Abstract Reusing low to moderately excavated soils within land redevelopment projects necessitates among others to verify the geochemical compatibility of these soils with the receiving site. Optimizing excavated soil reuse at redevelopment project or urban scale is compelled by comparing their concentrations with threshold values based on quarter or urban geochemical background. Because urban soil geochemistry varies both at horizontal and vertical scale, background might vary spatially. It is thus necessary to consider also the vertical dimension of the geochemical background. However, available in depth data on urban soils consist mainly in pollution diagnoses. The representability of these data is then called into question, along with the method employed to determine geochemical background. To answer this question, we compare herein three standard statistical computation algorithms and examine their sensitivity to various parameters: probability distribution, number of data, proportion of values below the limit of quantification, and sampling schema heterogeneity. After performing a set of theoretical computations, simulations are run and application tests are conducted on actual datasets. Results reveal a variability in the calculated thresholds depending not only on the applied statistical methods used, but also on the distribution law, number of samples and sampling heterogeneity. It appears to be impossible therefore to apply these statistical methods on datasets that are not dedicated to the geochemical background without conducting a preliminary data study, ultimately followed by a data sort. It is in fact necessary to verify dataset consistency with the notion of an anthropogenic geochemical background. In addition, according to the objectives associated to the valorisation of excavated soils (economic benefit of excavated soils and/or environmental/health protection being prioritized), the thresholds (based on background) targeted may be more or less conservative by virtue of adapting both the computational method and algorithm used.

中文翻译：

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预测地球化学兼容性以在城市范围内重新利用挖掘出的土壤：通常的统计工具是否有效？

摘要 在土地再开发项目中再利用低到中等开挖的土壤，需要验证这些土壤与接收地点的地球化学相容性。通过将其浓度与基于季度或城市地球化学背景的阈值进行比较，可以强制优化再开发项目或城市规模的挖掘土壤再利用。由于城市土壤地球化学在水平和垂直尺度上都不同，背景可能会在空间上发生变化。因此，还需要考虑地球化学背景的垂直维度。然而，可用的城市土壤深度数据主要包括污染诊断。然后对这些数据的可表示性以及用于确定地球化学背景的方法提出质疑。要回答这个问题，我们在这里比较了三种标准的统计计算算法，并检查它们对各种参数的敏感性：概率分布、数据数量、低于量化限制的值的比例以及采样模式异质性。在执行一组理论计算后，运行模拟并在实际数据集上进行应用测试。结果揭示了计算阈值的可变性，不仅取决于所使用的应用统计方法，还取决于分布规​​律、样本数量和采样异质性。因此，如果不进行初步数据研究，最后进行数据分类，就不可能将这些统计方法应用于并非专门用于地球化学背景的数据集。事实上，有必要验证数据集与人为地球化学背景概念的一致性。此外，根据与挖掘土壤增值相关的目标（挖掘土壤的经济效益和/或环境/健康保护被优先考虑），目标阈值（基于背景）可能或多或少保守，因为两者都适用使用的计算方法和算法。