An integrated indexical approach in assessing water quality for drinking purposes in the Nkalagu area, southeastern, Nigeria

doi:10.21203/rs.3.rs-2007700/v1

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Research Article

An integrated indexical approach in assessing water quality for drinking purposes in the Nkalagu area, southeastern, Nigeria

https://doi.org/10.21203/rs.3.rs-2007700/v1

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published 06 Mar, 2023

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In this study, water supplies for the two distinct climatic seasons; wet and dry seasons were evaluated. This was carried out to determine the degree of contamination and whether the water supplies were safe for human consumption. In light of this, four water quality indices; the vector modulus of pollution index (PI_vector), entropy-weighted water quality index (EWQI), integrated water quality index (IWQI), and modified water quality index (MWQI) were integrated. Based on the results, the pH of the analyzed water samples varied from 6.02 to 7.92 with a mean value of 7.2 during the wet season and from 5.25 to 8.25 with a mean value of 6.82 during the dry season. A generic quality assessment study revealed that Pb, As, during the wet season and Pb, As, and Mn during the dry season has the greatest effects on the region's water supplies. According to the indices, PI_vector classified approximately 71.43% of the water during the wet season as unpolluted and 28.57% as polluted, and 37.14% of the water during the dry season as unpolluted and 62.86% as polluted. Similarly, the EWQI classified 65.71% as safe water and 34.28% as unfit for human consumption during the wet season, whereas, 34.28% and 65.72% as safe and unfit respectively for the dry season. According to IWQI, during the wet season, 25.7% of the water is considered acceptable for drinking, while 74.3% is unsafe, and during the dry season, 14.85% is acceptable and 85.15% is unsafe. The MWQI results indicated that the water samples for the wet season were classed as "fair" water suggesting they are occasionally threatened while, for the dry season as “marginal” water indicating they are frequently threatened. Summarily, the results show that the water resources are safer and less contaminated in the wet season than in the dry season, which is attributed to the impact of rainfall, which reduces the mobility of contaminants, as well as the influence of vegetation cover.

Water resources

Indexical approach

Entropy-weighted water quality index

Integrated water quality index

Modified water quality index

Given that water is the foundation of all life, it is one of the most valuable natural resources on the planet (White and Eman 2020). The sustainability of human health is driving more demand for it (Unigwe et al. 2022). On a worldwide scale, water is seen as being crucially significant for domestic, industrial, and agricultural purposes (Niu et al. 2017). Water has a significant role in maintaining global food security, according to Wada et al. (2014). Nevertheless, depending on the intended use, the quantity and quality of the available water resources become a worldwide concern and may impede the ecological balance and sustainable development of a region. Water quality is highly influenced by anthropogenic activities, regional and local hydrogeological conditions, and hydrochemical processes that take place in saturated and unsaturated zones (Liu et al. 2003). Excessive water withdrawal can contaminate water supplies, and untreated mine water outflow can also impair water quality (Omonona et al. 2021).

The Nkalagu region, in southeast Nigeria, is well-known for its enormous limestone deposit. Open-cast mining technique is typically used to extract this material, and as a result, numerous pits have been produced throughout the years that are typically abandoned without rehabilitation. Ordinarily, abandoned mine pits are left untreated, and mine water is discharged straight into the region's surface water bodies. These pits during the peak dry season serve as sources of drinking water as well as for domestic and agricultural purposes for the teaming population without certainty of the quality which has resulted in an alarming outbreak of diarrhoea, typhoid fever and cholera in the area, leaving no fewer than eight people dead and several others hospitalized. Previous studies in the area (Eyankware et al. 2018; Onwe et al. 2019; Onwe et al. 2021) do not seem to have addressed the sources of the problem, especially concerning using modified water quality indices. They discovered that several of the available water sources that the inhabitants used had very dubious drinking water, which they attributed to the region's extensive mining activities.

The following drinking water indices will be integrated into this study: (i) modified water quality index (MWQI), (ii) integrated water quality index (IWQI), (iii) entropy-weighted water quality index (EWQI), and (iv) vector modulus of pollution index (PI_vector). A number of indices, including the Water Quality Index (WQI), Pollution Index of Groundwater (PIG), Synthetic Pollution Index (SPI), Overall Index of Pollution (OIP), Modified Water Quality Index (MWQI), Integrated Water Quality Index (IWQI), Entropy-weighted Water Quality Index (EWQI), and Vector Modulus of Pollution Index, are used over time to appraise the quality of drinking water (PIvector). As a method for transforming a wide range of parametric data into whole numbers, the development of numerical models for evaluating water quality has drawn attention. Recent research by Egbueri et al. (2020) and Unigwe and Egbueri (2021) has shown how different models, including PIvector, EWQI, IWQI, and MWQI, have substantially increased water quality monitoring and evaluation initiatives. No study in the region has analyzed the outcomes of these water quality investigations concurrently. Due to this, the purpose of this study was to evaluate the region's water quality for human consumption utilizing the indexical model approaches of PIvector, EWQI, IWQI, and MWQI as well as to find important contaminants in the water resources. The purpose of this study is to inform local and global water quality researchers more about the region's water quality as well as the validity and sensitivity of the performance of the chosen models in assessing water quality.

1.2 General description of the study area

The research area is situated in Ebonyi State, southeast Nigeria, between latitudes 6°21′N and 6°40′N and longitudes 7°35′E and 7°50′E. (Fig. 1). Nkalagu is accessible along the Enugu-Abakaliki expressway, approximately 50 kilometres northeast of Enugu. Based on the 2006 census, the area has 6000 inhabitants, the majority of whom are employed in agriculture and mining. Vegetables were grown next to streams, while food crops such as rice, cassava, and maize were grown on various farmlands strewn over the area and mine water is utilized for irrigation when surface water levels decline. The area of study experiences two distinct seasons: rainy and dry. The wet season runs from March to October, whereas November through February is considered the dry season. The Dry Harmattan Wind, a north-east trade wind from the Sahara Desert, which dominates during the dry season, and the SW Trade Wind, a marine wind from the Atlantic Ocean, which ushers in the rainy season, are the two dominant winds that blow across the country at various times throughout the year and are responsible for these two distinct seasons. The Nkalagu hamlet is situated in moderately hilly terrain with elevations ranging from 125 to 250 m above mean sea level (Inyang 1975). The ephemeral tributaries of the Ebonyi River drain the region. The average annual temperature ranges from 20 to 28°C, and the average annual precipitation ranges from 1750 to 2250 mm. Without a doubt, the regional climate facilitates the spread of toxins associated with mining.

1.3 Geology and Hydrogeology

The two formations that make up the region's geological bedrock are the Eze-Aku Formation and the Asu River Group, both belonging to the Albian and Turonian ages. The Asu River Group is composed of the Abakaliki Shale and volcanic sandstone, sandstone, and sandy limestone lenses (Benkheil 1989). The Abakaliki Shale averages 500 meters in thickness, is extensively folded, and has a few minor fractures (Omonona et al. 2021). The overburdened strata consist of lateritic soil and weathered shale that are between 18.5 and 20.0 m thick. This study's shale unit has a NE-SW trend and a dip that varies in angle from 4 to 32 degrees in the direction of the NW-SE. The black and micritic limestone lithology is mainly composed of grapestones, infralimeclasts, and brown algal remnants (Oko 2002). The Turonian Eze-Aku Formation is situated above the Asu River Group. In this formation, limestone and sandstone are secondary to hazy, grey, or black shales (Reyment 1965). Calcareous shales and a series of thick limestone or sandstone strata alternate in areas where the Eze-Aku Formation is visible (Ikhane et al. 2009). An area that satisfies the aforementioned requirements is the Nkalagu area, which is up to 6 m thick and is designated as the Nkalagu Formation by Petters (1991) and Umeji (2000). The existence of baked shales, pillow lava structures, and pyroclastics in the Abakaliki area during the Cretaceous period is proof that the southern Benue Trough had intense magmatic activity.

The hydrogeology of the Nkalagu region is characterized by poor aquifer conditions. The predominately shale units (Aquiclude) are to account for this since they are neither porous nor permeable and do not transmit water to nearby wells. The Nkalagu region's hydrogeology is characterized by poor aquifer conditions. The predominate shale units (Aquiclude) are to account for this since they are neither porous nor permeable and do not transfer water to nearby wells. Groundwater is frequently drawn from small fractured shale aquifers at depths of 45 to 68 m. This makes regional groundwater detection in the region impossible. But there may be conditions for groundwater presence in small fractured zones or locations where sandstone intercalates. The groundwater usually gets refilled by rains.

2.1 Sampling and laboratory analysis

In two seasons (rainy and wet), from March and August 2019, 35 water samples were collected from diverse places and classified as AM (abandoned mines), CP (catch pits), RV (rivers), and HDW (hand dug wells), as shown in Fig. 1. Before being put into distilled water that had been acidified with 1.0 mL of HNO₃ for three days as a precaution, the sample bottles and beakers were thoroughly cleaned. They were vigorously rinsed with the water sources at least three times and rinsed with diluted HNO₃ before taking a water sample from the area. To obtain a composite sample, waters were taken after being cleared of any observable wastes. They were vigorously rinsed with the water sources at least three times and rinsed with diluted HNO3 before taking a water sample from the area. To obtain a composite sample, waters were taken after being cleared of any observable wastes. The water samples were preserved clean by running them through a 0.45-m filter and injecting pH-2 ultrapure nitric acid to acidify them. To prevent sorption, this procedure was necessary. Electrical conductivity (EC), pH, and total dissolved solids (TDS) were a few of the quick observations performed at the places of collection. The samples were taken to the lab within 24 hours of being obtained for physicochemical characteristics and trace metal content examination. To keep the samples' temperature stable during shipping, the samples were initially kept in ice-packed containers.

To measure EC and TDS, a conductivity (HACH) and TDS meter (model HQ14D53000000, USA) were utilized. The pH was to be measured using a portable pH meter (Hach sens ION + 5050T Portable Combination pH Electrode and Hach sens ION + PH1 Portable pH Meter). We measured the potassium (K+), sodium (Na+), and calcium (Ca2+) ion concentrations using a Jenway Clinical flame photometer (PFP7 model). Using the appropriate titrimetric methods stipulated by APHA (2012), the concentrations of the ions magnesium (Mg2+), bicarbonate (HCO3-), and chloride (Cl-) were measured. The concentration of sulphate (SO42-) was measured using turbidimetry using a UV-Vis spectrometer and spectra manager software, and the nitrate (NO3-) concentration was determined using the hydrazine reduction method. Lead (Pb2+), zine (Zn2+), manganese (Mn2+), arsenic (As3+), cadmium (Cd2+), and iron (Fe2+) trace elements were analyzed using an atomic absorption spectrophotometer (AAS) on a Perkin Elmer Analyst 400 spectrophotometer. The accuracy of the hydrochemical analysis was confirmed by calculating the ion balance, which was within 0.01% when performed throughout (Aleke et al. 2016).

2.2 Water quality assessment

2.2.1 Vector modulus of pollution index (PI_vector)

The pollution index (PI), an indexical approach that shows the amount of contamination displayed separately by the measured parameter, determines the level of pollution risk (Kowalska et al. 2018; Egbueri and Unigwe 2020; Egbueri et al. 2020). The final summation of all the evaluated parameters is then calculated using the PI_vector, which measures the pollution for the entire sample. Kowalska et al. (2018) state that Equations (1) and (2) are used to calculate the PI and PI_vector.

PI = \(\frac{Cn}{GB}\) (1)

PI_{vector =} \(\surd \frac{1}{n}\sum {PI}^{2}\) (2)

Where Cn is the concentration of the analysed parameter and GB is the background value of the analysed parameter.

2.2.2 Entropy weighted-water quality index (EWQI)

The entropy-weighted-water quality index (EWQI) is a method for empirically assessing the water quality of sets of water samples. Li and coworkers created it (2010). It was developed by Li et al. (2010). The following are the stages involved in computing the EWQI:

The computation of the evaluation matrix "X" follows Eq. (3)

X = \(\left[\begin{array}{ccc}\begin{array}{c}{x}_{11}\\ {x}_{\begin{array}{c}21\\ \dots \end{array}}\end{array}& \begin{array}{c}{x}_{12\dots }\\ {x}_{\begin{array}{c}22\\ \dots \end{array}}\dots \end{array}& \begin{array}{c}{x}_{1n}\\ {x}_{\begin{array}{c}2n\\ ⋮\end{array}}\end{array}\\ {x}_{m1}& {x}_{m2}\dots & {x}_{mn}\end{array}\right]\) (3)

Where "n" is the number of parameters that were examined and "m" denotes the number of samples.

Using Eqs. (4) and (5), the standardization procedure for "yij" and "Y" is determined.

y_ij = \(\frac{{x}_{ij} - \left({\text{x}}_{ij}\right)\text{m}\text{i}\text{n}}{{\left({\text{x}}_{ij}\right)}_{max}-{ \left({\text{x}}_{ij}\right)}_{max}}\) (4)

Y = \(\left[\begin{array}{ccc}\begin{array}{c}{y}_{11}\\ {y}_{\begin{array}{c}21\\ \dots \end{array}}\end{array}& \begin{array}{c}{y}_{12\dots }\\ {y}_{\begin{array}{c}22\\ \dots \end{array}}\dots \end{array}& \begin{array}{c}{y}_{1n}\\ {y}_{\begin{array}{c}2n\\ ⋮\end{array}}\end{array}\\ {y}_{m1}& {y}_{m2}\dots & {y}_{mn}\end{array}\right]\) (5)

Where (xij) min and (xij) max indicate the minimum and maximum values of the examined parameters of the samples, and xij represents the initial matrix (Unigwe and Egbueri 2021).

The initial stage in the EWQI process is to calculate the information entropy (ej) for each chemical parameter as indicated in Eq. (6).

ej = \(-\frac{1}{Inm }\sum _{i=1}^{m}{P}_{ij}{InP}_{ij}\) (6)

Where Pij stands for the probability that the parameter j's normalized value, as defined in Eq. (7), will occur:

\({P}_{ij}\) = \(\frac{{P}_{ij}}{\sum {P}_{ij}}\) (7)

The second step entails computing the entropy weight (wj) for each chemical parameter as indicated in Eq. (8)

wj = \(\frac{1 -ej}{\sum _{j=1}^{n}1 -ej}\) (8)

In the third phase, the quality rating scale (qj) for each chemical parameter expressed in Eq. (9) is calculated

qj = \(\frac{Cj}{Sj} \times 100\) (9)

Where Sj is the standard allowable limit (mg/L) and Cj is the concentration of chemical parameters (mg/L). The SON (2015) was used as the reference limit in this research.

The calculation of the EWQI, as stated in Eq. (10) is the final step.

EWQI = \(\sum _{j=1}^{n}wj. qj\) (10)

2.2.3 Integrated water quality index (IWQI)

The IWQI was established by Mukate et al. (2019). It is a thorough and in-depth measure of water quality that takes into account both the desired and permissible limitations of parameters. It equally takes the parameter's shortcomings and overuse into account. The IWQI features a unique step mechanism, as demonstrated by the following:

The calculation for the range

The SON (2015) was regarded as a standard limit and is displayed in Table 1. It is calculated utilizing Eq. (11).

Range = Permissible limit (PL) − Desirable limit (DL) (11)

The calculation for modified permissible limits (MPL)

The SON (2015), which is shown in Table 1, was also taken into account when establishing the MPL. Eq. (12) is used to compute it.

MPL = PL − (20% of Range) (12)

Any parameter concentration that is below the Desirable limit (DL) or above the Permissible limits (PL) in the IWQI computation is considered unfit for consumption. Additionally, the concentration that is above the modified permissible limit (MPL) and below the minimum required concentration will have an impact on the water quality.

Determination of the subindex (SI)

When calculating the sub-index, three various parameters are factored. The list is as follows.

SI₁ = 0 (13)

When the observed value of the ith parameter (Pi) is > DL but < MPL, then SI₁ is expressed as zero.

SI₂ = \(\frac{(\text{D}\text{L}-\text{P}\text{i})}{\text{D}\text{L}}\) (14)

When the value of the ith parameter is smaller than the Desirable limit, Eq. 14 is taken into account (DL); i.e. Pi ≤ DL.

SI₃ = \(\frac{(\text{P}\text{i}-\text{M}\text{P}\text{L})}{\text{M}\text{P}\text{L}}\) (15)

When Pi exceeds the modified allowable limit, Eq. 11 is taken into consideration (MPL); i.e. Pi ≥ MPL.

The final IWQI is assessed using Eq. (16);

IWQI = \(\sum _{\text{i}=1}^{\text{n}}{\text{S}\text{I}}_{\text{i}\text{j}}\) (16)

Where \({\text{S}\text{I}}_{\text{i}\text{j}}\) is the sub-index value of the ith sample and jth water quality parameter (Mukate et al. 2019; Unigwe and Egbueri 2021).

Table 1

Table showing the DL, PL, MPL and weight factors utilized in this study
Parameter	Desirable Limit (DL)	Permissible Limit (PL)	Range	MPL	Weight factor
pH	6.5	8.5	2.0	8.1	1
TDS	500	1000	500	900	1
EC	500	1000	500	900	1
TH	75	150	75	135	1
SO₄	100	200	100	180	1
NO₃	20	50	30	44	3
HCO₃	200	250	50	240	1
Cl	200	250	50	240	2
Mg	20	50	30	44	1
Ca	100	200	100	180	1
Na	20	50	30	44	1
K	6	12	6	10.8	3
Fe	0.1	0.3	0.2	0.26	2
Zn	1.0	3.0	2.0	2.6	2
Mn	0.1	0.2	0.1	0.18	2
Pb	0.005	0.01	0.005	0.009	4
Cd	0.01	0.03	0.02	0.026	4
As	0.005	0.01	0.005	0.009	4

2.2.4 Modified water quality index (MWQI)

The MWQI entails the following three fundamental steps: First, choose the benchmarks and parameters for evaluating the quality of the water: The WHO (2017) and the SON (2015) were taken into consideration as the appropriate benchmarks while identifying the eighteen water quality criteria with their sub-indices for this study.

Secondly, selecting appropriate weighting factors for significant input parameters: At this level, emphasis was concentrated on the factors that posed the greatest risks to a person's health. Table 1 displays the weights that have been allocated to each parameter.

Third and last step, MWQI calculation: Three important factors: the scope factor (FS), frequency factor (FF), and amplitude factor (FA) are taken into account while calculating according to Shankar and Sreevidya (2019) and Unigwe and Egbueri (2021). The FS represents the number of parameters that deviate from benchmarks, the FF represents the number of measurements that deviate from benchmarks, and the FA represents the degree of deviation from benchmarks in the failed measurements. Eqs. (17) and (18) are used respectively, to determine the FS and FF:

F_S =\(\frac{\sum _{\text{j}=1}^{\text{n}}{\text{W}}_{\text{q}\text{j}} }{\sum _{\text{i}=1}^{\text{m}}{\text{W}}_{\text{q}\text{i}}}\), i = 1, 2. .. . m; j = 1, 2. . ., n (17)

F_F = \(\frac{\sum _{\text{j}=1}^{\text{n}}({\text{W}}_{\text{q}\text{j}} \times {\text{W}}_{\text{q}\text{i}})}{\sum _{\text{i}=1}^{\text{m}}({\text{W}}_{\text{q}\text{j}} \times {\text{N}}_{\text{q}\text{j}})}\) (18)

Where Wqi stands for the weight factor of an input parameter, Wqj for the weight factor of a violator parameter, and Nqi for the total number of measurements for the input parameter; Nqj stands for the number of measurements for the violator parameters; m stands for the number of input parameters, and n for the number of violator parameters.

The normalized sum of excursions is calculated for FA determination after the excursion amount of violating parameters (NSE). Eqs. (19) and (20) below are used in calculating NSE.

E_qi = \(\frac{{\text{C}}_{\text{q}\text{i}}}{{\text{S}\text{V}}_{\text{q}\text{i}}}-1\) (19)

NSE = \(\frac{\sum _{\text{j}=1}^{\text{n}}({\text{W}}_{\text{q}\text{j}} \times {\text{E}}_{\text{q}\text{j}})}{\sum _{\text{i}=1}^{\text{m}}({\text{W}}_{\text{q}\text{i}} \times {\text{W}}_{\text{q}\text{i}})}\) (20)

Where Cqi represents the concentration of the violator parameter; SVqi represents the standard value of the violator parameter; and Eqj, the excursion amount of the violator parameter. According to Unigwe and Egbueri (2021), the FA and the MWQI are calculated using equations Eqs. (21) and (22) respectively:

F_A = \(\frac{\text{N}\text{S}\text{E}}{0.01 \text{N}\text{S}\text{E}+0.01}\) (21)

MWQI = \(100-\frac{\sqrt{{{\text{F}}_{\text{S}}}^{2}+{{\text{F}}_{\text{F}}}^{2}+{{\text{F}}_{\text{A}}}^{2}}}{1.734}\) (22)

The classification scheme has been presented in Table 2.

Table 2

Classification criteria for MWQI according to Shankar and Sreevidya (2019)
Rank	MWQI value	Description
Excellent	95–100	Water quality is protected with a virtual absence of threat or impairment conditions very close to natural or pristine levels. All measurements are within objectives virtually all of the time.
Very good	89–94	Water quality is protected with a slight presence of threat or impairment conditions close to natural or pristine levels.
Good	80–88	Water quality is protected with only a minor degree of threat or impairment; conditions rarely depart from natural or desirable levels. Measurements rarely depart from desirable levels.
Fair	65–79	Water quality is usually protected but occasionally threatened or impaired conditions sometimes depart from natural or desirable levels. Measurements sometimes depart from desirable levels
Marginal	45–64	Water quality is frequently threatened or impaired; conditions often depart from natural or desirable levels. Measurements often depart from desirable levels
Poor	0–44	Water quality is almost always threatened or impaired; conditions usually depart from natural or desirable levels. Measurements usually depart from desirable levels.

3.1 General description of water quality

The results of the physiochemical and trace metal analyses for dry and wet seasons were a statistical summary and presented in Table 3.

The pH of water samples during the dry season in the area varies from 5.25 to 8.25 with a mean value of 6.82, while during the wet season, it varies from 6.02 to 7.92 with a mean value of 7.20 (Tables 3 and 4) signifying that they were slightly acidic to basic of 6.5 to 8.5 (WHO 2017). The majority of the sample locations, according to Table 3, are within the WHO (2017) recommended safe water intake range. However, over 46% (AM6, AM10, RV1, RV2, RV3, RV4, HDW1, HDW3, HDW6, HDW8, HDW10, HDW11, HDW12, HDW13, HDW14, and HDW15) during the dry season and 23% (HDW1, HDW6, HDW7, HDW9, HDW10, HDW11, HDW12, and HDW15) during wet season fall within the acidic pH range (Figs. 2). Drinking acidic water, according to Egbueri et al. (2021), may cause many negative health effects, as well as expose soft oral tissues to the acid and erode tooth enamel. The study area's lithography maybe accountable for the acidic water's composition.

Table 3

Statistical summary of the physiochemical and trace metal parameters.
Parameter	A: Wet season			B: Dry season
Parameter	Min	Max	Mean	Min	Max	Mean	WHO (2017)
pH	6.02	7.92	7.2	5.25	8.25	6.82	7
EC (µS/cm)	24.00	884.00	393.08	8.00	2754.00	860.42	1400
TDS (mg/L)	20.00	439.00	193.91	75.00	1372.00	443.31	500
TH (mg/L)	6.94	192.93	82.92	32.25	600.02	187.30	200
SO₄ (mg/L)	4.44	89.45	41.95	11.55	285.35	70.82	250
NO₃ (mg/L)	1.03	20.14	9.40	1.39	65.47	18.29	50
HCO₃ (mg/L)	2.52	146.00	36.65	12.08	254.10	79.48	120
Cl (mg/L)	2.42	103.00	37.93	12.31	354.05	96.51	250
Mg (mg/L)	0.48	16.65	7.10	4.08	62.34	21.22	30
Ca (mg/L)	1.94	58.71	21.51	5.08	137.56	39.66	75
Na (mg/L)	1.59	78.35	22.24	5.15	205.40	46.09	200
K (mg/L)	0.44	8.67	4.10	0.75	25.54	6.36	10
Pb (mg/L)	0.01	0.08	0.03	0.01	0.09	0.04	0.01
Zn (mg/L)	0.01	0.80	0.27	0.01	1.60	0.65	3
Mn (mg/L)	0.01	0.61	0.10	0.01	1.30	0.41	0.5
As (mg/L)	0.01	0.43	0.10	0.002	0.11	0.02	0.01
Cd (mg/L)	0.001	0.033	0.003	0.001	0.10	0.01	0.003
Fe (mg/L)	0.01	0.43	0.10	0.01	1.49	0.42	0.3

Electrical conductivity (EC)

EC is influenced by the level of dissolved salts in the water. Table 3 shows that EC ranges from 8.00 to 2754.00 S/cm with a mean value of 860.42 S/cm during the dry season and from 24.00 to 884.00 S/cm with a mean value of 393.08 S/cm during the rainy season. All of the samples in the dry season were within the WHO's (2017) acceptable level for drinking water quality, except for 6 samples (HDW4, HDW5, HDW6, HDW10, HDW11, and HDW12) (Tables 4). The likely causes of the elevated EC values in these sample locations are mineral dissolution and the high chemical activity present in the active abandoned mines. These figures agree with Eyankware et al. (2020).

Table 4

Summary of numbers of samples above the World Health Organization standard
		Water samples above WHO standard
Parameters	WHO	Dry season	Wet season
EC (µS/cm)	1400	6 (HDW4, HDW5, HDW6, HDW10, HDW11 and HDW12)	Nil
TDS (mg/L)	500	6 (HDW4, HDW5, HDW6, HDW10, HDW11 and HDW12)	Nil
TH (mg/L)	200	9 (HDW4, HDW5, HDW6, HDW7, HDW8, HDW9, HDW10, HDW11 and HDW12)	Nil
Ca (mg/L)	75	4 (HDW5, HDW10, HDW11 and HDW12)	Nil
Na (mg/L)	200	HDW10	Nil
Mg (mg/L)	30	5 (HDW5, HDW6, HDW10, HDW11 and HDW12)	Nil
Cl (mg/L)	250	2 (HDW10 and HDW12)	Nil
SO₄ (mg/L)	250	2 (HDW11 and HDW12)	Nil
HCO₃ (mg/L)	120	6 (HDW6, HDW7, HDW8, HDW9, HDW10 and HDW11)	Nil
NO₃ (mg/L)	50	2 (HDW11 and HDW12)	Nil
Pb (mg/L)	0.01	24 (AM1, AM2, AM3, AM7, AM8, AM9, AM10, AM11, AM12, AM13, HDW1, HDW2, HDW3, HDW4, HDW5, HDW6, HDW7, HDW8, HDW10, HDW11, HDW12, HDW13, HDW14 and HDW15)	18 (AM1, AM2, AM3, AM5, AM6, AM7, HDW1, HDW3, HDW4, HDW5, HDW6, HDW7, HDW8, HDW10, HDW12, HDW13, HDW14 and HDW15)
Mn (mg/L)	0.5	14 (AM4, AM5, AM6, AM7, AM8, AM9, CP3, RV2, HDW7, HDW8, HDW9, HDW10, HDW11 and HDW15)	HDW15
As (mg/L)	0.01	7 (AM2, AM9, AM11, AM12, AM13, HDW7 and HDW8)	10 (AM2, AM4, AM5, AM7, AM8, AM9, AM12, AM13, HDW7 and HDW8)
Cd (mg/L)	0.003	8 (HDW5, HDW6, HDW7, HDW8, HDW9, HDW10, HDW11 and HDW12)	1 (HDW10)
Fe (mg/L)	0.3	17 (AM1, AM2, AM3, AM5, AM6, AM7, AM8, AM9, CP1, HDW2, HDW4, HDW5, HDW6, HDW7, HDW8, HDW13 and HDW14)	8 (AM2, AM3, AM5, AM7, AM8, CP1, HDW4 and HDW8)

Total dissolved solids (TDS)

Similarly, TDS values vary from 75.00 to 1372.00 mg/L with a mean value of 443.31 mg/L during the dry season and 20.00 and from 439.00 mg/L with a mean value of 193.91 mg/L during the wet season (Table 3). Results indicated evidence of fresh to moderately mineralized water and it is in line with the observation of Ifediegwu et al. (2019). TDS values fell within WHO (2017) permissible limits for drinking water quality in both seasons except 6 samples (HDW 4, HDW5, HDW6, HDW10, HDW11, and HDW12) during the dry season that was slightly above the permissible limit (Table 4). The entire water samples were classified into fresh and desirable water for drinking water quality except for 9 samples (HDW1, HDW2, HDW4, HDW5, HDW6, HDW7, HDW8, HDW9, and HDW13) that are permissible water (Table 5) based on the salinity classification suggested by Davis and DeWiest (1966) and Freeze and Cherry (1979).

Total Hardness (TH)

The value of TH varies from 32.25 to 600.02 mg/L with a mean value of 187.30 mg/L during the dry season and from 6.94 to 192.93mg/L with a mean value of 82.92 mg/L during the wet season. The values of TH in the area are generally < 200 mg/L WHO (2017) in both seasons, except for 9 samples (HDW 4, HDW5, HDW6, HDW7, HDW8, HDW9, HDW10, HDW11, and HDW12) during the dry season that was above the permissible limit for drinking water quality (Table 4). The high values in these locations can be attributed to the influence of bedrock geology and anthropogenic activities in these locations. The hardness of water is the amount of calcium and magnesium, and iron to a lesser extent in water is commonly expressed as a milligram of calcium carbonate equivalent per liter. According to Sawyer and McCarty (1967), water quality classification based on TH values reveals that about 7 and 20 water samples fall within the soft water class in the dry season and wet season respectively (Table 5), 4 and 6 water samples fall in the moderately hard class in the dry season and wet season respectively, 19 and 9 water samples fall in the hard class in the dry season and wet season while 5 water samples fall in the very hard class in the dry season only. This is in line with findings conducted in the study area by Eyankware et al. (2018) and Onwe et al. (2021).

Table 5

Classification of the waters based on TDS, EC, and TH
Parameter	Range	Quality of water	Dry season	Wet season
Total Dissolved solids (TDS)	< 500	Desirable for drinking	26	35
	500–1000	Permissible for drinking	9	-
	> 3000	Unfit for drinking and irrigation	-	-
Electrical Conductivity (EC)	0–333	Excellent	13	24
	333–500	Good	10	11
	500–1100	Permissible	12	-
	1100–1500	Brackish	-	-
	1500–10,000	Saline	-	-
Total Hardness (TH)	< 60	Soft	7	20
	60–120	Moderate	4	6
	120–180	Hard	19	9
	> 180	Very hard	5	-

Sodium and potassium (Na ⁺ and K⁺)

Estimated Na⁺ content from collected water samples varies from 5.15 to 205.40mg/L with a mean value of 46.09 mg/L during the dry season and from 1.59 to 70.35 mg/L with a mean value of 22.24mg/L during the wet season (Tables 3 and 4). Except for one sample (HDW10) that exceeds the recommended limit standard during the dry season, virtually all water samples are within the maximum permissible limit level for drinking water as set forth by the WHO (2017). According to Prasanth et al. (2012) and Ramesh and Elango (2011), if the Na⁺ is over a safe range, it may cause salty taste as well as health problems such as hypertension, kidney stones, arteriosclerosis, oedema, and hyperosmolarity.

In the dry season, estimated K⁺ tested water samples range from 0.75 to 25.54 mg/L, with a mean value of 6.36 mg/L, and from 0.44 to 8.67 mg/L, with a mean value of 4.10 mg/L, in the wet season. According to the WHO (2017) standard, the K⁺ measurements were all within the suggested limits (10 mg/L) for drinking water quality during both the dry and wet seasons.

Calcium and magnesium (Ca ²⁺ and Mg²⁺)

During the dry season, the Ca²⁺ levels in the study region range from 5.08 to 137.56 mg/L, with a mean value of 39.66 mg/L, and between 1.94 and 58.71 mg/L, with a mean value of 21.51 mg/L. (Table 3). According to WHO, the maximum allowable level of calcium in water is 75 mg/L. (2017). Only four water samples (HDW5, HDW10, HDW11, and HDW12) during the dry season exceeded the WHO's (2017) upper limit for drinkable water quality. In sedimentary regions like the study area's limestone rocks, calcium is present as carbonates. Annapoorna and Janardhana also claim that ion exchange pathways allow calcium to enter the water (2015).

In the dry season, the Mg²⁺ levels range from 4.04 to 62.34 mg/L, with a mean value of 21.22 mg/L, and from 0.48 to 16.65 mg/L, with a mean value of 7.10 mg/L, in the wet season. WHO (2017) recommends that Mg²⁺ should not be more than 30 mg/L. Except for 5 water samples (HDW5, HDW6, HDW10, HDW11, and HDW12) from the dry season, all water samples from both seasons were within the prescribed maximum permissible limit (Table 4). Changes in the concentrations of Ca²⁺ and Mg²⁺ may be caused by the weathering of rocks and the minerals that each ion is made up of, including the acidity of the water. The lithography may have caused this.

Chloride and sulphate (Cl ⁻ and SO₄²⁻)

WHO (2017) states that the optimal concentration of chloride in water is 250 mg/L. In the dry season, Cl⁻ has a mean value of 96.51 mg/L and varies from 12.31 to 354.05 mg/L in the wet season, where it has a mean value of 37.93 mg/L. (Table 3). The water samples HDW10 and HDW12 exceeded the WHO's (2017) tolerable standard for dry season drinking water quality. WHO (2017) recommends limiting the amount of sulfate in water to 200 mg/L. With a mean value of 70.82 mg/L during the dry season and a range of 4.44 to 89.45 mg/L with a mean value of 41.95 mg/L during the rainy season, the results show that the value of SO₄²⁻ changes throughout the year. The permissible level for the quality of drinking water was found to be exceeded in only two water samples (HDW11 and HDW12) collected during the dry season; otherwise, all other water samples were determined to be far below the permitted limit. The extensive amount of water contamination in the area is caused by acid mine drainages (AMDs), which occur there.

Bicarbonate and Nitrate (HCO ₃ ⁻ and NO₃⁻)

The value of HCO₃⁻ in the water samples varies from 12.08 to 254.10 mg/L with a mean value of 79.48 mg/L during the dry season and from 2.52 to 146.00 mg/L with a mean value of 36.65 mg/L during the wet season (Table 3 ). All water samples, except for the six during the dry season (HDW6, HDW7, HDW8, HDW9, HDW10, and HDW11) that exceeded the desired level for drinking water quality, were within the desirable limit of 120 mg/L and had no known harmful impacts on health (Table 4). This may be linked to the weathering-dissolution of carbonate minerals in the studied area. In the dry season, NO₃⁻ levels in water samples vary from 1.39 to 65.47 mg/L, with a mean value of 18.29 mg/L, while in the wet season, they vary from 1.03 to 20.14 mg/L, with a mean value of 9.40 mg/L. Water nitrate must not exceed 50 mg/L, according to WHO (2017). In the dry season, two water samples (HDW11 and HDW12) were found to be of a quality that exceeded the level considered appropriate for drinking. Agricultural activities in the study area are responsible for this.

Lead (Pb ²⁺ ) and Zine (Zn ²⁺ )

During the dry season, Pb²⁺ readings range from 0.01 to 0.09 mg/L with a mean value of 0.04 mg/L, while during the rainy season, they range from 0.01 to 0.08 mg/L with a mean value of 0.03 mg/L. (Table 3). WHO states that the maximum allowable amount of lead in water is 0.01 mg/L. (2017). This finding demonstrates that 24 samples during the dry season and 18 samples during the wet season surpassed the maximum permitted limit for drinking water quality (Table 4). This indicates that there is a significant amount of lead contamination in the water samples and that the lead gangues and mining wastes (galena) in the area have a significant impact on the quality of the water sources. According to the ATSDR, in addition to local rubbish and gangues, the low pH, salinity, and presence of CO₂ in the water sources speed up the dissolution of lead in water (2007). Lead is quite mobile in water, particularly at low pH, as can be observed in the research area. The high lead concentration in the water of mining sites may be caused by the metal's high immobility, according to Davies et al. (2005) findings, which were backed by Pb²⁺ results in the area. The findings of the Zn²⁺ analysis range from 0.01 to 1.60 mg/L during the dry season, with a mean value of 0.65 mg/L, and from 0.01 to 0.80 mg/L during the rainy season, with a mean value of 0.27 mg/L. (Table 3). The WHO's tolerable threshold for drinking water quality, 3 mg/L, was met by all of the water samples (2017).

Manganese (Mn ²⁺ ) and Arsenic (As ³⁺ )

During the dry season, Mn²⁺ ranges from 0.01 to 1.30mg/L with a mean value of 0.41mg/L, and during the wet season, it ranges from 0.01 to 0.61mg/L with a mean value of 0.10mg/L. (Table 3). This result showed that 14 water samples collected during the dry season and 1 sample collected during the wet season had values greater than the WHO's 2017 recommendation for the quality of drinking water, which is 0.5 mg/L. (Table 4). The high manganese concentration in the area, according to Clewell et al. (2003), can be attributed to the chalcopyrite and siderite ores that underlie the area and are governed by the solubility, pH, and Eh (oxidation-reduction potential).

The analysis shows that As³⁺ levels ranged from 0.002 to 0.110 mg/L with a mean value of 0.020 mg/L during the dry season and from 0.01 to 0.43 mg/L with a mean value of 0.10 mg/L during the rainy season (Table 3). This result showed that 7 water samples collected during the dry season and 10 during the wet season both had values greater than the WHO's 2017 standard for drinking water quality of 0.01 mg/L. (Table 4). Following the ATSDR's 2007 report, arsenic is connected to metal-bearing ores, such as those that include copper and lead, and it can also be a result of both natural geologic processes and human activity.

Cadmium (Cd ²⁺ ) and Iron (Fe ²⁺ )

According to the analysis, Cd²⁺ fluctuates from 0.001 to 0.100 mg/L in the dry season and has a mean value of 0.010 mg/L in the rainy season (Table 3). This result demonstrates that 8 water samples obtained during the dry season and 1 sample taken during the wet season both had values greater than the WHO's (2017) recommendation of 0.003 mg/L for drinking water quality (Table 4). According to the EPA (2003), cadmium can be released into the environment naturally as well as through processes such as burning fossil fuels, burning municipal or industrial waste, or applying fertilizer or sewage sludge to the ground. The extremely high levels of cadmium in the area are likely responsible for the weathering and subsequent disintegration of the chalcopyrite and pyrite ores in the vicinity.

According to the study, Fe²⁺ fluctuates from 0.01 to 1.49 mg/L in the dry season and from 0.01 to 0.43 mg/L with a mean value of 0.10 mg/L in the wet season (Table 3). This finding indicates that 8 water samples collected during the wet season and 17 during the dry season both had values greater than the WHO's (2017) recommendation of 0.3 mg/L for the quality of drinking water (Table 4). These high quantities are thought to be the result of dissolved iron ions (Fe²⁺) in the water. Hem (1991) asserts that regions with high concentrations of dissolved ferrous iron in the solution can decrease or oxidize ferrous sulphides or ferric oxyhydroxides.

3.2 Indexical approach to assessing water quality

3.2.1 Vector modulus of pollution index (PI_vector)

The PI_vector results for this study are displayed in Table 6, and Table 7 summarizes the quality rating based on the categorization criteria. Before moving on to compute the vector modulus of the pollution index, the pollution index (PI) was first calculated (PI_vector). According to the results, Pb and As were found to have relatively high values during the wet season, nevertheless, the highest concentrations were found during the dry season for Pb, As, and Mn. The mean results for the PI are shown in Fig. 3. This reveals that the primary contaminants in the water supply during both seasons are Pb and As. According to the PI_vector results, which are shown in Table 6, the obtained values for the dry season vary between 0.498 and 11.73 with a mean value of 4.576, while those for the wet season vary between 0.573 and 7.67 with a mean value of 2.908. Kowalska et al. (2018) proposed PI_vector classification system (Table 7), PIvector is categorized as follows: PI_vector < 1 (low pollution); 1 ≤ PI_vector < 3 (moderate pollution); 3 ≤ PI_vector < 6 (considerable pollution); and PI_vector ≥ 6 (very high pollution). According to the findings, for the wet season, 14.29% of the water samples have low pollution, 57.14% have moderate pollution, 8.57% have significant pollution, and 20% have extremely high pollution. However, during the dry season, the percentages are 8.57% for low pollution, 28.57% for moderate pollution, 34.39% for significant pollution, and 31.43% for extremely high pollution. This shows that the dry season is more contaminated than the wet season because rain reduces contaminant mobility.

3.2.2 Entropy weighted-water quality index (EWQI)

In this study, Table 8 shows the EWQI findings for specific water samples, and Table 9 shows the information entropy (ej) and entropy weight (wj) values. The EWQI classification scheme, classified water quality into five classes as follows; excellent water quality (EWQI < 50); good water quality (EWQI 50 − 100); average quality water (EWQI 100 − 150); poor quality water (EWQI 150 − 200); and extremely poor quality water (EWQI > 200) (Wu et al. 2011; Singh et al. 2019; Adimalla et al. 2020; Unigwe and Egbueri 2021). According to the EWQI results shown in Table 8, the wet season had a range of 20.18 to 507 and a mean of 122.15, while the dry season had a range of 20.1 to 545.39 and a mean of 184.48. According to the EWQI categorization standards shown in Table 7, % of the water samples were excellent, 25.71% are good, 5.71% are average, 2.85% are poor, and 25.72% are extremely poor during the wet season. In contrast, during the dry season, 17.14% of the samples were of excellent quality, 17.14% are good, 14.29% are of average class, 5.71% are of low quality, and 45.71% were of really poor quality. However, it was observed that water samples in the average category can only be used for domestic purposes and are not recommended for drinking purposes after the study by Unigwe and Egbueri (2021). The parameter with the highest information entropy (ej) and entropy weight (wj), as determined by Gorgij et al. (2017) analysis of the EWQI, will have the most impact on the water quality. Considering the result shown in Table 9, it was found that Pb and As have the highest impact on the quality of water resources during the wet season, while Pb, Mn, and As have the greatest impact during the dry season. This was consistent with the study's pollution index (PI) results (Fig. 3).

Table 6

PIvector's summary results and its quality rating
Sample ID	A: Dry Season		B: Wet Season
Sample ID	PI_vector	Pollution level	PI_vector	Pollution level
AM1	2.337	Moderate	2.109	Moderate
AM2	8.676	Very high	6.116	Very high
AM3	3.630	Considerable	2.865	Moderate
AM4	2.563	Moderate	5.291	Considerable
AM5	8.344	Very high	4.872	Considerable
AM6	2.670	Moderate	2.926	Moderate
AM7	9.617	Very high	6.113	Very high
AM8	3.428	Considerable	4.441	Considerable
AM9	8.246	Very high	6.970	Very high
AM10	2.486	Moderate	1.919	Moderate
AM11	4.976	Considerable	1.647	Moderate
AM12	7.603	Very high	6.513	Very high
AM13	8.663	Very high	7.145	Very high
CP1	2.000	Moderate	1.238	Moderate
CP2	1.545	Moderate	1.019	Moderate
CP3	1.675	Moderate	1.101	Moderate
RV1	0.498	Low	0.635	Low
RV2	1.784	Moderate	0.634	Low
RV3	0.655	Low	0.579	Low
RV4	0.607	Low	0.573	Low
HDW1	3.013	Considerable	1.046	Moderate
HDW2	3.159	Considerable	0.793	Low
HDW3	2.876	Moderate	1.677	Moderate
HDW4	5.720	Considerable	2.070	Moderate
HDW5	6.475	Very high	2.989	Moderate
HDW6	6.066	Very high	1.618	Moderate
HDW7	11.728	Very high	7.669	Very high
HDW8	4.593	Considerable	6.365	Very high
HDW9	3.509	Considerable	1.481	Moderate
HDW10	7.775	Very high	1.821	Moderate
HDW11	5.293	Considerable	1.642	Moderate
HDW12	6.175	Very high	1.644	Moderate
HDW13	4.479	Considerable	1.852	Moderate
HDW14	2.924	Moderate	1.856	Moderate
HDW15	4.372	Considerable	2.558	Moderate

Table 7

Classification criteria and sample % for PI_vector, IWQI and EWQI for dry and wet seasons
Index	Class	Value Range	Quality Rank	Sample % Dry season	Sample % Wet season
PIvector	I	< 1	Low	8.57	14.29
	II	1 − 3	Moderate	28.57	57.14
	III	3 − 6	Considerable	34.29	8.57
	IV	≥ 6	Very High	31.43	20
IWQI	I	< 1	Excellent	-	20
	II	1 − 2	Good	12	2.85
	III	3 − 4	Poor	5.71	5.71
	IV	> 5	Unacceptable	82.30	71.43
EWQI	I	< 50	Excellent	17.14	40
	II	50 − 100	Good	17.14	25.71
	III	100 − 150	Average	14.29	5.71
	IV	150 − 200	Poor	8.57	2.85
	V	> 200	Extremely poor	42.85	25.72

This demonstrates that consumers of these water supplies may be exposed to cancer and other serious ailments due to a high intake of harmful carcinogens like Pb and As (Unigwe and Egbueri 2021). In conclusion, it was found that the wet season produced better water quality than the dry season. This may be attributable to the impact of heavy rainfall, which decreases the mobility of contaminants, as well as the influence of vegetation cover, which may also help absorb some toxins or toxicants.

Table 8

Presents the IWQI, EWQI, and their quality ratings in summary form
	A : Dry Season				B : Wet Season
Sample ID	IWQI Value	IWQI Rank	EWQI Value	EWQI Rank	IWQI Value	IWQI Rank	EWQI Value	EWQI Rank
AM1	11.15	Unacceptable	81.70	Good	5.27	Unacceptable	78.17	Good
AM2	15.35	Unacceptable	289.74	Extremely poor	20.75	Unacceptable	255.39	Extremely poor
AM3	11.10	Unacceptable	118.91	Average	5.52	Unacceptable	105.14	Average
AM4	5.25	Unacceptable	386.76	Extremely poor	2.41	Good	241.52	Extremely poor
AM5	3.25	Acceptable	389.00	Extremely poor	1.03	Excellent	293.69	Extremely poor
AM6	8.65	Unacceptable	277.70	Extremely poor	1.36	Excellent	102.69	Average
AM7	9.26	Unacceptable	338.87	Extremely poor	18.93	Unacceptable	264.02	Extremely poor
AM8	4.18	Poor	202.48	Extremely poor	10.69	Unacceptable	195.93	Poor
AM9	43.84	Unacceptable	393.71	Extremely poor	33.34	Unacceptable	215.57	Extremely poor
AM10	6.75	Unacceptable	100.63	Good	3.53	Acceptable	78.31	Good
AM11	16.75	Unacceptable	212.02	Extremely poor	4.13	Poor	67.06	Good
AM12	48.38	Unacceptable	194.07	Poor	41.73	Unacceptable	441.96	Extremely poor
AM13	33.91	Unacceptable	321.51	Extremely poor	31.33	Unacceptable	218.68	Extremely poor
CP1	9.85	Unacceptable	64.33	Good	8.23	Unacceptable	42.23	Excellent
CP2	10.43	Unacceptable	40.75	Excellent	8.43	Unacceptable	36.20	Excellent
CP3	10.16	Unacceptable	36.44	Excellent	7.87	Unacceptable	38.44	Excellent
RV1	11.02	Unacceptable	20.10	Excellent	8.36	Unacceptable	22.53	Excellent
RV2	11.23	Unacceptable	21.43	Excellent	8.43	Unacceptable	21.67	Excellent
RV3	11.70	Unacceptable	23.92	Excellent	10.46	Unacceptable	20.21	Excellent
RV4	12.12	Unacceptable	21.80	Excellent	10.56	Unacceptable	20.18	Excellent
HDW1	5.44	Unacceptable	95.78	Good	6.02	Unacceptable	32.47	Excellent
HDW2	2.70	Good	108.16	Average	5.75	Unacceptable	24.49	Excellent
HDW3	14.60	Unacceptable	96.42	Good	10.27	Unacceptable	52.89	Good
HDW4	16.00	Unacceptable	188.29	Poor	6.45	Unacceptable	59.03	Good
HDW5	15.69	Unacceptable	221.01	Extremely poor	6.71	Unacceptable	84.35	Good
HDW6	14.42	Unacceptable	216.23	Extremely poor	0.42	Excellent	48.26	Excellent
HDW7	57.81	Unacceptable	554.49	Extremely poor	45.16	Unacceptable	507.00	Extremely poor
HDW8	65.94	Unacceptable	545.39	Extremely poor	51.02	Unacceptable	356.69	Extremely poor
HDW9	2.05	Good	120.07	Average	1.45	Excellent	45.56	Excellent
HDW10	2.20	Good	304.31	Extremely poor	-3.63	Excellent	49.18	Excellent
HDW11	6.13	Unacceptable	206.26	Extremely poor	-0.47	Excellent	44.62	Excellent
HDW12	12.62	Unacceptable	235.84	Extremely poor	1.56	Excellent	44.03	Excellent
HDW13	11.29	Unacceptable	138.82	Average	7.26	Unacceptable	53.28	Good
HDW14	9.31	Unacceptable	86.37	Good	8.62	Unacceptable	54.64	Good
HDW15	11.50	Unacceptable	103.52	Average	9.78	Unacceptable	59.18	Good

Table 9

Results of information entropy (ej) and entropy weight (wj)
Parameter	A: Dry Season		B: Wet Season
Parameter	ej	wj	ej	wj
pH	0.0256	0.0551	0.0274	0.0550
EC (µS/cm)	0.0173	0.0556	0.0031	0.0564
TDS (mg/L)	0.0159	0.0557	0.0185	0.0555
TH (mg/L)	0.0197	0.0554	0.0207	0.0554
SO₄ (mg/L)	0.0156	0.0557	0.0153	0.0557
NO₃ (mg/L)	0.0216	0.0553	0.0153	0.0557
HCO₃ (mg/L)	0.0160	0.0556	0.0210	0.0554
Cl (mg/L)	0.0204	0.0554	0.0216	0.0554
Mg (mg/L)	0.0220	0.0553	0.0235	0.0552
Ca (mg/L)	0.0177	0.0556	0.0190	0.0555
Na (mg/L)	0.0132	0.0558	0.0148	0.0557
K (mg/L)	0.0175	0.0556	0.0282	0.0550
Pb (mg/L)	0.0375	0.0544	0.0343	0.0546

3.2.3 Integrated water quality index (IWQI)

The IWQI results from this study are summarized in Table 8. The four different quality classes for IWQI that Mukate et al. (2019) proposed are as follows; IWQI < 1 (Excellent water); IWQI 1–2 (Good water); IWQI 2–3 (Acceptable water); IWQI 3–4 (Poor water); IWQI > 5 (Unacceptable water) (Unigwe and Egbueri 2021). According to the IWQI results, it ranged from − 3.63 to 51.02 and a mean of 11.39 during the wet season, and from 2.05 to 65.94 and a mean of 12.06 during the dry season (Table 8). Following the classification criteria shown in Table 7, almost 20% of the samples fall into the excellent quality category, 2.85% into the good, acceptable, and bad quality categories, and 71.43% fall into the unacceptable quality category during the wet season. However, during the dry season, 2.85% of the water samples are each of acceptable and poor quality, whereas 12% of the samples are of high quality, and 85.71% are of unacceptable quality (Table 7). This suggests that a larger percentage of the water samples in the area are unfit for human consumption and that the dry season is significantly more polluted than the wet season, as evidenced by the PI_vector and EWQI indices. Additionally, it was shown that similar water samples HDW7, HDW8, AM9, AM12, and AM13 received higher concentrations of these contaminants as a result of their proximity to the mining and waste sites.

3.2.4 Modified water quality index (MWQI)

Parameters such as Pb, As, Cd, NO₃, Zn, Fe, and Mn were regarded as the violators' parameters in this study, while other analysed parameters (pH, EC, TDS, TH, SO4, HCO₃, Cl, Mg, Ca, Na, and K) were regarded as the input parameters. Table 2 describes the structure of the MWQI classification system, and Table 10 shows the study's results. The results showed that during the wet season, the FS (number of parameters that excurse benchmarks) is 0.6, the FF (number of measurements that excurse benchmarks) is 1.5, the NSE (normalized sum of excursions) is 8.804 and the FA (amount of excursion from benchmarks in the failed measurements) is 38.68. However, the final MWQI was observed as 70.60 which falls with the “fair rank” signifying that the water quality is often safeguarded, occasionally it can be threatened or suffer from situations that are not ideal or natural. Measurements occasionally deviate from ideal values. For the dry season, the obtained values are as follows; FS (number of parameters that excurse benchmarks) is 0.6, the FF (the number of measurements that excurse benchmarks) is 1.5, the NSE (normalized sum of excursions) is 12.68, while the FA (the amount of excursion from benchmarks in the failed measurements) is 42.73 and final MWQI was observed as 63.68 (Table 10) which falls with the “marginal rank” based on the MWQI classification scheme shown in Table 2 and it suggests that the water quality is frequently in danger or compromised and that things frequently deviate from ideal or natural conditions. Measurements frequently deviate from ideal values. These findings generally support the observation in the preceding computed indices (PI_vector, EWQI, and IWQI), which indicated that the water supplies are more in danger during the dry season than during the wet season.

Table 10

Summary result of the MWQI
S/N	Terms of Index	MWQI values	Rank	Quality
A: Dry Season
1	Scope − F_S	0.6	Marginal	Water quality is frequently threatened or impaired; conditions often depart from natural or desirable levels. Measurements often depart from desirable
2	Frequency − F_F	1.5		levels
3	NSE	12.68
4	Amplitude − F_A	42.73
5	MWQI	63.68
B: Wet Season
1	Scope − F_S	0.6	Fair	Water quality is usually protected but occasionally threatened or impaired conditions sometimes depart from natural or desirable levels.
2	Frequency − F_F	1.5		Measurements sometimes depart from desirable levels.
3	NSE	8.804
4	Amplitude − F_A	38.68
5	MWQI	70.60

Four water quality indices (PIvector, EWQI and IWQI, and MWQI) were used in this study to assess the Nkalagu region's drinking water supplies in southern Benue Trough, Nigeria. In this study, thirty-five water samples were collected from different locations in two seasons and analyzed for physicochemical parameters and trace metals using standard methods. From this research, it was observed that 46% (AM6, AM10, RV1, RV2, RV3, RV4, HDW1, HDW3, HDW6, HDW8, HDW10, HDW11, HDW12, HDW13, HDW14, and HDW15) in the dry season and 23% (HDW1, HDW6, HDW7, HDW9, HDW10, HDW11, HDW12, and HDW15) in the wet season were within acidic range while remaining samples were within WHO drinking water quality standard. Results indicate that the water samples are fresh to moderately mineralized water based on TDS and it ranges from soft water to very hard water. Results from physicochemical parameters and trace metal showed that EC, TDS, TH, Mg²⁺, Ca²⁺, Pb²⁺, and Cd²⁺ values were above WHO drinking water quality standards in a water sample (HDW4, HDW5, HDW6, HDW10, HDW11, and HDW12) in the dry season and Cl⁻, SO4²⁻ and HCO3⁻ values were above WHO drinking water quality standard in a water sample (HDW10 and HDW12) in the dry season. PI_vector result reveals that the primary contaminants in the water supply during both seasons are Pb and As and in comparison to the wet season, the dry season shows higher levels of contamination in water sources. Based on EWQI result, it was found that Pb and As have the highest impact on the quality of water sources during the wet season, while Pb, Mn, and As have the greatest impact during the dry season and are in line with PI_vector result. The IWQI finding suggests that during the dry season, a larger percentage of the water samples are unsafe for human consumption and are significantly more polluted than in the wet season, as evidenced by the PI_vector and EWQI indices. Additionally, it also showed that water samples (HDW7, HDW8, AM9, AM12, and AM13) will receive higher concentrations of these contaminants as a result of their proximity to the mining and waste sites. The final MWQI was observed as 70.60 which falls with the “fair rank” in the wet season and as 63.68 which falls with the “marginal rank” in the dry season. These findings generally support the observation in the preceding computed indices (PI_vector, EWQI, and IWQI), which indicated that the water supplies are more in danger during the dry season than during the wet season.

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You are reading this latest preprint version

An integrated indexical approach in assessing water quality for drinking purposes in the Nkalagu area, southeastern, Nigeria

Status:

Journal Publication

Version 1

Abstract

Figures

1.0 Introduction

1.2 General description of the study area

1.3 Geology and Hydrogeology

2.0 Materials and methods

2.1 Sampling and laboratory analysis

2.2 Water quality assessment

2.2.1 Vector modulus of pollution index (PI_vector)

2.2.2 Entropy weighted-water quality index (EWQI)

2.2.3 Integrated water quality index (IWQI)

2.2.4 Modified water quality index (MWQI)

3.0 Results and discussion

3.1 General description of water quality

3.2 Indexical approach to assessing water quality

3.2.1 Vector modulus of pollution index (PI_vector)

3.2.2 Entropy weighted-water quality index (EWQI)

3.2.3 Integrated water quality index (IWQI)

3.2.4 Modified water quality index (MWQI)

4 Conclusions

References

Additional Declarations

Status:

Journal Publication

Version 1

An integrated indexical approach in assessing water quality for drinking purposes in the Nkalagu area, southeastern, Nigeria

Status:

Journal Publication

Version 1

Abstract

Figures

1.0 Introduction

1.2 General description of the study area

1.3 Geology and Hydrogeology

2.0 Materials and methods

2.1 Sampling and laboratory analysis

2.2 Water quality assessment

2.2.1 Vector modulus of pollution index (PIvector)

2.2.2 Entropy weighted-water quality index (EWQI)

2.2.3 Integrated water quality index (IWQI)

2.2.4 Modified water quality index (MWQI)

3.0 Results and discussion

3.1 General description of water quality

3.2 Indexical approach to assessing water quality

3.2.1 Vector modulus of pollution index (PIvector)

3.2.2 Entropy weighted-water quality index (EWQI)

3.2.3 Integrated water quality index (IWQI)

3.2.4 Modified water quality index (MWQI)

4 Conclusions

References

Additional Declarations

Status:

Journal Publication

Version 1

2.2.1 Vector modulus of pollution index (PI_vector)

3.2.1 Vector modulus of pollution index (PI_vector)