A source-resolved three-dimensional chemical transport model, PMCAMx-SR (Particulate Matter Comprehensive Air-quality Model with extensions – Source Resolved), was
applied in the continental US to investigate the contribution of the
various components (primary and secondary) of biomass burning organic
aerosol (bbOA) to organic aerosol levels. Two different schemes based on the
volatility basis set were used for the simulation of the bbOA during
different seasons. The first is the default scheme of PMCAMx-SR, and the
second is a recently developed scheme based on laboratory experiments of the
bbOA evolution.
The simulations with the alternative bbOA scheme predict much higher total
bbOA concentrations when compared with the base case ones. This is mainly
due to the high emissions of intermediate-volatility organic compounds
(IVOCs) assumed in the alternative scheme. The oxidation of these compounds
is predicted to be a significant source of secondary organic aerosol. The
impact of the other parameters that differ in the two schemes is low to
negligible. The monthly average maximum predicted concentrations of the
alternative bbOA scheme were approximately an order of magnitude higher than
those of the default scheme during all seasons.
The performance of the two schemes was evaluated against observed total
organic aerosol concentrations from several measurement sites across the US.
The results were different for the different seasons examined. The default
scheme performed better during July and September, while the alternative
scheme performed a little better during April. These results illustrate the
uncertainty of the corresponding predictions and the need to quantify the
emissions and reactions of IVOCs from specific biomass sources and to
better constrain the total (primary and secondary) bbOA levels.
Introduction
Over the past decades, atmospheric aerosols, also known as particulate
matter (PM), have been at the forefront of atmospheric chemistry research due to
their adverse impacts on human health, climate change, and visibility. More
specifically, fine particulate matter with an aerodynamic diameter less than
2.5 µm (PM2.5) is associated with decreased lung function
(Gauderman et al., 2000), bronchitis incidents (Dockery et al., 1996),
respiratory diseases (Pope, 1991; Schwartz et al., 1996; Wang et al., 2008),
and eventually increases in mortality (Dockery et al., 1993). PM2.5
also affects the planet's energy balance (Schwartz et al., 1996) and causes
visibility reduction not only in urban centers but also rural areas (Seinfeld and
Pandis, 2006).
One of the most important components of fine PM almost everywhere is organic
aerosol (OA) (Andreae and Crutzen, 1997; Roberts et al., 2001; Kanakidou et
al., 2005). Despite its importance, OA remains poorly understood due to its
physicochemical complexity (Goldstein and Galbally, 2007). OA is
traditionally separated into primary (POA), which is emitted directly into
the atmosphere as particles, and secondary OA (SOA), which is OA that is
formed from gaseous precursors that form
organic particulate matter after oxidation and condensation (Seinfeld and Pandis, 2006). SOA includes
components produced during the oxidation of semi-volatile organic compounds
(called SOA-sv), of intermediate-volatility organic compounds (SOA-iv), and
of volatile organic compounds (SOA-v). POA and SOA are further categorized
into anthropogenic (aPOA, aSOA) and biogenic (bPOA, bSOA) based on their
sources. The terms POA and SOA (without a prefix for anthropogenic or
biogenic) are used to denote the totals, that is, the sum of the
anthropogenic and biogenic components. The term bbOA is also used for the
sum of primary and secondary biomass burning OA (bbOA=bbPOA+bbSOA).
Biomass burning is an important global source of OA (Puxbaum et al., 2007;
Gelencser et al., 2007; Chen et al., 2017; Gunsch et al., 2018) and other
pollutants such as nitrogen oxides, carbon monoxide, and volatile organic
compounds. This source contributes around 75 % of global combustion POA
(Bond et al., 2004). In this work, the term biomass burning includes
wildfires in forests and other areas; prescribed burning, which is a small
wildfire set intentionally (Tian et al., 2008; Chiodi et al., 2018) in order
to decrease the likelihood of major wildfires; agricultural waste burning;
and residential burning.
The simulation of bbOA has been the topic of numerous studies, all of them
concluding that it is an important source of fine particles (Tian et al.,
2009). Most of them assumed that bbOA is nonvolatile and inert (Chung and
Seinfeld, 2002; Kanakidou et al., 2005). Alvarado et al. (2015) used the
Aerosol Simulation Program, which incorporates updates to the gas-phase
chemistry and SOA formation modules using observations from a biomass
burning plume from a prescribed fire in California. A method was presented
for simultaneously accounting for the impact of the unidentified
intermediate-volatility, semi-volatile, and extremely low-volatility organic
compounds on the formation of OA, based on the volatility basis set (VBS)
approach (Robinson et al., 2007) for modeling OA and the concept of the
mechanistic reactivity of a mixture of organic compounds (Carter, 1994).
Bergström et al. (2012) concluded that residential wood combustion and
wildfires are a major source of aerosol over large parts of Europe. However,
the simulated results are sensitive to the parameters used in the VBS
framework. Posner et al. (2019), using the standard version of PMCAMx (Particulate Matter Comprehensive Air-quality Model with extensions), which
incorporates the VBS scheme, estimated that bbSOA from semi-volatile and
intermediate-volatility organic compounds emitted during biomass burning is
one of the most important components of bbOA in the US.
Fountoukis et al. (2014) performed simulations in Europe using the PMCAMx
model during 2008–2009. The largest discrepancies of average PM1 OA
concentrations between model and measurements were found during the winter.
Ciarelli et al. (2017a, b) proposed an alternative parameterization that was
derived from biomass burning experiments conducted with emissions from
woodstoves and was based on the VBS scheme (Koo et al., 2014). This
alternative parameterization was applied only to the residential heating
sector. The applicability of this parameterization to other biomass burning
sources such as wildfires and prescribed burning will be investigated in the
present study. The alternative framework was evaluated using CAMx for
February–March 2009. The new scheme narrowed the difference between
predictions and observations compared to previous studies (Fountoukis et
al., 2014) but still underpredicted the observed SOA, whereas the bbPOA was
generally overpredicted. The same scheme was evaluated for 2011 in Europe
using CAMx 6.3 (Jiang et al., 2019). The authors concluded that the modified
parameterization improved the model performance for total OA as well as the
OA components especially during the winter.
The aim of the current study is to implement the alternative VBS scheme
proposed by Ciarelli et al. (2017a, b) in the PMCAMx-SR (Source Resolved) model during
different periods. These periods have already been investigated by
Theodoritsi et al. (2020b) using the default PMCAMx-SR scheme. That study
concluded that during spring the PMCAMx-SR performance is good according to
the criteria proposed by Morris et al. (2005), but the model tends to
underpredict the observed OA in the PM2.5 size range. During the
modeled summer period the PMCAMx-SR performance was average with a tendency
towards overprediction of the observed PM2.5 OA. Finally, during the
fall, the model performance was average to problematic because the model
overpredicted the OA levels. The OA overprediction during this period was
mainly due to the probable overprediction of the bbOA (primary and
secondary), which was the dominant OA component according to the model. We
aim to further investigate whether the application of this new
parameterization that has improved bbOA predictions in Europe will close the
gap between predictions and observations in the US too.
In most modeling studies so far biomass burning OA (bbOA) is grouped with
the rest of the primary and secondary OA components and is simulated in
exactly the same way. In this study, PMCAMx-SR, the three-dimensional
chemical transport model (CTM) used, simulates bbOA components separately
from the rest of the OA allowing the use of volatility distributions, aging
schemes, etc., that are specific to this source (Theodoritsi and Pandis, 2019).
At the same time, this enhanced model (extension of PMCAMx) allows direct
predictions of bbOA concentrations since it tracks these species separately.
Theodoritsi et al. (2020b) used PMCAMx-SR to quantify the importance of bbOA
from prescribed burning activities in the US on air quality and human
health.
In the current study we will study in detail the impact of the different
partitioning parameters implemented in bbPOA description and bbSOA formation
and evolution as proposed by Ciarelli et al. (2017a, b). While the previous
study of Theodoritsi et al. (2020b) focused on the role of prescribed burning
as a source of bbOA, in this study all biomass burning sources are grouped
together.
The chemical transport model PMCAMx-SR
PMCAMx-SR is a source-resolved version of the three-dimensional CTM PMCAMx
(Murphy and Pandis, 2009; Tsimpidi et al., 2010; Karydis et al., 2010).
PMCAMx lumps all anthropogenic OA components and biomass burning OA
together, so it does not explicitly keep track of their sources and by
necessity uses source-independent parameterizations for the OA. PMCAMx-SR
uses different variables to describe the OA from different sources and
therefore allows the different treatment (e.g., volatility distributions,
partitioning parameters like enthalpy of vaporization, chemical aging
schemes) of OA from on-road transportation and from biomass
burning. Both PMCAMx and PMCAMx-SR simulate emissions, advection, turbulent
dispersion, removal by wet and dry deposition, chemistry in the gas, aqueous
and particulate phases, and aerosol dynamics using the same computational
modules. They differ in the treatment of OA. Different gas-phase chemistry
mechanisms can be selected by the user. In this study the Carbon Bond 5
mechanism (Yarwood et al., 2005; ENVIRON, 2015) is used expanded for the
treatment of secondary organic aerosol production. The extended version of
the mechanism used simulates the concentrations of 103 gas-phase stable
species and of 13 free radicals using 269 chemical reactions. The
aerosol-size composition distribution is simulated using the sectional
method with eight size bins for the diameter range from 40 nm to 10 µm
and two more for larger sizes used for particles that have grown to cloud
droplets. In total, PMCAMx-SR in this study simulates 67 aerosol components,
both inorganic and organic. PMCAMx-SR is flexible and its user can select
which OA source to treat independently of the others (biomass burning is
selected here) and also which OA parameterizations to employ.
Simulation of organic aerosol (base scheme)
PMCAMx-SR uses the VBS framework (Donahue et al., 2006; Stanier et al.,
2008) for the simulation of the various components of OA (as does PMCAMx).
The VBS treats all primary and secondary OA components as semi-volatile, simulating their partitioning between the vapor and particle phases. It
also treats all of them as reactive allowing the simulation of both the
initial stage of formation of SOA but also later generations of reactions
(often called “chemical aging”). Volatility is expressed in the VBS using
the effective saturation concentration at 298 K, C∗, and the
volatility distribution is split in logarithmically spaced volatility bins
(differences of factors of 10).
The emitted primary organic compounds include volatile organic compounds
(VOCs; C*≥106µg m-3), intermediate-volatility organic
compounds (IVOCs; C* bins of 103, 104, 105, and 106µg m-3), semi-volatile organic compounds (SVOCs; in the 1, 10, and 100 µg m-3C* bins), and finally low-volatility organic compounds (LVOCs; C*≤ 0.1 µg m-3) (Donahue et al., 2009). PMCAMx-SR uses the
generic POA volatility distribution proposed by Robinson et al. (2007) to
simulate the anthropogenic OA emissions from all sources except biomass
burning. The total VBS emissions are assumed to be 2.5 times the original
nonvolatile POA emissions in the traditional inventory used for regulatory
purposes (Robinson et al., 2007; Murphy and Pandis, 2009, 2010). This
default volatility distribution in previous studies using PMCAMx was
implemented for all sources of OA including biomass burning.
In PMCAMx-SR, the fresh and secondary bbOA components are modeled
separately from the other OA components, which are simulated with the default
PMCAMx parameters. The gas–particle partitioning parameters used for bbPOA
species are the ones proposed by May et al. (2013). However, the volatility
distribution proposed in that study only includes compounds up to a
volatility bin of 104µg m-3. The total emissions of the
bbPOA components in the 0.1–104C* bins are assumed to
be equal to the nonvolatile bbPOA emissions in the traditional inventory.
Following the approach of Theodoritsi et al. (2020b), the total emissions of
the more volatile IVOCs (C* values of 105 to 106µg m-3)
are set equal to 0.5 times the original nonvolatile POA emissions.
Therefore, the total biomass burning organic emissions used in this study
are 1.5 times the original POA emissions.
SOA from anthropogenic volatile organic compounds (aSOA-v) and SOA from
biogenic volatile organic compounds (bSOA-v) are represented by four
volatility bins with C* values ranging from 1 to
103µg m-3 at 298 K. Long-range transport OA (lrtOA) is assumed to be heavily oxidized
OA and is treated in PMCAMx-SR as nonvolatile and nonreactive. Overall, the
OA components included explicitly in PMCAMx-SR are fresh primary
anthropogenic OA (POA), fresh primary bbOA (bbPOA), anthropogenic SOA from
VOCs (aSOA), biogenic SOA (bSOA), SOA from semi-volatile anthropogenic
organic compounds (SOA-sv), SOA from intermediate-volatility anthropogenic
organic compounds (SOA-iv), bbSOA from semi-volatile organic compounds
(bbSOA-sv), bbSOA from intermediate-volatility organic compounds (bbSOA-iv),
and long-range transport OA.
All OA components (except from long-range transport OA) are treated as
chemically reactive in PMCAMx-SR. The rate constant used for the chemical
aging reactions with the OH radical is the same as the one currently used
for all primary organic vapors in the VBS and has a value of
4 × 10-11 cm3 molec.-1 s-1. SOA-sv, SOA-iv, bbSOA-sv, and bbSOA-iv components are assumed to further react with OH radicals in the gas
phase, resulting in the formation of lower-volatility SOA and bbSOA
components. All aSOA components are assumed to react with OH in the gas
phase with a rate constant of 1 × 10-11 cm3 molec.-1 s-1 (Atkinson and Arey, 2003). Chemical aging of bSOA (both
homogeneous and heterogeneous reactions) is assumed to lead to a small net
change in mass and is neglected (Murphy and Pandis, 2010). All the aging
reactions mentioned above are assumed to take place only in the gas phase
and to reduce the volatility of the reacted vapor by 1 order of magnitude.
These reactions are assumed to result in an increase in the OA mass of 7.5 % due to the added oxygen.
Table 1 summarizes the VBS parameters of all OA species in the base
PMCAMx-SR simulation. The average molecular weight of all POA and bbPOA components is assumed to be 250 g mol-1, that of aSOA components is assumed to be 150 g mol-1, and that of bSOA species is assumed to be 180 g mol-1. The effective enthalpies
of vaporization of both POA and bbPOA species are based on fits of diesel
and woodsmoke partitioning data (Lipsky and Robinson, 2006; Shrivastava et
al., 2006).
Parameters used to simulate bbPOA, bbSOA-sv, and bbSOA-iv in
PMCAMx-SR.
C* at 298 K (µg m-3)10-1100101102103104105106Base scheme Fraction of bbPOA emissions0.20.10.10.20.10.30.250.25ΔH(kJ mol-1)bbPOA, bbSOA-sv, bbSOA-iv106100948882767064MW (g mol-1)bbPOA, bbSOA-sv, bbSOA-iv250250250250250250250250Alternative bbOA scheme Fraction of bbPOA emissions0.20.10.10.20.4004.75ΔH(kJ mol-1)bbPOA–70594837––64bbSOA-sv3535353535353535bbSOA-iv3535353535353535MW (g mol-1)bbPOA216216216216215215215113bbSOA-sv194189184179179179179179bbSOA-iv149144140135131131131131Alternative bbOA scheme
The scheme of Ciarelli et al. (2017a, b) for the simulation of the emissions
of organics from residential heating biomass burning and their evolution in
the atmosphere during winter was also implemented in PMCAMx-SR. The organic
PM emissions (assumed to be nonvolatile in the original inventory) are distributed
in this scheme across five volatility bins with saturation concentrations
values ranging from 10-1 to 103µg m-3 following the
volatility distribution and enthalpy of vaporization proposed by May et al. (2013). Organic vapors in this volatility range are assumed to react with OH
forming semi-volatile oxidation products with an order-of-magnitude-lower
volatility:
bbPOGi+OH→bbSOGi-1,
where i is the corresponding volatility bin, bbPOGi is the primary
emissions in the gas phase, and bbSOGi is their oxidation products.
Fragmentation processes are implicitly assumed to balance the effect of the
increase in oxygen content of the reacting molecules. Both schemes (base
case and alternative) do not explicitly simulate the functionalization and
fragmentation reactions. The alternative scheme of Ciarelli et al. (2017a,
b) assumes that these two processes in a sense balance each other leading to
a mass stoichiometric yield equal to unity in the corresponding net reaction.
All emitted IVOCs in this bbOA scheme are assumed to have a C* value of
106µg m-3 (Ciarelli et al., 2017a, b), which is at the high
end of the IVOC saturation concentration range. The emission rate of these
IVOCs is assumed to be 4.75 times the primary OA emissions in the original
inventory. The IVOCs are assumed to react according to the following
reaction:
bbPOG106+OH→0.143bbSOG103+0.097bbSOG102+0.069bbSOG101+0.011bbSOG100,
yielding secondary products with saturation concentration ranging from
C*=1 to 103µg m-3. In this reaction
bbPOG106 stands for the primary
emissions in the volatility bin with a C∗ value equal to 106µg m-3, whereas bbSOG103
to bbSOG100 are the secondary gas-phase oxidation products of the IVOCs with C∗ values ranging from
103 to 100µg m-3. For both primary and secondary
compounds, aging is simulated assuming a gas-phase reaction rate constant
with OH of 4 × 10-11 cm3 molec.-1 s-1. The lowest
volatility secondary bbPOA components in this scheme have C*=10-1µg m-3 since the C*=1µg m-3 species can react with OH to
form lower-volatility products.
Table 1 also summarizes the volatility distribution, the molecular weights,
and the enthalpies of vaporization of all bbOA species used in the alternative
bbOA modeling scheme in this study. The enthalpies of vaporization used
in this bbOA scheme are the ones proposed in Ciarelli et al. (2017a, b). The
structure of the VBS combined with the modular structure of PMCAMx-SR allows
the user to change the corresponding parameters easily (volatility
distributions, enthalpies of vaporization, aging scheme, etc.) and therefore
change the OA parameterization for the source of interest.
Model application
In this study PMCAMx-SR is used to simulate three seasonally representative
months (April, July, and September) during 2008 for the continental US. The
modeling domain also included southern Canada and northern Mexico. The first
2 d of each simulation were excluded from our analysis to allow for
model spin-up, but the corresponding results are shown in time series plots.
The modeling domain covers a region of 5328 × 4032 km2 with
36 × 36 km grid cell resolution and 25 vertical layers extending up
to 19 km (Fig. 1). An annual CAMx simulation was performed for the same
domain to obtain the necessary initial conditions used in our simulations
for each month (ENVIRON, 2013).
PMCAMx-SR-predicted ground-level concentrations of (a) fresh
bbPOA, (b) SV-bbSOA-sv, and (c) SV-bbSOA-iv from all biomass burning sources
during April 2008. Left column refers to the base case simulations and right
column to the simulations with the alternative bbOA scheme. All
concentrations are in µg m-3.
The Weather Research and Forecast Model (WRF) version 3.3.1 (NCAR, 2012) was
used to produce the meteorological inputs needed by PMCAMx-SR. The land-use
data were based on the U.S. Geological Survey Geographic Information
Retrieval and Analysis System (USGS GIRAS) database. The photolysis rate
input data were produced by the NCAR Tropospheric Ultraviolet and Visible
(TUV) radiation model. The chemical boundary conditions were based on
simulations using the MOZART global CTM (Emmons et al., 2010). Additional
details about the model inputs can be found in Posner et al. (2019) and
Theodoritsi et al. (2020b).
The emission inventory used in the current study tracks separately the
biomass burning emissions from the emissions from other sources. The latter
are based on the US National Emissions Inventory (NEI TSD, 2008). Biomass
burning emissions include emissions of prescribed burning, agricultural
burning, and wildfires, and the methods used for their estimation inventory
be found in WRAP (2013). The fire activity data used are described in
Ruminski et al. (2006), Eidenshink et al. (2007), and Mavko and Randall
(2008). The approach used for the preparation, processing, and validation of
fire activity data were similar to those of Wiedinmyer et al. (2006) and
Raffuse et al. (2009). For fire consumption estimates CONSUME3 (Joint Fire
Science Program, 2009) was used for all biomass burning sources except
agricultural burns for which the method from the WRAP 2002 emissions
inventory was employed (WRAP, 2005).
During all three examined periods, based on the emissions inventories used
biomass burning was a significant POA source mainly in the southeast US
(Posner et al., 2019; Theodoritsi et al., 2020b). Specifically, during April,
July, and September, respectively, this source represents approximately 25 %,
65 %, and 37 % of the total POA emissions. During April 19 % of the
domain-averaged bbPOA emissions rate is due to agricultural burning, 47 %
to prescribed burning, and 34 % to wildfires. During July, due to the very
high wildfire emissions mainly in northern California, the domain-averaged
bbPOA emissions are mostly (96 %) due to this source. Agricultural burning
contributed 1 % and prescribed burning the remaining 3 %. For September,
wildfires in the west were still the dominant source, and they were
responsible for 73 % of the domain bbPOA emissions. Prescribed burning was
a significant source (22 % of the bbPOA emission), while agricultural
burning was responsible for 5 % of the emissions. Posner et al. (2019) and
Theodoritsi et al. (2020b) have presented analyses of the spatial
distribution and magnitude of these bbPOA emissions.
Predicted bbOA concentrations
In this section the predictions of PMCAMx-SR for the base case and the
alternative bbOA scheme are analyzed. In this work bbOA is defined as the
sum of primary (bbPOA) and secondary (bbSOA) OA. The latter is the sum of
bbSOA originating from semi-volatile organic compounds (bbSOA-sv) and from
IVOCs (bbSOA-iv). The small SOA contribution from VOCs (Posner et al., 2019)
is not explicitly accounted in the bbSOA but is included in the aSOA and
bSOA simulated by the model. The results of the PMCAMx-SR simulations with
the two schemes are shown in Figs. 1–3.
During April both schemes predict approximately the same bbPOA
concentrations (Fig. 1) that were as high as 3.5 µg m-3 on a
monthly average basis in the southeastern US. These high levels were mainly
due to prescribed burning. The differences in predicted bbPOA levels by the
two models were less than 0.1 µg m-3 (maximum difference in
average levels) in all areas of the domain (Fig. 4) something expected
given that they use the same volatility distributions for the primary LVOCs
and SVOCs. Predicted average ground bbPOA levels over the US were
approximately 0.02 µg m-3 (average of ground concentrations over
the whole domain). The domain and simulation average bbPOA values are quite
low given that fires often have a significant effect for only a few days for
a limited area. These values are provided here mainly to facilitate the
comparison of the two parameterizations. The predicted bbSOA-sv
concentration fields were also quite similar (differences of less than 0.1 µg m-3) for the two schemes (Fig. 1). This is also the consequence of
the similarity of the volatility distributions and chemical aging
parameterizations used by the two schemes in the SVOC volatility range of
the biomass burning emissions. While the average bbSOA-sv levels over the
domain were quite similar to those of the bbPOA (around 0.02 µg m-3), the peak levels were lower with a maximum monthly average
concentration of 0.5 µg m-3. This spreading of the bbSOA-sv
further from the fires is the result of the time needed for the
corresponding reactions to take place. The predictions of the two schemes
are quite different though for bbSOA-iv (Fig. 1). For the base scheme, the
bbSOA-iv is as important as the bbPOA and the bbSOA-iv contributes on
average 0.02 µg m-3 of OA over the domain. The peak monthly
average bbSOA-iv concentration is predicted to be approximately 0.2 µg m-3 in the southeast. The predictions for bbSOA-iv for the alternative
scheme are approximately an order of magnitude higher, with a maximum
average of 2 µg m-3 and a domain average of 0.2 µg m-3
(Fig. 1). Even if the IVOC emissions are assumed to be more volatile in
the alternative scheme, their high emission rate allows the production of
significant concentrations of secondary OA from biomass burning that extend
over the eastern half of the country during this photochemically active
period.
Both models predict that during April the bbSOA is the dominant component of
bbOA on average over the domain, and even if it peaks in South Carolina with
high levels in North Carolina and Georgia, it has average concentrations
above 0.1 µg m-3 in most areas of the eastern US (Fig. 5a). The
alternative scheme predicts that this bbSOA contribution is a factor of 5–10
higher and around or above 1 µg m-3 in the eastern US. Adding
everything together the alternative scheme predicts an average bbOA
concentration of 0.3 µg m-3 that is a factor of 5 higher than the
average predicted by the base scheme (Fig. 6a).
During July, several major wildfires occurred in California, and consequently
bbOA levels were particularly high in the western US (Fig. 2a) reaching
levels around 100 µg m-3. This presents a very different situation
compared to the spring month discussed above. Once more, the predictions of
the two schemes for bbPOA were quite similar (differences of less than 20 %),
even if the concentration levels at least in California were much higher.
Despite the intensity of the fires in California, the low emissions in the
rest of the country resulted in similar average bbPOA levels over the domain
as in April (0.15 µg m-3) for both schemes. Both schemes
predicted similarly high bbSOA-sv levels with monthly average values of up to
15 µg m-3 and domain average values of 0.2 µg m-3 (Fig. 2b). The alternative aging scheme predicts high bbSOA-iv that
dominates the overall bbOA in the domain with an average of 2 µg m-3. The average bbSOA-iv but also the peak levels predicted by the
base scheme are more than an order of magnitude lower (Fig. 2c). The
average bbSOA predicted by the base scheme was approximately a factor of 7
lower (0.3 versus 2 µg m-3) for the domain (Fig. 5), while the
total bbOA was a factor of 5 lower (Fig. 6). The differences between the
two schemes exceeded 10 µg m-3 on a monthly average basis over
California and were above 1 µg m-3 over a large part of the
western US (Fig. S1).
PMCAMx-SR-predicted ground-level concentrations of (a) fresh
bbPOA, (b) SV-bbSOA-sv, and (c) SV-bbSOA-iv from all biomass burning sources
during July 2008. Left column refers to the base case simulations and right
column to the simulations with the alternative bbOA scheme. All
concentrations are in µg m-3.
PMCAMx-SR-predicted ground-level concentrations of (a) fresh
bbPOA, (b) SV-bbSOA-sv, and (c) SV-bbSOA-iv from all biomass burning sources
during September 2008. Left column refers to the base case simulations and
right column to the simulations with the alternative bbOA scheme. All
concentrations are in µg m-3.
During September there were major wildfires once more in California but
also in Oregon (Fig. 3). Smaller fires were present in New Mexico and in
several southeastern states. The predicted bbPOA average concentration,
similar for both schemes, were the lowest of the three simulated periods
with a value of approximately 0.1 µg m-3. The local monthly maxima
were 65 and 75 µg m-3 for the base case and the alternative aging
scheme, respectively (Fig. 3a). The average bbSOA-sv concentration based on
the predictions of both schemes was a factor of 6 higher (around 0.6 µg m-3) than the average bbPOA concentration. The average bbSOA-sv
during the month exceeded 0.1 µg m-3 over a wide region covering
most of the western coast of the US and parts of the Pacific. The peak
monthly average bbSOA-sv concentration was 7 µg m-3 for both
simulations. Finally, for the bbSOA-iv the alternative scheme predicted both
domain average and peak concentrations that were approximately an order of
magnitude higher than the base scheme (Fig. 3c). For the base case
simulation, bbSOA-iv was as high as 4 µg m-3 with a monthly
average value of approximately 0.05 µg m-3, whereas the same values
for the alternative aging scheme were 45 and 0.7 µg m-3, respectively. As a result, the alternative scheme predicts average
bbSOA levels that are a factor of 7 higher than the base case (0.1 versus
0.7 µg m-3) (Fig. 5c) and total bbOA levels that are a factor of
4 higher (Fig. 6c). For the peak monthly average concentrations, the
differences are a factor of 5 for bbSOA and a factor of 1.5 for bbOA (given
that the bbPOA is a dominant component near the fires).
Average predicted absolute (µg m-3) difference
(alternative aging scheme minus base case) of ground-level PM2.5 bbPOA,
bbSOA-sv, and bbSOA-iv concentrations from PMCAMx-SR base case and
alternative aging scheme simulations during the modeled periods. Positive
values indicate that the PMCAMx-SR alternative aging scheme simulations
predicts higher concentrations.
PMCAMx-SR-predicted ground-level concentrations of bbSOA-sv and
bbSOA-iv from all biomass burning sources during (a) April, (b) July, and (c)
September 2008. Left column refers to the base case simulations and right
column to the simulations with the alternative bbOA scheme. All
concentrations are in µg m-3.
PMCAMx-SR-predicted ground-level concentrations of bbOA from
all biomass burning sources during (a) April, (b) July, and (c) September
2008. Left column refers to the base case simulations and right column to
the simulations with the alternative bbOA scheme. All concentrations are in
µg m-3.
Model evaluation with field measurements
The predictions of PMCAMx-SR for daily average PM2.5 OA were compared
to the corresponding measurements in 161 Chemical Speciation Network (CSN)
sites (located mainly in urban areas) and 162 Interagency Monitoring of
Protected Visual Environments (IMPROVE) sites (located mostly in rural and
remote areas). These daily average measurements were collected once every
3 or once every 6 d and include both the PM2.5 mass
concentration and its composition. The organic carbon (OC)/organic aerosol
(OA) measurements are used here given our focus on biomass burning OA. The
OC of PM2.5 aerosol samples collected on quartz fiber filters is
measured using thermal optical analysis with the corresponding temperature
protocol (Chow et al., 2007). Most measurements were collected in periods
during which the corresponding site was not impacted by biomass burning;
therefore the use of the complete data set would complicate the
interpretation of the evaluation results. To avoid this complication, we
have followed Posner et al. (2019) and selected only the periods during
which the base case of PMCAMx-SR predicts daily average concentrations
higher than a threshold value. Three such thresholds were used to denote all
periods with even a low biomass burning impact (threshold 0.1 µg m-3), all periods with intermediate or higher impact (threshold 0.5 µg m-3), and periods with high impact (threshold 1 µg m-3). The model prediction for the day and location of the measurement
is compared directly to the corresponding measurement.
The statistical metrics that were used for the evaluation of the two schemes
are the mean bias (MB), the mean absolute gross error (MAGE), the fractional
bias (FBIAS), and the fractional error (FERROR) (Fountoukis et al., 2011):
3MB=1/n∑i=1n(Pi-Oi),4MAGE=1/n∑i=1nPi-Oi,5FBIAS=2/n∑i=1n(Pi-Oi)/(Pi+Oi),6FERROR=2/n∑i=1nPi-Oi/(Pi+Oi),
where Pi is the predicted value of the pollutant concentration,
Oi is the corresponding observed value, and n is the total number of data
points used for the comparison.
Theodoritsi et al. (2020b) have already analyzed the performance of the base
scheme of PMCAMx-SR for the same three periods. They concluded that during
April the performance of the base scheme is good according to the Morris et al. (2005) criteria and the model tends to underpredict OA (fractional bias
-0.16, fractional error 0.51 for the low threshold). PMCAMx-SR showed little
bias (3 %–6 %) during July but had a relatively high fractional error
(around 55 %), so its summer performance was considered average for the
periods affected by biomass burning. Finally, the model overpredicted the OA
levels in September with the errors increasing when the predicted bbOA
concentration increased. This made its performance average to problematic
during this period. The metrics of this evaluation by Theodoritsi et al. (2020b) for the base case PMCAMx-SR simulation can also be found in Table S1
for completeness.
The bbOA predictions of the alternative scheme are in general higher than
those of the base scheme. This leads to a small improvement of the
performance of PMCAMx-SR during April especially for the low bbOA threshold
(Table 2). The model now tends to overpredict OA, while the base scheme
underpredicted. For this case, the fractional bias is reduced (in absolute
terms) from -0.16 to 0.11 and the fractional error from 0.51 to 0.48. The
improvements are minor for the medium threshold, while for the high
threshold the fractional bias increases (from -0.14 to 0.28) while the
fractional error decreases (from 0.53 to 0.5). So overall, the use of the
alternative scheme appears to lead to a small improvement of the PMCAMx-SR
predictions during this period but with a tendency towards overprediction
especially close to the sources of biomass burning.
PMCAMx-SR alternative scheme OA prediction skill metrics against
observed values from CSN and IMPROVE networks at biomass-impacted sites.
During July, the base scheme reproduced the OA observations in areas
affected by biomass burning with little bias. The alternative scheme
predicts a significantly higher SOA-iv production during this period and
results in a substantial overprediction of the OA levels in areas with bbOA
above all three thresholds (Table 2). The bias increases for the areas
closer to the fires (higher threshold). These results strongly suggest that
the alternative scheme is too aggressive in the production of SOA-iv during
this summertime period with intensive wildfires.
PMCAMx-SR using the base scheme has difficulties reproducing the OA
concentrations in areas affected by fires in the fall. Given that the base scheme
already overpredicts OA levels, the increased SOA-iv predicted by the
alternative scheme leads to additional deterioration of the model
performance. The alternative scheme substantially overpredicts OA and the
fractional bias increases closer to the sources of biomass burning. Overall,
the performance of the alternative scheme during September is like that
during July.
Importance of the VBS parameters used in the two bbOA schemes
The difference in the IVOC emissions and aging schemes appears to explain a
large fraction of the differences in the predictions of the two schemes in
the simulated periods. However, there are other potentially important
differences in the parameters used in the two schemes. These different
parameters include the enthalpy of vaporization and the molecular weights of
the various bbOA components. The effect of these together with the effect of
the assumed volatility distributions of the emitted bbOA components and the
assumed aging schemes was investigated. Sensitivity tests were performed for
one of the three periods (April 2008) to quantify the individual effect of
these parameters on the predictions of PMCAMx-SR. The results of these tests
and their comparison with the base case results are analyzed in the
subsequent sections.
Enthalpy of vaporization
In this first sensitivity test, we changed the effective enthalpies of
vaporization of the bbOA components (bbPOA, bbSOA-sv, bbSOA-iv) in the base
scheme from their original values that varied from 64 to 106 kJ mol-1
to those of the alternative scheme (Table 1). The new values were equal to
35 kJ mol-1 for the bbSOA components and varied from 37 to 70 kJ mol-1 for the bbPOA. This test allows us to quantify the importance of
the significantly lower enthalpies used in the alternative scheme based on
the work of Ciarelli et al. (2017a, b). All other parameters of the base
scheme were kept the same.
The changes in the predictions of the model were small, a few percent or
less (Fig. S2). The use of the higher original enthalpies of vaporization
resulted in a little higher concentration for all bbOA components. The
maximum monthly average changes were 0.3 µg m-3 for bbPOA, 0.03 µg m-3 for bbPOA-sv, 0.03 µg m-3 bbSOA-iv, and 0.4 µg m-3 for total bbOA all near Savannah, Georgia. However, for most of
the US the change in total bbOA was less than 0.05 µg m-3.
Therefore, the major differences in bbSOA-iv predictions of the base and
alternative scheme were not due to their different enthalpies of
vaporization.
Molecular weights
The base scheme assumes a molecular weight of 250 g mol-1 for all bbOA
components, while a range of molecular weights from 113 to 216 g mol-1
is used in the alternative scheme (Table 1). These variable molecular
weights are also intended to account for fragmentation effects and are
accompanied by a stoichiometric coefficient equal to unity (instead of 1.075
in the base scheme). We replaced the molecular weights of the base scheme
with those of the alternative, changed the stoichiometric coefficients in
the aging reactions from 1.075 to 1, kept everything else the same, and
repeated the April simulation.
The impact of these changes in the molecular weight values and
stoichiometric coefficients was small (Fig. 7). The maximum concentration
changes for the monthly average concentrations were 0.02 µg m-3
for bbPOA, 0.03 µg m-3 for bbSOA-sv, 0.1 µg m-3 for
bbPOA-iv, and 0.1 µg m-3 for total bbOA all in the borders between
South Carolina and Georgia. The use of the Ciarelli et al. (2017a, b) parameters
(molecular weights and aging stoichiometric coefficients) led to very small
reductions in the bbPOA and bbSOA-sv levels and small increases in the
bbSOA-iv levels. The latter dominated the overall bbOA change which
increased by 0.01 to 0.03 µg m-3 in large parts of the eastern US
and by 0.03–0.1 µg m-3 in South Carolina and Georgia. These
changes are still only a few percent. This small impact of the changes is
partially due to the fact that they cancel each other to a large extent. The
decrease in molecular weights leads to increased partitioning towards the
particle phase and therefore higher bbOA levels, where the decrease in the
aging stoichiometric coefficients has the opposite effect for the secondary
components.
Average predicted increase (µg m-3) of the predictions
of the base PMCAMx-SR scheme when the molecular weights and aging
stoichiometric coefficient of Ciarelli et al., 2017a, b) are used compared to the
predictions with the default values for ground-level PM2.5(a) bbPOA,
(b) bbSOA-sv, (c) bbSOA-iv, and (d) bbOA during April 2008. Positive values
indicate that the PMCAMx-SR base scheme with the molecular
weights or stoichiometric coefficients of Ciarelli et al. (2017a, b) predicts
higher concentrations.
Volatility distribution of biomass burning emissions
In this test, the emissions of the various organic compounds in the VBS from
biomass burning were changed from these of the base scheme to those of
Ciarelli et al. (2017a, b) (Table 1). This change does not affect the LVOC
emissions and the SVOC emissions for C* less or equal than 102µg m-3. However, it increases the emissions of the 103µg m-3 volatility bin (by adding to these emissions those that are in the
104µg m-3 bin) and also significantly increases the
emissions of the IVOCs in the 106µg m-3, while it zeros those
in the 105µg m-3 bin.
The use of the Ciarelli et al. (2017a, b) volatility distributions leads to
significant changes in the predicted bbOA concentration levels (Fig. 8).
In all areas and for all bbOA components it predicts higher concentrations.
The maximum concentration differences between the two simulations were 0.1 µg m-3 for bbPOA, 0.1 µg m-3 for bbSOA-sv, and 1.5 µg m-3 for bbSOA-iv. These differences are quite similar in magnitude
to those of the base and alternative schemes (Fig. 4a). This strongly
suggests that the differences in the assumed bbOA volatility-resolved
emissions is mainly responsible for the differences in the bbOA predictions
of the two schemes. For example, for the average total bbOA in the modeling
domain the change in the volatility distributions led to an increase in the
base case results of 0.14 µg m-3. This should be compared with 0.2 µg m-3, which is the difference between the average bbOA
predicted by the base and alternative schemes.
Average predicted increase (µg m-3) in the predictions
of the base PMCAMx-SR scheme when the volatility distribution of Ciarelli et al. (2017a, b) is used for the biomass burning emissions compared to the predictions
with the default values for ground-level PM2.5(a) bbPOA, (b) bbSOA-sv, (c) bbSOA-iv, and (d) bbOA during April 2008. Positive values indicate that
the PMCAMx-SR base scheme with the volatility distribution of Ciarelli et al. (2017a, b) predicts higher concentrations.
The most important difference is the change in the IVOC emissions resulting
in significant changes in the bbSOA-iv. The predicted bbSOA-iv of PMCAMx-SR
with the base scheme using the default and the Ciarelli et al. (2017a, b) bbOA
volatility distributions is depicted in Fig. 9. The monthly maximum
concentration was predicted to be 0.2 and 1.5 µm m-3 for the base
case and the alternative bbOA scheme, respectively, in South Carolina. This is
also consistent, with our conclusion that the difference in the IVOC
emissions is the leading cause of the differences in the predictions of the
base and alternative schemes.
PMCAMx-SR-predicted ground-level concentrations (µg m-3) of bbSOA-iv for the base scheme using (a) the base case volatility
distribution and (b) the Ciarelli et al. (2017a, b) volatility distribution.
Conclusions
An alternative bbOA scheme based on the work of Ciarelli et al. (2017a, b)
has been used in PMCAMx-SR to quantify the impact of bbOA on ambient
particulate-matter levels across the continental US during April, July, and
September 2008. The alternative parameterization was originally developed
based on residential heating biomass burning experiments (i.e., combustion in
stoves). In this study we test its applicability for the simulation of the
bbOA from other sources (wildfires, prescribed and agricultural burning) in
different periods.
In general, the alternative scheme predicts much higher bbOA levels than the
baseline scheme for all seasons. Both schemes suggest that secondary
production is a major process for the average bbOA levels over the US in all
examined periods. However, the alternative scheme predicts that the
production of secondary aerosol from intermediate-volatility organic
compounds emitted during biomass burning is a factor of 5–10 higher than
that of the base scheme. The differences in the predictions of the other
bbOA components (primary bbOA and bbOA from semi-volatile compounds) are low
to modest.
A set of sensitivity tests showed that the most important difference between
the two schemes is the assumed emission rate of intermediate-volatility
organic compounds together with their oxidation to form secondary organic
aerosol. The impact of other different parameters, including the assumed
enthalpies of vaporization and molecular weights, was small.
The performance of PMCAMx-SR using the two schemes was evaluated against
observed values obtained from 161 CSN and 162 IMPROVE network measurement
sites across the US. During April the use of the alternative scheme leads to
a small improvement of the performance of PMCAMx-SR. However, during the
more photochemically active periods of July and September, with intense wildfires, the PMCAMx-SR performance for OA deteriorates when the alternative
scheme is used instead of the base scheme. This strongly suggests that the
production of SOA-iv under these conditions is too aggressive. Fragmentation
reactions may become more important under these conditions leading to lower
production of secondary organic aerosol. Our analysis suggests that the
alternative scheme could be used during the spring-like conditions, but it
should probably be avoided during summer-like periods characterized by
intensive wildfire activities.
The alternative scheme considered here has been derived based on experiments
using residential heating emissions. An assumption used in most biomass
burning OA simulation efforts so far is that the same parameterization can
be used for the different burning types: wildfires, agricultural burning,
residential heating, prescribed burning, etc. Our work provides some support
for the hypothesis that different parameterizations may be needed for
residential heating and wildfires. This is clearly an issue that deserves
additional attention in future modeling efforts.
Code availability
The PMCAMx-SRv1.0 code is available in Zenodo at 10.5281/zenodo.4071362 (Theodoritsi et al., 2020a).
Data availability
The data in the study are available from the authors upon request
(spyros@chemeng.upatras.gr).
The supplement related to this article is available online at: https://doi.org/10.5194/gmd-14-2041-2021-supplement.
Author contributions
GNT wrote the code, conducted the simulations, analyzed the results, and
wrote the paper. GC contributed to the design of the code, analysis of the
results, and the writing of the paper. SNP was responsible for the design of
the study and the synthesis of the results and contributed to the writing of
the paper.
Competing interests
The authors declare that they have no conflict of interest.
Financial support
This work has received funding from the European Union's Horizon 2020
research and innovation program under project FORCeS, grant agreement no.
821205.
Review statement
This paper was edited by Havala Pye and reviewed by two anonymous referees.
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