Introduction
The geographical characteristics, specific atmospheric conditions and large
range of natural and anthropogenic sources in the Mediterranean basin
create a complex environmental situation contributing to the aerosol load.
The major motivations for characterizing aerosols in the Mediterranean are
their subsequent climate forcing (Nabat et al., 2014), as well as relevant
air quality and health issues (Rodríguez et al., 2002; Medina et al.,
2004). During summertime, regional circulation phenomena and increased
photochemistry favour the accumulation and secondary formation of
atmospheric aerosols (Millán et al., 1997; Rodríguez et al., 2002; Pey
et al., 2013). The atmosphere over the Aegean Archipelago (also referred to as
the Aegean Sea), part of the eastern Mediterranean (EM), is frequently
affected by strong northern winds during the warm period. These winds are
often bound to the Etesian flow (Maheras, 1986; Kotroni et al., 2001; Tylris
and Lelieveld, 2013; Anagnostopoulou et al., 2014), which is the most common
synoptic situation over the Aegean Sea (AS) during summer, transporting dry
and cool air masses downwind of southern Russia, Ukraine, central/eastern
Europe, the Balkan states and Turkey (Vrekoussis et al., 2005; Bryant et
al., 2006; Sciare et al., 2008). The emissions from biomass burning and
important urban and industrial centres situated in these regions, combined
with the intense photochemical ageing of the arriving plumes and the
decreased deposition of species in the marine environment, make the atmosphere
above the AS a favourable area for aerosol investigation, particularly during
regional-range transport phenomena observed in summer.
Previous aerosol modelling studies covering the AS (Lazaridis et al., 2005;
Fountoukis et al., 2011; Im et al., 2012) have shown the predominance of non
sea-salt sulfate in the fine aerosol mode, in agreement with previous
ground-based observations (Mihalopoulos et al., 1997; Bardouki et al., 2003;
Kanakidou et al., 2011), unlike anywhere else in Europe. Together with the
high degree of oxidation of the organic matter (Hildebrandt et al., 2010),
these findings are both consistent with the atmospheric conditions stated
above. In addition, the important role of natural aerosol sources (sea-salt
production and long-range transported dust plumes) has been investigated,
not only on the total PM10 mass levels (particulate matter with
aerodynamic diameter < 10 µm) and on model performance, but
also on the gas–aerosol interactions towards the modification of inorganic
aerosol composition (Kallos et al., 2007; Astitha and Kallos, 2008;
Athanasopoulou et al., 2008; Im, 2013). Another common output of model
applications over this archipelago is the exogenous influence (short-,
medium- and long-range transport) on aerosol chemical composition, PM10
concentration levels (European limit exceedances) and regional climate, in
comparison with the contribution of local sources (Lazaridis et al., 2005;
Kallos et al., 2007; Im and Kanakidou, 2012).
The relation between meteorology and aerosol load over the EM is less
understood, and it has only been until recently that people have started studying
it using atmospheric models. Im et al. (2012) and Megaritis et al. (2013)
have studied the influence of temperature increases of up to 5 K on the
chemical composition of aerosol particles. Their results are
contradictory for sulfate (negative and positive changes in mass
concentrations, respectively), but they are in agreement for nitrate
(decrease in mass concentrations) and organics (increase in mass
concentrations). Inversely, the effect of aerosols on regional climate has
been investigated by Solomos et al. (2011) and Kallos et al. (2014), who
showed that the properties of atmospheric particles can modify cloud
structure and precipitation during a heavy rainfall event over the EM. Given
the complexity of the aerosol mixture in the Mediterranean basin, further
studies on the chemical characterization and size distribution of the
aerosol mass will elucidate the interactions between airborne particles,
meteorology and climate in the region.
A satisfactory representation of aerosol chemical species by model
applications is a challenging task. Predictions over the AS from the
aforementioned studies have been evaluated against concurrent or past
measurements (Chabas and Lefevre, 2000; Kouvarakis et al., 2001; Smolik et
al., 2003; Eleftheriadis et al., 2006; Gerasopoulos et al., 2006, 2007;
Sciare et al., 2003, 2008; Koulouri et al., 2008; Pikridas et al., 2010; Im
et al., 2012). Comparisons showed a moderate to large underestimation of the
simulated PM10 (Lazaridis et al., 2005; Im and Kanakidou, 2012) or
organic mass concentration (Fountoukis et al., 2011), despite inorganic
species being well represented (Astitha and Kallos, 2008; Athanasopoulou et
al., 2008; Fountoukis et al., 2011). Improved model performance for
precipitation is achieved when cloud condensation nuclei activation of
aerosols is included (Kallos et al., 2014).
Most of the above modelling studies focus on the surface representation of
aerosols and are compared against ground-based observational data from the
station of Finokalia in Crete (south AS). A few modelling studies that
investigated the vertical profiles of dust and sea-salt aerosols found that
particles over the EM did not elevate higher than 2 km (Astitha
and Kallos, 2008; Solomos et al., 2011). The latter study, which was
compared against airborne measurements conducted near the Israeli coast,
showed a good correlation between modelled and airborne measurements of
aerosol mass concentrations. An earlier airborne experiment over the Aegean
Archipelago (not bound to a regional model application), showed that the
atmosphere 3.5 km above sea level (a.s.l.) is almost completely depleted of
particles during Etesians (Formenti et al., 2002). This study also confirmed
that additional quantities of aged aerosols from fossil fuel combustion and
forest fires are transported southward. Recently, four clustered airborne
campaigns, performed during a 10-day period of strong northern winds
(including Etesians), provided amongst others a unique data set, including
measurements of the chemical composition of submicron particles above the AS
and western Turkey. The first results from these measurements are
selectively presented in Bezantakos et al. (2013) and Tombrou et al. (2013,
2015).
The present study provides predictions of the size distribution and the
chemical composition of aerosol particles observed over the wider region of
the Aegean Archipelago during the same 10-day period (August–September 2011), taking full advantage of the aforementioned airborne data set and
supportive ground-based aerosol observations. In order to capture the airflows over the Aegean basin more
efficiently, a comprehensive coupling of gases and aerosols between the PMCAMx
and GEOS-CHEM chemical transport models (CTM) is performed and applied here for the first time. Outputs from
the PMCAMx model are compared against the complete set of experimental
aerosol data, providing the most extensive evaluation of aerosol simulation
performances over a wide region of the Mediterranean basin. The large number
of prediction–observation pairs enables the investigation of the parameters
that significantly affect aerosol model performance. An inter-comparison
among different scenarios is performed with respect to the airborne
observations, in order to improve predictions of the organic aerosol
fraction in the marine environment. This combined use of CTMs and monitoring
data, which is emphasized by the latest European air quality directive, is
taking advantage of the capabilities of the applied model system. The
current model applications presented here complement the newly existing
aerosol data set regarding the origin and chemical ageing of the organic
matter (primary, oxygenated, anthropogenic and biogenic), the chemical
composition and particle size distribution and the role of non-local sources
of air pollution on the mass of each aerosol species under different paths
of northern transport.
Experimental data
Airborne measurements
Airborne data from four EUFAR (http://www.eufar.net/) campaigns (i.e.
AEGEAN-GAME, ACEMED, CarbonExp and CIMS) are utilized in this study. The
measurements were conducted using the UK BAe-146-301 Atmospheric Research
Aircraft, which was operated through the Facility for Airborne Atmospheric
Measurements (FAAM, http://www.faam.ac.uk/). Nine flights were
performed between 31 August and 09 September 2011 (cf. Fig. 1). In all
flights, the aircraft took off from and landed at the airport of Chania
in northwest (NW) Crete. Five of the flights passed over the AS (on 1, 2, 4, and 7 September),
one oriented towards Thessaloniki passing over Athens (8 September), while
the aircraft flew over the western coast of Turkey up to the southwest
(SW) coast of the Black Sea during the rest of the flights. With the exception of the last flight on 8 September, all flights were performed from 08:00 to 15:00 UTC. Flight paths
in the Greek airspace were at altitudes up to 5 km a.s.l., while those over
Turkey were above 2 and up to 7.6 km a.s.l.
(a) Geographical map of the PMCAMx model domain covering the
greater area of the Aegean Sea, showing also the trajectories for the nine
flights during the modelling period (29 August–09 September 2011). All
flights took off and landed at the airport of Chania. The aircraft over the
Aegean Sea moved anti-clockwise. The ground monitoring sites are indicated
by the bold fonts. The rest indicate areas discussed within the text. (b) The
aircraft altitude during the time frame of each flight.
![]()
The airborne measurements during these campaigns provided, among other
atmospheric parameters, the chemical composition of aerosols. High-time
resolution measurements of the sulfate (SO42-), nitrate
(NO3-), ammonium (NH4+), chloride (Cl-) and organic
(OA) content of the sub-micron particles (PM1) were performed by an
airborne compact time-of-flight aerosol mass spectrometer (cToF-AMS)
(Canagaratna et al., 2007; Morgan et al., 2010). Aerosol mass concentrations
are reported at ambient temperature and pressure (i.e. a conversion from
standard temperature and pressure to ambient conditions has been applied).
In common with other AMS measurements, these measurements nominally
represent the submicron, non-refractory component of the aerosols, therefore
do not include any sulfate, nitrate or chloride associated with sea salt or
dust particles. The collection efficiency (CE) was estimated based on the
parameterization described by Middlebrook et al. (2012), which was close to
unity based on the acidic nature of the particles. Because no on-board
validation of this chemical data was available (no other composition data
were obtained and the possible presence of sea salt particles would confound
a comparison with the particle sizing instruments), it is prudent to assign
an uncertainty of ca. 30–35 % to the AMS measurements, as suggested by
Bahreini et al. (2009).
Wind speed and direction, air temperature and water vapour mixing ratio were
also available and used here for model evaluation (Sect. 4.1). More
information on the flights, instrumentation and measured data during this
period can be found in Bezantakos et al. (2013) and Tombrou et al. (2015).
Ground measurements
Ground-based measurements of the chemical composition and physical
properties of the particles in the region were conducted at two remote
stations located at Vigla (39∘58′ N, 25∘04′ E; 420 m a.s.l.) on the
island of Lemnos and Finokalia, (35∘20′ N, 25∘40′ E; 150 m a.s.l.) on
the island of Crete, between 29 August and 09 September 2011. Both sites are far from
major cities and local anthropogenic sources (Fig. 1a). To determine the
aerosol chemical composition observed at Vigla and Finokalia, particles were
collected every 1, 6 or 8 h using PM10 and PM1 samplers. The
ground aerosol data used in this study are the PM10 elemental (EC) and
organic carbon (OC) (6 h samples), the PM1 SO42- (hourly
samples) and the total PM10 mass (8 h samples).
OC and EC concentrations on the collected samples were measured with a
Sunset lab instrument (Sunset Laboratory Inc.; OR, USA) implemented with the
EUSAAR-2 protocol (Cavalli et al., 2010). Analytical procedures and
detection limits of the methods are reported in detail by Paraskevopoulou et
al. (2014). Finally, measurements of anions in PM1 were performed at
Finokalia using a particle-into-liquid sampler (PILS) (Orsini et al., 2003)
running at 15.5 L min-1 and coupled with an ion chromatograph (IC). More
information on the PILS-IC settings used here is available in Sciare et al. (2011).
Wind speed, wind direction, as well as the concentrations of ozone (O3)
and nitrogen oxides (NOx: NO+NO2) measured at the Finokalia
station are also used herein (cf. Sect. 4.1).
Methodology
Model framework
The model system used in this study is comprised of the regional aerosol
model PMCAMx, the regional meteorological model WRF/ARW (hereafter referred
as WRF, Skamarock et al., 2008) and the global chemistry transport model
GEOS-CHEM (Bey et al., 2001), following the methodology described by Tombrou
et al. (2009). The setup of the modelsis given in Table 1 and Sect. S1 in the Supplement. All
air quality model results presented in this study correspond to the PMCAMx
runs.
Main characteristics of the WRF meso-scale meteorological model,
the GEOS-Chem v8-03-01 global and the PMCAMx regional CTM applications.
WRF
GEOS-Chem
PMCAMx
Chemical and physical mechanisms
Planetary boundary layer (PBL) parameterization: YSU (Hong et al., 2006) (standard run). BOULAC (Bougeault and Lacarrére, 1989) and QNSE (Sukoriansky et al., 2005) are used in two additional scenarios.
NOx–Ox–hydrocarbon-aerosol species module (the SOA module by Chung and Seinfeld, 2002 and Henze et al., 2008 is included). The mechanism is combined to the ISORROPIA II aerosol thermodynamics (Fountoukis and Nenes, 2007).
Gaseous chemistry: SAPRC99 (Carter, 1990), inorganic aerosol chemistry: ISORROPIA II (Fountoukis and Nenes, 2007), organic aerosol chemistry: VBS (Shrivastava et al., 2008; Lane et al., 2008)
Initial, lateral and boundary conditions
National Centers for Environmental Prediction (NCEP) operational Global Final Analyses (1.0∘ × 1.0∘)
From the global GEOS-chem simulation (4.0∘ × 5.0∘)
From the global GEOS-chem simulation (0.5∘ × 0.667∘)
Input data
Sea surface temperature (SST): real-time global SST analysis data (0.5∘ × 0.5∘) Land use categories: 24 Soil categories: 16 (US Geological Survey)
Anthropogenic emissions: Wang et al. (1998), Benkovitz et al. (1996), Yevich and Logan (2003), Piccot et al. (1992) Natural emissions: Price and Rind (1992) Meteorological data: Goddard Earth Observing System (GEOS-5)/NASA Global Modeling and Assimilation Office
Anthropogenic, agricultural, forests: Hellenic Ministry of Environment, EMEP, Sea-salt and dust: Athanasopoulou et al. (2008, 2010) Meteorological data: from the WRF simulation (0.056∘ × 0.056∘)
Vertical grid
35 sigma levels (from ca. 10 m a.g.l. to 50 hPa)
47 hybrid eta levels (from ca. 50 m a.g.l. to 0.01 hPa)
14 levels (from surface to ca. 5.8 km)
Parent and nesting domains (extended areas of)
(A) Europe (0.5∘ × 0.5∘) (B) Greece and Italy (0.167∘ × 0.167∘) (C) Aegean Archipelago (0.056∘ × 0.056∘)
(A) Global domain (4∘ × 5∘) (B) Europe (0.5∘ × 0.667∘)
Aegean Archipelago (0.056∘ × 0.056∘)
PMCAMx is the research version of a former version (v.4) of the publicly
available 3-D, Eulerian chemical transport model CAMx (ENVIRON, 2003).
Aerosols therein, are represented by a detailed chemical composition:
potassium (K+), calcium (Ca2+), magnesium (Mg2+),
NH4+, sodium (Na+), SO42-, NO3, Cl-,
water (H2O), EC, reactive and inert primary organic aerosols (APO and
POA, respectively), oxidized APO (AOO) and secondary organic aerosols of
anthropogenic (ASOA) and biogenic (BSOA) origin. All these species are
distributed over 10 discrete and internally mixed size sections, in the
diameter range of 0.04 to 40 µm (cut-off diameters: 0.04, 0.08, 0.1, 0.3,
0.6, 1.2, 2.5, 5, 10, 20, 40 µm). This chemical and size treatment
results in 400 aerosol model components in total.
The aerosol-related dynamical processes considered in PMCAMx include primary
emissions, new particle formation by nucleation, condensation, evaporation,
wet and dry deposition, coagulation and chemistry. The incorporated chemical
modules are shown in Table 1. The ageing rate constants for primary and
secondary organic aerosols (both anthropogenic and biogenic) are 4 × 10-11 and 1 × 10-11 cm3 mol-1 s-1,
respectively (Murphy et al., 2011).
Model coupling
The chemical coupling (in ppb) between the PMCAMx (SAPRC,
ISORROPIAII and VBS mechanisms) and the GEOS-CHEM (SOA mechanism) model.
Aerosols are shown in bold. The numbers next to PMCAMx aerosol species
correspond to their size bins. PM2.5 in PMCAMx corresponds to the bins
1 to 6, while the rest of the bins (7 to 10) are PM2.5-40. Sea-salt aerosols
(SSA) in GEOS-CHEM are simulated in two bins (effective diameter ranges 0.2
to 5 and 5 to 8 µm), while dust particles (DST) are split into 4 bins
(effective diameters 1.4, 2.8, 4.8 and 9 µm).
No
PMCAMx
GEOS-CHEM
No
PMCAMx
GEOS-CHEM
1
NO2
NOX
30
SO2
SO2
2
O3
NOX–OX
31
Sulfuric acid (SULF)
SULF+methyl sulfonic acid
3
Peroxyacetyl nitrate (PAN)
PAN
32
NH3
NH3
4
Higher peroxyacetyl nitrate
Lumped peroxypropionyl nitrate
33
Hydrogen peroxide (H2O2)
H2O2
5
PAN compound from methacrolein
Peroxymethacroyl nitrate
34–37
ASOA gaseous precursors (CAS1–4)
Oxidized aromatics
6
Organic nitrate
Lumped alkyl nitrate
38
BSOA gaseous precursors (CBS1)
Oxidized a-pinene, b-pinene, sabinene, carene, terpenoid ketones, limonene, terpenes
7
Peroxynitric acid (HNO4)
HNO4
39
BSOA gaseous precursors (CBS2)
Oxidized myrcene, terpenoid alcohols, ocimene
8
Formaldehyde (HCHO)
HCHO
40
BSOA gaseous precursors (CBS3)
Oxidized sesquiterpenes
9
Acetaldehyde (CCHO)
Acetaldehyde ALD2
41
BSOA gaseous precursors (CBS4)
Oxidized isoprene
10
Higher aldehyde (RCHO)
RCHO
1–3
APO4-6
Organic carbon (OCPI+OCPO)
11
Isoprene (ISOP)
0.2ISOP
4–7
ASOA1-4
Aerosol aromatics
12
Methylvinyl ketone (MVK)
MVK
8
BSOA1
aerosol a-pinene etc
13
Methacrolein (METH)
Methacrolein (MACR)
9
BSOA2
aerosol myrcene etc
14
Terpene
A-pinene, B-pinene, sabinene, carene, terpenoid ketones, limonene, myrcene, terpenoid alcohols, ocimene
10
BSOA3
aerosol sesquiterpenes
15
HNO3
HNO3
11
BSOA4
aerosol isoprene
16
Acetone (ACET)
0.3 ACET
12-14
PEC4-6
black carbon (BCPI+BCPO)
17
Methylethyl ketone (MEK)
0.3 MEK
15–20
NO34–9
NO3+ssNO3+0.009 SSA1-2
18
Methyl hydroperoxide (COOH)
Methyl hydroperoxide (MP)
21–23
NH4 4–6
NH4
19
CO
CO
24–26
SO44–6
SO4+0.25 ssSO4+0.03 SSA1
20
Lumped alkanes 1
0.5 ethane
27-29
SO4 7-9
0.75 ssSO4+0.03 SSA2
21
Lumped alkanes 2
0.33 propane
30–35
Cl 4-9
0.42 SSA1-2+0.01 DST1-4
22
Lumped alkanes 3
0.09 lumped alkanes
36–38
Na 4–6
0.46 SSAf1+0.05 DST1-2
23
Lumped alkanes 4
0.09 lumped alkanes
39–41
Na 7–9
0.46 SSA2+0.04 DST3-4
24
Lumped alkanes 5
0.09 lumped alkanes
42–44
Ca 4–6
0.009 SSA1+0.07 DST1-2
25
Lumped aromatics 1
0.16 benzene +0.14 toluene
45–47
Ca 7–9
0.04 DST3-4
26
Lumped aromatics 2
0.13 xylene
48–53
K 4–9
0.009 SSA1-2+0.03 DST1-4
27
Lumped olefins 1
0.17 lumped alkenes
54–59
Mg 4–9
0.06 SSA1-2+0.05 DST1-4
28
Lumped olefins 2
0.17 lumped alkenes
60
Si, Al (CRST6)
0.81 DST1-2
29
NXOY
N2O5
61–63
Si, Al (CRST 7–9)
0.83 DST3-4
In the frame of this study, the two chemical models are coupled offline, so
that GEOS-CHEM provides concentrations for a series of species at the
boundaries (lateral and top; boundary conditions – BCs) of the PMCAMx domain for each hour of the
simulation period. A three-dimensional initialization field (29 August 2011,
00:00 Local Standard Time; LST) is also extracted from GEOS-CHEM and used by
PMCAMx (ICs). Differences in the chemistry and spatial resolution used by
the two models demanded a chemical and three-dimensional matching between
the two models with respect to the gas and aerosol fields. The chemical
linking between the two air quality models (Table 2) involves 41 gaseous
species (20 of which are VOCs) and 63 aerosol species (SO42-,
NO3-, NH4+, APO, ASOA, BSOA, EC, Na+, Cl-,
Mg2+, Ca2+ and others, distributed over the size bins treated by
PMCAMx). A conversion factor of 2.1 was used to calculate total organic
aerosols (OA) from the OC GEOS-CHEM outputs, of which value is suitable for
non-urban areas (Turpin and Lim, 2001) and has already been reported in the
literature for OA over Crete (Sciare et al., 2005; Hildbrandt et al., 2010).
In order to assess the relative contribution of the different OA precursors
to the total SOA transported from outside the PMCAMx modelling domain
(GEOS-CHEM BCs), each of the five lumped SOA species treated by the
volatility basis set (VBS) scheme in PMCAMx is coupled to each unique
oxidative product treated by GEOS-CHEM (instead to that of the uniform
distribution of their mixture, Sect. 4.3). Sea-salt and dust species treated
by GEOS-CHEM are chemically resolved to the PMCAMx species following
Athanasopoulou et al. (2008) and Kandler et al. (2007) respectively (cf.
Table 2).
The hourly meteorological fields provided offline by the WRF to the PMCAMx,
include horizontal wind speed, temperature, diffusion coefficients
(Kv), pressure and water vapour, cloud optical depth, cloud and
precipitated water. Kv values are calculated directly during the WRF run,
and are then adjusted for the heights under 100 m, which is found to benefit
air quality predictions (ENVIRON, 2011). Minimum Kv value is set to 0.1 m2 s-1.
Simulations setup
The PMCAMx simulation domain is the greater area of the Aegean Archipelago
(Fig. 1a; 34.1 to 42.5∘ N, 18.4 to 29∘ E) with 0.056∘ (∼ 6.2 km) horizontal grid resolution and 14 vertical layers
with their domain-averaged layer top at 20.9, 29.3, 69.7, 129, 169, 228,
531, 869, 1256, 1696, 2166, 3252, 4496 and 575 m above ground level (a.g.l.).
The simulations are realized during the period from 29 August to 09 September 2011, so that they are directly comparable with measurements.
Model outputs are extracted on hourly basis. The first 2 days are used as
a spin-up.
Emissions from the anthropogenic, agricultural activities and forests used
by the PMCAMx model for the area of Greece are based on a National database
provided by the Ministry of Environment for the year 2002. The emission
rates for the area of Turkey are retrieved from the EMEP emission data set
(http://www.ceip.at/webdab-emission-database/officially-reported-emission-data/).
Analytical information on the setup for the WRF and GEOS-CHEM simulations,
as well as on the emissions treated by the chemical models is given in the
Supplement (Sect. S1).
The standard model application that provides the base-case outputs follows
the modelling configuration described so far. The first applied scenario aims
at investigating the exogenous aerosol fraction over the Aegean Archipelago
(trans-boundary pollution). This is captured by the coupling between PMCAMx
and GEOS-CHEM models and was identified through a combination of two
simulations: the standard run (i.e. BCs provided by GEOS-CHEM) and a
scenario with constant, minimum BCs (scenario 1). The different contribution
to aerosol levels from sources in Greece and the Turkish area (covered by
the simulation domain) is calculated by switching off the emissions from
Turkey in scenario 1 (scenario 2) (Sects. 4.2 to 4.4).
To assess the sensitivity of organic aerosol simulation performance, a
series of model scenarios was performed. Here, results on the OA sensitivity
to their ageing process are presented, following previous model studies
(Tsimpidi et al., 2010; Fountoukis et al., 2011): one scenario with the BSOA
ageing switched off (scenario 3) and another with the ASOA ageing constant
multiplied by 4 (scenario 4) (Sect. 4.3).
The sensitivity of simulated aerosol mass loading on modelled meteorology is
also examined. Apart from the standard model setup, where WRF uses the YSU
planetary boundary layer (PBL) parameterization (Table 1), two additional
PMCAMx simulations were performed using WRF inputs from an application with
the Bougeault–Lacarrère (BOULAC) PBL parameterization scheme (Bougeault
and Lacarrère, 1989) (scenario 5) and with quasi-normal scale
elimination (QNSE) (Sukoriansky et al., 2005) (scenario 6). This selection
was based on wind speed differences between seven different PBL schemes
(Dandou et al., 2014) (Sects. 4.2 to 4.3).
In order to compare predicted versus measured nitrate aerosol fractions
(i.e. using the AMS data), a sea-salt aerosols-free case was applied
(scenario 7) (Sect. 4.5). A summary of all model applications is given in
Table 3.
Description of the modelling scenarios performed by the PMCAMx model
during 29 August–09 September 2011.
Scenario
Scenario's description
Objective
Other information
Base-case
Standard run
Aerosol chemistry over the Aegean Archipelago (or Aegean Sea)
Inputs by WRF/YSU; PMCAMx/GEOS-CHEMcoupling; SOA ageing constant is1 × 10-11 cm3 mol-1 s-1
1
Constant, minimum* boundary conditions
Exogenous/trans-boundary aerosol fraction
PMCAMx is not coupled with GEOS-CHEM
2
Scenario 1 without emissions from the Turkish area of the domain
Aerosol fraction from sources in Greece/Turkey (covered by the domain)
3
BSOA ageing switched off
Sensitivity of organic aerosol performance to SOA ageing
4
ASOA ageing × 4
Only 29 August–02 September
5
Inputs by WRF/BOULAC
Sensitivity of aerosols to meteorology
YSU, BOULAC and QNSE schemes differ in the wind field predictions
6
Inputs by WRF/QNSE
7
SSA-free simulation
Direct comparison of nitrate predictions and observations
AMS detects the non-refractory, submicron aerosol fraction
* aerosol species concentrations are equal to 10-9 µg m-3.
Model evaluation statistics
Aerosol predictions are compared against AMS measurements using the
statistical measures of mean bias (MB) and error (ME), mean fractional bias
(MFB) and error (MFE), normalized mean bias (NMB) and error (NME), root mean
square error (RMSE) and correlation coefficient (r and r2). The
formulas of these indices are given in Table S2 in the Supplement. The airborne observational
data that fall within a computational cell during a model time step (hour)
are averaged, in order to be directly comparable with the model outputs.
In order to estimate which parameters systematically affect the model
discrepancies, a multiple linear regression was used for each aerosol
species among the model biases and basic, local variables (e.g.
co-ordinates, day/flight, time, wind speed and wind direction). Based on the
regression results, paired samples were created between the model biases and
each parameter that significantly affects them (e.g. the model biases and
the observed wind speeds were paired and formed one sample).
Each of these paired samples were subdivided into two samples, on the basis
of thresholds considering the model performance; i.e. the threshold is set
for the parameter value where performance goals are met (or not) for the
∼ 75 % of the predicted values of the one (or the other)
sub-sample. In particular, the threshold regarding altitude is estimated to
be at 2.2 km, close to the PBL height over the domain. Other thresholds set
for the paired samples are the longitude of 27∘ that
separates the Aegean Sea from Turkey, the 0-degree winds that divide NW
from NE (northeast) sectors and the wind speed of 9 m s-1. The statistical
hypothesis tests (F and t tests) confirmed that for all cases, the two
sub-samples were significantly different from each other. This procedure
specified under which conditions (e.g. wind speed values and direction)
aerosol model performance over the AS is systematically good or poor and is
presented in Sects. 4.2 to 4.4.
Results and discussion
The following sections analyse the model results with respect to the
measurements. In parallel, measurement findings are supported by the
capabilities of the current model system. Model outputs are thoroughly
evaluated against airborne AMS and ground-based observations. MFB and MFE
were selected as the most appropriate metrics to summarize aerosol model
(PMCAMx) performance (Boylan and Russell, 2006). The calculated values are
compared against the proposed goals and criteria for each aerosol species
(Table S2), in order to characterize model performance as good (the level of
accuracy that is considered to be close to the best a model can be expected
to achieve) or average (the level of accuracy that is considered to be
acceptable for modelling applications). When the standards are not met for
one or more species, the model skills (with regard to these species) are
characterized as poor, and the reasons for the model discrepancies are
further investigated.
Comparison of PMCAMx results (green continuous line) with AMS
airborne measurements (black continuous line) for total PM1: (a)
sulfate; the legend applies for all succeeding graphs, (b) organics (green
dashed line for scenario 3 is also shown), (c) hourly particulate ammonium
concentrations (µg m-3) for all flights in the frame of the
AEGEAN-GAME, ACEMED, CarbonExp and CIMS campaigns, during 31 August–09 September 2011. Data from the flights over the Aegean Sea, via Athens and
over west Turkey are discriminated by the horizontal blue, dashed-blue and
red lines, respectively. The yellow shaded area indicates mass
concentrations below 2.2 km a.s.l. The flight numbers and dates are shown
at
the bottom. More detailed flight information is embedded in Fig. 1. On the
right of each graph, the vertical profile of each species averaged per 100 m
(error bars with minimum and maximum values) is shown.
![]()
The WRF model was evaluated following the model-evaluation benchmarks
suggested by Tesche et al. (2001) and Emery et al. (2001). In particular,
mean absolute gross error (MAGE), MB, RMSE and index of agreement (IA) are
compared against the proposed benchmark values (Table S2).
Meteorology and gas-phase chemistry
Strong northerly winds dominated during the simulated period, as also shown
by Tombrou et al. (2015). In most cases, both the predicted and the observed
winds were NE and NW, which seems to depend on the latitude. Nevertheless,
local surface winds observed at the site of Finokalia exhibit a strong
westerly component (Fig. S1a). This pattern is attributed to the effect of
local topography, while predictions reflect a representative value of a grid
cell (an area of ∼ 38 km2), mainly covered by sea.
An overall good agreement is found between the airborne measured and
simulated values over the archipelago, as far as humidity, air temperature
and wind direction are concerned (cf. Table S3). Regarding wind speed, model
performance is weaker (two out of the three proposed benchmarks are reached, as
shown in Table S2). The predicted mean value (8.0 m s-1) along all
flight tracks is in good agreement with the measured one (8.4 m s-1;
Table S3). More specifically, the average (maximum) predicted value was 9.0
(16.5) and 7.5 (19.5) m s-1 upon the flight tracks below and above 2.2 km a.s.l., respectively. The corresponding measured wind speeds were 9.7 (22.4)
and 7.8 (24.1) m s-1. As far as the surface-wind speed (at 10 m a.g.l.) is
concerned, the 9-day average (minimum to maximum) surface wind speed
predictions at Finokalia were 7.5 (3.0 to 10.7) m s-1, while the
respective measurements were 6.8 (1.1 to 9.1) m s-1. The different
scale between point measurements and model results, which represent volume
averages, contributes to this divergence.
Fourth and seventh September 2011 were typical Etesian days with strong-channelled
northeasterly surface winds (> 15 m s-1) over the
archipelago (Tyrlis and Lelieveld, 2012). Under such conditions, the
afternoon marine atmospheric boundary layer was around 1000, 700 and 500 m
in the north, SW and SE (southeast) Aegean, respectively, successfully represented by
the PBL schemes used in this study (Tombrou et al., 2015; Dandou et al.,
2014).
Gas-phase comparisons between PMCAMx and ground concentration measurements
do not exhibit any significant inconsistencies. The 12-day average (minimum
to maximum) NOx and O3 predictions at Finokalia were 0.4 (0.1 to
2.8) and 62 (42 to 72) ppbv, while the respective measurements were 0.5 (0
to 1.4) and 66 (41 to 89) ppbv. The temporal correlation between predictions
and measurements is also good, i.e. NME of the hourly data series is 55 and
10 %, respectively (Fig. S1b and c in the Supplement).
Overall, the aerosol model performance during the studied period is
independent of any systematic and important meteorological and/or gaseous
inconsistencies.
Sulfate aerosols (PM<inline-formula><mml:math display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="bold">1</mml:mn></mml:msub></mml:math></inline-formula> SO<inline-formula><mml:math display="inline"><mml:mrow><mml:msub><mml:mi/><mml:mn mathvariant="bold">4</mml:mn></mml:msub><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula>
Model evaluation
Figure 2a shows all available prediction–observation pairs of the airborne
PM1 SO4 in the greater area of the archipelago. The average
profile of airborne sulfate is rather homogeneous up to 2.2 km a.s.l. and shows
average modelled (measured) concentration of 5.8 (5.5) µg m-3.
Concentrations smoothly decline aloft, reaching lower values (< 1 µg m-3) above 4.2 km. The high uniformity and content in the
vertical is a first indication that the low troposphere above the AS is a
receptor of distant industrial plumes and medium-range sources, especially
under strong NE winds (Fig. S1a). This also explains the higher sulfate
concentration values in the lower troposphere above the AS (modelled: 5 µg m-3
and measured: 4.7 µg m-3) than above Turkey (which are 3.6
and 3.7 µg m-3, respectively).
The average model performance statistics have satisfactory values with
77 % of the data pairs being within the 2:1 and 1:2 lines (Table S3). The
MFB and MFE, when compared with the goals, rate the sulfate model system
performance as good, with only 15 % of the MFE values calculated for each
data pair being outside the criteria lines.
The good model performance is also supported by checking each ground data
pairs. Figure 3 shows all predictions of PM1 SO4 against the
respective available (PILS-IC) measurements from ground level, while Table S4 embeds the average ground statistics. The average modelled (measured)
concentration is 5.9 (6.4) µg m-3, representative of the
aforementioned domain-wide average within the PBL over the archipelago. Most
of MFE values meet the criteria with few outliers observed (14 % of the
cases). Evidently, there is no clear diurnal cycle of sulfate during the
studied period (Fig. 3). This is attributed to the lack of strong local
sulfur dioxide (SO2) sources (Pikridas et al., 2010), as well as to the
continuous dispersion of the overflying plumes, during their transport over
the sea.
Comparison of PMCAMx (total shaded area) with hourly measurements
(black dotted line) of total PM1 sulfate concentrations (µg m-3) over Finokalia during 31 August–09 September 2011. The
contribution of PMCAMx predicted trans-boundary (standard run – scenario 1,
in light red) and local (scenario 1, in dark red) to the total PM1
sulfate mass is also shown. The ability of the model to reproduce
observations is estimated through the calculation of the mean fractional
biases (MFB), shown at the bottom. Model performance is average (good), when
MFB values are within the red (green) lines (Boylan and Russell, 2006).
![]()
Sulfate is the dominant species of the atmospheric aerosols, as indicated
both by observations and predictions. This is in line with the majority of
earlier long term observations and campaigns in the region (Sciare et al.,
2008; Pikridas et al., 2010; Im et al., 2012). PM1 SO4 production
is related to its gaseous precursors (SO2), mostly emitted from the
industrialized areas in the Balkans, Turkey and eastern Europe (Sciare et al.,
2003a, b; Pikridas et al., 2010), which is converted to sulfuric acid
(H2SO4) that has low vapour pressure and nucleates or condenses
mainly in the aerosol fine mode (Mihalopoulos et al., 2007). The
satisfactory comparison between model predictions and spatially divergent
observations of sulfate over the greater area of the AS proves the
representation of its sources and processes in the applied model system to be good.
Exogenous influences
Confidence in this model system allows the provision of supplementary
information on the role of sulfur transport from outside the PMCAMx domain,
not provided by the measurements. The origin of sulfate from the hot spot
regions upwind of the archipelago is tracked by the calculation of the
transported mass to the total PM1 SO4 predictions (standard run –
scenario 1, light red shaded area in Fig. 3). It is found that a notable
part of area-wide episodic events is attributed by about 85 % to
trans-boundary transport of sulfate particles and its gaseous precursor
during most of the studied period. The spatial distribution of daily mean
sulfate concentrations over the domain of interest together with the
contribution of the trans-boundary transport (standard run – scenario 1,
iso-lines) is given for a representative Etesian day (Fig. 4a). The exact
origin of SO4, determined by back-trajectory calculations (Bezantakos
et al., 2013), is the eastern Europe and the wider Black Sea region. The
remaining 10–15 % SO4 of PM1 is equally formed by sulfur
emissions in the western continental part of Greece and sources in the
Turkish area of the domain (scenario 2 - scenario 1).
Daily average PM1 lowest level concentration fields (µg m-3) of sulfate, organics and ammonium species, during: (a), (c), (e)
NE winds (04 September 2011) and (b), (d), (f) NW winds (31 August 2011),
blowing over the Aegean Sea. Iso-lines show the contribution of
trans-boundary sources to the total aerosol mass [(standard run –
scenario 1)/standard run].
![]()
A different pattern is observed on 31 August 2011, when the observed winds
at Finokalia change to NW (Fig. S1a). The concentration map of this episode
indicates that the air parcels passing over continental Greece (Athens and
Peloponnese) head towards the south AS (Fig. 4b). During that day,
trans-boundary pollution in the area is less important compared to the rest
of the studied period. In particular, the submicron sulfate over the south
AS (Finokalia) is equally shaped by the local (domain-wide) and by the
exogenous sources, with 80 % of the former originating from the Greek
territory (scenario 2 - scenario 1). Interestingly, the peak values
(∼ 10 µg m-3) at Finokalia observed during the
sulfur transport from Greek power plants towards the south AS (Fig. 3) are
lower than those related to the transport from the Balkans and from further
NE (∼ 12 µg m-3). Model inconsistencies during this
plume transport (Fig. 3) are related to the strong gradient from near-source
to background, which is not accurately resolved and captured by the model's
grid resolution. The exogenous influence on SO4 concentrations in the
north Aegean remains high (70 %) on 31 August and originates from the
continental area between the Black and the Caspian Sea (Bezantakos et al.,
2013).
Sensitivity of model performance
The high spatial and temporal resolution of airborne measurements, allows
for an extended diagnostic evaluation and may help to better address poor
model system performance over the EM. Increased model discrepancies are
mostly attributable to the lower and the higher ends of airborne PM1
SO4 concentrations distribution (minimum and maximum observed values
shown in Fig. 2a). Poor model performance for lower aerosol concentrations
is explained below, although it is typical for aerosol concentrations below
2 µg m-3 (Boylan and Russell, 2006). The largest model
underestimations occur mainly in the area of Chania, where measurements
frequently exceed 14 µg m-3. Measurements during take-offs and
landings are contaminated by local airport emissions, while predictions
cannot ideally reproduce concentrated plumes, but are representative of a
much wider area (∼ 38 km2) and time scales (1 h). Indeed,
sulfate model predictions in the greater area of Chania (6 to 10 µg m-3) are much closer to previous measurements (7 to 9 µg m-3)
in a nearby, suburban area (Kopanakis et al., 2012). The maximum sulfate
aerosol concentration (23.4 µg m-3) is observed in the lower
troposphere (∼ 1.7 km) over Athens (02 September 2011, around
10:00 UTC). Here, the model under-prediction (4.6 µg m-3) is
intensified by the narrow shape of the Athens pollution plume (relative to the
size of the model grid size), as well as by the spatial and temporal changes
in actual conditions and fuels used for transportation in the greater Athens
area, that are not captured in emission inventories.
Mean and mean fractional error (MFE) values for the complete sample
and for paired sub-samples of the airborne model-measurement data set. Paired
sampling is based on the methodology described in Sect. 3.4. MFE with bold
italic (italic) fonts indicate good (poor) model performance, according to
the selected evaluation criteria (cf. Table S2). The rest of the model outputs (MFE
with black fonts) are acceptable (average model performance).
Airborne PM1
Sulfate (µg m-3)
Ammonium (µg m-3)
Organics (µg m-3)
PMCAMx mean (min–max)
4.8 (0.3–12.1)
1.1 (0.05–4.2)
1.4 (0.01–6.8)
AMS mean (min–max)
5 (0.2–23.4)
1 (0.05–5.2)
2.4 (0.05–10.7)
MFE ( % meets goals/criteria)
55 (56/73)
63 (70/79)
83 (51)
1st pair of samples
< 2.2
> 2.2 km a.s.l.
< 2.2
> 2.2 km a.s.l.
PMCAMx mean (min–max)
5.5 (1.1–12.1)
3.8 (0.3–9.7)
2.3 (0.2–6.8)
0.9 (0.01–4.8)
AMS mean (min–max)
5.8 (0.2–23.4)
3.7 (0.2–15)
4.4 (0.1–10.7)
1.1 (0.05–9.4)
MFE (% meets goals/criteria)
44 (64/82)
72 (< 50/59)
74 (< 50/58)
89 (< 50/50)
2nd pair of samples
Aegean
Turkey
Aegean
Turkey
Aegean
Turkey
PMCAMx mean (min–max)
5 (0.5–12.1)
3.3 (0.3–10.7)
1.3 (0.06–4.2)
0.8 (0.05–2.7)
2 (0.1–5.6)
0.5 (0.01–4.8)
AMS mean (min–max)
4.7 (0.2–20.4)
3.5 (0.2–19.6)
1.1 (0.05–4)
0.4 (0.05–2)
3.4 (0.05–9.3)
0.7 (0.05–8)
MFE (% meets goals/criteria)
48 (63/79)
85 (< 40/46)
54 (79/87)
71 (62/76)
77 (< 60/60)
92 (< 30/36)
3rd pair of samples
NE
NW winds
NE
NW winds
PMCAMx mean (min–max)
5.4 (0.7–11.5)
4 (0.3/12.1)
1.9 (0.02–5.6)
1 (0.01–6.8)
AMS mean (min–max)
5.6 (0.2–23.4)
4.1 (0.2–20.4)
3.2 (0.05–10.4)
1.6 (0.05–10.7)
MFE (% meets goals/criteria)
51 (60/77)
61 (< 60/69)
76 (< 60/62)
89 (< 40/42)
4th pair of samples
U > 9
< 9 m s-1
U > 9
< 9 m s-1
PMCAMx mean (min–max)
5.1 (0.4–12.1)
4.5 (0.3–11.3)
1.5 (0.06–4.2)
0.8 (0.05–3.1)
AMS mean (min–max)
5.5 (0.2–23.4)
4.5 (0.2–20.42)
1.2 (0.05–4.3)
0.9 (0.05–5.2)
MFE (% meets goals/criteria)
46 (62/82)
63 (< 60/65)
52 (80/100)
71 (61/72)
For more in-depth examinations regarding model system skills for sulfate
predictions, MFE for airborne data are broken down for those parameters
significantly affecting model performance (Table 4). As shown in this table,
sulfate model performance is not consistent throughout the troposphere: it
meets the goals at altitudes lower than 2.2 km a.s.l., but is poor at higher
altitudes. This is more pronounced over Turkey (25 % of the total number
of data pairs over 27 to 29∘ E, correspond to altitudes
from 4 to 7 km a.s.l.) and it is because a few large deviations between low
concentration values (below 1 µg m-3) can have a significant
impact on the overall performance assessment.
The other two parameters affecting sulfate model skills are related to the
wind conditions. Good model performance is observed under strong
(> 9 m s-1) NE winds above the archipelago (local
measurements along the flight tracks), that are typical of an Etesian
pattern (Tombrou et al., 2015). Under NW and/or winds of lower intensity,
sulfate predictions are still acceptable. The sensitivity of sulfate on the
simulated wind is further examined by scenarios 5 (BOULAC PBL scheme) and 6
(QNSE PBL scheme), providing the lowest and strongest wind speeds
respectively, below 2.2 km altitude. The average value inside the PBL layer
ranges between 5.3 and 5.8 µg m-3, for an average wind speed
variation from 8.6 (BOULAC) to 9.8 (QNSE) m s-1 (below 2.2 km altitude)
among the runs. Changes among concentration fields are anti-correlated with
the wind fields, due to the higher dispersion of pollution that is
associated with stronger winds. The aerosol model skills are rather
insensitive to these variations, although scenario 6 exhibited the lowest
MFB and MFE values (12.9 and 56.3 %, respectively) and the highest
correlation with measurements (r2=0.4). Lastly, sulfate predictions
showed a similar performance for all days (flights), independently of the
time of day and latitude.
Organic aerosols (PM<inline-formula><mml:math display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="bold">1</mml:mn></mml:msub></mml:math></inline-formula> OA and PM<inline-formula><mml:math display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="bold">10</mml:mn></mml:msub></mml:math></inline-formula> OC)
Model evaluation
Experimentally determined concentrations of the organic fraction of the
submicron particles over the AS (Fig. 2b) are much lower than sulfate. In
particular, the average measured concentration below 2.2 km a.s.l. is 4.2 µg m-3, with peaks ranging from 7 to 11 µg m-3. Similar
findings have previously been observed in the AMS measurements at Finokalia
(Hildebrandt et al., 2010; Pikridas et al., 2010).
Measurements of organic compounds over the archipelago below 2.2 km altitude
are moderately underestimated by this model system (average predicted
PM1 OA value is 2.3 µg m-3), which is consistent with
findings reported by many modelling studies (e.g. Zhang et al., 2014, and
references therein). Also, the comparison of GEOS-CHEM results with
integrated global airborne observations resulted in the underestimation of
the median OA concentrations in 13 of the 17 aircraft campaigns over central
Europe, North America and western Africa (Heald et al., 2011). The main
reasons for such underestimations were the poor model representation of SOA,
as well as the lack of important sources and sinks of OA. The sources of
error that may have contributed to the unaccounted OA mass in the current
model system are investigated in Sects. 4.3.3, 4.3.4 and 4.3.6.
The calculated organic aerosol model skills in the PBL show an acceptable
performance, with 58 % of the model predictions meeting the
performance criteria (Table S3). Also, PBL model predictions are better
correlated to the observed aerosol distribution for organics (r2=0.6) than for sulfate (r2=0.3). This can be explained by the fact
that the injection heights of the sulfuric compounds emitted from the
industry range from 0 to 1 km a.g.l. (e.g. Mailler et al., 2013), whereas the
large oxygenated fraction of organics in the AS troposphere creates a more
homogeneous field.
Modelled organic concentrations (PM10 OA) are divided here by a factor
of 2.1, to extract the organic carbon mass concentrations over non-urban
areas (see explanation in Sect. 3.2), which can be compared to ground
PM10 OC measurements. Model performance is good (Fig. 5 and Table S4),
with 41 (82) % of the model predictions meeting the performance goals
(criteria). The average PM10 OC concentration values are similar over
the north and south AS (2.3 and 2.9 µg m-3, respectively),
indicating the absence of major local OA sources, which can also explain the
smaller range of their spatial variability.
Comparison of PMCAMx (grey lines) with 6-h measurements (black
dotted line) of total PM10 OC concentrations (µg m-3) over
Vigla (left) and Finokalia (right) during 31 August–09 September 2011.
The relative contribution of PMCAMx predicted trans-boundary (standard run –
scenario 1, red line) to the total PM10 OA mass is also shown. Green
shaded areas represent the chemical composition of PM1 OA predictions,
grey shaded area shows the organics in the PM1-2.5 size range, while
the remaining (white shaded area) represents the organics in the coarse
fraction (PM2.5-10). All areas are percentage values. The ability of
the model to reproduce observations is estimated through the calculation of
the mean fractional biases (MFB), shown at the bottom. Model performance is
average (good), when MFB values are within the red (green) lines (Boylan and
Russell, 2006). The predicted OC is acquired by dividing OA by 2.1 (Turpin
and Lim, 2001).
![]()
OA analysis
The experimental data obtained during this study cannot separate SOA from
OA, their biogenic from their anthropogenic part, as well as the fine from
the coarse organic PM10 fraction. Model outputs are used to help
untangle these contributions (cf. Fig. 5). Similar predictions over both
measurement sites suggest again the large spatial homogeneity of organic
particles over the archipelago. Up to 40 % of PM10 organics are
located between 1 and 10 µm (which is slightly higher compared to the
25 % experimentally determined at Finokalia by Sciare et al., 2003a), 75 % of which is coarse (PM2.5-10). Submicron OA over the AS are
mainly secondary (95 %) and originate primarily (80 %) from biogenic
sources, which is explained in the next paragraph. Most of these results are
consistent with previous studies covering the region of the AS (Hildebrandt
et al., 2010; Athanasopoulou et al., 2013), and are explained by the aged
nature of the OA over the sea, especially during the summer period.
Uncertainties in OA treatment
Previous PMCAMx applications over Europe using the VBS scheme for SOA
formation (Fountoukis et al., 2011, 2014), have been shown to be competent in
predicting realistic levels of PM1 OA over the south Aegean Archipelago
(Finokalia). Those applications neglected the chemical ageing of BSOA
assuming that it is not expected to significantly contribute to the OA
concentration levels. Interestingly, our results indicate that the
activation chemical ageing of BSOA (standard run) leads to a significant
improvement of the OA levels over the Aegean Sea (cf. continuous green line
in Fig. 2b). In particular, the BSOA oxidation in the troposphere over the
AS increases the total OA mass predictions by 50 to 80 % during the whole
simulation period. The reason that BSOA are likely to undergo atmospheric
ageing lies in the sufficient quantities of anthropogenic nitrogen and
sulfur pollutants in the atmosphere over the AS (NOx=1 to 2 ppb,
mean molar ratio NH4+ / SO42-≤2), which facilitates
BSOA oxidation (cf. Zhao et al., 2013, and references therein). As a
consequence, deactivating BSOA ageing (scenario 3; dotted green line in Fig. 2b) changed the model skills for organics from average to poor (average
predicted value from scenario 3 is 0.7 µg m-3 for the atmosphere
up to 2.2 km a.s.l. over the AS).
The sensitivity of the model results on ASOA ageing (standard run – scenario
4) was limited to 5 % both for the average OA concentration predictions
and their chemical composition. In particular, the faster oxidation rate of
anthropogenic SOA resulted in a minor increase (up to 10 %) of the
predicted OA during the whole simulation period of scenario 4. Such a
different model response to BSOA/ASOA changes stems from the
isoprene / aromatics concentration ratio from GEOS-CHEM (ICs), which takes the
average value of 9/1 over the AS. Scenario 4 had a positive though minor
effect on performance metrics.
A possible error in OA predictions introduced by the VBS mechanism is that
species with similar volatilities can have different properties and
reactivities. Nevertheless, the development of more complex VBS schemes with
respect to these issues (Donahue et al., 2011) has already shown no
significant improvements in OA performance over Europe. This is probably due
to uncertainties in our understanding of SOA evolution in the atmosphere
(Murphy et al., 2012).
In case the VBS chemical module would have introduced significant errors,
then OA estimations would have performed similarly throughout the
troposphere. In contrast, the OC model performance at both ground locations
has been rated as “good” (cf. Sect. 4.3.1), while the calculated statistics
for the paired sampling for airborne organics revealed an inconsistent
behaviour of biases throughout the troposphere (Table 4). The model
performance in the upper atmosphere and especially in the area above Turkey
(elevated flight paths, low concentration values, as described in the
previous section) is rated as “poor” and deteriorates the overall organic
model performance.
Overall, the well-established VBS scheme is investigated and revisited, so
that it better describes OA behaviour over the southeastern Mediterranean
during summertime. The current SOA treatment is found to be satisfactory and it is
not regarded as introducing important errors in OA predictions.
Biomass burning component
Biomass burning (BB) plumes may enter the free troposphere and be advected
over very long distances, especially under strong winds. Back trajectory
calculations from 400 to 4500 m a.s.l. (Bezantakos et al., 2013) show that the
air masses arriving over the archipelago during the studied period, mostly
originate from (or pass over) the eastern Balkan area and the west coastline
of the Black Sea, where there is evidence (satellite observations) of fire
activity during (and prior to) the study period (Fig. S2). Consistent with
these observations, the comparison between the current model outputs and
measurements of OA when NE winds prevail shows an average difference of 1.3 µg m-3 (Table 4). When the prevailing winds have a NW direction
(the air masses arriving over the AS basin do not seem to originate/pass
from fire spots, according to the same back-trajectory analysis), the
difference between OA values from the model and observations is lower (0.6 µg m-3; cf. Table 4).
Based on this evidence, bb particles are found to be an important component
of PM1 OA over the AS during summer, which can largely explain the
PM1 OA underestimation (ca. 50 %) by the current model application,
which lacks representation of fire emissions (cf. Sect. S2). This is a quite
realistic hypothesis, given the observations reported by Sciare et al. (2008) and Bougiatioti et al. (2014). In particular, the systematic
measurements of aerosols in the southern AS region (Finokalia) during late
summer have shown that 30–35 % of OA comes from biomass burning in the
eastern Balkans and at the European countries surrounding the Black Sea.
Bossioli et al. (2014) have shown that the wildfire emissions sector
contributes on average ca. 50–60 % to the total PM1 OA mass
predictions in the AS region during the summer, which further supports our
speculation.
Other exogenous influences
Organic aerosol mass and gaseous (VOC) precursors from the Balkans and
further north, shapes more than 90 % of their total concentration levels
over the AS during the Etesian event (Fig. 4c). The effect of organic-rich
plumes from continental Greece during the non-Etesian event (with prevailing
NW winds, Fig. 4d), as well as the domain-wide photochemistry, decrease
slightly the role of exogenous sources (now 70 % on average) over the
whole region of the AS. Examining the different chemical constituents of the
transported SOA and precursors (34 to 41 gaseous and 4 to 11 aerosol paired
species as listed in Table 2) in the studied domain, shows that the
exogenous organic mass primarily originates from isoprene (mean NE
boundary concentration values of 1 to 2 µg m-3), while aromatics
are 2 to 6 times lower. The rest of the transported organic species (a-pinene,
myrcene, sesquiterpenes etc) are insignificant. It should be noted that
these findings correspond to the accounted sources of OA particles (and
their precursors) by the current model setup, which do not reflect BB, as
discussed in the previous section.
Other model performance issues
In order to further investigate the underestimation of the model to other
important sources of OA, we performed a series of sensitivity tests.
Independent artificial increases in emissions from the road transport,
maritime and industrial sectors showed insignificant changes in the organic
aerosol predictions. An additional scenario (increased values) for the
applied BCs from GEOS-CHEM resulted in unrealistically high OA concentration
outputs. In general, although some uncertainty in the emission inventory as
well as in GEOS-CHEM performance cannot be excluded, these do not contribute
substantially to the OA underestimation, pointing again to the fire activity
to be the main deficiency in the current model application with respect to
OA results.
Wind also affects the quality of organic aerosol predictions, but only
regarding direction, as already explained in Sect. 4.3.4 . The day, time of
day, latitude and wind speed do not affect organic aerosol model
performance. The latter is also confirmed by scenarios 5 and 6 (MFE = 72 to 80 %), although scenario 5, which was based on slightly lower winds
(i.e. lower dispersion), produced slightly higher concentration values
having the subsequent (though minor) reduced model bias.
Comparison of PMCAMx (grey lines) with 6-h measurements (black
dots) of PM10 elemental carbon concentrations (µg m-3) over
Vigla (top) and Finokalia (bottom) during 31 August–09 September 2011.
The ability of the model to reproduce observations is estimated through the
calculation of the mean fractional biases (MFB), shown at the bottom of each
graph. Model performance is average (good), when MFB values are within the
red (green) lines (Boylan and Russell, 2006). OC versus EC (grey/PMCAMx and
black/measurements data points and lines) in PM10 is shown on the right
of each graph. The black dashed line in Finokalia reflects the slope of OC
to EC for the measurements during the NW transport from continental Greek
sources (black empty circles, 31 August 2011).
![]()
Ammonium aerosols (PM<inline-formula><mml:math display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="bold">1</mml:mn></mml:msub></mml:math></inline-formula> NH<inline-formula><mml:math display="inline"><mml:mrow><mml:msub><mml:mi/><mml:mn mathvariant="bold">4</mml:mn></mml:msub><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula>
The hourly variation of airborne ammonium concentration predictions is
consistent with the observations (Fig. 2c). Apart from the expected inconsistencies
in the observed peak values already discussed, performance
issues are tied with sulfate inconsistencies (e.g. during 04 September 2011).
This is related to the fact that during summer most of the ammonium is
associated with the sulfate rather than the nitrate fraction.
The average predicted (observed) PBL concentration of PM1 NH4 is
1.6 (1.4) µg m-3, which is consistent with the ground ammonium
concentrations in earlier measurements at Finokalia (Kouvarakis et al.,
2001; Metzger et al., 2006; Pikridas et al., 2010). Regardless of the high
uncertainties in ammonia emissions usually incorporated in the photochemical
models (Skjøth et al., 2011), the reproduction of the observed data by
the current model system is high, i.e. 70 (79) % of the MFE values
meet the goals (criteria). The overall model performance for the ammonium
species is good and optimized over the archipelago and under strong winds
(Table 4). The rest of the examined parameters (flight/day, time of day,
altitude, latitude and wind direction) do not seem to affect model
performance with respect to ammonium.
Air parcels arriving in the simulation domain are predicted to contribute
ca. 70 % of the average PM1 NH4 concentrations when originating
from a NE direction (Fig. 4e) and by a lower percentage (though above 50 %)
when originating from NW (Fig. 4f).
Nitrate and chloride aerosols (PM<inline-formula><mml:math display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="bold">1</mml:mn></mml:msub></mml:math></inline-formula> NO<inline-formula><mml:math display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="bold">3</mml:mn></mml:msub></mml:math></inline-formula> and Cl)
The measured non-refractory submicron nitrate concentrations below 2.2 km a.s.l. (0.2 µg m-3 in average) are strongly overestimated by the
model system (1.9 µg m-3) (as shown in Table S3). This is
attributed to two distinct reasons: the sea-salt component of nitrate
(standard run – scenario 7), which is not captured by the AMS measurements,
accounts for the 54 % of PM1 NO3- predictions. Also, the
average exogenous contribution from upwind (standard run – scenario 1) is ca.
1 µg m-3, which is unrealistic according to current measurements.
When subtracting these mass fractions from total PM1 NO3
predictions, the model results become more realistic. Nevertheless, it
should be kept in mind that performance issues are commonly tied to low
concentration (< 1 µg m-3) cases, not only because they
greatly degrade normalized model performance, but also due to the higher
uncertainty of measurements.
Measured (submicron non-refractory) and modelled chloride is low (< 0.8 µg m-3), because of the insignificant sea-salt content in the
submicron fraction, the inability of AMS to measure sea-salt chloride, as
well as its gradual displacement by nitrate and sulfate ions. Nevertheless,
model performance is found to be good (Table S3).
Particulate elemental carbon (PM<inline-formula><mml:math display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="bold">10</mml:mn></mml:msub></mml:math></inline-formula> EC)
Comparison of PMCAMx (total shaded area) with 8-h measurements
(black dotted line) of PM10 concentrations (µg m-3) over
Finokalia during 31 August–09 September 2011. Colour shaded areas
represent the chemical composition of PM1 predictions, the grey shaded
area shows the PM1-2.5, while the remaining (white shaded area)
represents the coarse fraction (PM2.5-10). The ability of the model to
reproduce observations is estimated through the calculation of the mean
fractional biases (MFB), shown at the bottom. Model performance is average
(good), when MFB values are within the red (green) lines (Boylan and
Russell, 2006).
![]()
Figure 6 shows the diurnal variation of the PM10 EC for the ground
sites of the south and north Aegean basin. Both the absolute values and
their temporal evolution are well reproduced by the model. In particular,
the average measured (and modelled) concentration is ca. 0.3 µg m-3, and therefore the model performance is rated as good (Table S4),
with a few exceptional outliers. Elemental carbon is dominated by combustion
sources. Thus, it can be assumed that the fossil fuel sources are well
represented by the emission data sets used by this model system.
As for sulfate, the footprint of continental Greek sources (mainly from the
Athens metropolitan area) is apparent in EC concentrations at Finokalia
during the prevailing NW directions (0.6 to 1.2 µg m-3). During
the rest of the period, EC levels fluctuate at similar levels at both sites.
Unlike PM1 OA performance, no model underestimation related to the fire
activity upwind is observed for surface PM10 EC. This is mainly because
the long-range transport of fire plumes is more efficient in higher
altitudes due to the lack of surface deposition and stronger winds.
Likewise, the PM10 OC measurements near ground, although slightly
underestimated by the model system (cf. Table S4), perform much better
than PM1 OA in and above the PBL (cf. Table 4). In parallel, the signal
of fires on the low levels of ground EC (average values of observations and
predictions are largely below 1 µg m-3), already reported by
Sciare et al. (2008), is most probably within the biases (ca. the 40 % of
them is above 0.2 µg m-3) over the AS during summertime. The OC / EC
ratio from the measured (modelled) data during this period is as high as 4.7
(4.0) and 4.0 (7.1) at the north (Vigla) and south (Finokalia) AS (Fig. 6),
being at similar levels with those measured previously in Finokalia (Koulouri
et al., 2008; Pikridas et al., 2010; Im et al., 2012). OC / EC slopes greater
than 2 suggest the significant fraction of secondary species in the organic
aerosol mass in background areas, as predicted and previously discussed in
Sect. 4.3. The lower OC / EC slope at Finokalia during 31 August (1.9) is
close to previous findings in urban areas (Favez et al., 2008; Theodosi et
al., 2010) and is related to the higher EC levels during the urban plume
transport from the NW. The latter was also depicted in the spatial distribution
of sulfate (Fig. 4b).
Particulate matter (PM<inline-formula><mml:math display="inline"><mml:mrow><mml:msub><mml:mi/><mml:mn mathvariant="bold">10</mml:mn></mml:msub><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula>
The average predicted total PM10 mass at Finokalia during the simulated
period is found to be 30.1 µg m-3. This value is very close to the
average of the concurrent observations (29 µg m-3), as well as to
previous measurements in non-urban areas of the Mediterranean region
(Rodriguez et al., 2001; Gerasopoulos et al., 2006; Lazaridis et al., 2008;
Koulouri et al., 2008; Kopanakis et al., 2012). The performance skills of
the model system on PM10 predictions are rated as good (Table S4) and
the daily evolution of PM10 predictions is satisfactory (Fig. 7).
Atypically high PM10 concentration levels observed at Finokalia on 01 September suggest that the quality of sampling on this particular day might
be questionable.
The combined use of measurement and modelling techniques during this period
is useful for the estimation of the chemical composition and the size
distribution of PM10 measurements at Finokalia (Fig. 7). Sulfate
account for the 45 % of PM1 mass, followed by OM. The latter
represents the 20 % of PM1 (2.6 µg m-3), which is similar
to measurements at Finokalia (Pikridas et al., 2010). The predicted
submicron ammonium content at Finokalia (1.7 µg m-3) is consistent
either with the current airborne or with the past ground-based observations
at this site (Sect. 4.4). The contribution of the rest aerosol species (Cl
and EC) is minor (2 %). The levels of the submicron nitrate are greatly
overestimated by the model system (Sect. 4.5).
Submicron aerosol is the largest fraction of the PM2.5 mass (76 %),
but accounts for 42 % of the total PM10. This is mostly related to
the elevated coarse aerosol concentrations (14.2 µg m-3), which
shape the PM2.5 / PM10 ratio around 54 %. Previous ground-based
observations over the EM have resulted in fractions ca. 50 % (Kanakidou et
al., 2011), further supporting the satisfactory aerosol predictions over the
whole size range by this model system.
Given the similar levels of ground and airborne measurements over the AS and
below 2.2 km a.s.l. (discussed in Sects. 4.2–4.5), it can be stated that the
current analysis of the ground PM10 measurements performed by the
model is representative of the PBL above the archipelago during strong
northern winds.
Summary and conclusions
A recently applied model system consisting of three well-established
atmospheric models (namely, PMCAMx, WRF and GEOS-CHEM), and a unique aerosol
data set collected in the EM are synergistically used in the frame of this
study during a 10-day period characterized by strong northern winds
(August–September 2011). The aircraft data set used represents a spatially
diverse set of aerosol observations (covering the horizontal area of ca.
3 × 105 km2 and extending from the sea surface to 7.5 km
aloft), employed to perform the most extensive – to our knowledge – model
evaluation of major aerosol chemical component concentrations over the EM to
date (> 1300 observation-prediction samples per species).
The vertical resolution in the measurements allowed the exploration of the
aerosol profiles above the Aegean Sea. The PBL above the archipelago
(< 2.2 km a.s.l.) is homogenously enriched in sulfate (average modelled
and measured PM1 SO4 of 5.5 and 5.8 µg m-3,
respectively), followed by organics (2.3 and 4.4 µg m-3) and
ammonium (1.5 and 1.7 µg m-3). Aerosol concentrations smoothly
decline aloft, reaching low values (< 1 µg m-3) above 4.2 km.
Aerosol model performance within the PBL is largely within an acceptable
level of accuracy (for all major chemical species except for nitrate), or
even close to the best level of accuracy (sulfate, ammonium and chloride
satisfy the criteria), with 50 to 80 % reproduction of these standards.
Comparison with the ground-based observations (356 observation-prediction
samples in total) suggested an even higher model quality, with a good
reproducibility of all studied species and a few outliers (< 15 %
outside the criteria lines).
Wide and commonly found under-predictions in sulfate, elemental carbon and
coarse aerosols (cf. Nopmongcol et al., 2012) are not observed in the
current study. Also, in contrast to the uncertainties in ammonia emissions
usually reported in air quality modelling (e.g. Skjøth et al., 2011), the
observed ammonium levels are well reproduced here. These findings support
that the power plants, motorways and natural aerosol sources, including
agricultural activities of the surrounding area of the archipelago and
upwind, are well represented and treated by this model system.
Relatively high OC / EC ratios (4 to 5) from the ground observations are
successively reproduced by the PMCAMx model (OC / EC: 4 to 7), suggesting the
large oxygenation rate of the organic matter above the archipelago, nicely
represented by the employed OA chemical module. The activation of the
chemical ageing of BSOA in this formulation, greatly improves model
performance due to the sufficient NOx concentration and the
sulfate-rich Aegean environment. On the other hand, OA predictions showed
minor (or unrealistic) response to anthropogenic emissions and variations in BCs.
The fire activity, not taken into account by the current model
application, is the main cause of OA underestimation (ca. 50 %), which is
consistent with local measurements of the fire-induced OA fraction (e.g.
Bougiatioti et al., 2014). This finding serves as a challenge for future
model development.
Model performance was also dependent on the altitude (below and above 2.2 km), the longitude (western and eastern than 27∘ E, i.e.
above the AS and western Turkey, respectively), the wind speed (above and below 9 m s-1) and wind direction (NE and NW) over the studied area. The (time
of) day and latitude did not affect model biases. The sensitivity of aerosol
predictions on different PBL schemes showed a minor effect on aerosol
concentrations (e.g. 5.3 to 5.8 µg m-3 and 2.1 to 2.4 µg m-3 for airborne sulfate and organics, respectively), and did not
change model performance. Overall, aerosol predictions within the PBL over
the archipelago under strong NE winds showed the best performance.
More than 70 % of the predicted aerosol mass over the AS during the
Etesians is associated with the transport of aerosols and their precursors
from outside the PMCAMx modelling domain. In the case of organics, this mass
originates primarily from the oxidation of isoprene. These findings
underline the significance of the detailed gaseous and aerosol model
coupling developed in this study, towards more accurate model predictions.
The origin of the transported plume during NW winds, distinctively
identified from the model simulations (Greek industrialized areas) and the
daily evolution of sulfate, EC (and OC / EC) and total PM10, shapes half
of the total sulfate mass, the rest being attributed to the exogenous
sources. Also, the observed peak in submicron sulfate during this event at
Finokalia (10 µg m-3) is lower than the concentrations during the
Etesian flow (12 to 14 µg m-3). Therefore, developing abatement
strategies to reduce aerosol levels in the EM is both a national and
transnational task. Key findings from the current and similar applications
can provide information on the origin of air parcels and the contribution of
local and exogenous sources, thus on the effective design of air policies.
A forthcoming application of the same model system aims at investigating its
performance, as well as aerosol levels and interactions during recent
Saharan dust intrusions in the troposphere over the Aegean Sea.