Evaluation of algorithms developed for adaptive grid air quality modeling using surface elevation data

An adaptive grid model is being developed to reduce the resolution-related uncertainty in air quality predictions. By clustering the grid nodes in regions where errors in pollutant concentrations would potentially be large, the model is expected to generate much more accurate results than its fixed, uniform grid counterparts. The repositioning of grid nodes is performed automatically using a weight function that assumes large values when the curvature (change of slope) of the pollutant fields is large. Despite the movement of the nodes, the structure of the grid does not change: each node retains its connectivity to the same neighboring nodes. Since there is no a priori knowledge of the grid movement, the input data must be re-gridded after each adaptation step, throughout the simulation. Emissions are one of the major inputs and mapping them to the adapted grid is a computationally intensive task. Efficient intersection algorithms are being developed that take advantage of the unchanging grid structure. Here, the grid node repositioning and intersection algorithms are evaluated using surface elevation data. Two elevation data sets are reduced to one-fourth of their sizes using uniform 0198-9715/$ see front matter 2004 Published by Elsevier Ltd. doi:10.1016/j.compenvurbsys.2004.08.002 * Corresponding author. Tel.: +1 404 363 7101. E-mail address: [email protected] (M.N. Khan). M.N. Khan et al. / Comput., Environ. and Urban Systems 29 (2005) 718–734 719 as well as adaptive grids. The first data set contains important terrain features near the boundaries while the second has all of its features far away from the boundaries. The compression of the first data set using grid node repositioning results in a maximum error that is 25% smaller compared to a uniform grid with the same number of nodes. The maximum error associated with the adaptive grid compression of the second data set is 60% smaller compared to the uniform grid compression. These results show that the adaptive grid algorithm has the potential of significantly improving the accuracy of air quality predictions, especially when the regions of changing slope are far away from the boundaries. Indeed, in a preliminary air quality application, the adaptive grid displayed superior performance in capturing the details of plumes from a large number of emission sources. The algorithms are computationally efficient and the overhead involved in repositioning the grid nodes and intersecting the grid cells with emission sources is not limiting in air quality simulations. 2004 Published by Elsevier Ltd.