We measured a large suite of gas- and particle-phase multi-functional organic compounds with a Filter Inlet for Gases and AEROsols (FIGAERO) coupled to a high-resolution time-of-flight chemical ionization mass spectrometer (HR-ToF-CIMS) developed at the University of Washington. The instrument was deployed on environmental simulation chambers to study monoterpene oxidation as a secondary organic aerosol (SOA) source. We focus here on results from experiments utilizing an ionization method most selective towards acids (acetate negative ion proton transfer), but our conclusions are based on more general physical and chemical properties of the SOA. Hundreds of compounds were observed in both gas and particle phases, the latter being detected by temperature-programmed thermal desorption of collected particles. Particulate organic compounds detected by the FIGAERO–HR-ToF-CIMS are highly correlated with, and explain at least 25–50 % of, the organic aerosol mass measured by an Aerodyne aerosol mass spectrometer (AMS). Reproducible multi-modal structures in the thermograms for individual compounds of a given elemental composition reveal a significant SOA mass contribution from high molecular weight organics and/or oligomers (i.e., multi-phase accretion reaction products). Approximately 50 % of the HR-ToF-CIMS particle-phase mass is associated with compounds having effective vapor pressures 4 or more orders of magnitude lower than commonly measured monoterpene oxidation products. The relative importance of these accretion-type and other extremely low volatility products appears to vary with photochemical conditions. We present a desorption-temperature-based framework for apportionment of thermogram signals into volatility bins. The volatility-based apportionment greatly improves agreement between measured and modeled gas-particle partitioning for select major and minor components of the SOA, consistent with thermal decomposition during desorption causing the conversion of lower volatility components into the detected higher volatility compounds.
The sources, oxidation pathways, and chemical properties of secondary organic aerosol (SOA) remain highly uncertain on a molecular basis. These uncertainties can lead to large errors between modeled and measured aerosol loadings (Heald et al., 2010; Volkamer et al., 2006) and ultimately limit our ability to confidently predict changes in aerosol properties under a warming climate (Hallquist et al., 2009) or in response to other anthropogenic perturbations such as emissions of nitrogen oxides and sulfur dioxide. To develop adequate model parameterizations of organic aerosol (OA) formation, growth, and loss, there remains a need to improve and evaluate chemical mechanisms that involve conversion and partitioning of organic compounds between gas and condensed phases (Roldin et al., 2014). These needs are likely to be at least partially addressed by a more detailed understanding of molecular composition in both phases at higher time resolution from which mechanistic insights are more easily discerned.
While SOA is ubiquitous in the lower atmosphere, our understanding of its composition and properties, from nucleation and growth of freshly formed particles to background ambient particles, is still lacking (Donahue et al., 2011; Ehn et al., 2014; Riccobono et al., 2014; Riipinen et al., 2012; Ziemann, 2002). Identifying the sources and functional groups of organic molecules within SOA remains a difficult analytical challenge, given that their inherent low volatility makes routine online analysis by mass spectrometry impossible without perturbation (thermal desorption, dissolution, derivatization, etc.) and that the actual source molecules initially condensing into the particle phase may have been transformed via condensed phase chemistry, such as acid–base reactions or various organic accretion processes (Smith et al., 2010; Ziemann and Atkinson, 2012).
Recently, in situ measurement methods have been developed which can address the volatility distribution or molecular composition of organic aerosol. volatility tandem differential mobility analyzers allow the measurement of kinetic evaporation in a series of ovens, which can be used to constrain the bulk volatility of compounds present in the aerosol (Cappa, 2010). Similarly, thermal denuders have been coupled to aerosol mass spectrometers to examine the loss organic aerosol mass during transit through a programmatically heated oven with some molecular information derived from factor analysis (Cappa and Jimenez, 2010). Other chemically speciated measurements, for example the TAG (thermal desorption aerosol gas chromatograph) (Williams et al., 2006), measure the molecular composition of thermally desorbed compounds but lack a direct measure of the aerosol volatility and rather use functional group dependencies to infer the volatility distribution based on detected compositions. Holzinger et al. (2010) coupled an analogous collection-thermal-desorption inlet to a PTR-MS (proton transfer reaction mass spectrometer) to detect organic and inorganic compounds while also providing thermogram information, but this type of chemical ionization often leads to fragmentation and dehydration (Holzinger et al., 2010). We recently developed the FIGAERO (Filter Inlet for Gases and AEROsol), which allows both the separation of components in a volatility space, via a temperature-programmed thermal desorption, and the determination of the corresponding molecular composition on an hourly timescale (Lopez-Hilfiker et al., 2014).
We present measurements of compounds produced from
A prototype FIGAERO–HR-ToF-CIMS was coupled to both the Jülich Plant Atmosphere Chamber (JPAC), Jülich Forschungszentrum, Germany (Mentel et al., 2009), and a smaller chamber with similar conditions at the University of Washington (UW). Below we provide detailed descriptions of the coupling of the FIGAERO–HR-ToF-CIMS to the JPAC and UW chambers.
Most data presented here were obtained at the JPAC (Mentel et al., 2009), which
consists of a series of glass chambers housed in separate temperature-controlled rooms. We used a 1450 L borosilicate glass chamber housed in a
temperature-controlled room held at 15
Here we present measurements at the end of the PANDA campaign when we
utilized the FIGAERO inlet system. Concurrent measurements were made of
gas-phase extremely low volatility organic compounds (ELVOC) (Ehn et al., 2014) using a nitrate CIMS (Jokinen et
al., 2012), 1–3 nm sized particles with a particle size magnifier (Airmodus, Finland), the number size distribution from 3 to 600 nm using a
scanning mobility particle sizer (SMPS, TSI model 3071), monoterpene
concentrations using a quadrupole PTR-MS (Ionicon, Austria), and
non-refractory particle composition with an high-resolution aerosol mass
spectrometer (HR-AMS, Aerodyne, USA) (DeCarlo et al., 2006). We used the
composition measured from the AMS and total particle volume from a SMPS to
calculate particle mass of sulfate and SOA present in the chamber. The
chamber was run in steady-state mode with constant addition of 30 ppb
A University of Washington chamber was also used for optimization and
initial testing of the FIGAERO in steady-state conditions as well as batch
mode experiments to explore the time dependence of oxidation products. The
chamber consists of a 0.7 m
The FIGAERO and its HR-ToF-CIMS coupling has been described in detail elsewhere (Lopez-Hilfiker et al., 2014), and therefore only a review of its general operation and differences from the standard operation is described here. The FIGAERO is essentially a multi-port inlet assembly that allows sampling of either gas-phase components or thermally desorbed particulate compounds into a high-resolution time of flight mass spectrometer (HR-ToF-CIMS) with selective detection by chemical ionization. Here we present results using acetate negative-ion proton transfer ionization (Veres et al., 2008). The instrument is continuously cycled between gas and particle analysis modes with periodic determinations of the particle and gas-phase background signals using a secondary filter (Lopez-Hilfiker et al., 2014).
A prototype of the FIGAERO–HR-ToF-CIMS was deployed at JPAC and installed
directly under the chamber in the temperature-controlled chamber room. As
the FIGAERO was primarily designed for ambient sampling at high flows, for
faster particle concentration on the filter we reduced the flow rate
across each of the inlets and changed the inlet tube diameters to be more
appropriate for the low-flow conditions required on the chamber, which has a
finite fill rate. At JPAC, chamber air was drawn at 10 standard L min
To assess the particle background due to adsorption and/or absorption of
gases onto the Teflon filter, we manually placed an identical filter in a
Teflon filter holder immediately upstream of the FIGAERO filter. Particle
backgrounds were conducted at each photochemical condition in the chamber,
usually multiple times, to obtain representative backgrounds at each stage.
Gas-phase backgrounds were assessed at the point prior to desorption when
the instrument is sampling UHP N
The measurements reported here were
obtained using acetate ionization, employed as described previously
(Mohr et al., 2013; Veres et al., 2008; Yatavelli
et al., 2012). The dominant ionization mechanism is expected to be proton
abstraction governed by the gas-phase
The reagent ion cluster distribution is unknown for our conditions, but
tests changing the collisional declustering after the ionization region
indicate
The acetate chemical ionization scheme is still relatively novel, and as
such its selectivity and sensitivity towards a large range of compounds and
functional groups remains to be characterized. For this reason, we performed
a series of calibrations after the measurement campaign to investigate the
sensitivity of acetate ionization to a series of carboxylic acids, RC(O)OH,
and related functional groups, such as peroxy acids, RC(O)O–OH, and a diacyl
peroxide (benzoyl peroxide) (see Fig. 1). In all cases, compounds were
calibrated as described previously (Mohr et al., 2013). For these tests,
the walls of the IMR region were heated to 60
The distribution of observed sensitivities to carboxylic acids using
acetate reagent ions. Error bars are 1
For compounds with a carboxyl-related group, there appears to be a convergence of sensitivity towards a maximum value similar to that for formic acid (see Fig. 1). Given the available evidence, using this maximum sensitivity value for the entire spectrum would presumably lead to a lower-limit estimate of the mass concentrations of such compounds in the chamber. Clearly, further investigation and optimization of acetate ionization selectivity is needed, as is care attributing any signal to a specific functionality measured with acetate ionization. That said, our main conclusions are not dependent upon the exact functional groups acetate detects.
An overview of the experiments conducted in the JPAC chamber. Green and red lines are total organic and sulfate mass concentrations measured by an HR-AMS. The total organic aerosol mass detected by the AMS responded to changes in seed concentrations, increasing the inferred SOA yield. Size selected ammonium sulfate additions were performed to increase the particle surface area relative to chamber walls. The sum FIGAERO–HR-ToF-CIMS particulate mass using acetate reagent ions is shown in green circles using the formic acid sensitivity for all detected compositions (see text for details). Inset: correlation between the HR-AMS-derived total organic aerosol mass concentrations and the FIGAERO–HR-ToF-CIMS-derived total “acyl”-containing compound mass concentrations shows that across all chamber conditions the FIGAERO measurements explain 25–50 % of the total organic aerosol mass.
Figure 2 shows an overview of the key aerosol measurements during the time
when the FIGAERO was installed at JPAC. As the surface area concentration of
the seed particles increased, the detected AMS organic aerosol mass
increased while the amount of monoterpene reacted remained constant.
Consequently, during ozonolysis conditions, the inferred SOA mass yield
increased from 1 to 10 % even though the
To make the comparison between the FIGAERO–HR-ToF-CIMS and AMS quantitative,
we used the sensitivity of the instrument to formic acid (ions s
As the FIGAERO allows measurement of compounds in both the gas and particle
phase, we are able to assess the phase partitioning directly of individual
molecular compositions, after accounting for the volumetric concentration in
the particle-phase inlet relative to the instantaneous gas-phase
measurement. To calculate the phase partitioning, we follow a similar
procedure to that of Yatavelli et al. (2014), defining fraction in the particle phase (
In Fig. 3 (top panels), we show the thermal desorption profiles for two
molecular compositions, C
Top panels: thermograms for two ion compositions, each showing two
distinct modes in the thermogram, are plotted showing the results of fitting
desorption profiles characteristic of an individual compound with a specific
enthalpy of sublimation. The first, lower temperature, modes are consistent
with the corresponding carboxylic acid desorbing as a non-interacting
component of the collected organic aerosol. We attribute the second, higher
temperature modes, to thermal decomposition of lower volatility compounds
(such as oligomers or highly functionalized monomers) which are thermally
unstable and which presumably do not affect the partitioning of the primary
acids between the gas and particle phases. Lower Panels: the time series of
the individual peak integrations within a fitted thermogram for the two
different ion compositions across all chamber conditions. A significant
fraction of the total detected signal arises from thermal decomposition (red)
during desorption. Evident is the nearly complete disappearance of the second
mode in C
To further investigate the sources of, and thus information carried by, the
more complex desorption features observed in the
Typical results from this thermogram-fitting routine are shown in the top
panels of Fig. 3. Two or three separate particle-phase components are
desorbing as C
In the bottom panels of Fig. 3 we show the time series of the fractional
contribution each of the fitted desorption peaks makes to the overall
thermogram area. Interestingly, the lower volatility secondary modes in the
thermal desorption of norpinic and pinic acids are more prominent during
ozonolysis than OH-dominated oxidation of
With the above insights into the thermal desorption process, we can more
accurately assess gas-particle partitioning. We use equilibrium partitioning
theory first described by Pankow (1994) to model the phase
partitioning of three monoterpene-derived acids: pinic, norpinic, and
pinonic acids. Equation (2) shows the transformation of equilibrium
partitioning theory to a formulation of fraction in the particle phase
(
Based on the thermogram analysis presented above, it is highly unlikely that
norpinic and pinic acid partitioning would be well described by an
equilibrium partitioning model that assumed all of the measured particle
mass (thermogram area) carried in those two acids was in equilibrium with
the corresponding gas-phase acids. We demonstrate this issue in Fig. 4. In
the top panels of Fig. 4, we show the thermal desorption profiles for the
three molecular compositions, C
Top panels: thermogram shapes for ozonolysis conditions (black)
and OH oxidation (red) for the ion compositions corresponding to those of
pinic, pinonic, and norpinic acids (left to right). Middle panels: comparison
between modeled and measured gas-particle partitioning (
Previous comparisons to adsorptive partitioning models have used the full
integrated thermogram arising from thermal desorption to compare with the
model prediction and found varying levels of agreement (Yatavelli et
al., 2014; Zhao et al., 2013). In the middle panels of Fig. 4, we plot the
measured
In the lower panels of Fig. 4 we show the results of deconvolving the
thermograms into the different modes using the above fitting approach for a
more direct test of the actual partitioning of these three acids into the
particle phase. Based on our previous work, compounds with compositions
similar to those under consideration here, C
The revised partitioning calculation, using the thermogram-fitting approach,
does not improve the measurement–model agreement for pinonic acid. Pinonic
acid desorbs as a single peak under all conditions, therefore providing no
basis for selecting a smaller portion of the thermogram area. Yet, pinonic
acid clearly desorbs from the
We conclude this section by noting that thermal decomposition of particulate organic material likely occurs in any technique that utilizes heat to drive compounds into the gas phase for analysis. However, by utilizing a calibrated relationship between molecular composition and desorption temperature, together with a slow desorption-temperature ramp rate, the effects of thermal decomposition on inferred vapor-pressure-driven partitioning can be addressed and even utilized to arrive at a more complete view of SOA composition and volatility as we demonstrate below.
Above we provided specific examples of how a consistent framework can be
used to relate SOA molecular composition and vapor-pressure-driven
partitioning to thermal desorption measurements. However, as shown in Fig. 5 (top), we observe many (hundreds) of compounds which desorb at much higher
temperatures from
The simplest way to organize and reduce the information in Fig. 5 (top) is by
summing the calibration adjusted signal across all detected compounds. We
show an example of the resulting sum thermogram in Fig. 5 (bottom). Using the
thermogram-fitting approach discussed above, as well as our previously determined
volatility axis based on desorption temperature, we can examine the bulk
volatility of the
Top: points are plotted sized by the square root of their particle-phase
desorption signal. Evident is a secondary mode in abundance space between
carbon number 15 and 20. Each compound is colored by the temperature of maximum
desorption signal, which is related to a compound's enthalpy of sublimation
(saturation vapor pressure) as shown previously (Lopez-Hilfiker et al.,
2014). Bottom: a sum thermogram, i.e., the entire mass spectral signal at
each measurement time (using selection criteria detailed in the text), is
summed and plotted versus desorption temperature. Clearly SOA from
The above estimates of
From an analysis of molecular thermograms of
Our findings are broadly similar with other online and offline molecular
characterizations of
The addition of OH and UV light suppresses the secondary low volatility
modes in the thermograms of pinic and norpinic acids, which suggests that
the products formed via accretion chemistry may be susceptible to photolysis
and reaction with OH or involve a precursor formed only during ozonolysis.
The presence of peroxides as macromolecule linkages is certainly consistent
with our results given that the secondary modes in thermograms of many
compounds arise at temperatures above the O–O bond strength, the weakest
covalent bond. That there might be a significant source of low-volatility
peroxide compounds in
We use an estimate of the O–O bond strength to put desorption temperature
into a chemical bond context, but the O–O bond strength will depend upon
molecular structure, and thus there is likely a distribution across the
desorption-temperature space of molecular fragments desorbing at higher
temperatures than their composition would suggest due to O–O bond cleavage.
Additionally, there may be other bond scission pathways beyond the O–O
functionality which we have not yet identified. The pinonic acid thermogram
is a possible example, desorbing from the
Previous studies to characterize the molecular composition and partitioning of atmospheric aerosol have observed small-oxygenated organics that were present in larger-than-expected concentrations based on ideal partitioning. We show thermal decomposition of large molecules can be a significant bias in thermal desorption techniques, resulting in smaller stable fragments that are then detected. The process of thermal decomposition is likely common to any instrument which uses heat to drive aerosol components into the gas phase for analysis (Holzinger et al., 2010; Smith et al., 2010; Williams et al., 2006; Yatavelli et al., 2012; Zhao et al., 2013). Our measurements utilizing the FIGAERO indicate that “small acids” present in higher than expected concentrations in SOA are likely entirely due to thermal decomposition of much lower volatility components of the aerosol.
We have explored the contribution, composition, and volatility of acyl-containing organic compounds present in
That we infer a significant fraction of the SOA is comprised of oligomeric
compounds given the relatively short residence times of the JPAC and UW
chambers (45–60 min) and the relatively small amount of
We would like to thank the supporting staff and scientists at the Jülich Research Facility. This work was supported by the US Department of Energy through awards from the Atmospheric System Research (DOE Grant DE-SC0006867) and SBIR (DE-SC0004577) programs. Edited by: A. Nenes