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Monte Carlo sensitivity analysis of an Eulerian large-scale air pollution model

Author

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  • Dimov, I.
  • Georgieva, R.
  • Ostromsky, Tz.

Abstract

Variance-based approaches for global sensitivity analysis have been applied and analyzed to study the sensitivity of air pollutant concentrations according to variations of rates of chemical reactions. The Unified Danish Eulerian Model has been used as a mathematical model simulating a remote transport of air pollutants. Various Monte Carlo algorithms for numerical integration have been applied to compute Sobol's global sensitivity indices. A newly developed Monte Carlo algorithm based on Sobol's quasi-random points MCA-MSS has been applied for numerical integration. It has been compared with some existing approaches, namely Sobol's ΛΠτ sequences, an adaptive Monte Carlo algorithm, the plain Monte Carlo algorithm, as well as, eFAST and Sobol's sensitivity approaches both implemented in SIMLAB software. The analysis and numerical results show advantages of MCA-MSS for relatively small sensitivity indices in terms of accuracy and efficiency. Practical guidelines on the estimation of Sobol's global sensitivity indices in the presence of computational difficulties have been provided.

Suggested Citation

  • Dimov, I. & Georgieva, R. & Ostromsky, Tz., 2012. "Monte Carlo sensitivity analysis of an Eulerian large-scale air pollution model," Reliability Engineering and System Safety, Elsevier, vol. 107(C), pages 23-28.
    • Handle: RePEc:eee:reensy:v:107:y:2012:i:c:p:23-28
    DOI: 10.1016/j.ress.2011.06.007
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    References listed on IDEAS

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    1. Pierre L'Ecuyer & Christian Lécot & Bruno Tuffin, 2008. "A Randomized Quasi-Monte Carlo Simulation Method for Markov Chains," Operations Research, INFORMS, vol. 56(4), pages 958-975, August.
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    Cited by:

    1. Hou, Tianfeng & Nuyens, Dirk & Roels, Staf & Janssen, Hans, 2019. "Quasi-Monte Carlo based uncertainty analysis: Sampling efficiency and error estimation in engineering applications," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    2. Pengfei Wei & Zhenzhou Lu & Jingwen Song, 2014. "Moment‐Independent Sensitivity Analysis Using Copula," Risk Analysis, John Wiley & Sons, vol. 34(2), pages 210-222, February.

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