Smoke Emissions and Dispersion from Savanna

Smoke Dispersion modelling
Mick Meyer
The Centre for Australian Weather and Climate Research
A partnership between CSIRO and the Bureau of Meteorology
The Objectives
1. Include bushfire smoke in air quality modelling systems
•
•
•
Emissions
Verification of the dispersion
Exploration of potential for remote sensing
2. Process
•
Case studies of extensive fires
3. Human health impacts
1.
2.
Exposure
Health impacts
(integration with fire DST)
(Epidemiologists PhD candidate)
4. Improving current estimates of PM and GHG emission factors
5. Tools for planning of burning programs
The Centre for Australian Weather and Climate Research
A partnership between CSIRO and the Bureau of Meteorology
Programs and Deliverables
• 3 Case Studies
•
•
•
•
2006/7 Alpine fires
Kilmore East (Black Saturday) and scenarios
Regeneration burning in the Huon Valley
(2003 alpine fires)
(Complete)
(Complete)
(TAPM complete)
• EF studies
• Pyrotron simulations
• 1 field measurement set
(Complete, data analysis)
(Complete)
• Health Impacts
• PhD is progressing well
• Exposure: Fire DST
• Technology transfer
• How to use the current systems
The Centre for Australian Weather and Climate Research
A partnership between CSIRO and the Bureau of Meteorology
Deliverables
• 3 Papers
• 1 complete, 2 are in draft
• Firenotes
• 3 are in near complete drafts.
• Reviews and reports
• Review of models is complete
• EF studies delayed and now progressing
• Technology transfer reports
• Configuring TAPM
• Inverse modelling to assess relative risk of impact for different source locations
The Centre for Australian Weather and Climate Research
A partnership between CSIRO and the Bureau of Meteorology
MONTHLY EMISSIONS- DEC 06
FIRE EMISSIONS METHODOLOGY
1.0E+07
EMISSIONS (tonnes)
BACKGROUND
Fire area: daily area burnt; DSE
Fuel load: VAST 1.5 (litter, coarse-wd);
Efficiency: NGGI factors;
Diurnal : Parameterised;
Emission Factors: Literature;
tonnes/hour
CARBON MONOXIDE
Fire
Anthropogenic
10000
1000
100
10
4
6
8
10
12
14
16
18
Day Number
20
1.0E+05
1.0E+04
NOx
HOURLY EMISSIONS- DEC 06
2
Fire
1.0E+06
1.0E+03
Plume Rise: prescribed
100000
Anthropogenic
22
24
26
28
30
CO
VOC
PM25
POLLUTANT
SO2
TRANSPORT MODELLING METHODOLOGY
Emissions: tracer;
600
Meteorology:
Unified Model;
Transport: Hysplit
Brighton PM2.5
obs_conc
no fires
2000 m
3000 m
1000 m
400
OR
Emissions:
speciated;
200
Meteorology: TAPM or CCAM;
Transport:
CTM
0
0
48
96
144
192
240
288
Prescribed plume rise
336
384
Time (h)
432
480
528
576
624
672
Elemental
Carbon
Brighton: ozone
160
120
obs_conc
no f ires
2000m
3000 m
1000 m
20061204_1448
80
8TH December- hr 14
40
0
0
48
96
144
192
240
288
336
Time (h)
384
432
480
528
576
624
672
MODEL SENSITIVIES
Plume Rise
Emissions
(transport)
PM2.5 (mg m-3)
(1000m – 2000 m)
PM2.5 – peak daily for December 2006
O3- peak 1-h for December 2006
Hysplit- Hour 14 UTC 8th December 2006
O3 (ppb)
(VOCext - VOCsav)
Persistence
Smoke Dispersion from Kilmore East
12
16
20
13
17
21
14
15
18
19
22
23
The Centre for Australian Weather and Climate Research
A partnership between CSIRO and the Bureau of Meteorology
Emission factor sampling
Woozle
Pyrotron
Backpacks
EFs
CH4
9
EF_CH4
(% emitted C)
kulgnuki
8
flaming
7
smoldering
6
kulgnuki_smouldering
Logs
5
PM, total VOC
Cow pats
Woodheaters: VOC
50
Woodheaters:PM10
4
2
grass
1
0
0.60
0.70
0.80
0.90
1.00
MCE
October - Eucalypt Woodland
July -Eucalypt Woodland
October - Sandstone Woodland
July- Sandstone Woodland
October- Spinifex
July- Spinifex
October- Sorghum
N2O
1.4
EF N2O (% emitted N)
1.2
1.0
0.8
40
35
30
25
20
15
10
5
0
0.65
0.7
0.75
0.8
0.85
0.9
Combustion efficiency
0.6
0.4
0.2
0.0
0.86
Emission Factor (g (kg fuel)-1)
litter
3
Kulnguki VOC
45
0.88
0.90
0.92
0.94
MCE
0.96
0.98
1.00
0.95
1
3.0
2.5
2.0
Logs
1.5
1.4
1.0
Litter
0.5
0.0
0.70
0.75
0.80
0.85
0.90
0.95
Modified Combustion Efficiency
MCE ~ EF CO
1.00
N2O emission factor
(% fuel N emitted as N2O)
EF CH4
(% burned carbon emitted as CH4)
Field Measurements Heyfield
1.2
1.0
0.8
0.6
0.4
0.2
0.0
0.70
0.75
0.80
0.85
0.90
0.95
Modified Combustion Efficiency
The Centre for Australian Weather and Climate Research
A partnership between CSIRO and the Bureau of Meteorology
1.00
Heyfield compares with Savanna
10
Gulf- Flaming
Gulf -Logs
Gulf - Cow pats
Stirling curve regression
Heyfield
EF CH4 (% emitted C)
8
6
4
2
0
0.65
0.70
0.75
0.80
0.85
0.90
0.95
1.00
MCE
The Centre for Australian Weather and Climate Research
A partnership between CSIRO and the Bureau of Meteorology
Lessons Learned
1. The models perform well
2. The challenge is to link the components of a system together
•
•
Incompatible input & output formats
Range of models with different strengths an weaknesses
3. The model run slowly/ require large computing power
•
Need a large UNIX cluster (~1000 processor machine)
4. Output from the review of models was that the Bluesky framework
was worth exploring further.
The Centre for Australian Weather and Climate Research
A partnership between CSIRO and the Bureau of Meteorology

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