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Rivers are among the most vulnerable water bodies to pollution because of their role in carrying municipal and industrial wastes and run-offs from agricultural lands in their vast drainage basins. Detailed hydro-chemical research is needed to evaluate the different processes and mechanisms involved in polluting water (Helena et. al., 1999). Furthermore, due to temporal and spatial variations in water qualities, monitoring programs that involve a large number of physicochemical parameters and frequent water samplings at various sites are mandatory to produce reliable estimated topographies of surface water qualities (Dixon & Chiswell, 1996). The results are usually compiled into a large data matrix, which requires sophisticated data interpretations (Chapman, 1992). Water quality monitoring has one of the highest priorities in environmental protection policy (Simeonov et al., 2002). The main objective is to control and minimise the incidence of pollutant-oriented problems, and to provide water of appropriate quality to serve various environmental purposes. The quality of water is identified in terms of its physical, chemical and biological parameters (Sargaonkar & Deshpande, 2003). The particular problem in the case of water quality monitoring is the complexity associated with analysing the large number of measured variables (Saffran, 2001). Belonging to the class of a tide-dominated wetland (Selvam, 2003), the Sundarban wetland is comprised of a complex network of estuaries, tidal inlets, tidal creeks and a large number of islands. Most of the creeks act as the pathway for the to-and-from movement of tidal water and downstream flow of river systems. The present day sedimentation of the Ganga-Brahmaputra systems (with average water discharge of 970 km

Water Quality Analysis of the Coastal Regions of Sundarban Mangrove Wetland, India Using Multivariate Statistical Techniques