Investigations on Particulate Matter PM10 in Graz

Transcription

1 Investigations on Particulate Matter PM10 in Graz S.Hörmann B.Pfeiler E.Stadlober Ernst Stadlober, Siegfried Hörmann, Brigitte Pfeiler 1

2 Contents Facts on particulate matter PM10 Chemical composition, threshold values, health effects, polluter Situation in Graz, empirical analysis Meteorological factors, anthropogenic factors Prediction model Ernst Stadlober, Siegfried Hörmann, Brigitte Pfeiler 2

3 What is particulate matter? Mixture of midget dust particles Primary particles Exhausted by burning processes Mechanical abrasion of tyres, brakes, tarmac Pollen, crushing rocks, soil Secondary particles Particles formed in the atmosphere, arise from aerially pollutants Ernst Stadlober, Siegfried Hörmann, Brigitte Pfeiler 3

4 Threshold values: PM10: particles with diameter < 10 µm Concentration measured in µg/m³ Immissionsschutzgesetz Luft (IG-L) Daily average of 50 µg/m³ must not be exceeded on more than 30 days/year Annual average < 40 µg/m³ Gaging station Graz-Mitte 2003: 136 exceedances, nearly 85% in winter-season Ernst Stadlober, Siegfried Hörmann, Brigitte Pfeiler 4

5 Size of particles The smaller, the more toxic! Assuming same density 1 PM10-particle = 1000 PM1-particles Ernst Stadlober, Siegfried Hörmann, Brigitte Pfeiler 5

6 Health effects: Increasing mortality through heart- and lounge deceases Chronic obstructive pulmonary disease (COPD) Asthma Ernst Stadlober, Siegfried Hörmann, Brigitte Pfeiler 6

7 Department of Statistics Technical University Graz Situation in Graz Data: Location: Graz Nord (Urban residential area) Graz Mitte Don-Bosco Süd }(Traffic areas) Ernst Stadlober, Siegfried Hörmann, Brigitte Pfeiler 7

8 Unfavorable meteorological conditions Low wind velocities Low precipitation Many days with temperature inversion Usual anthropogenic factors Traffic, domestic fuel, industry Consequence During the winter season the threshold value is exceeded regularly Ernst Stadlober, Siegfried Hörmann, Brigitte Pfeiler 8

9 Monthly averages: Location Graz-Nord Graz-Mitte Graz-DB Graz-Süd Mean PM10 (µg/m³) January February March April May June July August September October November December Ernst Stadlober, Siegfried Hörmann, Brigitte Pfeiler 9

10 Domestic fuel Industry and Probably mainly through traffic This is indicated by type of day PM10-load on Su/Ho about 70% of working day Traffic about 60% of Human impact 95% CI PM10 (µg/m³) working day Mo-Fr Sa Su/Ho Location Graz-Nord Graz-M itte Graz-DB Graz-Süd Ernst Stadlober, Siegfried Hörmann, Brigitte Pfeiler 10

11 100 Day Mo-Fr Sa Su/Ho 80 95% CI PM10 (µg/m³) January February March April May June July August September October November December Ernst Stadlober, Siegfried Hörmann, Brigitte Pfeiler 11

12 Graz-Nord Graz-Mitte Graz-DB Graz-Süd Type of day Working day Sun/ Holyday Working Day Sun/ Holyday Working day Sun/ Holyday Working day Sun/ Holyday Exceedance 18 % 10 % 34 % 17 % 46 % 23 % 26 % 19 % Graz- Nord Graz- Mitte Graz- DB Graz- Süd MO-FR SO/FE On working days an exceedance of the threshold value is twice as probable as on Sun/Holydays! Ernst Stadlober, Siegfried Hörmann, Brigitte Pfeiler 12

13 Human impact has to be treated carefully! This may radically vary between different locations First investigations show: completely different behavior in Klagenfurt Graz has not much industry Community heating is widespread Ernst Stadlober, Siegfried Hörmann, Brigitte Pfeiler 13

14 Temperature inversion Plays very important role Temperature on the ground lower than above the ground Implicates a bad air convection Inversion occurs often, mainly during winter days We measure temperature difference between Graz (~350m) and Kalkleiten (~700m) Ernst Stadlober, Siegfried Hörmann, Brigitte Pfeiler 14

15 Balloon probe (6 am 8 pm) Temperatur [ C ] am in Graz-Gries, Firma Roche PM10 Konzentration [ µg/m³ ] am in Graz-Gries, Firma Roche Hoehe ueber Grund [m] Hoehe ueber Grund [m] no data Inversionsobergrenze [ m ] h 0700h 0800h 0900h 1000h 1100h 1200h 1300h 1400h 1510h Zeit MEZ 1600h 1700h 1800h 1900h 2015h h 0700h 0800h 0900h 1000h 1100h 1200h 1300h 1400h 1510h Zeit MEZ 1600h 1700h 1800h 1900h 2015h Ernst Stadlober, Siegfried Hörmann, Brigitte Pfeiler 15

16 End of inversion about noon Mittelwert PM10 (µg/m³) Winter 2003/2004: Graz-Mitte Tag Mo-Fr Sa So 30 0:00-4:00 4:00-8:00 8:00-12:00 12:00-16:00 16:00-20:00 20:00-24:00 Zeit Ernst Stadlober, Siegfried Hörmann, Brigitte Pfeiler 16

17 100 Location Graz-Nord Graz-Mitte Graz-DB Location Graz-Nord Graz-Mitte Graz-DB 95% CI PM10 (µg/m³) % CI PM10 (µg/m³) no Inversion Inversion no Inversion Inversion Comparison of inversion/no inversion Left plot: whole year Right plot: winter season ( ) Ernst Stadlober, Siegfried Hörmann, Brigitte Pfeiler 17

18 Graz-Nord Graz-Mitte Graz-DB Inversion Inversion No Inversion Inversion No Inversion Inversion No Inversion Exceedances 30 % 11 % 80 % 20 % 81 % 26 % Graz- Nord Graz- Mitte Graz-DB Inversion keine Inversion On days with temperature inversion an exceedance of the threshold value is 3-4 times more likely than on days without inversion. Ernst Stadlober, Siegfried Hörmann, Brigitte Pfeiler 18

19 Precipitation and Wind Both help to reduce the load of PM10 Unfortunately: low wind velocities and rare days with precipitation Ernst Stadlober, Siegfried Hörmann, Brigitte Pfeiler 19

20 Department of Statistics Technical University Graz Prediction Model Prediction of PM10 load of subsequent day with information available on current day Predictions for Graz Mitte in the winter season The model should be simple and manageable in praxis Reliable predictions, but not necessarily precise Ernst Stadlober, Siegfried Hörmann, Brigitte Pfeiler 20

21 Prediction via a multiple linear regression model Model contains 5 Parameters Metric variable PM10 average of last 24 hours from 12am-12am Categorical variable Type of day (Mo-Fr/Sa/Su-Ho) Meteorological forecasts Precipitation (yes/no) Wind velocity Inversion We need no information about traffic Ernst Stadlober, Siegfried Hörmann, Brigitte Pfeiler 21

22 Regression Model p 1, p 2, p 3, observed values of wind, inversion and precipitation (categorical value) on day x+1. v 1 category of day x+1, v 2 24h PM10 mean: day x-1 to day x. 3 2 PM10 α p + β v + ε ε ~ N(0, σ ) = i i i= 1 2 j= 1 j j Ernst Stadlober, Siegfried Hörmann, Brigitte Pfeiler 22

23 Adjusted R²: 0.67 Ernst Stadlober, Siegfried Hörmann, Brigitte Pfeiler 23

24 Verification of model assumptions 3,0 3 Normal Q-Q Plot of Unstandardized Residual Unstandardized Residual 2,0 1,0 0,0-1,0-2,0 Expected Normal Value ,0 2,0 4,0 6,0 8,0 10,0 12,0 14,0 Unstandardized Predicted Value Observed Value Ernst Stadlober, Siegfried Hörmann, Brigitte Pfeiler 24

25 Observed (PM10) Prediction (PM10) Ernst Stadlober, Siegfried Hörmann, Brigitte Pfeiler 25

26 Prediction > 75 µg/m³ Observation > 75 µg/m³ Oberserved (PM10) Oberserved > Prediction > Prediction (PM10) Ernst Stadlober, Siegfried Hörmann, Brigitte Pfeiler 26

27 October 04 April 05 Test Run Meteorological forecasts by the ZAMG Steiermark during the morning Prediction available until 1 pm and in the WWW as a traffic light system Ernst Stadlober, Siegfried Hörmann, Brigitte Pfeiler 27

28 Experiences Forecasts for wind difficult Forecasts for precipitation and inversion acceptable Quality of predictions satisfactory Ernst Stadlober, Siegfried Hörmann, Brigitte Pfeiler 28

29 Observed (PM10) Prediction (PM10) Ernst Stadlober, Siegfried Hörmann, Brigitte Pfeiler 29

30 Ernst Stadlober, Siegfried Hörmann, Brigitte Pfeiler 30