Simulating the Formation of Semivolatile Primary and Secondary Organic Aerosol in a Regional Chemical Transport Model

Abstract

Recent developments in our understanding of atmospheric organic particulate matter formation have been used to develop a state-of-the-art organic aerosol module for regional chemical transport models (CTMs). The module has been added to the regional CTM, PMCAMx, and has been evaluated against observations in the eastern U.S. The new module uses the volatility basis set framework to simulate primary organic aerosol (POA) partitioning between the gas and particulate phases and the gas-phase oxidation of the corresponding vapors. The formation and chemical aging of secondary organic aerosol (SOA) are modeled using the same volatility distribution approach. The module uses recent results from smog chamber studies for the formation of SOA from anthropogenic and biogenic hydrocarbons. Hourly organic aerosol predictions are evaluated using data from the Pittsburgh Air Quality Study (PAQS), and daily averaged predictions for July 2001 are compared to ambient measurements from the EPA Speciated Trends Network (STN) and the Interagency Monitoring of Protected Visual Environments (IMPROVE). The model reproduces both the absolute organic aerosol concentrations in urban and rural locations as well as the high degree of oxidation of these compounds. The chemical aging of anthropogenic SOA is consistent with the ambient organic aerosol concentration field and has a significant impact on the absolute ground-level concentrations of these compounds.

Formation of highly oxygenated organic aerosol in the atmosphere: Insights from the Finokalia Aerosol Measurement Experiments

“…The isoprene and terpene emissions in Greece had similar ratios (C. Pilinis, unpublished results, 2010). However, preliminary model results using the PMCAMx-2008 model [Murphy and Pandis, 2009] suggest that the contribution of biogenic SOA to total OA sampled at Finokalia were similar during the two campaigns (about 50%) (C. Fountoukis, unpublished results, 2010). Thus, the lower biogenic VOC emissions during the winter cannot explain the difference in OA composition between the two campaigns.…”

Verification of Chemical Transport Models for PM2.5 Chemical Composition Using Simultaneous Measurement Data over Japan

“…One sensitivity simulation was conducted with the AERO6 model, and two sensitivity simulations were conducted with the AERO6VBS model (Table 1): a standard version and a revised version. In the standard version (AERO6VBS-std), aging of SVOCs from biogenic VOCs was not considered, as in Murphy and Pandis (2009). The revised sensitivity simulation (AERO6VBS-rev) considered an aging reaction between OH radicals and SVOCs produced from biogenic VOCs with a reaction rate of 2 × 10 -11 cm 3 molecule -1 s -1…”

“…Recently, a volatility basis set (VBS) model that can deal with direct emissions of semivolatile and intermediate-volatility organic compounds (SVOCs and IVOCs, respectively), with the oxidation of these compounds, and with SVOC aging was developed (Donahue et al, 2006;Robinson et al, 2007). The VBS model has been widely used in the United States (Lane et al, 2008;Murphy and Pandis, 2009;Tessum et al, 2015), Europe (Bergstrom et al, 2012;, and Asia (Matsui et al, 2014;Han et al, 2015;Morino et al, 2015), and has improved simulations of OA concentrations. Morino et al (2014) compared the results obtained with five SOA models over the TMA and found that the VBS model reproduced the observed SOA concentrations well, whereas other models, including SOA yield models, underestimated SOA.…”

ORACLE 2-D (v2.0): an efficient module to compute the volatility and oxygen content of organic aerosol with a global chemistry–climate model

“…3370 A. P. : an efficient module to compute volatility and oxygen content approach, semi-volatile primary emissions, chemical aging, and SOA formation were unified within a common framework that is ideally suited for regional and global chemical modeling. Since 2006, many regional (Lane et al, 2008;Murphy and Pandis, 2009;Tsimpidi et al, , 2011Ahmadov et al, 2012;Athanasopoulou et al, 2013;Koo et al, 2014;Fountoukis et al, 2014;Ciarelli et al, 2017;Gao et al, 2017) and global (Pye and Seinfeld, 2010;Jathar et al, 2011;Jo et al, 2013;Tsimpidi et al, 2014;Hodzic et al, 2016) modeling studies have used the VBS to account for the semi-volatile nature and chemical aging of organic compounds, demonstrating improvements in reproducing the OA budget and its chemical resolution.…”