Efficient Stochastic Algorithms for the Sensitivity Analysis Problem in the Air Pollution Modelling
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Sensitivity analysis of the results of large and complicated mathematical models is rather tuff and time-consuming task. However, this is quite an important problem as far as their critical applications are concerned. There are many such applications in the area of air pollution modelling. On the other hand, there are lots of natural uncertainties in the input data sets and parameters of a large-scale air pollution model. Such a model, the Danish Eulerian Model with its up-to-date high-performance implementations, is under consideration in this work. Its advanced chemical scheme (the Condensed CBM IV) takes into account a large number of chemical species and numerous reactions between them.
Four efficient stochastic algorithms have been used and compared by their accuracy in studying the sensitivity of ammonia and ozone concentration results with respect to the input emission levels and some chemical reactions rate parameters. The results of our numerical experiments show that the stochastic algorithms under consideration are quite efficient for the purpose of our sensitivity studies.
The authors would like to thank Rayna Georgieva for her help. The work is supported in parts by the Bulgarian NSF under Projects DN 12/5-2017 “Efficient Stochastic Methods and Algorithms for Large-Scale Problems” and DN 12/4-2017 “Advanced Analytical and Numerical Methods for Nonlinear Differential Equations with Applications in Finance and Environmental Pollution”, and the Bilateral Project Bulgaria-Russia DNTS 02/12-2018 “Development and investigation of finite-difference schemes of higher order of accuracy for solving applied problems of fluid and gas mechanics and ecology”, by the NSP “Information and Communication Technologies for a Single Digital Market in Science, Education and Security” (ICTinSES), financed by the Ministry of Education and Science.
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