Mapping Of Ferrimagnetic Susceptibility For Screening Of Fly Ash Deposition
Free (open access)
379 - 393
C. Fürst, C. Lorz, D. Zirlewagen & F. Makeschin
This article presents a case study in the industrial triangle Leipzig-Halle- Bitterfeld, the purpose of which was to assess the actual fly ash load in forest soils and to test if ferrimagnetic susceptibility can be used for a fast and cost efficient screening of deposited elements. Ferrimagnetic susceptibility was mapped in a raster of 1x1 km² and correlated with key nutrients, selected metals/ heavy metals and Black Carbon. The predictive value of magnetic susceptibility was tested on the basis of linear regression models. Furthermore, multipleregionalization techniques were used to model the spatial variation of fly ash. This includes an analysis of which environmental parameters are most important for the spatial model. The correlation between ferrimagnetic susceptibility, base saturation and the contents in Ca, Mg, Fe, Al and Cd (humus layers) was comparably high. The correlation with the content in Mn was weaker and the correlation with Black Carbon (humus layers) showed no clear trend. Linear regression based models with sufficient precision could be found for Ca, Mg and Mn, with lower precision for Cd and Black Carbon. No prediction was possible for Fe and Al. Multiple regression based modelling of the spatial variation of fly ash deposition was possible with a very high precision. A slightly differing set of model parameters was selected for different depth levels in the humus layer and mineral soil, comprising topographical and soil parameters and to a much lesser extent stand parameters. In conclusion, the usability of the proxy indicator ferrimagnetic susceptibility for screening of the deposited elements was proved. Keywords: fly ash deposition, proxy indicator, ferrimagnetic susceptibility, linear regression models, spatial modelling, multiple regression based models.
fly ash deposition, proxy indicator, ferrimagnetic susceptibility,linear regression models, spatial modelling, multiple regression based models