Predicting Air Pollution using TAPM
-
Upload
martin-stein -
Category
Documents
-
view
20 -
download
0
description
Transcript of Predicting Air Pollution using TAPM
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
PTC
MTC ALP
PTHGLS
GVD
PLP BXH
5740
5760
5780
5800
5820
5840
NO
RT
HIN
G (
km)
LIGHT
HEAVY
NORTH EAST
HOUR
IND
EX
NORTH EAST
HOUR
IND
EX
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
0.3
0.6
0.9
1.2
1.5
1.8
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
1 4 7
10 13 16 19 22S1
S8
S15
S22
S29
0
20000
40000
60000
80000
100000
120000
140000
160000
180000
200000
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
1 4 7 10 13 16 19 22 25
S1
S5
S9
S13
S17
S21
S25
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80 0.7-0.8
0.6-0.7
0.5-0.6
0.4-0.5
0.3-0.4
0.2-0.3
0.1-0.2
0-0.1
Vegetation emissions
Ozone max, vicinity of Lima January 2000
CMAX(ppb)
-
20
40
60
80
100
120
0 24 48 72 96 120 144 168 192 216 240 264 288 312 336 360 384 408 432 456 480 504 528 552 576 600 624 648 672 696 720 744
Co
nce
ntr
atio
n
CMAX(ppb)
-
20
40
60
80
100
120
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?
0
2
4
6
8
10
12
0 24 48 72 96 120Hour
Win
d S
pee
d (
m s
-1)
0
90
180
270
360
0 24 48 72 96 120Hour
Win
d D
irec
tio
n (
o)
-5
5
15
25
35
45
0 24 48 72 96 120Hour
Tem
per
atu
re (
oC
)
0
20
40
60
80
100
0 24 48 72 96 120
HourR
elat
ive
Hu
mid
ity
(%)
-200
0
200
400
600
800
1000
0 24 48 72 96 120Hour
To
tal S
ola
r R
adia
tio
n (
W m
-2)
-200
0
200
400
600
800
1000
0 24 48 72 96 120Hour
Net
Rad
iati
on
(W
m-2)
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!