ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-18-4911-2018Primary aerosol and secondary inorganic aerosol budget over the Mediterranean Basin during 2012 and 2013Primary and secondary inorganic aerosol budget over the Mediterranean BasinGuthJonathanjonathan.guth@meteo.frhttps://orcid.org/0000-0001-5768-1992MarécalVirginieJosseBéatriceArtetaJoaquimHamerPaulCentre National de Recherches Météorologiques, CNRS–Météo-France, UMR3589, Toulouse, FranceNILU – Norwegian Institute for Air Research, P.O. Box 100 2027, Kjeller, NorwayJonathan Guth (jonathan.guth@meteo.fr)11April20181874911493418July201725August20172March20189March2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://acp.copernicus.org/articles/18/4911/2018/acp-18-4911-2018.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/18/4911/2018/acp-18-4911-2018.pdf
In the frame of the Chemistry-Aerosol Mediterranean Experiment (ChArMEx), we
analyse the budget of primary aerosols and secondary inorganic aerosols over
the Mediterranean Basin during the years 2012 and 2013. To do this, we use
two year-long numerical simulations with the chemistry-transport model MOCAGE
validated against satellite- and ground-based measurements. The budget is
presented on an annual and a monthly basis on a domain covering 29 to 47∘ N latitude and 10∘ W to 38∘ E longitude.
The years 2012 and 2013 show similar seasonal variations. The desert dust is
the main contributor to the annual aerosol burden in the Mediterranean region
with a peak in spring, and sea salt being the second most important
contributor. The secondary inorganic aerosols, taken as a whole, contribute
a similar level to sea salt. The results show that all of the considered
aerosol types, except for sea salt aerosols, experience net export out of our
Mediterranean Basin model domain, and thus this area should be considered as
a source region for aerosols globally. Our study showed that 11% of the
desert dust, 22.8 to 39.5% of the carbonaceous aerosols, 35% of the
sulfate and 9% of the ammonium emitted or produced into the study domain
are exported. The main sources of variability for aerosols between 2012 and
2013 are weather-related variations, acting on emissions processes, and the
episodic import of aerosols from North American fires.
In order to assess the importance of the anthropogenic emissions of the
marine and the coastal areas which are central for the economy of the
Mediterranean Basin, we made a sensitivity test simulation. This simulation
is similar to the reference simulation but with the removal of the
international shipping emissions and the anthropogenic emissions over a
50km wide band inland along the coast. We showed that around 30%
of the emissions of carbonaceous aerosols and 35 to 60% of the
exported carbonaceous aerosols originates from the marine and coastal areas.
The formation of 23, 27 and 27%, respectively of, ammonium,
nitrate and sulfate aerosols is due to the emissions within the marine and
coastal area.
Introduction
Atmospheric pollution is an environmental problem our modern societies have
to face. It has impacts on human health , agriculture
, ecosystems and even on
buildings . It also has an impact on weather and climate
.
The Mediterranean Basin region is a region subject to atmospheric pollution,
especially for air quality issues , because of the high
population density on the Mediterranean coast. The emission sources are
various, with most of the anthropogenic and biogenic sources in the northern
part of the basin and large mineral dust emissions in the south. The
Mediterranean Basin also experiences sporadic pollution from forest fires.
The accumulation of pollutants is favoured by the synoptic-scale flows along
with the complex topography of the area. Moreover, the climate simulations
tend to show that the climate of the Mediterranean Basin will become dryer
and warmer, especially during the summer .
In this context, the ChArMEx project aims to acquire knowledge about the
present and the future chemical composition of air in the Mediterranean area and
aims to understand its various impacts . In the
framework of ChArMEx, three intensive observation periods took place in the
summer months in 2012 and 2013. In 2012, the TRAQA campaign (transport and
air quality above the Mediterranean Basin) aimed to characterize the
dynamical processes exporting polluted air masses from source regions of the
Mediterranean Basin. During the TRAQA campaign, 20 June–13 July 2012,
meteorological conditions mainly favoured continental outflow to the
Mediterranean Basin from different source regions .
The data collected during this airborne campaign were radiative properties of
aerosols (absorption and scattering coefficients), particle number
concentration and particle composition. Two intensive campaigns, ADRIMED and
SAFMED, were conducted in 2013. The first one, ADRIMED (Aerosol Direct
Radiative impact on the regional climate in the MEDiterranean region) took
place between 11 June and 5 July 2013 . The
first part of this campaign is characterized by changes in the synoptic flux:
easterly (16 June), southerly (19 June) and north-westerly (29 June) for
example. The ADRIMED measurement strategy was mainly composed of two in situ
super-sites (Ersa and Lampedusa) and aircraft and balloon measurements.
Super-sites measure aerosol number; cloud condensation nuclei and mass
concentrations; aerosol composition; and radiation measurements such as
absorbing, scattering and extinction coefficients. Aircraft-based
measurements were composed of aerosol size distribution, number concentration
and radiation measurements. The SAFMED campaign (Secondary Aerosol Formation
in the MEDiterranean) took place between 24 July and 1 August
. The meteorological conditions during this campaign
can be divided into two periods. The first period corresponds to a stable
anticyclone located on the western part of the basin until 26 July,
possibly causing an accumulation of pollutants in the area. Then, the basin
was affected by a cyclonic system on 28–29 July, leading to very clean
conditions. During the SAFMED campaign, the measured parameters were similar
to those during ADRIMED, being size distribution and particle number
concentration and radiative properties.
The aerosol contributions during summer 2012 were analysed by
using the chemistry-transport model (CTM) CHIMERE
. They show that the Euro-Mediterranean region was
largely influenced by mineral dust. Indeed, surface PM10 was
composed of 62% mineral dust while anthropogenic aerosols were the second
largest contributor (19%). For PM2.5, the anthropogenic
emissions were the major part of the surface PM2.5 composition
(52%). The mineral dust was the second contributor, with 17%. Biogenic
sources also played a significant role in PM2.5. This result is
consistent with , who show the importance of desert
dust aerosols in the Mediterranean Basin. When looking at the aerosol optical
depth (AOD), an indicator of the total column of aerosols,
showed that anthropogenic sources accounted for
34% of the total AOD, while mineral dust accounted for 23% and biogenic sources
for 14%. analysed the ozone and aerosol
variability between 1 June and 15 July 2013. They show this
period was not very polluted, mainly due to several precipitation events.
Aerosols in the boundary layer, were dominated by sea salt, sulfate and
mineral dust in this case. The column of aerosols was mainly composed of
mineral dust.
The past studies focused on the summer season. Here we go a step further by
analysing the aerosols over the Mediterranean region based on two year-long
simulations that includes the intensive periods (2012 and 2013). Our objective
is to establish the budget of the primary aerosols and secondary inorganic
aerosols in this region for these two years including an analysis of its
seasonal variability. Because particulate pollution is an issue there, we
also analyse, using a sensitivity simulation, the contribution of the
anthropogenic emissions from the Mediterranean coast and from
international shipping emissions to the aerosol budget. The years 2012 and
2013 have different mean meteorological conditions, and this allows us to
quantify the impact of this year-to-year meteorological variability on the
aerosol budget. With this work being part of the ChArMEx project, the choice
of the years 2012 and 2013 for this study was linked to the possible
availability of ChArMEx data, such as aerosol composition, in the long term
that could be compared to the simulation results. Unfortunately, to date,
these data are not available. Nevertheless, these years are of interest
because of their different meteorology and also it makes it possible to link our
results to other ChArMEx studies published in the present issue and to any
future studies of this time period. Our study is based on the
chemistry-transport model MOCAGE
and the use of a wide range of observations.
The paper is organized as follows. Section presents the
MOCAGE model and the simulation set-up. The simulation is then evaluated in
Sect. . In Sect. , we analyse
the budget and the variability of the aerosols over the Mediterranean Basin.
Section presents the results of a sensitivity test
aiming to quantify the impact of the anthropogenic emissions over the
Mediterranean Sea and its coast. Finally, Sect. concludes
this article.
Configuration of the MOCAGE simulation
This section presents the MOCAGE model used in this study and the set-up of
the simulation discussed in Sects. and .
The MOCAGE model
MOCAGE (Modele de Chimie Atmospherique à Grande Echelle) is an offline
global chemistry transport model with grid-nesting capability used for
research at Météo-France in a wide range of scientific studies on
tropospheric and stratospheric chemistry, at various spatial and temporal
scales. MOCAGE has been used, for example, for studying the impact of climate
on air composition or
tropospheric–stratospheric exchanges using data assimilation .
MOCAGE is also used for daily operational air quality forecasts in the
framework of the French platform Prev'Air (,
, http://www2.prevair.org/, last access: 4 April 2018) and in the European
CAMS project (Copernicus Atmospheric Monitoring Service). In CAMS, the MOCAGE
model is one of the seven models contributing to the regional ensemble
forecasting system over Europe (,
, http://macc-raq-op.meteo.fr/index.php, last access: 4 April 2018).
The version of MOCAGE used in this study is fully detailed in
and . Two chemical schemes are
implemented in order to represent both the tropospheric and the stratospheric
air composition in MOCAGE. The Regional Atmospheric Chemistry Mechanism
(RACM) is used in the troposphere. For the
stratosphere, it is the REPROBUS scheme (REactive Processes Ruling the Ozone
BUdget in the Stratosphere) which is implemented
. Regarding aerosols, the version of the model
used in the present study includes desert dust, sea salt, primary organic
aerosols, black carbon and secondary inorganic aerosols (SIA) managed through
the ISORROPIA module , including the interactions with
sea salt aerosols. The model uses a sectional representation with six size
bins for each aerosol type. The sizes range from 2nm to
50µm and there is no effect of ageing on aerosol size in the
model concerning aerosol interactions. Yet, aerosol size distribution is
affected by deposition and emission or production processes.
The version of MOCAGE model used in this study does not include secondary
organic aerosols (SOA). SOA are currently in development in MOCAGE and are
not yet validated. In winter, carbonaceous aerosols are mainly composed of
primary aerosols from biomass burning and fossil fuel combustion
. showed by a regional model
simulation that in summer, PM10 aerosols are dominated by dust and
secondary inorganic aerosols over the Mediterranean Basin. The fraction of
SOA varies between 3 and 16% of the total PM10 mass. Yet, SOA can be
a significant contributor to aerosols. However, organic aerosol made up about
a half of the measured PM1 fine aerosol at Cape Corsica
during the SOP2 field experiment in summer 2013.
Nevertheless, our study analyses the mass budget of aerosol of all sizes in
which the fine-mode aerosol contribution to total mass is low. From all these
studies we can expect that SOA contributions to the mass of total aerosol is
small but non-negligible and could lead to negative biases compared to
observations.
Map of the annual modified normalized mean bias (MNMB) of the
aerosol optical depth against MODIS observations for the year 2012 (a, c) and
2013 (b, d) for MODIS (a, b) and Deep Blue (c, d).
Set-up of the simulation
For this study, the model is run using a global domain at 2∘×2∘ resolution and a nested domain over the Mediterranean Basin at
0.2∘×0.2∘ resolution. This second domain extends from
16 to 52∘ N and from 20∘ W to
40∘ E. The domain simulated is larger than the zone of interest
and in order to focus on the basin, we use a sub-domain centred on the
Mediterranean Basin, represented in Fig. by the red square.
This domain covers the 29 to 47∘ N latitude and
10∘ W to 38∘ E longitude region and will be called the
“budget domain”.
The MOCAGE model uses 47 vertical levels, in σ-pressure coordinates,
from the surface up to 5hPa. Simulations are run with a spin-up
period of 3 months and are driven by the meteorological fields from ARPEGE
operational analyses .
Emissions
At the global scale, the anthropogenic emissions used are the MACCity
emissions representative for 2013 given at a 0.5∘×0.5∘
resolution
.
The biogenic emissions are based on for the volatile
organic compounds. They are at a 0.5∘×0.5∘ resolution,
monthly and representative for 2010. NOx emissions from the soil
come from the GEIA dataset while nitrogen oxides
from lightning are taken into account following . GFAS
emissions (Global Fire Assimilation System, ) giving
daily biomass burning emissions based on satellite data are used here. The
natural aerosols emissions are dynamically computed using
and for mineral dust and for sea salt.
The anthropogenic emissions used at the regional scale are from the MACC-III
project emission inventory, representative for the year 2011. It corresponds
to the latest update of the MACC-II emission inventory
. This emission inventory, at a 7×7km
resolution, covers the European continent and the Mediterranean Sea. It is completed over the African continent by the MACCity emissions that
are also used at the global scale. The other types of emissions are the same
as those used at the global scale. In the model, all emissions are
distributed on the five lowest model layers using an exponential decay with a
decay constant of 5. The temporal distribution is based on the monthly
variations provided in the chosen inventory, on top of which are added the
variations linked to the day of the week and the time of the day (following
EMEP profiles). The speciation of volatile organic compounds follows
based on .
Evaluation of the simulation
Before the simulation results were analysed, their evaluation has been performed
against various observation sources and is presented in this section.
Unfortunately, the few ChArMEx measurements over long periods of time were
not available at the time of the present study for use in the model
evaluation. The statistical indicators used in this section are defined and
explained in the Appendix .
Comparison with MODIS aerosol optical depth
MODIS daily mean AODs were used to evaluate the model simulations. For this
purpose, we select both the daily MODIS and Deep Blue data level 3 (L3,
collection 6) for the year 2012 and 2013 and perform an additional quality
control and screening as presented in and
.
Mean statistics of the comparison between the MODIS AOD and the
MOCAGE simulation for the years 2012 and 2013. See Appendix for information about statistical
indicators.
YearBiasMNMBFGECorrelationMODIS 2012-0.04-0.090.390.392013-0.06-0.230.430.57Deep Blue 2012-0.13-0.370.610.392013-0.15-0.480.680.46
Mean statistics of the comparison between the MODIS AOD and the
MOCAGE simulation for the years 2012 and 2013 over the budget
domain.
YearBiasMNMBFGECorrelationMODIS 2012-0.03-0.090.370.392013-0.05-0.220.400.51Deep Blue 2012-0.08-0.180.510.342013-0.09-0.280.580.40
AODs in MOCAGE are calculated at 550nm using Mie theory with
refractive indices taken from the Global Aerosol Data Set
and extinction efficiencies derived with Wiscombe's Mie scattering code for
homogeneous spherical particles .
Figure presents the maps of the annual MNMB (modified
normalized mean bias, dimensionless) of the AOD simulated with the MOCAGE
model against the MODIS and the Deep Blue AODs for the years 2012 and 2013.
The simulated AOD shows a good agreement with the MODIS AOD with a MNMB close
to 0 in a large area, especially over the Mediterranean Sea. The MNMB is
slightly negative over the Mediterranean Sea for the year 2013, but not for
the year 2012. When considering Deep Blue observations, the MNMB is lower,
between -0.5 and -1, over the Red sea, the north of Africa and off the
African Atlantic coast, meaning a slight underestimation by the model. The
MNMB is higher, between 0.5 and 1, north of the Black sea. The negative
bias over the north of Africa can be due to an underestimation of desert dust
aerosols that may not be transported far enough, and to a lack of secondary
organic aerosols, especially for the coastal regions. Indeed, organic
aerosols can represent a significant part of the fine-mode aerosols
and they hence have a noteworthy contribution to AOD in
the visible range.
Tables and present the statistics
for the comparison between the MODIS and Deep Blue AOD data and the MOCAGE
simulations for 2012 and 2013 over the whole simulated domain and the budget
domain, respectively. They show that the model is able to simulate well the
aerosol optical depth over this period and region. The statistical indicators
are similar between the two domains considered here. Hence the following
discussion will focus on the whole simulated domain comparison (Table ).
There is a slightly different behaviour between the two
years. MNMB and FGE (fractional gross error, dimensionless) are lower for
the 2012 simulation, but the correlation is better for the 2013 simulation.
Mean statistics of the comparison between the MODIS AOD and the
MOCAGE simulation for the different seasons included in the years 2012 and
2013. See Appendix for information about statistical
indicators.
YearBiasMNMBFGECorrelationMODIS MAM 2012-0.05-0.130.400.34JJA 2012-0.05-0.080.400.41SON 2012-0.030.010.380.53DJF 2012/2013-0.06-0.240.440.23MAM 2013-0.06-0.230.420.35JJA 2013-0.06-0.200.410.52SON 2013-0.05-0.170.390.45Deep Blue MAM 2012-0.13-0.330.570.37JJA 2012-0.17-0.350.590.30SON 2012-0.13-0.360.630.46DJF 2012/2013-0.12-0.570.740.30MAM 2013-0.14-0.440.660.40JJA 2013-0.19-0.440.660.36SON 2013-0.14-0.500.690.44
Table presents the statistics for the comparison
between the MODIS AOD data and the MOCAGE simulations for the different
complete seasons included in the years 2012 and 2013 over the whole simulated
domain. These numbers show a lower correlation in spring that can be related
to the desert dust episodes that may be related to a spatial or temporal
shift in the model compared to observations that can decrease the
correlations. These numbers are consistent with those of
for the summer of 2012, despite a lower correlation
(0.41 for 2012 in this study versus 0.68). The bias and error metrics do
not show a seasonal behaviour. The statistics over the budget domain are very
similar to those of the whole simulated domain and are not presented here.
Map of the annual mean aerosol optical depth simulated with the
model MOCAGE with superimposed AERONET observations (circles) for the years
2012 (a) and 2013 (b).
Comparison with AERONET data
AERONET (AErosol RObotics NETwork) measures ground-based AOD from automated
stations with an accuracy of ±0.01 . The AERONET data
are used here for the simulation evaluation as in . In
this comparison, we used 33 AERONET stations for 2012 and 40 for 2013.
Mean statistics of the comparison between the AERONET AOD data and
the MOCAGE simulation for the years 2012 and 2013. See Appendix for information about statistical indicators.
Figure presents the comparison between the annual aerosol
optical depth simulated by MOCAGE and the annual aerosol optical depth
measured by the AERONET stations for the years 2012 and 2013. It shows a good
agreement. This figure exhibits generally similar patterns on the mean AODs
simulated by the model for 2012 and 2013. AODs are highest in 2012 over
northern
Africa and are higher in 2013 than in 2012 over the north-east of the domain,
especially over Romania and Ukraine. In the eastern Mediterranean, the AODs
are underestimated in 2013. The anthropogenic emissions in this area are not
well known and are likely underestimated, leading to a systematic negative bias
of the model over this region. The difference in behaviour between the years
2012 and 2013 might be due to weather differences. The year 2012 has more
rainfall in this area, leading to more wet deposition (Fig. ). The mean concentrations in 2012, and also
the AOD, are lower and closer in the simulation to reality since wet
deposition reduces the impact of emission uncertainty. This partly hides the
underestimation of the emission inventory compared to 2013. This can also be
seen in Fig. when comparing MOCAGE to MODIS and Deep Blue
AOD.
Table presents the statistics of the comparison between
the MOCAGE simulated AODs and the AERONET observed AODs. The model compares
very well to this observation set, with very low MNMB for both years (0.10
and 0.02) and high correlations (0.69 and 0.67). These numbers here are
coherent with those of and
.
As for the MODIS AODs, this comparison shows a good agreement between the
model simulation and the AERONET AOD measurements. Also, both comparisons
reveal coherent patterns such as an underestimation of the modelled AOD over
the east coast of the Mediterranean Basin.
Comparison with European air quality monitoring stations from the AQeR databases
The description of the data used in this section is available in Appendix .
Table presents the statistics for
the comparison between the MOCAGE simulations and Air Quality e-Reporting (AQeR) hourly data for the
year 2012 and 2013, and for PM10 and PM2.5. This table shows a
similar behaviour for 2012 and 2013. Aerosol concentrations are
underestimated, and MNMBs for PM10 are -0.64 and -0.58,
respectively, for 2012 and 2013, with correlations of 0.62 and 0.59.
PM2.5 concentrations are well represented with lower MNMB (-0.19
and -0.27) and higher correlations (0.71 and 0.68). The aerosol
underestimation can be explained at least partly by the lack of secondary
organic aerosols in the model MOCAGE, but also by uncertainties in the
anthropogenic emission inventories, particularly in the eastern part of the
Mediterranean region. The location of the AQeR stations, mostly in the
northern part of the simulation domain, makes the influence of natural
aerosols very small.
Statistics of the comparison between the MOCAGE simulation and the
AQeR database measurements, corresponding to classes 1 to 5 from the
classification of for the years 2012 and 2013. The table
presents results for PM10 and
PM2.5.
Statistics of the comparison between the MOCAGE simulation and the
AQeR database measurements, corresponding to classes 1 to 5 from the
classification of for the years 2012 and 2013 on the
budget domain. The table presents results for PM10 and
PM2.5.
Table presents the same statistics as for Table
but over the budget domain. In terms of bias, the results
are very similar, with a negative bias of about -10 µgm-3 for
PM10 and -4 µgm-3 for PM2.5. The errors and
the correlation are slightly worse than for the statistics over the whole
modelled domain with, for example, a correlation of 0.49 for PM10
in 2012 against a correlation of 0.62 for the whole set of measures. We should note that for
PM2.5 there are only 24 and 29 stations in the budget domain for
the year 2012 and 2013, respectively. Therefore the statistics are not as
solid for the budget domain as for the whole simulation domain.
Table presents the statistics for the comparison
between MOCAGE simulations and AQeR hourly data for the different seasons
included in the years 2012 and 2013. It shows that the model is in better
agreement with the measurements in winter periods compared to summer periods.
For example the PM2.5 correlation is 0.71 for the DJF period while it
is 0.44 for the JJA 2013 period, while the MNMB is higher for summer than
for winter. This is probably due to the lack of secondary organic aerosols
which are mainly produced in summer.
Statistics of the comparison between the MOCAGE simulation and the
AQeR database measurements, corresponding to classes 1 to 5 from the
classification of for the different seasons included in
the years 2012 and 2013. The table presents results for PM10 and
PM2.5.
The comparison with AODs in Sect. showed
better agreements between the model and measurements with a smaller bias.
This can come from a bad representation of the vertical aerosol distribution, for example if the total amount of aerosols is correct but the aerosols are
not concentrated enough in the boundary layer. Also, the size distribution
can affect the AOD computation, which is sensitive to the aerosol size, while
the PM10 indicator is less sensitive to this aspect as long as the
aerosols are smaller than 10 µm. Yet it is difficult to analyse because
the AOD and the surface stations do not represent the same quantities.
Indeed, the AOD is a measure of the integrated column of aerosol quantities
over a large area (for MODIS), while surface stations evaluate the aerosol
concentrations at the surface only and on a limited amount of locations. When
compared to AERONET stations, the AOD is also representative of a limited
amount of locations, but locations that are very different from those with the surface stations.
Location of the EMEP stations used in this study for the year 2013.
Comparison with the EMEP database
In order to characterize the model behaviour against aerosol composition, we
use the EMEP programme measurements (downloaded from the website
http://ebas.nilu.no/, last access: 4 April 2018).
From this set of measurements, we use measurements of concentrations of
secondary inorganic aerosols, carbonaceous aerosols and sodium, and wet
deposition of secondary inorganic aerosols and sodium, in order to check the
behaviour of the model regarding these simulated components. We do not
present chloride comparisons because the chemistry of HCl with
volatile organic compounds is not represented in the model. Gaseous
HCl is evaporated when nitrate is condensed on sea salt aerosols.
We only give the results for the year 2013 since there are too few stations
available in 2012 to be statistically significant. Nevertheless, results for
2012 on this limited set of data are similar to those for 2013. Figure
represents the location of the stations used in this
study. This figure highlights the lack of these types of measurements outside
Europe. Indeed, the EMEP network only allows us to characterize the north-west part of the Mediterranean region. This is a limitation of the
comparison.
Statistics of the comparison between the MOCAGE simulation and the
EMEP measurement database for the year 2013.
Table presents the statistics for the comparison between the
EMEP measurements and the MOCAGE simulation for the year 2013. Secondary
inorganic aerosol compounds are slightly underestimated, with MNMBs of
-0.11 for sulfate, -0.17 for nitrate and -0.19 for ammonium.
Correlations are slightly better for sulfate (0.58) than for ammonium
(0.53) and nitrate (0.49). The results presented here are similar to
for the MOCAGE simulations over the whole European
continent. This shows the ability of the model to represent the composition
of the SIA over the European part of the domain.
The comparison for black and organic carbon aerosols is made over only seven
stations, six of which are included in the budget domain. Black carbon aerosol
concentrations simulated with MOCAGE are overestimated and exhibit a positive
bias of 0.59µgm-3, with a correlation of 0.66. Organic
carbon aerosol concentrations are underestimated by the model simulations.
The bias is of -2.05 µgm-3, but the correlation is good, with
0.66. The overestimation of black carbon aerosols and the underestimation
of organic carbon aerosols can be due to errors in the speciation of the
anthropogenic aerosol emissions. Also, part of the underestimation of the
organic carbon aerosols can be linked to the lack of secondary organic
aerosols in MOCAGE.
Sodium concentrations are compared to eight measuring station observations, all located within the study domain.
Sodium concentrations are slightly overestimated by the model with a MNMB of 0.31 and a correlation of 0.47.
Chloride concentrations show a larger overestimation, with a MNMB of 0.80 and a lower correlation of 0.27.
Statistics of the comparison between the MOCAGE simulation and the
EMEP wet deposition measurements database for the year 2013.
stationsBiasMNMBFGECorrelationSulfate total (bias in mgSL-1) 440.14-0.571.40-0.17Nitrate (bias in mgNL-1) 440.33-0.421.39-0.2Ammonium (bias in mgNL-1) 440.77-0.281.40-0.15Sodium (bias in mgL-1) 448.900.171.480.05Wet deposition measurements
Table presents the statistics for the comparison between
the EMEP wet deposition measurements and MOCAGE simulation for the year 2013.
The wet deposition is underestimated for secondary inorganic aerosol
compounds, with a MNMB varying from -0.57 for sulfate to -0.28 for
ammonium. This is related to the underestimation of SIA concentrations. The
comparison for sodium wet depositions presents an overestimation with a MNMB
of 0.36 for sodium. These results are consistent with the overestimation of
sodium concentrations. The high FGE (around 1.40) and the low correlation
for all aerosol compounds show there are large variations between the model
and the measurements. Nevertheless, the low MNMBs allow us to be confident in
the mean quantity of deposited aerosol.
Conclusion on the evaluation
In this section, we used different sets of observations to evaluate the
results of the model. Firstly, we used aerosol optical depth measurements
which provide vertically integrated measurements over a large part of the
simulated domain. The model shows good results with respect to the MODIS and
AERONET observations. When comparing to AERONET data, we show for example
very low biases (MNMB of 0.10 for 2012 and 0.02 for 2013) and good
correlations (0.69 for 2012 and 0.67 for 2013). However, as shown by
for summer, organic aerosols can represent up to half of
the PM1 aerosols. They can then play a significant role in the visible
AOD. The fact that the bias is low here can be a sign of compensating errors
since the SOA are not taken into account in this study.
Secondly, we compared the simulations to in situ surface observations.
The comparison to the AQeR database in terms of particulate matter shows a larger bias in summer related to the lack of SOA in the MOCAGE model.
This is consistent with the negative bias when comparing the MOCAGE simulation to organic aerosol measurements from the EMEP database.
Finally we compared MOCAGE simulation to wet deposition measurements from the EMEP database. We showed a good agreement in terms of bias.
From this evaluation, we show that the MOCAGE simulation give realistic results compared to observations.
Nevertheless, there are large regions, especially in northern Africa, where we do not have in situ measurements available to evaluate the model.
Aerosol budget and variability over the Mediterranean Basin
The simulation presented and evaluated in Sect. is now used to characterize the budget of the
aerosols over the Mediterranean Basin. Note that the information presented
concerning the aerosol budget refers to the total aerosol mass.
Methodology
Over the domain considered, using hourly outputs, for a given aerosol
species, we define its budget for a chosen time period by the equation:
Δburden=Em+Pr-Loss-Dep+Tran,
with Δburden the difference in the atmospheric burden between the
end and the beginning of the time period. “Em” is the emission, “Pr” the
chemical production, “Loss” the chemical loss, “Dep” the deposition terms
(dry and wet deposition, and sedimentation) and “Tran” the import or export of
the aerosol in the budget domain. This last term is positive when aerosols
are imported into the domain and negative when exported. All terms are
estimated or directly computable from the simulation outputs. For the
advection, the MOCAGE model uses a semi-Lagrangian transport scheme. It means
that for each model grid point, the transport over a time step is done by
determining the location from which the air mass originated at the beginning
of the time step and the associated concentration of aerosol species at this
location. This approach is used in order to be able to use long time steps
for the transport. For the MOCAGE model, the transport time step is set to
1 h. Because of the use of a semi-Lagrangian approach in MOCAGE, the
Tran term cannot be directly estimated since there is no Eulerian flux
computed in the transport scheme. We therefore use an indirect estimation of
the Tran term by calculating the difference in the burden before and after
the transport into the budget domain at each time step. Note that the
separation between the inward flux and the outward flux of transported
particulate matter cannot be done in the Tran term.
From this definition, the Tran term implicitly includes the transport but
also the model errors due in particular to possible mass imbalance. Since
mass conservation is insured at the global domain that serves to force the
boundaries of the regional domain, the model error due to mass imbalance is
expected to be small compared to transport.
Annual budget of the total mass of aerosols for the year 2012. The
residual mass term corresponds to the values obtained when closing the
budget. The different components are in teragrams (Tg) except the mean burden
which is in gigagrams (Gg).
Using all these terms we calculate the residual mass, corresponding to the model error that is computed using the following:
Resid=Em+Pr-Loss-Dep+Tran-Δburden.
We have calculated this residual model error term. It is about 1% of
Em
or Pr for black carbon and primary organic carbon and sulfate, about 4%
of Em for desert dust and sea salt, and about 0.1% of Pr for ammonium
and nitrate. Therefore it is small and does not affect our budget analysis.
When summing up the budget terms, the reader will then find that the budget
is not fully closed. This is due to the error made in the semi-Lagrangian
transport scheme that has a smoothing effect on the strong peak in aerosols
concentrations, usually leading to a gain of mass, but this error remains
small.
Results of the aerosol budget over the Mediterranean Basin
In this section, we present the aerosol budget over the 2-year period
2012–2013, on an annual and month basis in order to discuss the seasonal
variability. All the following results are presented on the budget domain
covering 29 to 47∘ N latitude and 10∘ W
to 38∘ E longitude over the entirety of the vertical extent of
the model. All the source terms are positive (emission, production), while
the sinks are negative (deposition, chemical destruction). The horizontal
transport term is positive for import in the domain and negative for export.
Annual budget
Tables and present the annual budget, on
the budget domain, of the aerosols for the year 2012 and 2013, respectively.
Note that the unit of the burden term is the teragram (Tg) while the other term's
unit is gigagram (Gg). For ammonium, nitrate and sulfate there are no emissions
in the model. The first column corresponds to the quantity of secondary
aerosol condensed. In the same way, the column “chemical loss” shows the
evaporation of the secondary aerosols. One can see the similar behaviour for
both years, and especially for black carbon. Desert dusts and sea salt are
the most abundant aerosols in the region with a burden of about 900Gg for desert dust and 150Gg for sea salt. Other aerosols
have a mean burden between 13 and 75Gg. But altogether the
different SIA components (secondary inorganic aerosols, which are sulfate,
nitrate and ammonium) add up to a burden similar to sea salt. The year 2012
shows higher concentrations of primary carbonaceous aerosols and secondary
inorganic aerosols, while the year 2013 is characterized more by natural
aerosols (desert dust and sea salt). For both years, one can see that all
types of aerosols experience net export out of the budget domain, except for
sea salt. One can also note that the chemical destruction term for the
sulfate is equal to zero. This is because ISORROPIA assumes all of the
sulfuric acid is condensed into the aerosol phase, whatever the thermodynamic
conditions, because of the very low vapour pressure of sulfuric acid.
Yearly mean of the total column of aerosols for the year 2012. The
red square on the figures represents the budget
domain.
Yearly mean of the total column of aerosols for the year 2013. The
red square on the figures represents the budget
domain.
Yearly mean of precipitation rate (top panels) and wind vectors at
200 m above the surface (bottom panels) for the year 2012 (a, c) and 2013 (b, d).
The red square on the figures represents the budget domain.
Monthly budget for the total mass of primary aerosols for the year 2012 (dashed lines) and 2013 (solid line). The green lines correspond to the
emissions, the blue ones to deposition, the pink ones to the import or export
part and the brown ones the burden.
Monthly budget for the total mass of secondary aerosols for the
years 2012 (dashed lines) and 2013 (solid line). The green lines correspond to the
emissions, the red ones to the chemical loss, the blue ones to deposition,
the pink ones to the import or export part and the brown ones the
burden.
Tables and present the annual
budget of the aerosols for the year 2012 and 2013, respectively, as a
percentage of the emission or the production. This allows us to easily
identify which proportion of the aerosol goes preferentially to each term of
the budget. found that wet deposition is the major
sink for carbonaceous aerosols, ammonium, nitrate and sulfate aerosols,
while desert dust and sea salt experience mainly dry deposition.
Consistently, here wet deposition is the major sink for primary organic
carbon while desert dust and sea salt are mainly deposited by dry processes.
This is due to the size of the emitted aerosol, which is larger than for the
other types of aerosols. The sedimentation is thus more effective. For
ammonium and nitrate, the main sink is evaporation, which is not described as
a separate term of the budget in . Wet deposition is
the main sink in for sulfate, but sedimentation and
dry deposition are the main sinks in this study. This can be explained by the
difference in the simulated domains in both studies. The domain in our study
is more southern and has high sulfate aerosol concentrations in the eastern
part of the basin associated with less precipitation. For black carbon
aerosols, the main sink in our study is export. The difference between the
two studies can be explained, similarly to sulfate aerosols, to the
difference in domain location and weather conditions, especially in the
eastern Mediterranean.
Concerning the export, we can note that 11% of the desert dust is
exported, while this percentage raises between 22.8 to 39.5% for the
carbonaceous aerosols and the sulfate, 9% for the ammonium, and 2% for
the nitrate. For the sea salt it is 0.7% for 2012 and 2% for 2013, but
the residual mass in the budget calculation is of the same order as the
Tran term. We can then consider the global behaviour of sea salt as if
there is almost no flux. Nitrate aerosols export is low compared to both
ammonium and sulfate. It can be explained by the fact that nitrate and sea
salt are linked by the ISORROPIA module. Indeed, with our domain being
largely over a maritime surface, there are a lot of sea salt aerosols on
which nitrate condenses rapidly due to the thermodynamic equilibrium
assumption. conducted an analysis of the budget of
pollutants, including aerosols, over Europe for the year 2008. They found
that horizontal advection is a sink for nitrate, ammonium, sulfate and
carbonaceous aerosols. Hence, our study is consistent with their results.
These results are also consistent with .
showed that the nitrate aerosol partitioning is sensitive
to the pH. The pH is taken into account in the ISORROPIA calculation using
ion concentrations and the water content as input. Although the pH has not
been validated extensively in our model, showed the pH is
well predicted by ISORROPIA. The water content input is provided by the
numerical weather prediction simulation, in which data assimilation is conducted.
Therefore, we assume the input information is as precise as possible.
Moreover, the use of alkaline components in the SIA computation might change
the results. A work is in progress to include it into the ISORROPIA
implementation in MOCAGE. The inclusion of these compounds would increase pH
and shift the partitioning of HNO3 to the aerosol phase.
showed that the inclusion of dust-related alkaline
components improves the behaviour of the model, over Europe, by reducing the
PM10 bias of 6 to 10% when comparing results to selected EMEP
stations. By working around the Mediterranean Basin, we expect a greater
impact due to the close desert dust sources inducing more frequent and more
important dust outbreaks over the domain. Moreover,
showed the bias of the total nitrate aerosol is
reduced by about 20% when including crustal species in the computation.
Nevertheless, pointed out that the uptake
coefficients usually used are too important. Hence the previous numbers might
overestimated.
To further facilitate the analysis of the import or export of the aerosols,
Figs. and
present the yearly mean of the total column of the different aerosols for the
year 2012 and 2013, respectively. These panels also present the red square
representing the budget domain. Figure
depicts the precipitation rate and the wind fields at 200 m above the
surface for the years 2012 and 2013. One can see the mark of the desert dust
emissions along the southern boundary of the domain (Fig. 6a). These
emissions are transported with the dominant easterly and north-easterly
winds, thus explaining the general export behaviour of the desert dusts. We
observe the same phenomenon for carbonaceous aerosols and sulfate whose
concentrations are at a maximum in the eastern part of the basin and
associated with westerly winds, which export the aerosols across the eastern
border. The high concentrations of sulfate in the eastern Mediterranean are
consistent with who conducted a study on the
aerosol budget over Europe for the year 2006.
explain this high particulate sulfate abundance on the eastern Mediterranean Basin by the westerly winds transporting sulfur-rich air mass from central
Europe and the intense solar radiation enhancing the SO2 conversion.
By comparing the precipitation rates and the wind fields for 2012 and 2013,
we can note differences in the meteorology between the two years. In the
western part of the basin, the year 2013 presents higher wind speed values,
especially over the Gulf of Lion and northern Africa. This explains the higher
values of desert dust and sea salt aerosols in this region in 2013 compared
to 2012. In the eastern part of the basin, the year 2012 presents higher wind
speed values over the sea, explaining higher sea salt aerosol levels here.
Desert dust presents higher total column concentrations in 2013 and a bigger
extent towards the north-east of the domain. Associated with lower
precipitation in the area between Tunisia and Turkey in 2013, it explains the
lower wet deposition of desert dust aerosols in 2013 compared to 2012 despite
larger emissions. In this region, we can also see higher concentrations of
carbonaceous aerosols in 2012, which can be explained by higher speeds of the
wind advecting the pollution from the coast of Aegean Sea over the basin.
Maps of organic carbon emission from biomass burning (a, b) and total
column of primary organic carbon aerosols (c, d) for July 2012 (a, c) and
July 2013 (b, d).
Monthly budget
In this subsection, we examine the aerosol budget at the monthly temporal
scale. Figure presents the monthly variations of
budgets for the primary aerosols while Fig. shows
the monthly variations of budgets for the secondary inorganic aerosols. In
Fig. , sea salt aerosols present very similar
monthly variations between the two years. Sea salt aerosols show a slight
annual cycle with more emissions in the winter months, which are related to
higher wind speeds. Desert dusts have a similar behaviour between both years,
with high levels of dust between January and June and lower dust levels
during the second half of the year. Nevertheless, we can note the large
differences in desert dust emissions between the years 2012 and 2013, also
seen on other budget terms. This illustrates the important inter-annual
variability that can be seen over the Mediterranean Basin
. The 2012 dust period starts and finishes earlier,
while the dust period in 2013 involved higher emissions, and thus more
deposition, export and burden. For 2013, we can explain this phenomenon by
looking at the winds during the more active dust season (not shown). In 2013,
the average low-level winds were stronger over northern Africa, leading to
higher desert dust emissions, and thus to higher values for all the terms in
the budget.
Concerning anthropogenic aerosols, black carbon presents a very similar
behaviour between the two years, with a slight annual cycle having higher
emissions in autumn and winter than in summer. This is consistent with the
monthly variations of the emission inventory used. Organic carbon also presents
a similar behaviour for both years, except in summer when there is a
higher or similar burden in 2013 despite lower emissions. This comes from the
import of aerosols from outside the budget domain. Figure presents the total column and the biomass burning
emissions for July 2012 and July 2013. The figure illustrates that there were more
fires during summer 2013 in North America compared to summer 2012. These
fires exported a large amount of aerosols from the North American continent
into the budget domain, explaining the difference in behaviour for organic
carbon aerosols in summer
between 2012 and 2013.
The budget for secondary inorganic aerosols is presented in Fig. . There is a similar behaviour for all secondary
inorganic aerosols for both years. Nitrate and ammonium show small seasonal
variations. The burden of sulfate aerosols has a strong annual cycle with
maximum burden in summer despite a lower production during this season. The
summer maximum for sulfate aerosols is consistent with
, saying that the eastern Mediterranean Basin is
influenced by eastern Europe countries favouring sulfuric acid formation. The
reason for the increase in the burden is the lower deposition in summer than
in winter.
Conclusions on the aerosol budget
To conclude this section on the aerosol budget over the Mediterranean Basin,
we highlight several points. Firstly, there are very large differences in the
atmospheric loading of the different aerosol types. The burden of desert dust
and sea salt is much higher than that of other aerosols, but not only in
summer, as already shown by and
, but also throughout the whole year, leading to
their predominance in the annual budget. The use of the results of the two
simulated years allowed us to observe inter-annual differences, with the year 2012
having higher anthropogenic aerosol concentrations, while the year 2013
presents higher natural aerosol concentrations. Secondly, we saw that while
dry deposition processes are the main sink for natural aerosols and sulfate,
wet deposition is the main sink for primary organic carbon, transport
(export) for black carbon and aerosol evaporation for ammonium and nitrate.
We find that all aerosols are exported on average from the domain of study,
except for sea salt. and studies
on the European aerosol budget in 2006 and 2008, respectively, showed that
Europe is a net exporter of anthropogenic aerosols. These results are not
directly comparable to our study because of the difference in simulated
domain, but it allows some confidence in our results. Based on concentration
and wind maps, we showed that desert dust aerosols are exported out of the
study domain through the southern boundary, while carbonaceous aerosols and
sulfate are mainly exported out of the study domain through the eastern
boundary.
The monthly budget shows an annual cycle that is more (desert dust) or less
pronounced (sea salt, carbonaceous aerosols) depending on the type of
aerosols. This annual cycle is mainly due to the annual cycle of the
emissions. It can be related to the weather conditions, influencing directly
the amount of emitted particles (desert dust and sea salt). Forest fire
emissions from other continents, inducing aerosol import in the Mediterranean Basin, are another source of variability for the aerosol budget. It is
especially the case for the primary organic carbon in summer 2013.
Annual budget for the year 2012 of the total mass of aerosols for
the sensitivity test simulation. The different components are in teragrams
(Tg)
except the mean burden which is in gigagrams (Gg).
YearEmission orSedimentation andWetChemicalImport (>0) orMean2013chemical productiondry depositiondepositionlossexport (<0)burdenPrimary organic C-0.76-0.31-0.370.00-0.1030.62Black carbon-0.64-0.20-0.220.00-0.2410.69Ammonium-7.68-0.24-0.58-6.76-0.4325.38Nitrate-20.90-3.48-1.34-16.05-0.0464.45Sulfate-4.54-2.42-1.050.00-1.1154.26Sensitivity study: impact of international shipping and coastal anthropogenic emissions
The Mediterranean Basin has a large population density in the coastal areas
and high maritime traffic associated with high harbour economic activity
linked to the shipping business. In this section, we assess the impact of the
anthropogenic emissions in the coastal area and the international shipping
emissions in the Mediterranean Basin. To address this, we made a second
simulation where we removed the anthropogenic emissions over the sea and over
a 50km wide band along the coast. All the other parameters of the
simulation remain the same. Figure presents the mask used to
remove the anthropogenic emissions for this sensitivity test along with the
budget domain (in red) that is the same as in Sect. .
Since the natural aerosols are not impacted by the changes made, we will not
include them in this analysis.
Map of the mask used to cancel the anthropogenic emission in the
sensitivity test simulation in cyan and the budget domain in red.
Tables and present the annual
budget for the sensitivity simulation for 2012 and 2013, respectively.
Concerning the black carbon aerosol, we can note a similar behaviour between
the two simulated years, which is consistent with what we found in
Sect. . Concerning primary organic carbon, we can still see the
impact of the biomass burning from North America in summer 2013 on this
aerosol. Secondary inorganic aerosols present a similar behaviour between the
two years.
Annual budget for the year 2012 of the total mass of aerosols. The
terms corresponds to the relative difference between the reference simulation
and the sensitivity test. A negative value means the value is smaller in the
test simulation.
In order to compare the results between the two simulations, Tables and present the
relative differences, between the reference simulation and the test
simulation, respectively for the years 2012 and 2013. These relative
differences, for the parameter A, are computed as follows:
Adiff=Asen-ArefAref,
with Aref the value of the parameter A in the reference
simulation and Asen the value of A in the sensibility test
simulation. A negative value means the parameter is smaller, in absolute
value, in the test simulation than in the reference simulation. The reader
may find some small differences while computing the terms of these tables
because they are computed with the real values while the numbers in the
tables are rounded. As for the import or export terms, the values are always
negative or null; a negative value of the difference means the export is less
pronounced in the test than in the reference.
For the black carbon aerosols, we see a similar behaviour between 2012 and
2013. The mean burden is reduced by about 17% while the emissions are
reduced by 30% and the export by 35% (for 2013) to 40% (for 2012).
This is due to the high black carbon emissions in the eastern part of the
domain in the highly populated areas near the coast that are largely
exported. Primary organic carbon aerosols have a mean burden reduced by
7.5% while the emissions are reduced by 27 and 29%, respectively,
for 2012 and 2013. Here we can see the impact of the high aerosol
concentrations coming from the biomass burning in North America. The
differences in the imported term for each year, between the reference
simulation and the test simulation, are very similar, with 0.15Tg for
2012 and 0.14Tg 2013. This represents the reduction of the export of
aerosols from local sources. The reference simulation presents an export of
0.32Tg for 2012 and 0.24Tg for 2013. Then, when calculating
the relative difference, the year 2013 gives a higher number.
Concerning SIA, both years are similar, with a decrease in the mean burden of
about 16% for ammonium, 12% for nitrate and 17% for sulfate. The
decrease in SIA formation is between 23.2% for ammonium and 36.4% for
sulfate and the export decreases by 55% for sulfates and 54% for
ammonium. Figure presents the total annual emission for
the SIA precursors, computed over the budget domain, in the reference
simulation and the sensitivity test simulation. This figure presents the
numbers for 2013, but they are very similar for the year 2012. We can see
that the SO2 emissions are reduced by approximately 40%, which is
coherent with the 36.4% sulfate formation decrease.
shows that 54% of the summertime sulfate aerosol
burden over the Mediterranean originates from ship emissions. In our study,
the estimation of the proportion of sulfate aerosols originating from
maritime and coastal emission is lower because our proportion is calculated
for the annual quantity. The decrease in the NOx emissions is about
50%, while the formation of nitrate aerosol is lowered by only 26.7%.
The precursor of nitrate aerosols is nitric acid but there are different
chemical pathways NOx can take, explaining the difference between the
NOx emission reduction and the nitrate formation decrease. Moreover,
the decrease in sulfuric and nitric acid can also lead to a change in the
aerosol pH and then for the aerosol partitioning. Ammonium formation is
lowered by 23.2% while ammonia emissions are only lowered by about 20%.
This is explained by the fact that ammonium is condensed onto sulfate and
nitrate particles to neutralize the solution. The decrease in sulfate and
nitrate then becomes a limiting factor for the formation of ammonium
aerosols.
Annual emissions of SIA precursors, NOx, NH3 and
SO2 for the year 2013 computed over the budget domain in the
reference simulation in blue and the sensitivity test in red.
As a conclusion we can note the high importance of the coastal area, which
includes many major cities, in this region. Indeed, our sensitivity test
shows that 23% (ammonium) to 36% (sulfate) of the emission or
production of anthropogenic primary aerosols and secondary inorganic aerosols
in the Mediterranean originate from the marine or coastal area. Also,
they account for 35% (black carbon) to 90% (nitrate) of the exported
aerosols outside the budget domain. We do not show the monthly budgets here
since they do not give additional information.
Conclusions
This study aimed at establishing the budget of the primary aerosols and
secondary inorganic aerosols on the Mediterranean Basin based on numerical
simulations of the years 2012 and 2013 using the MOCAGE model. We also
studied its seasonal variability, its year-to-year variability with
meteorological conditions, and the contribution of local anthropogenic
emissions from the populated coastal area and from international shipping in
the Mediterranean Basin. Firstly, we compared the simulation to observations
in order to do an evaluation of the simulation. We showed the model was able
to well represent the aerosol optical depth on the Mediterranean Basin using
MODIS, Deep Blue and AERONET data. Secondly, we compared the model
simulations to in situ concentration data from the AQeR and EMEP database.
This comparison shows that the model represents well the secondary inorganic
aerosols while the lack of secondary organic aerosols is clearly apparent
both in seasonal particulate matter observations and in aerosol composition
observations. Also, this comparison highlights a lack of observations in the
southern part of the domain, and especially in northern Africa, to fully evaluate
the model using in situ surface measurements.
Secondly we use the two year-long simulations to compute the aerosol budget
over the Mediterranean Basin on a annual and monthly basis. The budget domain
we chose aimed to capture the economic activities over the Mediterranean Sea and the associated harbour activities while being rectangular for
simplicity of treatment and analysis. The two year-long simulations allowed us to
illustrate the inter-annual variability of the aerosol budget. While the year 2012
presents more anthropogenic aerosols, the year 2013 has more natural
aerosols. We showed that all aerosols considered in this study, except for
sea salt, experience net export in our domain of study. Hence this area
should be considered as a source region for these aerosols. These results are
consistent with and . For desert
dust, this result strongly depends on the domain used to do the calculations.
We showed that the export of desert dust out of the study domain is due to
the position of the southern border including desert dust emissions
associated with north-east winds exporting these emissions out of the domain.
For the other aerosols, the results are more robust to the location of the
limit of the domain used. Our study showed that 11% of the desert dust,
22.8 to 39.5% of the carbonaceous aerosols, 35% of the sulfate and
9% of the ammonium emitted or chemically produced into the study domain
are exported.
We observed an annual cycle for the natural aerosols budget that is due to
the influence of meteorological conditions, modulating the emissions of
desert dust and sea salt. The annual cycle can also be affected by the
differences in primary anthropogenic aerosol emissions or variations in the
import of aerosols from outside (especially biomass burning events). We also
show that natural aerosols (desert dust and sea salt) are predominant over
this region throughout the year, as found in and
for the summer of 2012 and 2013.
In this study we did not include crustal species interactions in the
computation of the SIA, which can have an important impact
. It would be interesting to add
these interactions because they change the size distribution of the SIA,
and hence the budget will be changed through the different physical processes,
such as sedimentation, which is sensitive to the aerosol size.
Then, we made a sensitivity test to assess the importance of the marine and
coastal regions of the Mediterranean Basin. To do this, we removed the
international shipping emissions and the anthropogenic emissions over the sea
and over a 50km wide band along the coast. We showed that around
30% of the emissions of carbonaceous aerosols and 35 to 60% of the
exported carbonaceous aerosols originates from this region. The formation of
23, 27 and 27%, respectively, of ammonium, nitrate and sulfate
aerosols is due to the emissions within the marine and coastal area. We
showed nonlinear interaction between ammonium and nitrate aerosols and their
precursors, as the decrease in their formation does not follow the precursor
emissions decrease as is the case for sulfate aerosols.
The focus of this study is on primary aerosols and secondary inorganic
aerosols. Once the SOA development is validated in MOCAGE, it would be
interesting to do the budget for this type of aerosol too. Also, this would
give an opportunity to analyse the gaseous phase compounds and their budget
over the Mediterranean Basin. This study will also use the sensitivity test
simulation to compare the differences in behaviour between aerosols and
gaseous compounds.
We showed in Sect. differences in the average
meteorology between 2012 and 2013, and a direct link between the weather
conditions and the aerosol concentrations, such as the effect of the wind
speed. To go a step further, we propose in a future paper to analyse the
aerosol distribution using a more detailed meteorological analysis, based on
the concept of weather regimes. Weather conditions can be classified into
weather regimes that correspond to idealized meteorological situations. These
weather regimes can be used to gather similar meteorological conditions and
to analyse the “aerosol regime” associated with each weather regime. This kind
of methodology could also be used in climate simulations to assess the
expected behaviour of aerosols in the future.
Data can be requested from the corresponding author (jonathan.guth@meteo.fr).
Metrics used for evaluation
Several statistical indicators can be used for model evaluation against in
situ data. state that past model performance evaluations
have generally used observations to normalize the error and the bias. This
approach can be misleading when the denominator is small compared to the
numerator. Following , we chose to use the fractional bias
and the fractional gross error instead of the bias and the root-mean-square
error (RMSE).
The fractional bias, also called modified normalized mean bias (MNMB) or mean
fractional bias (MFB), is used to quantify, for N observations, the mean
between modelled (f) and observed (o) quantities. Fractional bias is a dimensionless
quantity and is defined as follows:
MNMB=2N∑i=1Nfi-oifi+oi.
The fractional bias ranges between -2 and 2, varying symmetrically with respect to under and overestimation.
The fractional gross error (FGE), also called mean fractional error (MFE), aims to quantify the model error.
It varies between 0 and 2 and is a dimensionless quantity, defined as follows:
FGE=2N∑i=1Nfi-oifi+oi.
The correlation coefficient r indicates the extent to which patterns in the model match those in the observations and is defined as follows:
r=1N∑i=1Nfi-f‾oi-o‾σfσo,
where σf and σo are standard deviations,
respectively, from the modelled and the observed time series and f‾ and o‾ their mean values.
Description of the AQeR database
A dense measurement network is used for air quality monitoring in Europe.
Data are gathered into a database named AIRBASE. It is managed by the
European Topic Centre on Air Pollution and Climate Change Mitigation on
behalf of the European Environment Agency (EEA). AIRBASE data are used in
this study to evaluate the performance of the model for PM10 and
PM2.5. From 2013, EEA changed their observation database which is now
called Air Quality e-Reporting (AQeR).
For this study, we use the latest version (version 8) of the
AIRBASE database for the year 2012, and the AQeR database for the year 2013.
For simplicity, we will use AQeR to designate both databases.
Monitoring stations from the AQeR database are located on various sites that are representative of rural, peri-urban or urban conditions.
For model evaluation, we select the stations which are representative of the model resolution.
Following , each station is characterized by a class between 1 and 10 according to its statistical characteristics.
Classes 1 and 2 correspond to a fully rural behaviour while 9 and 10 to a
traffic behaviour. Then, as in , only the stations
corresponding to classes 1 to 5 are kept in order to assure a set of
representative sites.
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
We would like to thank the Chemistry-Aerosol Mediterranean Experiment
project (ChArMEx, http://charmex.lsce.ipsl.fr, last access: 4 April 2018), which is the atmospheric
component of the French multidisciplinary program MISTRALS (Mediterranean
Integrated Studies aT Regional And Local Scales). ChArMEx-France was
principally funded by INSU, ADEME, ANR, CNES, CTC (Corsica region), EU/FEDER,
Météo-France and CEA. This work has been possible thanks to the
AIRBASE, EMEP database and EBAS database infrastructure. We also
acknowledge the MODIS mission team and scientists for the production of the
data used in this study. The authors would also like to thank the AERONET PIs
and their staff for establishing and maintaining the sites used in this
investigation.
Edited by: Matthias Beekmann
Reviewed by: three anonymous referees
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