Fire Emissions InventoryFire Emissions InventoryDoug FoxDoug Fox

Outline of the datasets and models that are needed to estimate smoke.

SETS -- StrategicSETS -- Tactical

SETS -- Operational

SETS -- Evaluation

Vegetation growth & compositionincluding fire effects modelsFire behavior & combustion

models

Smoke EmissionsModels

Smoke EmissionsModels

Archived& On-Site Met Data

Archived& On-Site Met Data

Local wind fielddiagnostic modelsLocal wind field

diagnostic models

SmokeConcentrationpatterns inspace & time

LandCover &ConditionDatabases

Met. MesoscaleForecast Models Air

PollutantDispersionModels

SETS -- Strategic

“SETS” need to be populated with some different tools at each level of activity but tools must be able to interact between the activity levels.

•Because fire emissions contribute to degraded visibility we need:

•Better understanding of how emitted chemicals (organics) affect visibility;

•Better accounting of fire emissions •a national database of fire occurrence;

•Appropriately reviewed•Quality assured •Annually updated

•A national fire emissions model(s).

ConclusiConclusionsons

Monitoring DataMonitoring Data

• IMPROVE program measures visibility & speciated aerosol data representing Class I areas & relates them to each other for the visibility rule;

•Majority of fine particle species emitted from fires are organic and elemental carbon. Chemistry of gas to particle conversion is poorly understood.

•Wildland fire contributes to the 20% worst visibility days, especially in the western US

                                                                                                       

IMPROVE Timelines - Monthly OC

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2 1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 91

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Q1Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3Q4Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

1988 1989 1990 1991 1992 1993 1994 1995 1996

CO

NV

UT

WY

parameters OC

Obs Value Average

Yr Quarter Month

State Province Code

Acres consumed by wildfire in Colorado (bold black line) and OC mass concentrations at selected IMPROVE monitoring locations in Colorado. The OC mass concentrations are shown as annual means for Mesa Verde (MEVE), Weminuche (WEMI), Great Sand Dunes (GRSA) and Rocky Mountain (ROMO) at the U20, M20, and L20 fine mass concentration groups.

OC ApportionmentCalifornia -Yosemite, Lassen

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Year

OC

(n

g/m

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Wildfire

Background

Wildland fire and background group 90 OC components at IMPROVE monitoring sites in California from regression model.

Fire effects visibilityFire effects visibility

• Monthly OC contribution to total fine mass reaches 80% in some western US locations, longer term 10-30%

• IMPROVE monitoring suggests a range of 10%-40% of OC (organic carbon contribution to PM2.5) on the high mass days (20% worst visibility) may be from wild fires.

Characteristics of Characteristics of Regional Air Quality Regional Air Quality

ModelsModels• Multi-component simulation models,

including:– Point, area, mobile & biogenic emissions;– Mesoscale to regional scale

meteorological simulation;– Dispersion processes;– Gaseous and aerosol chemical formation

processes;– Removal mechanisms.

More Characteristics of More Characteristics of Regional Air Quality Regional Air Quality

ModelsModels • Outputs include:

– Spatial patterns of air quality with varying resolution upward from 1km to 50 km;

– Time series of these spatial patterns from hourly up to multiples of years;

– Concentration of all the National Ambient Air Quality Standards ‘Criteria Pollutants’;

– Concentration of aerosols affecting visibility, including sulfates, nitrates, soil, elemental and organic carbon;

More Characteristics of More Characteristics of Regional Air Quality Regional Air Quality

ModelsModels• Outputs are used to developing

State (Tribal) Implementation Plans (S/TIPs) which:– Examine alternative control strategies

to achieve & maintain NAAQS & visibiity;– Set emissions limitations for individual

and classes of sources;– Are codified as the legal tool for States

and local government to manage air quality and enforce the Clean Air Act.

What we What we (EPA/NPS/CIRA) are (EPA/NPS/CIRA) are

trying to do…trying to do… • Goal: to build a tool to generate

emissions from forest burning with the following characteristics:

– Scale is regional to national coverage with resolution ranging from 1 km to 36 km;

– Temporal resolution from hourly to multi-year;

– Chemical species including all NAAQS & visibility components & their precursors;

– Accuracy equivalent to other emissions estimates.

Assumptions about our Assumptions about our approach…approach…

• Build a 1st order tool capable of estimating needed information from existing data & information sources;

• Accuracy & scale needed are compatible with the National Fire Danger Rating System (NFDR);

• Based on historical fire data;• Meteorological data generated from

MM5 &/or higher resolution diagnostic models.

Approach outlineApproach outline

1. Identify fire boundaries;2. Identify vegetation & fuels involved;3. Calculate fuel moisture content;4. Calculate fuel consumption;5. Calculate fire emissions;6. Speciate fire emissions & calculate

plume rise.

MM5 outputs2pm local time Temperature; Relative humidity; Cloud cover; Wind speedDailyTemperature range; Relative humidity rangePast 7 daysPrecipitation; Same as above

MM5 outputs2pm local time Temperature; Relative humidity; Cloud cover; Wind speedDailyTemperature range; Relative humidity rangePast 7 daysPrecipitation; Same as above

Identify vegetation cover & fuel loadings(1 km resolution)

Read from NFDR fuel model coverage Modify with National FCC coverage

Generate species Emissions & Plume Rise(hourly, regional model resolution)

Develop emissions profiles to scale species from EPM generated emissions & to generate hourly emissions distributions.

Estimate plume rise based on Briggs at appropriate resolution for the spatial scale of emissions.

Calculate Fuel Consumption(daily, regional model resolution)

Utilize CONSUME2.1 to generate fuel consumption and EPM to estimate emissions & heat release rate for each fire.

Calculate Fuel Moisture Content(daily, weekly, regional model resolution) NFDR calculations based on MM5 input for range of variables at 36 km resolution

Fire Generator(hourly, 1km resolution)

Identify Fire Boundaries(daily, 1 km resolution)Read from National Fire Occurrence database

FIREFIRESS

Identify fire boundariesIdentify fire boundaries

• Time, location, & size of fires determined from National Fire Occurrence Database (Hardy, et.al. Missoula Fire Lab.)

• Federal & most State fires, from 1986-1996, at 1km resolution in a daily GIS database .

Fire Generator(hourly, 1km resolution)

Identify Fire Boundaries(daily, 1 km resolution)

Read from National Fire Occurrence database

FIRESFIRES

• Identify NFDR fuel model at 1 km resolution from Bergen, et.al., 1998

• Modify fuel loading, if necessary, using fuel National Current Condition Class coverage (Hardy, et.al. Missoula Fire Lab.)

Identify vegetation & Identify vegetation & fuelsfuels

Identify vegetation cover & fuel loadings(1 km resolution)

Read from NFDR fuel model coverage Modify with National FCC coverage

Calculate fuel moisture Calculate fuel moisture contentcontent

• Use NFDR equations based on data from MM5 including daily temperature & RH range, wind speed, cloud cover, precip.

• Drought indices from MM5

• Resolution from MM5

Calculate Fuel Moisture Content

(daily, weekly, regional model resolution) NFDR calculations based on MM5 input for range of

variables at 36 km resolution

Calculate fuel & Calculate fuel & emissionsemissions

• Use CONSUME 2.1 with NFDR model estimates of fuel loading & moisture content.

• Use EPM to generate PM10, PM2.5, CO & heat release rate.

Calculate Fuel Consumption(daily, regional model resolution)

Utilize CONSUME2.1 to generate fuel consumption and EPM to estimate emissions & heat release rate for each fire.

Speciate emissions & Speciate emissions & calculate plume risecalculate plume rise

• Develop emissions profiles from ratios of species to calculated CO emissions from current research results.

• Calculate plume rise using Briggs per SASEM

Generate species Emissions & Plume Rise

(hourly, regional model resolution)

Develop emissions profiles to scale species from EPM generated emissions & to generate hourly emissions distributions.

Estimate plume rise based on Briggs (modified by Brown et.al.) at appropriate resolution for the spatial scale of emissions.

Emissions speciationEmissions speciationCO2 1521 1833*CE

CO 144 961 - (984*CE)

CH4 6.8 42.7 – (43.2*CE)

PM2.5 12 67.4 – (66.8*CE)

PM10 14 1.18*PM2.5

EC 0.7 (0.7) 0.072*PM2.5

OC 5.8 (5.8) 0.54*PM2.5

NOx 3.1 (2.0) 16.8*MCE-13.1

NH4 0.6 0.012*CE

VOC 6.8 (5.3) 0.085*CE

SO2 0.8 (0.8)

Etc, etc.

CE = DCOCE = DCO22 / {DCO+DCO / {DCO+DCO22+DCH+DCH44+DCother}+DCother}MCE = 0.15+.86*CEMCE = 0.15+.86*CE

Current statusCurrent status

• Doug Fox is working on overall architecture of the system;

• Mike Sestak is coding linkages between fuel models, Consume 2.0 & EPM;

• Rodger Ames is linking the national Fire Occurrence data base with the NFDR coverage & estimating smoke contributions to IMPROVE measurements;

• Mike Barna will be introducing smoke emissions into REMSAD.

Challenges remainingChallenges remaining

• Demonstrating model functionality; • Coding the system into appropriate

emissions processors, i.e. ‘SMOKE’;• Testing sensitivities & simulating 1996

fire emissions;• Comparing simulated emissions with

WRAP Fire Emissions results;• Adding smoke emissions into regional

modeling (REMSAD & CMAQ).

Wildland fires 1996Wildland fires 1996

Forest fire emissionsat 1200 on3 July 1996

WRAP JFEFdata set

SMOKE StructureSMOKE Structure

GRID

INPUT

MOBILE

TEMPORAL

SPECIATE

PLUME RISE

BIOGENICS

CONTROLS

MERGE

PROJECTION

GRID

MOBILE

TEMPORAL

SPECIATE

CONTROLS

PLUME RISE

BIOGENICS

INPUT

MERGE

PROJECTION

METEOROLOGY DATA

Type Description Range Default

A Unit Region [WW,EW,WO,EO]1 none

A Unit Ownership [N,O,P]N = National ForestO = Other FederalP = Private

none

F Degrees North Latitude [0-90] see default table

F Degrees West Longitude [0-180] see default table

I Area of burn site (acres) [1-9999] none

A harvest date [9999|YYMM]2 burn date - 24 mo.

A Snow off month [00,01,02..11,12]3 04

A Predominant Species [D,P,M,H]4D = Douglas FirP = PineM = Mixed ConiferH = Hardwood

see default table

F Loading, 0-¼" fuel (tons/acre) [0-25] see default table

F Loading, ¼-1" fuel (tons/acre) [0-25] see default table

F Loading, 1-3" fuel (tons/acre) [0-50] see default table

F Loading, 3-9" fuel (tons/acre) [0-100] see default table

F Loading, 9-20"fuel (tons/acre) [0-150] see default table

F Loading, 20+" fuel (tons/acre) [0-200] see default table

F Duff Depth (inces) [0-9.9] see default table

I Terrain Slope (%) [0-200] 20

Type

Description Range Default

A Burn date [yymmdd] system date + 1

A Ignition time of day [0001,0002,..2400]

1200

I Number of ignition periods [1-10] {n} 2

I Area ignited, period 1 (acres) [1-9999] 10% of total

I Ignition duration, period 1 (min) [1-999] none

I Wait duration, period 1 (min) [1-999] 0

.

*3 entries for each of {n} periods*

.

I Area ignited in period{n} (acres) [1-9999]

I Ignition duration, period{n}(min) [1-999]

I Wait duration, period{n} (min) [1-999]

I 1000-hour fuel moisture (%) [0-99] 32

A Fuel moisture measurement method [N,A,W] N

I 10-hour fuel moisture (%) [1-30] 10

I Days since significant rainfall [0-99] 5

I Surface wind speed (mph) [0-35] 2

I Reporting interval for EPM (min) [1-59] 2

I Max. report length for EPM (min) [1-99,999] 600

Fire impacts on monitoring sites

• Aerosol monitoring (IMPROVE 1988- present) Organic carbon (OC) and elemental carbon (EC), organic matter by hydrogen (OMH), gravimetric fine mass (MF) Smoke tracers – Knon, all vegetative burning

• Wildland fires (Colin Hardy, Fire Occurrence data, 1986-1996)

Acres burned, fire locations, date discovered, date contained Species flux (can be) derived from emissions models (as in GCVTC)

Wildland fires 1996

Wildland fires 1994

Wildland fires 1995

Wildland fires 1996

PDM and PING

MCIP

JPROC

ICON

BCON

Episode

Domain

Projection

Grid Size

Vertical Structure

Chemical Mechanism

Options Selected

MM5 Meteorology

SMOKEEmissions

LUPROC

Community Chemical Transport Model

CMAQ System

Fire in SMOKE:How might data be

organized? Forestry data sets• Land cover• Species, density• Burn history• Soil information• Etc.

Meteorological data• Temperature• Soil moisture• Precipitation, etc.

SMOKEdata gridder

Fire emission model simulator

SMOKESpeciate emission data and merge with emissions of other sources