The present research work investigates the impact of natural and anthropogenic inputs on the chemistry and quality of the groundwater in the Beenaganj-Chachura block of Madhya Pradesh, India. A total of 50 groundwater samples were examined for nitrates, fluoride, chlorides, total dissolved solids, calcium, magnesium, pH, total hardness, and conductivity, and their impact on entropy-weighted water quality index and pollution index of groundwater (PIG) was investigated via the response surface methodology (RSM) using the central composite design. According to analytical findings, Ca, Mg, Cl, SO42−, and NO3 exceed the desired limit and permitted limit set by the Bureau of Indian Standards (BIS) and the World Health Organization (WHO). According to PIG findings, 76, 16, and 8% of groundwater samples, respectively, fell into the insignificant, low, and moderate pollution categories. The regression coefficients of the quadratic RSM models for the experimental data provided excellent results. Thus, RSM provides an excellent means to obtain the optimized values of input parameters to minimize the PIG values.

  • Entropy-weighted water quality index (EWQI) and pollution index of groundwater (PIG) of Beenaganj-Chachura India were studied for the first time.

  • Ca, Mg, Cl, SO42−, and NO3 exceed the desired limit and permitted limit set by the Bureau of Indian Standards and World Health Organization (WHO).

  • Distribution maps of EWQI and PIG are obtained for the area.

  • The quadratic response surface methodology model was found appropriate for the prediction of the groundwater pollution index of this region.

  • There were major implications for water resource management of the area.

Millions of people worldwide depend on groundwater, especially in arid and semi-arid regions (Mukherjee & Singh 2019). It supports residential water, irrigation, and industrial operations, providing food security, economic growth, and public health (Suthar et al. 2009). However, natural and anthropogenic processes are progressively threatening this essential resource (Subba Rao et al. 2015). Groundwater contamination from geological formations, agricultural operations, poor wastewater disposal, and industrial activity degrades water quality and poses health concerns to communities (Dutta & Deb 2010). Providing safe drinking water in rural areas of developing countries such as India is very difficult (APHA 2017; Singh et al. 2017). Effective groundwater management in these places requires understanding water quality parameters and identifying potential contamination sources.

Groundwater quality evaluation requires reliable, standardized methodologies. The entropy-weighted water quality index (EWQI) and pollution index of groundwater (PIG) are extensively used (Kumar & Kumar 2017; Rao & Rao 2017). The EWQI provides a holistic approach by including many physico-chemical characteristics. It calculates a weighted score based on pH, total dissolved solids (TDS), and ion concentrations including chloride, nitrate, and fluoride to assess water suitability (Adimalla et al. 2019a). Its capacity to account for the synergistic impacts of several water quality criteria makes it a more thorough assessment than examining individual parameters (Rao & Rao 2018). PIG is a quantitative tool for assessing groundwater pollution. By weighting water quality parameters, it creates a numerical index of groundwater contamination (Kumar et al. 2021). This technique helps detect polluted locations and prioritize remediation (Singh et al. 2021).

Numerous studies across India have stressed the need of groundwater quality testing, especially in rural areas with limited access to safe drinking water (Rao et al. 2009). For example, Sheikh (Sheikh 2018) recommended using the EWQI method to test groundwater quality in rural Jammu and Kashmir. Their data indicated excessive iron and fluoride levels, increasing public health concerns. Further, Mittal et al. (2021) used the PIG approach to assess groundwater quality in Punjab, India's agricultural heartland, for environmental monitoring and assessment. Their investigation found that agricultural and industrial discharges pollute groundwater.

This study focuses on the Beenaganj-Chachura block, which is located in the rural Guna district of Madhya Pradesh, India. Agriculture is the lifeline of the majority of the population. This block represents a rural area dependent on groundwater for several purposes. However, growing urbanization, intensive agriculture, and prospective industrialization threaten groundwater quality (APHA 2017). A thorough groundwater quality assessment is needed to develop effective management plans and preserve this essential resource for future generations. Many studies have examined groundwater quality in India, but few have focused on the Beenaganj-Chachura block in Guna, Madhya Pradesh (Srivastava 2023). This research will fill this knowledge gap by revealing groundwater quality geographic distribution and block contamination sources.

The purpose of this study is to address the information gaps that have been identified and to improve our understanding of the dynamics of groundwater. Not only does this research attempt to quantify groundwater quality but it also seeks to evaluate pollution sources and vulnerable distribution patterns. This is accomplished through the utilization of a multidisciplinary strategy that incorporates hydrochemistry, geographical distribution analysis, and multivariate approaches. We hope that by making these efforts, we will be able to make a significant contribution to the area of hydrology and provide evidence-based treatments for the management of groundwater in a sustainable manner. Based on these studies, the goals of the present research are as follows:

  • To determine the quality of groundwater for drinking purpose on the basis of EWQI and PIG and develop the distribution maps to identify the groundwater quality zones for various purposes based on EWQI and PIG.

  • To assess the results of PIG and EWQI by comparing them to the standard value of the World Health Organization (WHO) and Bureau of Indian Standards (BIS) limits after being carried out at a standard temperature and under a standard set of circumstances according to Alpha's standard procedures.

  • To determine the predominant factors affecting the EWQI and PIG of Beenaganj-Chachura block and obtain the surface plots of various factors affecting PIG using response surface methodology (RSM).

Study area

This study area is located in Guna district, India, and is geographically located in the NE of the Malwa Plateau along the Parbati River. The latitude is N 24° 1756′–N 25° 06′ and longitude E 76° 9996′–E 78° 16′) (Figure 1). This region has a population of 21,860 with a density of 1,850 per km. All activities in the villages, such as irrigation and domestic and commercial requirements, depend entirely on groundwater. The annual rainfall was ∼108.72 cm and received maximum rainfall (∼ 86%) through the SW monsoon (from June to September). The atmospheric temperature ranged from 4.8 (in winter) to 46 °C (in summer) as per the Indian Meteorological Department (IMD) report (2011–2020).
Figure 1

Study area Beenaganj-Chachura in Guna district, Madhya Pradesh.

Figure 1

Study area Beenaganj-Chachura in Guna district, Madhya Pradesh.

Close modal

In the Beenaganj-Chachura block, a total of 50 groundwater samples were taken from the 50 different villages using a variety of hand pumps, tube wells, and bore wells. All the samples were collected in the month of March 2022. In all the sources, sample collection commenced only after pumping water for 20 min. Sample collection, transportation, storage, and evaluation of groundwater samples conform to the American Public Health Association standards (APHA 2017). Portable digital meters (Hanna HI 9811–5) were used to assess the concentration of hydrogen ions (pH) and electrical conductivity (EC) in situ. These samples were then taken to the labs where they were tested for various ions such as calcium (Ca), magnesium (Mg), sodium (Na), and potassium (K), as well as carbonate (), bicarbonate (), sulfate (), nitrate (), and chloride (Cl), were determined. Using a factor of 0.65, TDS was calculated from EC. The detection methods for these parameters are presented in Table 1.

Table 1

Details of laboratory detection methods and relevant IS code

S.No.ParameterLaboratory detection methodIS code
Calcium (Ca) Titration with EDTA IS 3025 Part 11:1983 
Magnesium (Mg) Titration with EDTA IS 3025 Part 17:1983 
Sodium (Na) Flame Photometry IS 3025 Part 16:1984 
Potassium (K) Flame photometry IS 3025 Part 18:1984 
Carbonate (Acidification and titration – 
Bicarbonate (Titration – 
Sulfate (Titration IS 3025 Part 36:1987 
Nitrate (Cadmium reduction followed by Colorimetry IS 3025 Part 33:1988 
Chloride (ClTitration IS 3025 Part 32:1988 
10 Electrical Conductivity Conductivity meter IS 3025 Part 14:1984 
11 pH pH meter IS 3025 Part 14:1984 
S.No.ParameterLaboratory detection methodIS code
Calcium (Ca) Titration with EDTA IS 3025 Part 11:1983 
Magnesium (Mg) Titration with EDTA IS 3025 Part 17:1983 
Sodium (Na) Flame Photometry IS 3025 Part 16:1984 
Potassium (K) Flame photometry IS 3025 Part 18:1984 
Carbonate (Acidification and titration – 
Bicarbonate (Titration – 
Sulfate (Titration IS 3025 Part 36:1987 
Nitrate (Cadmium reduction followed by Colorimetry IS 3025 Part 33:1988 
Chloride (ClTitration IS 3025 Part 32:1988 
10 Electrical Conductivity Conductivity meter IS 3025 Part 14:1984 
11 pH pH meter IS 3025 Part 14:1984 

Research scope and research significance

This work focuses on evaluating groundwater contamination in Beenaganj-Chachura block of Madhya Pradesh, India. The study provides insights into factors that influence the groundwater quality, assisting in the development of efficient pollution mitigation methods. This is achieved by experimentally analyzing 50 groundwater samples collected for parametric evaluation of various ions present. To understand the overall quality of groundwater (OQG), there are several indices and techniques. These include EWQI, PIG, groundwater quality index, multivariate analysis, geochemical modeling, and fuzzy logic-based techniques. The analysis in the current work used EWQI and PIG. The reasons for choosing these methods over other mentioned methods include simplicity and interpretability, comprehensiveness, applicability, and ease of implementation. The selection of a groundwater quality assessment method is contingent upon various elements, including the research objectives, the availability of data, computational resources, and the requirements of stakeholders. Due to the compromise between simplicity, comprehensiveness, and applicability, EWQI and PIG are viable options for numerous groundwater quality studies.

The significance of conducting this work lies in several key aspects:

  • Water Security: In dry and semi-arid locations such as the Beenaganj-Chachura block, groundwater provides drinking water and irrigation. Groundwater quality assessment is essential for water security and public health. This research informs sustainable water management and community health by assessing groundwater contamination in the region.

  • Identifying Source of Contamination: By studying how various factors affect groundwater quality, we can identify the main causes of contamination. Targeted efforts to reduce pollution and groundwater deterioration require this knowledge.

  • Improving Methodologies: Modern methods such as EWQI, PIG, and RSM help us examine groundwater quality more thoroughly. We improve water quality evaluation methods by using these methods, boosting scientific understanding and resource management.

  • Local Relevance: Studying the Beenaganj-Chachura block gives communities and stakeholders important regional insights. Local legislators, water resource managers, and community leaders can use the data to make educated decisions and take necessary measures about groundwater quality concerns and opportunities.

Entropy-weighted water quality index

The EWQI can combine all physicochemical data to indicate water quality. EWQI computation follows these steps (Rao et al. 2009; Suthar et al. 2009; Singh et al. 2017):

Step 1: The eigen value matrix ‘X,’ associated with all hydro-chemical parameters and estimated by Equation (1), is calculated:
formula
(1)
where ‘m’ (i = 1, 2, 3, 4, …, m) represents the groundwater samples; n (j = 1, 2, 3, 4, ……., n) signifies the number of hydrochemical parameters of each sample.
Step 2: The standardization process ‘yij’ can be evaluated and then standard evaluation matrix ‘Y’ can be obtained following Equations (2) and (3), respectively.
formula
(2)
formula
(3)
where xij is the initial matrix; xijmin and xijmax are the minimum and maximum values of the hydrochemical parameters of the samples, respectively.
Step 3: The third step is to compute the entropy ‘ej’ and entropy weight ‘wj ’ by the following equations:
formula
(4)
formula
(5)
formula
(6)
Step 4: The fourth step is to compute the quality rating scale ‘qj’ of the ‘j’ parameter by following Equation (7):
formula
(7)
where ‘Cj ‘ is the concentration of chemical parameters ‘j’ (mg/L), ‘Sj ‘ is the permissible limit of WHO standards of parameter ‘j’ (mg/L), ‘CpH’ represents the value of pH, ‘SpH’ is the permissible limit of pH (6.5–8.5), if the measured pH is larger than 7, ‘SpH’ is to be taken 8.5, while the pH is smaller than 7, and ‘SpH’ is equal to 6.5 to confirm the value of ‘qj ‘ is positive.
Step 5: Finally, EWQI is calculated by using Equation (8):
formula
(8)

The EWQI indicates exceptional quality <25, good quality 25–50, medium quality 50–100, bad quality 100–150, and extremely low quality >150. Table 2 shows groundwater quality and EWQI ratings (Adimalla & Qian 2019; Adimalla et al. 2019b).

Table 2

Classification standards of groundwater quality according to EWQI

EWQIRankQuality of water
<25 Excellent 
25–50 Good 
50–100 Medium 
100–150 Poor 
>150 Very poor 
EWQIRankQuality of water
<25 Excellent 
25–50 Good 
50–100 Medium 
100–150 Poor 
>150 Very poor 

Pollution index of groundwater

The PIG method was proposed by Subba Rao as a tool for evaluating the status of several individual chemical variables (e.g., pH, EC, TDS, TH, Ca2+, Mg2+, Na+, K+, Cl, , , and F) to evaluate the OQG for drinking purposes. The general approach described by Rao et al. (2009) was followed to compute the PIG, as follows:

Step 1: The relative weight (RW) from 1 to 5 is assigned to each chemical parameter, depending on their relative impact on the overall quality of water for drinking purposes. The highest RW ‘5’ is assigned to parameters, which naturally have the foremost effects (, F, , and Cl), and a minimum RW ‘1’ is assigned to parameters, which are fewer effects (K+ and ). Moreover, RW ‘4’ is assigned to pH, TDS, Na+, and TH, and ‘2’ is assigned to Ca2+ and Mg2+ (Table 3).

Table 3

Relative weight, weight parameters, and drinking water standards used for PIG calculation

Chemical parameterRelative weight (RW)UnitsWeight parameters (WP)Drinking water standards (Ds)
pH – 0.094 7.5 
EC mg/l 0.038 500 
TDS mg/l 0.094 500 
TH mg/l 0.094 300 
Ca mg/l 0.038 75 
Mg mg/l 0.094 30 
Na mg/l 0.075 200 
mg/l 0.019 10 
Cl- mg/l 0.075 250 
F- mg/l 0.094 1.5 
 mg/l 0.057 300 
 mg/l 0.094 150 
 mg/l 0.094 45 
Sum (Σ53   
Chemical parameterRelative weight (RW)UnitsWeight parameters (WP)Drinking water standards (Ds)
pH – 0.094 7.5 
EC mg/l 0.038 500 
TDS mg/l 0.094 500 
TH mg/l 0.094 300 
Ca mg/l 0.038 75 
Mg mg/l 0.094 30 
Na mg/l 0.075 200 
mg/l 0.019 10 
Cl- mg/l 0.075 250 
F- mg/l 0.094 1.5 
 mg/l 0.057 300 
 mg/l 0.094 150 
 mg/l 0.094 45 
Sum (Σ53   

Step 2: The weight parameter (WP) is the ratio of RW of every chemical water quality measure to the sum of all relative weights [Σ (RW)]. The WP is estimated by the following equation (Equation (9)):
formula
(9)
Step 3: The status of concentration (SOC) is computed by dividing each chemical variable concentration () of each groundwater sample by its respective Drinking Water Quality Standards (DWQS). SOC is calculated as shown in Equation (10):
formula
(10)
Step 4: OQG for drinking purposes is computed by Equation (11):
formula
(11)
where WP indicates the WP and SOC signifies the SOC.
Step 5: To ascertain the influence of contaminants on the groundwater quality, PIG is computed by taking the sum of all OQG values (Equation (12)):
formula
(12)

The PIG is classified into five categories: insignificant pollution (PIG 2.5). The detailed classification is presented in Table 4.

Table 4

Classification standards of groundwater quality according to PIG

PIGLevel of pollution
<1 Insignificant 
1–1.5 Low 
1.5–2.0 Moderate 
2.0–2.5 High 
>2.5 Very high 
PIGLevel of pollution
<1 Insignificant 
1–1.5 Low 
1.5–2.0 Moderate 
2.0–2.5 High 
>2.5 Very high 

Response surface methodology

Depending on the range of the EWQI and PIG obtained, the results are computed and divided into categories presented in Tables 1 and 4. In RSM, surface plots are obtained by analyzing the data with the aid of a Design Expert v22.0. The results obtained were analyzed, and the most predominate factor is determined.

The RSM uses a number of mathematical and statistical techniques to explain how a group of data is related to one another. To simultaneously optimize the responses, this connection is expressed using a polynomial equation that correlates with the experimental data. The three-level complete factorial designs, namely, central composite design (CCD), the Doehlert design (DD), and the Box-Behnken design (BBD), are the most popular Design of Experiments types utilized for the RSM analysis. The CCD, DD, and BBD are modifications of two-level factorial designs (2n) that provide three-level designs that are appropriate for higher degree polynomials in RSM. The 2n design provides a linear function of the response (Y) without the center-points, as indicated, which does not allow for second-degree polynomial equations since it does not account for curvatures.
formula
(13)
where β0 represents the constant term and βᵢ represents the coefficient of the linear parameters respectively. Xi represents the factor and ε is the residual from the treatments. The critical points in the RSM plot are obtained from the following quadratic polynomial equation:
formula
(14)
Xi and Xj represent the factors, βii represents the coefficient of the quadratic parameter, and βij represents the coefficient for the interaction parameters.

The equation in terms of actual factors can be used to make predictions about the response for given levels of each factor. Here, the levels should be specified in the original units for each factor. This equation should not be used to determine the relative impact of each factor because the coefficients are scaled to accommodate the units of each factor and the intercept is not at the center of the design space. Numerous researchers have employed these methods to comprehend the chemical composition of water, the origins of water pollutants, and the relationships between various factors that affect water quality.

RSM describes the links between the independent variables (factors) and the dependent variables (responses) using mathematical models created utilizing data from experimental design. These models are employed for process optimization as well as the analysis of the impact of independent factors and their interactions on the responses. Typically, the results are displayed as 3D plots and 2D contours. Statistical experimental design, linear regression modeling, and optimization techniques are required when using the RSM. The use of RSM as an optimization method involves several stages/steps. These include:

  • (i) choosing the independent variables and their ranges,

  • (ii) choosing the experimental design and performing the experiments,

  • (iii) creating the equation for the linear regression model based on the results of the experiments,

  • (iv) confirming the suitability of the model, and

  • (v) choosing the experimental design.

In this section, analysis of the results obtained from EWQI and PIG are discussed first. Next, deliberations on the distribution of groundwater quality based on EWQI and PIG are presented. Finally, based on the results of RSM, the major governing factors affecting the groundwater quality are earmarked.

The chemical compositions of groundwater of various villages of Beenaganj-Chachura block are presented in Table 5. The pH of groundwater was in the range of 7.1–8.9, with an average of 7.82 (Table 4). Thus, in general, the groundwater in this block is alkaline and is mostly potable as per WHO and BIS standards. The desirable and permissible range of EC of potable water is 500 and 1,500 mhos/cm, respectively, according to WHO, while these values for Beenaganj-Chachura are 261 and 1,819 mhos/cm, respectively, with an average value of 822.73 mhos/cm. Thus, while in some villages, the value lies much less than the desired limit (DL), in some villages, EC is higher than the permitted limit (PL), indicating higher concentration of dissolved and charged particles.

Table 5

Chemical composition of ground water from various of Binagunj-Chachoda

S.NOLocationNitratesFluorideChloridesTDSAlkalinityCalciumMagnesiumpHTurbidityTotal hardnessConductivity
Khekheh 0.6 76 518 105 68.8 26.4 7.7 1.9 282 796 
Borda 0.7 33.5 246 65 33.6 17.28 7.9 156 378 
Faknehru 13 0.8 42 468 90 53.6 25.44 7.6 1.4 240 720 
Navalpura 10 0.4 49 443 75 48.8 24.48 7.7 1.6 224 
Lakhori 10 0.7 92 516 120 63.2 27.84 7.9 1.2 274 793 
Kakruaa 0.4 69 592 110 68.8 23.04 7.4 1.9 268 910 
Dehri 11 0.7 56 471 90 48.8 23.04 7.9 218 724 
Bitakhedi 10 0.6 70 531 105 47.2 24.48 1.6 210 816 
Bapcha Lahariya 18 0.4 149 1,176 210 116.8 25.44 8.1 1.4 398 1,819 
10 Ramtedi 0.5 32 499 85 46.4 21.12 7.6 204 767 
11 Dokriyakedi 21 0.6 114 1,142 175 124.8 27.36 7.7 1.3 426 1,756 
12 Pakhariyapura 0.7 29.5 352 65 44 26.4 7.5 1.4 214 541 
13 Teligav 0.4 93 892 130 60.6 24.48 7.9 1.4 254 1,372 
14 Purabkanya 0.6 19.5 170 45 28 11.52 7.7 1.3 118 261 
15 Bor Ka Keda 13 0.7 63 869 105 50.4 26.4 7.8 1.5 236 1,336 
16 Kali Karar 10 0.6 54 353 80 40.8 23.52 7.7 1.2 200 543 
17 Mohmmdpur 16 0.8 46 375 75 49.6 21.12 8.1 1.4 212 576 
18 Fulukhdi 0.4 31 220 60 45.6 16.8 2.2 182 338 
19 Gulwada 14 0.3 78 741 125 65.6 19.68 8.1 2.1 246 1,140 
20 Khedikla 0.5 47 267 80 32.8 19.6 7.4 2.3 164 410 
21 Pechi 0.9 63 420 105 64.8 18.72 7.9 1.4 240 420 
22 Kikhada 0.8 65 396 145 91.2 41.16 7.8 1.4 402 612 
23 Kotra 0.5 298 817 95 114.4 25.44 8.1 1.9 392 1,256 
24 Khatoli 16 0.7 493 1,034 230 291.2 32.16 7.59 1.9 1,590 
25 Todi 0.8 386 853 245 97.6 24.48 8.1 3.4 342 1,312 
26 Sagar 0.6 93 421 105 98.4 33.12 7.61 1.4 384 647 
27 Kekriya 0.8 128 524 145 72 43.6 8.1 1.6 362 806 
28 Batawda 0.5 178 559 35 24 7.2 1.4 90 860 
29 Kudhampura 0.8 128 524 145 72 43.68 1.6 362 806 
30 Badhagav 0.6 25 207 55 42.4 17.28 7.8 178 318 
31 Golyiakhedi 0.4 43 403 95 40.8 22.56 7.4 1.3 196 620 
32 Moiya 0.7 57 534 100 54.4 22 7.6 1.4 228 821 
33 Sagodi 0.6 39 310 80 46.4 70.76 7.7 1.9 190 476 
34 Gunjari 10 0.7 78 755 135 76.4 49.92 7.8 2.3 394 1,168 
35 Jamoniya Kla 0.5 61 490 95 57.6 23.52 7.9 2.4 242 753 
36 Barkheda Khurd 0.7 114 1,052 170 145.6 39.36 7.6 528 1,618 
37 Pipliya Moti 12 0.6 73 443 110 55.2 23.04 1.6 234 681 
38 Khanpur 10 0.7 52 415 90 60.8 23.04 8.1 1.1 248 638 
39 Patondi 13 0.6 41 375 85 47.2 20.64 7.1 1.9 204 604 
40 Tlavli 0.7 29.5 253 65 48 14.4 7.5 2.3 180 389 
41 Jukhara 0.7 98 695 130 73.6 47.52 7.4 1.4 382 1,069 
42 Umarthana 0.6 74 616 120 134.4 12.96 7.9 1.7 390 947 
43 Dedla 0.4 60 425 95 56.8 27.36 1.9 256 653 
44 Maheshpura 0.4 43 397 65 60 21.12 8.1 238 610 
45 Barkhedi Mafi 0.5 105 897 140 145.6 30.72 7.6 1.6 492 1,380 
46 Amaser 13 0.6 54 415 85 60.8 24.92 7.8 1.9 260 638 
47 Khejra Kla Rani 10 0.7 42 355 70 49.6 21.12 7.7 1.5 212 546 
48 Kekadhiya 0.4 73 548 135 56.8 22.56 7.4 1.4 236 843 
49 Basahedha 13 0.3 61 453 105 52.2 24.48 7.7 1.9 240 696 
50 Bethda 0.6 86 352 120 64.8 24 7.8 262 541 
S.NOLocationNitratesFluorideChloridesTDSAlkalinityCalciumMagnesiumpHTurbidityTotal hardnessConductivity
Khekheh 0.6 76 518 105 68.8 26.4 7.7 1.9 282 796 
Borda 0.7 33.5 246 65 33.6 17.28 7.9 156 378 
Faknehru 13 0.8 42 468 90 53.6 25.44 7.6 1.4 240 720 
Navalpura 10 0.4 49 443 75 48.8 24.48 7.7 1.6 224 
Lakhori 10 0.7 92 516 120 63.2 27.84 7.9 1.2 274 793 
Kakruaa 0.4 69 592 110 68.8 23.04 7.4 1.9 268 910 
Dehri 11 0.7 56 471 90 48.8 23.04 7.9 218 724 
Bitakhedi 10 0.6 70 531 105 47.2 24.48 1.6 210 816 
Bapcha Lahariya 18 0.4 149 1,176 210 116.8 25.44 8.1 1.4 398 1,819 
10 Ramtedi 0.5 32 499 85 46.4 21.12 7.6 204 767 
11 Dokriyakedi 21 0.6 114 1,142 175 124.8 27.36 7.7 1.3 426 1,756 
12 Pakhariyapura 0.7 29.5 352 65 44 26.4 7.5 1.4 214 541 
13 Teligav 0.4 93 892 130 60.6 24.48 7.9 1.4 254 1,372 
14 Purabkanya 0.6 19.5 170 45 28 11.52 7.7 1.3 118 261 
15 Bor Ka Keda 13 0.7 63 869 105 50.4 26.4 7.8 1.5 236 1,336 
16 Kali Karar 10 0.6 54 353 80 40.8 23.52 7.7 1.2 200 543 
17 Mohmmdpur 16 0.8 46 375 75 49.6 21.12 8.1 1.4 212 576 
18 Fulukhdi 0.4 31 220 60 45.6 16.8 2.2 182 338 
19 Gulwada 14 0.3 78 741 125 65.6 19.68 8.1 2.1 246 1,140 
20 Khedikla 0.5 47 267 80 32.8 19.6 7.4 2.3 164 410 
21 Pechi 0.9 63 420 105 64.8 18.72 7.9 1.4 240 420 
22 Kikhada 0.8 65 396 145 91.2 41.16 7.8 1.4 402 612 
23 Kotra 0.5 298 817 95 114.4 25.44 8.1 1.9 392 1,256 
24 Khatoli 16 0.7 493 1,034 230 291.2 32.16 7.59 1.9 1,590 
25 Todi 0.8 386 853 245 97.6 24.48 8.1 3.4 342 1,312 
26 Sagar 0.6 93 421 105 98.4 33.12 7.61 1.4 384 647 
27 Kekriya 0.8 128 524 145 72 43.6 8.1 1.6 362 806 
28 Batawda 0.5 178 559 35 24 7.2 1.4 90 860 
29 Kudhampura 0.8 128 524 145 72 43.68 1.6 362 806 
30 Badhagav 0.6 25 207 55 42.4 17.28 7.8 178 318 
31 Golyiakhedi 0.4 43 403 95 40.8 22.56 7.4 1.3 196 620 
32 Moiya 0.7 57 534 100 54.4 22 7.6 1.4 228 821 
33 Sagodi 0.6 39 310 80 46.4 70.76 7.7 1.9 190 476 
34 Gunjari 10 0.7 78 755 135 76.4 49.92 7.8 2.3 394 1,168 
35 Jamoniya Kla 0.5 61 490 95 57.6 23.52 7.9 2.4 242 753 
36 Barkheda Khurd 0.7 114 1,052 170 145.6 39.36 7.6 528 1,618 
37 Pipliya Moti 12 0.6 73 443 110 55.2 23.04 1.6 234 681 
38 Khanpur 10 0.7 52 415 90 60.8 23.04 8.1 1.1 248 638 
39 Patondi 13 0.6 41 375 85 47.2 20.64 7.1 1.9 204 604 
40 Tlavli 0.7 29.5 253 65 48 14.4 7.5 2.3 180 389 
41 Jukhara 0.7 98 695 130 73.6 47.52 7.4 1.4 382 1,069 
42 Umarthana 0.6 74 616 120 134.4 12.96 7.9 1.7 390 947 
43 Dedla 0.4 60 425 95 56.8 27.36 1.9 256 653 
44 Maheshpura 0.4 43 397 65 60 21.12 8.1 238 610 
45 Barkhedi Mafi 0.5 105 897 140 145.6 30.72 7.6 1.6 492 1,380 
46 Amaser 13 0.6 54 415 85 60.8 24.92 7.8 1.9 260 638 
47 Khejra Kla Rani 10 0.7 42 355 70 49.6 21.12 7.7 1.5 212 546 
48 Kekadhiya 0.4 73 548 135 56.8 22.56 7.4 1.4 236 843 
49 Basahedha 13 0.3 61 453 105 52.2 24.48 7.7 1.9 240 696 
50 Bethda 0.6 86 352 120 64.8 24 7.8 262 541 

Note: * indicates that the particular test was not done.

EC measures the ability of water to conduct an electrical current. The higher the concentration of dissolved charged chemicals (also known as salts) in the water, the greater the electrical current that can be conducted. In general, TDS consists of inorganic salts and low quantities of organic matters. Thus, the salinity of water and its appropriateness for domestic use largely depend on TDS (Karunanidhi et al. 2020). The TDS values for various villages of Beenaganj-Chachura block were well within the limits prescribed by both WHO and BIS.

The average concentrations of various major ions are also presented in Table 6. The average concentrations of Ca2+, Mg2+, Na+, K+, Cl, , , and F in groundwater were 69.864, 27.62, 111.2, 2.49, 87.68, 111.9, 9.5, and 0.6483 mg/l, respectively. Hence, based on the average concentrations, the ions can be arranged in the following ascending order: > Na+ >Cl > Ca2+ >Mg2+> > K+ > F. Finally, the TH of the samples expressed as the sum of Ca2+ and Mg2+ dissolved in the groundwater samples was obtained in the units of mg/L CaCO3 ranged from 90 to 528. This value is well within the guidelines of BIS but slightly deviates the norms of WHO, as the PL is 500, while in this case, the value for the village Barkheda Khurd was 528. In general, the concentrations of most of the villages for all the parameters were within the recommendations of WHO and BIS.

Table 6

The physicochemical characteristics and their comparability to drinking standards set by the Bureau of Indian Standards (BIS)

ParameterWHO limit (DL-PL)*BIS limit (DL-PL)RangeAverage% of sample above DL% of samples above PL
pH 6.5–8.5 6.5–8.5 7.1–8.9 7.82 100 
EC 500–1,500 – 261–1,819 822.735 – – 
TDS 500–1,500 500–2,000 170–1,576 543.58 44 
Ca 75–200 75–200 24–291.2 69.864 22 
Mg 50–100 30–100 7.2–110 27.62 28 
Na 200–600 200 36.2–501.4 111.2 15 
10 12 0.3–8.3 2.49 
Cl 250–500 250–1,000 19.5–549 87.68 
SO4 200–250 200–400 31.54–261.41 111.9 10 
NO3 45 45 2–51 9.5 
1–1.5 1–1.5 0.3–1.8 0.6483 
TH 100–500 300–600 90–528 271.22 96 
ParameterWHO limit (DL-PL)*BIS limit (DL-PL)RangeAverage% of sample above DL% of samples above PL
pH 6.5–8.5 6.5–8.5 7.1–8.9 7.82 100 
EC 500–1,500 – 261–1,819 822.735 – – 
TDS 500–1,500 500–2,000 170–1,576 543.58 44 
Ca 75–200 75–200 24–291.2 69.864 22 
Mg 50–100 30–100 7.2–110 27.62 28 
Na 200–600 200 36.2–501.4 111.2 15 
10 12 0.3–8.3 2.49 
Cl 250–500 250–1,000 19.5–549 87.68 
SO4 200–250 200–400 31.54–261.41 111.9 10 
NO3 45 45 2–51 9.5 
1–1.5 1–1.5 0.3–1.8 0.6483 
TH 100–500 300–600 90–528 271.22 96 

Note: DL, desired limit; PL, permissible limit.

EWQI-based quality assessment of groundwater

The evaluation of the quality of potable drinking water using EWQI is considered to be one of the most unbiased, simple, and yet comprehensive techniques (Sheikh 2018). Table 7 presents the village, EWQI, water quality, and EWQI rank for the Beenaganj-Chachura block. It is seen that village Jukhara had an EWQI value of 176.93 and was hence categorized with rank 5 and are hence indexed as villages with extremely poor quality of water.

Table 7

Location-wise EWQI, water quality, and EWQI rank

S.No.VillageEWQIWater qualityEWQI rank
Khekheh 84.10 Medium 
Borda 54.59 Medium 
Faknehru 98.31 Medium 
Navalpura 68.44 Medium 
Lakhori 67.92 Medium 
Kakruaa 141.16 Poor 
Dehri 64.39 Medium 
Bitakhedi 57.85 Medium 
Bapcha Lahariya 73.91 Medium 
10 Ramtedi 86.24 Medium 
11 Dokriyakedi 118.11 Poor 
12 Pakhariyapura 103.84 Poor 
13 Teligav 71.31 Medium 
14 Purabkanya 59.43 Medium 
15 Bor Ka Keda 83.40 Medium 
16 Kali Karar 73.93 Medium 
17 Mohmmdpur 52.60 Medium 
18 Fulukhdi 45.18 Good 
19 Gulwada 56.50 Medium 
20 Khedikla 116.70 Poor 
21 Pechi 63.72 Medium 
22 Kikhada 81.40 Medium 
23 Kotra 64.11 Medium 
24 Khatoli 141.26 Poor 
25 Todi 72.27 Medium 
26 Sagar 100.20 Poor 
27 Kekriya 61.56 Medium 
28 Batawda 49.51 Good 
29 Kudhampura 67.44 Medium 
30 Badhagav 58.72 Medium 
31 Golyiakhedi 123.44 Poor 
32 Moiya 94.64 Medium 
33 Sagodi 80.01 Medium 
34 Gunjari 91.14 Medium 
35 Jamoniya Kla 63.38 Medium 
36 Barkheda Khurd 139.00 Poor 
37 Pipliya Moti 57.62 Medium 
38 Khanpur 52.14 Medium 
39 Patondi 123.52 Medium 
40 Tlavli 99.24 Medium 
41 Jukhara 167.93 Extremely Poor 
42 Umarthana 71.79 Medium 
43 Dedla 53.89 Medium 
44 Maheshpura 47.51 Good 
45 Barkhedi Mafi 125.58 Poor 
46 Amaser 71.89 Medium 
47 Khejra Kla Rani 76.26 Medium 
48 Kekadhiya 137.81 Poor 
49 Basahedha 77.21 Medium 
50 Bethda 67.93 Medium 
S.No.VillageEWQIWater qualityEWQI rank
Khekheh 84.10 Medium 
Borda 54.59 Medium 
Faknehru 98.31 Medium 
Navalpura 68.44 Medium 
Lakhori 67.92 Medium 
Kakruaa 141.16 Poor 
Dehri 64.39 Medium 
Bitakhedi 57.85 Medium 
Bapcha Lahariya 73.91 Medium 
10 Ramtedi 86.24 Medium 
11 Dokriyakedi 118.11 Poor 
12 Pakhariyapura 103.84 Poor 
13 Teligav 71.31 Medium 
14 Purabkanya 59.43 Medium 
15 Bor Ka Keda 83.40 Medium 
16 Kali Karar 73.93 Medium 
17 Mohmmdpur 52.60 Medium 
18 Fulukhdi 45.18 Good 
19 Gulwada 56.50 Medium 
20 Khedikla 116.70 Poor 
21 Pechi 63.72 Medium 
22 Kikhada 81.40 Medium 
23 Kotra 64.11 Medium 
24 Khatoli 141.26 Poor 
25 Todi 72.27 Medium 
26 Sagar 100.20 Poor 
27 Kekriya 61.56 Medium 
28 Batawda 49.51 Good 
29 Kudhampura 67.44 Medium 
30 Badhagav 58.72 Medium 
31 Golyiakhedi 123.44 Poor 
32 Moiya 94.64 Medium 
33 Sagodi 80.01 Medium 
34 Gunjari 91.14 Medium 
35 Jamoniya Kla 63.38 Medium 
36 Barkheda Khurd 139.00 Poor 
37 Pipliya Moti 57.62 Medium 
38 Khanpur 52.14 Medium 
39 Patondi 123.52 Medium 
40 Tlavli 99.24 Medium 
41 Jukhara 167.93 Extremely Poor 
42 Umarthana 71.79 Medium 
43 Dedla 53.89 Medium 
44 Maheshpura 47.51 Good 
45 Barkhedi Mafi 125.58 Poor 
46 Amaser 71.89 Medium 
47 Khejra Kla Rani 76.26 Medium 
48 Kekadhiya 137.81 Poor 
49 Basahedha 77.21 Medium 
50 Bethda 67.93 Medium 

Ten villages fall in the category of poor water quality, while 38 villages have medium water quality. Fulukhedi, Batawda, and Maheshpura villages rank 2 as per EWQI and are categorized as good. None of the villages could be categorized as having excellent water quality. The groundwater quality distribution map of Beenaganj-Chachura block according to EWQI is shown in Figure 2.
Figure 2

Groundwater quality distribution based on EWQI.

Figure 2

Groundwater quality distribution based on EWQI.

Close modal
Figure 3

Groundwater quality distribution based on PIG.

Figure 3

Groundwater quality distribution based on PIG.

Close modal

PIG-based quality assessment of groundwater

The PIG was established to consolidate the extensive variety of physicochemical data into one numerical value, which provides adequate information about the groundwater's overall quality. In addition, the classification of groundwater through the use of PIG contributes to the evaluation of the chemical appropriateness of water that is consumed for drinking purposes (Figure 3). The PIG values that were estimated ranged from 0.426 to 1.67 (Figure 4), with a value of 0.750 representing the average (Table 6). According to the findings of this research, the groundwater in the area under investigation can be classified into one of three levels of water contamination: negligible contamination, low contamination, or significant contamination.
Figure 4

Contour plots of EWQI for various parameters.

Figure 4

Contour plots of EWQI for various parameters.

Close modal
According to the findings of PIG, 63% of the groundwater samples exhibit ‘insignificant pollution.’ This was determined by analyzing the samples. As a result, the drinking quality of the groundwater in these settlements has been evaluated and found to be satisfactory (Table 6). The percentage of groundwater samples that fall into the ‘low pollution’ category is around 27%, whereas the percentage that falls into the ‘moderate pollution’ category is approximately 10% (Figure 4; Table 6). According to the findings of the investigation that made use of the PIG method, the groundwater in the area under the study is only somewhat appropriate for consumption. The many classifications of groundwater quality that were determined using the PIG technique are presented in Figure 5, which shows their distribution over the study area. Note that the places in the study area that are identified as having extremely poor water quality according to the EWQI classification technique are the same areas that are identified as having moderate water quality according to the PIG classification method. This is because both classification methods use the same criteria to determine which areas have moderate or poor water quality.
Figure 5

Contour plots of PIG for various parameters.

Figure 5

Contour plots of PIG for various parameters.

Close modal

However, there are also some inconsistencies in the results obtained by EWQI and PIG. For example, the quality is good and/or medium around the area of Maheshpura according to the EWQI method, but it is moderate according to the PIG method. As a comparison, at the Jakhuaa area, it is extremely poor quality by EWQI but low pollution by PIG. These discrepancies between EWQI and PIG results may have numerous causes as follows:

  • Weighting of parameters: EWQI and PIG weight physicochemical characteristics differently. If specific parameters affect one index more than the other, water quality assessments may differ. EWQI prioritizes water quality parameters, while PIG may prioritize health-related contaminants.

  • Parameter selection: EWQI and PIG assessments use various parameters. If one technique omits or underweight's key parameters, it can affect water quality evaluation.

  • Methodology: EWQI and PIG measure groundwater quality differently. The two methodologies may yield different results due to differences in computation methods, weighting schemes, and index interpretation criteria.

Further, there are certain limitations of both the methods. EWQI might overlook harmful pollutants or health metrics as the weighing scheme of EWQI is based on subjective judgments or expert opinions, which may cause bias or ambiguity. EWQI may not detect local water quality differences or solve site-specific challenges. On the other hand, PIG may overlook water quality and geological considerations in favor of pollution concentrations. It might fail to recognize parameter interactions and water quality impacts. Pollutant levels and regulatory criteria are needed for sophisticated PIG computations. EWQI may be better for assessing water quality and identifying trends over a vast area. However, PIG may be more useful for analyzing pollution levels, pollutants, and regulatory compliance. Researchers may use both strategies to better understand groundwater quality.

Given the primary objective of the present work on Binaganj-Chachaura block as quality assessment of groundwater for drinking purposes, the authors suggest to adopt PIG over EWQI. PIG evaluates groundwater quality based on a comprehensive set of physicochemical parameters, including those relevant to human health. It considers the concentration of individual pollutants relative to regulatory standards, providing insights into potential risks to water users. PIG allows for the identification of specific contaminants contributing to groundwater pollution, enabling targeted remediation strategies.

Response surface methodology

By choosing two operational parameters each time, the surface and contour plots of the response parameter (EWQI) are shown in Figure 4. They are displayed using a quadratic equation. One of the remaining seven parameters has been chosen to serve as the second parameter in each plot. While plotting these graphs, the values of the other parameters have been taken as constants. In these charts, it is possible to see the trend that the response parameter follows in relation to an increase or reduction in the operational parameters. In every subfigure of Figure 4, the apparent reduction in the RSM caused by the fluctuation in total hardness, EC, TDS, nitrate, and fluoride is visible. The remaining plots may also be used to compare and examine how the second parameter affects the response. For instance, it can be observed that the influence of total hardness is less pronounced than that of EC when it is changed from its lower level to its greater level. Similar to this, a decline in EWQI with a rise in Ca and a fall in TDS is also observed. The contour plots of EWQI and PIG with various input parameters are presented in Figures 4 and 5, respectively.

The investigation conducted in the Beenaganj-Chauchora region offers crucial insights into groundwater quality assessment. Through the utilization of EWQI, PIG, and RSM, several significant findings have emerged, contributing substantially to the field of hydrology and water resource management of the area. Key highlights from this study include:

  • The high nitrate contents are found in 4% of samples in some villages of the block. PIG results inferred that 76, 16, and 8% groundwater samples come under insignificant, low, and moderate, and none of the samples are in high category.

  • Ten villages fall in the category of poor water quality, while 38 villages have medium water quality. Fulukhedi, Batawda, and Maheshpura villages rank 2 as per EWQI and are categorized as good. None of the village could be categorized as having excellent water quality.

  • The utilization of EWQI and PIG allows for the categorization of groundwater quality. Some communities showcase favorable to moderate quality, while others display substandard quality. In certain cases, both the methods showcase contradictory results.

  • PIG is more significantly impacted by changes in nitrite, fluoride, total hardness, and magnesium than by changes in the other factors. Surface plots have all been used to analyze the outcomes of the selected model.

  • The authors suggest using PIG over EWQI to analyze groundwater quality for drinking in the Binaganj-Chachaura block as it uses many physicochemical characteristics to assess groundwater quality.

The authors wish to thank to the faculties and staff of Department of Civil Engineering at Jaypee University of Engineering and Technology, Guna, for the technical support.

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

SKA: conceptualization, data curation, and software; YIM: formal analysis, validation, visualization, and writing – original draft; NKS: writing – review and editing

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

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