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Community Smoke Emissions Model

This document discusses the development of the Community Smoke Emissions Model and its potential applications in regional air quality modeling, smoke management, and haze planning. It also highlights the importance of accurately estimating fire emissions and their impact on visibility and fine particle pollution.

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Community Smoke Emissions Model

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  1. Community Smoke Emissions Model WRAP Fire Emissions Forum Meeting December 2002 Douglas Fox Cooperative Institute for Research in the Atmosphere Colorado State University Ft. Collins, CO 80524-1375 fox@cira.colostate.edu

  2. Based on work & presentations developed in cooperation with Dr. Mike Sestak (an independent consultant), Dr. Susan O’Neill (research scientist in PNW Seattle FERA group), Dr. Sue Ferguson (PNW FERA), Dr. Jason Ching (EPA/NOAA research) & Dr. Al Riebau (FS research)

  3. History • Technically Advanced Smoke Estimation Tools (TASET) • JFSP project (Fox & Riebau, 1998-2000) • Establish FCAMMS Cooperative Agreement (2000-continuing) • NPS Air Quality Division/CIRA (1999) • Evaluate applicability of Models3/CMAQ in Western US (WRAP….) • R&D on fire emissions

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

  5. USFS/Fire Consortia for Advanced Modeling of Smoke and Meteorology (FCAMMS) will implement smoke management using “BlueSky” by 2003.

  6. BlueSky: Smoke Modeling Framework • Regional application; • Automated, centralized processing; • Emission tracking; • Prediction of surface concentrations; • Quantitative verification; • Community model development; • Web-access control and output products; • www.BlueSkyRAINS.org/

  7. Needs for a Community Smoke Emissions Model • Fire smoke is significant • Current emissions inventories are labor intensive • GCVTC, WRAP • Potential Applications • Regional Air Quality Modeling • Regional Haze planning & SIP development • PM2.5 • Smoke Management • Blue Sky Framework • State based regulatory programs • Land Manager inventory & evaluations

  8. Monitoring Data • IMPROVE program measures visibility & speciated aerosol data representing Class I areas & relates them to each other for the regional haze rule; • Majority of fine particle species emitted from fires are organic and elemental carbon & secondary organic aerosol formation is poorly understood. • Wildland fire contributes to the 20% worst visibility days, especially in the west

  9. 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.

  10. On-going research is attempting to quantify fire’s contribution to organic aerosols p Organic Carbon % contribution to total extinction Elemental Carbon % contribution to total extinction

  11. Preliminary research results from the NPS Yosemite field study August 2002, on particle chemical composition • Organics accounted for about 80% of the non-soil fine mass during the summer of 2002. • The summertime organics in 1996-98 account for about 60% of the non-soil fine mass. • Organics come from biomass & fossil fuels DRAFT not for publication IMPROVE Data 1996-98

  12. Why a Community Smoke Emissions Model? • Common fire data • Inputs not readily available • Common modeling heritage • Fire Behavior - BEHAVE • Fuel Consumption - CONSUME • Emissions Production – EPM • Emissions Factors • Variety of applications • Different objectives drive different accuracy & resolution needs

  13. Consumption Emissions Production Speciation Community Smoke Emissions Modeling Identify Fires Identify Fuels Meteorology Plume Rise Input for Regional AQ model

  14. What we CSEM istrying to do… • Goal: to build a tool to generate emissions from forest burning for use in regional air quality modeling with the following characteristics: • Scale is regional to national 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.

  15. FIRES Fire Generator (hourly, 1km resolution) Identify Fire Boundaries (daily, 1 km resolution) Read from National Fire Occurrence database Identify vegetation cover & fuel loadings (1 km resolution) Read from NFDR fuel model coverage Modify with National FCC coverage MM5 Meteorology 2pm local time Temperature; Relative humidity; Cloud cover; Wind speed Daily Temperature range; Relative humidity range Past 7 days Precipitation; Same as above 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 Moisture Content (daily, weekly, regional model resolution) NFDR calculations based onMM5 input for range of variables at 36 km resolution Calculate Fuel Consumption (daily, regional model resolution) Utilize CONSUME to generate fuel consumption and EPM to estimate emissions & heat release rate for each fire.

  16. Assumptions about our 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.

  17. Approach outline • Identify fire boundaries; • Identify vegetation & fuels involved; • Calculate fuel moisture content; • Calculate fuel consumption; • Calculate fire emissions; • Speciate fire emissions & calculate plume rise.

  18. FIRES Fire Generator (hourly, 1km resolution) Identify Fire Boundaries (daily, 1 km resolution) Read from National Fire Occurrence database Identify 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 .

  19. Identify vegetation & fuels • 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 cover & fuel loadings (1 km resolution) Read from NFDR fuel model coverage Modify with National FCC coverage

  20. Optional modifier for NFDR fuel loadings, if needed to replicate WRAP ’96 fire emissions

  21. 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 onMM5 input for range of variables at 36 km resolution Calculate fuel moisture content

  22. Calculate fuel & emissions • Use CONSUME 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 CONSUME to generate fuel consumption and EPM to estimate emissions & heat release rate for each fire.

  23. 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 at appropriate resolution for the spatial scale of emissions. Speciate emissions & calculate plume rise

  24. Emissions speciation CE = DCO2 / {DCO+DCO2+DCH4+DCother} MCE = 0.15+.86*CE

  25. Preliminary Results • Comparative data inputs from 2002 Oregon fire (actual vs. 1996 met) BlueSky/FASTRACS CSEM • Area of Burnsite [acre] 500 500 • 0 - 0.25 inch fuel [tons/acre]1.0 2.9 • 0.25 - 1 inch fuel [tons/acre]2.2 2.3 • 1 - 3 inch fuel [tons/acre]1.6 5.6 • 3 - 9 inch fuel [tons/acre] 5.4 13.2 • 9 - 20 inch fuel [tons/acre] 24.6 0 • 20+ inch fuel [tons/acre]0.1 0 • Duff 8.0 2.5 • Burn-site slope [percent]50 50 • Ignition time [HHMM]1400 1400 • 10-hr fuel moisture 9 13.5 • Surface wind speed (mph) 6 5.5

  26. Preliminary Results • Comparative emissions from 2002 Oregon fire (actual vs. 1996 met) Bluesky CIRA • Time Heat Rel PM-10 Heat Rel PM-10 • 60 1.448E+07 9079.0 1.521E+07 9054.1 • 120 1.495E+07 9416.7 1.533E+07 9144.7 • 180 1.497E+07 9429.6 1.533E+07 9145.7 • 240 1.497E+07 9430.0 1.533E+07 9145.8 • 300 1.497E+07 9430.1 1.533E+07 9145.8 • 360 1.497E+07 9430.1 1.533E+07 9145.8 • 420 4.890E+05 351.0 116867.1 91.6 • 480 1.868E+04 13.4 1287.9 1.0 • 540 7.137E+02 .5 14.2 0.0 • 600 2.727E+01 .0 0.2 0.0

  27. Preliminary Results

  28. CSEM Summary • A rational approach to generating forest fire emissions for regional scale modeling has been developed. • Results appear to be consistent with site specific emissions estimates (BlueSky) but more testing is needed. • Plans exist to incorporate CSEM into the SMOKE processor.

  29. Challenges remaining • Coding CSEM into appropriate emissions processors, i.e. ‘SMOKE’; • Testing sensitivities & simulating WRAP ‘96 fire emissions; • Compare simulated emissions with WRAP ’96 Fire Emissions results; • Adding smoke emissions into regional modeling (REMSAD & CMAQ); • Finding adequate input data for years since 1996.

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