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.

Highlights

  • Population exposure to atmospheric particulate matter (PM) increases premature mortality from cardiovascular and respiratory diseases (Pope et al, 2002; IARC, 2016; Cohen et al, 2017)

  • The number of bins that can be used in the analysis of thermodenuder data is in general determined by the ambient Organic aerosol (OA) concentration, the number of temperature steps used in the analysis, and the maximum fraction of the OA evaporated during the analysis

  • The availability of measurements at 25, 60, 80 and 100 ◦C means a maximum of four bins are possible; since the OA was on the order 5 μg m−3, the thermograms contain little information on the partitioning of compounds with saturation concentration exceeding 100 μg m−3

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Summary

Introduction

Population exposure to atmospheric particulate matter (PM) increases premature mortality from cardiovascular and respiratory diseases (Pope et al, 2002; IARC, 2016; Cohen et al, 2017). Volatility measurements are mostly carried out using heated laminar flow reactors, known as thermodenuders (TDs) (Burtscher et al, 2001; An et al, 2007) or isothermal dilution (Grieshop et al, 2009) In these systems, changes in OA mass concentration are related to the OA evaporation rate and its volatility can be estimated. Clearly linked to volatility, the MFR depends on the enthalpy of vaporization ( Hvap), the aerosol concentration, the heating section residence time, the particle size distribution, and potential particle-to-gas mass transfer resistances All these parameters complicate the linking of the measured MFR to the volatility. Lopez-Hilfiker et al (2016) suggested that the IEPOX SOA had a very low saturation concentration with C∗ = 10−4 μg m−3, based on the Filter Inlet for Gases and AEROsols coupled to a Chemical-Ionization Mass Spectrometer (FIGAERO-CIMS) signals of C5H12O4 and C5H10O3. We proceed to associate the hygroscopicity parameters estimated by Cerully et al (2015) with the volatility distributions and test their consistency with the Nakao (2017) theoretical framework

Measurement site and campaign
Instrumentation
PMF and elemental ratios
TD losses
Volatility distribution estimation
Hygroscopicity
Volatility of organic aerosol
Volatility of OA components
Sensitivity analysis
Accommodation coefficient
TD collection efficiency
Comparisons with other studies
Link to the 2D-VBS framework
Conclusions
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