Introduction

Deterioration of groundwater quality has strong correlation with population growth and industrialization. Most of the world’s largest cities are located either near the rivers or the coasts. However, river water is insufficient to meet the ever-increasing demand of the cities. This scarcity of water has increased the overexploitation of groundwater. Groundwater serves as major and natural source of water for domestic and agricultural purposes in many cities (Mondal et al. 2010). However, groundwater quality in the coastal region is often under tremendous threat due to seawater intrusion (Somay and Gemici 2009; Melloul and Goldenberg 1997; Jørgensen et al. 2008; Cobaner et al. 2012; Werner et al. 2012). In the coastal regions, freshwater–saline water interface is highly mobile depending on the difference in hydraulic head. Overexploitation of freshwater through uncontrolled pumping is the normally encountered process that destroys the saline water–freshwater equilibrium. This naturally leads to salinization in the coastal regions (Pulido-Laboeuf 2004; Milnes and Renard 2004; Demirel 2004; Shammas and Jacks 2007; Kouzana et al. 2009). Salinization, consecutively, leads to an overall change in chemical composition of the groundwater (Richter and Kreitler 1993; Somay and Gemici 2009), especially proliferates the concentration of dissolved solids (TDS) and some chemical constituents such as Cl, Na, Mg, and SO4 (Sukhija et al.1996; Giménez and Morell 1997; Park et al. 2005).

The coastal aquifer systems are complex environments where geological heterogeneity, spatial and temporal variability in the flow field and surface water/groundwater interactions all play a crucial role in governing the distribution of fresh and saline waters (McInnis and Silliman 2010) Numerous studies were performed by researchers worldwide to identify and quantify seawater intrusion in coastal aquifers (Goldman et al. 1991; Yakirevich et al. 1998; Sivan et al. 2005; Prieto et al. 2006; Khublaryan et al. 2008). Multifarious methods are employed to study this widespread form of pollution, hydrogeochemical (Milnes and Renard 2004; Sarwade et al. 2007; Kouzana et al. 2009), Geophysical (Duque et al. 2008; de Franco et al. 2009; Zarroca et al. 2011), Isotope tracers (Jørgensen et al. 2008; Gattacceca et al. 2009; Han et al. 2011), numerical modeling (Abarca et al. 2007; Cobaner et al. 2012) and statistical analysis (Giménez and Morell 1997; Kim et al. 2005; Mondal et al. 2010).

The specific case of saline water–groundwater interaction was studied by Mondal et al. (2010) in a coastal watershed in South India using major ion chemistry. They have reported that freshwater with seawater mixed with a seasonal variability from 4.82 to 7.86% throughout the watershed. Another study by Kim et al. (2009) at Jeju Island, South Korea emphasized the usefulness of time-series data of EC and temperature at various depths for better understanding of the interaction processes between fresh and saline water. Sarwade et al. (2007) studied the seawater–groundwater mixing in a coral island system. Saline intrusion was identified as the main reason for the severe deterioration in groundwater quality.

The objective of the present study, carried out in a coastal aquifer located in the East Coast of South India, was to understand the groundwater–saline water mixing process. Multiple approaches including hydrochemical analysis, HFE-D plot, statistical techniques, hydrochemical Ionic changes and Salinity Mixing Index (SMI) were employed in the current study to ensure the accurate interpretation of the results.

Description of the study area

The study area is located in the East Coast of Tamil Nadu constituting the coastal regions of Cuddalore and Chidambaram Taluks. Geographically, this area lies between east longitude 79°22′00″–79°52′00″ and north latitude 11°10′00″–12°50′00″ (Fig. 1). Climate of this region is hot tropical with temperature ranging from 25 °C (December) to 31 °C (April). The summer season (March–May) is very humid. The annual normal rainfall for the period (1901–2000) ranges from 1050 to 1400 mm. The South West monsoon follows till September and North East monsoon extents from October to December (CGWB 2009). The major rivers that drain the study area are Gadilam and Pennaiyar rivers in the North, Vellar and Coleroon in the South. They generally flow from West towards East and the pattern is mainly sub-parallel. All these rivers are ephemeral and carry floods during monsoon. Vellar, is another seasonal river, which drains the major portion in the southern part of the district. Manimuktha, Gomukhi and Mayura form the major tributaries which join the Vellar River (CGWB 2009).

Fig. 1
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The study area map showing a India. b Tamil Nadu. c Google earth Image of the study area and sampling points

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A major portion of the study area is covered by eastern coastal plain, which is predominantly occupied by the flood plain of fluvial origin formed under the influence of Penniyar, Vellar and Coleroon river systems. Marine sedimentary plain is noted all along the eastern coastal region. In between the marine sedimentary plain and fluvial flood plains, fluvio-marine deposits are noted, which consist of sand dunes and back swamp areas. Black soils are observed in parts of Chidambaram Taluk. The younger alluvial soils are found as small patches along the stream and river courses in the district. Red sandy soil is seen covering the Cuddalore sandstone, laterite and lateritic gravels occurring in parts of Cuddalore Taluk (CGWB 2009). As the study area is typically a coastal region, the quaternary formations in the area consist of sediments of fluvial fluvio-marine and marine facies. It includes various types of soil, fine to coarse-grained sands, silts, clays laterite and lateritic gravels the semi consolidated formations are essentially argillaceous, comprising silts, clay stones, calcareous sandstones, siliceous limestones and algal limestones.

Materials and methods

Groundwater sampling and analytical techniques

Forty-nine groundwater samples were collected from the study area during January 2010. Each sample was located using a handheld GPS (HC Gramin). Wells were pumped out till the in situ parameters were stabilized. On-field measurements were conducted to determine the parameter such as electrical conductivity (EC) and pH. Alkalinity was measured by titration with 0.02 N H2SO4 prior to the groundwater sampling. Groundwater was collected in polyethylene bottles (1 L capacity); bottles were sealed and brought to the laboratory for analysis and stored properly (4 °C) before analysis. Analysis was carried out as per the standard methods suggested by APHA (1998). Major ions like Ca, Mg, Na, K, Cl, SO4, NO3 and F were analysed. Ca and Mg were analysed using titration with EDTA. Cl concentration was determined using Argenometric titration. UV visible spectrophotometer was used for analysis of sulphate. Sodium and potassium were analysed using flame photometer. The analytical precision of the measurements of cations and anions is indicated by the ionic balance error, which has been computed on the basis of ions expressed in milliequivalent per liter (meq/L). The values were observed to be within the standard limit of ±5%.

Hydrochemical facies evaluation

Conventional Piper plot (Piper 1953) and the more complicated HFE-D plot (Gimenez Forcada 2010) were employed to analyse the hydrochemical facies changes during the saline water–freshwater mixing in the study area. The HFE-D plot has 16 subdivisions, representing the various processes 1: Na–HCO3/SO4, 2: Na–MixHCO3/MixSO4, 3: Na–MixCl, 4: Na–Cl, 5: MixNa–HCO3/SO4, 6: MixNa–MixHCO3/MixSO4, 7: MixNa–MixCl, 8: MixNa–Cl, 9: MixCa–HCO3/SO4, 10: MixCa–MixHCO3/MixSO4, 11: MixCa–MixCl, 12: MixCa–Cl, 13: Ca–HCO3/SO4, 14: Ca–MixHCO3/MixSO4, 15: Ca–MixCl, 16: Ca–Cl. HFE-D can represent the main processes occurring during the intrusion and freshening stages in the evolution of the hydrochemical facies (Gimenez Forcada 2010; Ghiglieri et al. 2012), which is not possible in the usual triangular plots including Piper trilinier diagram.

Principal component analysis (PCA)

Principal component analysis quantifies the relationship between the variables by computing the matrix of correlations for the entire data set. This helps to summarize the data set without losing much information (Rao et al. 2006). In the initial step, data sets were standardized and correlation matrix created. The eigenvalues and factor loadings for the correlation matrix were determined and scree plot was drawn. The extraction factors were based on the variances and co-variances of the variables. The eigenvalues and eigenvectors are evaluated, which represent the amount of variance explained by each factor. Eigenvalue greater than 1 was set as a criterion to extract factors (Kaiser 1958; Liu et al. 2003). Finally, by the process of rotation, the loading of each variable on one of the extracted factors is maximized and the loadings of all the other factors are minimized. These factor loadings are useful in grouping the water quality parameters and providing information for interpreting the data. This study considered pH, EC, Na, K Ca, Mg, Cl, CO3, HCO3 and SO4 as water quality parameters. SPSS 16 was used for the Statistical analysis.

Hydrochemical ionic changes

Freshwater–seawater displacement can be evaluated depending on the calculation of the expected composition based on conservative mixing of seawater and freshwater in comparison to the result with actual compositions found in the studied groundwater samples (Appelo and Postma 2005). The seawater contribution was used to calculate the concentration of each ion (i) in the conservative mixing of seawater and freshwater (Eq. 1)

$${\text{e}}_{i(mix)} = {\text{f}}_{sea} \times {\text{e}}_{i(sea)} + \, (1 \, - {\text{f}}_{sea} ) \, \times {\text{e}}_{i(fresh)}$$
(1)

where e i (in meq/L) is the concentration of specific ion (i), f sea is the fraction of seawater in mixed freshwater–seawater, and subscripts mix, sea and fresh indicate the conservative mixture of seawater and freshwater. Any change in concentration (ionic change; e i(change)), as a result of chemical reaction, can be expressed as shown in Eq. 2 (Fidelibus et al. 1993; Pulido-Laboeuf 2004).

$${\text{e}}_{i \, (Change)} = \left[ {{\text{e}}_{i(sample)} - {\text{e}}_{i(mix)} } \right] = \, \left[ {{\text{e}}_{i(sample)} - {\text{ f}}_{sea} \times {\text{e}}_{i(sea)} + \, \left( { 1- {\text{ f}}_{sea} } \right) \, \times {\text{e}}_{i(fresh)} } \right],$$
(2)

where e i(sample) is the actual observed concentration of specific ion in the water sample. The fraction of seawater is normally based on Cl, which is a conservative ion with high solubility (Appelo and Postma 2005). The theoretical seawater fraction was calculated by considering the seawater contribution from the sample Cl concentration (e Cl(sample)), the freshwater Cl concentration (e Cl(fresh)) and seawater Cl concentration (e Cl(sea)) (see Eq. 3) where, Cl concentration is expressed in milliequivalents per liter (meq/L) (Appelo and Postma 2005).

$${\text{f}}_{sea} = \left[ {\left( {{\text{e}}_{{{\text{Cl}}(sample)}} {-}{\text{e}}_{{{\text{Cl}}(fresh)}} } \right)/\left( {{\text{e}}_{{{\text{Cl}}(sea)}} {-}{\text{e}}_{{{\text{Cl}}(fresh)}} } \right)} \right]$$
(3)

Seawater Mixing Index (SMI)

Seawater Mixing Index (SMI) is first suggested by Park et al. (2005). This parameter is based on the concentration of four major ionic constituents in seawater such as Na, Cl, Mg, and SO4. It can be calculated using Eq. 4

$$SMI = a \times \frac{{C_{\text{Na}} }}{{T_{\text{Na}} }} + b \times \frac{{C_{\text{Mg}} }}{{T_{\text{Mg}} }} + c \times \frac{{C_{\text{Cl}} }}{{T_{\text{Cl}} }} + d \times \frac{{C_{{{\text{SO}}_{4} }} }}{{T_{{{\text{SO}}_{4} }} }}$$
(4)

where the constants a, b, c and d denote the relative concentration proportion of Na, Mg, Cl and SO4 in seawater. C is the measured concentration in mg/L; and T represents the regional threshold values of the considered ions which can be estimated from the interpretation of cumulative probability curves.

Results and discussion

Hydrogeochemistry and its controlling mechanisms

The composition of the groundwater in the study area is presented in Table 1. Water quality of the region has varied considerably with range in TDS from 382 to 6032 mg/L. Among the 12 samples, 67% (n = 8) has crossed the guideline value for drinking 100 mg/L (WHO, 2011). All the groundwater samples were alkaline in nature (pH 8.00–9.00). Electrical conductivity, as a direct measure of salinity, showed significantly high values (up to 9630) in some of the wells adjoining the coast. These samples have undoubtedly affected by the seawater intrusion. In the major ion chemistry, Na, Mg, Cl and SO4, in those wells located near the sea, were higher in concentration than the guideline values (WHO 2011). As expected for a coastal area, when the concentration of Ca becomes lower than the Na concentration, it represents the natural discharge zone. The distribution of the statistical parameters of the groundwater in the study region is presented in Fig. 2. A Gibbs plot (Gibbs 1970) was drawn to identify the dominant mechanism controlling the hydrochemistry in the aquifer. As seen in Fig. 3, samples were plotted mainly in the rock dominant and evaporation dominant region in the plot. This shows the control of the aquifer lithology (rock dominance) and seawater mixing (evaporation dominance) on groundwater chemical composition.

Table 1 Results of the hydrogeochemical parameters of individual samples with respective geographical coordinates
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Fig. 2
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Box plot displaying the distribution of statistical parameters in groundwater of the study area

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Fig. 3
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Gibbs plot showing the mechanisms controlling the hydrochemistry in the groundwater of the study area

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Evaluation of hydrochemical facies using Piper plot and HFE-diagram

The geochemical evolution of groundwater can be understood by plotting the concentrations of major cations and anions in the Piper trilinear diagram (Chidambaram et al. 2011). Hydrogeochemical facies in the groundwater of the study area is shown in a Piper plot (Fig. 4). Among the water types, 75% of the wells represented Na–Cl type indicating the influence of seawater mixing with fresh groundwater. The remaining samples plotted in the Na–Ca–HCO3 and Ca–Mg–Cl type field. However, a detailed recognition of facies evolution sequence during recharge and encroachment events was not possible with Piper diagram (Ghiglieri et al. 2012). This difficulty is successfully overcome by the Hydrochemical Facies Evaluation diagram HFE-D suggested by Gimenez Forcada (2010). Groundwater samples from this study, which have been plotted in HFE-D plot, are presented in Fig. 5. In this figure, seawater (4) and freshwater fields (13) are connected through a mixing line. Other fields represent mixing sequences of major facies Na–Cl and Ca–HCO3. The missing line represents the simple mixing between fresh groundwater and seawater (Samples 10–11–12) in the study area. Samples 3 and 7 are plotted in the seawater dominant filed, while 4 & 5 showed a Na–MixCl nature. Sample 1,3,6,8 and 9 showed an assorted origin of facies; possibly dominated by the Na–Ca cation exchange process. The complexity of the salinization and the related hydrogeochemical processes in the study area is evident from this analysis, which needs to be studied more accurately as done in the forth section.

Fig. 4
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Piper trilinier plot showing the groundwater types in the study area

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Fig. 5
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Hydrochemical facies Evaluation diagram (HEF-D) explaining the seawater–groundwater mixing process

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Principal component analysis (PCA) of the hydrochemical data

Correlation coefficient hydrogeochemical parameters (n = 10) for the groundwater samples (n = 12) are presented in Table 2. Strong positive correlations were found; pH with K (r = 0.52) and (r = 0.82), EC with Ca (r = 0.62), Mg (r = 0.52), Na (r = 0.99), Cl (r = 0.98), SO4 (r = 0.92) and HCO3 (r = 0.51), Na with Cl (r = 0.96), SO4 (r = 0.95) and HCO3 (r = 0.51), K with CO3 (r = 0.81) and HCO3 (r = 0.66), SO4 with Cl (r = 0.84), CO3 (r = 0.61), and HCO3 (r = 0.74), CO3 with HCO3 (r = 0.74) etc. The strong positive correlation was observed for the ions such as Na, Cl, Mg and SO4 with EC showing the dominance of these ions in the hydrogeochemistry of this area. However, the strong positive correlation of Ca with SO4 is indicative of the dissolution of gypsum ion the aquifer.

Table 2 Correlation matrix for the hydrogeochemical parameters in the groundwater of the study area
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Three principal components evolved as the hydrochemical parameters in the study area (see Fig. 6). The first component has higher factor loadings (>0.5) for ions such as Na, Mg, Cl and SO4. These are the representative ions of seawater. The component 1 is responsible for 44% of the total variance in the dataset, indicating that these ions are dominating the groundwater chemistry. However, component 2 is responsible for 31% of the total variance in the analysed dataset. The dominant ions in this group are Ca, K, HCO3 and CO3, demonstrating the dominance of rock–water interactions as well as the dissolution of carbonate minerals present in the aquifer. Mostly these ions have a terrestrial origin than marine dominance. This versatile result hints the mixing of waters from both terrestrial and marine origin. Component 3 showed strong negative correlation with Mg, HCO3 and pH. These results have no definite trend that could be explained as a controlling factor of any of the process. However, this component can explain 16% of the total variance in the overall data.

Fig. 6
figure 6

Principle components of the hydrochemical parameters of the groundwater in the study area

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Source evaluation of critical hydrochemical parameters

Ions like Na and Cl have a definite role in the evaluation of salinization process in the coastal regions (Wen et al. 2011; Shammas and Jacks 2007). Based on this common principle, the Na and Cl concentrations were drawn in a bivariate plot with a theoretical mixing line of freshwater and saline water (see Fig. 7). This analysis shows that many samples are plotted on or near the mixing line, indicating the influence of seawater mixing in the aquifer. Those samples deviated from the general trend, which can be attributed to the other sources of Na or the cation exchange process modifying the groundwater chemistry by replacing it with Ca/and Mg.

Fig. 7
figure 7

Bivariate plot showing the relation between Na and Cl molar ratio. A theoretical mixing line is shown to differentiate the samples affected by seawater mixing and other processes in the groundwater of the study area

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The origin of Ca, Mg, HCO3 and SO4 will have a close relation if they are originated from dissolution of carbonate minerals (Wen et al. 2011). In their common origin, the groundwater samples will be plotted on or close to the Ca + Mg vs. HCO3 diagram. In the study area, this plot shows a shift towards the lower upper part of 1:1 line (see Fig. 8). Those samples shifted down due to the excess of Ca + Mg compared to HCO3, representing reverse ion exchange process. On the other hand, those samples shifted above the line due to the deficiency of Ca + Mg vs. HCO3 + SO4, indicating the normal ion exchange process. However, there are samples plotted o or near the 1:1 line showing the origin from dissolution of carbonate minerals. The positive correlations among these four minerals support the effect of carbonate minerals.

Fig. 8
figure 8

Bivariate plot showing the relation between Ca + Mg and HCO3 + SO4, deciphering the control of carbonate dissolution and ion exchange process in the groundwater of the study area

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Ionic changes during seawater–groundwater mixing

The results of the major ion chemistry, Piper diagram, principle component analysis and the Na vs. Cl cross plot indicate that hydrochemistry of the studied coastal aquifer is highly influenced by the seawater–groundwater mixing. The expected composition of the water during the freshwater–saline water displacement can be calculated based on the conservative mixing of seawater and freshwater. Later on, a comparison of the measured concentration with the actual seawater can effectively used as tool for describing the hydrochemical processes in the mixing zone (Aris et al. 2009). Using the Eqs. 1–3, based on the fact that Cl is a conservative tracer, seawater fraction in each sample was calculated (see Table 3; Fig. 9). Seawater fraction in each sample was utilized to calculate the ionic changes in Na, Ca, Mg, K, SO4 and HCO3 occurred during the interaction (Fig. 10). A positive seawater fraction (f. sample ) was observed in 50% of the samples (S.nos: 3, 4, 5,7,10 and 11). Presence of seawater fraction in these samples is a clear indication of the mixing process. Since the Na content in the normal freshwater is less, Na change will be positive in the fresh groundwater. In this study, the sample numbers 3, 7, 10 and 11 showed negative values indicative of the contribution of seawater to the groundwater chemistry. This is in agreement with the result of f sample , except sample numbers 4 and 5 as these samples are located slightly away from the coast. The most befitting explanation of this difference will be the cation exchange process which is already identified in “Principal component analysis (PCA) of the hydrochemical data”. However, being a conservative tracer, the Cl concentration remains unaltered with any external chemical reactions. It should be noted that the Ca change was positive in all the samples including those which showed negative Na change . The chemistry of the natural groundwater in the coastal area will be dominated by Ca and HCO3 due to the carbonate dissolution (Mondal et al. 2011). In contrary to this, seawater will be dominated by Na and Cl ions will be exchanged to the aquifer matrix during intrusion (Appelo and Postma 2005). However, this could be appropriately explained by the cation exchange in the costal aquifer, which is much more significant and better defined, and produces an inverse exchange between Na and Ca–Mg (Pulido-Laboeuf 2004). A negative K change also supports the samples showed negative, Na change and positive Ca change . The declining trend in both Na and K, with increasing seawater fraction, indicates that the groundwater samples are dominated by seawater (Aris et al. 2009).

Table 3 Composition of seawater and freshwater (Appelo and Postma 2005) and fraction of seawater in individual samples
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Fig. 9
figure 9

Plot showing the seawater fraction in individual samples. Positive values of f sample indicate seawater mixing with fresh groundwater

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Fig. 10
figure 10

Major ionic changes (e change ) in groundwater samples of the study area

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Evaluation of Seawater Mixing Index (SMI)

Seawater–groundwater mixing in the study area has been evaluated using the salinity mixing index (SMI). As input to the SMI equation expresses in Eq. 4, regional threshold values (T) of the critical parameters (Na, Mg, Cl and SO4) have been calculated. Figure 11 illustrates the method of calculation of threshold values for the selected parameters. Cumulative probability percentages of the parameters were plotted against their logarithmic concentration value. The intersection point of each plot represents the T values of the corresponding ion (Sinclair 1974; Park et al. 2005). The determined T values of Na, Mg, Cl and SO4 were 200, 71, 251 and 178 mg/L respectively. These T vales were substituted in Eq. 4 along with the relative concentration proportions (a = 0.31, b = 0.04, c = 0.57, d = 0.08) and the SMI values for each groundwater samples were deduced. If the calculated SMI value is greater than 1, the water may be considered to unmistakably record the effect of seawater mixing (Park et al. 2005). The distribution of SMI in individual groundwater samples is presented in Fig. 12. Altogether, (4–5–7–10–11) 5 samples have showed an elevated SMI (>1), suggesting the impact of seawater missing on the groundwater chemistry of the region. These results coincide with those of hydrochemical ionic change process, i.e. the same wells showed a positive seawater fraction (fsea), except sample 3. This can be considered as further refinement in the result of ionic changes, supported by the lower salinity value (1270 µS/cm) as compared to the other high saline wells.

Fig. 11
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Determination of the regional threshold values (T) of Na, Mg, Cl and SO4 in the groundwater of the study area

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Fig. 12
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Plot showing the Salinity Mixing Index (SMI) values in groundwater of the study area. SMII values greater than one indicating the influence of seawater mixing with fresh groundwater

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Conclusions

Groundwater chemistry in the coastal aquifer (part of Cuddalore and Chidambaram Taluks) shows that groundwater chemistry of the southern region, and to certain extends in the central region, is largely affected by the seawater mixing with the groundwater. The complexity of the hydrochemical processes in the study area is taken into account and a multidisciplinary approach was adopted for the accurate understanding. Though Piper diagram, a better delineation of the hydrochemical facies has been done using a complex HFE-D diagram. This plot was useful in differentiating the samples (nos: 3–4–5–7–10–11–12) affected by seawater mixing from the other processes like cation exchange. PCA analysis, demonstrates the positive correlation for EC with Ca (r = 0.62), Mg (r = 0.52), Na (r = 0.99), Cl (r = 0.98), SO4 (r = 0.92) that illustrates 44% of the total variance of the total data set. This reveals that these variables are chiefly responsible for the groundwater chemistry of study area. However, the second component was useful in indicating more natural groundwater (Ca, K, HCO3 and CO3) with 31% of the total variability. Gibbs plot of the groundwater samples showed a definite trend for evaporation and rock water interaction dominance. This confirms the bivariate plots of Na vs. Cl and Ca + Mg vs. HCO3 + SO4 that salinization of the aquifer is a dominant process with active contribution from carbonate dissolution and cation exchange processes. Hydrochemical ionic changes and seawater mixing index (SMI) were applied particularly to demarcate the wells that have been influenced by the seawater–groundwater mixing. The seawater fraction in individual samples were calculated based on Cl (conservative tracer) concentration and identified that samples (3, 4, 5, 7, 10 & 11) had a positive seawater fraction. Moreover, the negative e change for Na in these samples indicate the mixing of the seawater in these locations. Higher Salinity Mixing Index (SMI) values (>1) were observed for the same samples; except sample 3, that showed slightly lower value than 1. However, this further confirms the dominance of seawater–groundwater mixing in the samples 4, 5, 7, 10 & 11.

All the methods used in this study proved instrumental in the exact demarcation of the seawater–freshwater mixing. A slight discrepancy was observed in the sample 12, which fell under the seawater–groundwater mixing line of HFE-Diagram and later left out from the seawater dominant wells grouped by Hydrochemical ionic changes and SMI. The location of this well (far away from the coast) and the permissible TDS value (457 mg/L) supported these argument. Another modification found is that sample 3 is excluded from the salinity affected wells reported by SMI, which was included in the ionic changes approach. It is clear that adopting manifold methods for a single problem will be helpful in comparing the results and obviously reducing the error in the assessment. This study concludes that all the wells located near the coast are affected by seawater intrusion except sample no. 12. Groundwater quality has deteriorated seriously in the southern end of the study area and to certain extent, in the central region. Overexploitation must be averted to protect the water quality and also to conserve a sustainable ecosystem.