Multidimensional Air Pollution Sensitivity Analysis for Intelligent Decision Modeling by Means of Quasi-Monte Carlo Sequences

In this paper, we introduce a sophisticated method of sensitivity analysis for air pollution simulation, employing cutting-edge stochastic techniques within a comprehensive, long-distance air pollutant transport model, specifically the Unified Danish Eulerian Model (UNI-DEM). This model plays a crucial role in examining the detrimental impacts of elevated air pollution levels. We employ these stochastic algorithms to calculate global Sobol sensitivity indices, which help in understanding the impact of different input variables on key air pollutants in several European cities, each with its distinct geographical context. The aim of this study is to enhance our comprehension of the elements influencing air pollution and to aid in developing effective approaches to reduce its negative impact on the environment.