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首页> 外文会议>International Conference on Heavy Metals in the Environment >Exploring the Relationship between Surface and Subsurface Soil Concentrations of Heavy Metals using Geographically Weighted Regression
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Exploring the Relationship between Surface and Subsurface Soil Concentrations of Heavy Metals using Geographically Weighted Regression

机译：使用地理加权回归探索重金属表面与地下土壤浓度的关系

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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 publicly- available 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.
机译：地理加权回归（GWR）用于分析表面与地下（B地平线）土壤金属浓度之间关系的空间变异性。我们使用加拿大省政府网站的公开土壤样本。两层浓度水平的浓度水平之间的相关性为0.51，对于CD，0.33，对于CO，Ni 0.52，Ni为0.52，Pb为0.23。虽然相关结果表明，两层似乎与相关的相关性，但GWR分析表明，其他因素可能在预测这些金属的表面土壤浓度方面发挥重要作用。例如，只有砷（R2 = 0.34）在残差中显示不显示空间自相关。本研究提出了除了地下浓度本身之外的因素（自然和人为）控制该数据集中所有研究金属的浓度表面水平。

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