Predicting Air Pollution using TAPM

1. Predicting Air Pollution using TAPM Peter Manins CSIRO Marine and Atmospheric Research Australia WMO GURME SAG member

2. Needs and Issues for an AQ Forecast • Excellent meteorology base – accuracy of trajectories is important • Inventory of emissions of pollutants • Spatial AND temporal variation. • Airborne particles • Photochemical smog is not emitted, need to tackle chemistry. • Background & initial conditions important for air pollution prediction

3. 5840 5820 ALP MTC PLP BXH ) m k ( G 5800 PTC N HEAVY I H T R FTS O N PSY 5780 BRI PTH GLS DND GVD 5760 PORT PHILLIP BAY 5740 LIGHT 260 280 300 320 340 360 EASTING (km) Prognostic forecasting for resolution, exceptional conditions Tomorrow will be fine and sunny -with moderate to heavy air pollution AIR QUALITY FORECAST- MELBOURNE MODERATE

4. Prog. Air Pollution Modelling METEOROLOGY WEATHER PREDICTIONS For days • Windspeed investigated • Sunlight • Temperature • Humidity • Turbulence LANDUSE POLLUTION EMISSIONS TOPOGRAPHY DISPERSION ESTIMATES PREDICTIONS From Landuse - Transport - • Transport, mixing Emissions Model • Photochemical change AIR QUALITY PREDICTIONS Validation FOR REGION Ground level concentrations IMPLICATIONS FOR POLICY PLANNING AIR QUALITY METRICS POPULATION Population DATA exposure to pollutants

5. Meteorology Data for Modelling • TAPM requirements: 3D fields of Vwe, Vsn, Ta, RHa on eg 100 km grid. • TAPM can run directly off NCEP forecast fields • TAPM can also run off a single NCEP analysis via a model such as CCAM • We use local observational data for verification (it is possible to assimilate wind data in TAPM)

6. % Vehicles Industry Domestic, • Motor vehicles • Industry • Domestic, Commercial Commercial Emissions NOx 82 13 5 VOCs 49 10 41 Particles 31 36 33 CO 91 2 7 SO 14 64 22 2 NOx Particles VOCs CO SO 2 Sources of Urban Air Pollution(Sydney Source: SOE 1996) Can see from this that “Particles” is too hard to be easily characterised by vehicles/population!

7. Emissions Data for Modelling • Detailed emissions inventory? • Population-based first estimate? • Need size of city, population, vehicle estimates, information on special issues • Distribute as per population in Gaussian distribn. • E.g. Perth: NOx=57 g/day/capita VOC=72 g/day/capita • Take reactivity of VOCs 0.0067 ppm/ppmC • Impose diurnal profiles, etc a refinement • Biogenic emissions • Vegetation-fraction distribn. At 30C & PAR of 1000 µmol/m2/s 0.11x10-5 g/m2/s (isoprene) • Industry emissions • Handle big ones explicitly.

8. TAPM Run Basics • Set up the TAPM Programs: model + GIS • Set up the emissions data • Running the model • Analysing results • Interpreting the results • (Comparing with monitoring data)

9. Working Facts for Lima, Peru • January of 2000; have Eric Concepcion data. • Location: 12o04’S, 77o03’W • Year: 2000 (PISA emissions inventory, Saturation Study – AQ in Lima) • Population: Lima+Callao City 7,510,000 • Vehicles: 780,000 (9.5 people/vehicle) • ~50% cars are no-catalyst vehicles • Vehicles: • NOx: 60,758 + 25% x 19,837 = 65,717 t/yr • VOC: no data, but IVE (2003) says 73,000 t/yr • Industrial/commercial/domestic: • NOx: 6000 t/yr; VOC: ~4000 t/yr Stats: petrol 25% higher than inventory

12. From Eric Concepcion, SENAMHI

13. From Eric Concepcion, SENAMHI

14. PISA 2000

15. Ellipse: -50o from E 36 km long, 14 km short axes From Eric Concepcion, SENAMHI

16. Ellipse: -50o from E 36 km long, 14 km short axes

18. Emissions for Lima Peru • January of 2000, since Eric Concepcion data. • Total emissions in 2000 of NOx and VOC for Lima-Callao City were 65.7 and ~93.1 ktonne. • Divide the total emission by the total population to give an emissions factor for each pollutant. • 24 g/day/capita for NOx • 34 g/day/capita for VOC •  approx 40% of the values for Perth. • The significant difference is attributable to the much lower vehicle ownership per capita—at 105 vehicles/ thousand capita, Lima vehicle ownership is approximately 1/6 of that in Australia

19. Emissions details for TAPM • Vehicles: • NOx: 60,758 + 25% x 19,837 = 65,717 t/yr • VOC: no data, but IVE (2003) says 73,000 t/yr • Industrial/commercial/domestic: • NOx: 6000 t/yr; VOC: ~4000 t/yr Estimate. 50% cars, non-catalyst, many LCVs buses

20. Vehicle diurnal Profile (IVE)

21. Background- and Initial- Conditions • Meteorology: TAPM takes a day to “spin-up” so use only from second day. That way, we allow time for the predictions to adjust to the local geographic forcing • Predict Ozone concentrations (PM is too hard because of so many unknown sources) • Background Ozone ~20 ppb and a back-ground smog level to account for missing reactions • Include biogenics as per supplied land-use (effect is ~15% maximum ozone for Lima

22. Vegetation emissions

23. Ozone max, vicinity of Lima January 2000 7 January 2000

24. Date Range: 5–9 January 2000 LIMA

25. Run TAPM for Lima

26. Winds and Trajs on 7 Jan 2000

27. Ozone,NO2 (ppb) on 7 Jan 2000 Day 2 = 7 Jan, 1400 hr Ozone NO2

28. Ozone,NO2 (ppb) on 8 Jan 2000 Day 3 = 8 Jan, 1800 hr Ozone NO2

29. ¿Why was O3 high on 7 Jan ’00?

30. Profiles near centre • 6 Jan – mixing height ~ 300 m, no heating • 7 Jan – mixing height ~ 500 m, strong mixing throughout day, stronger winds • 8 Jan – weak inversion, little mixing in morning

31. Conclusions • TAPM is a great way to get started for air quality forecasting. • Use a large-scale numerical weather forecast; TAPM for local wind predictions. • Use population-weighted emissions distribution – Gaussian approximation is good! •  a powerful air pollution forecasting system for didactic purposes or much more!