Molecular corridors and kinetic regimes in the multiphase chemical evolution of secondary organic aerosol

Abstract

The dominant component of atmospheric, organic aerosol is that derived from the oxidation of volatile organic compounds (VOCs), so-called secondary organic aerosol (SOA). SOA consists of a multitude of organic compounds, only a small fraction of which has historically been identified. Formation and evolution of SOA is a complex process involving coupled chemical reaction and mass transport in the gas and particle phases. Current SOA models do not embody the full spectrum of reaction and transport processes, nor do they identify the dominant rate-limiting steps in SOA formation. Based on molecular identification of SOA oxidation products, we show here that the chemical evolution of SOA from a variety of VOC precursors adheres to characteristic "molecular corridors" with a tight inverse correlation between volatility and molar mass. The slope of these corridors corresponds to the increase in molar mass required to decrease volatility by one order of magnitude (-dM/dlogC(0)). It varies in the range of 10-30 g mol(-1), depending on the molecular size of the SOA precursor and the O:C ratio of the reaction products. Sequential and parallel reaction pathways of oxidation and dimerization or oligomerization progressing along these corridors pass through characteristic regimes of reaction-, diffusion-, or accommodation-limited multiphase chemical kinetics that can be classified according to reaction location, degree of saturation, and extent of heterogeneity of gas and particle phases. The molecular corridors and kinetic regimes help to constrain and describe the properties of the products, pathways, and rates of SOA evolution, thereby facilitating the further development of aerosol models for air quality and climate

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

“…3), the MW calculated by Eq. (7) is consistent with the molecular corridor approach (Shiraiwa et al, 2014), which suggests a tight inverse correlation between volatility and molar mass constrained by boundary lines of low and high O : C ratios. Organic compound emissions from anthropogenic fuel combustion and open biomass burning include LVOCs (with C * at 298 K equal to 10 −2 µg m −3 ), SVOCs (with C * at 298 K equal to 10 0 and 10 2 µg m −3 ), and IVOCs (with C * at 298 K equal to 10 4 and 10 6 µg m −3 ).…”

Technical Note: Development of chemoinformatic tools to enumerate functional groups in molecules for organic aerosol characterization

“…secondary OA formation, additional mechanisms (e.g., in the condensed phase) are necessary to introduce low-volatility organic compounds (LVOCs) as observed in atmospheric and controlled chamber observations (Ehn et al, 2014;Shiraiwa et al, 2014). Higher oxidation states than for compounds in the GC-MS set are observed on account of the larger number of functional groups containing electronegative atoms (oxygen and nitrogen) bonded to carbon.…”

Nanoparticle growth by particle-phase chemistry

“…Some examples include oligomers in biogenic SOA formed by accretion reactions (Barsanti and Pankow, 2004;Kalberer et al, 2004;Tolocka et al, 2004a), imine related species formed by the reaction of dicarbonyls with ammonia or amines (Galloway et al, 2014;De Haan et al, 2011;Lee et al, 2013;Stangl and Johnston, 2017), and organosulfates (Riva et al, 2016;Surratt et al, 2007;Wong et al, 2015;Xu et al, 2015). Reactions such as these increase the aerosol yield by forming additional SOA beyond what would be expected from partitioning alone, if they form non-volatile products from semi-volatile reactants in the particle phase (Lopez-Hilfiker et al, 2016;Shiraiwa et al, 2014). Experimental measurements have shown that oligomers can constitute up to about 50 % of the mass of SOA produced from biogenic precursors in laboratory reactors, though it is not clear how much of the oligomeric matter is produced from semi-volatile vs. non-volatile precursors (Hall IV and Johnston, 2011).…”

“…Owing to the high molecular weight and corresponding low volatility of oligomer products (Shiraiwa et al, 2014), early work assumed an irreversible process (Vesterinen et al, 2007), which proved effective for predicting the yields of freshly formed aerosol in chamber experiments and estimating the magnitude of the oligomerization rate constant needed for the process to impact yields. More recent models have included reversibility (Roldin et al, 2014;Trump and Donahue, 2014), which is needed to reproduce perturbations of freshly formed SOA such as changes induced by isothermal dilution, thermal degradation, and/or aging.…”