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IER Universität Stuttgart Institut für Energiewirtschaft und Rationelle Energieanwendung Multi-Pollutant Multi-Effect Modelling of European Air Pollution.

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Präsentation zum Thema: "IER Universität Stuttgart Institut für Energiewirtschaft und Rationelle Energieanwendung Multi-Pollutant Multi-Effect Modelling of European Air Pollution."—  Präsentation transkript:

1 IER Universität Stuttgart Institut für Energiewirtschaft und Rationelle Energieanwendung Multi-Pollutant Multi-Effect Modelling of European Air Pollution Control Strategies - an Integrated Approach (funded by DG Research 5th FP)

2 IER Universität Stuttgart Institut für Energiewirtschaft und Rationelle Energieanwendung The MERLIN team: IER University of Stuttgart (Co-ordinator) Aristotle University of Thessaloniki, Laboratory for Heat Transfer and Environmental Engineering (AUT/LHTEE) University College London (UCL) Norwegian Meteorological Institute (met.no) ECOFYS Energy and Environment Institute for Ecology of Industrial Areas (IETU) Energy Research Center (ERC) of Ostrava Technical University National Institute of Meteorology and Hydrology (NIMH) University of Ploiesti (Ploiesti, Romania)

3 IER Universität Stuttgart Institut für Energiewirtschaft und Rationelle Energieanwendung Objectives Development and application of methodologies and tools for an integrated assessment of European air pollution control strategies multi-pollutant, multi-effect assessment cost-effectiveness and cost-benefit analysis application of advanced optimisation methods inclusion of non-technical measures assesment of sectoral reduction potentials macroeconomic effects and distributional burdens of air pollution control inclusion of accession countries Features

4 IER Universität Stuttgart Institut für Energiewirtschaft und Rationelle Energieanwendung Acidification Eutrophication Tropospheric Ozone Global Warming primary & secondary Aerosols Urban Air Quality NO x SO 2 NMVOC CO CO 2 CH 4 NH 3 N2ON2O Particulate Matter (PM 2.5 / PM 10 ) Multi-Pollutant Multi-Effect Analysis

5 IER Universität Stuttgart Institut für Energiewirtschaft und Rationelle Energieanwendung Reference Description  EF 1  EF 2..  EF n Costs Meta-Information Measure-Database (MDB) Reference Description Stock (S) Activity (A) EF 1 EF 2.. EF n Meta-Information Stock-Activity-Database (SADB) unique ID e = A * EF i E = S * e techn. measures (affecting EFs) non-tech. measures (affecting S or A) Information on implementation, interdependencies, i.e. AND, OR, XOR,... EF= Emission Factor S = Stock (e.g. # of vehicles) A= Activity (e.g. km/yr) e = source emissions E = source-group emissions Costs of implementation (typically with reference to Stock or Activity) The Measure-Matrix approach illustrated

6 IER Universität Stuttgart Institut für Energiewirtschaft und Rationelle Energieanwendung Calculation example (I) Stock Activities Emission Factors Emissions X No. of vehicles: # [GER, PC, EURO2, gasoline, 2000] annual mileage: km/vehicle*year [GER, PC, EURO2, gasoline, 2000] g/km [CO2, NOx, CO, NMVOC, PM2.5, PM10,...] t/year [CO2, NOx, CO, NMVOC, PM2.5, PM10,...] concentrations/deposition impacts exceedances

7 IER Universität Stuttgart Institut für Energiewirtschaft und Rationelle Energieanwendung Calculation example (II) Stock Activities Emission Factors  Emissions = f(M1) X No. of vehicles: # [GER, PC, EURO2, gasoline, 2000] annual mileage: km/vehicle*year [GER, PC, EURO2, gasoline, 2000] g/km [CO2, NOx, CO, NMVOC, PM2.5, PM10,...] t/year [CO2, NOx, CO, NMVOC, PM2.5, PM10,...] Measure (M1) DPF PM   concentrations/deposition  impacts costs benefits CEA CBA exceedances compliance?

8 IER Universität Stuttgart Institut für Energiewirtschaft und Rationelle Energieanwendung Calculation example (III) Stock Activities Emission Factors  Emissions = f(M1,M2) X No. of vehicles: # [GER, PC, EURO1, gasoline, 2000] annual mileage: km/vehicle*year [GER, PC, EURO1, gasoline, 2000] g/km [CO2, NOx, CO, NMVOC, PM2.5, PM10,...] t/year [CO2, NOx, CO, NMVOC, PM2.5, PM10,...] Measure (M1) DPF PM  Measure (M2) Scrapping of EURO1 vehicles #   concentrations/deposition  impacts benefits CEA CBA costs exceedances compliance?

9 IER Universität Stuttgart Institut für Energiewirtschaft und Rationelle Energieanwendung Non-Technical Measures (I) In MERLIN, non-technical measures such as: (a) Higher motor fuel taxes (b) Road congestion pricing (c) Higher taxes on motor vehicle ownership (d) Restructuring vehicle fuels taxes ( eg the balance between diesel fuel and petrol) (e) Accelerated scrapping incentives (f) Parking charges (g) Public transport subsidy are evaluated, which affect activity levels by increasing relative prices.

10 IER Universität Stuttgart Institut für Energiewirtschaft und Rationelle Energieanwendung Non-Technical Measures (II) example for a price increase on fuel: Dead weight loss (DWL) as an means to assess the costs of implementation for a 10% increase in fuel tax

11 IER Universität Stuttgart Institut für Energiewirtschaft und Rationelle Energieanwendung Non-Technical Measures (III) Different effects on increased fuel tax is distributed into modal change: change from private to public transport (urban/inter-urban rail/bus) activity reduction: reduction of annual mileage („not driving“) technology change: effects on fuel efficiency (l/km)  long term effect investigate joint assessment of technical and non-technical measures in the same framework, avoiding double-counting and over/underestimation of effects

12 IER Universität Stuttgart Institut für Energiewirtschaft und Rationelle Energieanwendung Sectoral Studies potential to investigate questions such as the general spatial, temporal and speciation differences of emissions of individual source sectors, e.g. environmental impacts of gasoline vs diesel vehicles emission control in mobile vs stationary sources (emission height, VOC split etc.) impacts of structural changes in the energy sector on air pollutant emissions and GHG emissions for this purpose, calculation of sector-specific Source-Receptor Matrices road transport (PC, LDV, HDV, MP, MC, evaporation) public power plants (by fuel)...

13 IER Universität Stuttgart Institut für Energiewirtschaft und Rationelle Energieanwendung CEEC Information detailed analysis of implementation of environmental policies in Accession countries

14 IER Universität Stuttgart Institut für Energiewirtschaft und Rationelle Energieanwendung Calibration/Evaluation first comparison of fuel consumption data for road transport fuels (gasoline & diesel); R 2 =

15 IER Universität Stuttgart Institut für Energiewirtschaft und Rationelle Energieanwendung Calculate the resulting emissions for each country Calculate the resulting emissions for each country Crossbreed and mutate the surviving strategies Crossbreed and mutate the surviving strategies Remove strategies with worst performance Remove strategies with worst performance Evaluate results (emissions, concentrations, costs and benefits) Calculate concentration changes on a 50 * 50 km grid Calculate concentration changes on a 50 * 50 km grid Generate the first set of solutions at random Compiling a set of measures from the measure database Source receptor Matrices (EMEP) End optimisation, if targets are achieved (evaluation with EMEP Uni) MERLIN Optimisation Approach (Genetic Algorithm) – the OMEGA Model Stock activity database modified by measure set

16 IER Universität Stuttgart Institut für Energiewirtschaft und Rationelle Energieanwendung Model results (Ia): Ozone AOT40 crops [ppb.h] 2010 CLE (2003 met) AOT40c (-50% NO x -50% NMVOC)  AOT40c (-50% NO x -50% NMVOC)

17 IER Universität Stuttgart Institut für Energiewirtschaft und Rationelle Energieanwendung Model results (Ib): Ozone AOT40 crops [ppb.h] 2010 CLE (2003 met) AOT40c (-50% NO x -50% NMVOC)  AOT40c (-50% NO x -50% NMVOC)

18 IER Universität Stuttgart Institut für Energiewirtschaft und Rationelle Energieanwendung Model results (Ic): Ozone AOT40 crops [ppb.h] 2010 CLE (2003 met)  AOT40c (-50% NO x -50% NMVOC) AOT40c (-50% NO x -50% NMVOC)

19 IER Universität Stuttgart Institut für Energiewirtschaft und Rationelle Energieanwendung Model results (IIa): Acid-Deposition [mg/m 2 ] 2010 CLE (2003 met) S-Deposition (-50% NO x -50% SO 2 )  S-Deposition (-50% NO x -50% SO 2 )

20 IER Universität Stuttgart Institut für Energiewirtschaft und Rationelle Energieanwendung Model results (IIb): Acid-Deposition [mg/m 2 ] 2010 CLE (2003 met) S-Deposition (-50% NO x -50% SO 2 )  S-Deposition (-50% NO x -50% SO 2 )

21 IER Universität Stuttgart Institut für Energiewirtschaft und Rationelle Energieanwendung Model results (IIb): Acid-Deposition [mg/m 2 ] 2010 CLE (2003 met) S-Deposition (-50% NO x -50% SO 2 )  S-Deposition (-50% NO x -50% SO 2 )

22 IER Universität Stuttgart Institut für Energiewirtschaft und Rationelle Energieanwendung Model results (IIIa): N-Deposition [mg/m 2 ] 2010 CLE (2003 met) N-Deposition (-50% NO x -50% NH 3 )  N-Deposition (-50% NO x -50% NH 3 )

23 IER Universität Stuttgart Institut für Energiewirtschaft und Rationelle Energieanwendung Model results (IIIb): N-Deposition [mg/m 2 ] N-Deposition (-50% NO x -50% NH 3 ) 2010 CLE (2003 met)  N-Deposition (-50% NO x -50% NH 3 )

24 IER Universität Stuttgart Institut für Energiewirtschaft und Rationelle Energieanwendung Model results (IIIc): N-Deposition [mg/m 2 ]  N-Deposition (-50% NO x -50% NH 3 ) 2010 CLE (2003 met) N-Deposition (-50% NO x -50% NH 3 )

25 IER Universität Stuttgart Institut für Energiewirtschaft und Rationelle Energieanwendung Model results (IVa): Optimisation example for 4 pollutants UK Italy Germany France

26 IER Universität Stuttgart Institut für Energiewirtschaft und Rationelle Energieanwendung Model results (IVb): Optimisation example for 4 pollutants France

27 IER Universität Stuttgart Institut für Energiewirtschaft und Rationelle Energieanwendung Conclusions OMEGA Model up and running Source-receptor matrices for more years needed to investigate scenarios on the basis of averaged values calibration/evaluation of stock, activity and emission factors vs. latest baseline scenario data vital to ensure comparability of results sectoral matrices for road transport and power plants (first) to allow for detailed sector assessment runs final evaluation of measure data with data available from EGTEI, Baseline Scenarios, experts To do: define sets of targets (air quality & GHG emissions) and run, run, run, run run...


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