Predicting Air Pollution using TAPM

Peter Manins

CSIRO Marine and Atmospheric Research

Australia

WMO GURME SAG member

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

Prognostic forecasting for resolution, exceptional conditions

MODERATE

AIR QUALITY FORECAST- AIR QUALITY FORECAST- MELBOURNEMELBOURNE

AIR QUALITY FORECAST- AIR QUALITY FORECAST- MELBOURNEMELBOURNE

PORT PHILLIP BAY

260 280 300 320 340 360

EASTING (km)

DND

BRI

FTSPSY

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MTC ALP

PTHGLS

GVD

PLP BXH

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Tomorrow will be fine and sunnyTomorrow will be fine and sunny-with moderate to heavy air pollution-with moderate to heavy air pollution

Prog. Air Pollution ModellingMETEOROLOGY 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

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)

Sources of Urban Air Pollution(Sydney Source: SOE 1996)

Motor vehicles Industry Domestic,

Commercial

%

Emissions

Vehicles Industry Domestic, Commercial

NOx 82 13 5 VOCs 49 10 41 Particles 31 36 33 CO 91 2 7 SO2 14 64 22

NOx ParticlesVOCs CO SO 2

Can see from this that “Particles” is too hard to be easily characterised by vehicles/population!

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.

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)

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

From Eric Concepcion, SENAMHI

From Eric Concepcion, SENAMHI

Vehicle growth in Lima-Callao City

0

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1990 1995 2000 2005 2010 2015

Vehi

cles (

millio

ns)

024681012141618

Inha

bita

nts/V

ehicl

e

Vehicles

Inhab/Veh

1990 1995 2000 2005 2010 2015Vehicles 0.39 0.58 0.78 1.01 1.24 1.54

Inhab/Veh 15.4 11.65 9.53 8.07 7.08 6.07

PISA 2000

From Eric Concepcion, SENAMHI

Ellipse: -50o from E36 km long, 14 km short axes

Ellipse: -50o from E36 km long, 14 km short axes

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Lima population180000-200000160000-180000140000-160000120000-140000100000-12000080000-10000060000-8000040000-6000020000-400000-20000

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

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

g/day/ca Perth Melbourne Santiago IVE Lima Lima 2000NOx 57 50 32 47 24 VOC 72 106 47 26 34

Industry/Commercial/DomesticNOx 2VOC 1.5

Estimate. 50% cars,

non-catalyst, many LCVs

buses

Vehicle diurnal Profile (IVE)

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

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Vegetation emissions

Ozone max, vicinity of Lima January 2000

CMAX(ppb)

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0 24 48 72 96 120 144 168 192 216 240 264 288 312 336 360 384 408 432 456 480 504 528 552 576 600 624 648 672 696 720 744Hour

7 January 2000

Date Range: 5–9 January 2000

LIMA

Run TAPM for Lima

Winds and Trajs on 7 Jan 2000

Ozone,NO2 (ppb) on 7 Jan 2000

Day 2 = 7 Jan, 1400 hr

Ozone

NO2

NO2

Ozone

Ozone,NO2 (ppb) on 8 Jan 2000

Day 3 = 8 Jan, 1800 hr

¿Why was O3 high on 7 Jan ’00?

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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

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!