Oil-sands (OS) operations in Alberta, Canada, are a large
source of secondary organic aerosol (SOA). However, the SOA formation
process from OS-related precursors remains poorly understood. In this work,
a newly developed oxidation flow reactor (OFR), the Environment and Climate
Change Canada OFR (ECCC-OFR), was characterized and used to study the yields
and composition of SOA formed from OH oxidation of α-pinene,
selected alkanes, and the vapors evolved from five OS-related samples (OS
ore, naphtha, tailings pond water, bitumen, and dilbit). The derived SOA
yields from α-pinene and selected alkanes using the ECCC-OFR were in
good agreement with those of traditional smog chamber experiments but
significantly higher than those of other OFR studies under similar
conditions. The results also suggest that gas-phase reactions leading to
fragmentation (i.e., C–C bond cleavage) have a relatively small impact on
the SOA yields in the ECCC-OFR at high photochemical ages, in contrast to
other previously reported OFR results. Translating the impact of
fragmentation reactions in the ECCC-OFR to ambient atmospheric conditions
reduces its impact on SOA formation even further. These results highlight
the importance of careful evaluation of OFR data, particularly when using
such data to provide empirical factors for the fragmentation process in
models. Application of the ECCC-OFR to OS-related precursor mixtures
demonstrated that the SOA yields from OS ore and bitumen vapors (maximum of
∼0.6–0.7) are significantly higher than those from the vapors
from solvent use (naphtha), effluent from OS processing (tailings pond water),
and from the solvent diluted bitumen (dilbit; maximum of ∼0.2–0.3), likely due to the volatility of each precursor mixture. A
comparison of the yields and elemental ratios (H/C and O/C) of the SOA from
the OS-related precursors to those of linear and cyclic alkane precursors of
similar carbon numbers suggests that cyclic alkanes play an important role
in the SOA formation in the OS. The analysis further indicates that the
majority of the SOA formed downwind of OS facilities is derived from
open-pit mining operations (i.e., OS ore evaporative emissions) rather than
from higher-volatility precursors from solvent use during processing and/or
tailings management. The current results have implications for improving the
regional modeling of SOA from OS sources, for the potential mitigation of OS
precursor emissions responsible for observed SOA downwind of OS operations,
and for the understanding of petrochemical- and alkane-derived SOA in
general.
Introduction
Over the last several decades, oil production from unconventional sources
has increased significantly and is expected to continue to increase into the
future due to its abundant reserves, particularly in North America
(Alboudwarej et al., 2006; Mohr and Evans, 2010; Owen et al., 2010). The
Alberta oil sands (OS) are a large unconventional crude oil deposit, which are
extracted through both open-pit mining and in situ steam-assisted
techniques. Considering the scale of OS oil production, a number of
environmental concerns associated with OS air emissions have arisen,
including the potential for ecosystem toxicity (Kirk et al., 2014; Harner
et al., 2018) and acid deposition (Jung et al., 2011; Makar et al., 2018).
Recent field measurements have shown that OS mining and processing
facilities are a large source of volatile organic compounds (VOCs; Simpson et al., 2010; Li et al., 2017). Such gaseous air pollutants are
rapidly transformed into secondary organic aerosol (SOA), for which the OS
has been shown to be a large source (Liggio et al., 2016). Despite the
large SOA formation rates observed in the OS (∼45–84 t d-1; Liggio et al., 2016), the emission sources, chemical
compositions, volatilities, and SOA-forming potentials of the precursors
remain unclear. Understanding the impact of SOA on the regional PM2.5 burden, air quality and potentially regional climate requires accurate
model predictions of SOA, which have been limited by the lack of data on OS
source-specific SOA precursors and their SOA-forming potential
(Stroud et al., 2018).
Investigating SOA-forming potentials of hydrocarbons is generally
accomplished through targeted experiments of single-precursor compounds of
interest to derive a yield (Odum et al., 1996; Kroll and Seinfeld, 2008).
However, in the OS, SOA precursors are highly complex mixtures with
volatilities spanning the range of VOCs (saturation concentration C*>3×106µg m-3) to semi-volatile organic compounds (SVOCs; C*=0.3–300 µg m-3; Donahue et al., 2012; Liggio et al., 2016; Tokarek et al.,
2018). As a result, using a single-species approach for studying SOA
formation from OS is unrepresentative. In addition, the mix of precursors
(and hence chemical properties) varies by source within any given OS
facility. Precursor emissions occur throughout the OS surface mining and
processing production cycle, and they originate from sources including open-pit surface mines, processing plants, and tailings ponds. The organic gases
evaporated from these OS sources are mainly alkanes of diverse structure
(e.g., linear, branched, and cyclic; Simpson et al., 2010; Li et al.,
2017), which primarily react with hydroxyl radicals (OH) in the atmosphere,
as their reactions with the NO3 radical and O3 are very slow
(Atkinson and Arey, 2003). Consequently, the mixture of vapors
evolved from the above sources is ideally suited to experimental studies of
their overall SOA-forming potentials and yields with oxidation flow reactors
(OFRs), where ozone is often utilized as an OH radical precursor.
The development of OFRs has recently provided a complementary approach to
traditional large-volume smog chambers to investigate SOA formation
processes (Kang et al., 2007; Lambe et al., 2011; Bruns et al., 2015). The
advantages associated with the use of OFRs include their ability to probe
the SOA-forming potentials of a real-world mixture of precursors and to
study SOA formation on short timescales, simulating up to several weeks of
OH radical exposure (Lambe et al., 2015; Bruns et al., 2015; Palm et al.,
2016). OFRs have been utilized in numerous studies to investigate the SOA-forming potentials of bulk gasoline and diesel emissions (Tkacik et al.,
2014; Karjalainen et al., 2016; Jathar et al., 2017; Simonen et al., 2017),
biomass burning emissions (Ortega et al., 2013; Bruns et al., 2015),
ambient air at numerous locations (Ortega et al., 2016; Palm et al., 2016),
and single precursors (Kang et al., 2011; Lambe et al., 2011,
2012, 2015). The results of several OFR studies have also been
used to infer the presence of gas-phase fragmentation reactions (i.e., C–C
bond cleavage; Jimenez et al., 2009), the transition between
functionalization (i.e., oxygen addition) and fragmentation (Lambe et
al., 2012), and the corresponding impact of these processes on SOA formation
yields of single precursors and complex mixtures (Lambe et al.,
2012; Tkacik et al., 2014). However, results from OFR studies vary, with some
single-precursor experiments noting significantly lower SOA yields from OFRs
compared to smog chambers (e.g., isoprene and m-xylene; Lambe et al.,
2011, 2015) and others indicating similar but consistently
lower yields than traditional smog chamber results (e.g., α-pinene; Bruns et al., 2015; Lambe et al., 2015). Additionally, studies of vehicle
exhaust mixtures in OFRs generally exhibit reduced SOA potential relative to
smog chambers at similar photochemical ages (Tkacik et al., 2014; Jathar
et al., 2017; Simonen et al., 2017). Similarly, the impact of fragmentation
on SOA yields in OFRs has been reported to be relatively large at moderate
to high OH exposures in some studies (Lambe et al., 2012,
2015) but negligible in others (Bruns et al., 2015).
Although the use of OFRs has been suggested as a complementary approach to
smog chambers, such disparities between OFR experiments, and between OFR and
chamber results, are likely to make the interpretation of OFR SOA yields and
their application to air quality modeling systems difficult. This is
particularly relevant to the use of OFRs with a complex mixture of
precursors.
In this study, the application of a newly developed OFR (the Environment and
Climate Change Canada OFR; ECCC-OFR) to single compounds (alkanes and
α-pinene) and complex precursor mixtures is described. Alkanes are
the main component of OS emissions, while α-pinene is a
representative biogenic precursor which likely contributes significantly to
the background SOA observed in the OS region (Liggio et al., 2016). The
derived SOA yields for these single compounds are compared with those of
other OFRs and smog chambers, providing improved confidence in the use of
OFRs for the determination of SOA yields and in the understanding of the
relative importance of fragmentation processes to SOA formation. The
ECCC-OFR is further used here to study the OH-initiated formation of SOA
from various OS-derived complex mixtures under low-NOx conditions.
These mixtures are representative of the potential pollution from distinct
stages of the OS production cycle and/or sources. This new information on
the yields of SOA from these varied OS sources and other single compounds
will improve the understanding of SOA formation from this large industrial
sector, advance the modeling of the OS SOA formation in regional air quality
models, and improve the overall understanding of alkane-derived SOA in
general.
Methods
SOA formation was investigated using a custom-made OFR (ECCC-OFR), which is
shown schematically in Supplement Fig. S1. The design of the ECCC-OFR was partially
based on recent OFRs designs (Lambe et al., 2011; Huang et al.,
2017; Simonen et al., 2017), with several specific differences described
further in the Supplement (Sect. 1). Briefly, the reactor is a fused quartz
cylinder with a cone-shaped diffusion inlet. The length of the cylinder is
50.8 cm, with an inner diameter of 20.3 cm. The length of the cone inlet is
35.6 cm, with a full cone angle of 30∘. The conical inlet is
designed to minimize the establishment of jetting and recirculation in the
OFR (Huang et al., 2017), which were noted for straight OFR inlets
(Huang et al., 2017; Mitroo et al., 2018). There are seven openings at the
output end of the ECCC-OFR; six of them (I.D. of 0.25 in; I.D. – inner diameter) are equally spaced
around the perimeter to provide side flow as exhaust with a distance to the
walls of 2.5 cm, intended to reduce the impact of gas and particle wall
losses on sampling. A stainless steel sampling port (O.D. of 0.25 in; O.D. – outer diameter; I.D. of 0.18 in) is located in the center of the reactor, protruding 12.7 cm
into the ECCC-OFR to minimize the influence of any potential turbulent
eddies induced at the end of the reactor. Computational fluid dynamics (CFD)
simulations and residence time distribution (RTD) measurements were
conducted for this OFR, and the results indicate that only a small area is
affected by recirculation and that a near laminar flow is likely achieved (Sect. S2 and Figs. S4 and S5). The volume from the inlet of the
cone to the sampling port is approximately 16 L. The total flow rate for
experiments is 8 L min-1, resulting in a residence time of 120 s. The
sampling flow rate is approximately 1.6 L min-1 (determined by the flow
of instruments connected), with an additional flow (6.4 L min-1) pushed
out of the reactor through the side ports as exhaust. Four ozone-free
mercury UV lamps (BHK, Inc.) used to generate the OH radical are located in four
open-ended fused quartz tubes that are parallel and external to the OFR (1.5 cm). The lamps are purged by a large flow of air (∼30 L min-1) through the quartz tubes to remove the heat generated by lamps,
resulting in a working temperature of approximately 25 ∘C, which
is slightly higher than room temperature. The entire reactor is contained in
an internally mirrored polycarbonate enclosure to direct all produced light
towards the reactor.
OH radicals were generated by photolysis of O3 (∼12 ppm)
at 254 nm followed by reaction with water vapor, a commonly used method in
many OFRs (Kang et al., 2007; Lambe et al., 2012; Liu et al., 2014). Recent
OFR applications and modeling studies have demonstrated the utility of 185 nm radiation in OFRs due to ease of use in the field and due to additional
OH and HO2 generation (Li et al., 2015; Palm et al., 2016). However,
the fused quartz tubes of ECCC-OFR limit the application of such lamps due
to the low transmittance of 185 nm radiation (∼5 %), and
placement of lamps on the interior of the OFR is likely to increase
turbulence and wall losses within the OFR and limit overall OFR temperature
control. Consequently, 254 nm radiation lamps were used. The maximum photon
flux (with four lamps on) was determined based on the measured ozone decay
and OH exposure (without precursor injection) combined with a photochemical
box model characterizing radical chemistry in OFRs (Oxidation Flow Reactor
Exposure Estimator 3.1; Li et al., 2015; Peng et al., 2018). The input
photon flux of the model was adjusted to match the measured ozone decay and
OH exposure, which resulted in a maximum photon flux estimate of
∼1.9×1016 photons cm-2 s-1. This
photon flux is similar to the PEAR (Photochemical Emission Aging flow tube Reactor) OFR (2.3×1016 photons cm-2 s-1) using the same estimation method (Ihalainen et al., 2019) and about 3 times that reported for the PAM (Potential Aerosol Mass) reactor (6.4×1015 photons cm-2 s-1; Lambe et al., 2017). The
relative humidity was detected at the outlet (side flow) of the reactor with
a humidity sensor (Vaisala) and was maintained at 37%±2% at
room temperature (21±1∘C) by controlling the flow of dry
and wet zero air into the reactor. The OH exposure (photochemical age)
inside the reactor was estimated through the decay of CO due to its reaction
with OH (Li et al., 2015). The CO was introduced into the ECCC-OFR during
separate experiments to characterize OH exposure offline. The CO
concentration was measured with a CO analyzer (LGR CO-23r) with a high
precision (0.1 ppb). A low initial concentration of CO (∼0.5 ppm) was used to minimize the external OH reactivity introduced by CO, hence
increasing the accuracy of OH exposure estimation (Li et al., 2015). The
OH radical concentration was adjusted through changes in the UV light
intensity by varying the voltage applied to the lamps. The OH exposure
during experiments ranged from 1.2×1010–2.3×1012 molec cm-3 s, which corresponds to 0.1–18 d of
photochemical age, assuming a global average OH concentration of
1.5×106 molec cm-3 (Mao et al., 2009). However, the
equivalent aging time is significantly shorter for urban areas and OS
production regions because of their typically higher ambient OH
concentrations (Hofzumahaus et al., 2009; Stone et al., 2012). For
example, the OH exposure range is equivalent to 20 min–2.7 d if
assuming an average OH concentration of 1×107 molec cm-3, as has been estimated for the plumes originating from Alberta OS
operations (Liggio et al., 2016).
Vapors from α-pinene, individual alkanes (n-heptane, n-decane,
n-dodecane, cyclodecane, and decalin), and various OS-related samples (with the
exception of the tailings pond sample) were introduced into the ECCC-OFR by
a small flow of zero air (0.5–2 mL min-1) passing over the headspace of
the sample material, which was placed in a glass U tube and maintained at
room temperature. The OS samples included freshly mined OS ore (original and
unprocessed), bitumen (processed heavy oil product), naphtha (a solvent used
in OS processing), diluted bitumen (dilbit: a mixture of bitumen and solvent
for transport in pipelines), and tailings pond water (waste water from the
mining and processing; see Sect. S3 in the Supplement for details). The
tailings pond sample (∼2 L) was placed into a 4 L glass
bottle and was bubbled into the ECCC-OFR. For some samples with high
volatilities (e.g., naphtha and n-heptane), the gas phase was further diluted
before being injected into the reactor. The total hydrocarbon (THC)
concentration entering the ECCC-OFR was determined by passing the input gas
stream (in offline experiments) through a Pt-based catalytic converter
maintained at 400 ∘C and measuring the subsequently evolved
CO2 (LI-COR LI-840A) as described by Veres et al. (2010). The evolved CO2 concentration (ppb) is converted to the total carbon
concentration (ppbC; see Table 1). The conversion efficiency of this THC
system was measured to be 100%±2% for several hydrocarbons in the
range of C7–C18 (see Sect. S7 and Fig. S2) but
has been shown to be equally efficient at lower carbon numbers
(Veres et al., 2010). The THC concentration was measured
before and after each experiment, resulting in differences of less than
5 %. In addition, the magnitude of SOA formed for repeated experiments at
the same light intensity varied by less than 5 %, further indicating the
stability of the precursor concentration over time. For complex OS precursor
mixtures introduced into the OFR, a volatility distribution (VD) was
measured by collecting the vapor-phase compounds onto desorption tubes containing
Tenax (Gerstel) followed by analysis with a thermo-desorption gas
chromatography–mass spectrometer (TD-GC-MS; Agilent) utilizing a method
described previously (Liggio et al., 2016). The chromatogram and the
derived VD of the OS-related precursors are shown in Figs. S3 and 1 and
discussed in detail in Sect. S4.
Volatility distribution of the OS-related precursors
binned by carbon number (a) and effective saturation concentration
C*(b).
Initial concentrations, maximum SOA mass concentrations, and
maximum yields of OS-related precursors and selected compounds.
a The number shown in the brackets is the corresponding OH exposure
(1011 molec cm-3 s).
b The SOA yield does not reach a maximum over the OH exposure range, as
the highest OH exposure is shown here.
Particle size distributions at the exit of the OFR were measured with a
scanning mobility particle sizer (SMPS; TSI), which were used to determine
SOA yields. For a subset of experiments, ammonium sulfate (AS) seed
particles were generated with an atomizer, dried with a diffusion dryer, and
introduced into the reactor without size selecting. The mass concentration
of the AS seed particles was approximately 20 µg m-3 for most
experiments with a number-weighted mode diameter of approximately 50 nm
(mass-weighted mode diameter ∼90 nm). For OS ore and naphtha,
additional seed concentration experiments (∼10 and 40 µg m-3) were also performed to investigate the impact of seed
concentration on SOA formation. Particle composition was determined using a
long time-of-flight aerosol mass spectrometer (LToF-AMS; Aerodyne) with a
mass resolution of 6000–8000 in V mode. The mass spectra and elemental
properties of the SOA were determined using the AMS analysis software
Squirrel (version 1.57I) and Pika (version 1.16I). The elemental ratios (H/C and O/C) were estimated using the improved ambient method described
previously (Canagaratna et al., 2015). The SOA
mass concentration was calculated by multiplying the integrated volume
concentration from the SMPS (after subtracting the AS volume concentration
for seeded experiments) by the effective particle density. The effective
density (ρ; 1.35–1.6 for different precursors) was calculated from the
vacuum aerodynamic diameter (Dva; obtained from the AMS) and the
electric mobility diameter (Dm; obtained from the SMPS) for unseeded experiments using the equation ρ=Dva/Dm (Lambe et al., 2015). The same density was used
for seeded and unseeded experiments.
In the current study, only low-NOx experiments were performed for all
precursors, in which the reaction with HO2 radical dominates the fate
of the peroxy radical (RO2) formed in the initial OH reaction. Such
conditions are likely to represent the atmospherically relevant scenario
where OS emissions have been transported significantly downwind of the OS
region (and NO consumed), over boreal forest areas, where there were few
NOx sources. In addition, the low-NOx condition is a typical
oxidation pathway parameterized in regional air quality models. The
formation of OS-derived SOA under high-NOx conditions (closer to
sources) is the topic of a forthcoming publication.
Results and discussionCharacterization of the ECCC-OFRWall losses
Previous OFR studies have indicated that the wall losses of both gaseous
precursors and formed particles are potentially important factors in
influencing the SOA yield results from OFRs (Lambe et al., 2011, 2015; Huang et al., 2017; Simonen et al., 2017). The particle wall losses
for the ECCC-OFR were assessed by measuring size-selected (50, 100,
150, and 200 nm diameter) inorganic (AS; Huang
et al., 2017) and organic (bis(2-ethylhexyl) sebacate – BES; Lambe et
al., 2011; Simonen et al., 2017) aerosol number concentrations before
entering and after exiting the reactor. As shown in Fig. 2, the
concentration of AS aerosols after the reactor is within ±3% of
the concentration before the reactor. For BES, the particle transmission
efficiency (Ptrans) is 92 % at 50 nm and increases to ∼100% for 100 nm and larger particles. This indicates that inorganic and
organic particle wall losses of the ECCC-OFR were very small for the flow
conditions and particle size range in the experiments and hence were not
considered in further SOA yield calculations. The Ptrans of other OFRs
is also shown in Fig. 2 for comparison and indicates that the current
Ptrans is similar to that of the TSAR (TUT Secondary Aerosol Reactor; Simonen et al., 2017) and PEAR (Ihalainen et al., 2019),
likely due to the similarity in design (i.e., cone-shaped inlet and sampling
from the centerline; see Sect. S1). Conversely, the
Ptrans of the TPOT (Toronto Photo-Oxidation Tube), PAM glass (PAM
reactor with glass wall; Lambe et al.,
2011), PAM metal (PAM reactor with metal wall; Karjalainen et al.,
2016), and CPOT (Caltech Photooxidation Flow Tube; Huang et al.,
2017) is 15 %–85 %, 20 %–60 %, 10 %–25 %, and 20 %–45 % lower, respectively,
than the ECCC-OFR across a range of particle sizes. Potential causes of
these discrepancies include recirculation and turbulence induced by a
straight inlet and/or output sampling end
(Lambe et al., 2011), non-centerline
sampling (Huang et al., 2017), and longer residence times
(Huang et al., 2017) in the other OFRs (see Sect. S1), which have been noted as potential factors previously (Lambe
et al., 2011; Simonen et al., 2017; Mitroo et al., 2018).
Particle (left and bottom axis) and gas (right and top
axis) transmission efficiencies (Ptrans and Gtrans) for
the ECCC-OFR. Particle transmission efficiencies of other OFRs are shown for
comparison: PAM glass and TPOT (Lambe et
al., 2011), PAM metal (Karjalainen et al., 2016), TSAR (Simonen et
al., 2017), CPOT (Huang et al., 2017), and PEAR (Ihalainen
et al., 2019).
The transmission efficiencies of the ECCC-OFR for gaseous hydrocarbons
(Gtrans) in the volatile to intermediately volatile ranges were also
determined using the THC conversion methodology described above to measure
the concentration immediately before entering and after exiting the reactor.
The Gtrans results for three n-alkanes, specifically n-heptane (C7),
n-decane (C10), and n-dodecane (C12), are shown in Fig. 2 and are
approximately 100%±3%. The passivation time was 5–10 min, and
the mixing ratio was 300–500 ppb for these alkanes. Measurement data with
respect to hydrocarbon transmission efficiency for the other OFRs are not
currently available for comparison. While the loss of hydrocarbon precursors
in the ECCC-OFR may be minimal, one cannot easily measure the losses of
lower-volatility oxygenated compounds directly, particularly those of
intermediate products of oxidation, which largely influence measured SOA
yields in smog chambers and the other OFRs (Zhang et al., 2014; Palm et
al., 2016). Alternatively, we use the secondary formation of sulfuric acid
to evaluate the wall losses of gas-phase products, which is described below.
The OH oxidation of SO2 was performed in the ECCC-OFR. The SO2
concentrations used were in the range of 24–63 ppb and the OH exposure was
in the range of 3–10×1011 molec cm-3 s, which are
similar to those used in a previous PAM study
(Lambe et al., 2011). The yield of sulfuric
acid was calculated using a method described previously
(Lambe et al., 2011) and is shown in detail
in Sect. S5. As shown in Fig. S6, the yield of sulfuric
acid is 100%±4% in this study, which is in agreement with the
expected yield (Sect. S5). The yield here is significantly
higher than that obtained in previous OFR study using similar SO2
concentrations and OH exposures (PAM and TPOT), which are mainly in the
range of ∼15 %–50 %
(Lambe et al., 2011). This may be a result
of lower wall losses in the current OFR for gas-phase sulfuric acid and/or
particles. Given that sulfuric acid is not impacted by photolysis or
fragmentation, the results here suggest that wall losses and interactions within
the ECCC-OFR are significantly lower than previous OFRs that utilize
straight inlets (PAM and TPOT).
Low-NOx SOA yields of α-pinene (a), n-decane (C10), and n-dodecane (C12) (b) compared to previous
studies using OFRs and smog chambers (SCs; Ng et al., 2007;Eddingsaas et
al., 2012; Lambe et al., 2012, 2015; Chen et al., 2013; Loza et al., 2014; Bruns et al., 2015; Han et al., 2016). The details regarding these
comparisons are shown in Table 2. (c) SOA carbon and oxygen yields (YC
and YO) for single precursors for unseeded experiments in the current
study and in a previous study (Lambe et al., 2012). Dashed and solid
arrows indicate the maximum of YC and YO, respectively.
SOA yields and fragmentation
An important performance characteristic of an OFR is the ability to derive
SOA yields consistent with previous results in traditional chamber
experiments (Bruns et al., 2015; Lambe et al., 2015). The SOA yields from
the ECCC-OFR (under low-NOx conditions) for selected individual
compounds (α-pinene, n-decane (C10), and n-dodecane (C12)), as a
function of photochemical age or OH exposure and in the presence or absence
of AS seed aerosol, are provided in Fig. 3. Under the operating conditions
used here for α-pinene experiments, OH reaction contributes
64 %–98 % of the α-pinene gaseous loss across the entire OH
exposure range and >90 % after 3 equivalent days, with
the α-pinene +O3 reaction playing a minor role. The SOA yields (Y) in
Fig. 3 are calculated using the mass concentration of organic aerosols
(ΔMO) and reacted parent hydrocarbons (ΔHC; see Sect. S5 for details), where Y=ΔMO/ΔHC. Figure 3 also shows the yields from other recent smog chamber and OFR
studies for the same individual precursors under low-NOx conditions
(see Table 2 for details; Ng et al., 2007; Eddingsaas et al., 2012; Lambe
et al., 2012, 2015; Chen et al., 2013; Loza et al., 2014; Bruns et
al., 2015; Han et al., 2016).
Comparison of experimental conditions and SOA yields with
previous studies.
Precursor nameMseedPrecursorMSOASOA yieldOHexpReactorcReference(µg m-3)(ppb)(µg m-3)(1011 molec cm-3 s)α-Pinene041–100–0.35a5.57OFRLambe et al. (2015)050.6900.32a6.6OFRChen et al. (2013)013.737.90.50b5.44OFRThis study13–1944.5–47.763.5–76.60.26–0.290.91SCEddingsaas et al. (2012)14–2113.8–47.529.3–121.30.38–0.461.21SCNg et al. (2007)12.619.634.10.350.52SCHan et al. (2016)2113.721.80.29b1OFRThis study10–60192–200540–5700.55–0.562.6–3.6SCBruns et al. (2015)10–60137–347200–10000.31–0.673.5–11.9OFRBruns et al. (2015)2113.741.90.55b3.9OFRThis studyn-Decane01022310.39a5.3OFRLambe et al. (2012)023.430.40.25b5.2OFRThis studyn-Dodecane17–248.2–341.8–650.03–0.283.24SCLoza et al. (2014)219.624.30.37b2.72OFRThis study
a Maximum SOA yield.
b The SOA yield at the OH exposure similar to above studies.
c OFR: oxidation flow reactor. SC: smog chamber.
As most previous smog chamber studies are carried out at relatively low OH
exposures, limited data can be used for comparison, and the majority of
chamber data reside in the photochemical age of less than 3 equivalent days
(3.9×1011 molec OH cm-3 s-1; Table 2).
However, in addition to the OH exposure level, numerous other factors may
affect the SOA yield comparisons between OFR and chambers. These factors
include the concentration of the gas-phase precursor utilized, the presence or
absence of seed aerosol, and the mass of SOA formed during experiments
(Odum et al., 1996; Donahue et al., 2006; Kroll et al., 2007; Kroll and
Seinfeld, 2008; Hallquist et al., 2009). Nonetheless, the α-pinene
SOA yields in the ECCC-OFR are similar to previous chamber experiments at
similar OH exposures (Fig. 3a; Table 2). Given the known dependence of yield
on SOA mass and precursor concentration (Odum et al., 1996; Kroll and
Seinfeld, 2008), slightly higher yields for α-pinene are expected
from chamber studies (and observed), as some experiments were performed at
SOA mass levels and gaseous precursor concentrations 3–14 and 3–15 times
(Ng et al., 2007; Eddingsaas et al., 2012; Bruns et al., 2015) greater than
the current study (22–42 µg m-3 and 13.7 ppb; see Table 2 for
details). Considering the impact of these conditions on yields, the ECCC-OFR
SOA yields of α-pinene are in reasonable agreement with those
derived from chamber studies. However, in the case of alkanes, the agreement
is significantly different. While the initial n-dodecane concentration and OA
concentration (upper limit) in a previous study
(Loza et al., 2014) were ∼3
times higher than this study (Table 2), the corresponding SOA yields were
significantly lower (Fig. 3b) than the current results. The known impact of
gaseous wall loses on SOA yields in environmental chambers (Zhang
et al., 2014) suggests that the long residence time of those particular
experiments (∼36 h; Loza et
al., 2014) likely resulted in significant intermediate gaseous product wall
losses and correspondingly low SOA yields compared to the ECCC-OFR (which
has minimal wall losses).
While the SOA yields for single precursors from the present study are in
reasonable agreement with traditional chamber data, they are significantly
larger than those of other OFR data sets (Lambe et al., 2012, 2015; Chen et al.,
2013; Bruns et al., 2015; Fig. 3a and b). With the
exception of the lowest OH exposure data point for α-pinene
oxidation, the SOA yields quickly diverge from each other after
approximately 2 equivalent photochemical days (a factor of 4 larger in this
study after ∼10 equivalent days) for unseeded experiments,
despite initial concentrations of α-pinene (41–100 ppb) and SOA mass
(90 µg m-3) in previous OFR experiments (Chen et al.,
2013; Lambe et al., 2015) being considerably higher than the current study
(13.3 ppb and 37.9 µg m-3) at similar photochemical ages (Table
2). For seeded experiments of α-pinene, the current SOA yields are
higher than those reported by Bruns et al. (2015),
despite their precursor concentration and SOA mass being 10–25 and 5–24
times higher than this study (13.7 ppb and 41.9 µg m-3; Table 2).
Similarly, the present SOA yields for n-decane (C10) diverge from
previously reported results (Lambe et al., 2012; Fig. 3b), with the
present SOA yields being up to a factor of 4 higher after ∼10
equivalent photochemical days (1.3×1012 molec cm-3 s OH exposure). It is noteworthy that the yields for n-decane from the
present study and reported by Lambe et al. (2012) are in reasonable
agreement for up to 2 equivalent days (2.6×1011 molec cm-3 s OH exposure). However, this is likely fortuitous, as the SOA
mass concentration and precursor concentration in the study by Lambe et
al. (2012; 231 µg m-3 and 102 ppb) was an order of magnitude
higher than in the present study (30.4 µg m-3 and 23.4 ppb; Table 2), which will enhance the gas–particle partitioning process and lead to
higher yields. Such an effect has been observed in C15 SOA experiments
(Lambe et al., 2012), where decreasing the aerosol mass concentration
from 100 to 16 µg m-3 reduced the SOA yield from
0.69 to 0.21.
The decrease in yield at longer photochemical ages (higher OH exposures) in
previous OFR studies (Fig. 3a and b) has been attributed to gas-phase
fragmentation leading to higher-volatility SOA products, with a transition
point between functionalization and fragmentation being observed at the maximum
carbon yield (Lambe et al., 2012). The SOA carbon and oxygen yields
(YC and YO) for α-pinene and n-decane from the current
experiments are shown in Fig. 3c following the approach outlined elsewhere
(Kroll et al., 2009; Lambe et al., 2012) and presented in detail in the
Supplement (Sect. S5). In the absence of gaseous wall losses, the impact of
fragmentation may be indicated by a relatively larger decrease in YC at higher OH exposure compared to YO (Kroll et al., 2009; Lambe et
al., 2012). Such an effect is observed in the present results for both
α-pinene and n-decane (Fig. 3c), with YC decreasing by 38 %
and 15 % over 7 and 13 photochemical days, respectively. The maximum
YO is at a higher photochemical age compared to YC for SOA formed
from both precursors (∼9 and 4 photochemical days for α-pinene; ∼13 and 6 photochemical days for n-decane), further
consistent with a transition from functionalization to fragmentation in
these experiments, as indicated in Fig. 3c. However, the relative impact of
fragmentation on the overall SOA yields here is in contrast to that
suggested previously (Lambe et al., 2012; Fig. 3a and b). The maximum
YC for n-decane here is observed at a higher photochemical age of 6 d, compared to 4 d seen by Lambe et al. (2012), and the decrease in
YC and overall Y is also significantly less (15 % vs. ∼95 % for YC; <5 % vs. ∼95 % for Y).
Given the similarity in the OH exposure range used between studies, and the
generally higher SOA mass concentration (and precursor concentration) in
previous OFR studies (Lambe et al., 2012, 2015; Chen et al., 2013), the present results suggest that gaseous wall losses during the
oxidation process may have reduced previously observed yields in their OFRs,
thereby leading to an overemphasis on the importance of fragmentation in SOA
formation. It is notable that the relative impact of fragmentation here,
although small, may also not be fully applicable to the ambient atmosphere
due to the fate of low-volatility organic compounds (LVOCs) in the OFRs.
Accounting for the fate of LVOCs reduces the potential importance of
fragmentation to SOA formation in this study and the ambient atmosphere even
further, as is described below (Sect. 3.1.3).
Fate of LVOCs
Previous studies have demonstrated that SOA yields derived in OFRs at high
OH exposures (and other conditions) have likely been underestimated due to
differences between the fates of LVOCs in OFRs and the ambient atmosphere
(Palm et al., 2016). There are four possible fates associated
with LVOCs in an OFR: condensation to aerosol, reaction with OH,
condensation to the OFR walls, and exiting the OFR (then lost on sampling
walls). However, in the ambient atmosphere, condensation to aerosol is the
dominant fate of LVOCs, indicating that the other three possible fates are
limitations of the OFR (Palm et al., 2016). To characterize the
ECCC-OFR with respect to the fate of LVOC and improve the subsequent
applicability of the data to the ambient atmosphere, we modeled the fate of
LVOCs under conditions specific to these experiments, following the approach
of Palm et al. (2016), as described further in the Supplement
(Sect. S6).
(a, b) The modeled fate of LVOCs in the current OFR as a
function of photochemical age, for α-pinene oxidation, in the
absence (a) and presence (b) of AS seed particles. (c, d) Fraction of LVOCs
that condense on aerosol (Faerosol) in the OFR during the oxidation of
the single precursors (c) and various OS-related precursors (d) (blue:
seeded experiments; red: unseeded experiments).
The modeled fates of LVOCs in the ECCC-OFR for unseeded and AS-seeded
conditions are shown in Fig. 4a and b, using the parameters (OH
concentration and aerosol size distribution) from α-pinene
experiments. Figure 4a indicates that condensation on aerosol surfaces (in
the absence of seed particles, for α-pinene-derived SOA) accounts
for 70 %–80 % of the LVOC fate between ∼1–6 photochemical
days, decreasing to 40 %–50 % at 16 photochemical days. These fractions
are similar to an ambient OFR study conducted in Los Angeles
(∼40 %–80 %; Ortega et al., 2016) but
higher than the fraction obtained at a forested site (∼10 %–70 %; Palm et al., 2016). OH oxidation accounts for 5 %–50 % of
the LVOC loss in the ECCC-OFR, increasing in importance at higher
photochemical age, while LVOC wall losses and OFR-exiting fates are very
small, generally at less than 5 %. For experiments using 20 µg m-3
of AS seed particles, the fraction that condenses onto aerosol
(∼70 %–95 %; Fig. 4b) is significantly higher than that for
unseeded experiments due to the presence of a higher condensational sink.
The fraction of LVOCs that condenses on aerosol (Faerosol) for single
precursors (α-pinene, n-decane, and n-dodecane) and various OS-related
precursor mixtures (their yields will be discussed in the following section)
is shown in Fig. 4c and d. The Faerosol are very similar to each other
in the presence of AS seed particles regardless of the precursor, accounting
for ∼95 % of the LVOC fate at less than 1 photochemical day
and ∼70 % at ∼16 photochemical days.
Conversely, the range of Faerosol is much wider for unseeded
experiments (Fig. 4c and d), from ∼40 % to 80 %. The
results suggest that the OFR experiments under the seeded conditions here
are the most relevant to the ambient atmosphere, particularly at less than 4
photochemical days, with yields potentially requiring a relatively small
upwards adjustment (∼30 %) even at >14 photochemical days. The model also suggests that the impact of fragmentation
reactions on SOA yields (derived from this OFR), when translated to the
atmosphere, is likely to be very small, as the OH reactions of LVOC never
dominate the overall fate (Fig. 4b).
Given the results of Fig. 4, future OFR studies investigating SOA yields
should be conducted in the presence of pre-existing seed particles to reduce
uncertainties, as theoretically suggested previously (Palm et
al., 2016). The estimated fate of LVOCs for seeded experiments here is used
to apply an upwards correction to α-pinene (Fig. S8) and OS-derived
SOA yields (discussed in Sect. 3.2) assuming an LVOCs fraction of 80 % in
SOA (see Sect. S6 for details). As OH concentrations in
smog chambers are generally much lower than studies with the OFRs, the LVOCs
in smog chamber will mostly condense on aerosols, which is similar to the
real atmosphere. Hence, when comparing the OFR yields to smog chambers, an
LVOC-fate correction should be applied. As shown in Fig. S8, the SOA yields
from α-pinene in the current OFR after correction are in good
agreement with previous smog chamber results despite the lower SOA mass
concentration and precursor concentration.
(a) SOA yields of OS-related precursors (OS ore,
naphtha, tailings pond water, bitumen, and dilbit) for unseeded experiments
as a function of equivalent photochemical age and OH exposure. SOA yields of
C7, C10, and C12n-alkanes; cyclodecane; and decalin are also
shown for comparison. Representative error bars indicate ±1σ
uncertainty in measurements. (b) SOA carbon and oxygen yields
(YC and YO) for the OS precursors of lowest and highest volatility
(OS ore and naphtha solvent) compared to normalized YC and YO for
diesel and crude oil (Lambe et al., 2012). (c) SOA yields as in
(a) in the presence of ammonium sulfate seed particles.
(d) LVOC-fate-corrected SOA yields of OS-related precursors and
alkanes for seeded experiments. Note that the y axis ranges are different in (a), (c), and (d).
SOA yields of OS-related precursors
The ECCC-OFR was used to investigate the SOA yields of complex precursor
mixtures, specifically those derived from OS sources (see Methods). The SOA
yields of these OS-related precursor mixtures are shown in Fig. 5a for
unseeded experiments performed in an atmospherically relevant SOA mass
concentration range (<50µg m-3; Table 1). The SOA yield
in this case is defined similarly to that in Sect. 3.1.2 but accounts for
the calculated H/C ratio (Table S1) and measured carbon number distribution
of emissions (Fig. 1a), as described in detail in Sect. S5. Briefly, the H/C ratios of precursors were used to calculate the
initial precursor mass concentrations from the measured total carbon
concentration. The reacted mass concentrations were calculated using the
rate constant with OH of corresponding n-alkanes that have the same carbon
number as the average value of carbon number distributions. As demonstrated
in Fig. 5a, the freshly mined OS ore results in the highest yields among the
five precursor mixtures, with a maximum of 0.44±0.05 at approximately
11 atmospheric equivalent photochemical days (1.4×1012 molec cm-3 s OH exposure, corresponding to approximately 1.6 d
in OS plumes; Liggio et al., 2016), followed by processed bitumen, with
slightly lower yields over the entire range of photochemical age (with a
maximum of 0.35±0.03). The SOA yields of naphtha, dilbit, and tailings
pond emissions are significantly lower, with maximum SOA yields of
approximately 0.1±0.01 to 0.13±0.01. The difference in yields
between source mixtures (Fig. 5a) can be qualitatively explained by the
volatility distributions (VDs) of these precursors (Fig. 1), with precursors
of lower volatility (higher carbon number) having higher SOA yields (Lim
and Ziemann, 2005, 2009). In this case, naphtha solvent and
OS ore emissions represent volatility endpoints (high and low, respectively),
with other precursor mixtures being derived from a combination of these (see
Sect. S4 for details). Although these precursors have very
different SOA yields, their AMS mass spectra (Fig. S9) are similar,
indicating a similar main precursor composition (alkanes).
SOA yields from several straight chain pure compounds (C7, C10,
and C12) were also investigated in the ECCC-OFR to provide additional
information on the nature of the OS-related precursor mixtures and are
depicted in Fig. 5a. These single compounds were selected for comparison
based on the VD of the OS precursors (Fig. 1a), where heptane (C7)
represents the maximum of the VD of naphtha and dilbit, decane (C10)
the approximate average volatility of OS ore (see Sect. S4), and dodecane (C12) a compound representative of the lower
end of the VD of OS ore and processed bitumen. As shown in Fig. 5a, despite
naphtha and dilbit vapors being dominated by compounds with an equivalent
volatility to heptane (Fig. 1a), their SOA yields (0.11±0.01) are
significantly higher than the yield of heptane (0.044±0.006). Similarly,
OS ore emissions result in higher yields than decane, despite a comparable
volatility but lower yields than C12. This suggests that alkanes with a
higher carbon number (and hence lower volatility and higher yield)
contribute disproportionately to the overall SOA yields relative to their
proportions in the precursor emissions (Fig. 1a). Alternatively, cyclic
hydrocarbons in the OS-related precursors could also contribute
significantly to the overall yields, as experiments for cyclodecane and
decalin (a bicyclic C10 alkane; Fig. 5a) result in much higher yields
than decane. This is consistent with previous studies that demonstrated that
cyclic alkanes have much higher yields than n-alkanes in general (Lim and
Ziemann, 2009; Tkacik et al., 2012; Hunter et al., 2014). While the yields for
single species alone cannot be used to distinguish between the contributions
of cyclic and acyclic compounds to the observed OS-derived SOA, elemental
ratios of the SOA suggest that cyclic species may be an important
contributor (see Sect. 3.3).
The SOA carbon and oxygen yields (YC and YO) for the least and
most volatile precursor mixtures (OS ore and naphtha solvent, respectively)
are shown in Fig. 5b as an indicator of the impact of fragmentation on the
derived SOA yields. Both YC and YO for OS ore and naphtha reach a
maximum at approximately 11 equivalent photochemical days and then decrease
with increasing photochemical age. The decrease in YO for OS ore and
naphtha is ∼1 % per equivalent day from 11 to
∼15–17 d. However, the YC values for OS ore and naphtha
decrease by ∼2 %–4 % per day, which is higher than the
decrease in YO. This suggests that fragmentation reactions increasingly
influence SOA yields at higher photochemical ages for OS-related precursors,
although a significant relationship between the degree of fragmentation and
carbon number cannot be determined. Regardless, the overall impact of the
competition between functionalization and fragmentation on the SOA yields
here is small across all OS-derived precursors. This is in contrast to other
types of fuel products, specifically diesel and southern Louisiana crude oil
(Fig. 5b), which were shown to have SOA yields that are highly affected by
fragmentation reactions (Lambe et al., 2012), although those studies were
likely impacted by wall losses.
The results of experiments conducted using 20 µg m-3 solid
AS seed particles are shown in Fig. 5c. Experiments with
10 and 40 µg m-3 AS seed particles were also performed for OS ore
and naphtha but exhibited no SOA yield dependence on seed concentration
(not shown), with the same SOA yields derived in all cases. Generally, the
SOA yields for all precursors are enhanced significantly in the presence of
AS seed particles, with maximum yields of 0.58±0.03 and 0.18±0.02 for the least and most volatile OS precursors. This effect is more
clearly depicted as a yield enhancement ratio (Yseeded/Yunseeded)
in Fig. 6. Based on Fig. 6, it is evident that SOA from precursors with
higher volatilities is more impacted by the presence of AS seed particles;
SOA yield enhancement ratios for naphtha and dilbit (∼60 %)
are higher than OS ore and bitumen (∼30 %) after
approximately 2 equivalent photochemical days, with the ratio of tailings pond
SOA being between them. It is also evident that the enhancement factor is somewhat
larger during the initial stages of oxidation (up to >100 % at
<2 equivalent photochemical days). This is likely a result of the
different LVOC fates for seeded and unseeded experiments. As shown in Sect. 3.1.3 and Fig. 4d, the fraction of LVOCs that condenses on aerosol
(Faerosol) at <2 equivalent photochemical days for unseeded
experiments is much lower than that for seeded experiments, which will lead
to a larger yield enhancement ratio in the presence of seed particles. The
finding that the presence of seeds can enhance the SOA yields is in
agreement with various previous work (Kroll et al., 2007; Hildebrandt et
al., 2009; Zhang et al., 2014; Lambe et al., 2015; Li et al., 2018). In
addition to the difference in the LVOC fate discussed above, the enhanced SOA
yield in the presence of seed particles can also be due to increased aerosol
surface area that competes with other sinks (e.g., vapor wall losses for
smog chambers) and enhances the gas–particle partitioning of semi-volatile and intermediate-volatility organic compounds (S/IVOCs), as
suggested previously (Hildebrandt et al., 2009; Zhang et al., 2014; Li et
al., 2018).
Yield enhancement factor due to seed particles for
OS-related precursors. Dashed lines are exponential fittings for naphtha and
OS ore data; error bars indicate ±1σ uncertainty in
measurements.
The OS precursor SOA yields for seeded experiments are adjusted upwards to
account for the fate of formed LVOCs through normalization by the
Faerosol above (Sect. 3.1.3), with the results of this correction being shown
in Fig. 5d. Here, we assume that 80% of the SOA is LVOCs, while the other
20% is S/IVOCs (see Sect. S6 for details). Relative to
the yields of Fig. 5c, the LVOC-fate-adjusted SOA yields of Fig. 5d are
∼4 % to 37 % larger for all precursors, depending on
the OH exposure. As noted above, the fate of LVOC in seeded experiments is
primarily condensation to the aerosols, requiring a relatively small
adjustment. As a result, the seeded experiment data in Fig. 5d represent our
best estimate of the SOA yields for the precursors, applicable to the
ambient atmosphere (under these conditions). In this case, the maximum SOA
yield for the least and most volatile precursor mixtures (OS ore and
naphtha) increased from 0.58±0.03 to 0.71±0.04 and 0.18±0.02 to 0.23±0.02, respectively, after adjustment (Fig. 5d). In
addition, applying an LVOC-fate adjustment results in SOA yields for most OS
precursors, α-pinene, and n-alkanes generally increasing with
increasing OH exposure (Figs. S8 and 5d). This further suggests, as
noted above, that the fragmentation reactions will not significantly
decrease the SOA yields for these species in the ambient atmosphere even
after 16 equivalent photochemical days. However, uncertainties still remain
when using OFRs to simulate the SOA formation processes in the real
atmosphere, likely from differing fates of intermediate radicals (e.g.,
RO2), especially at high OH exposure, as suggested very recently
(Peng et al., 2019).
Elemental ratios of OS-related SOA
The elemental H/C and O/C ratios of SOA particles are illustrated in a Van
Krevelen diagram (Heald et al., 2010) in Fig. 7. Figure 7 indicates that the elemental ratios of SOA from OS ore and
bitumen (and its photochemical evolution) are very similar (O/C: 0.45–0.8,
H/C: 1.4–1.6), as are the elemental ratios of SOA formed from naphtha,
dilbit, and tailings pond water (O/C: 0.6–0.9, H/C: 1.5–1.7). This is
analogous to the similarity in the yields between the same precursors as
discussed above (Fig. 5) and consistent with the volatility of the
precursors (Fig. 1). The lower O/C ratios of OS ore and bitumen SOA are
probably due to their larger molecular size, with higher-carbon-number
(i.e., lower volatility) precursors requiring less oxygen (hence fewer
oxidation steps) to partition into the particle phase
(Tkacik et al., 2012). The H/C ratios are also lower
for SOA formed from lower-volatility precursor mixtures, which is likely a
result of different H/C of the precursors, with generally lower H/C for
higher-carbon-number hydrocarbons. Assuming a linear relationship in Fig. 7,
the y intercept is indicative of the average H/C of the precursor mixture
(Fig. S10). The intercept of naphtha and dilbit SOA (∼2.1) is
higher than OS ore and bitumen SOA (∼1.8), indicating a
higher H/C ratio for those precursors.
Van Krevelen diagram for the SOA formed from OS-related
precursors, selected alkanes, and recent aircraft data in OS plumes
(Liggio et al., 2016). The shaded area represents the elemental ratio
space associated with ambient OOA (Ng et al., 2011).
Similar inferences are made when comparing the evolution of the elemental
ratios of SOA from various single alkane species in Van Krevelen space to
that of OS precursors (Fig. 7). For example, SOA from parent n-alkanes with
a successively higher carbon number (and lower volatility) move towards the
bottom left of the Van Krevelen diagram. However, the position of OS-related
SOA in Van Krevelen space is not consistent with the corresponding
n-alkanes; naphtha, dilbit, and tailings SOA reside below n-heptane (C7),
despite having a very similar volatility (Fig. 1a). Similarly, OS ore and
bitumen reside below n-dodecane (C12), despite C12 volatility
compounds contributing little to the overall volatility distribution of
precursors (Fig. 1a). This discrepancy may be explained by the contribution
of cyclic alkanes, since SOA formed from cyclic structures tends to reside
below acyclic alkane SOA in Van Krevelen space and near that of OS-derived
SOA (e.g., cyclodecane and decalin relative to decane SOA and OS ore SOA in
Fig. 7). Recent aircraft measurement indicated that the cycloalkanes
contribute 13 %–27 % of the total alkanes (Li et al.,
2017) for Suncor and CNRL facilities (where the OS samples were collected),
which will contribute a large proportion of SOA after considering their high
SOA yields (Fig. 5a, c, and d). A lower H/C ratio for SOA derived from
cyclic alkanes is consistent with the parent hydrocarbon having lower H/C.
The linear regression results of H/C vs. O/C for alkane precursors are listed
in Table S1, from which the relationship between precursor H/C and intercept
is obtained (see Sect. S5 for details). A comparison
between the H/C ratios of alkanes and OS precursors demonstrates that the
H/C ratios of the OS precursors are generally lower than that of the
corresponding n-alkane (e.g., ∼2.2 for naphtha and dilbit,
∼2.3 for C7; ∼2 for OS ore,
and ∼2.2 for C10), which is likely from the contribution of cyclic
alkanes. Aromatics may also play a role in the decrease of H/C ratio of
precursors; however, their contributions are likely small, according to
recent aircraft measurement by Li et al. (2017; e.g.,
3.7 % aromatics compared to alkanes for CNRL). In addition, the presence
of aromatics will not decrease the observed H/C and O/C of SOA; for example,
the H/C and O/C of toluene SOA (1.67 and 0.85; Canagaratna et al., 2015) is similar to that
of heptane SOA observed here. While the current data cannot quantitatively
apportion OS precursors to various structures (cyclic vs.
n-alkane or branched), the above Van Krevelen analysis suggests that cyclic
compounds are an important contributor to the observed SOA.
The locations of two broad types of SOA, SV-OOA and LV-OOA (semi-volatile
and low-volatility oxidized organic aerosol), from various studies (Ng et
al., 2011; Canagaratna et al., 2015) and the location of the SOA downwind of the oil
sands from previous aircraft measurements (Liggio et al., 2016) in Van
Krevelen space are also shown in Fig. 7. The positions of SOA formed from
OS-related precursors in the ECCC-OFR are generally in the range of previous
ambient OOA. They are in good agreement with SV-OOA and LV-OOA for
experiments simulating ∼2 photochemical days (∼2.6×1011 molec cm-3 s OH exposure) and
∼2 weeks (∼2×1012 molec cm-3 s OH exposure), respectively. Furthermore, SOA derived from OS ore and bitumen is more similar to ambient SV-OOA and LV-OOA than naphtha-,
dilbit-, and tailings-pond-water-derived SOA (Fig. 7). This highlights the
contribution of intermediate-volatility alkanes to ambient SOA in the oil
sands, particularly since the SOA formed from OS ore and bitumen is in good
agreement with the aircraft data (Liggio et al., 2016; Fig. 7). Hence,
these results indicate that low-volatility precursors from open-pit mining
sources (i.e., OS ore) are likely the largest contributors to the SOA formed
downwind of the Alberta OS region, while precursors of high volatility play
a minor role, likely due to their lower SOA yields.
Conclusions and implications
In this study, a newly designed oxidation flow reactor (ECCC-OFR) was
applied to the investigation of SOA formation from single-precursor
compounds (α-pinene, n-alkanes, and cyclic alkanes) and complex
mixtures (OS-related precursors). The SOA yields for α-pinene and
alkanes obtained in the ECCC-OFR are similar to previous smog chamber
studies but significantly higher than other OFRs. The current results
provide SOA yield information for alkane precursors for which limited data
are available, especially at moderate to high photochemical ages (Tkacik
et al., 2012; Lambe et al., 2012). In addition, the differences in yields
between the current and other OFRs suggest that while OFRs can provide
insight into SOA mechanisms, care must be taken in deriving quantitative
results from OFRs, which are often designed with slightly different
geometries and operated under a variety of conditions. For example, recent
OFR modeling results (Peng et al., 2019) demonstrated that the
working conditions (e.g., light intensity and wavelength, humidity, and
external OH reactivity) could influence the RO2 fate and result in less
atmospherically relevant chemical mechanisms for SOA formation in the OFR.
Variability in the qualitative and/or mechanistic SOA information derived from OFRs
is also possible. In particular, previous OFR studies (Lambe et al.,
2012; Chen et al., 2013; Tkacik et al., 2014; Lambe et al., 2015; Ortega et al., 2016; Palm et al., 2016) have attributed large decreases in SOA yields at
moderate to high photochemical age (typically after 4–5 equivalent days) to
the dominant role of gas-phase fragmentation reactions. However, the current
study indicates that the impact of fragmentation on SOA yields from various
sources is minimal in the ECCC-OFR, likely due to reduced wall losses
relative to other OFRs, whose fluid dynamics are not entirely laminar as
suggested previously (Huang et al., 2017; Mitroo et al., 2018). Accounting
for the fate of LVOCs (Palm et al., 2016) in the ECCC-OFR
further indicates that the impact of fragmentation on SOA yields in the
ambient atmosphere will be even smaller than that within the OFR. This
implies that modeling SOA formation to include the impacts of fragmentation
should be carefully evaluated, especially if using OFR data to provide
empirical factors for fragmentation (Chen et al., 2013).
However, the current data also indicate that the impact of fragmentation on
SOA yields in OFRs can be significantly reduced through the use of seed
particles, which increase the fraction of LVOCs which condense on aerosols
(Faerosol). This suggests that future laboratory OFR experiments
studying SOA yields should be conducted with seed particles to obtain more
relevant qualitative and quantitative data.
Application of the ECCC-OFR to OS-related precursor mixtures indicates that
lower-volatility OS ore and bitumen vapors have significantly higher yields
(maximum of ∼0.6–0.7 for seeded experiments after LVOC-fate
correction) than those from higher-volatility naphtha, dilbit, and tailings
pond vapors (maximum of ∼0.2–0.3 under the same conditions).
The relatively high SOA yields from OS ore, together with the similar
elemental ratios between ambient measurements and OFR experiments, are
consistent with open-pit mining activities being the largest contributor to
the observed SOA downwind of the OS operations (Liggio et al.,
2016). The SOA yields and elemental ratio analysis also suggest that cyclic
alkanes are import contributors to OS-related SOA. The OS SOA information
derived here, for the range of precursor mixtures encountered in the oil
sands, can be used to improve parameterizations of SOA for the OS region
through source-specific inputs of SOA precursor properties and SOA yields
and to evaluate the subsequent regional modeling of SOA (Stroud et
al., 2018). The attribution of observed industrial SOA in the oil sands to
specific sources (i.e., OS ore emissions from open-pit mining) supports the
potential for future mitigation strategies for reducing SOA from this
sector.
Data availability
The data used in this study are available from the
corresponding author upon request (john.liggio@canada.ca).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-19-9715-2019-supplement.
Author contributions
KL and JL designed the OFR and the experiments; KL conducted the
experiments; PL and KL measured the volatility distributions; KL analyzed
the data and wrote the paper, with contributions from JL; and PL, CH, QL, and SML
commented on the paper.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
The authors acknowledge funding support from the Air Pollution program of
Environment and Climate Change Canada (ECCC) and the Oil Sands Monitoring (OSM) program . We further thank the Canada's Oil Sands Innovation Alliance
(COSIA) for the organization and provision of oil-sands-related samples used
in this paper.
Financial support
This research has been supported by the Air Pollution program of Environment and Climate Change Canada (CCAP) and the Oil Sands Monitoring (OSM) program.
Review statement
This paper was edited by Daniel Knopf and reviewed by two anonymous referees.
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