Assessment of hydrogeochemical characteristics of ...

Environment, Development and Sustainability https://doi.org/10.1007/s10668-018-0219-7

Assessment of hydrogeochemical characteristics of groundwater in the lower Vellar river basin: using Geographical Information System (GIS) and Water Quality Index (WQI) G. Gnanachandrasamy1,2 · C. Dushiyanthan3 · T. Jeyavel Rajakumar3 · Yongzhang Zhou1,2 Received: 29 December 2017 / Accepted: 8 July 2018 © Springer Nature B.V. 2018

Abstract The lower Vellar river basin is the study site for the assessment of hydrogeochemical characters of groundwater using GIS and Water Quality Index (WQI). The study site is entirely covered by the sediment topography of alluvium and Cuddalore sandstone. The site faces the water scarcity and water quality problem when rainfall failure occurs. Under these situations, a GIS- and WQI-based groundwater quality has been deliberate in this basin. To appraise the groundwater geochemical characteristics, in total eighty samples were collected, viz., PREM (pre-monsoon) and POSTM (post-monsoon), and examined for important physicochemical (Na+, Mg+, Ca++, K+, Cl−, HCO3, NO3, SO4, SiO2, TDS, EC, and pH) parameters. The results of the sample analysis and interpretation of groundwater data reveal that the maximum samples fall in Ca-Cl2, Ca-SO4 followed by Na-Cl2 water type. Gibb’s diagram shows that most samples plotted in weathering and followed by evaporation field. Percentage of sodium (Na%) results indicate that 18% samples were poor; 8.75%—permissible; and 72.5%—good category. According to SAR classification, 80% of the groundwater samples fall under C3S1 followed by C2S1 and C4S1 water type. The water quality index (WQI) shows that 70% of the samples fall good, 21.25% of the samples fall poor, and 8.75% of the samples fall excellent category. Hence, the study site is an alarming stage to become deterioration of the groundwater quality and could be problem to the public health. Keywords Groundwater quality · Water Quality Index · GIS · Hydrogeochemistry · Vellar river

* C. Dushiyanthan [email protected] Extended author information available on the last page of the article

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G. Gnanachandrasamy et al.

1 Introduction In sedimentary terrain, groundwater is a very crucial resource for various (industrial, irrigation, and domestic) uses. Owing to the rapid and modern development, demand for groundwater increases every day and also a scarcity of accessible surface water. In this connection, the understanding groundwater system is an important for sustainable management of water resources (World Bank 2010). The most utilized groundwater resources occur in sedimentary basins. Because of the continuity of stratigraphy, foreland and intra-mountain basins host the biggest groundwater flow systems (Ma et al. 2005; Hofmann and Cartwright 2013). The groundwater is contaminated by different anthropogenic and natural processes in the form of geology and geochemical processes such as rock weathering, ion exchange, evaporation, and dissolution. Natural sources are most important for the variations in the groundwater quality, which differs with time and space (Narany et al. 2014; Chung et al. 2014). Anthropogenic sources, specifically improperly treated industrial waste, agricultural activities, and municipal wastewater, are additional factors of water quality degradation (Mondal et al. 2010; Kim et al. 2012; Sivasubramanian et al. 2013; Matta et al. 2017). Contamination of groundwater affects the quality of the water, public health, and environment (Milovanovic 2007). In general, river basins are seriously susceptible to contamination by transportation and absorption processes of different sources. Hence, there is a necessity for monitoring the water quality and water pollution (Simeonov et al. 2003). Different techniques are used for the assessment of groundwater quality based on physicochemical parameters such as GIS (IDW, kriging method, cokriging, spline, etc.), geostatistics, and WQI. Among the various techniques, GIS and WQI methods are commonly used, and various researchers applied these techniques for their groundwater quality studies around the world (Venkatramanan et al. 2014; Gnanachandrasamy et al. 2015; Anitha et al. 2011; Nas and Berktay 2010; Singh et al. 2017; El-Hames et al. 2011; Vijith and Satheesh 2007; Chung et al. 2014; Saeedi et al. 2010; Yammani 2007). In such studies, GIS and WQI are utilized to know the groundwater quality with respect to purposes of irrigation and domestic usages. However, groundwater quality hydrogeochemical processes plays a very significant role. Under this scenario, the present study addresses the groundwater quality assessment using GIS and WQI in lower Vellar river basin based on different hydrogeochemical parameters.

2 Overview of the study site 2.1 Location The study site (Fig. 1) is the sub-basins of Vellar River with an area of 355 sq.km. It lies between 11°25′–11°35′N latitude and 79°30′–79°50′E longitude, and it is located in the SOI maps 58 M/7, 10 and 14, 11 and 15. The altitude is roughly ± 5 m above MSL. The lower Vellar sub-basin covers fully and partly the administrative blocks of Bhuvanagiri, Portonovo, Keerapalayam, Kammapuram, and Kurinjipadi, which come under Chidambaram, Virudhachalam, and Kurinjipadi taluks of Cuddalore prefecture, Tamil Nadu, India.

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Assessment of hydrogeochemical characteristics of groundwater…

Fig. 1 Sampling location of the lower Vellar river basin

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2.2 Geological settings Geologically, the sub-basin consists of quaternary and tertiary formation of sedimentary rocks. Alluvium, fluvial marine, and marine sediments are the major quaternary formations. The maximum area is enclosed with alluvium and the east side covered by marine sediments. A patch of fluvio marine sediment noticed in between alluvium and marine sediments and Cuddalore sandstone of tertiary formation noticed northern side of the boundary. Geomorphologically, the basin is classified as pediplain, alluvial plain, coastal plain, and flood plain. The flood plain was noticed along the river, and the coastal plain and pediplain were noticed along the eastern and northwestern boundaries, respectively. The types of the soil in the study site are black and coastal alluvium. The foremost soil groups are ustipsamments, Chromusterts, Haplustalfs, udifluvents, udipsamments, Ustrochepts, and Ustropepts.

2.3 Drainage, climate, and rainfall In the lower Vellar sub-basin, no major tributaries contribute to water supply; nevertheless, Walaja tank and Perumal Lake are the two large surface reservoirs that discharge large quantity of water to the sub-basin during monsoons as well as off seasons. The drainage systems of the sub-basin are man-made, which were constructed a few centuries back, and they show a parallel pattern in the middle part and sub-parallel pattern in the other parts. The river travels nearly 48 km in the sub-basin. The movement of the drainages observed from west-to-east direction is common, and little drainages differ. In the present study, only four rain gauge stations, namely Portonovo, Sethiathoppu, Kothavacheri, and Bhuvanagiri, are within the sub-basin. The rainfall data were collected from Public Works Department (PWD), Tamil Nadu, for the period from 2001 to 2012. From the annual rainfall, it is known that nearly 2000 mm was recorded during the years of 2005 and 2008 in Portonovo station. In the years 2003 and 2012, low rainfall of less than 1000 mm was recorded and overall rainfall trend showed a fluctuating pattern. In Sethiathoppu station, 2000 mm was recorded during the year 2005 and less than 1000 mm was recorded in the years 2001, 2003, and 2012. In stations Kothavacheri and Bhuvanagiri has shown a gradual variation where most of the years recorded nearly 1000 mm from the year 2001 to 2012. From the observation, rainfall was comparatively very low in the year 2012 in all four stations.

3 Methodology 3.1 Analytical methods In total, eighty groundwater samples were collected from borewells during PREM (40 samples, June 2012) and POSTM (40 samples, January 2013) season following standard procedures. The GARMIN GPS was used to locate the accurate coordinates of the sample location to continuous monitoring purposes. The parameters of TDS, EC, and pH were noted instantly in the field using handy meters (HANNA HI-8733 & HI 8414). The major parameters C a++, − + − + + l were determined by standard titration methods, and N a , K analyzed Mg , HCO3 , and C by a flame photometer (ELCO CL36), whereas S O4− analyzed by spectrum photometer with standard procedure. The accuracy of the analysis was checked by ionic error percentage,

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which is generally within ± − 5%. Piper diagrams were plotted using Rockworks (version 16) software, whereas USSL and Gibbs diagram were plotted by WATCLAST programming (Chidambaram et al. 2003). Total hardness, percentage of sodium (Na%), chloride classification and corrosivity ratio were calculated by mathematical formulas. The overall results of the samples are shown in Table 1.

3.2 Water Quality Index (WQI) WQI is very useful for assessing groundwater quality for the suitability of agricultural and domestic purposes. It is one of the successful tools to supply comprehensive information on the quality of the groundwater with reference to the group of hydrogeochemical parameters (Namibian 2007; Yisa and Jimoh 2010). Therefore, many researchers have developed different indices, technically referred to as WQI (Lermontov et al. 2009). Generally, WQI is a practical and comparatively simple approach of evaluating the composite influence of overall pollution and hardly provides evidences in pollution sources (Venkatramanan et al. 2013). In this study, WHO standards were used to compute the hydrogeochemical parameters. First, WQI was calculated by assigning weightage to the measured parameters. The highest weightage ‘5’ was O4, whereas minimum weightage ‘1’ was assigned to bicarbonate assigned to TDS, C l2, and S because it does not contribute to contamination of groundwater (Chung et al. 2014; Sanjai Kumar et al. 2013; Selvam et al. 2014). The following formula was used to know the relative weight (Wi).

Wi = wi

n /∑

(1)

wi ,

i=1

Table 1 Physicochemical parameters (min, max, and avge) compared with WHO (1998, 2004) standards Parameters

Pre-monsoon Min

Ca Mg Na K Cl HCO3 SO4 NO3 SiO2 pH EC TDS TH SAR

Post-monsoon

Max

Avge

Min

Max

Avge

19.2

125.0

54.6

19.8

96.5

48.6

11.2 11.9 1.0 68.6 10.5 1.2 102.0 0.5 7.0 285.0 229.2 167.5 0.3

117.5 95.0 130.0 320.0 268.0 88.0 262.0 5.5 9.1 6662.0 3090.6 580.9 2.5

43.2 49.7 23.6 183.4 145.9 30.8 163.3 2.5 7.7 2166.2 1030.0 310.7 1.3

6.0 10.7 1.2 92.1 34.0 14.0 1.0 4.1 6.0 661.0 423.2 111.1 0.4

66.4 137.1 54.0 488.0 426.0 86.9 36.0 67.5 8.0 5610.0 7546.5 413.0 3.2

32.7 40.6 14.6 184.0 149.8 44.8 8.2 24.2 7.2 1887.0 1467.0 254.3 1.1

WHO Stds (2014) 75 30 200 20 250 300 200 45 – 7.5 1500 500 200 –

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where wi is the weight of each individual parameter and n the total measured parameters. Third step, involves the calculation of the quality rating scale (qi) for each parameter for each of analyzed sample by its respective standard according to the guidelines (WHO 2014) and the result is multiplied by 100: ( / ) qi = Ci Si × 100, (2) where qi is the relative weight, Ci the concentration of each individual parameter, and Si the standard value of each individual parameter (WHO 2004). In the final step, for calculating WQI, the SI is first calculated for each parameter and then used to identify the WQI for each groundwater sample.

SIi = Wi ∗ qi WQI =

∑

SIi ,

(3) (4)

where SI is sub-index, qi ranking according to the concentration of ith parameter, and n the number of parameters.

3.3 Spatial interpolation ArcGIS software (9.3) was used to demarcate the sampling locations and to know the characteristics and spatial distribution pattern of groundwater throughout the study period. In this work, inverse distance weighting (IDW) interpolation technique was applied. This technique estimates a value for individual cell by calculating surrounding point within the user-defined boundary (Sarath Prasanth et al. 2012; Burrough and Mc Donell 1998).

4 Results and discussion 4.1 Physical parameters 4.1.1 pH Normally, pH (hydrogen ion) of the water sample depends on the relative contents of CO3 and CO3, HCO3−, and Ca. The change is also controlled by the amount of free H CO2. In this study, the pH level for the PREM and POSTM varied from 7.0 to 9.1 and 6.0 to 8.0, respectively. The highest pH was recorded at Periyapattu (location 35) and the lowest at Orathur (location 39) during the PREM period. Similarly, during POSTM period, the minimum pH value was observed at Kothavacheri (location 30) and the maximum at Ariyakoshti (location 28). The pH values were within the standards during PREM as prescribed as 6.5–8.5 in WHO (2014) standards for drinking water. Nevertheless, it was noticed below 6.5 in four locations (24, 30, 33, and 37) and the remaining locations were within the specified limit during POSTM. Overall, pH of the study site indicates slightly acidic to alkaline nature and is possibly attributed to anthropogenic activities.

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4.1.2 Electrical conductivity (EC) Electrical conductivity of groundwater based on ionic concentration, types of ions and temperature present in the water (Hem 1985). If the concentration of TDS increases, conductivity also increases. In the study site, during the PREM and POSTM periods, EC varied from 285 to 6662 µS/cm and 661 to 5610 µS/cm, respectively. The highest EC was noticed at Ariyakoshti (location 28) during PREM period and Adinarayanapuram (location 37) during POSTM period. Similarly, the low EC was observed during PREM and POSTM periods in Tanur (location 36) and Ambalpuram (location 3), respectively. The higher conductivity indicates the higher level of chloride present in the studied samples. In this study, 20 groundwater samples of PREM period and 18 samples of POSTM period were identified above the WHO standards (1500 µs/cm). The elevated level of EC in groundwater might affect the human by gastrointestinal irritation (Singh et al. 2008). Further, the EC has been categorized based on Wilcox (1955) classification. From the classification, 53% of PREM and 68% of POSTM samples were represented as permissible category; 23% of PREM and 25% of POSTM samples—doubtful; 15% of PREM and 5% of POSTM samples—good; and 10% of PREM and 2% of POSTM samples—unsuitable. The higher EC in groundwater is due to the influence of sewage water and agricultural activities (Gnanachandrasamy et al. 2012).

4.1.3 Total dissolved solids (TDS) In the current study, TDS varied from 229.2 mg/l (Poovanikuppam, location 38) to 3090.6 mg/l (Ariyakoshti, location 28) during PREM season, whereas during POSTM season, the minimum value was 423.15 mg/l (Ambalpuram, location 3) and the maximum value was 7546.5 mg/l (Tanur, location 36). High values of TDS suggest the hydrolysis of potassium or sodium silicate as a countable factor in the groundwater chemistry (Chae et al. 2007). In addition, as the host rocks belongs to alluvial sands, there can be some reduction and oxidation processes in groundwater and surface water, thereby also causing enrichment in the TDS (Thiyagarajan and Baskaran 2011). According to classification of USSL (1954), the study site TDS has been classified as 60% samples—useful category; 22.5%—permissible; and 5%—unfit during the PREM, whereas during the POSTM 62.5%—useful; 7.5%—permissible; and 5%—unfit category. Further, based on Freeze and Cherry (1979) classification 60% were classified as freshwater and 40%—brackish water during PREM. During POSTM season, 50% sample falls in freshwater and 50% falls in brackish water category. TDS higher than 500 mg/l in drinking water could cause an adverse physiological reaction in the transient consumer and gastrointestinal infections (Dar et al. 2011).

4.1.4 Total hardness (TH) Normally, the occurrence of Ca and Mg ions is referred to as hardness. In this study, during PREM TH varies from 167.5 to 580.9 mg/l with an average of 310.7 mg/l. The maximum value was observed at Maduvankarai (location 26), and the minimum value was observed at Vilakapadi (location 16). During POSTM, it varied from 111.1 to 413 mg/l, where minimum value was obtained from Tanur (location 36) and maximum value was obtained from

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Ariyakoshti (location 28). As per WHO (2012) standards, the majority of the samples were within allowable limit except for two locations Maduvankarai (location 26) and Periyapattu (location 35).

4.2 Chemical parameters 4.2.1 Calcium (Ca) and magnesium (Mg) Ca and Mg ions are important to human. Insufficient intakes of Ca have been connected with increased causes of several diseases such as kidney stones, hypertension, stroke, osteoporosis, and colorectal cancer (WHO 2009). In the present study, Ca concentration ranged from 19.2 mg/l (Chinnakumatti, location 29) to 125 mg/l (Chokkankollai, location 40) with an average value of 54.6 mg/l during PREM, whereas during POSTM season, it ranged from 19.8 mg/l (Alamelumangapuram, location 32) to 96.5 mg/l (Bhuvanagiri, location 10) with an average of 48.6 mg/l. The allowable limit of Ca is 75 mg/l as per WHO standards. In the study site, 7 locations (18, 19, 23, 30, 35, 36, and 40) during PREM and 3 locations (9, 10, and 40) during POSTM season were found above the drinking water standard. The Ca concentration is due to weathering process from rocks and minerals (Brindha and Elango 2012). Magnesium (Mg) is the fourth major ion in the human body as well as most important cation in groundwater. In this study, concentration of Mg varied from 11.2 to 117.5 mg/l with an average of 43.2 mg/l during the PREM. During POSTM, it varied from 5.9 to 66.3 mg/l with an average of 32.7 mg/l. The low and high concentrations observed during PREM and POSTM are at Maduvankarai (location 26), Tanur (location 36), and Kothavacheri (location 30), Vaiyalamur (location 24), respectively. The 20% groundwater samples above the permissible limit and 80% samples below limit of 30mg/l set for magnesium (WHO 2008). Common sources of Mg were the dissolution of calcite, magnesium calcite, and dolomite from host rocks (Vasanthavigar et al. 2010). Magnesium deficiency affects neurological, neuromuscular function, resulting in anorexia, muscular weakness, lethargy and unsteady gait (WHO 2009).

4.2.2 Sodium (Na+) and potassium (K+) Sodium (Na+) is a most imperative nutrient, and sufficient levels of N a+ are needed for fine + health. However, higher level of Na affects human by way of convulsion, hypertension, and vomiting (Elton et al. 1963). The Na+ concentration in the present study varied from 11.9 to 95 mg/l with an average of 49.7 mg/l during the PREM. The minimum concentration was found at Sirupalaiyur (location 34), and the maximum was found at Manambadi (location 27). During POSTM season, they were found to be 10.7 mg/l (Ambalpuram, location 3) and 137 mg/l (Chokkankollai, location 40) with an average value of 40.6 mg/l. As per WHO (2012) standards, both seasons’ samples were within the acceptable limit. In the case of potassium (K+), concentration varied from 1.0 mg/l to 130 mg/l and the average value was 17.7 mg/l during the study period. The maximum value was noted at Manambadi (location 27), and the minimum value was noted at Periyapattu (location 35). As per the WHO (1993) drinking water standards K+ is specified as 25 mg/l. In this study, 82.5% of the samples were within the permissible limit and 17.5% of the samples were above the limit. The major sources of pottassium in groundwater include rainwater, weathering of potash silicate group of minerals and application of potash fertilizer (Singh et al. 2014).

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Potassium is an important element in humans; however higher level of potassium could result in significant health effect in people with kidney disease or other conditions.

4.2.3 Chloride, bicarbonate, and sulfate The minimum, maximum and average concentrations of chloride (Cl−) in the present study were found to be 68.6 mg/l (Vilakapadi, location 16), 320 mg/l (Keerapalayam, location 21), and 183.4 mg/4 during PREM, whereas they were 92.1 mg/l (Ambalpuram, location 3), 488 mg/l (Chokkankollai, location 40), and 184 mg/l during POSTM season, respectively. Cl− was higher in POSTM (488 mg/l) due to the leaching from top soil layers derived from industrial, domestic, and dry climates (Srinivasamoorthy et al. 2008). As per WHO (1993) standards, the acceptable limit of C l− concentration is specified as 600 mg/l and all the locations are within the limit. As far as bicarbonate (HCO3) is concerned, throughout the study, the concentration varied from 10.5 to 426 mg/l with an average value of 147.7 mg/l. The higher and lower concentrations were noticed at Viramudaiyanatham (location 14) and Ambalpuram (location 3), respectively. HCO3 was higher during POSTM (426 mg/l) may be due to action of CO2 upon the basic materials of soil and rock. Normally, HCO3 concentration present in groundwater is because of carbonate weathering and carbonic acid dissolution in the aquifer systems (Jeevanandam et al. 2006; Alaya et al. 2014; Kumar et al. 2009). Spatial distribution patterns of pH, EC, TDS, Ca, Mg, Na, Cl, and HCO3 are shown in Figs. 2, 3, 4, 5, 6, 7, 8, and 9. Regarding sulfate (SO4), the concentration varied from 1.2 mg/l (Tiruppaninatham, 20) to 88.0 mg/l (Ayipettai, location 19) with an average of 30.8 mg/l during PREM season, and during POSTM it ranged from 14.0 mg/l (Bhuvanagiri, location 10) to 86.9 mg/l (Kizhamanakudi, location 13) with an average of 44.8 mg/l. In this study, the S O4 concentrations are within the limit of WHO (250 mg/l) standards. The higher values of sulfate may be from domestic waste, untreated industrial water, and their effluents (Baruah et al. 2008; Krishna Kumar et al. 2014).

4.3 Hydrogeochemical facies Hydrogeochemical facies (piper) diagram is extensively used to categorize the geochemical characteristics of groundwater based on the presence of major ions (Pierre et al. 2005; Piper 1953; Rasouli et al. 2012). The piper diagram contains a combination of one rhombus-shaped field and two triangular fields. The shape of rhombus field contains mixed combinations of both ions and categorized the groundwater type. The results of this diagram (Fig. 10) reveal that the maximum number of the samples falls in the field of Ca-Cl2, Ca-SO4 water type with a minor representation of Na-Cl2 type throughout the study. It suggested the dissolution of halite, carbonate-bearing minerals, domestic effluents and ionic processes (Magesh et al. 2013; Venkatramanan et al. 2017).

4.4 Mechanism controlling the chemistry of groundwater The mechanism of Gibb’s plot is an action between groundwater and aquifer minerals. It has a significant role in water quality which is helpful to identify the origin of water (Subramani et al. 2009; Gibbs 1970; Gnanachandrasamy et al. 2015; Srinivasamoorthy et al. 2012). Gibb’s plot (Fig. 11) represented the ratios of Na + K/Na + K+Ca and Cl/ Cl + HCO3 as a function of TDS and are widely employed to evaluate the functional

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Fig. 2 Spatial variation of pH: a pre–monsoon; b post-monsoon

sources of dissolved chemical constituents, such as evaporation dominance, rock dominace and precipitation dominance. In PREM season 68% of samples fall in the weathering dominance and 32% of samples fall in evaporation dominance. Whereas POSTM season, 51% of samples fall in weathering dominance and 45% samples fall in evaporation dominance. It indicates that weathering field may be because of the chemical

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Fig. 3 Spatial variation of EC: a pre–monsoon; b post-monsoon

weathering with the dissolution of rock-forming minerals and the evaporation field due to the contribution from surface water, rainwater infiltration and also anthropogenic activities (Li et al. 2016; Srinivasamoorthy et al. 2008).

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Fig. 4 Spatial variation of TDS: a pre-monsoon; b post-monsoon

4.5 Sodium adsorption ratio (SAR) The SAR is useful for examining the quality of water for the use of the agricultural purposes (Todd 1980). Richards (1954) SAR classification was used in this study; SAR ranges between 0.284 and 0.549 during PREM and between 0.368 and 3.161 during

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Fig. 5 Spatial variation of Ca: a pre-monsoon; b post-monsoon

POSTM with an average of 1.25 and 1.083. Table 2 shows all the samples of this study have been classified as excellent water category. USSL (United States Salinity Laboratory 1954) classification is an additional method of identification for suitability of irrigation. In this method, SAR and EC reciprocally can be used to evaluate irrigation water quality. Based on salinity hazard, the water types can be classified as low salinity

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Fig. 6 Spatial variation of Na: a pre-monsoon; b post-monsoon

(C1), medium salinity (C2), high salinity (C3), very high salinity (C4) and as low (S1), medium (S2), high (S3) and very high (S4) based on sodium hazard. In the present study, maximum numbers of the sample (83%) fall in C3S1 category, whereas few samples (15%) fall in the C2S1 and only one sample falls in the C4S1 category during PREM. During POSTM season, 93% of the samples have fallen in C3S1 category and

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Fig. 7 Spatial variation of Cl: a pre-monsoon; b post-monsoon

three samples have fallen in the C2S1 category. It indicates that low sodium (S1) can be used for the purpose of irrigation with minor causes of increasing concentration of exchangeable Na+ (Khodapanah et al. 2009; Ketata et al. 2011). High salinity class (C3) is satisfactory for plants having moderate salt tolerance, on soils of moderate permeability with leaching (Alaya et al. 2014; Jeevanandam et al. 2012). S1C2 represents

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Fig. 8 Spatial variation of H CO3: a pre-monsoon; b post-monsoon

low sodium and medium salinity type of water. It demonstrates that this water is fit for irrigation without salinity control (Narany et al. 2014). The USSL classification of the Vellar river basin groundwater samples is shown in Fig. 12 (Tables 3, 4).

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Fig. 9 Spatial variation of S O4: a pre-monsoon; b post-monsoon

4.6 Percentage of sodium (Na%) Percentage of sodium (Na%) in water is most crucial parameter to the evaluation of irrigation suitability. Excess Na in combination with Cl forms the saline soils, whereas with CO3, alkaline soils are formed. Either of the soils does not support the growth of the crops

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Fig. 10 Hydrogeochemcial facies (Piper 1953) diagram: a pre-monsoon; b post-monsoon

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Fig. 11 Mechanism controlling groundwater chemistry (Gibbs plot)—post-monsoon

(Venkatramanan et al. 2014). Percentage of sodium is commonly expressed by the following formula: /[ ] Na % = [Na + K] ∗ 100 (Ca + Mg + Na + K) (meq∕l).

Based on the Na%, 10% of samples fall excellent; 10% of samples fall permissible, and 80% of samples fall good category during PREM season. During POSTM season, 28% of the samples were classified as excellent category, while 65% of samples were classified as good category and 7.5% were classified as permissible category. When compared to POSTM, the PREM season had higher Na%, due to long residence time of water, dissolution of minerals from lithological compositions, and the addition of chemical fertilizers by the irrigation waters (Qiyan and Baoping 2002; Subba Rao and Devadas 2003).

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Table 2 Various classifications based on hydrological parameters Parameters

Classification

Value range

Pre-monsoon Post-monsoon

EC (values in µS/cm) (Wilcox 1955)

Excellent

5000 0–1000 1000–10,000 10,000–100,000 > 100,000 3000 26 80 564

6 21 9 4 24 16 Nil Nil Nil 9 24 6&1 40 Nil Nil Nil 4 32 4 Nil Nil Nil Nil 14 25 01 Nil Nil Nil

2 27 10 1 20 20 Nil Nil Nil 3 25 10 & 2 40 Nil Nil Nil 11 26 3 Nil Nil Nil Nil 18 19 3 Nil Nil Nil

TDS (values in mg/l) (Freeze and Cherry)

USSL—TDS classification

SAR (Richards 1954)

Sodium % (values in epm) (Wilcox 1948)

Chloride classification (values in epm) (Stuyfzand)

4.7 Corrosivity ratio (CR) The corrosivity ratio is important to know whether the water can be transported in metallic pipes or not. In this regard, there is a need to find the water quality based on corrosive character of the sample. The index of the CR was calculated by Ryznre (1944) method. According to index values, the CR 1, the water is corrosive and not suitable for transport through metal pipes (Raman 1983). From the results of CR it is observed that about 90% of the samples were 300 (not suitable for drinking purpose). In this study, 11 water quality variables (Na, Mg, K, Ca, Cl, SO4, HCO3, TH, TDS, EC, and pH) were used to know the WQI. The WQI results of this study are represented in Tables 3 and 4. The results ranged between 37.0 and 242.1. The minimum value of 37.0 was observed at Ambalpuram (location 3), and the maximum value of 242.1 was observed at Tanur (location 36) during POSTM period. Out of eighty samples water quality of 56 samples (70%) were classified as good; 17 (21.25%) samples as poor; and 7 samples (8.75%) as excellent in the sampling period.

5 Multivariate statistical approach 5.1 Correlation analysis The Pearson’s correlation analyses of the 14 variables for Vellar river sub-basin groundwater samples are presented in Table 5. The most significant positive correlation (r > 0.7) was found between Na and SAR (r = 0.95); Mg and TH (r = 0.82) during the study period. Strong significant correlation of TH between Ca (r = 0.55) and Cl (r = 0.46); EC and TDS (r = 0.44); Mg and Cl (r = 0.38) during pre-monsoon. During post-monsoon, Ca versus TH

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Assessment of hydrogeochemical characteristics of groundwater… Table 4 Groundwater quality of the lower Vellar river basin S. no

Village name

Usage

Pre-monsoon

Quality

1

Kummadimulai

Irrigation

105.0

Poor

2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40

Krishnapuram (vada) Ambalpuram Mutlur.C Manjakkollai Budarayampettai Chinna Nerkunam Vandarayampattu Melmungilladi Bhuvanagiri (TP) Agara Alambadi Sethiathoppu (TP) Kizhamungiladi Viramudaiyanatham Siruvarappur Vilakapadi Tharmanallur Kiliyanur Ayipettai Tiruppaninatham Keerapalayam Adhivaraganallur Kizhamanakudi Vaiyalamur Tachakkadu Maduvankarai Manambadi Ariyakoshti Chinnakumatti Kothavacheri Kundiyamallur Alamelumangapuram Villiyanallur Sirupalaiyur Periyapattu Tanur Adinarayanapuram Poovanikuppam Orathur Chokkankollai

Domestic Domestic Domestic Domestic Domestic Irrigation Domestic Domestic Domestic Irrigation Domestic Domestic Domestic Domestic Domestic Domestic Domestic Irrigation Irrigation Domestic Domestic Domestic Domestic Domestic Irrigation Domestic Domestic Domestic Irrigation Irrigation Domestic Domestic Domestic Domestic Domestic Domestic Domestic Domestic Domestic

87.7 101.6 54.7 48.5 85.7 79.2 63.4 54.2 70.8 96.6 57.2 94.9 63.2 41.7 40.2 50.3 57.3 78.6 46.6 72.6 90.6 90.3 75.4 51.3 98.1 90.6 163.0 46.2 111.9 191.7 98.1 87.8 106.9 70.3 79.2 51.3 53.1 73.5 147.6

Good Poor Good Excellent Good Good Good Good Good Good Good Good Good Excellent Excellent Good Good Good Excellent Good Good Good Good Good Good Good Poor Excellent Poor Poor Good Good Poor Good Good Good Good Good Poor

Post-monsoon 97.7 107.1 37.0 104.3 63.5 54.4 60.3 52.2 60.8 63.2 93.9 111.1 65.8 80.2 79.5 47.8 124.3 64.3 93.7 109.7 62.9 85.4 62.3 87.4 49.1 60.1 72.0 106.5 75.2 77.5 53.7 55.5 62.8 50.5 55.7 242.1 152.6 115.4 58.2 128.0

Quality Good Poor Excellent Poor Good Good Good Good Good Good Good Poor Good Good Good Good Poor Good Good Poor Good Good Good Good Excellent Good Good Poor Good Good Good Good Good Good Good Poor Poor Poor Good Poor

(r = 0.53) and Cl (r = 0.51); Na versus Cl (r = 0.66), K versus Cl (r = 0.46), Cl versus TH (r = 0.59) and SAR (r = 0.43), EC versus TDS (r = 0.53), and TDS versus SAR (r = 0.43) were found. It shows that Na, Ca, and K had good positive correlation with Cl ions in the

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Table 5 Correlation matrix of groundwater parameters in Vellar river sub-basin Pre-mon

Ca

Ca

1.00

Mg Na K Cl HCO3 SO4 NO3 SiO2 pH EC TDS TH SAR

– – – – – – – – – – – 0.55 –

Post-mon Ca Ca

1

Mg Na K Cl HCO3 SO4 NO3 SiO2 pH EC TDS TH SAR

– – – – – – – – – – – 0.53 –

Mg

Na

K

Cl

1.00 – – – – – – – – – – 0.79 –

1.00 – – – – – – – – – – 0.95

1.00 – – – – – – – – – –

1.00 – – – – – – – 0.46 –

Mg

Na

K

Cl

1.00 0.46 – – – – – – – – –

1.00 – – – – – – – 0.59 0.43

1.00 – – – – – –

1.00 – 0.66 – – – – – – – – – – 0.82 – – 0.92

HCO3 SO4

1.00 – – – – – – –

1.00 – – – – – – –

HCO3 SO4

1.00 – – – – – – –

1.00 – – – – – – –

NO3 SiO2 pH

EC

1.00 – – – – – –

1.00 0.44 1.00 – – – –

1.00 – 0.48 – – –

1.00 – – – –

TDS TH

NO3 SiO2 pH

EC

1.00 – – – – – –

1.00 0.53 1.00 – – – 0.43

1.00 – – – – –

1.00 – – – –

SAR

1.00 – 1.00

TDS TH

SAR

1.00 – 1.00

groundwater samples. It exhibits that the dissolution of halite was important in regulating the concentration between Na and Cl in the present samples. In addition, chloride ions of Na and Ca ( CaCl2 and NaCl) also increase the salinity in the present samples. In addition, detergents and domestic wastewater might have increased the concentrations of Na, K, and Cl in the groundwater (Vetrimurugan et al. 2013; Jiang et al. 2009).

5.2 Factor analysis In order to identify the sources, the variance and covariance of the different variables are calculated and are shown in Table 6. Factor analysis was applied to distinguish the partial contributions (Rao et al. 2013). During PREM, Factor I represents 17.95% of total variance with loadings of Ca, Mg, Cl, and TH and indicates the high level of

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Assessment of hydrogeochemical characteristics of groundwater… Table 6 Factor analysis for the chemical composition of groundwater in the study area Pre-mon Elements Ca Mg Na K Cl HCO3 SO4 NO3 SiO2 pH EC TDS TH SAR Total % of Variance Cumulative %

Post-mon Factor I

Factor II

0.463

− 0.078

0.396

− 0.059 0.878 0.36 0.108 0.074 0.492 0.262 0.018 0.301 0.108 0.123 − 0.098 0.925 2.219 15.85 33.806

− 0.312 − 0.134 0.131 0.104 − 0.094 0.069 − 0.318 0.508 0.164 0.834 0.695 − 0.018 − 0.157 1.902 13.584 47.391

0.78 0.295 − 0.12 0.689 − 0.007 − 0.011 0.188 − 0.001 0.419 − 0.029 0.145 0.938 0.027 2.514 17.957 17.957

Factor III

Elements Ca Mg Na K Cl HCO3 SO4 NO3 SIO2 pH EC TDS TH SAR Total % of Variance Cumulative %

Factor I 0.457

0.641 − 0.021 0.476 0.324 0.381 − 0.251 0.108 − 0.181 − 0.026 − 0.592 − 0.799 0.807 − 0.322 2.948 21.057 21.057

Factor II 0.486

0.089 0.928 0.39 0.796 − 0.054 − 0.053 0.193 0.124 − 0.01 0.087 0.267 0.357 0.838 2.859 20.421 41.477

Factor III 0.377 − 0.492 − 0.024 − 0.064 − 0.248 0.522 − 0.161 − 0.563 0.749 0.31 0.071 0.076 − 0.2 0.033 1.775 12.677 54.155

Bold values indicate high loading > 0.4

ions, influenced by both natural and anthropogenic activities in the study area. Factor II was represented by Na, S O4, and SAR with total variance of 15.85%. This indicates the influence of farming activities from leaching of plant nutrient and nitrate fertilizers (Madison and Brunett 1984). However, during POSTM, Factors I and II were represented with 21.05 and 20.42% of total variance by Mg and Ca and, Na, Cl, and SAR, respectively. Ca and Mg from ion exchange process and Cl− represents anthropogenic impact as POSTM (Vasanthavigar et al. 2012).

5.3 Cluster analysis The dendogram of the 14 variables based on the cluster analysis is shown in Fig. 13. Based on this dendogram, variables were grouped into three major clusters. First cluster group shows close relations between Ca, Na, Mg, K, S O4, pH, SAR, and SiO2 during pre- and post-monsoon seasons. Second and third cluster groups show the relationship between HCO3, NO3, Cl, TH, EC, and TDS in the groundwater samples during the study period and it reflected the influence of agricultural and anthropogenic activities. Generally, various hydrogeological processes such as ion exchange and weathering, and anthropogenic activities are the major sources that identify the hydrogeochemistry in the study site. Eventually, cluster analyses corroborate the results of earlier part and support better confidence in data analysis.

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Fig. 13 Cluster diagram (Ward’s method): a pre-monsoon; b post-monsoon

6 Conclusions This study focused on the assessment of groundwater quality and hydrogeochemical characteristics of the lower Vellar river basin using GIS and WQI. Overall, eighty groundwater samples were collected during PREM and POSTM seasons and analyzed various hydrogeochemical parameters. From the analytical measurements, the level of pH is moderately alkaline in nature; EC—47% samples showed values above WHO standards. TDS classification shows 60% of the samples are freshwater and 40% of the samples are brackish water. As per WHO drinking water standards, water from few sampling locations are apt for drinking and remains were to be treated. Hydrogeochemical facies reveals that most of the samples were Ca-Cl2, Ca-SO4 water type. Based on Gibbs plot higher amount of samples falls in weathering dominance followed by evaporation dominance. Regarding irrigation water quality, SAR, USSL, and Na% results reveal that 83% fall in C3S1 followed by C2S1 category; it denotes that the water is well suited for irrigation. Regarding CR, it is observed

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that about 90% of the groundwater samples were within the limit (