1. Fire Emissions Inventory Doug Fox

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

3. SETS -- Strategic SETS -- Tactical SETS -- Operational SETS -- Evaluation Vegetation growth & composition including fire effects models Fire behavior & combustion models Smoke Emissions Models Smoke Emissions Models Archived & On-Site Met Data Archived & On-Site Met Data Local wind field diagnostic models Local wind field diagnostic models Smoke Concentration patterns in space & time Land Cover & Condition Databases Met. Mesoscale Forecast Models Air Pollutant Dispersion Models 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.

4. 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). Conclusions

5. Monitoring 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

13. IMPROVE Timelines - Monthly OC

14. 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.

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

16. Fire 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.

17. Characteristics of Regional Air Quality Models 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.

18. More Characteristics of Regional Air Quality Models 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;

19. More Characteristics of Regional Air Quality Models 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.

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

21. Assumptions about our approach… Build a 1 st 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.

22. Approach 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.

23. MM5 outputs 2pm local time Temperature; Relative humidity; Cloud cover; Wind speed Daily Temperature range; Relative humidity range Past 7 days Precipitation; Same as above MM5 outputs 2pm local time Temperature; Relative humidity; Cloud cover; Wind speed Daily Temperature range; Relative humidity range Past 7 days Precipitation; 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 FIRES

24. Identify fire boundaries Time, location, & size of fires determined from National Fire Occurrence Database (Hardy, et.al. Missoula Fire Lab.) National Fire Occurrence Database 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 FIRES

26. Identify NFDR fuel model at 1 km resolution from Bergen, et.al., 1998 Bergen, et.al., 1998 Modify fuel loading, if necessary, using fuel National Current Condition Class coverage (Hardy, et.al. Missoula Fire Lab.)National Current Condition Class coverage Identify vegetation & fuels Identify vegetation cover & fuel loadings (1 km resolution) Read from NFDR fuel model coverage Modify with National FCC coverage

30. Calculate fuel moisture content 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

32. Calculate fuel & emissions 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.

33. Speciate emissions & calculate 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.

34. Emissions speciation CO215211833*CE CO144961 - (984*CE) CH46.842.7 – (43.2*CE) PM2.51267.4 – (66.8*CE) PM10141.18*PM2.5 EC0.7 (0.7)0.072*PM2.5 OC5.8 (5.8)0.54*PM2.5 NOx3.1 (2.0)16.8*MCE-13.1 NH40.60.012*CE VOC6.8 (5.3)0.085*CE SO20.8 (0.8) Etc, etc. CE = DCO 2 / {DCO+DCO 2 +DCH 4 +DCother} MCE = 0.15+.86*CE

35. Current 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.

36. Challenges 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).

37. Wildland fires 1996

38. Forest fire emissions at 1200 on 3 July 1996 WRAP JFEF data set

39. SMOKE 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

44. TypeDescriptionRangeDefault AUnit Region[WW,EW,WO,EO]1none AUnit Ownership[N,O,P] N = National Forest O = Other Federal P = Private none FDegrees North Latitude[0-90]see default table FDegrees West Longitude[0-180]see default table IArea of burn site (acres)[1-9999]none Aharvest date[9999|YYMM]2burn date - 24 mo. ASnow off month[00,01,02..11,12]304 APredominant Species[D,P,M,H]4 D = Douglas Fir P = Pine M = Mixed Conifer H = Hardwood see default table FLoading, 0-¼" fuel (tons/acre)[0-25]see default table FLoading, ¼-1" fuel (tons/acre)[0-25]see default table FLoading, 1-3" fuel (tons/acre)[0-50]see default table FLoading, 3-9" fuel (tons/acre)[0-100]see default table FLoading, 9-20"fuel (tons/acre)[0-150]see default table FLoading, 20+" fuel (tons/acre)[0-200]see default table FDuff Depth (inces)[0-9.9]see default table ITerrain Slope (%)[0-200]20

45. Typ e DescriptionRangeDefault ABurn date[yymmdd]system date + 1 AIgnition time of day[0001,0002,..2400]1200 INumber of ignition periods[1-10] {n}2 IArea ignited, period 1 (acres)[1-9999]10% of total IIgnition duration, period 1 (min)[1-999]none IWait duration, period 1 (min)[1-999]0. *3 entries for each of {n} periods*. IArea ignited in period{n} (acres)[1-9999] IIgnition duration, period{n}(min)[1-999] IWait duration, period{n} (min)[1-999] I1000-hour fuel moisture (%)[0-99]32 AFuel moisture measurement method[N,A,W]N I10-hour fuel moisture (%)[1-30]10 IDays since significant rainfall[0-99]5 ISurface wind speed (mph)[0-35]2 IReporting interval for EPM (min)[1-59]2 IMax. report length for EPM (min)[1-99,999]600

46. 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 – K non, 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)

47. Wildland fires 1996

48. Wildland fires 1994

49. Wildland fires 1995

50. Wildland fires 1996

52. PDM and PING MCIP JPROC ICON BCON Episode Domain Projection Grid Size Vertical Structure Chemical Mechanism Options Selected MM5 Meteorology SMOKE Emissions LUPROC Community Chemical Transport Model CMAQ System

53. 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. SMOKE data gridder Fire emission model simulator SMOKE Speciate emission data and merge with emissions of other sources