The VBS-GECKO (volatility basis set – Generator for Explicit Chemistry and Kinetics of Organics
in the Atmosphere) parameterization for secondary organic
aerosol (SOA) formation was integrated into the chemistry-transport model
CHIMERE. Concentrations of organic aerosol (OA) and SOA were simulated over
Europe for the July–August 2013 period. Simulated concentrations with VBS-GECKO were compared to results obtained with the former H2O
parameterization implemented in CHIMERE and to observations from EMEP,
ACTRIS and other observations available in the EBAS database. The model
configuration using the VBS-GECKO parameterization slightly improves the
performances compared to the model configuration using the former H2O
parameterization. The VBS-GECKO model configuration performs well for
stations showing a large SOA concentration from biogenic sources, especially
in northern Europe, but underestimates OA concentrations over stations close
to urban areas. Simulated OA was found to be mainly secondary
(∼85 %) and from terpene oxidation. Simulations show
negligible contribution of the oxidation of mono-aromatic compounds to SOA
production. Tests performed to examine the sensitivity of simulated OA
concentrations to hydro-solubility, volatility, aging rates and NOx
regime have shown that the VBS-GECKO parameterization provides consistent
results, with a weak sensitivity to changes in the parameters provided by
the gas-phase mechanism included in CHIMERE (e.g., HOx or NOx
concentrations). Different scenarios considering intermediate-volatility
organic compound (IVOC) emissions were tested to examine the contribution of
IVOC oxidation to SOA production. At the continental scale, these
simulations show a weak sensitivity of OA concentrations to IVOC emission
variations. At the local scale, accounting for IVOC emissions was found to
lead to a substantial increase in OA concentrations in the plume from urban
areas. This additional OA source remains too small to explain the gap
between simulated and measured values at stations where anthropogenic
sources are dominant.
Introduction
For the past 20 years, fine particulate matter or PM2.5 (particles with diameters smaller than 2.5 µm) has been regulated due to its
health impacts and the resulting costs (e.g., Lim et al., 2012; WHO Regional
Office for Europe and OECD, 2015). Furthermore, fine particles degrade
visibility (e.g., Han et al., 2012) and influence climate change (e.g.,
Boucher et al., 2013). Organic aerosol (OA) represents a large fraction of
the total fine particle mass (e.g., Jimenez et al., 2009). This OA is either
primary (directly emitted into the atmosphere) or secondary (formed by
gas–particle partitioning of low volatility and/or highly soluble species
produced during the oxidation of gaseous organic compounds) (e.g., Carlton et
al., 2009; Kroll and Seinfeld, 2008). The secondary organic aerosol (SOA)
dominates the primary organic aerosol (POA) in most environments (e.g.,
Gelencsér et al., 2007; Jimenez et al., 2009).
Chemistry-transport models (CTMs) are used to investigate and identify air
quality regulation policies. Parameterizations are developed and used in
CTMs to represent SOA formation. Different approaches have been followed to
describe SOA formation as the two-product model (e.g., Odum et al., 1996;
Schell et al., 2001), the molecular approach (e.g., Pun et al., 2002, 2003),
the volatility basis set (VBS) approach (e.g., Donahue et al., 2006, 2012) or
the statistical oxidation model (SOM) (e.g., Cappa and Wilson, 2012; Jathar
et al., 2015). Parameterizations are constantly improved and additional
processes were included in the parameterizations to improve the simulations
of SOA concentrations, such as gas-phase aging of organic species (e.g.,
Rudich et al., 2007) and more comprehensive emissions and multiphase chemistry.
Robinson et al. (2007) have indeed shown that POA provided in emission
inventories is in part composed of semivolatile organic compounds (SVOCs)
(existing both in particle and gas phases) and that a fraction of emitted
organic compounds were missing from these inventories: the
intermediate-volatility organic compounds (IVOCs) (forming SOA after several
oxidation stages) (e.g., Ots et al., 2016; Robinson et al., 2007; Woody et
al., 2015). Numerous experimental and modeling studies have since explored
the volatility distribution of SVOCs from POA emissions and of IVOC
emissions depending on the emission source (e.g., Akherati et al., 2019;
Grieshop et al., 2009; Hatch et al., 2018; Jathar et al., 2017; Louvaris et
al., 2017; Lu et al., 2018; May et al., 2013a, b, c; Woody et al.,
2016).
New gas-phase reaction pathways are also expected to play a large role in
SOA formation, like autoxidation reactions leading to a rapid formation of
highly oxygenated compounds with low volatility (e.g., Crounse et al., 2013;
Ehn et al., 2014; Molteni et al., 2018; Rissanen et al., 2015; Wang et al.,
2017). Some SOA parameterizations already integrate these reaction pathways
(e.g., Chrit et al., 2017). Other studies have highlighted the important role
played by condensed-phase processes in SOA formation, in particular the
reactivity of hydrophilic products in the condensed phase (e.g., Couvidat et
al., 2012; Couvidat and Seigneur, 2011; Knote et al., 2014; Paulot et al.,
2009; Pun et al., 2006b; Surratt et al., 2010), the oligomerization of SVOCs
in the aerosol (e.g., Aksoyoglu et al., 2011; Couvidat et al., 2012;
Denkenberger et al., 2007; Dommen et al., 2006; Kalberer et al., 2006;
Lemaire et al., 2016; Trump and Donahue, 2014), the nonideal behavior of
the organic aerosol (Couvidat et al., 2012; Couvidat and Sartelet, 2015; Pun
et al., 2006b; Pye et al., 2018) or the effect of the aerosol viscosity
(Couvidat and Sartelet, 2015; Shiraiwa et al., 2013). Comparisons with field
observations have shown that CTMs using these parameterizations fall short
to reproduce SOA concentration spatial and temporal variability (e.g.,
Aksoyoglu et al., 2011; Bessagnet et al., 2016; Ciarelli et al., 2016;
Couvidat et al., 2012; Heald et al., 2005; Im et al., 2015; Petetin et al.,
2014; Pun et al., 2006a; Solazzo et al., 2012; Tsigaridis et al., 2014;
Volkamer et al., 2006).
Most of these SOA parameterizations are optimized and built on the basis of
atmospheric chamber data. Experiments are, however, limited in number and are
usually performed under conditions that differ from the atmosphere. In
addition, SOA formation experiments can be subject to potential artifacts
from chamber wall surfaces, such as aerosol and gaseous compound wall losses
(e.g., La et al., 2016; Matsunaga and Ziemann, 2010; McMurry and Grosjean,
1985). Considering or not these artifacts for the parameterization
development directly impacts SOA representation in air quality models (e.g.,
Cappa et al., 2016).
The development of the VBS-GECKO (volatility basis set – Generator for Explicit Chemistry and Kinetics of Organics in the Atmosphere) parameterization explores another track
using the results of an explicit model to represent the organic gas-phase
chemistry and gas–particle mass transfer instead of atmospheric chamber
data. The VBS-GECKO parameterization for SOA formation (Lannuque et al.,
2018a) is a VBS-type parameterization with gaseous aging. VBS-GECKO was
optimized based on box modeling results using explicit oxidation mechanisms
generated with the Generator for Explicit Chemistry and Kinetics of Organics
in the Atmosphere (GECKO-A) modeling tool (Aumont et al., 2005; Camredon et
al., 2007). Lannuque et al. (2018a) have shown that the VBS-GECKO
parameterization is coherent compared to the explicit GECKO-A chemical
mechanism. The reliability of the VBS-GECKO parameterization is by design
directly linked to the accuracy of the GECKO-A mechanisms. The accuracy of
the GECKO-A mechanisms to represent SOA formation has been evaluated against
around 50 chamber experiments (Denjean et al., 2015; La et al., 2016; McVay
et al., 2016; Valorso et al., 2011). Some processes relevant for SOA formation
in the atmosphere can, however, be misrepresented in the GECKO-A mechanisms or are just not included (such as gaseous autoxidation reactions or the
reactivity in the condensed phase). The reliability of the VBS-GECKO
parameterization to represent SOA formation observed in the atmosphere has
thus to be evaluated.
The objectives of this study are (i) to evaluate the behavior of the
VBS-GECKO parameterization in the CTM CHIMERE (Menut et al., 2013; Mailler
et al., 2017) by comparison with field measurements and previous simulations
obtained with the H2O parameterization (Couvidat et al., 2012) already
implemented in CHIMERE, (ii) to explore the sensitivity of simulated SOA
concentrations to organic compound properties (volatility, solubility, aging
rates or NOx regime) and (iii) to test the sensitivity of OA
concentrations to the uncertainties in IVOC emission fluxes from traffic.
The setup of the CHIMERE model and the implementation of VBS-GECKO in
the CTM are described in Sect. 2. In Sect. 3, the VBS-GECKO, and is evaluated
over Europe for a 2-month summer period, the sensitivity to organic
compound properties is explored in Sect. 4, and the sensitivity to the
uncertainties in IVOC emission fluxes from traffic is investigated in
Sect. 5. Finally, results on simulated OA sources and concentrations are
discussed in Sect. 6.
MethodThe CHIMERE chemical transport model
The evaluation of the VBS-GECKO parameterization and the exploration of SOA
sensitivity were performed using the CHIMERE 2017 β version. This
version is based on the CHIMERE 2013 version (Menut et al., 2013), which was
modified to improve the representation of particles with the implementation
of a new aerosol module. Details of the CHIMERE 2017 β version and
its evaluation are given in Couvidat et al. (2018).
Briefly, the CHIMERE 2017 β version uses the MELCHIOR2 gas-phase
chemical scheme, involving 44 species reacting according to 120 reactions.
MELCHIOR2 is a reduced version of the MELCHIOR1 mechanism, obtained by the
Carter's surrogate molecule method (Carter, 1990). In CHIMERE, the aerosol
evolution is described by a sectional aerosol module (e.g., Bessagnet et al.,
2004, 2009; Schmidt et al., 2001). The size distribution of aerosol
particles is here represented using nine bins, ranging from 10 nm to
10 µm. Aerosol formation is represented in the model by nucleation for sulfuric
acid (Kulmala et al., 1998), coagulation between particles (e.g., Debry et
al., 2007; Jacobson et al., 1994) and condensation/evaporation via
absorption according to the “bulk equilibrium” approach (e.g., Pandis et
al., 1993). For inorganic species, the gas–particle equilibrium
concentrations are calculated using the ISORROPIA v2.1 (Fountoukis and
Nenes, 2007) thermodynamic module. For organic species, the equilibrium
concentrations are calculated using the SOAP (Secondary Organic Aerosol
Processor) thermodynamic module (Couvidat and Sartelet, 2015). The gaseous
formation of secondary organic species able to partition between the gas and
the condensed phases (so leading to SOA formation) are represented in the
CHIMERE β version using the H2O mechanism. Here, the VBS-GECKO
parameterization was also implemented. The H2O and VBS-GECKO organic
aerosol modules are described hereafter.
The chemical speciation of emitted nonmethane volatile organic compounds
(NMVOCs) is taken from Passant (2002) as described in Menut et al. (2013).
POA from emission inventories are considered SVOC. A factor of 5 is applied to
residential POA emissions, as wood burning emissions are underestimated in
emission inventories (e.g., Denier Van Der Gon et al., 2015). This factor was
shown to give satisfactory results on OA estimations (Couvidat et al., 2012,
2018). Biogenic emissions are computed with the
Model of Emissions of Gases and Aerosols from Nature MEGAN 2.1 algorithm (Guenther et al., 2012). Dry
deposition for gaseous organic species is described using the Wesely (1989)
parameterization and according to their Henry's law constants, as described
by Bessagnet et al. (2010).
The organic aerosol modules
The purpose of the comparison between the H2O and GECKO-VBS mechanisms
for SOA formation is to evaluate the reliability of the VBS-GECKO
parameterization. The simulations performed with CHIMERE were therefore
setup using the same configuration of the model (meteorological data,
emissions, deposition, inorganic and organic gaseous chemical mechanism,
inorganic and organic gas–particle partitioning, etc.) but
implementing either the H2O or the GECKO-VBS parameterizations. The
implementation of a given parameterization for SOA formation induces anyway
some differences related to the primary compounds considered and/or the
processes taken into account in the parameterization. The differences
between the H2O and VBS-GECKO are mentioned in the following
parameterization presentation sections.
The H2O reference mechanism
The H2O mechanism, described in detail by Couvidat et al. (2018),
considers SOA formation from the partitioning of hydrophilic species
(condensing on an aqueous phase and an organic particulate phase) and
hydrophobic species (condensing only on an organic particulate phase owing
to their low affinity with water). Distinction between hydrophobic and
hydrophilic compounds is based on their octanol/water coefficient (Pun et
al., 2006b) or their partitioning between the organic and aqueous phases
(Couvidat and Seigneur, 2011). H2O considers the formation of
hydrophilic and/or hydrophobic species from the gaseous oxidation of
isoprene, monoterpenes (α-pinene, β-pinene, the limonene and
ocimene), sesquiterpenes (humulene) and mono-aromatic precursors (toluene
and xylenes). Note that in H2O, limonene mechanism is used as a
surrogate mechanism for ocimene (ocimene having its own OH, NO3 and
O3 reaction rates). Each emitted SOA precursor is linked to a species
of the H2O mechanism. POA provided by emissions inventories are split
into three emitted SVOCs having different volatilities (saturation vapor
pressures at 298 K of 8.9×10-11, 8.4×10-9 and
3.2×10-7 atm, respectively) with a fraction that follows the
volatility distribution of POA emissions given by Robinson et al. (2007). In
H2O, gaseous oxidation of these three compounds with OH leads to
hydrophobic species with a lower volatility. No gaseous oxidation is
considered for hydrophilic and hydrophobic species in H2O. Activity
coefficients for the H2O species are computed with the thermodynamic
model UNIFAC (UNIversal Functional group Activity Coefficient; Fredenslund
et al., 1975). H2O has been evaluated over Europe (Couvidat et al.,
2012, 2018) and the Paris area (Couvidat et al., 2013; Zhu et al., 2016a,
b). The H2O reference mechanism is presented in Table S1 of
the Supplement.
The VBS-GECKO parameterization
The VBS-GECKO parameterization is described in detail in a previous paper
by Lannuque et al. (2018a). Briefly, VBS-GECKO is a volatility basis set
(VBS) type parameterization that represents SOA formation from the
partitioning of organic compounds having a low volatility onto an organic
aerosol phase. The VBS-GECKO parameterization takes into account for the
oxidation of a precursor k (precuk) (1) the formation of 7 VBk,i,
where i is the number of the volatility bin (1 being the most volatile and 7
the less volatile) (Reactions R1, R2 and R3), (2) the gas-phase aging of the
VBk,i (except for the lowest volatility bin 7) with OH redistributing
the matter between the VBk,i (Reaction R4) and by photolysis leading
to a loss of carbon matter (Reaction R4) and (3) the gas–particle partitioning
of the precursor k (Reaction R6) and of the VBk,i (Reaction R7). The VBS-GECKO follows
this structure for a given precursor k:
R1precuk(g)+OH→ak,RRR,1VBk,1+ak,RRR,2VBk,2+…+ak,RRR,nVBk,7kprecuk+OH,R2precuk(g)+O3→bk,RRR,1VBk,1+bk,RRR,2VBk,2+…+bk,RRR,nVBk,7kprecuk+O3,R3precuk(g)+NO3→ck,RRR,1VBk,1+ck,RRR,2VBk,2+…+ck,RRR,nVBk,7kprecuk+NO3,R4VBk,i(g)+OH→dk,RRR,i,1VBk,1+dk,RRR,i,2VBk,2+…+dk,RRR,i,nVBk,7∀i≠7kOH=4×10-11cm3molec.-1s-1,R5VBk,i(g)+hν→carbonlost∀i≠7φkJacetone,R6precuk(g)↔precuk(p),R7VBk,i(g)↔VBk,i(p).
In VBS-GECKO, the production and gaseous aging of the VBk,i for a
precursor k are adjusted by stoichiometric coefficients (ak,RRR,i, bk,RRR,i, ck,RRR,i, dk,RRR,i, for Reactions R1 to R4, respectively), which depend on the NOx regime. The formation of more
volatile and less volatile bins can be assimilated to fragmentation and
functionalization processes, respectively. The stoichiometric coefficients
depend on the NOx according to the reaction rate ratio (RRR) of
RO2 with NO:
RRR=kRO2+NONOkRO2+NONO+kRO2+HO2HO2,
where kRO2+NO (set to 9.0×10-12 cm3 molec.-1 s-1 according to Jenkin et al., 1997 at 298 K) and kRO2+HO2 (set to
2.2×10-11 cm3 molec.-1 s-1 according to Boyd
et al., 2003, assuming a large carbon skeleton for RO2 at 298 K) are
the rate constants for the reactions of the peroxy radicals with NO and
HO2, respectively, and [NO] and [HO2] are the concentration of the
radicals. The entire RRR range is covered by linear interpolation of the
coefficients between the two closest tabulated values. The photolysis is
considered a limiting process for SOA formation, leading to a loss of
matter. The photolysis rates of the VBk,i are based on the acetone one
multiplied by an optimized factor φk, different for each
precursor k. Precursors and VBk,i condense on an organic particulate
phase according to an equilibrium between the gas and the organic
particulate phase that follows the Raoult's law (Reactions R6 and R7).
List of the VBS-GECKO species and associated properties.
a Properties of the bins do not depend on the precursor k (see Lannuque et al., 2018a). b Gas–particle partitioning is implemented in CHIMERE for species with a * only.
The properties of the 7 VBk,i were considered to be independent of the
precursor k and set for each volatility bin i to the mean values simulated
with explicit GECKO-A simulations. Table 1 gives the molar weights (Mw),
saturation vapor pressures (Psat) at 298 K, effective Henry's law
constants (Heff) at 298 K and vaporization enthalpies (ΔHvap) used for each VBk,i VBS-GECKO species. The stoichiometric
coefficients and factors φk were optimized on explicit GECKO-A
simulations of gas-phase oxidation and SOA formation. The stoichiometric
coefficients were optimized for five RRR values: 0, 0.1, 0.5, 0.9 and 1
(Lannuque et al., 2018a). Precursors considered in the current VBS-GECKO
parameterization are mono-aromatic compounds (benzene; toluene; and o-, m-,
and p-xylenes) and n-alkanes (decane, tetradecane, octadecane, docosane and
hexacosane) reacting with OH and monoterpenes (α-pinene, β-pinene and limonene) and linear 1-alkenes (decene, tetradecene,
octadecene, docosene and hexacosene) reacting with OH, O3 and NO3.
Note that (1) the parameterization does not represent SOA formation from the
partitioning of hydrophilic species, (2) recently identified chemical
processes, such as autoxidation reactions or acid-catalyzed pathways, not
included in the GECKO-A mechanisms, are thus not considered in the VBS-GECKO
parameterization and (3) the high value of the reaction rate of the
VBk,i (kOH=4×10-11 cm3 molec.-1 s-1) was
fixed before optimization and is compensated by lower or higher values of
optimized coefficients (see details in Lannuque et al., 2018a). Tables of
optimized stoichiometric coefficients are available in the supplementary
material of Lannuque et al. (2018a).
Distribution of the NMVOC emission in the VBS-GECKO species.
VBS-GECKOEmitted NMVOC in CHIMERE (Passant, 2002)DecaneC10 alkanes; C10 cycloalkanes; 75 % of C11 alkanes; 50 % of C12 alkanes; 25 % of C13 alkanesTetradecane25 % of C11 alkanes; 50 % of C12 alkanes; 75 % of C13 alkanesDeceneC10 alkenesBenzeneBenzeneToluene25 % of C9, C10, C13 and unspeciated aromatic hydrocarbons; ethylbenzene; isopropylbenzene; propylbenzene; phenol; toluene; and styreneo-Xylene25 % of C9, C10, C13 and unspeciated aromatic hydrocarbons; 2-ethyltoluene; indan; 33 % of ethyltoluene; 33 % of methylpropylbenzene; o-xylenem-Xylene25 % of C9, C10, C13 and unspeciated aromatic hydrocarbons; tetramethylbenzene; trimethylbenzene; 1-methyl-3-isopropylbenzene; 3-ethyltoluene; ethyldimethylbenzene; 33 % of ethyltoluene; 33 % of methylpropylbenzene; m-xylenep-Xylene25 % of C9, C10, C13 and unspeciated aromatic hydrocarbons; 1-methyl-4-isopropylbenzene; 4-ethyltoluene; 33 % of ethyltoluene; 33 % of methylpropylbenzene; p-xylene
For SOA production from NMVOC oxidation, the former H2O
parameterization in CHIMERE was replaced by the VBS-GECKO parameterization
for terpenes and mono-aromatic compounds. The VBS-GECKO mechanisms were also
implemented in CHIMERE for SOA formation from C10
to C13 alkanes and
alkenes, gaseous species usually not considered in 3D models as SOA
precursors. Each emitted SOA precursor not present in the VBS-GECKO was
linked to a VBS-GECKO species. As in the H2O mechanism, the VBS-GECKO
parameterization for limonene was used as a surrogate mechanism for ocimene.
The VBS-GECKO parameterizations for benzene; toluene; and o-, m-, and p-xylenes
were also used as surrogate mechanisms for other emitted mono-aromatic
compounds according to their SOA yield and reactivity with OH. The n-dodecane
and tetradecane VBS-GECKO species were used to lump emitted alkanes with 10
to 13 atoms of carbon, according to their carbon chain length. The VBS-GECKO
mechanism for 1-decene was applied for all emitted C10 alkenes. The lumping
scheme between emitted NMVOCs and VBS-GECKO species is given in Table 2. The
current VBS-GECKO version does not represent SOA production from the
oxidation of isoprene and sesquiterpenes. The H2O parameterizations for
isoprene and humulene were therefore left unchanged in CHIMERE to account
for this SOA production. For SOA production for SVOC oxidation distributed
from POA emissions, the H2O approach was kept unchanged (i.e.,
distribution of POA emissions into three SVOC species and representation of
their SOA production using the H2O mechanism). In CHIMERE, RRR is
calculated in each box at each chemical time step following Eq. (1).
Activity coefficients for the condensation of the VBS-GECKO species into the
aerosol particulate phase are fixed to 1 (i.e., ideality of the organic
particulate phase is considered). This implementation of the VBS-GECKO in
CHIMERE was selected here as the reference configuration and is denoted
ref-VBS-GECKO hereafter. The ref-VBS-GECKO mechanism is presented in Table S2 in the Supplement and evaluated in Sect. 3.
Changes were then applied to this reference configuration to perform
sensitivity tests of SOA formation on secondary organic compound properties
(solubility, reactivity with OH, NOx/HO2 condition dependency and
volatility) or IVOC emission fluxes from traffic. For a better readability,
the details of these modifications are presented for each sensitivity test
in Sect. 4 (properties) and Sect. 5 (IVOC emissions).
Simulation setup and field measurements
The model was run to simulate the concentrations of OA over Europe (from
25∘ W to 45∘ E in longitude and from 30 to
70∘ N in latitude) with a horizontal resolution of 0.25∘×0.25∘ during the July–August 2013 period, SOA formation
being expected to be important during summertime. Meteorology was obtained
from the Integrated Forecasting System (IFS) model of the
European Centre for Medium-Range Weather Forecasts (ECMWF). This meteorology has been evaluated
in Bessagnet et al. (2016) for the model intercomparison project
EURODELTA-III. ECMWF-IFS in the EURODELTA-III project has been shown to be
one of the most reliable models to represent meteorological conditions over
Europe. Anthropogenic emissions of gases and particles were taken from the
European Monitoring and Evaluation Programme (EMEP) inventory (methodology
described in Vestreng, 2003) and boundary conditions were generated from the
Model for OZone And Related chemical Tracers (MOZART v4.0; Emmons et al., 2010).
Wildfire emissions were not considered.
Location of rural background stations used for (a) the
statistical evaluations and (b) time series comparisons.
The VBS-GECKO mechanism was evaluated by comparing the simulated results to
the H2O mechanism and particulate-phase measurements available in the
EBAS database (http://ebas.nilu.no/, last access: 20 April 2020). EBAS is a database hosting observation
data of atmospheric chemical composition and physical properties in support
of a number of national and international programs ranging from monitoring
activities to research projects. EBAS is developed and operated by the
Norwegian Institute for Air Research (NILU). This database is populated, for
example, by the EMEP measurements (Tørseth et al., 2012) or the Aerosols,
Clouds and Trace gases Research Infrastructure (ACTRIS;
http://www.actris.eu/, last access: 20 April 2020) ones. The 48 rural background stations provide
measurements for fine particulate matter and were thus selected here for a
statistical evaluation: 36 stations for PM2.5, 13 for OCPM2.5
(organic carbon in PM2.5, obtained by filter calcinations) and 6 for
OMPM1 (organic matter in PM1, obtained with aerosol chemical speciation monitors (ACSMs)). For the
comparisons with OC measurements, the OM : OC ratio of the VBS-GECKO
volatility bins were assumed to be equal to 1.8, in agreement with typical
observed values given by Canagaratna et al. (2015). The location of the
selected stations is shown in Fig. 1a. Among these stations, seven stations
were used for time series comparisons:
the Cabauw (NL0644R, the Netherlands), Melpitz (DE0044R, Germany) and
Palaiseau (FR0020R, SIRTA, France) rural background stations, located in
areas dominantly impacted by anthropogenic air masses (see Fig. S1 presenting the mean of the simulated ratios between
toluene and α-pinene emission fluxes for the studied period).
the Birkenes II (NO0002R, Norway), Diabla Gora (PL0005R, Poland),
Hyytiälä (FI0050R, Finland) and Iskrba (SI0008R, Slovenia) rural
background stations, located in areas dominantly impacted by biogenic
emissions (see Fig. S1).
These seven stations were selected among the 48 background station, because the
measurements at the stations provide (1) direct information on the organic
fraction of fine particles, i.e., OMPM1 and OCPM2.5 measurements,
and (2) enough data over the studied period to perform time series
comparisons. The location of the seven selected stations is shown in Fig. 1b.
Various statistical indicators were computed to evaluate the VBS-GECKO
mechanism, including the root mean square error (RMSE), the correlation
coefficient, the mean fractional error (MFE) and the mean fractional bias
(MFB). MFB and MFE are calculated as
2MFB=1N∑i=1NCimod-CiobsCimod+Ciobs2,3MFE=1N∑i=1NCimod-CiobsCimod+Ciobs2,
where cimod and ciobs are the simulated and observed, respectively,
concentrations of the studied component at the time i and N being the number of
available in situ measurement values. Boylan and Russell (2006) defined two
criteria to evaluate the performances of a model. The model performance
criteria (described as the level of accuracy that is considered to be
acceptable for modeling applications) is reached when MFE ≤75 % and
|MFB|≤50 %, whereas the performance goal (described
as the level of accuracy that is considered to be close to the best values a
model can be expected to achieve) is reached when MFE ≤50 % and
|MFB|≤30 %. These criteria are currently used to
evaluate the reliability of the models (e.g., Ciarelli et al., 2017; Couvidat
et al., 2018; Lecœur and Seigneur, 2013; Mircea et al., 2019).
Evaluation of the ref-VBS-GECKO parameterization
Figure 2a shows the mean OA mass concentrations simulated with the
ref-VBS-GECKO version for the July–August 2013 period. The simulated mean OA
concentrations range from ∼0µg m-3 in remote
oceanic areas to ∼12µg m-3 around the Adriatic
Sea and in northern Italy, and they are coherent with the expected orders of
magnitude and spatial distributions over Europe (Aksoyoglu et al., 2011;
Crippa et al., 2014). Figure 2b presents the relative difference between
mean OA mass concentrations simulated with ref-VBS-GECKO and with H2O.
The ref-VBS-GECKO produces more OA than H2O, with a mean OA mass
concentration around 30 % higher on average over Europe. The increase is
particularly important over northern Europe, with maximum differences
reaching around +60 %.
Mean OA mass concentrations simulated with the ref-VBS-GECKO
model configuration over Europe for the July–August 2013 period (a) and
relative difference of the simulated mean OA mass concentrations between the
ref-VBS-GECKO and the H2O configuration (b).
Statistical results calculated on daily averaged concentrations
for the ref-VBS-GECKO simulations and differences between ref-VBS-GECKO and
H2O statistical indicators.
Observations VBS-GECKO results Differences with H2O results NumberMeanMeanRMSErMFBMFEΔmeanΔRMSEΔrΔMFBΔMFE–(µg m-3)(µg m-3)(µg m-3)–––(%)(%)–(dist from 0)–PM2.520029.077.656.140.42-0.090.37+5.52-1.76+0.01-0.05-0.01OMPM12373.241.932.280.79-0.470.57+31.3-11.6-0.04-0.25-0.17OCPM2.52352.522.591.750.57-0.160.51+15.6+5.42+0.02-0.17-0.08
Table 3 gathers the statistical results calculated on daily averaged
concentrations for ref-VBS-GECKO at the 48 stations (RMSE, Pearson's r, MFB
and MFE), as well as the difference of this statistical indicator between
ref-VBS-GECKO and H2O. Statistical indicators show a high
spatiotemporal correlation between ref-VBS-GECKO and measurements for daily
OMPM1 and OCPM2.5 with r>0.5 (0.79 and 0.57,
respectively). These r values are in the standard of what has been found in
previous modeling studies for Europe (Bergström et al., 2012; Ciarelli
et al., 2017) or USA (Ahmadov et al., 2012; Murphy et al., 2017). For daily
averaged measurements of PM2.5, the correlation is smaller (0.42). This
lower correlation for PM2.5 has already been highlighted in summertime
during the EURODELTA-III intercomparison campaign (Bessagnet et al., 2016).
MFE and MFB satisfy the performance criteria of Boylan and Russel (2006) for
all the measurements. However, daily averaged PM2.5 values, and especially the
organic fraction (OMPM1 and OCPM2.5), appear to be systematically
underestimated by the ref-VBS-GECKO model.
Comparing ref-VBS-GECKO statistical results with H2O statistical
results, the simulated daily averaged PM2.5 concentrations over the 36
stations appear to be weakly sensitive to the SOA formation mechanism used
in the model. Only a slight improvement due to an increase in simulated
PM2.5 concentrations of about 5.5 % is observed with the
ref-VBS-GECKO model configuration. Concerning simulated OCPM2.5 over
the 13 measurement stations, the ref-VBS-GECKO parameterization leads to an
increase in the simulated concentration (+15.6 %), ultimately leading
to a clear improvement of MFE, MFB and correlation coefficient. Nevertheless, using the
ref-VBS-GECKO configuration instead of the H2O configuration increases
RMSE (+5 %), owing to a substantial overestimation of OA. The main
differences between the two organic aerosol modules are reached for
OMPM1 with simulated ref-VBS-GECKO concentrations higher than H2O
by 31.5 %. As simulated OMPM1 concentrations were highly
underestimated using the former H2O configuration compared to
observations (six stations), the ref-VBS-GECKO configuration improves RMSE,
MFB and MFE.
Measured (black) and simulated (with H2O in blue and
ref-VBS-GECKO in red) temporal evolution of daily averaged OMPM1 concentrations (a, b, d, e) OCPM2.5 concentrations (c, f, h) and PM2.5 concentrations (g). Top panels are for
stations influenced by anthropogenic sources in France (Palaiseau station,
a), Germany (Melpitz station, b), and the Netherlands (Cabauw station, c); middle
panels are stations in remote areas in Finland (Hyytiälä station,
d), Norway (Birkenes II station, e), and Poland (Diabla Gora station, f); and
bottom panels are for a station located in a remote area in Slovenia
(Iskrba, g, h).
Figure 3 shows comparisons between the measured and the simulated daily
averaged temporal evolutions of OMPM1, OCPM2.5 and/or PM2.5
concentrations at the seven selected stations. Figure 4 shows the measured and
simulated mean diurnal profiles at the four stations providing OMPM1.
Simulations capture qualitatively the observed feature of the daily averaged
time series for PM2.5, OCPM2.5 and OMPM1, as well as the mean
diurnal profiles for OMPM1. At stations dominantly impacted by biogenic
sources, OA concentrations simulated with ref-VBS-GECKO are higher than
those simulated with H2O, leading to a better agreement with
measurements (see Figs. 3d to h and 4c and d). However, day–night
variations of OMPM1 seem to be overestimated. At stations influenced
by anthropogenic air masses, OA concentrations are weakly influenced by the
organic aerosol module (see Figs. 3a to c and 4a and b). OA
concentrations simulated with ref-VBS-GECKO are substantially
underestimated: differences exceeding -50 % for OMPM1 concentrations
at the Palaiseau and Melpitz stations as well as for OCPM2.5
concentrations at the Cabauw station.
Measured (black) and simulated mean diurnal profile (in UTC)
with the H2O model configuration (blue) and the ref-VBS-GECKO model
configuration (red) for OMPM1 concentration at stations influenced
dominantly by anthropogenic sources (Palaiseau (a) and Melpitz (b)) and by
biogenic sources (Hyytiälä (c) and Birkenes II (d)).
Sensitivity to the parameterization properties
Sensitivity tests were performed to assess the VBS-GECKO parameterization,
evaluate the consistency of the modeling results and examine some hypotheses
that may explain the gaps between measurement and simulated values.
Sensitivities to hydro-solubility, gaseous aging, NOx regimes and
volatility were studied comparing results to the nonmodified ref-VBS-GECKO
version.
Sensitivity tests to hydro-solubility and Heff
SOA formation from the gas–particle partitioning of hydro-soluble organic
compounds into an aqueous phase is now well recognized (e.g.,
Bregonzio-Rozier et al., 2016; Carlton et al., 2009; Knote et al., 2014).
The effective Henry's law constant (Heff) is the key parameter which
controls this hydrophilic partitioning. Hydro-soluble organic compounds can
also be lost at the surface by dry deposition. In CHIMERE, and according to
the deposition scheme of Wesely (1989), the stomatal resistance of organic
compounds depends on Heff. To analyze the sensitivity of the simulated
OA to hydrophilic partitioning and values of Heff, the following two
simulations were run:
Hydro-VBS-GECKO. In this model configuration, VBk,i
can condense both on organic and aqueous phases of particles. Aqueous-phase
partitioning is computed according to Henry's law, assuming the particle
phase behaves as an ideal well-mixed homogeneous aqueous phase. Deposition of
VBk,i was already taken into account in the reference model
configuration and was kept unchanged.
Hydro-VBS-GECKO-high. This model configuration is identical to the
hydro-VBS-GECKO configuration above, except that the original Heff values of
each VBk,i are multiplied by 100. The new Heff values correspond
to the upper values of the Heff distribution of secondary organic
compounds contributing to a given volatility bin (see Lannuque et al.,
2018a).
The relative difference in the simulated mean OA concentrations between
Hydro-VBS-GECKO (respectively Hydro-VBS-GECKO-high) and ref-VBS-GECKO is
given in Fig. 5a (respectively Fig. 5b) for the 2-month period. Figure 5a
shows that considering aqueous-phase partitioning of the VBS-GECKO species
leads to variations in the simulated mean OA concentrations below ±0.5 %. Table 4 shows no significant modification in the statistical
results for this simulation. The values of Heff set to each volatility
bin increase when the volatility decreases (see Table 1), meaning that the
less volatile species are also more prone to condense into the aqueous
phase. Adding a hydrophilic partitioning does therefore not increase
substantially the concentrations of organic species in the condensed phases.
Relative differences on the simulated mean OA concentrations
between (a) the Hydro-VBS-GECKO and the ref-VBS-GECKO and (b) the
Hydro-VBS-GECKO-high and the ref-VBS-GECKO model configurations for the
2-month period. Bottom panels represent relative differences on the
simulated mean OA concentrations between the Hydro-VBS-GECKO-high and the
ref-VBS-GECKO due to variation in deposition (c) or partitioning (d).
Statistical results calculated on daily averaged concentrations
simulated with the various model configurations and differences with the
ref-VBS-GECKO configuration statistical indicators given Table 3.
Model configurationSensitivity test results Differences to ref-VBS-GECKO results MeanRMSErMFBMFEΔ meanΔRMSEΔrΔ MFBΔ MFE(µg m-3)(µg m-3)–––(%)(%)–(dist from 0)–hydro-VBS-GECKOOMPM11.932.280.79-0.470.570.000.000.000.000.00OCPM2.52.591.750.57-0.160.510.000.000.000.000.00hydro-VBS-GECKO-highOMPM12.072.170.79-0.410.52+7.43-4.580.00-0.06-0.05OCPM2.52.711.730.57-0.110.48+4.80-0.650.00-0.05-0.03kOH-VBS-GECKO-lowOMPM11.872.320.78-0.490.59-2.70+1.90-0.01+0.02+0.02OCPM2.52.521.730.57-0.190.53-2.40-0.650.00+0.03+0.02kOH-VB-GECKO-highOMPM12.022.210.79-0.430.54+4.72-2.670.00-0.04-0.03OCPM2.52.701.760.57-0.120.49+4.32+0.650.00-0.04-0.02RRR-VBS-GECKO-lowOMPM12.112.130.80-0.410.52+9.45-6.480.01-0.06-0.05OCPM2.52.821.810.57-0.080.48+9.13+3.920.00-0.08-0.03RRR-VBS-GECKO-highOMPM11.732.420.78-0.540.64-10.1+6.48-0.01+0.07+0.07OCPM2.52.361.720.56-0.250.56-8.65-1.30-0.01+0.09+0.05Psat-VBS-GECKO-lowOMPM12.421.920.80-0.310.44+25.6-15.6+0.01-0.16-0.13OCPM2.53.171.990.580.020.45+22.5+13.7+0.01-0.18-0.06Psat-VBS-GECKO-highOMPM11.472.630.76-0.650.73-23.6+15.6-0.03+0.18+0.16OCPM2.52.071.760.56-0.360.63-19.7+0.65-0.01+0.20+0.12
The Hydro-VBS-GECKO-high configuration increases the mean simulated OA
concentrations by ∼10 %, with a maximal increase reached
over the Belgium–the-Netherlands–Luxembourg area (called Benelux hereafter, around
+20 %; see Fig. 5b). The contribution of the deposition and the
partitioning processes are shown in Fig. 5c and d, respectively. Changes
due to deposition appear negligible (below ±0.2 %) compared to the
changes due to the aqueous partitioning (∼+10 %).
According to the Wesely (1989) parameterization used for deposition, water
solubility contributes to the surface resistance only. Knote et al. (2014)
have shown that deposition is not limited by the surface resistance for
Heff greater than 108 mol L-1 atm-1. In
ref-VBS-GECKO, this threshold corresponds to the VBk,(3–7) nominal
Heff values, i.e., to the volatility bins partitioning mainly to OA. OA
concentrations are therefore not sensitive to an increase in the Heff
values. The increase in Heff by a factor of 100 makes possible
hydrophilic partitioning of the most volatile bins that would not have
condensed otherwise, and it leads to an increase in simulated OA concentrations.
The maximum relative changes simulated over Benelux are mainly linked to the
high relative humidity encountered in this area and the low simulated OA
concentrations (see Fig. 2a). This model configuration improves slightly
the RMSE, MFB and MFE calculated on OMPM1 (-4.58 %, -0.06 and -0.05,
respectively) and OCPM2.5 (-0.65 %, -0.05 and -0.03, respectively)
mass concentrations (see Table 4). According to these tests, SOA production
due to the hydrophilic partitioning of the various VBk,i of the
VBS-GECKO parameterization is expected to be a minor process.
Sensitivity test to gaseous aging rates and OH radical concentrations
In the VBS-GECKO parameterization, the same rate constant is set for the
VBk,i reactions with OH (kOH=4.0×10-11 cm3 molec.-1 s-1). The timescale for gaseous aging is therefore driven by
the OH concentrations simulated by the CTM. Simulated OH concentrations
depend on the gas-phase chemical mechanisms used in the CTM, with
differences in OH concentrations reaching up to 45 % between mechanisms
(Sarwar et al., 2013). Two simulations were run with modified kOH to
examine the sensitivity of SOA production to the rate of chemical aging:
kOH-VBS-GECKO-low. In this model
configuration, the VBk,i+OH rate constants are divided by a factor 2,
i.e., kOHlow=2.0×10-11 cm3 molec.-1 s-1.
kOH-VBS-GECKO-high. In this model
configuration, the VBk,i+OH rate constants are multiplied by a factor
2, i.e., kOHhigh=8.0×10-11 cm3 molec.-1 s-1.
The relative difference in the simulated mean OA concentrations between
kOH-VBS-GECKO-low (respectively kOH-VBS-GECKO-high) and
ref-VBS-GECKO is given Fig. 6a (respectively Fig. 6b). A slight variation
of simulated OA concentrations is found (lower than ±10 %), with
simulated OA concentrations decreasing with the decrease in aging rates and
vice versa. This result highlights that the gas-phase aging of volatility bins in the
VBS-GECKO parameterization promotes functionalization (formation of less
volatile bins) rather than fragmentation (formation of more volatile bins),
as already shown with tests conducted in a box model (Lannuque et al., 2018a).
The highest relative differences are located over the Mediterranean Sea and
northern Africa, i.e., areas showing high OH and low OA concentrations (below 4 µg m-3; see Fig. 2a). The kOH-VBS-GECKO-high configuration
improves statistics, due to an overall increase in the simulated OA
concentrations (and contrariwise for the kOH-VBS-GECKO-low
configuration) (see Table 4). However, the sensitivity of SOA to the
gas-phase aging of the VBS-GECKO volatility bins remains weak, and aging
rates are likely not a major source of uncertainty.
Relative differences on the simulated mean OA concentrations
between (a) the kOH-VBS-GECKO-low and the ref-VBS-GECKO model
configurations and (b) the kOH-VBS-GECKO-high and the ref-VBS-GECKO
model configurations for the 2-month period.
Sensitivity test to the NOx regime
Similar to the OH discussion above, simulated HO2 and NO concentrations
in CTMs are linked to the gas-phase chemical mechanism used. The
concentrations of these two species determine the value of the RRR ratio and
therefore drive the aging of the various VBk,i (Lannuque et al., 2018a).
A sensitivity test was performed to examine the sensitivity of the simulated
OA to the chemical regime. HO2 or NO concentrations can hardly be
modified without changing all the simulation conditions. Here, two
simulations were run modifying the kRO2+HO2 value used to calculate the
RRR.
RRR-VBS-GECKO-low. In this model configuration, RRR ratio
is calculated with kRO2+HO2 multiplied by 2, i.e.,
kRO2+HO2RRRlow=4.4×10-11 cm3 molec.-1 s-1.
RRR-VBS-GECKO-high. In this model configuration, RRR ratio is
calculated with kRO2+HO2 divided by 2, i.e., kRO2+HO2RRRhigh=1.1×10-11 cm3 molec.-1 s-1.
Figure 7 presents the mean RRR ratio during the 2-month period for both
RRR-VBS-GECKO-low (Fig. 7a) and RRR-VBS-GECKO-high model configurations
(Fig. 7b). The entire range of RRR ratio (from remote NOx conditions
to high NOx conditions) is covered over Europe with the both model
configuration. As expected, the urban, industrial and intense shipping
transport areas such as Paris, the Channel, Benelux, northern Italy or
Moscow are systematically in the high NOx regime (RRR close to 1),
whereas remote areas over the seas (away from shipping tracks) are
systematically in the remote NOx regime (RRR close to 0). Between these
two extremes, the RRR ratio depends on the environmental and meteorological
conditions at the location and, in this sensitivity study, on the model
configuration for the RRR calculation. Current parameterizations for SOA
formation only consider two extreme regimes corresponding to a high-NOx
and a low-NOx condition. Criteria used to define high and low NOx
differ from a study to another one but the parameterizations are usually
optimized at NOx values typical of rural conditions for low NOx
(corresponding to a RRR ratio of ∼0.6) and typical of urban
conditions for high NOx (corresponding to a RRR ratio of
∼1) (e.g., Hodzic et al., 2014; Lane et al., 2008). The range
of RRR between 0.0 and 0.6 is therefore not considered in most of the
parameterizations, although substantial changes in SOA formation were found
within this range of RRR (Lannuque et al., 2018a).
Mean RRR over Europe during the 2-month period for (a) the
RRR-VBS-GECKO-low and (b) the RRR-VBS-GECKO-high model configurations.
Relative differences on the simulated mean OA concentrations
between (a) the RRR-VBS-GECKO-low and the ref-VBS-GECKO model configurations
and (b) between the RRR-VBS-GECKO-high and the ref-VBS-GECKO model
configurations for the 2-month period.
The relative difference in the simulated mean OA concentrations between
RRR-VBS-GECKO-low (respectively RRR-VBS-GECKO-high) and ref-VBS-GECKO is
given Fig. 8a (respectively Fig. 8b). Results show variations in simulated
mean OA concentrations smaller than ∼15 %. In agreement
with previous studies, an increase (decrease) of RRR ratio leads to a
decrease (increase) of the simulated OA concentrations (e.g., Donahue et al.,
2005; Lannuque et al., 2018a; Ng et al., 2007). As expected, the variation is
weaker over areas having either an RRR ratio close to 0 or 1, the NOx
regime remaining unchanged among the model configurations. The highest
relative differences on OA are found over continental rural areas, i.e.,
areas showing the largest variation in RRR among the model configurations.
Large relative differences are also found over the Mediterranean Sea, owing
in part to the low simulated OA concentrations. Similar to the kOH
sensitivity tests, the RRR-VBS-GECKO-low configuration increases the overall
OA concentrations and improves statistical indicators, and contrariwise for
the RRR-VBS-GECKO-high configuration. Sensitivity on RRR values appears weak
enough to likely not be a major source of uncertainty for the VBS-GECKO
parameterization.
Sensitivity test to volatility and Psat
In the explicit GECKO-A simulations used for the VBS-GECKO optimization, the
saturation vapor pressure, Psat, of secondary organic compounds was
estimated using structure activity relationships (SAR) (see Lannuque et al.,
2018a). Estimated Psat can typically vary within 1 order of magnitude
according to the SAR used (e.g., Valorso et al., 2011). Two simulations were
run to examine the sensitivity of SOA to the uncertainties in Psat:
Psat-VBS-GECKO-low. In this model
configuration, the nominal Psat values of VBk,i are divided by 10.
Psat-VBS-GECKO-high. In this model
configuration, the nominal Psat values of VBk,i are multiplied by
10.
As OA concentration directly contributes to the partitioning, these two
simulations can also be considered a sensitivity test to the simulated OA
concentrations.
Relative difference in the simulated mean OA concentrations
between (a) the Psat-VBS-GECKO-low and the ref-VBS-GECKO model
configurations and (b) between the Psat-VBS-GECKO-high and the
ref-VBS-GECKO model configurations for the 2-month period.
The relative difference in the simulated mean OA concentrations between
Psat-VBS-GECKO-low (respectively Psat-VBS-GECKO-high) and
ref-VBS-GECKO is given Fig. 9a (respectively Fig. 9b). Shifting the
volatility of the VBk,i by 1 order of magnitude leads to an overall
change in the simulated mean OA concentrations of about -25 % (+25 %)
when Psat is increased (decreased). A weaker sensitivity is observed
over urban areas, such as Paris or Moscow. This behavior is mainly linked to
the simulated volatility of OA in the ref-VBS-GECKO simulations. Figure 10
shows the mean volatility of OA over Europe for the reference configuration.
Simulated OA contributors are mainly low-volatility species (with mean
P298Ksat between 10-10 and 10-14 atm), with the highest
values being found over urban areas (less aged OA) and the lowest values
found over areas close to the boundaries of the domain (linked to a boundary
effect in the model). A shift in volatilities over these two types of site
has a lower impact on OA concentrations, as OA mean volatilities being
either too high (mean P298Ksat≈10-10 atm, upon
urban areas) or too low (mean P298Ksat≈10-14 atm,
upon boundary areas) for a change in Psat to substantially impact the
partitioning. The largest effect is typically observed over central Europe
where OA contributors show intermediate mean volatilities (mean
P298Ksat≈10-12 atm).
Simulated average volatility of OA in term of
P298Ksat upon Europe during the July–August 2013 period for the
ref-VBS-GECKO model configuration.
Statistically, the Psat-VBS-GECKO-low configuration is the only
configuration matching the performance goal for all the simulated OA
concentrations (OCPM2.5 and OMPM1) (see Table 4). For
OCPM2.5, RMSE is, however, higher than in the reference configuration.
Simulated OA concentrations appear to be sensitive to uncertainties in the
estimated saturation vapor pressures of the numerous OA contributors
considered during the development of the VBS-GECKO parameterization.
Sensitivity to IVOC emission fluxes from traffic and transport sources
IVOCs have been shown to be a substantial source of SOA in the plume of
megacities (e.g., Hodzic et al., 2010; Tsimpidi et al., 2010). Even if
several recent studies have been performed to identify the IVOC speciation
of different individual emission sources (e.g., Akherati et al., 2019;
Grieshop et al., 2009; Hatch et al., 2018; Jathar et al., 2017; Louvaris et
al., 2017; Lu et al., 2018; May et al., 2013a, b, c; Woody et al.,
2016), a comprehensive inventory is still not available to represent IVOC
emissions by activity sector (gathering several individual emission
sources). A large fraction of these IVOCs is thus still not considered in
emission inventories. In this section, only IVOC emissions from traffic and
transportation sources are treated. Robinson et al. (2007) assumed that IVOC
emissions for small off-road diesel engines were equal to 150 % of POA
emissions, consistent with the Schauer et al. (1999) emission data for 1995
medium-duty diesel vehicles. Recent studies have measured IVOC emissions
from (i) exhausts of light-duty gasoline vehicles and (ii) exhausts of both
heavy-duty and medium-duty diesel vehicles (Zhao et al., 2015, 2016).
Experiments on gasoline exhausts were processed on 42 vehicles and
experiments on diesel vehicles on 6 vehicles, with the selected vehicles being
representative of the transportation fleet in North America. In both cases,
Zhao et al. (2015, 2016) have shown that a stronger correlation can be found
between IVOC and NMVOC emissions (R2 equal to 0.92 and 0.98 for
gasoline and diesel exhausts, respectively) than between IVOC and POA
emissions (R2 equal to 0.76 and 0.61 for gasoline and diesel exhausts,
respectively). Zhao et al. (2015, 2016) have estimated that IVOC emissions
represent about 4 % of NMVOC emissions in cold-start cycle to about 16 %
in hot-start cycle for light-duty gasoline vehicles and about 60±10 % of NMVOC emissions for heavy-duty and medium-duty diesel vehicles.
In this study, the VBS-GECKO parameterization was used to examine the
sensitivity of SOA to IVOC emissions from road traffic (SNAP 7) and other
mobile sources and machinery (SNAP 8). The following five model
configurations, based on different IVOC emission fluxes, were designed for
that purpose.
IVOC150POA: in this model configuration, IVOC
emissions are set to 150 % of the semivolatile POA emissions, based on
Robinson et al. (2007).
IVOC4VOC: in this configuration, IVOC emissions are
set to 4 % of NMVOC emissions, based on Zhao et al. (2016) for gasoline
vehicles in cold-start cycle.
IVOC16VOC: in this configuration, IVOC emissions are
set to 16 % of NMVOC emissions, based on Zhao et al. (2016) for gasoline
vehicles in hot-start cycle.
IVOC30VOC: in this configuration, emissions are set
to 30 % of NMVOC emissions, assuming a mixing of diesel and gasoline
vehicle fleets.
IVOC65VOC: in this configuration, IVOC emissions are
set to 65 % of NMVOC emissions, based on Zhao et al. (2015) for diesel
vehicles.
As in the reference model configuration, POA species are considered SVOC in these
sensitivity tests for traffic and transport emissions. Primary SVOCs and
IVOCs (S/IVOCs) constitute a complex mixture of linear, branched, and cyclic
alkanes; alkenes; and aromatics (Fraser et al., 1997; Gentner et al., 2012;
Lu et al., 2018; Schauer et al., 1999, 2002). The molecular composition of
S/IVOCs emitted in the atmosphere by fossil fuel combustion is, however, still
poorly documented. S/IVOCs at emission were thus considered to be
distributed into the nine volatility bins given by Robinson et al. (2007), with
the provided fraction of primary SVOCs in each SVOC volatility bin, and of
estimated primary IVOCs in each IVOC volatility bin. The VBS-GECKO
parameterizations for C14, C18, C22 and C26 1-alkenes
and n-alkanes were used as surrogate mechanisms for S/IVOCs (C14 and
C18 for IVOCs and C18, C22 and C26 for SVOCs). The
C14 to C26 VBS-GECKO's n-alkanes and 1-alkenes were distributed
according to their volatility into the nine volatility bins of Robinson et al. (2007). Correspondences are shown in Fig. 11 for the example of the
IVOC150POA model configuration. The distribution of alkanes and alkenes
was estimated based on (i) the EMEP guidebook
(https://www.eea.europa.eu/publications/emep-eea-guidebook-2016, last access: 20 April 2020), providing
speciation data for emissions for various types of vehicles, and (ii) the
COPERT4 software (Ntziachristos et al., 2009) providing data for a vehicle
fleet. Data are only available for light compounds and are here extrapolated
to the heavy ones for the needs of the study. Thus, 75 % of the primary
S/IVOCs are here assumed to be alkanes and 25 % alkenes. The primary SVOC
total emissions and distributions over volatility bins are unchanged between
each simulation. The distribution of IVOCs among volatility bins is also
unchanged but the total IVOC emissions are modulated according to the five IVOC
emission scenarios described before (i.e., IVOC150POA, IVOC4VOC,
IVOC16VOC, IVOC30VOC and IVOC65VOC). Table 5 gives the
speciation of VBS-GECKO species for the various model configurations and the
VBS-GECKO mechanism for S/IVOCs presented in Table S3.
Distribution of the VBS-GECKO species into the S/IVOC
volatility bins by Robinson et al. (2007). Normalized emission factors by
POA emissions for IVOCs are used for the IVOC150POA model
configuration.
Distribution of the VBS-GECKO surrogate species for IVOC emission
in the various model configurations.
ModelSpecies configurationC14C18C22C26IVOC150POA105 % of POA80 % POA40 % of POA25 % of POAIVOC4VOC2.8 % of NMVOCs35 % of POA +1.2 % of NMVOCs40 % of POA25 % of POAIVOC16VOC11.2 % of NMVOCs35 % of POA +4.8 % of NMVOCs40 % of POA25 % of POAIVOC30VOC21 % of NMVOCs35 % of POA +9 % of NMVOCs40 % of POA25 % of POAIVOC65VOC45.5 % of NMVOCs35 % of POA +19.5 % of NMVOCs40 % of POA25 % of POA
Mean OA mass concentrations simulated with the model
configurations including the IVOC emissions for the July–August 2013 period
over Europe (second column), and absolute and relative differences with the
ref-VBS-GECKO model configurations (left and right columns, respectively).
Results are given for the following model configurations: IVOC150POA
(first row), IVOC4VOC (second row), IVOC16VOC (third row),
IVOC30VOC (fourth row) and IVOC65VOC (fifth row).
Statistical results calculated on daily averaged concentrations
simulated with VBS-GECKO considering IVOC emissions and differences with
those of the ref-VBS-GECKO (without IVOCs) from Table 3.
ModelVBS-GECKO with IVOC results Differences with ref-VBS-GECKO configurationMeanRMSErMFBMFEΔMeanΔRMSEΔrΔMFBΔMFE(µg m-3)(µg m-3)–––%%–(dist from 0)–IVOC150POAOMPM12.022.180.80-0.430.53+4.66-4.38+0.01-0.04-0.04OCPM2.52.721.830.57-0.110.5+5.01+4.570.00-0.05-0.01IVOC4VOCOMPM11.962.240.79-0.460.56+1.55-1.750.00-0.01-0.01OCPM2.52.641.790.57-0.150.51+1.93+2.280.00-0.010.00IVOC16VOCOMPM12.012.200.8-0.440.54+4.14-3.50+0.01-0.03-0.03OCPM2.52.731.880.57-0.120.51+5.40+7.420.00-0.040.00IVOC30VOCOMPM12.062.150.80-0.420.53+6.73-5.70+0.01-0.05-0.04OCPM2.52.841.980.57-0.090.51+9.65+13.10.00-0.070.00IVOC65VOCOMPM12.182.040.81-0.370.50+12.9-10.5+0.02-0.10-0.07OCPM2.53.102.290.56-0.020.50+19.6+30.8-0.01-0.14-0.01
Figure 12 shows the mean OA mass concentrations simulated for the five IVOC
emission configurations and the absolute and relative differences with the
ref-VBS-GECKO simulation without IVOC emissions. Table 6 presents the
statistical results calculated on daily averaged concentrations (RMSE,
Pearson's correlation coefficient, MFE and MFB) for the different IVOC
emission configurations and their difference with those of the
ref-VBS-GECKO configuration. As discussed previously, the highest
concentrations are simulated over northern Italy (see Fig. 2). For this
area, accounting for IVOC emissions increases the simulated concentrations
of OA up to 3 µg m-3 with the IVOC65VOC model
configuration. As expected, OA concentration increases when IVOC emissions
over Europe are taken into account during the simulated period, with an
overall mean increase of about 12 %, 2 %, 5 %, 10 % and 20 % for the
IVOC150POA, IVOC4VOC, IVOC16VOC, IVOC30VOC and
IVOC65VOC configurations, respectively. The relative differences show
large increases of OA concentrations (reaching +40 %) over a wide area
including the North Sea and Benelux for the IVOC65VOC configuration, owing
to the low simulated OA concentrations with the ref-VBS-GECKO configuration.
The IVOC150POA configuration leads to mean OA mass concentrations lying
between the IVOC16VOC and the IVOC30VOC configurations. Areas
showing substantial changes in simulated OA are, however, different between
these model configurations. In the IVOC150POA configuration, the
largest OA concentration increase is simulated over the Channel and
Gibraltar's Detroit (up to +80 %). These results were expected for this
model configuration based on POA emissions. Indeed, ships are one of the
most important sources of POA but emit a relatively small amount of NMVOCs.
For example, the EMEP inventory for 2013 estimates an average NMVOC/POA
emission ratio of ∼4 for road traffic in Europe and
∼0.4 for shipping in the studied domain.
Taking into account SOA formation from IVOC precursors improves the
statistical indicators for the simulated concentrations of OMPM1. As
discussed previously, the ref-VBS-GECKO configuration underestimates
OMPM1. Including IVOC emission increases the mean OMPM1 concentrations at the stations of about 5 %, 2 %, 5 %, 7 % or 13 % for the
IVOC150POA, IVOC4VOC, IVOC16VOC, IVOC30VOC or
IVOC65VOC configuration, respectively. Increasing IVOC emissions
provide better statistical indicators for OMPM1, with MFE and MFB
significantly closer to the performance goal (MFE decreases by 0.06 and
|MFB| decreases by 0.09 between IVOC4VOC and
IVOC65VOC configurations; see Table 6). For OCPM2.5, however, the
opposite trend is observed with a degradation of the statistical indicators
(Table 6). The ref-VBS-GECKO configuration leads to a slight overestimation
of OCPM2.5 concentrations over some stations (e.g., Iskrba; see Fig. 3)
and adding the SOA source from IVOCs strengthens the deviation (up to about
+30 % of RMSE for the IVOC65VOC configuration), even if the
correlation is not significantly modified.
Measured and simulated (for the ref-VBS-GECKO configuration
without IVOC and the different model configuration considering IVOC
emissions) temporal evolution of daily averaged OMPM1 concentrations (a, c) and OCPM2.5 concentrations (b, d). Top panels are for stations close to anthropogenic sources in Germany (Melpitz station, a) and the Netherlands (Cabauw station, b) and bottom panels for stations in remote areas in Norway (Birkenes II station, c) and Poland
(Diabla Gora station, d).
IVOC oxidation appears to be a significant SOA source at some locations
(e.g., the Cabauw station), especially in the IVOC30VOC and
IVOC65VOC configuration. However, the resulting OA increase remains too
weak to fill in the gaps between observations and simulated data (maximum
increase around +40 %). For example, time series presented in Fig. 13
show that adding IVOC emission increases systematically the simulated OA
concentrations, but it is not enough to explain the OA peaks recorded at the
anthropogenic stations (see Fig. 13b). Moreover, accounting for IVOC
emission strengthens the disagreement of the simulated concentrations with
observations over other areas (e.g., at Iskrba station).
The various IVOC emission configurations are aimed to answer the
following question: with constant POA and NMVOC emissions for the traffic, do IVOC
emissions typical of diesel vehicles (upper limit) or gasoline vehicles
(lower limit) significantly change OA concentrations in Europe, and in
particular in anthropogenic areas? At a local scale where anthropogenic
sources are dominant, IVOC emissions from traffic and transportation sources
appear to be a significant source of OA and simulated OA concentrations are
dependent to the IVOC emission configuration (∼+3µg m-3 in northern Italy for IVOC65VOC against IVOC4VOC). At a
continental scale outside anthropogenic areas, the low variations observed
on simulated OA concentrations between the different IVOC emission
configurations suggest that IVOCs from traffic and transportation sources
are likely not a major source of SOA.
Tracking OA sources
Apportionment of OA sources is investigated in this section. The study takes
into account OA formation from IVOC oxidation and is based on the
IVOC30VOC model configuration. Figure 14 shows the contribution of the
various OA sources to the simulated OA concentrations during the July–August
2013 period and the mean daily profiles at two stations located in areas
dominantly impacted by anthropogenic air masses (Cabauw and Palaiseau) and
two stations located in areas dominantly impacted by biogenic air masses
(Birkenes II and Iskrba). SOA constitutes the main fraction of OA whatever
the environment. This secondary fraction typically grows from anthropogenically
impacted areas (about 70 % at Palaiseau station) to remote areas (about
95 % at Iskrba station). This trend is in agreement with what is usually
observed or simulated for summertime periods (e.g., Aksoyoglu et al., 2011;
Belis et al., 2013). For the remote stations Birkenes II and Iskrba,
respectively 82 % and 67 % of the simulated OA concentrations come from a
biogenic source. Contrariwise, anthropogenic sources are the major OA
contributors at anthropogenically impacted stations (65 % and 60 % of OA at the
Cabauw and Palaiseau stations, respectively).
Evolution of simulated OA concentrations and distribution
function of sources with the IVOC30VOC model configuration. Panels (a),
(c), (e) and (g) present evolutions of daily averaged concentrations during the
July–August 2013. Panels (b), (d), (f) and (h) present mean daily profiles. Results
are shown at two stations influenced by anthropogenic sources in the Netherlands
(Cabauw, a, b) and in France (Palaiseau, c, d) and at two stations
influenced by biogenic sources in Norway (Birkenes II, e, f) and Slovenia
(Iskrba, g, h). Primary and secondary BBOA include compounds from
biomass burning. Traffic SVOC includes C14 to C26 VBS-GECKO
alkanes and alkenes and SOA from traffic SVOC+IVOC oxidation includes
their oxidation products. SOA from terpenes includes all species produced by
α-pinene, β-pinene, limonene, ocimene and humulene oxidation.
Among OA biogenic sources, terpene oxidation is clearly found as the major
contributor of OA during the summer period, contributing from 35 % (at
anthropogenic impacted stations) to 80 % (at remote stations) of the total
OA mass. The 60 % increase in OA mass concentration observed in northern
Europe between H2O and VBS-GECKO parameterizations (see Fig. 2) is also
mainly related to SOA formation from terpene, especially ocimene and
limonene. In our simulation, SOA produced by isoprene oxidation does not
represent a substantial fraction of OA at the selected measurement stations.
The major contribution of isoprene SOA to OA reaches about 5 % (see Fig. 14h) and is observed at the Iskrba station during diurnal conditions.
The anthropogenic fraction of OA is found to be dominated by residential
biomass burning OA sources (BBOA). Indeed, according to the temporal factors
used in CHIMERE (based on GENEMIS; Ebel et al., 1997; Friedrich, 2000),
4 % of annual emissions of residential BBOA occurs during July–August,
leading to a non-negligible amount of residential BBOA during summer. This
result remains, however, subject to caution, owing to the large uncertainties
in the temporalization of biomass burning emissions in the model. The
primary organic fraction (i.e., condensed primary SVOCs) from traffic
emissions is found to be substantial in the OA budget only at night in urban
areas. On the other hand, the secondary organic fraction produced by traffic
emissions can represent about 50 % of diurnal anthropogenic OA at stations
near urban areas (i.e., Palaiseau and Cabauw). OA formed by the oxidation of
mono-aromatic species is found to be negligible over Europe (less than 0.025 µg m-3 on average over the studied domain). Figure 15 shows the
contribution of traffic emission to the simulated OA concentrations for the
July–August 2013 period for three categories of precursors: SVOCs, IVOCs and
mono-aromatic compounds. As mentioned above, the OA concentrations from
mono-aromatic compound oxidation are negligible compared to concentrations
from traffic S/IVOC oxidation. Globally, in our study over Europe, OA
concentrations produced from traffic S/IVOC oxidation are of the same order
of magnitude. OA from primary SVOCs is locally more important close to
sources (i.e., northern Italy, Moscow, Paris, Gibraltar, etc.). OA from IVOC
is globally higher far away from the sources, with a higher dispersion over
Europe (Fig. 15). This higher dispersion is expected owing to the larger
timescale required to produce low-volatility species via multistep oxidation
processes in the plumes of high emission areas.
Mean simulated anthropogenic OA mass concentration formed by
the partitioning of species produced from the oxidation of emitted traffic
SVOCs (a), traffic IVOCs (b) and mono-aromatic compound (c) for July–August
2013 over Europe (data from IVOC30VOC simulation).
The distributions of OA within the volatility bins (given in Fig. S2) show similar features from one station to another.
The results suggest that OA over Europe has relatively low volatility during
summertime. Indeed, the VBS-GECKO contributors to OA have very low
volatility: ∼80 % of the OA contributors from VBS-GECKO are
volatility bins 7 to 5 (VBk,7, VBk,6 and VBk,5 species), i.e.,
having saturation vapor pressure at 298 K of 10-14, 10-12 and
10-11 atm, respectively.
Conclusions
The VBS-GECKO parameterization for SOA production was developed based on
explicit mechanisms generated with the GECKO-A tool. The VBS-GECKO
parameterization was fitted using box modeling results for a selected set
of parent compounds including terpenes, mono-aromatic compounds, linear
alkanes and alkenes and for various environmental conditions, including
different NOx regimes, temperatures, and OA loads (Lannuque et al., 2018a).
In this study, the VBS-GECKO parameterization was evaluated in the CHIMERE β 2017 CTM over Europe during summertime.
The VBS-GECKO parameterization shows good performances to simulate OA
concentrations over Europe in the summer. Calculated mean fractional biases
and mean fractional errors on PM2.5, OCPM2.5 and OMPM1
satisfy the performance criteria of Boylan and Russel (2006). The model
configuration including the VBS-GECKO parameterization yields to higher OA
concentrations compared to the former reference configuration including the
H2O parameterization. The deviations between the two configurations are
especially marked over northern Europe, with an increase factor of
∼60 %. Outside this area, the OA increases obtained with
the VBS-GECKO configuration are slight. Statistically, the use of the
VBS-GECKO improves the overall MFB, MFE and RMSE and does not modify
significantly correlation coefficients. Tests performed to examine the
sensitivity of simulated OA concentrations to hydro-solubility, volatility,
aging rates and NOx regimes have shown that the VBS-GECKO
parameterization provides consistent results that are not subject to large
deviations induced by parameters provided by the gas-phase mechanism
included in the CTM (e.g., HOx or NOx concentrations). However, the
OA concentrations remain underestimated with the VBS-GECKO model
configuration, especially in areas with a significant contribution of
anthropogenic sources (e.g., reaching a factor of 2.5 for OCPM2.5 at the
NL0644R station in the Netherlands). None of the conducted sensitivity tests
leads to OA variations large enough to fill the gaps between measurements
and simulated concentrations at the anthropogenic stations.
The analysis of simulated OA shows that, during summertime, the main
fraction is made of secondary matter which represents ∼85 %
of the total mean OA concentration. A large fraction of the simulated OA
comes from biogenic sources (between 30 % and 85 % of the total OA),
especially from terpene oxidation which represents ∼95 % of
these biogenic sources. For the conditions examined in this study, OA formed
by the oxidation of mono-aromatic compounds appears to be negligible with
maximum mean concentrations of 0.025 µg m-3 over the North Sea and
Benelux. Note that ignoring SOA production from these precursors in the
model would substantially reduce the number of VBk,i species currently
considered in the VBS-GECKO parameterization. The simulated OA was found to
be made of species having low and extremely low volatilities in remote
areas but also of SVOCs closer to major anthropogenic sources.
Finally, IVOC oxidation was added to examine the contribution of this
additional source to the SOA budget. Five model configurations with distinct
IVOC emissions from traffic were tested and compared using the VBS-GECKO
parameterization in CHIMERE. As expected, considering the emission of IVOCs
by traffic and transport sources was found to globally increase background
OA concentrations. Although SOA production from traffic IVOC oxidation can
locally be significant (up to ∼+3 µg m-3 in
northern Italy, assuming IVOC emissions represents 65 % of NMVOC
emissions), this additional OA source remains too small to explain the gap
between simulated and measured values at stations where anthropogenic
sources are dominant. This first application of this new VBS-GECKO
parameterization has been shown to provide consistent results. This outcome
creates motivation to extend the exploration to wintertime conditions and
to expand the list of parent compounds considered, in particular to include SOA
formation from oxidation of isoprene, sesquiterpenes or organics species
emitted by residential biomass burning, a prerequisite to extend the
evaluation and analysis to wintertime when this source is dominant. This is
the subject of ongoing studies. The VBS-GECKO is a heavy parameterization in
term of species number. Calculation time is multiplied by 2 using the
complete VBS-GECKO scheme with IVOCs compared to H2O. This study has
shown that the number of species can be optimized. For example, because of
the low influence on OA concentrations, the representation of the SOA formed
by the oxidation of mono-aromatic species can be highly simplified and
C10 precursors even removed.
Data availability
Daily averages and mean day profiles for the 17 model configurations
presented in this article have been made available on Zenodo:
10.5281/zenodo.1654297 (Lannuque et al., 2018b).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-20-4905-2020-supplement.
Author contributions
VL implemented the parameterization in the air quality model, conducted the simulations and the sensitivity tests, and wrote the article. FC conducted the reference simulation. VL, FC, MC, BA and BB contributed to design the research, develop the parameterization, interpret the data and revise the article.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
The authors gratefully acknowledge Wenche Aas, Olivier Favez, Liine Heikkinen and Laurent Poulain for providing ACSM data realized in the
framework of ACTRIS. Simulations were
performed using the TGCC-CCRT supercomputers.
Financial support
This research has been supported by the French Environment and Energy Management Agency (ADEME), INERIS and the French Ministry of Ecology.
Review statement
This paper was edited by Gordon McFiggans and reviewed by two anonymous referees.
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