Application of Sparse Matrix Techniques in the Chemical Part of a Large Air Pollution Model

Large-scale air pollution models are represented by large systems of partial diierential equations. After some discretization and splitting procedures these are transformed into several huge systems of ODE's, which are to be handled numerically during many time-steps. The use of both fast numerical methods and high speed computers is crucial for such models. The chemical part of the model is normally the most time-consuming module. It consists of a huge number of stii ODE systems, which can be treated as parallel tasks by using diierent partitioning procedures. One can also try to exploit the sparsity of the Jacobian matrix in order to improve further the eeciency of the computations. A special sparsity algorithm for the chemical part of a large air pollution model is described and tested in this paper.