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Beispielbild
Training Programme
Air Quality Monitoring, Emission Inventory and Source Apportionment Studies
Source Dispersion Modelling
Andreas KerschbaumerFreie Universität Berlin, Institut für Meteorologie
2 A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
Overview
3 A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
Overview
- General Introduction- Statistical Models,- Deterministic Models
- Chemistry Transport Models- Theoretical aspects- Input data- Validation
4 A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
Overview
- Application of Chemistry Transport Models
- Emission Scenarios,- Source Apportionment- Process Analysis
5 A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
General Aspects- Statistical approach
- Receptor Models- Based on measured pollutant concentrations- Valid for non-reactive (or slowly reactive) species
- Chemical Mass Balance (CMB)- for source apportionments
- Principal Component Analysis (PCA)- for source identification
- Empirical Orthogonal Functions (EOF)- for location and strength of emittors.
6 A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
General Aspects- Statistical approach
- Receptor Models- Based on measured pollutant concentrations- Valid for non-reactive (or slowly reactive) species
- Air parcel trajectory analysis
7 A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
- CMB
aij source emission signature (composition)
sj source contribution m = number of sources
- Constant source emission composition- Non-reactive species- Sources contribute to concentration- Uncertainties are un-related- Number of sources ≤ number of species- Measurement errors random
Statistical approach
nisacm
j jiji ,...,11
8 A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
- PCA
Statistical approach
0
1
1
1...
.........
...1
1
xIA
xAx
cccc
kr
r
r
Ak
l j
jlj
i
iliij
ji
ij
A = correlation matrix between species ci and cj (over range k)
x = Eigenvectors
λ = Eigenvalues
I = unity
9 A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
- EOF
Statistical approach
10 A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
- Air Parcel Trajectories
Statistical approach
11 A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
Deterministic Models CHEMISTRY-TRANSPORT-MODELS
12 A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
Deterministic Models Box model
13 A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
Deterministic Models Lagrange model
14 A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
Deterministic Models Gaussian models
15 A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
Deterministic Models Gaussian models
where
C(x, y, z) : pollutant concentration at point ( x, y, z );
U: wind speed (in the x "downwind" direction, m/s)
σ: standard deviation of the concentration in the x and y direction, i.e., in the wind direction and cross-wind, in meters;
Q is the emission strength (g/s)
h is the emission release height,
²2
²exp
²2
²exp
²2
²exp
2),,,(
zzyzy
hzhzy
u
QtzyxC
16 A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
17 A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
18 A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
Chem. Transport Model3-D Grid Modelling
19 A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
Chem. Transport Model3-D Grid Modelling
20 A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
Chem. Transport Model
21 A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
Chem. Transport Model
22 A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
Tropospheric chemistry
23 A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
Tropospheric chemistry
24 A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
Emissions
Anthropogenic PM2.5 Emissions for Europe
25 A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
Emissions
26 A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
Meteorology
27 A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
Meteorology
28 A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
Landuse
29 A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
Validation
30 A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
ValidationREM_Calgrid: Ozone Validation at rual background station 1997
31 A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
Validation
32 A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
Validatione
Neukoelln Jahresmittel [µg/m³]
10.5 (41%)
3.0 (12%)
1.8 (7%)
3.6 (14%)0.6 (2%)
2.2 (8%)
4.2 (16%)
Sulf Ammo Nitr Rest OM EC Seesalz
Anorganisches PM10 Kohlenstoffhaltiges PM10
33 A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
Validation
34 A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
APPLICATIONSREPRESENTATION OF CURRENT STATE
• spatially homogenous
35 A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
APPLICATIONSREPRESENTATION OF CURRENT STATE
36 A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
APPLICATIONSREPRESENTATION OF CURRENT STATE
37 A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
APPLICATIONSREPRESENTATION OF CURRENT STATE
• temporally continuous
38 A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
APPLICATIONSEmission Scenarios D2005 – MFR2020 [kt/yr]
SNAP SO2 NOx NMVOC PM10 PM2.5 NH3
01 Combustion in energy and transformation industries 78 135 0 5 4 0
02 Non-industrial combustion plants 47 28 30 15 14 0
03 Combustion in manufacturing industry 3 46 -1 1 1 0
04 Production processes 42 38 7 11 5 1
05 Extraction and distribution of fossil fuels 2 0 13 1 0 0
06 Solvent and other product use 0 0 8 0 0 0
07 Road transport 0 544 73 17 18 3
08 Other mobil sources and machinery 0 68 25 8 8 0
09 Waste treatment and disposal 0 0 0 0 0 0
10 Agriculture 0 5 5 1 1 67
11 Other sources and sinks 0 0 0 2 0 0
Sum 172 864 160 61 51 71
39 A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
APPLICATIONSEmission Scenarios
40 A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
APPLICATIONSEmission Scenarios
41 A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
APPLICATIONSEmission Scenarios
42 A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
APPLICATIONSEmission Scenarios
43 A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
APPLICATIONSSource Apportionment
from EMEP
44 A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
APPLICATIONSSource Apportionment
from EMEP
45 A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
APPLICATIONSSource Apportionment
Konzentrationsbeiträge zu den berechneten PM10-Tagesmittelwerten : Berlin-Silbersteinstr.
0
10
20
30
40
50
60
70
80
90
Jan-
01
Jan-
15
Jan-
29
Feb-1
2
Feb-2
6
MAR12
MAR26
Apr-0
9
Apr-2
3
MAY07
MAY21
Jun-
04
Jun-
18
Jul-0
2
Jul-1
6
Jul-3
0
Aug-1
3
Aug-2
9
Sep-1
2
Sep-2
6
OCT10
OCT24
Nov-0
7
Nov-2
1
DEC05
DEC19
µg/
m3
Hintergrund+ Stadt+ Straße: 95 Überschreitungstage
46 A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
APPLICATIONSSource Apportionment
47 A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
APPLICATIONSSource Apportionment
48 A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
APPLICATIONSProcess Analysis
49 A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
APPLICATIONSProcess Analysis
50 A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
APPLICATIONSProcess Analysis net transport contribution
AmmoEC
Sulf
OM Nitr
SOA
Rest Rest
51 A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
APPLICATIONSProcess Analysis wind direction influence
14.2
SULF
NITR
19.0
13.9
3.820.0
11.63.8
3.8
11.4
14.3
13.118.6
5.45.3
19.53.8
52 A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
LITERATURESeinfeld and Pandis, Atmospheric Chemistry and Physics, John Wileyd Sons, 1998
Peter Warneck, Chemistry of the Natural Atmosphere, Academic Press, Inc., 1988
R. B. Stull, An Introduction to Boundary Layer Meteorology, Springer-Verlag, 1988
Bruno Sportisse, Air Pollution Modelling and Simulaiton, Springer-Verlag, 2001