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Street Emission Ceilings (SEC) exercise

Street Emission Ceilings (SEC) exercise. Task leader: Nicolas Moussiopoulos Aristotle University Thessaloniki Team: Dick van den Hout, TNO Steinar Larssen, NILU Frank de Leeuw, RIVM Zissis Samaras, AUT/LAT. Many thanks to: Roel van Aalst

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Street Emission Ceilings (SEC) exercise

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  1. Street Emission Ceilings (SEC) exercise Task leader: Nicolas Moussiopoulos Aristotle University Thessaloniki Team: Dick van den Hout, TNO Steinar Larssen, NILU Frank de Leeuw, RIVM Zissis Samaras, AUT/LAT

  2. Many thanks to: Roel van Aalst Leonor Tarrason et al. Ruwim Berkowicz Ioannis Douros, Liana Kalognomou, Christos Naneris, Apostolos Papathanasiou Acknowledgements SEC-EIONET-Oslo-Oct03

  3. 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. Objectives SEC-EIONET-Oslo-Oct03

  4. Review of relevant existing studies Design of city and street typologies Analysis of excess concentrations (PM10, PM2.5 ,NO2) at selected traffic air monitoring stations by comparing with the urban background Interpretation of the above analysis in terms of local emission estimates Demonstration of model application potential So far activities SEC-EIONET-Oslo-Oct03

  5. Review of relevant simple and state-of-the art models Expand application of suitable urban and local scale models to a limited number of well-documented cases Synthesis of results, presentation to a wider audience, first ideas on how to generalize Planned activities SEC-EIONET-Oslo-Oct03

  6. Field campaigns related to source apportionment Monitoring data analysis associated with the characteristics of hotspots Resuspension studies Modelling studies leading to source-receptor relationships Emission patterns in busy streets and associated key parameters Review of relevant existing studies SEC-EIONET-Oslo-Oct03

  7. Why city and street typology? To develop a method for determining at which emissions in streets (depending on street and city type) limit values are reached. Such a method will • allow taking the street level into account in CAFE’s IA modelling • help local authorities in identifying critical hotspots SEC-EIONET-Oslo-Oct03

  8. Quantification of Regional background contribution … using EMEP model results Urban background contribution … setting-up a city typology Hotspot contribution … setting-up a street typology First tentative typification SEC-EIONET-Oslo-Oct03

  9. Key parameters: Population (continuous) Region and local climate: enclosed / open West/North; Central; South Type of predominant emission sources: major industry no major industry Towards a city typology SEC-EIONET-Oslo-Oct03

  10. Key parameters: Street emission (continuous) Average wind speed nearby:  3.5 m/s > 3.5 m/s Configuration: Open rural terrain Non-canyon streets in built-up areas Wide street canyon (W/H > 1.5) Narrow street canyon (W/H 1.5) Towards a street typology SEC-EIONET-Oslo-Oct03

  11. 25 20 15 12.5 10 7.5 5 2.5 0 Type C1a Urban background concentrations Type C1b Type C2a 1.80 Type C2b Type C3a Type C3b Type C4a concentration Urban background Type C4b Type C5a 0.00 Type C5b 0 800000 Type C6a Population Type C6b Type S1a Street concentrations Type S1b Type S2a 1000.00 Type S2b Type S3a Type S3b Type S4a Type S4b Street concentration 0.00 0 800000 Street emission Illustration of typification City typology defining f(pop) Street typology defining g(emi) SEC-EIONET-Oslo-Oct03

  12. From concentrations to SECs Concentration in a street of j-th type in a city of i-th type: c(Ci,Sj) = cRBG + fi(pop) + gj(emi) Street emission ceiling for this street: SECij = Gj(cLV – [cRBG + fi(pop)]) SEC-EIONET-Oslo-Oct03

  13. The aim of this subtask is to: contribute to knowledge of (relative) emission factors for vehicles, by comparing PM and NOx concentrations, as functions of vehicle distribution in traffic contribute to analysis of the road dust resuspension source, by comparing PM2.5, PM10 and NOx concentrations, together with meteo data contribute to relationships between street/traffic parameters and resultinh concentrations provide a basis for model-measurement comparisons / model validation. Analysis of excess concentrations SEC-EIONET-Oslo-Oct03

  14. Intention to study excess concentrations at hotspot stations. Analysis includes: deltaC (hotspot – urban background), hourly time series hourly time series of the other parameters average, percentiles, max (hour and day) separate in work-days and weekend days compare deltaC at the various stations, explain differences in terms of traffic, meteo, strength of resuspension source,.. Data analysis (1/2) SEC-EIONET-Oslo-Oct03

  15. Furthermore: Study deltaC as a function of time of day Study deltaC as a function of wind direction Study ratio PM/NOx (hour, day): plot time series in parallel look for peaks & variations analyse scatter plots to find ratios and outliers/different domains in the data (e.g. dry/wet road surface, poor dispersion) Data analysis (2/2) SEC-EIONET-Oslo-Oct03

  16. Review reports are being finalized. Up to now 5 stations pairs have been analysed: Hornsgatan, Stockholm Skårersletta, Oslo Marylebone Road, London Ermou,Thessaloniki Vrsovice, Prague Frankfurter Allee, Berlin Copenhagen Madrid Hannover Milano Progress of data analysis SEC-EIONET-Oslo-Oct03

  17. Hornsgatan, Stockholm (1/3) SEC-EIONET-Oslo-Oct03

  18. R N 70 m S 90 m Hornsgatan, Stockholm (2/3) SEC-EIONET-Oslo-Oct03

  19. Hornsgatan, traffic data SEC-EIONET-Oslo-Oct03

  20. Hornsgatan station pair, monthly averages SEC-EIONET-Oslo-Oct03

  21. PM10 - Hornsgatan station vs. urban and rural background, annual variation (3 years) SEC-EIONET-Oslo-Oct03

  22. Hornsgatan, average daily PM10 SEC-EIONET-Oslo-Oct03

  23. Hornsgatan, average daily PM2.5 SEC-EIONET-Oslo-Oct03

  24. Hornsgatan, average daily NOx SEC-EIONET-Oslo-Oct03

  25. Interpretation via emissions estimates Calculation of emissions for CO, NOx, using COPERT 3 methodology, taking into account: • Traffic volume and speed • Fleet composition (% HDV) • Road characteristics • Vehicle classification using TRENDS model results. SEC-EIONET-Oslo-Oct03

  26. Ratios dPM10/dNOx dPM2.5/dNOx 0,45 0,40 0,35 0,30 Ratio 0,25 0,20 0,15 0,10 0,05 0,00 1 2 3 4 5 6 7 8 9 10 11 12 Month Comparison with data analysisresults • Results are in good agreement with ratios derived from the data analysis. • Only hot emissions are assumed as the cold start effect is assumed to be negligible in the specific street canyon. SEC-EIONET-Oslo-Oct03

  27. Ermou str., Thessaloniki: area features SEC-EIONET-Oslo-Oct03

  28. Ermou str., average daily PM10, NO2 and NOx SEC-EIONET-Oslo-Oct03

  29. Ermou str., CO vs. NOx SEC-EIONET-Oslo-Oct03

  30. Ermou str., PM vs. NOx SEC-EIONET-Oslo-Oct03

  31. So far data analysis has been very instructive (in terms of excess concentrations and PM/NOx ratios). Comparison between emission and concentration ratios has shown variable results so far. Experience with model application is encouraging. Yet, data has been hard to find (station pairs and PM2.5 concentrations) and is still being processed, the aim being to improve European coverage. Model application should be expanded to more cities and more model systems. Conclusions SEC-EIONET-Oslo-Oct03

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