1 Introduction

Groundwater is found almost anywhere below the ground's surface of the earth, but it is not concentrated in a single large aquifer; rather, it is scattered among hundreds of minor aquifer systems and pockets with comparable properties [1]. Due to differences in monsoonal rainfall and over-draft of groundwater resources, understanding the appearances, restocking and restoration of groundwater is particularly important in coastal regions [2]. Groundwater quality is impacted by subsurface geochemical processes, inland surface water and recharged water quality [3]. The source and composition of recharged water might fluctuate over time due to hydrologic, anthropogenic, and temporal factors, causing periodic fluctuations in the quality of groundwater. The surface and subsurface terrains of many locations have been changed by urbanization and unchecked population expansion. Groundwater quality and quantity are directly impacted by changes in the local terrain and drainage system [4]. Significant water pollution has been caused by seawater intrusion [5,6,7].

Groundwater quality is influenced by subsurface geological processes, atmosphere precipitation, inland water bodies, and recharged water quality. Periodic variations in groundwater quality may result from hydrologic, human, and temporal factors in the source and makeup of the replenished water. Human health, economic growth, and social prosperity are all threatened by water pollution in addition to affecting water quality [8]. Hence, controlling water pollution and monitoring water quality is important because coastal regions are extremely sensitive to contamination due to seawater intrusion [9, 10].

A better knowledge of the water quality parameters is made possible by the application of several geostatistical methods for the interpretation of complicated data sets [11,12,13]. Risk assessment is determining the chance of such hazards occurring and the dangers connected with a certain event, activity, or condition [14]. Therefore, assessing the amount and quality of groundwater and creating a database is crucial for the advancement of civilization as well as for plans to enhance water resources [15]. Water quality forecasting has been achieved by the use of groundwater chemistry for a range of purposes [16, 17]. An important tool for evaluating groundwater quality and the suitability for human consumption is the water quality index [4, 18, 19].

The term “Water Quality Index” refers to a technique of grading that assesses the total quality of water [20] for human use by combining the effects of many water quality parameters such as TDS, Turbidity, pH, Alkalinity, Hardness, Na, K, Fe, Zn, Cl, Ca, Cl− and NO3, etc. Table 1. The water quality index can be computed using, the guidelines proposed by the WHO [21] and IS 10500 [22] Table 2. A map of groundwater quality is crucial for any city's drinking and irrigation needs as well as a preventative indicator of prospective environmental health issues [23, 24]. A number of papers have been written about the use of resistivity studies and remote sensing data to identify potential groundwater zones; however, a systematic, current publication or database for a water quality index map for coastal regions is needed [25]. Hence this study has been taken up to estimate the water quality index in the Sindhudurg District of Maharashtra state and also to spatio-temporally assess the area affected by seawater intrusion by mapping the water quality parameters.

2 Study area

The Sindhudurg District in Maharashtra, India, is an investigation area for this research project. It is an arid coastal region (figures 1 and 2). The research region is located between longitudes 73° 19′ and 74° 13′ East and latitudes 15° 40′ and 16° 44′ North. The total geographic area is 1,750 km2, and average annual rainfall is 4234 mm. The basin's terrain is between 300 and 400 metres above mean sea level. May has an average high temperature of 39.5°C, while December has an average low temperature of 11.5°C. For this study, groundwater samples from 37 well locations are collected during the post-monsoon season.

Figure 1
figure 1

Study area location map.

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Figure 2
figure 2

Study Area with Talukas.

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3 Materials and methods

During the post-monsoon season (October 2022 to January 2023), water samples were collected from 37 observation wells. To prevent unforeseen changes in characteristics, groundwater samples were taken using plastic bottles that had been acid cleaned, by normal practises. The water quality parameters such as TDS, Turbidity, pH, Alkalinity, Hardness, Na, K, Fe, Zn, Cl, Ca, Cl− and NO3, were examined in order to calculate the water quality index. The detailed methodology adopted in this study is described in the following segments and shown in figure 3. The locations of the sampling sites along with their latitude and longitude are described in table 3 [26].

Figure 3
figure 3

Methodology Flow chart.

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Table 1 Standard values of parameters are according to (IS: 10800, 2012).
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3.1 Collection of samples

From the study region, 37 different groundwater samples were taken for the post-monsoon season from October 2022 to January 2023. For collection of ground water samples from study area, a random sampling method was used. 16 groundwater samples from the Devgad Taluka, 14 groundwater samples from the Malvan Taluka and 7 groundwater samples were collected from the Vengurla Taluka.

3.2 Analysis of the water quality samples

The groundwater samples were taken using plastic bottles that had been acid cleaned, by normal practices. In order to estimate the water quality index, following water quality characteristics were investigated at: pH, Turbidity, TDS, Alkalinity, Hardness, Na, K, Fe, Zn, Cl, Ca, Cl−, and NO3. The groundwater sample points were grouped according to their distance from the shore. Samples were divided into six groups of 5–5 km distance from seashore.

3.3 Water quality index

The Water Quality Index (WQI) was first created in the United States by Horton (1965) using ten of the most often used water quality variables, such as dissolved oxygen (DO), pH, coliforms, specific conductance, alkalinity, and chloride. Since then, it has been extensively used and approved in nations of Europe, Africa, and Asia. The assigned weight significantly affects the index and reflects the importance of a parameter for a certain purpose. Moreover, the Brown group created a new WQI based on weights to specific parameters in 1970 that was comparable to Horton's index. Many changes to the WQI idea have recently been proposed by a number of scientists and professionals. The term "water quality index" (WQI) refers to ranking method that shows the combined effect of many water quality parameters on the total water quality. It is estimated with human consumption in mind [16, 18]. The quality index of the water can be used to evaluate its quality and suitability for drinking. WQI was calculated taking into account the norms for drinking [21]. This technique assumes that the suggested standards for the associated parameters weighted averages are inversely proportional to those standards [27]. The steps described in equations (1), (2) and (3) are involved in the calculation.

First, determine the ith parameter's weight. The second equation computes the quality rating for every parameter related to water quality, while the third equation sums all these sub-indexes to get the total index. The ith parameter's weight,

$${\text{W}}_{{\text{i}}} \, = {\text{ k}}/{\text{S}}_{{\text{i}}}$$
(1)

Where Si is the suggested level for the ith parameter (i = 1–6) and Wi is the unit weightage, k is the constant of proportionality.

The expression assigns a grade of individual quality,

$${\text{Q}}_{{\text{i}}} = {1}00{\text{V}}_{{\text{i}}} /{\text{S}}_{{\text{i}}}$$
(2)

Where Si is the standard or allowable limit for the ith parameter, Vi is the observed amount of the ith parameter in µg/l, and Qi is the sub-index of the ith parameter.

The Water Quality Index (WQI) is then calculated as:

$${\text{WQI}} = \mathop \sum \limits_{i = 1}^{n} \left( {{\text{Q}}_{{\text{i}}} {\text{W}}_{{\text{i}}} } \right)/ \mathop \sum \limits_{i = 1}^{n} \left( {{\text{W}}_{{\text{i}}} } \right)$$
(3)

Where, Qi is ith parameter’s sub-index. The number of parameters taken into consideration is n, and Wi is unit weightage for the ith parameter [28, 29].

Table 2 WQI Status Categories.
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3.4 Spatial interpolation of water quality index (WQI)

The various spatial distribution maps for pH, Turbidity, TDS, Hardness, Alkalinity, Na, K, Fe, Zn, Cl, Ca, Cl− and NO3 have been generated using inverse distance weighted (IDW) method in QGIS software [30]. The point-wise data of groundwater parameters were spatially interpolated using inverse distance weighted (IDW) approach which is an approach to multivariate predictable interpolation using a known dispersed gathering of points for all 37 wells in the research region as shown in figure 4. The values assigned to the unknown points are calculated using inverse distance weighted (IDW) method, which takes a weighted mean of the values found at the known places [31,32,33].

Figure 4
figure 4

Location of Sample well points in Study area.

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3.5 Inverse distance weighted (IDW) interpolation

A sequence of sample points is combined with linear weights to form cell values, which are then used in the accurate and convex process of IDW interpolation. This is predicated on the idea that the effect of a variable decreases with increasing distance from the sampling site. The fact that all anticipated values of IDW interpolation fall within the known points' maximum and minimum values is a crucial feature. Equation (4) represents a general IDW method:

$${\text{Zo}} = \frac{{\mathop \sum \nolimits_{i = 0}^{s} Zi \left( {1/d\begin{array}{*{20}c} k \\ i \\ \end{array} } \right)}}{{\mathop \sum \nolimits_{i = 0}^{s} \left( {1/d\begin{array}{*{20}c} k \\ i \\ \end{array} } \right)}}$$
(4)

Zi is Z value at known point i, di is distance between known point i and known point o, and Zo is estimated value at point o. In this case, s is number of known points used in estimation, k is indicated power. The extent of the local effect is determined by power k. Value changes between points in a linear interpolation to power of 1.0 occur at a consistent rate. According to power of 2.0 or above, rate of change in the data increases at a given point and levels out away from it. Amount of points that are known utilised in the estimate affects the amount of local effect as well (table 3).

Table 3 Location details of observation wells in post-monsoon with latitude and longitude.
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4 Results and discussion

4.1 Analysis of water quality parameters

The statistical analysis of water quality parameters is represented in table 4. It was observed from the analysis that groundwater was generally acidic to alkaline with pH ranging from 6.1 to 8.7 in post-monsoon season in three Talukas of Sindhudurg District. The TDS ranged from 26 mg/l to 1495 mg/l, at some sample points it is of poor quality meaning above 1000 mg/l. Sodium ranged from 8.50 to 17.30 mg/l and at below permissible levels. Potassium content ranged from 0.96 to 10 mg/l and it is within desirable limit. The iron level at most of the locations was below the desirable limit except one location and it ranged from 0.02 to 0.49 mg/l. The concentration of Zinc, Chlorine and Calcium ranged from 0.01 to 0.08 mg/l, 1 to 3 mg/l and 2 to 80 mg/l respectively which is within the desirable levels. Chlorides and Nitrates range from 2 to 670 mg/l and 1 to 2 mg/l, respectively out of which chlorides at some locations was below permissible level. Turbidity was found to be higher at some locations in Vengurla Taluka and levels ranged from 0.30 NTU to 8.10 NTU. The study area's water samples had a pH that varied from 6.1 to 8.7. Shriramvadi has highly alkaline water with a pH of 8.7, which is caused by weathering of minerals, rocks, and organic debris. Except for Huda in Vengurla Taluka, which contains 670 mg/l of chloride, the majority of the locations have chloride concentrations that are below acceptable levels. Due to the existence of a crowded residential area and poor agricultural practices, Huda and Shriramvadi had the highest concentrations of total dissolved solids, with 1495 mg/l and 371 mg/l, respectively. Huda (392 mg/l) has a high concentration of total hardness since it is an agriculturally used area. Due to weathering, salt seeping into the groundwater, and extensive residential human activity, the other surrounding locations displayed a moderate range of hardness levels.

Table 4 Chemical parameter statistics during the post-monsoon season in all samples.
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Table 5 contains the results of an evaluation of the correlation between the physicochemical parameters for the post-monsoon season using a correlation matrix. Strong correlation (r > 0.75), moderate (r = 0.50 to 0.75) and low (r = 0.30 to 0.50) were the three categories into which the correlation values were divided. According to the correlation data, during the post-monsoon season, there is a very significant positive association between TDS and Hardness (0.99), Ca and Hardness (0.98), and Cl− and Hardness (0.96). During the post-monsoon season, there was a somewhat favourable connection between TDS and alkalinity (0.56), pH and alkalinity (0.62), Cl and NO3 (0.53), and Ca and alkalinity (0.63). From the above discussion, it may be observed that dolomite or gypsum mineral dissolution may be a common source of calcium, hardness, and alkalinity in groundwater.

Table 5 Correlation matrix of various groundwater parameters.
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The sample locations were further divided into six groups according to their distances from the seashore (table 6) to evaluate the impact of the water quality as we move away from the seashore which may imply seawater intrusion. It was found that the total dissolved solids levels values were unacceptable in locations at 0 to 5 km distance from the shore (figure 5) and the same was observed at Vengurla Taluka. However, the calcium, chloride and hardness levels were above the permissible limit at this location. The turbidity level was beyond the permissible limit at sample location no. 16 and location no. 34 at a distance of 6 to 10 km from the seashore (figure 6). The TDS level was also nearer to the permissible limit in group B. It was observed that the maximum observation wells near to seashore showed values beyond the permissible limit and as we went away from the seashore the water Quality was good (figures 7, 8, 9, 10).

Table 6 Group-wise distribution of water quality parameters.
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Figure 5
figure 5

Graphical representation of parameters of Groundwater points at a distance 0 to 5 km from the seashore.

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Figure 6
figure 6

Graphical representation of parameters of Groundwater points at a distance 6 to 10 km from the seashore.

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Figure 7
figure 7

Graphical representation of parameters of Groundwater points at a distance 11 to 15 km from the seashore.

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Figure 8
figure 8

Graphical representation of parameters of Groundwater points at a distance 16 to 20 km from the seashore.

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Figure 9
figure 9

Graphical representation of parameters of Groundwater points at a distance 21 to 25 km from the seashore.

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Figure 10
figure 10

Graphical representation of parameters of Groundwater points at a distance 26 to 30 km from the seashore.

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4.2 Spatial-temporal mapping of water quality parameters and WQI

The spatiao-temporal mapping of water quality parameters and water quality index was carried out as mentioned in the methodology section. The assessment indicated that ground water in study area varied from excellent to good. The turbidity levels measured in the study area were below the desirable limit except at locations Math and Vadakhol in Vengurla Taluka. In these two locations the turbidity levels were not permissible figure 11(a). The total dissolved solids levels measured were unacceptable at location Huda in Vengurla Taluka figure 11(b). The pH is mainly acidic to alkaline figure 11(c). Sodium was below the permissible limit but at eight locations it was at the maximum permissible limit figure 11(d). The maps indicated that potassium, iron, zinc, chlorine, calcium, nitrates and alkalinity are within permissible limits in study area figures 11(e), (f), (g), (h), (i), (k), (l).

Figure 11
figure 11figure 11

(a) Distribution of Turbidity levels in study area. (b) Distribution of Total Dissolved Solids levels in study area. (c) Distribution of pH levels in study area. (d) Distribution of Sodium levels in study area. (e) Distribution of Potassium levels in study area. (f) Distribution of Iron levels in study area. (g) Distribution of Zinc levels in study area. (h) Distribution of Chlorine levels in study area. (i) Distribution of Calcium levels in study area. (j) Distribution of Chloride levels in study area. (k) Distribution of Nitrates levels in study area. (l) Distribution of Alkalinity levels in study area. (m) Distribution of Hardness levels in study area.

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The spatially interpolated maps of the groundwater quality parameters reveal that the pH level was neutral in the total study area except at two stations (Tendoli and Mahalunge) which was alkaline and at one station it was acidic. The concentration of TDS was found to be in poor to unacceptable limit in about 50 km2 in the study area. The Na, K, Fe, Zn, Cl, NO3, and Alkalinity were within permissible limits in the whole study area. The chloride level was not a permissible limit in nearly 44 km2 of the Vengurla Taluka of the study area. The hardness and turbidity levels were not in permissible limit to about 45 km2 in the study area where there is a possibility of seawater intrusion.

To calculate the Water Quality Index (WQI), weights of different water quality parameters were determined using the Indian Standard (IS) 10800:2012, as shown in table 7. The analysis of WQI revealed that water samples in study area varied in quality from excellent to good (figure 12). The WQI values for Vengurla (SR.33) and Vadakhol (SR.36) were calculated as 25 and 26.23, respectively. The high WQI values at these stations were attributed to the higher levels of total dissolved solids (TDS), sodium, iron, zinc, and turbidity in the groundwater. The higher TDS and turbidity were significantly related to seawater intrusion in that area.

Table 7 Unit weighting of factors according to the standard for drinking water in India (IS: 10800, 2012).
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Figure 12
figure 12

Study area's Water Quality Index map.

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5 Conclusions

This study has shown that GIS and laboratory analysis can be used together to assess and map groundwater quality. The spatial distribution of Sodium, pH, Potassium, Iron, Zinc, Chlorine, Calcium, Nitrates, and Alkalinity all show that these parameters are within the permissible limit in the study area. However, TDS analysis of water quality shows that more than 10% of ground water in study area is unfit for human consumption. The regional distribution map of hardness concentrations shows that majority of samples are within the allowable limit, with the exception of one sampling point, which is located in Vengurla Taluka and is 2.44 km from the seashore. Calcium is also within the maximum permissible limits, with the exception of one sample, which shows calcium concentration exceeding permissible limit. The spatial distribution map of alkalinity concentrations illustrates that all samples were in desirable limit. Only one site in the study area's groundwater has TDS and Chloride concentrations over the maximum allowable limit; this same sample also exceeds the allowable limit for Calcium. With a few exceptions of the surroundings of samples 34 and 37, the WQI score for the majority of the region in the current investigation was less than 20. This indicates that the groundwater is generally suitable for drinking and domestic purposes, with the exception of the areas around Huda and Vadakhol. The high value of water quality index at these stations is likely due to the higher values of Hardness, Calcium, Chloride, and Total dissolved solids. This indicates that the groundwater in these areas may be contaminated with sea water. The study also found that the observation wells near the seashore are slowly being affected by sea water intrusion. This is a concern, as it could lead to the contamination of more groundwater resources in the future. The authors of the study recommend that people in the area be made aware of the excessive exploitation of groundwater and subsequent sea water intrusion.