Vertical distribution of heavy metals in grain size fractions in sedimentary rocks: Mosina-Krajkowo water well field, Poland.

The paper presents the results of heavy metals determination in samples of sedimentary rocks from the Mosina-Krajkowo water well field (Poland). The concentration of heavy metals was analysed by type of rock (sand, gravel, warp, silt, till, and clay). Variation of heavy metal concentrations with depth was studied taking into account the age series of the rocks (fluvial sediments of the modern Warta River valley, sediments of the Baltic Glaciation, tills of the Middle-Polish Glaciation, sediments of the Masovian Interglacial (Holstein), tills of the Poznan series) and granulometric fractions. The grain sizes considered included: >2.0, 2.0-1.0, 1.0-0.5, 0.5-0.25, 0.25-0.1, 0.1-0.063, and <0.063 mm. The concentrations of the heavy metals studied were found to change with the type of rock, age series, and granulometric fraction. The levels of the metals were determined by the technique of atomic absorption spectrometry with flame atomisation (F-AAS) and inductively coupled plasma-atomic emission spectrometry (ICP-AES).

Application of multivariate statistical techniques to evaluation of water quality in the Mala Welna River (Western Poland).

The paper presents the results of determinations of physico-chemical parameters of the Mala Welna waters, a river situated in Wielkopolska voivodeship (Western Poland). Samples for the physico-chemical analysis were taken in eight gauging cross-sections once a month between May and November 2006. To assess the physico-chemical composition of surface water, use was made of multivariate statistical methods of data analysis, viz. cluster analysis (CA), factor analysis (FA), principal components analysis (PCA), and discriminant analysis (DA). They made it possible to observe similarities and differences in the physico-chemical composition of water in the gauging cross-sections, to identify water quality indicators suitable for characterising its temporal and spatial variability, to uncover hidden factors accounting for the structure of the data, and to assess the impact of man-made sources of water pollution.