Determination of groundwater quality index of a highland village of Kerala (India) using geographical information system.

Author:Rejith, P.G.


Fresh water is a precious and finite resource central to sustainable development, economic growth, social stability, and poverty alleviation. Fresh water quality, quantity, and security have grown to become the major international issues in recent years. Global environmental changes induced by natural variability and human activities influence both water quantity and quality at regional and local scales as well as at the global scale (Chang, 2004). Urban growth, increased industrial activities, intensive farming, and overuse of fertilizers in agricultural production have been identified as drivers responsible for these changes (Patwardhan, 2003). It is a well-known fact that a polluted environment has a detrimental effect on the health of people, animal life, and vegetation (Sujatha & Reddy, 2003). Hence, the maintenance of water quality at acceptable levels is an essential requirement for successful use of these water resources.

Shallow groundwater is the main source of drinking water in rural areas, but reliable data on its quality are currently insufficient (Melian, Myrlian, Gouriev, Moraru, & Radstake, 1999), especially from developing countries. In India, more than 90% of rural and nearly 30% of urban populations depend on groundwater for their drinking and domestic requirements (Jaiswal, Mukherjee, Krishnamurthy, & Saxena 2003; Reddy, Vinod, & Seshadri, 1996). More than 50% of the population of Kerala uses dug-well water for drinking (Kerala Water Authority, 1991; Pillai & Ouseph, 2000; Roy, 2004). Further, the density of dug-wells in the highlands is 25 No.s/ Km2 with a total of approximately three million wells in Kerala. The present situation calls for developing systems of conservation, sustainable use, and equitable sharing of water in the country as a whole. In view of this fact, water quality monitoring becomes essential for identifying problems and formulating measures to minimize deterioration of water quality. Environmental issues are likely to aggravate in the future; an evaluation of the present situation is therefore highly necessary.

Geographical Information Systems (GIS) are designed to collect diverse data to represent spatially variable phenomena by applying a series of overlay analysis of data that are in spatial register (Bonham Carter, 1996). GIS has grown rapidly in groundwater management and research. The spatial patterns of chemical constituents are useful in deciding the water use strategies for various purposes (Bonham-Carter, 1996).

The present study is an attempt to generate baseline groundwater quality data of a highland village and to apply a GIS-based tool to determine the water quality index of the study area.

Study Area

The highland village chosen for the present study is located in the high altitude headwaters region in Western Ghats, which comes under Idukki district of Kerala, India. The study area is located between 9[degrees]48'00" to 9[degrees]52'00" N and 77[degrees]06'00" to 77[degrees]15'00" E, covering an area of 71.95 [km.sup.2] (Figure 1). Geologically, the majority of the area is covered with acid charnockite. The measured rainfall in this area varies from 350 mm to 900 mm per day during the southwest monsoon (June-August) and northeast monsoon (October-December) seasons. The major river flowing through the area is the Kallar River, which joins into the Periyar River. The elevation varies between 400 m in the west to 1100 m above mean sea level in the east with a temperature range from 60.8[degrees]F in winter to 89.6[degrees]F in summer.


Spatial Database Construction and Selection of Sampling Wells

Survey of India (SOI) toposheet on 1:50,000 scale was used to prepare the base map and drainage map and to understand the general nature of the study area. The toposheet was scanned and digitized to generate a digital output using ArcInfo GIS software to obtain a baseline data. A 5 km x 5 km grid was overlaid in the panchayat boundary map and one sampling location was fixed from each grid. A total of 14 representative locations were selected from the entire study area (Figure 1).

Sample Collection and Analysis

In the field, representative sampling wells were identified and a total of 28 water samples from 14 wells were collected (January-March 2007) and analyzed in the laboratory for various physico-chemical and biological parameters with health significance as described by the American Public Health Association (1998). A Global Positioning System (Garmin GPS MAP 76) was used to map the location of each sampling wells. The parameters analyzed include pH, total hardness, nitrate chloride, sulfate and phosphate, trace metals (cadmium, lead, zinc, and copper), and fecal coliforms (FC). The physical, chemical, and biological parameters were compared with the limits set up by the Bureau of Indian Standards (BIS) for drinking water while the well water samples were analyzed strictly for drinking water quality.


Statistical Analysis

The statistical software GraphPad Prism 5 trial version was used to perform statistical analysis. The interdependency of the various parameters was studied using Pearson's coefficient of correlation (r). To measure the extent or strength of the association that exists between two parameters, coefficient of determination ([r.sup.2)] was calculated. Variation in the values of water quality parameters between seasons and samples was analyzed by two-factor ANOVA.

Spatial Pattern Recognition and Estimation of Water Quality Index (WQI)

The spatial interpolation technique through the Inverse Distance Weighted (IDW) approach has been used to delineate the locational distribution of water pollutants or constituents. The location of each well was recorded into the GIS environment and the results of each parameter analyzed from these wells were attributed to the concerned wells given in the GIS environment. Only those parameters that exceeded the drinking water quality standards specified by BIS were considered in classifying the study area in terms of water quality. The spatial and the attribute databases generated were integrated for the generation of spatial distribution maps of selected water quality parameters like pH, cadmium, and fecal coliforms (Figures 2 and 3). The water quality data (attribute) is linked to the sampling locations (spatial). Maps showing spatial distribution were prepared to easily identify the variation in concentrations of the above parameters, using spatial analyst, an extended module of ArcGIS 8.3.

A Water Quality Index (WQI) is a very useful and efficient method for assessing the quality of water (Asadi, Vuppala, & Reddy, 2007). It is also a very useful tool...

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