Evaluation of recently-proposed secondary organic aerosol models for a case study in Mexico City
- ISSN: 1680-7316
- ISBN: 1680-7316
Recent field studies have found large discrepancies in the measured vs. modeled SOA mass loadings in both urban and regional polluted atmospheres. The reasons for these large differences are unclear. Here we revisit a case study of SOA formation in Mexico City described by Volkamer et al. (2006), during a photochemically active period when the impact of regional biomass burning is minor or negligible, and show that the observed increase in OA/Delta CO is consistent with results from several groups during MILAGRO 2006. Then we use the case study to evaluate three new SOA models: 1) the update of aromatic SOA yields from recent chamber experiments (Ng et al., 2007); 2) the formation of SOA from glyoxal (Volkamer et al., 2007a); and 3) the formation of SOA from primary semivolatile and intermediate volatility species (P-S/IVOC) (Robinson et al., 2007). We also evaluate the effect of reduced partitioning of SOA into POA (Song et al., 2007). Traditional SOA precursors (mainly aromatics) by themselves still fail to produce enough SOA to match the observations by a factor of similar to similar to 7. The new low-NOx aromatic pathways with very high SOA yields make a very small contribution in this high-NOx urban environment as the RO2 center dot+NO reaction dominates the fate of the RO2 center dot radicals. Glyoxal contributes several mu g m(-3) to SOA formation, with similar timing as the measurements. P-S/IVOC are estimated from equilibrium with emitted POA, and introduce a large amount of gas-phase oxidizable carbon that was not in models before. With the formulation in Robinson et al. (2007) these species have a high SOA yield, and this mechanism can close the gap in SOA mass between measurements and models in our case study. However the volatility of SOA produced in the model is too high and the O/C ratio is somewhat lower than observations. Glyoxal SOA helps to bring the O/C ratio of predicted and observed SOA into better agreement. The sensitivities of the model to some key uncertain parameters are evaluated.