A Comparison of Quasi-Monte Carlo Methods Based on Faure and Sobol Sequences for Multidimensional Integrals in Air Pollution Modeling

摘要

Air pollution and meteorological models are examples of mathematical models with a lot of natural uncertainties in their input data sets and parameters. Sensitivity analysis is a powerful tool for studying and improving the reliability of such models. In this paper we present the results of a global sensitivity study of the Unified Danish Eulerian Model (UNI-DEM). One of the most important features of UNI-DEM is its advanced chemical scheme the Condensed CBM IV, which considers a large number of chemicals, and various reactions between them, of which the ozone is the most important pollutant because it is used in many practical applications. The stochastic methods based on Faure and Sobol sequences are used for computing the sensitivity measures. The numerical experiments show that the stochastic algorithms for the multidimensional integrals under consideration are efficient methods for computing the small value sensitivity indices.