Reduction of simulation times by application of a kernel method in a high-resolution Lagrangian particle dispersion model

Abstract

The role of modelling the atmospheric dispersion of pollutants at microscale, the scale that allows to resolve explicitly the presence of obstacles, is becoming increasingly important for performing air quality assessments in cities, as well as for regulatory purposes and for the design of pollution control strategies. However, the use of microscale models can be computationally demanding, both in terms of time and CPUs required, especially if the computational domain considers wide spatial extension and the simulation considers long time periods. This article proposes the application of a kernel method as the concentration calculation methodology inside microscale Lagrangian particle dispersion models (LPDMs) in order to reduce the required computational time. In these models, the concentration is normally estimated with the box-counting method, while the use of this alternative method, based on the use of the statistical technique of kernel density estimation, allows for a reduction of numerical particles emitted during the simulation, while guaranteeing a similar accuracy to that of the box-counting method. It therefore enables an optimization of computational efficiency. In an earlier manuscript, the kernel method was applied inside the LPDM of the PMSS (Parallel-Micro-SWIFT-SPRAY) system to perform high-resolution simulations of line sources, enabling an 80% simulation time reduction. In this article, additional features of this method are developed within the Micro-SPRAY model and tested through two test cases. The kernel method has been applied to estimate the pollutant concentrations of point sources as well as to compute the corresponding deposition at building-resolving scale. The results with tiled and nested configurations of domains are also verified.