Abstract
Abstract. Secondary organic aerosol (SOA) formation from mixtures of volatile precursors may be influenced by the molecular interactions of the components of the mixture. Here, we report measurements of the volatility distribution of SOA formed from the photo-oxidation of o-cresol, α-pinene, and their mixtures, representative anthropogenic and biogenic precursors, in an atmospheric simulation chamber. The combination of two independent thermal techniques (thermal denuder, TD, and the Filter Inlet for Gases and Aerosols coupled to a high-resolution time-of-flight chemical ionization mass spectrometer, FIGAERO-CIMS) to measure the particle volatility, along with detailed gas- and particle-phase composition measurements, provides links between the chemical composition of the mixture and the resultant SOA particle volatility. The SOA particle volatility obtained by the two independent techniques showed substantial discrepancies. The particle volatility obtained by the TD was wider, spanning across the LVOC and SVOC range, while the respective FIGAERO-CIMS derived using two different methods (i.e. calibrated Tmax and partitioning calculations) was substantially higher (mainly in the SVOC and IVOC, respectively) and narrow. Although the quantification of the SOA particle volatility was challenging, both techniques and methods showed similar trends, with the volatility of the SOA formed from the photo-oxidation of α-pinene being higher than that measured in the o-cresol system, while the volatility of the SOA particles of the mixture was between those measured at the single-precursor systems. This behaviour could be explained by two opposite effects, the scavenging of the larger molecules with lower volatility produced in the single-precursor experiments that led to an increase in the average volatility and the formation of unique-to-the-mixture products that had higher O:C, MW, OSc‾ and, consequently, lower volatility compared to those derived from the individual precursors. We further discuss the potential limitations of FIGAERO-CIMS to report quantitative volatilities and their implications for the reported results, and we show that the particle volatility changes can be qualitatively assessed, while caution should be taken when linking the chemical composition to the particle volatility. These results present the first detailed observations of SOA particle volatility and composition in mixed anthropogenic and biogenic systems and provide an analytical context that can be used to explore particle volatility in chamber experiments.
Highlights
Aerosol particles are ubiquitous in the atmosphere, with substantial impacts on climate (Ramanathan et al, 2001) and human health (Lelieveld et al, 2015; Brunekreef and Holgate, 2002)
We employ two independent thermal techniques, and we aim to provide the first observational quantification of the volatility of secondary organic aerosol (SOA) formed from the mixing of an anthropogenic VOC (aVOC) and a biogenic VOC (bVOC) precursor with modest and high yields, respectively
Many climate/air quality prediction models express SOA volatility based on single-precursor experiments and neglect the potential interactions of the molecules derived from various precursors that are emitted in the atmosphere and lead to the SOA formation (Yahya et al, 2017; Tsimpidi et al, 2018)
Summary
Aerosol particles are ubiquitous in the atmosphere, with substantial impacts on climate (Ramanathan et al, 2001) and human health (Lelieveld et al, 2015; Brunekreef and Holgate, 2002). These particles may contain a wide variety of com-. SOA is formed by the oxidation of volatile organic compounds (VOCs) in the atmosphere These reactions result in a variety of products that can be more or less volatile than their precursors, depending on the underlying chemical processes in the gas and/or condensed phase (Donahue et al, 2012). Our current mechanistic understanding and the resulting SOA representation in predictive models remain inadequate (Shrivastava et al, 2017; McFiggans et al, 2019), leading to a significant uncertainty in the assessment of their impacts
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