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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

Andreas.Kerschbaumer@FU-Berlin.de

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

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