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AUT / LHTEE Street Emission Ceilings (SEC) exercise Task leader: Nicolas Moussiopoulos Aristotle University Thessaloniki Team: Dick van den Hout, TNO Steinar.

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Presentation on theme: "AUT / LHTEE Street Emission Ceilings (SEC) exercise Task leader: Nicolas Moussiopoulos Aristotle University Thessaloniki Team: Dick van den Hout, TNO Steinar."— Presentation transcript:

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

3 AUT / LHTEE 3 SEC-EIONET-Oslo-Oct03 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.

4 AUT / LHTEE 4 SEC-EIONET-Oslo-Oct03 So far activities Review of relevant existing studies Design of city and street typologies Analysis of excess concentrations (PM 10, PM 2.5,NO 2 ) 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

5 AUT / LHTEE 5 SEC-EIONET-Oslo-Oct03 Planned activities 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

6 AUT / LHTEE 6 SEC-EIONET-Oslo-Oct03 Review of relevant existing studies 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

7 AUT / LHTEE 7 SEC-EIONET-Oslo-Oct03 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 CAFEs IA modelling help local authorities in identifying critical hotspots

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

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

10 AUT / LHTEE 10 SEC-EIONET-Oslo-Oct03 street Towards a street typology 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)

11 AUT / LHTEE 11 SEC-EIONET-Oslo-Oct Urban background concentrations Population Urban background concentration Type C1a Type C1b Type C2a Type C2b Type C3a Type C3b Type C4a Type C4b Type C5a Type C5b Type C6a Type C6b Street concentrations Street emission Street concentration Type S1a Type S1b Type S2a Type S2b Type S3a Type S3b Type S4a Type S4b Illustration of typification City typology defining f(pop ) Street typology defining g(emi)

12 AUT / LHTEE 12 SEC-EIONET-Oslo-Oct03 From concentrations to SECs Concentration in a street of j-th type in a city of i-th type: c(C i,S j ) = c RBG + f i (pop) + g j (emi) Street emission ceiling for this street: SEC ij = G j ( c LV – [c RBG + f i (pop)] )

13 AUT / LHTEE 13 SEC-EIONET-Oslo-Oct03 Analysis of excess concentrations The aim of this subtask is to: contribute to knowledge of (relative) emission factors for vehicles, by comparing PM and NO x concentrations, as functions of vehicle distribution in traffic contribute to analysis of the road dust resuspension source, by comparing PM 2.5, PM 10 and NO x concentrations, together with meteo data contribute to relationships between street/traffic parameters and resultinh concentrations provide a basis for model-measurement comparisons / model validation.

14 AUT / LHTEE 14 SEC-EIONET-Oslo-Oct03 Data analysis (1/2) 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,..

15 AUT / LHTEE 15 SEC-EIONET-Oslo-Oct03 Data analysis (2/2) Furthermore: Study deltaC as a function of time of day Study deltaC as a function of wind direction Study ratio PM/NO x (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)

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

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

18 AUT / LHTEE 18 SEC-EIONET-Oslo-Oct03 90 m 70 mN S R Hornsgatan, Stockholm (2/3)

19 AUT / LHTEE 19 SEC-EIONET-Oslo-Oct03 Hornsgatan, traffic data

20 AUT / LHTEE 20 SEC-EIONET-Oslo-Oct03 Hornsgatan station pair, monthly averages

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

22 AUT / LHTEE 22 SEC-EIONET-Oslo-Oct03 Hornsgatan, average daily PM 10

23 AUT / LHTEE 23 SEC-EIONET-Oslo-Oct03 Hornsgatan, average daily PM 2.5

24 AUT / LHTEE 24 SEC-EIONET-Oslo-Oct03 Hornsgatan, average daily NO x

25 AUT / LHTEE 25 SEC-EIONET-Oslo-Oct03 Interpretation via emissions estimates Calculation of emissions for CO, NO x, using COPERT 3 methodology, taking into account: Traffic volume and speed Fleet composition (% HDV) Road characteristics Vehicle classification using TRENDS model results.

26 AUT / LHTEE 26 SEC-EIONET-Oslo-Oct03 Comparison with data analysis results 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.

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

28 AUT / LHTEE 28 SEC-EIONET-Oslo-Oct03 Ermou str., average daily PM 10, NO 2 and NO x

29 AUT / LHTEE 29 SEC-EIONET-Oslo-Oct03 Ermou str., CO vs. NO x

30 AUT / LHTEE 30 SEC-EIONET-Oslo-Oct03 Ermou str., PM vs. NO x

31 AUT / LHTEE 31 SEC-EIONET-Oslo-Oct03 Conclusions So far data analysis has been very instructive (in terms of excess concentrations and PM/NO x 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 PM 2.5 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.


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