A modelling system for predicting urban PM 2.5 concentrations. Numerical results and evaluation against the data in Helsinki

Transcription

1 A modelling system for predicting urban PM 2. concentrations. Numerical results and evaluation against the data in Helsinki Ari Karppinen, Jaakko Kukkonen, Mari Kauhaniemi, Jari Härkönen, FMI Tarja Koskentalo, Anu Kousa YTV

2 1. Aims 2. The measurements 3. The modelling system Emission modelling Dispersion modelling Backgroud modelling 4. Results and Conclusions spatial concentration distributions comparison with measurements problems & further work

3 Aims Development and validation of a modelling system for predicting urban PM 2. concentrations

4 Measurements YTV monitoring network (continuous) EMEP stations (continuous) Measurement campaigns

5 PM MONITORING SITES in HELSINKI METROPOLITAN AREA Site Site type Vehicles /day Töölö Urban traffic Vallila Urban traffic Leppävaara Suburban traffic Kallio Urban background Distance from street Measured average hourly quantities 12 - m PM m PM 2. & PM m PM 8 8 m PM 2. & PM since '99 Luukki Regional background 4 8 m PM

6 MM? Modelling systems (FMI) Traffic simulation Macro- EMME/2 (YTV) Micro- HUTSIM (TKK) Activity Model Geographical information Emission characteristics Measurements Meteorology, traffic, pollution Emission inventories VTT, LIISA Corinair Exposure model Weather prediction, met. pre-processing HIRLAM, MPP- FMI Dispersion of pollutants stationary sources: UDM-FMI roadside: CAR-FMI street canyon: OSPM (NERI) forecasting air quality: API-FMI Statistical Analysis GIS MapInfo Visualisation

7 Model for urban fine particles PM 2. (r,t)= PM 2. tr (r,t) + PM 2. st (r,t) + PM 2. bg (t) PM 2. (r,t) is the total measured concentration at time t, at spatial coordinate r st : stationary sources tr : vehicular traffic (exh+non-exh) bg: background (LRT)

8 Assumptions: Exhaust traffic emissions purely PM 2. Other traffic related emissions are directly proportional to exhaust emissions Regional and long-range transported background purely PM 2. Ion-sum is a good proxy for LRT

9 Most important model components 1. Emission model for PM 2. coldstarts taken into account 2. Roadside dispersion model CAR-FMI 3. Statistical model for regional and long-range transported PM 2.

10 Daily averaged PM 2. line source emissions (kg/d/km) in the Helsinki Metropolitan Area in 22 Weekday weekday 3 to to 3. to 1.1 to. to.1

11 Daily averaged PM 2. line source emissions (kg/d/km) in the Helsinki Metropolitan Area in 22 Saturday 3 to to 3. to 1.1 to. to.1

12 Daily averaged PM 2. line source emissions (kg/d/km) in the Helsinki Metropolitan Area in 22 Sunday 3 to to 3. to 1.1 to. to.1

13 T > C Weekday Daily averaged cold start emissions of PM 2. (kg/d/km²) in the Helsinki Metropolitan Area in to 7.6 (4) 1 to 4 (38). to 1 (3).3 to. (4).1 to.3 (9) to.1 (62) T < C, 41% preheating Weekday 4 to 14.3 (1) 1 to 4 (). to 1 (69).3 to. (8).1 to.3 (4) to.1 (41)

14 T > C Saturday Daily averaged cold start emissions of PM 2. (kg/d/km²) in the Helsinki Metropolitan Area in to 2.3 (14). to 1 (23).3 to. (17).1 to.3 (112) to.1 (117) T < C, 41% preheating Saturday 4 to 4.7 (2) 1 to 4 (33). to 1 (31).3 to. (48).1 to.3 (6) to.1 (63)

15 T > C Sunday Daily averaged cold start emissions of PM 2. (kg/d/km²) in the Helsinki Metropolitan Area in to 1.9 (7). to 1 (24).3 to. (17).1 to.3 (111) to.1 (124) T < C, 41% preheating Sunday 1 to 3.9 (3). to 1 (26).3 to. ().1 to.3 (4) to.1 (68)

16 Solely exhaust emissions from local traffic Annual average PM 2. concentrations (µg/m³) in the Helsinki Metropolitan Area in 22 > 1. to 1.1 to. <.1 All emissions from local traffic > 1 to. to 1.1 to. <.1

17 Annual average PM 2. concentrations (µg/m³) in the Helsinki Metropolitan Area in 22 All local emissions and regional background > 9 to 8 to 9 7 to 8 < 7

18 Solely exhaust emissions from local traffic Maximum hourly PM 2. concentrations (µg/m³) in the Helsinki Metropolitan Area in 22 > 2 to 2 to 1 to < 1 All emissions from local traffic > 2 to to 2 to 1 to < 1

19 Maximum hourly PM 2. concentrations (µg/m³) in the Helsinki Metropolitan Area in 22 All local emissions and regional background > 8 to 6 to 8 4 to 6 < 4

20 Predicted vs. observed daily mean PM 2. concentrations in Helsinki in 22 Computations by mainframe version of CAR-FMI line source model Observations from YTV monitoring stations at Vallila and Kallio YTV, 23

21 Location of YTV monitoring stations Kallio station Vallila station 2 m N YTV, 22

22 observed Predicted vs. observed daily mean PM 2. concentrations scatter plot & IA VALLILA R 2 = 1 y =.97x -.7 R 2 = predicted KALLIO observed R 2 = 1 y =.9x R 2 = predicted VALLILA: R 2 =.7, IA =.84 KALLIO: R 2 =.6, IA =.86

23 observed Predicted vs. observed daily mean PM 2. concentration in Vallila scatter plot in terms of wind direction y = 1.7x -.69 R 2 =.63 VALLILA y =.94x R 2 = predicted downwind side Linear (downwind side) downwind upwind R 2 =1 upwind side Linear (upwind side) % of wind direction (deg) N 2 W NW SW 2 1 S NE SE E Pasila 22 downwind < 18 deg upwind > 18 deg

24 PM2. (µg/m 3 ) PM2. (µg/m 3 ) Predicted and observed daily mean PM 2. concentrations in Vallila seasonal variation VALLILA predicted observed Winter (January, February, December) predicted observed VALLILA Summer (June, July, August) PM2. (µg/m 3 ) VALLILA 3 4 Spring (March, April, May) VALLILA PM2. (µg/m 3 ) predicted observed predicted observed Autumn (September, October, November)

25 PM2. (µg/m 3 ) PM2. (µg/m 3 ) Predicted and observed daily mean PM 2. concentrations in Kallio seasonal variation KALLIO predicted observed Winter (January, February, December) predicted observed KALLIO Summer (June, July, August) PM2. (µg/m 3 ) KALLIO 3 4 Spring (March, April, May) KALLIO PM2. (µg/m 3 ) predicted observed predicted observed 9 11 Autumn (September, October, November)

26 Conclusions Modelling system has been developed for urban PM 2. Applicable also for other European cities (emission coefficients country-specific) Includes also the evaluation of regional background PM 2. Spatial concentration distributions of PM 2. The influence of traffic and LRT on total concentrations The annual average, maximum hourly and guideline concentrations Evaluation of the model performance against the results of the urban monitoring network Good statistical agreement of the predicted and measured daily concentrations

27 Challenges for future research PM emission modelling especially noncombustion and cold start emissions, and suspension (studied in SAPPHIRE, OSCAR) The contribution of LRT is important Direct regional PM 2. measurements would be welcome; continental scale PM modelling Modelling of the aerosol processes, including size distributions and chemical composition (studied in SAPPHIRE)

28 References Tiitta, P., Raunemaa, T., Tissari, J., Yli-Tuomi, T., Leskinen, A., Kukkonen, J., Härkönen, J. and Karppinen, A., 22. Measurements and Modelling of PM 2. Concentrations Near a Major Road in Kuopio, Finland. Atmospheric Environment 36, pp Pohjola, M.A., Kousa, A., Kukkonen, J., Härkönen, J., Karppinen, A., Aarnio, P., Koskentalo, T., 22. The Spatial and Temporal Variation of Measured Urban PM and PM 2. concentrations in the Helsinki Metropolitan Area. International Journal on Water, Air and Soil Pollution: Focus 2 (-6), pp Karppinen, A., Härkönen, J., Kukkonen, J., Aarnio, P. and Koskentalo, T., 24. Statistical model for assessing the portion of fine particulate matter transported regionally and long-range to urban air. Scandinavian Journal of Work, Environment & Health 24 (s3).

29 CREDITS Academy of Finland This is the end FMI Dispersion Modelling Group YTV Environmental Office