Analyzing experimental data and model parameters: implications for predictions of SOA using chemical transport models

Despite critical importance for air quality and climate predictions, accurate representation of secondary organic aerosol (SOA) formation remains elusive. An essential addition to the ongoing discussion of improving model predictions is an acknowledgement of the linkages between experimental conditions, parameter optimization and model output, as well as the linkage between empirically-derived partitioning parameters and the physicochemical properties of SOA they represent in models. In this work, a best available set of SOA modeling parameters is selected by comparing predicted SOA yields and mass concentrations with observed yields and mass concentrations from a comprehensive list of published smog chamber studies. Evaluated SOA model parameters include existing parameters for two product (2p) and volatility basis set (VBS) modeling frameworks, and new 2p-VBS parameters; 2p-VBS parameters are developed to exploit advantages of the VBS approach within the computationally-economical and widely-used 2p framework. Fine particulate matter (PM2.5) and SOA mass concentrations are simulated for the continental United States using CMAQv.4.7.1; results are compared for a base case (with default CMAQ parameters) and two best available parameter cases to illustrate the high-and low-NOx limits of biogenic SOA formation from monoterpenes. Results are discussed in terms of implications for current chemical transport model simulations and recommendations are provided for future modeling and measurement efforts. The comparisons of SOA yield predictions with data from 22 published chamber studies illustrate that: (1) SOA yields for naphthalene, and cyclic and >C5 straight-chain/branched alkanes are not well represented using either the newly developed or existing parameters for low-yield aromatics and lumped alkanes, respectively; and (2) for four of seven volatile organic compound+oxidant systems, the 2p-VBS parameters better represent chamber data than do the default CMAQ v.4.7.1 parameters. Using the best available parameters (combination of published 2p and newly derived 2p-VBS), predicted SOA mass and PM2.5 concentrations increase by up to 15 % and 7 %, respectively, for the high-NOx case and up to 215 % (similar to 3 mu gm(-3)) and 55 %, respectively, for the low-NOx case. Percent bias between model-based and observationally-based secondary organic carbon (SOC) improved from -63 % for the base case to -15 % for the low-NOx case. The ability to robustly assign best available parameters in all volatile organic compound+oxidant systems, however, is critically limited due to insufficient data; particularly for photo-oxidation of diverse monoterpenes, sesquiterpenes, and alkanes under a range of atmospherically relevant conditions.