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Some experiences from modelling Hornsgata data Gunnar Omstedt, SMHI

Some experiences from modelling Hornsgata data Gunnar Omstedt, SMHI. two different methods for testing emission models p roblems with NOx emissions r esults from using these methods on the new NORTRIP data Hornsgatan 2008 - 2009 with SMHIs PM emission model

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Some experiences from modelling Hornsgata data Gunnar Omstedt, SMHI

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  1. NORTRIP 2011 10 26 Some experiences from modelling Hornsgata dataGunnar Omstedt, SMHI • two different methods for testing • emission models • problems with NOx emissions • results from using these methods on the • new NORTRIP data Hornsgatan 2008-2009 • with SMHIs PM emission model • discussion and presentation of results using • SLB:s road wet moisture index measurements • statistical evaluation using FAIRMODEs • Delta tool for • (comparison between SMHI:s and NILU:s PM • emission models using Delta tool)

  2. NORTRIP 2011 10 26 The tracer method scaling with measured NOx concentrations + based only on measured concentrations and therefore not dependent on modelling dispersion processes - the emissions from the tracer must be known, NOx)

  3. NORTRIP 2011 10 26 • Real word emissions of NOx are higher than currently-used emission factors • The decreasing trend in NOx concentrations is not so large as expected by currently-used emission factors. Emissions data from 72,000 individual vehicles have been analysed from vehicle remote sensing detector (RSD). These data have been compared with emission factor estimates from the ‘Swiss/German Handbook on Emission Factors’ (HBEFA) and COPERT. (Carslaw,D.,Williams, M., et al,2011. Trends in NOx and NO2 emissions and ambient measurements in the UK). For Hornsgatan the decrease of NOxconcentrations between 2000 to 2009 was about 30%, which can be compared with the decrease in emission factor of about 56%.

  4. NORTRIP 2011 10 26 Dispersion modellingOSPM- A parameterized street pollution model + State-of-the-art model that has been used for about 20 years in many different countries Kakosimos K.E., Hertel O., Ketzel M. and Berkowicz R. (2010): Operational Street Pollution Model (OSPM) - a review of performed validation studies, and future prospects. Environmental Chemistry, 7, 485-503. + Input data simple: wind data on roof level, emission data, background concentrations and street geometry data - Some problems with describing stable winter conditions

  5. NORTRIP 2011 10 26 How to estimate real word emission factors-dispersion modelling can be a tool a) b) Calculated hourly NOx concentrations from Hornsgatan in Stockholm using OSPM, based on a constant emissionfactor for NOx of 0.6234 g/vkm. a) time period 1July 2008 to 31 Dec 2009 b) time period 1April 2009 to 30 September 2009 • Strong underestimation of modelled concentrations

  6. NORTRIP 2011 10 26 • But even if we select a much larger constant • emissionfactor we will not have perfect fit • A constant emission factor is too rough assumption, due to time variations in vehicle types and speed, driving pattern etc. • Emissiondata therefore needs to be improved But maybe this is another project!

  7. NORTRIP 2011 10 26 Modelling PM10 concentration using both the tracer method and the OSPM model Data: Hornsgatan_2008-2009_input_data_use110608.xlsx prepared by Michael Norman for the NORTRIP project. Met data from TorkelKnutssongatan but precipitation from Högdalenand cloud cover data from Mesan. Methods: Tracer method scaling with NOx and a constant emissionfactor for NOx as suggested by Michael of 0.62 g/vkm Dispersion calculation using OSPM PM emission calculated by SMHI model (Omstedt et al.,2005), no sand, eref(summer)=0.1 g/vkm

  8. NORTRIP 2011 10 26 Results using meteorological data to calculate road surface moisture

  9. NORTRIP 2011 10 26 Results using SLB:s wet index measurements

  10. NORTRIP 2011 10 26 Road surface moisture comparison modelled moisture modelledmoisture ”measured” moisture

  11. NORTRIP 2011 10 26 Comparing SMHI:s and NILU:s modelstracer method, modelled moisture

  12. NORTRIP 2011 10 26 SLB:s road wet moisture index no correlation? index data from Hornsgatan but precipitation data from Högdalen

  13. NORTRIP 2011 10 26 Comparing SLB:s index with calculated road moisture Similarities but SLB:s index indicate faster drying up and also maybe (?) some more wet conditions Include new processes in the road surface model: drying up caused by traffic and condensations due to cold roads?

  14. NORTRIP 2011 10 26 Statistical evaluation using FAIRMODsDelta tool Lists on available statistical indicators and diagrams

  15. Summary statistics using OSPM NORTRIP 2011 10 26

  16. NORTRIP 2011 10 26 Summary statistics using the Tracer method

  17. NORTRIP 2011 10 26 Summary and conclusions • Uncertainties in NOx emissions and trends lead to problems using the tracer method. Modelling hourly data with such data will give uncertain results. • The two methods (dispersion modelling and the tracer method) give good and similar results for local daily mean PM10 concentrations for the data used in this study. • SLB:s wet index data show that road surface moisture is a important parameter that can improve the modelling. The correlation coefficient improved from 0.65 to 0.85 • More work need to be done for understanding how SLB:s wet index is related to road surface moisture, the parameter used in the PMemission models. • FARMODEs Delta tool is a fast and harmonized tool for comparing models and • can therefore be used for comparing NILU:s and SMHI:s PMemission models

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