Groundwater quality assessment and spatio-seasonal variation using GIS and statistical analysis in parts of Dindigul district, Tamil Nadu, India

Abstract

Groundwater is the primary source of domestic and agricultural use in Chinnalapatti Firka, Dindigul district, Tamil Nadu, India. This study assesses the suitability of groundwater for drinking and irrigation. A total of 120 samples were collected and analysed for physicochemical parameters during the pre-monsoon (PRM) and post-monsoon (POM) seasons, and the contaminants were mapped using GIS-based interpolation. The potential of hydrogen, electrical conductivity, total dissolved solids, total hardness, calcium, magnesium, sodium, potassium, chloride, sulphate, bicarbonate, and fluoride exceed the most desirable limit in both seasons. The Piper diagram indicates that the PRM water type is Mixed HCO₃^--Cl⁻-SO₄²⁻, whereas in POM, the water type is Na⁺-K⁺-SO₄²⁻-Cl⁻. Gibbs indicates that rock-water interaction is the primary mechanism controlling the groundwater chemistry in both seasons. The Pearson correlation highlights seasonal shifts in groundwater chemistry, revealing natural processes in PRM and potential anthropogenic influences during the POM season. The groundwater quality index indicates 53% poor and 47% good water in the PRM season, whereas in POM, 70% good and 30% poor water, indicating improved water quality during POM. Wilcox and other irrigation indices indicate improved groundwater suitability for irrigation during the POM season, with reduced sodium and chloride hazards. In contrast, a slight sulphate-related risk arises from increased filtration and oxidation. Overall, weathering, rock-water interactions, and anthropogenic activities are significant factors that affect groundwater chemistry in the study area.

Introduction

In India and worldwide, groundwater is the most widely used freshwater resource, meeting over 85% of rural, 50% of urban, and 62% of agricultural water demands^1,2. Its consistent availability and generally consistent quality make it a popular choice for drinking and irrigation. However, groundwater quality degradation is being accelerated by a combination of geogenic influences, such as mineral dissolution and rock weathering, and increasing anthropogenic pressures, such as uncontrolled fertilizer use, industrial waste discharge, and over-extraction³.

Natural processes of evaporation and monsoonal recharge produce seasonal variability, a key factor in altering groundwater geochemistry. During the PRM (Pre-monsoon season), elevated levels of fluoride, nitrates, and total dissolved solids (TDS) are observed due to longer residence times and reduced recharge^4,5. During the POM (Post-monsoon season), the ion dilution improves water quality. Natural rock-water interactions, along with fertilizer leaching, increase fluoride levels by dissolving fluoride-bearing minerals such as apatite and biotite⁶.

Aquifers dominated by charnockite, migmatite release Ca²⁺, Mg²⁺, and F⁻ through mineral dissolution processes⁷. These ions alter soil permeability, salinity, and crop productivity, influencing potability and the appropriateness of irrigation. Thus, sustainable agriculture requires groundwater assessment using irrigation indices such as the Sodium Adsorption Ratio (SAR), Kelly’s Ratio (KR), Magnesium Ratio (MR), and Permeability Index (PI)^8,9.

Recent advances in geospatial approaches, including GIS-based spatial modelling and interpolation methods (such as IDW), can more efficiently detect contamination hotspots¹⁰. Furthermore, the comprehension of hydrogeochemical processes, including ion exchange, silicate weathering, and anthropogenic pollution, is enhanced by the integration of statistical techniques with hydrochemical plots (Piper, Gibbs)^3–5,7,8,11.

In light of this, the current study examines the cyclic fluctuations in groundwater quality in Chinnalapatti Firka, Dindigul district. This area is marked by intense agricultural practices and underlain by high-grade metamorphic terrains. The study aims to assess (1) the groundwater quality by analyzing physicochemical parameters, (2) mapping contaminants using GIS, (3) dominant water types and suitability indices, and (4) applying a multivariate statistical tool to identify interrelationships and key factors.

Study area

Chinnalapatti firka consists of nine villages in the Athoor taluk, and the study area is 13 km from Dindigul town. The latitude and longitude extensions are 10°15′0ʺ- 10°20’ʺN and 77°53′20ʺ—77°58′20ʺE, covering an area of 55.69 km² as shown in Fig. 1. The summer months had a relatively high mean temperature of 27–39 °C from April to June. During these periods, warm air contained more water vapour, increased evaporation from water bodies, soil, and vegetation, and decreased water availability¹¹. From November to January, temperatures range from 22 to 31 °C. This district receives annual rainfall ranging from 700 to 1600 mm, with minimal rainfall in and around Palani (709 mm) and Vedasandur (732.4 mm). It slowly increased towards the south and reached a maximum at Kodaikanal (1608.8 mm)¹².

Fig. 1.

Study area map (Map is generated using ArcGIS 10.6 software,).

The study area comprises migmatite and charnockite gneiss complexes. High-grade metamorphic rocks like charnockite are usually abundant in feldspar, quartz, and pyroxene. It is recognized for its hardness and weather-resistant nature, and it undergoes deep crustal processes. In contrast, the migmatites are mixed rocks formed by partial melting, indicating high-temperature metamorphic conditions. Migmatites have low porosity, but secondary porosity develops from weathering and fractures, providing moderate groundwater storage¹³. Charnockites are crystalline and massive, with limited porosity, and they mainly occur in joints, fractures, and weathered zones. Groundwater in charnockite occurs in the weathered mantle, with flow and storage controlled by the degree of weathering and fracturing¹⁴. The study area comprises structural hills, pediment plains, and valleys, which influence hydrology. The plains and valleys improve groundwater recharge, while the steep hills increase surface runoff, which affects both groundwater storage and occurrence. These features, integrated with lithology, play a significant role in controlling groundwater movement and the regional spatial distribution^15–17.

Methodology

Groundwater sampling methods and evaluation

Groundwater samples were collected during the PRM (May 2024) and POM (December 2024) seasons of 2024 to evaluate physicochemical parameters. About 120 groundwater samples were obtained from the borewells. All samples were collected in 1 L bottles. The potential of hydrogen (pH), total dissolved solids (TDS), and electrical conductivity (EC) were measured using handheld instruments (pH: ECO pH TEST 1-EUTECH 01X460901; TDS & EC: ECO TDS TEST-EUTECH). All the bottles were washed 2–3 times with distilled water, and the wells were pumped for 5–10 min before sample collection. Groundwater sample bottles were sealed tightly, transported to the laboratory, and examined after storage at 4 °C. To ensure data quality, 10% of duplicate samples were collected in the study area, and field blanks were analysed to assess contamination during handling and sampling. Before each sampling, the instruments were calibrated using standard buffer solutions according to the manufacturer’s guidelines. All the readings were taken three times to minimize instrument error and ensure accuracy. Standard recommended techniques were used to determine physical and chemical parameters, including Ca²⁺, Mg²⁺, Cl-, CO₃₋ and HCO₃₋ levels, which were measured by volumetric titration¹⁸. A flame photometer (Elico CL0378) and spectrometer (Elico SL 207 mini) were used to determine Na⁺, K⁺, and SO₄²⁻concentrations. Fluoride was analyzed using a fluorimeter (Thermo Orion star A214 series pH/ISE benchtop meter kit), and nitrate was analyzed using a nitrate electrode meter (Lmion-40). Groundwater samples collected during the pre- and post-monsoon seasons were analysed for physicochemical parameters and validated using ion balance error. The Groundwater data were used to compute the GWQI and irrigation indices; these were integrated in ArcGIS 10.6 software using the IDW method. Statistical correlations were carried out. Finally, results were compiled and interpreted to ensure groundwater suitability.

The total hardness of CaCO₃ was calculated using the following Eq. (1):

1

The estimated ionic balance error is within the permitted range of ± 10%, as shown in Eq. (2):

2

Groundwater water quality index (GWQI)

The groundwater quality index was calculated using the weighted arithmetic index method to evaluate water quality¹⁹. This is the easiest and most accurate method for calculating the water quality index for a particular location, as shown in Eq. 3–6.

For each parameter, the relative weight (W_r) was calculated using Eq. 3 below:

3

The quality rating (qi) for each parameter was determined using Eq. 4, the formula:

4

Vi is the parameter’s observed value, V_ideal is the parameter’s ideal value, and V_Standard is the parameter’s permissible value according to²⁰ guidelines. Every parameter is assigned a relative weight (W_r) based on the significance of water quality. The formula used to calculate the subindex (S_i) of each parameter is as follows Eq. 5:

5

The WQI is obtained by calculating all sub-indices, and the WQI is calculated using Eq. 6, the formula listed below:

6

∑W_r is the sum of all calculated relative weights, and ∑S_i is the sum of all calculated sub-indices (Table 1).

Table 1.

Lists the relative and assigned weights.

Parameter	Unit	Assigned weight (Wa)	Relative weight (Wr)
pH		4	0.105
Electrical conductivity (EC)	µS/cm	3	0.079
Total Dissolved Solids (TDS)	mg/L	4	0.105
Total Hardness (TH)	mg/L	2	0.053
Calcium (Ca2⁺)	mg/L	2	0.053
Magnesium (Mg2⁺)	mg/L	2	0.053
Sodium (Na⁺)	mg/L	3	0.079
Potassium (K⁺)	mg/L	2	0.053
Chloride (Cl⁻)	mg/L	3	0.079
Sulphate (SO₄2⁻)	mg/L	3	0.079
Nitrate (NO₃⁻)	mg/L	5	0.132
Bicarbonate (HCO₃⁻)	mg/L	2	0.053
Fluoride (F⁻)	mg/L	3	0.079
Total		38	1.000

The WQI is divided into “Excellent < 50”, “Good 50 -100”, “Poor 100—200”, “Very Poor 200—300”, and “unsuitable for drinking > 300” to evaluate water quality²¹.

Irrigation water quality indices

The groundwater suitability for irrigation purposes was calculated using various indices, including the Kelly ratio (KR), sodium percentage (Na%), chloride hazard index (CHI), magnesium adsorption ratio (MAR), sulphate hazard index (SHI), and Sodium adsorption ratio (SAR), using Eqs. (7) – (12).

7

8

9

10

11

12

Spatial analysis using inverse distance weighting

The inverse distance weighting method is commonly used for spatial interpolation to assess environmental factors like air quality, groundwater pollution, and contaminant distribution. IDW estimates values at unmeasured locations based on the proximity of known values, with points closer receiving higher weights²². This method has proven effective for mapping pollutants and evaluating their potential impacts on society’s health and the environment²³ introduced an enhanced version of IDW, inverse path distance weighting (IPDW), which improves spatial mapping accuracy by accounting for barriers such as peninsulas and islands in coastal water quality studies. These IDW advancements and variants provide valuable insights into environmental monitoring and resource management^22,23.

Statistical analysis

A comprehensive statistical examination of the groundwater characteristics’ highest, lowest, mean, and standard deviation provided a complete view of their range. Using SPSS multivariate techniques, such as Pearson’s correlation, to examine correlations across each parameter. It is used to identify trends in different groundwater quality parameters; values of + 1 or − 1 indicate positive or negative correlations, respectively. These help identify the major factors affecting groundwater quality. The correlation heatmap provides visual cues to clarify relationships. The detailed methodology is shown in Fig. 2, which represents the workflow used in the study.

Fig. 2.

Methodology flowchart.

Results and discussion

Hydrochemical characteristics

Physical characteristics of groundwater

Tables 2 and 3 provide a statistical outline of the groundwater physicochemical results for the PRM and POM seasons.

Table 2.

A qualitative statistical study of groundwater quality characteristics (PRM).

Parameters (PRM)	Min	Max	Mean	Std Dev	Skewness	Kurtosis	Median	20		% of Samples Exceed the Limit
								Most Desirable	Not Permissible
pH	7	8.6	7.68	0.38	0.23	−0.35	7.65	6.5–8.5	< 6.5 and > 8.5	2
EC	823	3861	1764.92	659.96	1.05	0.866	1646	< 1500	> 1500	57
TDS	576.1	2702.7	1235.44	461.97	1.05	0.866	1152	< 500	> 1500	20
TH	526.2	1474	990.8	205.1	−0.28	−0.26	1022	< 100	> 500	100
Ca2+	132.41	313.42	214.75	43.21	0.29	−0.33	209.3	< 75	> 200	62
Mg2+	30.14	230.17	110.3	37.29	0.19	0.94	109.17	< 50	> 150	10
Na+	63	265.1	155.73	47.63	0.11	−0.47	159.85	< 200	> 200	18
K+	6	186	20.13	12.15	2.53	9.64	18.5	< 10	> 10	88
Cl–	128.7	556.47	321.73	105.24	0.13	−0.88	320.39	< 250	> 250	67
NO3−	19.9	40.9	30.19	5.85	0.23	−0.97	28.99	< 45	> 45	-
SO4 2–	133.3	718.4	416.5	135.3	0.09	−0.64	413.64	< 400	> 400	55
HCO3−	285.9	924	599.2	133.9	−0.26	0.07	604.4	< 300	> 600	55
F−	1	3	1.6	0.46	1.64	3.42	1.5	< 1.5	> 1.5	22

Table 3.

A qualitative statistical study of groundwater quality characteristics (POM).

Parameters (POM)	Min	Max	Mean	Std Dev	Skewness	Kurtosis	Median	20		% of Samples exceed the limit
								Most desirable	Not permissible
pH	7.7	9.1	8.3	0.33	0.52	0.08	8.23	6.5–8.5	< 6.5 and > 8.5	15
EC	532	3270	1251.46	610.19	1.75	4.22	1014	< 1500	> 1500	20
TDS	372.4	2289	876	427.13	1.75	4.22	709.8	< 500	> 1500	7
TH	115	690	280	121.92	1.45	−0.24	255.5	< 100	> 500	27
Ca2+	102.78	199.17	143.23	24.92	0.66	4.97	141.53	< 75	> 200	10
Mg2+	34.43	128.51	74.57	22.91	0.70	0.36	71.16	< 50	> 150	7
Na+	74.65	314.14	165.65	54.34	0.69	−0.56	164.65	< 200	> 200	38
K+	6.92	214.6	32.75	41.73	3.25	23.19	18.52	< 10	> 10	92
Cl–	87.5	350	196.3	57.67	0.36	0.80	192.93	< 250	> 250	30
NO3−	11	29	21.25	4.34	-0.34	−0.24	21.5	< 45	> 45	-
SO4 2–	134.39	574.49	334.8	111	0.15	−0.81	326.91	< 400	> 400	52
HCO3−	117	744.68	524	144.45	-0.71	0.27	531.92	< 300	> 600	33
F−	0.8	1.5	1.06	0.16	1.91	2.11	1	< 1.5	> 1.5	7

During the PRM, the pH varied between 7 and 8.6 (Table 2), indicating that 2% of samples exceeded the most desirable limit, whereas in the POM season, the pH varied between 7.5 and 9.15 (Table 3), indicating that 15% of the samples exceeded the most desirable limit, indicating a 13% increase. Dilution and evaporation significantly alter pH levels during the PRM and POM. Areas with high evaporation rates, like Chinnalapatti Firka, have more concentrated groundwater minerals, which elevate the pH. The study area population mainly depends on agricultural practices, including the excessive use of fertilizers, especially calcium nitrate and ammonium compounds, which increase groundwater alkalinity, particularly in rural areas such as Chinnalapatti Firka²⁴. The pH spatial distribution maps for the PRM and POM seasons are shown in Fig. 3a, b. The study area groundwater is affected by both anthropogenic and geogenic sources. The common factors affecting are the dissolution of mineral-rich aquifers, rock-water interaction, evaporation, while waste-water infiltration and agricultural fertilizer leaching are significant anthropogenic factors. These factors drive the parameters such as electrical conductivity (EC). Total dissolved solids (TDS) and total hardness (TH), however, have different impacts^2,3. During the PRM season, the EC varied between 823 to 3861µS/cm (Table  2), indicating that 57% of the samples exceeded the most desirable limit, whereas, in POM season, the EC varied from 456 to 2890 µS/cm (Table 3), indicating that 20% of the samples exceeded the most desirable limit, indicating a 37% reduction. The EC spatial distribution maps for the PRM and POM seasons are shown in Fig. 3c, d. These elevated EC values are due to evaporative concentration and rock-water interactions, reflecting ionic enrichment in hard-rock aquifers²⁵. During the PRM season, TDS ranged from 576 to 2702 mg/L (Table 2), indicating that 20% of the samples exceeded the most desirable limit. In contrast, during the POM season, TDS varied from 372.4 to 2301.6 mg/L (Table 3), with 7% of samples exceeding the most desirable limit, indicating a 13% reduction. Agricultural return flows are minor inputs, and the dissolution of silicate-, carbonate-, and chloride-bearing minerals is the major factor for the elevated TDS^3,26. Prolonged consumption of water with higher TDS levels can lead to the formation of kidney stones, hypertension, and cardiovascular disease²⁷. The TDS spatial distribution maps for the PRM and POM seasons are shown in Fig. 3e, f.

Fig. 3.

Spatial interpolation maps of water quality parameters during the PRM season: (a) pH, (c) EC, (e) TDS, and (g) TH; and during the POM season: (b) pH, (d) EC, (f) TDS, and (h) TH. (Maps are generated using ArcGIS 10.6 software,)https://desktop.acgis.com/en/quick-start-guides/10.7/arcgis.desktop.quick.start.guide.htm).

During the PRM season, TH ranged from 240 to 1768 mg/L (Table 2), indicating that 100% of the samples exceeded the most desirable limit. In contrast, during the POM, it ranges from 140 to 1025 mg/L (Table 3), representing 27% samples exceeding the most desirable limit, indicating a 73% reduction. The primary contributor to the increased hardness is the leaching of magnesium and calcium from pyroxene and feldspars in charnockite-migmatites^1,28. The TH spatial distribution maps for the PRM and POM seasons are shown in Fig. 3g, h.

Chemical characteristics of groundwater

Major cations during the PRM, calcium levels ranged from 44 to 284.4 mg/L (Table 2), indicating that 62% of the samples exceeded the most desirable limit. In contrast, during the POM season, they varied from 28 to 235 mg/L (Table 3), with 10% of the samples exceeding the most desirable limit, indicating a 47% reduction. The excess calcium in the study area is due to the weathering of charnockite and migmatite rocks, which are rich in minerals such as feldspar and pyroxene, and to natural rock-water interactions, which release calcium ions into the water. Additionally, seasonal variations fluctuate the calcium concentrations, which are the primary geogenic inputs^24,28. Continuous consumption of calcium-rich water can lead to gastrointestinal issues, kidney disease, and corrosion of water supply pipes²⁹. The Calcium spatial distribution maps for the PRM and POM seasons are shown in Fig. 4a, b.

Fig. 4.

Spatial interpolation maps of water quality parameters during the PRM season: (a) Ca²⁺, (c) Mg²⁺, (e) Na⁺, and (g) K⁺; and during the POM season: (b) Ca²⁺, (d) Mg²⁺, (f) Na⁺, and (h) K⁺. (Maps are generated using ArcGIS 10.6 software https://desktop.acgis.com/en/quick-start-guides/10.7/arcgis.desktop.quick.start.guide.htm).

During the PRM season, magnesium levels ranged from 8 to 280 mg/L (Table 2), indicating that 10% of the samples exceeded the most desirable limit. In the POM season, they ranged from 12 to 132 mg/L (Table 3), with 7% of the samples exceeding the most desirable limit, indicating a 3% reduction. Charnockite and migmatite are metamorphic rocks rich in silicate minerals that contain high amounts of magnesium-bearing minerals, such as pyroxene and amphibole, and release significant amounts of magnesium ions through chemical weathering, thereby increasing magnesium levels as a geogenic input^30,31. The Magnesium spatial distribution of PRM and POM seasons is shown in Fig. 4c, d.

During PRM season, sodium levels ranged from 48.9 to 354.7 mg/L (Table 2), indicating that 18% of the samples exceeded the most desirable limit. In contrast, during the POM season, they ranged from 45 to 125 mg/L (Table 3), with 38% of samples exceeding the most desirable limit, indicating a 20% reduction. These are due to the extensive rainfall; there will be increased rock-water interaction and dissolution of feldspar-rich rocks such as charnockite and migmatite, which release sodium ions into groundwater³⁰. The charnockite and migmatite terrains are rich in silicate minerals such as feldspar and amphibole. These contain significant amounts of sodium-bearing minerals, and chemical weathering releases sodium ions into groundwater³². Surface water with a higher sodium concentration quickly infiltrates the ground, where the water table is shallow, contributing to higher sodium levels³³. The spatial distribution map of sodium for the PRM and POM seasons is shown in Fig. 4e, f.

During the PRM, potassium levels ranged from 6 to 186 mg/L (Table 2), indicating that 88% of the samples exceeded the most desirable limit. In contrast, during the POM season, they ranged from 1.2 to 37 mg/L (Table 3). 92% of samples exceeded the most desirable limit, indicating a 4% reduction. The primary anthropogenic sources of the high potassium concentration are agricultural fertilizers, especially in the study area, where grape cultivation is prominent, as these require potassium-rich fertilizers for optimal growth. Due to intense rainfall, these potassium-based fertilizers leach into the groundwater³⁴. The potassium spatial distribution maps for the PRM and POM seasons are shown in Fig. 4g, h.

Major anions during the PRM (Pre-monsoon), chloride levels ranged from 168 to 764 mg/L (Table 2), indicating that 67% of the samples exceeded the most desirable limit. In contrast, during the POM (Post-monsoon) season, they varied from 71 to 410 mg/L (Table 3), with 30% of the samples exceeding the most desirable limit, indicating a 37% reduction. The primary anthropogenic sources are excessive use of fertilizers such as potassium chloride, runoff from irrigation and rainfall, and the overextraction of groundwater from deep aquifers, which intrude it and raise chloride concentrations^35–37. The geogenic source for increasing chloride levels is the mineral weathering of charnockite and migmatite, which is significantly smaller than the anthropogenic source; however, it adds up to an overall amount³⁸. The Chloride spatial distribution maps for the PRM and POM seasons are shown in Fig. 5a, b. During the PRM season, bicarbonate levels ranged from 128 to 874 mg/L (Table 2), indicating that 55% of the samples exceeded the most desirable limit. In contrast, during the POM season, they ranged from 36.6 to 671 mg/L (Table 3), with 33% of samples exceeding the most desirable limit, indicating a 22% reduction. During the PRM, rainfall will be minimal. Agricultural runoff, leaching of carbonate-rich fertilizers into surface water, and discharge of wastewater or partially treated water due to urbanization and industrial activities are the major anthropogenic contributors to increased bicarbonate levels^39,40. The bicarbonate spatial distribution maps for the PRM and POM seasons are shown in Fig. 5c, d.

Fig. 5.

Spatial interpolation maps of water quality parameters during the PRM season: (a) Cl⁻, (c) HCO₃⁻, (e) SO₄²⁻, and (g) F⁻; and during the POM season: (b) Cl⁻, (d) HCO₃⁻, (f) SO₄²⁻, and (h) F⁻. (Maps are generated using ArcGIS 10.6 software,https://desktop.acgis.com/en/quick-start-guides/10.7/arcgis.desktop.quick.start.guide.htm).

During the PRM season, sulphate levels varied between 139.2—454 mg/L (Table 2), indicating that 55% of samples exceeded the most desirable limit, whereas in the POM season, they varied from 24–432 mg/L (Table 3), representing 52% of samples that exceeded the most desirable limit, indicating a 3% reduction. However, levels remained the same in some elevated areas. Both charnockite and migmatite in the study area contain pyrite. When it undergoes oxidation, it releases sulphuric compounds that form sulphates, which are the major geogenic inputs⁴¹. Anthropogenic inputs, particularly pyrite oxidation, significantly contribute to higher sulphate concentrations during the pre-monsoon, driven by higher evaporation rates and agricultural runoff⁴². High sulphate concentrations in water can cause health effects like bitterness, movement concerns, and gastrointestinal disturbances. The sulphate spatial distribution maps for the PRM and POM seasons are shown in Fig. 5e, f.

During the PRM season, nitrate levels varied between 20 and 41 mg/L (Table 2). In contrast, during the POM season, they varied from 12.3 to 35.2 mg/L (Table 3), indicating that each sample falls within the most desirable limit for both PRM and POM seasons.

During the PRM season, fluoride levels ranged from 1 to 3 mg/L (Table 2), indicating that 22% of the samples exceeded the most desirable limit. In contrast, during POM season, they varied from 0.62 to 2.01 mg/L (Table 3), with 7% of samples exceeding the most desirable limit, indicating a 15% reduction. However, levels remained the same in some elevated areas. The geogenic sources like charnockite and migmatite contain biotite, hornblende, and apatite, which release fluoride into groundwater through the weathering and leaching process, and during the prolonged contact between the groundwater and fluoride-rich minerals in deep aquifers, especially in granitic and metamorphic terrains like charnockite, increases fluoride concentration^43,44. The spatial maps of fluoride for the PRM and POM seasons are shown in Fig. 5g, h, respectively.

Hydrochemical evolution and mechanisms controlling groundwater chemistry

Piper diagram

The Piper diagram is valuable for detecting water quality and suitability for drinking, irrigation, and industrial purposes⁴⁵. From comparing both seasons, the samples in PRM show no dominant cation or anion type, and overall they are Mixed HCO₃–Cl–SO_₄²⁻, with variable water types. These are due to high evaporation and mineral dissolution (Fig. 6a) ^46,47.

Fig. 6.

Piper plot (a) PRM, (b) POM.

In POM season, the cation dominance is also mixed, with Na⁺ and K⁺, due to the weathering of silicate minerals. In anion the dominance is Predominantly Cl⁻-SO₄²⁻, suggesting pollution and the water type is Mostly Na⁺- K⁺- SO₄²⁻- Cl⁻. These are due to dilution, as there will be more rainfall and increased contamination (Fig. 6b) ⁴⁸.

Gibbs interpretation

The Gibbs diagram determines the relationship between water composition and aquifer lithological properties⁴⁹. The two images show the results for the PRM and POM seasons (Fig. 7a, b). During PRM season, most data points fall into the rock dominance category, with some extending towards the evaporation dominance zone. It indicates that rock-water interactions majorly influence water chemistry⁵⁰.

Fig. 7.

Gibbs plot, (a) PRM, (b) POM.

During the POM, data points still show rock dominance, but there is a slight shift toward reduced evaporation dominance. This suggests that monsoon rains have diluted the water chemistry, reduced salinity, and decreased evaporative effects. Both seasons are dominated by rock-water interaction, meaning minerals’ dissolution from geological formations significantly influences water chemistry. Similar results have been reported in various studies, where rock-water interaction is predominant, and evaporation is reduced due to dilution from seasonal rainfall during the POM season.

Suitability for drinking

Groundwater water quality index (GWQI)

During the PRM, the GWQI values of the study area ranged between 67.9 and 172, revealing that 47% of samples fall under good water, while 53% were in poor water due to high evaporation rates, which elevates the concentration of dissolved ions as shown in Table 4⁵¹. During the POM season, the GWQI values in the study area ranged from 60.7 to 174, with 70% of samples falling into the good water category and 30% into the poor water category (Table 4). Compared to the PRM season, water quality in POM has further improved due to rainfall dilution and reduced evaporation, as observed in similar studies in Tamil Nadu^52,53. The GWQI spatial distribution map for the PRM and POM seasons is shown in Fig. 8, b.

Table 4.

Categorization of GWQI water samples for PRM and POM seasons.

GWQI classification range	Water quality classification categories	No. of samples PRM)	% Of samples (PRM)	No. of samples (POM)	% Of samples (POM)
< 50	Excellent	0	0	0	0
50–100	Good water	28	47	42	70
100–200	Poor water	32	53	18	30
200–300	Poor water	0	0	0	0
> 300	Unsuitable	0	0	0	0
100				100

Fig. 8.

Groundwater quality index spatial maps, (a) PRM, (b) POM (Maps are generated using ArcGIS 10.6 software, https://desktop.acgis.com/en/quick-start guides/guides/10.7/arcgis.desktop.quick.start.guide.htm ).

Suitability for irrigation

Wilcox’s interpretation

Wilcox used salt percentage and specific conductance to measure water’s ability to conduct electricity and serve as indicators of salinity to assess groundwater suitability for irrigation⁵⁴. PRM results indicate that 74% of samples fall under the Good to Permissible category, 18% in the Doubtful to Unsuitable category, and 5% in the Permissible to Doubtful category due to the deprivation of rainfall during the summer season. It elevated the concentration of solids, as shown in Fig. 9a. In comparison, the remaining 3% are in the Unsuitable category, possibly due to local geological formations and agricultural activities. Similar findings have been reported by^52–55.

Fig. 9.

Wilcox plot, (a) PRM, (b) POM.

In contrast, the POM result indicates that 92% of samples fall into the Good to Permissible category, 3% into the Excellent to Good category, and 2% into the Doubtful to Unsuitable category. In comparison, the remaining 3% is in the Unsuitable category, and there is an improvement in water quality compared to the PRM season due to rainfall, which dilutes the dissolved solids and enhances groundwater recharge, as shown in Fig. 9b. Studies reported similar findings^56,57. The relationship between Na% and EC was analysed using the Wilcox diagram⁵⁸.

Sodium percentage

During the PRM season, the results indicate that 72 of the samples fall in the Good category, 23% in the Permissible category, 3% in the Excellent category, and 2% in the Doubtful category (Table 5). In contrast, during the POM season, the results indicate 67% of samples fall in the Good category, 28% in the Permissible category, 3% in the Excellent category, and 2% in the Doubtful category, and the effect of sodium is reduced when compared to PRM due to dilution effects (Table 5). The spatial distribution map of sodium percentage for the PRM and POM seasons is shown in Fig. 10a, b.

Table 5.

Classification of various irrigation indices.

Irrigation Indices	Classification	Range (meq/L)	% of samples
			PRM	POM
Sodium percentage (Na%) Wilcox (1955)	Excellent	< 20%	3	3
	Good	20–40%	72	67
	Permissible	40–60%	23	28
	Doubtful	60–80%	2	2
	Unsuitable	> 80%	–	–
Kelly’s ratio (KR) Kelly (1940)	Safe	< 1	97	95
	Unsuitable	> 1	3	5
Magnesium adsorption ratio (MAR) Paliwal (1972)	Acceptable	< 50%	65	68
	Unsuitable	> 50%	35	32
Chloride hazard index (CHI) Doneen (1964)	Safe	< 1	93	98
	Moderate	1–2	–	–
	High hazard	> 2	–	–
Sulphate hazard index (SHI) Doneen (1964)	Safe	< 1	100	93
	Moderate	1–2	–	–
	High hazard	> 2	–	7
Sodium adsorption ratio (SAR) (1954)	Excellent	< 10	100	100
	Good	10–18	–	–
	Doubtful	18–26	–	–
	Unsuitable	> 26	–	–

Fig. 10.

Spatial variation maps of irrigation suitability: 10a & b Na% during PRM & POM; (Maps are generated using ArcGIS 10.6 software, https://desktop.acgis.com/en/quick-start-guides/10.7/arcgis.desktop.quick.start.guide.htm).

Kelly ratio

Kelly’s ratio measures the degree of sodium’s effect on irrigation water quality⁵⁹. During the PRM season, 97% of samples fall into the Suitable category, whereas in the POM season, this percentage decreased to 95% (Table 5) ⁶⁰. The Kelly ratio spatial distribution map for the PRM and POM seasons is shown in Fig. 11a, b.

Fig. 11.

Spatial interpolation maps of water quality parameters during the PRM season: (a) KR, (c) MAR, (e) CHI, and (g) SAR; and during the POM season: (b) KR, (d) MAR, (f) CHI, and (h) SAR. (Maps are generated using ArcGIS 10.6 software).

Magnesium adsorption ratio

The magnesium adsorption ratio determines crop development and nutrient uptake in irrigation⁶¹. During the PRM season, 35% of samples fall under the Unsuitable category, while during the POM season, this percentage reduced to 32% (Table 5). The spatial distribution map of the Magnesium adsorption ratio for the PRM and POM seasons is shown in Fig. 11c, d.

Chloride hazard index

The chloride hazard index is used to assess the effect of chloride concentration in irrigation water on crop growth⁶². It is essential in plants for photosynthesis, stomatal regulation, osmotic adjustment, and nutrient transport⁶³. In the PRM season, 93% of samples fall into the Safe category. In contrast, this percentage increased to 98% of the Safe category in the POM season (Table 5). The remaining fall in the moderate category is due to leaching from agricultural fields, rock-water interactions, and dissolution of evaporated salts. The chloride hazard index spatial distribution map for the PRM and POM seasons is shown in Fig. 11e, f.

Sulphate hazard index

The sulphate hazard index significantly contributes to the overall salinity of irrigation water, which impacts soil health and crop growth⁶⁴. Increased soil salinity, reduced plant water uptake, and lower crop yields are the impacts of elevated sulphate concentration⁶⁵. Furthermore, high sulphate levels may disrupt cationic equilibrium in plants by promoting the uptake of sodium and potassium and restricting the uptake of vital minerals such as calcium⁶⁶. During the PRM season indicates no sample falls in the High-hazard category (Table 5). In contrast, during the POM season, this percentage increased to 7% due to increased filtration, which intensifies oxidation and elevates sulphate levels (Table 5). The sulphate hazard index spatial distribution map for the PRM and POM seasons is shown in Fig. 11g, h.

Sodium adsorption ratio

The Sodium Adsorption Ratio is calculated from the relative concentrations of sodium, calcium, and magnesium to assess irrigation suitability⁶⁷. During the PRM and POM season, results indicate that each sample falls within the Safe zone, indicating a low sodium hazard and suitability for irrigation, as shown in Table 5. The spatial distribution map of the sodium adsorption ratio for the PRM and POM seasons is shown in Fig. 11i, j.

Statistical analysis

Correlation

The Pearson correlation coefficient was used to assess the strength and direction of relationships with all water quality parameters using a correlation matrix. The statistical study shows a correlation and interaction among groundwater quality parameters, as shown in Fig. 12a, b⁶⁸. The pH shows a weaker negative correlation with K + (-0.32), indicating a more natural interaction between pH and less external influence in the PRM and POM. However, in POM, the correlation between pH and other parameters, such as K⁺ (-0.80), Na⁺ (-0.61), and SO₄²⁻ (-1.00), shows a strong negative relationship. This suggests that as pH increases, the concentration of these ions decreases significantly; this may be due to water treatment processes such as softening or ion exchange (Ravichandran & Jayaprakash, 2011). When comparing the relationship between Na + and K + , a stronger correlation (0.83) suggests that natural variability in Na + and K + levels predates any external modifications during the PRM season⁵².

Fig. 12.

Heat map of Pearson correlation, (a) PRM, (b) POM.

In contrast, the near-perfect correlation between Na⁺ and K⁺ (0.99) implies a common influencing factor, such as salinity intrusion from deeper aquifers in the POM season⁵¹. When comparing the Ca²⁺, Mg²⁺, and total hardness relationships for both seasons, a more balanced contribution from Ca²⁺ (0.78) and Mg²⁺(0.67) to total hardness reflects typical natural water chemistry in the PRM season⁶⁹. During the POM season, magnesium shows a stronger correlation with total hardness (0.77) than Ca²⁺ (0.32), indicating that Mg²⁺ contributes more to water hardness, possibly due to selective mineral removal⁶⁹. The relationship between HCO₃⁻ and NO₃⁻ in both seasons shows a weaker correlation (0.48), indicating that these parameters behave more independently in the natural environment at PRM⁶⁹. A strong positive correlation (0.75) suggests that agricultural runoff and specific treatment methods influence HCO₃⁻ and NO₃⁻ levels⁵¹.

The SO₄²⁻ and F⁻ relationship shows moderate correlations (SO₄²⁻ vs F⁻at 0.64). It reflects fewer interventions, indicating naturally occurring variations in these ion concentrations in the PRM season⁵¹. During the POM season, the perfect negative correlations (-1.00) between SO₄²⁻, F⁻, and most other parameters suggest targeted removal, possibly through reverse osmosis or other advanced treatment technologies⁶⁹.

Impact on the local community and mitigation

The primary source of drinking water and irrigation in the study area is groundwater. The region comprises 9 villages with a population of 26,000 people⁷⁰, and the majority of households depend on borewells. The increased concentration of (EC, TDS, Sodium, chloride, sulphate, potassium, total hardness) during PRM indicates both anthropogenic (domestic effluents and agricultural runoff) and geogenic (mineral dissolution and weathering of charnockite and migmatite rocks) sources.

In POM, it shows improved water quality due to reduced evaporation and dilution from the rainfall recharge. However, a few locations still exceed the permissible limits for salinity and fluoride, suggesting contamination persists. These affect public health (Fluorosis, toxicity) and agricultural productivity (reduced yields, effects on crop growth, soil salinization). Mitigation measures are required, such as defluorination, rainwater harvesting to improve groundwater, reducing overextraction, controlling agricultural runoff, and regular monitoring.

Conclusion

Groundwater in the Chinnalapatti Firka is essential for residential and farming purposes. The current study assesses groundwater pollution levels in southern India’s charnockite and migmatite terrain. Water quality metrics that conform to WHO (2017) standards represent the most desirable and non-permissible limits on a spatial distribution map. The findings were summarised below.

• Groundwater contamination levels increased during the PRM compared to the POM season, and the dominant water type was sodium chloride, as shown by the Piper plot. The Gibbs diagram indicated that groundwater chemistry is primarily influenced by rock-water interaction.

• Wilcox classification showed that unsuitable irrigation water decreased from 5% PRM to 3% POM. For drinking purposes, the groundwater quality index indicated Poor during the PRM, with 53% of water Poor, 47% Good, and 8% Unsuitable, rendering it unfit. Immediate actions are required to prevent further degradation. Some techniques, such as promoting rooftop rainwater harvesting to reduce overextraction and installing percolation pits to improve aquifer recharge, were recommended. The irrigation indices revealed moderate suitability for agricultural use, with minor significant challenges.

• Groundwater quality throughout the research area was degraded, rendering it Unsuitable for drinking and irrigation. Additionally, long-term monitoring and regulation of contaminant sources, especially agricultural runoff, were essential for sustainable water management.

Author contributions

Pragadeeshwaran Kannan : Conceptualisation, Methodology, Investigation, interpretation of data Writing- Original draft preparation; Gurugnanam Balasubramaniyan : Supervision, Validation, Writing- Reviewing and Editing; Shankar Karuppannan : Data curation, Formal Analysis, Validation, Visualisation, Writing- Reviewing and Editing; Bairavi Swaminathan : Software, Data Curation, Map correction; Bagyaraj Murugesan : Map preparation, Review and editing. Karunanidhi Duraisamy: Review in writing. All authors have read and agreed to the published version of the manuscript.

Funding

This research has not received any funding from any source.

Data availability

The data that support the findings of this study are available on request from the corresponding author.

Declarations

Competing interests

The authors declare no competing interests.

Ethics approval

The manuscript is conducted in the ethical manner advised by the targeted journal.

Consent for publication

The research is scientifically consented to be published. We have carefully reviewed all the images in our manuscript and confirm that no human faces are present.