Optimizing Air Pollution Modeling with a Highly-Convergent Quasi-Monte Carlo Method: A Case Study on the UNI-DEM Framework

In this study, we present the development of an advanced air pollution modeling approach, which incorporates cutting-edge stochastic techniques for large-scale simulations of long-range air pollutant transportation. The Unified Danish Eulerian Model (UNI-DEM) serves as a crucial mathematical framework with numerous applications in studies concerning the detrimental effects of heightened air pollution levels. We employ the UNI-DEM model in our research to obtain trustworthy insights into critical questions pertaining to environmental preservation. Our proposed methodology is a highly convergent quasi-Monte Carlo technique that relies on a unique symmetrization lattice rule. By fusing the concepts of special functions and optimal generating vectors, we create a novel algorithm grounded in the component-by-component construction method, which has been recently introduced. This amalgamation yields particularly impressive outcomes for lower-dimensional cases, substantially enhancing the performance of the most advanced existing methods for calculating the Sobol sensitivity indices of the UNI-DEM model. This improvement is vital, as these indices form an essential component of the digital ecosystem for environmental analysis.

Automated summary

In this study, an advanced air pollution modeling approach incorporating stochastic techniques for large-scale simulations of long-range air pollutant transportation is proposed. The Unified Danish Eulerian Model (UNI-DEM) is utilized as a crucial mathematical framework with numerous applications in studying the detrimental effects of heightened air pollution levels. The proposed methodology employs a highly convergent quasi-Monte Carlo technique that relies on a unique symmetrization lattice rule, combining the concepts of special functions and optimal generating vectors to enhance the performance of calculating the Sobol sensitivity indices of the UNI-DEM model.