Impacts of Stabilized Criegee Intermediates, surface uptake processes and higher aromatic secondary organic aerosol yields on predicted PM2.5 concentrations in the Mexico City Metropolitan Zone

Abstract

The Community Multiscale Air Quality Model (CMAQ) with the SAPRC-99 gas phase photochemical mechanism and the AERO5 aerosol module was applied to model gases and particulate matter (PM) concentrations in the Mexico City Metropolitan Zone (MCMZ) and the surrounding regions for March 2006 using the official 2006 emission inventories, along with emissions from biogenic sources, biomass burning, windblown dust, the Tula Industrial Complex and the Popocatépetl volcano. The base case model was capable of reproducing the observed hourly concentrations of O3 and attaining CO, NO2 and NOx performance similar to previous modeling studies. Although the base case model performance of hourly PM2.5 and PM10 meets the model performance criteria, under-prediction of high PM2.5 concentrations in late morning indicates that secondary PM, such as sulfate and secondary organic aerosol (SOA), might be under-predicted. Several potential pathways to increase SOA and secondary sulfate were investigated, including Stabilized Criegee Intermediates (SCIs) from ozonolysis reactions of unsaturated hydrocarbons and their reactions with SO2, the reactive uptake processes of SO2, glyoxal and methylglyoxal on particle surface and higher SOA formation due to higher mass yields of aromatic SOA precursors. Averaging over the entire episode, the glyoxal and methylglyoxal reactive uptake and higher aromatics SOA yields contribute to ∼0.9 μg m−3 and ∼1.25 μg m−3 of SOA, respectively. Episode average SOA in the MCMZ reaches ∼3 μg m−3. The SCI pathway increases PM2.5 sulfate by 0.2–0.4 μg m−3 or approximately 10–15%. The relative amount of sulfate increase due to SCI agrees with previous studies in summer eastern US. Surface SO2 uptake significantly increases sulfate concentration in MCMZ by 1–3 μg m−3 or approximately 50–60%. The higher SOA and sulfate leads to improved PM2.5 and PM10 model performance.