Aristotle University of Thessaloniki
Laboratory of Atmospheric Physics, Thessaloniki,
Greece
ACCENT Plus
ATMOSPHERIC COMPOSITION CHANGE THE
EUROPEAN NETWORK
ACCENT-Plus Symposium
September 17-20, 2013
Particulate matter emissions from natural sources
in Europe - A case study of the impact of natural
sources to PM pollution levels
Natalia Liora1, Konstantinos Markakis1,2, Anastasia Poupkou1,
Spiros Dimopoulos1, Theodoros Giannaros1, Dimitrios Melas1
1 Aristotle University of Thessaloniki, Department of Physics, Laboratory of Atmospheric Physics, 54124 Thessaloniki,
Greece. (lioranat@auth.gr)
2 Laboratoire de Meteorologie Dynamique/Institut Pierre-Simon Laplace, Centre National de la Recherche Scientifique,
Paris, France
Thursday 19 September
ACCENT Plus Symposium 2013
Urbino, Italy
MAIN OBJECTIVES – PRESENTATION OUTLINE
Main objectives :
 Quantification of the emissions of particulate matter (PM) originated from
natural sources in Europe
 Comparison of natural with anthropogenic PM emissions.
 Study of the contribution of natural sources to PM pollution levels in the
study area
Presentation Outline :
1. General Description – Model application
2. Short Description of methodology
3. Emissions Results
4. Air Quality Simulations
5. Conclusions
2
Model Application
•European Region
•30km spatial resolution grid
•4230x4020 km²
Natural Emissions

Natural Emission Model (NEMO)
(developed in Laboratory of
Atmospheric Physics (LAP), AUTH
(Markakis et al (2009), Poupkou et al
(2010) ).

Fortran90

Sources : BVOCs, Windblown Dust,
Sea Salt, PBAPs

Application year : 2010

Hourly Temporal Analysis
Meteorology

Mesoscale Meteorological model
WRF (version 3.2.1)
Anthropogenic Emissions

TNO - MACC Emissions (Kuenen
et al., 2011) (resolution : 1/8o x
1/16o) (year 2007).

MOSESS (Markakis et al., 2013) :
spatial and temporal resolution 3
PM Concentrations

CAMx Air Quality Model (version 5.3)
WRF-CAMx
runs for 12-21 July 2010
Methodology – Short Description
Windblown Dust
Natural Emissions

Primarily based on the methodology used on the LOTOS-EUROS model
developed by the Netherlands Organization (TNO) (Schaap et al.,2009)
 Parameterization of threshold friction velocity as a function of soil particle
size, soil moisture and drag partitioning
 Soil texture map compiled from the European Soil Database (Van Liedekerke
and Panagos, 2006)
 Meteorological data : 10m Wind Speed, precipitation, air temperature
Sea Salt

The SS production is dependent on wind speed, sea surface temperature and
water salinity (Sofiev et al.,2011)
 Relative humidity dependency (Lewis&Schwartz,2006)
Primary Biological Aerosol Particles (PBAPs)
Emission factors for plant debris and fungal spores emissions (Winiwarter et al.,
2009).

USGS (United States Geological Survey) land Cover Database – 1km spatial resolution
4
Natural and Anthropogenic
Emissions
5
Sea Salt
Windblown Dust
Spatial Distribution of Annual Natural PM10 Emissions
Dust emissions are maximum
in the Mediterranean
countries, mainly due to the
low precipitation levels and
where wind speed is also
high.
• The soil moisture is lower in
southern Europe
 Sea salt emissions are
highest in the Mediterranean
Sea where sea temperature
is maximum
 In the Northern part of
Atlantic ocean and in Baltic
sea , sea salt emissions are
the lowest
6
Comparison of Natural with Anthropogenic PM Annual
Emissions
NEMO
2010
PM10
(ktn)
PM2.5
(ktn)
Windblown
Dust
942
66
Sea Salt (dry)
4103
1057
PBAPs
117
-
Total Natural
Sources
Anthropogenic
Sources
5162
1123
4592
2954
PM10
PM2.5
25.9
%
42.1
%
47.1
%
72.5
%
1.2%
1.6%
9.7%
NATAIR
2003
PM10
(ktn)
Windblown
Dust
1500
Sea Salt
(dry)
4490
PM10 emissions
originated from natural
sources are 10% higher than
those of anthropogenic
sources due to the high sea
salt emissions which are
comparable with the
anthropogenic ones
PBAPs
135
 The contribution of
windblown dust is about 10%
 PM2.5 emissions
originate mainly from
anthropogenic
sources (72%)
 Sea salt emissions
represent about 25%
of PM2.5 emissions
* Reference: NATAIR, 2007
7
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
PM10
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
PM2.5
% Contribution of natural and
anthropogenic sources to total
PM2.5 emissions
% Contribution of natural and
anthropogenic sources to total
PM10 emissions
% Contribution of natural and anthropogenic sources
to total monthly PM emissions
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
PM2.5 Emissions (ktn)
PM10 Emissions (ktn)
3000
2000
1000
0
winter
spring
summer
autumn
 Sea Salt is the major contributor to total
emissions during summer and autumn
 Windblown Dust peak in February (31%)
(higher emissions in winter and spring)
 Windblown dust and sea salt have
almost the same contribution in winter
1000
500
0
winter
spring
summer
autumn
 Anthropogenic sources are the
major contributor the whole year
 During summer sea salt and
anthropogenic emissions are
comparable
No significant contribution of
windblown dust
8
Air Quality Modelling
9
Air Quality Simulations
 CAMx simulated the Emission, Dispersion,
Chemical reaction and Removal of pollutants in
Europe
 Emission for the following sources:
• Anthropogenic sources
•Windblown Dust
•Sea Salt
•Biogenic volatile organic compounds
(BVOCs)
 Chemical Boundary Conditions (BCs) : IFSMOZART model
 WRF – CAMx runs were performed for the
period 12 to 21 July 2010 for the following
emission scenarios:
1) Scenario 1: anthropogenic + natural
emissions
2) Scenario 2: Scenario1 without
windblown dust
3) Scenario 3: Scenario1 without sea
salt
10
Windblown Dust PM10 Emissions
12 to 21 July 2010
Windblown Dust
10m Wind Speed
Rain
 Dust events appear in limited areas over Europe (central and southern Europe)
 high wind speed, no precipitation episodes
11
Sea Salt PM10 Emissions - 12 to 21 July 2010
Sea Salt
Salinity
4%
0.9%
1.8%
3.8%
10m Wind Speed
SST
 SS emissions
show a similar
spatial
distribution with
annual emissions
 Lower wind
speed, salinity or
SST leads to
lower sea salt
production
12
Contribution of Windblown Dust to PM10 levels
12 to 21 July 2010
1st - 2nd
Scenario
% Difference of PM10 levels due
to windblown dust emissions
1st Scenario
% Contribution of Windblown Dust
to PM10 levels
PM10 Dust Emissions
The contribution of
windblown dust
emissions inside the
domain to PM10
concentrations is
relatively small and
ranges from 1 to 10 %
The contribution of windblown
dust to PM10 levels is moderate
in central and northern Europe
while in the southern and
eastern part is significant
associated mainly with the dust
transport from Africa and Asia
13
1st Scenario
Sea Salt contributes highly at the northern part
of the domain to PM2.5 levels due to BCs while
in the southern part of the domain sea salt
contribution is smaller and it is linked mainly to
the local sea salt emissions
PM10 Emissions
% Contribution of Sea Salt
to PM2.5 levels
Anthropogenic
Sources
% Difference of PM10 levels due
to sea salt emissions
1st - 3rd
Scenario
% Difference of PM2.5 levels due
to sea salt emissions
Sea Salt
Contribution of Sea Salt to PM levels
12 to 21 July 2010
 A small increase of 5 to15 % in
PM2.5 levels is observed in the
western Mediterranean when SS
emissions are included while an
increase of 20 to 40 % in PM10
concentrations is found.
 In the Aegean Sea, the increase
in PM10 concentrations reaches
+110% (+40% for PM2.5)
14
Conclusions
 Considering the PM emissions in Europe,
• Natural sources represent about 53% (sea salt 42%) of PM10 emissions
and 28% of PM2.5 emissions on an annual basis
• Sea Salt is a dominant PM10 emission source in summer (63%) and
autumn (59%)
• Windblown Dust emissions contribute mainly in winter (23%) and spring
(12%)
 Considering the PM concentrations from a 10-day run during July 2010,
• The impact of sea salt emissions to PM levels in the Mediterranean Sea is
considerable
• In the northern part of the domain sea salt contribution is high mostly due
to BCs
• The impact of windblown dust emissions is identified over limited areas in
Europe
• The contribution of windblown dust to PM10 levels over Europe is mostly
related to the dust transport from the boundaries
15
Acknowledgments: This work was supported by the FP7 EU project MACC II
(Monitoring Atmospheric Composition and Climate Interim Implementation: Grant
agreement no 283576). We would like to thank for the IFS-MOZART model data and
for the TNO anthropogenic emission data provided in the framework of MACC II.
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