Predicting Threshold Exceedance by Local Block Means in Soil Pollution Surveys |
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| Authors: | Christoph Hofer Andreas Papritz |
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| Institution: | 1.Institute of Terrestrial Ecosystems,ETH Zurich,Zürich,Switzerland |
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| Abstract: | Soil contamination by heavy metals and organic pollutants around industrial premises is a problem in many countries around
the world. Delineating zones where pollutants exceed tolerable levels is a necessity for successfully mitigating related health
risks. Predictions of pollutants are usually required for blocks because remediation or regulatory decisions are imposed for
entire parcels. Parcel areas typically exceed the observation support, but are smaller than the survey domain. Mapping soil
pollution therefore involves a local change of support. The goal of this work is to find a simple, robust, and precise method
for predicting block means (linear predictions) and threshold exceedance by block means (nonlinear predictions) from data
observed at points that show a spatial trend. By simulations, we compared the performance of universal block kriging (UK),
Gaussian conditional simulations (CS), constrained (CK), and covariance-matching constrained kriging (CMCK), for linear and
nonlinear local change of support prediction problems. We considered Gaussian and positively skewed spatial processes with
a nonstationary mean function and various scenarios for the autocorrelated error. The linear predictions were assessed by
bias and mean square prediction error and the nonlinear predictions by bias and Peirce skill scores. |
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