Fire emission uncertainties and their effect on smoke dispersion predictions: a case study at Eglin Air Force Base, Florida, USA
Aika Y. Davis A , Roger Ottmar B , Yongqiang Liu C , Scott Goodrick C , Gary Achtemeier C , Brian Gullett D , Johanna Aurell D , William Stevens E , Roby Greenwald F , Yongtao Hu A , Armistead Russell A , J. Kevin Hiers G and M. Talat Odman A H
+ Author Affiliations
- Author Affiliations
A School of Civil and Environmental Engineering, Georgia Institute of Technology, 790 Atlantic Drive, Atlanta, GA 30332, USA.
B USDA Forest Service, Pacific Northwest Research Station, Pacific Wildland Fire Sciences Laboratory, 400 N 34th Street, Seattle, WA 98103, USA.
C USDA Forest Service, Southern Research Station, Forestry Sciences Laboratory, 320 Green Street, Athens, GA 30602, USA.
D US Environmental Protection Agency Office of Research and Development, National Risk Management Research Laboratory, 109 T. W. Alexander Drive, Research Triangle Park, NC 27711, USA.
E Campus Box 2056, Kentucky Christian University, 100 Academic Parkway, Grayson, KY 41143, USA.
F Rollins School of Public Health, Emory University, 1518 Clifton Road, Atlanta, GA 30322, USA.
G Environmental Stewardship and Sustainability, University of the South, 735 University Avenue, Sewanee, TN 37383, USA.
H Corresponding author. Email: talat.odman@ce.gatech.edu
International Journal of Wildland Fire 24(2) 276-285 https://doi.org/10.1071/WF13071
Submitted: 25 May 2013 Accepted: 23 October 2014 Published: 3 February 2015
Abstract
Prescribed burning is practiced to benefit ecosystems but the resulting emissions can adversely affect air quality. A better understanding of the uncertainties in emission estimates and how these uncertainties affect smoke predictions is critical for model-based decision making. This study examined uncertainties associated with estimating fire emissions and how they affected smoke concentrations downwind from a prescribed burn that was conducted at Eglin Air Force Base in Florida, US. Estimated variables used in the modelled emission calculation were compared with field measurements. Fuel loadings, fuel consumption and emission factors were simulated using Photo Series, Consume, and previously published values. A plume dispersion model was used to study the effect of uncertainty in emissions on ground concentration prediction. The fire emission models predicted fuel loading, fuel consumption and emission factor within 15% of measurements. Approximately 18% uncertainty in field measurements of PM2.5 emissions and 36% uncertainty attributed to variability in emission estimating models resulted respectively in 20% and 42% ground level PM2.5 concentration uncertainties in dispersion modelling using Daysmoke. Uncertainty in input emissions influences the concentrations predicted by the smoke dispersion model to the same degree as does the model’s inherent uncertainty due to turbulence.
References
Achtemeier GL (2008) Effects of moisture released during forest burning on fog formation and implications for visibility. Journal of Applied Meteorology and Climatology 47, 1287–1296.| Effects of moisture released during forest burning on fog formation and implications for visibility.Crossref | GoogleScholarGoogle Scholar |
Achtemeier G, Goodrick S, Liu Y, Garcia-Menendez F, Hu Y, Odman M (2011) Modeling smoke plume-rise and dispersion from southern United States prescribed burns with daysmoke. Atmosphere (Toronto) 2, 358–388.
| Modeling smoke plume-rise and dispersion from southern United States prescribed burns with daysmoke.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3MXhtFyhtL7P&md5=a65dbfa7c89f0da47098f93de1bdbd6cCAS |
Achtemeier G, Goodrick S, Liu Y (2012) Modeling multiple-core updraft plume rise for an aerial ignition prescribed burn by coupling Daysmoke with a cellular automata fire model. Atmosphere (Toronto) 3, 352–376.
| Modeling multiple-core updraft plume rise for an aerial ignition prescribed burn by coupling Daysmoke with a cellular automata fire model.Crossref | GoogleScholarGoogle Scholar |
Anderson G, Sandberg D, Norheim R (2004) Fire Emission Production Simulator (FEPS) User’s Guide. Version 1.0. Available at http://www.fs.fed.us/pnw/fera/feps/FEPS_users_guide.pdf [Verified 25 November 2014]
Andrews PL, Bevins CD, Seli RC (2003) BehavePlus fire modelling system, version 2.0: User’s Guide. USDA Forest Service, Rocky Mountain Research Station, General Technical Report RMRS-GTR-106WWW. (Ogden, UT)
Aurell J, Gullett BK (2013) Emission factors from aerial and ground measurements of field and laboratory forest burns in the southeastern US: PM2.5, black and brown carbon, VOC, and PCDD/PCDF. Environmental Science & Technology 47, 8443–8452.
Boylan JW, Russell AG (2006) PM and light extinction model performance metrics, goals, and criteria for three-dimensional air quality models. Atmospheric Environment 40, 4946–4959.
| PM and light extinction model performance metrics, goals, and criteria for three-dimensional air quality models.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD28Xnt1KrtLY%3D&md5=a523745550e37fed009e53ebadb411a5CAS |
Brown JK (1974). Handbook for inventorying downed woody material. USDA Forest Service, Intermountain Forest and Range Experiment Station, Gen. Tech. Rep. INT-16, (Ogden, UT)
Fernandes PM, Davies GM, Ascoli D, Fernández C, Moreira F, Rigolot E, Stoof CR, Vega JA, Molina D (2013) Prescribed burning in southern Europe: developing fire management in a dynamic landscape. Frontiers in Ecology and the Environment 11, e4–e14.
| Prescribed burning in southern Europe: developing fire management in a dynamic landscape.Crossref | GoogleScholarGoogle Scholar |
French N, Goovaerts P, Kasischke E (2004) Uncertainty in estimating carbon emissions from boreal forest fires. Journal of Geophysical Research, D, Atmospheres 109, D14S08
| Uncertainty in estimating carbon emissions from boreal forest fires.Crossref | GoogleScholarGoogle Scholar |
Garcia‐Menendez F, Hu Y, Odman MT (2013) Simulating smoke transport from wildland fires with a regional‐scale air quality model: sensitivity to uncertain wind fields. Journal of Geophysical Research, D, Atmospheres 118, 6493–6504.
| Simulating smoke transport from wildland fires with a regional‐scale air quality model: sensitivity to uncertain wind fields.Crossref | GoogleScholarGoogle Scholar |
GoogleEarth7.1 (2013). Eglin Air Force Base, Florida USA 30°38′46.17″N, 86°16′36.03″W, elevation 52M. Available at http://www.google.com/earth/index.html. [Verified 20 December 2013]
Grandesso E, Gullett B, Touati A, Tabor D (2011) Effect of moisture, charge size, and chlorine concentration on pcdd/f emissions from simulated open burning of forest biomass. Environmental Science & Technology 45, 3887–3894.
| Effect of moisture, charge size, and chlorine concentration on pcdd/f emissions from simulated open burning of forest biomass.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3MXkt1ylurw%3D&md5=62976217660f46346cbe24409efb8c1dCAS |
Holton JR (2004). ‘An Introduction to Dynamic Meteorology.’ (Elsevier/Academic Press: London)
Hu Y, Odman MT, Chang ME, Jackson W, Lee S, Edgerton ES, Baumann K, Russell AG (2008) Simulation of air quality impacts from prescribed fires on an urban area. Environmental Science & Technology 42, 3676–3682.
| Simulation of air quality impacts from prescribed fires on an urban area.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1cXksFaktbk%3D&md5=588d764b100629b23b8f1debbec54fa1CAS |
Jain R, Vaughan J, Heitkamp K, Ramos C, Claiborn C, Schreuder M, Schaaf M, Lamb B (2007) Development of the ClearSky smoke dispersion forecast system for agricultural field burning in the Pacific Northwest. Atmospheric Environment 41, 6745–6761.
| Development of the ClearSky smoke dispersion forecast system for agricultural field burning in the Pacific Northwest.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2sXhtVKrurbJ&md5=eb0723af7114e39c4aef99ec704d2610CAS |
Keane RE (2012) Describing wildland surface fuel loading for fire management: a review of approaches, methods and systems. International Journal of Wildland Fire 22, 51–62.
| Describing wildland surface fuel loading for fire management: a review of approaches, methods and systems.Crossref | GoogleScholarGoogle Scholar |
Larkin NK, O’Neill SM, Solomon R, Raffuse S, Strand T, Sullivan DC, Krull C, Rorig M, Peterson J, Ferguson SA (2009) The BlueSky smoke modeling framework. International Journal of Wildland Fire 18, 906–920.
| The BlueSky smoke modeling framework.Crossref | GoogleScholarGoogle Scholar |
Laursen KK, Ferek RJ, Hobbs PV, Rasmussen RA (1992) Emission factors for particles, elemental carbon, and trace gases from the Kuwait oil fires. Journal of Geophysical Research, D, Atmospheres 97, 14491–14497.
| Emission factors for particles, elemental carbon, and trace gases from the Kuwait oil fires.Crossref | GoogleScholarGoogle Scholar |
Liu Y, Goodrick S, Achtemeier G, Jackson WA, Qu JJ, Wang W (2009) Smoke incursions into urban areas: simulation of a Georgia prescribed burn. International Journal of Wildland Fire 18, 336–348.
| Smoke incursions into urban areas: simulation of a Georgia prescribed burn.Crossref | GoogleScholarGoogle Scholar |
Liu Y, Goodrick S, Achtemeier G, Forbus K, Combs D (2013) Smoke plume height measurement of prescribed burns in the south-eastern United States. International Journal of Wildland Fire 22, 130–147.
| Smoke plume height measurement of prescribed burns in the south-eastern United States.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3sXks1OmtL8%3D&md5=b3019f0477f8c49eac6dadc75cb219e8CAS |
Mercer G, Weber R (2001) Fire plumes. In ‘Fire Plumes.’ (Eds EA Johnson, K Miyanishi) pp. 229–251. (Academic Press: San Diego)
Montiel C, Kraus DT (2010). ‘Best Practices of Fire Use: Prescribed Burning and Suppression: Fire Programmes in Selected Case-study Regions in Europe.’ (European Forest Institute: Joensuu, Finland)
Ottmar RD (2014) Wildland fire emissions, carbon, and climate: modeling fuel consumption. Forest Ecology and Management 317, 41–50.
| Wildland fire emissions, carbon, and climate: modeling fuel consumption.Crossref | GoogleScholarGoogle Scholar |
Ottmar R, Vihnanek R, Mathey J (2003) Stereo photo series for quantifying natural fuels. Volume VIa: sand hill, sand pine scrub, and hardwood with white pine types in the Southeast United States with supplemental sites for Volume VI. (Ed. ID Boise) National Wildfire Coordinating Group, National Interagency Fire Center, Report PMS 838, p. 78.
Prichard S, Ottmar R, Anderson G (2007). Consume 3.0 user’s guide and scientific documentation. USDA Forest Service. Pacific Wildland Fire Sciences Laboratory (Seattle, WA). Available at http://www.fs.fed.us/pnw/fera/research/smoke/consume/consume30_users_guide.pdf [Verified 25 November 2014]
Reid J, Koppmann R, Eck T, Eleuterio D (2005) A review of biomass burning emissions part II: intensive physical properties of biomass burning particles. Atmospheric Chemistry and Physics 5, 799–825.
| A review of biomass burning emissions part II: intensive physical properties of biomass burning particles.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2MXktlyrt7s%3D&md5=2f3ff806465f346d8c9a5b938cba0737CAS |
Seiler W, Crutzen P (1980) Estimates of gross and net fluxes of carbon between the biosphere and the atmosphere from biomass burning. Climatic Change 2, 207–247.
| Estimates of gross and net fluxes of carbon between the biosphere and the atmosphere from biomass burning.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DyaL3cXlsFelt7c%3D&md5=a1573b188f0425514f06e074b7aab43cCAS |
Skamarock WC, Klemp JB (2008) A time-split nonhydrostatic atmospheric model for weather research and forecasting applications. Journal of Computational Physics 227, 3465–3485.
| A time-split nonhydrostatic atmospheric model for weather research and forecasting applications.Crossref | GoogleScholarGoogle Scholar |
Skamarock J, Klemp W, Dudhia J, Gill D, Barker D, Duda M, Huang X, Wang W, Powers J (2008) A description of the advanced research WRF version 3, NCAR technical note. (NCAR/TN–475+ STR) Available at http://www2.mmm.ucar.edu/wrf/users/docs/arw_v2.pdf [Verified 25 November 2014]
Tian D, Hu Y, Wang Y, Boylan JW, Zheng M, Russell AG (2009) Assessment of biomass burning emissions and their impacts on urban and regional PM2. 5: a Georgia case study. Environmental Science & Technology 43, 299–305.
| Assessment of biomass burning emissions and their impacts on urban and regional PM2. 5: a Georgia case study.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1cXhsV2nurvJ&md5=e74db17a1bbd27cfd0a142112355927cCAS |
Urbanski SP, Hao WM, Baker S (2008) Chemical composition of wildland fire emissions. Developments in Environmental Science 8, 79–107.
| Chemical composition of wildland fire emissions.Crossref | GoogleScholarGoogle Scholar |
Urbanski S, Hao W, Nordgren B (2011) The wildland fire emission inventory: western United States emission estimates and an evaluation of uncertainty. Atmospheric Chemistry and Physics 11, 12973–13000.
| The wildland fire emission inventory: western United States emission estimates and an evaluation of uncertainty.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC38XjsV2ru7c%3D&md5=9f16e79f1f86788f237d58cb203d2775CAS |
US EPA (1995) Compilation of Air Pollutant Emission Factors, AP-42, 5th edn, Vol. 1: Stationary Point and Area Sources. (US EPA: Research Triangle Park, NC)
US EPA (2013). The 2011 National Emissions Inventory. Available at http://www.epa.gov/ttn/chief/net/2011inventory.html [Verified 25 November 2014]
Weise DR, Wright CS (2014) Wildland fire emissions, carbon and climate: characterizing wildland fuels. Forest Ecology and Management 317, 26–40.
| Wildland fire emissions, carbon and climate: characterizing wildland fuels.Crossref | GoogleScholarGoogle Scholar |
Wiedinmyer C, Quayle B, Geron C, Belote A, McKenzie D, Zhang XY, O’Neill S, Wynne KK (2006) Estimating emissions from fires in North America for air quality modeling. Atmospheric Environment 40, 3419–3432.
| Estimating emissions from fires in North America for air quality modeling.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD28XltVyhtrY%3D&md5=272941fc0237e7adc249c6f966badab4CAS |
Wilson J, Sawford B (1996) Review of Lagrangian stochastic models for trajectories in the turbulent atmosphere. Boundary-Layer Meteorology 78, 191–210.
| Review of Lagrangian stochastic models for trajectories in the turbulent atmosphere.Crossref | GoogleScholarGoogle Scholar |
