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Multi-Pollutant Multi-Effect Modelling of European Air Pollution Control Strategies

The MERLIN team aims to develop and apply methodologies for an integrated assessment of European air pollution control strategies. The project includes multi-pollutant and multi-effect assessment, cost-effectiveness and cost-benefit analysis, and the inclusion of non-technical measures. The team also considers the macroeconomic effects and distributional burdens of air pollution control, as well as the inclusion of accession countries.

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Multi-Pollutant Multi-Effect Modelling of European Air Pollution Control Strategies

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  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) Extension in the frame of the EC call to include partners from NAS: • Energy Research Center (ERC) of Ostrava TechnicalUniversity • 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 • 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. W IIOptimisation Model costs benefits (by country, sector, ...) distributional analysis burden sharing policy deployment Macroeconomic Model Abatement measures Stock/Activity data Emissions SR-Relationships Basemaps, ... EMEPEulerian Model boundary conditions W IIOptimisation Model Urban Air Quality Models Urban Air Quality Models costs benefits (by country, sector, ...) distributional analysis burden sharing policy deployment data flow Macroeconomic Model regional scale urban scale

  6. Introductory Remarks • Core aims of the data collection on stock, activities, abatement measures and costs: • compile a consistent dataset on activities, stock and measures for modelling; • provide information to calculate emissions as E = A * EF for • scenarios (2010 BAU, NECD, Kyoto, ..., and beyond) and to find • optimal reduction options and pathways to achieve a set of env. goals at once. Prime Objective: The datasets shall be comprehensive enough to allow for „realistic“ modelling, butat the same time transparent and on an aggregation level that keeps it manageable. • harmonisation with official scenarios and datasets essential (CAFE baseline, PRIMES, TRENDS etc.) • ongoing improvement and QA/QC by providing transparency and facilitating reviews and checks shall be possible (no static dataset)

  7. MERLIN Model Framework Databases compiled for 2000, 2010, 2020 • Optimal Strategies, including: • Emissions by country • Concentrations by gridcell • Abatement costs by country and sector • Avoided damage costs • ... SADB MDB Stock Activities MeasureData Scenario-ToolScenarion development and data management OMEGA Changing Stock and Activity by implementing measures Stock +Activities MeasureData Optimisation SADB* MDB* Scenarios Modified databases • Emission Scenarios for future years, giving • Emissions by country and sector • Implementation degrees of measures • Changes in emissions relative to the base case by country and sector

  8. 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

  9. Stock/Activity and technical abatement measures, Status 05/2003: • Data collection for a full draft dataset completed for • Power plants (NFR1) • Households (NFR2) • Solvent Use (NFR6) • Road Transport (NFR 7) • Work being finalised for • Industry (combustion & processes, NFR3 & 4) • Waste handling and treatment (NFR9) • Agriculture (NFR10) • Other mobile sources (NFR8, focus on off-road engines, rail and air travel) • comprehensive dataset available for model development • stepwise improvement of data, QA/QC project-internal and with external experts now possible (web-access, launch 05-06/2003) • timeline: finalise dataset and improve/quality check data until 08/2003

  10. MERLIN database web-access prototype

  11. MERLIN database-query (I): HDV, Diesel, EURO I compliant, NL 2000

  12. MERLIN database-query (II): emission calculation

  13. Road Transport Calibration (I)

  14. Road Transport Calibration (II)

  15. Road Transport BAU-Development to 2010

  16. Road Transport BAU-Development to 2020

  17. Non-Technical Measures: • 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. • Next step: derive useable data for modelling from existing transport related studies (e.g. AUTO-OIL I/II) • Subsequently, measures for other sectors (energy, households) to be assessed.

  18. 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. MERLIN Optimisation Approach (Genetic Algorithm)

  19. Example of a 2-Point-Crossover First parent strategy First child strategy Second parent strategy Second child strategy individual measures Recombination and propagation of ‚genes‘ from parent to child strategies

  20. Target OMEGA test runs to assess the Genetic Algorithm converging quickly improving still Starting population, up to now: random choice

  21. Approaches being tested to improve performance: • use lower resolution of the EMEP-grid for initial search (pre-opt) • leave out values close to zero (e.g. the changes of concentrations in grid cells of Norway for ozone due to some changes of NOx emissions in Luxembourg could be insignificant and left out) • favour young solutions (to give them enough mutation time to find a local optimum nearby) • use diversity increasing operators (to prevent premature convergence to a local optimum) • prefer mates of the same neighbourhood (to stress local search) • use several populations and immigration operators

  22. GAParameters Genetic algorithms are driven by a number of parameters. Among these, the following are assessed with regard to their ability to improve the models‘ performance (speed, quality of best strategy, diversity etc.): • Size of population, • Mates per generation, • Mutations per generation, • Number of measures selected and dropped per mutation. • Most improvement include other parameters, as for example the • amount of fitness added to favour young solutions, the • total number of populations (1 .. x) and the • value of the immigration factor.

  23. MERLIN Networking • MERLIN maintains close links to ongoing activities, such as the • UNECE Task Force Integrated Assessment Modelling (TFIAM), • UNECE Task Force Emission Inventories and Projection (TFEIP), • UNECE Expert Group on Techno-Economic Issues (EGTEI), • CITY-DELTA, MIDAIR, ExternE, DIEM, … • TREMOVE • CAFÉ • in order to make best efforts to harmonise work and take into account best available data sources.

  24. MERLIN Dissemination & Discussion • EnviroInfo 2002, Sept 2002, Vienna/A: • Presentation of the MERLIN measure-matrix approach • ISESS 2003, May 27-30 2003, Semmering/A • Presentation of the MERLIN measure-matrix approach and evaluation of first results from optimisation using GAs • MERLIN Website: http://www.merlin-project.info

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