From 1991 to 1996 intensive investigations ("Leitprojekt Elbe 2000") were performed on the water and sediment quality of the German Elbe tributaries. Schwarze Elster, the Mulde river system (Freiberger Mulde, Zwickauer Mulde, and Vereinigte Mulde), Saale and its tributaries Ilm and Unstrut, Weiße Elster, and Havel and Spree were considered.These important tributaries have a catchment area of approximately 86 000 km 2 and they are together more than 2 500 km long. The concentrations of different metals (e.g., Cd, Cr, Cu, Fe, Hg, Pb, or Zn) were determined. Furthermore, the alkali and alkaline earth elements and the concentrations of anions such as Cl -, NO 3 -, and SO 4 2-were analyzed in the water samples. In addition, conductivity, pH, redox potential, and temperature were measured directly at the sampling location. Together 21 700 pieces of data for water samples and 8 300 pieces of data for sediment samples formed the base for the statistical evaluation and interpretation. The water samples of the German Elbe tributaries are characterized by high salt loads (Ca, Mg, Na, Cl -, and conductivity), resulting from mining in the southern Harz region. In the sediments the concentrations of As, Cd, Pb, and Zn were identified as the most important anthropogenic parameters. The elements Co, Fe, Mn, and Ni were detected as typical background elements. Die Situation der deutschen Elbenebenflüsse -Entwicklung der Belastung in den letzten 10 JahrenIm Zeitraum von 1991 bis 1996 ermöglichte das "Leitprojekt Elbe 2000" eine in diesem Umfang erstmalige Bestandsaufnahme der aktuellen Belastungssituation der deutschen Elbenebenflüsse. Dabei

Environmental pollution data are often ranked in rule-based classification systems. These environmental data are separated in predetermined classes of a classification system for a better and smarter characterization of the state of pollution. Often the measured values are transformed, e.g. in pseudocolor maps, and can then be presented in maps. For some environmental compartments different classification systems for evaluating environmental loadings are used. Because of the dissimilarity of the various classification systems direct visual comparison is difficult. However, by means of information theory an objective comparison of these various classification systems based on their information content enables a decision to be made about which system is the most informative for objective assessment of the state of pollution. By means of the new measure "multiple medium information content" (multiple entropy) objective and simultaneous comparison of all channels (in an environmental classification system: pollutants) of each classification system is now possible. Furthermore the development of the state of pollution over the whole investigation period can be detected by means of information theory. On the basis of the conditions of the established rule-based systems the use of information theory enables definition of new ranges of classes in order to reach the optimum of information during conversion into the environmental classification system.

Environmental data are highly variable. They also include uncertainties resulting from all steps of the analytical process e. g. sampling, or sampling pre-treatment. However, a lot of information is unfortunately often lost because only univariate statistical methods are used for data evaluation and interpretation. This neglects correlation between different pollutants and relationships among various sampling points. It is therefore necessary to apply additional methods of analysis that can accommodate such relationships. This ability is provided by the established, and by the more modern, multivariate statistical methods because they can analyze complex sets of multidimensional data. These methods are used to visualize large amounts of data and to extract latent information (e. g. differently polluted areas, dischargers, or interactions between different environmental compartments). The goal of this paper is to present the use of established statistical techniques, like cluster or factor analysis, and the progress made in basic modern techniques (e. g. cluster imaging, multiway-partial least squares regression, projection pursuit, or information theory) and to demonstrate each with examples and illustrations.Keywords: Chemometric method / cluster analysis / cluster imaging / factor analysis / information theory / multiway-PLS regression / pollution / projection pursuit /

The article contains sections titled: 1 Subject and Scope 2 Working Fields 3 Contamination and Decontamination 4 Quantitation of Trace‐Analytical Measurements and Their Quality Assurance 4.1 Basic Considerations 4.2 Uncertainty Concept 4.3 Calibration 4.4 Validation 4.5 Limit of Decision, Detection, and Determination 4.6 Analytical Quality Assurance 5 Sampling, Sample Preservation, and Sample Preparation 5.1 Sampling and Sample Preservation 5.2 Sample Preparation 5.2.1 General Aspects 5.2.2 Sample Preparation for Inorganic Trace Analysis 5.2.3 Sample Preparation for Organic Trace Analysis 6 Inorganic Trace Analysis 6.1 Introduction 6.2 Spectroscopic Methods 6.2.1 Atomic Absorption Spectroscopy (AAS) 6.2.2 Inductively Coupled Plasma Optical Emission Spectroscopy (ICP‐OES) 6.2.3 Inductively Coupled Plasma Mass Spectroscopy (ICP‐MS) 6.2.4 Comparison of AAS, ICP‐OES, and ICP‐MS 6.2.5 X‐ray Fluorescence Spectroscopy (XRF) 6.3 Electrochemical Analytical Methods 6.3.1 Potentiometry–Ion‐selective Electrodes (ISE) 6.3.2 Voltammetry/Polarography 6.3.3 Comparison of Electrochemical Analytical Methods 6.4 Neutron Activation Analysis (NAA) 7 Organic Trace Analysis 7.1 Introduction 7.2 Gas Chromatography (GC) 7.3 High Performance Liquid Chromatography (HPLC) 8 Other Techniques for Trace Analysis 8.1 Methods of Classical Chemical Analysis 8.2 Methods of the Determination of Group and Sum Parameters 8.3 Enzyme and Immunochemical Analysis 9 Examples of Trace Analysis 9.1 Analysis of Fresh Water for Trace Elements by ICP‐MS 9.2 Determination of Pt in Soil Samples by GFAAS 9.3 Determination of Polyaromatic Hydrocarbons by GC/MS 9.4 Analysis of Explosives by HPLC/UV 10 Perspective References