Initial Application of the Adaptive Grid Air Quality Model

Grid size (or resolution), when inadequate, can be an important source of uncertainty for air quality model (AQM) simulations. Coarse grids used because of computational limitations may artificially diffuse the emissions, leading to significant errors in the concentrations of pollutant species, especially those that are formed via non-linear chemical reactions. Further, coarse grids may result in large numerical errors. To address this issue, multi-scale modeling and grid nesting techniques have been developed (Odman and Russell, 1991; Odman et al., 1997). These techniques use finer grids in areas that are presumed to be of interest (e.g., cities) and coarser grids elsewhere (e.g., rural locations). Limitations include loss in accuracy due to grid interface problems and inability to adjust to dynamic changes in resolution requirements. Adaptive grids are not subject to such limitations and do not require a priori knowledge of where to place finer grids. Using grid clustering or grid enrichment techniques, they automatically allocate fine resolution to areas of interest. They are thus able to capture the physical and chemical processes that occur in the atmosphere much more efficiently than their fixed grid counterparts. The adaptive grid methodology used here is based on the Dynamic Solution Adaptive Grid Algorithm (DSAGA) of Benson and McRae (1991), which was later extended for use in air quality modeling by Srivastava et al. (2000). It employs a structured grid with a constant number of grid nodes. The modeling domain is partitioned into M N × quadrilateral grid cells. The grid nodes are re-positioned in a twodimensional space throughout the simulation according to a weight function which represents the resolution requirements. The areas of the grid cells change due to grid node movements but the connectivity of the grid nodes remains the same. Further, since the number of grid nodes is fixed, refinement of grid scales in some regions is accompanied by coarsening in other regions where the weight function has smaller values. This results