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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. Overview. Overview. General Introduction Statistical Models,
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Training ProgrammeAir Quality Monitoring, Emission Inventory and Source Apportionment StudiesSource Dispersion ModellingAndreas KerschbaumerFreie Universität Berlin, Institut für MeteorologieAndreas.Kerschbaumer@FU-Berlin.de
Overview A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
Overview • General Introduction • Statistical Models, • Deterministic Models • Chemistry Transport Models • Theoretical aspects • Input data • Validation A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
Overview • Application of Chemistry Transport Models • Emission Scenarios, • Source Apportionment • Process Analysis 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. 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 A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
Statistical approach • 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 A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
Statistical approach • PCA A = correlation matrix between species ci and cj (over range k) x = Eigenvectors λ = Eigenvalues I = unity A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
Statistical approach • EOF A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
Statistical approach • Air Parcel Trajectories A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
Deterministic Models • CHEMISTRY-TRANSPORT-MODELS A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
Deterministic Models • Box model A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
Deterministic Models • Lagrange model A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
Deterministic Models • Gaussian models 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) • his the emission release height, A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
Chem. Transport Model 3-D Grid Modelling A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
Chem. Transport Model 3-D Grid Modelling A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
Chem. Transport Model A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
Chem. Transport Model A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
Tropospheric chemistry A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
Tropospheric chemistry A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
Emissions Anthropogenic PM2.5 Emissions for Europe A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
Emissions A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
Meteorology A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
Meteorology A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
Landuse A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
Validation A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
Validation REM_Calgrid: Ozone Validation at rual background station 1997 A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
Validation A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
Validation e Anorganisches PM10 Kohlenstoffhaltiges PM10 A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
Validation A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
APPLICATIONS REPRESENTATION OF CURRENT STATE • spatially homogenous A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
APPLICATIONS REPRESENTATION OF CURRENT STATE A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
APPLICATIONS REPRESENTATION OF CURRENT STATE A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
APPLICATIONS REPRESENTATION OF CURRENT STATE • temporally continuous A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
APPLICATIONS Emission ScenariosD2005 – MFR2020 [kt/yr] A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
APPLICATIONS Emission Scenarios A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
APPLICATIONS Emission Scenarios A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
APPLICATIONS Emission Scenarios A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
APPLICATIONS Emission Scenarios A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
APPLICATIONS Source Apportionment from EMEP A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
APPLICATIONS Source Apportionment from EMEP A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
APPLICATIONS Source Apportionment A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
APPLICATIONS Source Apportionment A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
APPLICATIONS Source Apportionment A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
APPLICATIONS Process Analysis A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
APPLICATIONS Process Analysis A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling
APPLICATIONS Process Analysisnet transport contribution Rest Rest Ammo EC OM Nitr Sulf SOA A. Kerschbaumer, 20.11.2009 Source Dispersion Modelling