Introduction
Atmospheric particulate matter (PM) negatively affects human health (e.g., Kampa and Castanas, 2008), impedes
visibility (e.g., Appel et al., 1985), and
impacts the global energy balance through direct radiative forcing or by
acting as cloud condensation nuclei (e.g., Kanakidou
et al., 2005). Organic aerosol (OA) particles comprise 20–90 % of submicron
PM (PM1) and may consist of thousands of distinct organic compounds (Goldstein
and Galbally, 2007; Ng et al., 2010; Zhang et al., 2007). Given the
multitude of organic compounds in the atmosphere and the numerous chemical
reactions they can experience during atmospheric processing (e.g., Goldstein and
Galbally, 2007; Kroll et al., 2009), laboratory studies are needed to fully
understand the chemical composition and oxidative evolution of
source-specific primary OA (POA, aerosol emitted directly into the
atmosphere) and secondary OA (SOA, formed from gas-phase material that
partition into the particle phase following photooxidation).
Biomass burning organic aerosol (BBOA) may contribute up to 90 % of
global combustion OA and 75 % of combustion POA (Bond et al., 2004; May
et al., 2013). Previous BBOA molecular speciation studies over the past
several decades have focused on the chemical composition of primary emissions
(e.g., Fine et al., 2002; Oros and Simoneit, 1999; Rogge et al., 1998;
Simoneit et al., 2000). Recently, improved understanding of SOA formation in
BBOA plumes has motivated the use of oxidation chambers in laboratory BBOA
experiments (e.g., Cubison et al., 2011; Grieshop et al., 2009; Ortega et
al., 2013). Some of these BBOA photooxidation studies have demonstrated that
OA production can exceed decay under certain conditions due to oxidation and
phase partitioning of gas-phase semivolatile and intermediately volatile
compounds (SVOCs and IVOCs, respectively; Grieshop et al., 2009). Other field
measurements show minimal OA enhancement with aging of primary biomass plumes
(Capes et al., 2008). During the third Fire Lab at Missoula Experiment
(FLAME-3) campaign (2013), OA enhancements following photooxidation varied
widely depending on the biomass source; although BBOA from some sources
doubled in mass after photochemical aging, other types of BBOA were depleted
(Ortega et al., 2013). The variation in OA enhancement observed by Ortega et
al. (2013) in the FLAME-3 study suggests that the amount of SOA from biomass
emissions depends on the fuel type, illustrating the need for source-specific
oxidation studies to investigate reactions and products leading to SOA
formation.
Previous BBOA oxidation studies (e.g., Grieshop et al., 2009; Ortega et al.,
2013) have utilized a high-resolution time-of-flight aerosol mass
spectrometer (HR-ToF-AMS, Aerodyne, Inc., Billerica, MA). The aerosol mass
spectrometer (AMS) obtains chemical information on bulk aerosol including
total mass concentrations and high-resolution ion signals, allowing for
determination of bulk aerosol chemical composition (Canagaratna et al., 2007;
DeCarlo et al., 2006). Hydrogen-to-carbon ratios (H : C) and
oxygen-to-carbon ratios (O : C) can also be calculated using
high-resolution AMS data, which are incorporated into estimations of an
average carbon oxidation state (OSC‾≈2 × H : C - O : C; Kroll et al., 2011). Although the AMS provides
real-time measurements of ensemble-averaged properties for submicron
nonrefractory aerosol, it does not achieve molecular speciation and thus
cannot be used to identify individual compounds present in OA. Typical AMS
BBOA studies use m/z 60 (C2H4O2+) and m/z 44
(CO2+) signals to quantify primary and aged emissions, respectively
(e.g., Cubison et al., 2011; Ng et al., 2010, 2011a, b). Levoglucosan, a cellulose decomposition product often used as a
molecular tracer for freshly emitted BBOA (e.g., Simoneit et al., 1999,
2004), is frequently considered to be a primary contributor to m/z 60 in
AMS laboratory and field studies (e.g., Lee et al., 2010; Ng et al., 2011b).
However, although levoglucosan has traditionally been understood to remain
stable over relevant timescales (Fraser and Lakshmanan, 2000; Locker, 1988;
Simoneit et al., 1999), multiple laboratory studies suggest that hydroxyl
radical (OH)-driven levoglucosan decay occurs on a timescale similar to
transport and deposition timescales (Hennigan et al., 2010; Hoffmann et al.,
2010; Lai et al., 2014). Additionally, recent measurements demonstrate that
m/z 60 abundances may remain above background levels with sufficient
atmospheric processing, suggesting that not all m/z 60 originates from BBOA
(Cubison et al., 2011; Ortega et al., 2013). These two considerations
highlight the need for in situ molecular speciation measurements to
complement bulk aerosol chemical data supplied by the AMS.
The thermal desorption aerosol gas chromatograph (TAG) pairs automated
aerosol collection and thermal desorption with gas chromatography and mass
spectrometry (GC-MS), providing molecular-level speciation with in situ analysis
and hourly time resolution (Williams et al.,
2006). The TAG has been used in field studies to identify molecular tracers
in ambient air and to link different chemical profiles to unique sources (e.g.,
Kreisberg et al., 2009; Lambe et al., 2009; Williams et al., 2007, 2010,
2014; Worton et al., 2011; Zhang et al., 2014, 2016). The TAG is capable of
providing speciated compound measurements for approximately 20 % of total
organic aerosol mass on average, depending on the type of aerosol collected (Williams et al., 2006). Although the TAG
reliably detects a high fraction (up to 100 %) of hydrocarbon OA mass,
which is typical of POA, the analyzed fraction of oxidized OA mass is often
much lower (Williams
et al., 2010, 2016; Zhang et al., 2014). This discrepancy is attributed to
low mass throughput of oxidized species through the 30 m nonpolar gas chromatography (GC)
capillary column (Williams
et al., 2006, 2016) and presents a disadvantage for TAG analysis of oxidized
components typical of SOA.
An example TAG chromatogram with GC oven and TAG collection and
thermal desorption (CTD) cell temperature ramp programs.
Recent advances have expanded the TAG's analytical capability. Traditional
GC utilizes a solvent delay to prevent detector damage
from large solvent or water signals. In the TAG, much of the solvent can be
purged prior to sample injection, and the solvent delay is no longer
applied. The lack of a solvent delay allows volatile components and aerosol
thermal decomposition products to reach the detector during thermal sample
desorption (5–15 min of TAG GC program) from the TAG collection cell to
the GC column. The mass-spectral signal within this period, called the
thermal decomposition window, typically features an air signal (e.g., m/z 32
for O2+, m/z 40 for Ar+, m/z 44 for CO2+), but can also
contain ions characteristic of decomposing nitrates (m/z 30 for NO+, m/z 46
for NO2+), sulfates (m/z 48 for SO+, m/z 64 for SO2+),
and organics (m/z 44 for CO2+). These ion signals were shown to
correlate with corresponding AMS ions for ambient data collected during the
Saint Louis Air Quality Regional Study in 2013 (Williams
et al., 2016). However, the TAG thermal decomposition window has only
recently been used to analyze ambient data, and more laboratory studies are
needed to explore the thermal decomposition products of OA from unique
sources.
In this work, we present results from laboratory studies aimed at
characterizing BBOA chemical composition using both the TAG compound window
(minutes 20–55 of the chromatogram; Fig. 1) and the TAG thermal
decomposition window (minutes 6–16 of the chromatogram; Fig. 1) in
parallel with an AMS. A custom-built emissions and combustion chamber was
used to generate BBOA, and a potential aerosol mass (PAM) oxidative flow
reactor (OFR), which can mimic up to 16 days of atmospheric aging with
residence times on the order of 100 s (Kang
et al., 2007; Lambe et al., 2011), was used to oxidize laboratory-generated
BBOA plumes at different levels of accelerated photochemistry. Our
experiments addressed three primary objectives. First, the chemical
composition of laboratory-generated BBOA was explored to identify molecular
tracers from the leaf and heartwood of the American white oak (Quercus alba).
Recently developed chromatogram-binning Positive Matrix Factorization (PMF)
techniques (Zhang et
al., 2014, 2016) were applied to the TAG compound window to determine the
prevalence of different compound classes and functionalities unique to BBOA
from each fuel type. Trends in compounds and compound classes with oxidation
were evaluated using both individual compound integrations and chromatogram
binning PMF results. Second, the TAG thermal decomposition window was used
to investigate how the chemical composition of thermally decomposing BBOA
varies with PAM aging. Concurrent AMS measurements were taken to complement
TAG decomposition window data, providing OSC‾ estimations and
high-resolution ion signals for bulk BBOA samples. These AMS parameters were
used to inform interpretation of TAG decomposition ion signals, particularly
the variation of TAG decomposition m/z 44 and m/z 60 signals with extent of
oxidation in the PAM chamber. Chromatogram-binning PMF techniques (Zhang et
al., 2014, 2016) were also applied to the decomposition window to
investigate the presence and covariance of key ion signals. Finally, trends
in TAG and AMS m/z 60 signals with PAM aging were explored to evaluate the
utility of m/z 60 as a tracer for freshly emitted BBOA. We present evidence
that, depending on biomass source and atmospheric conditions, a significant
fraction of AMS m/z 60, which is typically used to track primary BBOA in the
atmosphere, may be attributed to aged OA mass.
Materials and methods
Emissions and combustion chamber
A flow diagram of the experimental setup and a diagram of the custom-built
emissions and combustion chamber are given in the Supplement (Sect. S1,
Figs. S1 and S2, respectively). A complete description of the emissions and
combustion chamber is available elsewhere (Mellott, 2012). The chamber is a
rectangular 1.48 m3 chamber made of 0.635 cm thick tempered glass
panels secured by aluminum framing (80/20, Inc., Columbia City, IN). The
chamber is divided into two compartments, separated by an aluminum baffle
with a central hole 3 cm in diameter. In the first compartment, biomass
samples are resistively heated in
proportional–integral–derivative (PID)-controlled stainless-steel cups
installed along the chamber floor. The second compartment serves as a mixing
chamber from which primary gases and particles are sampled at
10 L min-1. Air was treated with a HEPA filter (Pall Corporation, Port
Washington, NY) and a hydrocarbon trap (Model BHT-4, Agilent Technologies,
Santa Clara, CA), then supplied to the heating compartment of the chamber to
promote mixing. Both compartments are extensively vented between experiments
to clear the chamber of gases and particles.
Devolatilization and combustion experiments
White oak (Q. alba) heartwood and leaves were chosen for these
studies because of their abundance in the oak–hickory forests of Missouri
and the southeastern United States. Although comparing different tree species
is also of interest, two different plant fractions of the same species are
studied here to investigate different types of wildfire or controlled
combustion processes, some of which may only impact leaf litter fall and
others would have wood available as a fuel. The white oak biomass samples
used in this study were collected at the Tyson Research Center in Eureka, MO,
located approximately 32 km outside of St. Louis, MO. An oak trunk segment
was taken from the site, and heartwood samples were collected by drilling
into the center of the trunk segment. Oak leaves were clipped from a single
branch that was taken directly from a live tree. The leaf samples were
air-dried for at least 1 week and milled into fine pieces using a tobacco
grinder prior to running the experiment. All biomass was stored at room
temperature (20–25 ∘C), and moisture content was not controlled for
either fuel type.
Samples of oak heartwood or leaf were preweighed (0.2–0.5 g), placed in the
emissions chamber cup, and spread evenly across the bottom rim. The cup was
heated for 3.5 min, with typical ignition temperatures of 300 ∘C. In this work, we use the term “devolatilization” to describe the
noncombustive release of emissions from biomass fuels at elevated
temperatures. During the heat pulse, the biomass sample was first
devolatilized, with smoldering embers observed in the final minute of the
heat ramp. No flaming combustion occurred during any of the emissions
experiments.
To ensure the TAG and AMS collected particles within a similar size range,
primary emissions were passed through a PM1 cyclone (Thermo Fisher
Scientific, Waltham, MA) operated at 16.7 L min-1 to remove particles
too large to be sampled by the AMS (DeCarlo et al., 2006). Because dilution
drives partitioning of SVOCs and IVOCs from the particle phase into the gas
phase in BBOA plumes (Grieshop et al., 2009; Ortega et al., 2013), dilution
was minimized in the system during devolatilization and combustion
experiments. Dilution air, purified using separate zero air generators
(Model 737, Aadco Instruments, Cleves, OH), was supplied before the PM1
cyclone (6.7 L min-1) and after the PAM chamber (4 L min-1) to
provide sufficient flow to the cyclone and to all instruments (Fig. S1),
giving a net dilution ratio of approximately 5 for all experiments.
PAM reactor operation
Particulate and gas-phase emissions were treated together in the PAM flow
reactor. A detailed description of the PAM reactor is given elsewhere (Kang
et al., 2007; Lambe et. al. 2011). The reactor consists of a 13 L
cylindrical aluminum chamber coated internally with Iridite 14-2 (MacDermid,
Inc., Waterbury, CT), a chromate conversion film designed to decrease charge
buildup and thereby inhibit losses of charged particles to the walls of the
reactor. Within the PAM chamber, low-pressure mercury lamps emit light at two
wavelengths (185 and 254 nm) in the UV range, and different OH
concentrations are produced by adjusting the intensity of the UV irradiation
(Kang et al., 2007). Ozone (O3) is produced externally by irradiating
0.4 L min-1 of pure O2 with mercury lamps
(λ = 185 nm; BHK, Inc., Ontario, CA) to produce 4 ppm
externally added O3. Water vapor is introduced into the PAM reactor with
4.6 L min-1 of humidified N2. A total flow rate of
10 L min-1 was maintained throughout the experiments, giving an
average residence time of 78 s within the reactor. To achieve consistent OH
formation, the relative humidity (RH) inside of the reactor was kept at
30.0 % ± 3.7 % (1 standard deviation), measured with a relative
humidity and temperature probe with manufacturer-specified accuracy of
1.5 % (Vaisala, Inc., Woburn, MA). The reactor water concentration, and
therefore RH, was altered by controlling N2 flow through a Nafion
membrane humidifier (Perma Pure LLC, Lakewood, NJ). The role of water
concentration in OH formation is discussed in detail in the Supplement
(Sect. S2.1, Fig. S3).
OH exposures (OHexp) within the PAM reactor were calculated using the
offline sulfur dioxide (SO2) calibration method described in previous
work (Kang et al., 2007). During reactor calibration, SO2 concentrations
(Airgas, Inc., Radnor, PA) were measured with an SO2 monitor
(Model 43i-TLE analyzer, Thermo Fisher Scientific, Waltham, MA) at varied UV
lamp intensities; similarly, O3 was measured downstream of the PAM
reactor by UV photometry (Model 49i, Thermo Fisher Scientific, Waltham, MA).
Equivalent atmospheric aging times from the SO2 calibrations were
calculated assuming an average atmospheric OH concentration of
1.5 × 106 molec cm-3 (Mao et al., 2009) and are provided
as the upper limit on the equivalent aging time ranges obtained for the
system (Table 1). PAM reactor calibration details and results are provided in
the Supplement (Sect. S2). For both heartwood and leaf fuels, experiments
were performed at two level of photochemical aging in addition to a baseline
without OH exposure. Henceforward, the different photochemical aging
conditions will be denoted by the corresponding equivalent aging time ranges
(Table 1).
Previous PAM reactor studies have demonstrated that high concentrations of
volatile organic compounds (VOCs) can suppress OH reactivity (Li
et al., 2015; Peng et al., 2015). This suppression occurs because VOCs drive
rapid conversion of OH to HO2, and recycling of HO2 back to OH can
be slow without addition of sufficient O3 (Peng
et al., 2015, 2016). External OH reactivity (OHRext, s-1) is
defined as the sum of the products of concentrations of externally reacting
species (Ci for a compound i) and corresponding OH reaction rate
constants (ki; Peng
et al., 2016):
OHRext=∑kiCi.
This metric is used to describe the potential for interfering gases to react
with OH and suppress heterogeneous oxidation. The external production of
O3 featured in our system is expected to reduce OH suppression by
introducing additional O3 to promote recycling of HO2 back to OH (Peng
et al., 2015).
Qualitative levels of PAM-reactor oxidation with corresponding OH
exposure (OHexp) estimations and equivalent aging times. The
OHexp estimations were made using methods described in the Supplement
(Methods: PAM Calibrations and Equivalent Aging Estimations).
Qualitative level
OHexp
Equivalent aging
of oxidation
(molec cm-3 s)
time (days)
Low to mid-level
1.7 × 1011–4.4 × 1011
1–3
High level
7.7 × 1011–1.3 × 1012
6–10
Due to a lack of gas-phase measurements, OHRext values were not
calculated during TAG and AMS collections. However, supplementary experiments
were conducted to approximate OHRext by repeating the fuel
burning procedure and measuring resulting CO emissions with a CO monitor
(Peak Laboratories, Mountain View, CA). During these experiments, emissions
were sampled alternately through the PAM chamber, set to approximately 3 days
of equivalent aging according to the most recent offline SO2
calibration, and a bypass line. We observed little difference in CO
OHRext between PAM-aged emissions (maximum
OHRext = 0.558 s-1) and bypassed emissions (maximum
OHRext = 0.516 s-1). Additionally, we estimated total
OHRext by scaling trace gas emission factors (EFs) from previous
laboratory-generated oak biomass combustion VOC measurements (Burling et al.,
2010) to our measured CO concentrations. Using this method, we approximate a
total OHRext of 2.2 s-1. This OHRext value is
assumed for subsequent OHexp and equivalent aging estimations. A
detailed description of the experimental methods, as well as a discussion of
the limitations of this OHRext estimation approach, is available
in the Supplement (Sect. S2.2). Averaged CO concentrations for aged and
unaged leaf BBOA are provided in Fig. S4.
Based on an RH of 30 %, a typical internally produced output O3
range of 0.3–1.7 ppm (measured during reactor calibrations), and an
OHRext of 2.2 s-1, we estimated OHexp ranges for each
PAM UV light setting using the Oxidation Flow Reactor Exposure Estimator
version 2.3 developed by Peng et al. (2016), available for download at
http://sites.google.com/site/pamwiki/hardware/estimation-equations
(Peng et al., 2015, 2016). Results obtained using this spreadsheet are given
in the Supplement (Table S1). The “condition type”, which indicates whether
VOC suppression is significant under the input conditions, was found to be
“safer”, indicating that chemical interferences from VOCs are minimal based
on input measurements and assumptions.
Flow field simulations and chemical tracer tests have demonstrated that the
PAM reactor used in this study is approximately well mixed if sufficient
time (at least 15 min) is given prior to sample collection to establish
a well-mixed and near steady-state concentration throughout the combustion
chamber and PAM chamber (Mitroo,
2017; Mitroo et al., 2017). The TAG therefore consistently collected 30 min after the biomass heat pulse to minimize particle concentration
gradients within the reactor.
Photobleaching of BBOA, particularly at 254 nm, has been reported in
previous literature (e.g., Sumlin et al., 2017; Wong et al., 2017; Zhao et
al., 2015) and therefore should be considered when estimating oxidative
aging. With the spreadsheet provided by Peng et al. (2016), we estimate 254
and 185 nm exposure ratios (ratio of photon flux, photons cm-2, to
OHexp; Peng et al., 2016) to be 1.2 × 105 and
8.1 × 102 cm s-1, respectively, at a measured internally
generated O3 concentration of 1.7 ppm (at the highest PAM UV lamp
intensity), a water mixing ratio of 1 % (RH = 30 %), and assuming
a maximum OHRext value of 1 (Peng et al., 2016). Using Figs. 1
and 2 of Peng et al. (2016) to interpret these values, we find that at both
185 and 254 nm, photolysis rates are likely less than 10 % for species
of interest.
Instrumentation and data analysis
The TAG and the AMS were used to collect complementary chemical composition
data. A scanning mobility particle sizer (SMPS; Model 3081 DMA, Model 3022A
CPC, TSI, Inc., Shoreview, MN) was used to measure aerosol size
distributions and volume concentrations.
The devolatilization and combustion experiments were performed in two
distinct experimentation periods. In the first period, the procedure was
done at each level of PAM oxidation using 0.2 g biomass. Triplicate
experiments were done with the TAG and the SMPS during this period to ensure
repeatability of the devolatilization and combustion cycle. In the second
experimentation period, experiments were performed once more at each level
of oxidation to obtain simultaneous TAG, SMPS, and AMS measurements. For
these experiments, the devolatilization and combustion procedure was done
with more biomass fuel (0.5 g) so the AMS could obtain sufficient signal.
Thermal desorption aerosol gas chromatograph (TAG)
A full description of the TAG system is provided in previous literature
(Williams et al., 2006). Particles are collected via humidification and
inertial impaction at a typical flow rate of 9.3 L min-1, with a
particle cutoff (dp50) of approximately 70 nm (Williams et al., 2006).
Following sample collection, the collection and thermal desorption (CTD) cell
is heated to 310 ∘C at a typical rate of 50 ∘C min-1
to thermally desorb the collected OA. The desorbed sample is flushed through
a heated transfer line over helium and transported to a gas chromatography
column for separation and mass spectral detection. An Agilent 6890 GC
(Agilent Technologies, Santa Clara, CA) with a 30 m long 0.25 mm inner
diameter RTX5-MS nonpolar fused silica capillary
column (Restek Corporation, Bellefonte, PA) was used to achieve
chromatographic separation. A 70 eV electron ionization quadrupole mass
spectrometer (5973 MSD, Agilent Technologies, Santa Clara, CA), operated to
scan between 29–450 m/z, provided mass spectral detection. TAG performance
was evaluated regularly (once every 1–3 days) using a 5 ng
C12–C40 even alkane standard mixture (Sigma Aldrich, St. Louis,
MO) manually injected onto the CTD cell and thermally desorbed onto the GC
column via a helium carrier stream (Kreisberg et al., 2009).
The TAG system developed by Isaacman et al. (2014) features an online
derivatization technique designed to improve analysis of oxidized species,
including methoxyphenols, levoglucosan, and other compounds unique to BBOA
(Isaacman et al., 2014). Although this technique presents multiple analytical
advantages, it was developed for a metal filter collection cell and is not
suitable for the impactor-style CTD cell used in these experiments. We chose
to use the impactor-style CTD cell to allow analysis of the thermal
decomposition window, since other collection cells purge this material when
transferring to a secondary trap. Additionally, we were interested to
identify new molecular marker compounds that could be associated with these
source types. We therefore performed all experiments without sample
derivatization prior to chromatographic analysis.
TAG data were collected during the first experimentation period using 0.2 g
biomass in the heat pulse. For all the oak leaf and heartwood experiments,
particles were collected on the TAG for 4 min, 30 min after
the heat pulse was performed in the emissions chamber. The TAG collected two
additional samples over the course of 3 h to ensure that both the
emissions chamber and the PAM reactor were clean prior to the subsequent
devolatilization cycle.
In this work, the TAG compound and thermal decomposition time windows were
analyzed as complementary sets of chemical data (Fig. 1). As defined for this
study, the thermal decomposition window occurs between minutes 6 and 16 of GC
analysis, which coincides with the thermal desorption of the sample from the CTD
cell. The compound window consists of material eluting from minutes 20–55 of
analysis following condensation of desorbed sample at the column head. This
window contains information on OA components that have been successfully
desorbed, transferred, and separated.
Prior to each experiment, a system blank chromatogram was obtained by
sampling from the empty emissions chamber through the PAM reactor, with the
PAM UV lamps set to the voltage corresponding to the subsequent equivalent
aging time to be tested. A system blank was subtracted from each
chromatogram prior to data processing to correct for both TAG system
artifacts (e.g., air signal and column bleed) and sampling system (PAM
reactor and emissions and combustion chamber) artifacts. Additionally, to
isolate changes in aerosol chemical properties from changes in aerosol mass
with photochemical aging, each blank-subtracted chromatogram was normalized
to volume concentration by dividing the abundance at each scan by the
maximum volume concentration (nm3 cm-3) obtained by the SMPS for
each devolatilization cycle (Table S2 and Fig. S5 in the Supplement). This blank subtraction and normalization process was done for
all total ion count (TIC) chromatograms and single-ion chromatograms (SICs)
presented in this work.
TAG positive matrix factorization
Positive matrix factorization (PMF) was performed on TAG chromatograms to
identify source-specific major compounds and compound classes present in the
heartwood and leaf BBOA. TAG chromatograms were binned by retention time
according to the method outlined in previous work (Zhang et
al., 2014, 2016). Prior to chromatogram binning, each chromatogram was
blank subtracted to minimize the contribution of background noise in PMF
calculations. An instrument error of 10 %, chosen based on a typical
average TAG instrument error of 10 % (Williams et al., 2006), was assumed during
PMF calculations.
The GC-resolved mass spectral PMF method for binned TAG data was developed
to separate compounds in TAG chromatograms into chemically similar factors,
improving analysis efficiency (Zhang et al., 2014). With this
method, mass spectral data are supplied to the PMF model, and solutions are
obtained using the PMF2 algorithm (Paatero,
1997). Each resulting factor consists of a mass spectrum corresponding to a
compound or class of compounds present in the TAG chromatograms (Zhang et al., 2014). This PMF
method was performed on the compound and decomposition analytical windows
separately for data obtained from both BBOA types. PMF output and solutions
were evaluated using custom-built pre- and postprocessing analysis software
in conjunction with the PMF Evaluation Tool (version 3.00A; Ulbrich et
al., 2009) in Igor Pro version 6.38Beta01 (WaveMetrics, Inc.). Mass spectral
identification of different factors was aided by the NIST MS Search Program
version 2.0, available for download at http://chemdata.nist.gov/mass-spc/ms-search/.
The number of appropriate PMF factors was determined for each solution based
on two considerations. First, in a typical PMF analysis, the optimal number
of factors in a solution is selected based on the objective function Q, which
is the sum of weighed squared residuals (Paatero, 1997). The Q/Qexp value, or the
ratio of the actual objective function to the expected objective function
assuming normally distributed residuals, should ideally approach 1; too few
factors may result in a large Q/Qexp, indicating that errors have been
underestimated in PMF calculations (Ulbrich et
al., 2009). Additionally, if too many factors are specified, the solution
may feature split factors, where information from a compound or compound
class is distributed across multiple factors. In this work, the number of
factors presented for each analysis was selected to minimize split factors
while maximizing identifiable factors. Because of the TAG data's high
chromatographic resolution, low rotational ambiguity was assumed, and all
calculations were performed with fpeak = 0. This assumption is
supported by previous work, where TAG data were not sensitive to
fpeak or starting point (seeds) during PMF analysis (Williams
et al., 2010).
AMS
The AMS data presented in this work were obtained using 0.5 g of biomass in
the heat pulse instead of 0.2 g to ensure the AMS received sufficient
signal. The AMS was operated in V mode throughout all experiments (DeCarlo et al., 2006). AMS data
were processed in Igor Pro version 6.38Beta01 using the SQUIRREL version 1.57 toolkit for unit mass resolution analysis and the PIKA version 1.16
toolkit for high-resolution analysis. Both AMS data analysis tools are
available for download at http://cires1.colorado.edu/jimenez-group/ToFAMSResources/ToFSoftware/index.html.
Results and discussion
AMS measurements
Average AMS mass spectra and van Krevelen plots are provided in the Supplement (Figs. S6 and S8, respectively). In addition, AMS-measured
concentrations of key species, including total organics, sulfate, and
potassium (K+), are provided in Fig. S7 and Table S3.
According to AMS mass spectra, the BBOA measured in these experiments is
chemically consistent with BBOA from similar oak fuel sources, though with
key differences related to combustion conditions (Cubison et al., 2011;
Ortega et al., 2013; Reece et al., 2017; Weimer et al., 2008). Detailed
analysis and contextualization of the AMS chemical composition data is given
in the Supplement (Sect. S4).
Individual compound analysis
The mass spectral dot product method proposed by Stein and Scott was used to
determine chemical similarity between each chromatogram and to evaluate
inter-test variability. For each blank-subtracted TAG chromatogram, a summed
mass spectrum was obtained by summing all ions (m/z 33–m/z 450) across all scans
(retention times) in the chromatogram and converting the resulting mass
spectral vector into a unit vector. To assess the similarity of two mass
spectra, the dot product of the mass spectral unit vectors was calculated; a
dot product of 1 signifies a perfect mass spectral match, and a dot product
of 0 indicates a complete mismatch (Stein and Scott, 1994). Within a
fuel type and an oxidation condition, the dot product was assessed for two
TAG chromatograms at a time for a total of 3 dot product values. These
values are given in Table S4.
For both leaf and heartwood BBOA, key molecules identified within the
compound window of the TAG chromatograms are given in the Supplement (Table S5). Corresponding molecular structures for the compounds
used in individual compound analysis are also provided (Fig. S9).
Identification certainty (“Certainty of ID”) was classified for each
compound according to the following criteria: (A) the compound was positively
identified based on external standard injections; (B) the compound was
identified based on a high match quality (MQ > 75 %) using
available mass spectral libraries; (C) the compound was identified based on a
low-to-moderate match quality (MQ <75 %) using available mass
spectral libraries; and (D) no adequate mass spectral library match was
available for the compound, so the compound structure was inferred by
retention time and manually evaluating possible fragmentation patterns.
Identification method (D) was particularly relevant for long-chain aliphatic
compounds, including alkenes and even-carbon aldehydes. For these compounds,
the parent ion was first determined, then major ions were identified (e.g.,
in tetracosanal, m/z 334 corresponds to C24H46+ following loss
of H2O). The feasibility of the identified structure was confirmed
based on predicted vapor pressures and retention times from even alkane
standards.
Subcooled liquid vapor pressures at 25 ∘C were predicted for each
compound using the Advanced Chemistry Development (ACD/Labs) Software V11.02
(© 1994–2017 ACD/Labs), available for use on the SciFinder website
(ACD/Labs, 2017).
Trends in individual compounds with photochemical aging
Leaf and heartwood BBOA chromatograms at three levels of photochemical aging
are overlaid for comparison in Fig. 2. Raw peak integration values with
standard deviations are provided for each compound at each level of
equivalent aging (Table S6). Each chromatogram constitutes an average of the
triplicate blank-subtracted measurements, with each chromatogram normalized
to the maximum total volume concentration measured during the experiment. For
these plots, the averaged, normalized chromatograms at each level of aging
were further normalized to the point of highest abundance in the unaged
(“0 days”) average chromatogram. In the leaf BBOA chromatograms (Fig. 2a),
many of the low-volatility species eluting after minute 35 of the GC analysis
are long-chain alkanes, alcohols, aldehydes, and terpenoids, compounds
commonly found in the leaf's waxy exterior coating (Gülz and Boor, 1992).
Based on even-numbered alkane standard injections, compounds eluting after
minute 35 exhibit approximate saturation vapor pressures not exceeding that
of docosane (approximately 3.64 × 10-3 Pa at 25 ∘C),
which corresponds approximately to log10(C∗) = 2.76 (Table S5
in the Supplement; ACD/Labs, 2017).
Chromatograms for (a) leaf BBOA and (b) heartwood
BBOA at different levels of oxidation. Corresponding names and structures for
numbered compounds are given in Table S5 and Fig. S9. For each plot, all
traces are normalized to the point of highest abundance within the average
unaged chromatogram.
Relative changes in integrated abundance as a function of equivalent
aging time (per SO2 calibrations) for primary compounds identified in
(a) oak leaf BBOA chromatograms and (b) oak heartwood BBOA
chromatograms. For each compound, the integrated abundances were first
normalized to appropriate volume concentrations, then subsequently normalized
to corresponding abundances at no oxidation (“0 days”). Compounds that
decrease in abundance are indicated with solid lines, and compounds that
deviate from this trend are given with dotted lines. Raw compound abundances
are provided in the Supplement (Table S6). The x-axis error bars denote
equivalent aging time ranges calculated for this study and are applicable to
all TAG data presented here, though they are only included on one compound
per panel to preserve figure readability.
To illustrate the relative rates of decay that each compound experiences in
the PAM reactor, Fig. 3a provides integrated abundances for nine compounds of
interest. The integrated abundances were first normalized to appropriate
volume concentrations, then to the corresponding abundances at no oxidation.
Nearly all compounds identified after 35 min decrease in relative abundance
with photochemical aging. Notably, we have identified an even-carbon
aliphatic aldehyde series based on [M-18]+ and [M-28]+ (where M
is the parent mass) peaks present in the mass spectra of each of the
compounds (Watson and Sparkman, 2007). As the carbon number (nC)
increases, the aldehyde abundance decreases more readily with oxidation. To
our knowledge, rate constants for the reaction of long-chain (nC≥C20+) condensed-phase aliphatic aldehydes with OH have not been
reported. However, previous studies on short-chain (nC ≤ C14) condensed-phase aliphatic aldehydes demonstrate that OH reaction
rate constants increase with increasing carbon chain length (D'Anna et al.,
2001; Niki et al., 1978). Although aliphatic aldehydes, particularly C26
and C28 aldehydes, have been characterized as components of oak leaf
waxes (Gülz and Boor, 1992), these aldehydes have not been reported as
components of oak leaf BBOA and may therefore serve as novel tracer species
in future field experiments. To confirm the presence of aldehydes in the leaf
waxes, solvent extractions were performed on oak leaves and were manually
injected onto the TAG CTD cell (Sect. S5.1 and Fig. S10 in the Supplement).
Analysis of these extractions confirm that the aldehydes are present in the
leaf wax prior to devolatilization and combustion.
Literature information available for hydrocarbon particle- and gas-phase OH
kinetics indicates that the trends observed in leaf BBOA alkane and aldehyde
abundances are consistent with heterogeneous OH oxidation. For example, Smith
et al. (2009) report approximately 70 % decay of squalane (a C30
branched alkane) particles when exposed to an OHexp of
1.1 × 1012 molec cm-3 s-1 (approximately 10 days
of equivalent aging; Smith et al., 2009), a figure approximately consistent
with the observed C29 alkane decay of 75 % at 6–10 days of
equivalent aging. Additionally, based on parameters provided by Kwok and
Atkinson (1995), gas-phase OH reaction rate constants at 298 K are estimated
to be 2.5 × 10-11, 2.7 × 10-11, and
3.1 × 10-11 cm3 molec-1 s-1 for C23,
C25, and C29 alkanes, respectively (Kwok and Atkinson, 1995).
Taking these rate constants into account, if purely gas-phase chemistry is
assumed, all three alkanes would react nearly 100 % before 1–3 days of
equivalent aging. A similar analysis on relevant aldehydes gave estimated
gas-rate constants of 2.5 × 10-11, 2.8 × 10-11,
and 3.0 × 10-11 cm3 molec-1 s-1 for C24,
C26, and C28 aldehydes, respectively (Kwok and Atkinson, 1995),
which in all cases would lead to complete depletion by 1–3 days of
equivalent aging if gas-phase chemistry is assumed.
Compounds characteristic of heartwood primary BBOA are typically more
volatile than those found in the leaf primary BBOA, eluting between minutes
28 and 35 of the GC analysis (Fig. 2b). Based on even alkane standard
injections, compounds eluting within this time window exhibit approximate
vapor pressures within
6.03 × 10-1–3.64 × 10-3 Pa at 25 ∘C
(log10(C∗)≈ 4.85–2.76; Table S5 in the Supplement;
ACD/Labs, 2017). The compound with the highest abundance in unoxidized wood
BBOA chromatograms is sinapaldehyde (4-hydroxy-3,5-dimethoxycinnamaldehyde),
a phenolic compound derived from lignin. Of the compounds examined,
sinapaldehyde decays most rapidly in the PAM reactor, with the normalized
average integrated peak area decreasing by approximately 70 % from 0 to
1–3 days of equivalent aging (Fig. 3b). Based on a rapid gas-phase OH
reaction rate constant of
2.7 × 10-12 cm3 molec-1 s-1, the observed
sinapaldehyde decay is likely occurring in the particle phase. Other
compounds, including methyl-β-D-glucopyranoside, galactoheptulose, and
acetylgalactosamine, also exhibit decreases in abundance. Relative rates of
decay for these and other wood BBOA tracers are given in Fig. 3b.
Syringol (2,6-dimethoxy-phenol), syringaldehyde
(4-hydroxy-3,5-dimethoxy-benzaldehyde), and vanillin
(4-hydroxy-3-methoxy-benzaldehyde) increase in abundance from 0 to 1–3 days of equivalent aging and are depleted with 6–10 days of equivalent
aging. Since the average volume concentration for runs at 1–3 days of aging
were larger than those at 0 days of aging by a factor of approximately 1.3
(Table S2 in the Supplement), the factor of ∼ 2
increase in syringol and syringaldehyde integrated abundances could occur
due to partitioning from the gas phase into the particle phase. To estimate
phase partitioning for these compounds, particle-phase fractions for
syringol, syringaldehyde, and vanillin (ξi) were calculated based
on AMS total organic concentrations (COA, µg m-3; Table S3 in the Supplement) and effective saturation concentrations
(Ci∗, µg m-3) using a basic partitioning equation (Donahue et al., 2006; Table S5 in the Supplement):
ξi=1+Ci∗COA-1.
Resulting particle-phase fractions are tabulated in the Supplement
(Table S7). Based on these approximations, syringol, syringaldehyde, and
vanillin are expected to partition primarily to the gas phase. For these
compounds, the increase in abundances at low to mid-levels of oxidation could
therefore result from increased SOA formation driving these compounds into
the particle phase. This observation is consistent with previous measurements
where maximum SOA concentrations were observed at similar levels of
OHexp for aerosol generated from oxidation of a single precursor (Lambe
et al., 2012; Ortega et al., 2016).
Average binned chromatograms and mass spectra for
factors 1–9 + 12 (F1–9 + 12) in PMF 15-factor solution on TAG oak
leaf BBOA compound window data. Relevant plots obtained in PMF calculations
are provided in the Supplement (Figs. S12a and S13a). These chromatograms
were obtained from PMF calculations by averaging binned data corresponding to
triplicate chromatograms at each level of oxidation. The triplicate-averaged
binned chromatograms at each equivalent aging time are displayed in one
trace; different aging times are demarcated with vertical lines across the
x axis. Average binned chromatograms and mass spectra for factors 10–15
(F10–15) in PMF 15-factor solution on TAG oak leaf BBOA compound window
data.
Average binned chromatograms and mass spectra for factors 1–8
(F1–8) in PMF 18-factor solution on TAG oak heartwood BBOA compound window
data. Relevant plots obtained in PMF calculations are provided in the
Supplement (Figs. S12b and S13b). These chromatograms were obtained from PMF
calculations by averaging binned data corresponding to triplicate
chromatograms at each level of oxidation. The triplicate-averaged binned
chromatograms at each equivalent aging time are displayed in one trace;
different aging times are demarcated with vertical lines across the x axis.
Average binned chromatograms and mass spectra for factors 9–18 (F9–18) in
PMF 18-factor solution on TAG oak heartwood BBOA compound window data.
Relevant plots obtained in PMF calculations are provided in the Supplement
(Figs. S12b and S13b).
Although phase partitioning may contribute to the trend in vanillin with
photochemical aging, the nearly eight-fold increase in vanillin integrated
abundance from 0 to 1–3 days of aging could suggest an alternative
formation mechanism driven by reactions occurring in the PAM reactor. One
potential mechanism for the formation of aldehydes from larger lignin
decomposition products involves the cleavage of the Cα–Cβ unsaturated bond on the benzyl substituent following formation and
fragmentation of a peroxide radical intermediate (Wong et al., 2010; Fig. S11 in the Supplement). The presence of OH in the PAM reactor may drive a similar
process, leading to increases in vanillin abundance at moderate OHexp.
Compound window PMF analysis
GC-MS PMF results are provided for both leaf and wood BBOA chromatograms
using data collected within the TAG compound window (Figs. 4 and 5).
Q/Qexp and residual plots are provided in the Supplement (Figs. S12 and S13, respectively). The chromatograms are displayed as averages of
binned data from triplicate measurements at each level of oxidation and are
displayed in one trace; different equivalent aging times are demarcated with
vertical lines along the x axis. Corresponding mass spectra are identified
and displayed with key ions labeled. High factor solutions (≥ 15) were
used for compound window data to best deconvolve the large and complex
mixture of compounds. However, in some cases, factor splitting resulted in
the distribution of ions between two or more factors, made evident by
similarities in retention times. Wherever possible, split factors were
recombined by summing the binned chromatograms and the mass spectra and are
labeled accordingly (e.g., “F10 + F12” indicates that factor 10 and factor
12 have been recombined). In general, for the compound window, factor
solutions were chosen to maximize the number of identifiable factors while
minimizing the number of split factors.
A 15-factor solution was chosen to deconvolve leaf BBOA compound window
chromatograms (Fig. 4; additional information provided in Figs. S12a and
S13a). This solution provided enough factors to resolve the lowest-abundance
components (e.g., F1), and increasing the number of factors past 15 led to
greater factor splitting without providing additional insight into the
chromatograms. Among the factors identifiable with this solution include
quinic acid (Factor 2, F2), sugars and anhydrosugars (e.g., mannose; F3),
alcohols and alkenes (F6), aldehydes (F10), terpenoids (e.g., friedelin;
F11), and column bleed (F13 + F14). Other factors (F1, F5 + F7, F9 + F12,
F15) correspond to different classes of unresolved complex mixtures (UCMs) and
have been tentatively identified by considering the closest matches in the
NIST mass spectral database. Factor 4 (F4) is identified as a split factor,
exhibiting mass spectral characteristics of multiple factors, including
acids (m/z 129) and anhydrosugars (m/z 116). Factors 13 and 14 demonstrate
contributions from both terpenoid-like UCMs and column bleed and are
therefore combined. The presence of alkylbenzenes (F8), dominated by m/z 91
(C7H7+) and m/z 92 (C7H8+), is noteworthy, as
alkylbenzenes are typical of anthropogenic materials (e.g., detergent
precursors produced from petroleum; Forman et al., 2014) and have not
been reported as components of biomass. Since the leaves were not cleaned
after they were collected, the alkylbenzenes could come from deposition of
fuel combustion aerosol onto the leaves' surface prior to biomass sample
collection. The presence of alkylbenzenes on the surface of the leaf was
confirmed with TAG analysis of solvent-extracted leaf surface components
(Fig. S14), supporting the interpretation of deposition of anthropogenic
compounds on the leaf's exterior.
An 18-factor solution was applied to deconvolve compounds in the wood BBOA
chromatograms (Fig. 5; additional information provided in Figs. S12b and
S13b). Notable factors correspond to levoglucosan (F1), guaiacol (F4),
vanillin and guaiacyl compounds (F7), syringol (F8), syringaldehyde (F10),
sinapaldehyde (F11), and column bleed (F18). Based on retention time and
mass spectral characteristics (e.g., m/z 77), factor 5 (F5) corresponds to
aromatic species and is not matched to a single compound. Factor 6 (F6) is
featured in multiple aromatic compounds, but is also present in levoglucosan
in very low abundances. Several types of UCM (F2, F3, F9, F12 + F13 + F14,
F15, F16) were deconvolved and tentatively identified using the top matches
from the mass spectral database. Factor 16 (F16) is predominated by
siloxanes (e.g., m/z 73, m/z 281, m/z 341), though some UCM has been split from other
factors. Finally, factor 17 (F17) exhibits characteristics of multiple
classes of compounds and is therefore identified as a split factor.
Nearly all factors obtained in the leaf BBOA compound window analysis
decrease with photochemical aging, including quinic acid (F2), sugars and
anhydrosugars (F3), alkanes and long-chain aliphatics (F6, F10, F15),
alkylbenzenes (F8), terpenoid components (F11), and various classes of UCM
(F1, F4, F5 + F7, F9 + F12). This trend agrees well with the individual
compound analysis and further indicates that primary components undergo
increased fragmentation at higher OHexp. In the heartwood BBOA, some
primary components decrease steadily with photochemical aging, including
sinapaldehyde (F11), aromatics (F5), and various classes of UCM
(F12 + F13 + F14, F15, F17). Other factors, including guaiacol (F4),
vanillin (F7), syringol (F8), and syringaldehyde, exhibit a strong increase
in abundance at 1–3 days of aging followed by a decrease at 6–10 days of
aging, possibly due to changes in partitioning as described previously.
Levoglucosan (F1) also appears to increase slightly in abundance at 1–3 days
of equivalent aging, though this is likely due to differences in aerosol
mass produced between experiments. Results from both types of BBOA show
changes in column bleed (F13 + 14 and F18 for leaf and wood BBOA,
respectively) from unaged chromatograms to 6–10 days of aging. Although the
column bleed decreases with photochemical aging in both cases, this trend is
due to differences in blank subtractions from run to run and is not related
to changes in photochemical aging.
TAG thermal decomposition window
The TAG thermal decomposition window has been used in previous work to
assess contributions of inorganic (nitrates, sulfates, etc.) and organic
species present in atmospheric aerosol (Williams
et al., 2016). In this work, we provide evidence that the TAG thermal
decomposition window can be used to evaluate the relative level of oxidation
of bulk OA samples using the m/z 44 (CO2+) ion. In addition, we
demonstrate that other fragments within the decomposition window may give
insight into the chemical composition of aged, thermally labile BBOA.
Replicable, quantitative TAG data were not obtained during experiments that
used 0.5 g biomass, potentially due to a minor system leak. However, the TAG
chromatograms that were obtained using 0.5 g biomass were chemically similar
to the triplicate TAG chromatograms obtained using 0.2 g biomass, and we
therefore compare all AMS data with TAG chromatograms collected using 0.2 g
biomass in subsequent analysis. Chemical similarity between chromatograms
was confirmed using the dot product mass spectral comparison method outlined
by Stein and Scott (1994). The dot product was determined for two chromatograms, one
obtained with 0.5 g biomass and one obtained with 0.2 g biomass, at each
level of oxidation. The resulting dot products for both leaf and wood oak
are all above 0.75 and are provided in the Supplement (Fig. S15;
Table S8).
m/z 44 as a tracer for aged OA
Figure 6a and b show m/z 44 TAG decomposition SICs for leaf and wood BBOA,
respectively. Raw SICs, along with blanks, are provided in Fig. S16. At
each oxidation condition, SICs from the triplicate chromatograms were blank
subtracted, normalized to maximum volume concentrations, and averaged to
obtain the displayed trace. Within each plot, the chromatograms have been
further normalized to the point of highest abundance within the unaged (“0 days”) m/z 44 signal. The m/z 44 signals were also summed across the entire
decomposition window following blank subtraction, normalization to
appropriate volume concentrations, and triplicate averaging, and are
provided as functions of equivalent aging time (± 1 standard
deviation) in Fig. 6c. The upward trend in the m/z 44 signal between minutes
6 and 10 of GC analysis coincides with the CTD temperature ramp from
45 to 310 ∘C and is thus consistent with gradual
increase in OA thermal decomposition as the temperature rises. The
subsequent decrease in m/z 44 signal from minutes 10 to 16 reflects the thermal
decomposition of remaining material as the CTD cell is held at
310 ∘C. For both types of BBOA, the decomposition m/z 44 integrated
signal increases overall from 0 to 6–10 days of equivalent aging,
indicating an increase in OA material that can thermally decompose with
increased PAM oxidation. This trend is consistent with relative increased
decomposition of highly oxidized aerosol formed within the PAM reactor, as
demonstrated in previous ambient aerosol observations (Williams et al.,
2016). In the leaf BBOA chromatograms, the increase in integrated m/z 44 signal
is most pronounced from 0 to 1–3 days of equivalent aging, while the
heartwood BBOA data exhibit the most dramatic increase from 1–3 to 6–10 days. The variation in the shape of the decomposition m/z signal between the
two types of biomass likely reflects differences in thermal lability between
different types of OA.
(a) Average m/z 44 single-ion chromatograms (SICs) across
distinct levels of photochemical aging for leaf BBOA, normalized to the point
of highest abundance within the averaged unaged chromatogram (“0 days”).
(b) Average m/z 44 SICs across
different levels of photochemical aging for heartwood BBOA, normalized to the
point of highest abundance within the averaged unaged chromatogram.
(c) Summed relative m/z 44 decomposition signal as a function of
photochemical aging for both fuels (± 1 standard deviation). These
values were obtained by averaging triplicate m/z 44 decomposition signals
at each level of photochemical aging. For each fuel type, all summed
abundances are normalized to the unaged m/z 44 signal (“0 days”). The
x-axis error bars denote the equivalent aging time range and are applicable
for all measurements obtained in this study.
AMS OSC‾ values calculated for both types of biomass range from
-1.5 to -0.2 (Fig. 7). In both types of BBOA, an increase in relative
integrated TAG decomposition m/z 44 signal coincides with an increase in
OSC‾ from 0 to 6–10 days of photochemical aging. A linear
correlation between decomposition m/z 44 and AMS OSC‾ for wood BBOA
(r2 = 1) indicates that under these experimental conditions, the TAG
thermal decomposition window has the potential to provide quantitative
measurements of bulk OA oxidation levels. By contrast, leaf BBOA
decomposition m/z 44 and AMS OSC‾ correlate poorly (r2 = 0.8 for a linear fit). The nonlinear trend in TAG decomposition m/z 44 for
leaf BBOA may indicate a shift in the dominant oxidation mechanisms between
moderate and high levels of OH within the PAM chamber; at the highest
OHexp, primary gas and/or particle-phase components may undergo
increased fragmentation, leading to a net decrease in production of the aged
OA that thermally decomposes during TAG analysis, along with an increase in
highly volatile fragmentation products that are not captured by the TAG.
However, the mechanisms behind this trend remain unclear and merit further
investigation.
TAG decomposition m/z 44 integrated relative abundances for
PAM-aged leaf and heartwood BBOA as functions of AMS
OSC‾. Here, all TAG data have been normalized
to the unaged (“0 days”) wood BBOA integrated m/z 44 abundance.
AMS and TAG f44 vs. f43 at different levels of
photochemical aging for (a) leaf and (b) heartwood BBOA.
TAG f44 and f43 values were obtained using Eq. (3). To minimize
noise, AMS data are plotted only for points where sufficient total organic
concentrations were achieved, around the peak of the concentration profile.
The triangles formed by the blue dotted lines provide visual guidelines for
the evolution of OA chemical composition across f44 vs. f43space;
the apex of the triangle indicates the direction of OA photochemical
oxidation (Ng et al., 2010).
For each fuel type, AMS f44 vs. f43 data have been plotted at each
level of equivalent aging (Fig. 8). To further explore the TAG's analytical
capability in relation to AMS bulk chemical data, TAG-integrated ion
fractions (fion) are also provided in these plots. These
fractions are defined as the blank-subtracted integrated ion signal divided
by the blank-subtracted integrated TIC signal. For example, for a
chromatogram i, the TAG f44 signal is defined as
follows:
f44,i=A44i-A44blankATICi-ATICblank
Here, (A44)i is the integrated m/z 44 signal across all (i.e., TAG
total chromatogram) or part (i.e., TAG compound window) of i,
(A44)blank is the integrated m/z 44 signal across a blank
chromatogram, (ATIC)i is the integrated TIC across all or
part of i, and (ATIC)blank is the integrated TIC across the same
blank. For heartwood BBOA, although AMS f44 increases and f43
decreases with photochemical aging, both TAG f44 and f43 increase
with increasing oxidation, particularly when the decomposition window is
included in analysis (i.e., TAG total chromatogram). However, TAG fractions
from the leaf BBOA data are more varied and do not exhibit a clear trend. In
general, the TAG fractions tend to fall to the left of AMS f44 vs.
f43 data points, indicating that the TAG excels at throughput of
less-oxygenated hydrocarbon OA and struggles with throughput of oxidized
species in the compound window. However, the increase in TAG f44 with
inclusion of decomposition window material shows a clearer oxidation trend
that is in greater agreement with the AMS oxidation trend. This
interpretation relies on the assumption that the m/z 43 and m/z 44 signals obtained
in the TAG decomposition window from sample thermal desorption at
310 ∘C are similar in nature to those obtained when aerosol is
flash vaporized at 600 ∘C in the AMS.
Average binned chromatograms and mass spectra for factors 1–4
(F1–4) in PMF 4-factor solution on TAG oak leaf BBOA decomposition window
data. Relevant plots obtained in PMF calculations are provided in the
Supplement (Figs. S12c and S13c). These chromatograms were obtained from PMF
calculations by averaging binned data corresponding to triplicate
chromatograms at each level of oxidation. The triplicate-averaged binned
chromatograms at each equivalent aging time are displayed in one trace;
different aging times are demarcated with vertical lines across the
x axis.
Average binned chromatograms and mass spectra for factors 1–5
(F1–5) in PMF 5-factor solution on TAG heartwood BBOA decomposition window
data. Relevant plots obtained in PMF calculations are provided in the
Supplement (Figs. S12d and S13d). These chromatograms were obtained from PMF
calculations by averaging binned data corresponding to triplicate
chromatograms at each level of oxidation. The triplicate-averaged binned
chromatograms at each equivalent aging time are displayed in one trace;
different aging times are demarcated with vertical lines along the x axis.
Decomposition window PMF analysis
To aid identification of key thermal decomposition products, the binning
deconvolution PMF method was applied to the TAG chromatogram decomposition
window (Figs. 9 and 10). Details of the PMF analyses are provided in the Supplement (Figs. S12 and S13). Tentative identification of
different factors was facilitated by the NIST mass spectral database, though
standard injections are needed to adequately quantify the decomposition
window signal and identify the factors with complete confidence. As with the
compound window PMF results, chromatograms are displayed as triplicate
averages of binned data at each level of oxidation and are demarcated by
vertical lines across the x axis. Key ions are labeled, and tentative
identifications are provided above each mass spectrum.
For the leaf BBOA chromatograms, a 4-factor solution gave several
distinguishable factors (Fig. 9; additional information provided in
Figs. S12c and S13c), including the m/z 44 (CO2+) signal previously
identified as originating from thermal decomposition oxidized organics (F1).
Factor 3 (F3), dominated by m/z 78 (possibly C6H6+) with
smaller contributions from m/z 39 (C3H3+) and m/z 51
(C4H3+), could indicate decomposing aromatics. Factor 2 (F2)
matches with nitrogenated compounds in the mass spectral database, and the
co-elution of m/z 43 (possibly C2H3O+) and m/z 79
(possibly C4H3N2O+) could signal the presence of
nitrogenated oxidized organics. Finally, factor 4 (F4) is dominated by
multiple fragments characteristic of less-oxidized or unsaturated organic
material, including m/z 55 (C4H7+), m/z 67
(C5H7+), and m/z 91 (C7H7+); this factor may also
include contributions from air (m/z 40; Ar+) and m/z 79 split from
factor 3.
A 5-factor solution was chosen for the wood BBOA chromatograms (Fig. 10;
additional information provided in Figs. S12d and S13d). Factor 1 (F1) is
dominated by m/z 44, attributed to decomposing oxidized organics
(CO2+). Acetic acid was identifiable in factor 2 (F2) based on
relative abundances of m/z 43 (C2H3O+), m/z 45 (CHO2+),
and m/z 60 (C2H4O2+), suggesting that organic acids
comprise part of the thermal decomposition OA. Factor 3 (F3) features m/z 50
and m/z 52 (possibly CH335Cl+ and CH337Cl+,
respectively) in the 3 : 1 isotopic ratio characteristic of chlorine,
indicating that the wood BBOA may contain chlorinated organics. Based on
comparison of retention times, the large contribution of m/z 44 to factor 3 may
be due to splitting from factor 1. Factor 4 is dominated by ions
characteristic of less-oxygenated or unsaturated organic material, including
m/z 55 (C4H7+), m/z 72 (C4H8O+), and m/z 84
(C5H8O+). Lastly, factor 5 (F5) has been identified as
furfural using the mass spectral database, which has been previously
reported in gas-phase mass spectral measurements of biomass burning
emissions (Stockwell et al., 2015).
Because of the lack of chemical resolution in the thermal decomposition
window, trends in factors with oxidative aging remain challenging to
interpret. Notably, the factors featuring m/z 44 (F1 in both Figs. 9 and 10)
increase with photochemical aging, consistent with an increase in oxidized
OA. In the heartwood BBOA, F2 (acetic acid) and F4 (less-oxidized organics)
appear to peak at 1–3 days of equivalent aging, though the mechanisms
driving this change remain uncertain. The PMF results obtained in this study
will be used to develop appropriate standards for the TAG thermal
decomposition window, allowing for more quantitative analysis and easier
identification of mass spectral fragments in future field and laboratory
work.
m/z 60 as a tracer for both primary and aged BBOA
The signal eluting between minutes 27 and 32 of GC analysis results from the
co-elution of multiple compounds, including levoglucosan. Many of these
co-eluting species exhibit m/z 60 (dominated by the C2H4O+ ion)
as a major fragment in their mass spectra. These compounds are poorly
resolved because the nonpolar GC column is not designed to resolve such
polar compounds. SICs at different levels of oxidation reveal that each
compound within this retention time window reacts at a unique rate, allowing
for the identification of different co-eluting species.
Heartwood and leaf BBOA m/z 60 SICs at each level of oxidation are given in
Fig. 11, and relative abundances of key m/z 60 fragmenting species in the
TAG compound window are provided in the Supplement (Tables S9 and S10). In
the unaged heartwood BBOA chromatograms, approximately 82 % of the TAG
compound window m/z 60 signal has been identified as levoglucosan
(retention time determined from authentic standards; Fig. S17 in the
Supplement), though other sugars and anhydrosugars exist in lower abundances.
Although some levoglucosan (between 8.35 and 3.20 %) is present in the
leaf BBOA chromatograms, up to 60 % of the TAG compound m/z 60 signal
comes from quinic acid, which elutes beginning at minute 29 (retention time
determined from authentic standards; Fig. S17). The differences in sources of
m/z 60 between types of biomass illustrate that the m/z 60 signal in any
given BBOA sample may be highly complex and dependent on the type of biomass
burned. Additionally, the presence of m/z 60 is likely dependent on the
combustion characteristics, as combustion processes can influence the
emission and phase of different compounds.
Average m/z 60 single-ion chromatograms (SICs) across the compound
window for (a) leaf BBOA and (b) heartwood BBOA. For each
plot, all traces are normalized to the point of highest abundance within the
average unaged chromatogram. Individual compounds are labeled according to
identifications provided in the Supplement (Fig. S9; Table S5).
Relative changes in abundance for different m/z 60 fragmenting
species in (a) leaf and (b) heartwood BBOA;
(c) TAG and AMS m/z 60 species as a function of OHexp.
Levoglucosan (LG) decay rates were calculated using two different literature
kLG values (Hennigan et al., 2010; Kessler et al., 2010) with an
assumed typical outdoor OH concentration of
1.5 × 10-6 molec cm-3 (Mao et al., 2009). Additionally,
normalized AMS f60 values for turkey oak (Quercus laevis) BBOA
obtained during the FLAME-3 campaign were adapted from Fig. 10b in Ortega et
al. (2013) and are included for comparison. The x-axis error bars denote
the equivalent aging time range and are applicable for all measurements
obtained in this study, though they are only included in panel (c)
to preserve figure readability.
In the leaf and heartwood BBOA, an increase in the m/z 60 signal was observed
in the decomposition window from 0 to 6–10 days of equivalent aging (Fig. 12). Deconvolution PMF results demonstrate that the m/z 60 decomposition signal
co-elutes with m/z 43 and m/z 45 signals, which likely correspond to
C2H3O+ and CHO2+, respectively, and is distinct
from the mass spectrum of levoglucosan (Fig. S18 in the Supplement). The co-elution of these three fragments and their relative
integrated abundances provides evidence that organic acids constitute a
portion of the decomposing OA. Further, the increase in the m/z 60 integrated
signal suggests that these acids are formed during oxidative reactions
occurring in the PAM chamber, either through heterogeneous oxidation of
primary BBOA or condensation of oxidized SOA material.
Relative rates of decay for TAG integrated m/z 60 fragmenting species are
given in Fig. 12. For leaf BBOA (Fig. 12a), these compounds include
levoglucosan, quinic acid, mannose, and octadecanoic acid, and for heartwood
BBOA (Fig. 12b), these include levoglucosan, methyl-β-D-glucopyranoside, galactoheptulose, n-acetyl-d-galactosamine, and
1,6-anhydro-α-d-galactofuranose. The TAG decomposition window
m/z 60 signal, total TAG compound window m/z 60 signal, and AMS f60
(the ratio of m/z 60 to the total signal; Ng et al., 2011b) are also
included in Fig. 12a and b for comparison. All values have been normalized to
the signal obtained at 0 days of equivalent aging. The normalized abundances
for TAG species were obtained by integrating each compound's m/z 60 signal
at each level of oxidation, then dividing each peak area by the peak area
obtained in the unaged chromatograms (“0 days”). As with TAG species, AMS
f60 has been normalized at each level of oxidation to the AMS f60
obtained without photochemical aging.
Primary TAG species generally decrease in abundance with photochemical aging,
though rates of decay vary depending on the compound. By contrast, in both
heartwood and leaf BBOA, the TAG decomposition m/z 60 summed signal
increases overall from zero to 6–10 days of equivalent aging, peaking at
1–3 days of aging. In the leaf BBOA, the AMS m/z 60 signal decreases by
approximately 10 % at 6–10 days of aging, while the AMS f60 in the
wood BBOA is reduced to 50 % of its original value at the highest level
of oxidation. These trends in AMS f60 may reflect the combined effects
of the oxidative decay of primary BBOA compounds, including sugars and
anhydrosugars, and the formation of organic acids with functionalization
reactions in the PAM chamber. Previous BBOA chemical characterization studies
have identified organic acids as BBOA tracers (Falkovich et al., 2005; Lin et
al., 2016; Mazzoleni et al., 2007), and Ortega et al. (2013) report that
organic acids formed through OFR-driven oxidation may contribute to net AMS
m/z 60 (Ortega et al., 2013).
Figure 12c displays experimental relative abundances as functions of
equivalent aging time for various TAG and AMS markers observed during wood
BBOA oxidation, along with levoglucosan decay rates calculated using
kLG values obtained in previous studies (Hennigan et al., 2010; Kessler
et al., 2010). In addition, AMS f60 values obtained for PAM-aged turkey
oak (Q. laevis) BBOA during the FLAME-3 campaign (Ortega
et al., 2013) are overlaid for comparison; the values plotted correspond to
f60 = 0.028 at OHexp = 0 molec cm-3 s and f60 = 0.016 at OHexp = 5.6 × 1011 molec cm-3 s
(approximately 4 days of equivalent aging based on their PAM reactor
calibration), with each point normalized to f60 = 0.028 (Ortega
et al., 2013).
The OH-driven oxidation kinetics of levoglucosan in BBOA have been
investigated in previous chamber oxidation studies. For example, Kessler et
al. (2010) obtained a second-order rate constant of
kLG = (3.09 ± 0.18) × 10-13 cm3 molec-1 s-1
from AMS measurements of OFR-oxidized levoglucosan particles (Kessler et al.,
2010), while Hennigan et al. (2010) obtained a rate constant of
kLG = (1.1 ± 0.5) × 10-11 cm3 molec-1 s-1
from smog chamber experiments (Hennigan et al., 2010). Lai et al. (2014)
obtained expressions for kLG as a function of relative humidity
and temperature in their own smog chamber experiments; at 25 ∘C and
30 % relative humidity,
kLG = 1.107 × 10-11 cm3 molec-1 s-1,
a value in good agreement with Hennigan et al. (2010)'s results (Lai et al.,
2014). Lai et al. (2014) attribute the discrepancy between Kessler et
al. (2010)'s and Hennigan et al. (2010)'s calculated kLG to
differences in both the levoglucosan detection method and experimental OH
concentration ranges. First, while Hennigan et al. (2010) used offline filter
collections to determine levoglucosan concentrations, Kessler et al. (2010)
took online measurements using an AMS and used m/z 144 as the marker
fragment for levoglucosan. Lai et al. (2014) suggest that because the parent
ion of m/z 162 was not used as the marker fragment in Kessler et al.'s AMS
measurements, any potential effects from reaction products cannot be fully
isolated, possibly leading to an underestimate of levoglucosan decay.
However, our chromatographic methods are not subject to this mass spectral
interference, and in the case of the heartwood BBOA, the TAG-measured
levoglucosan decay matches the decay predicted by Kessler et al. (2010).
Additionally, Lai et al. (2014) suggest that their own results may differ
from those obtained by Kessler et al. (2010) because they operated at much
lower OH concentrations. During these experiments, OH concentrations ([OH])
ranged from 109–1010 molec cm-3, closer to the operating
conditions of Kessler et al. (2010)
([OH] = 109–2 × 1011 molec cm-3) than Lai et
al. (2014) ([OH] = 3.50 × 107 molec cm-3).
Although levoglucosan decays rapidly in the leaf BBOA with increasing
OHexp, levoglucosan in the heartwood BBOA is depleted more slowly.
Levoglucosan is classified as semivolatile (at 25 ∘C,
pL∘ ∼ 2.41 × 10-5 Pa; ACD/Labs,
2017) and is therefore expected to partition between the gas and particle
phases. To approximate phase partitioning, particle-phase fractions for
levoglucosan (ξLG) were calculated based on AMS total organic
concentrations and effective saturation concentrations (CLG∗, µg m-3) using Eq. (2). The resulting values and relevant
parameters are reported in Table S12. For each fuel, little variance is
expected in levoglucosan particle-phase fraction between oxidation
conditions, so we conclude that phase partitioning is unlikely to be driving
trends in levoglucosan abundances observed in these experiments. Based on the
partitioning approximations, the leaf BBOA is expected to contain a higher
percentage of levoglucosan in the particle phase than the heartwood BBOA
(91.1 ± 1.65 % vs. 77.8 % ± 2.26 %), though in both
cases, gas-phase levoglucosan concentrations are likely to remain low. The
prevalence of levoglucosan in the particle phase during photochemical aging
is consistent with previous laboratory measurements of aged levoglucosan
particles (Kessler et al., 2010). Considering that heartwood BBOA exhibited
lower total organic concentrations than the leaf BBOA, the slower depletion
of levoglucosan in the heartwood samples is perhaps consistent with OH
suppression effects, wherein OH experiences increased reactivity with
gas-phase species at the particle surface.
The AMS m/z 60 signal agrees well with the levoglucosan decay rate
calculated using Kessler et al. (2010)'s kLG and decreases with
increasing OHexp, though it displays less overall decay compared to
levoglucosan measured by the TAG. Our results demonstrate that although
m/z 60 may be an effective tracer for levoglucosan and primary BBOA under
certain conditions, the formation of organic acids through photochemical
aging may also impact AMS m/z 60 and should be considered when using the
AMS to track levoglucosan and primary BBOA in future studies. Furthermore,
these results illustrate the utility of TAG data in interpreting AMS bulk OA
measurements, as it gives both molecular characterization as well as
additional insight on the chemical makeup of the most aged OA through
evaluation of thermal decomposition components.
Conclusions and atmospheric implications
The experimental methods presented in this work allow repeatable collection,
oxidation, and molecular-level analysis of source-specific BBOA. The
identification of molecular tracers unique to leaf and wood fuels can aid
apportionment of BBOA to different plant fractions. For example, based on
our results, a BBOA plume exhibiting high concentrations of aliphatic leaf
wax components may be attributed to canopy or leaf litter devolatilization
and combustion, while a plume with high concentrations of levoglucosan and
lignin decomposition products could be attributed to heartwood combustion.
Additionally, our results suggest that certain molecular components present
in freshly emitted BBOA may persist after 3 days of equivalent aging and
could even increase in abundance with atmospheric aging due to reaction or
gas-to-particle partitioning. The relative rates of OH-driven decay obtained
from TAG measurements may thus inform future field observations where
molecular speciation information is obtained for photochemically aged
plumes.
The PMF deconvolution results support the identification and analysis of
individual compounds present in heartwood and leaf BBOA. Because each
chromatogram may contain hundreds of compounds, a general knowledge of the
compound classes characteristic of each BBOA type can greatly reduce
individual compound analysis time and ensure that chromatograms are
characterized as completely as possible. The results presented in this study
therefore confirm that the chromatogram binning method coupled with PMF, as
developed by Zhang et al. (2014, 2016), can aid molecular tracer analysis by elucidating different
compound classes of interest present in BBOA. The compound window PMF
results provide information on characteristic mass spectral signatures
within leaf and wood primary BBOA and may be compared to results obtained in
future BBOA studies to more fully characterize how different compounds
evolve with photochemical aging in the atmosphere.
Based on previous studies, combustion conditions are expected to
significantly impact the chemical composition of both primary and secondary
BBOA (Ortega et al., 2013; Reece et al., 2017; Weimer et al., 2008; see “AMS
Chemical Characterization” in the Supplement). The resistive heating
technique applied in these experiments allows for the isolation of
devolatilization (precombustion) and low-temperature (≤ 300 ∘C) smoldering conditions, which is difficult to achieve in
combustion chambers that require ignition of a flame. For example, Tian et
al. (2015) designed a chamber that allows the user to control the relative
contributions of smoldering and flaming combustion, though smoldering
combustion is only achieved in this chamber following the introduction of a
flame to the biomass fuel (Tian et al., 2015). The devolatilization and
combustion procedure presented here is thus advantageous for investigating
aerosol from small masses of biomass fuel under tightly controlled
conditions. However, these results alone are likely not representative of a
real-world system, where smoldering combustion often occurs alongside flaming
combustion. Our results may therefore serve to complement field measurements,
where either smoldering or flaming combustion may dominate, as well as
laboratory studies where combustion conditions are controlled.
Future work will focus on characterizing sources of bias to improve
quantification of material in both the TAG compound and decomposition window.
For example, particle matrix effects, whereby certain compounds exhibit
enhanced or diminished recovery due to the presence of a particle matrix,
have been reported to influence compound responses in previous work with the
TAG and other thermal desorption GC systems, particularly for large molecular
weight compounds (Lambe et al., 2009; Lavrich and Hays, 2007). Lambe et
al. (2009) quantified this effect for the TAG by co-injecting a constant
C30 deuterated alkane standard with 0–60 µg motor oil and
found that the presence of the motor oil matrix enhanced recovery of the
standard by a factor of 2–3 (Lambe et al., 2009). In these experiments, the
TAG collected estimated ranges of 6–16 µg particles for leaf BBOA
and 22–36 µg particles for heartwood BBOA. Based on these mass
ranges, we do not expect these matrix effects to contribute significantly to
our results, especially for the lower molecular weight compounds. However,
future work will incorporate an evaluation of matrix effects to minimize bias
in TAG measurements. Although the TAG's OA analysis capability has
historically been limited by poor mass throughput of highly oxygenated
species, we demonstrate here that the TAG decomposition window can be used to
gain a better understanding of the molecular composition of oxidized BBOA.
Though the decomposition window does not provide chemical composition
information with molecular resolution, the chromatogram binning PMF results
allow identification of different co-eluting factors, many of which
correspond to molecular fragments that could be used as source-specific BBOA
tracers in future field studies.
The utility of the thermal decomposition window is limited by a lack of
adequate analytical standards, particularly for organic components. Although
ammonium sulfate and ammonium nitrate standards have been used to quantify
sulfate and nitrate particles in previous work (Williams
et al., 2016), the development of satisfactory standards for decomposing
organics remains difficult for several reasons. Fragments eluting in the
decomposition window may be tentatively identified using available mass
spectral identification tools, though we often cannot infer the source of
the fragments, since they are products of compound thermal decomposition
rather than volatilization. Many of the compounds undergoing decomposition
during sample desorption may therefore be too involatile for typical GC-MS
analysis. Despite these challenges, analytical standards are currently under
development to aid identification and interpretation of decomposition window
results based on molecular functionality. For both types of BBOA, the m/z 44
signal in the TAG decomposition window increases with photochemical aging,
confirming that this signal indicates the presence of thermally labile
oxygenated OA. The increase in m/z 44 with oxidation in both the TAG
decomposition window and the AMS mass spectra is consistent with results
from previous studies (Williams
et al., 2016). However, our observations suggest that the utility of
decomposition m/z 44 as a quantifiable tracer for aged OA varies depending on
OA type. For the heartwood BBOA, the TAG decomposition m/z 44 signal correlates
well with AMS OSC‾, suggesting that for this type of BBOA, the
decomposition m/z 44 abundance could be used to estimate the aerosol's
oxidation state. By contrast, the correlation between TAG decomposition
m/z 44 and AMS OSC‾ is not significant for PAM-aged oak leaf BBOA,
perhaps because compounds formed with photochemical aging of leaf BBOA are
less thermally labile and more resistant to thermal decomposition than those
found in aged heartwood BBOA. In addition, without mass-based standard
calibrations for the decomposition window, distinguishing between an
increase in thermally labile mass (i.e. due to SOA formation) and a relative
increase in thermally decomposing OA due to changes in chemical composition
(i.e. due to heterogeneous oxidation and functionalization) remains
challenging.
From the TAG data, we observe two competing effects driving the overall
m/z 60 signal measured in the AMS. While many primary BBOA components
exhibiting a characteristic m/z 60 fragment, including anhydrosugars like
levoglucosan, were depleted with photochemical aging, an enhanced m/z 60 signal
in the decomposition window indicates increased formation of organic acids
in the PAM reactor. Both processes have been reported in previous
literature, though the oxidative depletion of primary BBOA is most typically
thought to drive AMS m/z 60 trends in field and laboratory studies. Our data
suggest that although AMS measurements provide useful chemical composition
information on bulk OA, laboratory studies with molecular-level measurements
are needed to complement AMS data and provide a more complete understanding
of processes occurring in the atmosphere.
The mechanisms driving compositional changes in BBOA remain challenging to
interpret. Although many compounds observed in this study are clearly
depleted through functionalization reactions, some species may be subjected
to phase partitioning effects in addition to PAM-driven oxidation. In
particular, the enhancement in TAG thermal decomposition m/z 44 and m/z 60 may
occur due to formation of SOA through oxidation and condensation of
low-volatility gases, heterogeneous functionalization of compounds in the
particle phase, or a combination of these processes. Future studies will
focus on investigating the role of phase partitioning in OA chemical
composition within BBOA plumes, with emphasis on the thermally labile
material eluting in the TAG thermal decomposition window. In addition,
different types of biomass will be tested to explore the dependence of phase
partitioning and photochemical aging effects on fuel type, broadening the
applicability of these techniques to future field measurements.