Groundwater Quality and Potential Health Risk Assessment for Potable Use

Abstract

The Ramganga River basin, comprising three rivers, the Dhela, Dhandi, and Ramganga, plays a vital role in groundwater recharge, sustaining numerous industries, urban areas, and rural communities reliant on these rivers for daily activities. The study’s primary purpose was to analyze the groundwater quality in the context of potability, irrigation, and health risks to the local inhabitants of the Ramganga River basin. In 2021–2022, 52 samples (26 × 2) were collected from 13 locations in two different seasons, i.e., pre-monsoon and post-monsoon, and 20 physico-chemical and heavy metal and metalloids were analyzed using the standard protocols. The result shows that heavy metal and metalloids and metalloid concentrations of Zn (0.309–1.787 and 0.613–1.633); Fe (0.290–0.965 and 0.253–1.720), Cd (0.001–0.002 and 0.001–0.002); As (0.001–0.002 and 0.001–0.002), Cr (0.009–0.027 and 0.011–0.029), and Pb (−0.001–0.010 and 0.00–0.010) values in mg/L are present in both seasons. The groundwater quality index (GWQI), heavy metal pollution Index (HPI), and heavy metal evaluation index (HEI) were used to assess the water quality and metal pollution in the basin area. As per GWQI values, water quality lies from excellent water quality (41.639 and 43.091) to good water quality (56.326 and 53.902); as per HPI values, it shows good (29.51 and 30.03) to poor quality (60.26 and 59.75) and HEI values show the low-level contamination (1.03–2.57 and 1.13–3.37) of heavy metal and metalloids in both seasons. According to the potential health risk assessment, infants show low risk in pre-monsoon and low risk to medium post-monsoon, while children and adults show low risk to high risk in both seasons. From the health risk perspective, it shows that children and adults have more concerns about non-carcinogenic effects, so adequate remedial measures and treatment are required to avoid the groundwater quality of the Ramganga River basin.

1. Introduction

The Outer Himalaya, also known as the Shiwalik Himalaya, embody a mountain range that spans a significant portion of Nepal, Tibet, Bhutan, India, and Pakistan; due to its relatively lower elevation, this range comprehends the Greater Himalaya and the Lesser Himalaya [1]. At the same time, the Shiwalik Himalaya embrace great ecological and geological significance, as they are recognized for their exceptional biodiversity and natural resources and act as the origin of several major rivers that supply water to millions of people in the Indian subcontinent by [2]. Moreover, the Shiwalik Himalaya act as a congenital buffer, safeguarding the plains of the Indian subcontinent from the rigorous impact of the higher Himalayan ranges. Additionally, they play a decisive role in regulating river flow, vital for agricultural practices, hydropower generation, and overall livelihoods in the region [3].

Uttarakhand, located in the northern region of India, possesses an abundance of rivers and streams, a substantial proportion of which are tributaries of the formidable Ganga River [4,5]. These watercourses, along with the Ganges River itself, form essential elements of Uttarakhand’s ecosystem. They function as habitats for various plant and animal life forms, facilitate agricultural pursuits, and supply freshwater to human populations. For centuries, the Ganges River and its tributaries in Uttarakhand have remained inseparable from the cultural and religious heritage of the region [6]. The Ganga River in Uttarakhand is nourished by several tributaries, enhancing its magnificence and importance in the area. Notable tributaries include the Yamuna River, Bhagirathi River, Mandakini River, Alaknanda River, and Pindar River [6]. One of the prominent tributaries of the Ganga River in Uttarakhand is the Ramganga River. The Ramganga River not only embellishes the landscape of Uttarakhand but also assumes a vital role in the state’s ecosystem, providing sustenance for diverse flora and fauna along its course [7]. Uttarakhand, renowned for its scenic beauty and pilgrimage sites, is likewise witnessing industrial growth in certain areas. Although specific industrial hubs are not mentioned in the provided text excerpts, traditionally, regions such as Haridwar, Rudrapur, and Pantnagar have been acknowledged for their industrial activities [8]. These zones commonly attract various industries, including pharmaceuticals, automotive, food processing, and electronics, owing to their strategic locations and the incentives offered by the state government to foster industrial expansion [9]. Over the past decade, the area surrounding the Ramganga River has undergone significant industrial development, transforming it into an industrial hub. Consequently, untreated and contaminated wastewater from these industries is discharged into the river, leading to the degradation of groundwater quality in the surrounding areas [1,10]. Groundwater pollution in the region is now a significant concern due to the potential health risks of consuming contaminated water. The impact of industrial discharge on the Ramganga River basin and groundwater quality is a complex issue that requires comprehensive analysis and understanding. Industrial activities contaminate river water and have far-reaching consequences for groundwater. Uncontrolled discharge of untreated and contaminated wastewater from industries poses a significant threat to the overall ecosystem and the health of local communities [11]. Despite the Shivalik Himalaya and the Ramganga river’ recognized importance in sustaining regional ecosystems and supporting human livelihoods, the rapid industrialization in the Kashipur region of Uttarakhand has raised concerns about the pollution of water sources, particularly groundwater. Many research studies have been conducted on the river water contamination in this area [12,13,14]. Still, there are not many systematic investigations on the detailed nature of the pollutants, their origin, and the health risk implications of this industrial process on the groundwater. This research aims to address this knowledge gap by undertaking a comprehensive assessment of the groundwater pollution status of the region; the nature and composition of pollutants and their effects on the environment and people’s well-being. This study therefore fills a research gap by combining aspects of spatial research, community mobilization, and quantitative health risk assessment to give a comprehensive solution for sustainable management of the problem. To fully comprehend the magnitude of this issue, it is essential to investigate the specific pollutants being released into the environment, their pathways, and their long-term effects on surface water and groundwater. Furthermore, thoroughly examining the potential health risks associated with consuming contaminated water from these sources is crucial for developing mitigation strategies and safeguarding the local population’s well-being [15]. In addition, analyzing industrial processes and identifying specific sources of pollution can provide valuable insights for implementing effective regulatory measures and promoting sustainable industrial practices. This comprehensive exploration will shed light on the multifaceted nature of the problem and contribute to the development of holistic solutions that prioritize both environmental protection and public health. A meticulous analysis will allow for a better understanding of the current state of the groundwater in the vicinity of the Ramganga River basin, thus enabling informed decisions concerning its sustainable management and protection. Hence, the chief goal of our research is to approach the pollution level of the Ramganga River basin and merge it with assessing the health risks that can arise from industrial and agricultural activities. A thorough investigation of the water quality parameters was adopted to find possible toxicants/pollutants in the area’s water sources and determine their effects. It can either be on the environment or human health. The study will also involve mapping the distribution of irrigation practices in the basin and understanding the prevalent agricultural activities. Through a multidisciplinary strategy incorporating spatial mapping and the inclusion of community voices, we strive to obtain a comprehensive knowledge of which factors impact water quality and the potential health consequences associated with these factors. Communicating with the community members and the planters can create an incomparable avenue to pick their views and opinions concerning irrigation strategies and water quality issues, among other contested topics.

2. Materials and Methods

2.1. Description of Study Area

The Ramganga River, a key Ganges tributary, emerges in the Kumaon Province of Uttarakhand, India. It flows around 600 km through a diversified topography before joining the Ganges near Kannauj, Uttar Pradesh. The Ramganga River basin supports a diverse range of ecosystems, extending from the snow-capped heights of the Mountains to the lush floodplains of the Ganges Valley. It is an essential supply of groundwater for the entire area. The river is fed by ice melting in the spring, which keeps it flowing all year. This makes it an essential source of irrigation, sustaining agriculture in the lush plains downstream. The river also plays a critical role in replenishing groundwater aquifers, a primary drinking water supply for many communities. The Ramganga basin consists of two major rivers, the Dhela River and Dhandi River, which originate from the Shivalik foothills and merge in the Kashipur block of Udham Singh Nagar, Uttarakhand. After merging at a point, the Dhela River travels downward and merges downstream of the Ramganga River near Moradabad. Kashipur is a newly developed industrial hub in Uttarakhand. This city has seen immense urbanization and industrialization in the last few decades. These industries discharge the treated and sometimes untreated water into the rivers, affecting the adjacent areas’ aquatic ecosystem and groundwater quality [1,16].

2.2. Geology and Aquifers

The Ramganga basin consists of various lithological features, mainly lesser Himalaya with bed rocks. The upper part comprises crystalline rocks such as schists, gneisses, and quartzites, while the lower part consists of Holocene and Precambrian age alluvium. The subsurface lithology entails clay with silt and sand on the surface while underlaid by sand and gravel [17,18]. The aquifers of the Ramganga basin are categorized into the northern upper part, i.e., Himalayan Mountain belt, and the southern lower part covering Gangetic alluvial aquifers. According to the central groundwater board, the northern part consists of hard rock aquifers where groundwater availability is limited with isolated aquifers. The southern part holds potential groundwater because of the Bhabar and Tarai region. Based on the lithological features, the southern part consists of two aquifer groups, i.e., Aquifer-I and Aquifer-II. Aquifer-I is shallow aquifer found in unconfined to semiconfined condition. The groundwater was up to 50 mbgl (meters below ground level) here. Meanwhile, Aquifer-II is thick, and typical clay layers were found in confined aquifers. The thickness of confined aquifers is 110–260 mbgl (meters below ground level), with an average thickness of 50–60 m [17,18,19].

2.3. Site Selection

The study area has been exploited by pollution and exploitation of natural resources, fast-expanding urbanization, and industrial development. The specific sampling locations selected in the Ramganga River basin were established following certain parameters such as the proximity of industrial areas, urban and agricultural areas where the quality of groundwater will be differently affected. For instance, the sampling sites around Kashipur were selected for the reason that this location is among the most rapidly industrializing zones in the state, especially in the regions of pharmaceuticals, food manufacturing, and manufacturing industries [13,14]. These industries also discharge both treated and untreated wastewater from nearby water sources, hence the groundwater pollution caused by these heavy metal and metalloids and other pollutants. The other sampling sites were chosen because of their proximity to cultivated fields to determine the effects of agrochemicals including the fertilizers and pesticides on the groundwater quality. Some of the widespread and dominant agricultural practices in these regions include applying a lot of chemical inputs into the field, which are well documented to find their way into the aquifer, thus increasing the levels of some contaminants such as nitrates and phosphates. This study examined groundwater contamination levels in the Ramganga River basin.

The analysis of groundwater sampling was conducted in this area due to the high reliance on groundwater for daily necessities such as drinking and irrigation. Many enterprises rely on groundwater to prepare their valued products. Groundwater quality suffers due to increased use and leaching of industrial wastewater. This study aimed to assess the groundwater quality in the Ramganga River basin. Samples were collected from the vicinity of three rivers: Dhela, Dhandi, and downstream Ramganga. Additionally, samples were obtained from areas adjacent to industries, urban centers, rural regions, and agricultural fields. The sampling locations in the Rampur Ganga basin are described in

Table 1. Geo-coordinates of selected sampling location of the Rampur Ganga basin.

Sampling Code	Sampling Locations	Site Description	Lat	Long	Source Type	Depth of Source Type
GW1	Thana Sabiq, Kashipur	Urban and Industrial	29.219	78.948	Handpump	25–35 m
GW2	Global Institute of Pharmaceutical Education and Research	Urban and Industrial	29.239	78.897	Borewell	25–35 m
GW3	Shree Collage of Nurshing	Urban and Industrial	29.196	78.889	Borewell	25–35 m
GW4	Kavinagar	Urban and Industrial	29.202	78.951	Borewell	25–35 m
GW5	Dharak Nagla	Rural and Agriculture Area	29.971	78.821	Borewell	25–35 m
GW6	Barahi-lalpur	Rural and Agriculture Area	29.060	78.883	Borewell	25–35 m
GW7	Ahmadpur	Rural and Agriculture Area	28.962	78.809	Handpump	25–35 m
GW8	Bhojpur	Rural and Agriculture Area	28.954	78.824	Borewell	25–35 m
GW9	Kothiwal Dental Hospital	Urban	28.896	78.728	Borewell	25–35 m
GW10	MDA colony MBD	Urban	28.864	78.755	Borewell	25–35 m
GW11	Hayatnagar MBD	Urban	28.825	78.792	Handpump	25–35 m
GW12	Pital Nagri MBD	Urban	28.820	78.797	Handpump	25–35 m
GW13	Old Shiv Mandir, Devapur—mustakam	Urban	28.797	78.805	Handpump	25–35 m

and depicted in Figure 1.

2.4. Sample Collection and Water Quality Analyses

During the study period, 52 (26 × 2) groundwater samples were collected in two seasons, namely pre-monsoon and post-monsoon, from 13 locations in 2021–2022. The grab sampling method was employed to collect water in polyvinyl chloride (PVC) bottles from designated sample points at depths ranging from 25 to 50 m. The sampling depth of 25–50 m below ground level has been selected as it covers the shallow aquifers which are more vulnerable to pollution from human activities and the area mainly consists of aquifer of this height only [17,18]. The tube wells, borewell, and other groundwater sources have only 25–50 m depth. Water samples were collected in PVC containers rinsed with a 5% nitric acid solution for heavy metal and metalloids analysis. Standard protocols outlined by [20,21] were followed to determine 20 water quality parameters, including pH, Total Dissolved Solids (TDSs), Total Hardness (TH), Alkalinity (A), Acidity (AC), Bicarbonates (HCO₃⁻), Nitrate (NO₃⁻), Phosphate (PO₄³⁻), Sulphate (SO₄²⁻), Chloride (Cl⁻), Calcium (Ca²⁺), Magnesium (Mg²⁺), Sodium (Na⁺), Potassium (K⁺), and heavy metal and metalloids such as Arsenic (As), Zinc (Zn), Lead (Pb), Iron (Fe), Cadmium (Cd), and Chromium (Cr), respectively. All parameters were measured in mg/L, except pH. A digital TOSHCON meter (TMULTI 27) measured pH and TDS. Total hardness (TH), Calcium (Ca²⁺), and Magnesium (Mg²⁺) were determined using the EDTA technique, while Alkalinity (A) and Bicarbonates (HCO₃⁻) were analyzed using the potentiometric titration method, Acidity (AC) by titration method, and Chloride (Cl⁻) was measured employing iodometric method. Sodium (Na⁺) and Potassium (K⁺) were assessed using a flame photometer (Model No. ESICO-1361), and Nitrate (NO₃⁻), Phosphate (PO₄³⁻), and Sulphate (SO₄⁻) were measured using a spectrophotometer (Model No. Systronics Visiscan 167). Heavy metal and metalloids were analyzed using atomic absorption spectrophotometry (AAS; Model No. Model No. AAS 4129 ECIL India). The detection limits of AAS for heavy metal and metalloids, namely As, Fe, Zn, Pb, Cd, and Cr, are 0.02, 0.005, 0.05, 0.002, and 0.02, respectively. The physico-chemical and heavy metal and metalloids were premeditated by using a standard procedure developed by [20,22,23]. The entire analysis of water quality parameters was conducted in replicates, and standard protocols and instruments were employed to minimize data errors.

2.5. Statistical and Mapping Analysis

The results of this study are presented as minimum, maximum, and averages with standard deviations to minimize ambiguity for both seasons. The collection was conducted using Microsoft Excel 2021 (Microsoft Corp., Redmond, WA, USA). To compare the data, we computed the Groundwater Quality Index (GWQI), Heavy metal Pollution Index (HPI), and Heavy metal Evaluation Index (HEI). Furthermore, we determined Irrigation Water Quality Parameters such as SAR and % Na. The HI was also calculated to establish possible health risks associated with consuming groundwater. For better understanding, the obtained data were mapped using QGIS software 3.34. The study area groundwater physicochemical properties were mapped through an inverse distance-weighted (IDW) interpolation approach [4]. This method is the most commonly used for interpolating unknown values from adjacent computed locations. The outcomes of the spatial variation map provided by IDW were established by overlapping the results attained by laboratory investigation. It is an effective and quite simple technique as it estimates the unknown value by using the data of close neighbors with considerably more weightage. However, it can also be highly sensitive to distribution that is not balanced or data that have been unevenly distributed.

2.6. Groundwater Quality Index (GWQI)

The Groundwater Quality Index (GWQI) serves as a numerical indicator assessing the overall state of groundwater, drawing from specific physico-chemical parameters [5,20]. GWQI aids in efficiently examining large datasets of water quality parameters, simplifying complex data and identifying trends across time and space [20]. The technique is selected for its capability of encompassing a plurality of water quality indicators into a single rating. Its use is especially appropriate for giving a general perspective of the situation of groundwater pollution in the studied region which may require a more detailed investigation or management. Over the past few decades, numerous scientists have developed Water Quality Index (WQI) methods to evaluate water quality status. This study utilized a modified GWQI. Parameters such as pH, Total Dissolved Solids (TDSs), Total Hardness (TH), Bicarbonates (HCO₃⁻), Nitrate (NO₃⁻), Phosphate (PO₄³⁻), Sulphate (SO₄²⁻), Chloride (Cl⁻), Calcium (Ca²⁺), Magnesium (Mg²⁺), Sodium (Na⁺), and Potassium (K⁺) were analyzed. Weightage was assigned to each parameter on a scale from 1 to 5 based on its significance. The collected water quality data were compared against standards provided by the Bureau of Indian Standards [24,25]. GWQI enables comparisons between different locations or periods by standardizing water quality data. The calculation formula and quality status are described in

Table 2. Calculation formula and status of water quality based on GWQI, HPI, HEI, SAR, % Na, PI, and Health Risk Index.

Indexing	Calculation Formula	Values	Status	References
Groundwater Quality Index (GWQI)	$\mathrm{Equation} (1): Q_i={\displaystyle \frac{C_i}{S_i}}\times 100$ Equation (2): $WQI=\sum _{i=0}^n{W}_{i }\times Q_i$ where Ci = Observed value, Si = Standard value, Wi = Relative weight, Qi = Sub-index value,	<50	Excellent water	[6,15]
		50–100	Good water
		100–200	Poor water
		200–300	Very poor water
		>300	Water unsuitable for drinking
Heavy metal Pollution Index (HPI)	$\mathrm{Equation} (1): Q_i={\displaystyle \frac{C_{i }-I}{S_i-I}}\times 100$ Equation (2): HPI $=\sum _{i=0}^n{\displaystyle \frac{W_i\times Q_i}{W_i}}$ where Ci = Observed value, Si = Standard value, Wi = Relative weight, Qi = Sub-index value,	0–25	Excellent	[8,26,27]
		26–50	Good
		51–75	Poor
		>75	Very Poor
Heavy metal Evaluation Index (HEI)	HEI $=\sum _{i=0}^n{\displaystyle \frac{C_i}{S_i}}$ where Ci = Observed value, Si = Standard value,	<40	Low-Level Contamination
		40–80	Medium-Level Contamination
		>80	High-Level Contamination
Sodium Adsorption Ratio (SAR)	$SAR={\displaystyle \frac{{Na}^{+}}{\sqrt{\frac{{Ca}^{2+}+{Mg}^{2+}}{2}}}}$	<10	Excellent	[15,27]
		10–18	Good
		18–26	Doubtful
		>26	Unsuitable
Sodium percentage	$\%Na={\displaystyle \frac{{Na}^{+}+K^{+}}{{Ca}^{2+}+{Mg}^{2+}+{Na}^{+}+K^{+}}}$	<20	Excellent	[15,27]
		20–40	Good
		40–60	Permissible
		60–80	Doubtful
		>80	Unsuitable
Health Risk Index	Equation (1): ${ADD}_{oral}$$={\displaystyle \frac{C_w\times IR\times EF\times ED}{BW\times AT}}$ Equation (2): HQ = ${\displaystyle \frac{ADD}{RfD}}$ Equation (3): HI = $\sum {HQ}_s$ where ADDoral = Average Daily Dose though oral, Cw = observed concentration, IR: Ingestion rate, EF = Exposure Frequency, ED = Exposure duration, BW= Body Weight, AT = Average Time, HQ = Hazard Quotient, RfD = Reference Dose, HI = Health Index	0 < HI < 1	No Risk	[6,15]
		1 < HI < 5	Low Risk
		5 < HI < 10	Moderate Risk
		HI > 10	High Risk

.

2.7. Heavy Metal Contamination Indices (HPI and HEI)

The Heavy metal Pollution Index (HPI) takes into account the quantities of several heavy metal and metalloids like lead (Pb), zinc (Zn), iron (Fe), chromium (Cr), cadmium (Cd) and arsenic (As), measuring the level of heavy metal and metalloids contamination. It offers a thorough evaluation of the levels of heavy metal and metalloids contamination, frequently using a mathematical model or scoring system to assist in setting priorities for remediation projects and guide environmental management choices. This study calculated HPI using the weighted-arithmetic water quality index. The obtained data were compared with the standards provided by [26,27] (

Table 3. Descriptive statistics data of selected water quality parameters of the Ramganga River basin.

Parameters	Symbol	Unit	Pre-Monsoon			Post-Monsoon			BIS (2012) and WHO (2017)
			Min	Max	Average ± S.D.	Min	Max	Average ± S.D.
pH			7.16	7.35	7.26 ± 0.06	7.19	7.27	7.23 ± 0.03	6.5–8.5
Total Dissolved Solids	TDS	mg/L	265.33	360.67	301.59 ± 28.07	285.33	335.33	301.97 ± 14.94	500
Total Hardness	TH	mg/L	184.36	235.65	207.57 ± 12.54	189.58	214.22	201.83 ± 6.42	300
Alkalinity	A	mg/L	227.08	293.22	256.13 ± 17.66	256.29	299.97	270.56 ± 14.06	250
Acidity	AC	mg/L	19.96	40.49	26.57 ± 5.83	21.19	38.41	25.50 ± 5.59	--
Bicarbonates	HCO3−	mg/L	189.77	230.62	208.34 ± 12.63	201.22	231.18	212.94 ± 9.31	244
Nitrates	NO3−	mg/L	0.87	1.45	1.21 ± 0.19	0.84	1.29	1.10 ± 0.14	45
Phosphate	PO43−	mg/L	0.73	2.76	1.87 ± 0.74	0.95	2.31	1.63 ± 0.48	01
Sulphate	SO42−	mg/L	23.67	52.33	33.66 ± 12.27	23.23	44.31	31.10 ± 8.50	200
Chloride	Cl−	mg/L	20.58	49.89	32.63 ± 10.84	22.16	45.00	30.98 ± 8.89	250
Calcium	Ca2+	mg/L	114.97	141.65	126.29 ± 8.02	114.88	137.82	123.61 ± 6.92	75
Magnesium	Mg2+	mg/L	18.83	37.22	26.07 ± 4.96	19.51	35.15	25.69 ± 4.35	30
Sodium	Na+	mg/L	21.13	39.97	30.70 ± 6.31	22.18	34.19	28.61 ± 4.20	200
Potassium	K+	mg/L	2.32	7.39	4.04 ± 1.59	3.01	5.88	4.48 ± 1.18	10
Zinc	Zn	mg/L	0.309	1.787	1.004 ± 0.524	0.613	1.633	1.122 ± 0.346	5
Iron	Fe	mg/L	0.290	0.965	0.560 ± 0.270	0.253	1.720	0.515 ± 0.397	0.3
Cadmium	Cd	mg/L	0.001	0.002	0.001 ± 0.001	0.001	0.002	0.001 ± 0.001	0.003
Arsenic	As	mg/L	0.001	0.002	0.001 ± 0.001	0.001	0.002	0.001 ± 0.001	0.01
Chromium	Cr	mg/L	0.009	0.027	0.019 ± 0.007	0.011	0.029	0.021 ± 0.005	0.05
Lead	Pb	mg/L	0.001	0.010	0.005 ± 0.004	0.000	0.010	0.004 ± 0.004	0.05

). Similar to the HPI, the Heavy metal Evaluation Index assesses the presence of heavy metal and metalloids contamination in a specific environment, albeit it could use a different set of criteria or methodology. By offering a standardized method for evaluating heavy metal and metalloids pollution, this index hopes to facilitate comparisons across various sites or eras and support creating mitigation plans for the adverse effects of heavy metal and metalloids pollution on ecosystems and public health. It is used due to the focus on the heavy metal and metalloids that are major pollutants with serious health impacts. HPI precisely measures the degree of contamination of the groundwater by relatively high-density metals; hence, it is useful for evaluating water sources that are likely to cause serious health effects. Whereas, HEI is used as it offers a more precise evaluation of contaminants than a straightforward enumeration of metals. It involves weights that reflect toxicity and environmental influence of metals. This index is appropriate to identify where the hazards are more lethal, and efforts to mitigate should be concentrated. The calculation formula and water quality status based on HPI and HEI are described in Table 2 in detail. To protect the environment and public health, both indices are essential for tracking and controlling heavy metal and metalloids pollution.

2.8. Irrigation Water Quality Parameters (Sodium Adsorption Ratio (SAR), and Sodium Percentage (% Na))

Sodium adsorption ratio (SAR) is a metric that compares the concentration of sodium ions to calcium and magnesium ions to determine whether water is suitable for irrigation. SAR was selected purposively for the study because of its practicality in assessing water for irrigation. It determines the ability of sodium to build up in the soil, which plays a role in soil structure and its capacity to carry water and nutrients for crop use. SAR is important in determining the extent of groundwater for agricultural use. Higher SAR values indicate a higher likelihood of soil sodification, which helps estimate the possibility of soil degradation owing to sodium accumulation [15]. Another metric for evaluating irrigation water quality is sodium percentage (% Na), which focuses on the ratio of sodium ions to the concentration of all cations. The percentage of sodium ions is computed by dividing the total cation (sodium, calcium, magnesium, and other cations) by 100. A greater percentage of Na values indicates a higher risk of soil sodification, which can be detrimental to plant growth and soil structure [15,26]. The permeability index indicates the possible effects of irrigation water on soil drainage and permeability. It is computed as a numerical index by utilizing the SAR and % Na values in Table 2. Increased sodium content in irrigation water causes worse soil permeability, as higher PI values indicate. Over time, this can result in waterlogging, lower crop output, and soil degradation.

2.9. Health Risk Index

HRI is employed in the assessment of health risks that may result from consumption of water that has been contaminated. Since this index considers both the concentration and toxicity of pollutants, it clearly gives an idea of the level of damage that may take place to human health and hence is of great importance in the assessment of public health. The Health Risk Index (HI), based on the ingestion of six water parameters including Nitrate (NO₃⁻), Arsenic (As), Iron (Fe), Lead (Pb), Zinc (Zn), Cadmium (Cd), and Chromium (Cr) was computed for local inhabitant including infants (0–1 year), children (1–15 year), and adults (>15 years) using equations 1, 2, and 3, depicted in Table 2. The equations utilize specific parameters such as Ingestion Rate (IR) values of 0.3, 0.78, and 2.5 in L/Day; Exposure Frequency (EF) set at 365 days; Exposure Duration (ED) of 1, 12, and 64 years; Body Weight (BW) values of 16.9, 18.7, and 57.50 in kg; and Average Time (AT) of 365, 4380, and 23,360 in days/years for infants, children, and adults, respectively. The Reference Dose (RfD) values employed were: NO₃⁻ (1.6); Fe (0.007); Zn (0.3); As (0.0003); Cd (0.0005); Cr (0.0003); and Pb (0.0036). The calculation formula and status of HRI are defined in Table 2 in detail.

3. Results

3.1. Water Quality Parameters of Ramganga River Basin

The groundwater quality of the Ramganga River basin is measured using pH, TDSs, and levels of cations and anions, nutrients, and heavy metal and metalloids. These metrics show how healthy and suitable the water is for different purposes, such as drinking and irrigation. The health of human populations and ecosystems depends on groundwater having clean, healthy water. Keeping an eye on these variables makes it easier to see possible risks, such as pollution from urban growth, farming, and industrial runoff. In the study parameters, namely pH, Total Dissolved Solids (TDSs), Total Hardness (TH), Alkalinity (A), Acidity (AC), Bicarbonates (HCO₃⁻), Nitrate (NO₃⁻), Phosphate (PO₄³⁻), Sulphate (SO₄²⁻), Chloride (Cl⁻), Calcium (Ca²⁺), Magnesium (Mg²⁺), Sodium (Na⁺), Potassium (K⁺), and heavy metal and metalloids such as Arsenic (As), Zinc (Zn), Lead (Pb), Iron (Fe), Cadmium (Cd), and Chromium (Cr), respectively, were analyzed. The descriptive statistics result based on the minimum, maximum, average, and standard deviation of two seasons (pre-monsoon and post-monsoon) of the year 2021–2022 is described in Table 3, while detailed obtained data are presented through IDW spatial maps (Figure 2, Figure 3, Figure 4, Figure 5, Figure 6, Figure 7, Figure 8, Figure 9, Figure 10 and Figure 11) and Tables S1 and S2 (Supplementary Files).

3.1.1. Water Quality in Pre-Monsoon Season

In the pre-monsoon season, pH values ranged from 7.16 to 7.35, with an average of 7.26 ± 0.06, demonstrating near-neutral to slightly alkaline water. The TDSs ranged from 265.33 to 360.67 mg/L, with an average of 301.59 ± 28.07 mg/L, signifying moderate mineralization. TH ranged from 184.36 to 235.65 mg/L, with an average of 207.57 ± 12.54 mg/L, signifying moderately hard water. The alkalinity ranged from 227.08 to 293.22 mg/L, with an average value of 256.13 ± 17.66 mg/L, indicating a good buffering capacity. Acidity ranged from 19.96 to 40.49 mg/L, averaging 26.57 ± 5.83 mg/L. The bicarbonates (HCO₃⁻) ranged from 189.77 to 230.62 mg/L, with an average of 208.34 ± 12.63 mg/L. Nitrates (NO₃⁻) fluctuated between 0.87 and 1.45 mg/L, with an average value of 1.21 ± 0.19 mg/L, representing low levels of nitrate contamination. The phosphate (PO₄³⁻) ranged from 0.73 to 2.76 mg/L, with an average of 1.87 ± 0.74 mg/L, signifying moderate phosphate levels. Sulphate (SO₄²⁻) ranged from 23.67 to 52.33 mg/L, averaging 33.66 ± 12.27 mg/L. Chloride (Cl⁻) ranged from 20.58 to 49.89 mg/L, averaging 32.63 ± 10.84 mg/L. Calcium (Ca²⁺) ranged from 114.97 to 141.65 mg/L, with an average of 126.29 ± 8.02 mg/L. Magnesium (Mg²⁺) ranged from 18.83 to 37.22 mg/L, averaging 26.07 ± 4.96 mg/L. Sodium (Na⁺) ranged from 21.13 to 39.97 mg/L, averaging 30.70 ± 6.31 mg/L. Potassium (K⁺) ranged from 2.32 to 7.39 mg/L, with an average of 4.04 ± 1.59 mg/L. Whereas, heavy metal and metalloids like zinc (Zn) and iron (Fe) show variation from 0.309 to 1.787 mg/L and 0.290 to 0.965 mg/L with an average of 1.004 ± 0.524 mg/L and 0.560 ± 0.270 mg/L, respectively. The obtained result of Fe exceeded the BIS and WHO standards, i.e., 0.3 mg/L, while Cadmium (Cd), Arsenic (As), Chromium (Cr), and Lead (Pb) were present in trace amounts.

3.1.2. Water Quality in Post-Monsoon Season

During the post-monsoon season, the water quality in the selected sampling location of the Ramganga River basin shows an average pH of 7.23 ± 0.03 and a range of 7.19 to 7.27. The results indicate considerable mineralization with an average TDS of 301.97 ± 14.94 mg/L and a range of 285.33 to 335.33 mg/L. The average TH was 201.83 ± 6.42 mg/L, with a range of 189.58 to 214.22 mg/L, suggesting reasonably hard water. The average alkalinity was 270.56 ± 14.06 mg/L, with a range of 256.29 to 299.97 mg/L, indicating a strong buffering capacity. Acidity ranged from 21.19 to 38.41 mg/L, averaging 25.50 ± 5.59 mg/L. Bicarbonates (HCO₃⁻) ranged from 201.22 to 231.18 mg/L, averaging 212.94 ± 9.31 mg/L. Nitrates (NO₃⁻) ranged from 0.84 to 1.29 mg/L, with an average of 1.10 ± 0.14 mg/L, indicating low levels of nitrate contamination. Phosphate (PO₄³⁻) ranged from 0.95 to 2.31 mg/L, averaging 1.63 ± 0.48 mg/L, suggesting moderate phosphate levels. Sulphate (SO₄²⁻) ranged from 23.23 to 44.31 mg/L, averaging 31.10 ± 8.50 mg/L. Chloride (Cl⁻) ranged from 22.16 to 45.00 mg/L, averaging 30.98 ± 8.89 mg/L. Calcium (Ca²⁺) ranged from 114.88 to 137.82 mg/L, averaging 123.61 ± 6.92 mg/L. Ca²⁺ content shows an exceeded concentration by [17], i.e., 75. Magnesium (Mg²⁺) ranged from 19.51 to 35.15 mg/L, averaging 25.69 ± 4.35 mg/L. Sodium (Na⁺) ranged from 22.18 to 34.19 mg/L, averaging 28.61 ± 4.20 mg/L. Potassium (K⁺) ranged from 3.01 to 5.88 mg/L, averaging 4.48 ± 1.18 mg/L, whereas heavy metal and metalloids like Zinc (Zn), Iron (Fe), show variation from 0.613 to 1.633 mg/L and 0.253 to 1.720 mg/L with an average of 1.004 ± 0.524 mg/L and 0.515 ± 0.397 mg/L, respectively. The obtained result of Fe exceeded the BIS and WHO standards, i.e., 0.3 mg/L, while Cadmium (Cd), Arsenic (As), Chromium (Cr), and Lead (Pb) were present in trace amounts.

In both the pre-monsoon and post-monsoon seasons, the Ramganga River basin’s water quality metrics demonstrated moderate levels of mineralization, hardness, and other ions, with some seasonal fluctuations in the concentrations of certain parameters. In both seasons, Fe shows a higher concentration level in the context of heavy metal and metalloids than the standards provided by [27]. The studies show that the water quality in some of the water sources in the area was bad and thus not suitable for drinking purposes, while others lie in between good to excellent [28,29]. Anthropological activities such as urbanization, industrialization, and agricultural drainage also affect water quality [30]. Monsoon dilution seems to affect water quality parameters, and most water qualities improve during the post-monsoon season [28,30].

3.1.3. Spatial Map Distribution of Water Quality Parameters during Pre-Monsoon and Post-Monsoon

A practical method for visualizing and analyzing spatial distributions of data is the inverse distance weighting (IDW) map. This type of spatial interpolation estimates values at unsampled places by using the weighted average of nearby measured values [15]. The spatial distribution of parameters for respective seasons, i.e., pre-monsoon and post-monsoon, is displayed in Figure 2, Figure 3, Figure 4, Figure 5, Figure 6, Figure 7, Figure 8, Figure 9, Figure 10 and Figure 11. The produced maps can be important for observation and prognosis of the condition and tendencies of groundwater quality contamination, increasing public awareness of water quality problems, and for the decision-making process regarding the sustainable use of water resources [31,32,33]. In Figure 2, the dark orange color shows low pH, dark blue shows high pH, while yellow shows a moderate pH concentration in the study area. The variations or trends in pH levels are depicted between the two time periods by comparing the pre-monsoon and post-monsoon pH measurements side by side. In pre-monsoon, the lowest pH can be seen in sites GW6 and GW13, whereas the highest are in GW3, GW8, and GW10, with the same result in post-monsoon. The Figure 3 map displays the spatial distribution of TDS levels across the study area during the two seasons. The IDW maps indicate the range of TDS values, with the lowest values in dark green in the north and southwest areas in sites GW1–GW4 and the highest in dark orange in GW7 and GW8. The post-monsoon map shows the lowest in the southwest direction in site GW3, and GW10 in the north, whereas the highest value can be seen the same as in the pre-monsoon in the central portion in sites GW7 and GW8.

Figure 4 represents the pre- and post-monsoon variation in SO₄²⁻, with low in peach color and high in blue. The IDW map of sulfate in pre-monsoon and post-monsoon shows the highest value inside GW5–GW8 in the central portion, and the lowest value shown by peach color is seen in sites GW2, GW4 in the north and GW9–GW13 in the southwest in monsoon, whereas in post-monsoon, the highest value was seen in GW2 and GW3 in the north and GW11, GW12, and GW13 in the north direction. Color variation of green for low and blue for high can be seen in Figure 5 for NO₃⁻ in pre-monsoon and post-monsoon. In both seasons, the highest value was seen in sites GW11, GW12, and GW13, whereas the lowest was seen in GW1, GW2, GW4, and GW9 in pre-monsoon and post-monsoon. The IDW map for Zn is depicted in Figure 6, showing dark green as low and orange as high, and the same can be seen for IDW for Fe, As, Cd, Cr, and Pb variation in Figure 7, Figure 8, Figure 9, Figure 10 and Figure 11, respectively. The lowest value of Zn was seen in sites GW1–GW4 shown by dark green color in pre-monsoon and the highest value in GW5, GW6, and GW13. Post-monsoon zinc was the maximum in GW11, GW12, and GW13, whereas minimum was found in GW2 and GW7. In case of Fe, in the pre-monsoon, the lowest Fe concentration was found in sites GW2, GW9, and GW10, whereas in post-monsoon, it was minimal in GW1–GW4. The highest value was seen in GW4, GW6 in pre-monsoon and GW13 in post-monsoon. Arsenic (As) can be seen as maximum in pre-monsoon in GW3, GW5, and GW6, while in sites GW9–GW13 in the southwest direction and GW2 in the northern direction. In the case of post-monsoon, the lowest value was seen in sites GW2, GW9, GW10, GW11 and GW12, whereas the highest value was seen in sites GW6 and GW7 in the central part. The Cadmium (Cd) concentration was found to be maximum in sites GW4, GW5, GW6, GW7, and GW13 in pre-monsoon and post-monsoon. The IDW representation of chromium (Cr) shows the highest concentration in sites G5–G7 in pre-monsoon and G6 in post-monsoon, whereas the lowest concentration in site G10 in pre-monsoon and GW10–GW12 in post-monsoon. The concentration of lead was seen as maximum in sites GW2 and GW9 in pre-monsoon and GW1, GW2, and GW4 in post-monsoon, whereas the highest value was seen in GW11–GW13 in both pre-monsoon and post-monsoon. Some studies by [34,35] showed that the heavy metal and metalloids in water and soil samples were higher in pre- and post-monsoon seasons. These variations may be the result of factors like industrial processes, surface runoff, dilution effects, etc.

3.2. Groundwater Quality Index

The Groundwater Quality Index (GWQI) thoroughly assesses groundwater suitability for irrigation, drinking, and other uses, supporting efficient resource management and pollution prevention strategies [36,37]. The results of GWQI are shown in

Table 4. Groundwater Quality Index (GWQI) in selected sampling locations in pre-monsoon and post-monsoon in the Ramganga River basin.

Sampling Location	Pre-Monsoon		Post-Monsoon
	GWQI		GWQI
	Value	Status	Value	Status
GW1	41.639	Excellent Water	43.091	Excellent Water
GW2	42.243	Excellent Water	43.752	Excellent Water
GW3	45.380	Excellent Water	43.377	Excellent Water
GW4	46.384	Excellent Water	46.500	Excellent Water
GW5	50.173	Good Water	48.108	Excellent Water
GW6	53.139	Good Water	50.426	Good Water
GW7	54.837	Good Water	53.692	Good Water
GW8	56.326	Good Water	53.902	Good Water
GW9	44.303	Excellent Water	45.759	Excellent Water
GW10	48.293	Excellent Water	48.219	Excellent Water
GW11	51.305	Good Water	48.091	Excellent Water
GW12	52.208	Good Water	48.788	Excellent Water
GW13	52.043	Good Water	48.715	Excellent Water
Min	41.639	Excellent Water	43.091	Excellent Water
Max	56.326	Good Water	53.902	Good Water
Mean ± S.D.	49.098 ± 4.773	Excellent Water	47.878 ± 3.475	Excellent Water

and Figure 12.

GWQI in Pre-Monsoon Season and Post-Monsoon Season

In the pre-monsoon season, the GWQI values ranged from 41.639 to 56.326, with a mean value of 49.098 ± 4.773. According to the result of GWQI classification, 7 sampling locations, i.e., GW1, GW2, GW3, GW4, GW9, GW10, and GW11, had Excellent Water quality, while 6 sampling locations, i.e., GW5, GW6, GW7, GW8, GW12, and GW13, had Good Water quality.

In the post-monsoon, the GWQI values ranged from 43.091 to 53.902, averaging 47.878 ± 3.475. According to the result of the GWQI classification, 10 sampling locations, i.e., GW1, GW2, GW3, GW4, GW5, GW9, GW10, GW11, GW12, and GW13, had Excellent Water quality, while 3 sampling locations, i.e., GW6, GW7, and GW8, had Good Water quality.

The IDW map also shows the color variation with low value < 50, i.e., excellent quality as green color and value 50–100 good quality as yellow color. The groundwater quality index shows the highest value at site GW7 and GW8 showing good quality water in pre-and post-monsoon, whereas in pre-monsoon, minimum value was seen with light green color at sites GW1–GW4, whereas with dark green color in post-monsoon in sites GW1 to GW4 showing excellent water.

GWQI has been used in numerous studies to evaluate water quality in many parts of India. According to [38], borewells in the same area were of medium quality, while a water treatment plant in Delhi had a WQI of 73–80, signifying good quality. The Sabour block of Bihar groundwater quality was evaluated by [39], who rated 1.69% of the samples as excellent and 47.45% as good. When [40] assessed the spring water quality at Bageshwar, Uttarakhand, most samples were rated excellent. During a thorough evaluation of the surface and subsurface water in the Balco industrial region, [40] discovered that the WQI varied depending on the location. This research demonstrates how crucial WQI is for evaluating and tracking water quality. Many studies by [41,42,43] highlight the effectiveness of WQI in evaluating water quality and its suitability for drinking and irrigation purposes.

3.3. Heavy Metal Pollution Index (HPI) and Heavy Metal Evaluation Index (HEI)

The degree of heavy metal and metalloids pollution in a particular environment, usually in soil or water, can be measured using the Heavy metal Pollution Index (HPI) and the Heavy metal Evaluation Index (HEI). The HPI and HEI results are depicted in

Table 5. Heavy metal Pollution Index (HPI) and Heavy metal Evaluation Index (HEI) in selected sampling locations in pre-monsoon and post-monsoon in the Ramganga River basin.

Sampling Location	Pre-Monsoon				Post-Monsoon
	HPI		HEI		HPI		HEI
	Value	Status	Value	Status	Value	Status	Value	Status
GW1	30.45	Good	1.17	Low Level	58.30	Poor	1.20	Low Level
GW2	29.97	Good	1.03	Low Level	30.03	Good	1.13	Low Level
GW3	39.44	Good	1.24	Low Level	58.97	Poor	1.35	Low Level
GW4	59.80	Poor	2.41	Low Level	30.54	Good	1.69	Low Level
GW5	59.69	Poor	2.54	Low Level	59.18	Poor	2.09	Low Level
GW6	59.83	Poor	2.57	Low Level	59.65	Poor	2.32	Low Level
GW7	60.26	Poor	2.31	Low Level	59.75	Poor	2.15	Low Level
GW8	30.84	Good	1.72	Low Level	30.64	Good	1.73	Low Level
GW9	29.51	Good	1.11	Low Level	30.50	Good	1.36	Low Level
GW10	30.18	Good	1.17	Low Level	30.20	Good	1.22	Low Level
GW11	30.28	Good	1.25	Low Level	30.30	Good	1.25	Low Level
GW12	59.22	Poor	2.01	Low Level	30.81	Good	2.01	Low Level
GW13	60.12	Poor	2.32	Low Level	59.46	Poor	3.37	Low Level
Min	29.51	Good	1.03	Low Level	30.03	Good	1.13	Low Level
Max	60.26	Poor	2.57	Low Level	59.75	Poor	3.37	Low Level
Mean ± S.D.	44.58 ±14.89	Good	1.74 ± 0.62	Low Level	43.72 ± 14.94	Good	1.76 ± 0.63	Low Level

and the IDW map in Figure 13 and Figure 14.

3.3.1. HPI in Pre-Monsoon and Post-Monsoon Seasons

The result of the HPI values ranged from 29.512 to 60.262, with an average of 44.584 ± 14.893. The HPI result depicted good quality or low pollution in 7 sampling sites (GW1, GW2, GW3, GW8, GW9, GW10, GW11), while 6 sampling locations (GW4, GW5, GW6, GW7, GW12, GW13) showed high pollution levels and poor quality.

The result of post-monsoon showed that the HPI values ranged from 30.029 to 59.754, with an average of 43.718 ± 14.943. According to the HPI results, 7 sampling sites, GW2, GW4, GW8, GW9, GW10, GW11, and GW12, had Low Pollution or good quality levels, while 6 sampling sites, GW1, GW3, GW5, GW6, GW7, and GW13, had high pollution levels or low quality. It has been noted that some sites like GW1 and GW3 showed poor quality in post-monsoon season while good quality in pre-monsoon seasons. Other sampling sites show little variation in heavy metal and metalloids concentration.

The IDW map of HPI in pre-monsoon shows values in the range of 26–50, i.e., Good Quality in yellow color showing low pollution in pre-monsoon and post-monsoon. In the case of highest value, 51–75, i.e., poor quality indicated in red color the highest value in pre-monsoon and post-monsoon.

A mean of 44.584 ± 14.893 was found among the study’s HPI values, which varied from 29.512 to 60.262, suggesting low pollution in specific sample sites and high pollution in others [2,38,44,45]. Similar findings were seen in the post-monsoon season, with some locations experiencing low pollution and others experiencing severe pollution [9,45,46]. These results align with the idea that agricultural practices impact air pollution in the post-monsoon season [46,47].

3.3.2. Heavy Metal Evaluation Index (HEI) in Pre-Monsoon and Post-Monsoon Seasons

The HEI values ranged from 1.03 to 2.57, with an average of 1.74 ± 0.62 pre-monsoon and 1.13 to 3.37 with an average of 1.74 ± 0.62 post-monsoon season. The HEI result shows low heavy metal and metalloids contamination in all selected Ramganga River basin sampling locations. Only Fe concentration was exceeded in some locations during both seasons.

The IDW maps were also prepared for HEI for both seasons, that is, pre-monsoon and post-monsoon, where the lowest value, i.e., <40 was seen with green color. These results show that seasonal variations in contamination trends are persistent, with some test sites exhibiting consistently high contamination levels. The contrast between low and high pollution locations highlights the variation in groundwater quality across the research area’s spatial dimensions. Significant levels of heavy metal and metalloids contamination were present in these particular areas over both seasons, indicating that the elevated levels were probably caused by geogenic causes or other localized sources of heavy metal and metalloids pollution. Such pollutants can be biogenic and abiotic, the sources can be both geographical and human-induced, such as industries, leachates, and agricultural products [43]. Neurological conditions, kidney damage, and cancer are just a few of the harmful health impacts that can result from exposure to heavy metal and metalloids like cadmium, lead, and chromium [6,43,48,49,50]. Moreover, toxicity of these metals results in pathophysiological changes which aggravate plant growth such as chlorosis of leaves, less orderly root formation, and less photosynthesis [43,50]. Furthermore, the buildup of heavy metal and metalloids in the food chain may have extensive ecological repercussions [51,52,53].

3.4. Result of Irrigation Water Quality Parameters (Sodium Adsorption Ratio (SAR) and Sodium Percentage (% Na))

Indicators such as the sodium adsorption ratio (SAR) and sodium percentage (% Na) are essential for assessing the water quality of any water bodies for irrigation purposes. They help determine if the water is suitable for irrigation, stop soil erosion, and guarantee sustainable land use practices. The results of SAR and % Na are depicted in

Table 6. Irrigation water quality based on SAR and % Na in selected sampling locations in pre-monsoon and post-monsoon in the Ramganga River basin.

Sampling Location	Pre-Monsoon				Post-Monsoon
	SAR		% Na		SAR		% Na
	Value	Status	Value	Status	Value	Status	Value	Status
GW1	2.53	Excellent	14.39	Excellent	2.67	Excellent	15.40	Excellent
GW2	2.64	Excellent	14.82	Excellent	2.96	Excellent	16.48	Excellent
GW3	3.72	Excellent	19.89	Excellent	3.65	Excellent	19.67	Excellent
GW4	3.63	Excellent	19.13	Excellent	3.86	Excellent	20.73	Good
GW5	4.24	Excellent	21.52	Good	4.00	Excellent	20.48	Good
GW6	4.28	Excellent	21.25	Good	3.92	Excellent	20.59	Good
GW7	3.00	Excellent	15.22	Excellent	3.52	Excellent	18.17	Excellent
GW8	3.53	Excellent	17.81	Excellent	3.34	Excellent	17.52	Excellent
GW9	2.80	Excellent	15.21	Excellent	3.29	Excellent	17.74	Excellent
GW10	2.91	Excellent	15.47	Excellent	3.23	Excellent	17.10	Excellent
GW11	3.46	Excellent	19.03	Excellent	2.79	Excellent	17.02	Excellent
GW12	4.41	Excellent	23.18	Good	2.81	Excellent	16.87	Excellent
GW13	4.59	Excellent	23.10	Good	3.01	Excellent	17.63	Excellent
Min	2.53	Excellent	14.39	Excellent	2.67	Excellent	15.40	Excellent
Max	4.59	Excellent	23.18	Good	4.00	Excellent	20.73	Good
Mean ± S.D.	3.52 ± 0.71	Excellent	18.46 ± 3.21	Excellent	3.31 ± 0.45	Excellent	18.11 ± 1.72	Excellent

, and the IDW map shows the variation in SAR and % Na values in selected sampling locations depicted in Figure 15 and Figure 16.

The SAR values ranged from 2.529 to 4.590, with an average of 3.52 ± 0.71 in pre-monsoon and 2.67 to 4.00 with an average of 3.31 ± 0.45 in post-monsoon seasons. All the sampling sites had Excellent SAR status. The % Na values ranged from 14.39% to 23.18%, with an average of 18.46 ± 3.21% in pre-monsoon and 15.40% to 20.73% with an average of 18.11 ± 1.72% in post-monsoon.

In the IDW map, the lowest value, i.e., <20, showing excellent % Na, is indicated by green color in pre-monsoon and post-monsoon and the value 20–40, i.e., good quality, is indicated in yellow color in pre-monsoon. At the same time, the SAR value was found to be excellent (<10) in all sites in both seasons.

The Ramganga River basin’s groundwater is generally suitable for irrigation, as evidenced by the region’s constant Excellent SAR rating and the prevalence of Excellent and Good % Na status during the pre-monsoon and post-monsoon seasons. The local farming community will find great value in these findings, as they offer significant insights into whether the groundwater resources are suitable for irrigation. The Ramganga River basin’s long-term agricultural production and food security depend on maintaining an efficient and sustainable use of its groundwater resources.

Excellent water quality is indicated by the study’s SAR and % Na readings, albeit there is some fluctuation throughout sampling sites. This aligns with earlier studies on water quality evaluation [54,55]. The studies together assess the quality of water for consumption, agriculture, and other uses such as in industries for SAR, % Na, and WQI. They were used to evaluate water quality [56,57,58]. It is said that these contaminants can affect water quality indicators [59]. The studies focus on water management and also on discussing the sources of pollutants and their impact on the quality of water resources [58,59].

As [60] pointed out, the existence of pollutants in some places can be a role in the fluctuation in water quality metrics. It is necessary to look into the origins of these contaminants and how they affect water quality.

3.5. Health Risk Index for Local Inhabitants

By combining exposure and toxicity data, the Health Risk Index (HRI) evaluates possible health risks related to exposure to environmental contaminants. This helps to prioritize mitigation activities and safeguard the general public’s health from the harmful impacts of pollution. To ensure healthier communities and environments, it assists in identifying areas of concern and directs regulatory steps to prevent human exposure to toxic substances [6,15].

Table 7. Health Risk Index for Infant, Child and Adult based on Ingestion in selected sampling locations in pre-monsoon and post-monsoon in the Ramganga River basin.

Sampling Location	Pre-Monsoon						Post-Monsoon
	Infant		Child		Adults		Infant		Child		Adults
	Total Health Index	Status	Total Health Index	Status	Total Health Index	Status	Total Health Index	Status	Total Health Index	Status	Total Health Index	Status
G1	2.13	Low Risk	5.02	Medium Risk	5.23	Medium Risk	2.09	Low Risk	4.92	Low Risk	5.13	Medium Risk
G2	1.77	Low Risk	4.16	Low Risk	4.33	Low Risk	1.95	Low Risk	4.59	Low Risk	4.79	Low Risk
G3	1.99	Low Risk	4.68	Low Risk	4.88	Low Risk	2.31	Low Risk	5.43	Medium Risk	5.66	Medium Risk
G4	4.12	Low Risk	9.69	Medium Risk	10.10	High Risk	2.37	Low Risk	5.57	Medium Risk	5.80	Medium Risk
G5	4.20	Low Risk	9.87	Medium Risk	10.29	High Risk	3.19	Low Risk	7.51	Medium Risk	7.82	Medium Risk
G6	4.19	Low Risk	9.84	Medium Risk	10.26	High Risk	3.77	Low Risk	8.85	Medium Risk	9.23	Medium Risk
G7	3.91	Low Risk	9.19	Medium Risk	9.58	Medium Risk	3.42	Low Risk	8.04	Medium Risk	8.38	Medium Risk
G8	3.00	Low Risk	7.06	Medium Risk	7.36	Medium Risk	3.02	Low Risk	7.10	Medium Risk	7.40	Medium Risk
G9	1.55	Low Risk	3.65	Low Risk	3.80	Low Risk	2.18	Low Risk	5.13	Medium Risk	5.35	Medium Risk
G10	1.51	Low Risk	3.54	Low Risk	3.69	Low Risk	1.55	Low Risk	3.65	Low Risk	3.80	Low Risk
G11	1.52	Low Risk	3.56	Low Risk	3.71	Low Risk	1.52	Low Risk	3.57	Low Risk	3.72	Low Risk
G12	2.73	Low Risk	6.41	Medium Risk	6.68	Medium Risk	2.56	Low Risk	6.02	Medium Risk	6.27	Medium Risk
G13	3.31	Low Risk	7.78	Medium Risk	8.11	Medium Risk	5.99	Medium Risk	14.07	High Risk	14.67	High Risk

and Figure 17, Figure 18 and Figure 19 and Supplementary Tables (Tables S3 and S4) represent the HI for infants, children, and adults based on ingestion in the selected sampling locations during the pre-monsoon and post-monsoon seasons in the Ramganga River basin. The values of HI are calculated to measure the potential health risks linked with the groundwater quality. The HI is categorized into different status levels: low risk (HI < 1), medium risk (1 ≤ HI < 5), and high risk (HI ≥ 5).

Result of HI in Pre-Monsoon and Post-Monsoon Seasons

Infants’ HI values in pre-monsoon range from 1.51 to 4.20, which means they are at a low risk. For children, the HI values range from 3.54, i.e., low risk, to 9.87, i.e., medium risk. Whereas for adults, the HI values range from 3.69, i.e., low risk, to 10.29, i.e., high risk. The HI values for infants in post-monsoon range from 1.52, which means low risk, to 5.99, which means medium risk. For children, the HI values range from 3.57, i.e., low risk, to 14.07, i.e., high risk. In adults, the HI values range from 3.72, i.e., low risk, to 14.67, i.e., high risk.

The results indicate that the groundwater quality in the Ramganga River basin poses a medium-to-high health risk, particularly for children and adults, based on the findings of water parameters during pre-monsoon and post-monsoon seasons. The sampling locations with the highest HI values require further attention and potential remediation measures to ensure the safety of the groundwater for human consumption. The water quality in the earlier studies also showed the deteriorating quality in the region [59,60,61,62].

Seasonal variations in the health risk index values for adults, children, and newborns show that the post-monsoon season has the most significant values. This implies that there may be a rise in health hazards during this time, especially for adults and children. Maternal height, parity, and pre-pregnancy BMI have all been linked to an increased risk of low-birth-weight babies [6]. Furthermore, the existence of high-risk traits in infants, such as low birth weight and a family history of disability, may increase the population’s health risks [7]. These results highlight the necessity of focused measures, especially in the post-monsoon season, to lessen the negative effects of these risk factors on the health of newborns, children, and adults. Science communication can play an important role in water quality management and associated health risks [61].

The result of HI is also shown in interpolation for the two seasons pre- and post-monsoon. Based on the obtained results, three categories were found, i.e., low risk (1 < HI < 5) indicated by green color, moderate risk (5 < HI < 10) indicated by yellow color, and high risk (HI > 10) indicated by red color. For infants, low risk was observed in all the locations pre-monsoon and post-monsoon except for site GW13, which showed moderate risk. For Child, HI was found in low risk and moderate risk in pre-monsoon, while in post-monsoon, it showed low risk, moderate risk, and high risk. For Adult, HI was found in low risk, moderate risk, and high risk in pre- and post-monsoon.

4. Conclusions

The current study deals with groundwater quality assessment for drinking portability, irrigation purposes, heavy metal and metalloids contamination, and their potential health risks on local inhabitants of the Ramganga River basin. The findings show that during pre-monsoon, pH of the water was near-neutral to slightly alkaline, TDSs showed moderate mineralization, moderately hard water. Meanwhile, the calcium and iron concentrations showed variations exceeding BIS- and WHO-recommended values, and other heavy metal and metalloids were detected only in traces. While post-monsoon pH was near to that of pre-monsoon, with slight variation in TDSs that indicates a slight decrease in mineralization, total hardness also showed slightly softer water compared to pre-monsoon with respect to the standards, but both calcium and iron were higher than the standards. The GWQI result shows that the water quality is excellent or good in both seasons. However, HPI shows that the water quality in the selected regions lies between good quality and poor quality in respect to heavy metal and metalloids contamination, while HEI shows a low contamination of heavy metal and metalloids, but Fe is slightly exceeded in both pre-monsoon and post-monsoon seasons. Additionally, sodium content analysis by using Sodium Adsorption Ratio (SAR) and sodium percentage (% Na) suggested that the water is appropriate for irrigation purposes, with a category of SAR that is excellent quality and the % Na ranging from excellent to good quality. The health risk index, which covered heavy metal and metalloids exposure, revealed that infants presented low health risk in the pre-monsoon and post-monsoon periods. Children and adults were categorized in low to high risk.

5. Recommendation

According to the result, the following points are recommended:

• Throughout the study, the GWQI values show excellent to good water quality, with slight changes noted between the pre- and post-monsoon seasons. Most exceptional water quality classifications highlight how groundwater is generally suitable for various uses, such as drinking and irrigation. To avoid any deterioration over time, attention should be made to areas with acceptable water quality.
• The results of the HPI and HEI show heavy metal and metalloids contamination in some of the sample sites, and the pollution levels are constant in both seasons. These results highlight the critical need for focused remediation initiatives and strict monitoring protocols to lessen the negative effects of heavy metal and metalloids contamination on the ecosystem and public health.
• Across the studied sites, the evaluation of SAR, % Na, and PI values shows excellent water quality suited for irrigation. However, to guarantee ideal soil permeability and guard against future soil degradation, attention should be given to areas with lower % Na levels.
• In agricultural-dominated areas, nitrate and phosphate concentrations are higher, as influenced by the use of fertilizers. These variables are useful signs of agricultural runoff; we should use them in tracking the effect that farming practices have on the water quality of an aquifer.
• Frequent concentrations of heavy metals like Lead (Pb), Cadmium (Cd), and Chromium(Cr) were noted in the industrial regions, and these may be attributed to untreated industrial effluence. These pollutants are very dangerous to human health, and we suggest that heavy metal analysis should be conducted in these regions, most especially in the periods of pre- and post-monsoon rains so that adequate treatment measures can be taken.
• Out of all the criteria, the main ones which are affected by urbanization consist of the imbalance of the Total Dissolved Solids (TDSs) and chlorides (Cl-), which are augmented by the output of urban waste. These values should be kept under check as a maximum, especially in areas where one’s main input is urban runoff.
• In the downstream area of the river, TDSs, chlorides, and some of the heavy metal concentrations, for example, zinc and lead, were observed to be higher, especially where industrial activities were involved. All these enrichments are likely to be due to the overall impacts of the sources of pollutants upstream. It is therefore imperative that these parameters be monitored downstream to determine the effects of industrial and urban development on the water quality.