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

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Presentation on theme: "Predicting Air Pollution using TAPM Peter Manins CSIRO Marine and Atmospheric Research Australia WMO GURME SAG member."— Presentation transcript:

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 Prognostic forecasting for resolution, exceptional conditions MODERATE AIR QUALITY FORECAST- MELBOURNE Tomorrow will be fine and sunny -with moderate to heavy air pollution

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

5 Levels of Complexity 1.Embedded in a National Weather Forecasting System – e.g. AAQFS 2.Stand-alone System fed from a forecast supplied from elsewhere – CCAM/TAPM 3.System fed from Mesoscale forecasts supplied from elsewhere – TAPM alone Focus on ozone smog forecasting –Winter particle forecasting is hard work! Then the hard part is the emissions inventory – need VOCs, NOx.

6 For AQFS application (2 GHz PC): Initialise by 21 MB NCEP t12Z analysis from ftp:// Two grids: 70 km effective – 8 day forecast : ½ hr run 5 km effective – 2 day forecast : 1 hr run – ozone forecast: 1 hr extra using TAPM CTM E.g. CCAM set up for Australia Routinely run 18 levels at 70 km effective; special applications run to 1 km effective. Global analyses from NCEP, ECMWF, BoM (Aus), JMA, KMA, CMA, …

7 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)

8 Sources of Urban Air Pollution (Sydney Source: SOE 1996 ) Motor vehicles Industry Domestic, Commercial NOx Particles VOCsCO SO 2 Can see from this that Particles is too hard to be easily characterised by vehicles/population!

9 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 ppm/ppmC –Impose diurnal profiles, etc a refinement Biogenic emissions –Vegetation-fraction distribn. At 30C & PAR of 1000 µmol/m 2 /s 0.11x10 -5 g/m 2 /s (isoprene) Industry emissions –Handle big ones explicitly.

10 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)

11 Working Facts for Lima, Peru January of 2000; have Eric Concepcion data. Location: 12 o 04S, 77 o 03W 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, % 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

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14 From Eric Concepcion, SENAMHI

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16 PISA 2000

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

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

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20 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 capitaat 105 vehicles/ thousand capita, Lima vehicle ownership is approximately 1/6 of that in Australia

21 Emissions details for TAPM Vehicles: –NOx: 60, % 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

22 Vehicle diurnal Profile (IVE)

23 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

24 Vegetation emissions

25 Ozone max, vicinity of Lima January January 2000

26 Date Range: 5–9 January 2000 LIMA

27 Run TAPM for Lima

28 Winds and Trajs on 7 Jan 2000

29 Ozone,NO 2 (ppb) on 7 Jan 2000 Day 2 = 7 Jan, 1400 hr Ozone NO 2

30 Ozone Ozone,NO 2 (ppb) on 8 Jan 2000 Day 3 = 8 Jan, 1800 hr

31 ¿Why was O 3 high on 7 Jan 00?

32 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

33 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!


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