PM10 modeling for the city of Klagenfurt, Austria

Transcription

1 PM10 modeling for the city of Klagenfurt, Austria Dietmar Öttl Christian Kurz Wolfgang Hafner 1 Peter Sturm Graz University of Technology 1 Department of Environment Protection Klagenfurt

2 Model aproach NEMO (Network Emission Model, Rexeis et al. 2005) enables detailed calculation of traffic emissions for different vehicle categories for large road networks. GRAMM (Graz Mesoscale Model, Oettl 2000) prognostic non-hydrostatic. terrain following grid (tetrahedronal, Almbauer 1995). GRAL (Graz Lagrangian Model, Oettl et al. 2000, 2002, 2003a,b,c) all wind speed (>70% u<1.5m/s), stability conditions topography line-sources, point-sources, tunnel portals, area sources.

3 Non-exhaust PM10 emission factors EF [g/km/veh] Völkerm. TSP 2003 Völkerm. TSP 2004 St. Veiter Str Völkerm. PM Jan Feb Mär Apr Mai Jun Jul Aug Sep Okt Nov Dez

4 Total PM10 emission factors Share of diesel PC in A: >50% Total PM10 EF [g/km/veh] Gehrig et al Omstedt et al Ketzel et al This study

5 Emission inventory for Klagenfurt Emission inventory for domestic heating for 2001 [kg/a/ha]

6 Wind field simulations

7 Wind field simulations

8 AQ simulations with GRAL Public transport not included yet. Relatively high uncertainty regarind non-exhaust PM10 EF. Evaluation using NOx has still to be done. Winter services are not considered yet.

9 AQ simulations with GRAL Domestic Traffic-exhaust Traffic-non-exhaust Trade Agriculturing heating

10 AQ simulations with GRAL Total PM10 annual mean [µg/m³]

11 Comparison with observed annual mean PM observed [µg/m³] y = 1.07x R 2 = GRAL (without background) [µg/m³]

12 Non-Exhaust 4.9% Traffic 2.5% AQ simulations with GRAL Domestic heating 3.8% Agriculture 5.6% Trade 1.7% Hörtendorf annual mean PM10 Non-Exhaust 10.7% Non-Exhaust 19.8% Non-Exhaust Traffic 30.0% 4.2% Total PM10 annual mean [µg/m³] Völkermarkterstraße Koschatstraße Südring annual annual mean mean PM10 PM10 Trade Trade Domestic heating 6.4% 5.3% Domestic 8.8% heating 6.5% 10.4% 11.8% Agriculture Agriculture 1.0% 0.2% 0.3% Exhaust 8.9% Traffic 11.8% Background 43.1% Background 52.8% Background 67.8% Background 81.5%

13 Brief summary Direct traffic contribution PM10 urban background - kerbside 15%-45% Regional background kerbside >50% urban background >70% More research necessary to get a better understanding of the formation of high regional background

14 Nowcasting System Klagenfurt Sonic anemometer GRAL Online background PM10 Hourly surface concentrations Data bank: Stored 2-D concentration fields for different sources and dispersion conditions (about 1000/source) Online-traffic data

15 Outlook Simulated mean wind speeds for the city of Graz

16 Chem. Analysen TU-Wien,, Puxbaum et al., Messstelle AKH-Wien (städt. Hintergrund) direkter Verkehrsbeitrag: 19% Auspuff: 8% Ruß: 5% org. Material: 3% Reifenabrieb: 1% org. Material: 1% Aufwirbelung: 10% Mineralien: 7% Ca, Mg: 3%

17 Chemical analysis in Vienna (Puxbaum et al., 2005) UNID Fe Carbonates Minor Ions OC µg m Jun-99 Jul-99 Aug-99 Sep-99 Oct-99 Nov-99 Dec-99 Jan-00 Feb-00 Mar-00 Apr-00 May-00

18 Windfeldsimulationen Windfeldsimulationen N NNE NE ENE E ESE SE SSE S SSW SW WSW W WNW NW NNW N NNE NE ENE E ESE SE SSE S SSW SW WSW W WNW NW NNW N NNE NE ENE E ESE SE SSE S SSW SW WSW W WNW NW NNW N NNE NE ENE E ESE SE SSE S SSW SW WSW W WNW NW NNW N NNE NE ENE E ESE SE SSE S SSW SW WSW W WNW NW NNW

19 Observed annual mean PM10 concentrations Annual mean PM10 [µg/m³] GW Local traffic Urban background Rural background Regional background 10 0 Salzburg Klagenfurt Graz

20 Observed annual mean PM10 concentrations 100% 90% 80% 70% 60% 50% 40% Local traffic Urban background Rural background Regional background 30% 20% 10% 0% Salzburg Klagenfurt Graz

21 Sunday Saturday Weekday/weekend analysis Relative concentration Monday Tuesday Wednesday Thursday Friday s = PM10 NO 1 1 C E Son Son C E Werk Werk

22 Weekday/weekend analysis Direct traffic contributions PM10 NOx Völkermarkterstreet 25-40% 75% Koschatstreet 20-30% 55% Decreasing concentrations on sundays not only due to less traffic, but also due to less economic activities

23 Situation in summer

24 Situation in winter 8 days > 50µg/m³ as daily mean PM10 at urban background AQM 75 days > 50µg/m³ as daily mean PM10 at kerb site AQM

25 Wind field simulations Prognostic non-hydrostatic wind field model GRAMM (Graz Mesoscale Model): 400 m horizontal resolution, 15 vertical levels (10m lowest cell height) Terrain following grid (tetrahedronal grid) Steady-state wind fields for classified meteorological situations derived from a single point observation in the domain Numerical instabilities in the region of the mountain Predigtstuhl Changed from explicit to implicit time integration Constant surface cooling in stable conditions (20W/m²) Spatial variable surface heat flux in convective conditions (shadowing effects of topography)