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

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 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.