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

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 Sobolʹs global sensitivity indices. A newly developed Monte Carlo algorithm based on Sobolʹs quasi-random points MCA-MSS has been applied for numerical integration. It has been compared with some existing approaches, namely Sobolʹs Λ Π τ sequences, an adaptive Monte Carlo algorithm, the plain Monte Carlo algorithm, as well as, eFAST and Sobolʹs 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 Sobolʹs global sensitivity indices in the presence of computational difficulties have been provided.