QUANTIFYING OLIGOMERIZATION IN ORGANIC AEROSOL THROUGH DESORPTION
THERMOGRAM MODELING
S. SCHOBESBERGER1,2, F.D. LOPEZ-HILFIKER1,3, E.L. D’AMBRO4 and J.A. THORNTON1
1
Department of Atmospheric Sciences, University of Washington, Seattle, Washington, USA.
2
Department of Applied Physics, University of Eastern Finland, Kuopio, Finland.
3
Laboratory of Atmospheric Chemistry, Paul Scherrer Institute, Villigen, Switzerland.
4
Department of Chemistry, University of Washington, Seattle, Washington, USA.
Keywords: ORGANIC AEROSOL, DESORPTION, OLIGOMERIZATION, VOLATILITY.
INTRODUCTION
Atmospheric aerosol particles adversely affect visibility and health, and they influence climate (e.g.
Pöschl, 2005). In particular the overall effect on climate is possibly strong yet highly uncertain (IPCC,
2013). Sub-micron particles are of particular interest, as they often occur in high number concentrations
and are able to act as cloud condensation nuclei (CCN), thus indirectly affecting the Earth’s radiation
balance (Lee et al., 2013). One of the most important constituents of these aerosol particles is organic
matter, making up 20% to 90% of their mass (Zhang et al., 2007). Organic aerosol can be emitted directly
(primary organic aerosol) or formed from gas-phase precursors (secondary organic aerosol, SOA), which
are typically the products of the oxidative processing of volatile organic compounds (VOC), such as
biogenically emitted isoprene and monoterpenes. The condensation or reactive uptake of these products
typically explains 90% of new particle growth to CCN within the boundary layer (Riipinen et al., 2012).
In atmospheric models, the growth of SOA particles is typically described by equilibrium partitioning
between gas and particle phase, based on the volatility of the involved compounds (Donahue et al., 2011).
However, these models have typically underpredicted observed SOA loadings (e.g. Heald et al., 2005;
Dzepina et al., 2009), and it was found that SOA evaporates more slowly than would be expected from the
volatility distributions derived from SOA growth experiments (Vaden et al., 2011). The inability of
commonly applied equilibrium partitioning models to replicate the observations may stem from an
underprediction of low volatility material, either from inaccurate descriptions of gas-phase radical
chemistry (e.g. Ehn et al., 2014) or multiphase accretion chemistry (e.g. Surratt et al., 2006), or invalid
assumptions of particle phase state (Virtanen et al., 2010), or a combination of these possibilities. Also
diffusion limitations within the particles could thus inhibit evaporation (Cappa and Wilson, 2011). As for
the underlying mechanisms, recent experimental and modeling studies indicate a major role of
oligomerization, which occurs rapidly (within minutes) upon SOA formation, and that oligomer
decomposition indeed controls SOA evaporation rates (Roldin et al., 2014; Kolesar et al., 2015b).
Recent advances in mass spectrometric techniques allow us to measure aerosol molecular composition at
sufficiently high time resolution to access those molecular-scale mechanisms. One such technique is the
Filter Inlet for Gas and AEROsol (FIGAERO), coupled to a high-resolution time-of-flight chemical
ionization mass spectrometer (CIMS) (Lopez-Hilfiker et al., 2014). Using the FIGAERO, the molecular
composition of a major fraction of an organic aerosol sample can be measured as a function of time and
temperature. The resulting composition-resolved thermograms (signal from desorbing compounds vs.
ramped temperature) show that a substantial fraction of SOA material is present at a state of lower
volatility than the volatility suggested by the composition of the molecules that desorb upon heating,
supporting the hypothesis of the pivotal role of oligomer formation and decomposition in determining
SOA properties (Lopez-Hilfiker et al., 2016).
In this study, we developed a model to simulate the temperature-controlled evaporation of organic aerosol
in the FIGAERO. We apply the model to FIGAERO measurements of SOA that was formed in chamber
experiments from the oxidation of monoterpenes (α-pinene and Δ3-carene), with the goal of quantifying
the kinetics that govern the formation and decomposition of oligomers.
METHODS
In our study, we aimed at providing a model description for the particle-phase measurements using the
FIGAERO, i.e. the desorption of molecules from an aerosol sample that had been collected on a PTFE
filter, while exposed to a flow of clean N2, and their subsequent transport into the CIMS. The temperature
of the N2 flow was usually ramped, from room temperature up to 200 °C. A detailed description of the
method is provided in Lopez-Hilfiker et al. (2014). We used a variety of experiments to determine model
parameters, including desorptions following the direct injections of calibrant solutions onto the filter, and
isothermal desorption experiments (e.g. at room temperature). Eventual application of the model was to
SOA formation experiments conducted during an intensive measurement campaign at the Pacific
Northwest National Laboratory’s 10.6 m3 environmental chamber (e.g. D'Ambro et al., 2017).
The model consists of a set of differential equations. The desorption rate for a certain compound i from a
deposited aerosol particle is described using the Hertz-Knudsen equation (Cappa et al., 2007), i.e. mainly
dependent on the compound’s absolute vapor pressure (C*), an evaporation coefficient (α) and the
condensed phase surface area. The resulting rate is reduced by multiplying with the mole fraction of the
compound in the particle phase, to account for Raoult’s Law, and by a factor that accounts for diffusion
limitation both within the particle and into the gas-phase. Two additional terms describe the production of
molecules of compound i by the dissociation of oligomers and their loss by formation of oligomers. They
are inspired by Kolesar et al. (2015a) and are temperature-dependent as in Arrhenius’ equation. By design
of the FIGAERO, desorbing molecules need to travel through the collection filter, before they can be
detected by the CIMS, hence introducing vapor-surface interactions that we accounted for in a fashion
similar to the treatment by Zhang et al. (2014) of reversible organic vapor wall loss in laboratory chamber
SOA experiments.
RESULTS & CONCLUSIONS
Our model output generally produced thermogram peaks similar to those observed in FIGAERO
experiments. Disregarding oligomerization at first, the main factors that determined the peak position and
shape were: particle-phase diffusivity, expressed as a reduction in α (e.g. Saleh et al., 2013), C*, the
enthalpy of vaporization ΔH, and the parameters governing vapor-surface interactions, comprising a time
scale τ and an equivalent wall organic aerosol concentration Cw. Through a set of isothermal evaporation
experiments, we determined τCw on the order of 5 mg m–3 s, agreeing with previous experimental findings
on interactions of organic vapors with PTFE surfaces (Matsunaga and Ziemann, 2010). Figure 1 shows a
model application to the desorption thermograms of a solution containing monocarboxylic acids, using
literature values for C* and ΔH (Lopez-Hilfiker et al., 2014) and α = 1. It shows good overall agreement.
The modeled peak position is sensitive to both C* and ΔH, e.g. a shift by ~10 °C for a change in C* by a
factor of 5. The observed divergence from experimental results of less than 10 °C is thus well within
uncertainties common in determining C*. Similar reasoning applies for uncertainties in ΔH, which also
affects the steepness of the peak’s slope and hence width.
When sampling SOA using the FIGAERO on the other hand, a substantial fraction of the signal for the
majority of compounds desorbed at higher temperatures than their elemental composition would suggest.
The corresponding thermograms usually exhibited an initial peak, followed by a plateau towards higher
temperatures, or even a second peak (Lopez-Hilfiker et al., 2016). Using our model, we are now able to
simulate these more complex peak shapes, when we include the oligomerization terms, and use the rate
constants and activation energies that control oligomer formation and dissociation as free parameters in
fitting the observed thermograms.
As a result, we gain a quantitative insight into the dynamics of the reversible oligomerization reactions
occurring in SOA, broken down into the desorbing molecular compositions, that in many cases were also
measured when sampling directly from the gas-phase.
Figure 1. Comparison of experimental results from injecting a solution containing monocarboxylic acids
(circles) with model results (lines).
We expect that the new information gained from this approach will greatly improve our understanding of
the gas- and particle-phase organic chemistry that control organic aerosol dynamics and properties, and
consequently improve the performance of existing and future model frameworks.
ACKNOWLEDGEMENTS
This work was supported by the U.S. Department of Energy under grant DE-SC0011791.
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