In 2019–2020, Australia experienced its largest wildfire season on
record. Smoke covered hundreds of square kilometers across the southeastern
coast and reached the site of the COALA-2020 (Characterizing Organics and
Aerosol Loading over Australia) field campaign in New South Wales. Using a
subset of nighttime observations made by a proton-transfer-reaction
time-of-flight mass spectrometer (PTR-ToF-MS), we calculate emission ratios
(ERs) and factors (EFs) for 15 volatile organic compounds (VOCs). We
restrict our analysis to VOCs with sufficiently long lifetimes to be
minimally impacted by oxidation over the ∼ 8 h between when
the smoke was emitted and when it arrived at the field site. We use oxidized
VOC to VOC ratios to assess the total amount of radical oxidation: maleic
anhydride / furan to assess OH oxidation, and (cis-2-butenediol + furanone) / furan to assess NO3 oxidation. We examine time series of
O3 and NO2 given their closely linked chemistry with wildfire
plumes and observe their trends during the smoke event. Then we compare ERs
calculated from the freshest portion of the plume to ERs calculated using
the entire nighttime period. Finding good agreement between the two, we are
able to extend our analysis to VOCs measured in more chemically aged
portions of the plume. Our analysis provides ERs and EFs for six compounds not
previously reported for temperate forests in Australia: acrolein (a compound
with significant health impacts), methyl propanoate, methyl methacrylate,
maleic anhydride, benzaldehyde, and creosol. We compare our results with two
studies in similar Australian biomes, and two studies focused on US
temperate forests. We find over half of our EFs are within a factor of 2.5
relative to those presented in Australian biome studies, with nearly all
within a factor of 5, indicating reasonable agreement. For US-focused
studies, we find similar results with over half our EFs within a factor of
2.5, and nearly all within a factor of 5, again indicating reasonably good agreement.
This suggests that comprehensive field measurements of biomass burning VOC
emissions in other regions may be applicable to Australian temperate
forests. Finally, we quantify the magnitude attributable to the primary
compounds contributing to OH reactivity from this plume, finding results
comparable to several US-based wildfire and laboratory studies.
Introduction
Wildfire smoke significantly affects atmospheric composition, chemistry,
human health, and radiative balance (Andreae and Merlet, 2001; Yokelson
et al., 2008; Akagi et al., 2011; Ford et al., 2018; Gregory et al., 2018;
Sokolik et al., 2019; Macsween et al., 2020). Wildfire season duration and
intensity are predicted to increase in the future, suggesting a growing
influence of biomass burning in coming decades (Fairman et al., 2015;
Donovan et al., 2017; Abatzoglou et al., 2019). Volatile organic compounds
(VOCs) emitted from biomass burning (BBVOCs) are directly harmful to human
health and can contribute to the formation of ozone and secondary organic
aerosol (SOA) (Akagi et al., 2012; Keywood et al., 2013; Lawson et al.,
2015; Sekimoto et al., 2017). Predictions of BBVOC emissions are complicated
by the complexity of combustion and fuel characteristics, and model
parametrizations are based on a limited number of field observations
(Hatch et al., 2015; Sekimoto et al., 2018).
Australia wildfires emit 7 %–8 % of global biomass burning emissions,
producing more volatilized carbon than the United States and Europe, with
smoke plumes significantly influencing local and even global air quality
(Ito and Penner, 2004; Van Der Werf et al., 2010; Keywood et al., 2013;
Lawson et al., 2015). In 2019–2020, Australia experienced its worst wildfire
season on record with an estimated 19 million hectares of land destroyed
(Filkov et al., 2020). This particular season is now
colloquially known as the Black Summer, due to its prolonged intensity and
length (October 2019–February 2020). Many of Australia's major cities were
blanketed in smoke for weeks at a time, leading to long-term exposure to
excessive concentrations of harmful atmospheric compounds (Borchers
Arriagada et al., 2020). These fires predominantly affected the temperate
forests of the state of New South Wales (NSW), burning the largest land area
of anywhere in the country (Davey and Sarre, 2020). Despite the impact on
atmospheric composition from Australian fires, its biomes remain
understudied, particularly these same NSW forests (Lawson et al., 2015). Given the
complexity and variability in biomass burning scenarios and the use of
emission factors (EFs, in units of kilograms of VOC emitted per kilogram fuel burnt) to inform
air quality models, this can lead to issues in effectively constraining
emissions. For example, Lawson et al. (2017) reported
a strongly non-linear response in simulated ozone (O3) when varying
biomass burning (BB) EFs, showing the resulting sensitivity from chemical
transport models (CTMs). This sparseness of measurements leads to the use of
North American EFs (such as those from Burling et al., 2011, or
Akagi et al., 2011) to inform CTMs, simulating
emissions of geographically separate biomes. Even among similar biomes (for
instance, the temperate forests of the US), fuel types differ and thus can
influence the speciation of VOCs emitted (Coggon et al., 2016; Hatch et
al., 2017; Guérette et al., 2018). Further evidence of this is found in
a study by Guérette et al. (2018) showing that
EFs of some VOCs (e.g., formic acid, ethane, monoterpenes, acetonitrile) can
be 3–5 times higher than those measured in the US, and attributing this
to fuel type.
A complicating factor in deriving EFs from field observations is accounting
for the influence of chemical processing. EFs are ideally based on
observations close to the fire. When this is not possible, indicators of
plume chemical age, such as oxidized VOC (OVOC) to VOC ratios, can be used
to diagnose the relative age of a plume. During the day, downwind VOC
concentrations are primarily influenced by OH-initiated oxidation. At night,
NO3-initiated oxidation can significantly influence observed VOC
concentrations (Decker et al., 2019; Kodros et al., 2020). There are
several methods in existence for assessing daytime oxidation, but fewer are
known for the night (De Gouw et al., 2006; Liu et al., 2016; Gregory et
al., 2018; Decker et al., 2019). In this work, we use the maleic
anhydride-to-furan ratio introduced in Gkatzelis et al. (2020) to assess
OH oxidation. We examine the use of a new OVOC/VOC ratio,
cis-2-butenediol + furanone-to-furan, as an indicator of nighttime
oxidation.
To further assess the effects of nighttime transport on biomass burning
emissions, we look at the magnitude of OH reactivity measured that results
from the compounds which most substantially contribute to it and determine
the relative contributions of the resulting chemical groups. Certain
categories of BBVOCs like furans or phenols, which are emitted in the
combustion process, are important as they enhance OH reactivity and
resultingly have high O3 and SOA forming potential, and are considered
to be understudied (Gilman et al., 2015; Hatch et al., 2017). Most
wildfire studies are conducted during the daytime, with plume oxidation
focused on interactions with the OH radical and O3 (Liu et al.,
2016; Coggon et al., 2019; Palm et al., 2020; Decker et al., 2021; Permar et
al., 2021). However, the plume studied here spent a significant amount of
time transported under nighttime conditions.
Additionally, we use time series to observe chemical trends in ozone
(O3) and nitrogen dioxide (NO2) as their emissions and chemical
behavior are intimately linked with biomass burning chemistry. O3
production in wildfire plumes is contingent on initial emissions, local
environment, and atmospheric processing during transportation. Wildfire
plumes emit significant precursors of O3, but there is not a general
consensus towards generation or depletion, with various campaigns reporting
measurements in either case, especially in the instance of processed,
downwind plumes (Verma et al., 2009; Alvarado et al., 2010; Jaffe and
Wigder, 2012; Lawson et al., 2015; Brey and Fischer, 2016; Müller et
al., 2016). NO2 is emitted during the combustion process and has a
non-linear relationship to O3 production via reactions with these VOC
precursors. However, the NO2 radical has additional chemical pathways
with OH, NO3, and phenolic compounds leading to a general
NOx-limited regime (Jaffe and Wigder, 2012; Liang et al., 2022;
Robinson et al., 2021). Furthermore, there are again fewer observations for
the effect of nighttime oxidation processes on O3 production with a
recent modeling study conducted by Decker et al. (2019). O3 production in wildfire smoke remains a significant source of
uncertainty in its contribution to the tropospheric O3 budget (Jaffe
and Wigder, 2012; Young et al., 2018; Xu et al., 2021).
Here, we use observations from a proton-transfer-reaction time-of-flight
mass spectrometer (PTR-ToF-MS) during the 2019–2020 Australian wildfire
season to derive EFs of 15 compounds, including 6 compounds for which there
are no previous observations. We examine a subset of smoke-influenced
nighttime observations made by a PTR-ToF-MS during the COALA-2020 field
campaign. NO3-initiated oxidation dominated the chemical processing
late in the night, as the plume traveled ∼ 8 h to the field
site from large, highly active fires to the south. We also use co-located
Fourier transform infrared (FTIR) measurements of CO2, CO and CH4 to derive these EFs for
nighttime longer-lived VOCs (τBBVOC+NO3≥ average transport
time). We compare these results with five related studies, two focused on
Australian temperate forests, two focused on US temperature forests, and one
reporting EFs used to represent temperate biomes across the globe. We find
generally good agreement across several of these studies and discuss
potential reasons for discrepancies seen in EFs for selected compounds.
Field site and instrument descriptionField site and active fires
The COALA-2020 field site was located in Cataract Scout Park (34.247∘ S, 150.825∘ E) at 400 m above sea level, 15 km inland, and 30 km to the
northwest of the nearest urban area (Wollongong, NSW). Figure 1 shows the
field site relative to the fires active between 1 and 5 February 2020. We use
the Suomi VIIRS thermal anomalies product filtering for points at high
confidence levels to avoid counting any reflective false positives from
plains or urban centers. Also plotted is the normalized difference
vegetative index (NDVI), which is determined from measurements aboard the
MODIS Terra satellite (Didan, 2021). The fires are primarily located
in temperate forests along the southeastern coast, with a small inland group
near Canberra. These forests consist of open, tall woodlands made up of
Eucalyptus species grouped generally as dry sclerophyll.
Active fires from 1–5 February 2020 and their proximity to the
COALA-2020 field site. Normalized Difference Vegetative Index (NDVI) is plotted at 250 m resolution from the MOD13A1
dataset acquired by measurements via the MODIS Terra satellite. Pixels have
been filtered to contain cloud coverage less than 30 % and VI usefulness
bits indicating top two tiers of data quality. Fire counts are plotted using
the VNP14IMGTDL_NRT data from Suomi VIIRS satellite imaging
overlaid with HYSPLIT back trajectories. Each tail represents a trajectory
12 h prior to reaching the site and is colored by its starting time. Circles
indicate 1 h intervals moving backwards from the start time.
PTR-ToF-MS and supporting observations
VOCs were measured using an Ionicon PTR-ToF-MS 4000, which operated with a
mass resolution between 2000–3000 FWHM m Δm-1 and at a mass range
spanning m/z= 18–256. The drift tube was held at a temperature of
70 ∘C, pressure at 2.60 mbar, and an E/N=120 Td (electric field to
molecular number density ratio; 1 townsend =10-21 V m2). The instrument was housed in a
climate-controlled unit, connected to a 15 m long, 1/4 in. outer diameter (OD)
PTFE insulated line attached to a 10 m tall mast, placing inlet height 0.5 m
above canopy height. The sample flowed through the inlet at 3 SLPM for a
residence time of 2.5 s. Peak separation of 1 min averaged spectra was
conducted in Ionicon's PTR-Viewer 3 software.
Calibrations were performed using two VOC cylinders designed by Airgas on 31 January 2020, three days before measuring the smoke event discussed here. A
second calibration was performed in the following week with little change in
instrument sensitivity. The cylinders contained 17 compounds spanning a mass
range of 33–154 Da and are shown in Table S1 in the Supplement. Many of these compounds are
reported in the final EFs list – methanol, acetonitrile, acetaldehyde,
acrolein, acetone, MVK + MACR, benzene, C8 aromatics, and C9 benzenes. All
compounds used either do not fragment under these drift tube conditions or
have known fragmentary peaks. Instrument zeros were determined using
ultra-zero air. Limits of detection (3σ) for calibrated species are
also given in Table S1 and range between 5–165 ppt. The raw counts per
second (cps) were corrected for instrument transmission, which was
determined using a subset of the species in the calibration standards.
Corrected cps are then normalized (ncps) to the reagent ion signal (H3O+ ccps × 106 ncps) using the methodology described by Sekimoto et al. (2017). For compounds of interest not
included in the calibration standards, we use the method described by
Sekimoto et al. (2017), which yields uncertainties at
100 %. Table S2 shows all compounds presented in this study alongside
whether they were included in the calibration standards and their respective
uncertainty.
In addition to the PTR-ToF-MS measurements, we use observations of CO,
CO2, and CH4 obtained from the collocated FTIR system. Information
of this instrument and its setup is provided in
Griffith et al. (2012).
Observed CO, VOC, and OVOC enhancements
Figure 2 shows the observations of CO and VOCs during a smoky period on 3–4 February 2020. CO and acetonitrile – long-lived tracers associated with
wildfires (Coggon et al., 2016) – are used to identify
the total period of time during which observations were impacted by smoke.
Enhancements in both species started at 17:30 LT on 3 February and lasted
until 19:00 LT on 4 February, when wind direction shifted.
VOCs and CO on 3–4 February 2020 with the shaded area representing
sunset to sunrise. The peak in CO after sunset (start of gray-shaded area)
is used to denote the beginning of the smoke event. We limit our analysis to
sunrise on the following day. The color labels A–D indicate individual times
used to calculate ERs (see Sect. 5.2 in main text). m/z 85 in the bottom
time series indicates the sum of furanone and cis-2-butenediol.
We use furan, a short-lived smoke tracer, and its oxidation products to
determine which periods of the smoke event represent the least oxidized
plume. Furan is highly reactive with OH (kOH+furan=4.04×10-11 cm3 molec.-1 s-1 at 298 K and 1 atm) and NO3
(kNO3+furan=1.36×10-12 cm3 molec.-1 s-1 at 298 K and 1 atm). OH-initiated oxidation produces maleic
anhydride, which has low reactivity with both OH and NO3 (τOH=3.99 d, τNO3=1.42 d with [OH]Avg=2×106 molec. cm-3 and [NO3]Avg=8×107 molec. cm-3, with reaction rate constants from Grosjean and Williams (1992) and Bierbach et al. (1994) and no reported direct emissions).
The ratio of maleic anhydride-to-furan therefore provides a relative measure
of the plume photochemical age. Using aircraft-based observations of
wildfire plumes in the western US, Gkatzelis et al. (2020) found that
maleic anhydride-to-furan ratios below 0.10 indicate the plume has undergone
little OH processing.
Nighttime in-plume furan oxidation is dominated by NO3, with
contributions from O3 (Decker et al., 2019).
While many BBVOCs are highly reactive with NO3, there is substantially
less research on indicators of NO3 oxidation.
Decker et al. (2019) track NO3 chemistry using
the ratio of total reactive nitrogen (NOy) to NOx, and
Kodros et al. (2020) examine NO3-reacted products such as
nitrocatechol and nitrophenol of phenolic compounds (e.g., phenol, catechol,
cresol). Measurements of NOy were not made during this field campaign,
and NO3 products of phenols were subject to high uncertainty due
to fragmentation in our PTR-ToF-MS measurement. We therefore examine a new
indicator of NO3 processing using furan's dominant NO3 products –
cis-2-butenediol and furanone (Berndt et al., 1997). Both products
are relatively long lived, with lifetimes estimated at τcis-2-butenediol=9 d and τfuranone=8 h assuming an
average concentration of [NO3] =8×107 molec. cm-3 (O'Dell et al., 2020). Lab-based studies and field
campaigns conducted in the US and Australia suggest that furan and furanone
EFs are comparable, with study-averaged values for furan ranging from 0.132–0.51 g kg-1 and 0.27–0.57 for furanone (Andreae and Merlet,
2001; Akagi et al., 2011; Hatch et al., 2015; Stockwell et al., 2015; Liu et
al., 2017; Koss et al., 2018; Selimovic et al., 2018). No furan EFs have
been reported for Australian temperate forests and only one furanone EF is
reported from Lawson et al. (2015) at
a comparable value at 0.57 g kg-1. Additionally, emissions modeled in
Decker et al. (2019) from wildfires suggest that furan
and furanone are emitted in roughly equal proportions. As such, we operate
not on the assumption of negligible OVOC emissions, but that variability in
OVOC/VOC ratios are driven by chemical aging. Cis-2-butenediol and furanone are both measured at m/z 85, and from here onwards will be denoted as such.
Figure 2 shows furan enhancements, which begin later on 3 February than
acetonitrile, maleic anhydride, and m/z 85 enhancements, indicating a less
oxidized plume was being sampled. Maleic anhydride concentrations are high
during the initial period of the smoke event, suggesting significant
OH-initiated processing throughout the day before the plume reached the
site. After sunrise, furan decays faster than CO, and maleic anhydride
concentrations begin to rise, again showing the impact of OH-initiated
oxidation. Enhancements in m/z 85 are seen when the
smoke arrives and vary throughout the night. Just prior to sunrise (04:00–06:15 LT), both OVOC/VOC ratios rapidly decrease (Fig. 3),
corresponding with a rise in furan, CO, and acetonitrile. Maleic
anhydride/furan drops to 0.05, which is within the lower range of the
chemically younger plumes reported by Gkatzelis et al. (2020). The ratio
of m/z 85 to furan is around 2.5. While we cannot use this to quantify plume
age since the two products are measured as a sum, we note that this period
constitutes the lowest ratio throughout the event, with surrounding periods
having ratios 1.6–2.8 times greater. We note that at a value of 2.5, this
plume has likely undergone significant aging, despite this being the
freshest smoke detected during the campaign. Further corroboration of these
results, determined via particulate matter (PM) composition, can be found in
Simmons et al. (2022). In their study, a ToF-ACSM was employed
and observed the ratio of PM1 mass fraction at mass-to-charge ratio 44
(f44), where a lower mass fraction indicates a less oxidized plume. A
similar decrease at f44 in the same timeframe as the m/z 85 and maleic
anhydride tracers is noted.
Product-to-reactant ratio for furan oxidation products. Both
ratios indicate the period just before sunrise is least oxidized. Again, the
color labels A–D indicate individual times used to calculate ERs. m/z 85
indicates the sum of furanone and cis-2-butenediol.
The rapid decreases in OVOC/VOC ratios are unlikely to result from shifts in
chemistry alone. Instead, this suggests a shift in meteorological conditions
which brings in smoke from a closer source, in agreement with measured wind
direction, which shifted from flowing northeast to north at this time. We
further investigate plume transport using a back-trajectory model.
Plume origin and transport time
We use a HYSPLIT back-trajectory model (Stein et al.,
2015) to determine the origin and transport time of the smoke arriving at
the site throughout the smoke event. The meteorological input used is the
Global Data Assimilation (GDAS) dataset. The model was set to assess
trajectories at three different altitudes at 10, 500, and 1500 m above
ground level (a.g.l.) to capture plume height. Our period of interest spans
from 17:00 LT on 3 February, just before CO enhancements are seen at the site, to 06:00 LT on 4 February when furan concentrations rapidly decrease. The model was set to
calculate a new 12 h trajectory every hour during this time. Back
trajectories are shown in Fig. 1. For every hour in the event (each
represented by a color), one can track the origin of the sampled air mass 12 h in advance of its arrival.
A shift in trajectories occurred between 17:00 and 18:00 LT on 3 February,
corresponding with the arrival of the smoke plume as indicated by observed
CO enhancements. Subsequent trajectories originate near the fires located
∼ 230–375 km from the field site on the southeast coast. The
model shows that air masses initially stayed at low altitude and were lofted
to ∼ 560 m a.g.l. when passing over the active fires
∼ 25 km to the south, near Canberra (Fig. S1 in the Supplement). The plume
descended to 10 m a.g.l. as it reached the coast. The model suggests smoke
sampled later in the evening (between 04:00–06:00 LT on 4 February) spent more time
over land compared to previous points in the event. This shift in
trajectories and the increasing intensity of fires near Canberra during this
time signify possible contributions to the decrease in OVOC to VOC age marker
ratios. Further investigation is conducted via HYSPLIT forward trajectories
in the supplement (Figs. S2 and S3). In short, during this period, plumes
from the Canberra fires were lofted to 2000 m a.g.l. well before crossing with
the SE fire plume, which attained a maximum altitude up to 560 m a.g.l. This
indicates little influence from the Canberra fires on our measurements.
Given that there are two major clusters approximately 70 km apart in the SE,
the influence of precipitation and wind speed (Figs. S4–S6) is
considered to determine whether combustion conditions were comparable. Both
fires experienced similar total precipitation in the month prior and
experienced similar wind speeds during this smoke plume event. As a result,
we conclude that combustion conditions are similar and that EFs derived from
this plume would be representative of a biome average. Over the entire
course of the event, HYSPLIT analysis suggests transport time from the fires
to the field site is around 8 h (>200 km), but potentially
shorter for the time frame immediately prior to sunrise.
O3 and NO2 time series
Detailed time series of O3 and NO2 are presented in this section
in Fig. 4. Information regarding instrumentation and corresponding setups
can be found in Sect. 2.1 of Simmons et al. (2022). Like Fig. 2, a CO time series is provided to outline the general trend of smoke during
the event.
Time series for O3, NO2, CO, and wind direction. (a) Wind direction is read as true north is 0∘ and east is 90∘. (b) O3
trends well with CO until sunrise occurs, wherein BBVOC + OH oxidation
combined with biogenic VOC emissions led to daily production. The close
trend with CO over nighttime indicates transport rather than local
formation. (c) NO2 also shows a similar trend but upon sunrise begins
to negatively correlate with CO and O3. (d) CO smoke tracer provided as
time series reference.
A non-smoke-influenced daytime and nighttime average (composed of 8 h
averages) was calculated for O3 and NO2 concentrations using data
from the month of March. Smoke around the continent had been either
transported or removed by rain by this time. O3 was calculated to have
a daytime concentration of 24.6 ppb and a nighttime concentration of 19.5 ppb. Respective concentrations were calculated for NO2 at 2.2 ppb in
the day and 3.3 ppb in the evening. Additionally, averages for a larger
suite of gas and aerosol phase variables over all smoke events sampled
during the COALA-2020 campaign can be found in Simmons et al. (2022).
As stated before, smoke-related enhancements are visible around 17:30 LT in Fig. 4d, with the hours prior being virtually devoid of tracers.
Enhancements pick up without a shift in wind direction, with winds at this
time traveling to the northwest, consistent with the HYSPLIT trajectories
presented in Fig. 2. As the wind approaches a more easterly direction,
enhancements in CO are maintained, and concentrations of more reactive BBVOCs
begin to increase. O3 concentration on 3 February reaches a maximum of
approximately 25 ppb around 14:00 LT and maintains this level until
sunset, when it decreases as biogenic sources are no longer emitting and
photolysis is halted. O3 concentration decreases to a minimum 15.6 ppb
and NO2 decreases down to 0.8 ppb, both around midnight and both below
the nighttime monthly average despite enhancements in CO. O3 has a
R2=0.48 with CO and, when considering the known transport time of
this smoke, indicates transportation rather than local production. Given the
comparatively low concentrations of both compounds at this time, it is
likely that this plume is depleting these species. This is compounded with
the low concentrations of NO2 in this temperate forest setting and,
despite emitting NOx, wildfire plumes being generally NOx-limited
(Jaffe and Wigder, 2012; Robinson et al., 2021).
Around 03:30, the wind shifts from traveling northwest to west,
significantly enhancing O3, NO2, CO, and total VOC concentrations,
corresponding to the least aged portion discussed in Sect. 3. Sunrise
occurs around 06:30 LT coinciding with a steady decline in highly reactive VOC
enhancements (Fig. 2) and NO2 (Fig. 4c). Liang et al. (2022)
found a significant correlation of R2=0.86 between NO2 and
maleic anhydride for a transported plume of similar age oxidized in the
daytime. The opposite trend is observed in our scenario despite our
measurements exhibiting comparable trends from maleic anhydride. The
NOx-limited environment and differences in biogenic VOC (BVOC)
quantities arising from the forest setting in this study and the urban
setting in theirs are likely responsible for the opposing trends in the
NO2 time series. Maleic anhydride similarly peaks around noon on 4 February, and both its production and the fast depletion of furan indicate
that OH chemical pathways generally oxidize this plume faster than NO3
reaction pathways. While O3 concentration continues to increase after
sunrise, it cannot be stated that this is dominantly due to BBVOC oxidation
given the strong source of BVOC emissions from the surrounding forest.
Isoprene nitrates sequester NO2, ultimately leading to O3
production. The diel cycle of O3 and isoprene on a non-smoke-affected
day strongly correlate to temperature and photoactive radiation. O3
does achieve a max concentration of 30 ppb at 12:00 LT on 4 February, which is
approximately 5.5 ppb above the daytime average and higher than the prior
day despite similar temperatures (23.6 ∘C on 3 February, and 24.5 ∘C on 4 February). This most likely results from the combination of transported
O3 compounded with enhanced reactivity from the plume plus local,
biogenic-related production. The plume is diluted at a consistent rate until
18:00 LT on 4 February when a shift in wind direction significantly reduces CO
enhancements and concludes the smoke event.
Emission factorsSpecies selection
To identify compounds which would be suitable for EF derivation, we compare
the list of measured ions with compounds identified in previous literature
such as Brilli et al. (2014), Hatch
et al. (2015), Gilman et al. (2015), Stockwell et al. (2015),
Bruns et al. (2017), Koss et al. (2018), and the PTR Library
(Pagonis et al., 2019). To corroborate species assignment, we
examine correlations of identifiable compounds with CO, acetonitrile,
furans, and phenolic compounds, which are well-established smoke tracers. We
also examine tracer–tracer relationships, for instance the anti-trend
between maleic anhydride and furan resulting from OH oxidation. We exclude
compounds with low proton affinities that are known to have
humidity-dependent calibration factors (e.g., HCHO, HCN). This results in
150 identified VOCs species measured during the smoke event.
We further filter our VOC list by two criteria. First, VOC + NO3
reaction rates must be included either in the NIST Chemical Kinetics
Database (Manion et al., 2015) or Master
Chemical Mechanism (v3.3.1) (Bloss et al., 2005; Jenkin et al., 1997, 2003; Saunders et al., 2003). Second, the VOC must have a significantly
long lifetime against NO3 oxidation to be minimally impacted over the 8 h transit time from the active fires to the field site (τBBVOC+NO3<8 h, again assuming [NO3] =8×107 molec. cm-3).
Calculating emission ratios
An ER is defined here as the slope of a regression of a given VOC to
CO (both in units of ppb). Following Guérette
et al. (2018), ERs are reported if correlation between a given VOC and CO
are well correlated, with R2≥0.5. High correlation minimizes the
impact of the choice of regression method (e.g., orthogonal, York) on
calculated slopes (Wu and Yu, 2018) and removes the need to
account for background corrections (additional discussion of surrounding
influential sources can be found in Simmons et al., 2022). We
use a reduced major axis regression to determine emission ratios. Given the
time component that affects our measurements, it should be noted that
compounds with low emission factors and high reactivity are likely to be
excluded as they have been reacted away before reaching the site, thus
exhibiting an insufficient CO correlation.
We first derive ERs using all data from the “freshest” portion of the
plume as determined from OVOC/VOC ratios (marked “D” in Fig. 2). This
produces 15 ERs that meet our criteria. We expect this period to provide the
most accurate representation of original VOC emissions. We then calculate
ERs for more aged portions of the smoke event (Periods A–C, Fig. 2),
performing regression analysis on the chemically distinct time periods. The
start and end time of each period is determined by visual inspection of
VOC/CO behaviors, which all exhibit similar distinct periods. Figure 5
provides an example of the analysis using acrolein. We average the slopes
from each of these lines to derive an average ER for the full smoke event
and compare to just the freshest portion of the plume (Period D). We find
that using only the freshest smoke compared to using all the data generates
very similar results for 9 of the 15 compounds (of which these 9 all have
multiple ERs over the evening). Relative differences of the resultant ERs
are within 1.5 %–47 % with two outliers: C8 aromatics (88 %) and
C3 benzenes (212 %). Three compounds have only one ER from all four periods
(maleic anhydride, benzaldehyde, and creosol) so there is no standard
deviation, but the remaining compounds from period D are captured within
1σ of ERs from periods A–D (shown in Fig. S7). Good agreement
between methods allows us to extend our analysis beyond the freshest part of
the plume and therefore allows us to report ERs for a larger number of
compounds. When focusing only on the freshest part of the plume, maleic
anhydride and benzaldehyde must be excluded due to insufficient R2
with CO. All ERs reported here and used in EF calculation use the “average
over evening” method and include these compounds. Additionally, only one ER
for CO2 and CH4 have been calculated using the dataset from
periods A–D. Both these compounds are long-lived, and from visual
inspection, they do not form distinct time periods like the VOC ERs (shown
in Fig. 4). A table with the resultant VOC ERs is also provided in the
Supplement (Table S3). We use the CO2 ER to determine an average
modified combustion efficiency with the following equation:
MCE=ERCO2ERCO2+ERCO,
where the ERCO is just unity and ERCO2/CO is 10.82 ppb CO2 ppb CO-1. This results in an MCE calculation of 0.92, indicating a less
efficient, even mixture of smoldering and flaming
(Akagi et al., 2011).
Example ER analysis (a) using acrolein, wherein the smoke event is
partitioned into four periods over the evening. Average ERs (slopes) from
periods A–C agree closely with those in the freshest portion of the plume
(D). Panels (b) and (c) show the singular ERs derived for CO2 and
CH4 using the entire nighttime dataset (A–D).
Calculating emission factors
Emission factors are defined as the mass of some trace gas emitted per mass
of dry biomass burnt. The most direct way of calculating this quantity is
capturing total emissions released from a fire as well as knowing the
quantity of fuel burnt. Unless experiments are conducted in a laboratory
setting, these quantities are not known. As such, emission factors are
calculated according to the carbon mass balance method (Akagi et al.,
2011; Selimovic et al., 2018), using CO as the reference gas for the 15
reported species, which produces the following equation:
EFX=Fcarbon×1000×MMx/MMC×ERX/CO/∑ERY/CO
where Fcarbon=0.5 and is the assumed carbon fractional content of the
fuel as used in previous studies (Akagi et al., 2011; Paton-Walsh et al.,
2014). MMX is the molar mass of compound X; MMC is the molar mass
of carbon; ERX/CO is the CO ER of X; and ∑ERY/CO is the sum
of ERCO2/CO, ERCH4/CO, and ERCO/CO. These ERs constitute the
major volatilized carbon components of the plume, but the resulting EFs may
be overestimated by 1 %–2 % (Andreae and Merlet, 2001) as this
method assumes all volatilized carbon is detected including particulate
carbon and VOCs.
EFs derived in this work are presented in Table 1 alongside results from two
eastern Australia-based studies by Lawson et al. (2015) and
Guérette et al. (2018), two western US-based
studies sampling emissions from corresponding temperate fuel types by Liu
et al. (2017) and Permar et al. (2021), and one
study by Akagi et al. (2011) that provides EFs
for general temperate zones. Additionally, Fig. S8 displays these results
via scatter plot.
EFs (g kg-1) derived in this work compared to two studies
conducted in the same or near temperate Australian forests, two US-based
aircraft campaigns sampling western temperate US fuels, and one study
reporting EFs across geographically distant temperate forests. Again, m/z 85 indicates the sum of furanone and cis-2-butenediol.
∗ Dashes indicate either EF or EF variability not reported in
study.
First, in comparison with the Australia-based studies,
Guérette et al. (2018) reports EFs notably
larger than those presented in this work, with only benzene and C8 aromatics
showing good agreement. Except for these two compounds and C3 benzenes,
Guérette et al. (2018) reports larger EFs than Lawson et al. (2015) and none within
agreement. Our results more closely agree with Lawson et al. (2015) with
methanol, acetone, and furanone EFs within 1σ, and acetonitrile and
acetaldehyde falling within a factor of 2. This agreement is likely due to
both this work and Lawson et al. (2015) examining opportunistically intercepted smoke plumes that experienced
some processing, whereas Guérette et al. (2018)
sampled near-source, controlled ground burns. Guérette et al. (2018) reports an acetonitrile
EF ∼ 4.5 times higher than this work and ∼ 3 times greater than Lawson et al. (2015) constituting one of the largest disparities. This is attributed to
the native and abundant acacias, which are N-fixing species located mainly in
forest understories. Their measurements likely had a higher proportion of
this foliage constituting the total fuel load due to both proximity to the
forest floor and resulting leaf litter. Another of the largest differences
is MVK + MACR, which shows a disparity of ∼ 6 times this work
and 3 times that of Lawson et al. (2015). This is also most likely explained by differences in sampling
approach in that proportional contributions of vegetation vary and plumes in
Guérette et al. (2018) did not undergo any
dilution or photochemical processing.
In comparison with US-based studies, methanol, acetonitrile, acetone, and
benzene agree across both studies within 1σ, with acrolein, methyl
propanoate, methyl methacrylate, C3 benzenes, and creosol agreeing very well
with values reported by Permar et al. (2021).
It should be noted though that within the estimated uncertainties, the value
for creosol reported by Permar et al. (2021) is
∼ 3.5 times greater than the value in this work, which
constitutes another of the largest disparities in this dataset.
Additionally, methanol agrees well with the value from
Akagi et al. (2011). The EF for m/z 85 in this
work is also expectedly larger than both other values presented here at
∼ 3 times greater than Permar et al. (2021). This is likely due to the plume sampled in this work undergoing
the longest transport of any plumes measured in other studies.
Perhaps an unexpected finding is that EFs derived in this work agree better
with observations in the US than the Guérette et al. (2018) study, which was
in the same region as the COALA-2020 measurements. It should be noted that
all studies except Guérette et al. (2018) are from plumes sampled several kilometers downwind. Differences previously characterized as arising from varying fuel
types may actually result from measurement approaches to deriving EFs and
proximity to emission source. Agreement across results from this work and
from the US-based studies lends credence to the use of newly presented EFs
for modeling purposes in temperate Australian forests. Further corroborating
this notion is the extremely good agreement (all EFs within uncertainty for
all three studies) found between EFs in this work and those presented in
Stockwell et al. (2015) and Koss et al. (2018). These results can
be seen in the Supplement in Fig. S9.
OH reactivity
As this plume has been shown to oxidize faster when exposed to the OH
radical as opposed to the NO3 radical, this indicates that the
nighttime transport of this plume would be able to comparably preserve OH
reactivity. We investigate this by first determining which compounds were
most significant in their enhancements and then determining their
corresponding OH reactivity.
First, a subset of the PTR-ToF-MS data was created by calculating ERs using
the methodology described in Sect. 6.2 over the same nighttime period.
However, we did not filter out compounds by their atmospheric lifetime, and
any unidentified species were not considered regardless of correlation
strength. This means the resulting OH reactivity is likely to be slightly
low, but this method ensures reactivity solely from compounds attributable
to BB emissions is being gauged. Then, an average for each compound was
calculated using the same period for ERs. These nighttime averages were then
compared with their diurnal cycles calculated using data from 1–19 March 2020 (ending date of PTR-ToF-MS sampling ambient air). If a compound's mean
over the smokey period is greater than the mean of its diel cycle plus
1σ over the same timeframe, this compound is considered in the
transported OH reactivity. Finally, we background correct the nighttime
concentrations using the March diurnal cycles and convert to reactivity
using Eq. (3):
ROH=∑[VOCi]⋅A⋅kVOCi+OH,
where [VOCi] is the concentration of the ith VOC in units of
parts per billion, A is the conversion factor to molec.i cm-3
(A=2.46×1010 in units of molec.air cm-3 ppb-1
at 1 atm and 25 ∘C), and kVOCi+OH is the OH rate constant for the
corresponding VOCi. Rate constants were again sourced from the same
databases as the NO3 rate constants. The rate constant used for m/z 85
was determined as an average of the constant provided in Koss et al. (2018) (kOH=44.2×10-12 cm3 molec.-1 s-1) and Bierbach et al. (1994)
(kOH=52.1×10-12 cm3 molec.-1 s-1) assuming
both compounds contributed equally to signal at this mass peak.
Ultimately, 26 compounds were determined to have the most significant
contributions, transporting an average OH reactivity of 5.25 s-1, with
a minimum of 3.15 s-1 occurring around 03:00 LT on 4 February and a maximum of
9.83 s-1 around 20:00 LT on 3 February, shown in Fig. 6. These values are well
within range of those seen in nighttime and aged daytime transported plumes
by Liang et al. (2022), who measured a total OH reactivity range from
approximately 4–26 s-1. We calculate an OH reactivity from the
primary biogenic VOCs (isoprene plus monoterpenes) for further comparison.
The maximum biogenic value, achieved around 12:00 LT on 4 February, is 6.35 s-1,
and the average biogenic reactivity over the course of the campaign is 5.90 s-1, indicating that the nighttime conditions allowed for the transport
of a reactivity quantity that approximately doubled OH reactivity at the
COALA-2020 field site. Additionally, there is little variability in the
relative contributions to reactivity across these different groups over the
course of the smoke event, indicating the plume experienced a consistent
oxidation over the course of its travel.
Selected compounds with significantly high smoke-related
enhancements are grouped into categories of varying reactivity based on
known reactivity groups, except for the “isoprene + monoterpenes” group,
which is the sum of isoprene (m/z 69) and monoterpene (m/z 137)
reactivities. This captures every compound included in this OH reactivity
calculation.
Compounds from the plume have been grouped into four categories to capture
their diversity. Expectedly, biogenic emissions contribute the most to total
reactivity (attributable dominantly to isoprene), but the furans group is
the most reactive with values from 1.24–3.93 s-1. This group contains
various furans (furan, 2-methylfuran, m/z 85, and furfural alcohol) wherein
m/z 85 is by far the most significant, contributing up to 69 % of the group
total. This high m/z 85 presence explains why this group is also the most OH
reactive as most furans are largely oxidized by NO3 during this
transport timeframe, except m/z 85, which has a long τNO3 but a
comparatively shorter τOH. The furan reactivity range is
comparable to lab-based values measured in Gilman et al. (2015), which ranged from 1.3–5.5 s-1. Both these studies find lower furan reactivities than lab
measurements made in Koss et al. (2018)
at an average reactivity of 14.2 s-1, where furans constitute the third
highest reactivity group. Aromatics make up the second most reactive group
(range of 0.66–2.14 s-1) in this study, with dominant contributions
from phenol (39 %), styrene (33 %), and catechol (32 %). Catechol's
contribution is likely less than this as other studies have revealed that it
shares a significant portion of its mass peak with 5-methyl furfural
(Stockwell et al., 2015; Koss et al., 2018). Despite their high NO3
reactivity, phenolic compounds still dominate the overall OH reactivity
contributions in this category. These compounds appear across other studies
as primary contributors to OH reactivity (Gilman et al., 2015; Hatch et
al., 2017; Koss et al., 2018; Sekimoto et al., 2018; Decker et al., 2019;
Liang et al., 2022). Alkenes (range of 0.86–1.83 s-1) are on par
with aromatics, for which their reactivity is largely attributable to
propene and butene, followed finally by non-aromatic oxygenates (range of 0.28–1.87 s-1), which contain compounds like methanol, acetaldehyde, and
acetic acid. The comparably low reactivity from this group is unexpected as
other studies have shown that the dominant contributions to reactivity come
from this group (Gilman et al., 2015; Koss et al., 2018; Liang et al.,
2022).
Conclusions
EFs were derived for a total of 15 trace gas species via measurements from a
PTR-ToF-MS and an FTIR spectrometer, the resulting OH reactivity of the
transported plume quantified, and O3 and NO2 time series
investigated. The COALA-2020 ground-based field campaign opportunistically
sampled a sustained biomass burning plume from 3–4 February 2020 during the
2019–2020 wildfire season in New South Wales, Australia. We determined via
HYSPLIT trajectories that the most likely pathway traveled by the plume was
from a distance ranging from ∼ 230–375 km south from fires
along the temperate forests of the east coast with contributions from more
inland fires near Canberra, Australia. This plume lofted to an altitude of
500 m a.g.l. as it passed over active fires ∼ 8 h out from the
field site, before descending to 10 m a.g.l. while traveling over the ocean and
reaching the site at 17:30 LT. All data used in the derivation of
EFs were limited from sunset on 3 February to sunrise on 4 February as this period
showed the greatest enhancements of reactive BB tracers like furan. Through
visual inspection, we partitioned this plume event into four portions and
calculated and averaged the individual ERs. We used two age marker ratios
derived from furan radical oxidation to determine the freshest portion of
the plume and found that ERs from this portion corresponded well with the
averaged ERs (within 1σ). Using EFs from the entire evening allowed
for the inclusion of three more VOC EFs into this analysis which, for the
freshest portion of the plume, did not meet the selection criteria for ERs.
We have further characterized wildfire emissions in Australia's temperate
region by providing a more comprehensive suite of biome-averaged VOC EFs.
This suite introduces new EFs for acrolein, methyl propanoate, methyl
methacrylate, maleic anhydride, benzaldehyde, and creosol. Of particular
note is acrolein, which has been shown to be a gas-phase variable posing
significant harm to human health (O'Dell et al., 2020; Simmons et al., 2022).
When compared with values reported from two Australian studies located in the
same or nearby temperate forests, we find mixed agreement with results from
Guérette et al. (2018), as only two values are
captured within our EF variability, with acetonitrile differing by a factor
of ∼ 4.5 and MVK + MACR differing by a factor of ∼ 6. However, two compounds are within the range of variability for Lawson et al. (2015) and two others are
well within a factor of 2, which indicates reasonable agreement.
Furthermore, comparison with two recent US studies that report data on
analogous temperate zones, as well as one report covering global temperate
regions, show generally good agreement for 9 of the 15 compounds, with
several others within a factor of 2, indicating very good agreement. This
closer agreement with these studies, as well as that of Lawson et al. (2015), is likely due to
the measurement approach when deriving EFs as both US-based studies were
aircraft campaigns, and the Australia-based study intercepted a transported
plume much like this work. Guérette et al. (2018) sampled controlled burns on a ground campaign virtually at the
emission source. This indicates that variability previously ascribed to
differing fuel types may be overshadowed by sampling approach and that
comprehensive measurements from US-based studies may be useful for studying
Australian biomes. Agreement with both Lawson et al. (2015) and the US-based
studies indicates that results here are valid for future use in Australian,
biome-specific biomass burning studies. Compounding this is the excellent
agreement found between EFs in this study and a comparison of two
laboratory, US-based, temperate fuel studies, indicating the potential for
lab-based results to be similarly applicable. Chemically comprehensive
near-source observations of Australian fuel types are needed to evaluate the
importance delineating temperate forest EFs in different regions across the
globe.
Probing the OH reactivity of the plume revealed that the nighttime
conditions, despite the long transport time, transported a quantity that
effectively doubled OH reactivity at the COALA-2020 field site, with
contributions arising from expected classes of compounds such as furans
(most contribution), aromatics (second), and alkenes (third). m/z 85
contributed most significantly of the furans measured, which is due to its
long NO3 lifetime but short OH lifetime. Other furans had largely been
reacted away before reaching the COALA-2020 field site. Phenol had the
largest contribution of the measurable phenolic compounds despite its high
NO3 reactivity. Alkenes and aromatics were found, as a group, to have
an on par reactivity and, unexpectedly, non-aromatic oxygenates contributed
the least.
Data availability
Data from time periods used for analysis in this work are available from
the PANGAEA archive at https://doi.org/10.1594/PANGAEA.927277 (Mouat et al.,
2021a) and https://doi.org/10.1594/PANGAEA.939407 (Mouat et al.,
2021b).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-22-11033-2022-supplement.
Author contributions
APM conducted PTR-ToF-MS measurements and subsequent data
analysis. JBS, CPW, and JRG
oversaw the maintenance and in-person operation of the PTR-ToF-MS for much
of the COALA-2020 field campaign. CO measurements were provided by DWTG. CPW led the COALA-2020 campaign, whilst JK led PTR-ToF-MS instrument deployment and data analysis. All coauthors
have provided substantial input during the process of drafting this work.
Competing interests
The contact author has declared that none of the authors has any competing interests.
Disclaimer
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
We thank Travis Naylor, Ian Galbally and all the UOW COALA-2020 team for their aid in conducting
measurements during the field campaign and all input thereafter. We
additionally gratefully acknowledge the NOAA Air Resources Laboratory (ARL)
for providing the HYSPLIT transport and dispersion model used for analysis
in this publication. We acknowledge the use of data and/or imagery from
NASA's Land, Atmosphere Near real-time Capability for EOS (LANCE) system
(https://earthdata.nasa.gov/lance, last access: 11 March 2022), part of
NASA's Earth Observing System Data and Information System (EOSDIS)
(Simmons et al., 2022).
Financial support
This research has been supported by the National Science Foundation (grant no. 2016646).
Review statement
This paper was edited by Ivan Kourtchev and reviewed by two anonymous referees.
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