Introduction
Wildfires in the western US have become larger and more frequent, and this
trend is expected to continue in the coming decades
(Dennison et al., 2014; Miller et al., 2009). This is due to historical wildfire
suppression, leading to high fuel loading and climate changes that include longer springs
and summers, earlier snowmelts, and prolonged droughts
(Dennison et al., 2014; Jolly et al., 2015; Spracklen et al., 2009; Westerling et al.,
2006). Smoke emissions from wildfires primarily contain carbon dioxide
(CO2), carbon monoxide (CO), and thousands of organic compounds in the
gas and particle phases. These organic compounds can significantly influence
atmospheric chemistry, cloud formation, regional visibility, and human
health. Thus, increased occurrences and magnitudes of wildfires will likely
lead to greater smoke impacts on regional and global environments.
The extent to which smoke will adversely impact human health and the
environment depends, in part, on the chemical composition and amount of
emissions produced. In general, biomass burning, which includes wildfires,
is the main global source of fine carbonaceous aerosol particles
(∼ 75 %) in the atmosphere
(Andreae and Merlet, 2001; Bond and Bergstrom, 2006; IPCC, 2014; Park et al., 2007).
Individual and categorized organic emissions from wildfires have previously
been identified and quantified
(Akagi et al., 2011; Andreae and Merlet, 2001; Hatch et al., 2015; Kim et al.,
2013; Koss et al., 2018; Liu et al., 2017; Mazzoleni et al., 2007; Naeher et
al., 2007; Oros et al., 2006; Oros and Simoneit, 2001a; Simoneit, 2002;
Stockwell et al., 2015; Yokelson et al., 2013). The bulk of previous studies
on speciated organic compound emissions focused on gas-phase volatile
organic compounds (VOCs). Particle-phase results are typically reported as
total organic carbon (OC) or particulate matter (PM) with aerodynamic
diameters less than 10 or 2.5 µm (PM10 and PM2.5).
These types of measurements provide no chemical specificity of the particle phase
and thus limit the ability to predict how smoke will age in the atmosphere
and impact the environment.
Several studies have examined specific particle-phase organic compounds in
smoke such as toxic retene and other polycyclic aromatic hydrocarbons (PAHs)
(e.g., Jayarathne et al., 2018; Kim et al., 2013; Naeher et al., 2007; Sullivan et
al., 2014) or abundant tracer compounds, like levoglucosan and vanillic acid
(Simoneit et al., 1999). Out of the likely thousands of unique compounds, roughly 400 known particle-phase
organic compounds and their amounts produced per mass of dry fuel burned (a
quantity known as the emission factor, EF) have been published and organized
by wildland type (Oros et al., 2006, 2001a, b).
These compounds span many chemical families
(i.e., functionalities), like sugars and methoxyphenols, and provide key
insights into how different wildland burns lead to different organic
particulate composition and EFs.
New advances in instrumentation, such as two-dimensional gas chromatography
or electrospray ionization coupled to high-resolution mass spectrometers
(Isaacman et al., 2011; Laskin et al., 2009), now allow for unprecedented levels of
molecular speciation of atmospheric aerosol particles that can further
identify and quantify the thousands of previously unreported biomass burning
compounds. Nevertheless, current fire and atmospheric chemistry models that
predict the amount of smoke produced, its atmospheric
transformation/transportation, and its physiochemical properties
(e.g., French et al., 2011; Reinhardt et al., 1997; Wiedinmyer et al., 2011) do not
model the thousands of organic compounds emitted from fires, due primarily to
limited computational resources. To address this deficiency, Alvarado et al. (2015)
used measured volatility distribution bins of organic compounds from fresh
smoke and modeled their atmospheric aging by assuming shifts in volatility
distribution via reactions with ozone and hydroxl radicals. Though this
approach does better predict secondary organic aerosol particle formation,
it still does not consider the wide variety of chemical compounds found in
smoke, thus limiting its ability to predict physiochemical properties of
aged smoke particles and their impacts on the environment. Therefore, a
better or estimable representation of the chemical composition in smoke
particles within models requires condensing the information from
molecular-level speciation into useable relationships that correlate typical
particle composition to a measurable burn variable.
The purpose of this study is to (1) identify, classify, and quantify organic
compounds in smoke particles produced during laboratory burns and (2) provide scalable
EFs of individual compounds and their chemical families
from various fuels as a function of fire conditions. A selection of fuels
and fuel combinations commonly consumed in western US wildland fires were
burned at the US Forest Service Fire Sciences Laboratory (FSL) in
Missoula, MT, during the NOAA Fire Influence on Regional and Global
Environments Experiment (FIREX) campaign in 2016
(Selimovic et al., 2018). Regression models representing EFs as a function of fire
conditions are provided for groupings of organic compounds that vary in
chemical complexity, from generalized organic carbon and total particulate
organic compounds to specific chemical families and individual compounds. In
addition, a mass spectral database, compatible with the National Institute
of Standards and Technology (NIST) Mass Spectral Search program, containing
the mass spectra, retention indices, and identities/compound classifications for
all the separated compounds observed from the various burns, is included.
This database will be a valuable resource for the community for identifying
specific chemicals in air masses impacted by biomass burning plumes and
understanding the dominant source materials burned, fire characteristics,
and atmospheric transformations.
Materials and methods
A total of 34 different fuels were combusted in 75 “stack” burns during the
2016 FIREX campaign at FSL (Selimovic et al., 2018). Most fuels were
representative of common biomass components found in the western US wildlands. Non-western US wildland fuels were also burned and are used to
demonstrate the applicability of the reported regression models across a
wider range of fuels. A detailed description of the FSL combustion room can
be found elsewhere (Christian et al., 2004; Stockwell et al., 2014) with
pertinent details described here. The
12.5 m × 12.5 m × 22 m combustion room contained a fuel
bed on the floor. Fuels were placed in the fuel bed and ignited by
resistance-heated coils. Above the fuel bed was a 3.6 m inverted funnel
connected to a 1.6 m diameter exhaust stack that vented through the roof of
the combustion room. The room was kept at positive pressure to provide a
constant air flow that diluted and carried the smoke up the stack. A platform
was located 17 m above the fuel bed and allowed instrument sampling access
into the stack (see Fig. S1 in the Supplement). The samples studied here were
collected from the platform and thus represent fresh emissions.
Smoke from the stack was pulled through a custom-built sampler known as
DEFCON, Direct Emission Fire CONcentrator (see diagram in Fig. S2). DEFCON's
inlet was a 20.3 cm × 1.3 cm OD stainless steel tube that reached
15.2 cm into the stack. 10.3 LPM of smoke was pulled through the inlet, with
two 150 ccm flows branching off from the main sample flow. These low-flow
channels went to two parallel flows consisting of a Teflon filter followed by
sorbent tube for gas-phase sample collection; analysis of those samples will
be described in future publications. The remaining 10 LPM was passed through
a 1.0 µm cutoff cyclone before being sampled onto a 10 cm quartz
fiber filter (Pallflex Tissuquartz). Total residence time was ∼ 2 s.
One quartz fiber filter collected both particles and likely low volatility
gases for the duration of each fire which lasted ∼ 5–50 min. These
filters were analyzed for this study. A few fires were terminated “early”
when a small amount of fuel and smoldering combustion remained. Prior to
collection, filters were baked at 550 ∘C for 12 h and packed in
similarly baked aluminum foil inside Mylar bags. The flows were monitored to
ensure constant flow rates. Flow paths within DEFCON were passivated with
Inertium® (Advanced Materials Components
Express, Lemont, PA) which has been shown to reduce losses of oxygenated
organics (Williams et al., 2006). After each burn, the inlet of DEFCON was
replaced with a clean tube and the remainder of the system was purged with
clean air. A background filter sample was collected each morning prior to the
burns to estimate background contributions from sampling components and room
air.
List of fuels analyzed, with the number of compounds separated and
quantified from each burn. Note conifer fuel type refers to a realistic
mixture of a coniferous ecosystem unless otherwise noted.
Fuel description
Burn no.
Fuel type
Number of
MCE
compounds
Engelmann spruce
9
Conifer
714
0.9334
Engelmann duff
12
Coniferous duff
751
0.859
Ponderosa pine rotten log
13
Woody debris
709
0.9778
Ponderosa pine litter
16
Coniferous litter
687
0.9607
Engelmann spruce canopy
17
Conifer
403
0.8953
Douglas fir litter
22
Coniferous litter
585
0.9501
Engelmann spruce duff
26
Coniferous duff
398
0.8474
Manzanita canopy
28
Shrub
679
0.9789
Douglas fir rotten log
31
Woody debris
776
0.7785
Manzanita canopy
33
Shrub
570
0.9788
Engelmann spruce duff
36
Coniferous duff
596
0.8773
Ponderosa pine
37
Conifer
811
0.9403
Lodgepole pine canopy
40
Conifer
444
0.9231
Lodgepole pine
42
Conifer
634
0.9524
Chamise canopy
46
Shrub
128
0.9566
Subalpine fir
47
Conifer
596
0.9396
Excelsior
49
Wood
173
0.9712
Yak dung
50
Dung
515
0.9016
Peat, Kalimantan
55
Peat
392
0.8405
Subalpine fir duff
56
Coniferous duff
522
0.8874
Rice straw
60
Grass
288
0.951
Excelsior
61
Wood
230
0.9508
Bear grass
62
Grass
656
0.9036
Lodgepole pine
63
Conifer
834
0.938
Jeffery pine duff
65
Coniferous duff
472
0.8833
Sage
66
Shrub
328
0.9191
Juniper canopy
68
Conifer
522
0.9293
Kiln-dried lumber
70
Wood
209
0.953
Ceanothus canopy
74
Shrub
97
0.9748
A total of 29 fire-integrated smoke filter samples, including one from each
specific fuel (with some replicates), were selected and analyzed using a
thermal desorption, two-dimensional gas chromatograph with online
derivatization coupled to an electron ionization/vacuum ultraviolet
ionization high-resolution time-of-flight mass spectrometer
(TD-GC × GC-EI/VUV-HRToFMS) (Isaacman et al., 2012; Worton et al.,
2017). A list of analyzed fuels is given in Table 1. Chromatograms from
replicate burns showed minor variation; thus the remaining 46 burns were not
analyzed in detail. Punched samples of each filter (0.21–1.64 cm2)
were thermally desorbed at 320 ∘C under a helium flow using a
thermal desorption system (TDS3 and TDSA2, Gerstel). Desorbed samples were
then mixed with a gaseous derivatization agent, MSFTA
(N-methyl-N-(trimethylsilyl)trifluoroacetamide). MSFTA replaces the hydrogen
in polar hydroxyl, amino, and thiol groups with the trimethylsilyl group,
creating a less polar and thus elutable compound. Derivatized samples then
were focused on a quartz wool glass liner at 30 ∘C (cooled injection
system, CIS4, Gerstel) before rapid heating to 320 ∘C for injection
into the gas chromatograph (GC, Agilent 7890). GC × GC separation
was achieved with a 60 m × 0.25 mm × 0.25 µm
semi-nonpolar capillary column (Rxi-5Sil MS, Restek) followed by a
medium-polarity, second-dimension column
(1 m × 0.25 mm × 0.25 µm, Rtx-200MS, Restek). A
dual-stage thermal modulator (Zoex), consisting of a guard column
(1 m × 0.25 mm, Rxi, Restek), was used to cryogenically focus the
effluent from the first column prior to heated injection onto the second
column (modulation period of 2.3 s). The main GC × GC oven ramped
at 3.5 ∘C min-1 from 40 to 320 ∘C and was held at the
final temperature for 5 min and the secondary oven ramped at the same rate
from 90 to 330 ∘C and was held for 40 min. Separated compounds were
then ionized either by traditional EI (70 eV) or VUV light (10.5 eV). The
HRToFMS (ToFWerk) was used to detect the ions and was operated with a
resolution of 4000 and transfer line and ionizer chamber temperatures of
270 ∘C. VUV light was provided by the Advanced Light Source,
beamline 9.0.2, at Lawrence Berkeley National Laboratories. During the VUV
experiments, the HRToFMS operated at a lower ionizer chamber temperature of
170 ∘C to further reduce molecular fragmentation (Isaacman et al.,
2012).
Punches from the filter samples were also analyzed for organic and elemental
carbon (OC and EC, respectively) using a Sunset Model 5 Lab OCEC Aerosol
Analyzer following the NIOSH870 protocol in the Air Quality Research Center
at the University of California, Davis. Thermal pyrolysis (charring) was
corrected using laser transmittance. OC and EC were also measured on the
background filters.
Emission factor calculations
The mass loadings for all separated compounds measured by TD-GC × GC-EI/VUV-HRToFMS
were determined using a set of calibration curves. Full
details of the data conversion to mass loadings and emission factors with associated uncertainties are provided in the Supplement with important steps
outlined here. The TD-GC × GC-EI/VUV-HRToFMS responses to a wide
range of standard compounds commonly found in biomass burning samples were
measured at varying mass loadings to create calibration curves. Measured
peaks from the filters were calibrated using a standard compound that
exhibited similar first- and second-dimension retention times and compound
classification. For example, a sampled compound classified as sugar was
quantified using the nearest sugar standard compound in the chromatogram.
Unknown compounds were matched to the nearest eluting standard compound,
similar to the approach taken by Zhang et al. (2018). The mass loadings of
all observed compounds were then background subtracted; however, the mass on
the background filter for all compounds was negligible. The compound's
emission factor (EFcompound) was then calculated by normalizing mass
loadings by background-corrected sampled CO2 mass. This ratio was
then multiplied by the corresponding EFCO2, as given in Selimovic et
al. (2018).
EFs for OC and EC were calculated similarly, using background-corrected OC
and EC mass loadings.
(a) Measured organic and elemental carbon (OC and EC,
respectively) as a function of modified combustion efficiency (MCE) and
(b) OC/EC as a function of MCE. Symbols indicate the
different fuel types.
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Results and discussion
Emission factors of organic and elemental carbon
OC and EC EFs were first related to the fire-integrated modified combustion
efficiency (MCE). MCE reflects the mix of combustion processes in the fire
and is defined as background-corrected values of CO2/(CO2+CO)
(Akagi et al., 2011; Ward and Radke, 1993). MCE values near 1 indicate almost
pure flaming, while values near 0.8 are almost pure smoldering, with 0.9
representing a roughly equal mix of these processes. Figure 1a shows the EFs
of OC and EC (EFOC and EFEC) as a function of MCE
across a variety of fuel types (see Table 1). Decreasing MCE (more
smoldering) results in increased OC and decreased EC emissions across all
studied fuel types. These observed trends are in general agreement with
previous studies (e.g., Christian et al., 2003; Hosseini et al., 2013). EFs
for OC and EC generally follow a logarithmic relationship such that
log(EFOC) is inversely proportional to MCE (slope of -9.506)
and log(EFEC) is directly proportional to MCE (slope 5.441).
Comparison of the slopes suggests that decreasing MCE of a fire will produce
an increasing amount of OC compared to EC. This is further confirmed by
examining the ratio of OC to EC (OC/EC) with MCE. Figure 1b
illustrates how OC/EC sharply increases with more smoldering fire
conditions (slope of -16.555). This trend also follows a similar inversely
proportional logarithmic relationship as EFOC vs. MCE but with
even stronger correlation (R2=0.85 compared to 0.66, respectively). Note
that values for Douglas fir rotten log (burn 31), peat (burn 55), rice straw
(burn 60), and Engelmann spruce duff (burn 26) fires are not shown due to
measured EC at background levels. In addition, significant losses
(∼ 40 %) of organic compounds were only observed for the Douglas
fir rotten log burn and were determined by comparing GC × GC
chromatograms taken ∼ 1 month after collection at FSL and
∼ 2 years after collection prior to OC/EC analysis. The combined
results clearly show that flaming combustion produces slightly more
particulate EC compared to OC, whereas smoldering combustion emits levels of
OC 1–2 orders of magnitude higher compared to EC.
Two-dimensional chromatogram of smoke collected from burning
lodgepole pine (burn 63). The first dimension separates compounds by their
volatility and the second dimension by their polarity. Each point
(∼ 800 in total) represents a separated compound with the colors
signifying the compound's classification. Size of a point approximately
scales with its emission factor (see Sect. S3).
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Identification and quantification of I/SVOCs
Filter samples were analyzed for intermediate and semi-volatile organic
compounds (I/SVOCs) using the TD-GC × GC EI-VUV-HRToFMS. Between 100
and 850 peaks (i.e., unique compounds) were separated in each fire-integrated
chromatogram, with fewer peaks observed for more flaming fires such as from
shrub fuels (see Table 1). An example two-dimensional chromatogram of a
lodgepole pine burn (burn 63) is shown in Fig. 2. Based on the
GC × GC configuration, all compounds elute between dodecane
(C∗ ∼ 106 µg m-3 and n-alkane
retention index, RI, of 1200) and hexatriacontane
(C∗ ∼ 10-1 µg m-3, RI = 3600) and
thus are classified as I/SVOCs, with a small fraction classified as
low-volatility organic compounds (Donahue et al., 2009). In total,
approximately 3000 unique compounds were separated across the 29 analyzed
burns (see Table 1). From those compounds, 149 compounds were identified
using a combination of matching authentic standards, RI, EI mass spectrum
(via NIST Mass Spectral database, 2014 version), and VUV parent and fragment
mass ions. True positive identification requires analysis of a standard
compound on the instrument; however comparing the NIST match to parent mass
determined from VUV mass spectrum analysis can also provide a level of
identification (Worton et al., 2017). Identified compounds account for
4 %–37 % of the total observed organic mass (mean of 20 % with a
standard deviation of 9 %). A table of these identified compounds with
their identifying methods (e.g., standard matching, previous literature, or
NIST Mass Spectral database), RI, five most abundant mass ions from the EI
mass spectra, and fuel source(s) is given in Table S1 in the Supplement.
To help reduce the chemical complexity from the 3000 observed compounds,
each separated compound was sorted into a chemical family. This was achieved
using a combination of parent ion mass (VUV), fragment ion mass spectra (VUV
and EI), RI, and second-dimension retention time to estimate the compound's
functionality. More details on the classification process and examples
within each category can be found in the Supplement. The chemical families were
broadly named and include non-cyclic aliphatic/oxygenated, sugars,
PAHs/methylated + oxygenated, resin acids/diterpenoids, sterols/
triterpenoids, organic nitrogen, oxygenated aromatic heterocycles,
oxygenated cyclic alkanes, methoxyphenols, substituted phenols, and
substituted benzoic acids. Almost 400 compounds, including the identified
and most frequently observed compounds in the analyzed burns, were grouped
into these families. The remainder of the compounds, which were both
uncategorizable and unidentifiable, were placed into the unknown category.
Figure 2 illustrates the chemical families (indicated by color) of all the
separated compounds emitted from an example lodgepole pine burn.
Despite many compounds remaining unknown, their defining traits such as mass
spectra or retention index (i.e., volatility) can be compared to atmospheric
samples to help the community better define the composition of
biomass-burning-derived, particle-phase organic compounds. As such, all
∼ 3000 observed compounds have been compiled into a publicly
available mass spectral database and first reported here as the University
of California, Berkeley, Goldstein Library of Organic Biogenic and
Environmental Spectra (UCB-GLOBES) for FIREX (see Supplement). This spectral library
is compatible with NIST Mass Spectral Search and contains mass spectra, n-alkane RI,
potential compound identification or chemical families, EFs as a function of
fire conditions, and fuel sources of all unique compounds detected from the
29 analyzed burns.
(a) Contributions of unknown mass to the total observed
mass for the 29 analyzed burns. (b) Mass fractions for each chemical
family compared to total classified mass. Fuels are grouped by type, and
numbers after fuel name indicate the burn number during the FIREX campaign.
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Average observed I/SVOC composition
The masses of observed I/SVOCs from each chemical family were summed over
each fire-integrated sample and normalized to either the total observed
I/SVOC mass or total classified I/SVOC mass. Figure 3a illustrates mass fractions of the unidentified and unclassified
(unknown) compounds out of the total observed mass from the 29 analyzed
burns. Mass fractions for each chemical family out of the total observed
mass are given in Table S5. Unknowns represent ∼ 35 %–90 %
of I/SVOCs mass emitted during the analyzed burns, with woody debris (rotten
logs) exhibiting the highest mass fraction of unknowns (∼ 90 %).
Since the compounds that make up the unknown mass fraction varied
between burns, differences in the mass fractions between fuel types are not
indicative of higher emissions of any particular compound. However, notably
the two woody debris burns showed similar unknown compounds (i.e., 99 % of
the unknown mass was of compounds found in both burns) but occurred under
two different fire conditions (burn 13 at MCE = 0.98 and burn 31 at
MCE = 0.78). In both cases, the unknown mass fractions were similar at
87 %–89 %. This observation provides some indication that fuel type plays a
larger role than MCE in determining the unknown organic mass fraction in
smoke particles.
Given that the unknown compounds typically varied between burns, the mass of
each classified chemical family was normalized to the total observed
classified mass (i.e., excluding the unknown mass) in order to better
compare classified compounds between burns. These results are shown in
Fig. 3b. Conifers, coniferous litter, and wood exhibited the highest
fraction of sugars (38 %, 29 %, and 44 %, respectively) compared to
other fuels (between 6 %–30 %). Furthermore, levoglucosan was the largest
single contributor to the sugars for these burns and ranged from 10 % to 40 %
of the total sugars. These observations are consistent with previous studies
that have shown high levoglucosan emissions from cellulose-rich wood samples
(Mazzoleni et al., 2007; Simoneit et al., 1999). Coniferous fuels also emitted higher
amounts of resin acids/diterpenoids (7 %, 16 %, 7 %, and 3 % for
conifers, coniferous litter, coniferous duff, and woody debris
respectively), as previously observed (Hays et al., 2002; Oros and Simoneit, 2001a; Schauer et al., 2001). Peat (from
Indonesia) emitted the largest fraction of aliphatic compounds (52 %)
compared to other fuels, in agreement with previous observations
(George et al., 2016; Iinuma et al., 2007; Jayarathne et al., 2018). Manzanita burns
produced the highest amounts of substituted phenols (34 % of total
classified mass compared to 1 %–4 % for other fuels), mostly as hydroquinone
(Hatch et al., 2018; Jen et al., 2018). Organic nitrogen compounds, most of
which were nitro-organics, also contributed significantly (up to 43 %) to
the total observed classified mass for all fuels. These compounds tend to
absorb light (Laskin et al., 2015) and may contribute to
observed brown carbon light absorption from these burns
(Selimovic et al., 2018). However, it should be noted that the instrument is not as
sensitive to this class of compounds. Thus, the EF uncertainty is high
(factor of 2) for compounds that are not positively identified with a
standard but are categorized as organic nitrogen.
Figure 3b also provides some evidence that fuels within the same type
generally show similar mass fractions of chemical families. For example,
conifers, which consist of a mixture of coniferous ecosystem fuel components
(e.g., canopy, duff, litter, and twigs), exhibit relatively similar mass
fractions, with sugars accounting for 30 %–50 %, 4 %–28 %
non-cyclic aliphatic, 13 %–30 % organic nitrogen, 2 %–20 %
resin acid/diterpenoids, and 1 %–4 % PAH/methyl + oxy across the
MCE range of 0.90–0.95. Coniferous duff (MCE = 0.85–0.89) exhibited a
lower sugar fraction (11 %–31 %) but higher non-cyclic aliphatics
(15 %–38 %) than the conifers. Burning grasses
(MCE = 0.90–0.95) produced roughly equal amounts of sugars and organic
nitrogen compounds (30 %) and higher amounts of oxygenated cyclic
compounds (3 %–11 %), like lactones, than the coniferous fuels
(∼ 1 %). Shrubs (MCE = 0.92–0.98) exhibited the largest ranges in
chemical family mass fractions (e.g., 0 %–42 % organic nitrogen
compounds and 2 %–43 % substituted phenols), suggesting that plants
in this fuel type are less similar to each other than coniferous fuels. This
may be due to a wider range of plant chemical composition for shrubs than for
the other fuel types. Overall, the I/SVOC mass fractions tend to be more
similar for fuels within a fuel type with the most variation exhibited for
fuel mixtures and shrubs.
Summed emission factors (EFs) within a chemical family for each burn
as a function of modified combustion efficiency (MCE). Each panel depicts a
different family with (a) total observed I/SVOC EF,
(b) unknowns, (c), sugars, (d) polycyclic aromatic
hydrocarbons (PAHs, including methylated and oxygenated forms),
(e) methoxyphenols, and (f) sterols/triterpenoids. Dashed
lines represent a log fit of the form log(EF) inversely proportional to MCE.
The dotted lines represented a factor of 2 above and below the model. Symbols
denote different fuel types.
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EFs as a function of fire conditions (MCE)
Unlike the dependence of chemical family mass fractions on fuel type, EFs for
each chemical family showed a correlation with MCE across all fuels examined
and to a much lesser extent than fuel type. Figure 4 presents EFs for total
observed organic compounds and five of the chemical families (unknowns,
sugars, PAHs/methyl/oxy, methoxyphenols, and sterols/triterpenoids, with
others given in Fig. S4) as a function of MCE. Fuels not found in the western
US are also included in these figures to demonstrate that their EFs generally
follow the trend with MCE. The notable exception is peat, a semi-fossilized
fuel (Stockwell et al., 2016), whose EFs for all chemical families are
roughly an order of magnitude lower than other fuels at similar MCE values,
except non-cyclic aliphatic/oxygenated EF, which is approximately equal to
burns at similar MCE (see Fig. S4). In general, EFs measured by the
TD-GC × GC-EI/VUV-HRToFMS agree with previous literature (Hays et
al., 2002; McDonald et al., 2000; Oros et al., 2006; Oros and Simoneit,
2001a, b). For example, Oros and Simoneit (2001a) provided the sum of
carboxylic acids and alkanes–alkanes–alkanols for conifer burns at ∼ 1 g kg-1, which is within our reported
range of 0.4–2.3 g kg-1 (MCE = 0.90–0.95) for non-cyclic
aliphatic/oxygenated emitted from burning conifers. Other chemical family EFs
presented here for conifers, including PAHs (0.4 g kg-1), diterpenoids
(1–3 g kg-1), and methoxyphenols (1 g kg-1), are also in good
agreement with those published in Oros and Simoneit (2001a). Hays et
al. (2002) reported EFs for unknown compounds from ponderosa pine of
∼ 20 g kg-1, higher than 11 g kg-1 (MCE = 0.94) for
the ponderosa pine burn studied here. This may be due to more compounds being
classified here than in Hays et al. (2002) or differences in MCE between the
studies. In contrast to previous work, EFs for chemical families reported
here were measured over a wider range of fire conditions and fuel types and
show a clear relationship with MCE.
Emission factors (EFs) of (a) levoglucosan (sugar),
(b) fluoranthene (PAH), (c) acetovanillone (methoxyphenol),
and (d) coniferyl aldehyde (methoxyphenol) for various fuel burns as a
function of MCE. Dashed lines indicate a log fit of the form log(EF)
inversely proportional to MCE. Dotted lines show a factor of 2 above and
below the model. Note that peat (open pentagon) is not included in any of the
fits. Different symbols represent fuel categories.
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Chemical family EFs span ∼ 3 orders of magnitude, and therefore
logarithmic fits (given as a dashed line in the semi-log graphs of Fig. 4)
were applied to all the measurements excluding peat. Slopes range between
-9.425 for sterols/triterpenoids and -14.637 for PAHs/methyl + oxy
(fitted slopes, intercepts, and their errors for all chemical families are
provided in Table S6). Three decimal places are provided for both the slope
and intercept in order to reproduce the regression line. The R2 values
for the sugars (Fig. 4) and resin acids/diterpenoids (Fig. S4) are noticeably
lower at 0.32 and 0.31, respectively, than the other chemical families
(R2∼0.4). This is primarily due to the high mass fraction of sugars
and resin acids/diterpenoids found in conifers, as stated above, and suggests
that high emissions of both types of compounds are indicative of burning
conifers. Also, coniferous litter emitted high amounts of resin
acids/diterpenoids. Removing conifers from the semi-logarithmic model for
sugars yields a log10(EFsugars)=-10.299(MCE) + 9.361
with R2=0.66, and removing conifers and coniferous litter for resin
acids/diterpenoids results in
log10(EFresin)=-19.598(MCE) + 16.360 with R2=0.77.
These R2 values are then more similar to those of other chemical
families. In general, these results indicate that MCE can be used to estimate
EFs for various chemicals across a broad range of fuels, including those not
found in the western US wildlands except peat, with a minimal dependence on
fuel type.
The goodness-of-fit for the multi-fuel regression models can be evaluated by
comparing the predicted EFs to those measured for the various fuel types in
this study and others (Liu et al., 2017). As evident in Fig. 4a, the
predicted total I/SVOCs to observed EFs are between 0.7 and 11 times higher
for shrubs, 0.90 and 0.97 for grasses, 0.22 and 0.74 for conifers, 0.63 and
3.0 for coniferous duff, and 0.28 and 0.85 for woody debris. The model is
also compared to previously reported EFs from wildfires. Specifically, Liu et
al. (2017) reported MCE values from three different California wildfires and
total organic aerosol (OA) particle EFs, which are the most equivalent to
total I/SVOC EFs measured here (though at MCE values of <0.8, we observed
higher IVOC mass loadings in our chromatograms which would likely not be
included in the OA EFs at lower particle mass loadings; May et al., 2013).
Liu et al. (2017) measured MCE values of 0.935, 0.877, and 0.923, with OA EFs
of 23.3, 30.9, and 18.8 g kg-1, respectively. The model given in
Fig. 4a predicts total I/SVOC EFs of 8, 35, and 10 g kg-1 for those
MCE values. The predicted EFs are within a factor of 2–3, consistent with
measured total I/SVOC EFs reported here. No other previous experiments report
chemical family EFs from wildfires with corresponding MCE values; thus the
accuracy of applying the chemical family regressions cannot be evaluated at
this time. Without this information, uncertainty in using the reported
regression models in predicting EFs of various chemical families is estimated
to be a factor of 3. However, this uncertainty in EFs is minor when compared
to uncertainties in estimating the amount of fuel burned in large-scale
carbon emission fire models (French et al., 2011; Urbanski et al., 2011),
which is primarily due to high spatial and temporal variations in fuel
loadings and lack of observational data. Thus, these regressions can be used
to approximate EFs of various chemical families for a wide range of fuels and
fuel mixtures from measured MCE values.
Figure 5 shows the fire-integrated EFs of four specific compounds,
levoglucosan (sugar), fluoranthene (PAH), acetovanillone (methoxyphenol), and
coniferyl aldehyde (methoxyphenol), as a function of MCE. Acetovanillone and
coniferyl aldehyde, both methoxyphenols, have been reported previously as
tracers for lignin pyrolysis and levoglucosan (and more broadly sugars) from
cellulose (Hawthorne et al., 1989; Oros and Simoneit, 2001a; Schauer et al.,
2001; Simoneit, 2002). In addition, fluoranthene and other PAHs are known
carcinogenic compounds (Boffetta et al., 1997; Kim et al., 2013). EFs for
levoglucosan, the most widely reported particulate tracer compound for
biomass burning (Mazzoleni et al., 2007; Simoneit et al., 1999; Sullivan et
al., 2014), range between ∼ 0.004 and 1 g kg-1 from this study.
Hosseini et al. (2013) reported EFlevo for chaparral ecosystems
at 0.02–0.1 g kg-1, similar to EFlevo for shrubs measured
(0.004–0.1) in this study. Schuaer et al. (2001) provided average
EFlevo for pine trees at 1.4 g kg-1, roughly a factor of 2
higher than the average 0.6 g kg-1 EFlevo for conifers of
this study. Oros and Simoneit (2001a) examined levoglucosan emissions from
various types of pine trees, with an average EFlevo of
0.02 g kg-1, a factor of 30 lower than reported here. Many reasons
could explain this difference, such as different smoke sampling and filter
extraction procedures and different MCE conditions during sampling.
Regardless, the levoglucosan EFs reported in this study generally fall within
the ranges measured by previous groups.
EFs for the compounds shown in Fig. 5 span 2–5 orders of magnitude across
fire conditions and fuels, including fuels found outside of the western US. Similar to the chemical family EFs, peat displays significantly lower EFs
(factor of ∼ 10) than the other fuels. Consequently, applied
logarithmic fits, given as dashed lines in Fig. 5, exclude peat. Slopes of
these fits range from -6.455 to -19.443 for the four displayed compounds.
Figure 5a also shows that the R2 value for levoglucosan is the lowest
(0.34) compared to the other compounds (0.40–0.63). This poorer correlation
with MCE is similar to that seen for the sugar EFs in Fig. 4c, which shows that EFs from
burning conifers were higher than the model predicted. Removing the conifers'
levoglucosan EFs results in log(EFlevo)=-9.547(MCE) + 8.041
and improves the correlation (R2=0.43). Nonetheless, these measurements
suggest compound EFs do depend to some extent on fuel type in addition to
MCE. However, the spread of measured EFs around the logarithmic fit in Fig. 5
indicates a factor of 3 uncertainty in estimating EFs from MCE.
In addition to the well-known biomass burning particulate compounds shown in
Fig. 5, these measurements provide useful models to estimate EFs for
hundreds of previously unreported compounds (not shown in Fig. 5). These
commonly detected compounds (i.e., found in >10 burns and occurring in almost all fuel types) exhibited EFs that were inversely proportional to
MCE. Regression parameters for compounds not displayed here are provided in
the UCB-GLOBES FIREX mass spectral library. Many of these compounds still
remain unidentified or unknown (see Fig. 3) but are now quantified as a
function of fire conditions. Future work can be done to identify these
compounds and ultimately, with the use of these regressions, estimate their
contribution to I/SVOC mass in fresh smoke and model how they chemically
transform in the atmosphere.
Conclusions
Smoke produced from burning a wide variety fuels, primarily from the western
US wildlands, was collected onto quartz fiber filters at the Fire Science
Laboratory and analyzed for elemental and organic carbon. The organic carbon
fraction was further separated, identified, classified, and quantified using
the TD-GC × GC-EI/VUV-HRToFMS with online derivatization. Each separated
compound's mass spectrum, n-alkane retention index, chemical family, EF
vs. MCE relationship, and fuel source are reported here in a publicly available
mass spectral library (UCB-GLOBES FIREX) for future comparisons and
identification of biomass burning organic compounds in atmospheric samples.
Between 10 % and 65 % of the I/SVOC mass for each burn could be specifically
identified or placed into a chemical family. Fuels within the same type
tended to exhibit similar mass fractions, regardless of fire condition (as
quantitated modified combustion efficiency, MCE). For example, similar
unknown compounds accounted for ∼ 90 % of the total observed
mass for the two woody debris burns (MCE = 0.78–0.98). Conifers exhibited
similar sugar and resin acid/diterpenoid mass fractions (out of total
classified mass) of 30 %–50 % and 2 %–20 %, respectively (MCE = 0.90–0.95).
Burns of coniferous duff (MCE = 0.85–0.89) emitted higher classified mass
fractions of methoxyphenols (6 %–18 %) than conifers. Peat, a
semi-fossilized fuel, displayed a high classified mass fraction of
non-cyclic aliphatic/oxy compounds (52 %). Shrubs showed the widest range
in mass fractions, indicating fuels in this type were the most dissimilar.
Unlike mass fractions which depend primarily on fuel type, measured emission
factors (EFs), classified into either organic carbon, chemical families, or
specific compounds, primarily depended on fire conditions (MCE). Regardless
of classification, EFs spanned 2–5 orders of magnitude from smoldering to
flaming conditions. EFs were shown to follow an inversely proportional
relationship to MCE across the wide variety of all fuels studied. However,
peat EFs for chemical families (except non-cyclic aliphatic compounds) and
specific compounds were approximately a factor of 10 lower than fuels at
similar MCE values. This is likely due to significant differences in the fuel
structure of peat. Furthermore, conifers exhibited higher sugar (factor of
5) and levoglucosan (factor of 3) emissions compared to other fuels within
the same MCE range. This indicates that fuel type and specific fuels play
some role in the EFs, though more minor compared to MCE. This is
particularly true for nitrogen species and fuel-specific tracer compounds,
i.e. compounds that are only emitted from a particular fuel, which will be
discussed in a forthcoming paper. However, in general, EFs for these
particulate compounds primarily depend on MCE and can be estimated from the
fire conditions.
To provide modelers with useful relationships in estimating particle-phase
I/SVOC emissions, logarithmic fits were applied to the measured EFs as a
function of MCE. These regression models can be used to approximate EFs of
I/SVOCs or their chemical families from average MCE of real wildfires, for
which fuel loadings, fuel types, and fuel mixtures are often unknown. For example,
comparison with Liu et al. (2017) shows the estimated particulate organics from the regression model to be
within a factor of 2–3 of those measured in that study. The comparison
between predicted and previously measured EFs is affected by methodology,
concentration regime, and the definitions of I/SVOC. Regardless, these
regression models provide approximate EFs (within a factor of 3) of numerous
chemical families and organic species as solely a function of fire
conditions across a wide variety of fuels. These regressions will allow
modelers and other experimentalists to better define the chemical composition
of smoke particles emitted from wildland burns in the western US and
potentially other parts of the world.