MULTI VARIATE ANALYSIS OF SATLUJ RIVER WATER QUALITY DATA CONSEQUENT TO DAM CONSTRUCTION

Abstract

The quality of river water at any point is largely affected by several major influences, including characteristics of the basin, atmospheric inputs, climatic conditions and anthropogenic inputs. In order to identify the area requiring such control the current status of water quality is required to be known and for that a monitoring programme can be a useful tool. This type of programme generates tremendous amount of data having high complexity which is difficult to analyse. Multivariate statistical techniques are used to analyse this data. These techniques render data manageable and easy to understand. Multivariate statistical techniques use dimensionality reduction tool which converts the data to manageable form. Factor Analysis (FA) and Cluster Analysis (CA) are such techniques which can be used as unbiased methods in analysis of water quality data for drawing out meaningful conclusions. FA and CA explore groups and sets of variables with similar properties, thus potentially allowing us to simplify our description of observations by finding the structure or patterns in the presence of chaotic or confusing data. Factor Analysis generates smaller number of factors that are capable of explaining the observed variance in the larger number of variables. The reduced factors can also be used for further analysis. Cluster Analysis assembles objects based on the characteristics they possess. It classifies objects on the basis of predetermined selection criterion so that each object is similar to the others within the cluster. In the present study cluster analysis (CA) and factor analysis (FA) have been applied to the water quality data of the Satluj River, generated during 3 years monitoring of 11 parameters at 6 different sites. The study aims to find out the factors or parameter that characterize peculiar patterns of the water quality of the stream in stretch of approximately 70 kilometres due to the dam construction and related project activities. Factor analysis was applied on the normalized data of individual sites and on the combined data of all sites. The result showed that the main parameters responsible for the pollution in the area were organic and ionic contaminants. The upstream of dam has higher concentrations of TDS, Na, CY and S042 and on downstream organic contaminants prevails. It has been iv concluded that ionic and organic contaminants prevails in the study region. Cluster Analysis resulted in 2 clusters of monitoring sites. First cluster grouped site 1-5-6 together which are loaded heavily by ions such as sulphate, sodium and chloride. The second cluster grouped site - 2-3-4 together which are contaminated by organic matter. Cluster Analysis confirmed the - output of Factor Analysis and on the basis of which this can be possible to reduce one site from each cluster. Thus, this study illustrates the usefulness of Factor Analysis and Cluster Analysis in the assessment, identification of pollution sources, understanding the water quality of the stream and in optimizing the monitoring network.