Assessment of Groundwater quality for drinking and irrigation purposes in Rohtak district, Northern India

doi:10.21203/rs.3.rs-3132694/v1

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Assessment of Groundwater quality for drinking and irrigation purposes in Rohtak district, Northern India

https://doi.org/10.21203/rs.3.rs-3132694/v1

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This study discusses about the suitability of groundwater for drinking and irrigation use in Rohtak district of Haryana state, India. Ninety-four groundwater samples were collected during Pre monsoon and Post monsoon periods and analyzed by several methods, including Entropy water quality index (EWQI), Sodium Absorption Ratio (SAR), Permeability Index (PI), Sodium Percentage (Na %), Kelly Ratio (KR), Magnesium Adsorption Ratio (MAR), US salinity diagram, and Gibbs diagram. The spatial distribution maps of EWQI and irrigation indices were also plotted using the IDW technique in ArcGIS. The result suggests that the groundwater quality in the central part extending towards the south-western part of the area can be categorized as poor. The major ion concentrations were reported in the following order: Mg²⁺ > Ca²⁺ > Na⁺ > K⁺, and Cl^- > SO₄^2- > NO₃^- > HCO₃^- > F^- > CO₃^2- respectively. Moreover, results revealed that most of groundwater samples exceeded the desirable limits for drinking water as suggested by World Health Organization. The EWQI assessment indicates that 23.4% and 38.4% of groundwater samples have extremely poor quality in Pre monsoon and Post monsoon, respectively. Analysis of water quality for irrigation shows that various indices like EC, SAR, PI, Na %, KR, and MAR exceed their standard limit, and mostly samples are unsuitable for irrigation. The Gibbs diagram and other bivariate plots demonstrate that various ions in groundwater can be attributed to silicate and carbonate weathering, which occur through water-rock interactions and ion exchange phenomena. Additionally, the US Salinity diagram verifies that most groundwater samples exhibit significant salinity hazards, particularly concerning high sodium levels. The results of this study are highly valuable for efficiently allocating and managing the resources within this particular district.

Hydrochemical parameters

Entropy water quality index (EWQI)

Irrigation indices

Groundwater quality

ArcGIS

Due to unequal distribution of freshwater resources worldwide, access to it is becoming increasingly scarce due to anthropogenic activities and population growth. As surface water resources dwindle, the utilization of groundwater becomes essential to meet the diverse needs of various countries (Saha & Paul, 2019). Groundwater is one of the most vital resources for living beings for both drinking and irrigation purposes (Abbasnia et al., 2019; Adimalla et al., 2018; Raheja et al., 2023). According to the most recent figures, about 65% of groundwater worldwide is used for drinking, 20% for irrigation, and 15% for industrial uses (Adimalla et al., 2018). Additionally, excessive groundwater exploitation and a string of recent droughts caused a decline in groundwater level and deteriorated its quality (Hosseinifard & Mirzaei Aminiyan, 2015). In Haryana's arid and semiarid parts, extensive farming and urban expansion have led to a high demand for groundwater resources. Since groundwater in Haryana has been recognized as a significant source of drinking and irrigation water, it is essential to note that monitoring the quality of these resources should be included as a crucial step in managing water resources. The key variables that can impact the chemical quality of groundwater are the amount of rainfall, the geological composition of the region, and the mineralogy of the aquifer (Bahrami & Zarei, 2023; Kalhor et al., 2019; Raheja et al., 2022a).

Therefore, its quality assessment is crucial before using groundwater for different purposes. Monitoring programs make an effort to include routine water samples collected from several pre-selected sites to accurately reflect the status of groundwater quality. Literature suggests several indexing techniques that combine all the water quality parameters with the water quality status in the form of a single numeric value. Some important water quality index like the water quality index (WQI), Entropy water quality index (EWQI), and others reduces the complexity of representing the water quality and makes it easier for management to understand it ( Kumar & Augustine, 2022; Roy et al., 2021). Similarly, the suitability of groundwater for irrigation purposes is also determined by various indices such as Sodium Absorption Ratio (SAR), Permeability Index (PI), Sodium Percentage (Na %), Kelly Ratio (KR), and Magnesium Adsorption Ratio (MAR) (Guo et al., 2021; Kouser et al., 2022; Kumar et al., 2021; Safari et al., 2021).

Keeping in view of the continuous evaluation of water quality over the study area, this study was carried out in a way to collect water samples during Pre and Post monsoon season. The aim of this study was to identify the hydrochemical characteristics of groundwater and evaluate its suitability for drinking and irrigation purposes. To judge the groundwater quality, the entropy water quality index (EWQI) was used. In addition, the Sodium Absorption Ratio (SAR), Permeability Index (PI), Sodium Percentage (Na %), Kelly Ratio (KR), Magnesium Adsorption Ratio (MAR), US salinity diagram, and Gibbs diagram were also used to determine the quality for irrigation purposes. Moreover, Spatial distribution maps of drinking and irrigation indices were also plotted using the IDW technique in ArcGIS. The outcomes of this study will be helpful to decision-makers in implementing improved science-based water quality programs. They can also provide potential information on the quality of water that can be used for drinking and irrigation in a region and similar problems elsewhere.

2.1 Study area

The study area, Rohtak (Fig. 1), is located in a part of Haryana State, India. The district covers an approximate geographical location of about 1,745 Km². The temperatures in Rohtak district are usually hot and dry from April to June (25°C to 45°C) and are generally cold in winter from December to February (5°C to 20°C). The district experiences a monsoon-influenced humid subtropical climate with average annual rainfall is around 537.5 mm. The district receives primary rainfall during the monsoon season (July to September).

2.2 Sample Collection and field analysis

To collect groundwater samples for this study, 47 different locations were selected in a way to incorporate all blocks in the Rohtak district. The groundwater samples collected in this study are primarily from the government hand pumps and domestic tube wells (Fig. 2) during the Pre monsoon and Post monsoon periods (2022) as per the standard procedures of the American Public Health Association (APHA, 2012). Before collecting the water samples for testing, hand pump and tube wells were allowed to run for 4–5 minutes to discard any water in the pipes. Each sample was collected in 1000 mL clean high-density polypropylene bottles. The latitude and longitude of all the sampling sites and the source were geocoded using a portable global positioning system (GPS). The pH, total dissolved solids (TDS), and electrical conductivity (EC) were measured on-site using portable equipment.

2.3 Laboratory analysis of samples

The samples were carefully transported to the laboratory in an icebox after collection to preserve their integrity and stop any degradation. As soon as they arrived, they were immediately stored at 4°C to ensure their preservation and readiness for further analysis. The major cations like Ca²⁺ and Mg²⁺ were determined by the Volumetric titration method using EDTA, and other Na⁺ and K⁺ were determined by the flame photometer method (APHA, 2012). The major anions NO₃⁻, SO₄²⁻, and F⁻ were determined using a spectrophotometer as described in APHA (2012). The other anions, like Cl⁻, CO₃²⁻and HCO₃⁻ were determined by using titration method. With the help of Eq. (1), the Ionic balance error (IBE) between the cations and anions was determined to verify the accuracy of a thorough hydrochemical analysis of each groundwater sample.

$$IBE=\frac{\sum Cations-\sum Anions}{\sum Cations+\sum Anions}\times 100\%$$

All the above concentrations of cations (Mg²⁺, Ca²⁺, Na⁺, K⁺) and anions (Cl⁻, SO₄²⁻, NO₃⁻, CO₃²⁻, HCO₃⁻, F⁻) are in mg/L. The IBE was observed for groundwater samples to fall within the acceptable limit of ± 5%.

2.4 Entropy Water Quality Index (EWQI)

Entropy water quality index (EWQI) helps in representing all hydrochemical information by one value that accurately depicts the water quality for drinking purposes (Adimalla, 2021; Amiri et al., 2014; Raheja et al., 2022b). The calculation of EWQI involves several steps, as described below

Step 1. The most significant parameter is an entropy weight, often connected with "$m$" groundwater samples, each with "$n$" hydrochemical parameters. The estimation of the eigenvalue matrix "$X$" which is linked to all hydrochemical parameters and calculated by the following equation:

$$X=\left[\begin{array}{ccc}{x}_{11}& {x}_{12}& {..x}_{1n}\\ {x}_{21}& {x}_{22}& {..x}_{2n}\\ {x}_{m1}& {x}_{m2}& ..{x}_{mn}\end{array}\right]$$

Where $"m"$ ($i=\text{1,2},3,\dots ..,m$) is the number of groundwater samples collected during the study, and $"n"$ ($j=\text{1,2},3,\dots ..,n$) is the number of hydrochemical parameter for each sample.

Step 2. Now standard evaluation matrix $"Y"$ and $"{y}_{ij}"$ is calculated by using equations (3) and (4).

$$Y=\left[\begin{array}{ccc}{y}_{11}& {y}_{12}& {..y}_{1n}\\ {y}_{21}& {y}_{22}& {..y}_{2n}\\ {y}_{m1}& {y}_{m2}& ..{y}_{mn}\end{array}\right]$$

$${y}_{ij}=\frac{{x}_{ij}-{\left({x}_{ij}\right)}_{min}}{{\left({x}_{ij}\right)}_{max}-{\left({x}_{ij}\right)}_{min}}$$

Where, ${x}_{ij}$ is the ${j}^{th}$ evaluation index of the ${i}^{th}$ groundwater sample, ${\left({x}_{ij}\right)}_{max}$ is the maximum value of hydrochemical parameter, and ${\left({x}_{ij}\right)}_{min}$ is the minimum value of a hydrochemical parameter.

Step 3. After calculating standardized value, a ratio of index value, and information entropy ${"e}_{j}"$ are calculated by following equations (5) and (6).

$${P}_{ij}=\frac{{y}_{ij}}{\sum _{i=1}^{m}{y}_{ij}}$$

$${e}_{j}=-\frac{1}{\text{ln}m}\sum _{i=1}^{m}{P}_{ij}\text{ln}{P}_{ij}$$

Step 4. Entropy weight $"{w}_{j}"$ and quality rating scale $"{q}_{j}"$ are computed by equations (7) & (8).

$${w}_{j}=\frac{1-{e}_{j}}{\sum _{j=1}^{n}(1-{e}_{j})}$$

$${q}_{j}=\frac{{c}_{j}}{{s}_{j}}\times 100$$

In which the term $"{c}_{j}"$ is the concentration of the hydrochemical parameter as in mg/L, and ${s}_{j}$ is the drinking water standard for each parameter (WHO, 2017).

Step 5. Finally, $EWQI$ is calculated by the following Eq. (9).

$$EWQI=\sum _{j=1}^{n}{w}_{j }{q}_{j}$$

The EWQI is rated as excellent if it is below 25, good between 26 and 50, medium between 51 and 75, poor between 76 and 100, and extremely poor if it is beyond 100. Additionally, Table 1 shows the classification and rankings for the EWQI and groundwater quality (Adimalla, 2021; Dashora et al., 2022).

Table 1

Classification of groundwater quality as per EWQI
EWQI scale	Rank	Groundwater quality
< 25	I	Excellent quality
26–50	II	Good quality
51–75	III	Medium quality
76–100	IV	Poor quality
> 100	V	Extremely Poor quality

2.5 Irrigation water quality

The various groundwater quality parameters, including Sodium Absorption Ratio (SAR), Permeability Index (PI), Sodium Percentage (Na %), Kelly Ratio (KR), and Magnesium Adsorption Ratio (MAR), were calculated to categorize the groundwater for irrigation purposes (Table 2).

Table 2

Irrigation groundwater quality based on various parameters.
Sr. No.	Parameters	Reference
1.	$SAR= \frac{{Na}^{+}}{\sqrt{\frac{{Ca}^{2+}+{Mg}^{2+}}{2}}}$	(Richards, 1954)
2.	$PI= \frac{{Na}^{+}\sqrt{{HCO}_{3}^{-}}}{{Ca}^{2+}+{Mg}^{2+}+{Na}^{+}}\times 100$	(Doneen, 1964)
3.	$Na \%= \frac{{Na}^{+}}{{Ca}^{2+}+{Mg}^{2+}+{Na}^{+}+{K}^{+}}\times 100$	(Wilcox, 1955)
4.	$RSC=\left({CO}_{3}^{2-}+{HCO}_{3}^{-}\right)-({Ca}^{2+}+{Mg}^{2+})$	(Raghunath, 1987)
5.	$KR=\frac{{Na}^{+}}{{Ca}^{2+}+{Mg}^{2+}}$	(Kelly, 1956)
6.	$MAR= \frac{{Mg}^{2+}}{{Ca}^{2+}+{Mg}^{2+}}$	(Paliwal KV, 1972)

2.6 Spatial interpolation method

The Geostatistical Analyst tool in ArcMap offers various interpolation methods to analyze and interpolate spatial data using Inverse Distance Weighting (IDW), Kriging, Natural Neighbor, Spline, Radial Basis Functions, and Local Polynomial Interpolation. In this study, IDW technique was used to draw spatial distribution diagrams. IDW is a deterministic interpolation method used to evaluate values at unmeasured locations based on the values of surrounding measured locations. The method calculates the weighted average of the measured values, where the weights decrease as the distance from the predicted location increases and represented by Eq. (10).

$${Z}_{p}=\frac{\sum _{i=1}^{n}{w}_{i}\times {Z}_{i}}{\sum _{i=1}^{n}{w}_{i} }$$

Where,${Z}_{p}$ is the estimated value, ${w}_{i}$ is assigned weight, $n$ is the number of measured values. Furthermore, to determine the hydrochemistry and functional sources of ions, various analyses such as major ions correlation, US salinity diagram, and Gibbs diagram were also plotted for the water samples.

3.1 Groundwater quality assessment using Hydrochemical parameters

Tables 3 and Table 4 display results for groundwater quality parameters, descriptive statistics, and a comparison with WHO (2017) recommendations. The pH of groundwater in the study area is found to lie in the range of 7.03–8.63, with an average of 7.77 during Pre monsoon, and 7.05–8.7 with an average of 7.66 in the Post monsoon period, indicating that groundwater of the region is neutral to alkaline in nature. Although pH does not directly impact human health, it influences all biological reactions (Subba Rao et al., 2017). Out of 47 groundwater samples, approximately 4.25% of samples had pH values above the recommended limits (WHO, 2017) for both periods. Total dissolved salts, or TDS, are a measure of the salinity of groundwater and its appropriateness for domestic uses (Howladar et al., 2018; Wu et al., 2017). When TDS values are compared to WHO norms, approximately 87.23% of samples exceed the established limit for both seasons. Total hardness (TH) represents the sum of calcium ions (Ca²⁺) and magnesium ions (Mg²⁺) dissolved in groundwater, normally expressed in mg/L of calcium carbonate equivalent. Hence, in the present study, the TH of the collected groundwater samples was found to be in the range of 102 mg/L to 3250 mg/L and 110 mg/L to 3660 mg/L during Pre monsoon and Post monsoon seasons, respectively. The mean concentrations of Ca²⁺, Mg²⁺, Na⁺, K⁺, CO₃²⁻, HCO₃⁻, Cl⁻, SO₄²⁻, NO₃⁻, and F⁻ in groundwater were 288.8 mg/L, 470.93 mg/L, 263.3 mg/L, 68.53 mg/L, 0.21 mg/L, 8.95 mg/L, 437.04 mg/L, 424.30 mg/L, 36.66 mg/L, 0.84 mg/L for Pre monsoon and 294.33 mg/L, 500.1 mg/L, 301.95 mg/L, 90.94 mg/L, 0.11 mg/L, 7.65 mg/L, 462.29 mg/L, 448.25 mg/L, 49.42 mg/L, 0.87 mg/L for Post monsoon respectively. The predominant of cations and anions based on their average values is in the following order: Mg²⁺ > Ca²⁺ > Na⁺ > K⁺, and Cl⁻ > SO₄²⁻ > NO₃⁻ > HCO₃⁻ > F⁻ > CO₃²⁻.

Table 3

Descriptive statistics and comparison with (WHO, 2017) standards during Pre monsoon.
Variables	Minimum	Mean	Maximum	(WHO, 2017)	Samples above WHO limits (%)
pH	7.03	7.77	8.63	6.5–8.5	4.25
TDS	220	2478.96	9655	1000	87.23
EC	56	3569.95	16545	1500	85.11
TH	102	759.81	3250	500	51.06
Ca²⁺	50	288.88	795	200	63.82
Mg²⁺	25	470.93	2700	150	78.72
Na⁺	46	263.30	456	200	65.95
K⁺	25	68.53	119	200	-
CO₃²⁻	0	0.21	1.63	*	-
HCO₃⁻	2.5	8.95	18.5	400	-
Cl⁻	45	437.04	818	250	70.21
SO₄²⁻	56	424.30	864	250	76.59
NO₃⁻	10	36.66	65	45	34.04
F⁻	0.23	0.84	1.6	1.5	4.25

Table 4

Descriptive statistics and comparison with (WHO, 2017) standards during Post monsoon.
Variables	Minimum	Mean	Maximum	(WHO, 2017)	Samples above WHO limits (%)
pH	7.05	7.66	8.7	6.5–8.5	4.25
TDS	229	2681.89	8544	1000	87.23
EC	329	4253.7	18400	1500	89.36
TH	110	794.38	3660	500	51.06
Ca²⁺	60	294.33	1060	200	61.70
Mg²⁺	50	500.1	2900	150	85.11
Na⁺	115	301.95	636	200	68.09
K⁺	29	90.94	163	200	-
CO₃²⁻	0	0.11	1.6	*	-
HCO₃⁻	1.8	7.65	14.9	400	-
Cl⁻	85	462.29	921	250	76.59
SO₄²⁻	75	448.25	965	250	78.72
NO₃⁻	15	49.42	85	45	48.93
F⁻	0.25	0.87	1.65	1.5	6.38

3.2 Groundwater quality assessment based on EWQI

The entropy water quality index (EWQI) is a method that has been used to evaluate and quantify the quality of groundwater for drinking purposes (Adimalla, 2021; Dashora et al., 2022). The classifications of the EWQI and the sampling locations are provided in Table 5, whereas; Fig. 3 provides the graphical representation of EWQI. The EWQI values for groundwater samples in the Rohtak district vary from 61.68 to 322.76 with a mean of 128.57 during Pre monsoon, and 61.01 to 330.83 with a mean of 145.80 for Post monsoon season (Table 5). A comparison of calculated values with standard values indicates eleven groundwater samples from Pre monsoon and eighteen samples from Post monsoon have extremely poor water quality for drinking.

Table 5

Classifications of Entropy water quality index (EWQI) values for groundwater samples collected from 47 different sites in the study area.
Site/Village name	Pre monsoon EWQI	Water quality	Post monsoon EWQI	Water quality
Lakhan Majra	70.52	Medium quality	73.07	Medium quality
Chandi	138.78	Poor quality	141.67	Poor quality
Lakhan Majra	113.07	Poor quality	110.44	Poor quality
Lakhan Majra	266.32	Extremely poor	275.01	Extremely poor
Bansi	158.05	Extremely poor	202.57	Extremely poor
Nindana	322.76	Extremely poor	330.83	Extremely poor
Nindana	119.02	Poor quality	112.19	Poor quality
Hanuman Brick Kiln	102.23	Poor quality	105.30	Poor quality
Kheri Makhan	217.69	Extremely poor	229.99	Extremely poor
Ganga Nagar	118.59	Poor quality	126.14	Poor quality
Shekh Pur Titri	129.83	Poor quality	172.76	Extremely poor
Bhran	76.17	Medium quality	92.05	Medium quality
Madina	72.97	Medium quality	83.72	Medium quality
Bahu Akabpur	82.50	Medium quality	94.13	Medium quality
Choti Bahu	160.24	Extremely poor	166.30	Extremely poor
Khrawar	114.86	Poor quality	126.93	Poor quality
Gandhara	135.92	Poor quality	156.16	Extremely poor
Gandhari	108.17	Poor quality	133.64	Poor quality
Ismalia	140.95	Poor quality	155.37	Extremely poor
Sampla	74.95	Medium quality	84.69	Medium quality
Kalanaur	80.98	Medium quality	100.48	Poor quality
Kalanaur	136.46	Extremely poor	149.36	Poor quality
Kalanaur	106.16	Extremely poor	140.52	Poor quality
Kharki Mor	255.83	Extremely poor	269.06	Extremely poor
Sample	83.29	Medium quality	109.77	Poor quality
Kansala	205.68	Extremely poor	265.84	Extremely poor
Balmba	101.34	Poor quality	131.26	Poor quality
Indian Oil Pump, Kalanaur	122.50	Poor quality	124.65	Poor quality
Mokhra	125.82	Poor quality	153.13	Extremely poor
Mokhra	131.00	Poor quality	165.94	Extremely poor
Lahali	61.68	Medium quality	61.01	Medium quality
Rohtak	203.43	Extremely poor	208.03	Extremely poor
Nasirpur	81.63	Medium quality	88.96	Medium quality
Rohtak	93.15	Medium quality	133.29	Poor quality
Aakrouli Khurd	108.78	Poor quality	132.29	Poor quality
Rohtak	111.40	Poor quality	152.11	Extremely poor
Baniyani	203.45	Extremely poor	189.76	Extremely poor
Hassan Garh	93.56	Medium quality	109.44	Poor quality
Dohab	80.32	Medium quality	101.18	Poor quality
Rohtak	85.05	Medium quality	154.94	Extremely poor
Chamaria	94.88	Medium quality	120.66	Poor quality
Rohtak	93.57	Medium quality	116.45	Poor quality
Makhroli Kalan	205.93	Extremely poor	203.43	Extremely poor
Sanghi	89.53	Medium quality	98.89	Medium quality
Jassia	74.48	Medium quality	90.55	Medium quality
Dhamar	118.11	Poor quality	126.90	Poor quality
Baland	171.02	Extremely poor	181.62	Extremely poor

Furthermore, as shown in Table 4, 40% of groundwater samples for both seasons were classified as poor water quality, suggesting they were unsuitable for drinking purposes. In addition, medium quality water was assigned to 17 Pre monsoon and 9 Post monsoon samples, indicating that the water quality at these locations is only marginally fit for human consumption. Figure 3 shows the distribution of the EWQI values for the groundwater samples obtained from the 47 different sites in the Rohtak district. It can be observed that none of the groundwater samples be classified as Excellent or good water quality. The EWQI classification system was used to estimate the spatial distribution of the water quality classifications throughout the Rohtak district, as shown in Figs. 4(A) and 4(B). The spatial distribution maps indicate that the distribution of medium water quality is not uniform and is relatively uneven. Large areas of the region, primarily in the western and southern parts, have Extremely poor groundwater quality, which is unfit for drinking consumption.

3.3 Groundwater Assessment for Irrigation

After studying the water quality for drinking purposes, another focus of this study was to evaluate the suitability of groundwater for irrigation. The following criteria are used to categorize the characteristics of groundwater used for irrigation as given below:

3.3.1 Electrical Conductivity (EC)

EC is a significant parameter to be considered for irrigation water quality. It is reported in µS/cm. The groundwater quality could also be grouped such as excellent with EC (< 250 µS/cm), good (250–750 µS/cm), permissible (750–2000 µS/cm), doubtful (2000–3000 µS/cm), and unsuitable (> 3000 µS/cm) for irrigation. The spatial distribution maps of Electrical conductivity (EC) for both seasons, Pre and Post monsoon were created and presented in Fig. 5. A higher concentration of EC during the Pre and Post monsoon in the southern, central, and western parts of this area and some eastern pockets show low EC values.

The concentration maps of groundwater suitability during Pre and Post monsoon seasons were prepared and shown in Fig. 6. In Pre monsoon season, 1 (2.12%) sample was categorized as good water, 6 (12.77%) samples into a permissible stage, 19 (40.42%) samples under doubtful, and 21 (44.68%) samples were found to be unsuitable for irrigation purposes. From a perusal of Fig. 6 (B), during Post monsoon period, 3 (6.38%), 8 (17.02%), 12 (25.53%), 24 (51.06%) of groundwater samples were in good, permissible, doubtful, and unsuitable range respectively. It can be concluded from these maps that EC values in most of the sampling locations were higher (3000 µS/cm). Moreover, it is also clear that the groundwater samples of this Rohtak district have a higher amount of EC during Pre and Post monsoon season.

3.3.2 Sodium Adsorption Ratio (SAR)

The results of the SAR classification (Fig. 7(A)) indicate that 8 samples (17.02%) are in the excellent, 14 groundwater samples (29.78%) are in good range, 6 samples (12.76%) are in doubtful range, and 19 samples (40.42%) in unsuitable range for irrigation during Pre monsoon. For Post monsoon study, 5 samples (10.64%) falls under excellent category, 10 groundwater samples (21.27%) in good range, 11 (23.40%) samples in doubtful category, and 21 (44.68%) samples are not suitable for irrigation use (Fig. 7B). A spatial variation map of SAR was also prepared and provided in Fig. 8. A comparison of Pre and Post monsoon maps indicates higher amount of SAR distribution in Post monsoon as compared with samples from Pre monsoon period. In addition, it is also clear that the High SAR value during Post monsoon lies mainly in the central part of the study area, and a lower SAR value was observed during the Post monsoon in the northern part of the study area.

As per the US Salinity Laboratory’s diagram (Richards, 1954) (Fig. 9), result confirms that a majority of the groundwater samples are found to have very high salinity hazards (C4) with very high sodium hazards (S4). Läuchli & Grattan (1990) suggested that salinity affects plant growth and development. Furthermore, salinity can also cause nutrient imbalances in plants, as excessive salt concentrations can lead to soil salinization, which can be harmful to plant growth and yield. To moderate the effects of salt, farmers can be asked to implement management practices such as leaching requirements, which involve applying excess water to the soil to wash out salts or planting salt-tolerant crops. Moreover, farmers may use desalination techniques to remove high salts from the water before irrigation (Aly et al., 2015; Raheja, Goel, & Bundela, 2019).

Figure 9. Classification of irrigation water using US Salinity diagram.

3.3.3 Permeability Index (PI)

Permeability Index (PI) is another criterion used to evaluate the suitability of groundwater for irrigation (Doneen, 1964). As per this classification scheme, groundwater quality is divided into three classes: excellent when PI < 75, good when PI lies from 25–75, and unsuitable when PI value is less than 25 for irrigation. The PI concentration maps for both Pre and Post monsoon groundwater samples are presented in Fig. 10. In Pre monsoon season, only 1 (2.13%) sample is categorized into excellent type water, 25 (53.19%) samples into good water type, and 21 (44.68%) samples in unsuitable category for irrigation purposes (Fig. 10A). In comparison to Pre monsoon samples, results from post monsoon samples suggest 1 (2.13%), 28 (59.57%), and 18 (38.30%) samples of groundwater are in excellent, good, and unsuitable type for irrigation, respectively (Fig. 10 (B)). Spatial variation maps of PI were also plotted for both Pre monsoon and Post monsoon periods (Fig. 11). These maps show that higher concentrations are mostly observed during the Pre monsoon in comparison to Post monsoon season. Results in terms of PI suggest that the study area's mostly groundwater samples have satisfactory quality.

3.3.4 Sodium Percentage (Na %)

Todd, (1995) suggested that a high amount of sodium in groundwater decreases soil permeability, plant growth and reduces production. The Na % classification for both Pre and Post monsoon groundwater samples is provided in Fig. 12. Based on these results, 9 (19.15%) and 19 (40.42%) samples were in excellent and good range during Pre monsoon, and only 4 (8.51%) of samples fell in the doubtful range. In Post monsoon season, 5 (10.63%), 18 (39.30%), 17 (36.17%), 7 (14.89%) of groundwater samples were in excellent, good, permissible, and doubtful range respectively (Fig. 12B). Spatial distribution maps of Sodium Percentage (Na %) during Pre and Post monsoon periods show low concentrations in the study area's northern and western portions and high concentrations in the central part of the study area (Fig. 13). Moreover, it is also clear that Na % values were below the permissible limit (80). These results show that the groundwater samples in the study area have satisfactory quality in terms of Na %. A graph has also been plotted between Na % and EC to determine the suitability of groundwater for irrigation (Fig. 14). This figure suggests that most groundwater samples are doubtful to unsuitable for irrigation uses during both periods.

3.3.5 Kelly’s Ratio (KR)

The Kelly’s ratio defines the water quality in two subcategories. If the KR value is < 1, water will be considered suitable for irrigation whereas if it is > 1, water will be treated as unsuitable for irrigation. Figure 14 provides the KR values of both seasons covering the entire study area. In Pre monsoon, the KR value is found to vary from 0.896 to 3.23. Out of total samples, 39 (82.97%) groundwater samples were found to be suitable for irrigation, whereas remaining 8 (17.03%) samples were unsuitable for irrigation uses (Fig. 15A). However, the value of KR in Post monsoon season ranges from 0.0625 to 2.96, with 38 (80.85%) groundwater samples being suitable for irrigation and 9 (19.15%) samples unsuitable for irrigation purposes (Fig. 15B). In addition, it is also observed that KR values are slightly higher in the Pre monsoon season compared to the Post monsoon season. A spatial distribution map of Kelly’s ratio (KR) during the Pre and Post monsoon seasons shows low concentrations in the northern, eastern, southern, and western portions of the study area and high concentrations in the small pockets of the central part of the study area (Fig. 16).

3.3.6 Magnesium adsorption ratio (MAR)

Magnesium (Mg²⁺) concentration is another important value affecting the groundwater quality for irrigation. High amount of Mg²⁺ ion present in groundwater reduces crop production, and the soil becomes more alkaline (Chaudhary & Kumar, 2018). High value of Magnesium adsorption ratio (> 50) causes harm to the soil (Gupta, S. K. ; Gupta, 1987). Highest MAR values identified in the study area were 92.48 and 87.2 during Pre and Post monsoon, respectively. Based on Fig. 17A, 19 (40.43%) of the groundwater samples were suitable for irrigation whereas 28 (59.52%) samples were unsuitable as magnesium ratio exceeds value of 50. During Post monsoon season, 11 (23.40%) of the samples were suitable and 36 (76.60%) samples were not suitable for irrigation (Fig. 17B). The spatial distribution map of MAR (Fig. 18) demonstrates a higher concentration during the Pre monsoon and Post monsoon seasons in the western and southern portion of the study region and a low concentration in the northeastern part of the study area.

3.4 Hydrogeochemistry characteristics

Gibbs diagrams are commonly used to assess the dissolved chemical constituents sources in groundwater (Gibbs, 1970). These are graphical representations to interpret the hydrogeochemical processes controlling the chemistry of water samples. It includes two diagrams: one shows the relative concentrations of Na⁺/ (Na⁺ + Ca²⁺) on the x-axis and total dissolved solids (TDS) on y-axis, and second diagram shows the ratio of TDS to the Cl⁻ / (Cl⁻ + HCO₃⁻) ions. Three zones represent the aquifer's chemistry mechanisms, such as Evaporation, Rock, and precipitation dominance (Gibbs, 1970). In the present study, Fig. 19 (A and B) shows that most groundwater samples fall in the zone of evaporation dominance and rock dominance, indicating that these two natural processes are the main controlling factor of the groundwater chemistry in the study area for both periods. Although some of the samples falls outside these three zones, that might be due to some anthropogenic activities. Gibbs’s diagram over the study area suggests that the chemical weathering of rock-forming minerals and evaporation influence the groundwater quality.

3.5 Ion ratio

Generally, groundwater quality is influenced by various factors, including natural processes such as the geology and mineral composition of the area, as well as anthropogenic factors. The weathering types in the area can also play a role in determining groundwater quality (Krishan et al., 2023). The major ions ratio can provide significant statistics about the hydrogeochemical processes that control the aquifer system (Liu et al., 2021). Figure 20 (A to F) shows the correlation between major ions in groundwater of the study area. Figure 20A suggests that maximum number of the points are lying on the higher side of the 1:1 line, meaning that groundwater in the study area has Na⁺ and Cl⁻ levels that cannot be accounted for by halite dissolution alone. It also suggests that there may be other sources, like weathering of rocks or anthropogenic activities of these ions in the groundwater (Gaur et al., 2022).

Figure 20B shows that most points lie below the 1:1 line, indicating that carbonate weathering is a significant source of calcium ions (Ca²⁺) in the water samples. In addition, Cation exchange is also a process that can release Ca²⁺ ions into the water. From Fig. 20C, most of the points fall near the 1:1 line and represent that hydrochemistry in groundwater is controlled by silicate and carbonate weathering. As shown in Fig. 20 (B, C, and D), the concentration of HCO₃⁻ (R² = 0.0063; 0.0025), Mg²⁺ (R² = 0.3085; 0.43), and SO₄²⁻ (R² = 0.0079; 0.0005) in groundwater have a weak relationship with Ca²⁺ concentration in Pre and Post monsoon periods. Similarly, Fig. 20 suggests a weak relationship between E, SO₄²⁻ + HCO₃⁻ with Ca²⁺ + Mg²⁺ ions in groundwater of study area. Cation exchange is a critical process that can significantly impact the aquifer chemical dynamics in an area (Li et al., 2016). Figure 20F suggests that most of the groundwater samples were far from the 1:1 line, representing that cation exchange is an essential process affecting the chemical dynamics of an aquifer system in the study area.

This study employs hydrogeochemical methods to recognize the hydrochemistry and controlling mechanisms of the groundwater in the Rohtak district of Haryana. Additionally, by the EWQI method and various indicators to estimate the groundwater suitability for drinking and irrigation purposes, the main conclusions are drawn as follows:

1. The groundwater was neutral to alkaline in nature. The hydrochemical analyses of the groundwater samples indicated Mg²⁺ > Ca²⁺ > Na⁺ > K⁺, and Cl^- > SO₄^2- > NO₃^- > HCO₃^- > F^- > CO₃^2- respectively. According to recommended guidelines, values for EC, TDS, TH, Mg²⁺, Ca²⁺, Na⁺, K⁺, Cl^-, and SO₄^2-exceeded the desirable limits in several study area locations.

2. According to the EWQI assessment, the overall quality of groundwater samples in study area varied greatly, from medium water quality (36.1%) to extremely poor quality for drinking (23.4%) during Pre monsoon and from medium water quality (19.1%) to extremely poor quality for (38.4%) during Post monsoon.

3. For irrigation water quality assessment, various indices were analyzed, and it found that EC, SAR, PI, Na %, KR, and MAR exceed the limit, mainly describing the salinity and magnesium hazards in groundwater.

4. US Salinity diagram confirms that most of the groundwater samples possess very high salinity hazards with very high sodium hazards. The spatial distribution maps of EWQI and irrigation parameters showed a low concentration of major ions in the northeastern part and a high concentration from the central part towards the south-western part of the regions.

5. Gibbs diagram and other bivariate plots explain that different ions in groundwater originated from silicate and carbonate weathering through water–rock interactions and ion exchange phenomena.

Based on all of these conclusions, it was concluded that most of the groundwater of the Rohtak area was unfit for drinking and irrigation purposes. Therefore, some solutions are put up to reduce the salinity of the groundwater in this area. The use of soil and water amendments, the blending of high-salinity water with stored rainwater, the implementation of restrictions on excessive groundwater extraction, the regulation of agricultural practices, and the use of organic fertilizers.

Acknowledgment: Hemant Raheja, the first author, is grateful to the Ministry of Education, Government of India, for funding the scholarship for this study. The authors acknowledged the National Institute of Technology, Kurukshetra, for providing various research facilities.

Funding: The Ministry of Education, Government of India, supported this project financially (Grant No. 2K19/NITK/PHD/61900011-Hemant Raheja).

Author contribution: Hemant Raheja was responsible for data processing, writing—original draft preparation, methodology, conceptualization, analysis, and editing. Arun Goel and Mahesh Pal were responsible for visualization, conceptualization, writing—reviewing, and editing.

Data availability: The raw/processed data required to reproduce these findings cannot be shared at this time as the data also forms part of an ongoing study.

Ethics approval: Not applicable since the manuscript has not been involved in using animal or human data or tissue.

Consent to participate: All authors gave informed consent to participate in the article.

Consent for publication: All authors consent to the publication of the article.

Conflict of interest: The authors declare no competing interests.

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Assessment of Groundwater quality for drinking and irrigation purposes in Rohtak district, Northern India

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Abstract

Figures

1 Introduction

2 Material and Methodology

2.1 Study area

2.2 Sample Collection and field analysis

2.3 Laboratory analysis of samples

2.4 Entropy Water Quality Index (EWQI)

2.5 Irrigation water quality

2.6 Spatial interpolation method

3 Result and Discussion

3.1 Groundwater quality assessment using Hydrochemical parameters

3.2 Groundwater quality assessment based on EWQI

3.3 Groundwater Assessment for Irrigation

3.3.1 Electrical Conductivity (EC)

3.3.2 Sodium Adsorption Ratio (SAR)

3.3.3 Permeability Index (PI)

3.3.4 Sodium Percentage (Na %)

3.3.5 Kelly’s Ratio (KR)

3.3.6 Magnesium adsorption ratio (MAR)

3.4 Hydrogeochemistry characteristics

3.5 Ion ratio

Conclusion

Declarations

References

Supplementary Files

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Sr. No.	Parameters	Reference
1.	\(SAR= \frac{{Na}^{+}}{\sqrt{\frac{{Ca}^{2+}+{Mg}^{2+}}{2}}}\)	(Richards, 1954)
2.	\(PI= \frac{{Na}^{+}\sqrt{{HCO}_{3}^{-}}}{{Ca}^{2+}+{Mg}^{2+}+{Na}^{+}}\times 100\)	(Doneen, 1964)
3.	\(Na \%= \frac{{Na}^{+}}{{Ca}^{2+}+{Mg}^{2+}+{Na}^{+}+{K}^{+}}\times 100\)	(Wilcox, 1955)
4.	\(RSC=\left({CO}_{3}^{2-}+{HCO}_{3}^{-}\right)-({Ca}^{2+}+{Mg}^{2+})\)	(Raghunath, 1987)
5.	\(KR=\frac{{Na}^{+}}{{Ca}^{2+}+{Mg}^{2+}}\)	(Kelly, 1956)
6.	\(MAR= \frac{{Mg}^{2+}}{{Ca}^{2+}+{Mg}^{2+}}\)	(Paliwal KV, 1972)