Introduction
Groundwater has traditionally been used as a source of drinking water, and it remains vital today. Ground water development has offered significant socioeconomic benefits to civilization [1]. It is projected to provide around 50% of existing drinking water supplies globally. Most people assume that groundwater is reasonably pure and free of pollution. Although most groundwater is still of high quality, there are notable exceptions, The purity of groundwater is becoming increasingly difficult to preserve [2]. Seawater intrusions are a major source of groundwater pollution. Saltwater intrusion in groundwater aquifers can be exacerbated by rising sea levels combined with increased groundwater pumping [3]. Ingress of saltwater into groundwater aquifers can raise treatment costs for drinking water facilities or leave groundwater wells inoperable. The coastal shore of Rameshwaram has focused attention on groundwater removal from aquifers during the previous decade. This is due to increased water demands from a growing population, as well as a climate change problem, particularly along coastal shallow groundwater in Rameshwaram coastline zone and Salinization of groundwater in drilled wells and deep boreholes is a serious challenge in drinking water quality and residential water supply management in the coastal region of Rameshwaram [4]. The primary goal of this research is to undertake a comprehensive assessment of the quality of groundwater resources in the Rameshwaram coastal zone. To classify groundwater in the area of spatial water quality classes, a strong water quality index (WQI) weighted for the main factors in domestic water quality evaluation. The samples were collected and tested to determine the water quality and the extent of seawater intrusion. The rotating component matrix is used to provide statistical interpretations [5], The primary analysis of principal component analysis is utilized to assess the correlation between each variable created by the water quality analysis, and kaiser normalization illustrations are used to anticipate the correlations between parameters and components, which are followed by box plots, which draw the lower and upper water quality parameters as horizontal lines either side of a rectangle shape, and dendrogram analyses, which provide the output in hierarchical cluster form overlay mapping using a Geographical Information System (GIS) in a comprehensive approach to visually identify the dispersion of water quality parameters revealed polluted areas for sustainable coastal resource management [6].
Groundwater has historically been a key source of drinking water and continues to be so today. Seawater intrusion poses a serious threat to the coastal ecosystem, especially considering the increased demand for groundwater by residents of coastal metropolitan areas. The environmental threats that the seawater intrusion phenomenon poses have been investigated by several scientists from around the world. Seawater incursions are a significant cause of groundwater pollution in coastal areas [7]. Rising sea levels and greater groundwater pumping may make saltwater intrusion in groundwater aquifers worse. Although most groundwater is still of high quality, there are notable exceptions, The purity of groundwater is becoming increasingly difficult to preserve in sea shore areas [8]. Groundwater wells may become inoperable or incur higher treatment expenses as a result of saltwater intrusion into groundwater aquifers. When groundwater levels fall below the mean sea level, the hydraulic gradient is reversed and seawater in the coastal aquifer is pushed to flow inland. The growth of industry and agriculture in the region is hampered by seawater intrusion, but it also lowers people’s quality of life.
Climate change and sea level rise are the two most important climatic factors that control seawater intrusion in coastal areas. Sea levels rise as a result of an increase in ocean temperature brought on by global warming, which also speeds up glacier melting. A rise in sea level results in the migration of the freshwater-seawater mixing zone to inland regions, which also raises the seawater head at the ocean’s edge. Human activity is another crucial factor; it has the greatest direct and indirect impact on all other factors that regulate seawater intrusion [9]. Overuse of groundwater resources is the most significant human-caused factor speeding up seawater intrusion in coastal zones. When groundwater with a significantly higher salt content is used in agricultural fields in areas where freshwater is under severe stress, the soil eventually becomes salinized [10]. Because it is challenging and not advantageous to remove salts from the soil, the saline soil becomes unusable for agricultural activities. Groundwater usage for agriculture, land reclamation, haphazard shrimp aquaculture, inland saltpans, inadequate or poorly maintained infrastructure, and inefficient water management systems, may cause the coastal aquifer to become salinized.
The coastal shore of Rameshwaram focused attention on groundwater removal from aquifers during the previous decade. This is due to increased water demands from a growing population, as well as a climate change problem, particularly along coastal shallow groundwater in Rameshwaram coastline zone and Salinization of groundwater in drilled wells and deep boreholes is a serious challenge in drinking water quality and residential water supply management in the coastal region of Rameshwaram [11]. The primary goal of this research is to undertake a comprehensive assessment of the quality of groundwater resources in the Rameshwaram coastal zone. The rotating component matrix and kaiser normalization illustrations are used to anticipate the correlations between parameters and components, which are followed by box plots and dendrogram analyses provide the output in hierarchical cluster form used to provide statistical interpretations, Overlay mapping using a Geographical Information System (GIS) in a comprehensive approach to visually identify the dispersion of water quality parameters revealed polluted areas for sustainable coastal resource management.
Materials and Methods
Study Area
Rameshwaram is a popular pilgrimage site in India. People come from all over the world to seek salvation and moksha. This research region is in the Indian state of Tamil Nadu, in the southeast [12]. At the point of the Indian peninsula, this location is separated from Sri Lanka by a narrow Palk Bay canal known as the Gulf of Mannar. The area is located between longitudes 79°12’30”E and 79°21’30”E, and latitudes 9°8’55”N to 9°19’25”N, with Pamban Bridge to the north and the Gulf of Mannar to the south [13]. Fig. 1 depicted an actual visual map of the research area. Rameshwaram is governed by a municipality that was created in 1994. The town has a total size of 53 km2 (20 sq mi). The majority of Rameshwaram workforce is employed in tourism and fishing [14]. The expansion of such activities necessitates a coordinated strategy for the use and development of water resources. Rameshwaram’s weather is hot, humid, windy, and cloudy. Throughout the year, the temperature normally ranges from 77ºF to 89ºF, with temperatures rarely falling below 75ºF or rising over 92ºF. The island is in a semi-arid zone where intermittent rains may occur [15]. A severe drought or a lack of rainfall can last for several years in some cases. This region is subject to two monsoons: the southwest monsoon from June to August and the northeast monsoon from October to December [16]. During the monsoon season, this area receives an average of 94 cm of rain. Cyclones are widespread in the coastal region, and they can have disastrous consequences. In recent years, pollution has become a major source of coral damage. This is due to rising population settlements in coastal towns in the Palk Bay region, which dump their untreated domestic and industrial pollutants into the oceans [17]. Tidal effect dominates pollutant load dilution around the island.
Sample Collection and Analysis
Ground water samples were collected from 40 selected bore wells during the southwest and northeast monsoons within a 20km radius of the coastline at appropriate distance intervals designated by the Tamil Nadu Water Supply and Drainage (TWARD) board for drinking and household uses [18]. Water samples were collected in pre-sanitized plastic containers. The physical properties were measured using calibrated digital equipment at the sampling site. The chemical constituents were evaluated in a laboratory utilizing American Public Health Association (APHA) and Bureau of Indian Standards (BIS) standard procedures [19]. At the time of sampling, the physicochemical parameters of water were assessed in the field using portable meters (electrical conductivity, pH, and temperature). GPS was utilized to record the latitude and longitude of the sample collecting site.
Water Quality Analysis
Standard techniques were used to examine water quality characteristics [20]. Temperature was recorded during sample collection using a thermometer, and specifics of several procedures important to parameters are presented in Table 1.
Table 1.
Various water quality parameters and approximated standards methods.
Statistical Analysis
Principle Component Matrix
SPSS software 14.0 was used to do statistical computations. Principal component analysis (PCA) is a useful approach for attempting to explain the variation of a large dataset of intercorrelated variables with a smaller collection of independent variables [21]. The eigenvalues and eigenvectors of the original variables are extracted using the PCA technique from the covariance matrix. The uncorrelated variables obtained by multiplying the original correlated variables with the eigenvector, which is a list of coefficients, are known as the Principal Components (PC) [22]. As a result, the PCs are linear weighted combinations of the original variables. PC gives information on the most important factors that describe the entire data collection while allowing for data reduction with minimal loss of original information [23]. The first major component is the linear combination of the original variables that provides the most variance to their overall variance. The residual variance is maximized by the second principal component, which is uncorrelated with the first, and so on until the total variance is examined [24]. Due to the method’s reliance on the total variance of the original variables, it works best when all variables are measured in the same units.
Rotation Method by Varimax with Kaiser Normalization
A varimax rotation condenses a sub-expression space into a few key elements. The real coordinate system remains unaltered, and the orthogonal basis is rotated to align with them [25]. Principal component analysis or factor analysis generate a dense subspace with several non-zero weights, making it challenging to comprehend. A subspace-invariant rotation is required for orthogonality. A variable has a high loading on a single factor but near-zero loadings on the remaining factors, while a factor is composed of only a few variables with extremely high loadings on this factor while the rest variables have near-zero loadings on this factor [26]. Kaiser normalization was used to identify potential sources. In PCA, an eigenvalue greater than one was used to select factors, and factor scores greater than 0.5 were considered significant. For all statistical analyses, a minimum significance level of 95% was adopted.
Cluster Analysis (CA)
Cluster analysis is a multivariate statistical technique that allows objects to be assembled based on their similarity. CA classifies things so that each object in the cluster is like the rest in the cluster in terms of a given selection criterion [27]. The most prevalent CA method is Rescaled Distance cluster combine cluster analysis. which, generally depicted by a dendrogram, shows intuitive similarity relationships between each one sample and the entire dataset [28]. The dendrogram is a visual representation of the clustering processes and providing a representation of the groupings and their vicinity with a dramatic reduction in the original data’s dimensionality.
GIS Mapping
Geographical analysis with GIS was used to capture the geographical variation of ground water quality in the research area. Toposheets for 40 sampling places were created for the creation of data bases [29]. Based on the location data, a location map of the study area has been created, with point features representing the position of 40 sample areas identified and a map for the same. To characterize surface reflectance, atmospheric correction removes the scattering and absorption effects from the environment [30]. Radiometric correction is used to reduce or rectify mistakes in satellite data’s digital values. The non-spatial database was prepared and evaluated in the laboratory using normal processes [31]. The ground WQI value was determined for each sampling point. The acquired ground water quality data serves as the attribute database for the current study. The produced spatial and non-spatial (attribute) data bases were combined to create spatial distribution maps of all water quality metrics, including the WQI map [32]. To build various thematic maps, the water quality data (non-spatial data) is linked to the sample location (spatial data).
Result and Discussion
Water Quality Analysis
The pH values, which averaged 7.6 and 7.5, mirrored the slightly alkaline quality of the water samples. The pH recorded in the Palkulam Nelli site was acidic, ranging between 6.87 and 6.88. Table 2 shows the sample location of Edayarvalsai bond with the greatest pH level of 8.73. All the sample’s pH levels were within the federal environmental protection agency and WHO guidelines [33]. Most of the samples were more than 7 levels alkaline, and the recurring likeness of alkalinity of sea water suggests sea water intrusions.
According to experimental results, the EC in CMRI, Mandabam site was greater than 13.73mg/L. The recorded range in Gandhi Nagar South Street-R-II was 10.12 mg/L, and the EC range in Mandapam Residence was around 10.58 mg/L. These three areas represent the EC alarm range, indicating that saltwater incursion persists. In general, the degree of water mineralisation in the direction of flow increases significantly. The highest EC values were found in three places in the east and along the seashore, whereas the lowest values were found in the west away from the sea level, indicating an increase in EC owing to seawater intrusion.
The Table 2 shows that TDS, as a main indicator of water quality, ranged between 240 and 2840 mg/L for all samples. 70% of the samples above the acceptable level set by WHO and the federal environmental protection agency. The TDS value range was 4103 mg/L at CMRI, Mandabam, which is close to the coastal area, while the lowest range was reported in Lotus Pond, which is west of the coastal zone. The wide range of TDS parameters listed in (Table 2) suggest that multiple pollution sources influence groundwater quality in the research area. Topography may be one of the credited variables that caused high TDS in coastal region groundwater sources [34]. The high TDS level in coastal groundwater is thought to be caused by a high rate of evapotranspiration and limited fresh water exchange, which leads to the creation, transport, and dissolution of salt layers in the research area.
Calcium hardness (Ca2+) predominates in all tested wells, particularly those near the coast, according to analyses. The ion concentration ranges from 60 to 355 mg/L. The mixing of fresh groundwater with seawater along the shore and in coastal transition zones may be the principal sources of calcium in aquifer systems [35]. The greatest range measured was 355 mg/L in CMRI, Mandabam, and the lowest range anticipated was 60 mg/L in the vicinity of Edaiyarvalsai Pond, as exposed in Table 2.
The concentration of Magnesium (Mg) ion in the zone costal region ranges from 50 to 560 mg/l, as shown in the Table 2. The greatest amount was 560 mg/L in CMRI, Mandabam, while the lowest level was around 50 mg/L in Maravatti valasai opposite. The primary source of magnesium hardness may be local rocks, The magnesium hardness in the coastal zone aquifer is implied by the erosion and transportation of rock components by waves in the seashore area. However, in some areas of the test region, the magnesium may be caused by the mixing of fresh groundwater with seawater along the shore.
The experimental study reveals a low range of 180 mg/L and a high value of 915 mg/L in the CMRI, Mandabam area, as revealed in the Table 2. On the other hand, overall hardness is connected to Ca2+ and Mg2+ ions, and calcium found in greater abundance than magnesium in numerous groundwater samples from the island and mainland locations. When seawater enters a fresh water aquifer, cation exchange occurs, with sodium being taken up by the exchanger and Ca2+ being discharged into groundwater. Significant fresh water degradation occurs in the study region as a result of abandoned or inadequately constructed wells and the development of saline water tanks for fish preservation [36]. This exchange mechanism eventually leaves a trace of high hardness in groundwater. Total hardness implies that all the samples were very hard.
The total solids represent the suspended and dissolved solids in the obtained sample. The laboratory studies show the total solids range in representative samples, as shown in the Table 2. The experimental study revealed the lowest range was 375 mg/L in Maravatti valasai opposite and the highest value was 4100 mg/L in CMRI,Mandabam region, from the experimental data 80% of the samples reveal Nacl type water, indicating that the sample locations are near the coastline and that there is an increase in Nacl ions in the groundwater due to seawater movement towards the landside [37]. The fact that 17% of the samples are freshwater implies that they are from the recharge zone, whereas the rest samples are mixed saline (Ca2+ -Mg2+ -Cl type). A low carbonate concentration with a comparatively high Cl and Mg concentration implies a rising rate of solid content in the water sample. It occurs as a result of saline water intrusion into the groundwater aquifer caused by the seashore’s effect.
The fluoride concentration in 59% of the samples above the WHO standard of 0.5-1.5 mg/L, whereas the remaining 41% were under the limit. The highest concentrations of fluoride were found in three locations: Palkulam, Kunjarvalasai-R, and Edaiyarvalasai, as shown in Table 2. The lowest levels were found in Enmanagundam, where they were in the 1.0mg/L range. Fluoride’s impact to groundwater in these samples is minor [38]. Fluoride levels in natural waters are low as a result of its tendency to be fixed by the nature of ground water and to be impacted by the increasing amount of fluoride. The fluoride level in groundwater sources appeared to indicate that most of the samples were unfit for drinking. As the sources at the coast are dominated by permanent fluoride pollution, particularly with high input from sea water intrusion.
Table 2.
Water quality characteristics of coastal zone of Rameshwaram.
Statistical Analysis
Rotation Component Matrix
Table 3 shows the major component analyses for the water quality analysis of the coastal area. It contains the loading for the rotated component matrix as well as the eigenvalues for each component, each component accounts for a certain percentage and a cumulative percentage of the variance were explained. It shows that the first three major components account for 90.52% of the total variation in the dataset. where the first major component accounts for 48.53% and the second principal component accounts for 67.64%. The third major component accounts for 90.52% of the overall variance [39]. The first three principal components’ eigenvalues can be utilized to assess the primary hydro geochemical processes. Positive loadings of pH, Ca2+, and TS are high (0.441-0.891), although Mg concentrations are low. For the first principal component, TH and F- have low positive loadings (0.846-0.889) (Joseph Dien et al., 2005). The values of EC and TDS for the third main component reveal high loadings of (–0.248- –0.221). Ca2+ values suggest a modest loading rate (2.867-02). pH, TDS, Mg, Ca2+, and TH concentrations demonstrate a low loading rate of (–0.248- –0.258).
Varimax with Kaiser Normalization
Table 4 expressed the Varimax with Kaiser Normalization for a coastal zone water sample. It comprises the varimax matrix’s loading, the valid N points for each component, the minimum, maximum, mean, and standard deviation per cent of varimax explained with Kaiser Normalization. It indicates that the first three varimax components account for 37 of the total variances in the dataset [40]. where the minimum Varimax data with Kaiser Normalization value ranged from 0.81-330.0. Varimax values might range from 8.73 to 25000.60. The mean deviation ranged from 1.8373 to 1661.0150, while the standard deviation ranged from 0.3264 to 3867.6154. The primary hydrogeochemical processes can be accessed via statistical analysis. The presence of high positive loading concentrations of pH, EC, TDS, Ca2+, Mg, TH, TS, and F- suggests the possibility of a sea water intrusion problem.
Table 3.
Rotated Component Matrix.
Table 4.
Extraction Method: Principal Component Analysis, Rotation Method: Varimax with Kaiser Normalization - Descriptive Statistics.
Cluster Analysis
This research looked at, Cluster analysis revealed a substantial regional and temporal connection based on fluctuations in major pollution indicators, indicating that the effects of sea water incursion on water quality varied spatially and temporally. The dendrogram depicts the level of pollution as well as the impact of contamination at the sampling sites. It depicts the groupings and their proximity, providing a visual summary of the clustering processes [41]. To find similarities between the forty sampling sites, cluster analysis was utilized. Cluster analysis produced a dendrogram, which classified the sample based on the percentage of similarity and dissimilarity of water quality indicators. The percentage similarity dendrogram of forty research sites in Fig. 2 depicts based on physicochemical considerations. The similarity of research sites was analyzed to determine the degree of relationships between sites as a cluster. The rescaled proximity cluster similarity analysis indicated an identity of 65% in one set of forty sampling locations and 35% in the remaining group. In contrast to these sites considering, it is located at the head of the stream, it has the greatest dissimilarity to other sites throughout the whole research period. Hence, the seawater intrusion has a significant impact on the seashore. Fig. 2 shows the dendrogram of percentage seasonal similarity. The dendrogram developed categorized the sampling sites based on water quality characteristics.
Interpretation by ArcGIS
The current study attempted to evaluate and map the groundwater quality surrounding Rameshwaram’s coastline zone. The estimated WQI is a simple technique to understand the overall effect of seawater intrusion [42]. The integration of several thematic layers using ArcGIS 9.3 is quite beneficial in establishing the acceptability of ground water quality for drinking purposes. The surface water determinant concentration in the donor region is indicated in the geographic variation maps interpolated for pH. The pH concentration is determined by the findings of the laboratory analysis, it was subsequently superimposed on the Palkulam Nelli research was acidic, ranging between 6.87 and 6.88. Fig. 3a) depicts the map interpretation of the sample site of Edayarvalsai bond with the highest pH level of 8.73 in alkaline nature distribution.
The EC specifies how salt and iron-free water may contain additional contaminants. From the results, the downstream places near the seashore had greater concentration values of 4.43 mg/L and 4.63 mg/L, whereas the western part of some areas had higher concentrations of 13.73 mg/L. The high EC concentration can be due to the limited mobility of water; as a result, EC concentration tends to rise. For EC concentration, the average lower values of EC were detected between upstream and downstream locations that operate as open water runoff. Also, Fig. 3b) shows that the receiving zone is more contaminated than the donating region in terms of EC concentration, which is well represented in the interpolated map.
Fig. 3c) depicts the geographic maps interpolated for TDS concentration in open water in the research area’s coastline zone. TDS exhibits a variable pattern, and its concentration is the result of laboratory analysis findings overlaid on the research area shapefile. TDS is a broad indicator of the quality of water. Most of the study area has TDS levels ranging from 240 mg/L to 805 mg/L. The slightly higher number showed the presence of contamination caused by seawater intrusion. The interpolated map clearly shows the TDS spread.
The presence of minerals associated with rock surfaces is primarily accountable to produce calcium hardness in water. Some areas had high calcium concentrations. Fig. 3d) depicts the spatial distribution of calcium hardness (Ca2+). Most of the location calcium hardness ranges from 60 mg/L to 195 mg/L. The greater value from the range of 355 mg/L was obtained in some sites. The precision of Ca2+ distribution in sampling areas is shown on the GIS interpolated map.
Most of the locations were discovered to be above the permitted range of 90-400 mg/L. However, in the CMRI, Mandabam area, the highest magnesium hardness was reported in the range of 560 mg/L, while much of the area magnesium hardness (Mg) ranges from 100 mg/L to 195 mg/L. Fig. 3e) represents the spatial distribution of Mg on a GIS map, regarding Total hardness the CMRI, Mandabam area has a high concentration of total hardness in the range of 915 mg/L. Most of the region was within the ideal level, ranging from 215 mg/L to 475 mg/L. Fig. 3f) represents the spatial distribution of Total Hardness.
Higher solid content observed mostly as a result of sewage effluent overapplication, septic tanks, open solid waste dump sites, and so on [43]. A higher level of solid content, around 4100mg/L, was not discovered in the research area at CMRI, Mandabam region. Total solid content ranges from 330 mg/L to 1800 mg/L. Fig. 3g) denotes the spatial distribution of total solids.
Fluoride dissolved through geological formation is commonly found in ground water. Most of the sampling sites were within the allowable level of 1.5mg/L. Fluoride levels in some areas surpass the allowable limit, as seen in the Fig. 3h). The highest level of fluoride detected was 1.78 mg/L in Edaiyarvalsai and in Edaiyarvalsai pond [44]. Fluoride poisoning can cause disorders such as dental fluorosis and skeletal fluorosis.
Conclusion
A wide variety of physio-chemical indicators revealed the groundwater quality in the research area is governed by the complex of seawater intrusion mechanisms. The statistical results of the Rotation component matrix demonstrated a positive loading of water quality indices. The Varimax with Kaiser Normalization statistical data in principle component analysis illustrates the standard and temporal deviations of parameters. The cluster analysis examined the relationships between spatiotemporal variability and water quality. Based on cluster condition, the range of ground water quality index found among all forty sample sites is 15% good quality, 20% medium quality, and 65% very poor-quality waters reported in coastal zone areas. According to a GIS mapping study, it also aids in water quality analysis. It indicates that the groundwater in the entire research region is not fit for direct drinking due to pH, EC, TDS, Ca2+, Mg2+, TH, TS, and F- levels that are not below acceptable limits in most places. The intervention and fluctuations in water quality caused by an issue with sea water infiltration, which was well predicted using GIS mapping. Understanding salinity diagram quality is made easier by the study. The floating population in the research area is increasing yearly since it is a popular destination for pilgrimages in India. Polluting activities along the coast contribute to climate change problems, which result in abrupt changes in the weather and unpredictable rainfall. To prevent pollution caused by salinization in terms of deteriorating water quality, strategies, planning, and control of groundwater use are required.
