ABSTRACT
Groundwater is a main resource of drinking water in several parts of India. Its degradation poses a significant risk to water availability and human health, highlighting the importance of regularly evaluating groundwater quality in these regions. Thus, the significant aim of this study is to examine and map groundwater quality and its vulnerability for drinking purposes using the EWQI, PIG, and GOD methods. The quality of groundwater in the study area is found to be generally alkaline in nature. More than 20% of samples exceeded the desirable limit of TH. Correlations of major ions revealed that groundwater samples were distributed in the areas of silicate weathering and dolomite dissolution. The EWQI values vary from 33.74 to 62.22, with an average value of 41.54. The spatial distribution diagrams of hydrochemical parameters and EWQI represent poor water quality in southern and southern-western areas. The PIG ranged from 0.49 to 0.84, with an average value of 0.59. Moreover, the GOD method indicates that the southern part of the region has moderate vulnerability and demonstrates that groundwater level is the significant factor for the calculation of groundwater vulnerability.
HIGHLIGHTS
To analyze the groundwater quality for drinking purposes in Kurukshetra district of Haryana (India).
Scatter plots with permissible limits and spatial distribution maps were plotted.
Groundwater quality is estimated by the EWQI method.
The PIG method was compared to determine overall groundwater quality.
GOD method is a significant factor for the calculation of groundwater vulnerability.
INTRODUCTION
Rapid population growth, industrialization, and better living standards have resulted in ever-increasing demands for groundwater in India. The groundwater covers approximately one-third of the Earth's area. It is a significant water resource in arid and semiarid areas, and many sources of pollution contaminate its quality, which may be natural and anthropogenic activities (Abtahi et al. 2015; Grimmeisen et al. 2017; Gao et al. 2020; Gani et al. 2023a; Raheja et al. 2024a).
In the recent past, numerous studies have estimated groundwater quality, including factors affecting water quality and anthropogenic sources of contamination, spread over many parts of India (Ram et al. 2021; Khan et al. 2022; Mahammad et al. 2022). Recently, water quality indices (WQIs) have been proposed and used to understand groundwater quality status around the globe. The application of entropy theory to assess water quality has proven to be more accurate than other indexing methods (Li et al. 2016; Kumar et al. 2022; Raheja et al. 2022a; Gani et al. 2023b, 2024a; Sharma et al. 2024). Zhang et al. (2021a) calculated the quality criteria for groundwater using the integrated-weight water quality index (IWQI) in the Jiaokou district, China and observed the effect of anthropogenic activities on groundwater quality. Salem et al. (2023) assess groundwater quality for irrigation in the Zeroud Basin, Kairouan Plain, Tunisia using extensive physicochemical examinations revealing the dominance of Na+, Ca2+, Mg2+, , , and Cl− ions. Raheja et al. (2022a) studied groundwater quality by using the entropy water quality index (EWQI) and WQI using data from various sites in Haryana (India) and found that the EWQI-based water quality prediction techniques work well in studying the suitability of groundwater for drinking. Shyam et al. (2022) evaluate the groundwater quality in Mewat district of Haryana for drinking and irrigation uses. They found that the majority of groundwater in the study region presents a high risk of salinization. Another study by Krishan et al. (2022) assessed the groundwater salinity status in the southwest zone of Punjab, India, and recommended salinity in groundwater arises from the combination of evaporation enrichment and salt dissolution. Matta et al. (2022) studied the Ganga River's water quality using the WQI and an overall index of pollution (OIP). Hosseini-Moghari et al. (2015) studied the groundwater quality in Saveh Plain, Iran. Approximately 35% of groundwater sample wells were not suitable for drinking purposes in their study. Adimalla & Venkatayogi (2018) have estimated groundwater quality in Telangana state, India, and concluded from the work that anthropogenic activities and geochemical processes usually control groundwater quality. These earlier studies specified that groundwater quality variation depends on various factors such as anthropogenic activities, geochemical factors, and dissolution processes (Maurya et al. 2023; Gani et al. 2024b; Hussain et al. 2024; Raheja et al. 2024b, 2024c). Another study conducted by Sharma et al. (2021), measured drinking water quality by using heavy metals assessments. Maurya et al. (2021) assessed the WQI of the upper Ganges River near Haridwar, Uttarakhand, correlating it with changing land use to infer its logical implications. Zhang et al. (2021b) calculated the quality criteria for groundwater using the IWQI in the Jiaokou district, China, and observed the effect of anthropogenic activities on groundwater quality. They found that the EWQI-based water quality prediction techniques work well in studying the suitability of groundwater for drinking purposes. Maurya et al. (2021) studied the Ganga River's water quality using WQI whereas Subba Rao (2012) assessed groundwater quality by estimating the pollution index of groundwater (PIG) of the Varaha River Basin in (Andhra Pradesh) India. Hamlat et al. (2017) investigated the use of 10 different WQIs in the Tafna basin and reported that the Canadian Council of Ministers of the Environment (CCME) WQI and British Columbia (BC) WQI were the suitable indices. Narsimha & Sudarshan (2018) have estimated groundwater quality in Telangana state, India, and concluded that anthropogenic activities and geochemical processes usually control groundwater quality. However, while evaluating the comprehensive chemical quality of groundwater, Krishan et al. (2023) employed an integrated approach to studying groundwater quality for drinking through hydrochemistry and WQI in Mewat district, northern India. These earlier studies specified that groundwater quality variation depends on several factors such as anthropogenic activities, geochemical factors, and dissolution processes.
Many researchers have also applied the geographic information system (GIS)-based spatial interpolation technique to monitor groundwater quality by using the inverse distance weighted (IDW) interpolation. Yadav et al. (2018) assess the groundwater quality for drinking purposes in Agra City, India by using a geo-informatic approach. Maurya et al. (2023) evaluate groundwater quality and its suitability for drinking and irrigation, analyzing major ions, and heavy metals, and employing GIS for spatial mapping in Jaunpur district, Uttar Pradesh, India. Results indicate elevated levels of EC, TDS, and TH, suggesting unsuitability for drinking and irrigation. GIS is concluded to be a cost-effective and time-efficient technique for transforming big data sets to create different spatial distribution maps (Harichandan et al. 2021; Masood et al. 2022; Raheja et al. 2022b, 2024d).
The vulnerability of groundwater refers to the ease with which contaminants can reach a specific position in the groundwater system after they are introduced at a different location above the uppermost aquifer (Foster et al. 2002). The vulnerability of groundwater is influenced by a variety of factors, including the characteristics of the aquifer, the soil and rock types that cover the aquifer, the depth and thickness of the soil and rock layers, the topography of the land surface, and the types and amounts of contaminants that are introduced into the system. So, it is important to understand the groundwater vulnerability to protect this valued resource from contamination and ensure its long-term sustainability. There are many techniques for determining a groundwater vulnerability, but the overlay and index methods are among the most efficient and straightforward (Akinlalu et al. 2021; Pathak et al. 2021; Patel et al. 2023). There are numerous overlay and index methods, but the most often used and well-known ones include DRASTIC (Aller & Thornhill 1987), GOD (Foster 1987), AVI (Van Stempvoort et al. 1993), and PI (Goldscheider et al. 2000). The GOD method performs effectively in locations where groundwater quality is somewhat too good and where groundwater pollution is more strongly influenced by the depth of the water table and the aquifer.
Groundwater is a dependable source for safe drinking purposes and is more widely used than other water sources. Recently studied that half the urban area population of India depends on groundwater only. A literature review has shown that very limited work has been carried out in the Kurukshetra district, where mainly groundwater is used for drinking purposes. Further, the study area is already in the dark zone, and the water table has been falling at an alarming rate for the last many years (CGWB 2013). Because of the above reasons, Kurukshetra district, Haryana state in India, has been selected as a study area to assess groundwater quality and its vulnerability. The main objectives of this study are (1) to calculate the hydrochemical properties of the groundwater (2) to study the hydrochemistry of ions and generate a spatial distribution map using ArcGIS (3) to determine the groundwater quality for drinking purposes by using the EWQI and PIG (4) to study the groundwater vulnerability by using GOD (groundwater occurrence and depth) method.
MATERIAL AND METHODS
Study area and description
Sample collection and analysis
Groundwater sampling in Kurukshetra district, Haryana, was collected during February 2021 from where 19 samples were taken from the tube wells. These samples were collected in 1-L High Density Polyethylene (HDPE) bottles, which were rinsed three to five times with groundwater at the sampling site to avoid any outside impurities. Samples of groundwater were collected only after allowing of pumping the water for about 5–10 min from each tube well and these samples were stored in an icebox at 4 °C before doing analysis. The sample analyses in the laboratory were carried out as per the American Public Health Association (APHA 2012). The pH, EC, and TDS were measured at each sampling site. Volumetric titration with Ethylenediaminetetraacetic acid (EDTA) was used to measure the Ca2+ and Mg2+ ions. Apparatuses, namely flame photometer, and spectrophotometer were used to determine the concentration of Na+, K+, , , and F− ions. Volumetric titration with H2SO4 solution was carried out to determine the concentration.
Entropy water quality index
EWQI is calculated to estimate groundwater quality for safe drinking (Amiri et al. 2014; Raheja et al. 2022a). The steps involved in the calculation of EWQI are as given in Table 1.
Step used . | Formula . |
---|---|
Normalization of initial matrix and standard matrix | |
Ratio of the index value | |
Information entropy | |
Entropy weight | |
Quality rating scale | |
Finally, |
Step used . | Formula . |
---|---|
Normalization of initial matrix and standard matrix | |
Ratio of the index value | |
Information entropy | |
Entropy weight | |
Quality rating scale | |
Finally, |
Kumar & Augustine (2022) described the EWQI into five categories as listed in Table 1. If the value of EWQI is less than 50, then excellent groundwater quality. If EWQI values lie between 50 and 100, then good. If EWQI values lie between 100 and 200, then poor groundwater quality. If EWQI values lie between 200 and 300, then very poor, and if the value of EWQI > 300, groundwater is not useful for drinking.
Pollution Index of Groundwater
The PIG has been used to evaluate groundwater quality for drinking purposes (Subba Rao 2012). These five steps are explained in the following paragraphs:
Step 1, Calculation of relative weight (RW). The RW is varied from 1 to 5 and assigned to each chemical parameter, depending upon the level of importance of groundwater for drinking purposes, as shown in Table 2.
Sr. No. . | Name of parameters . | Relative weight () . | Weight parameter . | WHO (2011) standards . |
---|---|---|---|---|
1 | pH | 4 | 0.095 | 7.5 |
2 | TDS | 4 | 0.095 | 500 |
3 | TH | 4 | 0.095 | 200 |
4 | Ca2+ | 2 | 0.048 | 75 |
5 | Mg2+ | 2 | 0.048 | 30 |
6 | Na+ | 4 | 0.095 | 200 |
7 | K+ | 1 | 0.024 | 12 |
8 | 1 | 0.024 | 300 | |
9 | Cl− | 5 | 0.119 | 250 |
10 | 5 | 0.119 | 200 | |
11 | 5 | 0.119 | 50 | |
12 | F− | 5 | 0.119 | 1.5 |
= 42 |
Sr. No. . | Name of parameters . | Relative weight () . | Weight parameter . | WHO (2011) standards . |
---|---|---|---|---|
1 | pH | 4 | 0.095 | 7.5 |
2 | TDS | 4 | 0.095 | 500 |
3 | TH | 4 | 0.095 | 200 |
4 | Ca2+ | 2 | 0.048 | 75 |
5 | Mg2+ | 2 | 0.048 | 30 |
6 | Na+ | 4 | 0.095 | 200 |
7 | K+ | 1 | 0.024 | 12 |
8 | 1 | 0.024 | 300 | |
9 | Cl− | 5 | 0.119 | 250 |
10 | 5 | 0.119 | 200 | |
11 | 5 | 0.119 | 50 | |
12 | F− | 5 | 0.119 | 1.5 |
= 42 |
The PIG value can be used to classify into five categories provided in Table 3 (Subba Rao 2012).
Sr. No. . | PIG classification . | Type of pollution . |
---|---|---|
1 | <1.0 | Insignificant pollution |
2 | 1.0–1.5 | Low pollution |
3 | 1.5–2.0 | Moderate pollution |
4 | 2.0–2.5 | High pollution |
5 | >2.5 | Very high pollution |
Sr. No. . | PIG classification . | Type of pollution . |
---|---|---|
1 | <1.0 | Insignificant pollution |
2 | 1.0–1.5 | Low pollution |
3 | 1.5–2.0 | Moderate pollution |
4 | 2.0–2.5 | High pollution |
5 | >2.5 | Very high pollution |
GOD method
Layer . | Type . | Weight . |
---|---|---|
Groundwater confinement (G) | Confined aquifer | 0.2 |
Semi-confined aquifer | 0.4 | |
Unconfined aquifer | 0.6 | |
Overlaying strata (O) | Basalt | 1.0 |
Karst limestone | 0.9 | |
Massive sandstone | 0.8 | |
Sand and gravel | 0.7 | |
Groundwater depth (D) | 0–2.0 m | 1.0 |
2.0–5.0 m | 0.9 | |
5.0–20.0 m | 0.8 | |
20.0–50.0 m | 0.7 | |
> 50 m | 0.6 |
Layer . | Type . | Weight . |
---|---|---|
Groundwater confinement (G) | Confined aquifer | 0.2 |
Semi-confined aquifer | 0.4 | |
Unconfined aquifer | 0.6 | |
Overlaying strata (O) | Basalt | 1.0 |
Karst limestone | 0.9 | |
Massive sandstone | 0.8 | |
Sand and gravel | 0.7 | |
Groundwater depth (D) | 0–2.0 m | 1.0 |
2.0–5.0 m | 0.9 | |
5.0–20.0 m | 0.8 | |
20.0–50.0 m | 0.7 | |
> 50 m | 0.6 |
If the value of the GOD index is between 0 and 0.1, then it falls in the negligible vulnerability class. If GOD index values lie between 0.1 and 0.3, then low. If GOD index values lie between 0.3 and 0.5, then moderate groundwater vulnerability. If GOD index values lie between 0.5 and 0.7, then high, and if the value of the GOD index lies between 0.7 and 1.0, groundwater vulnerability is very high, and very high groundwater vulnerability means that the groundwater in a specific region is higher risk of contamination or depletion (Ghazavi & Ebrahimi 2015; Patel et al. 2023).
Statistical analysis
The statistical parameters including mean, range, maximum, and minimum values for various parameters were computed using Microsoft Excel software. Spatial distribution maps of groundwater quality parameters were generated using ArcGIS software, which analyses geographic information to create layered and spatial maps. The spatial modeling utilized an IDW interpolation technique, calculating values for each grid node based on nearby data points within a user-defined search radius. All data points were incorporated in the interpolation process, with node values determined by averaging the weighted sum of all points (Yadav et al. 2018).
Quality assurance and quality control
RESULTS AND DISCUSSION
General characteristics of major ions
Sr. No. . | Name of parameter . | Minimum value . | Maximum value . | Mean value . | WHO (2011) . | Remarks . | |
---|---|---|---|---|---|---|---|
Desirable limit (DL) . | Maximum allowable limit (MAL) . | ||||||
1 | pH | 7.85 | 8.96 | 8.48 | 6.5 | 8.5 | Taste effects |
2 | TDS | 403 | 1,400 | 713.53 | 500 | 1,500 | Bad taste, leather formation reduced |
3 | TH | 177 | 276 | 235.80 | 200 | 600 | Stomach disorder and kidney issue |
4 | Ca2+ | 10 | 45 | 28.52 | 75 | 200 | Defective teeth, kidney stones, and colorectal cancer |
5 | Mg2+ | 14 | 45 | 25.79 | 50 | 150 | Laxative effect |
6 | Na+ | 34 | 187 | 83.74 | a | 200 | Unpleasant taste |
7 | K+ | 3.4 | 14.5 | 7.81 | a | 12 | Laxative effect |
8 | 155 | 745 | 294.75 | 300 | a | Laxative effect | |
9 | 1.5 | 22.6 | 6.08 | 10 | 45 | Methemoglobinemia or blue-baby disease | |
10 | Cl− | 12 | 75 | 37 | 250 | 600 | Heart or kidney problems |
11 | 10 | 65 | 37.21 | 200 | 400 | Laxative effect | |
12 | F− | 0.35 | 0.98 | 0.61 | 1.0 | 1.5 | Teeth problems |
Sr. No. . | Name of parameter . | Minimum value . | Maximum value . | Mean value . | WHO (2011) . | Remarks . | |
---|---|---|---|---|---|---|---|
Desirable limit (DL) . | Maximum allowable limit (MAL) . | ||||||
1 | pH | 7.85 | 8.96 | 8.48 | 6.5 | 8.5 | Taste effects |
2 | TDS | 403 | 1,400 | 713.53 | 500 | 1,500 | Bad taste, leather formation reduced |
3 | TH | 177 | 276 | 235.80 | 200 | 600 | Stomach disorder and kidney issue |
4 | Ca2+ | 10 | 45 | 28.52 | 75 | 200 | Defective teeth, kidney stones, and colorectal cancer |
5 | Mg2+ | 14 | 45 | 25.79 | 50 | 150 | Laxative effect |
6 | Na+ | 34 | 187 | 83.74 | a | 200 | Unpleasant taste |
7 | K+ | 3.4 | 14.5 | 7.81 | a | 12 | Laxative effect |
8 | 155 | 745 | 294.75 | 300 | a | Laxative effect | |
9 | 1.5 | 22.6 | 6.08 | 10 | 45 | Methemoglobinemia or blue-baby disease | |
10 | Cl− | 12 | 75 | 37 | 250 | 600 | Heart or kidney problems |
11 | 10 | 65 | 37.21 | 200 | 400 | Laxative effect | |
12 | F− | 0.35 | 0.98 | 0.61 | 1.0 | 1.5 | Teeth problems |
aImplies, not available.
Correlation of major ions
Assessment of groundwater quality based on EWQI and PIG
Groundwater vulnerability by GOD method
Implementing the findings of this groundwater quality study at the national level involves integrating its data and methodologies into larger water resource management strategies. National governments can use GIS-based maps to identify areas, such as the southern part of Kurukshetra district, where hydrochemical parameters indicate higher concentrations of pollutants. By targeting these specific areas, authorities can prioritize and allocate resources more effectively for remediation efforts and preventive measures. The study's findings on the relationship between TDS and TH help in establishing national drinking water standards and ensuring that groundwater meets these criteria. Additionally, public health campaigns can be launched to educate communities about the significance of water quality, encouraging practices that prevent contamination and promote sustainable groundwater use. The national implementation of such studies also entails the development of infrastructure for regular monitoring and maintenance of groundwater resources, ensuring that water quality remains within safe limits for human consumption. At the global level, the insights from this study can contribute to international efforts aimed at improving groundwater quality and reducing vulnerability. Organizations such as the WHO and the United Nations can use these findings to update global water quality guidelines and recommend best practices for groundwater management. The study's methodology, including the use of the EWQI and PIG classifications, can be standardized and promoted as a model for other regions facing similar challenges. Additionally, global funding mechanisms can support projects in vulnerable areas identified by such studies, helping to build infrastructure and capacity for better groundwater management. Transboundary water management agreements can benefit from the vulnerability assessments provided by methods like GOD, ensuring that regions with moderate vulnerability, such as the southern portion of the Kurukshetra district, receive adequate protection and that sustainable usage practices are enforced. This global implementation fosters a cooperative approach to safeguarding groundwater resources, essential for addressing the broader impacts of climate change and population growth on water security.
CONCLUSIONS
This study focuses on assessing groundwater quality for safe drinking purposes in the Kurukshetra district. Based on that several conclusions may be drawn as follows:
1. The groundwater in the study area is found to be alkaline in nature. Eighty-six percent of groundwater samples have a total dissolved solid (TDS) value of less than 1,000 mg/L, and 21% of groundwater samples collected from different locations exceeded the desirable limit of TH (WHO 2011). The relation between TDS and TH shows that 79% of samples fall under the very hard freshwater category.
2. GIS-based spatial distribution of hydrochemical parameters shows higher concentrations in the southern part of the study area. Moreover, the correlation of major ions indicates that the hydrogeochemical composition is dominated by silicate weathering and dolomite dissolution.
3. The EWQI indicates that 94.73% of groundwater samples have exhibited good water quality. The spatial distribution of EWQI has a higher concentration in the southern-western part of the study area.
4. The value of the PIG indicates that all groundwater samples collected from the study area have insignificant pollution (PIG < 1.0). Both classifications (EWQI and PIG) specify that the groundwater of the Kurukshetra district is good for human consumption.
5. Based on the GOD method for groundwater vulnerability, indicates that mostly southern portion of the study area has moderate vulnerability, and shows that groundwater level is the important parameter for the assessment of groundwater vulnerability.
The study concludes that assessing groundwater quality is beneficial for making informed decisions regarding the sustainable exploitation and management of groundwater resources.
ACKNOWLEDGEMENTS
The first author (Hemant Raheja) is thankful to the Ministry of Education, Government of India for providing the scholarship to do this research. The authors convey their thanks to the National Institute of Technology, Kurukshetra, for giving several research facilities.
FUNDING
This work was financially supported by the Ministry of Education, Government of India (Grant No. 2K19/NITK/PHD/61900011-Hemant Raheja).
AUTHOR CONTRIBUTIONS
H. R. developed the methodology, collected the samples, conceptualized the whole article, wrote the original manuscript, and arranged the software. A. G. reviewed the article, edited the manuscript, and rendered support in language embellishment. M. P. reviewed the article, edited the manuscript, arranged the software, and supervised the work.
DATA AVAILABILITY STATEMENT
All relevant data are included in the paper or its Supplementary Information.
CONFLICT OF INTEREST
The authors declare there is no conflict.