Introduction

Human health and groundwater (GW) quality are inextricably intertwined. The deterioration in quality of GW is primarily caused by geogenic and human-induced activities, including development occurring in agricultural activities and industrialization1,23. For drinking and irrigation, approximately 2.5 billion humans rely on GW regularly, highlighting the critical importance of GW quantity and quality4,5. Two major contaminants in GW that can have detrimental effects on human health are NO₃⁻ and F⁻. NO₃⁻ pollutant is particularly problematic in shallow GW due to its high toxicity, especially in agricultural regions with permeable soils and advantageous oxygen-related geochemical environments6,78.

Concentration of F⁻ in GW is predominantly linked to breakdown and leaching of F⁻ containing minerals in rocks. Minerals like fluorite (CaF2), Sellaite (MgF2), fluoroapatite (Ca5(PO4)3F), topaz (Al2(SiO4)F2), mica (K(LiAl)3(AlSi3O10)(O, OH, F)2), and crystalline igneous rocks contribute to F⁻ enrichment. Water-mineral interactions, especially under elevated levels of HCO₃⁻ and Na⁺ in GW, speedup dissolution of these minerals, thereby increasing F⁻ range in GW9. NO₃⁻ originates from numerous sources, including animal manure, wastewater effluent penetration, dairy lagoons, and septic systems. These diffuse pollution sources including manure, pesticides, fertilizers, and atmospheric deposition, also contribute significantly to the pollution of NO₃⁻10.

Many researchers have investigated the contamination of GW by NO₃⁻ and F⁻11,12,13,14,15. Also, GW salinization threatens agricultural soils by elevating salt ranges, which can adversely affect salt-sensitive crops. Therefore, understanding processes driving GW salinity in semi-arid regions is essential for sustainable GW resource management, to safeguard its quality, geoscientists are increasingly focused on assessing its chemical variations through advanced data-driven methods including analysis of variable association and principal component analysis (PCA)16,17,1819. To evaluate GW pollution, researchers have employed various methods, including Entropy based Water Quality Index (EWQI), which evaluates the general condition of GW and establishes its suitability for human consumption20,21,22,2324. Several indices have been devised to assess whether GW is suitable for irrigation, including Na%, RSC, KR, SAR, MHR, PI. These might not adequately capture the overall irrigation quality of GW, even while providing insightful information about aspects of WQ (water quality)25,26,2712,28. Ingestion of F⁻ and NO₃⁻ elevated water may lead to health complications, including: asthma, blue baby syndrome, dental fluorosis, anxiety, muscle spasm, polyuria, hypocalcemia, polydipsia, skeletal fluorosis, and even Alzheimer disease29,30.

Elevated ranges of NO₃⁻ and F⁻ infiltration in GW have been noted in several countries, including the Netherlands, Japan, Denmark, Lebanon, India, the United Kingdom, China, Australia, Costa Rica, New Zealand, various regions of Africa, Iran, the United States, Israel, Turkey, Morocco, and Saudi Arabia. Furthermore, the population in this nation is exposed to GW containing NO₃⁻ ranges exceeding 45 mg/L and F⁻ range of 1.5mg/L5,31,32,33,34. Within India, states like Andhra Pradesh, Gujarat, Utter Pradesh, Tamil Nadu, and Rajasthan face severe issues of F⁻ and NO₃⁻ contamination in GW, numerous studies in recent years have evaluated health risks aligned with F⁻ and NO₃⁻ susceptibility across extensive regions of the country. For instance, research conducted in Bichpuri block of Agra, Uttar Pradesh, revealed that over 92% GW samples contained F⁻ above the safe threshold35. In the Arjunanadhi River basin, Tamilnadu, India revealed that 34% of samples contained NO₃⁻ above the safe threshold due to the application of chemical fertilizers and leaching of improperly managed domestic waste31, posing significant non-carcinogenic health risks to residents. Such evidence highlights the widespread nature of the problem and its potential health implications. To quantify the associated non-carcinogenic risks, many studies have adopted the U.S. EPA hazard quotient approach for evaluating oral exposure through GW consumption36,37,38,39,4041.

In recent years, geo-informatics and GIS-based approaches have been extensively utilized across diverse environmental investigations, including soil erosion susceptibility mapping, analysis of land surface temperature, vegetation, water index dynamics, and GW management modelling, demonstrating the adaptability of spatial decision support frameworks42,4344. Although several studies have investigated GW quality in different regions of Tamil Nadu and around the world, in our earlier study29, we examined the distribution of NO₃⁻ in GW across Pambar Basin, south India, during the monsoon (Nov, 2024). This finding revealed NO₃⁻ was particularly high in Samalpatti-Sevathur region. Therefore, the present study specifically focuses on assessing major ion contamination in GW within this area. No comprehensive study has yet been carried out to evaluate the GW aptness for potable and irrigation uses in the Samalpatti and Sevathur carbonatite complexes. This research is therefore novel in integrating hydrogeochemical analysis, EWQI, and health risk assessment to provide a holistic understanding of GW quality in this unique geological setting. Therefore, this study principally targets (i) examining the hydrogeochemistry and occurrence of NO₃⁻ and F⁻ in GW, (ii) computing the EWQI to determine GW suitability for potable use, (iii) assessing irrigation WQ using multiple indices derived from GW chemistry, and (iv) calculating health risks associated with NO₃⁻ and F⁻ through mean daily intake (MDI) and hazard quotient (HQ) analysis.

Materials and methods

Research vicinity

The research vicinity is primarily oriented toward Sevathur and Samalpatti carbonatite complexes (12⁰ 17’ – 12⁰ 27’N, 78⁰ 26’ – 78⁰ 34’E), situated in the northwestern districts of Tamil Nadu, India, covering approximately 70 km2 (Fig. 1). On average, the area witnesses 800 mm rainfall per annum and falls under a semi-arid climate. The carbonatite complexes are part of high grade Proterozoic metamorphic rocks of Southern Granulite Terrain and the major rock types in this region are biotite hornblende gneiss, biotite hornblende syenite, pyroxenite, carbonatite, and porphyritic syenite (Fig. 2b). Dominant minerals are feldspars, quartz, fluoro-apatite, vermiculite, biotite, hornblende, and pyroxene29,36. Soil types in these regions consist mainly of calcareous and gravelly clay soil with smaller patches of loamy soil. Land use in the area is primarily agricultural, with additional coverage including vegetation, barren lands, water bodies, and residential areas (Fig. 2c).

Fig. 1
Fig. 1
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Key and groundwater samples location maps of the Samalpatti-Sevathur carbonatite complexes in the study area using QGIS 3.16.14.

Drainage (Fig. 2a) displays the stream network of the research vicinity, derived from the Digital Elevation Model using GIS and classified by Strahler’s method. Stream orders range from first to sixth, with first- and second-order streams forming fine tributaries that merge into higher orders toward the central and southern regions. The dendritic drainage pattern reflects homogeneous lithology with minimal structural disturbance. The pattern and stream distribution indicate efficient surface runoff and potential GW recharge zones within the region.

Fig. 2
Fig. 2
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(a) Drainage, (b) Geology, and (c) LULC maps of the Samalpatti-Sevathur carbonatite complexes in the study area using QGIS 3.16.14.

Sampling and testing

Fieldwork performed in February 2025 to assess GW pollution within the research vicinity. Around 75 groundwater (GW) samples were collected in 500 ml HDPE bottles, comprising 44 deep well (DW) samples (SS1 to SS44) and 31 shallow wells (SW) samples (OW1 to OW31), sampling locations were determined through a random approach to ensure comprehensive spatial representation across the research zone. Special consideration was given to two carbonatite-bearing zones to capture variations related to geological and hydrogeological conditions. In addition, factors including well accessibility, depth, and patterns of GW utilization for domestic and irrigation purposes were taken into account during site selection. Prior to sampling, the motorized pump of each well was operated for a few minutes to avoid stagnant water. Sample collecting bottles were rinsed thoroughly with respective GW before being filled and sealed suddenly, ensuring they were free of air bubbles were trapped to prevent contamination. Subsequently, GPS used to record the location of each collection point, including elevation. Water level measurement for shallow wells, which was determined from the surface with the help of measuring tape.

In-situ variables such as pH, bicarbonate (HCO₃⁻), TDS, carbonate (CO₃²⁻), and EC were directly measured in the field. Within 24 h, each sample was passed through 0.45-micron WHATMAN filter paper and preserved at 4 °C for subsequent chemical examination. Major ionic constituents, including calcium (Ca²⁺), potassium (K⁺), chloride (Cl⁻), sulfate (SO₄²⁻), magnesium (Mg²⁺), total hardness (TH), fluoride (F⁻), sodium (Na⁺), and nitrate (NO₃⁻) were analysed within six days following the completion of the sampling procedure, using standard APHA45 outlined procedures. The instrument used to analyse the sample is detailed in Table 1, and the procedure was discussed29. Na⁺ and K⁺ were estimated using flame photometer 128 (Systronics) due to its high accuracy for alkali metal detection, while NO₃⁻ and SO₄²⁻ were quantified using UV–visible spectrophotometer 119 (Systronics) because of its sensitivity and reliability. Fluoride was measured using Hach (HQ430d) ion-selective electrode, and in-situ parameters were recorded using PCS Testr-35 multiparameter probe. Major ions, including HCO₃⁻, Mg²⁺, Cl⁻, and Ca²⁺ were quantified using standard titrimetric methods. Each analytical technique was selected based on its suitability for hydrochemical analysis by APHA, with necessary calibrations performed to minimize instrument-specific limitations.

Obtained analytical data were cross-checked with WHO46 and BIS47 standards to ensure GW safety for consumption1.

The accuracy of analytical data was tested by calculating the balance of ionic error percentage (BIE%) for each sample, computed using Eq. (1). The maximum acceptable range for BIE % was within the \(\:\pm\:\:10\%\)5. For this computation, both anions and cations were converted to meq/L (milliequivalent per litre).

$$\:BIE=\:\frac{-\sum\:Anions+\:\sum\:Cations}{\sum\:Anions+\:\sum\:Cations}\:\:\times\:100$$
(1)
Table 1 Instrumentation and methods to identify the physico-chemical variables with their ranges in the Samalpatti-Sevathur carbonatite complexes.
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Water quality Index – Entropy method (EWQI)

The water quality index represents the overall condition of water in a single term. Many researchers have employed this term by adopting different methods23,24. Among these, the most commonly used method of water quality index is entropy weighted (EWQI) because it determines parameter weights objectively based on their variability, enabling a more precise and holistic evaluation of GW quality compared to traditional indices, where the weight of each variable is assigned based on entropy21,22. A few steps involved in computing the EWQI are outlined below20.

In step 1, select the water quality parameters for all samples. In an excel sheet, each row consists of all parameters for a single sample. This is illustrated by the \(\:{X}_{ns}\) matrix, as given in Eq. 2.

$$\:{X}_{ns}=\:\left(\begin{array}{ccc}{x}_{11}&\:\begin{array}{cc}{x}_{12}&\:{x}_{13}\end{array}&\:\begin{array}{cc}\cdots\:&\:{x}_{1d}\end{array}\\\:{x}_{21}&\:\begin{array}{cc}{x}_{22}&\:{x}_{23}\end{array}&\:\begin{array}{cc}\cdots\:&\:{x}_{2d}\end{array}\\\:\begin{array}{c}\vdots\\\:{x}_{c1}\end{array}&\:\begin{array}{cc}\begin{array}{c}\vdots\\\:{x}_{c2}\end{array}&\:\begin{array}{c}\vdots\\\:{x}_{c3}\end{array}\end{array}&\:\begin{array}{cc}\begin{array}{c}\ddots\:\\\:\cdots\:\end{array}&\:\begin{array}{c}\vdots\\\:{x}_{cd}\end{array}\end{array}\end{array}\right)\:$$
(2)

Where c indicates total samples and d represents the number of ions in the sample.

In step 2, \(\:{X}_{ns}\) matrix is normalized into \(\:{Y}_{ns}\) matrix by using Eq. 3.

$$\:{Y}_{ns}=\:\frac{{X}_{ns}-{\left({X}_{ns}\right)}_{min}}{{\left({X}_{ns}\right)}_{max}-{\left({X}_{ns}\right)}_{min}}$$
(3)

Where \(\:{\left({X}_{ns}\right)}_{max}\) is maximum range of ion s and \(\:{\left({X}_{ns}\right)}_{min}\) is minimum range of ion s in \(\:{X}_{ns}\) matrix. From using Eq. 3, normalized values are generated to all values which is shown as Y matrix in Eq. 4

$$\:Y=\:\left(\begin{array}{ccc}{y}_{11}&\:\begin{array}{cc}{y}_{12}&\:{y}_{13}\end{array}&\:\begin{array}{cc}\cdots\:&\:{y}_{1d}\end{array}\\\:{y}_{21}&\:\begin{array}{cc}{y}_{22}&\:{y}_{23}\end{array}&\:\begin{array}{cc}\cdots\:&\:{y}_{2d}\end{array}\\\:\begin{array}{c}\vdots\\\:{y}_{c1}\end{array}&\:\begin{array}{cc}\begin{array}{c}\vdots\\\:{y}_{c2}\end{array}&\:\begin{array}{c}\vdots\\\:{y}_{c3}\end{array}\end{array}&\:\begin{array}{cc}\begin{array}{c}\ddots\:\\\:\cdots\:\end{array}&\:\begin{array}{c}\vdots\\\:{y}_{cd}\end{array}\end{array}\end{array}\right)$$
(4)

In step 3, calculate the proportion of ion s value in a sample n \(\:\left({P}_{ns}\right)\), entropy of ion s \(\:\left({e}_{s}\right)\), and weight of ion s \(\:({w}_{s}\)) by using Eqs. 5, 6, and 7.

$$\:{P}_{ns}=\:\frac{{Y}_{ns}}{{\sum\:}_{n=1}^{c}{Y}_{ns}}$$
(5)
$$\:{e}_{s}=-\frac{1}{\text{ln}c}\sum\:_{n=1}^{c}{P}_{ns}ln{P}_{ns}$$
(6)
$$\:{w}_{s}=\:\frac{1-{e}_{s}}{\sum\:_{s=1}^{d}1-{e}_{s}}$$
(7)

In step 4, compute the rate of quality \(\:{q}_{s}\) using ion value of s \(\:({I}_{s}\)) in mg/L and WHO recommended permissible level of ion s \(\:({D}_{s}\)) in mg/L, as shown in Eq. 8

$$\:{q}_{s}=\:\frac{{I}_{s}}{{D}_{s}}\times\:100$$
(8)

In final step, water quality index assessed by entropy weighted is computed using Eq. 9

$$\:EWQI=\:\sum\:_{s=1}^{d}{w}_{s}{q}_{s}$$
(9)

The EWQI value is compared with a predefined scale to classify the quality category of water typically spans from excellent to unacceptable for potable use (Table 2)12.

Table 2 EWQI classification and potability assessment of groundwater in the Samalpatti-Sevathur carbonatite complexes.
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Statistical analysis

In this study, a multivariate statistical approach was applied to emphasize the relationships between toxic constituents detected in GW samples and the key variables influencing the transport and distribution of ionic contaminants. PCA was utilized to identify significant components among the physicochemical variable while retaining as musch of the original data variability as possible. Additionally, inter-ionic relationships were examined to gain insight into potential sources and migration pathways of ions within the particulate medium16.

To explore the degree of association among variables, Pearson’s correlation (r) was used to identify potential pollutant sources, where a higher r value indicates a stronger association between variables. Correlation values of r > 0.7, 0.5 < r < 0.7, and r < 0.5 were interpreted as strong, moderate, and weak relationships, respectively19,21,48.

Subsequently, PCA was performed to reduce data dimensionality and determine dominant factors controlling GW chemistry. Prior to PCA, data inventory was normalized and reformatted into an association coefficient matrix to ensure equal contribution from all variables by standardizing them to a common scale. This process involved mean-correcting the data (subtracting the mean) and scaling it by the standard deviation to prevent variables with larger magnitudes from prevailing principal components, thereby allowing the PCA to more effectively reveal the underlying patterns and interrelationships among variables. Eigenvalues and eigenvectors were subsequently derived from the correlation matrix, and less significant components were excluded. Components with eigenvalues greater than one were considered to make a substantial contribution to the total variance. To improve interpretability, the varimax rotation method was employed to factor loadings to orient them more closely to + 1, 0, or − 1, which represent positive, neutral, and negative associations, respectively16.

Irrigation indices of water quality

Chemical characteristics of water define its suitability, with sodicity, toxicity and salinity being major concerns for agricultural purposes12,25,26. Excess sodium can adversely affect soil composition and restrict water movement through it27,28. Commonly used variables help to evaluate the applicability of water for irrigation: Sodium %, Magnesium Hazard Ratio (MHR), Residual Sodium Carbonate (RSC), Electrical Conductivity (EC), Kelley’s Ratio (KR), Sodium Adsorption Ratio (SAR), and Permeability Index (PI), the ionic ranges used to calculate the irrigation indices are in meq/L20,49. The formula derived to compute irrigation indices of GW quality is given in Table 3.

Table 3 Standard equations to derive the irrigation indices of GW quality.
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Health risk assessment

Groundwater containing natural and man-induced pollutants poses substantial health impacts when regularly consumed through ingestion7,36. Potential health impacts linked with intake of contaminated ions were evaluated in compliance with guidelines prescribed by the United States Environmental Protection Agency (US EPA)57. Among various ions, \(\:{NO}_{3}^{-}\) and \(\:{F}^{-}\) were crossed WHO potable limits. Therefore, Mean Daily Intake (MDI) was estimated specifically for oral intake of \(\:{NO}_{3}^{-}\) and \(\:{F}^{-}\) across all samples for children, teenagers, men and women based on standard procedure4,6,8,11. MDI and hazard quotient (HQ) for \(\:{NO}_{3}^{-}\) and \(\:{F}^{-}\) were calculated separately using Eqs. 10 and 11 which were derived by USEPA57.

$$\:MDI=\:\frac{{C}_{s}\times\:RI\times\:{E}_{f}\times\:{E}_{d}}{{T}_{ae}\times\:ABW}$$
(10)

Where, MDI signifies \(\:{NO}_{3}^{-}\) and \(\:{F}^{-}\) consumed daily (mg/kg/day), \(\:{C}_{s}\) corresponds to the pollutant in groundwater (mg/L), \(\:RI\) and denotes ingestion (L/day), which is 1.0 for children, 1.7 for teens, 2.3 for women and 3 for men. \(\:{E}_{f}\) denotes exposure frequency (365 days/year for all). \(\:{E}_{d}\) is exposure duration (in years), 8 for children, 19 for teens, 40 for women and men. \(\:ABW\) (in kg) represents average body weight: 21, 38, 55, 75 for children, teens, women, and men. \(\:{T}_{ae}\) = time of average exposure in days [2920 (Children), 6935 (teens), 14600 (women and men)]. In MDI calculation, all parameters except \(\:{C}_{s}\) were assumed to be the same for both \(\:{NO}_{3}^{-}\) and \(\:{F}^{-}\)29,57,58.

$$\:HQ=\frac{MDI}{{R}_{f}d}$$
(11)

Where, \(\:HQ\) is hazard quotient of \(\:{NO}_{3}^{-}\) and \(\:{F}^{-}\) intake, MDI of \(\:{NO}_{3}^{-}\) and MDI of \(\:{F}^{-}\) is obtained from Eq. 10 and \(\:{R}_{f}d\) (reference dose in mg/kg/day) of \(\:{NO}_{3}^{-}\) is 1.6 and \(\:{F}^{-}\) is 0.0657,29,30. If HQ surpasses 1, indicates potential non-cancerous health impacts due to exposure of contaminants.

Ethical statement

The present study did not involve experiments on human participants, the use of human biological materials, or the collection of personal or individual-level human data. The health risk assessment was conducted using USEPA recommended exposure and risk assessment models, based solely on measured GW ranges, literature derived exposure parameters. As no human subjects were directly involved, approval from an institutional ethics committee and informed consent were not required for this study.

Results and Discussion

Descriptive Statistics of physico chemical variables

A total of 13 parameters from 75 GW samples were systematically analysed and compared with WHO46 and BIS47 guidelines for potability. Table 1, illustrates the statistics of GW samples, with pH varying from 7 to 8.72 (mean: 7.65). pH of GW reflects its acidic or base balance and represents its chemical equilibrium18. Only one sample crossed the acceptable limit of pH (Fig. 3a). When CO2 interacts with carbonate rocks and soils rich in carbonate minerals in the existence of fresh water, it leads to the formation of HCO₃⁻, which in turn contributes significantly to GW alkalinity14,38,59. CO₃²⁻ was present in only two samples (OW19 and OW20), ranging 78 mg/L and 46 mg/L, respectively. HCO₃⁻ ranged between 268.4 mg/L and 988.2 mg/L (mean of 571.45 mg/L); notably, 37.3% samples exceeded tolerable range (Fig. 3b). The elevated levels of Na⁺, Mg²⁺, Ca²⁺, and other dissolved salts in GW largely contribute to its TDS. Increased TDS concentrations within the aquifer suggest a high presence of ionic constituents and prolonged interactions between water and surrounding rock materials16. TDS varied from 477.14 to 1692.86 mg/L, with 93.3% (WHO46) and 100% (BIS47) samples falling within the limit of the drinking range (Fig. 3c). Primary concern associated with GW quality is its hardness, which mainly arises from Mg²⁺ and Ca²⁺ rich lithological formations. TH ranged 230–690 mg/l, with 20% (WHO46) and 8% (BIS47) samples crossing range for potability. Overall, all GW samples in the region fall under hard and very hard classes8. Mg²⁺ and Ca²⁺ concentrations ranged 29.2 to 128.9 mg/L and 32–140 mg/L, respectively, and all samples were within the limit for the purpose of drinking as per WHO46, but based on BIS47 6.67% samples crossed Mg²⁺ range for potability (Figs. 3 g and 3e). Enrichment of K⁺ in GW may results from leaching of K⁺-based fertilizers through soil, which allows K⁺ to infiltrate and accumulate in GW system22. K⁺ levels changed from 1.62 to 31.5 mg/L, average of 9.34 mg/L; however, 32% samples crossed WHO recommendations (Fig. 3 d). High K⁺ in GW intake may pose issues in gastrointestinal and adverse effects on the nervous system31,37,49. Na⁺ is a highly reactive alkali metal naturally present in the environment and plays an essential role in human health. It enters GW primarily by dissolution and weathering of Na⁺ bearing rocks and silicate minerals34,60. Na⁺ values were 6.3 mg/L to 344.6 mg/L (mean: 113.3 mg/L). About 20% of samples were not safe for drinking as per WHO46 (Fig. 3i). High Na⁺ in GW is often attributed to water-rock interactions, it can reduce soil permeability and alter its physical characteristics, and it may increase the risk of cardiac related disease58,60. SO₄²⁻ content is attributed to oxidizing conditions within the aquifer system. Breakdown of organic matter facilitates sulphide production, whereas the presence or dissolution of oxygen enhances the conversion of sulphides into SO₄²⁻18,60,61. Concentration of SO₄²⁻ ranged between 0.5 and 143.5 mg/L, average of 31.7 mg/L; all samples were safer to drink (Fig. 3 h), Cl⁻ in GW primarily comes from human-induced activities, including domestic sewage discharge, wastewater infiltration, agricultural runoff, leakage from septic systems, inadequate waste disposal, and industrial effluents5. Cl⁻ ranged from 28.36 to 709 mg/L (mean: 138.2) with 98.7% (WHO46) and 100% (BIS47) samples within acceptable limit (Fig. 3f).

Fig. 3
Fig. 3
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Spatial maps of Physico-Chemical varibales (a) pH, (b) HCO₃⁻, (c) TDS, (d) K⁺, (e) Ca²⁺, (f) Cl⁻, (g) Mg²⁺, (h) SO₄²⁻, (i) Na⁺, (j) NO₃⁻, and (k) F⁻ using QGIS 3.16.14.

NO₃⁻ and F⁻ in GW

At the research site, NO₃⁻ in GW spanned from 4.99 to 182.55 mg/L, with 54.09 mg/L mean range, respectively. Among the 75 GW samples analyzed, 32 crossed the suggested range of 45 mg/L for the purpose of human consumption (Fig. 3j). The main source of NO₃⁻ pollutants is due to human practices, particularly the application of nitrate rich fertilizers and the presence of animal feces in agricultural fields, these can infiltrate the soil and leach into GW39,29,62. Range of F⁻ ion in GW was 0.18–4.6.18.6 mg/L (mean: 1.76 mg/L). Notably, 50 samples exceeded the permissible limits for potability set by the WHO (Fig. 3k). The dissolution and leaching F⁻ hosting minerals like fluorite, vermiculite, biotite, apatite, and hornblende and the usage of phosphate-rich fertilizers such as Diammonium Phosphate, fungicides, insecticides may contribute as sources of F⁻ in GW40,30. The presence of NO₃⁻ and/or F⁻ in GW can pose serious health risks upon consumption15. One of the sources of NO₃⁻ on GW quality (poultry farms) is illustrated in Fig. 4.

Fig. 4
Fig. 4
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One of the sources of NO₃⁻ on GW quality in the Samalpatti-Sevathur carbonatite complexes.

The integration of drainage, geological, and LULC data with GW chemistry provides a better understanding of the factors influencing GW quality. Areas with low drainage density promote higher infiltration, allowing greater GW recharge and extended interaction between water and rocks. The lithology comprising syenite, carbonatite, and gneissic formations, plays a major role in defining GW chemistry through the dissolution and weathering of minerals, leading to increased concentrations of F⁻, Mg²⁺, and Ca²⁺. LULC distribution also affects GW quality, as agricultural lands show higher NO₃⁻ levels from animal manure and fertilizer leaching, while settlement areas experience localized pollution from domestic waste29. Conversely, scrub and fallow lands reflect limited human activity and are generally associated with relatively better GW quality; however, underlying rock formations contribute to elevated F⁻ in GW.

EWQI

Entropy water quality index was computed using 11 parameters from 75 GW samples, such as TDS, HCO₃⁻, pH, K⁺, Ca²⁺, Cl⁻, Mg²⁺, SO₄²⁻, Na⁺, F⁻, and NO₃⁻. EWQI outcomes are depicted in Fig. 5, providing an overall assessment of GW quality based on major ions. Within the Samalpatti-Sevathur complexes, EWQI ranged from 30.12 to 86.68, with a mean range of 54.8. Among the samples, 37.3% were classified as good and fit for drinking, while 62.7% were moderate quality, indicating the need for treatment before consumption21,41.

Fig. 5
Fig. 5
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Spatial distribution of EWQI for drinking purpose in the Samalpatti-Sevathur carbonatite complexes using QGIS 3.16.14.

Relationship among various ions

Correlation analysis illustrates the extent of association among various hydrochemical parameters, offering valuable insights into the geochemical and anthropogenic processes influencing GW composition (Table 4). A very strong positive connection between EC and TDS (r = 1.0) indicates that conductivity is largely governed by the concentration of dissolved ions, confirming that both parameters collectively reflect the overall water salinity. A strong association between Na⁺ and Cl⁻ (r = 0.6) suggests a common origin, most likely evaporation-induced, typical of semi-arid environments. A weaker association with Ca²⁺ and Mg²⁺ implies that carbonate equilibrium reactions, such as leaching of carbonate minerals, play an important role in controlling groundwater alkalinity and buffering capacity. K⁺ exhibits weak to moderate correlations with other ions, indicating limited mobility and localized anthropogenic inputs, potentially arising from fertilizer application or soil ion-exchange processes. F⁻ displays a moderate relationship with Na⁺ (r = 0.477) and HCO₃⁻ (r = 0.468), suggesting that its concentration is primarily geogenic, controlled by leaching of fluoride-bearing minerals rather than anthropogenic influences. Negative correlation of NO₃⁻ with Na⁺, SO₄²⁻, HCO₃⁻, and F⁻ and positive correlation of NO₃⁻ with Ca²⁺ reflect sources from animal manure leaching and agricultural input, particularly from calcium–nitrate fertilizers and associated leaching processes in recharge areas19,31.

Table 4 Relationship among major ions in GW.
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Principal component analysis (PCA)

PCA loadings (Fig. 6a) describe the extent to which each GW quality contributes to PC, thereby helping to identify dominant factors influencing GW chemistry. PC1, high positive loadings of EC (0.4), Na⁺ (0.41), and TDS (0.4) along with moderate contributions from Cl⁻ (0.34) and HCO₃⁻ (0.31), suggests this component represents salinity and ionic strength of GW, primarily governed by mineral dissolution and ion exchange processes. PC2 shows strong loadings for K⁺ (0.38), Cl⁻ (0.34), Mg²⁺ (0.48), and NO₃⁻ (0.39), indicating influence of anthropogenic activities such as agricultural return flow and fertilizer leaching, which contribute NO₃⁻ and K⁺ to GW. PC3, high positive loadings of K⁺ (0.62) and F⁻ (0.61) reflects geogenic processes, particularly silicate weathering and dissolution of fluoride rich minerals. PC4 is characterized by strong loading of pH and a negative loading of HCO₃⁻ (−0.43), representing acid–base balance and carbonate equilibrium in GW. PC5 exhibits high positive loadings of SO₄²⁻ (0.51) and K⁺ (0.49), signifying anthropogenic inputs from fertilizer application. PC6, dominated by high positive loading of Mg²⁺ (0.59), may correspond to silicate weathering processes.

In higher-order components (PC7–PC12), the loadings are generally smaller and more dispersed, reflecting minor or localized influences. For instance, NO₃⁻ and SO₄²⁻ display high positive loadings in PC7 and PC9, suggesting site-specific contamination, while Na⁺ dominance in PC11 may indicate cation exchange or evaporative concentration effects in certain locations. Overall, the PCA results indicate that GW chemistry in the research vicinity is primarily controlled by salinity, rock–water interaction sources of F⁻ pollution, and anthropogenic activities that contribute NO₃⁻ contamination, with secondary contributions from localized geochemical and hydrogeological variations16.

Hydrochemical processes

A Piper trilinear diagram was plotted to classify GW types, in which 75 GW samples were plotted (Fig. 6b). The diagram incorporates milliequivalent ranges of major ions in GW samples to interpret hydrochemical characteristics24. It comprises two triangular zones and a central-diamond zone representing overall GW type18,63. In the cation triangle, 52% GW (34.7% DW and 17.3% SW) fall within no dominant category, indicating mixed cationic character from complex water-rock relation. While 26.7% (17.3% DW and 9.3% SW) samples fall in Na⁺ type, 9.3% and 12% of samples fall within Ca²⁺ and Mg²⁺ type. In the anion phase, most samples, 94.7% (53.4% DW and 41.3% SW) GW samples fall in HCO₃⁻ zone, followed by 4% and 1.3% of DW samples falling under Cl⁻ and no dominant facies. The high bicarbonate and relatively low calcium concentrations suggest cation exchange, where Ca²⁺ is replaced by Na⁺ in groundwater. In the central diamond field, the majority of samples (68% GW) correspond to CaHCO₃ type, followed by CaNaHCO3 type (26.7% samples) indicating carbonate weathering and ion-exchange as the dominant geochemical processes. while only 4% and 1.3% of GW samples fall under Mixed CaMgCl and NaCl types, respectively, indicating minor contributions from evaporite dissolution and anthropogenic pollution and Gibbs (Fig. 6c and d) was used to assess the influence of three main mechanisms, rock weathering, evaporation, and precipitation zone on GW chemistry. All GW samples plot within the rock dominance field, indicating water-rock interactions are principal natural processes controlling GW hydro chemistry in the research vicinity7,20.

Fig. 6
Fig. 6
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Diagrammatic representation of (a) PCA, (b) Piper, and (c and d) Gibbs plots.

Irrigation indices

SAR

According to the sodium adsorption ratio, all GW samples from Samalpatti-Sevathur complexes range between 0.14 and 8.45 (Fig. 8a). Since all values are below 10, the water is considered excellent and indicates a low sodium hazard for irrigation purposes, based on USSL classification. USSL diagram (Fig. 7a) was plotted using SAR and EC values. In this region, EC varied from 668 µs/cm to 2370 µs/cm (Fig. 7c). As per irrigation suitability51, salinity hazard (C) is compartmentalized as follows: C1 (0–250 µs/cm) indicates excellent with weakly saline; C2 (250–750µs/cm) is good quality with an intermediate salinity range; C3 (750 to 2250µs/cm) represents permissible water with intense salinity; and above 2250µs/cm (C4) is classified as unsuitable due to very high hazard based on salinity. Based on these categories, 4%, 93.3%, and 2.7% of samples fall under the good, permissible, and not suitable categories for irrigation. According to USSL classification, 62.7% (n = 47) samples fall within the C3S1 category, indicating that water is medium quality; 30.7% (n = 23) fall under C3S2, representing poor quality, and 4% (n = 3) and 2.6% (n = 2) fall into C2S1 (good) and C4S2 (unsuitable) categories, respectively, based on combined salinity and sodium hazard. Overall, 33.3% of GW samples lacked the standards for irrigation purposes64.

RSC

Excess sodium bicarbonate and carbonate change soil texture through the breakdown of organic matter, which results in a black residue forming on the surface once the soil dries. This is accompanied by RSC and it reduces the permeability of soil49. TheRSC values in the study area range from − 6.8 to 8.6 (Fig. 8b). RSC levels below 1.25meq/L classify water as ideal for irrigation. In this study, 60% of samples falls below 1.25 and are perfectly ideal for irrigation, whereas 8% and 32% fall into category of medium and unfit for irrigation, respectively26.

Na %

Percent of Na⁺ in the samples varies between 5.2% and 73.3%. Of these, 36% and 25.3% fall under the category of excellent and good, while 28% and 10.7% fall within permissible and doubtful ranges (Fig. 8c). Na% and EC can be used to illustrate Wilcox54 shown in Fig. 7b, suggested that 74.7% of samples are suitable for irrigation, while 25.3% require treatment before agricultural use12.

MHR

MHR is determined by the proportion of Mg²⁺ relative to the combined values of Mg²⁺ and Ca²⁺. In the Samalpatti-Sevathur carbonatite complexes, MHR ranges between 31.3 and 80 (Fig. 8 d). Based on this, 21.3% of samples (n = 16) are considered suitable for irrigation, and 78.7% (n = 59) are not suitable, which could negatively impact the production of crops. Although most other irrigation indices fall within the suitable to moderate range, MHR has contrasting results. This is likely due to geological setting, as the aquifer materials in the carbonatite complexes are rich in magnesite and dolomite, leading to higher Mg²⁺ compared to Ca²⁺28.

KR

In irrigation water, Na⁺ existence can be determine using the ratio proposed by Kelley55. A KR value above 1 indicates inappropriateness for irrigation. In the research zone, 73.3% of GW samples are suitable for irrigation based on KR, while 26.7% exceed the recommended threshold and are thus not suitable (Fig. 8e)28.

PI

PI ranged 27.9% to 92% (Fig. 8f). Out of 75 samples, 16% and 84% were classified as suitable (> 75%) and good (25–75%) for irrigation use, respectively. Doneen56 developed a diagram to assess water permeability based on the total ion concentrations and PI values, as shown in Fig. 7d27.

Fig. 7
Fig. 7
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Diagrams representing irrigation suitability (a) USSL, (b) Wilcox, (d) Doneen and (c) spatial view of EC. The spatial maps were generated using QGIS version 3.16.14 (Hannover-https://qgis.org/).

Fig. 8
Fig. 8
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Spatial distributions of irrigation indices (a) SAR, (b) RSC, (c) Na%, (d) MHR, (e) KR, and (f) PI using QGIS 3.16.14.

Health risk assessment

Prolonged consumption of GW contaminated with geogenic and human-induced contaminants can pose severe health problems to humans765,6667. In the Samalpatti-Sevathur carbonatite complexes, contaminations of NO₃⁻ and F⁻ exceed the WHO recommended permissible limits for potable purposes. Based on these values, non-carcinogenic risk57 related to NO₃⁻ and F⁻ was computed for children, teens, women, and men by adopting Eqs. 10 and 11. MDI and HQ for NO₃⁻ and F⁻ are illustrated in Table 5.

Table 5 Statistical range of MDI and HQ for NO₃⁻ and F⁻ in the Samalpatti-Sevathur carbonatite complexes.
Full size table

HRA for NO₃⁻

The MDI ranged from 0.24 to 8.69 for children, 0.22 to 8.17 for teens, 0.20 to 7.3 for men, and 0.21 to 7.63 for women. Correspondingly, HQ values ranged between 0.15 and 5.43 (children), 0.14 and 5.10 (teens), 0.12 and 4.56 (men), and 0.13 and 4.77 (women). A significant proportion of the samples - about 60% [29.3% of DW (n = 22) and 30.7% of SW (n = 23)], 57.3% [28% of DW (n = 21) and 29.3% of SW (n = 22)], 50.7% [24% of DW (n = 18) and 26.7% of SW (n = 20)], and 52% [25.3% of DW (n = 19) and 26.7% of SW (n = 20)] for children, teens, men, and women exceeded HQ threshold andpose potential health risks such as childhood diabetes, cardiovascular diseases, blue baby syndrome, high blood pressure and low birth weight29.

HRA for F⁻

MDI varied from 0.008 to 0.219 (children), 0.0078 to 0.206 (teens), 0.007 to 0.184 (men), and 0.0073 to 0.192 (women). The corresponding HQ values varied between 0.139 and 3.65 (children), 0.13 and 3.43 (teens), 0.117 and 3.1 (men), and 0.122 and 3.21 (women). The percentage of samples exceeding the HQ threshold was 73.3% [49.3% of DW (n = 37) and 24% of SW (n = 18)] for children, 69.3% [46.65% of DW (n = 35) and 22.65% of SW (n = 17)] for teens, 66.7% [45.4% of DW (n = 34) and 21.3% of SW (n = 16)] for men, and 68% [45.3% of DW (n = 34) and 22.7% of SW (n = 17)] for women, indicating potential health risks relevant to F⁻ like anxiety, skeletal fluorosis, and Alzheimer’s disease, dental fluorosis30. Overall, NO₃⁻ and F⁻ related HQ analysis reveals that Children as the most at-risk group, surpassing teens, women, and men, with men exhibiting the lowest risk levels. NO₃⁻ related health risks were found to be comparatively higher in SW, while F⁻ related risks were more prominent in DW. High NO₃⁻ ranges in shallow aquifers are attributed to surface-derived contamination from agricultural runoff, infiltration of animal manure and fertilizer use. Conversely, higher F⁻ in deeper aquifers are primarily geogenic, resulting from prolonged water–rock interaction, alkaline conditions, and leaching of F⁻ enrich minerals such as fluorite and biotite. These contrasting patterns highlight the combined influence of anthropogenic inputs and geochemical processes on GW quality and associated health risks in the research vicinity. Figure 9 displays geographic distribution of HQ for NO₃⁻ and F⁻ among all age categories.

Fig. 9
Fig. 9
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Spatial variation of HQ for NO₃⁻ and F⁻ (a and d) Children, (b and e) teens, (c and f) women, and (g and h) men using QGIS 3.16.14.

Recommendations

In Samalpatti and Sevathur carbonatite complexes, elevated levels of both NO₃⁻ and F⁻ were observed in GW, indicating the combined effect of geogenic and man-induced activities contribute to contamination. To mitigate these contaminants, several sustainable measures are recommended.

  • Reducing the application of fertilizers high in chemicals can significantly give the positive result of NO₃⁻ reduction in GW29,31.

  • Managed Aquifer Recharge (MAR), constructing rainwater recharge structures including percolation tanks, especially in run-off zones or NO₃⁻ free areas can help to dilute F⁻ and enhance GW sustainability30.

Before implementing these two remedial measures, it is essential to understand the seasonal variation of GW in these regions to determine whether the natural processes influencing the quality of GW such as dilution and dissolution. Additionally, efforts should be made to minimize nitrate usage in these areas, as both NO₃⁻ and F⁻ have exceeded tolerable levels in most samples. During the recharge process, one contaminant may increase while other decreases, depending on the dominant geochemical mechanism.

  • Installation of common Reverse Osmosis (RO) water treatment systems can effectively reduce these contaminants and provide water to the local population.

Collectively, these strategies promote sustainable GW use, safeguard rural livelihoods, and contribute to achieving SDG 6.

Conclusions

The physicochemical analysis revealed elevated ranges of NO₃⁻ and F⁻ in the GW of the Samalpatti-Sevathur carbonatite complexes, posing potential health risks to local communities. NO₃⁻ ranged from 4.99 to 182.55 mg/L, averaging 54.09 mg/L. Elevated NO₃⁻ in GW are mainly linked to human-induced practices such as the use of animal waste and DAP fertilizer for irrigation. While F⁻ ranging 0.18 to 4.6 mg/L, averaging 1.76 mg/L and is predominantly from natural geochemistry, owing to the presence of F⁻ containing minerals in these carbonatite complexes. The results highlighted that, out of 75 samples, 42.3% exceeded the permissible limits for NO₃⁻ and 66.7% exceeded the limits for F⁻ for potable use. EWQI assessment showed that 37.3% of GW samples met drinking standards, whereas 62.7% were required treatment due to contamination of NO₃⁻ and F⁻. Regarding irrigation suitability, GW was generally suitable based on SAR, Na%, RSC and KR indices, although MHR indicated elevated Mg²⁺ over Ca²⁺ in more than half of the samples, likely influenced by carbonatite geological setting. Correlation and PCA analyses indicate that GW chemistry is mainly influenced by salinity, rock–water reactions, and anthropogenic inputs. F⁻ shows moderate correlation with Na⁺ and HCO₃⁻, suggesting a geogenic origin from dissolution processes, while NO₃⁻ contamination arises from human activities. Piper and Gibbs diagrams confirm that dominant CaHCO₃ and CaNaHCO₃ facies result from carbonate weathering, ion exchange, and water–rock interaction processes in region. The main theme of this research, the non-carcinogenic health risk assessment from consuming contaminated GW, showed that Hazard quotient of NO₃⁻ and F⁻ exposure are significant, especially among children. Health risks from NO₃⁻ were higher in SW due to surface contamination from agricultural runoff, fertilizer leaching, and animal waste infiltration. In contrast, elevated F⁻ in DW were of geogenic origin, linked to prolonged water–rock interaction, alkaline conditions, and dissolution of minerals enriched in F⁻ like fluorite and biotite. These findings emphasize that both anthropogenic activities and natural geochemical processes collectively influence GW quality and associated health risks in research vicinity. The research emphasizes the need for community-level awareness to minimize the usage of diammonium phosphate fertilizers, fungicides, and insecticides for irrigation purposes to reduce human-induced NO₃⁻ and F⁻ concentrations in GW. Additionally, it suggests implementing managed aquifer recharge as in-situ remediation strategy and installing public reverse osmosis systems as an ex-situ. Together, these measures can significantly enhance GW quality and ensure safe water availability. This study not only clarifies geochemical mechanisms influencing GW composition but also provides practical insights to guide sustainable water-resource management and public health protection in the research vicinity.