Using the power of information of sparse data for soil improvement management

摘要: 2 Introduction Data 4 Methods 7 Analysis of spatial dependency Ordinary Kriging 8 Conditional Simulation 10 Uncertainty Assessment 11 Results 15 Spatial Estimation 17 Consequences 19 Conclusion 20 Decision Relevance 21 Acknowledgements 22 References 23 1 Working Group: Environmental Decision-Making, Evaluation and Modeling Ute Schnabel, Olaf Tietje, & Roland W. Scholz July 2002 Abstract For a sustainable management natural resources, such as soil, the distribution environmental impacts is basic need for decision-making. However, interpolation in most cases only few data with skewed uncertain information about soil contamination are available whereas decisions high ”correctness” required. In order to assess power sparse site square km 76 samples was investigated. The cadmium contaminated predominantly due airborne emissions from metal smelter. A lognormal probability found appropriately estimate probabilistic contaminant. compares anisotropic kriging conditional simulation. resulting overall uncertainty sampling, sampling preparation, analytical measurement, numerical representation has been analyzed major component coarse heterogeneity. It shown that can be efficiently estimated by calculating percentiles function. This procedure also allows calculation local exceeding legal threshold values. Although prediction concentration rather high, yields what often required decision making: qualified rough contamination. Conclusively, predicting site-specific used roughly delineate prior areas improvement, remediation, or restricted area use, based on makers requirement.