Chemical Composition of Ground Water
The assessment findings of the hydro-chemical characteristics of groundwater samples in the research region are listed in Table 1. The measurements of pH range from 7.38 to 8.66 (Fig. 3a), with a mean value of 8.08, indicating the water samples contain a slightly neutral to alkaline nature. The entire pH levels were within the irrigation water thresholds of 6.5–8.5 (FAO, 1985), aside from five samples (Sample Wells 4, 5, 6, 9 and 12), which had a pH value of above 8.5, exceeding the permissible limit.
Table 1
Statistically review of the groundwater hydro-chemistry
Parameters | Minimum | Maximum | Median | Mean | Standard Deviation | FAO standards (1985) |
pH | 7.38 | 8.66 | 8.08 | 8.09 | 0.30 | 6.5–8.5 |
EC | 597.00 | 4046.00 | 1085.00 | 1286.76 | 673.21 | 3000 |
TDS | 364.81 | 3946.24 | 661.74 | 835.29 | 684.93 | 2000 |
Total hardness (CaCO3) | 75.00 | 395.00 | 200.00 | 213.64 | 85.23 | 712 |
Calcium (Ca2+) | 8.00 | 112.00 | 33.70 | 26.00 | 22.23 | 400 |
Magnesium (Mg2+) | 4.00 | 69.00 | 31.42 | 31.59 | 16.25 | 60 |
Sodium (Na+) | 52.75 | 660.50 | 186.84 | 135.40 | 134.99 | 900 |
Potassium (K2+) | 1.90 | 51.00 | 9.45 | 5.70 | 10.61 | 2 |
Bicarbonates (HCO3−) | 170.80 | 811.30 | 402.82 | 408.70 | 127.96 | 600 |
Chlorides (Cl−) | 21.27 | 999.69 | 225.03 | 155.98 | 200.34 | 1100 |
Nitrates (NO3−) | 0.00 | 24.00 | 6.42 | 5.00 | 7.32 | 10 |
Fluoride (F−) | 0.02 | 1.14 | 0.56 | 0.47 | 0.33 | 1.5 |
Sulphates (SO42−) | 0.00 | 37.81 | 7.10 | 4.60 | 8.83 | 1000 |
*Findings are in mg/L; EC in µS/cm.
The EC of groundwater fluctuates notably and spans from 597 to 4046 µS/cm (Fig. 3b), whose average value is 1286.76 µS/cm. Among the 33 samples, only sample wells 21 have shown an EC concentration above the permissible limit of 3000 (FAO 1985).
TDS values varied from 364.81 to 3946.24 mg/L (Fig. 3c), with an average concentration of 661.74 mg/L. A few numbers of samples (Sample Wells 21 & 28) displayed TDS values crossing the FAO acceptable margin of 2000 mg/L.
The range of TH (as CaCO3) values discovered was 75–395 mg/L (Fig. 3d), with a mean of 213.64 and a standard deviation of 85.64 mg/L. Entire the sample wells of TH value were within the FAO permissible limit of irrigation (FAO 1985), which denotes that the groundwater is portrayed by non-carbonated hardness (Chow 1964).
The concentrations of cations in groundwater takes place in the order specified: Na+≥ Mg2+≥Ca2+≥K2+. The concentration of sodium (Fig. 3e) and magnesium (Fig. 3f) were within the limits of 52.75 to 660.50 and 4 to 69 mg/L, respectively. Throughout the 33 sample wells, sodium concentrations exhibited inside the permissible limit of 900 mg/L, while 9.09% (Sample wells 10, 25 and 26) had high magnesium intensities that exceeded the tolerable limit of 60 mg/L (FAO 1985). The concentrations of calcium (Fig. 3g) and potassium (Fig. 3h) vary from 8 to 112 mg/L and 1.90 to 51 mg/L, respectively. All the 33 sample wells of calcium are considered to be within the allowable ranges of 400 mg/L (FAO 1985), whereas, almost all the sample wells (except Sample wells 5 and 3) we observed had potassium values that were higher than the permitted limit of 2 (FAO 1985).
Bicarbonate is the predominant anion, with subsequent chlorides, nitrates, sulfates and fluoride and their concentrations ranged between respectively, 170.80 to 811.30 (Fig. 3i), 21.27 to 999.69 (Fig. 3j), 0 t o 24 (Fig. 3k), 0 to 37.81(Fig. 3l) and 0.02 to 1.14 (Fig. 3m). All the 33 sample wells showed a bicarbonate, chlorides, sulfates and fluoride concentration within the FAO allowable limit of 600 mg/L, 1100 mg/L, 1000mg/L and 1.5 mg/L, respectively. As all the sample wells except nitrates of these anions concentrations are demonstrated within the limits allowed, the study area were having little bit low concern about aforesaid anions.
Multivariate statistical evaluation
Pearson’s Correlation
In this case, Pearson's correlation has been employed to properly determine how closely related the various hydrogeochemical parameters were (Fig. 4). A correlation coefficient (r) value below 0.5 indicates a weak correlation, one between 0.5 and 0.7 shows a moderate correlation, and a value greater than 0.7 indicates a strong correlation between two parameters. (Shyu et al. 2011).
Figure 4 displays the significant positive correlations between EC and TDS (0.70), Na+ (0.93), Cl− (0.94), Cl− and TDS (0.80), Na+ (0.80). The significant positive association among EC and TDS suggests that no charged less-soluble molecules in water could increase to total dissolved solids (Datta and Tyagi 1996). The notion that the chemistry of groundwater is driven forward by the dissolving of evaporates/involvement of saline causes is supported by the significant positive interaction between Na+ and Cl− ions (Thilagavathi et al. 2017). The remarkable correlation with other ions and cl− implies that secondary salts are being leached and that saline sources are contributing (Khan et al. 2020). A modest correlation has been found amongst TDS and Na+ (0.67), K2+ (0.50), Ca2+ and TH (0.62), K2+ (0.59), Na+ and HCO3− (0.64). Chemical weathering is indicated by the substantial correlation of HCO3− with Na+ (Chidambaram et al. 2008). The pH, Mg2+, HCO3−, and NO3− have a negative correlation with the other maximum parameters which reveals the geogenic basis (Jayakumar and Siraz 1997).
PCA analysis
PCA was carried out in the current study using varimax rotation and orthogonal Kaiser normalisation. Kaiser suggested limiting the set of factors to those with eigenvalues > 1 (Liu et al. 2003). The screeplot (Fig. 5) can also be used to quantify the number of significant eigenvalues (Cattell, 1966). After 6 iterations, five factors (Table 2) with eigenvalues ≥ 1 that accounted for a total variance of 84.406% appeared. Significant principal component loadings have been regarded for those whose absolute value was І0.5І (bold in Table 2).
Table 2
Principal component eigenvalues with varimax rotation component loading
Parameters | Components |
PC1 | PC2 | PC3 | PC4 | PC5 |
EC | 0.981 | 0.005 | -0.035 | 0.070 | -0.050 |
Na+ | 0.929 | -0.151 | -0.289 | 0.114 | -0.017 |
Cl− | 0.919 | 0.232 | 0.116 | -0.018 | -0.148 |
TDS | 0.847 | 0.262 | 0.113 | -0.167 | -0.137 |
Ca2+ | 0.138 | 0.841 | 0.176 | 0.316 | 0.011 |
K+ | 0.364 | 0.762 | -0.166 | 0.181 | -0.086 |
HCO3− | 0.482 | -0.680 | -0.309 | 0.252 | 0.077 |
Mg2+ | -0.075 | -0.133 | 0.948 | -0.072 | -0.090 |
TH | 0.031 | 0.443 | 0.861 | 0.148 | -0.064 |
NO3− | 0.058 | 0.146 | -0.189 | 0.790 | 0.244 |
SO42− | -0.071 | 0.149 | 0.301 | 0.688 | -0.304 |
F− | -0.131 | 0.077 | 0.044 | -0.077 | 0.865 |
pH | -0.118 | -0.284 | -0.319 | 0.186 | 0.636 |
Eigen value | 4.059 | 3.046 | 1.749 | 1.103 | 1.015 |
% variance | 31.226 | 23.430 | 13.457 | 8.481 | 7.810 |
Cumulative % | 31.226 | 54.657 | 68.114 | 76.596 | 84.406 |
*Bold values denote a significant relationship.
PC1 accounts for approximately 31.226% of overall variation, with EC, Na+, Cl−, and TDS having larger positive loadings. The above element perfectly illustrates that the saline repository is the source of the Na+ and Cl− throughout the research site (Voudouris et al. 1997; Ruiz et al. 1990), while EC and TDS suggest the effects of the aquifer's geological components' mineralizations (Khanoranga and Khalid 2019). PC2 explains approximately 23.43% of entire variances, indicating high loading of Ca2+, HCO3− and K+. The 3rd PC (PC3) is positively loaded with Mg2+ and TH, explaining for 13.46% of total variance. The dissolving of calcite through carbonic acid is the natural origin of the ions, as explained by the Ca2+ and Mg2+ factors (Islam et al. 2017), while, the dissolving of carbonate rock is the principal source of HCO3− ions in water (Avdullahi et al. 2013). PC4 has a total variance of 8.48%, with NO3− and SO42− dominating primarily. The NO3− is formed by leaching of NO3− along with percolate water as a result of numerous agricultural activities, including the widespread usage chemical fertilisers and pesticides in the research areas (Amiri et al. 2014), whereas, SO42− is derived from saline water (Khan et al. 2020). With a variance of 7.81%, PC5 exhibits pH and F− loadings, indicating the acidic medium's aggressive nature in ion dissolving as well as rock weathering containing fluoridated minerals (Khan et al, 2020; Hasan and Rai, 2020).
Ion Exchange
Although it is hard to control the dissolution of undesired elements in water during subsurface effluent, it is inevitable to understand the many modifications in chemical composites that groundwater undergoes in the course of its flowing in the subsurface (Johnson 1979; Sastri 1994). The exchange of ions within the aquifer and its host circumstances throughout the time of stay or travel, as postulated by Schoeller, is substantially shown by the chloro-alkaline indices CAI-1 & CAI-2 (1965, 1967 and 1977). Equations (14) and (15) are commonly used to express the two indices (Li et al. 2012), at which concentrations are in meq/L:
$$CAI 1=\frac{{Cl}^{-}-({Na}^{+}+{K}^{+})}{{Cl}^{-}}$$
14
$$CAI 2=\frac{{Cl}^{-}-({Na}^{+}+{K}^{+})}{({SO}_{4}^{{2}^{-}}+{HCO}_{3}^{-}+{CO}_{3}^{{2}^{-}}+{NO}_{3}^{-})}$$
15
Positive CAI imply a direct base (cation-anion) exchange mechanism that occurs once Na+ and K+ ions within groundwater switched places by Mg2+ and Ca2+ ions in aquifer constituents, When the event of an opposite ion exchange, both indices become negative as well as the exchange remains indirect, suggesting a chloro-alkaline mismatch or imbalance (Chidambaram et al. 2012). Most samples (75.76%) of the aquifer were found negative indices (Fig. 6) with an oblique base reaction process, that demonstrated substitution of Ca2+ and Mg2+ in water had swapped the Na+ and K+ inside the parent rocks.
Hydrochemical Facies
Piper Diagram
Piper trilinear diagrams were frequently utilised to understand the hydrogeochemical environment of a studied region (Piper 1944; Gao et al. 2019). A Piper trilinear diagram of the Indian Sundarban area was generated using Origin Pro 2023 software, which is shown in Fig. 7. There is one diamond and two triangles in this diagram. In terms of cations (left triangle), the bulk of the samples are concentrated in sections D (sodium and potassium types) and B (no dominant type), while anions are largely concentrated in the lower right angled triangle's regions E (bicarbonate types) and G (chloride types) which suggests that carbonate-dominated rocks are weathered (Gao et al. 2020). A “diamond field” is a graphical depiction of groundwater's general features. Within that field, circles are used to represent TDS concentration; the larger the circumference of the circle, the greater the TDS value (Sutradhar and Mondal, 2021). In this field, 12.12% of water samples are distributed in zone 5 (HCO3−-Ca2+, Mg2+ type), 27.27% of water samples are concentrated in zone 7 (Cl−-Na+ type), 9.09% of water samples are accounted in zone 8 (HCO3−-Na+ type), and the maximum of said water samples (51.51%) circulate in zone 9 (mixed type).
Gibbs Diagram
Three variables typically regulate groundwater chemistry: evaporation, rock-water co-action, and atmospheric precipitation (Gibbs 1970). Gibbs diagrams are frequently employed to pinpoint the dominant factor in groundwater hydrochemistry (Gao et al. 2020). Gibbs diagrams depicting two plots as a function of TDS: ratio 1 (Fig. 8a) with cations Na+/(Na+ + Ca2+) as well as ratio 2 (Fig. 8b) for anions Cl−/ (Cl− + HCO3−). The mass of groundwater sources in the research region are situated within the upper middle part, denoting that rock weathering and evaporation are the main factors affecting the major ion composition of the groundwater, as illustrated in Fig. 8a and Fig. 8b. The Gibbs diagram shows that when TDS rises, sodium and chloride dissolve, causing groundwater samples to switch from the rock dominance sector to the evaporation sector (Ndoye et al. 2018). Agricultural fertilisers and irrigation return flows, which are anthropogenic operations, continue to raise Na+ and Cl− and, consequently, TDS, resulting in an effect on evaporation (Ravikumar et al. 2010).
Variation of irrigation water quality indices for crop growth appraisal
The chemistry of groundwater and how it affects the crops and soil determines how well it is suitable for irrigation (Singh et al. 2015; Richards 1954). In this investigation, the Wilcox diagram (1948) and the United States Salinity Laboratory (USSL) diagram (1954) were used to determine the appropriateness of groundwater for irrigation, as well as 10 distinct indices. Table 3 contains descriptive summary statistics, while Fig. 9 depicts spatial variability maps.
The EC is a marker of the soluble ions within water since it often indicates the salt concentration in the water (Bozda 2014). Groundwater with an EC value of below 250 µS/cm is considered excellent, between 250 and 750 µS/cm is good, between 750 and 2250 µS/cm is doubtful, and beyond 2250 µS/cm is designated as unsuitable for irrigation, as previously stated (Wilcox, 1955). The current study discovered that EC concentrations vary from 597 to 4046 µS/cm (Fig. 3b & Table 3), with an average value of 1286.76 µS/cm. In accordance with the spatial variability of the EC (Fig. 3b) shows that 81.82% and 6.06% of the samples are rated as doubtful or dubious and unsuitable, respectively, while only 12.12% of the samples taken are regarded as good. None of the groundwater sources, nevertheless, received an excellent rating (Table 3). According to Sys et al. (1993), the irrigation salinity threshold values for the most widely grown crops in the region are: rice (4000 µS/cm), pulses (3000 µS/cm), oilseeds (7000 µS/cm), potato (5000 µS/cm), red chilli (4500 µS/cm), and vegetables (6500 µS/cm).
Among the most significant indicators of irrigation compatibility for identifying salt or alkali dangers is SAR (Bhunia et al., 2018). The soil gets harder and less permeable when sodium replaces Ca2+ and Mg2+ in the soil (Kaur et al., 2017; Roy et al., 2018). In the research area, the total SAR value varied from 1.36 to 25.47 meq, with an average value of 6.26 meq (Table 3). Approximately 84.89% of the samples collected in the studied region were considered excellent (SAR = < 10 meq), 9.09% were good (10–18 meq), and the remaining 6.06% fell into the doubtful (18–26 meq) category for irrigation (Table 3) (Fig. 9a).
In irrigation waters, the optimal KR value needs to be no higher than 1 meq (Kelly, 1963). KR has a range of 0.34 meq to 14.64 meq with an average value of 2.56 meq as well as a standard deviation of 3.0 meq in the study region (Table 3). Figure 9b shows a KR of 1 meq or less is present in around 24.24% of the samples, making them irrigation-ready. Approximately 36.36% of the samples indicate marginally acceptable for irrigation, while the remaining 39.39% are unsuitable (Table 3).
Table 3
Categorization of irrigation suitability indices and EWQII
Parameters | References | Value | Irrigation Suitability | % of Samples | Statistics |
Range | Mean | S.D |
EC (µS/cm) | Wilcox, 1955 | < 250 | Excellent | 0.00 | 597.00-4046.00 | 1286.76 | 673.21 |
250–750 | Good | 12.12 |
750–2250 | Doubtful | 81.82 |
> 2250 | Unsuitable | 6.06 |
SAR (meq) | United States salinity Laboratory, 1954 | < 10 | Excellent | 84.89 | 1.36–25.47 | 6.26 | 5.64 |
10–18 | Good | 9.09 |
18–26 | Doubtful | 6.06 |
> 26 | Unsuitable | 0.00 |
KR (meq) | Kelly, 1963 | < 1 | Suitable | 24.24 | 0.34–14.64 | 2.56 | 3.00 |
1–2 | Marginal suitable | 36.36 |
> 2 | Unsuitable | 39.39 |
RSC (meq) | Eaton, 1950 | < 5 | Good | 87.88 | -5.16-13.99 | 2.48 | 3.57 |
5–10 | Doubtful | 9.09 |
> 10 | Unsuitable | 3.03 |
RSBC (meq) | Gupta, 1983 | < 5 | Satisfactory | 54.55 | -1.21-12.90 | 4.92 | 2.78 |
5–10 | Marginal | 42.42 |
> 10 | Unsatisfactory | 3.03 |
%NA (%) | Wilcox, 1955 | < 20 | Excellent | 0.00 | 25.95–93.62 | 61.73 | 16.26 |
20–40 | Good | 6.06 |
40–60 | Permissible | 45.46 |
60–80 | Doubtful | 33.33 |
> 80 | Unsafe | 15.15 |
SSP (%) | Todd, 1959 | < 60 | Suitable | 51.52 | 25.23–93.41 | 59.82 | 16.29 |
> 60 | Unsuitable | 48.48 |
PI (%) | Doneen, 1964 | > 75 | Good | 69.70 | 41.09-111.88 | 84.02 | 15.52 |
25–75 | Marginal | 30.30 |
< 25 | Unsuitable | 0.00 |
MH (%) | Raghunath, 1987 | < 50 | Suitable | 18.18 | 12.95–85.85 | 60.53 | 17.73 |
> 50 | Unsuitable | 81.82 |
PS (meq) | Doneen, 1964 | < 5 | Excellent to Good | 54.55 | 0.61–28.26 | 6.43 | 5.66 |
5–10 | Good to Injurious | 33.33 |
> 10 | Injurious to Unsatisfactory | 12.12 |
EWQII | Singh et al., 2019 | < 25 | Very good | 0.00 | 26.59-371.38 | 90.49 | 76.99 |
25–50 | Good | 30.30 |
50–75 | Average | 30.30 |
> 75 | Poor | 39.40 |
RSC concentration in the Sundarban region ranges from − 5.16 to 13.99 meq, with an average of 2.48 meq (Table 3). For residual sodium carbonate, three classifications have been established: good (below 5 meq), doubtful (5–10 meq), and unsuitable (above 10 meq). Only 3.03% of samples are judged unsuitable, while 9.09% are deemed doubtful and 87.88% are considered good quality for irrigation (Table 3) (Fig. 9c).
The water samples that were examined for RSBC exhibited values ranging from − 1.21 meq to 12.90 meq, with a mean of 4.92 meq (Table 3). Gupta (1983) divided RSBC values into three categories: satisfactory (below 5 meq), marginal (5 to 10 meq), and unsatisfactory (above 10 meq). For irrigational purposes, 54.55% of samples are satisfactory, 42.42% are marginal, and only 3.03% are unsuitable (Table 3) (Fig. 9d).
Crop production is affected by how the soil and the Na% in water respond to modify the permeability & aeration of the soil (Vasanthavigar et al. 2010). As per statistical analysis, the value of %Na has a mean value of 61.73 and a range of 25.95 to 93.62 (Table 3). Water with beneath 20% Na is excellent for irrigation, followed by water with 20% – 40% good, 40% – 60% permissible, 60% – 80% dubious or doubtful, and water with more than 80% unsafe (Wilcox, 1955). Table 3 indicates that the excellent quality water sample is completely absent, while the good quality irrigational water sample is concentrated at only 6.06%. In addition, in contrast to the bulk of the samples collected (45.46%) fall through into permissible range, 15.15 percent and 33.33 percent of the samples taken settle into the unsafe & doubtful categories regarding irrigation use, respectively (Fig. 9e).
The SSP is a crucial measure for grading irrigation water according to soil permeability (Singh et al. 2019). Groundwater containing SSP levels less than 60 īs considered good for irrigation; however, levels greater than 60 are unsuitable for irrigation (Todd 1959). SSP has a mean of 59.82 and a range of 25.23 to 93.41 (Table 3). In accordance with SSP's spatial variation, 51.52% of the samples have been considered suitable for irrigation, while 48.48% are not (Table 3) (Fig. 9f).
The PI was used to calculate the soil’s ability to flow water relying on the proportions of HCO3−, Ca2+, Na+ and Mg2+ (Chidambaram et al. 2022). When a PI value of 75 or more is regarded as good, 25 to 75 is considered marginal, and 25 or lower is not suited for irrigation (Donnen, 1964). The PI levels varied from 41.09 to 111.88, with an average value of 84.02 (Table 3). Figure 9g and Table 3 explain that the spatial variance of the PI indicates that 69.70% of the samples are deemed good, 30.30% as marginal, and none of the test samples were determined to be unsuitable in terms of the PI.
Groundwater MH values trigger the soil to turn alkaline, which reduces agricultural production (Gautam et al., 2015). If the MH value is greater than 50%, the water is hazardous and shouldn't be used for irrigation (Raghunath, 1987). With a mean of 60.53%, the MH values for the examined samples ranged from 12.95–85.85%. Furthermore, only 18.18% of the samples remain suitable for irrigation, whereas the remaining samples are unsuitable. (Table 3) (Fig. 9h).
PS was utilized to estimate the quality of estuary irrigation water in particular (Ahmed et al., 2020), with values under 5 are regarded as excellent to good, values within 5 to 10 considered Good to Injurious, and values greater than 15 considered injurious to unsatisfactory for irrigation (Donnen, 1964). In this investigation, the average PS content in water samples was 6.43 meq, with amounts fluctuating from 0.61 meq to 28.26 meq (Table 3). In accordance with Table 3 and Fig. 9i, only 12.12% of samples are awful or injurious to unsatisfactory in support of irrigation, compared to 54.55% that are excellent to good, 33.33% that are good to harmful or injurious.
Wilcox diagram
The Wilcox diagram (1955) is a diagramming technique that also categorizes the key properties of hydrochemical compounds and displays water appropriateness in irrigation with reference to the solubility of salts and its impact on crops and soil (Mousazadeh et al. 2018). Irrigation water is divided into five groups in Wilcox's plot: excellent to good, good to permissible or legal, permissible to doubtful or dubious, doubtful to unsuitable, and unsuitable. Figure 10a depicts the results for 33 samples: 2 (6.06%) samples are excellent to good, 13 (39.39%) samples are good to permissible, 12 (36.36%) samples are acceptable to doubtful, 4 (12.12%) samples are doubtful to unsuitable, and 2 (6.06%) samples are inappropriate for irrigational usage.
USSL diagram
Plotting the EC and SAR values of water on the USSL graph (Fig. 10b) allows for a much more in-depth investigation of groundwater suitability for irrigation (Richards 1954). As stated in the USSL diagram (Fig. 10b), the largest of the groundwater sources (60.60%) are within the C3S1 zone, revealing a high salinity/low sodium form, implying that the water may be used for plants that survive modest concentrations of salt (Balasubramani et al. 2020). On the other hand, 6.06% of groundwater sources are labelled as C3S1, signalling a high-salinity/high-sodium type, and 15.15% are classified as C3S2, showing a high-salinity/moderate-sodium type. Additionally, 6.06% of said samples were classified as C2S1, demonstrating that modest salinity tolerance crops may be cultivated and water is possible to use when only minor leaching arises. Furthermore, 9.09% & 3.03% of the samples were classified as C4S4 and C4S3, indicating a high salinity/very high sodium type as well as a very high salinity/high sodium type, correspondingly, necessitating special salinity corrective actions (Balasubramani et al. 2020).
Irrigation suitability assessment based on EWQII
The EWQII was employed to estimate groundwater suitability for irrigation, and the findings are shown in Table 4, while the categorization is shown in Fig. 11. EWQII levels in the Sundarban area differ from 26.59 to 371.38, with a mean of 90.49 and a standard deviation of around 76.98 (Table 3). According to Singh et al. 2019, the EWQII range is divided into four classes: very good (< 25), good (25–50), average (50–75), and poor (> 75) (Table 3). According to the EWQII, 30.30%, 30.30%, and 39.40% of the sample wells, respectively, record good (low restriction), average (moderate restriction), and poor (severe restriction) irrigation water quality, while no samples fall into the very good (no restriction) category (Table 3) (Fig. 11). Sample 30 (Rajnagar) has the lowest entropy value (excellent quality of water), whereas sample 28 has the highest (poor quality of water) (Khanrapara) (Table 4) (Kumar and Augustine, 2021).
Table 4
Performance of groundwater peculiarity for irrigation using EWQII
Sample wells | Site name | EWQII | Rank | Water quality |
1 | Taki | 34.23 | 2 | Good |
2 | Haroa | 54.32 | 3 | Average |
3 | Bhebia | 57.08 | 3 | Average |
4 | Nawapara | 100.50 | 4 | Poor |
5 | RajapurUttarpara | 48.84 | 2 | Good |
6 | Sandaler Bill | 58.54 | 3 | Average |
7 | Baghirula | 49.98 | 2 | Good |
8 | Minakhan Batala | 100.27 | 4 | Poor |
9 | Nimichi | 96.77 | 4 | Poor |
10 | Chowmoha | 60.21 | 3 | Average |
11 | Sandeshkhali | 57.18 | 3 | Average |
12 | Bayermari | 73.21 | 3 | Average |
13 | Sarberia | 41.47 | 2 | Good |
14 | Goiler More | 59.48 | 3 | Average |
15 | Joygopalpur | 82.12 | 4 | Poor |
16 | Basanti | 117.15 | 4 | Poor |
17 | Jalabaria | 59.41 | 3 | Average |
18 | Nalia Khali | 36.84 | 2 | Good |
19 | Sharatpally More | 70.11 | 3 | Average |
20 | Akratala | 41.51 | 2 | Good |
21 | JotirampurFerryghat | 76.15 | 4 | Poor |
22 | Burarghat | 138.25 | 4 | Poor |
23 | NatunhatBakultala | 48.73 | 2 | Good |
24 | Dhoserhat | 75.30 | 4 | Poor |
25 | Ambikanagar | 57.91 | 3 | Average |
26 | Ramnagar | 85.86 | 4 | Poor |
27 | Raidighi | 38.59 | 2 | Good |
28 | Khanrapara | 371.38 | 4 | Poor |
29 | Malancha | 39.53 | 2 | Good |
30 | Rajnagar | 26.59 | 2 | Good |
31 | Digambari-Rudranagar | 193.85 | 4 | Poor |
32 | Kakdwip | 216.53 | 4 | Poor |
33 | Jaikhali | 318.21 | 4 | Poor |
The spatial variance of EWQII demonstrates that the quality of groundwater has been disseminated differently across the research region (Fig. 12). The maps of geographical allocation reveal a progressive tendency from southwest to northeast, despite the absence of very good irrigational water quality in the study location (Fig. 12). In isolated small patches, good (EWQII: 25–50) irrigational water quality is recorded in Taki, Rajapur, Uttarpara, Baghirula, Sarberia, Nalia-Khali, Akratala, Natunhat, Bakultala, Raidighi, Malancha, and Rajnagar, while, several sampling sites like Haroa, Bhebia, Sandaler Bill, Chowmoha, Sandeshkhali, Bayermari, Goiler More (Kalidanga), Jalabaria, and Ambikanagar have average (EWQII: 50–75) irrigational sources, they are situated in the northeastern and middle portion of the Sundarban region. Additionally, the remaining portion of the research region (enormously) possesses water of poor quality for irrigation, which could be influenced by anthropogenic factors and salt water intrusion and for which results in higher concentrations of TDS, Na+, Mg2+, Cl− than that of other pollutant concentrations (Subba Rao et al. 2018).