1. Development and evaluation of the suspension emission model Mari Kauhaniemi Research Scientist Finnish meteorological Institute, Air Quality, Dispersion modelling NORTRIP kick-off workshop (Stockholm) 26.-27.4.2010

2. Background Based on the PM emission model developed by Omstedt et al. (2005). Aim is to use it also in forecasting  slightly modified. Paper in progress: Development and evaluation of a vehicular suspension model for predicting the concentrations of PM10 in urban environments. Kauhaniemi, Kukkonen, Härkönen, Nikmo, Kangas, Omstedt, Ketzel, Kousa, Haakana, and Karppinen No measured suspension emissions available Evaluated against observed PM10 concentrations. Two dispersion models used: Street canyon model (OSPM) Open road line-source model (CAR-FMI) Study period: 8.1.-2.5.2004

3. Open roadside site (Vallila) Runeberg Street Hesperian Boulevard Measurement station Street canyon site (Runeberg Street) Measurement site

4. Runeberg Street Vallila Results: daily PM10 IA = 0.87 FB = 0.03 IA = 0.88 FB = 0.10 predicted (µg/m 3 ) observed (µg/m 3 )

5. Results: daily PM10 Runeberg Street Vallila Cleaning & dust binding Under-prediction: because traffic volume under- estimated, No on-site met. data. Over-prediction: due to the dust binding. Affects about 2 weeks, if good conditions. Not taken into account in the suspension model Over-prediction: due to the snowing/raining. No on-site met. data. Precipitation too light to be taken into account in the suspension model. Under-prediction: due to the cleaning of road surfaces. Can rise dust into the air in short time periods. Not taken into account in the suspension model.

6. Vallila All data: IA = 0.78 FB = 0.10 Low wind: IA = 0.45 FB = 0.38 High wind: IA = 0.91 FB = 0.01 predicted (µg/m 3 ) observed (µg/m 3 ) predicted (µg/m 3 ) observed (µg/m 3 ) 300 predicted (µg/m 3 ) observed (µg/m 3 ) predicted (µg/m 3 ) observed (µg/m 3 ) All data: IA = 0.83 FB = 0.02 Low wind: IA = 0.80 FB = 0.18 High wind: IA = 0.84 FB = -0.03 Runeberg Street Results: hourly PM10 u ≤ 2 m/s u > 2 m/s u ≤ 2 m/s u > 2 m/s

7. Short-term PM10 concentrations can be predicted fairly well. Differences between predicted and observed concs could be caused because: No on-site measurements were available for: meteorological data (especially for precipitation), urban background data, and traffic volume (modelled data was used). Cleaning and dust binding processes are not taken into account in the suspension model. Sanding days are estimated only based on the meteorological parameters. Suspension model includes number of empirical factors that may be site specific. Uncertainties in dispersion modelling. Conclusions

8. Comparison of the modelled and measured suspension emission factors Measurements made by Pirjola et al. with SNIFFER Development of the suspension model, e.g. by utilising: parameters from the FMI Road weather model. data gathered in KAPU project, e.g. sanding, cleaning and dust binding days Evaluation of the forecasted PM10 concentrations. In general, for modelling purposes, time series of on-site background concentrations and meteorological data are required. Further work