ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-16-12961-2016Observations and regional modeling of aerosol optical properties, speciation and size distribution over Northern Africa and western EuropeMenutLaurentmenut@lmd.polytechnique.frhttps://orcid.org/0000-0001-9776-0812SiourGuillaumeMaillerSylvainCouvidatFlorianBessagnetBertrandLaboratoire de Météorologie Dynamique, UMR CNRS 8539, École Polytechnique, École Normale Supérieure, Université P.M.Curie, École Nationale des Ponts et Chaussées, Palaiseau, FranceLaboratoire Inter-Universitaire des Systèmes Atmosphériques, UMR CNRS 7583, Université Paris Est Créteil et Université Paris Diderot, Institut Pierre Simon Laplace, Créteil, FranceInstitut National de l'Environnement Industriel et des Risques, Verneuil en Halatte, 60550, Parc Technologique ALATA, FranceLaurent Menut (menut@lmd.polytechnique.fr)20October20161620129611298230March201627May201614September20166October2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/16/12961/2016/acp-16-12961-2016.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/16/12961/2016/acp-16-12961-2016.pdf
The aerosol speciation and size distribution is modeled during the summer
2013 and over a large area encompassing Africa, Mediterranean and western
Europe. The modeled aerosol is compared to available measurements such as the
AERONET aerosol optical depth (AOD) and aerosol size distribution (ASD) and
the EMEP network for surface concentrations of particulate matter PM2.5,
PM10 and inorganic species (nitrate, sulfate and ammonium). The main
goal of this study is to quantify the model ability to realistically model
the speciation and size distribution of the aerosol. Results first showed
that the long-range transport pathways are well reproduced and mainly
constituted by mineral dust: spatial correlation is ≈ 0.9 for AOD
and Ångström exponent, when temporal correlations show that the day-to-day
variability is more difficult to reproduce. Over Europe, PM2.5 and
PM10 have a mean temporal correlation of ≈ 0.4 but the lowest
spatial correlation (≈ 0.25 and 0.62, respectively), showing that
the fine particles are not well localized or transported. Being short-lived
species, the uncertainties on meteorology and emissions induce these
lowest scores. However, time series of PM2.5 with the speciation show a
good agreement between model and measurements and are useful for
discriminating
the aerosol composition. Using a classification from the south (Africa) to
the north (northern Europe), it is shown that mineral dust relative mass
contribution decreases from 50 to 10 % when nitrate increases from 0
to 20 % and all other species, sulfate, sea salt, ammonium, elemental carbon,
primary organic matter, are constant. The secondary organic aerosol
contribution is between 10 and 20 % with a maximum at the latitude of the
Mediterranean Sea (Spanish stations). For inorganic species, it is shown that
nitrate, sulfate and ammonium have a mean temporal correlation of 0.25, 0.37
and 0.17, respectively. The spatial correlation is better (0.25, 0.5 and
0.87), showing that the mean values may be biased but the spatial localization
of sulfate and ammonium is well reproduced. The size distribution is compared
to the AERONET product and it is shown that the model fairly reproduces the
main values for the fine and coarse mode. In particular, for the fine mode,
the model overestimates the aerosol mass in Africa and underestimates it in
Europe.
Introduction
For the World Health Organisation (WHO), air pollution is a major
environmental risk to health and particularly particulate matter (PM). The
most health-damaging particles are those with a diameter of 10 microns or
less (PM10) which can penetrate and lodge deep inside the lungs. PM is
responsible for a loss of life expectancy particularly when we consider
long-term exposure to PM2.5. Particles also play
a role on the evolution of climate via direct and indirect effects
. In Europe, PM is still a major problem for regional air
quality (AQ) , and the member states have to take measures to
reduce the exposure to comply with EU standards driven by international
guidelines and regulations. A fraction of PM exceedances number is due to
long-range transport of desert dust issued from the Saharan region,
. In the AQ directive 2008/50/EC ,
chemistry-transport models (CTMs) are often cited as a technique to be used to
assess AQ. The added value of using models for AQ management is
summarized in with, for instance, the possibility to subtract
days of PM exceedances due to a Saharan dust outbreak. These models are also
used for a better understanding of the atmospheric composition and the
radiative impact of aerosol over Europe and Africa
.
Even if the models are useful integrated tools, the measurements are the
mandatory step to really understand the processes involved in the aerosol
life cycle and thus its evolution in terms of composition and size
distribution. During the last 15 years, many field experiments and
long-term measurements in specific supersites were conducted. In Europe,
analyzed several ground PM2.5 and PM10
measurements to estimate the chemical composition of the aerosol. This
aerosol speciation was conducted to identify the relative contributions of
organic and elementary carbon (OC and EC), mineral dust, marine and
secondary inorganic aerosol. Depending on the measurements period and the
location of the instruments, they showed the very high variability of the
aerosol speciation in Europe. Also over Europe, analyzed a
large ensemble of surface measurements to estimate the chemical
characteristics of aerosol depending on the measurements location (from urban
to background sites). In the French Alps, studied the
inorganic components of the aerosol during a Saharan dust long-range
transport event. In Spain, statistically analyzed
surface PM10 measurements to extract the relative part of mineral dust
coming from Africa. reviewed several methodologies of
chemical speciation determination for the source apportionment. In the
eastern Mediterranean Basin for summer 2012, analyzed
the aerosol concentrations and their chemical compositions over the eastern
Mediterranean. The fine aerosol (PM1) was found to be dominated by
organic aerosol and sulfate. From all these studies, and as synthesized in
(after the European EUCARII project), one major conclusion
is the need to better understand the aerosols speciation and size
distribution. This need is also the conclusion of , who
list all existing methods to have better observations about the aerosol's
chemical composition.
In the field of aerosol modeling, many developments were recently done to
simulate these complex observations. At the global scale, and knowing the
importance of the aerosol load and composition on Earth's climate, models
were significantly improved and were able to accurately describe the
different steps in the aerosol formation using complex schemes for
nucleation, condensation and coagulation . These global
models are compared and their strength and weakness are quantified, as, for
example, in for the mineral dust emissions, transport and
deposition in the framework of the AEROCOM project. However, due to
computational cost, the global models have to use a limited number of modes
or bins to describe the aerosol distribution. In addition, the validation of
simulations is often restricted to datasets well documented over the globe,
i.e., surface PM concentrations (without speciation) and aerosol optical depth
(AOD), but with a low spatial resolution. CTMs at regional scale simulate the
same processes but usually with a more accurate description for the processes
involved in the aerosol formation and evolution.
At the regional scale, AQ models tend to underestimate PM and the
main discrepancies are often attributed to a lack of emissions or
difficulties to reproduce stable meteorological conditions during PM episodes
(). The chemistry of secondary organic
species and deposition is also a source of uncertainties
. More generally for aerosol, one part of
this uncertainty is linked to the fact that the size distribution modeling is
poorly addressed in the literature. This size distribution will directly
impact the aerosol behavior via the chemistry (nucleation, coagulation), the
dry deposition (the settling velocity) and the wet deposition (the
scavenging).
To go further in the aerosol life cycle understanding, it is now necessary to
develop more constrained frameworks for the model vs. observations
comparisons. The goal is to be able to answer new questions such as, (i) What
is the chemical composition of the aerosol during its complete life cycle
including emissions, transport and deposition? (ii) Is it possible to
accurately identify the relative contributions of anthropogenic and natural
emissions in the aerosol budget? (iii) If the surface PM surface
concentrations and AOD are well modeled, are we sure there are no
compensation errors in the chemical composition and radiative properties of
the aerosol?
To answer these questions, numerical simulations are performed for the 2
months of June and July 2013 and over a large domain encompassing Africa and
Europe. This period corresponds to the ADRIMED project presented in
. The simulations are performed using two models: (i) the
WRF meteorological model calculates the meteorological variables and (ii) the
CHIMERE CTM calculates the fields concentrations of
gaseous and aerosols using the meteorological fields. WRF and CHIMERE are
widely used for regional studies of atmospheric of gaseous and aerosol
species. Over this domain and for this period, the two models were already
used in , and and
showed realistic results for the modeling of gaseous and aerosol species. In
this study, the analysis is focused on the aerosol size distribution and its
speciation in Africa and Europe.
The observations data and the models used are described in Sects.
and . The comparisons between observed and modeled concentrations
are presented in Sect. for the aerosol optical depth and the
Ångström exponent (AE), Sect. for the surface concentrations
of PM2.5 and PM10, Sect. for sulfate, nitrate and
ammonium and Sect. for the aerosol size distribution (ASD).
Conclusions and perspectives are presented in Sect. .
Observations
Two types of observations are used in this study: (i) the surface
concentrations of aerosols species with the EMEP network data and (ii) the
column-integrated aerosol measurements with the AERONET (AErosol RObotic NETwork) network data, with
AOD and size distribution. All stations locations are
displayed in Fig. with the EMEP stations in red and the
AERONET stations in blue.
Locations of the measurements stations used in this study; in red
are
the EMEP stations and in blue are the AERONET stations. Names and coordinates of
these stations are listed in Table and
Table .
These stations were selected to cover the studied region: western Europe
and the Mediterranean Sea, with additional stations in Africa representative
of the mineral dust emissions before transport towards Europe.
The EMEP network surface concentrations
For the comparisons between observed and modeled concentrations, the
background stations measurements performed during campaigns or in a routine
way are used. All the stations are listed in Table and
displayed in Fig. . Depending on each station, several
pollutants are measured: O3, NO2, SO2, PM2.5 and
PM10. For some stations, the inorganic species sulfate, nitrate and
ammonium are used.
These measurements are available on the EBAS database
(http://ebas.nilu.no/) and are used here on a mean daily basis. Only
the background stations are used due to the coarse horizontal resolution of
the model. The altitude above sea level (a.s.l.) is presented for information.
The representativity of the station depends on the sub-grid-scale variability
of the model cell: the lower the variability, is the more representative the
station is. Over mountains areas, it is rare and, generally, stations at high
altitude a.s.l. cannot be considered as well representative of the first model
level for concentrations. In our case, this is probably the case for the
stations in the Alps. In this study, these stations were considered for the
scores calculations but, in the case of poor comparisons scores, this problem of
representativity could be a large part of the differences between model and
observations. This is discussed in each case and in the following sections.
Names and locations of the EMEP stations used for model
comparisons to aerosol surface concentrations. The stations are ordered from
south to north. The altitude above sea level (a.s.l.) is indicated because the
surface measurements are compared to the first model vertical level.
SiteAltitudeLongitudeLatitudeSiteAltitudeLongitudeLatitude(m a.s.l.)(∘)(∘)(m a.s.l.)(∘)(∘)Viznar1265-3.5337.23Revin3904.6349.90Barcarrola393-6.9238.47Schmucke93710.7650.65Zarra885-1.1039.08Sniezka160315.7350.73SanPablo917-4.3439.54Vredepeel285.8551.54Campisábalos1360-3.1441.28Harwell137-1.3151.57Penausende985-5.8641.28Jarczew18021.9851.81Els Torms4700.7141.40Valentia11-10.2451.93Cap de Creus233.3142.31Cabauw04.91651.99Noya683-8.9242.72Carnsore9-6.3652.18O Saviñao506-7.6943.23De Zilk44.5052.30Niembro134-4.8543.44Oak Park59-6.9252.86Peyrusse2000.1843.61Neuglobsow6213.0353.16Iskrba52014.8645.56Kollumerwaard16.2753.33Leova16628.2846.48Diabla Góra15722.0654.15La Tardière133-0.7546.65Zingst112.7354.43Payerne4896.9446.81Leba217.5354.75K-puszta12519.5846.96Westerland128.3054.92Tanikon5398.9047.47Malin Head20-7.3455.37Schauinsland12057.9047.91Risoe312.0855.69Chopok200819.5848.93Auchencorth260-3.2455.79Starina34522.2649.05Vavihill17513.1556.01Košetice53415.0849.58Ulborg108.4356.28Svratouch73716.0549.73Tange139.6056.35The AERONET data
Names and locations of the AERONET stations used for model
comparisons to AOD and ASD data. The stations are ordered from south to
north. The altitude a.s.l. is not presented, the measurements being
representative of the vertically integrated atmospheric column above ground
level (a.g.l.). The three codes are designed to clusterize the results at the
end of this study; the classification mainly depends on the latitude of the
station to split the domain into three main parts: Afr (for latitude below
≈ 30∘ N), Med (between latitude ≈ 30 and
≈ 45∘ N) and Eur (for latitude up to
≈ 45∘ N).
SiteLongitudeLatitudeCodeCountry∘∘Ilorin4.348.32AfrNigeriaCinzana-5.9313.27AfrMaliBanizoumbou2.6613.54AfrNigerZinder Airport8.9813.75AfrNigerDakar-16.9514.39AfrSenegalCabo Verde-22.9316.73AfrCabo VerdeTamanrasset5.5322.79AfrAlgeriaSaada-8.1531.61AfrAlgeriaIzana-16.4928.31MedTenerifeSanta Cruz de Tenerife-16.2428.47MedTenerifeLa Laguna-16.3228.48MedTenerifeForth Crete25.2735.31MedGreeceLampedusa12.6335.51MedItalyGranada-3.6037.16MedSpainAthens23.7737.98MedGreeceEvora-7.9138.56MedPortugalLecce University18.1140.33MedItalyBarcelona2.1141.38MedSpainRome Tor Vergata12.6441.84MedItalyBastia9.4442.69MedFranceVillefranche7.3243.68EurFrancePalaiseau2.2148.70EurFranceKarlsruhe8.42849.093EurDeutschlandLille3.14250.612EurFranceBrussels4.35050.783EurBelgiumChilbolton-1.43751.144EurUnited KingdomLeipzig12.43551.352EurDeutschlandCabauw4.92751.971EurNetherlands
The aerosol optical properties are compared between observations and model
using the AERONET measurements .
First, the comparison is done using the AOD measured
by the AERONET photometers and for a wavelength of λ=550 nm. The level
2 data are used. Second, the comparison is performed using the ASD product level 1.5, estimated after inversion of the
photometers data as described in . For each AERONET station
used in this study and listed in Table , the inversion
algorithm provides volume particle size distribution for 15 bins,
logarithmically distributed for radius between 0.05 and 15 µm.
Modeling
For the simulation performed in this study, two regional models are used: (i)
the WRF meteorological model calculates the meteorological variables and (ii)
the CHIMERE CTM calculates the fields concentrations of
gaseous and aerosols using the meteorological fields. The horizontal domain
is the same for the two models, with a constant horizontal resolution of 60 km × 60 km, as displayed in Fig. . This domain
was selected to be sure to have all sources producing gas and aerosol
concentrations around the Mediterranean Basin: European anthropogenic
emissions, mineral dust and vegetation fires emissions. These species are
mainly ozone and carbon monoxide for the gaseous species, mineral dust and
organic matter (due to vegetation fires) for the aerosol. The modeled period
ranged from 1 June to 30 July 2013. The results are presented from 10 June
to 30 July 2013 to account for a spin-up period.
The WRF meteorological model
The meteorological variables are modeled with the non-hydrostatic WRF
regional model in its version 3.6.1 . The global
meteorological analyses from NCEP/GFS are hourly read by WRF using nudging
techniques for the main atmospheric variables (pressure, temperature,
humidity, wind). In order to preserve both large-scale circulations and small-scale gradients and variability, the “spectral nudging” was selected. This
nudging was evaluated in regional models, as presented in
. In this study, the spectral nudging was selected to be
applied for the large-scale dynamics (wave numbers less than 3 in latitude
and longitude, for wind, temperature and humidity and only above 850 hPa
corresponding to all wavelength greater than 2000 km). This configuration
allows the regional model to create its own structures within the boundary
layer but makes sure it follows the large-scale meteorological fields.
The model is used with 28 vertical levels from the surface to 50 hPa. The
Single Moment-5 class microphysics scheme is used, allowing for mixed phase
processes and super cooled water . The radiation scheme is
RRTMG scheme with the MCICA method of random cloud overlap
. The surface layer scheme is based on Monin–Obukhov with
Carlson–Boland viscous sublayer. The surface physics is calculated using the
Noah Land Surface Model scheme with four soil temperature and moisture
layers . The planetary boundary layer physics is processed
using the Yonsei University scheme and the cumulus
parameterization uses the ensemble scheme of . The aerosol
direct effect is taken into account using the climatology.
The CHIMERE CTMOverview
CHIMERE is a CTM allowing the simulation of
concentrations fields of gaseous and aerosol species at a regional scale. It
is an offline model, driven by pre-calculated meteorological fields: for
this study, the hourly WRF meteorological fields and the version fully
described in is used. If the simulation is performed with
the same horizontal domain, the 28 vertical levels of the WRF simulations are
projected onto 20 levels from the surface up to 200 hPa for CHIMERE.
The chemical evolution of gaseous species is calculated using the MELCHIOR2
scheme. The photolysis rates are explicitly calculated using the FastJX
radiation module (version 7.0b) . The modeled AOD
is calculated by FastJX for the 600 nm wavelength over the whole atmospheric
column. A complete analysis of the improvement obtained in the model with
this online calculation is fully described in . The way
to redistribute the aerosol bins for the FastJX model is extensively
described in . At the boundaries of the domain,
climatologies from global model simulations are used. In this study, outputs
from LMDz-INCA are used for all gaseous and aerosols
species, except for mineral dust where the simulations from the GOCART model
are used .
The modeled aerosols
The aerosols are modeled using the scheme developed by .
This module takes into account sulfate, nitrate, ammonium, primary organic
matter (POM) and EC, secondary organic aerosol (SOA), sea
salt, dust and water. The aerosol size is represented using 10 bins, from
40 nm to 40 µm, in mean mass median diameter (MMMD). The aerosol life cycle
is completely represented with nucleation of sulfuric acid, coagulation,
absorption, wet and dry deposition and scavenging. The scavenging is
represented by in-cloud and sub-cloud scavenging.
The aerosol model species and their characteristics are displayed in
Table . It consists in 10 different types of
aerosols, some being a compound of several aerosol species.
Properties of the modeled aerosol species. The density
ρp is expressed as value ×103
kg m-3. “anth”, “bio” and “mineral” refer to the origin of the emission, respectively anthropogenic, biogenic and mineral dust.
The inorganic part constitutes the major part of the particulate matter in
the fine mode (for Dp<2.5µm). To determine the gas-particle
partitioning of these semivolatile species, the ISORROPIA model is used
.
In the model, some processes are certainly roughly or not well represented.
For the analysis, it is necessary to consider these approximations. This is
the case for the formation of the coarse nitrate aerosol. Coarse nitrate is
the result of chemical reaction of nitric acid with mineral dust and sea
salt. This process and its impact on the European PM10 surface
concentrations were studied in a previous version of CHIMERE,
. In this current version, this process is not yet
implemented due to missing information on the calcium carbonate mass. Thus,
the modeled nitrate could be underestimated compared to measurements.
Moreover, the formation of SOA formation from semivolatile organic compound
is not represented in this CHIMERE version, since the emission inventories
are not mature enough to account for this kind of emissions.
Emissions
Emissions are the only source in the atmospheric composition system and,
thus, represent a large part of the uncertainty in the modeled
concentrations. This uncertainty is related not only to the emitted mass flux itself
(for gases and aerosol) but also to the size distribution for the modeled
aerosol. In this model version, all kinds of anthropogenic and natural sources
are taken into account on an hourly basis: the anthropogenic emissions are
estimated using hourly time profiles and are thus hourly provided. The
biogenic and mineral dust emissions (calculated online in CHIMERE) are using meteorological data and are also hourly estimated.
Emission fluxes calculations
The anthropogenic emissions are estimated using the same methodology as the
one described in but using the global emission database
EDGAR-HTAP annual totals as input data. The EDGAR-HTAP project compiled a
global emission dataset with annual inventories at the national or regional
scale that are likely to be acceptable for policy makers in each region of
the world. This compilation of different official inventories from EMEP,
UNFCCC, EPA for USA, GAINS for China and REAS was first gap-filled with
global emission data . The version 2 of this emission
inventory was available for the year 2010. The available emitted species are
listed in Table . PPM corresponds to the non-chemically
reactive mass of particulate matter. The “fine” part of H2SO4
corresponds to 1 % of the SOx anthropogenic emissions and thus to
primary sulfuric acid. These emissions were already used in this region and
for this period in .
The biogenic emissions are calculated using the MEGAN emissions scheme
, which provides emission fluxes of nitrogen monoxide,
isoprene and monoterpenes. The mineral dust emissions are calculated using
new soil and surface databases described in and with a
spatial extension of potentially emitting areas in Europe. The dust
production model used is the one of . The sea salt emissions
are calculated following the parameterization. Note that
this scheme has its own size distribution. The “coarse” part of
H2SO4 corresponds to the sulfate fraction of sea salt.
Emission distributions in aerosols bins
The way to distribute the primary emissions into the model bins will have a
large impact on the finally modeled aerosols. For all aerosols, the primary
emissions are provided with three main modes: fine, coarse and big. For each
of these modes, a mean mass median diameter Dp is defined, with its
associated σ. Depending on the emission type (anthropogenic, dust, sea
salt, etc.), these parameters are different and are displayed in
Table .
Aerosols emissions with the three modes describing their size
distribution: fine, coarse and big. The mean mass median diameter (MMMD)
Dp is expressed in µm; σ is unitless. “Mo86” refers to the
parameterization of .
For the anthropogenic emissions, the species POM, EC and PPM are emitted only
in the fine and coarse mode, with MMMD of 0.2 and 4 µm,
respectively. SO4 is emitted in the fine mode only. Then, log-normal
distributions are applied for these two modes to project the emissions into
the model bins, as presented in Fig. . For the sea salt
emissions, the distribution is directly the one proposed by
.
Distribution factors used to project the three aerosols emitted modes on the CHIMERE bins size distribution.
Optical properties
In this paper, the first observations vs. model comparison is done for
AOD and AE. Correlations are
calculated on a daily basis between the AERONET product and the values
calculated in CHIMERE using the FastJX module as described in
.
Aerosol optical depth
The AOD calculated with CHIMERE does not correspond exactly to the available
AERONET data. For the comparison between model and observations, the modeled
AOD is interpolated on the AERONET wavelengths. For the region and studied
period, the most complete AERONET dataset was found for AOD at
λ=675 nm. The CHIMERE AOD useful for the interpolation are for
λ=600 and 999 nm. First, the AE is estimated as
A(λ1,λ2)=-logAOD(λ1)AOD(λ2)logλ1λ2,
where λ1 and λ2 are two wavelengths and
AOD(λ1) and AOD(λ2) the AOD corresponding to these two
wavelengths. In case of this study, λ1=600 nm and
λ2=999 nm with CHIMERE. Then, the interpolated AOD is obtained as
AOD(λ3)=AOD(λ2)exp(-A(λ1,λ2)×log(λ3/λ2)),
with λ3=675 nm for the comparison between CHIMERE and AERONET.
Scores for the comparisons between observations (AERONET) and
model (CHIMERE) for the aerosol optical depth (AOD). Results are presented
with N the number of daily mean available measurements for the period from
10 June to 30 July 2013, the temporal correlation (Rt), the root mean
square error (RMSE) and the bias (model minus observations). The last line
“average” represents the spatial correlation Rs between the mean
observed and modeled values, and the mean averaged values of temporal
correlation, RMSE and bias. Stations are sorted in increasing latitude, from
south to north.
SiteNAOD RtRMSEbiasObsModIlorin230.310.400.100.200.08Cinzana520.500.510.580.250.01Banizoumbou530.470.470.720.210.00Zinder Airport550.450.650.690.380.20Dakar440.540.810.570.420.28Cabo Verde410.470.670.480.350.19Tamanrasset600.360.420.010.320.06Izana600.060.210.440.370.16Santa Cruz de Tenerife590.130.210.480.330.08La Laguna540.120.230.480.350.10Saada580.220.400.320.570.18Forth Crete400.100.090.540.06-0.01Lampedusa430.150.250.630.260.10Granada170.100.090.760.06-0.01Athens470.120.100.470.08-0.02Evora560.090.060.250.08-0.03Lecce University460.120.100.170.10-0.03Barcelona490.150.100.300.09-0.04Rome Tor Vergata570.140.100.120.09-0.04Bastia520.140.100.140.11-0.04Villefranche370.130.090.090.13-0.04Palaiseau440.160.070.040.13-0.09Karlsruhe390.150.100.040.13-0.05Lille350.190.060.030.16-0.12Brussels300.190.06-0.140.18-0.14Chilbolton300.160.05-0.090.15-0.11Leipzig390.150.080.230.13-0.08Cabauw350.160.05-0.060.13-0.10AverageRs=0.900.300.210.02
For the period from 10 June to 30 July 2013, and for all stations listed in
Table , number of available data, correlations, root mean
square error (RMSE) and bias are presented in Table
for AOD. Generally, the bias is slightly positive for locations close to
mineral dust emissions (Banizoumbou, Cabo Verde, Dakar and Tamanrasset) and
negative for locations far from these sources. This bias ranges from -0.14
(Brussels) to 0.28 (Dakar) and thus represents up to 100 % of the AOD
value. Compared to the AOD absolute value, the correlation is better: the
temporal variability is better captured by the model than the mean average.
The temporal variability is primarily explained by the meteorology (for dust
emissions, transport and deposition of particles) and these correlations show
that the model is able to reproduce the majority of huge aerosol plumes over
the Mediterranean but failed for the north of Europe. The absolute value is
more difficult to model because of its calculation methodology: the model
uses a size distribution with a limited number of bins. Even if this approach
is more realistic to describe the complex behavior of aerosols, it has some
limitations: the number of bins and the values of the mean mass median
diameter of the primary particles have a direct impact on all modeled
processes (from the emissions to the deposition). The choice of the bins
properties has also an impact on the AOD calculation itself: the distribution
has to be projected on the extinction efficiency function, characterized by a
narrow spread around the measured value. Thus, it is not surprising to have a
large variability in AOD modeled values compared to measurements, but it does
not mean that the aerosol life cycle is not well represented in the model.
Finally, the last row of Table presents scores for all
stations at the same time. Rs represents the correlation between the
temporally averaged values of observed and modeled AOD. Rs shows that
the low/high AOD values are very well estimated by the model, where and when
they are observed by AERONET. The mean correlation is +0.3, showing that some
stations have low temporal correlations. The mean RMSE is 0.21 and the mean bias
is 0.02, showing that on average the positive bias (mainly in Africa)
compensates the negative one (mainly in Europe).
Ångström exponent
In addition to the AOD, the AE provides
derived information on the size distribution of the aerosols in the
vertically integrated atmospheric column. Depending on its value, one can
have a first look of the dominant aerosol size in the atmosphere: mainly fine
or mainly coarse. For low values, the atmospheric column is mainly composed
of coarse particles (mineral dust and sea salt) when for large values the
anthropogenic and biogenic contributions dominate.
After a complete screening of the available AERONET data, the most abundant
information is for A(440,870). In order to have the same information with
CHIMERE, the modeled AODs are first estimated following the interpolation
described in Eq. () and for wavelengths λ=440
and 870 nm. Then, the corresponding AE is estimated using
Eq. ().
Results are presented in Table . The mean averaged
temporal correlation is better than for AOD, with R=0.54. This means that
the size distribution (fine or coarse) is more accurately modeled than the
AOD value itself. The bias (model minus observations) is large for all
stations and negative. The bias increases with the latitude. This means that the model tends to diagnose too-low values of
AEs and thus atmospheric columns with too much mass of
particles in the coarse mode compared to the fine one. The mean spatial
correlation Rs is good, with Rs=0.96. This means that the long-range
transport and the locations of the aerosol plumes are correctly estimated by
the model.
Scores for the comparisons between observations (AERONET) and
model (CHIMERE) for the Ångström exponent. Results are presented with N
the number of daily mean available measurements for the period from 10 June
to 30 July 2013, the temporal correlation (Rt), the root mean
square error (RMSE) and the bias (model minus observations). The last line
“average”
represents the spatial correlation Rs between the mean observed and
modeled values, and the mean averaged values of temporal correlation, RMSE
and bias. Stations are sorted in increasing latitude, from south to north.
SiteNÅngström RtRMSEbiasObsModIlorin210.790.550.560.40-0.25Cinzana440.270.290.510.190.02Banizoumbou450.280.340.700.160.06Zinder Airport460.320.260.710.16-0.07Dakar440.260.090.670.22-0.17Cabo Verde360.170.090.720.11-0.09Tamanrasset510.160.080.570.11-0.08Izana510.610.320.750.38-0.29Santa Cruz de Tenerife500.670.320.510.48-0.35La Laguna460.600.300.500.44-0.30Saada490.370.260.630.22-0.10Forth Crete341.310.860.650.52-0.45Lampedusa431.170.640.800.61-0.53Granada80.810.520.950.32-0.29Athens381.610.970.750.68-0.64Evora491.310.700.320.68-0.61Lecce University461.591.110.720.54-0.48Barcelona421.490.720.230.82-0.76Rome Tor Vergata491.540.970.720.63-0.57Bastia441.531.020.590.59-0.51Villefranche331.560.930.690.67-0.62Palaiseau371.410.880.400.60-0.53Karlsruhe331.550.830.330.79-0.72Lille281.360.900.510.52-0.46Brussels251.470.970.040.57-0.51Chilbolton221.190.67-0.050.63-0.52Leipzig341.580.800.240.82-0.78Cabauw261.260.820.370.51-0.44AverageRs=0.960.540.48-0.39Optical properties maps
Maps for three different days – (left) 18 June, (middle) 4 July
and (right) 23 July – and for the AOD (top) and the Ångström exponent
(bottom). The AERONET measurements are superimposed to the modeled maps in
colored circles.
In order to have another view of the model results, measured AOD and
AE are overprinted on maps of these modeled variables in
Fig. . This enables us to identify several cases,
representative of the diversity of observed situations during this period of
June and July 2013. Three days are selected: 18 June, 4 and 23 July,
mainly because (i) 18 June corresponds to a strong peak discussed later in
the article and (ii) the two other days are, more or less, with a step of 2
weeks, leading to a correct temporal coverage for the discussion. These days
will be used as cases in the following parts of this study. The discussion is
focused on western Africa, Europe and Mediterranean Sea, where the ADRIMED
measurement campaign was performed .
For 18 June 2013, a large dust plume, issued from Africa, reaches the
western Europe, leading to large AOD values over France, Benelux and Germany.
The plume is not spatially large but with important absolute values of AOD.
In Africa, the model retrieves some high observed values, up to 0.5. In
Europe, the model presents also an intense plume, but the measured values are
less important, especially for 18 June 2013. The corresponding AE map
first shows the differences between model and measurements. The low values of
AE corresponding to the high values of AOD in the plume confirm the mineral
dust origin of the aerosol. In addition, AE shows that the low AOD over the
Mediterranean is not due to the absence of aerosols but to anthropogenic and
biogenic aerosol with AE values up to 1. At the south of the domain, high
AE values are also modeled, showing the African forest fires in Central
Africa.
For 4 July 2013, a very large area in Africa have high AOD values, up
to 0.5. Compared to the measurements, the model overestimates the AOD during
the 3 days. One can also observe a thin mineral dust plume (with low AE)
modeled over the Atlantic Ocean, coming from Africa and flowing until the
north of France and the south of United Kingdom. On 5 July, and over the
north of France, this aerosol plume appears on measurements a little further
north than expected in the model simulations. Over western Europe, the AE values
increase and values up to 1 cover the whole part of this region. Over
Africa, AE values are low, showing the mineral dust dominance.
For 23 July 2013, two plumes are observed from Africa: one to the west
and over the Atlantic sea and another one to the western Europe and over the
Mediterranean Sea. The values are less important than for the two other
studied days, but the plume has a larger spatial extent and covers the whole
western Mediterranean Basin. The model is in good agreement with the
measurements and the AOD values, between 0.1 and 0.5, are well located by the
model. As for the 4 July, the region composed by Germany and Benelux is
mainly driven by high AE values, corresponding more to fine than coarse
aerosol in the whole column: this result is both found for observations and
model.
Surface PM2.5 and PM10 concentrations
This section is dedicated to the comparison between modeled and observed
PM. PM2.5 and PM10 families correspond to the
sum of all aerosols described in Table , for mean
mass median diameter lower than Dp=2.5 and 10 µm,
respectively.
Scores for PM2.5 and PM10
Comparisons between observed and modeled surface concentrations of PM2.5
and PM10 are presented in Table . Scores are
calculated from 10 June to 30 July 2013, leading to a maximum of 51 daily
values. The results are presented for the EMEP stations with PM2.5 and PM10 measurements at the same time.
Scores for the comparisons between observations (EMEP) and model
(CHIMERE) for PM2.5 and PM10. Results are presented with N the
number of daily mean available measurements for the period from 10 June to 30
July 2013, the temporal correlation (Rt), the root mean
square error
(RMSE) and the bias (model minus observations). The last line “average”
represents the spatial correlation Rs between the mean observed and
modeled values, and the mean averaged values of temporal correlation, RMSE
and bias. Stations are sorted in increasing latitude, from south to north.
SitePM2.5PM10NObsModRtRMSEbiasNObsModRtRMSEbiasViznar4612.488.130.446.07-4.354822.0015.350.3914.84-6.65Barcarrola479.777.500.335.71-2.265016.9611.780.1316.50-5.18Zarra497.738.910.454.851.175014.8216.770.6016.631.95San Pablo518.006.400.483.53-1.605115.2010.120.2410.51-5.08Campisábalos439.567.490.584.01-2.074510.9810.780.388.35-0.20Penausende496.656.220.562.91-0.435011.067.820.385.80-3.24Els Torms468.3010.340.485.502.044914.5319.840.3721.575.31Cap de Creus469.3312.230.167.732.904618.3531.070.2642.7912.72O Saviñao4310.149.030.683.93-1.114313.2613.140.534.42-0.12Niembro488.2310.490.585.042.264817.0214.550.586.53-2.47Iskrba5110.829.310.475.47-1.515113.9611.120.339.08-2.84Payerne1210.778.480.474.47-2.285114.1312.730.4711.60-1.40Schauinsland489.659.610.097.22-0.044912.3512.210.1510.97-0.14Košetice2511.528.500.445.55-3.022511.169.430.495.70-1.73Schmucke518.117.950.415.02-0.155111.959.360.447.15-2.59Harwell517.817.960.633.840.155113.249.840.566.21-3.40Neuglobsow517.327.640.165.060.325011.058.510.146.11-2.54Diabla Góra508.226.240.523.52-1.985111.437.250.575.46-4.18Auchencorth415.227.890.483.862.6727.007.891.000.990.89AverageRs=0.250.444.91-0.49Rs=0.620.4211.12-1.10
The PM2.5 scores show an heterogeneous bias, depending on the location,
ranging from -4.35 to +3 µg m-3. Only five stations provide
measurements for all days. However, except for Payerne (with only 12 days of
measurements), all other stations provide more than 40 days on measurements,
leading to representative statistics. In general, the correlations are
satisfactory and around ≈ 0.5 on average for all stations.
For PM10 measurements, only 9 stations out of 25 provide complete times
series. The correlation is correct with a large spread in the values: the
worst correlation, R=-0.11, is calculated in Leova while the best
correlation,
R=0.6, is found at Zarra. For the majority of stations, the model
underestimates the concentrations.
More generally, these scores show that the processes leading to fine
particles (emissions, chemistry) are better reproduced that the ones at the
origin of large particles.
For these comparisons, the scores show that the model reproduces a large part
of the observed temporal variability. For the aerosol mass, non-negligible
biases appear with the simulation (≈ 20 % of the mass on average),
negative or positive, depending of the location. The last row of
Table presents the correlation, Rs, estimated
using the mean averaged values of observed and modeled concentrations. This
spatial correlation is better for PM10 (Rs=0.62) than for
PM2.5 (Rs=0.25). The mean averaged values of correlation are close
between PM2.5 and PM10 with 0.44 and 0.42, respectively. Finally,
the averaged bias is larger for PM10 (bias =-1.10 µg m-3) than
for PM2.5 (bias =-0.49), a logical result considering that the aerosol
mass is much larger with PM10. These scores show that the order of
magnitude of ground aerosol concentrations is correctly reproduced.
Time series of PM2.5 speciation
Time series of PM2.5 are presented to better explain the scores
presented in the previous section. For the discussion, six sites are
selected. The selection was made independently of the scores found but to be
as
representative of the largest region as possible. The precise location of
these sites is displayed in Fig. (red symbols).
Harwell (United Kingdom) and Diabla Góra (Poland) are chosen for the north of
Europe, Iskrba (Slovenia) and Schauinsland (Germany) for the center of
Europe, and
Campisábalos and Zarra (Spain) for the south of Europe.
Location of the sites: (red symbols) surface time series
of PM2.5 speciations are presented in Fig. ;
(green symbols) time series of PM10 for inorganic species are presented
in Fig. .
The time series of PM2.5 speciation are displayed in
Fig. . The symbols represent the PM2.5 EMEP
observations. For all sites, the cumulative concentrations until Dp<2.5µm of the model species show a good agreement in terms of mass and
temporal variability. The important peak of PM2.5 observed around 18
June is well reproduced by the model for stations Harwell, Diabla Góra and
Iskrba. This peak is overestimated in Schauinsland, mainly due to an
overestimation of modeled mineral dust. This peak is mainly due to mineral
dust except for at Iskrba, where this is mostly due to SOA and sulfate peak
(mineral dust concentration remains low).
The event of 4 July shows less important concentrations, meaning that the AOD
is related to long-range transport of aerosols in the troposphere and
not to surface concentrations due to local emissions or chemistry. This peak
is observed and modeled in Harwell and Campisábalos mainly. At the end of the
modeled period, for the event of 23 July, the model shows the observed
increase in surface concentrations in Harwell and Campisábalos but failed to
estimate the right concentrations in Zarra (overestimation).
The view of the aerosol speciation shows that aerosol peaks, even if they appear at the same period, are not always due to the same chemical species increase.
Time series of PM2.5 (µg m-3) with the model
aerosol speciation. The colors represent all constituents of the modeled
aerosol (for Dp<2.5µm) and the symbols represent the surface
measurements of PM2.5.
In order to quantify the relative contribution of each species in the
PM2.5 concentrations budget, percentages are presented for each EMEP
measurements site and in Fig. . Values are
presented for the stations where PM2.5 measurements were available. As
previously discussed for the PM2.5 time series, the chemical composition
is dominated by mineral dust and sulfate for all EMEP stations. If the
mineral dust and sulfate relative contributions vary a lot (from 10 to 50 %
for mineral dust and from 20 to 40 % for sulfate), the contribution of the
other species is less variable: ≈ 15 % for SOA, ≈ 10 %
for ammonium and less than 10 % for the other components.
Relative contribution of each chemical species in the budget of
the modeled PM2.5 surface concentrations for each EMEP station and on
average over the period from 10 June to 30 July 2013.
Surface inorganic species concentrations
The EMEP network provides surface measurements of nitrate, sulfate and
ammonium for aerosol size until 10 µm (PM10). This is a good
opportunity to evaluate the model capabilities to quantify these chemical
species and to determine if the results of the previous sections are not due
to error compensations.
From all EMEP stations listed in Table , the measurements of
these three species are not systematic and regular in time. To quantify the
model performance, statistical scores are calculated. The available
measurements being different for the three species, the results are presented
in different tables. The comparison is performed for ammonium, nitrate and
sulfate, respectively, with 21, 25 and 36 stations.
Scores for the comparisons between observations (EMEP) and model
(CHIMERE) for the NH4 surface concentrations (in µg m-3).
Results are presented with N the number of daily mean available
measurements for the period from 10 June to 30 July 2013, the observed and
modeled surface concentrations (“obs” and “mod”), the temporal correlation
(Rt), the root mean square error (RMSE) and the absolute bias (model
minus observations). The last line “average” represents the spatial
correlation Rs between the mean observed and modeled values, and the
mean averaged values of temporal correlation, RMSE and bias. Stations are
sorted in increasing latitude, from south to north.
For NH4 comparisons, the results in Table show a
large variability for the correlation. The worst score R=-0.18 is at Leova,
when the best score is at Viznar with R=0.80. The mean absolute values of
concentrations are between 0.4 (K-puszta) and 1.6 (Diabla Góra) and the RMSE
exhibits values with the same order of magnitude, showing a non-negligible
variability of the error. With values ranging between -0.87 (Diabla Góra) and
0.67 (De Zilk), the bias is important and also of the order of magnitude of
the mean absolute value. The row “average” in Table
shows that the spatial correlation of NH4 is very low with
Rs=0.17;
this means that the model is not able to retrieve the NH4 plumes of high
concentrations where and when they are observed. The mean averaged bias is
+0.16 and represents ≈ 20 % of the averaged concentrations,
highlighting a non-negligible bias with the model for this species.
Scores for the comparisons between observations (EMEP) and model
(CHIMERE) for the SO4. Results are presented with N the number of
daily mean available measurements for the period from 10 June to 30 July
2013, the temporal correlation (Rt), the root mean square error (RMSE)
and the bias (model minus observations). The last line “average” represents
the spatial correlation Rs between the mean observed and modeled values
and the mean averaged values of correlation, RMSE and bias. Stations are
sorted in increasing latitude, from south to north.
For SO4, in Table , results are better than for
ammonium. The correlation R ranges from -0.24 (K-puszta, but this is the only
station with a negative correlation) to 0.78 (O Saviñao). The mean values of
measured and modeled concentrations are larger than for ammonium and range
from ≈ 1 to ≈ 4 µg m-3. The RMSE is satisfactory
and never exceeds the half value of the mean concentration. The bias is
scattered ranging from negative (until -0.87 at Cap de Creus) to positive
values (until +1.23 at Chopok). The spatial correlation Rs=0.5 is better
than the one of NH4. The model is more able to retrieve the spatial
variability of this pollutant than the temporal variability with the mean
averaged correlation of 0.37. The mean bias is very low (+0.05) but the mean
RMSE is high (+1.20), showing that the model has the correct order of
magnitude for this species but the model variability remains high.
Scores for the comparisons between observations (EMEP) and model
(CHIMERE) for the nitrate. Results are presented with N the number of daily
mean available measurements for the period from 10 June to 30 July 2013, the
temporal correlation (Rt), the root mean
square error (RMSE) and the
bias (model minus observations). The last line “average” represents the
spatial correlation Rs between the mean observed and modeled values, and
the mean averaged values of correlation, RMSE and bias. Stations are sorted
in increasing latitude, from south to north.
SiteNNO3RtRMSEbiasObsModViznar491.020.050.171.08-0.97Barcarrola500.830.08-0.160.84-0.75Zarra501.390.070.111.41-1.32San Pablo510.540.05-0.100.60-0.49Campisábalos380.280.130.720.27-0.15Penausende500.640.12-0.020.61-0.51Els Torms490.470.25-0.070.54-0.21Cap de Creus461.400.100.091.46-1.29Noya400.900.160.140.91-0.74Niembro460.970.240.410.86-0.73O Saviñao410.740.180.280.62-0.56Leova510.710.050.410.79-0.66K-puszta510.690.09-0.040.69-0.60Chopok500.600.17-0.130.61-0.43Starina491.010.110.040.99-0.90Sniezka511.540.320.171.38-1.22Vredepeel264.532.190.014.40-2.34Jarczew461.170.220.081.11-0.95Carnsore501.600.400.212.12-1.21De Zilk253.731.590.783.95-2.14Oak Park511.300.550.711.14-0.75Neuglobsow510.650.800.211.010.15Diabla G´ra501.280.24-0.011.35-1.04Leba511.160.65-0.221.05-0.51Malin Head440.840.710.450.99-0.13AverageRs=0.870.171.23-0.82
Results for the nitrate are presented in Table . The
comparison between observation and model is not fair: the model strongly
underestimates the observed surface concentrations. In addition, the modeled
concentrations temporal variability is not satisfactorily, with low or
negative correlation values. These bad results are mainly due to the missing
formation of coarse nitrate. Viznar and Barcarrola illustrate this statement
with a strong underestimate of nitrate concentrations, correlated with high
simulated dust fraction in the PM10 concentrations.
In order to have more information about the temporal variability of these
inorganic species concentrations, time series are presented for specific
sites where the three species were measured simultaneously with a sufficient
number of data. Results are presented in Fig. for
Leba, Niembro, Starina, Viznar, K-puszta and Vredepeel. These locations are
reported in Fig. . Even if the performances of the
model seem poor, these time series show that the order of magnitude of
inorganic species is fairly reproduced (except for nitrate). It means that
even if the sources and the chemistry remain uncertain, the inorganic
equilibrium diagnosed using the ISORROPIA module works well to ensure
realistic inorganic chemistry and partitioning, whatever the location and the
period in summer 2013.
Time series of PM10 (µg m-3) for the modeled and measured surface inorganic species.
Another analysis of the results is presented in
Fig. . The three rows correspond to the 3 days of
18 June, 4 and 23 July 2013. The three columns are for sulfate, nitrate
and ammonium. For each map, the modeled surface concentrations are expressed
in µg m-3 over the whole simulation domain. Since the measurements
are restricted to Europe with the EMEP measurements, a magnification is done to focus
on Europe. For each time and each pollutant, the corresponding observed
ground concentrations is superimposed as colored circles on the map.
Maps of sulfate, nitrate and ammonium (µg m-3) for 18
June, 4 and 23 July 2013. A magnification is done over western Europe where EMEP
surface measurements (superimposed to the model) are available. All
concentrations values lower than 0.2 µg m-3 are considered to be nonsignificant and are not colored. The 10 m (a.g.l.) wind speed is
superimposed as vectors.
For sulfate, and for the 3 selected days, the surface concentrations are
higher than for nitrate and ammonium, as already discussed in the previous
section both for observations and modeling. The most important modeled
concentrations are found over the seas (Mediterranean Sea and English
Channel). Over land in Europe, the concentrations remain low and the model
reproduces well the observed concentrations. Some peaks corresponding to
advected plumes are observed and also well modeled as in Benelux and Italy
(18 June), north of Spain (7 and 23 July). For this species, the model
is able to reproduce the largest spatial patterns with the correct order of
magnitude of the concentrations.
For nitrate, the modeled concentrations are low and mainly concentrated in
the English Channel. This effectively corresponds to the largest measured
values as in the western United Kingdom (18 June) and Benelux (7 and 23
July). The addition of NOx shipping and anthropogenic emissions
(advected above the sea) is responsible for the formation of nitrate favored
by mild, humid conditions and low deposition over the Channel. For all other
parts of the modeled domain, the model estimates concentrations below 0.2 µg m-3
when the observations ranged between 0.1 and 1 µg m-3, highlighting a systematic underestimation of the model for
background values over land.
For ammonium, the modeled background concentrations are higher than for
nitrate and ranged from 0.2 to 1 µg m-3. This is in agreement with
the observed values and when the highest concentrations are observed, the
model simulates a plume close to these areas. Performances on ammonium follow
the ones of sulfate, most of the ammonium reacts with sulfuric acid to form
ammonium sulfate salts.
Aerosol size distribution
In the previous section, the speciation was studied only at the surface using
EMEP measurements. An additional way is to use the AERONET inversions to have
ASD to compare to model results. Two types of
comparisons are presented in this section: (i) direct comparison of ASD
between model and observations, where and when AERONET inversion products are
available; (ii) a comparison of fine and coarse mode values to quantify the
ability of the model to estimate the size distribution changes.
ASD speciation
As presented in Sect. , the AERONET inversion products provide ASD
for 15 bins, following a logarithmic distribution, ranging from 0.05 to 15 µm. In order to conserve all model information, the calculation is done
on the AERONET bins plus extra bins in the finest and coarsest sizes: five bins
are added below 0.05 µm with r=0.005, 0.01, 0.02, 0.03 and 0.04 µm
and three bins are added after 15 µm with r=20, 30 and 40 µm. The model
bins are interpolated on the AERONET bins and the column aerosol volume size
distribution is calculated for each bin i as in :
dV(ri)dlnri=∑k=1nlevels∑a=1naeroma(k,ri)×Δz(k)ρa×ln(ri,max/ri,min),
where ri is the mean mass median radius and ri,min and ri,max
the boundaries of the ith bin. ma(k,ri) is the aerosol mass
concentration (the mass of aerosol in cubic meter of air, in µg m-3)
for the naero modeled aerosols. ρa is the aerosol density (also in
µg m-3, the mass of the particle in its own volume). The aerosols
densities are fixed for each model species and displayed in
Table . Δz(k) is the model layer thickness
(for a total of n levels, here 20 vertical levels).
In order to conserve all model information, the calculation is done on the
AERONET bins plus extra bins in the finest and coarsest sizes: five bins are
added below 0.05 µm with r=0.005, 0.01, 0.02, 0.03 and 0.04 µm and
three bins are added after 15 µm with r=20, 30 and 40 µm.
Comparisons between observed (AERONET) and modeled (CHIMERE)
aerosol size distribution for 18 June, 4 and 23 July 2013. For the model
results, the aerosol speciation is displayed with different colors for each
species.
The model ASD calculation is done independently for each aerosol species in
order to have the chemical speciation. All aerosol ASD are cumulated and are
thus directly comparable to the AERONET ASD. Results are presented in
Fig. for the three selected periods and for several
AERONET stations (chosen to be representative of several locations in the
modeled domain).
For modeled and observed concentrations, two main modes are observed: a fine
mode with r≈0.1µm and a coarse mode with r≈1 to
5 µm. These modes differ a lot between days and locations. In these
examples, there is no systematic bias between the model and the observations
regarding the values of the modes radius. A more systematic comparison is
presented in the next section. The speciation is presented for the model and
cumulated over all species to have a direct comparison to the AERONET ASD.
For the fine mode, the main modeled species are SOA, sulfate and ammonium.
The composition varies a lot from one site to another: in Athens (18 June)
SOA and sulfate dominate, while in Evora (23 July) only SOA dominates with a
lowest contribution of PPM. For all days and stations, the fine mode is
underestimated by the model and exhibits a distribution larger than the
AERONET fine mode.
For the coarse mode, the main modeled species is mineral dust. For sites
close to this source, the ASD shows a correct order of magnitude (Banizoumbou
for 18 June, Cabo Verde for 4 July). Far from the African dust sources, the
mineral dust contribution may be under or overestimated by a factor of 2
(Evora for the 18 June, Barcelona for 23 July). The best comparisons are
obtained when the measured coarse mode is centered on r≈2µm
as, for example, in Banizoumbou (18 June), Izana and Santa Cruz Tenerife (23
July).
ASD fine and coarse modes
In order to have a global view of the model capability to estimate the
aerosol size distribution, a simple calculation of these distribution
characteristics is done for all sites and hours where AERONET measurements
are available. An example is displayed in Fig. .
Most of the AERONET ASD exhibit a two-mode distributions, with a “fine” and
a “coarse” mode. This is due to the AERONET inversion methodology itself,
searching for a local minimum of dV/dlog(r) between 0.439 and 0.992 µm
for the aerosol radius. The same analysis is done for the modeled ASD. From
these two local minimum values, the local maxima are quantified for the
“fine” and “coarse” mode.
Method for the local minima and maxima values estimation. This example corresponds to the ASD for Athens, 23 July 2013, 14:00 UTC.
The values of radius are compared between the model and the observations in
Fig. . Since the radius in the size distribution
is estimated using a logarithmic progression, the results are also presented
using a logarithmic scale. For the observed and modeled distributions, the
bins are discretized: this explains the few number of points on the
scatter plot, even if numerous data were analyzed.
The results are classified with three categories: “Africa”, “Europe” and
“Mediterranean”. This classification is related to the stations location (the
latitude as explained in Table ) and enables us to see if any
systematic trends appear. The results show a large variability of the
differences between model and observations, both for the “fine” and
“coarse”
modes.
For the two modes, this scatter plot first shows that the variability is
larger in the observations than in the model: for one observed specific
radius the model found three to four different radius, while for one modeled radii,
five to six different radii are found in the observations.
For the “fine” mode and for the stations denoted “Africa”, the model
overestimates the radius by a factor of 2: for the largest occurrences of
radius values, when the observations are around r≈0.1µm, the
corresponding model value is r≈0.2–0.3 µm. For the
“Mediterranean” stations, there is a large spread between model and
observations but no systematic bias: the fine mode is correctly modeled with
r≈0.1µm. For “Europe” stations, the trend is different and a
systematic bias appears: in this case, the model underestimates the observed
radius by a factor of 2.
For the “coarse” mode, the same behavior is observed as for the “fine” mode.
A large spread is observed between observations and model, but with well-marked trends, depending on the stations location. When the radius is
overestimated in Africa, it is well retrieved for Mediterranean stations and
underestimated in Europe.
Scatter plot of the radius found in observations and model for
the fine and coarse modes. The width of each symbol represents the occurrence
for each obs/model value (normalized with the highest value for each mode
“fine” and “coarse” at each location). The blue circles represent the scale
for the results, with examples for sizes representing 10, 50 and 100 %.
Another way to quantify the differences between the observed and modeled
modes is to sum the dV/dlog(r) values for the observations and the model
and independently for the “fine” and “coarse” modes. The modes are split
considering a constant radius of r=0.5µm. This choice of a constant
value is done to avoid the bias observed in the radius retrieval presented in
Fig. .
Results for this comparison are presented in Fig. . For
the fine mode, the cumulated mass of aerosol shows a clear tendency between
the three regions: the model overestimates the concentrations in Africa,
slightly underestimates the aerosol load over Mediterranean Sea and clearly
underestimates the values in Europe.
The results are less marked for the coarse mode but follow the same tendency.
In addition, the spread of the cumulated mass is larger than for the fine
mode. Over Africa, the model overestimates the aerosol mass, and this
concerns high mass values. However, the model tends to
underestimate the mass over the Mediterranean Sea and this corresponds to low
mass values. Over Europe, the model underestimates the low mass values, but
overestimates the highest mass values. Clearly, the case of the Mediterranean
stations corresponds to a mixture of anthropogenic and biogenic aerosol
(mainly emitted in Europe) and mineral dust aerosol (mainly emitted in
Africa).
Scatter plots for comparisons between the observations and the
model for the aerosol size distribution. Each plot corresponds to the sum of
the concentrations of aerosol for the “fine” mode (r<0.5µm) and the
“coarse” mode (r>0.5µm). Each point corresponds to an hour during
the whole simulation and a modeled concentration corresponding to an AERONET
site. The sites are split in three families: Africa (black symbols),
Europe (red symbols) and Mediterranean region (green symbols), following the
classification explained in Table .
Conclusions
Studying aerosol composition and size distribution is a
scientific challenge that can lead to a better understanding of the aerosol life cycle and
improve our understanding of the aerosol impact on health and climate.
This is also necessary if we want to split the relative contribution of
anthropogenic and biogenic parts in the aerosol to be able to adapt and have
more efficient rules of AQ legislation.
This modeling study presents the analysis of a simulation performed with the
WRF and CHIMERE models, over a large region including Africa, Mediterranean
region and western Europe. The simulation was performed for the 2 months of
June and July 2013 and includes all aerosol sources and chemical types. In
order to estimate the model accuracy, the AOD and AE
are compared to the AERONET photometers measurements. For AOD, it is shown
that the correlation varies a lot from south (Africa, with high correlations)
to north (Europe, with low correlations) with a mean averaged value of 0.3.
The spatial correlation is better, 0.9, and showed that if the events are not
temporally well modeled, the large spatial structures of dense plumes are
well estimated by the model. This is confirmed by the good scores with the
AE, showing that the origin of the air masses and thus the relative abundance
of fine/coarse aerosol is correctly retrieved by the model (spatial
correlation of 0.96). The PM2.5 and PM10 surface concentrations are
compared to the EMEP network measurements. A mean averaged correlation of
0.42 and 0.44 is found, with negative biases of -0.49 and -1.10 µg m-3.
To go further in the analysis, several additional measurements are added to
this observations vs. model comparison. First, this study takes advantages
of the availability of surface measurements of inorganic chemical species
such as nitrate, sulfate and ammonium. The equivalent species are modeled
with CHIMERE and it is shown that the mean averaged correlation is 0.25, 0.37
and 0.17, for these three species, respectively. The spatial correlation is
different and is 0.25, 0.5 and 0.87, respectively. This shows that if some
bias remain in the modeling of these species, the spatial localization of
sulfate and ammonium is well captured by the model. The modeling of the
nitrate is the weak point for these inorganic species, certainly due to
missing sources and processes such as the calculation of coarse nitrate.
Second, we take advantage of the AERONET inversion products to estimate the
model capability to retrieve the aerosol size distribution over this large
region. It is shown that the two main observed modes are well estimated: in
Africa, the model is able to correctly estimate the observed radius of the
AERONET distribution, while the largest variability is diagnosed in the
Mediterranean region and Europe. In mass, the aerosol fine mode is
overestimated in Africa but underestimated in Europe. As the Mediterranean
region has aerosol that is a mix between African sources (mainly mineral
dust), local sea salt and European sources, the modeled mass in the fine mode
exhibits a large variability compared to the measurements. Results in mass
are better for the coarse mode, but always with a slight model overestimation
in Africa and a model underestimation in Europe.
This study shows that the CTM CHIMERE reproduces the
main part of the observed aerosol composition variability over several
regions as Africa, the Mediterranean region and Europe. By splitting the analysis
in terms of chemical composition, it is shown that the scores obtained for
PM2.5 and PM10 are not due to model errors compensation, the order
of magnitude and time variability of inorganic species being correctly
reproduced. The next step will be to reduce the uncertainties on (i) the
mineral dust emissions in Africa, representing a large part of the model
error after long-range transport from Africa to Europe and (ii) the sources and
chemistry of nitrate.
Acknowledgements
INERIS is funded by the French Ministry of Ecology, Sustainable Development and Energy. The EBAS
database has largely been funded by the CLRTAP-EMEP programme, AMAP and
NILU internal resources. Specific developments have been possible due to
projects like EUSAAR (EBAS web interface), EBAS-Online (upgrading of database
platform) and HTAP (import and export routines to build a secondary
repository in support of www.htap.org. A large number of specific
projects have supported development of data and metadata reporting schemes in
dialog with data providers (CREATE, ACTRIS and others).
Edited by: J.-L. Attie
Reviewed by: two anonymous referees
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