Skeffington,R.A.(2006): Quantifying uncertainty in critical loads: (A) Literature review. Water, air, and Soil Pollution, 169, 3-24.

『臨界負荷の不確かさを定量化する:(A)文献レビュー』


Abstract
 Critical loads are the basis for policies controlling emissions of acidic substances in Europe. The implementation of these policies involves large expenditures, and it is reasonable for policymakers to ask what degree of certainty can be attached to the underlying critical load and exceedance estimates. This paper is a literature review of studies which attempt to estimate the uncertainty attached to critical loads. Critical load models and uncertainty analysis are briefly outlined. Most studies have used Monte Carlo analysis of some form to investigate the propagation of uncertainties in the definition of the input parameters through to uncertainties in critical loads. Though the input parameters are often poorly known, the critical load uncertainties are typically surprisingly small because of a “compensation of errors” mechanism. These results depend on the quality of the uncertainty estimates of the input parameters, and a “pedigree” classification for these is proposed. Sensitivity analysis shows that some input parameters are more important in influence critical load uncertainty than others, but there have not been enough studies to form a general picture. Methods used for dealing with spatial variation are briefly discussed. Application of alternative models to the same site or modifications of existing models can lead to widely differing critical loads, indicating that research into the underlying science needs to continue.

Keywords: acid deposition; emission control; environmental policy; GLUE; Monte Carlo analysis; sensitivity analysis; steady state mass balance model; steady state water chemistry model; uncertainty analysis』

1. Introduction
2. Definitions and methodology
 2.1. Critical load models
 2.2. Uncertainty analysis
3. Full uncertainty analyses
 3.1. Critical loads for soils
 3.2. Critical loads for waters
 3.3. Uncertainties in exceedance
4. Single parameter sensitivity analyses
5. Multiple model applications
6. Changes in model structure
7. Discussion
Acknowledgments
References


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