Biomass burning is a large source of volatile organic compounds
(VOCs) and many other trace species to the atmosphere, which can act as
precursors to secondary pollutants such as ozone and fine particles.
Measurements performed with a proton-transfer-reaction time-of-flight mass
spectrometer during the FIREX 2016 laboratory intensive were analyzed with
positive matrix factorization (PMF), in order to understand the
instantaneous variability in VOC emissions from biomass burning, and to
simplify the description of these types of emissions. Despite the complexity
and variability of emissions, we found that a solution including just two
emission profiles, which are mass spectral representations of the relative
abundances of emitted VOCs, explained on average 85 % of the VOC emissions
across various fuels representative of the western US (including various
coniferous and chaparral fuels). In addition, the profiles were remarkably
similar across almost all of the fuel types tested. For example, the
correlation coefficient
Biomass burning is a large source of volatile organic compounds (VOCs) and other trace species to the atmosphere. Reactions involving these VOCs produce ozone and fine particles, which are important air pollutants and radiative forcing agents (Alvarado et al., 2009, 2015; Yokelson et al., 2009; Jaffe et al., 2012). Some VOCs from fires also have direct health effects (Naeher et al., 2007; Roberts et al., 2011). Biomass burning occurs in wildfires, controlled burns of wildland and agricultural fuels, and in residential wood stoves and industrial processes. Given the variety of fuels and burning conditions, it is unsurprising that the VOC composition of biomass burning emissions varies greatly between different fire states, locations, and studies. Therefore, it is important to understand VOC emissions from biomass burning in detail and develop a predictive capability that explains some of the variability in VOC emissions.
Multiple complex processes take place in biomass burning, including (i) distillation with release of water vapor and terpenes, (ii) pyrolysis of
solid biomass giving off flammable gases, (iii) flaming combustion, and (iv) nonflaming processes loosely lumped with smoldering combustion such as
glowing (gasification) of biomass (Yokelson et al., 1996,
1997; Collard and Blin, 2014; Liu et al., 2016). The main source of VOC
emissions is pyrolysis of the polymers that form biomass such as cellulose,
hemicellulose, and lignin. The temperature of the reaction and the physical
characteristics of the biopolymer control which pyrolysis mechanism (e.g.,
depolymerization, fragmentation, or aromatization) is the main source of
emitted VOCs (Yokelson et al., 1996, 1997; Collard and
Blin, 2014; Liu et al., 2016). In a given fire, the processes (i)–(iv) occur
simultaneously, but the relative importance of each process and temperature
can change with time, which relates to the variability in integrated VOC
emissions between different fires. This variability is often parameterized
as a function of modified combustion efficiency (MCE
The National Oceanic and Atmospheric Administration (NOAA) led the Fire
Influence on Regional and Global Environments Experiment (FIREX) 2016
laboratory intensive conducted at the US Forest Service Fire Sciences
Laboratory in Missoula, Montana, to study emissions of trace gases and
aerosol from wildfires. Emissions from various fuels representative of the
western US were sampled under controlled conditions by extensive
instrumentation
(
The aims of this work are to understand the variation in gas-phase emissions both over the course of a fire and on a fire-integrated basis. Ultimately, this improved understanding of emissions variability could be used to simplify predictions of the emission of secondary organic aerosol (SOA) and ozone precursors. To do this, the VOCs observed by PTR-ToF-MS in stack burns were analyzed using positive matrix factorization (PMF). We show that much of the observed variability in VOCs can be explained by only two factors, and that these two factors are qualitatively related to the temperature of the pyrolysis processes, which are the main sources of the VOC emissions from biomass burning. Based on this result, the two factors are named as a high-temperature pyrolysis factor and a low-temperature pyrolysis factor. The two factors are compared between fuels. Importantly, the high-temperature factor is quantitatively similar between different fuels, and the same is true for the low-temperature factor. The VOCs present in each factor are discussed in terms of composition, reactivity with OH, and propensity to form secondary organic aerosol. The relative importance of high- and low-temperature pyrolysis factors is quantified for each fuel and discussed with respect to physical properties of the fuel and the burn dynamics. We also investigate how well VOC emissions in biomass burning can be modeled by the two PMF emission profiles through comparisons with previously reported data from laboratory burns and wildfires. Finally, emissions of some specific compounds are discussed.
Fire emissions were measured during the FIREX 2016 intensive at the Fire Sciences Laboratory in Missoula, Montana. The facility consists of a large combustion chamber and has been described in detail previously (Christian et al., 2003, 2004; Burling et al., 2010).
VOC measurements were performed using several instruments, including a
PTR-ToF-MS. This instrument employed a high-resolution ToF mass analyzer
(Aerodyne Research Inc, MA, USA; Tofwerk AG, Thun, Switzerland) and measured
with a time resolution of 2 Hz. VOCs and some inorganic compounds were
ionized by proton transfer from
(a) Data numbers and corresponding details of 15 different fuels used in PMF analysis. (b) Average MCE and fuel moisture content. (c) Residuals of two-factor PMF solutions. (d) Correlation with average VOC emission profile (Fig. 3).
A total of 15 types of natural fuel mixtures, most of which are representative of important western US ecosystems, were burned (Table 1). The names below are largely taken from the dominant plant species: (i) ponderosa pine, (ii) lodgepole pine, (iii) loblolly pine, (iv) Douglas fir, (v) Engelmann spruce, (vi) subalpine fir, (vii) juniper, (viii) bear grass, (ix) ceanothus, (x) chamise-contaminated, (xi) chamise-uncontaminated, (xii) manzanita-contaminated, (xiii) manzanita-uncontaminated, (xiv) sagebrush, and (xv) excelsior (aspen wood shavings). “Contaminated” chaparral fuels (manzanita and chamise) were collected from a heavily air-polluted site near San Dimas, CA, while “uncontaminated” fuels were collected from a cleaner site in North Mountain, CA. Individual components of various fuel complexes, including canopy, litter, duff, and rotten wood, were also burned separately. Fuel moisture content ranged from 0.6 to 55.6 %, and instantaneous MCE ranged from 0.75 to 1. Additional details on the fires and fuels are given by Selimovic et al. (2018), including pre- and postfire weight, weight of fuel components, and elemental composition (C, H, N, S, and Cl by weight). Each fuel type was burned several times. All fires consumed most of the fuel. The present experiments did not have a direct measurement of temperature within the fire, which is not homogeneous and therefore difficult to define. Rather, the air temperature of the emissions was measured by the FTIR instrument, located at the sampling inlet of the PTR-ToF-MS. The hot gases from the fire were mixed with air from the room, cooling the air significantly, but the trends in temperature are related to the initial temperature of the emitted gases.
Data from 51 burns measured by PTR-ToF-MS (Table 1) were analyzed using positive matrix factorization, a numerical method that can be used to determine major compositional categories of emissions, their compositional profiles, and their relative enhancements over time. PMF was conducted using the PMF Evaluation Tool v. 2.08A (Ulbrich et al., 2009). The basic principles of PMF and application to atmospheric chemistry measurements have been previously described (Ulbrich et al., 2009; Paatero and Tapper, 1994; Paatero, 1997).
More than 1000 ions were quantified in the PTR-ToF-MS mass spectra between
In this work, we applied PMF to extended time series, in which all fires of a particular fuel type (e.g., ponderosa pine) were consolidated into a single data matrix (Fig. S1 in the Supplement), as well as time series of single fire data. Each fuel type was burned several times. Some individual fires of a particular fuel did not necessarily capture the full possible range of high- and low-temperature fire conditions, because of variability in the relative amounts of fuel parts, fuel moisture content, when fuel was added, or other differences. PMF using the consolidated time series makes it possible to capture the widest possible range of fire conditions. This approach also simplifies the comparison of average emission profiles between different types of fuels. Details on preparation of ion signal and uncertainty datasets are described in the Supplement (Sect. S1).
The discussion in Sect. 3 is based on the two-factor PMF solutions. Out of the 574 ions, 434 ions were fitted well and together represented 99 % of the total ion signal. A total of 140 ions were not well fitted, as the difference between their measurements and the PMF reconstruction was higher than 50 %; these ions are excluded from the factors presented here. Ulbrich et al. (2009) suggest that poor retrieval of ions with less than 5 % of total signal is not uncommon.
To characterize key chemical properties of the emission profiles derived
from PMF analysis, we compare the OH reactivity and volatility of VOCs in
each profile. These calculations require conversion of the emission profiles
from instrument signal (ncps) to mixing ratio (ppbv). Fragment ions, cluster
ions, and ions not well fitted by PMF were excluded from the 574 ions used
in PMF analysis, and calibration factors were applied to the remaining 400
ions to convert them to mixing ratio. Of these, 156 have known VOC
contributors, and account for 90 % of the total instrument signal of
nonprimary and noncontaminant ions between
We quantified the importance of the 156 identified ions to OH chemistry by
multiplying the VOC
We also quantified volatility using the saturation concentration at 25
Figure 1a shows the time series of selected VOC ion signals from burning a representative mixture of ponderosa pine fuels. In these lab fires, total VOC emissions (red line in Fig. 1a) often increase immediately and substantially during the initial combustion (for 170 s after starting the burn in this example), and then total emissions gradually decrease as the flames die out. Emissions of individual VOCs can be seen to fall into two categories: (i) higher emissions during the first part of the fire, e.g., naphthalene, which correlates with the PMF factor we will largely attribute below to high-temperature pyrolysis (blue line in Fig. 1a), and (ii) higher emissions during the latter part of the fire, e.g., syringol, which correlates with the PMF factor we will attribute below to low-temperature pyrolysis (green line in Fig. 1a). This separation into two categories is typical for most fires, with a few exceptions discussed later (e.g., burns of duff and rotten wood).
Results for an example burn of ponderosa pine realistic mixture
(Fire no. 37).
These two PMF factors (Fig. 1b) describe the total VOC emissions
remarkably well for most fuels: residuals (the differences between the
measured ion signals and the calculated ion signals based on the PMF fits)
are less than 15 % on average, except for Douglas fir, Engelmann spruce,
and subalpine fir for which the residual average is 20–25 %. The residuals
for individual fuels are summarized in Table 1c. For most of the fuels, the
time series of the first and second factors are strongly correlated with
those of naphthalene and syringol, respectively (correlation coefficient
(
There are notable exceptions to the two-factor solution, including an
infrequently observed, but important, third factor that we call a
“distillation” factor, and a fourth profile observed during burns of duff.
Several fires contain a distillation phase, in which a brief burst of VOCs,
typically enriched in terpenes, is emitted immediately prior to ignition.
However, PMF captured this phase for only a limited number of burns in which
the distillation phase contained sufficient gas-phase emissions and lasted
long enough (
The mass spectral profiles of the relative abundances of emitted VOCs for
the individual PMF factors obtained from a given fuel type are similar for
replicate burns of the same fuel type. When comparing the PMF profiles for
two individual burns of the ponderosa pine realistic mixture, the
correlation coefficient (
Comparison of mass spectral profiles:
The compositional differences between the two profiles can be qualitatively explained by the temperature of the pyrolysis reactions thought to be the main production mechanism of the VOCs, such as depolymerization, fragmentation, and aromatization (Yokelson et al., 1996, 1997; Collard and Blin, 2014; Liu et al., 2016). This is illustrated by the relative contributions from the high-temperature versus low-temperature factors for most emitted VOCs. VOCs expected from high-temperature processes have a higher emissions contribution from the high-temperature factor, and likewise for low-temperature VOCs and the low-temperature factor.
Average VOC emission profiles of high- and low-temperature pyrolysis factors, obtained using consolidated PMF results of 15 different fuels.
Figure 4a shows the contribution of each factor to selected pyrolysis
products from major fuel biopolymers, i.e., hemicellulose, cellulose, and
lignin. The contributions of individual VOCs are expressed by their
normalized fractions (
These many diverse chemical processes are likely happening simultaneously
during a fire, and their relative intensities may change based on fuel
composition, fuel moisture content, or other as-yet poorly defined
parameters. However, the net result of all these variables is the emission
of just two major compositional groups. The VOCs that comprise these two
groups mostly consist of the pyrolysis products described above and their
analogs. During most of these fires, the emissions of any particular VOC can
be described by a linear combination of the high-temperature and
low-temperature pyrolysis time series. Some VOCs are emitted mainly from the
high-temperature pyrolysis, some mainly from the low-temperature profile,
and others have a mixed contribution. This is quantified by
Contributions, shown as normalized fractions, of VOCs relative to
the high- and low-temperature factors:
Several nitrogen (N)-containing compounds also fall into high- or low-temperature categories, consistent with behavior previously reported in the
literature. The main N-containing compounds detected by PTR-ToF-MS are
isocyanic acid (HNCO), nitrous acid (HONO), hydrogen cyanide (HCN), and
ammonia (
The present analysis predominantly focuses on VOCs. The VOC emissions from
biomass burning are dominated by pyrolysis reactions of biopolymers.
However, not all species are emitted from pyrolysis reactions. For example,
flaming combustion releases
The VOC emission profiles for the high- and low-temperature factors are
shown in Fig. 3 and they mainly consist of hydrocarbons, oxygenates with
VOC composition in the high- and low-temperature emission profiles.
VOCs emitted from biomass burning can be generally organized into major structural groups: furans, aromatics, oxygenated aromatics, aliphatic compounds, and so on. Within each structural category, compounds can have various functionalities, such as alcohol or alkene substituents (Hatch et al., 2015). VOC composition, classified by 11 structures and 17 functionalities, is shown in Figs. 7 and 8. Some VOCs have multiple functional groups. These are counted once in each relevant category. For example, guaiacol is counted in “Oxygenated aromatic” structural category as “Alcohol” and “Ether (methoxy)” functional groups.
The most dominant emissions are attributable to aliphatic oxygenates, i.e.,
62 % of molar emissions in the high-temperature profile and 60 % in the
low-temperature profile (Fig. 7). This is due to the specific compounds
(ii)–(vi) described above. The low-temperature profile is twice as rich in
aromatic oxygenates (
VOC composition in
VOC composition in high- and low-temperature pyrolysis emission profiles (Fig. 3) sorted by 17 functional groups. Each group includes various structures and elemental composition. Some VOCs have multiple functional groups. These are counted once in each relevant category. For example, guaiacol is counted in the categories of “Alcohol” and “Ether (methoxy)”.
The hydroxyl radical (OH) is an important driver of daytime oxidation
chemistry. Quantifying the VOC reactivity with OH provides insight into
which VOC emissions may be most important for ozone and secondary organic
aerosol formation. Interestingly, the two profiles have a similar
average per-molecule (weighted by abundance) rate constant with OH:
15.7
High- and low-temperature emission profiles compared by
Volatility is another important chemical characteristic affecting secondary
organic aerosol yield and formation rate. The low-temperature emission
profile contains more compounds that are of higher molecular weight, more
oxygenated, and of lower volatility (Fig. 9b). Oxygenated aromatics have
been shown to be important biomass burning SOA precursors (Bruns et al.,
2016), and while the SOA yields of many other compounds are unknown, the
lower volatility and higher oxygen content of the low-temperature profile
suggests a potentially more efficient SOA formation. SOA formation was also
studied during the FIREX 2016 campaign, by oxidizing emissions in a chamber,
and will be presented separately (Lim et al., 2018). We note that
the compounds with C
Ratios of fire-integrated molar emissions of total VOCs from
high- to low-temperature pyrolysis (“
To use the PMF profiles (Fig. 3) for estimates of VOC emissions from other
fires, it is necessary to know the relative fire-integrated contributions of
high- and low-temperature pyrolysis for those fires. As a step in this
direction, in the present work, we found that fire-integrated molar emission
ratios of total VOCs from high-temperature pyrolysis to low-temperature
pyrolysis,
In contrast, the burn of rotten wood was found to contain VOC emissions from
low-temperature pyrolysis only. Our brown rotten wood samples were enriched
in lignin (Kirk and Cowling, 1984). Lignin is relatively resistant to
thermal decomposition compared to cellulose and hemicellulose. The
temperature range where pyrolytic decomposition occurs significantly is
280–500
Previous studies have found a correlation between the emission factors of
certain VOCs and the fire-integrated modified combustion efficiency (MCE)
(Yokelson et al., 1996, 1997; Selimovic et al., 2018).
Thus, one might expect that the high- and low-temperature pyrolysis factors
would also show a strong relationship to MCE. However, MCE does not
parameterize the relative amounts of high- and low-temperature pyrolysis
products very well, either instantaneously or on a fire-integrated basis
(Fig. 11). The basic reason is that
The comparison of contribution of high-temperature factor versus
modified combustion efficiency (MCE).
The relative contributions from the high- and low-temperature processes
could be estimated from ratios of distinct marker species that are
consistently enhanced in the high- and low-temperature profiles. Several such
pairs were considered and the ratio of ethyne (
Studies of laboratory burns and wildfires have reported variable emission
ratios (or factors) for various VOCs as well as fire-integrated MCE, even
for similar fuel types. Here we investigate how well total VOC emissions in
biomass burning can be fit by the average VOC emission profiles (Fig. 3)
using emission factors and ratios reported in the literature for laboratory
and field burns (Gilman et al., 2015; Stockwell et al., 2015; Akagi et al.,
2011). When fitting the present high- and low-temperature factors to the
other biomass burning data, total VOC emissions can be described with
different relative fractions of the factors (Fig. S10). For example, the
best fit to a laboratory study by Gilman et al. (2015), using fuels from
the southwestern, southeastern, and northern US (e.g., pine, spruce, fir,
chaparral, mesquite, and oak) with MCE
At the beginning of many burn experiments, white smoke is visible immediately prior to ignition. This “distillation phase” does not result from pyrolysis or combustion, but rather a gradual heating and release of water and volatile compounds trapped within the biomass. This phase of the fire was not distinguished by PMF. The distillation phase from coniferous fuels is enriched in some compounds highly relevant to atmospheric chemistry, especially terpenes (Koss et al., 2018). But this phase lasts only a short time (typically less than 10 s), in which only a short spike in emissions is observed. Accordingly, PMF cannot capture this phase effectively even if a large number of factors is chosen. As an exception, the distillation phase of sagebrush, enriched in terpenes and a specific oxygenated monoterpene (camphor), can be distinguished as a third PMF factor, because that phase lasted longer than 30 s in that fire. The reported overall residual of 15 % includes the poorly fitted distillation phase, and we stress that it typically accounts for only a small portion of the overall emissions. Additionally, with the exception of terpenes, the composition of the distillation profile is similar to that of the high-temperature profile.
For some fuel burns other than coniferous fuels (e.g., manzanita), VOC emissions during the distillation phase are quite small, although distillation smoke is visible. In these cases, PMF incorporates this phase into the low-temperature pyrolysis factor. There may be a relationship between the VOC emission process coincident with distillation (low- or high-temperature) and the presence of visible smoke. For instance, perhaps here the temperatures are low enough that the compounds are able to recondense into visible smoke.
A fourth factor can be resolved from the PMF analysis of duff burns. The distribution of VOC structures and functionality in the duff emission profiles (Fig. 12a) is similar to the low-temperature pyrolysis profile (Fig. 12b). The major difference is much higher emission of aliphatic nitrogen-containing compounds: 56 % more of these compounds are emitted per ppbv of VOCs in the duff profile than in the low-temperature profile. The additional emissions are mostly nitriles and amides, especially hydrogen cyanide, acetonitrile, and acetamide. Pyrroles and pyridines are also enhanced, but are much less abundant overall.
The organic portion of duff is enriched in nitrogen relative to other
components of coniferous fuels. The nitrogen-to-carbon ratio in the
subalpine fir duff (N : C ratio
However, the nitrogen content cannot entirely explain why duff has a unique
emission profile. Other fuels, such as ceanothus and ponderosa pine litter,
have similar N : C ratios (0.025 and 0.022, respectively ) but are
explained well by the two-factor PMF solution consisting of high- and
low-temperature pyrolysis factors. The contradiction may be due to
differences in the speciation of nitrogen-containing organics. In woody and
leafy fuels, proteins and amino acids account for 80–85 % of the organic
nitrogen (Ren and Zhao, 2015). In soils, proteins account for typically only
40 % of organic nitrogen, and heterocyclic nitrogen compounds (pyrroles
and pyridines) account for 35 % (Schulten and Schnitzer, 1997). Pyrolysis
of nitrogen heterocycles releases HCN, while proteins and amino acids may
release more NH
When comparing emission profiles of individual fuels to the average profiles
shown in Fig. 3, there are some specific compounds whose emissions are
notably higher (> For ponderosa, lodgepole, and loblolly pines; Douglas and subalpine firs; and juniper,
the emission of benzoquinone ( For fuels other than coniferous fuels and sagebrush, i.e., bear grass,
excelsior, ceanothus, chamise, and manzanita, emissions of monoterpenes
( Excelsior emits especially low quantities of nitrogen-containing compounds,
especially nitriles (hydrogen cyanide, acetonitrile, acrylonitrile, and
propane nitrile) and pyridine, in the high-temperature pyrolysis (Fig. S4a-9). This is because the nitrogen content in excelsior is significantly
lower than other fuels. The excelsior N : C ratio (0.005 by weight) is 3.6 High-temperature pyrolysis of ceanothus produces quite high emission of
benzofuran-type compounds (Fig. S4a-10). Benzofuran
( Sagebrush specifically emits camphor ( There are a limited number of exceptions in low-temperature profiles (Fig. S4b). This means that low-temperature pyrolysis gives almost identical VOC
emissions, independent of fuel types.
This work focused on interpretation of VOC emissions from biomass burning.
We provided an understanding of VOC variability based on known chemical and
physical processes to release VOCs from fires. We explained most of the
observed variability between VOC emissions from fuel types and over the
course of a fire using just two emission profiles: (i) a high-temperature
pyrolysis profile and (ii) a low-temperature pyrolysis profile. The results
are summarized as follows:
The two profiles can explain the variability in VOC emissions composition
between different fuel types and over the course of individual fires, with
an average residual of < 15 %. The high-temperature profile is quantitatively similar between different
fuel types ( The two profiles are significantly different in terms of VOC composition,
volatility, and contributors to OH reactivity. The high-temperature
pyrolysis profile is enriched in aliphatic unsaturated hydrocarbons,
(polycyclic) aromatic hydrocarbons, terpenes (emitted from distillation),
HCN, HNCO, and HONO. The resulting OH reactivity is primarily attributed to
terpenes, aliphatic hydrocarbons, and nonaromatic oxygenates. The
low-temperature pyrolysis profile is enriched in aromatic oxygenates,
furans, and The fire-integrated molar emission ratios of total VOCs from
high-temperature pyrolysis to low-temperature pyrolysis are related to the
biopolymer composition and surface-to-volume ratios of fuels. Higher
surface-to-volume ratios lead to more total VOC emissions enriched in products
resulting from high-temperature pyrolysis than from those resulting from
low-temperature pyrolysis. The two VOC profiles can model previously reported VOC data for laboratory
and field burns ( MCE, which parameterizes flaming and smoldering combustion, is not
appropriate to estimate the high- and low-temperature pyrolysis VOC emissions.
This suggests that the high- and low-temperature pyrolysis profiles may
provide information on emissions that is not accessible with a broader
definition of smoldering combustion implicit in the use of MCE. Duff burns emit a specific VOC profile which is similar to that of
low-temperature pyrolysis, but additionally includes aliphatic
nitrogen-containing compounds, especially HCN, acetonitrile, and acetamide.
Our framework provides a way to understand VOC emissions variability in
other laboratory and field studies of biomass burning. We highlight two
areas of useful future work. First, external tracers should be found that
will allow the prediction of the relative contribution of individual
profiles. This could include specific chemical species, an understanding of
how fuel or burn characteristics relate to the relative contribution of the
two profiles, or a relationship between some measure of fire temperature and
the VOC profiles. Second, the SOA and ozone formation potential of the two
profiles should be determined. With this further work, the VOC profiles
could be widely useful to model VOC emissions from many types of biomass
burning in the western US, with additions to the framework being needed for
fires that burn a lot of duff.
Future work should also include a quantitative comparison of the VOC PMF results to measurements of aerosol, inorganic gases, and organic species not measured by PTR-ToF-MS. Such a comparison would help define the relationship between VOCs and characteristics of primary organic aerosol. We note that the primary aerosols have also been shown to have distinct profiles that correlate with different pyrolysis and combustion processes in the fire (Reece et al., 2017; Haslett et al., 2018).
Data are available from the CSD NOAA archive at
The supplement related to this article is available online at:
KS, ARK, CW, RJY, JMR, and JdG designed the research. KS, ARK, JBG, VS, MMC, KJZ, BY, BML, SSB, CW, RJY, and JdG performed the measurements and/or contributed to the data analysis. All authors contributed to the discussion and interpretation of the results and writing the paper.
The authors declare that they have no conflict of interest.
Kanako Sekimoto acknowledges the Postdoctoral Fellowships for Research Abroad from the Japan Society for the Promotion of Science (JSPS) and a Grant-in-Aid for Young Scientists (B) (15K16117) from the Ministry of Education, Culture, Sports, Science and Technology of Japan. Abigail R. Koss acknowledges support from the NSF Graduate Fellowship Program. Matthew M. Coggon acknowledges the Visiting Postdoctoral Fellowship from the Cooperative Institute for Research in Environmental Sciences (CIRES). Vanessa Selimovic and Robert J. Yokelson were supported by NOAA-CPO grant NA16OAR4310100. Joost de Gouw worked as a consultant for Aerodyne Research Inc. during part of the preparation phase of this paper. We thank for support from NOAA AC4 external funding, and thank the USFS Missoula Fire Sciences Laboratory for their assistance and cooperation. This work was also supported in part by NOAA's Climate Change and Health of the Atmosphere initiatives. Edited by: Jacqui Hamilton Reviewed by: two anonymous referees