Identifying organic aerosol sources by comparing functional group composition in chamber and atmospheric particles

Abstract

Measurements of submicron particles by Fourier transform infrared spectroscopy in 14 campaigns in North America, Asia, South America, and Europe were used to identify characteristic organic functional group compositions of fuel combustion, terrestrial vegetation, and ocean bubble bursting sources, each of which often accounts for more than a third of organic mass (OM), and some of which is secondary organic aerosol (SOA) from gas-phase precursors. The majority of the OM consists of alkane, carboxylic acid, hydroxyl, and carbonyl groups. The organic functional groups formed from combustion and vegetation emissions are similar to the secondary products identified in chamber studies. The near absence of carbonyl groups in the observed SOA associated with combustion is consistent with alkane rather than aromatic precursors, and the absence of organonitrate groups can be explained by their hydrolysis in humid ambient conditions. The remote forest observations have ratios of carboxylic acid, organic hydroxyl, and nonacid carbonyl groups similar to those observed for isoprene and monoterpene chamber studies, but in biogenic aerosols transported downwind of urban areas the formation of esters replaces the acid and hydroxyl groups and leaves only nonacid carbonyl groups. The carbonyl groups in SOA associated with vegetation emissions provides striking evidence for the mechanism of esterification as the pathway for possible oligomerization reactions in the atmosphere. Forest fires include biogenic emissions that produce SOA with organic components similar to isoprene and monoterpene chamber studies, also resulting in nonacid carbonyl groups in SOA.atmospheric aerosol | organic particles | smog chamber aerosol | alkane oxidation products | photochemical reactions R ecent literature reviews have highlighted significant advances in identifying compounds formed as "secondary" organic aerosol (SOA) in a wide range of laboratory-simulated atmospheric conditions, and field studies have identified several individual products from chamber studies (1-3). Paulot et al. have used global modeling to show that extrapolating these laboratory results for modeled global oxidant distributions helps explain biogenic SOA (4). However, the observations needed to confirm the proposed sources of organic aerosols (OAs) in global modelsnamely, quantitative field measurements of the proposed products to compare with the model predictions-remain elusive (5). Without such confirmation, it is difficult to identify which of the mechanisms proposed to explain controlled laboratory studies of simple volatile organic compound (VOC)-oxidant systems would satisfactorily capture those aspects of the chemistry that determine SOA formation in the more complex atmosphere.There has been significant progress in quantifying organic mass (OM, the particle-phase components of OA) from the fragments produced by online mass spectrometry of particles, with recent promulgation of techniques to quantify the resulting mixtures by two to four fragment-based positive matri...

Estimating the contribution of organic acids to northern hemispheric continental organic aerosol

“…Recent studies indicate organic acids can be a large fraction of SOA at locations highly influenced by biogenic emissions [Vogel et al, 2013], and it has been hypothesized that their fractional contribution to OA increases with increasing photochemical age [Heald et al, 2010;Levin et al, 2014]. However, quantitative determination of the fraction of submicron OA that is composed of organic acids is still limited despite indications that they can be a substantial fraction of OA [Russell et al, 2011]. Organic acids contribute substantially to the ion signal at mass-to-charge ratio (m/z) 44 (mostly from CO 2 + Here we show that total particle-phase organic acid concentration correlates well with AMS OA and m/z 44 in three different studies, a study in semipolluted temperate montane forest in Colorado, USA, and two studies at a mixed boreal forest in Hyytiälä, Finland, in different years and with different source contributions.…”

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

“…There have been many proposals for reducing representations in which a mixture of 10 000+ different types of molecules (Hamilton et al, 2004) are represented by some combination of their molecular size, carbon number, polarity, or elemental ratios (Pankow and Barsanti, 2009;Kroll et al, 2011;Daumit et al, 2013;Donahue et al, 2012), many of which are associated with observable quantities (e.g., by aerosol mass spectrometry (AMS; Jayne et al, 2000), gas chromatography-mass spectrometry (GC-MS and GCxGC-MS; Rogge et al, 1993;Hamilton et al, 2004)). Molecular bonds or organic functional groups (FGs), which are the focus of this manuscript, can also be used to provide reduced representations for mixtures and have been shown useful for organic mass (OM) quantification, source apportionment, and prediction of hygroscopicity and volatility (e.g., Russell, 2003;Donahue, 2011;Russell et al, 2011;Suda et al, 2014). Examples of property estimation methods include models for purecomponent vapor pressure (Pankow and Asher, 2008;Compernolle et al, 2011), UNIFAC, and its variations for activity coefficients and viscosity (Ming and Russell, 2001;Griffin et al, 2002;Zuend et al, 2008Zuend et al, , 2011.…”

Microscale spectrophotometric methods for quantification of functional groups in oxidized organic aerosol

“…Offline analysis of organic functional groups, including carbonyl, hydroxyl, carboxyl, sulfate, and nitrate, conducted most often using Fourier transform infrared (FTIR) spectroscopy, has provided useful information at a level of chemical detail intermediate to that obtained with other methods. Use of this method requires significant data analysis and interpretation skills, however, because overlapping spectral peaks can make identification and quantification of specific functional groups difficult (Russell et al 2011).…”