Quantifying Uncertainty in Critical Loads: (A) Literature Review

被引:0
|
作者
R. A. Skeffington
机构
[1] University of Reading,Aquatic Environments Research Centre, Department of Geography
来源
关键词
acid deposition; emission control; environmental policy; GLUE; Monte Carlo analysis; sensitivity analysis; steady state mass balance model; steady state water chemistry model; uncertainty analysis;
D O I
暂无
中图分类号
学科分类号
摘要
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 influencing 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.
引用
收藏
页码:3 / 24
页数:21
相关论文
共 50 条
  • [41] Benchmarking HR outsourcing literature: a critical literature review
    Sim, Siew-Chen
    Mohan, Avvari V.
    Kaliannan, Maniam
    Vinayan, Gowrie
    Harikirishanan, Davindran
    [J]. INTERNATIONAL JOURNAL OF LEARNING AND INTELLECTUAL CAPITAL, 2023, 20 (01) : 91 - 122
  • [42] Quantifying Information and Uncertainty
    Frankel, Alexander
    Kamenica, Emir
    [J]. AMERICAN ECONOMIC REVIEW, 2019, 109 (10): : 3650 - 3680
  • [43] Quantifying LOPA uncertainty
    Freeman, Raymond Randy
    [J]. PROCESS SAFETY PROGRESS, 2012, 31 (03) : 240 - 247
  • [44] Quantifying monsoon uncertainty
    Graham Simpkins
    [J]. Nature Reviews Earth & Environment, 2020, 1 : 436 - 436
  • [45] Quantifying uncertainty in CFD
    Karniadakis, G
    [J]. JOURNAL OF FLUIDS ENGINEERING-TRANSACTIONS OF THE ASME, 2002, 124 (01): : 2 - 3
  • [46] Quantifying the uncertainty of CovidSim
    Leung, Kathy
    Wu, Joseph T.
    [J]. NATURE COMPUTATIONAL SCIENCE, 2021, 1 (02): : 98 - 99
  • [47] Quantifying monsoon uncertainty
    Simpkins, Graham
    [J]. NATURE REVIEWS EARTH & ENVIRONMENT, 2020, 1 (09) : 436 - 436
  • [48] Quantifying the uncertainty in heritability
    Nicholas A Furlotte
    David Heckerman
    Christoph Lippert
    [J]. Journal of Human Genetics, 2014, 59 : 269 - 275
  • [49] Quantifying the uncertainty of CovidSim
    Kathy Leung
    Joseph T. Wu
    [J]. Nature Computational Science, 2021, 1 : 98 - 99
  • [50] Quantifying the uncertainty in heritability
    Furlotte, Nicholas A.
    Heckerman, David
    Lippert, Christoph
    [J]. JOURNAL OF HUMAN GENETICS, 2014, 59 (05) : 269 - 275