Introduction
Biomass burning (BB) is a year-round global phenomenon that plays an
important role in the budget of many species in atmospheric chemistry. BB can
be natural (e.g., wildfire) or anthropogenic (e.g., cooking and agricultural
fires) (Crutzen and Andreae, 1990). BB is the largest global source of fine
primary organic aerosol (OA), black carbon (BC), and brown carbon (BrC) (Bond
et al., 2004, 2013; Akagi et al., 2011) and the second-largest source of
CO2, total greenhouse gases, and non-methane organic gases (NMOGs)
(Yokelson et al., 2008, 2009), which are precursors for the formation of
ozone and OA. About 80 % of BB occurs in the tropics, but even the small
fraction of total BB in the western US is responsible for significant US air
quality impacts (Park et al., 2007; Liu et al., 2017). Record high
temperatures, drought, and fire-control practices over the last century have
culminated in a situation in which we can expect more frequent fires and
fires of a larger size and intensity in the western US and Canada (Yue et
al., 2015; Westerling et al., 2006). While wildfires are understood to be a
natural part of many ecosystems, modern-day practices have led to an
accumulation of fuels and a breakdown in the natural ecology of forests,
leading to a disequilibrium, notable in the form of increased fire risk and
fire behavior that is more difficult to control (Stevens et al., 2014).
Prescribing fires and reducing aggressive fire suppression techniques are
options to remedy the situation, but factors not related to the direct risk
of fire, such as atmospheric impacts of smoke on air quality, climate, and
health are still a concern. Despite these important atmospheric chemistry
issues, much of the emissions from BB remain either understudied or
completely unstudied. To date, most of the research on the emissions and
evolution of smoke from US fires has targeted prescribed fires (Burling et
al., 2011; Akagi et al., 2013; Yokelson et al., 2013; May et al., 2014;
Müller et al., 2016). However, wildfires burn a different mix of fuels in
a different season that has more intense photochemistry and different smoke
dispersion scenarios, and they typically consume more fuel per unit area than prescribed fires and can have
different emission factors (EFs, grams of compound emitted per kilogram of
fuel burned) (Campbell et al., 2007; Yokelson et al., 2013; Urbanski, 2013).
For instance, Liu et al. (2017) found that wildfires had an average EF for
PM1 (particulate matter with an aerodynamic diameter less than
1 µm) of more than 2 times that of prescribed fires and that
wildfire PM1 was more OA dominated. Despite the large BB emissions of
greenhouse gases and BC, it has been assumed that BB OA contributes to
negative radiative forcing by BB overall. However, the overall BB forcing
could be positive if the emitted weakly absorbing OA known as BrC is
sufficiently absorbing and long lived (Feng et al., 2013; Jacobsen, 2014;
Saleh et al., 2014; Forrister et al., 2015). This could generate a positive
feedback with the expected increase in BB due to a warming climate (Feng et
al., 2013; Doerr and Santin, 2016; Bowman et al., 2017). Thus, comprehensive
understanding of wildfire contributions to air quality and climate requires
further evaluation.
The Fire Influence on Regional and Global Environments Experiment (FIREX)
(https://www.esrl.noaa.gov/csd/projects/firex/) multiyear campaign led
by the National Oceanic and Atmospheric Administration (NOAA) aims to answer
research questions and critical unknowns about BB that can be addressed with
existing or new technologies, laboratory and field studies, and interpretive
efforts in order to understand and predict the impact of North American fires
on the atmosphere and ultimately support land management. The first phase of
this multiyear campaign took place at the US Forest Service Fire Sciences
Laboratory (FSL) in Missoula, Montana, in the fall of 2016. We deployed a
comprehensive suite of standard instrumentation as well as newer measurement
techniques and analysis methods to better assess BB emissions. Each approach
has its strengths and weaknesses and many uncertainties are difficult to
quantify based on data from a single instrument. Thus, combining results from
many techniques to develop a larger data set is critical to achieving the
fullest understanding of the capabilities of each method and to better
comprehend the full diversity of the emissions and their impacts. Laboratory
fires provide the most cost-effective opportunity to deploy a large suite of
instruments and test new instruments under conditions with realistic
concentration ranges and sample matrix effects such as interferences. Fuel
composition and the ambient conditions under which the fuel burned are better
known in a laboratory. Additionally, only in a laboratory setting can
essentially all of the smoke from a fire be sampled, so that sampling errors
are minimized. For these reasons, numerous laboratory studies have been
crucial to advance our understanding of BB emissions (e.g.,
Lobert et al., 1991; Yokelson et al., 1996; Lewis et al., 2008; McMeeking et
al., 2009, etc). However, accurate lab-based EFs are most valuable when they
result from burning realistically re-created fuels from complex flammable
ecosystems that produce emissions representative of field fires (Yokelson et
al., 2013). Thus, we simulated the fuel and combustion conditions of real
wildfires to the extent possible in hopes of obtaining the most relevant
emissions measurements.
As part of the first (laboratory) phase of FIREX we deployed an open-path
Fourier transform infrared spectrometer (OP-FTIR) and two photoacoustic
extinctiometers (PAXs) operating at 401 and 870 nm. In this paper, based on
these instruments, we report EFs for
23 trace gases and BC and scattering (EFscat) and absorption
(EFabs) at two wavelengths for 31 stack burns (stack burns are
defined later), along with the single-scattering albedos (SSAs) and the
Ångström absorption exponents (AAEs). We also report the trace gas
and BC EFs, along with EFscat, EFabs, and SSA at just
870 nm for another 44 stack fires. After the first 31 fires, our 401 nm PAX
was moved and sampled from a barrel as part of an intercomparison, while the
870 nm PAX stayed sampling the remaining stack fires. After all the stack
fires were finished, the 870 nm PAX moved to participate in an additional
intercomparison of aerosol optical property measurement techniques carried
out in BB aerosol. The intercomparison results will be reported elsewhere
(Manfred et al., 2018). In this paper, we examine how well we succeeded in
our goal of obtaining emissions data representative of real wildfires and how
the fuels influenced the emissions, and we highlight some of the important
species that we measured during FIREX that are still unmeasured in real
wildfires.
Experimental details
US Forest Service Fire Science Laboratory (FSL)
The FSL has a large indoor combustion room described in more detail elsewhere
(Christian et al., 2003; Burling et al., 2010). Briefly, the room is 12.5 m × 12.5 m × 22 m high with a 1.6 m diameter exhaust stack
and a 3.6 m inverted funnel opening approximately 2 m above a continuously
weighed fuel bed. The room can be pressurized to create a large constant flow
that dilutes and completely entrains the fire emissions while venting through
the stack. A sampling platform that can accommodate up to 1820 kg and
sampling ports surround the stack 17 m above the fuel bed. Other
instrumentation can be placed in adjacent rooms with sample lines pulling
from ports at the sampling platform. Previous studies concluded that the
temperature and mixing ratios are consistent across the width of the stack at
the height of the platform, confirming well-mixed emissions that can be
monitored by a number of different sample lines throughout the fire
(Christian et al., 2004).
Our simulated fires used two configurations. In the first configuration,
termed “stack burns”, fires were ignited below the stack and they burned
for a few minutes to half an hour. As the fire evolved, the emissions,
partially diluted and cooled by outside air, traveled up through the stack at
a constant flow rate (∼ 3.3 m s-1). At the platform height, the
well-mixed emissions were near ambient temperature, about 5 s old, and
monitored by a large range of instruments in real time.
In the second scenario, referred to as “room burns”, most of the
instruments were relocated to rooms adjacent to the combustion chamber and
used sample lines that extended well within the combustion room. The stack
was raised, the combustion room was sealed, and the fuels were burned for several
minutes. After about 15–20 min, the smoke from the whole fire was well
mixed vertically in the combustion room and was monitored under approximately
steady-state, low-light conditions for up to several hours, though some
infiltration and losses of gases and particles for some species occurred
(Stockwell et al., 2014). Despite the losses, the configuration is useful for
measurements requiring longer times. The OP-FTIR remained on the sampling
platform during room burns, which helped to document the initial rise of
flaming emissions and verified the overall mixing processes. Temperature and
relative humidity in the combustion room were recorded for all fires and both
stack and room burns were videotaped and stored in the NOAA archive.
Fuels
A team of experts collected fuels that represent fire-prone western US
ecosystems primarily from the Clearwater Wildlife Management Area
(http://fwp.mt.gov/fishAndWildlife/wma/siteDetail.html?id=39754079) and
the Lubrecht Experimental Forest (https://www.cfc.umt.edu/lubrecht/),
which are managed by the state of Montana and University of Montana,
respectively. Chaparral fuels and fuels for the Fire and Smoke Model
Evaluation Experiment (FASMEE, https://www.fasmee.net/) were sampled by
forest fire experts at locations in California and Utah, respectively, and
shipped overnight to the FSL. A few fuels representative of prescribed fires
were sampled by foresters at SE US military bases and burned for comparison
purposes and for the FASMEE project. Sagebrush and juniper were sampled
locally. Indonesian peat, aspen
shavings (also known as “excelsior”), and dung were sampled and burned
because of their global importance and/or to investigate the impact of fuel
chemistry (e.g., N content) on emissions. Fuel components for the forest
ecosystems included duff; litter; dead and down woody debris in different
size classes; herbaceous, shrub, and canopy fuels; and rotten logs from two
of the above ecosystems (ponderosa pine and Douglas fir). These fuel
components were burned both on their own and in realistic three-dimensional
mixtures to mimic the different fuel complexes for various ecosystems. The
first-order fire effects model (FOFEM) (Reinhardt et al., 1997) was used to
calculate the relative amount of each component that typically burns in
coniferous ecosystems, while pure components were burned to probe how they
affected the total emissions. The coniferous ecosystems modeled and burned
included ponderosa pine (Pinus ponderosa), lodgepole pine
(Pinus contorta), Engelmann spruce (Picea engelmanii),
Douglas fir (Pseudotsuga menziesii), and subalpine fir
(Abies lasiocarpa). Chaparral was represented by manzanita
(Arctostaphylos) and chamise (Adenostoma fasciculatum). A
full description of the fuels for each fire, including collection location;
C, H, N, S, and Cl content; dry weight of each component; ignition time; etc.
is included in Table S1 in the Supplement. Moisture content, ash data, and
other details for fuels and fire duration were also recorded and are
available in the NOAA archive or from the corresponding author.
Instrument details
Extensive instrumentation that monitored both the gas-phase and particle-phase emissions from BB was deployed during the FIREX laboratory study. A
table of all the instruments can be found at this URL
(https://www.esrl.noaa.gov/csd/projects/firex/firelab/instruments.html).
We reiterate that for the first 31 stack fires the two PAXs were the only
instruments measuring aerosol optical properties on the platform and only the
870 nm PAX measured optical properties on the sampling platform for the next
44 fires, which accounts for all the stack burns. The 401 nm PAX was
deployed with a BC intercomparison that measured subsamples of smoke in a
mixing barrel for fires 32–107. The 870 nm PAX was deployed with a large
group of aerosol instruments that characterized aerosol subsamples from the
room burns (fires 76–107). Other aerosol measurements on the platform during
the stack burns included filter sampling with off-line analysis of
non-methane organic compounds and AMS characterization of diluted smoke. Here
we present the PAX (and FTIR) measurements on the platform and the other
results will be described elsewhere.
Open-path Fourier transform spectrometer (OP-FTIR)
The OP-FTIR consisted of a Bruker MATRIX-M IR cube spectrometer with a
mercury cadmium telluride (MCT) liquid-nitrogen-cooled detector interfaced
with a 1.6 m base open-path White cell. The optical path length was 58 m
and IR spectra were collected at a resolution of 0.67 cm-1 from
600–4000 cm-1. During stack burns, the OP-FTIR was positioned on the
sampling platform so that the open path fully spanned the width of the stack.
This allowed continuous direct measurements across the rising emissions. A
pressure transducer and two temperature sensors were located directly
adjacent to the White cell optical path and were used for spectrum fitting
and to calculate mixing ratios from the IR spectra. For stack burns the time
resolution was approximately 1.37 s and the duty cycle was
> 95 %. For the room burns, where concentrations changed more
slowly, we increased the sensitivity by co-adding scans (time resolution of
approximately 5.48 s) and moved the OP-FTIR to the edge of the sampling
platform closest to the fires.
Excess
mixing ratios of 21 trace gases vs. time for a complete juniper canopy stack
burn (no. 75) as measured using the OP-FTIR. CO2 denotes flaming; CO denotes smoldering. 1,3-Butadiene is
shown as an example of lower signal-to-noise data but retained since there is
no evidence of bias.
Mixing ratios were determined for carbon dioxide (CO2), carbon monoxide
(CO), methane (CH4), acetylene (C2H2), ethylene
(C2H4), propylene (C3H6), 1,3-butadiene (C4H6),
formaldehyde (HCHO), formic acid (HCOOH), methanol (CH3OH), acetic acid
(CH3COOH), glycolaldehyde (C2H4O2), furan
(C4H4O), furaldehyde (C5H4O), phenol (C6H6O),
hydroxyacetone (C3H6O2), water (H2O), nitric oxide (NO),
nitrogen dioxide (NO2), nitrous acid (HONO), ammonia (NH3),
hydrogen cyanide (HCN), hydrogen chloride (HCl), and sulfur dioxide
(SO2). Mixing ratios were based on retrievals utilizing multicomponent
fits to specific sections of mid-IR transmission spectra with a synthetic
calibration nonlinear least-squares method (Griffith, 1996; Yokelson et al.,
2007) applying both the HITRAN spectral database and reference spectra
recorded at the Pacific Northwest National Laboratory (Rothman et al.,
2009; Sharpe et al., 2004; Johnson et al., 2010, 2013). The above species
were always or often enhanced in the smoke well above the real-time detection
limits, but some species such as 1,3-butadiene, furaldehyde, phenol, and HCl
were frequently not enhanced to more than 2–3 times the real-time detection
limit and are not reported in those cases. The uncertainties in the
individual mixing ratios varied by spectrum and molecule and were influenced
by uncertainty in the reference spectra (1–5 %) or the real-time
detection limit (0.5–20 ppb), whichever was larger. Typical stack
concentrations ranged from hundreds of parts per billion to thousands of parts per million depending on
the analyte (Fig. 1 and Stockwell et al., 2014).
Photoacoustic extinctiometers (PAX) at 870 and 401 nm
Particle absorption and scattering coefficients (Babs,
Bscat Mm-1) were measured directly at 1 s time resolution
using two PAXs (Droplet Measurement
Technologies, Inc., Longmont, CO; Lewis et al., 2008), and SSA at 401 and 870 nm and the AAEs were derived using those measurements. A 1 L min-1 aerosol
sample flow was drawn through each PAX using a downstream pump and split
internally between a nephelometer and photoacoustic resonator for
simultaneous measurement of light scattering and absorption. Scattering of
the PAX laser was measured using a wide-angle reciprocal nephelometer that
responds to all particle types regardless of chemical makeup, mixing state,
or morphology. For absorption measurements, the laser beam was directed
through the aerosol stream and modulated at a resonant frequency of the
acoustic chamber. Absorbing particles transferred heat to the surrounding
air, inducing pressure waves that were detected via a sensitive microphone.
Advantages of the PAX include direct in situ measurements, a fast response
time, continuous autonomous operation, and eliminating the need for filter
collection and the uncertainties that come with filter artifacts (Subramanian
et al., 2007).
We sampled stack burns through ∼ 2 m of 0.638 cm (o.d.) Cu tubing
that ran from the stack to a splitter that connected the two instruments.
From the splitter, each separate sample line encountered a scrubber to remove
UV-absorbing gases such as NO2 (Purafil SP Media, minimum removal
efficiency 99.5 %) and then a diffusion drier (silica gel 4–10 mesh) to
remove water, with this order ensuring that both instruments were sampling at
the same relative humidity (varying between 13 and 30 %). The scrubber
and drier were refreshed before any signs of deterioration were observed
(e.g., color change) and the diffusion-based designs should incur minimal
particle losses, but losses were not explicitly measured. After the drier,
each sample line featured a 1.0 µm size-cutoff cyclone and two
acoustic notch filters that reduced noise. Both PAX instruments were
calibrated before and after the experiment using the manufacturer-recommended
scattering and absorption calibration procedures utilizing ammonium sulfate
particles and a propane torch to generate pure scattering and strongly
absorbing aerosols, respectively. The estimated uncertainty in PAX absorption
and scattering measurements has been estimated as ∼ 4–11 %
(Nakayama et al., 2015).
Emission ratios (ERs), emission factors (EFs), and modified combustion
efficiency (MCE)
We convert the time series of mixing ratios for each analyte (Fig. 1) into a
form that is broadly useful to others for implementation in local to global
chemistry and climate models. For this, we produce emissions ratios (ERs) and
EFs. The process starts by calculating excess mixing ratios (denoted ΔX for each species “X”) for all 23 gas-phase species measured using the
OP-FTIR by subtracting the
relatively small average background mixing ratio measured before each fire
from all the mixing ratios observed during the burn. The molar ER for each
species X relative to CO2 (ΔX/ΔCO2) is the ratio
between the sum of the ΔX over the entire fire relative to the sum of
the ΔCO2 over the entire fire. A comparison of the sums is valid
because the large entrainment flow ensures a constant total flow. Molar ERs
to CO2 were calculated for all the species measured using the
OP-FTIR for all 75 stack burns and
the two most important room burns. For the other room burns, OP-FTIR data
were generated only for CO2, CO, CH4, C2H4,
C2H2, and H2O as losses in the room add uncertainty to the
mixing ratios for many NMOGs, NH3, etc. The ERs to CO2 were then used to derive
EFs calculated with the carbon mass balance (CMB) method, which assumes all
of the burned carbon is volatilized and that all of the major
carbon-containing species have been measured (Ward and Radke, 1993; Yokelson
et al., 1996, 1999; Burling et al., 2010, Stockwell et al., 2014):
EFXgkg-1=FC×1000×MMxAMC×ΔXΔCO2∑j=1nNCj×ΔCjΔCO2,
where FC is the measured carbon mass fraction of the fuel,
MMx is the molar mass of species X, AMC is the atomic mass
of carbon (12 g mol-1), NCj is the number of carbon atoms in each
species j, and ΔCj or ΔX referenced to ΔCO2
are the source-average molar ERs for the respective species. The
denominator of the last term in Eq. (1) estimates total carbon. Based on many
BB combustion sources measured in the past, the species CO2, CO, and
CH4 usually comprise 97–99 % of the total carbon emissions (Akagi
et al., 2011; Stockwell et al., 2015). Our estimate of total carbon in this
paper includes these three species and all the rest of the C-containing gases
measured with the OP-FTIR as well as the C in the particles (i.e., BC and OC)
based on the PAX data. Samples of each fuel component were analyzed for
moisture content by weighing until dry and for C, H, N, S, and Cl by a
commercial (ALS, Tucson) and an academic laboratory, whose results agreed
well with each other on several overlapping fuel samples. The fire-average
carbon mass fractions for mixed fuel beds were calculated from the average of
the relevant fuel component analyses weighted by dry mass (Table S1). The
usually small error in the CMB method caused by neglecting char formation (Bertschi
et al., 2003) tends to be canceled by more complete combustion of the
higher-C components (Santín et al., 2015) and both these effects are
ignored here but will be explored in more detail in a companion study.
Two major combustion processes are often recognized for open burning of
biomass: flaming and smoldering, where smoldering is an approximate term
for all non-flaming processes (e.g., glowing and pyrolysis) as explored in
more detail elsewhere (Yokelson et al., 1996; Koss et al., 2017). Combustion
efficiency is the fraction of fuel carbon converted to carbon as CO2,
which is maximized by flaming combustion, but the modified combustion
efficiency (MCE) is also a useful approach for characterizing the relative
amount of smoldering and flaming combustion by comparing the fuel carbon
converted to CO2 versus CO2+ CO. Although the two processes often
occur simultaneously throughout a fire, a high MCE (near 0.99) is an
indication of nearly pure flaming, while a lower MCE (∼ 0.8) is an
indication of nearly pure smoldering (Akagi et al., 2011) and an MCE of 0.9
would indicate roughly equal amounts of flaming and smoldering (i.e., a
flaming / smoldering ratio of ∼ 1):
MCE=ΔCO2ΔCO+ΔCO2.
In the PAX, the 870 nm laser is absorbed in situ by BC-containing
particles only without filter or
filter-loading effects that can be difficult to correct. We directly
measured aerosol absorption
(Babs, Mm-1) and used the literature-recommended mass
absorption coefficient (MAC) (4.74 ± 0.63 m2 g-1 at
870 nm) to calculate the BC concentration (µg m-3) (Bond and
Bergstrom, 2006), but the BC mass can be adjusted using different MAC values
if supported by future work. Because the PAXs also measured light scattering,
scattering and absorption values can be combined to directly calculate the
SSA (the ratio of scattering to total extinction). SSA is a useful input for
climate models, where an SSA closer to 1 indicates a more “cooling” highly
scattering aerosol:
SSA=scattering(λ)scattering(λ)+absorption(λ).
To a good approximation,
sp2-hybridized carbon (including BC) absorbs light proportional to
frequency (Bond and Bergstrom, 2006). Thus, the Babs contribution
from BC at 401 nm can be calculated from 2.17 times Babs at
870 nm, and any additional Babs at 401 nm can be assigned to
BrC subject to limitations due to “lensing” by coatings discussed elsewhere
(Pokhrel et al., 2016, 2017; Lack and Langridge, 2013; Lack and Cappa, 2010).
Pokhrel et al. (2017) found that coatings typically accounted for much less
than 30 % of absorption in room burn smoke 1–2 h old and coatings are
likely much less important in 5 s old stack burn smoke (Akagi et al., 2012).
Coating effects are very difficult to deconvolve from BrC effects even with
additional instruments that were not available during the stack burns
(Pokhrel et al., 2017). This adds some uncertainty to the BrC attribution
(±25 %) but not to the absorption measurements themselves. Absorption
by the BrC component of OA means that an approximate mass of OA can be
calculated using an OA MAC of 0.98 m2 g-1 (Lack and Langridge,
2013), but the MAC for OA is variable because BrC chemistry and BrC / OA
vary and the OA MAC is also highly dependent on the BC / OA ratio as
described elsewhere (Saleh et al., 2014). We use the qualitative OA to
calculate a small term in our CMB method that helps account for unmeasured
C species (assuming OA / OC of 1.6), but we do not report OA or OC in the
tables as quantitative species. Critically though, we do report the OA
absorption due mainly to BrC at 401 nm, a poorly characterized term that
needs to be improved in climate models to better estimate the radiative
forcing of BB aerosol (Feng et al., 2013). The mass ratio of BC to the CO2
measured using the FTIR was multiplied by the EFs for CO2 to determine mass EFs
for BC (g kg-1). The EFs for absorption and scattering optical cross
sections at 870 and 401 nm (EFabs, EFscat) were
calculated from the measured ratios of Babs and Bscat
to CO2 and reported in units of square meters per kilogram of dry fuel
burned. EFabs or EFscat are more precisely the
optical cross-section (m2) due to the particles produced when a kilogram
of fuel is burned if the emissions are mixed into a cubic meter of air. These
EFs enable direct calculation of the absorption or scattering coefficient
(m-1) by multiplication with a specified ratio of fuel burned to a
volume of air (kg m-3) (Bond et al., 1999; Moosmüller et al.,
2005). We also report the estimated portion of
Babs at 401 nm due to BrC. Finally, the AAE (401 and 870 nm)
can be calculated from the 401 and 870 nm data, where the AAE of pure BC is
close to 1 and larger values are indicative of smoke absorption more
dominated by BrC emissions:
AAE=-logBabs,1Babs,2logλ1λ2.
The AAE is useful as an indicator of BrC / BC, but in addition, the full
aerosol absorption spectrum is often approximated with a power-law function
(absorption = C × λ-AAE) and thus the AAE determined
with any wavelength pair can be used to approximately calculate the shape of
absorption across the UV–VIS range (Reid et al., 2005).
Average emission factors (g kg-1) of common western US
ecosystems measured in the lab.
Compound
Douglas
Engelmann
Lodgepole
Ponderosa
Subalpine
Chaparral –
Chaparral –
fir
spruce
pine
pine
fir
chamise (NM*)
manzanita (NM)
CO2
1685.99 (23.68)
1644.61 (55.81)
1688.52 (22.26)
1699.05 (23.11)
1659.79 (10.91)
1714.70 (14.78)
1698.45 (15.79)
CO
65.87 (12.66)
69.42 (18.47)
70.52 (9.67)
78.52 (10.90)
72.80 (5.07)
55.82 (4.96)
40.62 (0.72)
CH4
2.31 (0.39)
3.02 (1.38)
2.61 (0.32)
2.76 (0.85)
3.86 (1.34)
1.26 (0.10)
1.14 (0.07)
Methanol
0.73 (0.14)
1.34 (0.70)
0.86 (0.20)
1.31 (0.59)
1.28 (0.55)
0.40 (0.04)
0.53 (0.07)
(CH3OH)
Formaldehyde
1.53 (0.40)
1.56 (0.26)
1.67 (0.50)
1.79 (0.46)
1.92 (0.32)
0.55 (0.002)
0.46 (0.14)
(HCHO)
Hydrochloric
–
0.05
–
–
–
–
–
acid (HCl)
Acetylene
0.40 (0.11)
0.32 (0.07)
0.55(0.11)
0.47 (0.15)
0.50 (0.05)
0.31 (0.08)
0.22 (0.09)
(C2H2)
Ethylene
1.33 (0.31)
1.18 (0.21)
1.85 (0.35)
1.61 (0.47)
1.86 (0.53)
0.48 (0.05)
0.57 (0.18)
(C2H4)
Propene
0.35 (0.05)
0.45 (0.20)
0.71 (0.42)
0.52 (0.14)
0.68 (0.36)
0.11 (0.01)
0.17 (0.05)
(C3H6)
Ammonia
0.47 (0.07)
1.13 (0.70)
0.62 (0.13)
0.69 (0.22)
0.85 (0.57)
0.56 (0.02)
0.52 (0.03)
(NH3)
1,3-Butadiene
0.01
0.02
0.06 (0.04)
0.04 (0.02)
0.09 (0.03)
–
–
Acetic acid
1.14 (0.20)
1.71 (0.46)
1.12 (0.46)
1.64 (1.03)
1.99 (1.36)
0.74 (0.05)
1.75 (1.39)
(CH3COOH)
Formic acid
0.25 (0.07)
0.23 (0.02)
0.21 (0.05)
0.28 (0.09)
0.26 (0.06)
0.05 (0.002)
0.18 (0.16)
(CH2O2)
Furan
0.14 (0.05)
0.15 (0.11)
0.18 (0.04)
0.30 (0.10)
0.16 (0.03)
0.06 (0.03)
0.46 (0.59)
(C4H4O)
Hydroxyacetone
0.58 (0.07)
0.75 (0.16)
0.53 (0.29)
0.97 (0.29)
0.72 (0.09)
0.36 (0.07)
0.31 (0.08)
Phenol
0.46 (0.41)
0.62 (0.09)
0.42 (0.18)
0.89 (0.20)
0.61 (0.27)
0.49 (0.07)
0.31 (0.09)
Furaldehyde
0.68
0.72 (0.17)
0.73 (0.06)
0.95 (0.26)
0.58 (0.37)
0.53 (0.25)
0.72 (0.11)
NO
1.83 (0.24)
1.71 (0.11)
1.84 (0.14)
1.25 (0.40)
1.85 (0.12)
2.39 (0.05)
1.89 (0.01)
NO2
1.57 (0.32)
2.03 (0.44)
1.13 (0.32)
1.53 (0.70)
1.37 (0.19)
0.49 (0.11)
0.81 (0.10)
HONO
0.65 (0.18)
0.42 (0.16)
0.68 (0.05)
0.60 (0.19)
0.71 (0.05)
0.48 (0.11)
0.44 (0.01)
Glycolaldehyde
0.53 (0.06)
0.63 (0.06)
0.63 (0.10)
0.69 (0.17)
0.76 (0.14)
0.12
0.18
HCN
0.20 (0.02)
0.27 (0.05)
0.24 (0.05)
0.29 (0.08)
0.25 (0.05)
0.10 (0.03)
0.07
SO2
1.18 (0.06)
1.32 (0.19)
1.31 (0.15)
1.49 (0.50)
1.67 (0.48)
0.82 (0.05)
0.90
MCE
0.94 (0.01)
0.94 (0.02)
0.94 (0.01)
0.93 (0.01)
0.94 (0.01)
0.95 (0.01)
0.96 (0.001)
Values in brackets are (1σ) standard deviation.
* “NM” indicates the relatively unpolluted North Mountain sample
collection site.
Results and discussion
Overview of wildfire trace gas emissions
We sampled a total of 75 stack burns and 32 room burns at the FSL combustion
facility during October and November 2016. Figure 1 shows temporal profiles
for the excess mixing ratios of 21 gas-phase compounds (not including water)
measured with the OP-FTIR for a complete juniper canopy fire (fire 75).
Immediately after ignition, the fire is characterized by a large increase in
CO2, corresponding to flaming, followed by a slower increase in CO from
smoldering combustion. As is common to most fires, there is no clear
distinction between flaming and smoldering but rather an evolving mix of the
two processes. Fire-integrated ERs to CO2 and EFs were determined for all 75 stack fires based on
the whole fire. For room burns, we calculated EF based on integrating the
ΔX only up to the point at which emissions were well mixed to capture
the whole fire but also minimize the effect of wall losses and infiltration
(see Fig. 3 in Stockwell et al., 2014). The fire-integrated EFs for some of
the most common western US ecosystem fuel complexes sampled in this study are
summarized in Table 1. These are averages of the replicate fires (three to
four replicate measurements for each fuel type). Table 1 does not reveal a
strong ecosystem dependence across the coniferous ecosystems but does
indicate lower EFs for many pollutants emitted by the chaparral fires.
However, large wildfires often burn in multiple fuel types simultaneously.
For instance, the Rim Fire burned in pine, pine–oak, and chaparral fuels
simultaneously (Liu et al., 2017). These factors justify using a single set
of EFs for all wildfires, unless detailed fuel data are available that
warrant more precise EF estimates. The derivation of the best wildfire EFs is
explored in more detail in the next section. A summary of all the EFs we
measured with OP-FTIR and PAX can be found in Table S2, with the averages of
specific fuel components and complexes found in Table S3. Numerous additional
NMOGs that were measured using other instruments (e.g., H3O+
chemical ionization mass spectrometer (CIMS) and I- CIMS) will be
presented elsewhere (Koss et al., 2017). These additional species are often
reactive and very important in plume chemistry even though they have only a
small effect on the carbon mass balance. A few species were measured with
both OP-FTIR and MS and the preferred values depend on several issues such as
S / N (often better on MS), interference (often worse on MS),
“stickiness”, fragmentation, and proton affinity that are discussed in more
detail elsewhere (Koss et al., 2017).
Methane emissions from 75 stack fires plotted against
corresponding MCE and wildfire field methane emissions plotted against
corresponding wildfire field MCE. Also included are the field-average
methane emissions (blue) and the predicted methane emissions (purple) using
the linear regression shown and a field-average MCE of 0.912.
Comparison of laboratory EF to wildfire EF
It is important to compare our FIREX laboratory fire emissions data to field
measurements of real wildfires to assess how representative and useful the
lab-based data are, especially for the many species measured in the
laboratory but not the field. We assess representativeness by comparing the
EF results for species measured in both the field and our laboratory fires.
EF measurements on real wildfires are rare, but Liu et al. (2017) report
recent EFs for three wildfires sampled during the 2013 Studies of Emissions
and Atmospheric Composition, Clouds, and Climate Coupling by Regional
Surveys (SEAC4RS, https://espo.nasa.gov/missions/seac4rs) (Toon et al.,
2016) campaign, and the Biomass Burning Observation Project (BBOP,
https://www.arm.gov/research/campaigns/aaf2013bbop) campaign.
Summary of the comparison of emission factors (g kg-1)
measured in the lab and field.
Compound
Lab avg
Lab eqn
Lab eqn
Lab-based
Liu et al. (2017)
Predicted/
Lab avg/
EF
slopea
intercept
prediction
EF
field
field avg
CO2
1646.90
2804.24
-960.40
1600
1454
1.10
1.13
CO
78.16
-1049.297
1053.751
95.74
89.30
1.07
0.88
CH4
3.31
-81.531
79.112
4.76
4.90
0.97
0.68
NOx as NO
2.98
22.6627
-18.2162
2.47
0.49
5.04
6.08
Acetic acid
1.88
-32.3429
31.9418
2.41
–
–
–
NO
1.81
12.6048
-9.9742
1.53
0.11
13.91
16.45
Formaldehyde
1.68
-30.4300
29.9621
2.18
2.29
0.95
0.73
Ethylene
1.63
-16.6799
17.1354
1.91
0.91
2.10
1.79
SO2
1.37
-7.9297
8.7467
1.51
0.32
4.72
4.29
Methanol
1.32
-36.3839
35.1443
1.93
2.45
0.79
0.54
NO2
1.20
-4.9035
5.7873
1.31
0.58
2.26
2.07
Ammonia
1.10
-31.3876
30.2792
1.62
–
–
–
Furaldehyde
0.82
-13.9054
13.7561
1.06
–
–
–
Hydroxyacetone
0.80
-15.9636
15.6891
1.11
1.13
0.98
0.71
Glycolaldehyde
0.73
-11.4308
11.3395
0.90
–
–
–
Phenol
0.70
-15.0074
14.7376
1.03
–
–
–
Propene
0.61
-10.0850
9.9817
0.77
0.35
2.20
1.74
HONO
0.56
-2.4751
2.8703
0.61
–
–
–
Acetylene
0.45
-2.4893
2.7722
0.50
0.24
2.08
1.89
HCN
0.36
-7.3943
7.2227
0.47
0.34
1.38
1.06
Formic acid
0.27
-5.3701
5.2629
0.36
–
–
–
Furan
0.23
-5.3695
5.2244
0.32
0.51
0.63
0.45
1,3-Butadiene
0.17
-9.8599
9.3401
0.34
0.06
5.67
2.83
HCl
0.11
-2.5126
2.4661
0.17
0.004
35
27.5
Average ratio smoldering compoundsb
0.96
0.76
SD ratio
0.29
0.23
Fractional uncertainty
0.30
0.30
a The slope and intercept parameters enable calculation of EF at alternate MCE values.
b Average of less reactive and moderately reactive species: includes formaldehyde, methanol, hydroxyacetone, and HCN.
Reactive smoldering compounds were left out.
We compare our results from the FSL combustion studies to those reported by
Liu et al. in two ways. In method 1, we plot the lab-measured EFs against
their corresponding MCEs for all the fires and we fit the data with a linear
regression relationship for each compound. Using the slope and y intercept
of the linear regression, and the field-average MCE from Liu et al. of 0.912,
we calculate a lab-based prediction of EF at the field-average MCE for each
compound measured with the OP-FTIR.
Figure 2 shows an example of the procedure for CH4, comparing the
lab-predicted EF at the field-average MCE (4.76 g kg-1) to the average
field-measured wildfire EF (4.90 g kg-1). In method 2, we compared the
simple lab-average EF to the average field-measured wildfire EF. The results
of these two methods are shown in Table 2 and Fig. 2. Method 1 is generally
preferred because the laboratory fires had a higher average MCE (i.e., a
higher fire-integrated flaming / smoldering ratio) than the real
wildfires sampled to date, most likely due to some unavoidable drying of the
fuels during storage and some underrepresentation of the largest diameter
fuels (Table S1). The differences between the laboratory prediction at the
field-average MCE and the field-average emissions are probably mostly due to
the relative age of the smoke and the reactivity of compounds. The field
study included smoke samples up to 2 h old and elevated OH, HO2,
H2O2, O3, etc. have been observed in fresh smoke plumes (Hobbs
et al., 2003; Yokelson et al., 2009). Thus the more reactive species (e.g.,
SO2, HCl, NOx, and some NMOGs) have lower EFs in the field data.
For example, the lab / field ratio increases going from ethylene to
propene to 1,3-butadiene in accordance with, though not directly proportional
to, their increasing OH rate constants, and other chemistry, instrumental,
and sampling challenges are relevant for some species (e.g., Finlayson-Pitts
and Pitts, 2000; Apel et al., 2003; Fig. 7 in Hornbrook et al., 2011;
Burkholder et al., 2015). A few reactive species were measured in two older
airborne studies of fresh western US wildfire smoke and they agree
significantly better with our lab-based predictions (Radke et al., 1991;
Hobbs et al., 1996). For instance, Radke et al. (1991) report EFs for
NOx as NO (2.0 g kg-1), NH3 (2.0 g kg-1), and
C3H6 (0.70 g kg-1) for the Myrtle–Fall Creek wildfire that are all within 20 % of our
lab-predicted EFs. Hobbs et al. (1996) report an EF for SO2
(0.79 g kg-1) that is closer to our value than the Liu et al. value is
despite the much lower MCE (0.81).
Comparison of the lab-predicted EFs at the field-average MCE to
average field-measured EFs reported by Liu et al. (2017).
Trace gas emissions from a mixed Douglas fir ecosystem (including
sound and dead wood, but rotten log not included) and pure components. Sound
dead wood was not burned separately except as untreated lumber.
Trace gas emissions from a mixed ponderosa pine ecosystem (including
sound dead wood; rotten log not included) and pure components.
Figure 3 shows the comparison for method 1 from Table 2 graphically. From
Fig. 3 it is clear that for the main relatively stable compounds, including
formaldehyde, methanol, and hydroxyacetone, the lab-predicted EF falls within
21 % of the measured wildfire EF and all the emissions except NOx
and SO2 overlap within the observed variability. Also highlighted in
Fig. 3, many compounds such as HONO, acetic acid, ammonia, phenol,
glycolaldehyde, formic acid, etc. were measured only for our laboratory
fires. The lab-measured EFs for these OP-FTIR species and the data for many
NMOG species measured by MS and FIREX data in general can thus be used to
generate representative EFs or other data for real wildfires. Many of these
EFs are critically important to represent wildfire emissions well: e.g.,
NH3 (Benedict et al., 2017) and secondary organic aerosol or
peroxyacetyl nitrate precursors (Alvarado et al., 2015; Müller et al.,
2016). Other approaches to generate representative data that are not explored
in detail here but should work well include reporting the average for the
laboratory fires clustered around the field-average MCE (fires 8, 39, 45, 51,
59, and 66) or reporting ER to CO (e.g., Koss et al., 2017), where the latter
can also be converted to EF by coupling with the field-average EF of CO. For
example, if we take the average of six fires clustered around the
field-average MCE in the new CH4
plot shown in Fig. 2, we get an average EF for CH4 of 4.67, which is
close to the value of 4.90 reported by Liu et al. Alternatively, we can
calculate a molar ER for CH4 to CO for all the laboratory fires (0.108),
then utilize the wildfire-average EF of CO reported by Liu et al.
(89.3 g kg-1) to calculate a new EF. Using this method, we get an EF
for CH4 of 5.5, which is within 11 % of the field-average value.
Either of these methods should help reflect the field-average
flaming / smoldering ratio. In addition, positive matrix factorization
was found to be useful to model field and laboratory EFs for NMOGs as
discussed elsewhere (Sekimoto et al., 2018). Finally, given the small amount
of field sampling, more field work is clearly needed.
EF dependence on fuel
We burned individual fuel components (duff, litter, canopy, etc.) in addition
to mixtures of major components found in widespread western US coniferous
ecosystems for insights into fuel effects on emissions and to what degree
specific emissions were enhanced by a certain component. For example, Fig. 4
shows the EFs of 21 trace gases from the Douglas fir ecosystem fuel mixture
burns side by side with the EFs from burning pure Douglas fir components in
separate fires. Emissions of furaldehyde, formaldehyde, and methanol were
enhanced when burning a pure rotten log component, while acetylene, ethylene,
and propene, as well as other non-methane hydrocarbons (NMHCs), were more
prevalent in emissions from Douglas fir canopy. We did the same analysis for
a ponderosa pine ecosystem (Fig. 5). While the canopy component in ponderosa
pine produced enhanced emissions of NMHCs, the rotten log did not contribute
to the same level of enhancement in furaldehyde, formaldehyde, and methanol
because of a transition to flaming combustion during the second half of the
fire. Additionally, we observed an enhancement in NOx emissions from the
litter and canopy components in ponderosa pine. Coniferous ecosystem values
are fairly similar for both fuels and agree within 30 % for the majority
of compounds, excluding methanol, furan, and NOx.
We also note that while the mixed
Douglas fir and ponderosa pine ecosystem fuel mixtures that we burned contained canopy,
litter, and woody components in varying diameter classes, they did not
contain a rotten log since the latter component is not included in FOFEM. We
further investigate fuel variability by taking pure components from several
ecosystems and comparing them to one another. Figure 6 shows species emitted
by duff from three different coniferous ecosystems. Acetic acid and methanol
are strongly emitted by all three duff fuels, but ammonia enhancement occurs
in only Engelmann spruce and subalpine fir fuels. Jeffrey pine duff had a
lower EF for NH3 despite similar fuel N. This could possibly be due to
the age of the fuel as it was contained in storage longer than other fuels
and not fresh. Additional results for other fuel components (rotten log,
canopy, litter) are in Figs. S1, S2, and S3, respectively.
Trace gas emissions from pure duff of three different ecosystem
types.
Lab-average emission factors
(m2 kg-1) and fire-integrated optical properties for common
western US ecosystems.
Species
Douglas
Engelmann
Lodgepole
Ponderosa
Chaparral –
Chaparral –
fir
spruce
pine
pine
chamise
manzanita
Black carbon (g kg-1)
0.23 (0.06)
0.12 (0.07)
0.34 (0.14)
0.48 (0.25)
0.45 (0.16)
0.32 (0.04)
EFabs870
1.07 (0.29)
0.58 (0.32)
1.59 (0.67)
2.28 (1.20)
2.00 (0.68)
1.32 (0.15)
EFabs401
7.63 (1.11)
6.22 (0.19)
10.20 (1.12)
12.06 (1.08)
10.40
8.65
EFabs401 (BrC)
5.05 (0.70)
4.41 (0.27)
5.79 (0.77)
5.56 (0.76)
5.57
5.55
EFscat870
3.01 (1.34)
3.36 (2.66)
2.79 (1.40)
4.55 (1.50)
0.52 (0.16)
0.90 (0.51)
EFscat401
48.42 (7.27)
62.56 (7.40)
44.23 (7.03)
50.28 (9.92)
12.02
23.76
SSA 401
0.86 (0.01)
0.91 (0.01)
0.81 (0.02)
0.80 (0.04)
0.54
0.72
SSA 870
0.72 (0.08)
0.82 (0.09)
0.64 (0.07)
0.67 (0.11)
0.21
0.39
AAE
2.43 (0.09)
2.65 (0.30)
2.12 (0.19)
1.84 (0.18)
2.02
2.36
MCE
0.94 (0.01)
0.94 (0.02)
0.94 (0.01)
0.93 (0.01)
0.95 (0.01)
0.96 (0.001)
Values in brackets are (1σ) standard deviation.
SSA at both wavelengths (401 and 870 nm) and AAE (401 and 870 nm) against
MCE for 31 stack fires for which both 401 and 870 nm data were available. The
circle on the fit line represents the lab-predicted AAE using the wildfire
field-average MCE of 0.912. SSA is difficult to fit to MCE and fits better
to EC and OC data, which were not available (Liu et al., 2014; Pokhrel et
al., 2016).
Overview of optical properties
As mentioned previously, we measured absorption and scattering coefficients
directly at 401 and 870 nm. For the first 31 stack fires, which
included most of the studied fuel
types, we have both 401 and 870 nm data. For the remaining 44 stack fires,
we only report data at 870 nm as we used our 401 nm PAX for intercomparison
studies that will be reported elsewhere. Figure 7 plots the AAE and SSA at
both wavelengths of 31 stack fires as a function of MCE. High AAE is an
indicator of BrC and relates to smoldering, which is denoted by low MCE and
high SSA values. Smoldering is also associated with higher EFs for OA, most
NMOGs, and other gases such as NH3. Low AAE, along with low SSA and high
MCE values, indicates more flaming combustion, which is also generally
associated with higher EF for BC and “flaming compounds” such as CO2,
NOx, and SO2. The lab-based average fire-integrated optical
properties for some of the most common western US ecosystems are listed in
Table 3. Table 3 does not reveal a strong ecosystem dependence among
coniferous ecosystems tested for optical properties but does indicate that
chaparral fire aerosol has consistently lower SSA than coniferous fire
aerosol and that there are significant contributions of absorption by BrC at
401 nm among all ecosystems. The absorption by BrC is responsible for at
least half and up to two-thirds of the absorption at 401 nm even at higher
MCE. The laboratory-average AAE of 2.80 ± 1.57 across all 31 fires
confirms a role for BrC, while the lab-average SSA at both wavelengths
indicates the fresh lab-fire aerosol would have a
net warming influence in the atmosphere (SSA < 0.9; Praveen et al.,
2012), although SSA can increase with smoke age (Yokelson et al., 2009). The
absorption of BrC at 401 nm has several implications in atmospheric
chemistry, including impacts on UV-driven photochemical reactions producing
ozone, and the lifetime of NOx and HONO. Furthermore, because of its
absorbing nature, factoring in the BrC could mean the net radiative forcing
of BB is not cooling or neutral as often assumed, but warming if the BrC is
sufficiently long-lived as probed in other FIREX studies and previous papers
(e.g., Feng et al., 2013; Forrister et al., 2015).
Absorption emission factors measured at 401 nm for “BC plus BrC”
and for “BrC only” for 31 lab fires, Also shown are the fractional
contributions of BrC to total absorption at 401 nm predicted from the lab AAE
data at the field-average MCE (green), the Rim Fire MCE (blue), and the field-measured AAE (purple) (Forrister et al., 2015; Liu et al., 2017).
Comparison of laboratory optical properties to field optical
properties
There are very few field measurements of the optical properties of smoke from
US wildfires, but we can compare our results from the laboratory studies to
the initial aerosol optical properties for one wildfire (the Rim Fire)
reported by Liu et al. (2017) and Forrister et al. (2015). An AAE of 3.75 at
an MCE of 0.923 for the Rim Fire is reported between these two studies. With
the linear regression of the laboratory data shown in Fig. 7, we can predict
an AAE of 3.31 at the wildfire field-average MCE (0.912) and an AAE of 2.91
at the Rim Fire MCE (0.923) using prediction method 1 described in Sect. 3.2. At the wildfire field-average MCE, our calculated AAE represents
88 % of the reported Rim Fire AAE, while at the Rim Fire MCE, our
calculated AAE represents 78 % of the reported Rim Fire AAE. Although our
calculated values are relatively close to the reported value, a small change
in AAE implies a big change in the BrC / BC absorption ratio, but only a small
change in the percentage of absorption by BrC. Our AAE values imply that BrC accounts
for 77 to 82 % of the absorption at 401 nm. The average of the AAE from the
single Rim Fire measurement (3.75) and the AAE predicted from the more
extensive laboratory fires (3.31) is ∼ 3.5, which may be a reasonable
best guess at the AAE of fresh US wildfire smoke and implies that
∼ 86 % of absorption at 401 nm is due to BrC.
Summary of the comparison of optical properties and emission factors
(m2 kg-1) measured in lab to the Rim Fire.
Lab-based
prediction using
field average
Predicted/
Lab avg/
Species
Lab avg
Lab eqnh
r2g
MCE
Rim Fire
field
Rim Fire
Black carbonb (g kg-1)
0.68 (1.09)
y=1.7926x25.655
0.3237
0.169
0.187e
0.90
3.64
EFabs870b
3.21 (5.16)
y=8.497x25.655
0.3237
0.80
–
–
–
EFabs401c
11.16 (6.00)
y=11.385x1.7374
0.028
9.71
–
–
–
EFabs401 (BrC)c
7.15 (5.20)
y=-32.81x+37.53
0.0648
7.57
–
–
–
EFscat870b
10.15 (22.64)
y=0.9868x-17.48
0.2404
4.94
–
–
–
EFscat401c
70.37 (81.25)
y=-1343.6x+1314.7
0.4462
87.99
–
–
–
SSA (401)c
0.79 (0.13)
–
0.90d
–
–
–
SSA (870)b
0.64 (0.26)
–
0.91d
–
–
–
AAEc
2.80 (1.57)
y=-35.45x+35.64
0.8335
3.31
3.75f
0.78
0.75
a Values in brackets are (1σ) standard deviation.
b Average for all 75 stack fires for which 870 nm data are available.
c Average for 31 fires for which both 401 and 870 nm are available.
d SSA values calculated from Babs and Bscat
EF.
e Value not published (X. Liu, personal communication, 2017;
https://www.nasa.gov/mission_pages/seac4rs/index.html).
f From Forrister et al. (2015).
g The low r2 equations return reasonable values at the field-average MCE.
h In the equations below, “y” is the quantity in column 1 and “x” is
MCE.
In Fig. 8, we plot the initial percentage of absorption by BrC at 401 nm for the Rim
Fire measured AAE and for our lab-estimated AAE at the field-average MCE.
Figure 8 also shows the lab-measured total EFabs401 and the BrC
contribution to EFabs401 for 31 laboratory fires. BrC dominates
absorption at 401 nm at low MCE values, and as MCE increases, BrC absorption
remains a significant but variable component of overall absorption. The
variability is likely due to realistic “natural” fire-to-fire variability
in fuels, moisture content, etc.
In Table 4 we report the study averages for BC mass EF, absorption and scattering EFs, SSA,
and AAE. The quantities that require 401 nm data are averages for the 31
stack fires for which 401 and 870 nm data were obtained, while the
quantities that need just 870 nm data are averages for all 75 stack fires.
We also show the comparison of our lab-average and lab-predicted AAEs to the
AAE in Forrister et al. (2015) and our lab-average and lab-predicted BC EF to
the unpublished BC EF calculated as part of Liu et al. (2017). Table 4 also
presents a set of equations that can be used to fit lab-measured optical
properties and make predictions at any MCE. However, more measurements of
wildfires in the field and the laboratory (including aging) are needed to
asses wildfire aerosol optical properties.
Optical properties and emission factors (m2 kg-1) for
mixed coniferous ecosystems and ecosystem components.
Species
Mixed coniferous ecosystema
Canopyb
Litterc
Duffd
Rotten logf
Black carbon (g kg-1)
0.43 (0.33)
0.46 (0.37)
0.68 (0.53)
0.50 (0.79)
0.43 (0.59)
EFabs870
2.03 (1.58)
2.18 (1.77)
3.22 (2.51)
0.02 (0.007)
2.04 (2.84)
EFabs401
9.02 (2.61)
14.53 (6.37)
14.29 (7.58)
4.08e (0.09)
7.86 (1.46)
EFabs401 (BrC)
5.20 (0.61)
10.65 (5.14)
6.39 (2.84)
4.04e (0.10)
6.18 (3.73)
EFscat870
4.51 (2.51)
10.00 (7.80)
2.28 (1.12)
6.73 (1.85)
22.21 (5.86)
EFscat401
51.37 (7.87)
84.03 (55.92)
35.39 (11.14)
94.37e (2.45)
139.47 (153.27)
SSA 401
0.85 (0.05)
0.81 (0.05)
0.70 (0.17)
0.96e (< 0.01)
0.89 (0.10)
SSA 870
0.71 (0.08)
0.71 (0.13)
0.48 (0.27)
0.99e (< 0.01)
0.89 (0.15)
AAE
2.26 (0.36)
2.69 (0.36)
1.86 (0.20)
7.13e (0.06)
4.60 (3.73)
MCE
0.94 (< 0.01)
0.93 (0.01)
0.93 (0.02)
0.87 (0.02)
0.86 (0.12)
a Douglas fir, Engelmann spruce, lodgepole pine, ponderosa pine,
subalpine fir.
b Douglas fir, Engelmann spruce, lodgepole pine, ponderosa pine,
juniper, subalpine fir.
c Douglas fir, loblolly pine, lodgepole pine, ponderosa pine, subalpine
fir.
d Engelmann spruce, Jeffrey pine, ponderosa pine, subalpine fir.
e Engelmann spruce.
f Douglas fir, ponderosa pine.
Fuel dependence of aerosol optical properties
Burning individual fuel components in addition to mixtures found in typical,
widespread western US ecosystems allows us to investigate the extent to which
optical properties are either enhanced or diminished by certain components.
Table 5 lists the study-average BC EF and optical properties for all the
coniferous ecosystems shown in Table 3 and the study-average BC EF and
optical properties for the individual fuel components averaged across all the
coniferous ecosystems. The averages and standard deviations for each reported
quantity indicate that there is large variation among specific components and
a large coefficient of variation for the coniferous ecosystem average. The
variability could potentially depend on ecosystem type, fuel components, fuel
moisture, or other things as discussed for trace gases in section 3.3. While
there is considerable variation within each ecosystem type, the individual
ecosystem averages in Table 3 all agree within 38 % of the study average
for all the coniferous ecosystems shown in Table 5 and the AAEs are all
within 20 %. However, Table 5 also shows that the average AAE for some
ecosystem components is very different from the average AAE for all the
coniferous ecosystems (2.26). For instance, the largest contribution to a
high AAE per fuel component consumed comes from duff, where BrC accounts for
almost all of the absorption at 401 nm (AAE 7.13). The rotten log component
also contributes an anomalously high average AAE of 4.60. Thus, these
components contribute more BrC relative to BC in proportion to their fuel
consumption to the mixed ecosystem results, where AAE is 2.26 and BrC accounts
for just over half of the absorption at 401 nm. Conversely, litter
consumption would tend to lower a fuel mixture's AAE. However, AAE is a
measure of the shape of the aerosol absorption cross section and the
absorption EFs are a measure of total emissions of absorbing material. In
this respect, litter produces more BC absorption and more BrC absorption per
unit mass than duff though at a lower BrC / BC ratio than duff. This is
consistent with the lower SSA for litter. We conclude that the variability in
mixed ecosystem optical properties was likely due to variable consumption of
pure components, with a weaker dependence on the dominant tree species. For
example, much of the variability in ecosystem-average AAEs and the study-average AAE is linked to the varying amount of duff consumed in the mixed
fuel beds (Table S1). (The variability in actual duff consumption is likely
larger than the variability in duff loading shown as the amount of residual
material also varied.) Duff consumption in the field is increased by drought
conditions, which would contribute to variability in real fires (Davies et al.,
2013).
We can compare our duff results to previous measurements of optical
properties of duff-fire aerosol by Chakrabarty et al. (2010). These authors
identified tar balls as a major BrC species produced by duff combustion and
they measured an AAE of 4.2 (405 and 532 nm wavelength pair) for a ponderosa
pine duff sample from MT. Including their other duff sample (Alaskan
feather moss duff), they obtained a study-average duff-combustion AAE of 5.3.
We measured AAE on two much larger burns (∼ 4 times more fuel mass,
fire nos. 12 and 26) in Engelmann spruce duff, with different wavelengths,
and at much lower MCE (0.843 ± 0.036 versus ∼ 0.91). We obtained
a study-average duff combustion AAE of 7.13 (0.057). Both studies observed a
high AAE for duff combustion. Their lower AAE values could be related to
different wavelengths used, the possibility of some BrC absorption at 532 nm
(Bluvshtein et al., 2017), the different duff type, and/or their higher MCE,
which they attributed to sampling some flaming combustion during the ignition
process. The AAE calculated from our AAE versus MCE fit (for all fuels) at
their MCE of 0.91 is relatively closer to their value.
In summary, the results presented indicate that, in all cases, burning a
typical ecosystem mixture of components produces a significant amount of BrC.
As mentioned previously, this has several implications in regional
atmospheric chemistry and radiative forcing. Additional instruments were
deployed on room burn experiments, in which the fuels were also purposely
changed to investigate the effect on optical properties
and the results will be reported elsewhere (e.g., Manfred et al., 2018).
Trace gas and BC emissions of peat, dung, and rice straw
combustion
We also measured emissions from several fires of peat, rice straw, and dung
due to their widespread burning in Asia and their value as extreme examples
of fuel impacts (e.g., high smoldering-to-flaming ratio or high N or Cl content). Peat, which is
especially important in Southeast Asia (Stockwell et al., 2016a) is similar
to duff found in the western US in that it is often consumed by pure
smoldering combustion and produces high
AAE (Pokhrel et al., 2016), high HCN emissions, and low BC emissions.
Although we did not measure the AAE for our peat fire aerosol, we do report an MCE of 0.83, where a low MCE likely indicates a high
AAE. We also report EFs for CH4 (10.39 g kg-1), HCN
(3.97 g kg-1), acetic acid (4.44 g kg-1), and BC
(0.003 g kg-1). We compare these values to the field measurements
reported in Stockwell et al. (2016a): CH4
(9.51 ± 4.74 g kg-1), HCN (5.75 ± 1.60 g kg-1),
acetic acid (3.89 ± 1.65 g kg-1), and BC
(0.006 ±0.002 g kg-1) and find that our values agree well (EF of
BC extremely small compared to most BB (Akagi et al., 2011) and gases within
31 %) between peat fire measurements in the laboratory and the field. (A more detailed comparison
will follow planned field measurements.)
Additionally, we compare our dung MCE (0.90), CH4
(6.63 g kg-1), HCN (1.96 g kg-1), acetic acid
(6.36 g kg-1), and BC (0.01 g kg-1) values to those based on
field work in Nepal reported in Stockwell et al. (2016b): MCE (0.90),
CH4 (6.65 ± 0.46 g kg-1), HCN
(2.01 ± 1.25 g kg-1), acetic acid
(7.32 ± 6.59 g kg-1), and BC
(0.004 ± 0.003 g kg-1). We find excellent agreement between our
values (15 % for trace gases and a very small EF of BC) and those reported
from field measurements in Nepal.
Rice straw was burned because of its global importance in agricultural waste
burning and to probe the extremes of fuel chemistry (Akagi et al., 2011).
Grasses are usually very high in chlorine content (0.61 %, Table S1;
Lobert et al., 1999) and our EF for HCl of 0.65 g kg-1 for rice straw
was the highest of any fuel measured during the FIREX campaign. Furthermore,
our rice straw EF for HCl is comparable to Stockwell et al. 2015
(0.43 ± 0.29). The findings briefly summarized in this section further
suggest and reinforce the idea that simulated laboratory fires can probe fuel
effects and provide an accurate representation of measurements in the field,
even outside the scope of western US wildfires. More comprehensive, recent
discussions of these fuels can be found elsewhere (Stockwell et al., 2016a,
b; Jayarathne et al., 2018a, b).
Conclusions
We measured trace gas and aerosol emissions from 107 simulated western
wildfires during the FIREX campaign in the fall of 2016 using OP-FTIR and
PAX. For 31 stack fires, we report aerosol measurements based on both 401 and
870 nm, and for the remaining 44 stack fires we report aerosol
characteristics based on only 870 nm data. We provide the MCE and the mass
EF (g kg-1) for 23 different trace gases (not including water) and BC.
We also provide the scattering and absorption EF (m2 kg-1) at 870
and 401 nm along with the EFabs401 due to BrC only, SSA, and
AAE. We burned canopy, litter, duff, dead wood, and other fuels in
combinations using FOFEM to represent relevant ecosystems and as pure
components to investigate the effects of individual fuels. Full trace gas
data are reported for all 75 stack burns and two room burns, and CO2,
CO, CH4, C2H4, C2H2, and MCE were archived for the
remaining room burns. We found little variability in average trace gas EFs
across coniferous ecosystems, but the average EFs for two chaparral
plant species were similar to each other
and lower than in coniferous ecosystems for most pollutants, including
CH4 (1.20 ± 0.09 g kg-1), formaldehyde
(0.50 ± 0.06 g kg-1), glycolaldehyde (0.15 g kg-1), and
HCN (0.09 g kg-1) to name a few. Additionally, there was considerable
variability in the average trace gas EF for certain fuel components. For
instance, emissions of some NMOGs were enhanced from a Douglas fir rotten log
and emissions of NOx were enhanced from ponderosa pine litter and canopy
components.
In a similar fashion, there was little variation in the average optical
properties for the different mixed coniferous ecosystems, but individual fuel
components like duff and rotten logs contributed significantly on a per mass
basis to the relative importance of BrC and BC, with BrC accounting for
nearly 100 and 94 % of the absorption at 401 nm for these
fuel components (using data only from fires with measurements at two
wavelengths).The lab-average AAE for all 31 fires, including those burning
components like chaparral and coniferous canopy, which tend to burn more by
flaming, was 2.8 (Table 4), indicating that BrC absorption contributed to over
half (64 %) of the absorption at 401 nm for the laboratory fires on
average.
We compared the trace gas and aerosol emissions from the fires in our
laboratory-simulated western US ecosystems to those from real western US
wildfires measured in slightly aged smoke in the field as reported by Liu et
al. (2017) and Forrister et al. (2015). Despite some underrepresentation of
the largest diameter fuel class we were able to use a simple procedure to
account for the flaming-to-smoldering ratio and generate EF values from the
laboratory data that were in agreement with the field data for most
“stable” trace gases, including CH4 (within 3 %), formaldehyde
(within 5 %), methanol (within 21 %), and hydroxyacetone (within
2 %). Most of the EF discrepancies were due to the field smoke being more
aged. The excellent agreement suggests that FIREX data can be confidently
used in general to represent real fires, especially for species not measured
yet in the field. For instance, important compounds rarely or not previously
measured in the field for western wildfires but measured in this study
include ammonia (1.62 g kg-1), acetic acid (2.41 g kg-1), HONO,
and others (Fig. 3). Optical properties were not compared as extensively
because limited field data are available, which highlights the need for more
field measurements on true wildfires. However, a preliminary best guess for a
fresh wildfire smoke AAE of ∼ 3.5 is supported by averaging the
lab-based predictions and the more limited field data. Impacts on photochemical reactions producing
ozone and the lifetime of NOx and HONO are likely as a result of the
strong abundance of BrC. In addition, recognizing the presence of absorbing
BrC in BB plumes could alter the modeled contribution of BB to net radiative
forcing in a more positive direction. Finally, to investigate fuel chemistry
impacts and due to their widespread global importance, we also measured EFs
for fires in peat, dung, and rice straw and compared to field values reported
by Stockwell et al. (2015, 2016a, b). Our lab-based EFs for all three of
these fuels were in good agreement with the field studies. Overall, our
lab-simulated fires can provide important emissions data that are fairly
representative of real fires and used to accurately assess BB impacts.