Organic aerosol in the summertime southeastern United States: components and their link to volatility distribution, oxidation state and hygroscopicity

Abstract

Abstract. The volatility distribution of the organic aerosol (OA) and its sources during the Southern Oxidant and Aerosol Study (SOAS; Centreville, Alabama) was constrained using measurements from an Aerodyne high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS) and a thermodenuder (TD). Positive matrix factorization (PMF) analysis was applied on both the ambient and thermodenuded high-resolution mass spectra, leading to four factors: more oxidized oxygenated OA (MO-OOA), less oxidized oxygenated OA (LO-OOA), an isoprene epoxydiol (IEPOX)-related factor (isoprene-OA) and biomass burning OA (BBOA). BBOA had the highest mass fraction remaining (MFR) at 100 ∘C, followed by the isoprene-OA, and the LO-OOA. Surprisingly the MO-OOA evaporated the most in the TD. The estimated effective vaporization enthalpies assuming an evaporation coefficient equal to unity were 58 ± 13 kJ mol−1 for the LO-OOA, 89 ± 10 kJ mol−1 for the MO-OOA, 55 ± 11 kJ mol−1 for the BBOA, and 63 ± 15 kJ mol−1 for the isoprene-OA. The estimated volatility distribution of all factors covered a wide range including both semi-volatile and low-volatility components. BBOA had the lowest average volatility of all factors, even though it had the lowest O : C ratio among all factors. LO-OOA was the more volatile factor and its high MFR was due to its low enthalpy of vaporization according to the model. The isoprene-OA factor had intermediate volatility, quite higher than suggested by a few other studies. The analysis suggests that deducing the volatility of a factor only from its MFR could lead to erroneous conclusions. The oxygen content of the factors can be combined with their estimated volatility and hygroscopicity to provide a better view of their physical properties.

An omnipresent diversity and variability in the chemical composition of atmospheric functionalized organic aerosol

“…SOA parameterizations in 3-D chemistry and climate models should be informed by these results and incorporate strategic simplifications that capture key changes in molecular-level composition. Existing bulk composition metrics include elemental ratios 26 , bulk carbon oxidation state 34 , mass spectra fragments (e.g., m/z 43/44) 40 , functional group fractions 41 , or often generic statistically-derived source apportionment factors (e.g., more oxidized oxygenated OA (MO-OOA), less oxidized oxygenated OA (LO-OOA)) 42 . Nontargeted molecular-level speciation should be used to elucidate variations between seemingly similar bulk metric values within data sets, between studies, and in model evaluations.…”

“…Future work should determine which chemical/physical properties are most prone to changes with compositional variability, and how observed compositional changes at different locations will affect SOA properties. For example, while there is some correlation between hygroscopicity and average O/C 42,46 , O/C distribution is also important in determining volatility 42,47 . Additionally, metrics like O/C, which do not reflect changes in the isomeric composition of a mixture, may be misleading in characterizing an aerosol mixture because overall O/C may remain fairly consistent with time, and therefore seemingly indicate consistent chemical composition.…”

Summertime aerosol volatility measurements in Beijing, China

“…For example, Kostenidou et al (2018) found that SOA species with higher MFR can be more volatile because of lower enthalpy of vaporization. As a result, a mass transfer model taking into account during the dynamic evaporation of the aerosol all these properties that affect volatility as vaporization enthalpy residence time, particle size and OA concentration into account is needed for better interpretation of OA volatility measurements (Riipinen et al, 2010).…”

Supplementary material to "Summertime aerosol volatility measurements in Beijing, China"

“…Volatility distribution ∆H am 1 0 -8 10 -7 10 -6 10 -5 10 -4 10 -3 10 -2 10 -1 10 0 10 1 10 2 , 2008, . (Lee et al, 2010.…”

“…g Sampling site is Centreville, and the sampling time is June/July 2013 (Kostenidou et al, 2018). This estimation use the transfer model of Riipinen et al (2010), and the uncertainties are calculated by Karnezi et al (2014).…”