Data for the evaluation of groundwater quality using water quality index and regression analysis in parts of Nalgonda district, Telangana, Southern India

The main focus of this study is to evaluate the groundwater quality through drinking water quality index and regression analysis in semi-arid region and the results are examined with reference to the drinking water quality standards laid down by WHO. Water quality index (WQI) was determined from fourteen physicochemical parameters like pH, EC, TDS, total hardness, total alkalinity, sodium, potassium, calcium, magnesium, chloride, bromide, nitrate, sulphate and fluoride. The drinking water quality index values range from 32.8 - 442.4, indicating three categories i.e. poor, very poor and unfit, which are inappropriate for drinking. Regarding correlation analysis results, EC, TDS, TH, Na+, Mg2+, Ca2+ and Cl− shows high correlation. Most of the parameters are more or less correlated with each other, regression relations have the same correlation coefficients and pH, Na+, EC, TDS, Mg2+, Ca2+, Cl−, SO42−, CO32−, TH were significantly positively correlated (R>0.9), indicate the increase in the pollution load.


Specifications
Hydro Chemistry More specific subject area Water Quality Type of data Tables and figures How data was acquired GPS was used to mark the location and Ion chromatography was employed to analyses for various parameters such as pH, electrical conductivity (EC), Total dissolved solids (TDS), TH, Calcium (Ca 2 + ), Magnesium (Mg 2 + ), Sodium (Na + ), Potassium (K + ), Carbonate (CO 3 2-) , Bicarbonate (HCO 3 −) , Chloride (Cl − ), Sulfate (SO 4 2 − ) and Nitrate (NO 3 −) ions. Ion Chromatography is employed to analyze the cations and anions. The columns used were AS-14 and CS-17 for anions and cations, respectively. Carbonates and bicarbonates were measured by end-point titration method. ARC GIS 10.3 for producing distribution maps [1] . Data format Raw and analyzed Parameters for data collection Samples of ground water were collected in two litre bottles and stored in dark room under specified conditions. To analyse the concentration levels of various physico chemical Parameters using standard methods. Description of the data collection

Value of the Data
• The data represented is used to calculate water quality index which aids in assessment of groundwater in around semi-arid region, can help to better understanding the quality of groundwater and taking necessary steps to regular monitoring to avoid groundwater contamination and suggest proper remediation technologies. • The linear regression analyses are used for the water quality parameters and it measure higher and better levels of significance in their correlation coefficient. The systematic calculation of regression analysis provides indirect means for the fast monitoring of water quality. • Due to limited studies in the study region, this data would be useful for the researches, government and nongovernmental organizations to adopt effective planning methods and mitigation measures in limestone mining areas and aids in sustainable development of groundwater.

Study area
The area situated between 79 °35'30" E and 17 °05'00" N to 79 °40'35" E and 17 °10'00" N latitude ( Fig. 1 ) and located in and around Suryapet city of Nalgonda district, Telangana state. The study area experiences the semi-arid climate; where pollution and scarcity of groundwater resources has been observed due to anthropogenic activities and it is very significant in terms of rapid industrialization, pesticide usage, pharmaceutical, agro based industries, urban development and granite polishing [5][6][7][8]. Geologically, the study area is essentially composed of Archaean crystalline rocks such as granites/granite gneisses, and the dolerite dykes, pegmatite veins and quartz veins are intruded into the Archaean basement rock. The Cuddapah and Kurnool system is represented by consolidated meta sedimentary rocks like limestones,  quartzites and shales in the southern part of the Nalgonda district ( Fig. 2 ). The groundwater occurs in the crystalline aquifer at depths ranging from 6-15 m and in dug-cum-bore wells at a depth of 60m. The yield of irrigation wells range from 100 to150 m 3 /day. The fractures are randomly oriented and are observed at depth of 40-60 m.

Data
Normal statistics of water quality parameters of groundwater samples were indicated in Table 1 . WHO standards weight (W i) and calculated relative weight (Wi) for each parameters are shown in Table 2 . Spatial variation maps of physico-chemical parameters (3a to 3n) are shown in Fig. 3 . Water quality classification based on WQI values of the study area were depicted in Table 3 and Fig. 4 . Fig. 5 represents Piper tri linear diagram representing the chemical analysis of suryapet region. Correlation Matrices for water quality parameters are shown in Table 4 . Higher and lower levels of significant correlation coefficient values of the parameters are shown in Table 5 and Linear plots of various parameters are shown in Fig. 6 .

Materials and methods
Groundwater samples were collected from thirty locations ( ∼10 to 40 m deep) in post monsoon season. GPS was used to mark the location and ion chromatography was employed to analyze for various ionic and non-ionic parameters such as pH, EC (electrical conductivity), TDS (Total dissolved solids), TH (Total hardness), Cations and Anions ( Fig. 3 a to 3 j). Total fourteen parameters were considered to generate the Drinking Water Quality Index (DWQI), correlation analysis, correlation regression study along with water quality characteristics were determined.

Analytical procedures
WQI was calculated using the World Health Organization standards [2] and Indian Standards [3] in the following steps. Water quality index method for groundwater quality assessment is widely used around the world for assessment & management of groundwater [ 4 -7 ]. The WQI calculation was carried out using a weighted arithmetic index as shown below [ 9 , 10 ].
For computing WQI four steps are followed: The groundwater experimental study (WQI) ( Fig. 5 ) by means of fourteen physical and chemical parameters of the study area recognize with the purpose of water quality for drinking purpose, values range from 32.8 -442.4, ( Table 3 ) indicating three categories viz. poor, very poor and unfit.
Step: 1 The 14 parameters are allocated a weight (wi) and calculated relative weight (Wi) for each parameter of drinking water purposes is given in Table 1 . Nitrate assigned by weight (Max) of five parameters because their major significance in water quality assessment [11] . As per signifi- Fig. 3. Spatial variation maps of physico-chemical parameters (3a to 3j). cance in the complete quality of water for drinking purposes, the Na + , K + , Ca 2 + and Mg 2 + were allocated a weight between one and five.
Step: 2 The following equation is used to calculate the virtual weight (Wi), which is derived from weighed arithmetic index formula [ 10 , 12 -13 ] W i = wi/ n wi i = 1 Relative weight = Wi Weight of each parameter = wi Number of parameters = n Step: 3 Calculated each parameter concentration of each water sample = (Qi) Relevant standard = (Ci) and then multiplied by 100 = Si Step: 4 Sub Index (SIi) is determined for each chemical parameter

Correlation Coefficient (r):
The correlation coefficient r equation x and y be any two variables, (Xi, Yi) be n pairs of identified values of (I = 1, 2, 3……n ) variables and in equation r between the variables x and y. Where, the summations are taken above 1 to n (n = number of observations). The values of observed parameters 'a' and 'b' were considered with the help of Eqs. (2) and (3).
The regression analysis has been performed using by SPSS 11.0 Statistical Software.

Regression equation
SPSS 18 software used to study the correlation among different water quality parameters and the regression analysis.
It assesses the degree of association that exists among two variables, which shows how one variable predicts the other; it can be used for swift monitoring of water quality. A high correlation coefficient (nearly 1 or -1) reveals that the relationship between two variables is good.

Declaration of Competing Interest
None.