Sensitivity Analysis of an Air Pollution Model by Using Quasi-Monte Carlo Algorithms for Multidimensional Numerical Integration

  • Tzvetan OstromskyEmail author
  • Ivan Dimov
  • Venelin Todorov
  • Zahari Zlatev
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11189)


Sensitivity analysis is a powerful tool for studying and improving the reliability of large and complicated mathematical models. Air pollution and meteorological models are in front places among the examples of such models, with a lot of natural uncertainties in their input data sets and parameters. We present here some results of our global sensitivity study of the Unified Danish Eulerian Model (UNI-DEM). One of the most attractive features of UNI-DEM is its advanced chemical scheme – the Condensed CBM IV, which consider in detail a large number of chemical species and numerous reactions between them.

Four efficient stochastic algorithms (Sobol QMC, Halton QMC, Fibonacci lattice rule and Latin hypercube sampling) have been used and compared by their accuracy in studying the sensitivity of ammonia and ozone concentration results with respect to the emission levels and some chemical reactions rates. The numerical experiments show that the stochastic algorithms under consideration are quite efficient for this purpose, especially for evaluating the contribution of small by value sensitivity indices.



The authors would like to thank Rayna Georgieva for her help. This work is supported by the Bulgarian Academy of Sciences through the Program for Career Development of Young Scientists, Grant DFNP-17-88 /28.07.2017; as well as by the Bulgarian NSF under Projects DN 12/4 -2017 and DN 12/5 -2017.


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Authors and Affiliations

  • Tzvetan Ostromsky
    • 1
    Email author
  • Ivan Dimov
    • 1
  • Venelin Todorov
    • 1
    • 2
  • Zahari Zlatev
    • 3
  1. 1.Department of Parallel AlgorithmsInstitute of Information and Communication Technologies, Bulgarian Academy of Sciences (IICT-BAS)SofiaBulgaria
  2. 2.Department of Information ModellingInstitute of Mathematics and Informatics, Bulgarian Academy of Sciences (IMI-BAS)SofiaBulgaria
  3. 3.National Centre for Environment and EnergyUniversity of ÅrhusRoskildeDenmark

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