1. Refinement and evaluation of the suspension emission model Mari Kauhaniemi Research Scientist Finnish meteorological Institute, Air Quality, Dispersion modelling NORTRIP meeting (Arlanda) 16.11.2010

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: Refinement and evaluation of a road dust suspension model for predicting the concentrations of PM 10 in street canyon in Helsinki. Kauhaniemi, Kukkonen, Härkönen, Nikmo, Kangas, Omstedt, Ketzel, Kousa, Haakana, and Karppinen No measured suspension emissions available Evaluated against observed PM 10 concentrations PM10 concentration computed by a street canyon model (OSPM) Study period: 8.1.-2.5.2004 Study site: Runeberg Street

3. Kaisaniemi Runeberg Street Urban background measurement station Meteorological station Air quality measurement station Wind mast Measurement sites

4. Sensitivity analysis Influence of precipitation studied with: Kaisaniemi precipitation data (0-3.8 mm/h) No precipitation Maximum precipitation of Kaisaniemi data (3.8 mm/h) SF (Kaisaniemi data) is occasionally higher than SF (no precipitation) max 48%.

5. Sensitivity analysis Influence of sanding studied with: 20 sanding days 11 sanding days If Kaisaniemi precipitation data or no precipitation is used: SF (20 sanding days) max about 15 % higher than SF (11 sanding days) If maximum precipitation data is used: SF calculated with 20 or 11 sanding days have no difference.

6. FMI vs. SMHI suspension emission factors Normalised sand dust layer (ls) IA = 0.94 SF (FMI) is max 67% lower than SF (SMHI) Suspension emission factor (SF) SF (SMHI) is systematically higher than SF (FMI) because: LS increse is greater (SMHI: 0.048, FMI: 0.029) LS is increased more often (SMHI: 885 times, FMI: 20 times) LS is increased on different days and hours (e.g. SMHI: 2 Feb at 0, FMI: 1 Feb at 23)  reduction factors may influence differently on dust layer.

7. Cleaning & dust binding Under-prediction: possible because pedestrian ways cleaned after car lines, traffic volume under-estimated? No on-site meteorological data  suspension emission factors under-estimated? Over-prediction: due to the snowing/raining. No on-site meteorological data  suspension emission factors over-estimated? 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. IA = 0.87 FB = 0.03 F2 = 94% predicted (µg/m 3 ) observed (µg/m 3 ) Daily PM10 concentrations