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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, andVereinigte Mulde), Saale and its tributaries Ilm and Unstrut, Weise Elster, and Havel and Spree were considered. These important tributaries have a catchment area of approximately 86 000 km2 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, and SO 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 Elbenebenflusse — Entwicklung der Belastung in den letzten 10 Jahren Im Zeitraum von 1991 bis 1996 ermoglichte das „Leitprojekt Elbe 2000” eine in diesem Umfang erstmalige Bestandsaufnahme der aktuellen Belastungssituation der deutschen Elbenebenflusse. Dabei wurden die Schwarze Elster, das Muldensystem, die Saale einschlieslich Ilm, Unstrut und Weiser Elster sowie die Havel und die Spree beprobt. Das Einzugsgebiet dieser Flusse umfasst eine Flache von ca. 86 000 km2 und die Gesamtfliesstrecke summiert sich auf mehr als 2 500 km. Innerhalb dieser Untersuchung erfolgte die Bestimmung einer Vielzahl von Schwermetallen (z.B. Cd, Cr, Cu, Hg, Pb und Zn) sowohl im Sediment als auch im Filtrat. Weiterhin wurden die Gehalte an Alkali- und Erdalkalielementen sowie verschiedener Anionen, wie z.B. Cl—, NO, uand SO, im Filtrat ermittelt. Am Ort der Probennahme wurden sowohl die Leitfahigkeit, der pH-Wert, das Redoxpotential als auch die Temperatur gemessen. Insgesamt bilden 21 700 Filtrat- sowie 8 300 Sedimentdaten die Grundlage fur die weiterfuhrende statistische Auswertung und Interpretation. Die Filtrate der deutschen Elbenebenflusse sind insbesondere durch hohe Salzfrachten (Ca, Mg, Na, Cl—, Leitfahigkeit), bedingt vor allem durch die Kaliindustrie in der Region des Sudharzes, charakterisiert. Innerhalb der Sedimente liesen sich zum einen die uberwiegend anthropogenen Elemente As, Cd, Pb und Zn sowie die in erster Linie geogenen Elemente Co, Fe, Mn und Ni als typische Einflussgrosen identifizieren.

Soil pollution data is also strongly scattering at small scale. Sampling of composite samples, therefore, is recommended for pollution assessment. Different statistical methods are available to provide information about the accuracy of the sampling process. Autocorrelation and variogram analysis can be applied to investigate spatial relationships. Analysis of variance is a useful method for homogeneity testing. The main source of the total measurement uncertainty is the uncertainty arising from sampling. The sample mass required for analysis can also be estimated using an analysis of variance. The number of increments to be taken for a composite sample can be estimated by means of simple statistical formulae. Analytical results of composite samples obtained from different fusion procedures of increments can be compared by means of multiple mean comparison. The applicability of statistical methods and their advantages are demonstrated for a case study investigating metals in soil at a very small spatial scale. The paper describes important statistical tools for the quantitative assessment of the sampling process. Detailed results clearly depend on the purpose of sampling, the spatial scale of the object under investigation and the specific case study, and have to be determined for each particular case.

Geostatistical and multivariate methods of data analysis are used to describe patterns of soil pollution with inorganic contaminants in Celje County, Slovenia. Groups of contaminants and polluted sites were identified using cluster analysis and confirmed with multidimensional variance and discriminant analysis. Factor analysis yields an identification of not directly observable relationships between the contaminants. The spatial structure and distribution of contaminants were assessed by applying semivariogram analysis and kriging interpolation method. Zinc, Cd and Cu were identified as a pollutant emitted from the zinc smelter, Pb also from other sources, and Cr and Ni mostly from geological parent material.