co than those of Cd, Mn, Ni, and Cr.These heavy metals are contained in mostly sample 6 (pond) water followed by sample 2 (Stream) water in a nearby village around the mine area.Concentrations of heavy metals in water samples decreased in the order Sample 6 > Sample 5 > Sample 7 > Sample 1 > Sample 2 > Sample 4 > Sample 8 (Figure 3).Samples 1-3 and 8 are stream water samples, Samples 3-5 are borehole water samples, and Sample 6 is a pond water sample.

Pearson's correlation analysis and principal component analysis
Strengths of correlation relationship around water sources were tested using statistical techniques of Pearson's CA and PCA.A 0.01% confidence limit relationship between variables (Data S1) shows that significant correlations exist between Pb with Zn, NO 3 À , Zn with Fe, EC, TS, and TH.Fe correlated significantly with Cu, Cd, Mn, Cr, TSS, TS, turbidity, TH, and NO 3 À .Cu correlated significantly with Cd, Mn, turbidity, TH, and NO 3 À ; Cd correlated significantly with Mn, Ni, Cr, pH, TSS, turbidity, and NO 3 À .
Mn correlated significantly with Ni, Cr, pH, and NO 3 À .
Ni correlated significantly with Cr, pH, and NO 3 À .Temperature correlated significantly with Mg 2+ .The pH correlated significantly with NO 3 À ; EC correlated significantly with Mg 2+ .TDS correlated with Na + .TSS correlated with TS, turbidity, TH, and NO 3 À .Turbidity correlated with TH and NO 3 À .TH correlated with NO 3 À .
SO 4 2À correlated with Mg 2+ and K + .Ca 2+ correlated with Na + , and K + correlated with Mg 2+ .The relationships of variables at a confidence limit of 0.05% show significant correlations between Pb and Fe, Cu, TS, turbidity, and NO 3 À .Zn correlated with Cu, TSS, turbidity, and NO 3 À .Fe correlated with Ni, pH, and EC.Cu correlated with Ni, Cr, pH, TSS, and TS.Cd correlated with TS and TH.Mn correlated with TSS, turbidity, and TH.Cr correlated with TSS and turbidity.Temperature correlated with K + .The pH correlated with TSS and turbidity.EC correlated with TS and TH.PCA was used to find the effective factors that influence the hydrochemistry.It shows the importance of correlation between factors and variables of the data (Kaiser, 1960).The criterion was used to evaluate the number of factors for the data set showing positive and negative correlations between factors shown by high factor loadings of almost 1 or À1, respectively.On this basis, only factors with Eigen values exceeding or equal to 1 were acceptable as likely source of variation in the data.Loading values > 0.40 were used for interpreting the data (Liu et al., 2003).The factor with the highest Eigen vector sum is accorded precedence to the other factors (Table 2).Factors 1-4 explained 94% of the variation in the data (Table 2).Factor 1 accounted for 50.3% of the total variance with significant positive loadings of Pb, Zn, Fe, Cu, Cd, Mn, Ni, Cr, pH, EC, TSS, TS, turbidity, TH, and NO 3 À .Factor 2 explained 19.8% of the total variance with positive significant loadings of Zn, temperature, EC, SO 4 , Mg 2+ , and K + .Factor 3 explained 16.0% of the total variance with a positive significant loading of Cl À .Factor 4 accounted for 7.8% of the total variance with positive significant loadings of TS and C and a negative loading of PO 4 3À .Component plots in rotated space are shown in Figure 4.

Chemical composition of water samples
Figure 4 shows the chemical composition of water samples.Figure 4 shows the analysis of water with quite different compositions in Sample 1. SO 4 2À is of high concentration than the other anions.For the cation, Ca 2+ is of the least concentrations while Mg 2+ and Na + + K + are higher.Sample 2 shows almost equal concentrations of major anions and cations in water.Sample 3 has almost equal anions with very low concentrations, but SO 4 2À is a little higher.Among the cations, Na + + K + obtained higher levels of Ca 2+ and Mg 2+ .Sample 4 shows uniform concentrations of anions and cations with very low concentrations.In Figure 4, Sample 5 exhibited the highest level of SO 4 2À than the other anions and Ca 2+ is the least cation.Mg 2+ is a little higher than Na + + K + .Samples 6-8 show lower levels of anions and cations.Samples 6 and 7 show different variables while Sample 7 has different levels of anions.In Samples 6 and 7, cations have higher concentrations than anions.Figure 6 shows stiff map indicating the spatial distribution of samples and anionic species in the study area.In Figure 7, Sample 5 shows a higher concentration of SO 4 2À than the other samples.The trilinear plot in Figure 8 shows that the water samples fall under Ca 2+ + Mg 2+ , SO 4 2À hydrochemical facie.

Chemical speciation and distribution
Speciation modeling showing chemical species distribution in the samples is presented in Data S2.The data show the chemical components and their percentage of total component as they occur in water samples.Some occur as free metallic ions while some form complexes with other elements at different percentages.

Saturation indices
In

Water quality index
Table 3 shows WQI in sample locations.In Table 4, computed WQI show values of 35.0,63.8,68.8,47.8,88.7,121,103 and 45.5 in Locations 1,2,3,4,5,6,7 and 8,respectively.WQI was computed to determine the suitability of water sources for drinking, irrigation, and industrial uses.The comparison of WQI with standard water quality (Table 4) shows that WQI for Sample Location 1 has poor water for irrigation and industrial uses.Locations 2 and 3 water samples is of fair water quality status and suitable for irrigation and industrial uses.Sample 3 has fair water quality status and suitable for irrigation and industrial uses.Sample 4 has good water quality status and is suitable for drinking, irrigation, and industrial uses.Samples 6 and 7 are of very poor quality with restricted use for irrigation.Sample Location 7 has very poor water quality status and is suitable for irrigation.The water in Sample 8 is suitable for irrigation, drinking, and industrial uses.Tables 4 and 5 show WQI in each location and water quality rating, respectively.Figure 9 shows the spatial distribution of WQI in sample locations.

Concentrations of physical parameters in water
Levels of physical parameters are below standards recommended for potable water.Mean levels of turbidity (6.40 FTU) exceeded the WHO and NSDWQ standards of 5 FTU for potable water.This may be due to the weathering of mining and agricultural wastes into water sources.Turbidity is a water quality parameter caused by runoffs from clay materials (Freeze & Cherry, 1979).The pH obtained a mean value of 6.82, which is weakly acidic.Nnabo (2015aNnabo ( , 2015b) ) and Benibo et al. (2020) obtained pH values of 6.02 and 7.32-7.67,respectively, in the Eyingba and Ishiagu Pb-Zn mining areas.The pH values compared favorably with those obtained in this study.Opoke and Osayanda (2018)  pH < 7 is evidence of acidity due to acid mine drainage (Sikakwe et al., 2015).
The mean values of Pb (0.13 mg/L) and Cd (0.01 mg/L) exceeded permissible limits of 0.01 and 0.003 mg/L stipulated by the WHO and Standard Organization of Nigeria (SON) ( 2015) for potable water.The source of Pb could be from Pb mining activities around the study area.The presence of Cd could be due to the association of Cd with sulfide minerals (Levinson, 1974).Other heavy metals obtained mean levels below their permissible limits for potable water by world standards (Table 1).Teaching of heavy metals into the bottom sediments of water sources may be responsible for low concentrations of heavy metals in the water samples.Pb, Zn, Fe, and Cu recorded higher levels in Water Samples 1 and 5-7.Pb exposures in adults and children cause convulsion, renal failure, coma, death, and fetal damage in pregnant women (Tay et al., 2019).Pb and Cd are human carcinogens, and human exposure to Pb in elevated doses causes abortion and cardiovascular issues.Cd even of minute concentrations triggers metabolic poison (ATSAR, 2000;Guatam et al., 2015).High Cd levels cause kidney damage and high blood pressure (Rajappa et al., 2010).Nnabo (2015aNnabo ( , 2015b) ) established mean values of pH, Pb, and Cr of 6.02, 1.78 mg/L, and 4.02 mg/L, which are at variance with the values obtained in this study.

Relationships and sources of chemical constituents in water sources
Positive significant correlations between elements show that they control their concentrations in water by having the same source.Negative correlations between elements show that they do not have a common source and that they do not control their concentrations in water.The  source of elemental constituents in water in this study area could be from sources such as rock weathering and human activities such as mining, agriculture, and waste disposal.The source of Pb, Fe, and Cu could be from galena (PbS), pyrite (FeS), covellite (CuS), and pesticides in agricultural activities.Rotated component matrix of PCA shows PC 1 with significant correlations of Pb, Zn, Fe, Cu, Cd, Mn, Ni, Cr, pH, TSS, TS, TH, turbidity, and NO 3 À .This is evidence of weathering of clays, shale, sandstones, and waste disposal.Their sources could also be from mineralization and hydrochemical processes such as rock-water interaction, cation exchange, and wastewater (Huang et al., 2014).The presence of NO 3 À is from domestic wastes such as wastewater and fertilizer pollution indicator (Mohapatra et al., 2011).Turbidity is a water quality indicator arising from runoffs of weathered materials (Freeze & Cherry, 1979).TH is caused by water-rock interaction.The pH shows that this factor is controlled by acidity.The source of Pb, Zn, Fe, Cu, Cd, Mn, Ni, and Cr is from Pb-Zn mining, and other heavy metals are associative elements in Pb-Zn deposit.
Their sources could also be from mineralization of rocks, agricultural runoffs, phosphate fertilizers and pesticides, and poor management of household wastewater (Herojeet & Kishi, 2015).Turbidity is also due to farming activities, organic matter, and erosion resulting in suspended particles in water (Adjiri et al., 2019).In PC 2, temperature, EC, SO 4 , Mg 2+ , and K + correlated significantly, indicating that temperature controls the dissolution of Mg 2+ and K + in water, which are from weathering of silicate minerals.The source of SO 4 is dissolved gypsum, sulfate dissolution, organically formed matter, and anthropogenic activities (Nagaraju et al., 2016).EC influences mineral content of water caused by hydrochemical processes (Okongbo & Douglas, 2015).PC 3 shows significant correlations of TDS, Na + , and Cr.TDS reflects enriched minerals in water due to groundwater contamination by human and animal wastes (Hem, 1989).Ca 2+ and Na + are from weathering of sodic and calcic plagioclase minerals and saline water (Malick et al., 2018;Nagaraju et al., 2016).PC 4 shows a negative significant loading of phosphate indicating reduced concentration in water samples.PC 1 loadings have significant Pearson's correlations at a 0.01% confidence limit.Mn, Ni, Cr, pH, TSS, turbidity, and NO 3 À reflects weathering of aquifer rocks and solid waste materials weathered and washed by surface runoff infiltrated into the subsurface.

Clustering
Clustering of variables using hierarchical cluster analysis (HCA) classified water quality parameters such that variables in each cluster are similar pair of variables.Clusters with the most similar pair of variables are formed first.
The Wards method of linkage with squared Euclidean distance as dissimilarity measure was adopted to evaluate the similarity of variables.HCA helps to group hydrochemical variables to evaluate groundwater quality (Huang et al., 2014).Clustering helps to make categories have differences within the group to be comparable with the sub-group variation and distribution.The grouping of variables in a cluster is in agreement with Pearson's correlation and principal components (Data S1 and Figure 4).Variables in the same cluster possess similar hydrochemical properties.From Figure 5, TH belongs to Group 5 having distinct property.Cluster 1 probably explains water interaction and control mineralization.
Cluster 2 containing EC and TS reflects dissolution of salts causing salinity, which conforms to PC 3. Cluster 3 containing TDS relates to hydrochemical processes caused by human and animal waste (Hem, 1989).TSS in Cluster 4 is caused by weathering of rocks and soils and surface runoffs while Cluster 5 containing TH may be caused by Ca 2+ , Mg 2+ , and SO 4 ions in water.

Hydrochemistry of water
Orderly presentation of chemical data provides for visual inspection (Freeze & Cherry, 1979).The major ions composition in milligrams per liter is presented in Figure 6, known as stiff diagram, which enables a quick comparison based on their characteristic shapes.From the stiff diagram (Figure 6), Samples 1 and 2 display higher concentrations of SO T A B L E 5 Water quality rating as per arithmetic weighted arithmetic WQI (Dharani & Vidya, 2019;Ogbodo et al., 2020).

Speciation and complexation modeling of cations in water
The chemical data were modeled for speciation of cations (Pb, Zn, Fe, Cu, Cd, Mn, Ni, Cr, Mg 2+ , Na + , Ca 2+ , and K + ).Almost all the cations occur as free ions at 79%-99.9% and form few complexes at very low percentages.
Only Cr forms complexes of Cr(OH) 2 at about 98.9% in all the samples but occurs at a very low percentage as free metal.Anions such as PO 4 3À and SO 4 occur as free ions at 25%-50% and form few complexes.Most cations occur as free ionic species and are the bioavailable and noxious type of cations that occur in natural water (Ekwere & Edet, 2012).However, in all the water samples, Cr forms complex of Cr(OH) + at more than 90% and occurs as free ionic specie not more than 2%.This implies that Cr(OH) + is more mobile and soluble in the water samples than the free ion Cr (Levinson, 1974).On the other hand, other cations are more soluble and mobile as free ionic species in the waters.Mineral SI < 1 indicates undersaturation, SI = 0 represents equilibrium condition, and SI > 1 is evidence of oversaturation of the mineral specie in water.The mineral species occurring at SI > 1 include chlorophymorphite (c), chloropyromorphite (soil), anglesite, anhydrite, hydropyromorphite, tenorite (am), tenorite (c), tsumebite, and vivianite.Others are langite, larnakite, antlerite, atacamite, melanterite, and mirabite.PbPO 4 Cl À , tsumebite, and tenorite were oversaturated in Sample 2. This shows that they were precipitated in water due to oversaturation.Equilibrium saturation minerals are Pb(PO 4 ) 2 , MnHPO 4 , tenorite (am), and Pb(OH) 2 in Samples 2 and 5, showing that they are neither precipitating nor dissolved in water.The minerals chloropyromorphite (c) and chloropyromorphite (soil) were oversaturated in all the sample locations, showing that they were precipitated from water (Alexakis, 2011).Chloropyromorphite (soil) obtained SI values of 10.7 reflecting its supersaturation and precipitation from water.Sample treatment processes in which precipitated things are re-dissolved due to the prevailing pH and temperature conditions could explain its detection in water.

Water quality index
Comparisons of the computed WQI values with standard values by Ogbodo et al. (2020) show that about 30% of the water samples are good for drinking, irrigation, and industrial uses.About 20% of fair quality for irrigation and industrial uses.About 15% of poor quality suitable for irrigation use.About 35 % were of very poor status and suitable for irrigation use only.Nwakpu stream, Eyingba stream and borehole at front gate are of good I G U R E 9 Spatial distribution of water quality index (WQI) in sample locations.
status and suitable for drinking, irrigation, and industrial uses (Table 4).The pond (lake) at College of Education, Alike Stream, and FUNAI Zone A borehole are of very poor status and suitable for irrigation only.This may be due to the dissolution of anhydrite and Pb through weathering and water-rock interaction.Echara junction borehole water is of poor quality status and suitable for irrigation and industrial uses.This may be due to geogenic and anthropogenic activities contaminating the water sources.Magadam et al. (2017) established WQI in river water ranging from 68 to 593.This exceeds the values of WQI obtained in the study.Etim et al. (2013) recorded WQI of 34-36 in pipe-borne water in Akwa Ibom, Nigeria.These were all of good quality status suitable for drinking, irrigation, and industrial uses.Unlike in this study, only few samples fall under this category.This may be due to contamination activities by Pb-Zn mining in the area compared with the work done by Etim et al. (2013) in locations of no visible water contamination activity.Etim et al. (2013) also established WQI of 55-84 in the Niger Delta Area.These approximate the values obtained in this study.This may be because Niger Delta Area is in a zone of water contamination activities caused by crude oil exploration, drilling, and processing.

CONCLUSION
The quality of water sources can be better described using a comprehensive analysis of water from hydrochemical composition, speciation modeling, and WQIs.The uses of water for drinking, irrigation, and industrial purposes demand that the quality be ascertained to ensure that the water meets its requirements for its intended use.Due to the distance of the sample sites from the mine sites, the degree of water acidity did not really reflect pH levels of water that characterize mine drainage sites.However, the pH tends towards moderately acidic condition, which can influence water quality for drinking purpose.None of the sample locations contains water that is excellent for drinking, irrigation, and industrial uses.However, water sample locations at the community borehole, Nwakpu Stream, and Enyigba Stream are good for drinking, irrigation, and industrial uses.Other sample locations are described as fair or poor and can only be used for either irrigation or industrial purposes.
Chemical constituents such as turbidity, Pb, and Cr levels exceeded world standards for drinking purposes.The use of statistical analysis, geochemical modeling, and hydrochemical studies revealed that the water sources are sulfate dominated and that the major cations exhibit equal concentrations.Heavy metals exist mostly as free cations and soluble in water.However, Cr is more soluble and mobile in water as a complex of Cr(OH) 2 in contrast to other heavy metals.In all the locations, the water sources show hydrochemical facies of Ca + Mg 2+ , Cl À + SO 4 .Piper plot shows that the major cations have almost equal concentrations in water.Sulfate is the dominant anion in the water sources.Multivariate analysis shows that the sources of chemical species are more from geogenic than anthropogenic sources.Pearson's CA, PCA, and cluster analysis show reasonable agreement in the sources of the chemical species in water.
From SI, the undersaturated minerals in water are anglesite, anhydrite, zincite, vivianite, langite, larnakite, melanterite, and mirabite.These minerals dissolved to produce their corresponding ions in water such as Pb, Zn, Cu, Fe, and SO 4 that can degrade water quality.These ions need monitoring in the water sources.WQI shows that 25% of water sources are suitable for drinking and irrigation.On the other hand, 40% are suitable for irrigation only and 25% are suitable for irrigation and industrial uses.The method of water quality studies applied in this study is comprehensive and addresses the need of water quality investigation for diverse uses.Water quality studies around mine drainage areas have been understudied, and this study stands out among other water quality studies by focussing on water quality in mine drainage areas to determine the suitability of water for different uses.The method is suitable for investigation of water quality, which is useable by other scientists elsewhere to solve similar environmental problems.

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I G U R E 1 Study area, sample locations, and insert of map of Nigeria.
also recorded pH means of 5.60 and 5.35 in river waters during the dry and wet seasons, respectively.In boreholes, the study obtained pH means of 5.72 and 5.19 in the dry and wet seasons, respectively, in mining areas of Ebonyi State.Low pH values may be due to the distance of the sample points from the mine sites.Water pH is naturally high around mining areas, and F I G U R E 7 Stiff map of chemical species in water.

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I G U R E 8 Trilinear plot of chemical species in water.T A B L E 3 Water quality index (WQI) of water samples.
Descriptive statistics of physicochemical and heavy metals.United States Environmental Protection Agency (2012).
4 2À , Mg 2+ , and Na + + K + with little PO 4 3À .Ca 2+ is the least cation.This is due to higher dissolution of orthoclase and plagioclase feldspars and Mg 2+ than that of calcic plagioclase feldspars.The source of Mg 2+ is from amphiboles such as hornblende and olivine.Those samples are from Eyingba and Nwakpu Stream.Nwakpu stream drain the Pb-Zn mining area at Eyingba.
T A B L E 4 WQI in each sample location.Abbreviation: WQI, water quality index.