Cooking emissions account for a major fraction of urban
organic aerosol. It is therefore important to understand the atmospheric
evolution in the physical and chemical properties of organic compounds
emitted from cooking activities. In this work, we investigate the formation
of secondary organic aerosol (SOA) from oxidation of gas-phase organic
compounds from heated cooking oil. The chemical composition of cooking SOA
is analyzed using thermal desorption–gas chromatography–mass spectrometry (TD–GC–MS). While the particle-phase composition of SOA is a highly complex
mixture, we adopt a new method to achieve molecular speciation of the SOA.
All the GC-elutable material is classified by the constituent functional
groups, allowing us to provide a molecular description of its chemical
evolution upon oxidative aging. Our results demonstrate an increase in
average oxidation state (from -0.6 to -0.24) and decrease in average carbon
number (from 5.2 to 4.9) with increasing photochemical aging of cooking oil,
suggesting that fragmentation reactions are key processes in the oxidative
aging of cooking emissions within 2 d equivalent of ambient oxidant
exposure. Moreover, we estimate that aldehyde precursors from cooking
emissions account for a majority of the SOA formation and oxidation
products. Overall, our results provide insights into the atmospheric
evolution of cooking SOA, a majority of which is derived from gas-phase
oxidation of aldehydes.
Introduction
Organic aerosol (OA) has important impacts on air quality, climate, and human
health
(Hallquist et
al., 2009). OA is often composed of thousands of organic compounds formed
from a variety of sources. In urban areas, particulate emissions from food
cooking account for a significant fraction of OA
(Allan
et al., 2010; Crippa et al., 2013; Florou et al., 2017; Kostenidou et al.,
2015; Lee et al., 2015; Mohr et al., 2012; Sun et al., 2011). Furthermore,
volatile organic compounds (VOCs) are also emitted, and they can undergo
oxidation and form secondary organic aerosol (SOA). Recent studies have
reported the formation of SOA from meat charbroiling
(Kaltsonoudis et al., 2017) and
heated cooking oils
(Liu
et al., 2017a, c, 2018). Therefore, food cooking activities have
substantial impacts on air quality in and downwind of urban areas.
The emission of VOCs from cooking is highly variable and depends on a number
of factors such as cooking style, food, ingredients, and temperature
(Fullana
et al., 2004a, b; Klein et al., 2016a, b; Liu et al., 2017c; Schauer
et al., 1999, 2002). Of the different classes of VOCs characterized in these
studies, aldehydes have been shown to be the major group of VOCs emitted
from cooking oils. These VOCs are chemically produced upon heating via
peroxyl radical reactions of the fatty acids
(Choe and Min, 2007; Gardner,
1989). Klein et al. (2016b) investigated the composition of nonmethane organic gas (NMOG) emissions from boiling, charbroiling, and shallow and deep
frying of various vegetables, meats, and cooking oils heated under different
temperature conditions. The authors reported that emissions from shallow
frying, deep frying, and charbroiling are dominated by aldehydes, and the
relative amounts depend on the type of oil used during cooking
(Klein et al.,
2016b). C7 aldehydes are the major species in emissions from canola oil,
whereas C9 aldehydes are dominant from olive oil
(Klein et al.,
2016b). These differences in emission patterns of oils vary with composition
of triglycerides present in the oil (Choe and
Min, 2006). Katragadda et al. (2010)
demonstrated an increase of up to an order of magnitude in emissions upon reaching
the smoke point of cooking oils. In addition to emissions from cooking oil,
the addition of condiments (herbs and peppers) to cooking leads to
significant emissions of mono-, sesqui-, and diterpenes in the gas phase
(Klein et al., 2016a). Liu et
al. (2017b)
showed an increase of an order of magnitude in the emissions of VOCs when
stir-frying with spices. Therefore, factors like cooking style, food,
cooking temperature, and ingredients play a significant role in the chemical
profile of cooking emissions
(Fullana
et al., 2004a, b; Klein et al., 2016a, b; Liu et al., 2017b, c).
The VOCs emitted from cooking have been shown to produce a significant amount
of SOA rapidly in recent flow tube
(Liu et al., 2017a)
and smog chamber studies
(Kaltsonoudis
et al., 2017; Liu et al., 2017c, 2018). Kaltsonoudis et al. (2017) and Liu et al. (2017a,
2018) showed an increase in O:C ratio upon a few hours of atmospheric aging,
suggesting lightly oxidized cooking SOA. Furthermore, Liu et al. (2017a)
showed significant production of SOA with increasing OH exposure for
different cooking oils. Thus far studies have only focused on formation
potential of SOA from cooking emissions. Despite high emission rates of VOCs
from cooking, the understanding of SOA composition from these emissions
remains limited.
Source apportionment using aerosol mass spectrometry (AMS) data in urban
areas has often revealed a cooking organic aerosol (COA) factor, but it is
unclear how this factor is related to cooking emissions. Many studies
reported that the mass spectra associated with this factor resemble those of
the hydrocarbon-like organic aerosol (HOA) factor from other non-cooking sources
(Dall'Osto
et al., 2015; Hayes et al., 2013; Huang et al., 2010; Mohr et al., 2009,
2012). In addition, it is often unclear whether ambient COA represents
primary or secondary organic aerosol from cooking emissions
(Dall'Osto
et al., 2015; Florou et al., 2017; Kaltsonoudis et al., 2017; Kostenidou et
al., 2015). Laboratory studies
(Liu et al.,
2017a, 2018) showed that the mass spectra for primary cooking organic
aerosol exhibited strong correlation with ambient COA factor
(Lee et al., 2015), but the cooking SOA mass
spectra showed some similarities to ambient semi-volatile oxygenated OA
(SV-OOA) factor. These measurements highlight the challenges in assigning
COA factor without understanding the changes in chemical composition
occurring during oxidation of cooking emissions.
In general, there is a need to better understand the molecular composition
contributing to aged COA. In this study, we investigate detailed chemical
composition of cooking SOA at the molecular level. The objectives of this
study are to (i) understand the detailed chemical speciation of cooking SOA
using TD–GC–MS (thermal desorption–gas chromatography–mass spectrometry), (ii) describe chemical evolution in SOA upon atmospheric
aging, and (iii) attribute formation of SOA to different VOCs emitted from
food cooking emissions. In this work we use heated cooking oil as a model
for food cooking emissions. We show that the majority of the SOA is derived
from oxidation of aldehydes, and the oxidation mechanisms are dominated by
fragmentation reactions. Overall, our results provide useful insights into
the evolution of cooking SOA, which may be incorporated into chemical
transport models for better predicting OA formation from cooking emissions
in the atmosphere.
Experimental setup for oxidation of heated-cooking-oil emissions.
Experimental methodsFlow tube experiments
The experimental setup is shown in Fig. 1, and experimental conditions are
listed in Table S1. For each experiment, 30–40 mL of canola oil was heated
at 250 ∘C on an electric heating plate in a Pyrex bottle,
resulting in an average cooking oil temperature of 180 ∘C, as
measured by a thermocouple in direct contact with the heated oil. Purified
air flowed over the headspace of the heated oil at a rate of 0.2 L min-1 and was then diluted by a factor of 50. A total of 0.2 L min-1 of the
total diluted flow was passed through a Teflon filter to remove particles,
and the oil vapors were introduced into a custom-built 10 L quartz flow tube
reactor. A separate flow of oxygen (99.6 %) was irradiated in a UV ozone
generator (UVP 97006601) to produce ozone and was also introduced into the
flow tube reactor. In parallel, purified air was flowed through a water
bubbler into the reactor to provide water vapor. The combined flow rate
through the flow tube was set at 3 L min-1, resulting in an average
residence time of approximately 200 s.
In the flow tube, hydroxyl radicals were produced through the photolysis of
ozone irradiated by a UV lamp (λ=254 nm) in the presence of
water vapor. The integrated OH exposure was measured indirectly from the
loss of cyclopentane, which was monitored by a gas chromatography flame
ionization detector (GC-FID; model 8610C, SRI Instruments Inc.) equipped
with a Tenax TA trap sampling downstream of the flow tube at a rate of 0.15 L min-1. In this study, the experiments were conducted at different OH
exposures ranging from 5.77×1010 to 2.2×1011 molecules cm-3 s. OH exposure in this range is equivalent to
∼11 to 41 h of atmospheric oxidation, respectively, assuming
a 24 h average atmospheric OH concentration of 1.5×106 molecules cm-3 (Mao et al.,
2009). The effect of ozone on the SOA formation was found to be negligible
as the reaction timescales of aldehydes with ozone were calculated to be at
least 100 times longer than those with OH. A sample calculation for
methacrolein reaction timescales with OH and ozone is shown in the Supplement in Sect. S1.
Downstream of the flow tube, pre-baked quartz fiber filter and Tenax tube
samples were collected for offline chemical analysis. The changes in the
particle size distribution and volume concentration were monitored using a
scanning mobility particle sizer (SMPS) with a differential mobility
analyzer (TSI 3081) and a condensation particle counter (TSI 3781). A
constant density of 1.4 g cm-3 was assumed to convert particle volume
concentration into mass concentration
(Chan et al.,
2010). Relative humidity and temperature were monitored by an Omega HX94C
RH/T transmitter and were maintained at 65 %–70 % and 19–20 ∘C,
respectively, for all experiments. A fast stepping and scanning thermodenuder
(TD; Aerodyne Inc. Billerica, USA) was also placed downstream of the flow
tube to measure SOA evaporation rates. Details about TD operating conditions
and analysis can be found in Takhar et al. (2019). The TD was only operated during one
experiment in which the OH exposure was 9.23×1010 molecules cm-3 s. The SOA was systematically heated in a TD from 25
to 175 ∘C, and changes in particle volume concentrations and
corresponding mass fraction remaining (MFR) were measured using an SMPS. The
SOA size distribution during TD operation and volatility distribution are
shown in Figs. S1 and S2, respectively. A kinetic mass transfer model
developed by Riipinen et al. (2010)
was used to interpret the TD data. The inputs to the model are volatility
distribution of OA, enthalpy of vaporization, and mass accommodation
coefficients. Compound groups are translated into volatility distributions
by binning components according to their saturation concentrations
(Donahue et al., 2006).
Parameterization for enthalpy of vaporization was similar to that of Takhar
et al. (2019). We assume a surface tension of
0.05 N m-1 and gas-phase diffusion coefficients of 5×10-6 m2 s-1 for all simulations, similar to those reported in Riipinen et
al. (2010).
Chemical characterization of SOA
Tenax tube and quartz filter samples were analyzed separately by thermal
desorption–gas chromatography–mass spectrometry (TD–GC–MS) for detailed
chemical speciation of gas- and particle-phase organic compounds. The
analyses were performed using a thermal-desorption system (TDS 3, Gerstel)
combined with a gas chromatography (7890B, Agilent)–mass spectrometer
(5977A, Agilent). For gas-phase analysis, concentrations of aldehydes (C7 to
C10 n-alkanals, alkenals, and alkadienals) collected on Tenax tube samples
before photooxidation (downstream of the flow tube, with lights off) were
quantified. For particle-phase analysis, thermal desorption of quartz
filters was performed with in situ derivatization using
N-trimethylsilyl-N-methyl trifluoroacetamide (MSTFA). A known amount of
deuterated 3-hydroxy-1,5-pentanedioic-2,2,3,4,4-d5 acid, and
n-pentadecane-d32 (CDN isotopes) was injected, respectively, onto quartz
filter punches and Tenax tubes as internal standards before the samples
were desorbed in the TDS. All GC–MS analysis was performed using a non-polar
DB5 column (Rxi-5Sil MS, Restek). Details of the operating parameters (GC
column, GC and TDS temperature ramps, MS parameters) can be found in Sect. S2
of the Supplement.
With in situ derivatization, polar organic compounds react rapidly with MSTFA at
elevated temperatures during thermal desorption, and functional groups with
acidic hydrogen atoms (such as –OH) are replaced by a less polar
trimethylsilyl (TMS; [–OSi(CH3)3]) group. This reduction in
polarity allows the derivatized analyte to elute from a non-polar column and
be analyzed by subsequent electron impact (EI) at 70 eV. Derivatized compounds
produce a signature fragment ion at mass-to-charge (m/z) 73
(–Si(CH3)3+), arising from the scission of O–Si bond in
R–O–[Si(CH3)3]. In other words, all derivatized compounds produce
ions with m/z 73 during analysis. Therefore, the total signal at m/z 73 can be
taken as the total concentration of organic compounds with at least one
hydroxyl group (including both –OH and –C(O)OH) present in cooking SOA,
much like how m/z 57 represents total concentration of aliphatic compounds in
hydrocarbon mixtures (Zhao et al.,
2014,
2015). It should be noted that organic peroxides (R–OOH) were also found to
be derivatized, but the major reaction product formed is
R–O–[Si(CH3)3] (which is also formed from R–OH derivatization) as
shown in Fig. S3. Here we assume alcohols and acids are the major
components but will explore the potential role of ROOH on the overall
chemical composition in Sect. 3.1.
Highly complex mixture of canola oil SOA generated upon
photooxidation. With known signal and mass fragmentation, the signal of m/z 73
can be recreated based on pseudo-parent ions (e.g., M-15 used in this study).
As shown in Fig. 2, many compounds in cooking SOA contain at least one –OH
group, and the chromatogram of m/z 73 is typical of that for a highly complex
mixture or unresolved complex mixture (UCM). Using traditional analytical
techniques like GC–MS it is difficult to deconvolute the UCM. However,
knowledge about mass spectral fragmentation of TMS derivatives can be used
to understand the compounds contributing to the UCM. Table S2 shows a list
of compounds containing multiple functional groups, e.g., –COOH and –OH, resulting
in different combinations of compound classes like dicarboxylic acids,
hydroxy acids, hydroxy dicarboxylic acids, and dihydroxy dicarboxylic acids
with different carbon numbers. As mentioned earlier, we acknowledge the
potential contribution from ROOH but first assume the functional
groups shown in Table S2 here and consider ROOH in more detail in a later
section. The compound groups shown in Table S2 are expected to be formed
from oxidation of aldehydes and be derivatized by MSTFA. The TMS derivatives
of these compounds share common ion fragments in their EI mass spectra:
m/z 73 [Si(CH3)3].+, 75, 147
[(CH3)2Si=O(CH3)3].+, M-15 [M–CH3].+
(Jaoui
et al., 2004, 2005; Yu et al., 1998). Most importantly, all TMS derivatives
exhibit quantifiable peaks at m/z 73 (ubiquitous ion for all derivatives) and
M-15 (ion specific to each compound group, hereby referred to as the
pseudo-parent ion). We also obtained the characteristic ratio of these two
ions for each compound group (fM-15/73) from the National Institute of Standards and Technology (NIST) mass spectral
libraries and from analyzing authentic standards. To verify the validity of
this method, we calculate the total m/z 73 ion signal that is attributable to
these compound groups by taking the chromatograms of the pseudo-parent ion
for each compound group, dividing by its characteristic ratio fM-15/73, and then summing across all compound groups as shown in Eq. (1).
S73,tsum=∑iSM-15,i,tfM-15/73,i,
where S73,tsum is the m/z 73 ion signal at retention time t that is
attributable to all compound groups listed in Table S2, SM-15,i,t is
the signal of the pseudo-parent ion for compound group i at retention time
t, and fM-15/73,i is the characteristic ratio of the pseudo-parent ion to m/z 73.
This approach is similar to that described in
Isaacman-VanWertz et al. (2020).
As shown in Fig. 2, S73,tsum shows excellent agreement with the
measured m/z 73 ion signal, suggesting that the m/z 73 signal, which is
representative of all TMS derivatives, is almost entirely comprised of
contributions from the compound groups listed in Table S2. This agreement
between our bottom-up approach and measured signal provides confidence that
our method is able to provide information about the chemical composition of
a highly complex mixture.
With the signals from all the pseudo-parent ions for all compound groups,
the total mass of each compound group was then calculated using Eq. (2).
Mi=TAiRFi×1fM-15/73,i,
where Mi is the mass of compound group i, TAi is the total
integrated signal of the pseudo-parent ion for compound group i (normalized by
the signal of the deuterated internal standard), RF is the response factor
(calculated from calibration curves of fatty acid and dicarboxylic acid authentic standards) of compound group i, and fM-15/73,i is the
characteristic ratio of the pseudo-parent ion to m/z 73 for compound group i. A more
detailed, step-by-step description of the procedure can be found in the Supplement
in Sect. S3 and is illustrated in Fig. S4, with corresponding uncertainties in
the fitting procedure shown in Fig. S5.
Evolution in OSc‾–nc space for canola oil SOA
under different conditions of photochemical aging. As the oxidation
progresses in the atmosphere, more compounds are formed with smaller nc and
higher OSc‾, suggesting fragmentation to be a dominant pathway of
oxidation for cooking emissions in the atmosphere.
Results and discussionChemical evolution of SOA
As described in Sect. 2.2, components in cooking SOA were classified by
functional groups and carbon number. To describe the overall changes in SOA
composition with increasing OH exposure, we use the average carbon oxidation
state (OSc‾) as a metric for the evolving composition of a complex
mixture undergoing oxidation
(Kroll et al., 2011). Both
OSc‾ and number of carbon atoms (nc) for each compound group are
calculated from the GC-derived chemical composition. The total mole fraction
of C, H, and O was calculated for each sample, which was then used to
calculate the bulk OSc‾ using Eq. 2×O:C–H:C
(Kroll et al., 2011). The
evolution in this framework for canola oil SOA is shown in Fig. 3. The bulk
OSc‾ was observed to increase from -0.6 to -0.24 when OH exposure
increased from 5.77 to 22.0×1010 molecules cm-3 s for
canola oil SOA. For comparison, Liu et al. (2017a) showed an
initial decrease in OSc‾ and O:C, which gradually stabilized at OH
exposure greater than 9×1010 molecules cm-3 s. For the
OSc‾ range reported here, the OSc‾ of cooking SOA falls in the
range of SV-OOA as determined from factor analysis of AMS data
(Canagaratna
et al., 2015). This degree of oxygenation is greater than that of the COA
factor measured by AMS, which is reported to be around -1.37
(Canagaratna
et al., 2015). This difference suggests that the COA factor resolved using
positive matrix factorization (PMF) analysis is likely of primary origin and does not represent SOA formed
from atmospheric oxidation of cooking emissions. Furthermore, previous GC–MS
analysis showed for POA from cooking oils an OSc‾ of -1.66 (canola
oil) and -1.7 (beef tallow, olive oil; Takhar et al., 2019). These observations again
suggest that the COA factor measured by AMS represents primary cooking
emissions.
In addition to carbon oxidation state, knowledge about molecular composition
provides further insights into the oxidation mechanisms. Canola oil SOA at
an OH exposure of 5.77×1010 molecules cm-3 s is
comprised of ∼19 % larger (C8–C10) and less oxygenated
compounds; this fraction declined to ∼11 % at higher OH
exposures. Furthermore, the total fraction of C2–C7 products increased from
81 % to 89 % when OH exposure increased from 10.7 h to 1.7 d. Of this
fraction, the smaller-carbon-number compounds (C2–C4), which are indicative of
fragmentation processes, increased from 42 % at 10.7 h to ∼49 % at 1.7 d. An increase in smaller and more oxygenated compounds, along
with a decrease in larger and less oxygenated products, suggests that
fragmentation reactions are responsible for the shift towards formation of
smaller oxygenated compounds. As a result, oxidation simultaneously leads to
higher OSc‾ and lower carbon number on average. Based on the
compounds observable by our technique, this trend suggests that
fragmentation reactions are key processes in the oxidative evolution of
cooking emissions.
Van Krevelen diagram of canola oil SOA colored by
different OH exposure. In the background, average carbon oxidation state
(OSc‾) and functionalization slopes are shown for reference. The
slope of -0.19 for canola oil SOA corresponds to formation of both alcohol
and carboxylic acid, consistent with the chemical composition obtained from
TD–GC–MS.
The compounds observed here can also be compared to previously measured bulk
composition using elemental ratios, such as those presented in a Van
Krevelen (VK) diagram (Heald et
al., 2010). As shown in Fig. 4, the O:C ratio in our study ranged between
0.64 and 0.79 when OH exposure increased from 5.77×1010 to
22.0×1010 molecules cm-3 s. The O:C ratios measured
using an AMS
(Kaltsonoudis
et al., 2017; Liu et al., 2017a) ranged between 0.24–0.46, which is within a
factor of 2 of those measured in this study. Furthermore, the H:C versus O:C trend is
linear with a slope of -0.19, which lies between the slope of 0 measured for
low-NOx oxidation reported by Liu et al. (2017a) and -0.4 for
high-NOx conditions (Liu et al., 2018). Therefore,
based on elemental ratios, the evolution in SOA composition measured in this
study is comparable to that in bulk average properties estimated by AMS.
Furthermore, we use the two-dimensional volatility basis set (2D-VBS) framework developed by Donahue et al. (2012) to investigate OA chemistry and understand the evolution of cooking SOA through changes in the
volatility of the SOA system. The vapor pressures of the identified compounds
are calculated using the group contribution method
(Pankow
and Asher, 2008) where experimentally determined vapor pressures were
unavailable and reported in Table S2. The observed compounds in SOA have a
broad range of volatilities since they were formed from oxidation of a
complex ensemble of VOC precursors. As shown in Fig. S6, there is a minor
decrease in overall volatility of the mixture (change lies within 1 decade
in C*) irrespective of the presence of peroxides, while OSc‾ is
increasing with oxidation. This increase in oxidation state is coincident
with increasing fragmentation upon oxidation, and, as a result, the overall
change in the bulk volatility of canola oil SOA is relatively small.
As mentioned earlier in Sect. 2.2, there is a potential to misclassify ROOH
as ROH using our current GC–MS method. In Fig. S3, we show that
derivatization of cumene hydroperoxide forms the TMS of hydroxy-cumene in
our system. Here we further examine the chemical composition by assuming
that each –O–[Si(CH3)3] group observed originates from an –OOH
group in the SOA. It should be noted that replacing –OH with –OOH results
in a higher estimate of O:C (and OSc‾) but does not change H:C or
carbon number. Furthermore, since pseudo-molecular ion fraction
(fM-15/73) for organic peroxides (needed for quantification) is
unknown, we assume that it is similar to those presented in Table S2. As
shown in Fig. S7 if all observed –OH groups are –OOH groups, the VK slope
would be -0.15, which is similar to the value of -0.19 calculated based on the
no-peroxide assumption. Similarly, Fig. S6 shows that this uncertainty in
hydroxyl group identification has a negligible effect on estimation of vapor
pressure or volatility in the 2D-VBS framework. Therefore, this potential
misclassification of peroxide groups may lead to an underestimation in O:C
and OSc‾ but is not expected to affect estimates of volatility and
our general conclusions about the importance of fragmentation reactions. In
the future, analytical techniques such as extractive-electrospray-ionization
time-of-flight mass spectrometry
(Lopez-Hilfiker et al., 2019) may be
useful to better understand the composition of peroxides from cooking SOA.
While the misclassification of peroxides may have little impact on the bulk
properties such as average O:C ratios, there may be important implications
on understanding the reactivity of the SOA.
Evaporation rates of SOA
The volatility of the SOA is also probed by measuring the evaporation rates
in a heated thermodenuder and compared to the rates expected from the
measured composition. In order to derive the evaporation rates from the
measured chemical composition of cooking SOA, we use the kinetic mass transfer model developed by Riipinen et al. (2010). Among the inputs into the
model, the mass accommodation coefficient is a critical but uncertain
parameter that accounts for the mass transfer limitations in the system.
Mass thermogram of canola oil SOA at an OH exposure of
9.23×1010 molecules cm-3 s. The black line represents
model simulations using α=1, underpredicting the measured MFR.
The red line corresponds to model simulations using α=0.03,
predicting the measurements reasonably well, therefore implying kinetic
limitations in the system. The error bars represent ±1σ.
Figure 5 shows both measured and modeled mass thermograms for canola oil
SOA. We observe that for canola oil SOA, a mass accommodation coefficient of
0.03 is needed to predict the experimentally determined mass thermograms. An
accommodation coefficient of <1 suggests that mass transfer
limitations in the system likely occur in the condensed phase. Formation
of multifunctional organic compounds such as those observed in this study is
likely responsible for an increase in viscosity through increasing hydrogen
bonding and other polar interactions (Rothfuss and Petters,
2016). It should be noted that Takhar et al. (2019) reported similar magnitudes of mass accommodation coefficients for
heterogeneous oxidation of cooking oil particles. Due to similarity in the
type of functional groups present in both aging pathways, we believe the
decrease in mass accommodation coefficients for both systems undergoes similar
changes in phase and/or viscosity.
These measurements of evaporation rates are consistent with the volatilities
expected from our measured composition of SOA containing small oxygenated
compounds. Although mass accommodation coefficients are highly uncertain,
the mass accommodation coefficients for other SOA systems have been measured
to be even lower, on the order of 10-4
(Cappa and Wilson, 2011), which would
require the volatilities to be even higher to explain the measured
evaporation rates. Therefore, the TD measurements support the conclusion
that smaller oxygenated compounds are produced from oxidation of cooking oil
vapors and that fragmentation reactions are dominant. Furthermore, these
measurements provide useful inputs into chemical transport models for
predicting SOA formation and gas-particle partitioning. Our previous work
(Takhar et al., 2019) showed that even at
α=10-2, gas-particle partitioning timescales are short
(within hours), and the assumption of equilibrium partitioning still holds
for regional-scale SOA formation. Further work is needed to directly measure
the viscosity of cooking SOA and corresponding mixing timescales to better
constrain the physicochemical properties of cooking SOA.
Contribution of aldehydes to observed oxidation products and total
SOA
Since cooking oil vapors are comprised of a number of reactive aldehydes
that can lead to SOA formation, we conduct further experiments of SOA
formation from these precursors and identify the relative contributions to
observed oxidation products and to total SOA. These results are applied to
the heated-cooking-oil experiments to understand the role of aldehydes in
the overall production and evolution of cooking oil SOA.
Formation of particle-phase oxidation products
As described in the earlier sections, we are able to quantify the mass
concentrations of different compound groups (six different combinations of
functional groups, from C2 to C10, summarized in Table S2) in the particle
phase for all experiments. We denote the observed mass concentrations of
compound group i in SOA from canola oil photooxidation as Mioil. The
expected precursors to these oxidation products are likely aldehydes since
aldehydes are emitted in significant amounts and are highly reactive. To
examine this hypothesis, here we calculate the formation of these observed
compound groups from oxidation of aldehydes. For this calculation, heptanal,
trans-2-heptenal, trans-2-octenal, trans,trans-2,4-heptadienal, and trans,trans-2,4-decadienal (Sigma Aldrich
Co.) were considered because these aldehydes are the dominant VOC precursors
emitted from heated canola oil in our experiments as shown in Fig. S8. More
volatile aldehydes, such as acrolein and methacrolein, were likely present
but could not be captured and analyzed by our techniques. The molar amount
reacted for each aldehyde j in the canola oil oxidation experiments is
denoted as ΔVOCjoil and was calculated based on the
measured OH exposure.
Prediction of different compounds formed at an OH
exposure of 6.43×1010 molecules cm-3 s using product
molar yields of heptanal, heptenal, octenal, heptadienal, and decadienal.
The total aldehyde products can explain the observed oil SOA products
within a factor of 0.5, while the inconsistency in prediction of some SOA
products is likely caused by differences in gas-particle partitioning in
both photooxidation systems.
In order to estimate the contribution from oxidation of an aldehyde j in the
gas-phase mix to the formation of each compound group i, we conducted a
series of experiments in which a representative aldehyde was oxidized, and
the molar yields of the various compounds were measured:
γij=Mijind/MWiΔVOCjind,
where γij represents the molar yield of compound group i from
precursor j, Mijind denotes the mass concentration of compound i
observed in photooxidation experiments in which aldehyde j was the sole
precursor, MWi is the molecular weight of compound i, and ΔVOCjind is the amount of precursor j reacted in each experiment.
γij is then applied to the heated-cooking-oil experiments to
estimate the mass of oxidation products that would form from each
precursor:
Misum=∑jγijΔVOCjoilMWi.
A sample calculation for this analysis is presented in Sect. S4 of the Supplement. The
comparison between Misum (contribution of aldehyde oxidation to
formation of compound i) and Mioil (observed concentrations of
compound i) is shown in Fig. 6. Based on this methodology, oxidation of
aldehydes accounts for 63 µgm-3 (Misum) of the observed
75 µgm-3 (Mioil) (or 84 %) of particle-phase oxidation
products measured at an OH exposure of 6.43×1010 molecules cm-3 s. The contributions of alkanals (heptanal), alkenals (heptenal + octenal), and alkadienals (heptadienal + decadienal) are 7 %,
∼31 %, and 46 %, respectively.
While the mass of oxidation products expected from aldehydes is somewhat
lower than that observed in canola oil SOA, this difference may arise from
differences in gas-particle partitioning between single-aldehyde
photooxidation and canola oil photooxidation. As shown in Fig. 6, the
formation of higher-carbon-number products cannot be explained from the
photooxidation of aldehydes used to predict oil oxidation products, likely
due to the assumption of negligible particle-phase or oligomerization
reactions occurring in the condensed phase. In addition, higher-carbon-number
acids are likely present as primary vapors in the gas phase, which can then
partition to the condensed phase upon SOA formation. As shown in Fig. S9,
more oxygenated compounds (higher O:C and greater number of functional
groups) tend to be more abundant in the canola oil SOA than expected from
aldehyde photooxidation, suggesting that canola oil SOA is more favorable
for oxygenated compounds to partition than SOA from individual aldehydes. On
the other hand, there is no clear trend in partitioning with respect to
vapor pressures and carbon number. It should be noted that uncertainties in
the fitting procedure or estimation in the pseudo-molecular ion (refer to
Table S2 and Fig. S5) can also result in uncertainties between -40 % and
+20 %. Therefore, in summary, the quantified oxidation products from
canola oil SOA are generally consistent with those from aldehyde
photooxidation, and the relative masses may be subject to further changes
due to gas-particle partitioning.
Using the statistical oxidation model (SOM) framework
To further explore the evolution of canola oil SOA, we applied our results
to the statistical oxidation model (SOM) framework developed by Cappa and
Wilson
(Cappa
et al., 2013; Cappa and Wilson, 2012). SOM describes the oxidation chemistry
of a VOC precursor through multi-generational space defined by the number of
carbon and oxygen atoms present in the precursor and its possible SOA
product molecules. The SOM does not specifically track the product
composition in terms of functional groups but provides adequate details to
represent key atmospheric processes such as gas-particle partitioning,
fragmentation, functionalization, reactions with oxidants, and condensed-phase
chemistry. The model has been applied to chamber experiments to derive
parametrizations by fitting experimental data to both SOA mass concentration
and the bulk aerosol O:C ratio. Eluri et al. (2018) used the
chamber-derived parameterizations to predict the properties of SOA generated
from diesel exhaust in an oxidation flow tube reactor.
To the best of the authors' knowledge, there are no parameterizations for
the oxidation of aldehydes. Therefore, in this study we first derived the
parameterizations for aldehyde oxidation and then use these parameters to
predict the SOA mass concentrations. In order to obtain the parameters, we
fit the measured SOA concentration from oxidation of heptanal,
trans-2-heptenal, and trans,trans-2,4-heptadienal at different OH exposures to optimize the six
tunable parameters under low-NOx conditions (shown in Fig. S10). Best-fit SOM parameters indicate that photooxidation leads to fragmentation per
reaction with OH, as shown by a lower mfrag than compared to other systems, e.g.,
alkanes (≥2 for branched, cyclic, or n-alkane under low-NOx
conditions (Eluri et al., 2018)). Since a lower value for
mfrag represents greater fragmentation (Cappa
and Wilson, 2012), this again reflects the higher propensity for
fragmentation in this SOA system. The optimized parameters were then used to
predict the SOA concentration for canola oil photooxidation under different
aging conditions in the OH exposure range, similarly to that of aldehyde
photooxidation.
SOM prediction of SOA produced from different aldehydes
with increasing photochemical age. The model overpredicts SOA formation at
lower photochemical age, while it underpredicts SOA formation by
∼40 % at higher photochemical age, suggesting that
traditional VOC precursors cannot fully explain the SOA formation, and other
gas-phase precursors may be needed to better constrain the formation of SOA
at higher aging conditions. In addition, the SOM-predicted O:C is within
50 % of the measured O:C, suggesting that the overall change in chemical
composition of cooking SOA is predicted reasonably well.
Based on these established parameterizations for different aldehydes, model
simulations were conducted for canola oil having a mixture of aldehydes
under different photochemical-aging conditions. It should be noted that we
used parameterizations of heptanal for all alkanals, heptenal for all
alkenals, and heptadienal for alkadienals. As shown in Fig. 7, the model
generally captures the amount of SOA formed to up to 62 % but
overpredicts SOA formation at lower photochemical ages and underpredicts SOA
concentrations at higher photochemical ages. In addition, SOM also tracks
atomic O:C ratio, which was further compared with the measured O:C ratio.
SOM predicts an O:C around 0.51, which is within 50 % of the measured O:C, likely suggesting that the changes in chemical composition of cooking SOA are
in a reasonable agreement with the model predictions. Furthermore, the
unexplained SOA can likely arise from other unidentified S and IVOCs as
hypothesized by Liu et al. (2017c). However, unlike traffic emissions (Zhao
et al., 2014), S and IVOCs from cooking have not been positively identified. In
addition, small VOC precursors like acrolein and malondialdehyde, which have
been measured in large quantities from cooking emissions (Klein et al.,
2016b), may form SOA products having higher O:C ratios, which may better
explain the O:C ratios observed in our experiments.
One inconsistency between the model and measurements is the slope at which
SOA is being formed. The experimental data suggest a steeper trend of SOA
formation, while the model predicts a more gradual increase in SOA formation.
A potential explanation for this discrepancy is the contribution from other
unmeasured VOCs. These VOCs are less reactive than those considered in the
model such that they contribute to higher SOA at higher OH exposures.
Alternatively, these missing VOCs are more volatile such that more of their
SOA is formed at later generations of oxidation. For example, acrolein forms
SOA with measurable yields
(Chan et al.,
2010) and is emitted at large amounts from heated cooking oils
(Klein et al.,
2016b). Despite these limitations, these parameterizations generally capture
the amount of SOA formed and its degree of oxidation (O:C) on oxidation
timescales relevant to urban areas (within 2 d) and are useful for
representing cooking oil emissions in the chemical transport models.
Overall, the amount of SOA formed and the evolution upon oxidation can be
well described by photooxidation of aldehydes.
Conclusions and implications
In this work, we characterize the detailed chemical composition of SOA
generated from cooking oil vapors. We show that cooking SOA occurring as
a highly complex mixture can be deconvoluted using mass spectral fragmentation
pattern to extract useful information about the chemical identities of
organic compounds, such as functional groups and carbon number. Using this
detailed chemical composition of cooking SOA, we show that fragmentation
is an important pathway for oxidative processing of cooking emissions in the
atmosphere even within short timescales of oxidation. Furthermore, we show
that aldehydes can reasonably explain the formation of SOA generated from
cooking oil vapors and the oxidative evolution as described using a
multi-generational oxidation model. Our study, therefore, highlights the
importance of molecular composition in constraining the chemical properties
of cooking SOA as well as understanding the contribution of aldehydes in
the formation of SOA from cooking emissions.
Consistent with other studies, our work shows that aldehydes are an
important class of VOC precursors emitted from cooking emissions, and
substantial efforts have been made to measure their emission factors
depending on different cooking settings (heating temperature, cooking style,
food, ingredients)
(Klein
et al., 2016a, b). However, the contribution of aldehydes from cooking
emissions is underrepresented in chemical transport models. Recently,
McDonald et al. (2018) showed that
the ambient concentrations of OA were underpredicted when aldehydes were not
included in the box model calculations, suggesting that aldehydes, likely
from food cooking, play an important role in atmospheric oxidation
chemistry. Furthermore, Klein et al. (2019)
showed that heavy polluters like restaurants play a significant role in
contributing to the ambient cooking organic aerosol concentrations. In this
study, we show that a large fraction of the SOA is derived from aldehyde
precursors, with strong similarities in chemical composition. Therefore, it
is important to consider the contribution of aldehyde chemistry in
atmospheric models towards total OA budget. Furthermore, we demonstrate the
importance of fragmentation reactions and their influence on OA properties
such as volatility and chemical composition. Future work should therefore
focus on measuring not only the SOA formation but also the oxygenated VOCs
formed due to fragmentation upon aging to provide insights into aging of
cooking emissions.
Formation of SOA from cooking emissions in the atmosphere is likely
influenced by emissions of POA and other gas-phase precursors. Therefore,
inclusion of POA during atmospheric processing of cooking emissions will
likely influence the physicochemical properties of cooking SOA. For
instance, with cooking POA being much less functionalized than SOA,
inclusion of POA will likely decrease the system O:C (or OSc‾).
However, POA from cooking emissions can undergo heterogeneous reactions in
the atmosphere, thereby increasing O:C (or OSc‾). On the other hand,
there could potentially be contributions from other gas-phase precursors or
S and IVOCs emitted from cooking vapors that can result in SOA formation. These
precursors can potentially contribute to SOA formation from cooking
emissions, but their oxidative evolution in the atmosphere is not well
understood.
Gas-particle partitioning of SOA can be further affected by non-ideal
mixing as well as morphology of the particles
(Shiraiwa et al.,
2013; Zuend and Seinfeld, 2012). Future work should investigate the effect
of these parameters on cooking SOA properties and formation potential. To
account for thermodynamic mixing favorability of the particles, the Hansen
solubility framework developed by Ye et al. (2016) can be implemented to provide
insights into SOA mixing and yield enhancement. As shown in Ye et al. (2018), primary meat-cooking
emissions can enhance SOA yield from α-pinene due to similarity in
Hansen solubility parameters, suggesting that primary meat-cooking particles
are miscible with α-pinene SOA. It should be noted that the present
study did not investigate the effect of atmospherically relevant seed
particles as well as NOx levels, which are representative of typical
urban environments. Upon entering the atmosphere, emissions get mixed
with background air and other source emissions and diluted upon mixing, thereby
altering the gas-particle partitioning and thus the total OA loading.
Therefore, it is important to understand the changes in partitioning and
miscibility of cooking emissions as the composition continually evolves with
atmospheric processing. Additionally, as mentioned earlier, cooking SOA
undergoes large mass transfer limitations due to changes in the phase state
of the SOA particles, making it more important to experimentally
determine the corresponding viscosity of cooking SOA. Therefore, future work
should focus on measuring both the viscosity and miscibility of SOA derived
from cooking emissions.
Code availability
The SOM code used in this paper is cited in Sect. 3.3.2 and should be requested from Cappa and Wilson (2012) and Cappa et al. (2013)
Data availability
The data are available upon request to the corresponding author.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-21-5137-2021-supplement.
Author contributions
MT and AWHC designed the research. MT collected and analyzed the data. MT, YL, and
AWHC interpreted the results. MT and AWHC wrote the manuscript with input from
YL.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
The authors acknowledge Environment and Climate Change Canada (ECCC) for
funding support through the government of Canada Grants and Contributions program. The authors would like to thank Shao-Meng Li from ECCC for use of
the thermodenuder; Chris Cappa from UC Davis for help with SOM simulations; and Greg Evans, Jeff Brook, and Tengyu Liu from the University of Toronto for helpful
discussion.
Review statement
This paper was edited by Allan Bertram and reviewed by two anonymous referees.
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