Since the publication of the compilation of biomass
burning emission factors by Andreae and Merlet (2001), a large number of
studies have greatly expanded the amount of available data on emissions from
various types of biomass burning. Using essentially the same methodology as
Andreae and Merlet (2001), this paper presents an updated compilation of
emission factors. The data from over 370 published studies were critically
evaluated and integrated into a consistent format. Several new categories of
biomass burning were added, and the number of species for which emission
data are presented was increased from 93 to 121. Where field data are still
insufficient, estimates based on appropriate extrapolation techniques are
proposed. For key species, the updated emission factors are compared with
previously published values. Based on these emission factors and published
global activity estimates, I have derived estimates of pyrogenic emissions
for important species released by the various types of biomass burning.
Introduction
Biomass burning, in the form of open vegetation fires and indoor biofuel
use, is one of the largest sources of many trace gases and aerosols to the
global atmosphere. For some important atmospheric pollutants, like black
carbon (BC) and primary organic aerosol (POA), biomass burning is the
dominant global source; based on the estimates of Bond et al. (2013), it accounts for 59 % of BC emissions and 85 % of POA emissions
worldwide. Open vegetation fires alone represent about one-third to one-half
of global carbon monoxide (CO) and 20 % of nitrogen oxide (NOx)
emissions (Olivier et al., 2005; Wiedinmyer et al., 2011). Fires are also
a major source of greenhouse gases, including carbon dioxide (CO2),
methane (CH4), and nitrous oxide (N2O) (Ciais et al., 2013;
Tian et al., 2016; Le Quéré et al., 2018). While a significant
fraction of the emitted CO2 is taken up again by vegetation regrowth,
much of it remains in the atmosphere for years and potentially even up to
centuries, e.g., in the case of tropical deforestation fires or peat soil
burning (van der Werf et al., 2017). Model
simulations suggest that in the absence of fires, atmospheric CO2
concentrations would be about 40 ppm lower, indicating the importance of
fires for the atmospheric carbon budget and climate (Ward
et al., 2012). Biomass burning is the second largest global source of
non-methane organic gases (NMOGs, also referred to as volatile organic
compounds, VOCs) (Yokelson et al., 2008; Akagi et al., 2011). Numerous
other studies have reached similar conclusions about the importance of
biomass burning for atmospheric composition (e.g., Crutzen and Andreae,
1990; Andreae and Rosenfeld, 2008; Andreae et al., 2009; Kaiser et al.,
2012; van der Werf et al., 2017).
The resulting perturbations of the atmospheric burdens of trace gases and
aerosols have important consequences for climate, biogeochemical cycles, and
human health. Aerosols from biomass burning affect the regional and global
radiation balance and impact cloud properties and precipitation (Andreae
et al., 2004; Andreae and Rosenfeld, 2008; Rosenfeld et al., 2008, 2014; Ward et
al., 2012; Tosca et al., 2013; Jiang et al., 2016;
Braga et al., 2017; Cecchini et al., 2017; Hamilton et al., 2018; Thornhill
et al., 2018). By shifting the proportions of direct and indirect solar
radiation, they also influence primary productivity and thereby forest
growth and agricultural production (Artaxo et al., 2009; Rap et al.,
2015; Malavelle et al., 2019; McKendry et al., 2019). Fires mobilize
nutrients, such as nitrogen, phosphorus, and potassium, which can deplete
local ecosystem nutrient reservoirs on the one hand and provide nutrients to
other ecosystems by atmospheric transport on the other (Andreae, 1991;
Andreae et al., 1998; Mahowald et al., 2008; Y. Chen et al., 2010). The VOCs
and NOx in biomass smoke undergo smog photochemistry in the atmosphere,
leading to the production of ozone, secondary organic aerosols, and other
pollutants, which impact plant productivity (Crutzen and Andreae, 1990;
Andreae, 1991; Robinson et al., 2007; Jaffe and Wigder, 2012; May et al.,
2013; Pacifico et al., 2015; Hatch et al., 2017; Yue and Unger, 2018). These
gaseous pollutants, and even more so the particulate matter from biomass
burning, pose grave risks to human health (Naeher et al., 2007; Akagi et
al., 2014; Dennekamp et al., 2015; Knorr et al., 2017; Apte et al., 2018).
Recent estimates of global excess mortality from outdoor air pollution range
from 4.2 to 8.9 million annually (Cohen et al., 2017; Lelieveld and
Pöschl, 2017; Shiraiwa et al., 2017; Burnett et al., 2018; Lelieveld et
al., 2019), with smoke from open vegetation burning accounting for up to
600 000 premature deaths per year globally (75th
percentile of model estimates; Johnston et al., 2012). In addition to
outdoor exposure, pollution from indoor solid fuel use, much of it biofuel
burning, has been estimated to cause 2.8 million premature deaths annually
(Kodros et al., 2018).
In view of the immense impact of biomass burning emissions on climate,
ecosystem function, and human wellbeing, it is disconcerting that large
uncertainties persist regarding the amounts emitted and their spatial and
temporal distribution. For bottom-up emissions estimates, two basic types of
information are required: the amount of the various types of biomass burned
as a function of time and space and the emission factors for the various
emitted species, i.e., the amount of a given species emitted per unit mass
of biomass burned. Considerable effort has gone into quantifying the
magnitude of open biomass burning by remote-sensing approaches (Mouillot
et al., 2006; Reid et al., 2009; Mieville et al., 2010; Wiedinmyer et al.,
2011; Kaiser et al., 2012; Ichoku and Ellison, 2014; Darmenov and da Silva,
2015; Chuvieco et al., 2016; van der Werf et al., 2017), but the estimates
in these studies of the annual amounts of carbon released still range over a
factor of 3 from 1.5 to 4.7 Pg a-1. A model intercomparison based
on state-of-the-art dynamic global vegetation models (DVGMs) yielded an even
wider range of 1.0 to 4.9 Pg a-1 (F. Li et al., 2019).
Efforts to narrow the uncertainties in the emission factors for the large
number of species emitted from the diverse types of burning are ongoing in
the form of numerous field campaigns and laboratory studies. The results of
these studies are, however, widely dispersed among hundreds of papers in a
large number of journals, each dealing with a particular campaign or
experiment. Over the last two decades, there have been two efforts to
synthesize these data on a global scale, one by Andreae and Merlet (2001;
hereafter referred to as A&M2001) and the other by Akagi et al. (2011). The latter included more recent
data and additional species and burning types and is available in updated
form at http://bai.acom.ucar.edu/Data/fire/ (last access: 27 June 2019). As part of the
Fire INventory from NCAR (FINN) model, Wiedinmyer et al. (2011) selected data from these two sources into a “best estimate” set of
emission factors.
In the present study, I am presenting an updated set of
emission factors, which includes the results of studies published since the
writing of the two previous compilations. It provides emission estimates for
28 more chemical species, for which a sufficient amount of field data has
become available since A&M2001, as well as an extended set of burning
types. The extratropical forest category is differentiated into boreal and
temperate forest burning, domestic biofuel use is separated into non-dung
and dung burning, and peat fires and domestic waste burning are added as new
categories. Based on these emission data and recent activity estimates, I
present a compilation of global emission amounts and make some
recommendations regarding priorities for future investigations.
MethodsData selection
This paper applies the same methodological approach as A&M2001, and
therefore the Methods section will only provide a brief overview of the
definitions and calculation methods used and highlight those points where
the present approach differs from the previous one. For all other details,
the reader is referred to A&M2001. The original data, which form the
basis of the emission factor averages presented in Table 1, can be found in
an Excel spreadsheet in the Supplement.
Emission factors for pyrogenic species emitted from various types
of biomass burninga.
SpeciesBiofuels (without dung) Dung Charcoal making Charcoal burning Garbage burning Lab studies EMbaverageSDNaverageSDNaverageSDNaverageSDNaverageSDNaverageSDNCH3Cl0.1840.27862.22.23––00.011–10.430.16–0.7020.120.1312–CH3Br0.00070.0006–0.000820.00870.00493––0––00.002–10.0010.0005–0.0012COCH3I0.00010.0001–0.000120.00060.00023––0––00.0003–10.0060.000–0.0112COHg04.7E-056.8E-6–8.8E-52––0––0––0––03.7E-05–1–PM2.56.84.45816.513.0820.12.1–38.223.02.579.72.1310.511.023–TPM7.05.8276.16.0513.819.342.11.7–2.42––08.16.112COTC3.41.4288.19.94––02.02.056.9–06.59.17BEOC3.12.16610.29.460.8–12.22.045.55.3–5.724.94.628–BC or EC0.811.19660.310.2670.02–10.270.1531.45.130.560.3929–Levoglucosan0.500.42130.450.16–0.7420.06–10.79–10.400.25–0.5620.450.4413–K0.130.24220.090.1040.004–10.750.7930.020.01–0.0220.360.3420BECN2.9E+153.0E+154––0––04.9E+152.5E+15–7.2E+1525.4E+15–04.2E+156.7E+155COCCN (0.5 % SS)1.1E+15–0––0––0––0––08.0E+14–1CON (>∼0.12µm1.0E+15–0––0––0––0––0––0COdiameter)
a Emission factors are given in gram species per kilogram dry matter
burned. See text for the conventions used for reporting uncertainties.
Abbreviations are as follows: PM2.5, particulate matter <2.5 mm
diameter; TPM, total particulate matter; TC, total carbon; BC, black carbon;
CN, condensation nuclei;
CCN (0.5 % SS), cloud condensation nuclei at 0.5 % supersaturation; and
N (>∼0.12 mm diam), particles >∼0.12µm diameter. Values in italics represent
estimates for emission factors that have not been measured directly.
b Estimation method for emission factors for which no measurements are
available. See text Sect. 2.4 for details.
c Based on field measurements that only include varying (often incomplete) sets of identified species (see text for discussion).
d Sum of chemically identified and unidentified species, from online
updates to Akagi et al. (2011).
With few exceptions, and consistent with the approach used in A&M2001, I
only used results from field measurements in young fire plumes for the
compilation of the emission factor data in Table 1. Ideally, these
measurements had been made within minutes after the smoke was released from
the fires to avoid significant chemical changes during atmospheric aging,
especially in the case of reactive trace gases. This is only possible,
however, when sampling at the ground or from aircraft very close to the
fire. In many other cases, aircraft were sampling at some distance from the
fires, often without actually knowing the exact location of the fire. In
such cases, I have rejected the data for the more reactive trace gases. A
special case is presented by emission data calculated from remote sensing by
either satellite measurements or ground-based solar Fourier transform
infrared (FTIR) spectrometry. Here, the authors have often included a
correction for atmospheric transformations, using model calculations
involving transport times and reaction rates of the species concerned.
Because of their large spatial and temporal coverage, such measurements are
quite valuable, and I have therefore included some of them in this
assessment, as long as they were either dealing with long-lived species or
used appropriate correction methods (i.e., chemistry-transport model
calculations to correct for atmospheric transformations) (Rinsland et
al., 2007; Mebust et al., 2011; Tereszchuk et al., 2011,
2013; Schreier et al., 2015; Viatte et al., 2015; Lutsch et al., 2016; Adams
et al., 2019). They can be compared with in situ measurement results by
referring to the original data in the Supplement spreadsheet.
Another special case are the emission factors for gaseous elemental mercury
(Hg0). Here, only relatively few actual field emission measurements are
available for most of the combustion types listed in Table 1. Therefore, I
have followed the approach of Friedli et al. (2009) and
included Hg0 emission factors from studies that are based on the Hg
content of the fuels and the assumption of total volatilization of Hg from
the fuel during combustion, which appears well justified for this volatile
element.
Generally, the results from laboratory combustion studies have not been
included in the emission factor averages for the different fire types in
Table 1, but they are given for comparison in a separate column in Table 1.
The reason for this decision is that such experiments often do not reproduce
realistic burning conditions in the field. For example, it has been shown
that the emissions of many trace gases are strongly dependent on fuel
moisture, temperature, wind, and other fire environment parameters (e.g.,
L.-W. A. Chen et al., 2010; Robertson et al., 2014; Liu et al., 2017; Thompson et
al., 2019). The fuels in lab experiments, however, may be well aged and
dried and thus have a much lower moisture content than fuels in the field,
and the wind conditions in the field are impossible to reproduce in the lab.
This can be seen in the values of the modified combustion efficiency (MCE;
the ratio of ΔCO2/(ΔCO2+ΔCO)) in many
lab studies, which are much higher than those typical of field burns, an
extreme example being the study by Sirithian et al. (2018), who
reported a mean MCE of 0.9996 in a lab study on biofuel burning. Therefore,
lab results are only used in some special cases, where little or no field
data are available, and where the lab data appear representative based on
their MCE (e.g., Christian et al., 2003) or had
been adjusted to reflect field conditions using “overlap species”, emission ratios (ERs), or
MCE as discussed in Yokelson et al. (2013b). Some lab values are also used as estimates in Table 1; they are
shown in italics and indicated as “LV” in the last column.
The studies on emissions from biofuel burning for cooking or heating
represent a borderline case, as they are often conducted in a laboratory
environment but with an effort to simulate the actual fuel use conditions
and stove setups used in households. Here, I have favored studies performed
in actual households but also included results from lab studies that
appeared to realistically emulate field conditions. Results from modern
residential biofuel combustion units, such as automated pellet burners or
modern low-emission stoves, have not been included. A more detailed
analysis of emissions from different types of domestic biofuel use can be
found in Akagi et al. (2011), albeit without the
benefit of the numerous papers that have been published on these emissions
in the last decade. A special review on this issue would be desirable but
is beyond the scope of this paper.
In contrast to gaseous compounds, which are chemically well defined,
aerosols are complex and variable mixtures of organic and inorganic species
and comprise particles across a wide range of sizes. This affects in
particular the measurements of organic aerosol, black/elemental carbon, and
size-fractionated aerosol mass. Organic aerosol is usually measured either
by a variety of thermochemical or mass spectrometric methods, both of which
may have positive and negative artifacts, for which different authors have
applied different corrections. Since some techniques report the result as
organic aerosol mass and others as organic carbon mass concentrations, a
conversion had to be applied. To convert between organic carbon and organic
matter (OM), a default OM/OC mass ratio of 1.6 is used in the absence of
specific information. This value is based on the data from fresh biomass
smoke aerosol in the literature (Turpin and Lim, 2001; Aiken et al.,
2008; Yokelson et al., 2009; Takahama et al., 2011; Kostenidou et al., 2013;
Brito et al., 2014; Collier et al., 2016; Fang et al., 2017; Tkacik et al.,
2017; Ahern et al., 2019; Lim et al., 2019). Where only O/C ratios were
given, they were converted to OM/OC ratios using the relationship given in Aiken et al. (2008).
Black carbon (BC) and elemental carbon (EC) are an even more problematic
category. Various definitions for these species have been used (Andreae and Gelencsér, 2006), but most commonly BC refers to
carbon with specific optical properties (light absorption) and is measured
by optical techniques, whereas EC is defined by its chemical properties and
determined by a variety of thermochemical methods. Not all authors, however,
adhere to these definitions, and the terms soot, EC, and BC are often used
interchangeably. Unfortunately, while some techniques have been shown to
have less bias than others (H. Y. Li et al., 2019), there
is no general answer as to which technique is best, and which property,
optical or chemical, is more representative. In view of the lack of a better
alternative, both BC and EC data have been merged in the “BC” category
here.
The size distribution of biomass smoke aerosols is a continuum ranging from
tens of nanometers to millimeters (Reid et al., 2005), with
most of the mass present in a mode at a few hundred nanometers. Mass
concentration measurements are typically reported as PM1, PM2.5,
PM10, or TPM, referring to the size ranges below 1, 2.5, and 10 µm, and total mass, respectively. For convenience, data reported as PM1
and PM2.5 have been grouped together in the PM2.5 category, which
in view of the typical biomass burning (BB) aerosol size distribution is not expected to
result in significant bias. The same applies to the PM10 and TPM data,
which were grouped together in the TPM category.
Emission data for ionic species and trace metals are not included in this
data set. They are tabulated in Akagi et al. (2011),
and additional information can be found in a number of papers (e.g.,
Goetz et al., 2018; Jayarathne et al., 2018a, b).
Another problematic “species” is the total concentration of non-methane
organic gases (NMOGs), also referred to as volatile organic compounds (VOCs).
The diverse methods used for these compounds measure different sets of NMOGs,
which in some instances may be quite incomplete. In general, the more recent
studies from the last 5–7 years are much more comprehensive and show that
the early studies were severely underestimating the amounts of NMOGs emitted.
Regrettably, these new techniques have been so far used mostly in lab studies
and could therefore not be considered for the combustion category emission
estimates. To highlight this issue, I have added the NMOG emission factors
from the online updates to Akagi et al. (2011) in Tables 1 and 3.
Definitions
In the literature, emission information is generally found as either
emission ratios (ERs) or emission factors (EFs). Strictly speaking, most data
presented as “emission ratios” are actually enhancement ratios (EnR),
often also referred to as normalized excess mixing ratios
(NEMRs; Akagi et al., 2011). They are defined as the
ratio of the excess mixing ratio of the species of interest in the plume,
(ΔX), to the excess mixing ratio of a reference species, e.g.,
carbon monoxide (ΔCO):
EnRX/CO=ΔXΔCO=(X)plume-(X)backgr(CO)plume-(CO)backgr,
where Δ stands for the difference between the mixing ratio in the
plume and in the background atmosphere (in molar units). Because of its
abundance in fire emissions and its relatively low ambient background
concentration, CO is most commonly used as reference species, but other
gases, such as carbon dioxide (CO2), methane (CH4), or
acetonitrile have also been used. The use of CO2 can introduce large
errors because it also has strong surface sources and sinks, which can lead
to erroneous estimates of the background concentration, as discussed in
detail in Yokelson et al. (2013a). A statistical method using
multiple fire tracers (Mixed Effects Regression Emission Technique, MERET),
which can resolve the problems associated with variable CO2 background
concentrations, has recently been developed (Chatfield et al.,
2019).
An enhancement ratio can be interpreted as an emission ratio when it is
assured that the concentrations of both species X and the reference species
have not been affected by chemical production or loss since the emission
and that both concentrations have changed proportionally during dilution of
the plume with background air. In the case of very long-lived substances,
e.g., acetonitrile, EnRs can be very close to ERs even after days, while for
reactive compounds, e.g., nitric oxide (NO), significant changes can occur
in minutes. For very rapidly reacting species, it becomes difficult to
define an appropriate time after emission at which an EnR can be treated as
an effective ER. A good example is the emission of primary organic aerosol
mass, whereby the apparent EnR decreases substantially (by about a factor of
2) over the first few minutes to hours as a result of the evaporation of
semivolatile compounds during plume dilution (May et al., 2013; Konovalov
et al., 2019). Whether the ER at the moment of emission or the EnR after
cooling and dilution to typical ambient conditions is the more meaningful
value will depend on the intended application. In general, field
measurements are likely to represent somewhat more aged conditions (tens of
minutes to a few hours), whereas lab measurements often represent very fresh
emissions. For further discussion of the advantages and disadvantages of the
different reference gases, the effects of flaming vs. smoldering combustion,
and ground-based vs. airborne sampling, see A&M2001, Burling et al. (2011), and Akagi et al. (2011).
While the measurement of ERs is relatively straightforward in the field,
because it requires only the measurement of the atmospheric concentrations
of target and reference species, it is generally desirable to obtain the
amount of a species emitted per unit mass of fuel burned, i.e., the emission
factor, EF. For biomass burning, this is usually expressed as the mass of
target species X released per mass of dry fuel burned, in units of grams per kilogram (g kg-1). This, however, requires knowledge of the mass of fuel burned,
which can easily be measured in the lab but is difficult to obtain in the field. As
an alternative, the mass balance method can be used, whereby the mass of fuel
burned is approximated by the sum of carbon contained in the emitted carbon
species (CO2, CO, CH4, volatile organic compounds (VOCs), organic
aerosol carbon (OC), and elemental carbon (EC) or black carbon (BC)),
divided by the carbon fraction in the fuel. Often, the carbon mass is
approximated by the sum of CO2 and CO, and a default fuel carbon
content of 45 % is assumed.
To provide a uniform representation of the various types of data found in
the literature in the form most useful to modelers, all emission data were
converted to emission factors, in units of grams per kilogram (g kg-1) of dry fuel burned. Where
emission factors relative to other fuel mass indicators were given, e.g.,
the mass of carbon burned or released, I applied appropriate conversion
factors, such as the known or assumed carbon content of the fuel. Very
frequently in the literature, only EnRs or ERs in units of mole / mole are
provided. These can in principle be easily converted to EFs by the following
equation:
EFX=ER(X/Y)MWXMWYEFY,
where EFX is the emission factor for species X, ER(X/Y) is the
emission ratio of species X relative to the reference species Y, MWX
and MWY are the molecular weights of the species X and the reference
species Y, and EFY is the known or assumed emission factor of the
reference species (often CO or CH4). When the value of EFY was not
known for a specific study, the mean EFY for the appropriate type of
fire (forest, savanna, etc.) was applied to derive an estimate of EFX.
Estimates for which no data are available
For some combinations of fire type and emitted species, no suitable field
data are available to provide a basis for estimating EFs. Where possible, I
have used appropriate methods to derive estimates (shown in italic font in
Table 1) based on other information. For each species, the estimation method
is given in column EM. For species predominantly emitted during smoldering
combustion, e.g., most VOCs, I have based the estimate on the assumption
that their emission factors for the various fire categories are proportional
to those of CO for the same categories. The estimate was then obtained by
calculating the mean of the ratios EFX/EFCO for the fire
categories with available data and multiplying this mean ratio by the
EFCO of the fire category for which an estimate was needed (labeled CO
in column EM). Where no suitable ratios ERX/ERCO were available
from field studies, the lab ratio was used instead (labeled LV). For some
species containing heteroelements (N2O, SO2, DMS, and HCl), the
mean of the ERs from fire categories with available data, weighted by the
amounts of biomass globally burned in those categories, was used (labeled
AV). Subjective “best estimates” are labeled BE. Specifically, for missing
values of total particulate carbon emissions, the sum of OC and EC emissions
was used, and for aerosol potassium emissions in boreal forest fires I used
the temperate forest value.
Results and discussionCombustion process and pyrogenic emissions
Our fundamental understanding of the biomass combustion process has remained
unchanged since the 1990s, as reviewed in A&M2001 and other papers
(Lobert and Warnatz, 1993; Yokelson et al., 1996, 1997;
Akagi et al., 2011) and will thus be summarized here only briefly. As the
flaming or glowing front of a fire moves towards the uncombusted fuel, the
fuel is heated by radiative and sensible heat transfer, leading first to
evaporation of water and other volatiles, then to pyrolytic decomposition
and the release of volatile and semivolatile (tar) decomposition products
(Collard and Blin, 2014). When this released mixture ignites, flame
chemistry sets in, which breaks down the more complex pyrolysis compounds to
small molecules and radicals but also produces new larger molecules by
radical chemistry, such as alkynes, polycyclic aromatic hydrocarbons (PAHs),
soot, and organohalides. In addition to volatile matter being consumed by
flaming combustion, char undergoes gas–solid reactions between oxygen and
other gases and solid carbon at the fuel surface, called gasification or
“glowing” combustion, in which a large fraction of the fuel carbon is
released as CO, part of which is further oxidized to CO2. In a typical
vegetation fire, all these processes occur simultaneously as the fire
propagates through the fuel, so that the fire plumes at any place and time
contain mixtures of flaming and smoldering (vernacular for a changing mix of
distillation, pyrolysis, and glowing) combustion products in variable
proportions.
Depending on the vegetation type and burning conditions, the relative
amounts of fuel consumed by flaming and smoldering combustion can vary
considerably. Dry grassland fires, for example, are dominated by flaming
combustion and a rapid passage of the fire front, with little residual
smoldering. Forest fires, on the other hand, especially those in fuels with
relatively high fuel moisture and large diameters, have a long phase of
residual smoldering combustion (RSC), during which larger-diameter fuels are
consumed over time spans of up to several days (Ward and Hardy, 1991;
Ward et al., 1992; Yokelson et al., 1997; Bertschi et al., 2003; Hao and
Babbitt, 2007; Burling et al., 2011; Akagi et al., 2013; Geron and Hays,
2013; Urbanski, 2014; Reisen et al., 2018). The smoldering mode of
combustion can become dominant in peat fires, which often proceed without a
flaming phase and below ground (Bertschi et al., 2003; Stockwell et al.,
2016b).
Since the rate of heat release during RSC is relatively low, and much of it
occurs during nighttime, the resulting emissions tend to accumulate close to
the ground in the boundary layer. At nighttime, emissions are confined in a
nocturnal boundary layer, often less than 100 m thick, where the
fire-emitted CO2 becomes mixed with CO2 from biological
respiration. This presents serious problems for measuring accurate and
representative fire-integrated emission factors for fires where RSC
emissions are important (Bertschi et al., 2003).
Ground-based studies during the RSC phase can obtain ERs of trace species,
but these are difficult to relate to the corresponding amount of fuel
burned. Aircraft studies have trouble measuring the RSC component of these
emissions, as they are not lofted in the form of discrete plumes to aircraft
altitudes but only mixed upward during daytime convection (or fire
blow-ups) where they get distorted by mixing in the ambient atmosphere
(Guyon et al., 2005). The mixing of biogenic and
pyrogenic CO2 in fire plumes that entrain such boundary layer air into
a deeper mixed layer presents serious problems for deriving fire-integrated
ERs and EFs from aircraft measurements (Yokelson et al.,
2013a), which can potentially be addressed by the multi-tracer MERET
approach (Chatfield et al., 2019).
Because the flaming phase is characterized by CO2 being the dominant
combustion product by far, while the smoldering phase yields relatively
large amounts of CO (up to about 30 % of carbon burned), the MCE has been
established over the last two decades as the key metric representing the
relative role of flaming vs. smoldering combustion in vegetation fires,
spanning a range of 0.77 in peat fires to 0.98 in some grassland fires (see
Supplement). Mean MCE values for the different combustion categories are
presented in Table 1.
Scatter plots of the emission factors of ethene (a) and ethane (b)
against MCE, based on studies in the different combustion categories.
Since the MCE was introduced by Ward and Radke (1993), numerous
papers have used this metric and have shown significant negative
correlations for many trace gases between emission factors and MCE,
especially for the various VOCs that are emitted predominantly during
smoldering combustion (e.g., Korontzi et al., 2003; Yokelson et al.,
2003, 2008, 2013b; Soares Neto et al., 2009; Urbanski et al.,
2009; Burling et al., 2011; Urbanski, 2013, 2014; Liu et
al., 2014; Collier et al., 2016; Coffey et al., 2017;
Fortner et al., 2018; Hodgson et al., 2018; Reisen et al., 2018; Jen et al.,
2019). However, the correlation slopes between EFs and MCE vary considerably
between studies in different fuels and burning environments, so that a
global parameterization of all EFs based on observed or modeled MCE remains
problematic. As an illustration, I show in Fig. 1a and 1b plots of the EFs
of ethene (C2H4) and ethane (C2H6) vs. MCE, based on the
average values from the individual studies in the supplemental spreadsheet.
In both cases, the results scatter widely, and especially the data from the
lab studies, biofuel burning, peat fires, and RSC-dominated fires introduce
a large amount of scatter. The limitations in correlation between EFs and
MCE have been noted previously (Yokelson et al., 1997; Bertschi et al.,
2003; Burling et al., 2011; Urbanski, 2014). In the case of ethene, the
correlation using all data points is not significant (R2=0.07).
However, when only the data from open vegetation fires are included (and
after removing three outliers), the correlation improves to an R2 of
0.27. For ethane, the correlation coefficient is R2=0.38 for all
data but does not improve substantially by removing the peat fire data.
These results suggest that the level of aggregation at which MCE is useful
as a meaningful but rough predictor of EFs for at least some species is yet
to be determined. This approach is not pursued further here, but the data in
the original studies listed in the Supplement can be used by investigators
to derive such relationships for specific compounds and combustion types of
interest. An interesting and novel approach to generalizing VOC emissions is
provided by Sekimoto et al. (2018), who
showed that most of the variability in VOC emissions measured in a lab study
using a wide variety of fuels was explained by just two factors, related to
low and high temperature pyrolysis, respectively.
Using MCE as a predictor variable may be an alternative to providing
separate EFs for smoldering and flaming combustion, which has been
frequently requested by the modeling community but for which there are still
not enough data to provide robust estimates, as we already remarked
previously in A&M2001. However, once vegetation fire models are able to
provide estimates of the contribution of flaming and smoldering combustion
from a given fire, the resulting MCE could be predicted. This could then
form the basis of a more fire-specific prediction of trace gas and aerosol
emissions based on MCE correlations. An alternative approach was proposed by
Hoffa et al. (1999) and further developed by Korontzi et al. (2003), who showed a correlation
between vegetation greenness and MCE, which allowed the prediction of
seasonally dependent emissions from African savanna fires (Ito and
Penner, 2004; Korontzi et al., 2004; Korontzi, 2005). In view of the
limitations seen with regard to more general parameterizations, it appears
that for now one can keep using the category-average EFs, but be aware they
can vary considerably from region to region and from fire to fire.
Emission factors for chemical species from the various combustion
categories
In Table 1, I present the updated estimates of emission factors for the
combustion categories, savanna and grassland, tropical forest, temperate forest,
boreal forest, peat fires, open agricultural waste burning (in the fields),
biofuels (excluding dung), dung cakes, charcoal making, charcoal burning,
and garbage burning. As more data have become available, it was now possible
to split the extratropical forest category into temperate and boreal forest
burning. The transition between these two types is not always clear, but in
general, I have followed the authors' choice of category; where this was not
possible I have taken a latitude of 60∘ N as a boundary.
The large number of studies on residential biomass burning, which have been
published in the last two decades, has made it possible to separate dung
cakes from the other biofuels, such as fuel wood and agricultural residues.
As mentioned above, I only included studies that used fireplaces and
traditional or simple “improved” stoves, as are used in developing
countries, and not modern appliances, such as automated pellet stoves.
The publication of a few papers that provide emissions data for open garbage
burning, still quite prevalent in many countries and a serious source of
pollution especially in urban areas (Wiedinmyer et al., 2014),
has made it possible to provide EFs for this category.
Obviously, the categories used here are still quite highly aggregated, but
they correspond closely to the fire types used in many global modeling
studies, such as those involved in the Fire Modeling Intercomparison Project
(FireMIP) (F. Li et al., 2019) and in model- or satellite-based
emission inventories (Wiedinmyer et al., 2011; Kaiser et al., 2012;
Ichoku and Ellison, 2014; Darmenov and da Silva, 2015; van der Werf et al.,
2017). Should a reader require less highly aggregated data, they can use the
Supplement to split the data into subcategories or even use the supplemental
references to get back to the original literature. Valuable detail about the
various burning types and further breakdown of some categories, e.g.,
biofuel use, into relevant subcategories can be found in
Akagi et al. (2011).
For information purposes, I also include a column summarizing the results of
laboratory studies. The averages in this column can only be seen as general
indication of the magnitude of emission factors found in the lab studies,
since all types of fuels and burning methods are included in the statistics
presented here. However, the original data and references are provided in
the Supplement for readers interested in the details.
As in A&M2001 and in Akagi et al. (2011), the
amount of information for any given combination of species and fire category
varies greatly – for some combinations we have no measurements at all, and
for others there are as many as 50 values. Accordingly, the uncertainty of
the estimates is also highly variable. In Table 1, I am using the same
convention as in A&M2001 to represent the uncertainty: when three or more
values (based on independent references) are available for a given table
cell, the results are given as means and standard deviations (x±s).
In the case of two available measurements, they are given as a range, and
where only a single measurement is available, it is given without an
uncertainty estimate. For single measurements, it can usually be assumed
that the uncertainty is no less than a factor of 3.
In spite of the fact that this paper is based on data from over 370
publications, rather than the 130 papers that formed the basis for
A&M2001, Table 1 shows that there are still many species for which there
are little or no field data available. For example, there are still no field
measurements of the emission factors for the alkyl amines, which have
recently become implicated in aerosol nucleation and new particle formation
(Smith et al., 2010; Almeida et al., 2013; Kürten et al., 2014). In
view of the importance of the number concentrations of aerosol particles
(CN), especially cloud condensation nuclei (CCN), for climate change, it is
unfortunate that there have only been a few additional measurements of their
emission factors in the last two decades. The rapid coagulation of particles
very near the source makes it difficult to choose the most appropriate plume
age for such a measurement (Hobbs et al., 2003; Sakamoto et al., 2016;
Hodshire et al., 2019). However, a survey of available measurements suggests
that the ratio of excess particle number concentration (ΔCN or
ΔCCN) to ΔCO stabilizes at the scale of typical aircraft
measurements in plumes, as a consequence of the sharp decrease of the
coagulation rate with increasing dilution (Janhäll et al.,
2010). More field studies on the evolution of aerosol number concentrations
and size distributions as a function of plume age under different conditions
(fire size, wind speed, flux density, etc.) are warranted.
Another climate-relevant component, for which we have no field emission data
at this time, is brown carbon (BrC) (Andreae and Gelencsér,
2006), which has been shown to account for about half of the aerosol light
absorption by biomass smoke at 401 nm (Selimovic et al.,
2019) and 25 %–45 % at 550 nm (Tian et al., 2019). Providing EFs
for this species is problematic because of the very complex and variable
mixture of compounds that make up BrC, as well as its potential for rapid
change in abundance and optical properties during plume evolution
(Forrister et al., 2015; Fleming et al., 2019). To some extent, data on
the optical properties of BB aerosols can substitute direct measurements
of BrC (Stockwell et al., 2016a, b; Goetz et al.,
2018; Selimovic et al., 2018).
Regarding the role of vegetation fires in the global carbon cycle, the most
problematic uncertainty pertains to the emission factors of CO2 and CO
from forest fires, which is surprising in view of the many available
estimates. This uncertainty stems from the inadequate knowledge of the
contribution from RSC, which has already been referred to above, and which
may significantly contribute to large mismatches between bottom-up
predictions of CO emissions and remote-sensing measurements from satellite
(Pechony et al., 2013; Deeter et al., 2016). A representative measurement
of fire-average ΔCO/ΔCO2 emission ratios from large
forest fires is very difficult if not impossible, as ground-based
measurements in such violent fires are not possible, and aircraft
measurements are prone to undersampling the smoldering emissions, especially
the contributions from RSC. The uncertainty regarding the ΔCO/ΔCO2 emission ratio also seriously hampers our ability to
separate the influence of the emissions from deforestation burning from
those of biological carbon fluxes in regional carbon budgets (Andreae et al., 2012). For example, the uncertainty of
the ΔCO/ΔCO2 ratios of tropical forest burning is large
enough that it can even change the inferred sign of the net carbon flux
between the Amazon forest and the atmosphere (Gatti et al., 2014). A novel
multi-tracer statistical technique (MERET; Chatfield et al., 2019)
may be able to provide improved estimates of the CO ERs and EFs from such
fires.
Comparison between the emission factors for selected species
between this study and the values in Akagi et al. (2011).
Figure 2 presents a comparison between selected EFs from this study with
those published in Akagi et al. (2011) in the form of
ratios between the EFs from these studies. For this comparison, I have
selected species that are of major climatic or chemical significance or are
important BB tracers, and for which there are enough data to allow a
meaningful comparison. Data are presented for the combustion types with the
largest total global emissions, i.e., savanna and grassland, tropical,
temperate, and boreal fires, and biofuel use. In the case of biofuel use,
the comparison is made with Akagi et al.'s “open cooking” category
because its MCE shows good agreement with that for the “biofuel use”
category in this paper. Figure 2 shows close agreement for the main carbon
species CO2 and CO as well as for MCE, suggesting that the averages derived for both species
capture comparable combustion conditions. For most other species, the EF
ratios fall within a factor of 2, with no obvious systematic shift for
either the individual species or for the combustion types. A slight
exception are the EFs for savanna and grassland, which tend to be somewhat
higher in the present study. In one case (isoprene) this is the result of
higher values from an individual study, i.e., the lab-adjusted-to-field EFs
from Stockwell et al. (2015), but generally the differences
appear to be the result of including a larger set of studies from this
category in the present study. The lower EFs for glycolaldehyde in this
study are the result of corrections made by the Yokelson group to their data
based on improved spectral data (see https://www.atmos-chem-phys-discuss.net/12/C11864/2013/acpd-12-C11864-2013.pdf, last access: 27 June 2019),
which have been incorporated here and in the online updates to
Akagi et al. (2011), but for consistency the values
from the original paper were used for Fig. 2. The largest and most
systematic difference is seen for the NMOG category, where the values from
Akagi et al. (2011) are as much as a factor of 10
higher than the averages from the published field studies in Table 1. This
is largely due to differences in the analytical techniques used in the
original studies. Most of the older studies, especially in field campaigns,
were measuring only a very limited subset of NMOGs (e.g., non-methane
hydrocarbons), whereas Akagi et al. (2011) in the original paper and in the
subsequent updates used techniques that measured practically all NMOGs,
including unidentified species. To address this issue, I am including both
the field study averages (labeled VOCs) and the corresponding values from
the online updates to Akagi et al. (2011) (labeled
NMOGs) in Table 1. The latter values may be more appropriate as input for
modeling studies that require an estimate of total NMOGs.
Emissions from global biomass burning
In 2001, we estimated the total amount of biomass burned by all combustion
types to be 8.6 Pg dry matter annually, with an uncertainty of ±50 % (A&M2001). This estimate was based on bottom-up inventories and
had not yet benefitted from remote-sensing detection and quantification of
fires. At present, there are several operational fire detection and emission
estimation products based on remote sensing. Three of them (for example) use
an approach based on burned area and hotspot detection: Fire INventory from
NCAR (FINN; Wiedinmyer et al., 2011), Fire Locating and
Modeling of Burning Emissions (FLAMBE; Reid
et al., 2009), and Global Fire Emissions Database (GFED; van der Werf et al., 2017). The other
three products are based on fire radiative power (FRP): Quick Fire Emission
Dataset (QFED; Darmenov and da Silva, 2015), Global Fire
Assimilation System (GFAS; Kaiser et al., 2012), and Fire
Energetics and Emissions Research (FEER; Ichoku and Ellison,
2014). The amounts of biomass burned annually in open fires estimated by
these systems still span a wide range, from 4.3 Pg (GFAS) to 11.6 Pg
(FLAMBE) (for the FRP-based products, which do not use biomass burned in
their calculations, the biomass estimate was based on the stated emission of
carbon compounds and an assumed carbon fraction of 45 % in the biomass).
For domestic biofuel use, there are three recent global estimates: 2.1 Pg a-1 (Fernandes et al., 2007), 2.5 Pg a-1 (Steven J. Smith, personal communication, 2019, based on the Community Emissions Data
System (CEDS) model; Hoesly et al., 2018), and 2.3 Pg a-1 (Zbigniew Klimont, personal communication, 2019, based on the methodology
in Klimont et al., 2017). These recent estimates are all somewhat
lower than those of A&M2001 (2.9 Tg a-1) and Yevich and Logan (2003) (3.1 Tg a-1). For charcoal burning, I am also using the estimate
of 53 Tg a-1 given for 2014 by FAO Forestry Policy and Resources Division (2015), and for charcoal making
I am assuming a 25 % yield of charcoal relative to dry wood (Yevich
and Logan, 2003).
Estimates of biomass burned (Tg dry matter) annually in the various
fire categories.
SourceSavanna/TropicalTemperateBorealPeatAgriculturalTotalYearsgrasslandforestforestforestresiduesopen firesFINNa19203200260137–21057302005–2010GFED4.1sb298069010033016129045502005–2015GFAS1.2c25409101104601836342602003–2018QFEDd3690850280200––55602003–2012FEERe––––––93302000–2012FLAMBEf87087507501120–99115802005–2015ECLIPSE V6ag–––––530–2005–2010Average240028803004501722406440Wood etc.CharcoalCharcoalAgriculturalDungTotalmakingburningwastebiofuelFernandesh1350156395007521202000FAOi––53–––2014ECLIPSE V6ag1780–443508922702005–2010CEDSj1590–465808824902010Average157018045480842360Grand total from all biomass burning 8800
a Wiedinmyer et al. (2011).
b From http://www.geo.vu.nl/~gwerf/GFED/GFED4/tables/GFED4.1s_C.txt (last access: 27 June 2019) assuming 45 % C in biofuel.
c Imke Hüser, personal communication 2019, based on methodology in
Kaiser et al. (2012).
d Anton Darmenov, personal communication 2019, based on methodology in
Darmenov and da Silva (2015). Emissions from boreal fires were calculated
from extratropical fires north of 50∘ N, and temperate emissions
were calculated by subtracting boreal from extratropical emissions;
emissions from crop residue burning fires are included in the grassland fire
category.
e Ichoku and Ellison (2014), not included in category averages
because breakdown not available.
f Edward Hyer, personal communication 2019, based on methodology in Reid
et al. (2009). Temperate and boreal emissions were calculated by splitting
extratropical burning 40 % / 60 %.
g Zbigniew Klimont, personal communication 2019, based on methodology in
Klimont et al. (2017).
h Fernandes et al. (2007).
i FAO Forestry Policy and Resources Division (2015).
j Steven Smith, personal communication 2019, based on methodology in
Hoesly et al. (2019).
Combining these estimates of open and domestic burning yields a mean
estimate of 8.8 Pg (with a range of 6.4 to 14.1 Pg) dry biomass burned
annually. Interestingly, this is almost identical to the values given in
A&M2001: 8.6 Pg a-1, with an estimated range of 4.3 to 12.9 Pg a-1. Table 2 summarizes these emission estimates. For the various
categories of open burning, the satellite-derived emission estimates vary
greatly, in some cases by an order of magnitude. Differences in the
definitions of the burning categories between the different retrieval
algorithms, differing ability to detect small fires, and the fundamental
difference between the burned-area and FRP-based techniques may all play a
role here.
Global emission of selected species based on the emission factors
in Table 1 and the biomass burning estimates in Table 2 (Tg a-1).
∗ Using EFs from online updates to Akagi et al. (2011).
In Table 3, I use the average of the available estimates from the different
inventories shown in Table 2 as activity estimates for the combustion
categories to derive emission values for major species emitted from biomass
burning. For comparison, the last column in Table 3 shows the global total
emissions estimated in A&M2001. The totals of the major emitted carbon
species and many minor species remain fairly close to those in our previous
assessment. Given the large number of measurements for the emission factors
for the major species, CO2, CO, and CH4, the standard error of the
mean is much smaller than the standard deviation, and thus the relative
uncertainties of the mean for these emission factors are quite small,
1 %–3 % for CO2, 4 %–9 % for CO, and 6 %–18 % for CH4 from the
major burning categories savanna, forests, and biofuel. Consequently, the
global emission uncertainties for these species are completely dominated by
the large uncertainties in the activity estimates.
The best independent “reality check” for these emissions may still come
from the inverse modeling of the CO budget. This species is the most
appropriate for such a comparison because its emission factors are well
constrained, biomass burning is a large fraction of all global sources, and
there is a large body of measurements both from ground stations and remote
sensing. Estimates of CO emissions from the various inversion models range
from 190 to 560 Tg a-1 from biofuel burning and 360 to 610 Tg a-1
from open burning for the years around 2000 (Park et al., 2015,
and references therein). The model of Park et al. (2015), which
uses a joint inversion of CO concentrations and oxygen isotopic composition
and therefore is likely to be the most reliable in separating the different
source types, predicts CO emissions of 380 to 610 Tg a-1 from open
burning, 400 to 520 Tg a-1 from biofuel use, and 780 to 1130 Tg a-1 for all biomass burning. Using the EFs from Table 1 and the
activity estimates from Table 2, we obtain a range of 390 to 1210 Tg a-1 for the CO emissions from open burning, in reasonable agreement
with the inverse results. The range of biofuel CO emissions estimated from
Tables 1 and 2 is only 181–196 Tg a-1, accounting for less than
one-half of the inverse estimate. This suggests either that the amount of
biofuel use is significantly underestimated in present bottom-up budgets or
that the inversions attribute some of the open burning inaccurately to
biofuel use. This could likely be the case for agricultural burning, which
uses similar fuels and takes place in similar regions as biofuel use. The
inverse analyses may also be useful to indicate unlikely estimates based on
remote-sensing techniques. For example, the burning of 8750 Tg dry matter in
tropical forests estimated by FLAMBE, combined with the corresponding
EFCO (105 g kg-1), would produce CO emissions of 900 Tg a-1
from this biome alone, well above the range of inverse CO emission estimates
for all open burning (see also the comments by Reviewer 1;
10.5194/acp-2019-303-RC1).
Major differences between the present emission estimates and A&M2001 are
seen for the oxygenated volatile organic compounds and for HCN
(as already noted in Akagi et al., 2011), which all
are significantly greater in the present assessment than in A&M2001. This
is due to the large number of new and more accurate emission factor
measurements for these compounds, which have been made possible by
improvements in analytical techniques since the 1990s.
Conclusions
We are left with the somewhat frustrating conclusion that, in spite of the
great progress in emission factor measurements and detection and
quantification of fires, the overall uncertainty of global biomass burning
emissions has not decreased significantly for most substances since our previous analysis almost 20 years ago. Evidently, there is a great need for
improved accuracy in the activity estimates, both for open burning and
especially for biofuel use. For open burning, coordinated regional CO
studies in regions and at times of high biomass burning activity, including
both FRP- and burned-area-based remote-sensing approaches as well as
inversions, may be a way to resolve discrepancies and improve accuracy. This
would be of great benefit for testing and improving fire emission models,
which also give quite divergent results and have difficulties in capturing
interannual variations and temporal trends. For example, the modeled
estimates of carbon emitted from open burning in the nine models
participating in the FireMIP project span from 1.0 to 4.9 Pg a-1
(F. Li et al., 2019).
With regard to emission factors, Table 1 can serve as a guide to
prioritizing future research activities. Photochemically active species and
toxic compounds for which there are only a few measurements from important
fire types deserve more intense study. An example is the emission of PAHs,
for which we have only one study from boreal fires and none at all from tropical
forest fires. Given the toxicity of these compounds and the increasing
exposure of populations in these regions to biomass smoke as a result of
climate change and population growth, this seems an important knowledge gap.
Another example are the emissions of semivolatile and intermediate-volatile
compounds (I/SVOCs), which are important in the context of organic aerosol production
from biomass burning but for which at this time only laboratory
measurements are available (Hatch et al., 2018). I have already
referred to the lack of field measurements of alkyl amine emissions, which
may be of importance for new particle formation. In view of the grave health
risk associated with aerosol particles (see, e.g.,
Lelieveld et al., 2019, and references therein) and the growing exposure to
wildfire smoke in areas like the western USA, the accuracy and fire
condition dependence of PM emissions need to be improved. Emphasis should be
on field measurements under a variety of representative conditions, to
represent the influence of parameters like fuel moisture and fire weather.
While the approach in this paper is focused on global averages, future work
should also emphasize regional and seasonal differences in order to better
support more highly geographically resolved modeling. A spreadsheet containing Table 1, the underlying data, and the corresponding
references is available at 10.17617/3.26 (Andreae,
2019), where periodical updates will also be provided.
Data availability
A spreadsheet containing Table 1, the data on which the averages in Table 1
are based, and the corresponding references is available at 10.17617/3.26 (Andreae, 2019).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-19-8523-2019-supplement.
Competing interests
The author declares that there is no conflict of
interest.
Acknowledgements
I thank Johannes Kaiser, Imke Hüser, Zbigniew Klimont, Anton Darmenov,
Edward Hyer, and Steven Smith for providing estimates of biomass burned from
their respective databases, Maximilien Desserrvetaz for unpublished emission data,
and Robert Yokelson, Charles Ichoku, Nic Surawski, James Roberts, and an anonymous
reviewer for comments on the manuscript. This work was supported by the
German Max Planck Society.
Financial support
The article processing charges for this open-access publication were covered by the Max Planck Society.
Review statement
This paper was edited by Qiang Zhang and reviewed by Charles Ichoku, Robert Yokelson, and one anonymous referee.
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