Special Lattice and Digital Sequences for Multidimensional Air Pollution Modelling

This paper presents an advanced multidimensional sensitivity analysis using innovative stochastic approaches for air pollution modeling on a large-scale long-range transport model of air pollutants, specifically the Unified Danish Eulerian Model (UNI-DEM). This mathematical model is important for studying the harmful effects of high air pollution levels, and in this paper, we aim to use it to address critical environmental protection questions. We develop advanced Monte Carlo and quasi-Monte Carlo methods using special lattice and digital sequences to improve the computational efficiency of multidimensional numerical integration. We also enhance the existing stochastic approaches for digital ecosystem modeling. The study focuses on analyzing the sensitivity of UNI-DEM model output to variations in input emissions of anthropogenic pollutants and rates of several chemical reactions. The algorithms are applied to compute global Sobol sensitivity measures for several input parameters’ influence on important air pollutant concentrations in various European cities with different geographical locations. The research aims to improve understanding of the factors affecting air pollution and inform effective strategies for mitigating its harmful effects on the environment.