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Beispielbild Training Programme Air Quality Monitoring, Emission Inventory and Source Apportionment Studies Source Dispersion Modelling Andreas Kerschbaumer.

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Presentation on theme: "Beispielbild Training Programme Air Quality Monitoring, Emission Inventory and Source Apportionment Studies Source Dispersion Modelling Andreas Kerschbaumer."— Presentation transcript:

1 Beispielbild Training Programme Air Quality Monitoring, Emission Inventory and Source Apportionment Studies Source Dispersion Modelling Andreas Kerschbaumer Freie Universität Berlin, Institut für Meteorologie Andreas.Kerschbaumer@FU-Berlin.de

2 2 A. Kerschbaumer, 20.11.2009Source Dispersion Modelling Overview

3 3 A. Kerschbaumer, 20.11.2009Source Dispersion Modelling Overview - General Introduction - Statistical Models, - Deterministic Models - Chemistry Transport Models - Theoretical aspects - Input data - Validation

4 4 A. Kerschbaumer, 20.11.2009Source Dispersion Modelling Overview - Application of Chemistry Transport Models - Emission Scenarios, - Source Apportionment - Process Analysis

5 5 A. Kerschbaumer, 20.11.2009Source 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 6 A. Kerschbaumer, 20.11.2009Source 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 7 A. Kerschbaumer, 20.11.2009Source Dispersion Modelling - CMB a ij source emission signature (composition) s j 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

8 8 A. Kerschbaumer, 20.11.2009Source Dispersion Modelling - PCA Statistical approach A = correlation matrix between species c i and c j (over range k) x = Eigenvectors λ = Eigenvalues I = unity

9 9 A. Kerschbaumer, 20.11.2009Source Dispersion Modelling - EOF Statistical approach

10 10 A. Kerschbaumer, 20.11.2009Source Dispersion Modelling - Air Parcel Trajectories Statistical approach

11 11 A. Kerschbaumer, 20.11.2009Source Dispersion Modelling Deterministic Models CHEMISTRY-TRANSPORT-MODELS

12 12 A. Kerschbaumer, 20.11.2009Source Dispersion Modelling Deterministic Models Box model

13 13 A. Kerschbaumer, 20.11.2009Source Dispersion Modelling Deterministic Models Lagrange model

14 14 A. Kerschbaumer, 20.11.2009Source Dispersion Modelling Deterministic Models Gaussian models

15 15 A. Kerschbaumer, 20.11.2009Source 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,

16 16 A. Kerschbaumer, 20.11.2009Source Dispersion Modelling

17 17 A. Kerschbaumer, 20.11.2009Source Dispersion Modelling

18 18 A. Kerschbaumer, 20.11.2009Source Dispersion Modelling Chem. Transport Model 3-D Grid Modelling

19 19 A. Kerschbaumer, 20.11.2009Source Dispersion Modelling Chem. Transport Model 3-D Grid Modelling

20 20 A. Kerschbaumer, 20.11.2009Source Dispersion Modelling Chem. Transport Model

21 21 A. Kerschbaumer, 20.11.2009Source Dispersion Modelling Chem. Transport Model

22 22 A. Kerschbaumer, 20.11.2009Source Dispersion Modelling Tropospheric chemistry

23 23 A. Kerschbaumer, 20.11.2009Source Dispersion Modelling Tropospheric chemistry

24 24 A. Kerschbaumer, 20.11.2009Source Dispersion Modelling Emissions Anthropogenic PM2.5 Emissions for Europe

25 25 A. Kerschbaumer, 20.11.2009Source Dispersion Modelling Emissions

26 26 A. Kerschbaumer, 20.11.2009Source Dispersion Modelling Meteorology

27 27 A. Kerschbaumer, 20.11.2009Source Dispersion Modelling Meteorology

28 28 A. Kerschbaumer, 20.11.2009Source Dispersion Modelling Landuse

29 29 A. Kerschbaumer, 20.11.2009Source Dispersion Modelling Validation

30 30 A. Kerschbaumer, 20.11.2009Source Dispersion Modelling Validation REM_Calgrid: Ozone Validation at rual background station 1997

31 31 A. Kerschbaumer, 20.11.2009Source Dispersion Modelling Validation

32 32 A. Kerschbaumer, 20.11.2009Source Dispersion Modelling Validation e Anorganisches PM10 Kohlenstoffhaltiges PM10

33 33 A. Kerschbaumer, 20.11.2009Source Dispersion Modelling Validation

34 34 A. Kerschbaumer, 20.11.2009Source Dispersion Modelling APPLICATIONS REPRESENTATION OF CURRENT STATE spatially homogenous

35 35 A. Kerschbaumer, 20.11.2009Source Dispersion Modelling APPLICATIONS REPRESENTATION OF CURRENT STATE

36 36 A. Kerschbaumer, 20.11.2009Source Dispersion Modelling APPLICATIONS REPRESENTATION OF CURRENT STATE

37 37 A. Kerschbaumer, 20.11.2009Source Dispersion Modelling APPLICATIONS REPRESENTATION OF CURRENT STATE temporally continuous

38 38 A. Kerschbaumer, 20.11.2009Source Dispersion Modelling APPLICATIONS Emission Scenarios D2005 – MFR2020 [kt/yr] SNAPSO2NOxNMVOCPM10PM2.5NH3 01 Combustion in energy and transformation industries 781350540 02 Non-industrial combustion plants 47283015140 03 Combustion in manufacturing industry 346110 04 Production processes 423871151 05 Extraction and distribution of fossil fuels 2013100 06 Solvent and other product use 008000 07 Road transport 05447317183 08 Other mobil sources and machinery 06825880 09 Waste treatment and disposal 000000 10 Agriculture 0551167 11 Other sources and sinks 000200 Sum 172864160615171

39 39 A. Kerschbaumer, 20.11.2009Source Dispersion Modelling APPLICATIONS Emission Scenarios

40 40 A. Kerschbaumer, 20.11.2009Source Dispersion Modelling APPLICATIONS Emission Scenarios

41 41 A. Kerschbaumer, 20.11.2009Source Dispersion Modelling APPLICATIONS Emission Scenarios

42 42 A. Kerschbaumer, 20.11.2009Source Dispersion Modelling APPLICATIONS Emission Scenarios

43 43 A. Kerschbaumer, 20.11.2009Source Dispersion Modelling APPLICATIONS Source Apportionment from EMEP

44 44 A. Kerschbaumer, 20.11.2009Source Dispersion Modelling APPLICATIONS Source Apportionment from EMEP

45 45 A. Kerschbaumer, 20.11.2009Source Dispersion Modelling APPLICATIONS Source Apportionment

46 46 A. Kerschbaumer, 20.11.2009Source Dispersion Modelling APPLICATIONS Source Apportionment

47 47 A. Kerschbaumer, 20.11.2009Source Dispersion Modelling APPLICATIONS Source Apportionment

48 48 A. Kerschbaumer, 20.11.2009Source Dispersion Modelling APPLICATIONS Process Analysis

49 49 A. Kerschbaumer, 20.11.2009Source Dispersion Modelling APPLICATIONS Process Analysis

50 50 A. Kerschbaumer, 20.11.2009Source Dispersion Modelling APPLICATIONS Process Analysis net transport contribution Ammo EC Sulf OMNitr SOA Rest

51 51 A. Kerschbaumer, 20.11.2009Source Dispersion Modelling APPLICATIONS Process Analysis wind direction influence 14.2 SULF NITR 19.0 13.9 3.8 20.0 11.6 3.8 11.4 14.3 13.1 18.6 5.4 5.3 19.5 3.8

52 52 A. Kerschbaumer, 20.11.2009Source Dispersion Modelling LITERATURE Seinfeld 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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