Groundwater quality assessment for drinking, irrigation purposes and fuzzy logic-based water quality index for industrial suitability in Walajapet taluk, Ranipet district, Tamil Nadu, India

Water is the lifeblood of our planet, and groundwater, which is hidden under the surface of the earth, plays a critical role in maintaining the civilisation of humans. Groundwater quality is affected by natural geogenic and anthropogenic processes (Al-Abadi 2017). In general, groundwater quality is determined by the cations and anions present in the water, which are derived from soil and rocks present in the subsurface (Krishna Kumar et al. 2017). The interactions between water, rock, sediment, and soil have a major impact on the quality variance of groundwater (Stamatis et al. 2011). Other than natural processes, groundwater quality is also affected by anthropogenic activities such as improper disposal of solid wastes, sewage effluents, industrial effluents, and excessive use of fertilisers and pesticides (de Andrade et al. 2008). Discharging industrial effluents into water bodies without proper treatment is a primary cause of groundwater pollution in industrial zones (Lemessa et al. 2023). Approximately 80% of illnesses and fatalities in developing nations are linked to polluted water (Shayo et al. 2023). The physicochemical composition of the soil can be altered, and soil fertility can be reduced by extended usage of poor-quality groundwater in agricultural fields. This can significantly negatively influence the crop output (Ismail et al. 2023). The water distribution networks and instruments used in industries face scaling and corrosion problems due to poor quality of water. Generally, corrosion in pipes occurs due to low pH and high concentrations of strong acids like chloride, sulphate, and higher total dissolved solids (TDS) (Mukate et al. 2020). In contrast, scaling occurs when the concentration of bicarbonate is high (Omeka et al. 2022). It reduces the efficiency and longevity of industrial instruments (Agatemor & Okolo 2008). To avoid these kinds of problems, proper monitoring and assessment of water quality for domestic, agricultural, and industrial usage is necessary.

To analyse the drinking water quality of groundwater, drinking water quality index (DWQI) is widely employed due to its ease of use and flexibility, in addition to comparing the parameters with World Health Organization (WHO) criteria. DWQI is used by numerous researchers to simplify complex physicochemical features for easier understanding (Mohebbi et al. 2013; Jasmin & Mallikarjuna 2014; Jamshidzadeh 2020; Alfaleh et al. 2023). Thirumoorthy et al. (2024) evaluated the groundwater for drinking purposes using DWQI in Perundurai, south India, and found that more than 70% of samples were not appropriate for human consumption. Howladar et al. (2018) applied water quality index (WQI) to analyse groundwater for domestic usage in the industrial area and noted that 96% of the sample is good for drinking. Several scholars effectively analysed the appropriateness of groundwater for irrigation using indices such as sodium adsorption ratio (SAR), sodium percentage (Na%), residual sodium carbonate (RSC), Kelly ratio (KR), magnesium hazard ratio (MHR), and permeability index (PI) (Jasmin & Mallikarjuna 2015; Gad et al. 2020; Guo et al. 2021; Dhaoui et al. 2022). Monitoring and evaluating industrial water quality has not received enough attention throughout the years (Egbueri 2022). The scaling or corrosion tendency of the water is measured by various industrial water quality indices like the Langelier saturation index (LSI), Larson index (LI), Puckorius scaling index (PSI), Ryznar stability index (RSI), and corrosivity ratio (CR). These indices are developed based on the different water quality parameters that influence the scaling and corrosion potential of water. These indices are used by several researchers across the globe to determine the industrial suitability of groundwater (Vasanthavigar et al. 2012; Aghazadeh et al. 2017; Amiri et al. 2021; Kadam et al. 2021; Egbueri et al. 2023). Krishna & Achari (2024) assessed the industrial suitability of groundwater in a coastal aquifer in Kerala, India, using indices such as LSI, RSI, PSI, and LI, and found that most of the samples had a scaling tendency. Egbueri (2022) used machine learning algorithms and various industrial water quality indices to estimate the corrosion and scaling potential of the groundwater in the industrial region of Southeast Nigeria. They found that groundwater had high corrosivity. These results show that industrial water quality indices can be effective in classifying groundwater. However, because each indicator is based on a separate set of physicochemical properties, there is little disadvantage to employing all these indices simultaneously (Egbueri 2022). This may classify the water sample into two or more water quality categories. This will create uncertainty in the handling and management of water quality for industrial purposes. So, there is a need to develop a single index that can integrate these indices, and fuzzy logic was used for this purpose.

Fuzzy set theory has been extensively used in water resources engineering (Tayfur 2023). Fuzzy logic can address the inherent ambiguity and uncertainty associated with deterministic approaches (Vadiati et al. 2019). Many researchers used a fuzzy logic approach to find out the groundwater suitableness for irrigation and drinking purposes (Gharibi et al. 2012; Barzegar et al. 2023; Abidi et al. 2024; Loganathan et al. 2024). However, there is no integrated index model for industrial suitability assessment based on fuzzy logic. A novel fuzzy integrated industrial water quality index (IIndWQI) is therefore constructed in this study based on the Mamdani fuzzy inference method by combining four industrial water quality indices.

This research is focused on Walajapet taluk in Ranipet district, Tamil Nadu. Groundwater is the major source for drinking, irrigation, and industrial use because of the semi-arid nature of the region. The study area comprises around 250 medium- to large-scale industries and numerous small-scale industries. Some of these small-scale industries discharge untreated effluents into the nearby water bodies. This affects the groundwater quality (Srinivasa Gowd & Govil 2007; Sai Chaithanya et al. 2023; Veluprabakaran & Kavitha 2023). A detailed assessment of groundwater for domestic, agricultural, and industrial purposes is needed to ensure sustainable groundwater management in the region. The main objectives of this study are (1) to evaluate the groundwater quality for drinking purposes by DWQI, (2) to evaluate the groundwater aptness for irrigation purposes using irrigation quality indices such as SAR, Na%, KR, RSC, MHR, and PI, (3) industrial suitability assessment of groundwater using LSI, RSI, PSI, LI, and CR, and (4) to develop a single IIndWQI by combining the industrial water quality indices.

The semi-arid climate is prevailing in the area with a mean minimum temperature of 17.4 °C (January) to 26.8 °C (May) and a mean maximum temperature of 28.8 °C (January) to 38 °C (May). The study area has undulating topography, with elevations varying from 125 to 499 m above mean sea level. Geologically, the study region has both hard rocks and sedimentary formations. The most common hard rocks such as charnockite and gneisses are found throughout the study area. Sedimentary formations are present along the Palar and Ponnai river regions. The land use/land cover patterns of the area are mainly divided into built-up areas, irrigated cropland, surface water bodies, forests, and barren land. Groundwater is present in crystalline rocks as well as alluvial deposits under unconfined conditions. Major crops cultivated in the region are paddy, pearl millet, sorghum, maize, ragi, and groundnut.

Portable equipment is used on the site to measure the pH and electrical conductivity (EC). The EDTA titrimetric method was used to estimate calcium (Ca²⁺) and magnesium (Mg²⁺) in the samples. Sodium (Na⁺) and potassium (K⁺) concentrations are determined by using a flame photometer by preparing the standard sodium and potassium solution. Bicarbonate and carbonate were determined titrimetrically by H₂SO₄ using methyl orange and phenolphthalein as indicators.

The chloride (Cl⁻) concentration is evaluated by the AgNO₃ method. Nitrate ⁠, sulphate ⁠, and fluoride (F⁻) concentration were determined by using a double-beam UV-spectrophotometer. Charge balance error is used to calculate the precision of chemical analysis, and it is less than ±10% for all samples. A spatial distribution map of groundwater quality parameters was done using the inverse distance weightage tool in ArcGIS software.

The suitableness of groundwater for drinking usage was evaluated based on the DWQI. Many researchers have used DWQI to estimate groundwater suitability for domestic usage (Mohebbi et al. 2013; Elubid et al. 2019; Jesuraja et al. 2021). The DWQI is calculated by comparing the observed concentrations with the standard value. The water quality parameters such as pH, TDS, EC, total hardness, Ca²⁺, Mg²⁺, Na⁺, K⁺, Cl⁻, ⁠, ⁠, NO₃, and F⁻ are considered for calculating the DWQI value. The following steps are used to calculate the DWQI.

In the study area, groundwater is the primary source for agricultural usage. Hence, it is essential to analyse groundwater based on irrigation parameters. The irrigation suitability of water is assessed based on its EC, SAR, Na%, KR, RSC, MHR, and PI (Supplementary Table S2).

When low-quality water is used for industrial purposes, it can damage the water distribution network (Kadam et al. 2021). This type of water can lead to corrosion and scaling problems. In this study, indices such as LSI, RSI, PSI, LI, and CR were used to assess groundwater quality for industrial usage.

When the value of LSI < 0, it indicates undersaturation and a high possibility of corrosion in water-carrying pipes. If LSI = 0, groundwater is in the equilibrium stage, and there is less possibility of either corrosion or scaling in pipes. Positive LSI values show that groundwater is supersaturated and tends to precipitate CaCO₃. For this kind of water, scaling is highly possible.

All the concentration values are in mEq/L.

All the values are expressed in mg/L.

pH = 7.41, TDS = 712 mg/L, = 396.61 mg/L (or) 6.50 mEq/L, Cl⁻ = 204.93 mg/L (or) 5.779 mEq/L, = 81.46 mg/L (or) 1.69 mEq/L, = 0 mg/l, Ca²⁺ = 73.74 mg/L (or) 3.687 mEq/L, Mg²⁺ = 50.18 mg/L (or) 4.11 mEq/L, Na⁺ = 124.37 mg/L (or) 5.33 mEq/L, K⁺ = 2.87 mg/L (or) 0.073 mEq/L, alkalinity = 325.35 mg/L, temperature = 30 °C, A = 0.18, B = 1.99, C = 1.86, D = 2.51, pHs = 7.10, pHeq = 8.18.

SAR = 2.70, Na% = 40.94, RSC = −1.30 mEq/L, KR = 0.68, MHR = 52.74, PI = 60.03, LSI = 0.31, RSI = 6.79, PSI = 6.02, LI = 1.15, CR = 0.95.

The most popular centroid method was used for the defuzzification of fuzzy values. To avoid complexity, only four inputs (LSI, RSI, PSI, and LI) and one output (industrial quality) were selected for this study.

A statistical investigation of PRM and POM seasons for the samples is presented in Table 1. The results indicated that the majority of the parameters have a high skewness and kurtosis value in both seasons. This might be due to a high level of pollution in the industrial region, which causes several outlier values. The data were examined for normal distribution using histograms and QQPlots, and only pH, sodium, and bicarbonate showed a normal distribution. It was also discovered that there is no single dominant distribution pattern in the data for both seasons. The pH of the groundwater varies from 6.27 to 8.49 and 6.66 to 8.69 in PRM and POM seasons, respectively. The average pH values show that groundwater was mostly neutral to slightly alkaline. Except one sample in the POM season, all the samples were within permissible limits for human consumption. Groundwater samples in the study location had EC values ranging from 390 to 10,040 μS/cm in PRM and 318 to 10,700 μS/cm in POM. The average EC value slightly increased from 1,925 μS/cm in PRM to 1,993 μS/cm in the POM. The desirable value for EC in water is 500 μS/cm as per WHO (2017) standards. About 98% of samples in both seasons exceed the desirable limit (Table 2). Furthermore, 61% of the samples surpass the permitted limit of 1,500 μS/cm. The high concentration of EC might be due to the high salt content caused by water vaporisation and anthropogenic activities such as industrial wastewater contamination. TDS levels in the samples ranged from 140 to 6,020 mg/L, with an average of 1,122 mg/L during the PRM season, and from 101 to 6,000 mg/L, with an average of 1,109 mg/L during the POM season. The elevated concentration of TDS in some regions may be due to various salt inputs from industrial activities (Kadam et al. 2021). Almost 96% of samples in both seasons exceeded the desirable 500 mg/L limit. However, only 13 and 11% of samples from PRM and POM seasons exceed the permissible limit of TDS. Even though the average EC value increased in the POM season, the value of TDS decreased; this might be due to the increase in other EC-affecting factors in the POM season (Hasan 2017). The total hardness of samples ranged from 180 to 4,310 mg/L in PRM and 150 to 3,400 mg/L in POM season, averaging 609 and 628 mg/L, respectively. All samples in both seasons exceeded the desirable WHO (2017) standard limit of 100 mg/L. About 43% of samples in PRM and 59% in POM exceeded the maximum permissible limit of 500 mg/L for total hardness. Although excessive hardness is not proven to harm health, it can cause corrosion and blockage in water-carrying pipes (Egbueri et al. 2023).

Table 1

Statistical analysis of physicochemical parameters

Parameters .	PRM .						POM .
	Min .	Max .	Average .	Standard deviation .	Kurtosis .	Skewness .	Min .	Max .	Average .	Standard deviation .	Kurtosis .	Skewness .
pH	6.27	8.49	7.31	0.36	2.89	0.91	6.66	8.69	7.3287	0.44	0.16	0.28
TDS (mg/L)	140	6,020	1,122	782.91	29.20	4.74	101	6,000	1,110	811.93	25.41	4.42
EC (μS/cm)	390	10,040	1,925	1,386.99	29.84	4.82	318	10,700	1,993	1,347.44	25.16	4.38
Hardness (mg/L)	180	4,310	609	431.43	32.97	5.17	150	3,400	628	568.33	35.06	5.49
Ca2+ (mg/L)	25.05	1,170.29	146.5	146.10	38.49	5.83	24.05	1,110.17	137.64	152.35	39.99	5.94
Mg2+ (mg/L)	8.35	337.76	61.05	30.38	1.26	1.11	18.2	157.2	69.31	54.46	12.34	3.00
Na+ (mg/L)	7.83	392.91	150.68	72.29	2.92	0.92	17.13	440.77	169.49	67.97	2.15	0.79
K+ (mg/L)	0.54	88.97	7.12	7.20	17.58	3.78	0.44	44.04	4.27	13.56	26.60	4.82
Cl− (mg/L)	22.49	2,571.7	338.86	491.38	37.40	5.67	57.48	3,683.86	432.55	363.22	27.89	4.81
(mg/L)	27.49	1,229.11	188.85	212.04	9.27	2.98	27.49	1,128.68	176.12	193.23	16.81	3.70
(mg/L)	61.02	610.17	336.39	141.20	1.53	−0.28	91.53	915.26	504.29	135.79	−0.80	0.18
F− (mg/L)	0.11	1.18	0.45	0.50	−1.35	−0.15	0.19	1.76	0.89	0.23	1.84	1.29
(mg/L)	0.3	25	2.87	5.94	8.13	2.21	0.25	35.5	7.49	3.95	21.01	4.26

Parameters .	PRM .						POM .
	Min .	Max .	Average .	Standard deviation .	Kurtosis .	Skewness .	Min .	Max .	Average .	Standard deviation .	Kurtosis .	Skewness .
pH	6.27	8.49	7.31	0.36	2.89	0.91	6.66	8.69	7.3287	0.44	0.16	0.28
TDS (mg/L)	140	6,020	1,122	782.91	29.20	4.74	101	6,000	1,110	811.93	25.41	4.42
EC (μS/cm)	390	10,040	1,925	1,386.99	29.84	4.82	318	10,700	1,993	1,347.44	25.16	4.38
Hardness (mg/L)	180	4,310	609	431.43	32.97	5.17	150	3,400	628	568.33	35.06	5.49
Ca2+ (mg/L)	25.05	1,170.29	146.5	146.10	38.49	5.83	24.05	1,110.17	137.64	152.35	39.99	5.94
Mg2+ (mg/L)	8.35	337.76	61.05	30.38	1.26	1.11	18.2	157.2	69.31	54.46	12.34	3.00
Na+ (mg/L)	7.83	392.91	150.68	72.29	2.92	0.92	17.13	440.77	169.49	67.97	2.15	0.79
K+ (mg/L)	0.54	88.97	7.12	7.20	17.58	3.78	0.44	44.04	4.27	13.56	26.60	4.82
Cl− (mg/L)	22.49	2,571.7	338.86	491.38	37.40	5.67	57.48	3,683.86	432.55	363.22	27.89	4.81
(mg/L)	27.49	1,229.11	188.85	212.04	9.27	2.98	27.49	1,128.68	176.12	193.23	16.81	3.70
(mg/L)	61.02	610.17	336.39	141.20	1.53	−0.28	91.53	915.26	504.29	135.79	−0.80	0.18
F− (mg/L)	0.11	1.18	0.45	0.50	−1.35	−0.15	0.19	1.76	0.89	0.23	1.84	1.29
(mg/L)	0.3	25	2.87	5.94	8.13	2.21	0.25	35.5	7.49	3.95	21.01	4.26

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Table 2

Comparison of water quality measures with WHO's (2017) standard

Water quality parameter .	Desirable limit (DL) – permissible limit (PL) .	PRM .		POM .
		% Sample above (DL) .	% Sample above (PL) .	% Sample above (DL) .	% Sample above (PL) .
pH	6.5–8.5	0	0	2	2
TDS (mg/L)	500–1,500	96	13	94	11
EC (μS/cm)	500–1,500	98	61	98	61
Total hardness (mg/L)	100–500	100	43	100	59
Ca2+ (mg/L)	75–200	85	13	83	7
Mg2+ (mg/L)	50–100	48	13	67	13
Na+ (mg/L)	200–600	20	0	31	0
K+ (mg/L)	10–12	20	11	11	9
Cl− (mg/L)	250–500	52	13	69	24
(mg/L)	200–250	26	19	19	11
(mg/L)	200–500	83	15	96	57
F− (mg/L)	1–1.5	0	0	0	2
(mg/L)	45	0	0	0	0

Water quality parameter .	Desirable limit (DL) – permissible limit (PL) .	PRM .		POM .
		% Sample above (DL) .	% Sample above (PL) .	% Sample above (DL) .	% Sample above (PL) .
pH	6.5–8.5	0	0	2	2
TDS (mg/L)	500–1,500	96	13	94	11
EC (μS/cm)	500–1,500	98	61	98	61
Total hardness (mg/L)	100–500	100	43	100	59
Ca2+ (mg/L)	75–200	85	13	83	7
Mg2+ (mg/L)	50–100	48	13	67	13
Na+ (mg/L)	200–600	20	0	31	0
K+ (mg/L)	10–12	20	11	11	9
Cl− (mg/L)	250–500	52	13	69	24
(mg/L)	200–250	26	19	19	11
(mg/L)	200–500	83	15	96	57
F− (mg/L)	1–1.5	0	0	0	2
(mg/L)	45	0	0	0	0

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The dominant cation for both seasons is in the order of Na⁺ > Ca²⁺ > Mg²⁺ > K⁺. Sodium concentration in the study area varied from 7.83 to 393 mg/L in the PRM season, having a mean of 150.68 mg/L and 17.13 to 440.77 mg/L in the POM season, with a mean of 169.49 mg/L. Similar results were observed by Kumar (2014) in the Vaniyambadi industrial area, where the dominance of alkaline metal (Na⁺) concentration in both seasons showed the saline nature of the groundwater due to geogenic as well as anthropogenic sources such as domestic and industrial wastewater contamination. Approximately 20% of PRM and 31% of POM samples exceeded the WHO (2017) guideline limit of 200 mg/L for Na⁺. However, all the samples are within the permissible limit for drinking with respect to sodium concentration. The Ca²⁺ ion concentration in the study region varied from 25.05 to 1,170.29 mg/L, averaging 146.50 mg/L in PRM and from 24.05 to 1,110.17 mg/L, with a mean of 137.64 mg/L in POM season. Most samples from both seasons surpassed the recommended limit for calcium in groundwater. The average concentration of Mg²⁺ increased from 61.05 mg/L in PRM to 69.31 mg/L in the POM season. The high concentration of Ca²⁺ and Mg²⁺ ions is due to the dissolution of calcium and magnesium-bearing rock such as limestone, silicates, gypsum, and dolomite (Krishna Kumar et al. 2014). Potassium is the least prominent cation in the research area, ranging from 0.54 to 88.97 mg/L in PRM, having an average of 7.12 mg/L, and 0.44 to 44.04 mg/L with an average of 4.27 mg/L in POM season. The high concentration of potassium in some of the groundwater may be due to the application of fertiliser in the irrigation fields (Rajmohan & Elango 2005).

Based on the average value, the major anion in the PRM season is Cl⁻ followed by > > > F⁻. However, dominance changed in the order of > Cl⁻ > > > F⁻ in the POM season. The concentration of Cl⁻ ranges from 27.49 to 1,229.11 mg/L in the PRM and 57.48 to 3,683.86 mg/L in the POM season. The average concentration of chloride increased considerably in the POM season to 432.55 mg/L from 338.86 mg/L in the PRM season. The main reason for the high chloride content is the dissolution of minerals such as peninsular gneiss, charnockite, and khondalite (Mukherjee & Singh 2018). The high chloride concentration may also be due to domestic and industrial sewage mixing with waterbodies and percolating into groundwater. About 52% of samples in the PRM and 69% in the POM season surpassed the desirable limit of 250 mg/L for chloride. The ion concentration in samples varied from 61.02 to 610.17 mg/L in the PRM season and 91.53 to 915.26 mg/L in the POM season. During the POM season, the average ion concentration increased to 504.29 mg/L from 336.39 mg/L in the PRM season. The main source of bicarbonate is the dissolution of carbonate minerals such as calcite and dolomite by CO₂ during the rainy season (Chidambaram et al. 2011). Eighty-three percent of samples in the PRM season and 94% of samples in the POM season exceeded the standard permissible limit of 200 mg/L for bicarbonate (WHO 2017). The value of ranges from 27.49 to 1,229.11 mg/L for the PRM season and 27.49 to 1,128.68 mg/L in the POM season. High concentrations in some of the groundwater indicate anthropogenic activities and industrial processes (Krishna Kumar et al. 2014). The average value of sulphate slightly decreased in POM compared to PRM; this may be due to the dilution of groundwater. Based on the concentration, 26 and 19% of the samples are unsuitable for drinking in PRM and POM seasons, respectively. There is not much and F⁻ contamination in the study region.

Agriculture is one of the main occupations in the study region, and it includes major crops like paddy, cholam, and redgram. So, it is important to determine the suitability of groundwater for agricultural usage. The groundwater suitability for irrigation applications was evaluated by indices like SAR, KR, Na%, MHR, RSC, and PI (Table 4) and the United States Salinity Laboratory (USSL) diagram's graphical representation.

A high SAR value in water can decrease soil permeability and harm soil structure, making SAR an essential metric for assessing groundwater suitability for irrigation (Todd 1980). The sodium hazard of the water is expressed in terms of the SAR value. It is a measure of the soil's ability to release Ca²⁺ and Mg²⁺ ions and to adsorb Na⁺ ions. Based on the SAR value, all the samples in both seasons were considered excellent for irrigating all crops except sodium-sensitive crops. Like sodium hazards, salinity hazards for groundwater are expressed in terms of EC values.

KR compares the concentration of sodium (Na⁺) over calcium (Ca²⁺) and magnesium (Mg²⁺) in water (Kelly 1963). KR > 1 means the irrigation water contains a high concentration of salt, which can alter soil characteristics and impair soil permeability. So, groundwater with KR > 1 is unsuitable for agriculture. Based on the KR value, most samples (89%) from both seasons are appropriate for irrigation.

Water with a sodium percentage of more than 60% is not suitable for irrigation (Richards 1954). Based on the Na% value, in PRM season, only 26% of groundwater was suitable for agricultural usage, while 72% of samples were classified as doubtful category. In the POM season, the suitable category was reduced to 17%, and the doubtful category increased to 80% due to a slight increase in sodium value in the POM season. The high Na% value can reduce the soil permeability and affect the drainage of soil (Richards 1954).

Mg²⁺ and Ca²⁺ are essential nutrients for the crop's growth and generally maintain equilibrium in natural waters (Brindha & Elango 2014). MHR measures the excess concentration of Mg²⁺ over Ca²⁺ ions in the groundwater. Irrigation water with an MHR < 50% is acceptable for irrigation. According to the MHR value for the study region, 28% of groundwater in PRM and 46% of groundwater in POM seasons are not appropriate for agricultural application. A high magnesium value can affect the crop growth by increasing the alkalinity of the soil (Panaskar et al. 2016).

If the carbonate and bicarbonate concentration is high in the groundwater, it can precipitate alkaline earth like Ca²⁺ and Mg²⁺ in the soil. This leads to water becoming more saturated with Ca²⁺ and Mg²⁺, subsequently increasing the Na⁺ ion concentration and SAR value (Sadashivaiah et al. 2008). So, RSC, formulated by Richards (1954), is used to measure the negative effects of CO₃ and HCO₃ in water. According to Richards's classification, water with an RSC < 1.25 mEq/L is suitable for agricultural use. About 98% of the groundwater in PRM and 94% in POM are appropriate for irrigation in the study area. Only a few samples are unsuitable based on RSC classification.

The PI, formulated by Doneen (1964), is a critical tool to determine the groundwater's suitability for agricultural purposes. Repeated use of water with highly concentrated sodium, calcium, magnesium, and bicarbonate can severely affect soil permeability (Pivić et al. 2022). As per the PI classification, all the samples (except one in PRM) are classified as good to suitable for irrigation usage in both seasons.

RSI is an improved version of LSI for predicting the scaling and corrosion tendency of water (mostly scaling) (Siddha & Sahu 2022). For the study region, the RSI value varies from 5.06 to 8.46 in the PRM season, with an average of 6.65. During the POM season, the average value decreases to 6.36 and ranges from 4.05 to 9.13. If RSI < 6, there is a high possibility of heavy scaling in water pipes. The water is balanced, and there is less possibility of corrosion or scaling if the RSI value is between 6 and 6.8. If the RSI is more than 6.8, water shows a higher corrosion tendency. The spatial distribution map for the RSI demonstrates that the studied region's groundwater has moderate to high scaling tendency and water is unsuitable for industrial purposes (Figure 6(c) and 6(d)). Sajil Kumar (2019) found similar results in Thanjavur district, south India, with the majority of research regions showing a scaling trend.

PSI is used to measure the scaling potential of water. For the study area, the PSI value varied between 4.23 and 7.48, having a mean of 5.87 in PRM and ranges from 2.59 to 9.03, with a mean of 5.33 in POM season. If PSI < 6 in groundwater, there is a high possibility of scaling; if PSI is between 6 and 7, then there is a very low scaling or corrosion possibility; when PSI > 7 in groundwater, corrosion of pipes is highly possible. As per the spatial map, in the PRM, most of the groundwater has a low to high scaling tendency (Figure 6(e) and 6(f)). The scaling properties of groundwater increased after the rainy season. This might be due to increased bicarbonate alkalinity in the POM.

The high value of bicarbonate and sulphate in groundwater makes it more corrosive (Mukate et al. 2020). Water with a LI value less than 0.8 has no corrosive potential, and water with a LI value between 0.8 and 1.2 can have considerable corrosive potential. Groundwater with a LI greater than 1.2 has heavy corrosive potential. For the study area, the LI value varied between 0.36 and 21.78, averaging 2.81 in the PRM and between 0.39 and 8.48, averaging 1.84 in the POM season. Spatial distribution maps show that most of the study regions have very high corrosive potential, other than the small patches (Figure 6(g) and 6(h)). Metallic pipes used in industry can be severely damaged when this kind of water is utilised frequently.

Elevated levels of chloride and sulphate in groundwater raise the probability of corrosion and incrustation in water pipes used in industries (Siddha & Sahu 2022). In the current study, the CR value varied from 0.30 to 18.04, averaging 2.41 in the PRM and 0.32–7.03, with an average of 1.57 in the POM season. The spatial distribution map shows that the majority of the samples in the study region are unsuitable for industrial usage with CR values greater than 1 (Figure 6(i) and 6(j)). Therefore, it is recommended to use noncorrosive pipes, like PVC pipes and cement pies in the industries.

LSI is classified as follows:

• − 1.5 < LSI < 0: Heavy corrosion potential

• − 0.25 < LSI < 0.25: Very low corrosion potential

• 0 < LSI < 1.5: Heavy scaling potential

RSI is classified as follows:

• 0 < RSI < 6.2: Heavy scaling potential

• 5.6 < RSI < 7.4: Very low corrosion potential

• 6.8 < RSI < 20: Heavy corrosion potential

PSI is classified as follows:

• 0 < PSI < 6: Heavy scaling potential

• 5 < PSI < 8: Very low corrosion potential

• 7 < PSI < 20: Heavy corrosion potential

LI is classified as follows:

• 0 < LI < 0.8: Heavy scaling potential

• 0.4 < LI < 1.6: Very low corrosion potential

• 1.2 < LI < 25: Heavy corrosion potential

The output variable industrial quality is assigned with a range of 0–100, and it has been classified as follows:

• 0 < IIndWQI < 40: Heavy scaling potential, no corrosion.

• 40 < IIndWQI < 60: Slight corrosion potential, no scaling

• 60 < IIndWQI < 100: Heavy corrosion potential, no scaling

In this research, the groundwater quality of the industrial region in the Ranipet district of Tamil Nadu was evaluated for drinking, irrigation, and industrial purposes. Groundwater in the study region is characterised as rather slightly alkaline. Then quality of groundwater was compared with the drinking water quality standard given by WHO (2017). Most of the parameters surpassed the desirable and permissible limit of each parameter in both seasons. The average values of TDS, calcium (Ca²⁺), sulphate (⁠⁠), and potassium (K⁺) have decreased in the POM season, whereas the levels of pH, hardness, EC, sodium (Na⁺), chloride (Cl⁻), magnesium (Mg²⁺), bicarbonate (⁠⁠), fluoride (F⁻), and nitrate (⁠⁠) have increased. The high concentration in some samples is due to anthropogenic activities such as domestic and industrial wastewater contamination. The order of the dominating cation is Na⁺ > Ca²⁺ > Mg²⁺ > K⁺ in both seasons, but for anions, the dominance changed from Cl⁻ > > > > F⁻ in the PRM season to > Cl⁻ > > > F⁻ in the POM season. The Piper diagram revealed that the groundwater in the research area is predominantly mixed Ca-Mg-Cl and Ca-HCO₃ types. Based on the DWQI value for the groundwater, most of the samples in both seasons were classified as ‘poor’ and ‘very poor’ quality for domestic usage. There is not a considerable variation observed in the spatial distribution of DWQI in PRM and POM seasons. Based on the SAR value, groundwater is excellent for irrigation usage. Based on the KR value, the majority of the samples are appropriate for agricultural applications. The Na% index showed that groundwater was ‘suitable’ to ‘marginally suitable’ for irrigation purposes. The MHR demonstrated that 72% of groundwater PRM and 54% in the POM season are suitable for agricultural purposes. The USSL diagram showed that the groundwater in the study region has low sodium and a high saline character. So, it is recommended to cultivate salt-tolerant crops to increase the yield. The groundwater industrial purpose suitability was analysed based on indices like LSI, RSI, PSI, LI, and CR. As per the LSI value, most of the samples are supersaturated in terms of CaCO₃ and have a high possibility of scaling in water conduits. The high RSI and PSI values again confirmed the scaling nature of groundwater. The spatial variation map of the LI and CR showed that groundwater has a high corrosive tendency. To avoid bias created due to variation in the classification of samples, an integrated industrial WQI was developed using a fuzzy logic approach. The groundwater was more precisely classified for industrial use by this fuzzy approach. According to IIndWQI, there was an increase in groundwater's scaling tendency during the POM season from the PRM season. Overall, groundwater is mostly unsuitable for industrial usage in both seasons without pretreatment. Noncorrosive pipes made of PVC and cement are recommended for industrial use. This study comprehensively evaluates groundwater quality for beneficial domestic, agricultural, and industrial purposes. This study can serve as a basis for developing remedial actions to enhance groundwater quality in the studied area.

The authors thank Vellore Institute of Technology, Vellore, for providing research facilities for this research work.

The authors did not receive any funding for this research.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.