Global Sensitivity Analysis of the Regional Atmospheric Chemical Mechanism: An Application of Random Sampling-High Dimensional Model Representation to Urban Oxidation Chemistry

Abstract

Chemical mechanisms play a crucial part for the air quality modeling and pollution control decision-making. Parameters in a chemical mechanism have uncertainties, leading to the uncertainties of model predictions. A recently developed global sensitivity analysis (SA) method based on Random Sampling-High Dimensional Model Representation (RS-HDMR) was applied to the Regional Atmospheric Chemical Mechanism (RACM) within a zero-dimensional photochemical model to highlight the main uncertainty sources of atmospheric hydroxyl (OH) and hydroperoxyl (HO(2)) radicals. This global SA approach can be applied as a routine in zero-dimensional photochemical modeling to comprehensively assess model uncertainty and sensitivity under different conditions. It also highlights the parameters to which the model is most sensitive during periods when the model/measurement OH and HO(2) discrepancies are greatest. Uncertainties in 584 model parameters were assigned for measured constituents used to constrain the model, for photolysis and kinetic rate coefficients, and for product yields of the reactions. With simulations performed for the hourly field data of two typical days, modeled and measured OH and HO(2) generally agree better for polluted conditions than for cleaner conditions, except during morning rush hour. Sensitivity analysis shows that the modeled OH and HO(2) depend most critically on the reactions of xylenes and isoprene with OH, NO(2) with OH, NO with HO(2), and internal alkenes with O(3) and suggests that model/measurement discrepancies in OH and HO(2) would benefit from a closer examination of these reactions.