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Uncertainty Assessment for Management of Soil Contaminants with Sparse Data

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Abstract

In order for soil resources to be sustainably managed, it is necessary to have reliable, valid data on the spatial distribution of their environmental impact. However, in practice, one often has to cope with spatial interpolation achieved from few data that show a skewed distribution and uncertain information about soil contamination. We present a case study with 76 soil samples taken from a site of 15 square km in order to assess the usability of information gleaned from sparse data. The soil was contaminated with cadmium predominantly as a result of airborne emissions from a metal smelter. The spatial interpolation applies lognormal anisotropic kriging and conditional simulation for log-transformed data. The uncertainty of cadmium concentration acquired through data sampling, sample preparation, analytical measurement, and interpolation is factor 2 within 68.3 % confidence. Uncertainty predominantly results from the spatial interpolation necessitated by low sampling density and spatial heterogeneity. The interpolation data are shown in maps presenting likelihoods of exceeding threshold values as a result of a lognormal probability distribution. Although the results are not deterministic, this procedure yields a quantified and transparent estimation of the contamination, which can be used to delineate areas for soil improvement, remediation, or restricted area use, based on the decision-makers’ probability safety requirement.

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Acknowledgements

The work reported in this paper was conducted in the context of the Integrated Project Soil of the Swiss Priority Program Environment. We are grateful to Martin Fritsch, who initiated the project and guided it until 2000, and to the colleagues at UNS and within the IP Soil for the helpful discussions. We also appreciate the comment of three anonymous reviewers. The project was funded by the Swiss National Science of Foundation (Project number 5001-44758) and the Swiss Federal Institute of Technology (Project number 0048-41-2609-5).

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  1. Chair of Environmental Science - Natural and Social Science Interface, Swiss Federal Institute of Technology, Haldenbachstr. 44, 8092 Zurich, Switzerland

    Ute Schnabel, Olaf Tietje & Roland W. Scholz

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  1. Ute Schnabel
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  2. Olaf Tietje
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  3. Roland W. Scholz
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Correspondence to Ute Schnabel.

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Table 8 shows the number of pairs and semivariances for single lags of the variogram, calculated in equation (1) and shown in Figure 3.

The formal kriging system is:

Spherical function calculated for Figure 5 and Table 5 and 6:

where c 0 is the nugget effect, c 1 is the sill, h is the lag, and a the range.

The accuracy plot for the variogram model calculated in equation 1 is given in Figure 8. Dots indicate outliers being outside the 99% confidence limit of a normal distribution.

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Schnabel, U., Tietje, O. & Scholz, R. Uncertainty Assessment for Management of Soil Contaminants with Sparse Data . Environmental Management 33, 911–925 (2004). https://doi.org/10.1007/s00267-003-2971-0

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