ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-17-12509-2017Modelling organic aerosol concentrations and properties during ChArMEx summer campaigns of 2012 and 2013 in the western Mediterranean regionChritMounirmounir.chrit@enpc.frSarteletKarineSciareJeanPeyJorgehttps://orcid.org/0000-0002-5015-1742MarchandNicolashttps://orcid.org/0000-0001-9745-492XCouvidatFlorianSellegriKarineBeekmannMatthiasCEREA, Joint Laboratory École des Ponts ParisTech – EDF R&D,
Université Paris-Est, 77455 Champs-sur-Marne, FranceLSCE, CNRS-CEA-UVSQ, IPSL, Université Paris-Saclay, Gif-sur-Yvette, FranceAix-Marseille University, CNRS, LCE UMR 7376, Marseille, FranceINERIS, Verneuil-en-Halatte, FranceLaboratoire de Météorologie Physique (LaMP), UMR 6016 CNRS/UBP,
of the Observatoire de Physique du Globe de Clermont-Ferrand (OPGC), Aubière, FranceLISA, UMR CNRS 7583, IPSL, Université Paris-Est Créteil and Université Paris Diderot, FranceEEWRC, The Cyprus Institute, Nicosia, Cyprusnow at: The Geological Survey of Spain, IGME, 50006 Zaragoza, SpainMounir Chrit (mounir.chrit@enpc.fr)23October2017172012509125314April201729May201725August201715September2017This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/17/12509/2017/acp-17-12509-2017.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/17/12509/2017/acp-17-12509-2017.pdf
In the framework of the Chemistry-Aerosol Mediterranean
Experiment, a measurement site was set up at a remote site (Ersa) on Corsica
Island in the northwestern Mediterranean Sea. Measurement campaigns performed
during the summers of 2012 and 2013 showed high organic aerosol
concentrations, mostly from biogenic origin. This work aims to represent
the organic aerosol concentrations and properties (oxidation state and
hydrophilicity) using the air-quality model Polyphemus with a surrogate
approach for secondary organic aerosol (SOA) formation. Biogenic precursors
are isoprene, monoterpenes and sesquiterpenes. In this work, the following
model oxidation products of monoterpenes are added: (i) a carboxylic acid
(MBTCA) to represent multi-generation oxidation products in the low-NOx
regime, (ii) organic nitrate chemistry and (iii) extremely low-volatility
organic compounds (ELVOCs) formed by ozonolysis. The model shows good
agreement of measurements of organic concentrations for both 2012 and 2013
summer campaigns. The modelled oxidation property and hydrophilic organic
carbon properties of the organic aerosols also agree reasonably well with the
measurements. The influence of the different chemical processes added to the
model on the oxidation level of organics is studied. Measured and simulated
water-soluble organic carbon (WSOC) concentrations show that even at a remote site
next to the sea, about 64 % of the organic carbon is soluble. The
concentrations of WSOC vary with the origins of the air masses and the
composition of organic aerosols. The marine organic emissions only contribute
to a few percent of the organic mass in PM1, with maxima above the sea.
Introduction
The Mediterranean region is considered as one of the most prominent regions
to be detrimentally impacted by climate and air composition changes over
both southern Europe and northern Africa. Organic aerosols (OA) account for
about 20–50 % of the fine aerosol mass at continental mid-latitudes
and as high as 90 % in tropical forest areas
. They contribute to more than 50 % of EU-regulated PM2.5 concentrations in Europe . OA affect both climate
and human health. They influence the radiation budget by mostly scattering
sunlight resulting in negative direct radiative forcing
. Moreover, hydrophilic OA can act as a cloud condensation
nuclei and hence modify cloud microphysical properties and
lifetime. In terms of health effects, OA toxicity is linked to the oxidative stress which is induced by
the reactive oxygen species. The oxidative potential may differ for the different organic
precursors .
OA are usually classified either as primary (POA) or secondary
(SOA). POA are directly emitted in the atmosphere, often as
intermediate/semivolatile organic compounds (I/S-VOCs), which partition
between the gas and the particle phases . The
gas phase of I/S-VOC is missing from emission inventories . SOA are produced through chemical oxidation of volatile organic
compounds (VOCs) and I/S-VOCs and through condensation of I/S-VOCs.
A large fraction of emitted VOCs is biogenic, especially in the western
Mediterranean in summer, when solar radiation is high. Biogenic emissions may
age and form SOA as they are transported through different environments
. Using aerosol mass spectrometer (AMS) measurements
performed in an urban area in southern France (Marseille) and positive matrix
factorisation techniques,
attributed 80 % of the organic aerosol mass to biogenic secondary organic
aerosols (BSOA) and near 40 % of the BSOA to monoterpene
oxidation products. These high biogenic concentrations in an urban area may
be partly explained by the influence of anthropogenic emissions on biogenic
SOA formation .
Similar results were obtained through measurement campaigns in the Barcelona
region (Spain), where have found a
prevalence of non-fossil organic aerosol sources in remote and urban
environments and clear evidence of biogenic VOC oxidation products and
biogenic SOA formation under anthropogenic
stressors.
The ChArMEx (Chemistry and Aerosol Mediterranean Experiment: http://charmex.lsce.ipsl.fr) project has organised several summer campaigns to study
atmospheric chemistry and its impacts in the western Mediterranean region.
The TRAQA (Transport et Qualité de l'Air) campaign
was set up in summer 2012 to study the transport and impact of continental air on atmospheric
pollution over the basin . The ADRIMED (Aerosol Direct
Radiative Impact in the Mediterranean; ) campaign was set
up in June–July 2013 to assess the radiative impact of aerosols, while the
SAF-MED (Secondary Aerosol Formation in the MEDiterranean) campaign was set
up in July–early August 2013 to understand and characterise the concentrations
and properties of organic aerosols in the western Mediterranean and to figure
out the origins of the high concentrations observed outside urban areas.
Intensive ground-based in situ measurements were performed during the summer
of
2013, while airborne measurements were performed during the summers 2013 and
2014 . In agreement with the observations of
in Marseille on the French Mediterranean coast, the
ground-based in situ measurements performed at the remote site of Ersa on
Cape Corsica, the northern tip of Corsica Island (southeast of continental
France) showed that OA are mostly from biogenic origin. However, as over
urban areas, anthropogenic emissions from shipping or pollution plumes from
European big cities may influence biogenic SOA formation
.
The VOC biogenic precursors of SOA are isoprene, monoterpenes (MT) and
sesquiterpenes. Although sesquiterpenes emission factors are lower than those
of isoprene and MT over Europe, their SOA yields are high because
of their low saturation vapour pressures . For MT,
first-generation oxidation products, such as pinonaldehyde, pinic and pinonic
acids contribute to the formation of SOA, although their contributions may be
low . For α-pinene, further-generation oxidation
steps may lead to the formation of very low-volatility products, such as the
tricarboxylic acid 3-methyl-1,2,3-butanetricarboxylic acid (MBTCA;
) and oligomeric compounds.
and showed that highly oxidised organic compounds are
formed in the early stages of the oxidation of MT.
proposed a mechanistic description of these extremely
low-volatility organic compounds (ELVOCs) formation from the most
atmospherically abundant biogenic monoterpenes, such as α-pinene and
limonene. These ELVOCs have been observed during both chamber and in situ
measurements in Germany . Several studies showed the
importance of nighttime SOA formation from MT via nitrate radical
oxidation, resulting in the formation of organic nitrates
. showed the
importance of reactive nitrogen and pointed out the fact that organic nitrate
accounts for more than a half of the monoterpene oxidation products in the
particle phase over the US.
For isoprene, in low-NOx environments, recent studies have focused on the
formation of isoprene epoxydiols (IEPOX) in acidic aerosols
. However, using AMS
measurements, particle-phase IEPOX was not observed during the ChArMEx
campaign over an isoprene-emitting forest in the south of France, suggesting
it might have formed organosulfates . Several studies also
showed the importance of non-IEPOX pathway for isoprene oxidation in low-NOx
environment . Although ELVOCs may also form from
isoprene oxidation, the yields may be low .
OA can also be emitted from the sea because of phytoplankton activity;
according to , OA from marine origin can contribute
considerably to OA concentrations, especially near the biologically productive
waters. Recently derived parameterisations
relate the organic fraction of sea-salt emissions to the seawater
chlorophyll a (chl a) concentrations. However, the contribution of these species to
the organic budget over the Mediterranean Sea is not clear.
SOA modelling has undergone significant progress over the past few years due
to the rapid increase of experimental data on SOA yields and molecular
chemical composition resulting from the oxidation of a variety of VOC and
I/S-VOC. SOA models used in mesoscale models can be grouped into two major
categories: (1) models based on an empirical representation of SOA formation
and (2) models based on a mechanistic representation of SOA formation. Models
of the first category include the widely used two-compound Odum approach
, the more recent volatility basis set (VBS) approach
or the multi-generational oxidation model
. Models of the second category use experimental data on
the molecular composition of SOA and represent the formation of SOA using
surrogate molecules with representative physicochemical properties
. The surrogate approach
differentiates low-NOx and high-NOx regimes. The gas–particle partitioning
may include both absorption into hydrophobic organic particles and
dissolution into aqueous particles and take into account some of the
complexity involved in OA partitioning (such as non-ideality, multi-phase
partitioning). Although these two categories of models are fundamentally
different in their initial design (empirical vs. mechanistic), they aim to
describe the same processes. Furthermore, they tend to converge as they
continue to be developed and refined. For example, I/S-VOCs from
anthropogenic emissions, which are usually specified by volatility classes,
can be included in a mechanistic model ; also, the VBS scheme can take into account the oxidative state of
SOA , in particular its elemental O/C ratio
. The recently developed 1.5-VBS
assigned a molecular structure to VBS products. Both the
mechanistic and empirical approaches are scientifically valid and
complementary; as shown by , the most important aspect of an
SOA model is its comprehensiveness in terms of the precursors and processes
being treated (completeness of the precursor VOC list, importance of low-NOx
vs. high-NOx regimes, treatment of hydrophilic properties of the surrogates)
rather than its fundamental design. study how the VBS
approach can be used to represent the formation of SOA over the western
Mediterranean and point out the importance of taking into account
fragmentation and formation of nonvolatile SOA in this framework.
Precursors classes and the surrogate species used for SOA formation.
PrecursorsSurrogate speciesI/S-VOCsThree volatility bins: log(C*)=-0.04, 1.93, 3.5 with C* the saturation concentrationAromaticsToluene, xyleneIsopreneIsopreneMonoterpenesα-Pinene, β-pinene, limoneneSesquiterpenesHumulene
This paper aims to investigate the chemical processes and surrogates that
need to be taken into account in the mechanistic representation of SOA to
reproduce the concentrations and properties of the observed biogenic SOA at
the Ersa supersite in Corsica. The mechanistic representation
included in the air-quality model Polyphemus is modified
by including recent research progress on monoterpenes SOA formation (ELVOC,
MBTCA, organic nitrate). The influence of primary marine organic emissions is
also studied. A further evaluation of the model by comparison to airborne
measurements is presented in .
The paper is structured as follows. Section 2 presents the air-quality
model used and the improvements made in the mechanistic
representation. Section 3 details the model input data sets and the
measurement data. Section 4 compares the concentrations and the
properties of OA to measurements, as well as the influence of the different chemical
processes added to the model. Finally, Sect. 5 studies the impact of
the biological activity of the Mediterranean Sea on OA concentrations.
Model descriptionGeneral features
In order to simulate aerosol formation over the western Mediterranean, the
Polair3d/Polyphemus air-quality model is used . The
numerical algorithms used for transport and the parameterisations used for
dry and wet depositions are detailed in . Gas-phase
chemistry is modelled with the carbon bond 05 mechanism (CB05)
. Different reactions are added to CB05 to model the
formation of semivolatile organic compounds from five classes of SOA
precursors (intermediate and semivolatile organic compounds of anthropogenic
emissions, aromatic compounds, isoprene, monoterpenes and sesquiterpenes)
. For these classes of precursors, which
include a great number of species, only a few surrogates are used to
represent all the species.
As detailed in , isoprene may form tetrols and methyl
dihydroxy dihydroperoxide under low NOx, and methyl glyceric acids and
organic nitrates under high NOx. Oxidation of isoprene by the nitrate radical
NO3 is also modelled.
For monoterpenes and sesquiterpenes, the oxidation scheme is based on
. Humulene is used to represent all sesquiterpenes. For
monoterpenes, three precursors are used: API (for α-pinene and
sabinene), BPI (for β-pinene and δ3-carene) and LIM (for
limonene and other monoterpenes and terpenoids). Depending on the NOx
regimes, three surrogates are formed: pinonaldehyde, norpinic acid and pinic
acid. Although a simple parameterisation was developed to represent the
oligomerisation of pinonaldehyde as a function of pH in ,
it is not used here because its influence on SOA formation is not clear. As
detailed in , I/S-VOC emissions are emitted as three
primary surrogates of different volatilities (characterized by their
saturation concentrations C*: log(C*)=-0.04, 1.93, 3.5). The ageing
of each primary surrogate is represented through a single oxidation step,
without NOx dependance, to produce a secondary surrogate of lower
volatility (log(C*)=-2.4, -0.064, 1.5 respectively) but higher molecular
weight. For aromatic compounds, toluene and xylene are used as surrogate
precursors. The precursors react with OH to form radicals that may then react
differently under low-NOx and high-NOx conditions. Under low-NOx
conditions, the surrogate is not identified, but it is supposed to be
hydrophobic. Under high-NOx conditions, the surrogates formed are two
benzoic acids (methyl nitrobenzoic acid and methyl hydroxy benzoic acid).
Table describes the five classes of precursors used to
represent the SOA formation and the surrogates used.
The SIze REsolved Aerosol Model (SIREAM; ) is used for
simulating the dynamics of the aerosol size distribution by coagulation and
condensation/evaporation. SIREAM uses a sectional approach and the aerosol
distribution is described here using 20 sections of bound diameters: 0.01,
0.0141, 0.0199, 0.0281, 0.0398, 0.0562, 0.0794, 0.1121, 0.1585, 0.2512,
0.3981, 0.6310, 1.0, 1.2589, 1.5849, 1.9953, 2.5119, 3.5481, 5.0119, 7.0795
and 10.0 µm. The condensation/evaporation of inorganic aerosols is
determined using the thermodynamic model ISORROPIA with a
bulk equilibrium approach in order to compute partitioning between the
gaseous and condensed phases of particles.
For organic aerosols, the gas–particle partitioning of the surrogates is
computed using SOAP , and bulk equilibrium is also
assumed for SOA partitioning. The gas–particle partitioning of hydrophobic
surrogates is modelled following , with absorption by
the organic phase (hydrophobic surrogates). The gas–particle partitioning of
hydrophilic surrogates is computed using the Henry's law modified to
extrapolate infinite dilution conditions to all conditions using an
aqueous-phase partitioning coefficient, with absorption by the aqueous phase
(hydrophilic organics, inorganics and water). Activity coefficients are
computed with the thermodynamic model UNIFAC (UNIversal Functional group;
). After condensation/evaporation, the moving diameter
algorithm is used for mass redistribution among size bins.
ELVOCs
and showed that extremely low-volatility hydrophobic
molecules of high molecular weight form at the early
stage of oxidation of the monoterpenes α-pinene and limonene by ozone.
In the model, a unique gaseous precursor representing α-pinene and limonene is
used for ELVOC formation. The ELVOC yield is assumed to be 11 %, i.e. close to
the average of the yields of α-pinene and limonene according to
. suggested lower yields
(Table ). In this paper, sensitivity simulations with a
lower bound of 3 % and a upper bound of 18 % are also conducted.
Relying on known chemistry and experimental findings, and
provided a formation pathway from monoterpenes to ELVOCs
through the autoxidation process .
The ozonolysis reaction leads to the formation of peroxy radicals (RO2),
which are the starting point of ELVOC formation. These radicals
undergo a fairly rapid (∼ 1 s-1) sequential
intramolecular H-atom shift followed by O2 addition leading to the
formation of highly oxygenated peroxy radicals (RELVOCO2). This O2
addition leads not only to an increase in molecular weight but also to a
decrease in the radical volatilities. investigated these
reactions including the different steps, possible isomerisation as well as
the most likely chemical pathways leading to an enrichment of peroxy radicals
by oxygen. The oxygen-centred peroxy radical intermediates are internally
rearranged by intramolecular hydrogen shift reactions, enabling more oxygen
molecules to attach to the carbon backbone. Simultaneously, the sequential
H-shift mechanism competes with reactions between peroxy radicals, NO and
HO2. Subsequently, the termination reactions lead to the formation of
ELVOCs. ELVOC monomers and dimers are formed through reactions of
RELVOCO2 with RO2 and HO2. Following , the
reactions for the ozonolysis of α-pinene and limonene (MT), as
well as the kinetic constants used in the model (Table ), are included in the model as detailed in Appendix . The formation of organic nitrate from
RELVOCO2 peroxy radicals is not considered in the model, as the
reactions proposed by led to negligible concentrations.
The aerosol species introduced in the model for ELVOC formation and their
properties are detailed in Table . The enthalpy of
vaporisation of the monomer and dimer is set
to 50 KJ mol-1, and the saturation vapour pressure
is assumed to be very low and is taken equal to 10-14 torr at 298 K.
Species introduced in the extremely low-volatility
organic compounds (ELVOC), organic nitrate (orgNIT) and MBTCA kinetic models.
Organic nitrates are formed where biogenic VOCs and anthropogenic NOx
sources interact . We used here the
parameterisation of to account for the formation of organic
nitrate compounds from the oxidation by OH and NO3 of MT. The oxidation of MT by OH leads to the formation of a peroxy radical
TERPRO2, and the oxidation by NO3 leads to the formation of TERPNRO2
(nighttime chemistry). The peroxy radicals TERPRO2 may react with NO to
form organic nitrate with a molar yield of 20.1 %, while the oxidation of
TERPNRO2 leads to higher yields. The reactions, as well as the kinetic constants
(Table ) are described in
Appendix .
Following , the estimated vapour pressure of the condensing
organic nitrate species is assumed to be 5×10-6 torr , and the enthalpy of
vaporisation is taken as 40 KJ mol-1. The aerosol species (orgNIT)
introduced in the model and its properties are summarised in
Table . The organic nitrate is assumed to be
hydrophobic .
MBTCA: an aging product of the pinonic acid
It was shown in a set of studies that ozonolysis and OH-initiated reactions of
terpenes produce organic acids . identified MBTCA
(3-methyl-1,2,3-butanetricarboxylic acid) as the most relevant organic acid
for atmospheric SOA. It is produced by the OH oxidation of pinonic acid,
which is itself produced by the OH oxidation of α-pinene. The
OH oxidation of pinonic acid to form MBTCA is added to the model with a
kinetic constant k=9.0×10-12 cm3 s-1 and a yield of 0.0061 . Following
, MBTCA is supposed to be hydrophilic, with a
OM/OC ratio of 2.125 (Table ).
Model and measurement setup
The simulation domain and the input data are now detailed, as well as the measurements used in this study.
Model setupDomains
Two nested simulations are performed: one over Europe and one over a
Mediterranean domain centred around the Ersa supersite surroundings in Corsica (Fig. ).
The coordinates of the European southwestern-most point are 15∘ W,
35∘ N in longitude and latitude. The domain of simulation covers an area
of 50∘×35∘ with a uniform spatial step of 0.5∘
along both longitude and latitude. For the nested Mediterranean domain, the
southwestern-most point is 4∘ W, 39∘ N.
The domain of simulation covers an area of 11∘×8∘ with
a uniform spatial step of 0.125∘ (∼ 13 km) along both longitude
and latitude. Fourteen vertical levels are considered from the ground to 12 km. The
heights of the cell interfaces are 0, 30, 60, 100, 150, 200, 300, 500, 750,
1000, 1500, 2400, 3500, 6000 and 12 000 m.
The nested modelling domains: the nesting domain
over Europe and a nested domain over the northwestern Mediterranean, as
delimited by a black rectangular contour in the figure. The red point
indicates the Ersa station in Corsica Island.
The dates of simulation are chosen such as matching those of the
measurements. For 2012, the simulations are run between 2 June and 8 July
2012 for the nesting domains (6 June and 8 July 2012 for nested). For 2013, the simulations are run between 2 June and
10 August 2013) for the nesting
domains (6 June and 10 August 2013 for nested).
Boundary conditions for the European domain are obtained from the global
chemistry-transport model MOZART v4
(https://www.acom.ucar.edu/wrf-chem/mozart.shtml). The European simulation
provides initial and boundary conditions to the Mediterranean simulation.
Meteorological data
Meteorological data are provided by the European Centre for Medium-Range
Weather Forecasts (ECMWF) model. The vertical diffusion is computed using the
Troen and Mahrt parameterisation . The Global Land Cover
2000 (GLC-2000; http://forobs.jrc.ec.europa.eu/products/glc2000/data_access.php) data set is used for land
cover.
Emissions
Anthropogenic emissions are generated using the EDGAR-HTAP_V2 inventory for
2010 (http://edgar.jrc.ec.europa.eu/htap_v2/). The monthly and daily
temporal distribution for the different activity sectors are obtained from
and the hourly temporal distribution from
. Following , NOx emissions are
split in mass into 90 % of NO, 9.2 % of NO2 and 0.8 % of HONO. SOx
emissions are split into 98 % of SO2 and 2 % of H2SO4 (in molar
concentrations). For emissions of non-methane volatile organic compounds, the
speciation of is used. PM2.5 primary particle emissions
are speciated into dust, POA and black carbon. POA are assumed to be the particle phase of I/S-VOC. Total I/S-VOC
emissions (gas and particle phases) are estimated as detailed in
by multiplying POA by a fixed value and by assigning
them to species of different volatilities. The volatility distribution is
kept the same for all emission sectors, although more detailed volatility
distributions could be defined following the work of . In this study, the ratio I/S-VOC / POA is set to 2.5
and . Setting the ratio of semivolatile organic carbon to POA to 1, i.e. ignoring
I/S-VOC, has little impact on the organic concentrations, as shown in
Fig. . Particles of diameters higher than 2.5 µm are all
speciated into dust. Biogenic emissions are estimated with the Model of
Emissions of Gases and Aerosols from Nature (MEGAN; ).
Over the Mediterranean domain, during the period of the 2013 summer
simulation, the average emissions of sesquiterpenes, monoterpenes and
isoprene are 0.001, 0.019 and 0.024 µg m-2 s-1 respectively.
Hence, compared to isoprene and monoterpene emissions, the sesquiterpene
emissions are lower by a factor of 95.8 and 94.7 % respectively.
Sea-salt emissions are parameterised following , who modelled the generation of sea salt by the evaporation of sea spray produced by
bursting bubbles during whitecap formations due to the friction with surface
wind. The emitted sea-salt mass is assumed to be made of 30.61 % sodium
, 25.40 % chloride and 4.22 % sulfate following results
from measurements in mesocosms made in Corsica in July 2012
. The organic fraction of sea-salt emissions is not taken
into account in the simulation presented here. However, it is estimated in
Sect. , where the contribution of organic sea-salt emissions to
organic concentrations is assessed.
Relative difference (%) of OM1 concentrations simulated using
the emission ratio I/S-VOC / POA = 2.5 and 1.
Measured data
The model results are compared to observational data from ChArMEx campaigns
during the summers of 2012 and 2013. The station is located at the red point
in Fig. . The measurement site is located at Ersa
(42∘58′ N, 9∘21.8′ E), on a ridge at an altitude of
about 530 m above the sea level, and has an unimpeded view of the sea over
∼ 300∘ from the SSW to SSE . The ground-based
comparisons are performed by comparing the measured concentrations to the
simulated ones using the concentrations of the model cell the closest to the
station. The central coordinates of this cell at which concentrations are
computed are 42∘52 N, 9∘22′30′′ E, which is very close to the
station and with a similar altitude above sea level (494 m).
studied the difficulty to correctly represent in a
model the orography of Ersa site, which is a cape at the northern edge of
Corsica. They concluded that the representativeness error is about 10 % for
organic aerosols.
To evaluate the organic concentrations and oxidation properties, an ACSM
(aerosol chemical speciation monitor) was used to measure the real-time
chemical composition and mass loading of aerosols with aerodynamic diameters
between 70 and 1000 nm (sulfate, nitrate, ammonium, chloride and organic
compounds), between 8 June and 2 July 2012 and between 6 June and 3 August
2013. The ratio OM/OC and the oxidation state of organics are estimated using
the ACSM measurements following .
Other instruments were deployed in 2013 to evaluate the organic properties: a
PILS-TOC-UV to estimate the water-soluble fraction of organics
between 14 July and 5 August 2013 and a high-volume quartz
filter sampling DIGITEL for 14C measurements in organics between 16
and 30 July 2013.
A direct evaluation of the simulated concentrations of ELVOCs or organic nitrates cannot be done because
they were not measured during the campaigns.
Model–measurements comparison method
To evaluate a model, several approaches and performance scores can be used.
Here, we compare model simulation results to measurements using a set of
performance statistical indicators: the simulated mean (s‾), the
root mean square error (RMSE), the correlation coefficient, the mean
fractional bias (MFB) and the mean fractional error (MFE). They are defined in
Table of Appendix . Based on the MFB and the MFE,
proposed a performance and a goal evaluation criteria
as detailed in Table of Appendix .
Comparison to measurements
The concentrations of organic aerosols are compared to measurements for the
summers of 2012 and 2013. The origins of organic aerosols (fossil vs. non-fossil), and their properties (oxidation state, hydrophilic properties) are
compared to the measurements performed during the intensive measurement
period of the summer 2013. In the simulation presented here, the ELVOC yield
is assumed to be 11 %, as detailed in Sect. . Two
sensitivity simulations are performed using a lower bound yield (3 %) and an
upper bound yield (18 %). In Appendix , similarly to
what is presented in this section for the reference simulation, the
sensitivity simulations are compared to each other and to the measurements in
terms of the mass of OM1, the organic aerosol composition and the OM/OC and
O/C ratios.
Organic concentrations
The comparisons of the measured and modelled temporal profiles of the
concentration of the submicron organic mass (OM1) at Ersa are shown in
Fig. for the two summer campaigns of 2012 and 2013.
Comparison of measured and simulated daily OM1 concentration at
Ersa during the summer campaigns of 2012 (a) and 2013 (b).
The model shows satisfactory results at Ersa station, as shown by the
statistics in Table . Both the goal and the performance
criteria of are verified for both years (MFB<30 %
and MFE<50 %). The overall concentration of OM1 is reasonably well
modelled, although it is slightly underestimated 2.58 (3.71)
µg m-3 against 2.89 (4.14) µg m-3 in the
measurements for 2013 (2012). Overall, the model reproduces very
well the peaks and troughs of OM1 concentrations in both 2012 and 2013
with the exception of a few days in late June–early July 2013. This period is
a period with air trajectories passing over France , during
which aging processes of biogenic compounds may be particularly important
with formation of aged hydrophilic SOA. In the model, this process would lead
to the formation of aged carboxylic acids, with MBTCA used as surrogate.
However, the simulated concentration is low because the yield used is very
low (it corresponds to the yield of MBTCA only).
Statistics of model to measurements comparisons for hourly organic
concentrations in particles of diameters lower than 1 µm during the
summer campaigns of 2012 and 2013. o‾ and s‾ are the
observed and simulated means respectively. RMSE is the root mean square
error, and MFB and MFE are the mean fractional bias and error respectively (see
Appendix ).
Yearo‾ (µg m-3)s‾ (µg m-3)RMSE (µg m-3)Correlation (%)MFBMFE20124.143.712.0061.7-0.130.3920132.892.581.5367.3-0.150.49Sources of OA
In both 2012 (6 June to 8 July) and 2013 (6 June to 10 August), the modelled
organic mass is dominated essentially by biogenic particles (Fig. ). They represent
77 and 75 % of the organic mass. In the model, for comparisons to the 14C
measurements, the biogenic-origin organic compounds are assumed to be non-fossil, and the anthropogenic-origin organic compounds are assumed to be
fossil. Although in winter, some of the anthropogenic-origin organic compounds
may originate from wood combustion (residential heating) and be non-fossil, we
assume that the fraction of anthropogenic-origin organic compounds from
residential heating is low in summer.
The simulated OC is computed by dividing the modelled organic mass
of each model surrogate by the OM/OC ratio of the surrogate.
During the period when 14C measurements were performed (16 to 30
July), 75 % of the modelled organic mass is biogenic, in agreement with the
14C measurements, which estimated that 85 % is non-fossil. The
measured and simulated means are 2.5 and 1.9 µgC m-3, respectively, for non-fossil OC, and 0.5 and
0.6 µgC m-3, respectively, for fossil OC. Although fossil OC is well
modelled, non-fossil OC is slightly underestimated between 16 and 30 July.
The modelled average chemical compositions of OM1 at Ersa during the
summers of 2012 and 2013 are presented in Fig. . The chemical
composition is very similar between the years 2012 and 2013.
Simulated composition of OM1 during the
summer campaigns of 2012 (a) and 2013 (b).
Monoterpene oxidation products including ELVOCs and organic nitrate represent
a large part of biogenic aerosols (about 48 % in 2012 and 2013). ELVOCs and
organic nitrate are abundant. ELVOCs represent 10 % of OM1 in 2012 and
15 % in 2013. Organic nitrate represent 24 % of the organic mass in 2012 and
20 % in 2013. The route to organic particulate nitrate may essentially (but
not exclusively) be active during the night as NO3 efficiently photolyses
during the day and the production yields are more important during the night,
and higher organic-nitrate concentrations are observed at night
(Fig. ).
Diurnal variations in simulated organic nitrate concentrations during
summer 2013 at ERSA.
MBTCA, an oxidation product of MT, represents a tiny portion of
OM1, following the very low molar yield used. After MT, the most
important biogenic precursor is isoprene; its oxidation products represent
about 20 % of OM1 in 2012 and 16 % in 2013. Although sesquiterpenes
emissions are lower than isoprene and monoterpenes emissions, their oxidation
products represent about 10 % of OM1. Anthropogenic oxidation products
represent about 22 and 25 % of OM1 in 2012 and 2013 respectively. Most
of anthropogenic oxidation products originate from intermediate and
semivolatile organic emissions (about 19 % of OM1; they are referred to as
anthropogenic SOA and POA in Fig. ) and from aromatic
oxidation products (3 to 5 % of OM1).
Oxidation state of organics
The level of oxidation of ambient organic aerosols is assessed by the ratio of organic
matter to organic carbon (OM/OC) and the ratio of oxygen to carbon ratio
(O/C). OM is made up of many different molecular structures and it may include
not only particulate organic carbon but also oxygen, hydrogen, nitrogen and/or
sulfate. Hence, a high OM/OC ratio indicates a high degree of oxidation of
the organic aerosols and probably a high degree of hygroscopicity
. There is a variety of methods that have been used to
calculate OM/OC ratio as reported by . In our case, the
ambient OM/OC ratio is calculated by weighting the ratio (OM/OC)i of each
surrogate species i by the relative mass of the surrogate: OM/OC=∑i=1Nesp(OM/OC)i×OMi/OM, where Nesp is the number of
surrogate species. The ratio (OM/OC)i of the surrogate species i depends
only on the molecular structure of the species and the number of carbons in
the molecule. The O/C ratio allows for the degree of
oxygenation of the organics to also be considered.
The measured and simulated temporal evolutions of both OM/OC and O/C ratios
for submicron organic aerosols are shown in Fig. during the
whole summer 2013 campaign period. The contributions from ELVOCs, organic
nitrate and MBTCA are highlighted.
Daily variations of the ratios OM/OC (a) and O/C (b) during the 2013 campaign. The red line represents the
measurements. The blue line represents model results without
taking into account the concentrations of ELVOCs, MBTCA and organic
nitrate. The green and magenta lines represent the model results by also taking
into account ELVOCs (green) and MBTCA and organic nitrate (magenta). The
cyan line represents the model results when assuming that a surrogate from
isoprene oxidation is an organosulfate.
Relying on the measured values, the organic species over Ersa are highly
oxidised and oxygenated. In fact, the measured value of the OM/OC ratio (2.43) is
higher than the value of 2.1 suggested by for a
rural site like Kern Refuge (US). The measured O/C ratio is 0.99. In
agreement with the measurements, all simulations show a relatively stable
OM/OC ratio and O/C ratio during the simulated period.
Without taking into account the ELVOCs, MBTCA and organic nitrate species,
the model strongly underestimates both the OM/OC ratio and the O/C ratio.
This is because of the absence of highly oxidised species in the model, as
all the other modelled organic compounds with non-negligible mass tend to have
low OM/OC and O/C ratios.
Taking into account the formation of ELVOCs leads to improvements in the
predicted oxidation state of aerosols: the OM/OC ratio (O/C)
changes from 1.57 to 1.89 (0.51 to 0.65), although the monomer
and dimer ELVOCs only represent 15.7 % of the OM1 mass.
MBTCA has a low impact on the organics oxidation level, despite its high OM/OC
ratio, because it constitutes only a tiny part of the OM1 mass (0.2 %).
Taking into account organic nitrate leads to an improvement of both ratios.
The OM/OC increases from 1.89 to 2.18 and the O/C ratio increases from 0.65
to 0.73.
A possible way to explain the underestimation of the OM/OC ratio (2.17
simulated against 2.43 measured) and the O/C ratio (0.73 simulated and 0.99
measured) is to take into account the formation of organosulfate. As both
organic and sulfate are the major components of aerosols at Ersa
, there may be formation of organosulfate, as suggested
by the transmission electron microscopy measurements of in
the south of France.
The measurements performed at Ersa show a good correlation between sulfate
and organic OM1 concentrations, with a linear regression coefficient of
0.64. In agreement with the measurements, the simulated concentrations of
sulfate and organics are also well correlated with a linear regression
coefficient of 0.71. Although the formation of organosulfate is not modelled
here, the modelled correlation is high because both sulfate and organics are
formed by oxidation of precursors, and oxidant concentrations largely depend
on meteorological variables, such as temperature and dilution within a
variable mixing layer. Furthermore, a large part of biogenic SOA is
hydrophilic and therefore higher condensation of sulfate enhances their
partitioning into the particulate phase, as the mass of the aqueous phase
increases through the condensation of sulfate .
In laboratory, the formation of organosulfate was observed from the uptake of
monoterpene oxidation products (pinonaldehyde) on acidic sulfate aerosols
, from the uptake of ELVOC
and from the uptake of isoprene oxidation products
. Isoprene SOA may be formed via the reactive
uptake of IEPOX, a second-generation oxidation product
of isoprene, in the presence of hydrated sulfate
. Using AMS
measurements, estimated that IEPOX-OA makes a large fraction
of the OA (between 17 to 36 % in the US) outside urban areas, in agreement
with . In regions where aerosols are acidic,
IEPOX-derived OA may be strongly dependent on the sulfate concentration,
which acts as nucleophile and facilitates the ring-opening reaction of IEPOX
and organosulfate formation .
In order to take into account the influence of the formation of
organosulfates on OA properties, the surrogate products of the model are
modified. As Mediterranean aerosol composition displays large concentrations
of sulfate, isoprene oxidation
products may lead to the formation
of organosulfate. In the model, the components formed from the low-NOx
oxidation of isoprene are BiPER and BiDER. The
surrogate BiPER is supposed to be a methyl dihydroxy dihydroperoxide. Although
BiDER is not identified, it is assumed to have the properties of a methyl
tetrol . If we assumed that this compound has the
same properties as a sulfate ester of formula C6H11O3SO4, the
ratios OM/OC and O/C increase to get closer to measurements
(Fig. ). In fact, the average OM/OC ratio (O/C) increases
from 2.18 (0.73) to 2.37 (0.84), which compares very well with the
average measured ratios (2.43 for OM/OC and 0.99 for O/C). Even though the
ratio O/C still seems to be slightly underestimated, the discrepancies may be
explained by uncertainties in the measurements. Measurements performed at the
same place between 10 July and 6 August with a high-resolution time-of-flight AMS shown an average OM/OC ratio of 2.34 (±0.14) and an average
O/C ratio 0.92 (±0.11).
Water-soluble organics
Water-soluble organics constitute a major fraction of organic compounds. On
average between 15 and 31 July 2013, submicron water-soluble organic carbon
(WSOC) represents 64 % of the organic carbon in the measurements and 46 % in
the model. WSOC concentrations are well modelled on average (the measured mean
is 1.0 µgC m-3 and the modelled mean is 0.9 µgC m-3).
Figure shows the daily concentrations of WSOC in the model and
according to the measurements. Although the WSOC concentrations are well
modelled between 21 and 31 July, they are underestimated between 15 and 20
July. These differences between the periods in the ability of the model to
represent WSOC concentrations may be linked to differences in the organic
aerosol composition and in the origins of air masses. This is illustrated by
the comparison of 16 and 30 July. WSOC concentrations are underestimated on
16 July but are well modelled on 30 July. 16 July is characterised by
low/calm winds in a time when 30 July is characterised by winds from
southeast France. Figure shows the chemical composition of
modelled OM1 for both days. On 30 July, hydrophilic oxidation products of
isoprene constitute most of the mass: they represent 26 % of the
concentrations against 9 % on 16 July. However, organic nitrate (from
monoterpene oxidation) represents as much as 35 % of OM1 concentrations on
16 July, against only about 10 % on 30 July, because the low winds of 16 July
probably enhance the influence of NOx emissions from ships on pollutant
concentrations.
Although this organic nitrate is assumed to be hydrophobic, it may have
undergone hydrolysis resulting in nitric acid and nonvolatile secondary
organic aerosol that may change the hydrophilicity of organics and the
organic composition, as detailed in .
Measured and simulated submicron water soluble organic carbon (µgC m-3) at Ersa.
Simulated chemical composition of OM1 on 16 July (a) and
on 30 July 2013 (b).
(a) Temporal average of chl a in sea surface in units of
mg m-3 during the summer of 2013 (from 6 June to 3 August). (b) Organic mass fraction of emitted sea-spray aerosols of diameters
between 0.01 and 0.1585 µm. (c) Relative difference (%) in the
concentrations of OM1 between the base simulation and the simulation
including organic sea-salt emissions.
Impact of the biological activity of the Mediterranean Sea
According to , organic aerosols of marine origin can
contribute to organic OM1 concentrations especially near biologically
productive waters. Particles of diameters larger than 1 µm tend to
contain mostly inorganic compounds, and the fraction of organic increases
with decreasing diameter for particles of diameters smaller than 1 µm
.
Several studies found a correlation between the organic mass fraction of
sea-spray aerosol (OMSSA) and the concentrations of oceanic parameters
like chl a, which is used as a proxy for biological activity
and related ocean chemistry . Several
parameterisations exist to estimate the fraction of organics in primary
marine aerosols. Whereas in the parameterisation of ,
which is designed for Aitken mode aerosols, the organic fraction depends
not only on chl a but also on the 10 m wind speed and the particle diameter in
. OMSSA decreases with increasing 10 m wind speed,
because for strong wind speeds, bubbles are not enough enriched by organic
matter due to the wave breaking. Concerning the size dependence,
showed that the maximum organic fraction in the Aitken and
accumulation modes is about 80 to 90 %, while the fraction is less than 2 %
in the coarse mode.
Temporal averages of OMSSA near Ersa using the parameterisation
of as a function of aerosol particle diameter, over the summer of 2013 (from 6 June to 3 August).
Diameter range (µm)[0.01,0.1585][0.1585,1.0][1.0,2.5119][2.5119,10.0]Temporal average of OMSSA in Ersa0.310.220.020.01
The concentrations of the chl a are obtained from monthly averaged
MODIS/AQUA satellite data
(https://oceandata.sci.gsfc.nasa.gov/MODIS-Aqua/L3SMI/, ),
with a spatial resolution of 4 km × 4 km. They are shown in Fig. a.
Values are low, typical of oligotrophic conditions that characterise
stratified surface Mediterranean waters in summer. The highest chl a
concentrations are recorded around the coastal zones meaning shallow water,
places where sea currents bring cold waters with plants and nutrients from
sea floor or brought from the rivers to the surface due to the rising slope
of the sea floor.
Far from the coasts, the chl a is more or less uniform (less than 0.2 mg m-3). The chl a temporal average near Ersa is 0.14 mg m-3.
The emitted organic mass fraction of sea salt is shown in
Fig. b using the parameterisation of for
aerosols of diameters between 0.01 and 0.1585 µm. The organic fraction
map has almost the same spatial distribution as the chl a map. The fraction
of marine organic emissions is higher near the shores of the continent. The
temporal average of emitted OMSSA near Ersa is detailed in
Table for aerosols of different sizes. As expected, the
organic fraction is higher for aerosols in the Aitken mode (∼ 0.31) than
in the accumulation mode (∼ 0.22) than in the coarse one (∼ 0.01).
These simulated organic fractions are consistent with the fraction obtained
by the parameterisation for the Aitken mode (0.23). They
are also consistent with the average fraction of 0.24 found experimentally by
using pelagic mesocosms in the bay of Calvi (Corsica,
France) during the summer of 2012 (an average fraction of 0.24).
A simulation was performed using the organic fraction of primary marine
emissions computed with the parameterisation, in order to
assess the impact of the marine organics on the concentrations of organics
OM1. The organic emissions are affected to a new species called “SSorg”,
which is assumed to be hydrophobic and not very volatile. The properties are
detailed in Table . The values are assumed to be the
same as those taken for methyl nitrobenzoic acid in the model, following the
lack of data characterising the properties of these species. Secondary marine
OM formation is not taken into account.
Figure c shows the relative difference of
organic OM1 concentrations between the base simulation and the simulation
including organic sea salt. The contribution of the organic sea-salt
emissions to organic OM1 is small (a few percent at the maximum) and
localised above the sea. On average over the marine domain, the organic
sea-salt emissions contribute to about 1.8 % of the organic OM1
concentrations. Despite the larger chl a and organic fraction over the
Adriatic Sea, the contribution of SSorg to OM1 concentrations is not as
high as the one over the Mediterranean Sea in the south of France. This is
due to the fact that the surface wind-driven flux of sea salts over the
Adriatic Sea is not as important as over the Mediterranean Sea in the south
of France.
Conclusions
This paper presents comparisons of modelled organic concentrations and
properties to surface measurements performed at Ersa (Cape Corsica, France) during the
summers of 2012 and 2013. The air-quality model of the Polyphemus platform is
used with a surrogate approach to model SOA. The
previously published surrogate approach is modified to better represent
observed OA
properties, by taking into account the formation of ELVOCs and organic nitrate from monoterpene
oxidation. The concentrations of organic matter compare well to the
measurements performed with an ACSM.
During the summer 2013, the added surrogates led to a significant increase of
mass concentrations: they contributed to 15 % of the OM1 mass for ELVOC,
20 % for organic nitrate from monoterpene oxidation and 0.2 % for MBTCA. In
agreement with 14C measurements, most of the organic aerosol is from
non-fossil (biogenic) origin. The inclusion in the model of ELVOC and organic
nitrate formation greatly improves the modelled oxidation state of particles,
as assessed by the OM/OC and O/C ratios. Despite the model improvements,
these ratios remain underestimated compared to measurements (2.18 simulated
against 2.43 measured for OM/OC and 0.73 simulated against 0.99 measured for
O/C). However, the ratios are better modelled by assuming that a surrogate
species from isoprene oxidation is an organosulfate (2.37 simulated for
OM/OC and 0.84 for O/C), suggesting that further work should focus on a
better description of organosulfate formation. Although an organic acid,
MBTCA, was introduced as a second-generation product of MT, its
yield should be revisited to include the formation of several carboxylic
acids rather than a single species. Concerning the hydrophilic properties of
aerosols, as much as 64 % of organic carbon is soluble. Therefore, taking
into account the hydrophilic properties of aerosols is crucial to model the
partitioning of aerosols between the gas and particle phases. The average
concentration of water soluble organic carbon is relatively well modelled by
comparison to measurements performed using a PILS-TOC-UV (with a mean value
of 1.0 µgC m-3 in the measurements and 0.9 µgC m-3 in the
model) over the second half of July 2013. Daily comparisons to measurements
show that although organic nitrate is assumed hydrophobic here, its
hydrolysis should be modelled to better represent the hydrophilic properties
of organics. There are other pathways and mechanisms that are not considered
in the model, but that may change the concentrations and hydrophilicity of
organics . For example, salting effects (via activity
coefficients) are not considered, although inorganics provide a mass onto
which hydrophilic organic surrogates may condense. Furthermore, pathways such
as the aging chemistry of VOCs and I/S-VOCs from biomass burning (wildfires)
and organic cloud processing are not considered. However, these pathways may
be relatively low during the studied periods. Marine organic aerosols were
added to the model, with a parameterisation depending on chlorophyll a, 10 m
wind speed and particle diameters. Although the emitted organic fraction is
high for particles of small diameters (Aitken and accumulation modes), its
contribution to the total organic mass OM1 is only a few percents at the
maximum. Its contribution over the continent is always lower than 1 to 2 %.
Data can be requested from the corresponding author (mounir.chrit@enpc.fr).
Formation of ELVOC
The reactions involved in the formation of ELVOCs are
MT+O3k1→γRO2⋅RO2+(1-λ-γRO2)⋅R′O2+OH+0.001HO2R′O2kH/O2→α⋅R′′O2R′′O2kH/O2→α⋅R′′′O2R′′′O2kH/O2→α⋅RelvocO2RO2+R′O2k2→productsRO2+NOk3→δ⋅RO2RO2+HO2k4→productsRelvocO2+RO2(orHO2)k5→β⋅monomer+(1-β)⋅dimerRelvocO2+NOk6→χ⋅nitrateorg+ϕ⋅monomer
Kinetic constants used in the ELVOC kinetic model.
Kinetic constantValue (cm3 s-1 or s-1)k18.4×10-17k21.0×10-12k34.7×10-12k42.7×10-11k55.0×10-11k64.7×10-12kH/O20.5
After numerous combinations, the stoichiometric coefficients of Eq. () were determined, such as reproducing the
observations of for both the low-NOx and high-NOx regimes
(Table ).
For model validation, comparisons are made to the experiments of .
In the low-NOx regime, the experiments of lasted 45 min and
the initial ozone concentration was 80 ppb. In the model, we modified the
initial concentration of α-pinene from 0 to 11 ppb to reproduce the observations. Figure , which shows one-fifth of
the ELVOC concentrations as a function of the α-pinene reaction rate, reproduces
successfully the extended data in Fig. 10 of . According
to Fig. , the increase of peroxy radicals has a square root
dependence while ELVOC monomers and dimers evolve nearly linearly. Therefore,
the total ELVOC concentration has a near-linear dependence on the amount of
α-pinene reacting with O3 indicative of first-generation oxidation products.
These findings are consistent qualitatively and quantitatively with
measured and modelled results.
ELVOC concentrations as a function of the α-pinene reaction rate for the low-NOx experiment.
In the high-NOx regime, the experiments of also lasted
45 min and the initial ozone and α-pinene concentrations were 80 and
5 ppb respectively. The initial NO concentration was
changed gradually from 0.3 to 5 ppb. Figure , which shows
one-fifth of the ELVOC as a function of the RO2 concentration, reproduces
successfully the extended data in Fig. 10 of .
While increasing NO concentration, both monomer and dimer concentrations
decrease rapidly as expected because a fraction of peroxy radicals is consumed
by the NO reaction. Moreover, the dimer concentration decreases rapidly while
the monomer concentration decreases more slowly. Simultaneously, the organic
nitrate concentration increases with increasing NO.
ELVOC concentration as a function of the RO2 concentration in the high-NOx regime.
Formation of organic nitrate
The formation of organic nitrate (orgNIT) from MT is modelled
with the following reactions:
Terpene+OHk1′→TERPRO2TERPRO2+NOk2′→0.201orgNITMT+NO3k3′→TERPNRO2TERPNRO2+NOk4′→0.688orgNIT+productsTERPNRO2+HO2k5′→orgNIT+productsTERPNRO2+NO3k6′→0.422orgNIT+productsTERPNRO2+RO2k7′→(1-0.578δ)orgNIT+products where δ=50%
Kinetic constants used in the organic nitrate formation mechanism.
Kinetic constantValue (cm3 s-1 or s-1)k1′α-Pinene (1.21×10-11exp(444/T))Limonene (4.2010-11exp(401/T))β-Pinene (2.38×10-11exp(357/T))Humulene (2.93×10-10)k2′2.27×10-11exp(435/T)k3′α-Pinene (1.19×10-12exp(490/T))Limonene (1.22×10-11)β-Pinene (2.51×10-12)Humulene (1.33×10-12exp(490/T))k4′2.6×10-12exp(380/T)k5′2.65×10-13exp(1300/T)k6′2.3×10-12k7′3.5×10-14Statistic indicators and criteria
The statistic indicators used in this paper are described in
Table . The performance and goal criteria used in this paper are
described in Table .
Definitions of the statistics used in this work. (oi)i and
(ci)i are the observed and the simulated concentrations at time and
location i respectively. n is the number of data.
The ELVOC yield in the reference simulation is 11 %. Two sensitivity simulations
are performed, using a lower bound (3 %; ) and a higher bound (18 %; ).
The comparison of OM1 concentrations is shown in Fig.
and the statistical evaluation is shown in Table .
The correlation between the measurements and the simulation is not modified by
the ELVOC yield. However, the higher the yield is, the closer to zero the bias is
(it decreases from -26 to -7 %) and the lower the error MFE. However, the
lower RMSE is obtained with a yield of 11 % (1.53 µg m-3 with a yield
of 11 % against 1.54 µg m-3 with a yield of 3 % and 1.59 µg m-3 with a yield of 18 %).
Simulated daily concentrations of OM1 using a molar ELVOC yield of
11 % (blue plot, reference simulation), 18 % (magenta plot) and 3 % (green plot).
Statistics of model to measurements comparisons for organic
concentrations in particles of diameters lower than 1 µm during the
summer campaign of 2013 using an ELVOC molar yield of 3, 11 (reference) and
18 %. s‾ represents the simulated mean concentrations. The
observed mean concentration is o‾=2.89µg m-3.
The simulated composition of OM1 using ELVOC yields of 3 and 18 % are shown in Fig. .
Using an ELVOC yield of 3 % (18 %), the ELVOCs represent 4.7 %
(22.9 %) of the OM1 mass.
Simulated composition of OM1 using the upper (a) and
lower (b) bounds of ELVOCs molar
yields.
The OM/OC and O/C ratios are plotted using the three yields (3, 11 and 18 %) in Fig. .
The simulated means of the two ratios using the three ELVOC yields
(3, 11 and 18 %) are shown in Table . The ratios using the upper bound of ELVOC yields are the closest to the
measurements. The OM/OC and O/C ratios may be higher when organosulfate
is
considered (Sect. ).
Comparisons of the OM/OC ratio (a) and the O/C ratio (b) for simulations using an ELVOC molar yield of 3, 11 and
18 %.
Simulated means of the OM/OC and O/C ratios during the
summer campaign of 2013 using an ELVOC molar yield of 3, 11 and
18 %. The measured means of OM/OC and O/C are 2.43 and 0.99 respectively.
The authors declare that they have no conflict of interest.
This article is part of the special issue “CHemistry and AeRosols Mediterranean EXperiments (ChArMEx) (ACP/AMT inter-journal SI)”.
It is not associated with a conference.
Acknowledgements
This research has received funding from the French National Research Agency
(ANR) projects SAF-MED (grant ANR-12-BS06-0013). This work is part of the
ChArMEx project supported by ADEME, CEA, CNRS-INSU and Météo-France
through the multidisciplinary programme MISTRALS (Mediterranean Integrated
Studies aT Regional And Local Scales). The station at Ersa was partly
supported by the CORSiCA project funded by the Collectivité Territoriale de
Corse through the Fonds Européen de Développement Régional of the
European Operational Program 2007–2013 and the Contrat de Plan Etat-Région.
Eric Hamounou and François Dulac are acknowledged for their great help in
organising the campaigns at Ersa. CEREA is a member of the Institut Pierre-Simon
Laplace (IPSL).
Edited by: Barbara Ervens
Reviewed by: three anonymous referees
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