E3S Web of Conferences
Volume 1, 2013Proceedings of the 16th International Conference on Heavy Metals in the Environment
|Number of page(s)||4|
|Section||Heavy Metals in Soils III|
|Published online||23 April 2013|
Exploring the Relationship between Surface and Subsurface Soil Concentrations of Heavy Metals using Geographically Weighted Regression
1 Department of Geography, University of Victoria, Victoria, Canada
2 Cancer Care Ontario, Toronto, Canada
Geographically Weighted Regression (GWR) is used to analyze the spatial variability of the relationship between the surface and the subsurface (b horizon) soil metal concentration. We used publiclyavailable soil samples from provincial government websites in Canada. The correlation between the log of concentration levels of the two layers are 0.51 for As, 0.40 for Cd, 0.33 for Cr, 0.52 for Co, 0.38 for Ni, and 0.23 for Pb. Although the correlation results show that the two layers seem to be related, the GWR analysis suggests that other factors might play important role in predicting the surface soil concentration of these metals. For example, only arsenic (R2=0.34) shows no spatial autocorrelation in the residuals. This study proposes that factors (natural and anthropogenic) other than the subsurface concentration itself are controlling the concentration surface levels for all the studied metals in this dataset.
Key words: Heavy metals / spatial variability / GWR / soil / horizons
© Owned by the authors, published by EDP Sciences, 2013
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 2.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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