Study Area

The study area selected is the Thiruvallur district in Tamil Nadu, a mixture of urban and rural areas with a total area of 3422 sq km, as shown in (Fig. 1). The district is situated between the longitudes of 79^o15’ E to 80^o20’ E to latitudes of 12^o15’ N to 13^o15’ N. The district is characterized by sandy, red, non-calcareous, and coastal alluvial soil that has been blended with soda or alkali. In this region, the average annual rainfall is recorded at 1152.8 mm. The maximum and minimum temperatures range from 37.9ºC to 23.6ºC. This district is supported by several groundwater resources, with approximately 70 dug wells present in the area. For the selected blocks in Tiruvallur district, namely Madhavaram, Ponneri, Poonamallee, and Ambattur, a total of 11 dug wells have been identified in various villages. It includes Thiruthani, Thirvalangadu, Thiruvallur, Thirumullaivoiyil, Thiruverkadu, Madhavaram, Thiruvottriyur, Ennore, Periyapalayam, Gummidipoondi, and Redhills. The wells are selected in residential, industrial, and commercial places in the district. The latitudes and longitudes of sampling locations, along with sampling points within the study area, are illustrated in Table 1.

Fig. 1.

Study Area Map.

Physicochemical Analysis

Groundwater samples were collected from 20 groundwater wells at 11 different locations in the study area in the year 2022 to evaluate both groundwater quality and major ion chemistry. The collection, preservation, and analysis of the samples were carried out in accordance with the standard methods outlined by the Central Pollution Control Board (CPCB) in 2007 [30]. As per the CPCB guidelines, the sampling locations are selected such that the samples taken are representative of the source from which water is obtained. The sampling points should include those that yield samples representative of the conditions at the water system, particularly points of possible contamination upstream and downstream of significant pollution outfalls like city sewage drains and industrial effluent outfalls.

The samples were collected four times, once in pre-monsoon (PRMS), April-May, and thereafter at intervals of 3 months covering monsoon and post-monsoon (POMS) periods. For the groundwater study, sampling will be done in a tube well, dug well, and hand pump, which are in use. In the present study, the samples were composed of open-dug wells and tube wells. The sample from an open well is collected using bottles at a depth of 30 cm below the surface of the water. Tube wells were continuously pumped for a minimum of 10 minutes before collecting the samples, and then the samples were collected using 1 L HDPE (high-density polyethylene) bottles. Sample bottles were rinsed and washed properly three times with deionized water before the sample collection. The container is attached with an inscribed tag or label. The GPS of sampling stations, sample number, and the sampling date should be clearly marked on the tag and the samples brought to the laboratory. The methods for estimating the variations in physicochemical parameters are provided in Table 2.

The measured physio-chemical characteristics such as EC, pH, TDS, TH, Chlorides, Alkalinity, Calcium, Magnesium, Sulfate, Nitrate, Sodium, and Potassium of collected samples are as per the standard, and the test procedures are presented in Table 3.

The acquired data on groundwater quality serves as a basis for the current study. Table 4 illustrates the comparison between measured parameters and the recommended standard limits in drinking water specified by the Bureau of Indian Standards (BIS 10500, 2012) [31] and the World Health Organization (WHO, 1983) [32-33] for the purposes of drinking and public health. The methodology adopted for this study is shown in Fig. 2.

Preparation of Spatial Database Using QGIS

GIS is a software-based technology that utilizes hardware assistance and proves beneficial for a wide range of geospatial applications. GIS encompasses a multitude of functions like collecting, storing, overseeing, retrieving, transforming, examining, simulating, and presenting geographic data. It establishes a connection between data and maps, enabling the integration of location data with several forms of descriptive data. It establishes a fundamental basis for the process of mapping and analysis, which finds application in scientific research as well as various industries. The Geographic Information System (GIS) interpolates the groundwater quality of unknown locations by utilizing the water quality parameters of known regions. This process creates a continuous surface that illustrates the spatial distribution of water quality characteristics over a large area [34].

Quantum Geographic Information System (QGIS) is an open-source, cross-platform desktop GIS that performs as GIS software, enabling users to compose and export graphical maps as well as analyze and modify spatial data. The Geospatial database for the study is prepared from QGIS by creating spatial thematic maps. The water quality maps are created through interpolation techniques. In the present study, the measured characteristics obtained from the physicochemical assessment of groundwater samples at 11 locations are integrated with QGIS. The Inverse Distance Weight (IDW) method is used for generating groundwater quality maps for the proposed study locations in the Thiruvallur district. This process creates a continuous surface that illustrates the spatial distribution of water quality characteristics over large areas [34-35]. The spatial distribution of water quality is shown in Fig. 3

Fig. 2.

Flow diagram representing methodology.

The State Surface and Groundwater Resources Data Centre Chennai, Taramani, provided information on groundwater quality for the most recent ten years, from 2012 to 2022. It was categorized as secondary data for all the sample sites. The primary data on physio-chemical characteristics of groundwater samples collected from the study region were compared to the secondary data in this study [36]. The water quality comparative analysis diagrams generated using QGIS are illustrated in Fig. 4.

Generation of Water Quality Index Map

The Water Quality Index (WQI) is a numerical rating that reflects the quality of water based on several physical, chemical, and biological parameters. The overall Water Quality Index (WQI) is a valuable and unique assessment system obtained from the Weighted Arithmetic Index method that summarizes the water quality status. It is particularly useful in determining the best treatment method to satisfy specific water quality requirements [37].

A quality rating scale (Qi) was initially developed for each characteristic. The sub-index value of each characteristic is estimated by multiplying the rating scale with the respective estimated weighing factor. The sub-indices are combined to get the overall Water Quality Index. Table 5 displays the water quality rating assigned by the Bureau of Indian Standards (BIS) based on the Water Quality Index. According to the BIS guidelines, the WQI values are usually divided into five categories, each representing a range of water quality: less than 25 as ‘Excellent,’ 25–50 as ‘Good water,’ 50–75 as ‘Poor water,’ 76–100 as ‘Very Poor water,’ and greater than 100 (Fig. 5), which is unfit for drinking purposes.

Table 6 provides the calculated WQI alongside the corresponding status of chosen groundwater samples in the Tiruvallur district. The spatial databases built using QGIS were integrated with non-spatial data to create the maps depicting the spatial distribution of all water quality parameters as well as the WQI map, as shown in (Fig. 4). The calculated WQI values for the samples in the study area ranged from 11.71 to 84.83. The WQI results for groundwater in the Tiruvallur district showed that samples from 2 locations (18%), namely Thiruverkadu and Thiruvalangadu, fell into the ‘Excellent water’ category [38].

Samples from 7 locations (64%), namely Thiruthani, Thiruvallur, Madhavaram, Thiruvottriyur, Ennore, Periyapalayam, and Gummidipoondi, were classified as ‘Good’ water. The quality of water status in 2 locations, namely Redhills and Thirumullaivoiyil (18%), was categorized under the ‘Very Poor water’ category.

Hydrogen Ion Concentration (pH)

pH is the measure of acid-base equilibrium in natural water. The pH levels in the groundwater samples vary from 5.7 to 7.06, with a mean value of 6.54 in the area considered for the present study. The pH of Thirvalangadu was the highest at 7.06, and the lowest pH of 5.75 was noticed in Redhills. The comparison of results also revealed that all the locations except Thirvalangadu have a pH below 7. As the Thirvalangadu location is regarded as residential, a pH shift would have an impact on the local inhabitants. The diverse geomorphology variance within the soil composition can be attributed to Thirvalangadu’s higher pH level. Specifically, the areas with dense rocks and minerals with high alkalinity in the above region contribute to an overall increase in pH levels. The pH values of all the tested samples fall within the limits for drinking as well as irrigation (Table 4) prescribed by both BIS and WHO. The test results also indicate that a significant portion of the study region exhibits a pH value equal to or below 6.5, indicating a moderately acidic nature of the groundwater.

Total Dissolved Solids (TDS)

TDS refers to a quantification of the collective presence of both organic and inorganic compounds. It indicates aesthetic characteristics that show the existence of diverse chemical pollutants. TDS of collected samples varied between 324 and 1072 mg/l, with an average value of 634.36 mg/l. The TDS of each sample conforms to the permissible limits as per the BIS guidelines. However, samples from Thiruttani, Thiruvallur, Thiruverkadu, Thiruvottriyur, and Periyapalayam are greater than the WHO prescribed limits. It could be due to the leaching of salts from industrial effluents, the deposition of scale on the inner surface of the well, and other human-made sources like urban areas and agricultural fields. The increase in TDS due to scale formation can be minimized by regular cleaning and maintenance of the well. The analysis of TDS rate changes over a 10- year period starting from 2010 suggests that the wells in Thirvalangadu, Thiruverkadu, and Redhills (Fig. 4a) exhibit minimal variations in TDS rates and generally maintain low levels of TDS. These wells have been kept in good condition as a result of maintenance efforts, and rainfall is another element that significantly lowers TDS levels and keeps them within acceptable levels. The wells located in Thirumullaivoiyil and Gummidipoondi demonstrate a marginally higher TDS rate, surpassing the permissible limits recommended by the World Health Organization (WHO). It can be attributed to the presence of toxins and filthy water seeping into the groundwater source.

The measurement of the salinity level in groundwater is indicated by its electrical conductivity. It makes an estimate of the TDS or total dissolved solids in water. The natural conductivity of fresh water varies from very low values of 30µS/cm to very high values of 2000 µS/ cm, which is unsuitable for irrigation. The electrical conductivity varied from 540 to 1787 μs/cm with an average value of 1057 μs/cm in the selected study area. The wells in Thirumullaivoiyil, Thiruverkadu Thiruvottriyur, and Gummidipoondi exhibit notable differences in their electrical conductivity rates. However, these wells fall within the acceptable range of electrical conductivity. The electrical conductivity is lower in the areas such as Thiruvalangadu, Madhavaram, Redhills, and Ennore, and well below the permissible limit. This can primarily be attributed to the decrease in TDS within those specific regions.

Fig. 3a.

Spatial Patterns of PH Distribution. Fig. 3c. Spatial Patterns of TDS Distribution. Fig. 3b. Spatial Patterns of Electrical Conductivity Distribution. Fig. 3d. Spatial Patterns of Total Hardness Distribution. Fig. 3e. Spatial Patterns of Chlorides Distribution.

Fig. 4a.

Total Dissolved Solids Comparison in 2012 and 2022. Fig. 4b. Electrical Conductivity Comparison in 2012 and 2022. Fig. 4c. Comparison of Total Hardness in 2012 and 2022. Fig. 4d. Comparison of Chlorides in 2012 and 2022.

Fig. 5.

Water Quality Index Variation in the Thiruvallur district.

Electrical Conductivity (EC)

The spatial distribution of the Electrical Conductivity rate over a period of 10 years (Fig. 4b) demonstrates a similar pattern of increment in the electrical conductivity in water as observed in TDS. There has been a marginal increase in electrical conductivity in the areas of Thiruverkadu and Redhills in the last ten years. Nevertheless, due to their increased TDS value during the previous ten years, wells in Thiruthani, Thiruvallur, and Periyapalayam exhibit a sharp increase in electrical conductivity. It also exceeds the permissible limits prescribed in BIS standards.

Total Hardness

The hardness in water is caused by the presence of dissolved salts, primarily calcium and magnesium compounds. The groundwater samples exhibit hardness levels ranging from 152 to 640 parts per million (ppm), with an average hardness of 312 ppm in the study area. The hardness of Thiruthani was highest at about 640 ppm and the lowest at 152 ppm was noticed in Redhills. The wells in all the locations except Thiruthani are having hardness values within acceptable- permissible limits of BIS standards. With the exception of Thiruthani, the wells in the remaining locations have hardness values that fall within the acceptable and permissible limits set by the Bureau of Indian Standards (BIS) standards. This may be attributed to the excessive application of non-treated chemical and mining effluents in the Thiruthani zone. There has been a significant increase in water degradation in the above-mentioned zone compared to the conditions observed in 2010. Despite an increase in the number of industries, Thirvalangadu, Thirumullaivoiyil, Madhavaram, Gummidipoondi, and Redhills are among the places where water hardness (Fig. 4c) has decreased. This observation suggests that the effluents generated by industries in the vicinity of these areas have undergone effective treatment processes. There is a marginal increase in hardness observed in Redhills, which indicates a well-maintained dug well.

Chloride (CL^-)

Chloride is a prominent inorganic anion present in water. The primary source of groundwater contamination in the Thiruvallur district is the discharge of domestic sewage, with chloride concentrations ranging from 36 to 370 mg/l, as indicated in Tables 3 and 4, with a mean value of 160 mg/l in the area considered for the present study. The chloride concentration of Periyapalayam was the highest, about 370 mg/l, and the lowest value of 36 mg/l was noticed in Thirvalangadu. The Chloride levels in groundwater in the above region are safely within the BIS’s maximum desirable limit of 1000 mg/L. However, the wells in all the locations except Periyapalayam have chloride concentrations within permissible limits of WHO standards. The spatial distribution of chloride concentration rate over a period of ten years (Fig. 4d) indicates that the aforementioned parameter in water declines in places like Gummidipoondi, Thirvalangadu, Thirumullaivoiyil, Madhavaram, and Ennore. However, wells in Thiruthani, Thiruvallur, Thiruverkadu, Thiruvottriyur, and Redhills show a drastic increase in chloride concentration in groundwater in the last ten years but well below the acceptable-permissible limits of BIS and ISO Standards. The results of the computed Water Quality Index and the status of selected groundwater samples in the Tiruvallur district are presented in Table 6.

Alkalinity

The presence of carbonates, bicarbonates, and hydroxides contributes to the alkalinity of water. Alkalinity also indicates the water’s capacity to absorb hydrogen ions (H+). An excess of alkalinity in water can be detrimental to irrigation as it may lead to soil damage and a reduction in crop yields. The total alkalinity of the samples ranged from 230 mg/l to 488 mg/l, as indicated in Table 3. The Bicarbonate concentration of all the water samples in the Thiruvallur district was substantially below the permissible limits set by the Bureau of Indian Standards (BIS), as shown in Table 4. In this region, samples in the regions of Thirvalangadu, Thiruvallur, and Thirumullaivoiyil exhibit total alkalinity levels above the desirable limit set by the WHO, as illustrated in Table 4.

Calcium (Ca²⁺)

The calcium hardness in the groundwater of the Thiruvallur region ranged from 36 mg/L to 100 mg/L. All the groundwater samples collected from the above region fall within the permissible limits prescribed by the BIS, as shown in Table 4.

Magnesium (Mg²⁺)

The magnesium hardness in the groundwater of the Thiruvallur zone was measured to range from 17 mg/L to 92 mg/L. The magnesium content in all the groundwater samples falls within the BIS desirable limit, as presented in Table 4. However, the samples from Thiruthani, Thiruvallur, Thirumullaivoiyil, Thiruvarur, Madhavaram, Gummidipoondi, and Redhills have a magnesium level above the WHO acceptable limit.

Sulfates (SO4^-)

All types of water naturally contain sulfates. However, significant concentrations of sulfates in water are primarily attributed to drainage wastes. An excess of sodium and magnesium sulfate can lead to cathartic effects. The sulfate content of groundwater in the Thiruvallur district ranges from 29 mg/l to 182 mg/l, as indicated in Table 4. The sulfate content of collected groundwater samples was found to be within the acceptable limit set by the BIS.

Nitrate (NO₃^-)

Surface water has low nitrate levels, while high concentrations of nitrate levels are observed in some groundwater sources. The primary source of nitrate is the biological oxidation of nitrogen-containing compounds from sewage. It was ascribed that the nitrate concentrations in each sample ranged from 2 mg/l to 75 mg/l. The nitrate concentration of each water sample was within the permissible BIS limit, except in the zone of Thirvalangadu, as shown in Table 4.

Sodium

The sodium concentration of all the water samples was substantially below the detectable limits set by the WHO except in the Thiruvallur region, as shown in Table 4.

Potassium

The samples exhibited potassium levels ranging from 2 mg/L to 5 mg/L, clearly indicating that they are well below the WHO permissible limit, as shown in Table 4.

Kelly Index

The Kelly index (KI) for a groundwater sample is calculated as the ratio of the sodium concentration to the sum of the concentrations of calcium and magnesium. The KI of overall samples ranges from 0.67 to 2.26. The samples collected from locations in Thiruthani, Madhavaram, Thiruvottriyur, Gummidipoondi, and Redhills have a KI value of less than 1 and can be used for irrigation. The samples from Thirvalangadu, Thiruvallur, Thirumullaivoiyil, Thiruverkadu, and Ennore locations exhibit a KI between 1 and 2, suggesting they are marginally suitable for irrigation (Table 7). However, the Kelly index in the Periyapalayam region exceeds 2, rendering it unsuitable for irrigation.

Magnesium Hazard

The ratio of sodium concentration to the total calcium and magnesium concentrations determines the magnesium hazard of a groundwater sample. Table 7 illustrates the overall investigated samples’ magnesium ratio values range from 27.78 to 57.43. The groundwater samples from three regions, namely Thiruthani, Madhavaram, and Gummidipoondi, have a magnesium ratio exceeding 50, making them unfit for irrigation. Using this water can adversely affect crop yield by increasing the soil’s alkalinity.

Permeability Index

The permeability index of groundwater is determined as follows:

PI =[(Na + √HCO3) ∕ (Ca + Mg + Na)] x 100

The majority of the groundwater samples are classified as class 1, and the other samples as class 2. Therefore, all the samples are deemed suitable for agricultural uses (Fig. 7).

Hydrochemical Facies

The identification of hydrochemical facies serves as a valuable tool for assessing water chemistry.

Piper Plot

The geochemical composition of water samples, derived from surface or groundwater sources, is illustrated through graphical representations known as Piper diagrams. It also depicts the proportional composition of major ions within the solution. The water facies are categorized using the above trilinear diagram based on the dominating ions. This diagram also reveals that most water samples in these locations are characterized by calcium and magnesium dominance in cations and chloride or sulfate dominance in anions. There is a significant presence of mixed water types, suggesting complex water chemistry in this region.

The Piper Diagram (Fig 6.) consists of a geometric arrangement featuring two external triangles and a central diamondshaped quadrilateral. The diagram’s bottom left triangle represents the concentration of cations such as Calcium (Ca²⁺), Magnesium (Mg²⁺), Sodium (Na⁺), and Potassium (K⁺) in % mg/L. The total concentration of Ca²⁺, Mg²⁺, Na⁺, and K⁺ ions is represented by the width of the corresponding field in this triangle. Most of the samples are clustered towards the lower part of the triangle, indicating a higher proportion of calcium and magnesium dominance. There are fewer points in the upper part of the triangle, indicating that the cations are less dominant. The proportions of these cations can provide valuable insights into the origins of the ions and the interactions between water and rock.

Anions such as bicarbonate (HCO3^-), sulfate (SO4^2- ), and chloride (Cl^-) are displayed in the bottom right triangle of the diagram. The concentrations of anions reveal valuable information about the source of water and signify the presence of pollutants. The majority of the samples are towards the lower right part of the triangle, indicating a higher proportion of chloride. Fewer samples are in the upper part, showing these anions are less dominant.

Points obtained from the triangular plots of cations and anions are superimposed upon the inner diamond-shaped quadrilateral structure. The combined concentrations of HCO₃^-, SO4^2-, and Cl^- are represented by the width of the field within this quadrilateral. Many samples are concentrated in the Upper Left Quadrant, indicating water types rich in calcium, magnesium, and sulfate. This is typical of gypsum or dolomite waters. Some points are closer to the center, indicating mixed water types with no dominant cations or anions. It can further categorize the samples into five distinct regions. The Region I (Upper Left Quadrant) is characterized by the dominance of Ca²⁺- Mg²⁺ - SO4^2-. It is occupied by the samples from Thiruthani, Thirumullaivoiyil, and Thiruvottriyur, indicating a slight dominance of alkaline earth metals over alkali metals and the presence of strong acidic anion compared to weak acidic anion. Region II (Upper Right Quadrant) comprises the dominance of Ca²⁺- Mg²⁺ - Cl^- (Upper Right Quadrant). This category includes samples Thiruvallur, Thiruverkadu, Madhavaram, Periyapalayam, and Redhills. Region III is dominated by Na⁺- K⁺- SO₄^- whereas Region IV comprises Na⁺- K⁺- CL^- dominance. The regions considered for the present study did not belong to both of the zones. Region V is composed of mixed water types in the central region. Ennore, Gummidipoondi, and Thiruverkadu also appear in the Ca-Mg-Cl zone. The significant intersection of the HCO3^-/SO4^2- and Ca²⁺ /Mg²⁺ fields suggests that the water is of the bicarbonate type.

Wilcox Plot

The Wilcox diagram, a valuable tool, is utilized to categorize water samples according to their levels of sodium and salinity [39]. This classification aids in the assessment of sodium hazards in irrigation water. It also helps to prevent crop damage and soil degradation caused by excessive sodium. Typically, the diagram represents sodium percentage (Na%) on the x-axis and salinity (expressed as either electrical conductivity (EC) or sodium adsorption ratio (SAR)) on the y-axis. On the graph, each water sample is depicted as a point based on its sodium percentage (Na%) and salinity levels. The zones C1, C2, C3, and C4 represent water samples categorized by low, medium, high, and very high levels of sodium hazard and salinity. The electrical conductivity of various zones, such as Thiruvalangadu, Madhavaram Red Hills, and Ennore, ranges between 540 to 662 μs/cm. These zones fall under the C2 zone, characterized by medium salinity and low sodium hazard. It is conducive for cultivating a wide range of crops. Water in regions like Gummidipoondi, Thiruvottriyur, and Thiruthani exhibits low sodium hazard but high salinity levels. This may necessitate specialized management practices for specific crops. The areas of Thiruvallur, Thirumulaivoiyil, and Thiruverkadu, fall within a medium sodium and high salinity zone. Management practices may be needed to alleviate salinity-related challenges. Almost all locations are situated in zones with low to medium sodium hazards but high salinity except Periyapalayam, where water quality exhibits both high sodium hazard and high salinity. Hence, it may be appropriate for irrigation only with minimal management. It is ascribed that this illustration can assist farmers and water managers in making informed decisions regarding irrigation practices, such as choosing crops that can withstand high sodium levels or applying soil amendments to enhance water quality.

Fig. 6.

A trilinear diagram illustrating the relative cation and anion composition of groundwater samples.

Correlation Matrix

The trend in water quality can be determined using a correlation matrix approach. Any strong positive correlation can help reveal relationships between variables, allowing for a deeper understanding of water quality dynamics [40]. This information can then be used to develop targeted water treatment techniques and strategies to improve and maintain water quality. Python programming was employed to calculate the correlation matrix for five parameters, with the correlation coefficients presented in Fig. 8. A correlation value of less than 0.5 suggests a poor correlation, while a value greater than 0.5 is indicative of a strong correlation.

A very strong positive correlation between EC with TDS (1), Total hardness (0.86), and Cl-(0.87) except pH implies that this region is experiencing leaching of salts from groundwater, contamination due to human activities such as urbanization industrialization, overpopulation, deforestation, mining, intensification of tourism, and fishing, along with a reverse ion exchange processes.

There is a strong positive correlation between pH and total hardness in all the locations considered in the present study. The increase in total hardness has been impacted by shifts in environmental and geological conditions, such as the over-exploitation of groundwater, as well as pollution from human activities. Conversely, pH exhibits a poor correlation with Electrical Conductivity (EC) and Total Dissolved Solids (TDS). Further, it is noticed that the correlation between pH and chlorides is insignificant. This may be attributed to the lower concentration of chloride ions in groundwater in all locations.

Fig. 7.

Wilcox plot displaying the levels of sodium and salinity levels in groundwater samples.

Fig. 8.

Pearson Correlation matrix of the study area.

The correlation matrix also reveals a strong linear positive correlation between total dissolved solids (TDS) and electrical conductivity (EC) with a correlation coefficient equal to 1. TDS has an insignificant correlation with pH in all the places covering the region and a positive correlation with Total hardness and Cl-. It could be mainly due to manmade activities. Soil and water pollution is further intensified by agricultural practices, such as industrial metal mining, landfill waste, and the use of pesticides and herbicides in food production, along with animal and human waste and poor drainage systems.

An insignificant correlation between chloride and pH is observed, while a positive correlation with EC, Total Hardness, and TDS is seen in all the regions. This suggests the leaching of secondary salts, contamination from human activities, and the occurrence of reverse ion exchange.

Management Strategies

Rainwater harvesting (RWH) is gaining recognition as a vital part of urban water management, offering several benefits, such as increasing water supply, reducing flood risks, and enhancing environmental sustainability. To design an effective RWH system, it is crucial to select storage tanks or reservoirs that are appropriately sized based on factors like rainfall patterns, roof size, and water needs. The catchment area should be large enough and properly designed to efficiently direct rainwater to storage systems. Incorporating first-flush diverters and filters helps remove debris, dust, and contaminants before the water enters the storage system. Moreover, designing overflow systems is also essential. Prioritizing water usage is key, especially in areas with limited rainfall. In such regions, combining rainwater with other sources, such as treated wastewater or municipal water, can provide a more reliable water supply. Additionally, rainwater can be used for groundwater recharge by constructing infiltration wells, trenches, or percolation pits, which is particularly useful in areas with declining groundwater levels. Analyzing local rainfall patterns and historical data can optimize RWH system designs and help predict water availability. RWH management can be further enhanced by using rainfall data analysis and remote monitoring systems to track performance. By integrating these strategies, communities, industries, and governments can develop sustainable rainwater harvesting systems that conserve water, reduce reliance on traditional water sources, and promote environmental sustainability.

Land Use Statistics

The comparison of land use statistics in 2011 and 2021 as per the data provided by the revenue authorities during data collection is presented in Table 8. It is observed that the forest land coverage increased to 197.36 km² (5.77%), followed by expansions in barren land (135.69 km², 3.96%), Permanent Pastures and Other Grazing Lands (77.41 km², 2.26%), and Land Under Miscellaneous Tree Crops (66.31 km², 1.94%). These statistics highlight a rapid urban sprawl in the study area, driven by the growth of built-up areas (7.44%) and fallow land (7.19%). Conversely, there is a decline in agricultural growth, with Net Area Sown decreasing (Fig. 9) by 2.82%, Current Fallows by 2.13%, and Culturable Waste Land by 0.71% [41-42].

The increase in Forest land by 0.32% contributes positively by filtering runoff, preserving water clarity, and stabilizing dissolved oxygen (DO) levels. The expansion of non-agricultural land, such as a built-up area of about 7.44% over the past decade, is likely to impact TDS and chloride levels due to increased sewage discharge and stormwater runoff. A marginal increase in barren land can affect water quality parameters such as increased Total Dissolved Solids due to the leaching of minerals and salts, which may, in turn, indirectly affect chloride and hardness levels in groundwater. The reduction in agricultural land can lead to increased runoff of pollutants like oil, heavy metals, and other urban contaminants. This results in modification in Hardness, TDS, nitrates, phosphates, and chloride levels, impacting water clarity and quality. It also contributes to nutrient loading in water bodies. This analysis indicates that effective strategies for maintaining chloride and hardness levels within acceptable limits in the Tiruvallur district include managing agricultural runoff, implementing erosion control measures on barren lands, and implementing urban runoff treatment.

Comparison of Groundwater Quality in the Tiruvallur and Kancheepuram Districts

Table 9 presents the maximum and minimum concentrations of major cations and anions found in the groundwater of both the Tiruvallur and Kancheepuram districts. The TDS values range from 175 to 1453 mg/L in the Kancheepuram region [43], while in the Tiruvallur district, they vary between 324 and 1072 mg/L. Alkalinity levels in the Tiruvallur region range from 230 to 488 mg/L, while in the Kancheepuram region, they vary between 61 and 604 mg/L. Higher HCO₃ concentrations suggest contributions from silicate and carbonate weathering processes.

The cations follow an order of abundance as Na > Ca > Mg > K in both districts. The concentrations of calcium and magnesium in the study area are likely influenced by rock weathering processes. Magnesium might be originated from the dissolution of magnesium calcite, gypsum, and dolomite. The anions follow an order of abundance as SO₄ > Cl > HCO₃ in the Tiruvallur district and SO₄ > HCO₃ > Cl in the Kancheepuram district. The higher chloride content in groundwater in the Kancheepuram district may be attributed to soluble chloride from rock sources and saline water intrusion from the Bay of Bengal. The correlation results indicated a strong relationship between major anions and cations, suggesting that the samples originated from similar geological settings in both districts.

Fig. 9a.

Land use and land cover changes across the Tiruvallur district between 2011 and 2021. Fig. 9b. Variation in land use and land cover in the Tiruvallur district from 2011 to 2021.