ArticlePDF Available

High- and low-temperature pyrolysis profiles describe volatile organic compound emissions from western US wildfire fuels

Authors:

Abstract and Figures

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 r² of each profile between ponderosa pine (coniferous tree) and manzanita (chaparral) is higher than 0.84. The compositional differences between the two VOC profiles appear to be related to differences in pyrolysis processes of fuel biopolymers at high and low temperatures. These pyrolysis processes are thought to be the main source of VOC emissions. High-temperature and low-temperature pyrolysis processes do not correspond exactly to the commonly used flaming and smoldering categories as described by modified combustion efficiency (MCE). The average atmospheric properties (e.g., OH reactivity, volatility, etc) of the high- and low-temperature profiles are significantly different. We also found that the two VOC profiles can describe previously reported VOC data for laboratory and field burns.
Content may be subject to copyright.
Atmos. Chem. Phys., 18, 9263–9281, 2018
https://doi.org/10.5194/acp-18-9263-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
High- and low-temperature pyrolysis profiles describe volatile
organic compound emissions from western US wildfire fuels
Kanako Sekimoto1,2,3,*, Abigail R. Koss1,2,4,a,*, Jessica B. Gilman1, Vanessa Selimovic5, Matthew M. Coggon1,2,
Kyle J. Zarzana1,2, Bin Yuan1,2,6, Brian M. Lerner1,2,b, Steven S. Brown1,4, Carsten Warneke1,2, Robert J. Yokelson5,
James M. Roberts1, and Joost de Gouw1,2,4
1NOAA Earth System Research Laboratory (ESRL), Chemical Sciences Division, Boulder, CO 80305, USA
2Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO 80309, USA
3Graduate School of Nanobioscience, Yokohama City University, Yokohama, Kanagawa 236-0027, Japan
4Department of Chemistry and Biochemistry, University of Colorado Boulder, Boulder, CO 80302, USA
5Department of Chemistry, University of Montana, Missoula, MT 59812, USA
6Institute for Environment and Climate Research, Jinan University, Guangzhou, China
anow at: Department of Civil & Environmental Engineering, Massachusetts Institute of Technology,
Cambridge, MA 02142, USA
bnow at: Aerodyne Research, Inc., Billerica, MA 01821, USA
*These authors contributed equally to this work.
Correspondence: Kanako Sekimoto (sekimoto@yokohama-cu.ac.jp)
Received: 19 January 2018 – Discussion started: 19 February 2018
Revised: 28 May 2018 – Accepted: 18 June 2018 – Published: 3 July 2018
Abstract. Biomass burning is a large source of volatile or-
ganic compounds (VOCs) and many other trace species to the
atmosphere, which can act as precursors to secondary pol-
lutants such as ozone and fine particles. Measurements per-
formed 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 descrip-
tion 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 repre-
sentations of the relative abundances of emitted VOCs, ex-
plained on average 85% of the VOC emissions across var-
ious fuels representative of the western US (including var-
ious coniferous and chaparral fuels). In addition, the pro-
files were remarkably similar across almost all of the fuel
types tested. For example, the correlation coefficient r2of
each profile between ponderosa pine (coniferous tree) and
manzanita (chaparral) is higher than 0.84. The compositional
differences between the two VOC profiles appear to be re-
lated to differences in pyrolysis processes of fuel biopoly-
mers at high and low temperatures. These pyrolysis pro-
cesses are thought to be the main source of VOC emis-
sions. “High-temperature” and “low-temperature” pyrolysis
processes do not correspond exactly to the commonly used
“flaming” and “smoldering” categories as described by mod-
ified combustion efficiency (MCE). The average atmospheric
properties (e.g., OH reactivity, volatility, etc) of the high- and
low-temperature profiles are significantly different. We also
found that the two VOC profiles can describe previously re-
ported VOC data for laboratory and field burns.
1 Introduction
Biomass burning is a large source of volatile organic com-
pounds (VOCs) and other trace species to the atmosphere.
Reactions involving these VOCs produce ozone and fine par-
ticles, which are important air pollutants and radiative forc-
ing 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
Published by Copernicus Publications on behalf of the European Geosciences Union.
9264 K. Sekimoto et al.: High- and low-temperature pyrolysis profiles
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 compo-
sition of biomass burning emissions varies greatly between
different fire states, locations, and studies. Therefore, it is
important to understand VOC emissions from biomass burn-
ing in detail and develop a predictive capability that explains
some of the variability in VOC emissions.
Multiple complex processes take place in biomass burn-
ing, 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 pro-
cesses 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 charac-
teristics of the biopolymer control which pyrolysis mecha-
nism (e.g., depolymerization, fragmentation, or aromatiza-
tion) 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 inte-
grated VOC emissions between different fires. This variabil-
ity is often parameterized as a function of modified combus-
tion efficiency (MCE =1CO2/(1CO+1CO2)) (Yokelson et
al., 1996). CO2and CO are representative gases emitted from
the flaming and smoldering combustion processes, respec-
tively, and are measured in most biomass burning studies.
MCE is generally higher in flaming combustion (> 0.9) and
lower in smoldering combustion (<0.9) (Akagi et al., 2011).
The National Oceanic and Atmospheric Administration
(NOAA) led the Fire Influence on Regional and Global En-
vironments 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 repre-
sentative of the western US were sampled under controlled
conditions by extensive instrumentation (https://www.esrl.
noaa.gov/csd/projects/firex/firelab/instruments.html, last ac-
cess: 19 January 2018). Experiments included so-called stack
burns, in which emissions from an evolving burn were en-
trained into a large-diameter stack and sampled by vari-
ous instruments. VOCs were measured by several instru-
ments, including a PTR-ToF-MS (proton-transfer-reaction
time-of-flight mass spectrometer) which captured gas-phase
emissions with a fast time response during stack burns.
The measurements show variability in VOC composition as
the fire shifts between a dynamic mix of distillation, py-
rolysis, flaming combustion, and “smoldering” combustion
(here we use smoldering as a rough term to include various
“nonflame” processes such as gasification). Ions measured
with the PTR-ToF-MS were interpreted using a combination
of gas-chromatographic preseparation experiments, literature
review, time-series analysis, and comparison to other instru-
ments (Koss et al., 2018). Approximately 90% of the instru-
ment signal could be attributed to identified VOCs.
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 understand-
ing of emissions variability could be used to simplify predic-
tions 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 tempera-
ture of the pyrolysis processes, which are the main sources of
the VOC emissions from biomass burning. Based on this re-
sult, the two factors are named as a high-temperature pyrol-
ysis 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 sec-
ondary 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.
2 Methods
2.1 VOC measurements by PTR-ToF-MS
Fire emissions were measured during the FIREX 2016 inten-
sive 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 instru-
ments, including a PTR-ToF-MS. This instrument employed
a high-resolution ToF mass analyzer (Aerodyne Research
Inc, MA, USA; Tofwerk AG, Thun, Switzerland) and mea-
sured with a time resolution of 2 Hz. VOCs and some in-
organic compounds were ionized by proton transfer from
H3O+reagent ions. We include the inorganic compounds in
the discussion of VOCs. Species with a proton affinity higher
than that of water can be measured, which includes many un-
saturated and polar compounds. The mass resolution of the
instrument (3000–5000 FWHM m/1m) was sufficient to de-
termine the elemental composition of ions and separate many
isobaric compounds. Before each fire, background air in the
Atmos. Chem. Phys., 18, 9263–9281, 2018 www.atmos-chem-phys.net/18/9263/2018/
K. Sekimoto et al.: High- and low-temperature pyrolysis profiles 9265
combustion chamber was measured directly for several min-
utes. The instrument has been described in detail by Yuan et
al. (2016, 2017), and operation, calibration, and peak iden-
tification during the FIREX 2016 laboratory intensive were
described by Koss et al. (2018).
2.2 Fuel and biomass burn descriptions
A total of 15 types of natural fuel mixtures, most of
which are representative of important western US ecosys-
tems, 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 heav-
ily air-polluted site near San Dimas, CA, while “uncontam-
inated” fuels were collected from a cleaner site in North
Mountain, CA. Individual components of various fuel com-
plexes, 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 sev-
eral times. All fires consumed most of the fuel. The present
experiments did not have a direct measurement of tempera-
ture within the fire, which is not homogeneous and therefore
difficult to define. Rather, the air temperature of the emis-
sions 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 signif-
icantly, but the trends in temperature are related to the initial
temperature of the emitted gases.
2.3 PMF analysis
Data from 51 burns measured by PTR-ToF-MS (Table 1)
were analyzed using positive matrix factorization, a numer-
ical method that can be used to determine major composi-
tional categories of emissions, their compositional profiles,
and their relative enhancements over time. PMF was con-
ducted 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 m/z 12–217. Of these, 574 were se-
lected for PMF analysis (Table S1 in the Supplement). These
574 ions were resolved from neighboring peaks, were en-
hanced during at least one fire, and exclude primary (e.g.,
H3O+and H3O+(H2O)) and contaminant ions (e.g., Teflon
fragments and transition metals) (Koss et al., 2018). The ion
signals (in units of normalized counts per second; ncps),
which are normalized to the H3O+ion intensities and cor-
rected for ToF-duty cycle, humidity dependence, and H3O+
ion depletion as described by Koss et al. (2018), were ana-
lyzed using PMF. Typically, raw ion signals in units of counts
per second (cps) have been used for PMF analysis. However,
cps VOC ion signals are affected by temporal variability (de-
pletion and instability) in primary ion intensity and humidity
during the fire. To obtain PMF results that exclude instru-
ment effects, the normalized and corrected ion signals are
used in this analysis. The uncertainties of the normalized and
corrected ion signals were calculated based on those origi-
nating from the raw (cps) ion signals. We chose to use in-
strument signal rather than mixing ratio because many ion
masses cannot be unambiguously related to a single VOC
contributor: they have several contributors, or result from
fragmentation, and cannot be converted to mixing ratio. For
example, C7H+
13 (m/z 97.101) is a fragmentary product ion
of at least five different VOCs, whose relative contributions
are different between fires. However, variability in these ion
signals still contains information useful for PMF. To interpret
the PMF results, we did convert to mixing ratio where possi-
ble (Sect. 2.4). A total of 528 compounds were quantified, of
which 156 are identified VOCs. The PTR-ToF-MS measures
50–80 % of total emitted nonmethane VOC mass, with un-
certainty in this value due to semivolatile compounds (Hatch
et al., 2017).
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 possi-
ble range of high- and low-temperature fire conditions, be-
cause 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 ap-
proach also simplifies the comparison of average emission
profiles between different types of fuels. Details on prepara-
tion 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 to-
tal 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.
www.atmos-chem-phys.net/18/9263/2018/ Atmos. Chem. Phys., 18, 9263–9281, 2018
9266 K. Sekimoto et al.: High- and low-temperature pyrolysis profiles
Table 1. (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) Data number for consolidated PMF (b) Fire characteristics (c) Residual (%)a(d) Correlation with average VOC emission profile (Fig. 3)
High-temperature pyrolysis factor Low-temperature pyrolysis factor
Fuel Total Detail MCE Moisture content Average ±STDV Slope Correlation Slope Correlation
(maximum, minimum) coefficient (r2)coefficient (r2)
1. Ponderosa pine 10 Realistic 5 (Fire 01, 02, 37, 59, 72) 0.913–0.940 24.3–31.8 % 15.7 ±7.6 (28.9, 7.7) 0.976 ±0.004 0.9393 1.012 ±0.005 0.9245
Canopy 2 (Fire 19, 39) 0.904–0.935 40.4–51.1 %
Litter 1 (Fire 38) 0.945 6.2 %
Rotten log 2 (Fire 13, 73) 0.932–0.957 2.9–5.7 %
2. Lodgepole pine 7 Realistic 4 (Fire 06, 07, 58, 63) 0.927–0.943 20.3–24.4% 14.8 ±4.9 (23.3, 10.9) 0.990 ±0.004 0.9586 0.990 ±0.002 0.9716
Canopy 1 (Fire 40) 0.924 49.30 %
Litter 2 (Fire 21, 41) 0.925–0.938 7.0–10.5 %
3. Loblolly pine 2 Litter 2 (Fire 35, 53) 0.922–0.929 5.4–10.9 % 6.3 ±0.3 (6.6, 6.1) 0.989 ±0.007 0.8662 0.960 ±0.004 0.8862
4. Douglas fir 4 Realistic 2 (Fire 14, 57) 0.926–0.951 23.3–25.7 % 21.2 ±9.3 (34.9, 14.9) 0.996 ±0.004 0.9508 0.999 ±0.003 0.9563
Canopy 1 (Fire 18) 0.928 50.3 %
Litter 1 (Fire 43) 0.951 3.0 %
5. Engelmann spruce 3 Realistic 1 (Fire 08) 0.920 13.0 % 20.5±2.4 (22.2, 18.8)b0.999 ±0.006b0.9019b0.960 ±0.004b0.9004b
Canopy 1 (Fire 25) 0.950 34.0 %
Duff 1 (Fire 26) 0.817 0.6 % 82.0
6. Subalpine fir 6 Realistic 2 (Fire 47, 67) 0.932–0.942 32.8–35.6 % 23.0 ±14.1 (45.2, 9.1)b1.001 ±0.005b0.9359b0.999 ±0.003b0.9547b
Canopy 2 (Fire 15, 23) 0.886–0.947 17.6–55.5 %
Litter 1 (Fire 51) 0.906 6.6 %
Duff 1 (Fire 56) 0.886 0.9 % 87.0
7. Juniper 2 Canopy 2 (Fire 68, 75) 0.928–0.939 45.0–48.0 % 6.4 ±3.0 (8.5, 4.3) 1.016 ±0.006 0.8872 0.971 ±0.004 0.9010
8. Bear grass 1 (Fire 62) 0.897 55.1% 5.6 1.039 ±0.006 0.8847 1.006 ±0.004 0.9174
9. Excelsior 2 (Fire 49, 61) 0.945–0.971 3.9–5.4 % 6.2 ±3.0 (8.3, 4.0) 1.04±0.01 0.6806 1.012 ±0.007 0.8521
10. Ceanothus 2 Shrub 2 (Fire 69, 74) 0.942–0.947 17.7–27.9% 10.2 ±1.1 (11.0, 9.5) 1.000 ±0.007 0.8416 1.030 ±0.006 0.8985
11. Chamise 3 Canopy 3 (Fire 24, 29, 46) 0.948–0.959 10.9–16.1% 13.1 ±3.9 (16.0, 8.6) 1.037 ±0.006 0.8951 1.044 ±0.004 0.9477
(contaminated)
12. Chamise 3 Canopy 3 (Fire 27, 32, 48) 0.946–0.954 6.2–17.1 % 12.6 ±2.0 (14.2, 10.4) 1.017 ±0.005 0.9322 1.024 ±0.004 0.9299
(uncontaminated)
13. Manzanita 2 Canopy 2 (Fire 30, 33) 0.962–0.963 23.5–26.7 % 13.0 ±1.1 (13.8, 12.3) 0.997 ±0.004 0.9347 1.034 ±0.004 0.9504
(contaminated)
14. Manzanita 2 Canopy 2 (Fire 28, 34) 0.963–0.964 25.7–26.3 % 7.3 ±1.1 (8.0, 6.5) 1.015 ±0.005 0.9229 1.043 ±0.005 0.9224
(uncontaminated)
15. Sagebrush 2 Shrub 2 (Fire 66, 71) 0.919–0.922 37.8–54.2 % 7.0 ±2.1 (8.5, 5.6) 0.993 ±0.005 0.9046 1.011 ±0.004 0.9306
aResidual (%) =[total measured ion signal – total synthetic ion signal of high- and low-temperature factors] /total measured ion signal ×100. b“Duff” data are excluded.
Atmos. Chem. Phys., 18, 9263–9281, 2018 www.atmos-chem-phys.net/18/9263/2018/
K. Sekimoto et al.: High- and low-temperature pyrolysis profiles 9267
2.4 Calculations of OH reactivity and volatility
To characterize key chemical properties of the emission pro-
files derived from PMF analysis, we compare the OH reactiv-
ity 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 ac-
count for 90 % of the total instrument signal of nonprimary
and noncontaminant ions between m/z 12–217. (This corre-
sponds to an average of 92% of the total VOC concentration
detected by PTR-ToF-MS). Details on identification of the
VOC contributors to ion masses and calibration are described
by Koss et al. (2018).
We quantified the importance of the 156 identified ions
to OH chemistry by multiplying the VOC+OH reaction
rate coefficient (cm3molecule1s1) with the VOC frac-
tion in the profile (ppbv VOC ppbv1of total VOC emitted)
with a scaling factor to convert from VOC molar emission
(ppbv VOC) to number density (molecule cm3at experi-
mental conditions of 900 mbar and 26 C). The resulting OH
reactivity is in units of per second per ppbv of total VOCs
measured with PTR-ToF-MS (1 sppbv1of total VOC emit-
ted). For ions with more than one contributor, a weighted
average rate constant was determined. Rate constants were
taken from the literature (Atkinson and Arey, 2003; Manion
et al., 2017; Cicerone and Zellner, 1983; Gilman et al., 2015)
or estimated from structurally similar VOCs. Details can be
found elsewhere (Koss et al., 2018).
We also quantified volatility using the saturation concen-
tration at 25 C (C0, µg m3). Saturation concentrations were
taken from the literature (Rumble, 2017–2018; NIST Chem-
istry WebBook, 2017; Yaws, 2015) where possible, and oth-
erwise estimated based on the elemental composition of the
ion (Li et al., 2016). Volatility determined from elemen-
tal composition is uncertain, especially for compounds with
very low volatility where the uncertainty can be several or-
ders of magnitude (Li et al., 2016). We determined volatility
for the 400 nonfragmentary ions. We define volatility bins as
follows, after Li et al. (2016): volatile organic compounds
(C0> 3×106µg m3), intermediate volatility compounds
(IVOCs, 300 < C0< 3 ×106µg m3), and semivolatile com-
pounds (SVOCs, 0.3 < C0< 300 µg m3). Separation into
such volatility bins is commonly used as an aid to discus-
sion of SOA formation potential and gas–particle partition-
ing (Donahue et al., 2011).
3 Results and discussion
3.1 Two-factor parameterization of VOC emissions
from biomass burning
Figure 1a shows the time series of selected VOC ion sig-
nals 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 dur-
ing 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 be-
low 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).
These two PMF factors (Fig. 1b) describe the total VOC
emissions remarkably well for most fuels: residuals (the dif-
ferences between the measured ion signals and the calculated
ion signals based on the PMF fits) are less than 15 % on aver-
age, 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 (r2)>0.74). On the con-
trary, emissions of compounds mainly from flaming or non-
pyrolysis smoldering processes, such as CO, CO2, and NOx
(Fig. 1c), do not correlate well with the individual PMF fac-
tors (more detailed discussion is given in Sect. 3.5). This in-
dicates that the two PMF factors do not correspond to the
flaming and smoldering combustion processes that are de-
scribed by MCE and often referenced in biomass burning lit-
erature. The main source of VOC emissions is pyrolysis of
fuel biopolymers, and not the flaming and/or other combus-
tion processes. Therefore, we primarily attribute these two
factors to high-temperature pyrolysis and low-temperature
pyrolysis, respectively, and will use these names to describe
these factors in this work. Our association between the fac-
tors and pyrolysis temperature is related more rigorously to
the distribution of products observed as a function of pyroly-
sis temperature in the next section. When allowing more than
two factors in PMF, the time series and mass spectral pro-
files of the additional factors can be represented as an “inter-
mediate” or “splitting” of high- and/or low-temperature fac-
tors which can be described by a linear combination of the
two factors. As examples, Figs. S2 and S3 show the corre-
lation between n-factor solutions (n=3, 4) and PMF results
from high- and low-temperature factors for ponderosa pine
www.atmos-chem-phys.net/18/9263/2018/ Atmos. Chem. Phys., 18, 9263–9281, 2018
9268 K. Sekimoto et al.: High- and low-temperature pyrolysis profiles
Figure 1. Results for an example burn of ponderosa pine realis-
tic mixture (Fire no. 37). (a) Time series of ion signals of 574
ion peaks, naphthalene (C10H8·H+,m/z 129.070), and syringol
(C8H10O3·H+,m/z 155.070). (b) PMF results of two-factor solu-
tion. The grey and pink colors are stacked, not overlapped. (c) Time
series of mixing ratios of CO2, CO, and NOxmeasured by open-
path Fourier transform infrared (OP-FTIR) optical spectroscopy and
the modified combustion efficiency (MCE) (Selimovic et al., 2018).
The MCE trace is colored by the key and scale on the right.
datasets. This suggests that only two factors, i.e., high- and
low-temperature pyrolysis factors, were needed to explain
most of the variability we observed for the VOC emissions
from biomass burning.
There are notable exceptions to the two-factor solution, in-
cluding an infrequently observed, but important, third factor
that we call a “distillation” factor, and a fourth profile ob-
served during burns of duff. Several fires contain a distil-
lation phase, in which a brief burst of VOCs, typically en-
riched 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 (30 s). When a
two-factor solution is used, the terpenes are largely grouped
with the high-temperature pyrolysis factor. Duff is defined
as a “layer of moderately to highly decomposed leaves, nee-
dles, fine twigs, and other organic material found between
the mineral soil surface and litter layer of forest soil” (Rear-
don, 2007). The duff PMF solutions have residuals larger
than 80 % when solved with only two factors. This means
that duff burns have a unique VOC emission pattern that can-
not be explained by only high- and low-temperature factors.
These exceptions are discussed in more detail later.
3.2 VOC emission profiles of high- and
low-temperature pyrolysis factors
The mass spectral profiles of the relative abundances of emit-
ted 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 in-
dividual burns of the ponderosa pine realistic mixture, the
correlation coefficient (r2)is higher than 0.92 for both the
high- and low-temperature pyrolysis factors (Fig. 2a). Im-
portantly, the mass spectra for the high-temperature pyroly-
sis factor are also very similar between different fuels, and
the same is true for the low-temperature pyrolysis factor. For
example, the correlations of each profile between (i) Dou-
glas fir and ponderosa pine, (ii) manzanita (chaparral) and
ponderosa, and (iii) bear grass and ponderosa have a slope
near 1 and r20.83 (Fig. 2b–d). In contrast, the correlation
between the high- and low-temperature mass spectra is vi-
sually clearly lower (r2< 0.69, Fig. 2e). Figure 3 shows the
average VOC emission profiles of the two factors obtained
using PMF results of 15 different fuels. The fractions of in-
dividual ion peaks in the emission profiles are summarized in
Table S1. These average profiles are in good agreement with
profiles of individual fuels: a best fit of 0.96 < slope <1.04
and r2> 0.84, except for a high-temperature factor of excel-
sior with r2=0.68 (Table 1d and Fig. S4). Excelsior is an
unusual fuel in that it consists of fine shavings of a single
fuel component (wood). VOC composition in high- and low-
temperature profiles is discussed in Sect. 3.3.1.
The compositional differences between the two profiles
can be qualitatively explained by the temperature of the py-
rolysis reactions thought to be the main production mech-
anism of the VOCs, such as depolymerization, fragmenta-
tion, and aromatization (Yokelson et al., 1996, 1997; Col-
lard 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 ex-
pected from high-temperature processes have a higher emis-
sions contribution from the high-temperature factor, and like-
wise for low-temperature VOCs and the low-temperature fac-
tor.
Figure 4a shows the contribution of each factor to se-
lected pyrolysis products from major fuel biopolymers, i.e.,
hemicellulose, cellulose, and lignin. The contributions of
individual VOCs are expressed by their normalized fractions
(Fhigh-Tand Flow-T)of high- and low-temperature factors:
Fhigh-T=Fractionhigh-T/(Fractionhigh-T+Fractionlow-T)
and Flow-T=Fractionlow-T/(Fractionhigh-T+Fractionlow-T),
Atmos. Chem. Phys., 18, 9263–9281, 2018 www.atmos-chem-phys.net/18/9263/2018/
K. Sekimoto et al.: High- and low-temperature pyrolysis profiles 9269
Figure 2. Comparison of mass spectral profiles: (a) ponderosa pine realistic mixture (Fire no. 72) vs. ponderosa pine realistic mixture
(Fire no. 02) for high- and low-temperature pyrolysis factors. (In this case, PMF was separately performed for data of Fire no. 02 and
no. 72.) (b) Douglas fir vs. ponderosa pine for high- and low-temperature factors. (c) Manzanita (contaminated) vs. ponderosa pine for both
the factors. (d) Bear grass vs. ponderosa pine for both the factors. (e) Low- vs. high-temperature pyrolysis factor for ponderosa pine and
manzanita (contaminated). Data points in individual panels correspond to well-fitted 434 ion peaks. Slope and correlation coefficient (r2)are
obtained using logarithmic fraction, i.e., log(ncps per total VOC ncps).
where Fractionhigh-Tand Fractionlow-Tcorrespond to frac-
tions (in ncps per total VOC ncps) of individual species
in the high- and low-temperature VOC profiles (Fig. 3),
respectively. Figure 4b also shows the relationship be-
tween pyrolysis temperature and representative products
for individual biopolymers as reported in the literature
(Collard and Blin, 2014). During the heating of biomass,
different chemical bonds within the biopolymers are broken,
which results in the release of VOCs and in rearrangement
reactions within the matrix of the residue. Low-temperature
pyrolysis breaks the bonds between the monomer units of
the polymers. Depolymerization in lignin (300–500 C)
produces guaiacols, (iso)eugenol, and syringol. Furans
and furfurals are dominantly formed from cellulose and
hemicellulose (300–400 C). Emissions of these compounds
have a larger contribution from the low-temperature factor
(Flow-T=60–100 %). Higher temperatures allow reaction
of functional groups and covalent bonds in polymers and
monomers. The resulting fragmentation emits various VOCs:
for example, hydroxyacetone, acetaldehyde, and acetic acid
from depolymerization of cellulose and/or hemicellulose.
These VOCs have roughly equal contributions from low-
and high-temperature factors. The release of oxygenated
compounds during depolymerization and fragmentation
increases the carbon percentage of the residual biopolymers.
Benzene rings and aromatic polycyclic structures form,
which is termed char. Higher temperature pyrolysis breaks
progressively stronger bonds in char (>500 C). This arom-
atization process gives off aromatic compounds with short
substituents (e.g., phenol), nonsubstituted aromatics (e.g.,
benzene), and polycyclic aromatic hydrocarbons (PAHs,
such as naphthalene). Most of those aromatics have a large
contribution from the high-temperature factor (Fhigh-T=60–
100 %). As the temperature increases, substituents of the
aromatic rings disappear and PAHs are dominantly pro-
duced. This is consistent with the contribution of the
high-temperature factor to phenol (Fhigh-T=60 %), benzene
(77 %), and naphthalene (92 %).
www.atmos-chem-phys.net/18/9263/2018/ Atmos. Chem. Phys., 18, 9263–9281, 2018
9270 K. Sekimoto et al.: High- and low-temperature pyrolysis profiles
Figure 3. Average VOC emission profiles of high- and low-
temperature pyrolysis factors, obtained using consolidated PMF re-
sults of 15 different fuels.
These many diverse chemical processes are likely happen-
ing simultaneously during a fire, and their relative intensities
may change based on fuel composition, fuel moisture con-
tent, or other as-yet poorly defined parameters. However, the
net result of all these variables is the emission of just two ma-
jor 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 emis-
sions 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 Fhigh-Tas described above. We sorted
the VOCs by Fhigh-T, to show how the chemical composition
of emissions changes from high- to low-temperature pyrol-
ysis process. Figure 5 shows the chemical characteristics of
compounds that are mostly emitted in the high-temperature
pyrolysis (Fhigh-T=80–100 % in panel a), mostly emitted
in the low-temperature pyrolysis (Fhigh-T=0–20% in panel
e), or have mixed contributions from both types of pyrolysis
(Fhigh-T=60–80 % in panel b, 40–60 % in c, and 20–40 %
in d). Fhigh-Tof each individual VOC is shown in Fig. S5. In
the category emitted mostly by the high-temperature pyrol-
ysis, important compounds include alkyl-substituted aromat-
ics and aliphatic alkenes (Fig. 5a and b), whereas carbonyls
have more equal contributions from the high- and low-
temperature pyrolysis processes. It should be noted that ter-
penes (e.g., (oxygenated) monoterpenes and isoprene) emit-
ted from distillation are grouped with the high-temperature
pyrolysis (Fig. 5a and b; Sect. 3.6).
Several nitrogen (N)-containing compounds also fall into
high- or low-temperature categories, consistent with be-
havior previously reported in the literature. The main N-
containing compounds detected by PTR-ToF-MS are iso-
cyanic acid (HNCO), nitrous acid (HONO), hydrogen
cyanide (HCN), and ammonia (NH3). HNCO, HONO, and
HCN have a high contribution of the high-temperature factor
(Fhigh-T=80–100 % in Fig. 5a), while NH3falls into the cat-
egory with a large contribution from the low-temperature fac-
tor (Flow-T=86 % in Fig. 5e). Nitrogen in biomass typically
exists as amino acids or proteins and pyrrole or pyridine (aro-
matic N-heterocycles). During the pyrolysis of those N func-
tionalities at high temperature (700–1100 C), HCN is iden-
tified as the main product in most cases (Johnson and Kang,
1971; Haidar et al., 1981; Patterson et al., 1968; Houser et
al., 1980). NH3, resulting from the lower-temperature py-
rolysis of proteins, has been classified as smoldering com-
bustion gases and falls here into the low-temperature profile
(Yokelson et al., 1996).
The present analysis predominantly focuses on VOCs. The
VOC emissions from biomass burning are dominated by py-
rolysis reactions of biopolymers. However, not all species are
emitted from pyrolysis reactions. For example, flaming com-
bustion releases CO2, NOx, HONO, black carbon, etc. This
is a separate process and cannot be expected to be captured
by our VOC framework. In Sect. 3.5 we show that MCE,
which delineates flaming versus smoldering combustion, is
a poorer descriptor of VOC variability than the high- versus
low-temperature pyrolysis framework.
3.3 Chemical characteristics of VOC emissions
depending on pyrolysis temperature
3.3.1 VOC composition
The VOC emission profiles for the high- and low-
temperature factors are shown in Fig. 3 and they mainly
consist of hydrocarbons, oxygenates with n=1–7 oxygen
atoms, and nitrogen- and/or sulfur-containing hydrocarbons
(Fig. 6). In each emission profile, about half of the frac-
tion (in ppbv) is accounted for by a combination of the fol-
lowing seven compounds: (i) ethene (C2H4), (ii) formalde-
hyde (HCHO), (iii) methanol (CH3OH), (iv) acetalde-
hyde (CH3CHO), (v) acrolein (CH2=CHCHO), (vi) acetic
acid (CH3COOH) and glycolaldehyde (HOCH2CHO), and
(vii) ammonia (NH3). The other half includes several fun-
damental structures, with a variety of functionalities, as dis-
cussed later. Oxygenates with one oxygen are predominant in
both emission profiles, accounting for 39 % of molar emis-
Atmos. Chem. Phys., 18, 9263–9281, 2018 www.atmos-chem-phys.net/18/9263/2018/
K. Sekimoto et al.: High- and low-temperature pyrolysis profiles 9271
Figure 4. (a) Normalized fraction of factors for selected biomass pyrolysis products, obtained using PMF results of 15 different fuels. (b) Di-
agram of the relationship between pyrolysis temperature and products for hemicellulose, cellulose, and lignin, as reported in the literature
(Collard and Blin, 2014). Individual color bars show the temperature range to form specific products described by chemical structures.
sions in the high-temperature profile and 36 % in the low-
temperature profile. Emissions of highly oxygenated com-
pounds (2 oxygen atoms) and ammonia are higher in the
low-temperature profile than in the high-temperature pro-
file. The fractions of hydrocarbons and compounds that con-
tain both N and O, such as HNCO, are lower in the low-
temperature profile.
VOCs emitted from biomass burning can be generally or-
ganized into major structural groups: furans, aromatics, oxy-
genated aromatics, aliphatic compounds, and so on. Within
each structural category, compounds can have various func-
tionalities, 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 pro-
file (Fig. 7). This is due to the specific compounds (ii)–
(vi) described above. The low-temperature profile is twice as
rich in aromatic oxygenates (2 oxygen atoms) and furans
as the high-temperature profile, while the high-temperature
profile is enriched in aliphatic (mostly alkenes) and aro-
matic hydrocarbons. Terpenes (including isoprene, monoter-
penes, sesquiterpenes, and oxygenated monoterpenes) emit-
ted from distillation, not from pyrolysis, are dominantly
grouped with the high-temperature factor. Compared to
the low-temperature profile, the high-temperature profile is
enriched in the following functional groups: C-C double
bond (> C =C<), C-C triple bond (-CC-), diene (> C=C-
C=C<), polycyclic aromatic hydrocarbon (PAH), nitrile
(-C N), amide (-C(=O)-N-), nitro (-NO2), nitrate (-NO3),
thiol/sulfide (-S-(H)) (Fig. 8). The low-temperature profile is
enriched in alcohols (-OH), ethers (mostly methoxy groups: -
O-CH3), esters (-C(=O)-O-), and amines (-NH2; mostly am-
monia). The emissions of compounds with carbonyl groups
(> C =O) and acids (-C(=O)-OH-) are similar. These results
are consistent with the contributions of VOC to the high- and
low-temperature factors described in Sect. 3.2.
3.3.2 OH reactivity
The hydroxyl radical (OH) is an important driver of day-
time oxidation chemistry. Quantifying the VOC reactivity
with OH provides insight into which VOC emissions may
www.atmos-chem-phys.net/18/9263/2018/ Atmos. Chem. Phys., 18, 9263–9281, 2018
9272 K. Sekimoto et al.: High- and low-temperature pyrolysis profiles
Figure 5. Contributions, shown as normalized fractions, of VOCs relative to the high- and low-temperature factors: (a) FHigh-T=100–80
and FLow-T=0–20 %, (b) FHigh-T=80–60 and FLow-T=20–40 %, (c) FHigh-T=60–40 and FLow-T=40–60 %, (d) FHigh-T=40–20
and FLow-T=60–80 %, and (e) FHigh-T=20–0 and FLow-T=80–100 %. In this figure, molar emissions (in units of ppbv) of all the ion
peaks in VOC emission profiles (Fig. 2b) are described. The inner circle in each pie chart shows the elemental composition of the emissions.
The outer circle provides more detailed information on specific compounds, structures, and functionalities found in each group. Details of
molar fractions in each category are summarized in Table S2.
be most important for ozone and secondary organic aerosol
formation. Interestingly, the two profiles have a similar av-
erage per-molecule (weighted by abundance) rate constant
with OH: 15.7 ×1012 cm3molecule1s1for the high-
temperature profile and 15.8 ×1012 cm3molecule1s1
for the low-temperature profile. However, the reactivity is
provided by very different VOCs in each profile. Aliphatic
oxygenates are important in both profiles, but more so in
the high-temperature profile (30 % of reactivity) than in the
low-temperature profile (24% of reactivity). In the high-
temperature profile, the reactivity also has a large contribu-
tion from terpenes and aliphatic hydrocarbons, while in the
low-temperature profile, the reactivity is largely due to fu-
rans and aromatics (Fig. 9a). Since the total VOC emissions
in real-world fires come from a mixture of the high- and low-
temperature pyrolysis factors, the total OH reactivity of fresh
emissions should scale directly with VOC concentration.
3.3.3 Volatility
Volatility is another important chemical characteristic affect-
ing 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
Atmos. Chem. Phys., 18, 9263–9281, 2018 www.atmos-chem-phys.net/18/9263/2018/
K. Sekimoto et al.: High- and low-temperature pyrolysis profiles 9273
Figure 6. VOC composition in the high- and low-temperature emission profiles.
Figure 7. VOC composition in (a) high-temperature pyrolysis and (b) low-temperature pyrolysis emission profiles (Fig. 3) sorted by 11
structural categories and 17 functional groups. Some VOCs have multiple structures. These are counted once in each relevant category. For
example, benzofuran is counted in the structural categories of “Oxy. aromatic” and “Furans” as “Not substituted/alkyl” functional group.
Structures detected with low abundance (<0.002ppbv per total VOC ppbv) are mostly not-substituted or alkyl-substituted.
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 po-
tentially more efficient SOA formation. SOA formation was
also studied during the FIREX 2016 campaign, by oxidiz-
ing emissions in a chamber, and will be presented separately
(Lim et al., 2018). We note that the compounds with C0
< 102µg m3shown in Fig. 9b should be primarily in the
particle phase and not measurable by PTR-MS without long
delay times (Pagonis et al., 2017). However, the volatility
of these compounds (calculated from the elemental compo-
sition) has an uncertainty of several orders of magnitude.
Also, the cyclic compounds that are abundant in the low-
temperature profile, such as aromatic oxygenates, produce
multifunctional ring-opening-products that are known to be
www.atmos-chem-phys.net/18/9263/2018/ Atmos. Chem. Phys., 18, 9263–9281, 2018
9274 K. Sekimoto et al.: High- and low-temperature pyrolysis profiles
Figure 8. 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)”.
Figure 9. High- and low-temperature emission profiles compared
by (a) OH reactivity and (b) volatility, described by saturation con-
centration (µg m3).
efficient SOA precursors (Yee et al., 2013). In a similar man-
ner to the OH reactivity, the total volatility distribution can
be estimated from the relative importance of the high- and
low-temperature pyrolysis in a given fire.
3.4 Relationship of fuel characteristics to relative
importance of high- and low-temperature pyrolysis
factors
To use the PMF profiles (Fig. 3) for estimates of VOC emis-
sions 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, PVOChigh-T(in ppbv)/PVOClow-T
(in ppbv), are related to which parts of the plants are
burned (blue bars in Fig. 10). When leafy fuels (i.e., canopy,
shrub, and herbaceous fuels) are burned, the fraction of to-
tal VOC emissions originating from high-temperature py-
rolysis is higher than those from low-temperature pyroly-
Figure 10. Ratios of fire-integrated molar emissions
of total VOCs from high- to low-temperature pyrolysis
(“PVOCHigh-T/PVOCLow-T”) for different type fuel parts,
obtained using PMF results of 15 different fuels.
sis. These results imply that surface-to-volume ratios and the
content of biopolymers in a given fuel can strongly affect
the relative importance of high- and low-temperature pyrol-
ysis. Leaves have high surface-to-volume ratios and despite
higher fuel moisture, at least the surface may tend to heat
up easily, resulting in a higher contribution from the high-
temperature factor. The higher monoterpene content of fo-
liage may explain why low-temperature distillation products
like monoterpenes are associated with the high-temperature
pyrolysis factor.
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 sig-
Atmos. Chem. Phys., 18, 9263–9281, 2018 www.atmos-chem-phys.net/18/9263/2018/
K. Sekimoto et al.: High- and low-temperature pyrolysis profiles 9275
nificantly is 280–500 C for lignin, 240–350C for cellulose,
and 200–260 C for hemicellulose (Liu et al., 2016; Babu,
2008), as shown in Fig. 4b. In our laboratory fires, the rot-
ten wood first smoldered for an extended period, and then
flames were observed. However, only the low-temperature
profile was observed. This suggests that it is more diffi-
cult for lignin-rich fuels to reach temperatures high enough
to emit the “high-temperature pyrolysis” VOCs. Therefore,
we do not see the same gradient in pyrolysis products that
is observed for other fuel burns mainly consisting of cellu-
lose and hemicellulose. Nitrogen content and speciation also
vary between different biomass components, and tempera-
ture and differences in biopolymer content have been shown
to strongly affect the composition of nitrogen-containing
emissions (Hansson et al., 2004; Ren et al., 2011; Coggon
et al., 2016). This is consistent with the observed differences
in nitrogen speciation between the two profiles.
3.5 High- and low-temperature pyrolysis profiles
describe total VOC emissions
Previous studies have found a correlation between the emis-
sion factors of certain VOCs and the fire-integrated mod-
ified 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 pa-
rameterize 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 CO2as
well as NOxare emitted overwhelmingly from flaming com-
bustion, which is not the main source of most VOC emis-
sions, and these emissions are not expected to correlate with
a linear combination of the high- and low-temperature pyrol-
ysis processes, while CO emissions are reasonably well cor-
related with an average of high- and low-temperature emis-
sions (Figs. 1 and S6). This is especially clear in rotten log
burns, where CO2and the PMF profiles are not correlated.
The CO2emissions are enhanced by shifting from the smol-
dering to flaming combustion, but VOC emission patterns
are not changed from the low- to high-temperature pyrol-
ysis (Fig. S7). Consequently, CO2and MCE, which indi-
cate the separation between flaming and smoldering com-
bustions, are not appropriate to estimate the high- and low-
temperature pyrolysis VOC emissions. Our results indicate
that VOC emissions are even more closely correlated to the
biopolymer composition and the surface-to-volume ratios of
fuels, than to the MCE. It is also seen that for some fires the
air temperature correlates with the high-temperature contri-
bution (e.g., Fires no. 37 and no. 59 shown in Fig. S8a–c).
This suggests that the VOC emissions are certainly related
to the temperature within a fire. However, some other burns
did not have a good correlation between the temperature and
VOC emissions (e.g., Fire no. 38 shown in Fig. S8d), be-
cause the temperature measurement had some issues in the
present work: (i) background temperature for each burn was
different, (ii) some burns have colder temperature at the end
compared to the start, which means that the laboratory was
not controlled at constant temperature, and (iii) the increase
in air temperature often lagged behind the emissions, espe-
cially at the start of a fire.
The relative contributions from the high- and low-
temperature processes could be estimated from ratios of dis-
tinct marker species that are consistently enhanced in the
high- and low-temperature profiles. Several such pairs were
considered and the ratio of ethyne (C2H2) to furan (C4H4O)
can reasonably predict the ratio of high- to low-temperature
emissions as given in Eq. (1):
total VOC, high temperature (ppbv)
total VOC, low temperature (ppbv) =ethyne (ppbv)/0.0393
furan (ppbv)/0.0159 .(1)
The derivation and how the ethyne /furan ratio correlates
with the high-temperature /low-temperature emission ratio
are given in the Supplement (Sect. S2 and Fig. S9). How-
ever, this pair is not ideal because measurements of these
two species are not frequently available and furan has high
reactivity to both O3and NO3radicals. Future work should
assess non-PTR measurements in order to find appropriate
external markers.
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 burn-
ing 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; Stock-
well et al., 2015; Akagi et al., 2011). When fitting the present
high- and low-temperature factors to the other biomass burn-
ing data, total VOC emissions can be described with differ-
ent 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 north-
ern US (e.g., pine, spruce, fir, chaparral, mesquite, and oak)
with MCE =0.75–0.98, includes 32 % high-temperature and
68 % low-temperature VOC emissions; for another labo-
ratory study by Stockwell et al. (2015) including several
types of grass, spruce, and chaparral with MCE =0.68–0.99,
high temperatures of 59 % and low temperatures of 41 %
were found; temperate forest fires (MCE =0.95) reported
by Akagi et al. (2011) showed 77% high temperature and
23 % low temperature, while in the case of chaparral fires
(MCE =0.96), high temperatures of 48 % and low tempera-
tures of 52 % were found. The fitting can be done with high
correlation coefficient (r0.92) for all the literature data
(Fig. S10). This is further evidence that at most two factors
can explain the majority of VOC variability. Therefore, these
two factors could be used to fill in VOCs not measured in the
other studies which sometimes had less chemical detail. The
current study incorporated a wide range of MCEs and fuel
moisture contents (Table 1), so the two-factor description
www.atmos-chem-phys.net/18/9263/2018/ Atmos. Chem. Phys., 18, 9263–9281, 2018
9276 K. Sekimoto et al.: High- and low-temperature pyrolysis profiles
Figure 11. The comparison of contribution of high-temperature factor versus modified combustion efficiency (MCE). (a) Time series of Fire
no. 37 (ponderosa pine realistic mixture). (b) Scatter plot of instantaneous high-temperature contribution versus MCE for all ponderosa pine
fires. (c) Scatter plot of fire-integrated high-temperature contribution versus MCE for all fires. Contribution of high-temperature factor was
calculated by 6VOChigh-T/(6VOChigh-T+6VOClow-T)instantaneously or on a fire-integrated basis. T =temperature.
may be applicable under many conditions. However, some
other factors should be required for specific burns, as dis-
cussed below.
3.6 Emission of specific compounds
3.6.1 Distillation phase
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 rel-
evant 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 ef-
fectively even if a large number of factors is chosen. As an
exception, the distillation phase of sagebrush, enriched in ter-
penes 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 resid-
ual 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., man-
zanita), 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 pyrol-
ysis 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 com-
pounds are able to recondense into visible smoke.
3.6.2 Duff burn
A fourth factor can be resolved from the PMF analysis of
duff burns. The distribution of VOC structures and func-
tionality in the duff emission profiles (Fig. 12a) is similar
Atmos. Chem. Phys., 18, 9263–9281, 2018 www.atmos-chem-phys.net/18/9263/2018/
K. Sekimoto et al.: High- and low-temperature pyrolysis profiles 9277
Figure 12. (a) VOC emission profile of duff burn of Engelmann
spruce and subalpine fir. (b) Scatter plot of duff emission profile
(Engelmann spruce) versus average low-temperature pyrolysis pro-
file.
to the low-temperature pyrolysis profile (Fig. 12b). The ma-
jor 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 ni-
triles 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 =0.028 by
weight) was a factor of 2.1 higher than the average of other
subalpine fir components, and the Engelmann spruce duff
N : C ratio (0.022) was 1.3×higher than other Engelmann
spruce components. Coggon et al. (2016), who investigated
VOC emissions from the burning of herbaceous and arbora-
ceous fuels, also found that the nitrogen-containing fraction
of VOCs emitted from biomass burning increased with the
nitrogen content of the fuel.
However, the nitrogen content cannot entirely explain why
duff has a unique emission profile. Other fuels, such as cean-
othus 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 organ-
ics. In woody and leafy fuels, proteins and amino acids ac-
count 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 NH3
(Leppälahti and Koljonen, 1995). This is consistent with the
higher HCN and nitriles characteristic of the duff emission
profile.
3.6.3 Variation in specific VOCs between fuels
When comparing emission profiles of individual fuels to the
average profiles shown in Fig. 3, there are some specific com-
pounds whose emissions are notably higher (> ×5) or lower
(< ×0.2) than the average (Fig. S4). Here we highlight sev-
eral key features:
i. For ponderosa, lodgepole, and loblolly pines; Douglas
and subalpine firs; and juniper, the emission of ben-
zoquinone (C6H4O2·H+,m/z 109.028) is quite low
in the high-temperature pyrolysis: 7–21 % of the aver-
age emission for the pines and firs, and 2 % for juniper
(Fig. S4a-1–4, 6, and 7).
ii. For fuels other than coniferous fuels and sagebrush,
i.e., bear grass, excelsior, ceanothus, chamise, and
manzanita, emissions of monoterpenes (C10H16 ·H+,
m/z 137.132) are only 2–15 % of the average (Fig. S4a-
8–14).
iii. Excelsior emits especially low quantities of nitrogen-
containing compounds, especially nitriles (hydrogen
cyanide, acetonitrile, acrylonitrile, and propane ni-
trile) and pyridine, in the high-temperature pyrolysis
www.atmos-chem-phys.net/18/9263/2018/ Atmos. Chem. Phys., 18, 9263–9281, 2018
9278 K. Sekimoto et al.: High- and low-temperature pyrolysis profiles
(Fig. S4a-9). This is because the nitrogen content in ex-
celsior is significantly lower than other fuels. The excel-
sior N :C ratio (0.005 by weight) is 3.6×lower than the
average of other fuels (0.017±0.006).
iv. High-temperature pyrolysis of ceanothus produces
quite high emission of benzofuran-type compounds
(Fig. S4a-10). Benzofuran (C8H6O·H+,m/z 119.049)
and methylbenzofuran and possibly a methylbenzo-
furan isomer such as cinnamaldehyde (C9H8O·H+,
m/z 133.065) are 5.5 and 10.1×higher than the aver-
age, respectively.
v. Sagebrush specifically emits camphor (C10 H16O·H+,
m/z 153.127) in high-temperature pyrolysis (Fig. S4a-
15).
vi. There are a limited number of exceptions in low-
temperature profiles (Fig. S4b). This means that low-
temperature pyrolysis gives almost identical VOC emis-
sions, independent of fuel types.
4 Conclusions
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 ob-
served variability between VOC emissions from fuel types
and over the course of a fire using just two emission pro-
files: (i) a high-temperature pyrolysis profile and (ii) a low-
temperature pyrolysis profile. The results are summarized as
follows:
1. 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 %.
2. The high-temperature profile is quantitatively similar
between different fuel types (r2> 0.84), and likewise for
the low-temperature profile.
3. The two profiles are significantly different in terms of
VOC composition, volatility, and contributors to OH re-
activity. The high-temperature pyrolysis profile is en-
riched in aliphatic unsaturated hydrocarbons, (poly-
cyclic) aromatic hydrocarbons, terpenes (emitted from
distillation), HCN, HNCO, and HONO. The result-
ing OH reactivity is primarily attributed to terpenes,
aliphatic hydrocarbons, and nonaromatic oxygenates.
The low-temperature pyrolysis profile is enriched in
aromatic oxygenates, furans, and NH3. Furans and aro-
matics contribute significantly to the OH reactivity.
4. The fire-integrated molar emission ratios of total VOCs
from high-temperature pyrolysis to low-temperature py-
rolysis are related to the biopolymer composition and
surface-to-volume ratios of fuels. Higher surface-to-
volume ratios lead to more total VOC emissions en-
riched in products resulting from high-temperature py-
rolysis than from those resulting from low-temperature
pyrolysis.
5. The two VOC profiles can model previously reported
VOC data for laboratory and field burns (r0.92). This
suggests that these two profiles could be used to fill
in VOCs not actually measured in the previous studies
which sometimes had less chemical detail.
6. MCE, which parameterizes flaming and smoldering
combustion, is not appropriate to estimate the high- and
low-temperature pyrolysis VOC emissions. This sug-
gests that the high- and low-temperature pyrolysis pro-
files may provide information on emissions that is not
accessible with a broader definition of smoldering com-
bustion implicit in the use of MCE.
7. Duff burns emit a specific VOC profile which is sim-
ilar to that of low-temperature pyrolysis, but addition-
ally 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 compari-
son 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 relation-
ship 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 availability. Data are available from the CSD NOAA archive
at https://esrl.noaa.gov/csd/groups/csd7/measurements/2016firex/
FireLab/DataDownload/. The credentials of this archive can be
obtained by contacting the corresponding author.
Atmos. Chem. Phys., 18, 9263–9281, 2018 www.atmos-chem-phys.net/18/9263/2018/
K. Sekimoto et al.: High- and low-temperature pyrolysis profiles 9279
The Supplement related to this article is available online
at https://doi.org/10.5194/acp-18-9263-2018-supplement.
Author contributions. 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.
Competing interests. The authors declare that they have no conflict
of interest.
Acknowledgements. Kanako Sekimoto acknowledges the Post-
doctoral 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 Ed-
ucation, 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
References
Akagi, S. K., Yokelson, R. J., Wiedinmyer, C., Alvarado, M. J.,
Reid, J. S., Karl, T., Crounse, J. D., and Wennberg, P. O.: Emis-
sion factors for open and domestic biomass burning for use
in atmospheric models, Atmos. Chem. Phys., 11, 4039–4072,
https://doi.org/10.5194/acp-11-4039-2011, 2011.
Alvarado, M. J., Wang, C, and Prinn, R. G.: Formation of ozone and
growth of aerosols in young smoke plumes from biomass burn-
ing: 2. Three-dimensional Eulerian studies, J. Geophys. Res.,
114, D09307, https://doi.org/10.1029/2008JD011186, 2009.
Alvarado, M. J., Lonsdale, C. R., Yokelson, R. J., Akagi, S. K.,
Coe, H., Craven, J. S., Fischer, E. V., McMeeking, G. R., Se-
infeld, J. H., Soni, T., Taylor, J. W., Weise, D. R., and Wold,
C. E.: Investigating the links between ozone and organic aerosol
chemistry in a biomass burning plume from a prescribed fire
in California chaparral, Atmos. Chem. Phys., 15, 6667–6688,
https://doi.org/10.5194/acp-15-6667-2015, 2015.
Atkinson, R. and Arey, J.: Atmospheric degradation of
volatile organic compounds, Chem. Rev., 103, 4605–4638,
https://doi.org/10.1021/cr0206420, 2003.
Babu, B. V.: Biomass pyrolysis: a state-of-the-art review, Bio-
fuel. Bioprod. Bior., 2, 393–414, https://doi.org/10.1002/bbb.92,
2008.
Bruns, E. A., El Haddad, I., Slowik, J. G., Kilic, D., Klein,
F., Baltensperger, U., and Prévôt, A. S. H.: Identification
of significant precursor gases of secondary organic aerosols
from residential wood combustion, Sci. Rep., 6, 27881,
https://doi.org/10.1038/srep27881, 2016.
Burling, I. R., Yokelson, R. J., Griffith, D. W. T., Johnson, T. J.,
Veres, P., Roberts, J. M., Warneke, C., Urbanski, S. P., Rear-
don, J., Weise, D. R., Hao, W. M., and de Gouw, J.: Lab-
oratory measurements of trace gas emissions from biomass
burning of fuel types from the southeastern and southwest-
ern United States, Atmos. Chem. Phys., 10, 11115–11130,
https://doi.org/10.5194/acp-10-11115-2010, 2010.
Christian, T. J., Kleiss, B., Yokelson, R. J., Holzinger, R., Crutzen,
P. J., Hao, W. M., Saharjo, B. H., and Ward, D. E.: Compre-
hensive laboratory measurements of biomass-burning emissions:
1. Emissions from Indonesian, African and other fuels, J. Geo-
phys. Res., 108, 4719, https://doi.org/10.1029/2003JD003704,
2003.
Christian, T. J., Kleiss, B., Yokelson, R. J., Holzinger, R.,
Crutzen, P. J., Hao, W. M., Shirai, T., and Blake, D.
R.: Comprehensive laboratory measurements of biomass-
burning emissions: 2. First intercomparison of open-path
FTIR, and GC-MS/FID/ECD, J. Geophys. Res., 109, D02311,
https://doi.org/10.1029/2003JD003874, 2004.
Cicerone, R. J. and Zellner, R.: The atmospheric chemistry of hy-
drogen cyanide (HCN), J. Geophys. Res., 88, 10689–10696,
https://doi.org/10.1029/JC088iC15p10689, 1983.
Coggon, M. M., Veres, P. R., Yuan, B., Koss, A., Warneke, C.,
Gilman, J. B., Lerner, B. M., Peischl, J., Aikin, K. C., Stock-
well, C. E., Hatch, L. E., Ryerson, T. B., Roberts, J. M., Yokel-
son, R. J., and de Gouw, J. A.: Emissions of nitrogen-containing
organic compounds from the burning of herbaceous and arbora-
ceous biomass: Fuel composition dependence and the variability
of commonly used nitrile tracers, Geophys. Res. Lett., 43, 9903–
9912, https://doi.org/10.1002/2016GL070562, 2016.
Collard, F. X. and Blin, J.: A review on pyrolysis of biomass
constituents: Mechanisms and composition of the prod-
ucts obtained from the conversion of cellulose, hemicellu-
loses and lignin, Renew. Sust. Energ. Rev., 38, 594–608,
https://doi.org/10.1016/j.rser.2014.06.013, 2014.
Donahue, N. M., Epstein, S. A., Pandis, S. N., and Robinson, A.
L.: A two-dimensional volatility basis set: 1. organic-aerosol
mixing thermodynamics, Atmos. Chem. Phys., 11, 3303–3318,
https://doi.org/10.5194/acp-11-3303-2011, 2011.
Gilman, J. B., Lerner, B. M., Kuster, W. C., Goldan, P. D., Warneke,
C., Veres, P. R., Roberts, J. M., de Gouw, J. A., Burling, I.
R., and Yokelson, R. J.: Biomass burning emissions and po-
tential air quality impacts of volatile organic compounds and
other trace gases from f uels common in the US, Atmos. Chem.
Phys., 15, 13915–13938, https://doi.org/10.5194/acp-15-13915-
2015, 2015.
Haidar, N. F., Patterson, J. M., Moors, M., and Smith, W. T.: Effects
of structure on pyrolysis gases from amino acids, J. Agr. Food.
Chem., 29, 163–165, https://doi.org/10.1021/jf00103a040, 1981.
Hansson, K.-M., Samuelsson, J., Tullin, C., and
Åmand, L.-E.: Formation of HNCO, HCN, and NH3
www.atmos-chem-phys.net/18/9263/2018/ Atmos. Chem. Phys., 18, 9263–9281, 2018
9280 K. Sekimoto et al.: High- and low-temperature pyrolysis profiles
from the pyrolysis of bark and nitrogen-containing
model compounds, Combust. Flame, 137, 265–277,
https://doi.org/10.1016/J.COMBUSTFLAME.2004.01.005,
2004.
Haslett, S. L., Thomas, J. C., Morgan, W. T., Hadden, R., Liu, D.,
Allan, J. D., Williams, P. I., Keita, S., Liousse, C., and Coe, H.:
Highly controlled, reproducible measurements of aerosol emis-
sions from combustion of a common African biofuel source, At-
mos. Chem. Phys., 18, 385–403, https://doi.org/10.5194/acp-18-
385-2018, 2018.
Hatch, L. E., Luo, W., Pankow, J. F., Yokelson, R. J., Stock-
well, C. E., and Barsanti, K. C.: Identification and quantifi-
cation of gaseous organic compounds emitted from biomass
burning using two-dimensional gas chromatography-time-of-
flight mass spectrometry, Atmos. Chem. Phys., 15, 1865–1899,
https://doi.org/10.5194/acp-15-1865-2015, 2015.
Hatch, L. E., Yokelson, R. J., Stockwell, C. E., Veres, P. R., Simp-
son, I. J., Blake, D. R., Orlando, J. J., and Barsanti, K. C.:
Multi-instrument comparison and compilation of non-methane
organic gas emissions from biomass burning and implications
for smoke-derived secondary organic aerosol precursors, At-
mos. Chem. Phys., 17, 1471–1489, https://doi.org/10.5194/acp-
17-1471-2017, 2017.
Houser, T. J., McCarville, M. E., and Biftu, T.: Kinetics of the ther-
mal decomposition of pyridine in a flow system, Int. J. Chem.
Kinet., 12, 555–568, https://doi.org/10.1002/kin.550120806,
1980.
Jaffe, D. A. and Wigder, N. L.: Ozone production from
wildfires: A critical review, Atmos. Environ., 51, 1–10,
https://doi.org/10.1016/j.atmosenv.2011.11.063, 2012.
Johnson, W. R. and Kan, J. C.: Mechanisms of hydrogen
cyanide formation from the pyrolysis of amino acids
and related compounds, J. Org. Chem., 36, 189–192,
https://doi.org/10.1021/jo00800a038, 1971.
Kirk, T. K. and Cowling, E. B.: Biological decomposition of solid
wood, Adv. Chem., 207, 455–487, https://doi.org/10.1021/ba-
1984-0207.ch012, 1984.
Koss, A. R., Sekimoto, K., Gilman, J. B., Selimovic, V., Cog-
gon, M. M., Zarzana, K. J., Yuan, B., Lerner, B. M., Brown,
S. S., Jimenez, J. L., Krechmer, J., Roberts, J. M., Warneke,
C., Yokelson, R. J., and de Gouw, J.: Non-methane organic gas
emissions from biomass burning: identification, quantification,
and emission factors from PTR-ToF during the FIREX 2016
laboratory experiment, Atmos. Chem. Phys., 18, 3299–3319,
https://doi.org/10.5194/acp-18-3299-2018, 2018.
Leppälahti, J. and Koljonen, T.: Nitrogen evolution from
coal, peat and wood during gasification: Literature review,
Fuel Process. Technol., 43, 1–45, https://doi.org/10.1016/0378-
3820(94)00123-B, 1995.
Li, Y., Pöschl, U., and Shiraiwa, M.: Molecular corridors
and parameterizations of volatility in the chemical evolution
of organic aerosols, Atmos. Chem. Phys., 16, 3327–3344,
https://doi.org/10.5194/acp-16-3327-2016, 2016.
Lim, C., Hagan, D., Cappa, C., Coggon, M., Koss, A., Sekimoto,
K., de Gouw, J., Warneke, C., and Kroll, J.: Laboratory studies
on the aging of biomass burning emissions: chemical evolution
and secondary organic aerosol yield, in preparation, 2018.
Liu, W. -J., Li, W. -W., Jiang, H., and Yu, H.-Q.: Fates of chemical
elements in biomass during its pyrolysis, Chem. Rev., 117, 6367–
6398, https://doi.org/10.1021/acs.chemrev.6b00647, 2016.
Manion, J. A., Huie, R. E., Levin, R. D., Burgess Jr., D. R., Orkin,
V. L., Tsang, W., McGivern, W. S., Hudgens, J. W., Knyazev, V.
D., Atkinson, D. B., Chai, E., Tereza, A. M., Lin, C.-Y., Allison,
T. C., Mallard, W. G., Westley, F., Herron, J. T., Hampson, R.
F., and Frizzell, D. H.: NIST Chemical Kinetics Database, NIST
Standard Reference Database 17, Version 7.0 (Web Version), Re-
lease 1.6.8, Data version 2015.12, National Institute of Stan-
dards and Technology, Gaithersburg, Maryland, 20899–8320,
available at: https://kinetics.nist.gov/kinetics/index.jsp, last ac-
cess: 28 November 2017.
Naeher, L. P., Brauer, M., Lipsett, M., Zelikoff, J. T., Simp-
son, C. D., Koenig, J. Q., and Smith, K. R.: Woodsmoke
health effects: A review, Inhal. Toxicol., 19, 67–106,
https://doi.org/10.1080/08958370600985875, 2007.
NIST Chemistry WebBook: NIST Standard Reference Database
Number 69, available at: http://webbook.nist.gov/chemistry/, last
access: 28 November 2017.
Paatero, P.: Least squares formulation of robust non-negative
factor analysis, Chemometr. Intell. Lab., 37, 23–35,
https://doi.org/10.1016/S0169-7439(96)00044-5, 1997.
Paatero, P. and Tapper, U.: Positive matrix factorization: A
non-negative factor model with optimal utilization of er-
ror estimates of data values, Environmetrics, 5, 111–126,
https://doi.org/10.1002/env.3170050203, 1994.
Pagonis, D., Krechmer, J. E., de Gouw, J., Jimenez, J. L., and
Ziemann, P. J.: Effects of gas-wall partitioning in Teflon tub-
ing and instrumentation on time-resolved measurements of gas-
phase organic compounds, Atmos. Meas. Tech., 10, 4687–4696,
https://doi.org/10.5194/amt-10-4687-2017, 2017.
Patterson, J. M., Tsamasfyros, A., and Smith, W. T.: Py-
rolysis of pyrrole, J. Heterocyclic Chem., 5, 727–729,
https://doi.org/10.1002/jhet.5570050527, 1968.
Reardon, J.: Duff, in: FireWords: Fire Science Glossary [electronic],
U.S. Department of Agriculture, Forest Service, Rocky Mountina
Research Station, Fire Science Laboratory (Producer), available
at: http://www.firewords.net (last access: 28 November 2017),
2007.
Reece, S. M., Sinha, A., and Grieshop, A. P.: Primary and pho-
tochemically aged aerosol emissions from biomass cookstoves:
Chemical and physical characterization, Environ. Sci. Technol.,
51 , 9379–9390, https://doi.org/10.1021/acs.est.7b01881, 2017.
Ren, Q. and Zhao, C.: Evolution of fuel-N in gas phase dur-
ing biomass pyrolysis, Renew. Sust. Energ. Rev., 50, 408–418,
https://doi.org/10.1016/j.rser.2015.05.043, 2015.
Ren, Q., Zhao, C., Chen, X., Duan, L., Li, Y., and Ma, C.:
NOxand N2O precursors (NH3and HCN) from biomass
pyrolysis: Co-pyrolysis of amino acids and cellulose, hemi-
cellulose and lignin, P. Combust. Inst., 33, 1715–1722,
https://doi.org/10.1016/J.PROCI.2010.06.033, 2011.
Roberts, J. M, Veres, P. R., Cochran, A. K., Warneke, C.,
Burling, I. R., Yokelson, R. J., Lerner, B., Gilman, J. B.,
Kuster, W. C., Fall, R., and de Gouw, J.: Isocyanic acid
in the atmosphere and its possible link to smoke-related
health effects, P. Natl. Acad. Sci. USA, 208, 8966–8971,
https://doi.org/10.1073/pnas.1103352108, 2011.
Atmos. Chem. Phys., 18, 9263–9281, 2018 www.atmos-chem-phys.net/18/9263/2018/
K. Sekimoto et al.: High- and low-temperature pyrolysis profiles 9281
Rumble, J. R.: CRC Handbook of Chemistry and Physics, 98th
Edn., CRC Press, Boca Raton, Florida, USA, 2017–2018.
Schulten, H.-R. and Schnitzer, M.: The chemistry of soil organic
nitrogen: a review, Biol. Fert. Soils., 26, 1–15, 1997.
Selimovic, V., Yokelson, R. J., Warneke, C., Roberts, J. M., de
Gouw, J., Reardon, J., and Griffith, D. W. T.: Aerosol optical
properties and trace gas emissions by PAX and OP-FTIR for
laboratory-simulated western US wildfires during FIREX, At-
mos. Chem. Phys., 18, 2929–2948, https://doi.org/10.5194/acp-
18-2929-2018, 2018.
Stockwell, C. E., Veres, P. R., Williams, J., and Yokelson, R. J.:
Characterization of biomass burning emissions from cooking
fires, peat, crop residue, and other fuels with high-resolution
proton-transfer-reaction time-of-flight mass spectrometry, At-
mos. Chem. Phys., 15, 845–865, https://doi.org/10.5194/acp-15-
845-2015, 2015.
Ulbrich, I. M., Canagaratna, M. R., Zhang, Q., Worsnop, D. R., and
Jimenez, J. L.: Interpretation of organic components from Posi-
tive Matrix Factorization of aerosol mass spectrometric data, At-
mos. Chem. Phys., 9, 2891–2918, https://doi.org/10.5194/acp-9-
2891-2009, 2009.
Yaws, C. L.: The Yaws Handbook of Vapor Pressure: Antoine Co-
efficients, Elsevier Science, Amsterdam, the Netherlands, 2015.
Yee, L. D., Kautzman, K. E., Loza, C. L., Schilling, K. A., Cog-
gon, M. M., Chhabra, P. S., Chan, M. N., Chan, A. W. H.,
Hersey, S. P., Crounse, J. D., Wennberg, P. O., Flagan, R. C.,
and Seinfeld, J. H.: Secondary organic aerosol formation from
biomass burning intermediates: phenol and methoxyphenols, At-
mos. Chem. Phys., 13, 8019–8043, https://doi.org/10.5194/acp-
13-8019-2013, 2013.
Yokelson, R. J., Griffith, D. W. T., and Ward, D. E.: Open-
path Fourier transform infrared studies of large-scale labora-
tory biomass fires, J. Geophys. Res.-Atmos., 101, 21067–21080,
https://doi.org/10.1029/96JD01800, 1996.
Yokelson, R. J., Susott, R., Ward, D. E., Reardon, J., and Grif-
fith, D. W. T.: Emissions from smoldering combustion of
biomass measured by open-path Fourier transform infrared
spectroscopy, J. Geophys. Res.-Atmos., 102, 18865–18877,
https://doi.org/10.1029/97JD00852, 1997.
Yokelson, R. J., Crounse, J. D., DeCarlo, P. F., Karl, T., Urbanski,
S., Atlas, E., Campos, T., Shinozuka, Y., Kapustin, V., Clarke, A.
D., Weinheimer, A., Knapp, D. J., Montzka, D. D., Holloway, J.,
Weibring, P., Flocke, F., Zheng, W., Toohey, D., Wennberg, P. O.,
Wiedinmyer, C., Mauldin, L., Fried, A., Richter, D., Walega, J.,
Jimenez, J. L., Adachi, K., Buseck, P. R., Hall, S. R., and Shet-
ter, R.: Emissions from biomass burning in the Yucatan, Atmos.
Chem. Phys., 9, 5785–5812, https://doi.org/10.5194/acp-9-5785-
2009, 2009.
Yuan, B., Koss, A., Warneke, C., Gilman, J. B., Lerner, B. M.,
Stark, H., and de Gouw, J. A.: A high-resolution time-of-flight
chemical ionization mass spectrometer utilizing hydronium ions
(H3O+ToF-CIMS) for measurements of volatile organic com-
pounds in the atmosphere, Atmos. Meas. Tech., 9, 2735–2752,
https://doi.org/10.5194/amt-9-2735-2016, 2016.
Yuan, B., Koss, A., Warneke, C., Coggon, M., Sekimoto, K., and de
Gouw, J. A.: Proton-transfer-reaction mass spectrometry: Appli-
cations in atmospheric sciences, Chem. Rev., 117, 13187–13229,
https://doi.org/10.1021/acs.chemrev.7b00325, 2017.
www.atmos-chem-phys.net/18/9263/2018/ Atmos. Chem. Phys., 18, 9263–9281, 2018
... The primary components of wildland fuels are cellulose, hemicellulose and lignin (Shafizadeh 1982;Collard and Blin 2014). These components decompose thermally by a variety of pathways which are (a) temperature dependent (Shafizadeh 1982;Neves et al. 2011;Sekimoto et al. 2018) and (b) affected by the presence/absence of O 2 (e.g. Senneca et al. 2007). ...
... More HONO relative to NH 3 was observed at Ft. Jackson. Sekimoto et al. (2018) associated HONO with a hightemperature pyrolysis profile and NH 3 with a lowtemperature pyrolysis profile. Based on this, our results suggest that more of the Ft. ...
... More furan relative to naphthalene and more acetaldehyde relative to formaldehyde was observed in the oxidative pyrolysis gases at Ft. Jackson compared to the wind tunnel. The ratio of furan to acetylene has also been suggested as a possible measure to distinguish between high-and low-temperature pyrolysis factors (Sekimoto et al. 2018). Sekimoto et al. defined two factors that accounted for 'much of the observed variability in VOCs' as high-and low-temperature pyrolysis factors; air temperature was measured by an FTIR instrument at a sampling inlet after cooling and mixing with ambient air occurred. ...
Article
Background Fire models use pyrolysis data from ground samples and environments that differ from wildland conditions. Two analytical methods successfully measured oxidative pyrolysis gases in wind tunnel and field fires: Fourier transform infrared (FTIR) spectroscopy and gas chromatography with flame-ionisation detector (GC-FID). Compositional data require appropriate statistical analysis. Aims To determine if oxidative pyrolysis gas composition differed between analytical methods and locations (wind tunnel and field). Methods Oxidative pyrolysis gas sample composition collected in wind tunnel and prescribed fires was determined by FTIR and GC/FID. Proportionality between gases was tested. Analytical method and location effects on composition were tested using permutational multivariate analysis of variance and the Kruskal–Wallis test. Key results Gases proportional to each other were identified. The FTIR composition differed between locations. The subcomposition of common gases differed between analytical methods but not between locations. Relative amount of the primary fuel gases (CO, CH4) was not significantly affected by location. Conclusions Composition of trace gases differed between the analytical methods; however, each method yielded a comparable description of the primary fuel gases. Implications Both FTIR and GC/FID methods can be used to quantify primary pyrolysis fuel gases for physically-based fire models. Importance of the trace gases in combustion models remains to be determined.
... The latter (SOA) is a major constituent of atmospheric PM (Zhang et al., 2007). In order to predict the air quality impacts of wildfires, differences in emissions and their effects on chemistry and pollutant formation must be represented in models (Kochanski et al., 2015;Pavlovic et al., 2016; Wildfire emissions are dependent on a number of factors, such as combustion conditions (e.g., flaming vs. smoldering), fuel conditions (e.g., moisture content) and fuel type (e.g., species and component) (Goode et al., 2000;Urbanski, 2013;Liu et al., 2017;Stockwell et al., 2014Stockwell et al., , 2015Koss et al., 2018;Sekimoto et al., 2018;Hatch et al., 2019;Prichard et al., 2020). Differences in these factors can affect the total amount of emissions as well as the profile of emissions, i.e., the identities and quantities of individual chemical species. ...
... NMOC speciation profiles have been developed from both field and laboratory studies (Urbanski et al., 2008;Urbanski, 2014;Simpson et al., 2011;Holder et al., 2017;Andreae, 2019;Prichard et al., 2020). Laboratory studies offer some advantages over field studies in the context of controlling fuel species and fuel components; other variables, such as combustion conditions and fuel moisture, can be harder to control and can lead to differences in the identities and quantities of NMOCs emitted between laboratory and field studies (Yokelson et al., 2013;Stockwell et al., 2014;Liu et al., 2017;Sekimoto et al., 2018). Yokelson et al. (2013) presented an intercomparison of laboratory-and field-based emission factors (EFs) as well as approaches that combine the use of laboratory data to enhance the fundamental understanding of fire emissions with the use of field data to evaluate the representativeness of laboratory-based measurements. ...
Article
Full-text available
Wildfires have increased in frequency and intensity in the western United States (US) over the past decades, with negative consequences for air quality. Wildfires emit large quantities of particles and gases that serve as air pollutants and their precursors, and can lead to severe air quality conditions over large spatial and long temporal scales. Therefore, characterization of the chemical constituents in smoke as a function of combustion conditions, fuel type and fuel component is an important step towards improving the prediction of air quality effects from fires and evaluating mitigation strategies. Building on the comprehensive characterization of gaseous non-methane organic compounds (NMOCs) identified in laboratory and field studies, a supervised pattern recognition algorithm was developed that successfully identified unique chemical speciation profiles among similar fuel types common in western coniferous forests. The algorithm was developed using laboratory data from single fuel species and tested on simplified synthetic fuel mixtures. The fuel types in the synthetic mixtures were differentiated, but as the relative mixing proportions became more similar, the differentiation became poorer. Using the results from the pattern recognition algorithm, a classification model based on linear discriminant analysis was trained to differentiate smoke samples based on the contribution(s) of dominant fuel type(s). The classification model was applied to field data and, despite the complexity of the contributing fuels and the presence of fuels “unknown” to the classifier, the dominant sources/fuel types were identified. The pattern recognition and classification algorithms are a promising approach for identifying the types of fuels contributing to smoke samples and facilitating the selection of appropriate chemical speciation profiles for predictive air quality modeling using a highly reduced suite of measured NMOCs. The utility and performance of the pattern recognition and classification algorithms can be improved by expanding the training and test sets to include data from a broader range of single and mixed fuel types.
... We know that both cellulose and hemicellulose are typically slightly isotopically enriched (1 ‰-2 ‰) compared to the δ 13 C in the bulk plant, whereas lignin tends to be depleted by 2 ‰-7 ‰ (Benner et al., 1987;Leavitt et al., 1982;Loader et al., 2003;Steinbeiss et al., 2006;Weigt et al., 2015;Wilson and Grinsted, 1977;Zech et al., 2014). Different combustion phases are dominated by the consumption of different fuel sub-compounds and result in a different palette of combustion products (Sekimoto et al., 2018). We found CO 2 , CO, and CH 4 (representing > 95 % of the carbon emissions) to be heavier during FC than during RSC, which coincides with a shift from cellulose and hemicellulose to lignin. ...
Article
Full-text available
Landscape fires are a significant contributor to atmospheric burdens of greenhouse gases and aerosols. Although many studies have looked at biomass burning products and their fate in the atmosphere, estimating and tracing atmospheric pollution from landscape fires based on atmospheric measurements are challenging due to the large variability in fuel composition and burning conditions. Stable carbon isotopes in biomass burning (BB) emissions can be used to trace the contribution of C3 plants (e.g. trees or shrubs) and C4 plants (e.g. savanna grasses) to various combustion products. However, there are still many uncertainties regarding changes in isotopic composition (also known as fractionation) of the emitted carbon compared to the burnt fuel during the pyrolysis and combustion processes. To study BB isotope fractionation, we performed a series of laboratory fire experiments in which we burned pure C3 and C4 plants as well as mixtures of the two. Using isotope ratio mass spectrometry (IRMS), we measured stable carbon isotope signatures in the pre-fire fuels and post-fire residual char, as well as in the CO2, CO, CH4, organic carbon (OC), and elemental carbon (EC) emissions, which together constitute over 98 % of the post-fire carbon. Our laboratory tests indicated substantial isotopic fractionation in combustion products compared to the fuel, which varied between the measured fire products. CO2, EC, and residual char were the most reliable tracers of the fuel 13C signature. CO in particular showed a distinct dependence on burning conditions; flaming emissions were enriched in 13C compared to smouldering combustion emissions. For CH4 and OC, the fractionation was the other way round for C3 emissions (13C-enriched) and C4 emissions (13C-depleted). This indicates that while it is possible to distinguish between fires that were dominated by either C3 or C4 fuels using these tracers, it is more complicated to quantify their relative contribution to a mixed-fuel fire based on the δ13C signature of emissions. Besides laboratory experiments, we sampled gases and carbonaceous aerosols from prescribed fires in the Niassa Special Reserve (NSR) in Mozambique, using an unmanned aerial system (UAS)-mounted sampling set-up. We also provided a range of C3:C4 contributions to the fuel and measured the fuel isotopic signatures. While both OC and EC were useful tracers of the C3-to-C4 fuel ratio in mixed fires in the lab, we found particularly OC to be depleted compared to the calculated fuel signal in the field experiments. This suggests that either our fuel measurements were incomprehensive and underestimated the C3:C4 ratio in the field or other processes caused this depletion. Although additional field measurements are needed, our results indicate that C3-vs.-C4 source ratio estimation is possible with most BB products, albeit with varying uncertainty ranges.
... Whereas many studies have measured the composition of primary BB emissions, the formation and evolution of secondary species are less well characterized. Those studies that have examined secondary chemistry have focused largely on 50 the formation and properties of SOA (Lim et al., 2019;Ortega et al., 2013;Cubison et al., 2011) or individual species and pathways in BB photooxidation (Coggon et al., 2019;Koss et al., 2018;Sekimoto et al., 2018). These laboratory-based studies have revealed that atmospheric processing of BB emissions can lead to multiple generations of product formation as well as SOA formation. ...
Preprint
Full-text available
Biomass burning (BB) is a major source of reactive organic carbon into the atmosphere. Once in the atmosphere, these organic BB emissions, in both the gas and particle phases, are subject to atmospheric oxidation, though the nature and impact of the chemical transformations are not currently well constrained. Here we describe experiments carried out as part of the FIREX FireLab campaign, in which smoke from the combustion of fuels typical of the Western US was sampled into an environmental chamber and exposed to high concentrations of OH, to simulate the equivalent of up to two days of atmospheric oxidation. The evolution of the organic mixture was monitored using three real-time time-of-flight mass spectrometric instruments (a proton transfer reaction mass spectrometer, an iodide chemical ionization mass spectrometer, and an aerosol mass spectrometer), providing measurements of both individual species and ensemble properties of the mixture. The combined measurements from these instruments achieve a reasonable degree of carbon closure (within 15–35 %), indicating that most of the reactive organic carbon is measured by these instruments. Consistent with our previous studies of the oxidation of individual organic species, atmospheric oxidation of the complex organic mixture leads to the formation of species that on average are smaller and more oxidized than those in the unoxidized emissions. In addition, comparison of mass spectra from the different fuels indicates that the oxidative evolution of BB emissions proceeds largely independent of fuel type, with different fresh smoke mixtures ultimately converging into a common, aged distribution of gas-phase compounds. This distribution is characterized by high concentrations of several small, volatile oxygenates, formed from fragmentation reactions, as well as a complex pool of many minor oxidized species and secondary organic aerosol, likely formed via functionalization processes.
... Emissions ratios for 240 mass spectral signals were calculated and evaluated using PMF to identify two factors relating to high and low temperature pyrolysis. These factors corroborated the temperature dependent factors obtained by Sekimoto et al. (2018) from the FireLab. These factors indicate processes that cannot be discriminated using MCE. ...
Article
Full-text available
The NOAA/NASA Fire Influence on Regional to Global Environments and Air Quality (FIREX‐AQ) experiment was a multi‐agency, inter‐disciplinary research effort to: (a) obtain detailed measurements of trace gas and aerosol emissions from wildfires and prescribed fires using aircraft, satellites and ground‐based instruments, (b) make extensive suborbital remote sensing measurements of fire dynamics, (c) assess local, regional, and global modeling of fires, and (d) strengthen connections to observables on the ground such as fuels and fuel consumption and satellite products such as burned area and fire radiative power. From Boise, ID western wildfires were studied with the NASA DC‐8 and two NOAA Twin Otter aircraft. The high‐altitude NASA ER‐2 was deployed from Palmdale, CA to observe some of these fires in conjunction with satellite overpasses and the other aircraft. Further research was conducted on three mobile laboratories and ground sites, and 17 different modeling forecast and analyses products for fire, fuels and air quality and climate implications. From Salina, KS the DC‐8 investigated 87 smaller fires in the Southeast with remote and in‐situ data collection. Sampling by all platforms was designed to measure emissions of trace gases and aerosols with multiple transects to capture the chemical transformation of these emissions and perform remote sensing observations of fire and smoke plumes under day and night conditions. The emissions were linked to fuels consumed and fire radiative power using orbital and suborbital remote sensing observations collected during overflights of the fires and smoke plumes and ground sampling of fuels.
... Decades of fire suppression in the western US have also increased fuel load for fires, which when combined with changing climates further exacerbate fire risk (Marlon et al., 2012).These wildfires cause increasing economic and public health burdens (Kochi et al., 2012, Liu et al., 2015, Smith, 2020. Wildfires emit both gas phase chemicals and particulate matter (PM) into the atmosphere where local, regional, and long-range air quality impairments have been reported (Primbs et al., 2008, Sekimoto et al., 2018, Kang et al., 2014, Greenberg et al., 2006. The resulting impact of wildfire-associated fine PM (PM 2.5 , aerodynamic diameters smaller than or equal to 2.5 μm) on the planetin terms of the Earth's energy balance and the health of human populationsis a global environmental and public health concern (Burke et al., 2021), particularly since exposure to PM 2.5 has been linked to a wide variety of acute and chronic adverse health impacts (Xing et al., 2016, Williamson et al., 2016, Klepeis et al., 2001, Sharma et al., 2020. ...
Article
Full-text available
The increasing number and severity of wildfires is negatively impacting air quality for millions of California residents each year. Community exposure to PM2.5 in two main population centers (San Francisco Bay area and Los Angeles County area) was assessed using the low-cost PurpleAir sensor network for the record-setting 2020 California wildfire season. Estimated PM2.5 concentrations in each study area were compared to census tract-level environmental justice vulnerability indicators, including environmental, health, and demographic data. Higher PM2.5 concentrations were positively correlated with poverty, cardiovascular emergency department visits, and housing inequities. Sensors within 30 km of actively burning wildfires showed statistically significant increases in indoor (~800 %) and outdoor (~540 %) PM2.5 during the fires. Results indicate that wildfire emissions may exacerbate existing health disparities as well as the burden of pollution in disadvantaged communities, suggesting a need to improve monitoring and adaptive capacity among vulnerable populations.
Article
Full-text available
Biomass burning organic aerosol (BBOA) is one of the largest sources of organics in the atmosphere.
Article
Furans are predominant heterocyclic volatile organic compounds in the atmosphere from both primary and secondary sources, such as direct emissions from wildfires and atmospheric oxidation of dienes. The formation of secondary organic aerosols (SOAs) from the oxidation of furans has been reported. Previous research has shown that furan SOA generated from nighttime oxidation contributes to brown carbon (BrC) formation; however, how nighttime oxidant levels [represented by nitrate radical (NO3) levels] and pre-existing particles influence the SOA chemical composition and BrC optical properties is not well constrained. In this study, we conducted chamber experiments to systematically investigate the role of these two environmental factors in furan-derived secondary BrC formation during the nighttime. Our results suggest that the bulk compositions of SOA measured as ion fragment families by an aerosol mass spectrometer are unaffected by changes in NO3 levels but can be influenced by the presence of pre-existing ammonium sulfate particles. Based on the mass absorption coefficient profiles of SOA produced under different experimental conditions, BrC light absorption was enhanced by higher NO3 levels and reduced by the presence of pre-existing ammonium sulfate seed particles, suggesting that NO3-initiated oxidation of furan can promote the formation of light-absorbing products, while pre-existing particles may facilitate the partitioning of nonabsorbing organics in the aerosol phase. Furthermore, molecular-level compositional analysis reveals a similar pattern of chromophores under various studied environmental conditions, in which highly oxygenated monomers (e.g., C4H4O6 and C4H3NO7), dimers, and oligomers can all contribute to BrC chromophores. Taken together, the NO3 levels and pre-existing particles can influence secondary BrC formation by altering SOA compositions, which is critical for assessing BrC optical properties in a complex environment.
Article
We collected total suspended particulate (TSP) samples from January 2010 to December 2010 at Sapporo deciduous forest to understand the oxidation processes of biogenic volatile organic compounds (BVOCs). The gas chromatography-mass spectrometric technique was applied to determine biogenic secondary organic aerosols (BSOAs) in the TSP samples. We found the predominance of the isoprene SOA (iSOA) tracers (20.6 ng m-3) followed by α/β-pinene SOA (pSOA) tracers (8.25 ng m-3) and β-caryophyllene SOA (cSOA) tracer (1.53 ng m-3) in the forest aerosols. The results showed large isoprene fluxes and relatively high levels of oxidants in the forest atmosphere. The iSOA and pSOA tracers showed a clear seasonal trend with summer and autumn maxima and winter and spring minima. Their seasonal trends were mainly controlled by BVOCs emission from the local broadleaf deciduous forest. Additionally, the regional level of isoprene emissions from the oceanic sources may also be contributed during summertime aerosols. cSOA tracer showed high concentrations in the winter and spring, possibly due to an additional contribution of biomass burning (BB) aerosols from the local or regional BB activities. The biogenic secondary organic carbon (BSOC) was contributed mainly by the oxidation products of isoprene (136 ngC m-3) followed by β-caryophyllene (63.0 ngC m-3) and α/β-pinene (35.9 ngC m-3). The mass concentration ratio (0.92) of pinonic acid + pinic acid and 3-methyl-1,2,3-butanetricarboxylic acid ((PNA + PA)/3-MBTCA) indicates the photochemical transformation of first-generation oxidation products to the higher generation oxidation products. The average ratios of isoprene to α/β-pinene (1.64) and β-caryophyllene (18.6) oxidation products suggested a large difference in the emissions of isoprene and α/β-pinene compared to β-caryophyllene. The cSOA tracers in the forest aerosols are also contributed by BB during the winter and spring. Positive matrix factorization analyses of the BSOA tracers confirmed that organic aerosols of deciduous forests are mostly related to isoprene emissions. This study suggests that isoprene is a more significant precursor for the BSOA than α/β-pinene and β-caryophyllene in a broadleaf deciduous forest.
Article
Full-text available
The volatility distribution of organic emissions from biomass burning and other combustion sources can determine their atmospheric evolution due to partitioning/aging. The gap between measurements and models predicting secondary organic aerosol has been partially attributed to the absence of semi- and intermediate volatility organic compounds (S/I-VOC) in models and measurements. However, S/I-VOCs emitted from these sources and typically quantified using the volatility basis framework (VBS) are not well understood. For example, the amount and composition of S/I-VOCs and their variability across different biomass burning sources such as residential woodstoves, open field burns, and laboratory simulated open burning are uncertain. To address this, a novel filter-in-tube sorbent tube sampling method collected S/I-VOC samples from biomass burning experiments for a range of fuels and combustion conditions. Filter-in-tube samples were analyzed using thermal desorption-gas chromatography-mass spectrometry (TD/GC/MS) for compounds across a wide range of volatilities (saturation concentrations; -2 ≤ logC* ≤ 6). The S/I-VOC measurements were used to calculate volatility distributions for each emissions source. The distributions were broadly consistent across the sources with IVOCs accounting for 75% - 90% of the total captured organic matter, while SVOCs and LVOCs were responsible for 6% - 13% and 1% - 12%, respectively. The distributions and predicted partitioning were generally consistent with literature. Particulate matter emission factors spanned two orders of magnitude across the sources. This work highlights the potential of inferring gas-particle partitioning behavior of biomass burning emissions using filter-in-tube sorbent samples analyzed offline. This simplifies both sampling and analysis of S/I-VOCs for studies focused on capturing the full range of organics emitted.
Article
Full-text available
Volatile and intermediate-volatility non-methane organic gases (NMOGs) released from biomass burning were measured during laboratory-simulated wildfires by proton-transfer-reaction time-of-flight mass spectrometry (PTR-ToF). We identified NMOG contributors to more than 150 PTR ion masses using gas chromatography (GC) pre-separation with electron ionization, H3O⁺ chemical ionization, and NO⁺ chemical ionization, an extensive literature review, and time series correlation, providing higher certainty for ion identifications than has been previously available. Our interpretation of the PTR-ToF mass spectrum accounts for nearly 90 % of NMOG mass detected by PTR-ToF across all fuel types. The relative contributions of different NMOGs to individual exact ion masses are mostly similar across many fires and fuel types. The PTR-ToF measurements are compared to corresponding measurements from open-path Fourier transform infrared spectroscopy (OP-FTIR), broadband cavity-enhanced spectroscopy (ACES), and iodide ion chemical ionization mass spectrometry (I⁻ CIMS) where possible. The majority of comparisons have slopes near 1 and values of the linear correlation coefficient, R², of > 0.8, including compounds that are not frequently reported by PTR-MS such as ammonia, hydrogen cyanide (HCN), nitrous acid (HONO), and propene. The exceptions include methylglyoxal and compounds that are known to be difficult to measure with one or more of the deployed instruments. The fire-integrated emission ratios to CO and emission factors of NMOGs from 18 fuel types are provided. Finally, we provide an overview of the chemical characteristics of detected species. Non-aromatic oxygenated compounds are the most abundant. Furans and aromatics, while less abundant, comprise a large portion of the OH reactivity. The OH reactivity, its major contributors, and the volatility distribution of emissions can change considerably over the course of a fire.
Article
Full-text available
Western wildfires have a major impact on air quality in the US. In the fall of 2016, 107 test fires were burned in the large-scale combustion facility at the US Forest Service Missoula Fire Sciences Laboratory as part of the Fire Influence on Regional and Global Environments Experiment (FIREX). Canopy, litter, duff, dead wood, and other fuel components were burned in combinations that represented realistic fuel complexes for several important western US coniferous and chaparral ecosystems including ponderosa pine, Douglas fir, Engelmann spruce, lodgepole pine, subalpine fir, chamise, and manzanita. In addition, dung, Indonesian peat, and individual coniferous ecosystem fuel components were burned alone to investigate the effects of individual components (e.g., duff) and fuel chemistry on emissions. The smoke emissions were characterized by a large suite of state-of-the-art instruments. In this study we report emission factor (EF, grams of compound emitted per kilogram of fuel burned) measurements in fresh smoke of a diverse suite of critically important trace gases measured using open-path Fourier transform infrared spectroscopy (OP-FTIR). We also report aerosol optical properties (absorption EF; single-scattering albedo, SSA; and Ångström absorption exponent, AAE) as well as black carbon (BC) EF measured by photoacoustic extinctiometers (PAXs) at 870 and 401 nm. The average trace gas emissions were similar across the coniferous ecosystems tested and most of the variability observed in emissions could be attributed to differences in the consumption of components such as duff and litter, rather than the dominant tree species. Chaparral fuels produced lower EFs than mixed coniferous fuels for most trace gases except for NOx and acetylene. A careful comparison with available field measurements of wildfires confirms that several methods can be used to extract data representative of real wildfires from the FIREX laboratory fire data. This is especially valuable for species rarely or not yet measured in the field. For instance, the OP-FTIR data alone show that ammonia (1.62 g kg⁻¹), acetic acid (2.41 g kg⁻¹), nitrous acid (HONO, 0.61 g kg⁻¹), and other trace gases such as glycolaldehyde (0.90 g kg⁻¹) and formic acid (0.36 g kg⁻¹) are significant emissions that were poorly characterized or not characterized for US wildfires in previous work. The PAX measurements show that the ratio of brown carbon (BrC) absorption to BC absorption is strongly dependent on modified combustion efficiency (MCE) and that BrC absorption is most dominant for combustion of duff (AAE 7.13) and rotten wood (AAE 4.60): fuels that are consumed in greater amounts during wildfires than prescribed fires. Coupling our laboratory data with field data suggests that fresh wildfire smoke typically has an EF for BC near 0.2 g kg⁻¹, an SSA of ∼ 0.91, and an AAE of ∼ 3.50, with the latter implying that about 86 % of the aerosol absorption at 401 nm is due to BrC.
Article
Full-text available
Recent studies have demonstrated that organic compounds can partition from the gas phase to the walls in Teflon environmental chambers and that the process can be modeled as absorptive partitioning. Here these studies were extended to investigate gas–wall partitioning of organic compounds in Teflon tubing and inside a proton-transfer-reaction mass spectrometer (PTR-MS) used to monitor compound concentrations. Rapid partitioning of C8–C14 2-ketones and C11–C16 1-alkenes was observed for compounds with saturation concentrations (c∗) in the range of 3 × 104 to 1 × 107 µg m−3, causing delays in instrument response to step-function changes in the concentration of compounds being measured. These delays vary proportionally with tubing length and diameter and inversely with flow rate and c∗. The gas–wall partitioning process that occurs in tubing is similar to what occurs in a gas chromatography column, and the measured delay times (analogous to retention times) were accurately described using a linear chromatography model where the walls were treated as an equivalent absorbing mass that is consistent with values determined for Teflon environmental chambers. The effect of PTR-MS surfaces on delay times was also quantified and incorporated into the model. The model predicts delays of an hour or more for semivolatile compounds measured under commonly employed conditions. These results and the model can enable better quantitative design of sampling systems, in particular when fast response is needed, such as for rapid transients, aircraft, or eddy covariance measurements. They may also allow estimation of c∗ values for unidentified organic compounds detected by mass spectrometry and could be employed to introduce differences in time series of compounds for use with factor analysis methods. Best practices are suggested for sampling organic compounds through Teflon tubing.
Article
Full-text available
Particulate emissions from biomass burning can both alter the atmosphere's radiative balance and cause significant harm to human health. However, due to the large effect on emissions caused by even small alterations to the way in which a fuel burns, it is difficult to study particulate production of biomass combustion mechanistically and in a repeatable manner. In order to address this gap, in this study, small wood samples sourced from Côte D'Ivoire in West Africa were burned in a highly controlled laboratory environment. The shape and mass of samples, available airflow and surrounding thermal environment were carefully regulated. Organic aerosol and refractory black carbon emissions were measured in real time using an Aerosol Mass Spectrometer and a Single Particle Soot Photometer, respectively. This methodology produced remarkably repeatable results, allowing aerosol emissions to be mapped directly onto different phases of combustion. Emissions from pyrolysis were visible as a distinct phase before flaming was established. After flaming combustion was initiated, a black-carbon-dominant flame was observed during which very little organic aerosol was produced, followed by a period that was dominated by organic-carbon-producing smouldering combustion, despite the presence of residual flaming. During pyrolysis and smouldering, the two phases producing organic aerosol, distinct mass spectral signatures that correspond to previously reported variations in biofuel emissions measured in the atmosphere are found. Organic aerosol emission factors averaged over an entire combustion event were found to be representative of the time spent in the pyrolysis and smouldering phases, rather than reflecting a coupling between emissions and the mass loss of the sample. Further exploration of aerosol yields from similarly carefully controlled fires and a careful comparison with data from macroscopic fires and real-world emissions will help to deliver greater constraints on the variability of particulate emissions in atmospheric systems.
Article
Full-text available
Multiple trace-gas instruments were deployed during the fourth Fire Lab at Missoula Experiment (FLAME-4), including the first application of proton-transfer-reaction time-of-flight mass spectrometry (PTR-TOFMS) and comprehensive two-dimensional gas chromatography–time-of-flight mass spectrometry (GC × GC-TOFMS) for laboratory biomass burning (BB) measurements. Open-path Fourier transform infrared spectroscopy (OP-FTIR) was also deployed, as well as whole-air sampling (WAS) with one-dimensional gas chromatography–mass spectrometry (GC-MS) analysis. This combination of instruments provided an unprecedented level of detection and chemical speciation. The chemical composition and emission factors (EFs) determined by these four analytical techniques were compared for four representative fuels. The results demonstrate that the instruments are highly complementary, with each covering some unique and important ranges of compositional space, thus demonstrating the need for multi-instrument approaches to adequately characterize BB smoke emissions. Emission factors for overlapping compounds generally compared within experimental uncertainty, despite some outliers, including monoterpenes. Data from all measurements were synthesized into a single EF database that includes over 500 non-methane organic gases (NMOGs) to provide a comprehensive picture of speciated, gaseous BB emissions. The identified compounds were assessed as a function of volatility; 6–11 % of the total NMOG EF was associated with intermediate-volatility organic compounds (IVOCs). These atmospherically relevant compounds historically have been unresolved in BB smoke measurements and thus are largely missing from emission inventories. Additionally, the identified compounds were screened for published secondary organic aerosol (SOA) yields. Of the total reactive carbon (defined as EF scaled by the OH rate constant and carbon number of each compound) in the BB emissions, 55–77 % was associated with compounds for which SOA yields are unknown or understudied. The best candidates for future smog chamber experiments were identified based on the relative abundance and ubiquity of the understudied compounds, and they included furfural, 2-methyl furan, 2-furan methanol, and 1,3-cyclopentadiene. Laboratory study of these compounds will facilitate future modeling efforts.
Article
Full-text available
The formation of secondary organic aerosol from oxidation of phenol, guaiacol (2-methoxyphenol), and syringol (2,6-dimethoxyphenol), major components of biomass burning, is described. Photooxidation experiments were conducted in the Caltech laboratory chambers under low-NOx (< 10 ppb) conditions using H2O2 as the OH source. Secondary organic aerosol (SOA) yields (ratio of mass of SOA formed to mass of primary organic reacted) greater than 25% are observed. Aerosol growth is rapid and linear with the primary organic conversion, consistent with the formation of essentially non-volatile products. Gas- and aerosol-phase oxidation products from the guaiacol system provide insight into the chemical mechanisms responsible for SOA formation. Syringol SOA yields are lower than those of phenol and guaiacol, likely due to novel methoxy group chemistry that leads to early fragmentation in the gas-phase photooxidation. Atomic oxygen to carbon (O : C) ratios calculated from high-resolution-time-of-flight Aerodyne Aerosol Mass Spectrometer (HR-ToF-AMS) measurements of the SOA in all three systems are ~ 0.9, which represent among the highest such ratios achieved in laboratory chamber experiments and are similar to that of aged atmospheric organic aerosol. The global contribution of SOA from intermediate volatility and semivolatile organic compounds has been shown to be substantial (Pye and Seinfeld, 2010). An approach to representing SOA formation from biomass burning emissions in atmospheric models could involve one or more surrogate species for which aerosol formation under well-controlled conditions has been quantified. The present work provides data for such an approach.
Article
Full-text available
We deployed a high-resolution proton-transfer-reaction time-of-flight mass spectrometer (PTR-TOF-MS) to measure biomass-burning emissions from peat, crop residue, cooking fires, and many other fire types during the fourth Fire Lab at Missoula Experiment (FLAME-4) laboratory campaign. A combination of gas standard calibrations and composition sensitive, mass-dependent calibration curves was applied to quantify gas-phase non-methane organic compounds (NMOCs) observed in the complex mixture of fire emissions. We used several approaches to assign the best identities to most major "exact masses", including many high molecular mass species. Using these methods, approximately 80–96% of the total NMOC mass detected by the PTR-TOF-MS and Fourier transform infrared (FTIR) spectroscopy was positively or tentatively identified for major fuel types. We report data for many rarely measured or previously unmeasured emissions in several compound classes including aromatic hydrocarbons, phenolic compounds, and furans; many of these are suspected secondary organic aerosol precursors. A large set of new emission factors (EFs) for a range of globally significant biomass fuels is presented. Measurements show that oxygenated NMOCs accounted for the largest fraction of emissions of all compound classes. In a brief study of various traditional and advanced cooking methods, the EFs for these emissions groups were greatest for open three-stone cooking in comparison to their more advanced counterparts. Several little-studied nitrogen-containing organic compounds were detected from many fuel types, that together accounted for 0.1–8.7% of the fuel nitrogen, and some may play a role in new particle formation.
Article
Proton-transfer-reaction mass spectrometry (PTR-MS) has been widely used to study the emissions, distributions, and chemical evolution of volatile organic compounds (VOCs) in the atmosphere. The applications of PTR-MS have greatly promoted understanding of VOC sources and their roles in air-quality issues. In the past two decades, many new mass spectrometric techniques have been applied in PTR-MS instruments, and the performance of PTR-MS has improved significantly. This Review summarizes these developments and recent applications of PTR-MS in the atmospheric sciences. We discuss the latest instrument development and characterization work on PTR-MS instruments, including the use of time-of-flight mass analyzers and new types of ion guiding interfaces. Here we review what has been learned about the specificity of different product ion signals for important atmospheric VOCs. We present some of the recent highlights of VOC research using PTR-MS including new observations in urban air, biomass-burning plumes, forested regions, oil and natural gas production regions, agricultural facilities, the marine environment, laboratory studies, and indoor air. Finally, we will summarize some further instrument developments that are aimed at improving the sensitivity and specificity of PTR-MS and extending its use to other applications in atmospheric sciences, e.g., aerosol measurements and OH reactivity measurements.
Article
Secondary organic aerosol (SOA) formation during photo-oxidation of primary emissions from cookstoves used in developing countries may make important contributions to their climate and air quality impacts. We present results from laboratory experiments with a field portable oxidation flow reactor (F-OFR) to study the evolution of emissions over hours to weeks of equivalent atmospheric aging. Lab tests, using dry red oak, measured fresh and aged emissions from a 3 stone fire (TSF), a “rocket” natural draft stove (NDS) and a forced draft gasifier stove (FDGS), in order of increasing modified combustion efficiency (MCE) and decreasing particulate matter emission factors (EF). SOA production was observed for all stoves/tests; organic aerosol (OA) enhancement factor ranged from 1.2 to 3.1, decreasing with increased MCE. In primary emissions, OA mass spectral fragments associated with oxygenated species (primary biomass burning markers) increased (decreased) with MCE; fresh OA from FDGS combustion was especially oxygenated. OA oxygenation increased with further oxidation for all stove emissions, even where minimal enhancement was observed. More efficient stoves emit particles with greater net direct specific warming than TSFs, with the difference increasing with aging. Our results show that the properties and evolution of cookstove emissions are a strong function of combustion efficiency and atmospheric aging.
Article
Biomass is increasingly perceived as a renewable resource rather than as an organic solid waste today, as it can be converted to various chemicals, biofuels, and solid biochar using modern processes. In the past few years, pyrolysis has attracted growing interest as a promising versatile platform to convert biomass into valuable resources. However, an efficient and selective conversion process is still difficult to be realized due to the complex nature of biomass, which usually makes the products complicated. Furthermore, various contaminants and inorganic elements (e.g., heavy metals, nitrogen, phosphorus, sulfur, and chlorine) embodied in biomass may be transferred into pyrolysis products or released into the environment, arousing environmental pollution concerns. Understanding their behaviors in biomass pyrolysis is essential to optimizing the pyrolysis process for efficient resource recovery and less environmental pollution. However, there is no comprehensive review so far about the fates of chemical elements in biomass during its pyrolysis. Here, we provide a critical review about the fates of main chemical elements (C, H, O, N, P, Cl, S, and metals) in biomass during its pyrolysis. We overview the research advances about the emission, transformation, and distribution of elements in biomass pyrolysis, discuss the present challenges for resource-oriented conversion and pollution abatement, highlight the importance and significance of understanding the fate of elements during pyrolysis, and outlook the future development directions for process control. The review provides useful information for developing sustainable biomass pyrolysis processes with an improved efficiency and selectivity as well as minimized environmental impacts, and encourages more research efforts from the scientific communities of chemistry, the environment, and energy.