A MASS-CONSERVATIVE TEMPORAL SECOND ORDER AND SPATIAL FOURTH ORDER CHARACTERISTIC FINITE VOLUME METHOD FOR ATMOSPHERIC POLLUTION ADVECTION DIFFUSION PROBLEMS

摘要

In this paper, a mass-conservative temporal second order and spatial fourth order characteristic finite volume method (MC-T2S4-CFVM) for solving atmospheric pollution advection diffusion problems is developed. While the characteristics tracking is applied to treat the advection term, we use conservative interpolation to treat the advective integrals over the irregular tracking volume cells at the previous time level. A temporal second order discretization by averaging along the characteristics is proposed for the diffusion term, where the diffusion fluxes are approximated by high order spatial discrete operators that provide continuity of the discrete fluxes across the edges of volume cells and tracking volume cells. The developed characteristic finite volume method is mass conservative and has fourth order accuracy in space and second order accuracy in time. Numerical tests of Gaussian pulse moving verify the temporal and spatial accuracies of MC-T2S4-CFVM as well as the mass-conservative property. The proposed scheme has much better accuracy Over the classical characteristic schemes. Computational results of transporting a square of concentration 1 show the high accuracy of MC-T2S4-CFVM in preserving mass and volume. Atmospheric pollution simulations are further carried out by using the MC-T2S4-CFVM. Test results of local emission simulation exhibit the big impact of East Asian monsoon winds on the transport of local emission. The predicted results of PM2.5 concentrations in the realistic simulation are consistent with observed data in three metropolitan municipalities and the capitals of seven provinces in China. Investigating through the time series of wind vectors and PM2.5 concentration, the influence of wind transport has been numerically observed on the air quality of the cities and provinces. The developed high order mass-conservative characteristic method can be used to solve the large scale atmospheric pollution problems in real-world applications.