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The identification of buried soil horizons in agricultural landscapes helps to quantify sediment budgets and erosion-related carbon dynamics. High-resolution mapping of buried horizons using conventional soil surveys is destructive and time consuming. Geoelectrical sensors can offer a fast and non-destructive alternative for determining horizon positions and properties. In this paper, we compare the suitability of several geoelectrical methods for measuring the depth to buried horizons (Apb, Ahb and Hab) in the hummocky ground moraine landscape of northeastern Germany. Soil profile descriptions were developed for 269 locations within a 6-ha experimental field "CarboZALF-D". A stepwise linear discriminant analysis (LDA) estimated the lateral position of the buried horizons using electromagnetic induction data and terrain attributes. To predict the depth of a buried horizon, multiple linear regression (MLR) was used for both a 120-m transect and a 0.2-ha pseudo-three-dimensional (3D) area. At these scales, apparent electrical conductivity (ECa), electrical resistivity (ER) and terrain attributes were used as independent variables. The LDA accurately predicted Apb- and Ahb-horizons (a correct classification of 93%). The LDA of the Hab-horizon had a misclassification of 24%, which was probably related to the smaller test set and the higher depth of this horizon. The MLR predicted the depth of the Apb-, Ahb- and Hab-horizons with relative root mean square errors (RMSEs) of 7, 3 and 13%, respectively, in the pseudo-3D area. MLR had a lower accuracy for the 2D transect compared to the pseudo-3D area. Overall, the use of LDA and MLR has been an efficient methodological approach for predicting buried horizon positions. Highlights The suitability of geoelectrical measurements for digital modelling of diagnostic buried soil horizons was determined. LDA and MLR were used to detect multiple horizons with geoelectrical devices and terrain attributes. Geoelectrical variables were significant predictors of the position of the target soil horizons. The use of these tested digital technologies gives an opportunity to develop high-resolution soil mapping procedures.
Electrochemical methods offer the simple characterization of the synthesis of molecularly imprinted polymers (MIPs) and the readouts of target binding. The binding of electroinactive analytes can be detected indirectly by their modulating effect on the diffusional permeability of a redox marker through thin MIP films. However, this process generates an overall signal, which may include nonspecific interactions with the nonimprinted surface and adsorption at the electrode surface in addition to (specific) binding to the cavities. Redox-active low-molecular-weight targets and metalloproteins enable a more specific direct quantification of their binding to MIPs by measuring the faradaic current. The in situ characterization of enzymes, MIP-based mimics of redox enzymes or enzyme-labeled targets, is based on the indication of an electroactive product. This approach allows the determination of both the activity of the bio(mimetic) catalyst and of the substrate concentration.
Brownian motion and viscoelastic anomalous diffusion in homogeneous environments are intrinsically Gaussian processes. In a growing number of systems, however, non-Gaussian displacement distributions of these processes are being reported. The physical cause of the non-Gaussianity is typically seen in different forms of disorder. These include, for instance, imperfect "ensembles" of tracer particles, the presence of local variations of the tracer mobility in heteroegenous environments, or cases in which the speed or persistence of moving nematodes or cells are distributed. From a theoretical point of view stochastic descriptions based on distributed ("superstatistical") transport coefficients as well as time-dependent generalisations based on stochastic transport parameters with built-in finite correlation time are invoked. After a brief review of the history of Brownian motion and the famed Gaussian displacement distribution, we here provide a brief introduction to the phenomenon of non-Gaussianity and the stochastic modelling in terms of superstatistical and diffusing-diffusivity approaches.