ADAPTIVE GRIDS IN AIR POLLUTION MODELING: TOWARDS AN OPERATIONAL MODEL

摘要

We developed an adaptive grid algorithm for use in AQMs. It employs a structured grid where the nodes move throughout the simulation. The movement is controlled by a weight function whose value depends on a linear combination of the errors in various pollutant concentrations. The algorithm generates a continuous, multiscale grid where the scales change gradually, and makes optimal use of computational resources at all times. So far, we evaluated the algorithm in idealized model problems involving dispersion of power plant plumes and chemical reactions. The results are much more accurate than those achieved by uniform fixed grid models that use the same computational resources. The adaptation criterion we used so far gives equal weight to the errors in the concentrations of different pollutant species. We are developing criteria that would be more sensitive to reaction pathways that are more important in the formation of specific secondary pollutants, such as ozone. The incorporation of the algorithm into an operational air pollution model is under way. The movement of the grid nodes necessitates reprocessing of meteorological and emissions inputs after each adaptation step. While the ideal solution would be to have a meteorological model running in parallel, over the same grid, since there is no such model at present, we are interpolating data from a nested grid model onto the adaptive grid. We developed efficient intersection algorithms to assure proper allocation of point and line source emissions into each grid cell every time the nodes move.