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

Abstract

Abstract. A new module, ORACLE 2-D, simulating organic aerosol formation and evolution in the atmosphere has been developed and evaluated. The module calculates the concentrations of surrogate organic species in two-dimensional space defined by volatility and oxygen-to-carbon ratio. It is implemented into the EMAC global chemistry-climate model, and a comprehensive evaluation of its performance is conducted using an aerosol mass spectrometer (AMS) factor analysis dataset derived from almost all major field campaigns that took place globally during the period 2001-2010. ORACLE 2-D uses a simple photochemical aging scheme that efficiently simulates the net effects of fragmentation and functionalization of the organic compounds. The module predicts not only the mass concentration of organic aerosol (OA) components, but also their oxidation state (in terms of O : C), which allows for their classification into primary OA (POA, chemically unprocessed), fresh secondary OA (SOA, low oxygen content), and aged SOA (highly oxygenated). The explicit simulation of chemical OA conversion from freshly emitted compounds to a highly oxygenated state during photochemical aging enables the tracking of hygroscopicity changes in OA that result from these reactions. OR-ACLE 2-D can thus compute the ability of OA particles to act as cloud condensation nuclei and serves as a tool to quantify the climatic impact of OA.

Nonlinear behavior of organic aerosol in biomass burning plumes: a microphysical model analysis

“…Specifically, it has been estimated that BB emissions of primary OA (POA), which typically constitutes the predominant fraction of BB aerosol, contribute about 70 % of total POA emissions (Bond et al, 2013). In recent years, numerous studies have been aimed at investigating and modeling sources (e.g., May et al, 2013;Jathar et al, 2014;Konovalov et al, 2015;van der Werf et al, 2017), radiative effects (e.g., Saleh et al, 2013;Archer-Nicholls et al, 2016;Pokhrel et al, 2017;Yao et al, 2017), and atmospheric transformations (e.g., Cubison et al, 2011;Jolleys et al, 2012;Forrister et al, 2015;Shrivastava et al, 2015;Konovalov et al, 2015;Tsimpidi et al, 2018;Theodoritsi and Pandis, 2019 ) of BB OA and its components.…”

“…The SVOCs and IVOCs can also be distributed between several model types, depending, e.g., on their oxidation state (O:C ratio), origin (e.g., primary or secondary, anthropogenic or biogenic, etc. ), and photochemical age (Donahue et al, 2012a,b;Shrivastava et al, 2013;Tsimpidi et al, 2018). Representing the processes involving SVOCs and IVOCs within the VBS framework has been shown to allow improving the performance of simulations of OA from vegetation fires with respect to simulations using the "conventional" OA modeling framework, in which these processes are basically disregarded and only specific volatile https://doi.org/10.5194/acp-2019-425 Preprint.…”

“…Based on simulations using the VBS method, it has also been argued (Konovalov et al, 2015;) that disregard for the BB OA aging processes might be one of the main reasons for a strong underestimation of aerosol optical depth in BB plumes by chemistry transport models using the conventional representation of OA evolution (e.g., Tosca et al, 2013;Konovalov et al, 2014;Reddington et al, 2016;Petrenko et al, 2017). It should be noted, however, that the representation of the BB OA evolution within the VBS framework in chemistry transport models is still associated with major uncertainties: while a variety of VBS schemes of different complexities have been suggested for BB OA modeling (e.g., Grieshop et al, 2009;Koo et al, 2014;Shrivastava et al, 2015;Ciarelli et al, 2017a;Tsimpidi et al, 2018), any of these schemes have only partially been constrained by laboratory or ambient measurements. In view of these uncertainties, we performed our analysis using several different available VBS schemes.…”

Modeling organic aerosol concentrations and properties during winter 2014 in the northwestern Mediterranean region

“…Although the organic matter to organic carbon ratio (OM : OC) was first believed to lie between 1.2 and 1.4 (Grosjean and Friedlander, 1975), numerous studies (Turpin and Lim, 2001;El-Zanan et al, 2005;Aiken et al, 2008;Couvidat et al, 2012;Tost and Pringle, 2012;Canagaratna et al, 2015;Tsimpidi et al, 2018) show that OM : OC is approximately 1.6 for urban aerosols and 2.1 for non urban aerosols. Zhang et al (2005a) developed an algorithm to deconvolve the mass spectra of OAs obtained with an Aerodyne ™ aerosol mass spectrometer (AMS) in order to estimate the mass concentrations of hydrocarbon-like and oxygenated organic aerosols (HOAs and OOAs).…”

Predictions of diffusion rates of organic molecules in secondary organic aerosols using the Stokes-Einstein and fractional Stokes-Einstein relations

“…SOA is formed in the atmosphere when volatile organic molecules, emitted from both anthropogenic and natural sources, are oxidized and partition to the particle phase (Ervens et al, 2011;Hallquist et al, 2009). The exact chemical composition of SOA remains uncertain; however, measurements have shown that SOA contains 1000s of different organic molecules and the average oxygen-to-carbon (O:C) ratio of organic molecules in SOA ranges from 0.3 -1.0 or even higher (Aiken et al, 2008;Cappa and Wilson, 2012;DeCarlo et al, 2008;Ditto et al, 2018;Hawkins et al, 2010;Heald et al, 2010;Jimenez et al, 2009;Laskin et al, 2018;Ng et al, 2010;Nozière et al, 2015;Takahama et al, 2011;Tsimpidi et al, 2018). SOA also contains a range of organic functional groups including alcohols and carboxylic acids (Claeys et al, 2004(Claeys et al, , 2007Edney et al, 2005;Fisseha et al, 2004;Glasius et al, 2000;Liu et al, 2011;Surratt et al, 2006Surratt et al, , 2010.…”