Reduced chemical mechanisms for atmospheric pollution using Computational Singular Perturbation analysis

Abstract

This paper explores the derivation of reduced chemical mechanisms for atmospheric pollution using the Computational Singular Perturbation technique. The technique is based on an algorithmic selection of “fast-” and “slow-” reacting species, which can then guide the identification of appropriate steady-state approximations to reduce the number of variables that have to be solved. The chemical mechanism analyzed here is the Carbon-Bond Mechanism (CBM-IV), which is already a skeletal form of the detailed kinetics of the atmosphere. A box-model using CBM-IV and a set of measurements of mixing ratios to reflect realistic urban and rural conditions were used as initial conditions. It was found that the selection of steady-state species depends on the integration period, i.e. whether it is over a diurnal period, or during day only, or during night only. Reduced mechanisms with seven, 11 and 15 steady-state species (out of 28 species present) were tested and it was found that the predictions of CBM-IV under different scenarios were reproduced satisfactorily for most species, with inaccuracies increasing as the reduction level increases. The method selects the appropriate steady-state approximations and hence is suitable for analyzing more complex atmospheric pollution chemical mechanisms.