1 / 34

Air Pollution Modelling at the Urban and Local Scales

Air Pollution Modelling at the Urban and Local Scales. Nicolas Moussiopoulos Aristotle University Thessaloniki, Greece.

eamon
Download Presentation

Air Pollution Modelling at the Urban and Local Scales

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Air Pollution Modellingat the Urban and Local Scales Nicolas Moussiopoulos Aristotle University Thessaloniki, Greece

  2. Work on these scales started in EUROTRAC and particularly in the SATURN subproject. Currently, modelling tools are developed further in the frame of several FP5 and FP6 projects. Several of these constitute the CLEAR cluster (cf. http://www.nilu.no/clear/)

  3. University of Hertfordshire CLEAR,Basic Statistics • 11 Projects • ~ 12 Partners per project • ~ 200 Researchers across Europe • ~ 8% SME User partners • EC Contribution ~ €15M

  4. CLEAR 20 Partner Countries

  5. University of Hertfordshire Aim of CLEAR • Threefold aim: • To improve our scientific understanding of atmospheric processes, composition and pollution variabilities on local to regional scales • To provide next generation tools for end users and stakeholders for managing air pollution and responding to its impact • To help create a critical mass of expertise and ambition to address future research needs in the areas of air pollution, its impact and response strategies.

  6. University of Hertfordshire Synergy within CLEAR • Scientific and Technological Benefits - Sources, processes and atmospheric composition (NO, NO2, O3, aerosols, PAHs) - New generation models and tools - Micro to regional scales - short/long term calculations and forecasting - Impacts (health, environment and monuments) - Mitigations measures/policies - New datasets and databases

  7. University of Hertfordshire S & T Benefits Sources, Emissions, Processes and Composition ATREUS, OSCAR BOND SAPPHIRE URBAN AEROSOL URBAN EXPOSURE Exposure Assessment FUMAPEX URBAN EXPOSURE URBAN AEROSOL OSCAR SAPPHIRE ISHTAR New Generation Models Local to Regional ATREUS, OSCAR, FUMAPEX URBAN EXPOSURE ISHTAR, MERLIN Tools for Urban Governance and Response/Mitigation Strategies ALL PROJECTS

  8. The problem Bad urban air quality may result from • in-city pollution sources • long-range transport • Multi-scale interactions important (in particular regarding aerosols, in view of secondary particle formation) Emissions are mainly released within or shortly above the canopy layer. • Urban geometry of high importance

  9. Roof level wind • Background pollution Air exchange leeward side windward side recirculating air direct plume Multi-scale character • Local concentrations influenced by regional scale processes. • Urban air quality affected by mesoscale wind circulations. • Circulations created by the city itself affect pollutant dispersion. • Hotspot concentrations depend on street canyon scale effects. Source: Lutgens et al.  Regional-to-urban coupling needed Source: Britter Urban-to-local coupling needed

  10. Source: V.d.Hout Monitoring & modelling (1/2)

  11. Monitoring & modelling (2/2) • Data assimilation important. • Much effort was put on model QA/QC: • Refinement of model evaluation protocols • Organisation of further model validation and intercomparison activities • Need of wind tunnel experiments in support of model validation

  12. Uncertainties • Measured data uncertain because of • instrument inaccuracies • measuring concept shortcomings (e.g. lack of representativeness, too short averaging) • Model results uncertain because of • input data inaccuracies • model concept shortcomings (e.g. wrong assumptions, bad parameterisations)

  13. European Zooming Model (EZM) system

  14. (1-2)D multi-box model urban Wind direction suburban The Ozone Fine Structure (OFIS) model - Model concept (1/2) The OFIS model was developed in order to • allow authorities to assess urban air quality by means of a fast, simple and still reliable model • (ii) refine a regional model simulation by estimating the urban subgrid effect on pollution levels

  15. The Ozone Fine Structure (OFIS) model - Model concept (2/2) Pollutant transport and transformation downwind the city (along the prevailing wind direction) calculated with a 2-layer model.

  16. The Ozone Fine Structure (OFIS) model - Model features • EMEP MSC-W and CBM-IV chemical mechanisms • Two-mode aerosol module (log-normal distribution) assuming inorganics equilibrium between phases • Advection discretised using an upwind scheme • Mixing height and turbulent diffusivity estimated in a vertical column atmosphere/soil radiation budget model. • Requires a computation time < 4 hours for a full year simulation on a P4 2.0 GHz CPU, being more than 70 times faster than the 3D model MUSE

  17. Latest OFIS improvements • Capability for use of gridded emissions inventories, besides the default disaggregated ones • 3-hourly values are used for the meteorological and boundary conditions input, performing a 5 hour run for each 3-hour frame • Aerodynamic resistance approach to parameterise dry deposition for gases and particles • Use of an appropriate parameterisation for wet removal of gases and particles (Scott, 1979) • Biogenic emissions calculated according to land use

  18. OFIS – Geographical emission distribution used for Milan NOx emissions for 5th of May, 8:00 am, [kg/km2] Application within CityDelta I & II

  19. MIMO • 3D, prognostic microscale model. • Predicts air motion near building structures. • Solves conservation equations for: • Mass • Momentum • Scalar quantities like potential temperature, TKE & specific humidity • Heating module calculates heat transfer through: • Conduction • Convection • Radiation

  20. MIMO validation (1/3) • vs. wind tunnel experiments of Rafailidis (1997) for the isothermal case (cf. Assimakopoulos, 2001) • vs. field measurements of Panskus et.al. (2002) for the heated walls case • vs. wind tunnel experiments of Bezpalcová (2003) for pollutants dispersion.

  21. Isothermal T = 120K T = 80K Windward wall heated case MIMO validation (2/3) Isothermal T = 80K T = 120K Field measurements results MIMO model results

  22. MIMO validation (3/3) Pollutant dispersion case Wind tunnel results MIMO model results

  23. Flow field comparison for aspect ratio 1.0 for the isothermal case (a) MIMO (b) TASCflow

  24. Flow field comparison for aspect ratio 0.33 for the isothermal case (a) MIMO (b) TASCflow

  25. Isothermal Windward wall heated Leeward wall heated Effect of wall heating (ΔΤ = 15Κ) on the concentration fields for aspect ratio 0.4

  26. In – street canyon measurements equipment placed here (Both tests) Non treated tests Non - treated surfaces TiO2 treated surfaces Background measurements & meteorological equipment placed here The Guerville experiment (PICADA project)

  27. EEA’s “Street Emission Ceilings” (SEC) exercise – Main objectives Quantifying the influence of urban and local emissions and other smaller scale effects on concentrations at urban hotspots as a basis for measures for attaining compliance. Development and pilot application of a methodology for this purpose, also with relevance to health issues. SEC is expected to lead to recommendations on how hotspots may be considered in Integrated Assessment based on city and street typologies.

  28. Hornsgatan, Stockholm (1/2)

  29. Hornsgatan, Stockholm (2/2)

  30. Simulation character & chemistry

  31. Duration of the simulation

  32. PM2.5 annual mean concentration in 2000

  33. PM2.5 average daily variation in 2000

  34. PM2.5 monthly mean concentration in 2000

More Related