Reactive nitrogen (Nr, defined as all nitrogen-containing
compounds except for N2 and N2O) is one of the most important
classes of compounds emitted from wildfire, as Nr impacts both
atmospheric oxidation processes and particle formation chemistry. In
addition, several Nr compounds can contribute to health impacts from
wildfires. Understanding the impacts of wildfire on the atmosphere requires
a thorough description of Nr emissions. Total reactive nitrogen was
measured by catalytic conversion to NO and detection by NO–O3
chemiluminescence together with individual Nr species during a series
of laboratory fires of fuels characteristic of western US wildfires,
conducted as part of the FIREX Fire Lab 2016 study. Data from 75 stack fires
were analyzed to examine the systematics of nitrogen emissions. The measured
Nr/ total-carbon ratios averaged 0.37 % for fuels characteristic of
western North America, and these gas-phase emissions were compared with fuel
and residue N/C ratios and mass to estimate that a mean (±SD)
of 0.68 (±0.14) of fuel nitrogen was emitted as N2 and N2O.
The Nr detected as speciated individual compounds included the following: nitric
oxide (NO), nitrogen dioxide (NO2), nitrous acid (HONO), isocyanic acid
(HNCO), hydrogen cyanide (HCN), ammonia (NH3), and 44 nitrogen-containing volatile organic compounds (NVOCs). The sum of these
measured individual Nr compounds averaged 84.8 (±9.8) %
relative to the total Nr, and much of the 15.2 % “unaccounted”
Nr is expected to be particle-bound species, not included in this
analysis.
A number of key species, e.g., HNCO, HCN, and HONO, were confirmed not to
correlate with only flaming or with only smoldering combustion when using
modified combustion efficiency, MCE=CO2/(CO+CO2), as a
rough indicator. However, the systematic variations in the abundance of
these species relative to other nitrogen-containing species were
successfully modeled using positive matrix factorization (PMF). Three
distinct factors were found for the emissions from combined coniferous
fuels: a combustion factor (Comb-N) (800–1200 ∘C) with emissions
of the inorganic compounds NO, NO2, and HONO, and a minor contribution
from organic nitro compounds (R-NO2); a high-temperature pyrolysis
factor (HT-N) (500–800 ∘C) with emissions of HNCO, HCN, and
nitriles; and a low-temperature pyrolysis factor (LT-N) (<500∘C) with mostly ammonia and NVOCs. The temperature ranges
specified are based on known combustion and pyrolysis chemistry
considerations. The mix of emissions in the PMF factors from chaparral fuels
(manzanita and chamise) had a slightly different composition: the Comb-N
factor was also mostly NO, with small amounts of HNCO, HONO, and NH3;
the HT-N factor was dominated by NO2 and had HONO, HCN, and HNCO; and
the LT-N factor was mostly NH3 with a slight amount of NO contributing.
In both cases, the Comb-N factor correlated best with CO2
emission, while the HT-N factors from coniferous fuels correlated closely
with the high-temperature VOC factors recently reported by Sekimoto et al. (2018), and the LT-N had some correspondence to the LT-VOC factors. As a
consequence, CO2 is recommended as a marker for combustion Nr
emissions, HCN is recommended as a marker for HT-N emissions, and the family
NH3/ particle ammonium is recommended as a marker for LT-N emissions.
Introduction
Wildfires have severe impacts on the chemistry of the atmosphere from local
to global scales (Crutzen and Andreae, 1990). A warmer, drier climate
in western North America, coupled with policies that have allowed buildup
of fuels in forest ecosystems has led to increases in frequency and severity
of wildfires in this region (Abatzoglou and
Williams, 2016; Westerling et al., 2006). The new strategy for management of
wildfire in the US is to allow fire where possible and to fight fire where
needed (D. C. Lee et al., 2014). The science behind making
these decisions and understanding their consequences involves, in part, a
better understanding of the emissions from wildfires. The NOAA FIREX (Fire
Influence on Regional and Global Environments Experiment) Fire Lab experiment
was conducted in the fall of 2016, at the US Forest Service Fire Sciences
Laboratory in Missoula, Montana, to acquire detailed measurements of
particle and gas-phase emissions from fires involving fuels characteristic
of the western US (NOAA, 2020a). Several aspects of these
measurements dealing with VOC species and individual reactive nitrogen
species (Nr, defined as all nitrogen compounds except for N2 and
N2O) have already been published (Koss et al., 2018; Manfred et al.,
2018; Sekimoto et al., 2018; Selimovic et al., 2018; Zarzana et al., 2018),
including emissions factors for many of the Nr species
(Koss et al., 2018).
Schematic of the combustion chemistry of the small molecules that
are emitted from BB and represent sources and sinks of reactive nitrogen
(Nr), adapted from Glarborg et al. (2018), Lobert and Warnatz (1993),
Lucassen et al. (2012), and Manion et al. (2015). H2O(surf) denotes the
combination of H2O and a surface to facilitate the reaction. Red color
indicates the highest temperature (combustion) processes; blue indicates
intermediate temperature processes, and green indicates the lowest
temperature processes. The species that are measured in this work are shown
in bold and slightly larger text.
The Nr compounds emitted by natural-convection biomass burning (BB)
arise solely from the N in the fuels, since the combustion temperatures are
not high enough (<1200∘C) to produce oxides of nitrogen
(NOx) from N2 and O2 (the so-called Zeldovich or thermal nitrogen
cycle) (Lobert and Warnatz, 1993; Taylor et al., 2004; Wotton et al.,
2012). The fuel nitrogen cycles that pertain to BB flaming combustion are
shown schematically in Fig. 1 (Glarborg et al., 2018; Lobert and
Warnatz, 1993; Lucassen et al., 2012; Manion et al., 2015). Note that the
equations shown in Fig. 1 are meant to indicate the general flow of the
chemistry and do not always convey the mechanistic subtleties of the
reactions, which are fully covered in specialized references (Glarborg et
al., 2018; Manion et al., 2015). Nr compounds are emitted as small
molecules, hydrogen cyanide (HCN), isocyanic acid (HNCO), and ammonia
(NH3) resulting from pyrolysis of the fuel, with minor contributions
from larger N-containing organic species, especially at lower temperatures.
Flame chemistry converts those species to N2, N2O, nitric oxide
(NO), nitrogen dioxide (NO2), and nitrous acid (HONO) as a result of
radical chemistry. It has been recognized for some time that a significant
amount of denitrification (conversion of Nr compounds to N2)
occurs due to reactions of NO with NHi (where i=1, 2, or 3) or N
atoms, as confirmed experimentally (Kuhlbusch et al., 1991).
While N atoms are also intermediates in the thermal NOx cycle, and the
reaction N+O2→NO+O figures in to both the fuel and
thermal NOx cycles, the second reaction of the thermal NOx cycle,
O+N2→NO+N, is too slow at BB flame temperatures to
result in NOx production
(Manion et al., 2015). In
addition to the small molecules shown in Fig. 1, numerous
Nr compounds are emitted in roughly the following categories: amides,
amines, heterocyclic compounds, nitriles, isocyanates, and nitro compounds
(Andreae, 2019; Andreae and Merlet, 2001; Koss et al., 2018; Lobert et
al., 1991, 1990; Lobert and Warnatz, 1993; Stockwell et al.,
2015). These compounds are produced at much lower abundance from fuel
pyrolysis and partial reactions with the radical species in Fig. 1.
The emissions of N compounds from BB and wildfires in general have been the
subject of considerable research. Early studies by Lobert et al. (1990,
1991) and Lobert and Warnatz (1993) measured a wide range of Nr compounds in laboratory fires
and suggested that considerable denitrification (conversion of fuel nitrogen
to N2) was taking place, a process later confirmed in experiments
described by Kuhlbusch et al. (1991). Subsequent work on laboratory fires
has better-defined particle-phase nitrogen emissions
(McMeeking et al., 2009) and led to the
recognition of the importance of several inorganic Nr species, such as
HONO and HNCO (Burling et al., 2010; Roberts et al., 2011; Veres et al.,
2010), and the presence of a wider variety of organic Nr species
(Gilman et al., 2015; Stockwell et al., 2015; Warneke et al., 2011). A
number of studies have sought to summarize both real-world and laboratory
emissions of Nr compounds (Akagi et al., 2011; Andreae, 2019;
Andreae and Merlet, 2001; Coggon et al., 2016; Yokelson et al., 2013b, 2009). The known N compounds range in oxidation state from
NH3 to HNO3 and include N2 and N2O. Among the more
prominent and important Nr species are the following: NOx (NO and NO2),
which is a key player in the atmospheric oxidant cycle; NH3, which has a
major role in particle formation; HONO, which can be an important radical
source; HCN and acetonitrile (CH3CN), which are toxic at high
concentrations and represent valuable tracers for following fire transport;
and isocyanates, HNCO and methyl isocyanate (CH3NCO), which have unique
health impacts (Roberts et al., 2011). In addition,
nitro (R-NO2) or nitrogen heterocyclic compounds may contribute to
so-called brown carbon, aerosol organic compounds exhibiting optical
absorption in the near-UV or blue wavelength regions. Wildfire N emissions
also have very minor contributions from gas-phase nitric acid (HNO3).
Nitric acid is either not efficiently produced by BB or readily
incorporated into aerosol if it is produced in fresh wildfire plumes, as is
clear from the absence of HNO3 enhancements in several studies of BB
plumes (Liu et al., 2016; Yokelson et al.,
2009; Alvarado
et al., 2010); however nitrate (NO3-) has been shown to contribute
to aerosol mass particularly for inefficient combustion
(May et al., 2014). Flame chemistry
is inefficient in forming N2O, relative to the pathways that form
N2 (Andreae, 2019; Andreae and Merlet, 2001; Griffith et al., 1991;
Hao et al., 1991). The modeling of the emissions of these N compounds on a
large scale could benefit from a better understanding of the total budget of
these species as a function of fuel nitrogen content and the dependence of
the individual species on fuel type and combustion conditions.
The construction of Nr budgets in this work is made possible by the
inclusion of a total reactive nitrogen measurement (termed Nr herein),
a method by which all nitrogen compounds besides N2 and N2O are
converted to NO and detected by NO–O3 chemiluminescence. This
technology has been developed by a number of groups, typically using
precious metal or NiCr catalysts that have been shown to convert all Nr
compounds to NO (and to some extent NO2) at high temperatures
(750–825 ∘C) (Hardy and Knarr, 1982; Kashihira et al., 1982;
Marx et al., 2012; Roberts et al., 1988). There are also commercial
instruments that incorporate this technology (see for example Thermo
Scientific Model 17i). This technique has been applied to gas-phase
atmospheric measurements, principally to measure NH3 by difference
techniques (Saylor et al., 2010;
Schwab et al., 2007), and has also been used to observe wildfire plumes that
have impacted ambient air measurements (Benedict et al., 2017; Prenni et
al., 2014). We have recently developed a platinum/molybdenum oxide Nr
catalyst system and confirmed that it quantitatively converts Nr
compounds including all particle-bound nitrogen compounds
(Stockwell et al., 2018). To our knowledge
this technique has not been applied directly to BB emissions before.
This paper describes the total reactive nitrogen and individual Nr
compound measurements made during the Fire Lab 2016 experiment, with the
intent of providing information that can be used for analysis and modeling
of the impact of wildfire emissions on the atmosphere. The total Nr
measurements are combined with CO2, CO, and VOC measurements and fuel,
residue, and ash C and N content to estimate the amount of N lost to N2
and N2O. In addition systematics of the ratio Nr/ total carbon are
examined for simplifying relationships. Fire-integrated Nr is then
compared to fire-integrated measurements of individual compounds to
determine the fraction of unaccounted-for Nr. The systematic behavior
of individual Nr species and their fractional contribution to Nr
are examined with respect to fuel type, N content, and combustion processes.
A positive matrix factorization (PMF) technique is used to examine
commonalities between fires of different fuels under different conditions
and compared to the PMF analysis of the VOC emissions published by Sekimoto
et al. (2018). The results are used to arrive at suggested guidelines that
can be used estimate Nr emissions profiles for fires representative of
western North America.
Nitrogen compounds observed in the FIREX Fire Lab 2016 Study.
Compound/classImportanceMeasurement methodMethod referenceTotal reactive NTotal available for atmospheric reactionsCatalytic conversion NO/O3 chemiluminescenceStockwell et al. (2018)Nitric oxideMajor “flaming-stage” product, oxidant productionNO/O3 chemiluminescenceWilliams et al. (1998)OP-FTIR (open-path Fourier transform infrared)Selimovic et al. (2018)Nitrogen dioxideAtmospheric oxidant productionOP-FTIR, ACES (airborne cavity-enhanced spectrometer)Stockwell et al. (2014), Min et al. (2016), Zarzana et al. (2018)Nitrous acidHOx radical sourceOP-FTIR, ACESStockwell et al. (2014), Min et al. (2016), Zarzana et al. (2018)Nitric acidaParticle precursorOP-FTIRYokelson et al. (2009), McMeeking et al. (2009)Hydrogen cyanideFlame chemistry, atmospheric tracer, toxicityOP-FTIR, PTR-ToF-MS (proton transfer reaction time-of-flight mass spectrometer)Selimovic et al. (2018), Koss et al. (2018)Isocyanic acidFlame chemistry, toxicity, health effectsPTR-ToF-MSKoss et al. (2018)AmmoniaMajor “smoldering-stage” product, main atmospheric base, particle formationOP-FTIRSelimovic et al. (2018)NVOCs: Aminesb Amidesc Heterocyclicsd Nitrilese Nitro compoundsfBrown carbon, toxicity, tracersPTR-ToF-MS, GC/MS (gas chromatography mass spectrometry), I- CIMS (iodide ion chemical ionization mass spectrometer.Koss et al. (2018) Gilman et al. (2015) Lerner et al. (2017) B. H. Lee et al. (2014)
a The OP-FTIR has a 10 ppbv detection for gas-phase HNO3, but
HNO3 was not observed above detection limit.b Ethylamine, methanimine, propeneamine, sulfinylmethanamine,
trimethylamine, buteneamines.c Formamide, acetamide, methylmaleimide.dC2-pyrroles, dihydropyridine, ethynylpyrrole, methylpyridine,
methylpyrrole, pyridinealdehyde, 4-pyrindinol, vinylpyridine.e Acetonitrile, acrylonitrile, benzonitrile, butanenitrile, butynenitrile,
benzoacetonitrile, C7 acrylonitrile, C8-nitriles, heptylnitrile,
furancarbonitrile, methylbenzoacetonitrile, pentylnitriles, propanenitrile,
propynenitrile, butenenitrile, methylisocyanate.f Butenenitrates, nitrobenzene, nitroethane, nitroethene, nitrofuran,
nitromethane, nitropropanes, nitrotoluene.
Methodology
The Fire Lab 2016 study involved laboratory burns of fuels mostly
characteristic of western North American wildfires such as coniferous fuels
and chaparral fuels (important in central to southern California and the
southwestern US). We also measured some that have global significance such
as Indonesian peat and yak dung (important in areas above timberline or
where wood is scarce, such as India, Nepal, and Tibet). The procedures and
associated details of the study have been described previously by Selimovic
et al. (2018) and will be only briefly summarized here. The detailed data
on fuel types, amounts, and composition can be found in Table S1 in the Supplement and in the
Supplement of Selimovic et al. (2018). The laboratory burns
involved fuel samples, ranging in mass from 0.26 to 6.02 kg spread out on a
fuel bed roughly 1m×1m square. Fires were started without the addition of
any contaminants, using an electric igniter (a series of NiCr heating
elements that were flash-heated electrically), and typically lasted from
approximately 5 to 30 min. Seventy-five fires were conducted in the
“stack” burn configuration where the smoke was directed up the central
stack of the facility where it could be sampled simultaneously by all the
instruments that measured gas-phase species and some of the particle-phase
measurements. The sampling platform was about 15 m above the fire, and the
sampling took place in well-mixed smoke approximately 5 s after emission
(Christian et al., 2003). Thirty-one additional
fires were conducted as “room” burns on most of the same fuels, when the
stack was closed and the room was allowed to fill with smoke, permitting
sampling to be done over the course of several hours. The following analyses
will focus on the stack burns, as those measurements had little or no
interferences from surfaces, where room burns are known to be
compromised by the loss of materials, such as NH3, to the room walls at
long sample times (Stockwell et al.,
2014). Ash analyses were performed only on the residues from the room burns,
and those values will be used for the N and C budget calculations, with the
assumption that stack and room burns left similar ash, considering the
combustion conditions were the same for each type of fire. Table 1 lists the
compounds and associated techniques used to measure them during the Fire Lab 2016 study and describes the grouping of NVOCs measured by a proton transfer reaction time of flight mass spectrometer (PTR-ToF-MS) into
common categories, e.g., amines, nitriles, etc. We specifically note that the
OP-FTIR is capable of measuring gas-phase HNO3 with comparatively good
sensitivity (10 ppbv detection limit in fires where Nr can be 5 ppmv or
more), but HNO3 was not observed above detection limit in any of the
fires.
Nr and NO measurements by chemiluminescence
Total reactive N (Nr) was measured by catalytic conversion to NO,
followed by O3 chemiluminescence using an instrument described
previously (Williams et al.,
1998). Nr and NO were sampled from inlets inserted adjacent to the
inlet-less open-path Fourier transform infrared spectrometer (OP-FTIR)
instrument path during the stack burns (Selimovic
et al., 2018) and from a platform approximately 4 m off the floor in the
middle of the room during the room burns. The catalyst used for the Nr
channel, described in detail by Stockwell et al. (2018), consisted of a 11 mm i.d. quartz tube, packed with 36 platinum screens, heated to 750 ∘C. This tube was wrapped with high-temperature heating tape and insulated
inside a 7 cm o.d. stainless steel tube that was fitted to a bulkhead placed
through the wall of the stack. The Nr channel was diluted by a factor
of 5:1 (±3 %) using a flow of zero air added immediately downstream
of the Pt catalyst assembly. NO was sampled through a 6.3 mm o.d. stainless
steel inlet tube, which was placed through the bulkhead directly into the
free air stream of the stack and connected to a 50 mm Teflon filter holder
immediately outside the stack. The transfer lines for the Nr and NO
measurements consisted of 6.35 mm o.d., 1 mm wall thickness PFA tubing of
approximately 20 m in length. Nr and NO data were acquired at 1 s
frequency, but the flow rate through each inlet was 1 SLmin-1,
resulting in residence time in each inlet of 14 s. This time delay was
corrected in the data analysis. Any chemical effects of the inlet on the
sampled air stream were negligible since the analytes consisted of only NO
and NO2, and those are known to be transmitted by PFA Teflon tubing with
essentially no surface effects. However, there were possible effects of the
inlets on the temporal features of the measurement through diffusion or
turbulent mixing. Those effects were examined through comparison of the
temporal variations in the NO signal with the NO measured by the OP-FTIR
and comparison of the Nr signal under smoldering conditions with the
NH3 measured by the OP-FTIR. Both of these comparisons showed that the
NO and Nr inlets had effective time constants of 4 s, somewhat
slower than the diffusive relaxation time assuming solely laminar flow.
Examples of the estimate of diffusion and dispersion on NO and Nr
signals and the estimate of the effective time constant of these
measurements are presented in the Supplement.
The inlet streams were sampled by the NO instrument either directly (NO
channel) or after passing through a second catalyst of molybdenum oxide
(MoOx) to convert remaining NO2 to NO. The MoOx catalyst consisted of a
molybdenum tube at 350 ∘C to which a small flow of H2
(0.8 % v/v) was added to control the redox state of the surface. Both
channels of the instrument were “detuned” to keep raw photon count rates
below 4 MHz, by turning down the O3 flows and photo-multiplier tube (PMT) voltages.
Calibrations were performed with both a NO standard in N2
(Scott-Marrin) and 10.1 ppmv standard of HCN in nitrogen (Gasco). The Pt
catalyst was dismounted from the stack (or room) every few days and checked
for conversion efficiency by the addition of the HCN standard to the inlet.
Conversion efficiencies were found to be consistently high (>98 %) throughout the entire sampling period (5 October–12 November 2016). There were slight background signals (a few tens of ppbv) for both NO
and Nr in both the stack and room air prior to and after the burns, and
those were subtracted from the fires' signals prior to reporting the data.
The overall uncertainties in the NO and Nr data were ±10 % for
each measurement.
Other measurements
Measurements of individual species during the 2016 Fire Lab study have been
presented in several previous publications. The OP-FTIR measurements were
discussed by Selimovic et al. (2018), and the PTR-ToF measurements were
discussed by Koss et al. (2018). In addition, some of the calibration
methods and GC separation and identifications rely on additional analytical
work presented by Sekimoto et al. (2017) and Gilman et al. (2015). We
measured the mass and elemental content of the initial fuel and the mass of
unburned fuel for all the fires, and we measured the mass and the elemental
content of the ash during 21 room burns, which covered all the fuel types
discussed.
PMF analysis
Trace gas measurements from multiple instruments involved in the Fire Lab
study were combined and analyzed using positive matrix factorization (PMF).
PMF is a numerical method that was used in this case to partition the
compounds involved in a time-varying mixture of chemicals into a few groups,
or “factors”, where a compound can appear in more than one factor. A
factor represents a consistent profile of compounds that is representative
of one of the sources contributing to the total signal. The sum of all the
factors then ideally describes the total composition of the
measurements, which in this case is the emissions of Nr compounds. By
its nature, PMF assumes that the total signal is a linear combination of
individual sources that have a consistent composition, the relative
contribution of which is represented by the amount of each compound or
category found in each factor (Paatero and
Tapper, 1994; Ulbrich et al., 2009). We hypothesize that species with
dominant fractions in the same factor are related to each other via the same
formation processes. With knowledge of factor composition and the amount of
each factor at any given time, the original emissions measurements can be
reconstructed, and this approach provides an alternate source of profiles for
fire emissions. PMF has also been used by a number of groups to explore how
much various source profiles contribute to complex ambient measurements (see
for example Ulbrich et al., 2009) and was recently used to analyze
PTR-ToF-MS measurements from the Fire Lab
(Sekimoto et al., 2018). Here, PMF was
accomplished using the PMF Evaluation Tool v. 2.08A
(Ulbrich et al., 2009).
Compounds and compound classes used in the PMF analyses and their
corresponding errors.
The application of PMF to this data set is different than the instances
where it is applied to data from a single instrument in which compound
abundances are inherently scaled properly and error estimates are well
defined and self-consistent. For example, when applied to mass spectral data
from a single instrument, errors can be expected to scale as the square root
of ion counts based on fundamental counting statistics
(Sekimoto et al., 2018). In this work we
are including nitrogen measurements from several instruments, and thus we chose
to use mixing ratios as the unit of comparison. The error estimates required
by the PMF analysis were taken from the reported combined uncertainties: the
sum of the detection limit plus the estimated random error of the measured
value. For example, the uncertainty in a NO mixing ratio of 500 ppbv was
±51 ppbv. The variables that were used in this PMF analysis and their
units and corresponding errors are listed in Table 2. Where compound
categories are specified (e.g., nitriles), the values were the sum of the
measured compounds in that category as listed in the footnotes of Table 1.
The data were further adjusted by subtracting the ambient air background
before and after the fires, which was a relatively minor adjustment for most
compounds and categories. Any negative numbers that resulted were very small
compared to the fire emissions and were set to zero. In addition to the PMF
analysis for the species listed in Table 2, several exploratory runs were
tried with CO2, CO added (in units of ppmv), and total Nr (in units
of ppbv) added to the list in Table 2. No significant differences were
observed in the results for individual Nr compounds and classes, so
CO2, CO, and total Nr were not included in this analysis.
We applied PMF to single-fire data as well as extended time series that
included all fires of a particular fuel type, in line with the approach laid
out by Sekimoto et al. (2018). By
consolidating fuels from a particular vegetation type, the fire-to-fire
variability largely driven by differences in the fuel (e.g., moisture
content, structure, quantity) is constrained and the most representative
fire conditions are captured. Two fuel groups were analyzed in this way: the
western US coniferous ecosystem fuels, which included ponderosa pine,
lodgepole pine, Douglas fir, Engelmann spruce, and subalpine fir, and the
southwestern US chaparral ecosystem, which was represented by chamise and
manzanita. The consolidated time series for the coniferous ecosystems
included realistic mixtures, canopy only, and litter only, while duff and
rotten logs were analyzed separately and not included in the time series.
Results and discussion
The measurements of total Nr can be combined with N and C measurements
of fuel and ash to estimate N lost to N2 and N2O. The total
Nr emitted from laboratory fires combined with individual N compound
measurements allow us to construct a budget for Nr species that
define what the dominant forms of N are and how those emissions depend on
other fire parameters or different temperature combustion processes. The
systematics of N emissions found by PMF are compared to other fire
indicators, and PMF analyses previously conducted on VOCs allow the
formulation of simplifying relationships that can be used in atmospheric
models of wildfires.
Timelines of the (a)Nr and NO, (b)ΔCO2 andΔCO, and (c) MCE and (Nr-NO) /Nr measured during Fire 004, a ponderosa pine realistic mix sample. Note that ΔCO is plotted
at ×10 the measured abundance for clarity.
Comparison of Nr and total carbon in fire emissions
The total Nr and total carbon emissions were measured for 75 stack
fires in order to place the N emissions in the context of total carbon, which
has been widely estimated for wildfires. Example time series of NO, Nr,
ΔCO, and ΔCO2 (CO and CO2 corrected for their
backgrounds) are shown in Fig. 2, for a fire burning a sample of ponderosa
pine realistic mix (Fire 004). In addition to the chemical species, the
modified combustion efficiency (MCE) was also plotted. MCE is defined as
MCE=ΔCO2/(ΔCO2+ΔCO),
where ΔCO2 and ΔCO are the CO2 and CO levels above
the ambient. MCE has traditionally been used to indicate the relative amount
of flaming and smoldering combustion in a fire. The time series for Fire 004
(Fig. 2) shows a short initial smoldering/distillation phase (MCE 0.7 to
0.8) as heat pyrolyzes the fresh fuel and releases VOCs from existing pools
in the fuel, followed after ignition by a relatively efficient mix of flaming
and smoldering combustion (MCE 0.95 to 0.98) and then finally a subsequent
period of essentially pure smoldering (MCE ∼0.80). The
Nr and NO timelines had many features in common because NO is often the
most abundant Nr compound (see below). As a result, it is useful to
compare the quantities Nr-NO and (Nr-NO) /Nr to the other
measures of chemical species or combustion efficiency. As expected,
(Nr-NO) /Nr, in Fig. 2c is anticorrelated with MCE since
Nr is primarily NO at high MCE. In addition to the anticorrelation,
this non-NO fraction, like its approximate carbon analog CO/CO2, has a
wider dynamic range than MCE and will often suffer less from background
variability than carbon-based indices (Yokelson et al.,
2013a).
The concentration profiles of the background-corrected measurements of
Nr, CO2, CO, and all the carbon-containing species measured by the
FTIR (Selimovic et al., 2018) during the stack
burns were integrated over the entire time of the burn to obtain total
carbon, termed TC here, and total Nr. The additional carbon species
included methane and a number of other gas phase VOCs as well as organic-
and black-carbon aerosol. Altogether these carbon species should account for
≥98 % of emitted carbon
(McMeeking et al., 2009). Total Nr
is plotted in Fig. 3, versus TC (Fig. 3a) and versus nitrogen burned,
which is calculated from the %N in the fuel times the mass of fuel
consumed (Fig. 3b). The points in Fig. 3 are colored by the fuel N/C
mole % obtained from the elemental analysis of each fuel. The
relationship between Nr and TC in Fig. 3a clusters around the 0.37 %
line, and those points are from fuels most characteristic of the North
American biomes impacted by wildfire. There are clear outliers in the
correlation of Nr and TC; for example, yak dung and two samples of duff
(Engelmann spruce and subalpine fir) were high due to the fact that
they either have high fuel N/C ratios (dung; see Table S1 in the Supplement) or burned with
minimal flaming (whole-fire MCEs 0.86–0.89, duff and dung), hence
experienced less denitrification. The fuels that were low in Nr/TC in
Fig. 3a, ponderosa pine rotten log, subalpine fir, and excelsior, had low
fuel N/C, so when plotted versus nitrogen burned in Fig. 3b, they cluster
with the main group of characteristic fuels, i.e., they are no longer
“outliers” in the distribution.
Integrated Nr versus (a) integrated total carbon and
versus (b) nitrogen burned, based on fuel nitrogen content and mass of fuel
burned. The points are colored by fuel nitrogen to carbon ratio.
Note that the x and y scales in panel (a) are different by more than a
factor of 100.
Estimates of denitrification
The removal of N to forms that are inactive in the troposphere, N2 and
N2O, has importance in the biogeochemistry of forest ecosystems and
also determines how much N takes part in wildfire plume chemistry. The
points in Fig. 3a are all lower than the corresponding fuel N/C mole
ratio, due to the denitrification chemistry, shown in Fig. 1 and verified
in lab studies described by Kuhlbusch et al. (1991), and the production of
N2O, which is also not measured by the Nr technique. The sum of
N2 and N2O produced in the fires can be estimated from the
difference between the fuel N/C and the Nr/totalC emitted and the data
on C and N content remaining in the ash. The mass balance equations used for
these calculations are detailed in the Supplement.
The histogram of the fraction of N loss to N2 and N2O
estimated from the mass balance analysis described in the Supplement (52 burns).
The distribution of the N lost to N2 and N2O is shown in Fig. 4.
Chemical analyses were not done for all fuels during the stack burns, and
the analysis above assumes that the ash residues and ash / burned fuel ratios
from the stack burns were well represented by those for the same fuels used
in the room burns, for which mass yields and chemical analyses were done.
Data are missing for fuels that did not have a corresponding residue
analysis. The median fraction of N lost to N2 and N2O for
ash-corrected fires was 0.70, and the mean (± standard deviation) was
0.68 (±0.14). This fuel-based estimate is uncertain by approximately
25 % because of the above assumptions concerning the applicability of the
residue analyses from the room burns and because fuel moisture corrections
were assumed to apply to all of the materials burned, foliage vs. woody
biomass (see the Supplement for details). The emission of N2O relative to N2
is approximately 10 % or less for a wide range of fuels
(Andreae, 2019). Assuming the N remainder in our work is at
least 90 % N2 gives values that are somewhat higher than the N2
values reported by Kuhlbusch et al. (1991) where N2 accounted for
36 % of fuel N burned in flaming-stage fires. A closer inspection of
Kuhlbusch et al. (1991) showed a range of N2 yields of 40 %–54 % at
highest MCEs of 0.94–0.97. Possible reasons for these differences are that
the Kuhlbusch et al. (1991) fires were limited to grasses, hay, and pine
needles, and the fires were confined to a closed container and so may not
have experienced the convection and turbulence of typical biomass fires. In
addition, the fires analyzed in our work were somewhat weighted towards the
full canopy and higher temperature burning fuels, since ash analyses were
not done for peat, dung, and many of the “litter” samples, all of which
tend to burn less efficiently. Goode et al. (1999) estimated an N2
emission of 45±5 % for MCE values of 0.95 in grass and surface
fuels. The range of values determined in our work overlap with these
literature values but are on average higher. It should be noted that such
loss of reactive nitrogen can have implications for ecosystem N budgets, as
discussed by Kuhlbusch et al. (1991).
Timelines of (a)Nr and NO, (b)Nr minus NO and the sum of
all measures N species except for NO, (c) residual of Nr minus
all measured N species (Nr-NO-Sum N), (d) and MCE and (Nr-NO) /Nr for Fire 047, subalpine fir realistic
mix. The yellow box highlights the area of higher residual Nr that
corresponds to more smoldering emissions.
The budget of Nr and individual N-containing species
The composition of the N that does not get converted to N2 or N2O
is of intense importance in determining atmospheric impacts of fires, since
those compounds are involved in oxidation capacity (NOx), radical production
(HONO), and particle formation (NH3). Emission factors (EF, defined as mass of compound emitted per mass of fuel burn) for all the
individual Nr compounds identified in our work have been compiled and
reported in previous publications (Koss et al., 2018; Selimovic et al.,
2018), so this paper will focus on the Nr budget. The balance of
Nr budget for Fire 047, subalpine fir realistic mix, is shown in
Fig. 5, in which the timelines of Nr, NO, Nr-NO, sumN, and NVOC
are plotted along with MCE and (Nr-NO) /Nr. The quantity sumN is
the sum of all other non-NO compounds, and NVOC is the subset of sumN that
are organic nitrogen compounds measured by the PTR-ToF, as listed in Table 1. This fire had a mixture of flaming and smoldering combustion throughout
the fire as indicated by MCE and nitrogen profiles (Fig. 5d). The
comparison of Nr-NO with the sumN in Fig. 5b shows that much of the N
is accounted for. The major contributors to sumN for this fire were HNCO,
HCN, HONO, NO2, and NH3, while NVOC was a very small contributor
to sumN (Fig. 5b). Note that while HNO3 is measurable by FTIR with
good sensitivity, no HNO3 signals were observed above detection limit,
which was 10 ppbv. Fig. 5c shows the residual left after NO and sumN are
subtracted from Nr, corresponding to an integrated amount of 15.6±8 % of Nr. This residual is reasonable, considering typical
published particle Nr measurements (Akagi et al., 2012, 2011; Liu et al., 2017; May et al., 2014), and consistent with there
being some particle Nr from flaming, which is most likely organic
nitrates or nitro-organics, and particle ammonium from smoldering with
potassium or ammonium nitrate, potentially accounting for substantial
Nr.
Several fuels had much lower NO emissions and higher unaccounted-for
Nr. Yak dung was one such fuel, the emissions of which stand in contrast
to the fire shown above. The nitrogen emissions from Fire 050, yak dung, are
shown in Fig. S2 in the Supplement. This fuel produced mostly smoldering emissions as
exemplified by the low NO levels relative to Nr (panel a) and the low
MCEs observed (panel d). The sum of Nr species was somewhat correlated
with the quantity Nr-NO but was substantially lower, and the residual
Nr unaccounted for by the gas-phase measurements was 33.9±16 % of Nr (panel c). The majority of sumN was represented by HCN and
NH3, with acetonitrile (CH3CN) higher than any of the other
inorganics, HNCO, NO2, or HONO. The NVOCs were also a larger fraction of
Nr-NO than in the case of Fire 047 shown above, a feature that implies
that more semivolatile organic compounds (SVOCs) survive these types of
fires and could make a proportionally higher contribution to the Nr
budget in this fire relative to Fire 047. Fire Lab results of particle
organic carbon measurements (Jen et al., 2019)
and field measurements in environments with a lot of dung burning
(Jayarathne et al., 2018; Stockwell et al., 2016a) are consistent with a
higher EF for particle organic carbon and by extension particle NVOC
compounds. The quantity (Nr-NO) /Nr was relatively high and had
less dynamic range than for fires with more flaming combustion like Fire 047.
Summary of Xi/Nr measurements for all stack burnsa.
a Not every measurement was available for every fire; consequently the
values do not add up to exactly 100 %.
A histogram of the residual N for all the stack fires during the
2016 Fire Lab study for which there are FTIR, ACES, and PTR-ToF measurements
(n=43). The median is 0.143, and the mean (± SD) was 0.15 (±0.10).
An overall budget of Nr can be constructed for all of the stack fires
through integrating the time profile of all the compounds and compound
classes. The fire-integrated measurements of inorganic and NVOC species are
listed in the Supplement as ratios to Nr for each stack fire
(Table S1). The summary of all the fire integrated Xi/Nr fractions
(where Xi is the Nr species or quantity) is given in Table 3 for
all the fires for which we have a complete set of measurements (43 fires).
In general, NO was the major species, followed by NH3, and the other
inorganic Nr species, NO2, HNCO, HONO, and HCN had individual
contributions of 4.3 % to 9.4 %. NVOC species were less than 5 % of
Nr on average. The unaccounted-for Nr, defined as
(Nr-NO-sumN)/Nr had a median value of 14.3 % and a mean (± SD) of 15 (±10) %. Overall, 85 % of Nr was accounted
for by the gas-phase measurements. The distribution of whole-fire Nr
residuals is plotted as a histogram in Fig. 6. We expect the residual
Nr was composed of either semi- or low-volatility compounds or
particle-bound Nr compounds, which we know are converted efficiently by
the Nr catalyst (Stockwell et al., 2018) but not detected by the
instruments included in this analysis. Along these lines, there is some
indication that the residual has a systematic variation with whole-fire MCE,
with higher residuals (up to 30 %) observed at lower MCEs and higher
(Nr-NO) /Nr (see Fig. S3a, b), which would be consistent
with higher EF for SVOC at low MCE (Jen et
al., 2019) and particle Nr having a higher contribution from
NO3- (May et al., 2014)
and, perhaps, particle ammonium or reduced-Nr compounds. In general,
there is more particulate organic material emitted from fires at low MCE
(Jen et al., 2019), so we would expect more
particle N at low MCE to go along with that.
Systematic dependences of Nr composition on combustion processes
The features noted in fires shown above, as well as the anticorrelation of
MCE and (Nr-NO) /Nr lead to the question of whether there are
systematic dependences in Nr-compound composition on fire stage that
can be used to formally classify and/or potentially predict the relative
emissions of Nr compounds. MCE has been used as a rough indicator of
the relative amounts of flaming and smoldering combustion in a fire, with
high MCE (∼99 %) being “pure” flaming, low MCE
(∼80 %) being “pure smoldering,” and an MCE of
∼0.9 being roughly equal amounts of both (Sect. 2.1.1 in
Akagi et al., 2011). It should be understood that “smoldering” in this
framework is a lumped term that includes all non-flame processes such as
pyrolysis, glowing, and distillation, which are the processes that produce
gaseous fuel to support flaming (Yokelson et al., 1996). In addition, pure
flaming is essentially the efficient oxidation of smoldering products
before they enter the atmosphere. However, for MCE to predict flaming and
smoldering Nr species well, the variable fuel N must be considered. For
instance, NOx is clearly produced by flaming based on its temporal
profile, but fire-integrated EFNOx may not correlate with MCE due to
variable fuel N. In these cases, EFNOx/ fuel N or ΔNH3/ΔNOx may still correlate (or anticorrelate) well with MCE
(e.g., Fig. 4 in Burling et al., 2010, or Yokelson et al., 1996). Finally, the
flame chemistry involving NH3, HNCO, and HCN both produces and destroys
NO in a fashion that does not conserve Nr. This chemistry is explored
in Fig. 7, in which NOx, NH3, HNCO, HCN, HONO, and CH3CN ratios
to Nr are plotted vs. real-time MCE for Fire 047 as a typical example
for fires that have a substantial range of MCEs (e.g., from 0.8 to above
0.98). The relationship between NH3/Nr and MCE confirms that
NH3 is primarily a smoldering emission, and NOx/Nr increases
with increasing MCE in a nonlinear fashion that confirms it is primarily a
flaming compound. Such a nonlinear dependence has also been seen for other
flaming-related quantities such as elemental carbon/TC or EFHCl
(Christian et al., 2003; Stockwell et al., 2014). Most importantly, the
variations in HNCO/Nr, HCN/Nr, HONO/Nr, and
CH3CN/Nr versus MCE do not arise dominantly from either regime, as
these are species that are likely produced by multiple pathways (e.g., “incomplete flaming”, pyrolysis, or possibly glowing). By incomplete
flame chemistry we mean the production of incompletely oxidized products in
flames such as the complex system of reactions shown in Fig. 1. These
reactions involving HNCO, HCN, and NH3 both produce and destroy NO,
while HONO is produced from reactions of NO and NO2 that are faster
at slightly lower temperatures, for example the three-body association
reaction of NO with OH radical
(Manion et al., 2015).
Variable turbulence in the turbulent diffusion flames that are
characteristic of open BB likely contributes to varying temperatures and,
therefore, varying amounts of incomplete oxidation of the fuel N
(Shaddix et al., 1994).
(a) The relationships between NOx/Nr and NH3/Nr vs. MCE and (b) the HNCO/Nr, HCN/Nr, HONO/Nr, and
CH3CN/Nr vs. MCE for Fire 047.
(a) The measured Nr signal for Fire 063 (lodgepole
pine) (blue line), the sum of the signal reconstructed by the PMF (purple
points), and the three PMF factors – combustion (grey), high temperature
(green), and low temperature (red) – plotted in a stacked fashion (i.e., added
on top of one another). (b) The “residual” of the PMF fit consisting
of the measured Nr signal minus the Nr signal reconstructed by the
PMF, as a percentage of the Nr signal.
The PMF analysis of coniferous fuels
The complexity of the dependence of Nr speciation on combustion
chemistry suggests that MCE is an insufficient model to use for applying lab
results to real-world fire emissions (Stockwell et al., 2016a; Yokelson
et al., 2013b). Accordingly, we employed the positive matrix factorization
(PMF) method (see Methodology section) that has been used by a number of
groups to probe the sources contributing to complex mixtures (see for
example Ulbrich et al., 2009; Sekimoto et al., 2018). Our PMF results showed
several general features, irrespective of the inclusion or exclusion of
CO2, CO, and Nr. The emissions were best fit by three factors (with
approximate descriptive names justified below and prime species): (1) a
combustion (flaming) factor (abbreviated Comb-N), (2) a high-temperature
pyrolysis factor (HT-N), and (3) a low-temperature pyrolysis factor (LT-N).
We use these terms in part to harmonize our discussion with the VOC results
discussed by Sekimoto et al. (2018). An example time series for the PMF analysis of a
coniferous fuel with just the Nr species included is shown in Fig. 8
for a realistic mix of lodgepole pine (Fire 063). In this case, several
different Fpeak values were tried (-1, 0, +1) and runs with 100 different
seeds (initial factor profiles) were performed. The results of those
analyses (Fig. S4) show that a three-factor PMF result is robust. A PMF
analysis was performed on the consolidated time series of all coniferous
fuels fit using just the Nr species, as shown in Fig. S5. In this
case Fpeak=0 was used, and the Q/Qexpected showed an inflection for the
three-factor solution at a value of 5.3. The three factors successfully describe
the majority of the Nr emissions where the difference between the
measured and calculated mass is on average 5.1 % for coniferous fuels and
4.6 % for chaparrals as indicated in Table 4.
(a) The contributions of nitrogen species to the factors that simulate
the emissions from coniferous fuels shown in Fig. S2 and (b) the
fraction of each compound or class found in each factor.
Residuals of the PMF analyses by fuel, as percent of total signal.
Several metrics of the PMF analysis quantify how the compounds or compound
classes contribute to each factor. The “loadings” of the three different
factors, i.e., the contribution of compounds to each factor, for coniferous
fuels are shown in Fig. 9a, and the distribution of a given compound or
compound class amongst the three factors is shown in Fig. 9b as
normalized fraction. Normalized fraction is equal to the PMF-determined
contribution of a compound to a factor, divided by the sum of the
contribution of the compound to all three factors. The Comb-N factor
contained NO, NO2, and HONO; the HT-N factor had mostly HCN, HNCO, and
nitriles, with contributions from NO2 and nitro compounds; and the LT-N
factor contained NH3, amines, amides, and heterocyclics. Within the
Comb-N factor there is some evidence that the relative amounts of HONO and
NOx depend on fuel moisture. For example, the ratio HONO/NOx for whole fires
shows some correlation with needle moisture in coniferous fires that were
canopy fuels (foliage and small woody biomass), as shown in Fig. S6. This
may be due to flame processes that interconvert NOx and HONO in the presence
of water vapor or OH (see Fig. 1).
Literature values from studies where flame temperature was measured are
typically in the range of 1100–1200 ∘C
(Taylor et al., 2004; Wotton et al.,
2012), so we would assume that would constitute the upper range of our
Comb-N factor. The radical chemistry involving HCN, HNCO, and NH3 starts
to shut down below about 800–900 ∘C, according to the modeling of
Glarborg et al. (2018), so we set 800 ∘C as a lower limit for the
Comb-N factor. The HT-N factor species are known to be produced by the
intense pyrolysis of fuel Nr compounds (Hansson et al., 2004; Liu et
al., 2018; Ren et al., 2010), which for these compounds becomes important at
temperatures around 500–600 ∘C. Accordingly, we estimate the
temperature range for the HT-N factor at 500–800 ∘C. The
remaining LT-N factor results from mild pyrolysis and pertains to fire
conditions of roughly 500 ∘C and below, and it was dominated by
NH3, amines, amides, and some of the more complex organics
(Koss et al., 2018). The names and
temperature ranges are approximate and likely include processes that occur
inside flames as part of the flame proper, as turbulent diffusive flames are
highly variable in space and time.
Comparisons of the N-PMF combustion factor (Comb-N) with (a)CO2
and MCE for Fire 037 (ponderosa pine). Panel (b) shows the scatter
plot of the Comb-N factor versus CO2, and panel (c) shows the scatter
plot of Comb-N factor versus MCE.
The comparison of N-PMF factors to other fire parameters and VOC
emission factors
It is useful to explore the correlation of N-PMF factors with other fire
indicators to determine relationships for parameterizing Nr emissions
together with carbon and VOC emissions, in order to simplify how emissions
might be parameterized in models. The Comb-N factor for coniferous fuels,
which consisted of NOx and HONO, would be expected to correlate with
CO2 but not as well with MCE since the latter includes an indicator of
incomplete combustion. The time series of Comb-N along with CO2 and with
MCE for Fire 037 (ponderosa pine) are plotted in Fig. 10. As expected
they show an excellent correlation of Comb-N with CO2 (R2=0.942),
since all the species are flaming compounds, but nonlinear correlation of
Comb-N with MCE (R2=0.363), since the latter factors in a smoldering
compound (CO), similar to the NOx/Nr vs. MCE plot for Fire 047 in
Fig. 7. The excellent correlation of Comb-N with CO2 is a broadly
applicable result; the R2 parameters for all the fires shown in Fig. S5 had an average of 0.898 and ranged from 0.806 to 0.966. As a
consequence, we can conclude that CO2 would be the best tracer for
Comb-N in many western US ecosystems where conifers predominate, provided
ambient CO2 backgrounds can be properly accounted for as described by
Yokelson et al. (2013a).
Details of the PMF factors for Fire 037 (ponderosa pine). Panel (a) shows the total Nr signal (magenta) and the Comb-N factor (black);
panel (b) shows the HT-N factor (green) and HT- VOC factor (blue), and panel (c) shows the LT-N factor (red) and LT-VOC factor (orange). The insets (d) and (e) show the correlation of the two HT factors and the correlation
between the two LT factors, respectively.
Our Comb-N factor did not correspond to the high-temperature VOC factor
(HT-VOC) found by Sekimoto et al. (2018) in their pyrolysis study, because
our broader study includes flaming combustion, which produces NOx and HONO,
and almost none of the compounds classified as VOCs survive flaming
conditions. However, the HT-N and HT-VOC factors are both linked to
pyrolysis and were well correlated for many fires. An example of this is
shown in Fig. 11 for Fire 037, a sample that was broadly representative of
ponderosa pine (i.e., canopy and litter). This result can be rationalized by
the fact that while HT-VOC factors have large contributions from many more
compounds than the N compounds measured here, they also have large
contributions (>85 %) from HCN, HNCO, and HONO (in other
words > 85 % of HCN, HNCO, and HONO are found in the HT-VOC
factor). Since the HT-N factors are also heavily weighted by HCN and HNCO,
it is reassuring that both of these PMF analyses have independently
identified these species as important contributors to the HT fire regime.
The R2 correlation coefficients between the HT-N and HT-VOC factors for
the coniferous fires shown in Fig. S5 averaged 0.866 and ranged from 0.419
to 0.959. As a consequence of this correlation, we can conclude that HCN is
the best marker for the HT-N and HT-VOC factors in most western US
wildfires, since HCN is essentially inert on the timescales of fire plumes
(Li et al., 2000). It should be noted that other nitriles, particularly
acetonitrile, also show up in the HT-N factor, and acetonitrile has also
been used as a tracer of biomass combustion. However, it has been shown that
this acetonitrile signal can be obscured in urban or industrial areas by
solvent usage or can be quite small in woodstove emissions due to low N in
the fuel (Coggon et al., 2016).
The correlations of LT-N and LT-VOC factors were not particularly high for
most of the coniferous fires shown in Fig. S5. The average R2 was
0.427 with a range of between 0.072 and 0.827. The reasons for this lack of
correlation are not clear, as NH3, amines, and amides appear
predominantly in both LT factors, and the absolute concentrations of
NH3 are usually quite high in these fires relative to VOCs (Sekimoto et
al., 2018). However, the LT-VOC factor includes many more compounds with a
variety of functional groups not found in the LT-N factor, so it appears
that the VOC and N compounds have sufficiently different pyrolysis chemistry
that the LT factors do not show much correlation. We conclude that NH3
(and particle NH4+) will be the best marker for the LT-N factor in
western US coniferous wildfires, but the LT-VOC chemistry might not be
captured reliably by this marker.
PMF analysis of chaparral fuels
Chaparral is an important ecosystem of concern in wildfires that occur in
central and southern California and other areas of the southwestern US.
The emissions from burning chaparral fuels (manzanita and chamise) collected
at two sites in California were also analyzed as a group and yielded three
separate factors in a fashion similar to the coniferous fuels (see Fig. S7
for the PMF timeline). As with the coniferous fuels, there was essentially
no change in the three-factor solution with Fpeak, so Fpeak=0 was used, and the
Q/Qexp was 3.8. The chaparral factors had slightly different composition
(Fig. S8); the combustion factor was mostly NO, with small amounts of
HNCO, HONO, and NH3; the high-temperature factor was dominated by
NO2 and included HONO, HCN, and HNCO; and the low-temperature factor
was mostly NH3 with a slight amount of NO contributing. The NVOC
species were found in both the medium- and low-temperature factors.
There was less similarity between the Comb-N factor and CO2 emissions
for chaparral fuels compared to those found for coniferous fuels, with an
average correlation coefficient (R2) of 0.689, with a range from 0.244
and 0.950. As a result, there may not be a simple conserved tracer for the
combustion factor of these fuel types; however total odd nitrogen (NOy)
which is NOx and all the compounds that are produced from NOx in the
troposphere, may be useful as it is a reasonably conserved tracer in the
absence of wet or dry deposition of particles. Correlation coefficients
between the HT-N and HT-VOC factors were on average R2=0.551, with
a range 0.047–0.911. The correlations between LT-N and LT-VOC factors were
in the same range for chaparral fuels as for coniferous, with average R2=0.447, range 0.028–0.827.
There were some fuels that do not sustain flaming combustion well,
specifically duff, Yak dung, and Indonesian peat. These fires exhibited
little or no NO emission commensurate with minimal flaming combustion.
Instead the emissions were mostly the pyrolysis products NH3, (0.22–0.53 Nr fraction), and HCN (up to 0.32 Nr fraction for peat). It
was also apparent that these fires also had unaccounted-for Nr, close
to or just over 0.30 (Table S1). The distribution of Nr compounds in
the one peat fire that we measured (Fire 055) is in line with those reported
for fires measured in situ, which showed relatively high EFs for HCN and
NH3 (Stockwell et al., 2016b, 2015).
Application to real-world fires
The application of our Nr emissions results to real-world fires will
depend somewhat on the nature of the information available on a particular
fire or fire complex. As a good starting point, or in the absence of
detailed N and C analyses of fuels, a Nr/C ratio of 0.37 % appears to
capture most of the fires studied in this work. The Nr could be
apportioned according to the results summarized in Table 3. Adjustments to
those fractions can be made by scaling slightly by average MCE, with
the knowledge that intermediate species (HT-N pyrolysis species) such as HCN
and HNCO do not scale in the simple manner that NH3 and NOx do. If
measurements of marker compounds are available then CO2, HCN, and the
sum NH3+NH4+ can be used for the combustion,
high-temperature pyrolysis, and low-temperature pyrolysis factors,
respectively.
Conclusions
Seventy-five stack fire experiments were conducted during the FIREX Fire Lab
experiments in Fall 2016. A range of fuels characteristic of the western
US was burned under conditions and in mixtures meant to represent
authentic wildfire conditions, as closely as is possible in the laboratory.
Total reactive nitrogen (Nr: all N-containing compounds except
N2 and N2O) was measured along with a suite of N-containing
compounds in order to obtain a budget for Nr emissions and to examine
relationships between fuels, combustion conditions, and emissions chemistry.
Natural convection wildfires do not burn hot enough to produce NOx from
N2 and O2, so all Nr emissions come from the fuel N. Almost
all of the fires representative of North American ecosystems had emissions
that clustered around a Nr/C ratio of 0.37 %, which can serve as a
starting point for scaling emissions from these ecosystems. Comparing total
Nr and total carbon emissions with the N/C ratios of both the original
fuel and remaining ash allowed us to estimate that an average of 68 %
(±14 %) of the fuel nitrogen ends up as N2 and N2O. This
loss of nitrogen can be used to estimate how much fuel nitrogen ends up as
Nr. Of the remaining N emitted as Nr, approximately 85 %
(±10 %) was accounted for by individually measured gas-phase
species, while the rest was most likely particle-bound NH4+ and
NO3-, with a smaller contribution from low-volatility species or
other species such as cyanogen (Lobert and Warnatz, 1993), that
were not quantified by the instruments for individual measurements we used
in this study. The speciation and modeling of Nr we present promotes
accurate modeling of fire plume chemistry since the photochemistry of many
fire plumes is NOx-limited, and NH3 is an important contributor to
particle chemistry.
The individual Nr species composition normalized to total Nr, to
account for fuel N variability, correlated monotonically with flaming versus
vs. smoldering combustion as indicated by modified combustion efficiency
(MCE) for some species (e.g., NH3, NOx). Other species, such as HCN
and HNCO, peaked at intermediate MCE values. Positive matrix factorization
(PMF) showed that all the measured Nr emissions from the main two
categories of fuels, conifers, and chaparral, grouped into three mixtures
(factors), roughly attributed to temperature: combustion (NOx, HONO), high
temperature (HNCO, HCN, nitriles), and low-temperature (NH3, amines,
amides). Chemical kinetic and pyrolysis considerations set the temperature
ranges for these regimes at approximately 800–1200,
500–800, and <500∘C, respectively.
This paper connects mechanistic aspects of N combustion chemistry to the
budget of Nr emissions from biomass burning. The emission composition
measurements detailed here give useful information concerning what the
initial conditions will be in actual fire plumes. These results suggest that
for coniferous fuels characteristic of the western US, CO2 is the best
marker for flaming combustion, HCN is the best marker for high-temperature
pyrolysis processes, and NH3/NH4+ is the best marker for low-temperature pyrolysis processes. The HT-N and HT-VOC pyrolysis factors
showed a high degree of correlation especially for coniferous fuels, which can
simplify how these different classes of emissions can be estimated. Results
from less comprehensive field experiments can be combined with this
emissions information to improve the representation of Nr chemistry in
the modeling frameworks needed to predict fire plume chemistry and impacts.
Data availability
The FIREX Fire Lab 2016 data are available at https://esrl.noaa.gov/csd/groups/csd7/measurements/2016firex/FireLab/DataDownload/ (NOAA, 2020b).
The descriptions of the measurements can be found at https://esrl.noaa.gov/csd/groups/csd7/measurements/2016firex/FireLab/dataidtable.html (last access: 1 June 2020).
The complete ash analyses are available on request.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-20-8807-2020-supplement.
Author contributions
JMR, RJY, CW, and JdG designed the research. The measurements were conducted
by JMR, CES, CW, RJY, JdG, YL, VS, ARK, KS, MMC, BY, KJZ, SSB, CS, and SHD.
All authors contributed to the discussion and interpretation of the results
and writing the paper.
Competing interests
Joost de Gouw worked as a consultant for Aerodyne Research during part of
the preparation phase of this paper.
Disclaimer
Any mention of brand names or manufacturers is for information purposes only
and does not constitute an endorsement.
Acknowledgements
Abigail R. Koss acknowledges funding from the NSF Graduate Fellowship Program. Kanako Sekimoto acknowledges funding from the Postdoctoral Fellowships for Research
Abroad from Japan Society for the Promotion of Science (JSPS) and a
Grant-in-Aid for Young Scientists (B) (15K16117) from the Ministry of
Education, Culture, Sports, Science and Technology of Japan. Robert J. Yokelson and
Vanessa Selimovic were supported by NOAA-CPO grant no. NA16OAR4310100. Joost de Gouw was
supported by the NSF AGS grant no. 1748266 under a subcontract to the University
of Montana during the analysis phase of this work. We thank the USFS
Missoula Fire Sciences Laboratory for their help in conducting these
experiments, especially Shawn Urbanski and Thomas Dzomba. This work was also
supported by NOAA's Climate Research and Health of the Atmosphere
initiatives.
Financial support
This research has been supported by the Japanese Society for the promotion of Science (grant no. 15K16117), the NOAA Climate Program Office (grant no. NA16OAR4310100), and the National Science Foundation (grant no. NSF AGS1748266).
Review statement
This paper was edited by Andreas Hofzumahaus and reviewed by two anonymous referees.
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