Sensitivity Analysis of an Air Pollution Model by Using Quasi-Monte Carlo Algorithms for Multidimensional Numerical Integration
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.
- 3.Dimitrov, S., Dimov, I., Todorov, V.: Latin hypercube sampling and fibonacci based lattice method comparison for computation of multidimensional integrals. In: Dimov, I., Faragó, I., Vulkov, L. (eds.) NAA 2016. Lecture Notes in Computer Science, vol. 10187, pp. 296–303. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-57099-0_32CrossRefGoogle Scholar
- 4.Dimov, I., Georgieva, R., Ostromsky, Tz, Zlatev, Z.: Variance-based sensitivity analysis of the unified danish eulerian model according to variations of chemical rates. In: Dimov, I.T., Faragó, I., Vulkov, L. (eds.) NAA 2012. LNCS, vol. 8236, pp. 247–254. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41515-9_26CrossRefzbMATHGoogle Scholar
- 5.Dimov, I.T., Georgieva, R., Ostromsky, T., Zlatev, Z.: Sensitivity studies of pollutant concentrations calculated by UNI-DEM with respect to the input emissions. Centr. Eur. J. Math. 11(8), 1531–1545 (2013). https://doi.org/10.2478/s11533-013-0256-2. Numerical Methods for Large Scale Scientific ComputingMathSciNetCrossRefzbMATHGoogle Scholar
- 6.Dimov, I.T., Georgieva, R., Todorov, V., Ostromsky, Tz.: Efficient stochastic approaches for sensitivity studies of an Eulerian large-scale air pollution model. In: AIP Conference Proceedings, vol. 1895, no. 1, p. 050004 (2017). https://doi.org/10.1063/1.5007376
- 12.Ostromsky, T.z., Dimov, I., Georgieva, R., Zlatev, Z.: Parallel computation of sensitivity analysis data for the Danish Eulerian model. In: Lirkov, I., Margenov, S., Waśniewski, J. (eds.) LSSC 2011. LNCS, vol. 7116, pp. 307–315. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-29843-1_35CrossRefGoogle Scholar
- 13.Ostromsky, T.z., Dimov, I.T., Marinov, P., Georgieva, R., Zlatev, Z.: Advanced sensitivity analysis of the Danish Eulerian Model in parallel and grid environment. In: Proceedings of 3rd International Conference on AIP, AMiTaNS 2011, vol. 1404, pp. 225–232 (2011)Google Scholar