Multi-element soil analysis is now an established technique in archaeology. It has been used to locate archaeological sites and define the extent of human activity beyond the structural remains, and to aid interpretation of space use in and around archaeological remains. This study aimed to evaluate the consistency of these soil element signatures between sites and hence their potential usefulness in archaeological studies. Known contexts on abandoned farms across the UK were sampled to test the relationships between element concentrations and known functional area and to assess inter-site variability. The results clearly show that there are significant differences in the soil chemistry of contrasting functional areas, particularly for Ba, Ca, P, Zn, Cu, Sr and Pb. Despite significant site specific effects, which appear to reflect individual anthropogenic practices rather than geological influences, there is sufficient similarity in the pattern of element enhancement to allow reliable interpretation of former function using discriminant models. Relating these enhancements to precise soil inputs, however, is more problematic because many important soil inputs do not contain distinct element fingerprints and because there is mixing of materials within the soil. There is also a suggestion that charcoal and bone play an important role in both the loading and post-depositional retention of Ca, Sr, P, Zn, and Cu and thus may be significant in the formation of soil element concentration patterns.
Historic and prehistoric human activity can cause accumulation of elements in the soil. Multielement soil analysis has been used extensively over the last two decades to study element patterns of historic soil enrichment as a means of prospecting for sites and as an aid to interpretation of space use within archaeological structures. However, there have been surprisingly few of studies designed to assist with the interpretation of the analytical results. In this investigation soils from six abandoned farms with a known history of spatial use were sampled to determine if similar patterns of trace element enhancement occur between different farms. The preliminary results show significant differences in soil elemental concentrations between the functional areas, and highlight similar patterns of element enhancement between the farms. Concentrations of Ca, P, Sr, Ba, Zn and Pb are elevated in the buildings and fields of all the farms and provide valuable information about past human activity.
There is currently a significant need to improve our understanding of the factors that control a number of critical soil processes by integrating physical, chemical and biological measurements on soils at microscopic scales to help produce 3D maps of the related properties. Because of technological limitations, most chemical and biological measurements can be carried out only on exposed soil surfaces or 2-dimensional cuts through soil samples. Methods need to be developed to produce 3D maps of soil properties based on spatial sequences of 2D maps. In this general context, the objective of the research described here was to develop a method to generate 3D maps of soil chemical properties at the microscale by combining 2D SEM-EDX data with 3D X-ray computed tomography images. A statistical approach using the regression tree method and ordinary kriging applied to the residuals was developed and applied to predict the 3D spatial distribution of carbon, silicon, iron, and oxygen at the microscale. The spatial correlation between the X-ray grayscale intensities and the chemical maps made it possible to use a regression-tree model as an initial step to predict the 3D chemical composition. For chemical elements, e.g., iron, that are sparsely distributed in a soil sample, the regression-tree model provides a good prediction, explaining as much as 90% of the variability in some of the data. However, for chemical elements that are more homogenously distributed, such as carbon, silicon, or oxygen, the additional kriging of the regression tree residuals improved significantly the prediction with an increase in the R2 value from 0.221 to 0.324 for carbon, 0.312 to 0.423 for silicon, and 0.218 to 0.374 for oxygen, respectively. The present research develops for the first time an integrated experimental and theoretical framework, which combines geostatistical methods with imaging techniques to unveil the 3-D chemical structure of soil at very fine scales. The methodology presented in this study can be easily adapted and applied to other types of data such as bacterial or fungal population densities for the 3D characterization of microbial distribution.
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