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Development and evaluation of the suspension emission model

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. Background.

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Development and evaluation of the suspension emission model

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

  4. Results: daily PM10 Runeberg Street predicted (µg/m3) observed (µg/m3) IA = 0.87 FB = 0.03 Vallila predicted (µg/m3) observed (µg/m3) IA = 0.88 FB = 0.10

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

  6. predicted (µg/m3) predicted (µg/m3) 300 observed (µg/m3) observed (µg/m3) predicted (µg/m3) predicted (µg/m3) observed (µg/m3) observed (µg/m3) Results: hourly PM10 Runeberg Street All data: IA = 0.83 FB = 0.02 Low wind: IA = 0.80 FB = 0.18 High wind: IA = 0.84 FB = -0.03 u > 2 m/s u ≤ 2 m/s 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 u ≤ 2 m/s u > 2 m/s

  7. Conclusions • 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.

  8. Further work • 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.

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