Atmospheric chemistry mechanisms are the most computationally intensive components of photochemical air quality simulation models (PAQSMs). The development of a photochemical mechanism, that accurately describes atmospheric chemistry while being computationally efficient for use in PAQSMs, is a difficult undertaking that has traditionally been pursued through semiempirical ('diagnostic') lumping approaches. The limitations of these diagnostic approaches are often associated with inaccuracies due to the fact that the lumped mechanisms have typically been optimized to fit the concentration profile of a specific species. Formal mathematical methods for model reduction have the potential (demonstrated through past applications in other areas) to provide very effective solutions to the need for computational efficiency combined with accuracy. Such methods, that can be used to 'condense' a chemical mechanism, include 'kinetic lumping' and 'domain separation'. An application of the kinetic lumping method, using the direct constrained approximate lumping (DCAL) approach, to the atmospheric photochemistry of alkanes is presented in this work. It is shown that the lumped mechanism generated through the application of the DCAL method has the potential to overcome the limitations of existing semiempirical approaches, especially in relation to the consistent and accurate calculation of the time-concentration profiles of multiple species.
All Science Journal Classification (ASJC) codes
- Environmental Chemistry