Modeling regional-scale variability of organic aerosols in the atmosphere

Description

In the first part of this poster, simulations of carbonaceous and inorganic aerosols made by the Weather Research and Forecasting Chemistry (WRF-Chem) model with the MOSAIC aerosol model and a volatility basis set approach of representing secondary organic aerosol (SOA) are evaluated using measurements collected during the Carbonaceous Aerosols and Radiative Effects (CARES) field campaign in California during June 2010. When using the 2008 emission inventory for California, predictions of organic matter agree reasonably well with aerosol mass spectrometer (AMS) measurements in the Sacramento and San Joaquin Valleys. However, estimates of primary organic aerosol (POA) derived from the AMS data suggest simulated primary organic aerosol (POA) was too high and simulated SOA was too low. Simulated black carbon (BC) was also too high compared to surface and aircraft measurements. It is likely that simulated biogenic source of SOA was too low, since simulated isoprene was low by a factor of two. Shilling et al. found enhanced SOA formation when the Sacramento plume interacted with biogenic species, and possible mechanisms for this are being investigated with the model.

For the second part of this poster, we investigate the important issues of particle-phase changes in volatility and gas-phase fragmentation versus functionalization reactions affecting the formation and evolution of SOA. We show that under any realistic assumptions of mass accommodation coefficient, our analysis of measured evaporation rates of SOA imply significantly lower “effective volatility” than those derived from SOA growth in smog chamber, pointing towards the role of particle phase changes after SOA formation. Thus, models may need to use different parameters to describe SOA volatility during and after formation. Using both a box model and the 3D chemical transport model, we investigate the implications of low “effective volatility” of SOA and gas-phase fragmentation reactions. All our box model configurations using multi-generational gas-phase chemistry predict one-two orders of magnitude higher SOA loadings compared to the models that neglect this chemistry.

Previous models with multi-generational chemistry limited to functionalization reactions are known to eventually produce too much SOA, and including fragmentation reactions significantly reduces SOA production. The 3D model demonstrates complex variations in spatial and temporal distribution of SOA with varying degrees of fragmentation. In addition, the treatment of SOA as semi-volatile or non-volatile also causes variations in predicted SOA loadings in the atmosphere.