Wildfire Emissions in the Canadian GEM-MACH

Air Quality Forecast System

2011 International Workshop for Air Quality Forecast Research Washington DC

Environment Canada Jack Chen, Radenko Pavlovic, Al Pankratz, Sylvie Gravel

Canadian Forest Service/Natural Resources Canada Kerry Anderson

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Objective

• Air quality forecast with impact of wildfire emissions

▪ input for the Air Quality Health Index (AQHI)▪ smoke dispersion information▪ visibility information

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GEM-MACH System

• Meteorological & chemical models

– GEM-MACH runs twice daily (00z, 12z), resolution 15 km

– GEM-MACH emissions processed via SMOKE▪ Area sources▪ Mobile sources▪ Point sources

– Biogenics calculated online (BEIS3)

• Canadian wildfires ~1.3 million hectares/yr (2000-2010)

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4Edmonton without smoke

Grant MacEwan University

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5Edmonton, August 19, 2010, 11:35 AM

(Hourly PM2.5 ~80 μg/m3)

Grant MacEwan University

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6Edmonton, August 19, 2010, 2:01 PM

(Hourly PM2.5 ~250 μg/m3)

Grant MacEwan University ??

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Fire Information

• BlueSky (US Forest Service)

• CWFIS (Canadian Wildland Fire Information System)– runs daily during fire season;– Includes:

▪ landuse databases;▪ Hotspot detection;▪ FBP (Fire Behavior Prediction);▪ Fuel consumption / fire type

GEM-MACH domain

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• Canadian Wildland Fire Information System (cont’d)

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Approach

• FireWork framework

BLUESKY

Weather model output

Fire Information

Fuel loading

Consumption

Emissions(SMOKE)

GEM-MACH

CWFIS

Fuel loading

Consumption

US hotspot Canadian hotspot

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Current Limitations

• sensitivity to hotspot algorithms – Active burning emissions vs. smoldering emissions– Remote sensing is limited by cloud cover

• no fire spread/growth

• no active fire suppression

• fire size is empirically estimated

• operational considerations: reliability and timeliness of remote sensing data

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Proof-of-concept test

• Wildfire event in Central British Columbia (Cariboo Region)• Two week simulation: August 10 – 24 2010

• No changes to GEM-MACH model science• Fire emissions modelled as major point sources

– Brigg’s plume rise algorithm within GEM-MACH– Constant and uniform stack parameters

• PM2.5 results compared with operational forecast predictions

• Challenging event to model due to complex topography across the Rockies and Pacific Cordillera

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Fire Emissions

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Fire Emissions. Aug 10-24

BC+AB FireBC+AB

Anthropogenic

CO 654,122 126,541

VOC 155,514 33,415

PM10 70,337 41,552

PM2.5 59,608 9,032

Episode Emissions (tons)

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Aug. 10-24 average surface PM2.5 difference (fire minus no-fire)

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15Average hourly PM2.5 for 40 stations in BC

Average hourly PM2.5 for 25 stations in AB

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Obs. PM2.5 Avg.Operational Forecast

PM2.5 Avg.(NMB)

+ Fire Emissions PM2.5 Avg.

(NMB)N

59 μg/m35 μg/m3

(-11%)22 μg/m3

(-8%) 8

Aug 10-24 Stations Avg. PM2.5 > 45 ug/m3

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Obs. Avg.Operational Forecast

PM2.5 Avg.(NMB)

+ Fire Emissions PM2.5 Avg.

(NMB)N

7 μg/m37 μg/m3

(-0.3%)9 μg/m3

(+0.5%) 29

Aug 10-24 Stations Avg. PM2.5 < 10 ug/m3

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Conclusions & Future Work

• the goal of this project is to enhance the capacity of Environment Canada’s GEM-MACH operational air quality forecasting system to include wildfire emissions

• preliminary results show that fires producing large amount of emissions can significantly impact PM2.5 forecast results

• Initial examination: GEM-MACH captured general PM2.5 trends but

underestimated magnitudes, especially for receptors further downwind

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Future Work

• Improve Canada hotspot detection – consolidate pixels into fire events

• Update GEM-MACH science parameterizations– inline plume rise algorithm

– inline fire emission adjustment by weather▪ e.g. emissions in concert with rain, snow or humidity changes

– PM2.5 tracer to isolate fire plume from anthropogenic sources

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Acknowledgement

• BC Ministry of Environment – Steve Sakiyama• US Forest Service – Tara Strand• Sonoma Technology, Inc. – Kenneth Craig• Washington State University – Joe Vaughan• Environment Canada – GEM-MACH development team

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Thank you