The aim of this paper was to investigate the relationship between magnetic susceptibility of topsoil and content of heavy metal being the result of urban and industrial dust-fall. Tools for this study were some complementary statistic methods such as: correlation analysis using Pearson correlation coefficient, Spearman rank correlation coefficient, stepwise regression and .chi-kwadrat" test. The base for statistic analysis was dataset of ca. 600 topsoil samples (20 cm) form Upper Silesian Industrial Region, including content ofAs, Cd, Co, Cr, Cu, Fe, Mn, Ni and Pb as well as values of low-field specific magnetic susceptibility (x) measured for the same samples. The study clearly confirms a significant correlation between the level of inorganic contamination and the measured susceptibility value, although the correlations in soil are usually more sophisticated. The most often observed correlation coefficients between magnetic susceptibility and heavy metals content were on medium (r = 0.5--0.7) and high (r = 0.7--0.9) level. The statistic analysis of the studied parameters can not be based only on Pearson correlation coefficient. The use of some complementary statistic methods allows for more correct interpretation of existing relationships. The comparable values of Pearson linear correlation coefficient and Spearman rank the correlation coefficient, observed in studied dataset within the range of accuracy used, shows the existence of linear correlation. The similar conclusions have been drawn from the analysis of reverse stepwise regression. The observed model of linear multiple regression explains almost 80% of variability of the X value. Foregoing statistical analysis confirms some earlier observations that magnetometry based on topsoil magnetic susceptibility measurement could be a very interesting and alternative or complementary method for monitoring anthropogenic soil pollution and especially heavy metal contamination level.

The aim of the research was to study the influence of different tree stands on topsoil magnetic susceptibility and heavy metal contamination in the soil. The study was performed in the old park in Pruhonice (near Prague) in the Czech Republic. On the relatively small area of Pruhonice Park, five different coniferous tree species (pine, spruce, blue spruce, fir, Douglas fir) and five deciduous species (beech, red oak, common oak, hornbeam, birch) were found, growing in small clusters on the same geological background. Also other natural and anthropogenic factors such as distance from industrial and urban sources of pollution, type of soil, climate, etc. were similar. The magnetic susceptibility was measured directly in the field. Twenty topsoil cores 0.3 m long (2 under each tree species) were collected and also soil samples from under each tree (litter horizon) were taken. The magnetic susceptibility values of the topsoil profiles and of litter layer samples were obtained. Heavy metal analyses of surface samples (litter horizon) were also carried out. The field magnetic susceptibility (K) data are more or less comparable to the laboratory data (x). High heavy metal contents corresponding to high magnetic susceptibility values are observed in the litter horizon. A positive correlation between magnetic susceptibility and some heavy metals was observed. The results suggest that the type of forest may also influence the values of magnetic susceptibility and heavy metal content. Generally higher magnetic susceptibility values were observed in the coniferous forest, except for the surface layer (litter horizon) where the K values are lower than in the deciduous forest.

Coal is the main energy source in China, but its underground mining causes surface subsidence, which seriously damages the ecological and living environments. How to calculate subsidence accurately is a core issue in evaluating mining damage. At present, the most commonly used method of calculation is the Probability Integral Method (PIM), based on a normal distribution. However, this method has limitations in thick topsoil (thickness > 100 m), in that the extent of the calculated boundary of the subsidence basin is smaller than its real extent, and this has an undoubted impact on the accurate assessment of the extent of mining damage. Therefore, this paper introduces a calculation model for surface subsidence based on a Cauchy distribution for thick topsoil conditions. This not only improves the accuracy of calculation at the subsidence basin boundary, but also provides a universal method for the calculation of surface subsidence.