Chemical composition and volatility of atmospheric organic aerosol (OA) particles are interconnected. Functionalization of organic molecules can modify their vapor pressure by several orders of magnitude (Pankow and Asher, 2008; Donahue et al., 2011; Graham et al., 2023; Isaacman-VanWertz and Aumont, 2021; Kroll and Seinfeld, 2008). Volatility determines whether a compound partitions into or evaporates from the particle phase and thus influences particulate mass and lifetime in the atmosphere. Accurate predictions of atmospheric organic particle mass thus require a quantitative understanding of the chemical nature and volatility of its components. This is of special importance for oxygenated organic aerosol (OOA), most of which is of secondary origin (i.e., SOA) (Jimenez et al., 2009) and involves phase transitions and chemical reactions in all phases. Further, the conditions of the atmosphere, e.g., temperature, play a critical role. A semi-volatile compound may be in the gas phase in the boundary layer at 25 °C but may condense at an altitude of ∼ 4.5 km where the ambient temperature is ∼ 20 °C lower, and consequently its apparent volatility is lower by up to ∼ 2 orders of magnitude (Bardakov et al., 2021; Donahue et al., 2011; Epstein et al., 2010; Stolzenburg et al., 2018). The complex thermodynamics and chemistry of OOA and their changes during its lifetime in the atmosphere are challenging to measure and represent in modeling frameworks, resulting in simplified descriptions of volatility (Nozière et al., 2015; Hallquist et al., 2009). The development of the filter inlet for gases and aerosols coupled to a time-of-flight chemical ionization mass spectrometer (FIGAERO-CIMS) has enabled the combined analysis of molecular composition and volatility of OOA particles in near-real time (Thornton et al., 2020; Lopez-Hilfiker et al., 2014). OOA volatility can be derived in two ways from FIGAERO-CIMS: 1. The molecular information of OOA can be used to parametrize volatility via calculations of the effective saturation mass concentrations (Csat) of different organic compounds (Donahue et al., 2011; Li et al., 2016; Mohr et al., 2019; Isaacman-VanWertz and Aumont, 2021; Graham et al., 2023). There are various parametrization methods to calculate Csat, based on different assumptions, training datasets, or structurebased estimation methods (Isaacman-VanWertz and Aumont, 2021). However, calculated Csat from different parametrizations may vary: while the modified Li method (Li et al., 2016; Isaacman-VanWertz and Aumont, 2021) tends to estimate higher vapor pressures than expected for low-volatility species, the Daumit (Daumit et al., 2013) and Donahue methods (Donahue et al., 2011) (also the updated Mohr method; Mohr et al., 2019) are found to estimate lower vapor pressures (Isaacman-VanWertz and Aumont, 2021). The discrepancy in Csat among different parametrizations can span several orders of magnitude and becomes even larger for compounds with increasing nitrogen numbers, especially for organonitrates with multiple nitrate groups (Wu et al., 2021; Isaacman-VanWertz and Aumont, 2021; Graham et al., 2023). This could induce uncertainties associated with volatility estimates, particularly for the complex ambient particle matrix (O’Meara et al., 2014), in addition to the potential divergence from the sum of individual parametrized Csat due to non-ideal intermolecular interactions (Compernolle et al., 2011; Isaacman-VanWertz and Aumont, 2021). 2-While the particles are thermally desorbed within the FIGAERO for molecular composition analysis with CIMS, the instrument also yields a qualitative measure of particle volatility through the signal evolution as a
function of desorption temperature (i.e., thermograms) (Lopez-Hilfiker et al., 2014, 2015). A model framework has also been developed to reproduce the shape
of thermograms (Schobesberger et al., 2018). The desorption temperature at which a compound exhibits maxAtmos. Chem. Phys., 24, 2607–2624, 2024 https://doi.org/10.5194/acp-24-2607-2024 W. Huang et al.: Variation in chemical composition and volatility of OOA 2609 imum signal (Tmax) correlates with the compound’s enthalpy of sublimation and can be used to infer its saturation vapor pressure (Lopez-Hilfiker et al., 2015; Mohr et al., 2017). Evaporative behavior and hence thermogram shape, Tmax, and inferred volatility of a particlebound compound are subject to artifacts from thermal decomposition (Lopez-Hilfiker et al., 2015; Yang et al.,
2021), the heating rate (Yang et al., 2021; Ylisirniö et al., 2021; Thornton et al., 2020; Schobesberger et al., 2018), FIGAERO geometry (Ylisirniö et al., 2021), the
presence of isomers (Thompson et al., 2017; Masoud and Ruiz, 2021), and particle-phase diffusivity and viscosity (Yli-Juuti et al., 2017; Huang et al., 2018). As a consequence, thermogram shape and Tmax for an individual compound can vary among different chamber studies (D’Ambro et al., 2017; Huang et al., 2018; Wang and Hildebrandt Ruiz, 2018) and field measurements (Thompson et al., 2017; Huang et al., 2019b; Gaston et
al., 2016), which could also induce uncertainties associated with volatility estimates, similar to parametrization.
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