River Pollution Data Interpreted by Means of Chemometric Methods

Abstract Environmental data, including river pollution data, are characterized by high variability. Much information is lost by using only univariate graphical or statistical methods for data evaluation and interpretation. Chemometric methods, in particular methods of multivariate data analysis, help to extract the latent information in such data. The combination of cluster analysis as the first step and multivariate analysis of variance and discriminant analysis as the second step enables identification of similar locations in a river. Pollution sources and dischargers can be detected by means of factor analysis. The deposition–remobilization behavior of metals in a river can be described using partial least squares regression. Summarizing, it can be stated that methods of multivariate data analysis are powerful tools for the evaluation and interpretation of river pollution data.