(funded by DG Research 5th FP)

1. (funded by DG Research 5th FP) Multi-Pollutant Multi-Effect Modelling of European Air Pollution Control Strategies - an Integrated Approach

2. 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. Objectives Developmentandapplicationof methodologies and toolsfor an integrated assessment of European air pollution control strategies Features • 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

4. Acidification Global Warming Eutrophication N2O SO2 CH4 NOx CO2 NH3 CO NMVOC Urban Air Quality Particulate Matter (PM2.5 / PM10) TroposphericOzone primary & secondary Aerosols Multi-Pollutant Multi-Effect Analysis

5. The Measure-Matrix approach illustrated Costs of implementation(typically with reference to Stock or Activity) Measure-Database (MDB) Reference Description DEF1DEF2 .. DEFn Costs Meta-Information non-tech. measures(affecting S or A) techn. measures(affecting EFs) unique ID Information on implementation, interdependencies, i.e. AND, OR, XOR, ... Stock-Activity-Database (SADB) Reference Description Stock (S) Activity (A) EF1 EF2 .. EFn Meta-Information e = A * EFi EF = Emission Factor S = Stock (e.g. # of vehicles) A = Activity (e.g. km/yr) e = source emissions E = source-group emissions E = S * e

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

7. Calculation example (II) Stock Activities No. of vehicles: # [GER, PC, EURO2, gasoline, 2000] annual mileage: km/vehicle*year [GER, PC, EURO2, gasoline, 2000] X g/km [CO2, NOx, CO, NMVOC, PM2.5, PM10, ...] Emission Factors Measure (M1)DPF PM t/year[CO2, NOx, CO, NMVOC, PM2.5, PM10, ...] DEmissions = f(M1) CEA CBA Dconcentrations/deposition costs Dimpacts exceedancescompliance? benefits

8. Measure (M2)Scrapping ofEURO1 vehicles #  Calculation example (III) Stock Activities No. of vehicles: #[GER, PC, EURO1, gasoline, 2000] annual mileage: km/vehicle*year [GER, PC, EURO1, gasoline, 2000] X g/km [CO2, NOx, CO, NMVOC, PM2.5, PM10, ...] Emission Factors Measure (M1)DPF PM t/year[CO2, NOx, CO, NMVOC, PM2.5, PM10, ...] DEmissions = f(M1,M2) CEA CBA Dconcentrations/deposition costs Dimpacts exceedancescompliance? benefits

9. 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. 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. 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 andnon-technical measures in the same framework, avoiding double-counting and over/underestimationof effects

12. 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. CEEC Information • detailed analysis of implementation of environmental policies in Accession countries

14. Calibration/Evaluation • first comparison of fuel consumption data for road transport fuels (gasoline & diesel); R2 = 0.9867

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

16. Model results (Ia): Ozone AOT40 crops [ppb.h] AOT40c (-50% NOx -50% NMVOC) 2010 CLE (2003 met) DAOT40c (-50% NOx -50% NMVOC)

17. Model results (Ib): Ozone AOT40 crops [ppb.h] 2010 CLE (2003 met) AOT40c (-50% NOx -50% NMVOC) DAOT40c (-50% NOx -50% NMVOC)

18. Model results (Ic): Ozone AOT40 crops [ppb.h] 2010 CLE (2003 met) DAOT40c (-50% NOx -50% NMVOC) AOT40c (-50% NOx -50% NMVOC)

19. Model results (IIa): Acid-Deposition [mg/m2] S-Deposition (-50% NOx -50% SO2) 2010 CLE (2003 met) DS-Deposition (-50% NOx -50% SO2)

20. Model results (IIb): Acid-Deposition [mg/m2] S-Deposition (-50% NOx -50% SO2) 2010 CLE (2003 met) DS-Deposition (-50% NOx -50% SO2)

21. Model results (IIb): Acid-Deposition [mg/m2] S-Deposition (-50% NOx -50% SO2) 2010 CLE (2003 met) DS-Deposition (-50% NOx -50% SO2)

22. Model results (IIIa): N-Deposition [mg/m2] N-Deposition (-50% NOx -50% NH3) 2010 CLE (2003 met) DN-Deposition (-50% NOx -50% NH3)

23. Model results (IIIb): N-Deposition [mg/m2] 2010 CLE (2003 met) N-Deposition (-50% NOx -50% NH3) DN-Deposition (-50% NOx -50% NH3)

24. Model results (IIIc): N-Deposition [mg/m2] 2010 CLE (2003 met) DN-Deposition (-50% NOx -50% NH3) N-Deposition (-50% NOx -50% NH3)

25. Model results (IVa): Optimisation example for 4 pollutants Italy UK France Germany

26. Model results (IVb): Optimisation example for 4 pollutants France

27. 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 Conclusions To do: define sets of targets (air quality & GHG emissions) and ... ... run, run, run, run run ...