Assessment of Groundwater Quality Using GIS - A Case Study of the Churu District of Rajasthan - page 01

Noha Donia (2011), used the IDW for interpolating the samples values and uses equations: qi=100(vi/si), wqi=sigma (i=1 to n) (qi) and AWQI= sigma (qi)(i=1 to n)/n to calculate the index. The value between 0 to 100 is considered as good quality and above is not recommended for drinking use.

Ramakrishnaiah et al. (2009) and Ishaku (2011) have used the relative weight method to calculate the index value, i.e. a weight is assigned to each parameter is divided by the sum of all weights. A quality rating scale (Qi) for each parameter is assigned by dividing its concentration in each groundwater sample by its respective standard according to the guidelines by IS:10500 and the result is multiplied by 100 (Gebrehiwot et al., 2011). Then the Quality rating scale is multiplied by relative weight and summation of all sub indexes to get the quality index (QI). To get the final WQI, summation of all sub indexes is done. Where, Qi is the quality rating, Ci is the concentration of each parameter in each water sample, and Si is the WHO drinking water standard for each parameter. For computing the WQI, the SI is first determined for each parameter, which is then used to determine the WQI as indicated by the following equation (Reza and Singh, 2010): These equations are given below:


Where SIi is the sub index of ith parameter; Qi is the rating based on concentration of ith parameter and n is the number of parameters. QIj is the quality index of physical and chemical parameters.

Another set of equations used to calculate the WQI is given from equation B.1 to Eq B.3 (Tiwari and Manzoor, 1988).

The quality rating qi, for the ith water quality parameter can be obtained by the following relation,


Where Vi = observed value of the ith parameter at a given sampling site and Si = water quality standard. Thus, the larger the value of qi, the more polluted the water is, with respect to the corresponding standard value (mg/L).

The overall water quality index was calculated by aggregating these quality ratings linearly as follows,


Where n = number of parameters. The average water quality index (AWQI) for n parameters was calculated using this equation,


Katyal (2011) et al studied many quality indexes for water and suggested that every area has been affected by different parameters and that different weights could be used in various regions. F Ghadimi (2012) has used 2 indices and done statistical analysis of them to calculate the WQI of water. He also concluded that hydrochemical data were classified in to 2 main groups: 1 - natural and; 2 - mining or leaching sources. Mouna (2011) used the simple WQI equation to calculate the qi for water using the weightage for various parameters analysed for the samples. They categorised each parameter into 3 categories i.e. permissible limit, below limit and above limit and produced the spatial distribution map of all parameters.

From the literature it is clear that all have attempted a small area for their study with a maximum of 25 samples for calculating the WQI. It would be easier to handle complex equations with a smaller number of samples in calculating WQI but when a large area and huge number of samples will be used, then simple equations would be used to maintain control on measurement of WQI.

Cluster analysis (CA) is a simple approach for classification of groundwater quality into two or more mutually exclusive unknown groups based on the combination of interval variables (Hussein, 2004). The tool sorts out different objects into groups such that the degree of association between the objects is maximal if they belong to the same group (Hamzaoui-Azaza et al., 2009). The hierarchal cluster analysis according to Ward (1963), with squared Euclidean distances, was applied to detect multivariate similarities in groundwater quality.