Characterizing spatial variability of groundwater quality indices using GIS for Palar Basin Open Access

Water is essential for life, and groundwater is a critical source of freshwater, especially in arid and semi-arid regions. However, the over-exploitation of groundwater due to easy access and increasing energy availability has led to significant declines in groundwater levels over the past two decades. By 2025, global demand for drinking water is expected to rise by 44%, irrigation by 10%, and industrial water use by 81% (Burri et al. 2019). In India, issues related to groundwater quality are becoming increasingly severe, especially in water-scarce states like Tamil Nadu, where contamination from pollutants like fluoride poses major challenges. Studies conducted on the Palar River, one of the most contaminated rivers in India due to industrial pollution, highlight the water quality challenges in this region, with fluoride concentrations found between 0.18 and 0.22 mg/L, below the World Health Organization (WHO) 1.5 mg/L level (Kumar et al. 2019). Factors such as rapid population growth, industrialization, and pollution continue to exacerbate groundwater stress, threatening its availability for both drinking and agricultural purposes.

The Palar River has been heavily polluted by effluents from tanneries, particularly in Vellore District, where elevated chromium concentrations pose serious environmental and public health risks. This pollution has rendered the river unsuitable for consumption and agriculture, leading to widespread health issues, such as asthma, skin diseases, and stomach ailments, while thousands of acres of fertile land have been abandoned. In 2006, Ranipet, an industrial area along the river, was labeled one of the most polluted places in the world due to heavy contamination by salts and metals, including chromium (Pranavam et al. 2011).

Rapid urbanization in India has added complexity to the challenge of maintaining both the quantity and quality of groundwater. As a substantial portion of water demand is met by groundwater, urban growth and intensive agriculture continue to deteriorate water quality. Climate change further compounds these challenges by altering precipitation patterns, intensifying pressure on water resources (Awodumi & Akeasa 2017). Groundwater contamination, often due to wastewater discharge, poses significant risks to public health, and its detection is more challenging than surface water pollution (Zektser & Everett 2004; Singh & Kumar 2017).

To address these concerns, geographic information system (GIS) tools offer a powerful method for analyzing spatial patterns and trends in groundwater quality. GIS-based techniques help to monitor contamination and identify pollution sources, enabling informed decision-making for water resource management (Balakrishnan et al. 2011). In a study conducted in the flood plains of the upper Palar River, the analysis of 22 groundwater samples during the pre- and post-monsoon seasons revealed tannery effluents, solid wastes, and sewage as primary sources of pollution. Physico-chemical analysis indicated high alkali and sulfate (SO₄) content, with statistical analysis supporting the connection to industrial pollution (Kuppuraj et al. 2012).

Similar studies in India have assessed groundwater quality using GIS and water quality indices (WQIs). Ram et al. (2021) mapped groundwater quality in Mahoba District, Uttar Pradesh, using ‘inverse distance weighted’ (IDW) interpolation on 43 samples. They found most of the area had acceptable quality, with some pockets of poor quality, highlighting the usefulness of WQI for assessing groundwater suitability. Dandge & Patil (2022) conducted a similar study in Bhokardan, Maharashtra, using GIS and remote sensing to analyze 12 water quality parameters. Their findings showed poor water quality in the pre-monsoon season, with improvements post-monsoon, emphasizing the importance of seasonal monitoring and WQI for groundwater management.

WQIs are essential tools for assessing water quality and are particularly useful for monitoring both drinking water and irrigation water quality. Various indices such as sodium absorption ratio (SAR), residual sodium carbonate (RSC), sodium percentage (Na%), permeability index (PI), and Kelly's ratio (KR) provide valuable insights into the suitability of water for irrigation purposes, and their application in the study of groundwater contamination is critical for understanding its impact on agriculture (Jafari et al. 2018; Khalid 2019). Regular monitoring and evaluation of these parameters are necessary for effective water resource management (Prathumratana et al. 2008). Al-Amry (2008) assessed groundwater quality in the Al-Salameh area, Yemen, using 25 samples. The results showed that 17 samples were suitable for drinking, while irrigation suitability was determined based on parameters like SAR, KR, and RSC, indicating general suitability for irrigation.

Despite the growing body of research on groundwater contamination in the region, significant gaps remain in understanding the full extent of pollution and its long-term effects on human health and the environment. This study aims to fill these gaps by assessing the quality of groundwater in the Palar River Basin, focusing on the impact of industrial pollution and the suitability of groundwater for drinking and irrigation purposes.

The primary objectives of this investigation are as follows:

• 1. To visualize the spatial distribution of irrigation indices using GIS techniques across the study area.

• 2. To compare spatial distribution maps of irrigation indices in different years to assess changes over time and to evaluate groundwater suitability for irrigation purposes based on the spatial analysis.

• 3. To support effective water resource management and sustainable agricultural practices by providing a comprehensive understanding of groundwater suitability trends.

The Palar Basin stands out as one of the most significant among Tamil Nadu's 17 basins. The area selected for this study is located between 12°37′19″ N–13°00′12″ N latitudes and 78°28′07″ E–79°34′51″ E longitudes with an areal extend of 10,163.151 km² (Figure 1). The Palar Basin plays an essential role in Tamil Nadu's hydrology, agriculture, and water supply, making it highly relevant for assessing groundwater quality and irrigation suitability. As a transboundary basin shared with Karnataka and Andhra Pradesh, the Palar Basin supports a diverse range of agricultural activities and is a crucial source of groundwater for irrigation, drinking, and industrial use. Given its distinct geographical features – spanning the Eastern Ghats, plateau regions, and coastal plains – the basin experiences varied climatic and hydrological conditions, influencing groundwater recharge, quality, and availability.

By focusing on the Palar Basin, this study can reveal valuable insights into groundwater trends over time, providing a framework to manage and sustain water resources in a region where water demand is increasing and groundwater quality faces challenges from agricultural runoff and urbanization. The findings from this study will offer vital data to support informed decision-making for resource management, benefiting not only the Palar Basin but also contributing to broader water conservation and agricultural strategies across Tamil Nadu. The latitudes and longitudes of the sampling point in the study area are provided in Table 1.

The investigation of the quality of groundwater of the Palar Basin was undertaken for the years 1993, 1983, 1973, 2003, 2013, and 2022. The data collected from Central Ground Water Board (CGWB), Chennai includes the information related to the water quality parameter (WQP) values for pre- and post-monsoon. The study took into account various physicochemical parameters, including pH, electrical conductivity (EC), and the concentrations of key ions such as calcium (Ca), magnesium (Mg), sodium (Na), potassium (K), chloride (Cl), and SO₄. These parameters were utilized to compute the irrigation indices, which served as critical indicators for assessing water suitability for agricultural purposes.

The study examined a range of physicochemical parameters, including pH, EC, and concentrations of essential ions such as Ca, Mg, Na, K, Cl, and SO₄. These parameters were used for assessment of drinking water quality and also used to calculate irrigation indices, which are vital indicators for evaluating the suitability of water for agricultural use. Table 2 presents the maximum permissible limits for water quality parameters and their impact on irrigation water.

The quality of water for irrigation has been assessed using various irrigation indices. It includes SAR, RSC, Na%, PI, magnesium hazard (MH), KR, and potential salinity (PS). The equations for calculating irrigation indices are given in Table 3, where all ion concentrations used in the equations are in meq/L. The groundwater categorization on irrigation indices is visible in Table 4. SAR, RSC, Na%, PI, MH, KR, PS were chosen for their ability to provide a comprehensive assessment of irrigation water quality and its impact on soil and plant health. These indices address critical factors such as sodium toxicity, soil permeability, salinity, and the balance of essential ions. SAR and Na% evaluate the risk of sodium-induced soil degradation, while RSC and PI focus on the alkalinity and permeability effects on soil. MH helps assess Mg toxicity risks, and KR indicates the ionic balance in water that influences plant growth. PS evaluates the salinity potential, which can affect plant roots and crop yield. Together, these indices offer a holistic understanding of water quality, ensuring a thorough evaluation of its suitability for irrigation in varying hydrogeological conditions.

Table 3

Equations for the calculated irrigation indices

Irrigation indices .	Equation number .	References .
	(1)	Raghunath (1987)
	(2)	Nageswara Rao et al. (2022)
	(3)	Todd (1980)
	(4)	Doneen (1964)
	(5)	Al-Shammiri et al. (2005), Joshi et al. (2009).
	(6)	Kelly (1940)
	(7)	Doneen (1964)

Irrigation indices .	Equation number .	References .
	(1)	Raghunath (1987)
	(2)	Nageswara Rao et al. (2022)
	(3)	Todd (1980)
	(4)	Doneen (1964)
	(5)	Al-Shammiri et al. (2005), Joshi et al. (2009).
	(6)	Kelly (1940)
	(7)	Doneen (1964)

View Large

The WQI is essential for determining overall water quality, while irrigation indices like Na%, SAR, PI, and MH are critical for evaluating groundwater's suitability for irrigation. These indices help assess soil salinity and sodicity, which impact crop productivity and soil health, making a combined analysis with WQI vital for sustainable agricultural practices (Shukla et al. 2023).

A composite index, known as the WQI, was employed to evaluate the suitability of groundwater for various purposes. The primary objective is to consolidate extensive and intricate water quality data into a single, comprehensive index (Ramakrishnaiah et al. 2009; Varol & Davraz 2015). The WQI is a valuable tool developed to simplify the assessment of overall water quality by combining various parameters into a single value, facilitating effective management and categorization of water sources for their suitability in different uses. Initially developed by Horton in 1965, the WQI has evolved over time, incorporating complex datasets to provide insights for governmental, public, and regulatory bodies, despite the absence of a universally recognized index method (Rana & Ganguly 2020). The standard values and unit weight values of irrigation indices are given in Table 5.

Table 5

Standard values and unit weight values used for the calculation of WQI

Irrigation index .	Standard values (⁠⁠) .	Unit weight values (⁠⁠) .
SAR	10	0.052356
RSC	2.5	0.209424
Na%	60	0.008726
PI	25	0.020942
MH	50	0.010471
KR	1	0.52356
PS	3	0.17452
Total weightage		1

Irrigation index .	Standard values (⁠⁠) .	Unit weight values (⁠⁠) .
SAR	10	0.052356
RSC	2.5	0.209424
Na%	60	0.008726
PI	25	0.020942
MH	50	0.010471
KR	1	0.52356
PS	3	0.17452
Total weightage		1

View Large

The WQI has been calculated in three steps using weighted arithmetic index methods (Ramakrishnaiah et al. 2009). The categorization of WQI in accordance with restriction in using groundwater for irrigation purpose is given in Table 6.

The regional distribution of groundwater quality metrics was interpolated using spatial modeling, an advanced capability of ArcGIS 10.4. The spatial resolution of the groundwater quality data used in this GIS analysis is approximately 5.7 km × 5.7 km, suggesting that the points are not evenly distributed across the study area. Finer resolution in well-dense areas improves mapping accuracy, while coarser resolution in sparse zones balances efficiency and data availability. Regions with closely spaced wells yield more accurate representations of water quality, whereas sparsely monitored areas have lower accuracy.

The IDW interpolation method was specifically utilized to create detailed spatial distribution maps of water quality parameters. This technique effectively interpolates data to assess observations between measured points. Numerous researchers have adopted the IDW method for interpolation in their studies, demonstrating its widespread application and reliability (Mueller et al. 2004). In assessing WQI, the IDW method within GIS modeling offers valuable spatial insights by interpolating parameters like temperature, dissolved oxygen (DO), nitrates (NO₃), and total phosphorus across various depths. This integration with traditional methods allows for comprehensive spatial analysis of water quality, enhancing monitoring and understanding of pollution dynamics within water bodies (Vasistha & Ganguly 2022). In this study, IDW was selected for its effectiveness in estimating spatial variations in groundwater quality from scattered sample points. IDW assumes that closer points exert more influence, fitting well with our data distribution. Unlike Kriging, which requires a complex variogram model, IDW is computationally efficient and adaptable to irregular sampling patterns. Validated widely in groundwater studies, IDW aligns with established practices and provides reproducible, detailed maps. This method avoids the smoothing effect seen in Kriging, preserving small-scale quality variations and ensuring that local influences are accurately represented in our groundwater quality maps.

Salt accumulation in groundwater poses a major environmental issue across several regions of Tamil Nadu, particularly those with substantial industrial activities. Industrial effluents often contain high concentrations of dissolved solids, heavy metals, and various contaminants that infiltrate groundwater, leading to increased EC and degraded water quality (Kanagaraj & Elango 2016). Over time, this contamination not only raises groundwater salinity but also threatens public health and agricultural sustainability. Extended use of high-EC water for irrigation can lead to soil salinization, adversely affecting crop productivity and soil structure (Richards 1954).

The spatial distribution of WQI showed that the majority of water is coming under categories that have no restriction in pre- and post-monsoon periods except in 1993. An increase in water quality parameters like Ca, Mg, Na, and other parameters may contribute to the change in water quality. Contaminants dissolved in the runoff during monsoon reach the groundwater and at last, it joins with seawater resulting in the deterioration of water quality. Comparing the spatial distribution of the WQI for selected years (1973, 1983, 1993, 2003, and 2013) showed no drastic changes in the WQI up to 1993. In 1993, the greater part of the study area fell into the ‘slight restriction’ category. However, after 1993, it shifted to the ‘no restriction’ category. The western part of the region fell under the 'severe restriction' category during the pre-monsoon season of 1993.

Increased agricultural runoff, industrial discharges, or urbanization can introduce pollutants into groundwater, degrading its quality. Additionally, natural processes like mineral leaching and climate change impacts can alter groundwater composition, necessitating slight usage restrictions to protect human health and agricultural productivity. We have to take measures to control the decrease in the quality of water by strict enforcement of environmental regulations on industrial effluent discharge, improving the wastewater treatment facilities, and encouraging the construction of recharge structures in the desired areas.

From Table 7, it is evident that irrigation indices, such as RSC, MH, and PS, do not show significant seasonal variation (p > 0.05). Significant seasonal variations are observed in key irrigation indices such as SAR, Na%, PI, KR, and WQI (p < 0.05). These fluctuations indicate changes in water quality and soil interactions between the pre- and post-monsoon seasons. Specifically, the variations in sodium adsorption ratio, sodium percentage, and the balance of sodium with other cations may impact soil permeability and fertility, necessitating targeted adjustments in irrigation practices. The observed seasonal shifts in water quality emphasize the need for intensive management strategies during periods of significant change to ensure optimal soil and water health.

This research was designed to measure the spatial characterization of groundwater quality of the Palar basin for pre- and post-monsoon of the selected years. The study, relying on data from the CGWB, may be limited by its temporal resolution and spatial coverage, potentially affecting the granularity of the analysis. Although seasonal variations are considered, the complexity of seasonal factors influencing water quality, such as unrecorded weather events or extreme climate occurrences, may not be entirely captured. Additionally, the use of IDW interpolation for spatial analysis assumes uniformity in data distribution, which could introduce bias or inaccuracies in areas with sparse data points. Furthermore, the study may not fully address the impact of non-point source pollution, such as runoff from urban areas or diffuse agricultural runoff, which can significantly influence water quality in certain regions. On the contrary, it is important to recognize that assessing the quality of water in the context of climate change is the most effective way to understand the hydrological behavior of groundwater. Therefore, this research offers valuable insights into the spatial characteristics of water quality parameters of water resources, emphasizing that failure to maintain groundwater quality could have severe consequences for communities. As such, this study is highly relevant for water resources management, highlighting the need for effective measures to safeguard groundwater quality.

Groundwater quality and its relevance for irrigation purposes in the Palar Basin area have been evaluated. The water quality and pollution status of the Palar basin are critically important as they directly impact human health. Nearly 90% of diseases are caused by consuming contaminated water, and rivers serve as the primary water source. The Tamil Nadu government relies heavily on groundwater, using 80% of it for water supply. A change in water quality parameters over time was observed and irrigation indices were calculated.

In conclusion, the outcomes of the present study suggest that frequent and comprehensive groundwater analysis is essential to monitor the rate and types of pollution. Raising public awareness of the importance of preserving groundwater's highest standards of quality and purity is imperative.

This work was carried out in collaboration with all authors. All authors read and approved the final manuscript.

No funding was obtained for this study.

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

The authors declare there is no conflict.