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River pollution data are characterized by high variability. Multivariate statistical methods help to determine a complex set of these multidimensional data and to extract latent information (e.g. differently polluted areas, discharges). The chemometric methods can handle interactions between different pollutants and relationships among various sampling locations. This study presents an application...
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