References

ACP

Atmospheric Chemistry and Physics

ACP

1680-7324

Copernicus GmbH

Göttingen, Germany

10.5194/acp-11-12973-2011

The wildland fire emission inventory: western United States emission estimates and an evaluation of uncertainty

Urbanski

S. P.

¹ Hao

W. M.

¹ Nordgren

Missoula Fire Sciences Laboratory, Rocky Mountain Research Station, United States Forest Service, Missoula, Montana, USA

20 12 2011

11 24 12973 13000

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Biomass burning emission inventories serve as critical input for atmospheric chemical transport models that are used to understand the role of biomass fires in the chemical composition of the atmosphere, air quality, and the climate system. Significant progress has been achieved in the development of regional and global biomass burning emission inventories over the past decade using satellite remote sensing technology for fire detection and burned area mapping. However, agreement among biomass burning emission inventories is frequently poor. Furthermore, the uncertainties of the emission estimates are typically not well characterized, particularly at the spatio-temporal scales pertinent to regional air quality modeling. We present the Wildland Fire Emission Inventory (WFEI), a high resolution model for non-agricultural open biomass burning (hereafter referred to as wildland fires, WF) in the contiguous United States (CONUS). The model combines observations from the MODerate Resolution Imaging Spectroradiometer (MODIS) sensors on the Terra and Aqua satellites, meteorological analyses, fuel loading maps, an emission factor database, and fuel condition and fuel consumption models to estimate emissions from WF. WFEI was used to estimate emissions of CO (ECO) and PM2.5 (EPM2.5) for the western United States from 2003–2008. The uncertainties in the inventory estimates of ECO and EPM2.5 (uECO and uEPM2.5, respectively) have been explored across spatial and temporal scales relevant to regional and global modeling applications. In order to evaluate the uncertainty in our emission estimates across multiple scales we used a figure of merit, the half mass uncertainty, ũEX (where X = CO or PM2.5), defined such that for a given aggregation level 50% of total emissions occurred from elements with uEX ũEX. The sensitivity of the WFEI estimates of ECO and EPM2.5 to uncertainties in mapped fuel loading, fuel consumption, burned area and emission factors have also been examined. The estimated annual, domain wide ECO ranged from 436 Gg yr−1 in 2004 to 3107 Gg yr−1 in 2007. The extremes in estimated annual, domain wide EPM2.5 were 65 Gg yr−1 in 2004 and 454 Gg yr−1 in 2007. Annual WF emissions were a significant share of total emissions from non-WF sources (agriculture, dust, non-WF fire, fuel combustion, industrial processes, transportation, solvent, and miscellaneous) in the western United States as estimated in a national emission inventory. In the peak fire year of 2007, WF emissions were ~20% of total (WF + non-WF) CO emissions and ~39% of total PM2.5 emissions. During the months with the greatest fire activity, WF accounted for the majority of total CO and PM2.5 emitted across the study region. Uncertainties in annual, domain wide emissions was 28% to 51% for CO and 40% to 65% for PM2.5. Sensitivity of ũECO and ũEPM2.5 to the emission model components depended on scale. At scales relevant to regional modeling applications (Δx = 10 km, Δt = 1 day) WFEI estimates 50% of total ECO with an uncertainty <133% and half of total EPM2.5 with an uncertainty <146%. ũECO and ũEPM2.5 are reduced by more than half at the scale of global modeling applications (Δ x = 100 km, Δ t = 30 day) where 50% of total emissions are estimated with an uncertainty <50% for CO and <64% for PM2.5. Uncertainties in the estimates of burned area drives the emission uncertainties at regional scales. At global scales ũECO is most sensitive to uncertainties in the fuel load consumed while the uncertainty in the emission factor for PM2.5 plays the dominant role in ũEPM2.5. Our analysis indicates that the large scale aggregate uncertainties (e.g. the uncertainty in annual CO emitted for CONUS) typically reported for biomass burning emission inventories may not be appropriate for evaluating and interpreting results of regional scale modeling applications that employ the emission estimates. When feasible, biomass burning emission inventories should be evaluated and reported across the scales for which they are intended to be used.

References 1

Akagi, S. K., Yokelson, R. J., Wiedinmyer, C., Alvarado, M. J., Reid, J. S., Karl, T., Crounse, J. D., and Wennberg, P. O.: Emission factors for open and domestic biomass burning for use in atmospheric models, Atmos. Chem. Phys., 11, 4039–4072, http://dx.doi.org/10.5194/acp-11-4039-2011doi:10.5194/acp-11-4039-2011, 2011.

Albini, F. A.: Estimating wildfire behavior and effects, General Technical Report, INT-GTR-30, USDA Forest Service, Intermountain Forest and Range Experiment Station, Ogden Utah, 92~pp., available at: http://www.treesearch.fs.fed.us/pubs/29574, 1976.

Albini, F. A., Brown, J. K., Reinhardt, E. D., and Ottmar, R. D.: Calibration of a large fuel burnout model, Int. J. Wildland Fire, 5, 173–192, 1995.

Alvarado, M. J., Wang, C., and Prinn, R. G.: Formation of ozone and growth of aerosols in young smoke plumes from biomass burning: 2. Three-dimensional Eulerian studies, J. Geophys.Res., 114, D09307, doi:10.1029/2008JD011186, 2009.

Al-Saadi, J., Soja, A., Pierce, R. B., Szykman, J., Wiedinmyer, C., Emmons, L., Kondragunta, S., Zhang, X. Y., Kittaka, C., Schaack, T., and Bowman, K.: Intercomparison of near-real-time biomass burning emissions estimates constrained by satellite fire data, J. Appl. Remote. Sens., 2, doi:10.1117/1.2948785, 1–24, 2008.

Anderson, H. E.: Predicting wind-driven wildland fire size and shape, Research Paper INT-305, USDA Forest Service, Intermountain Forest and Range Experiment Station, Ogden, UT, 26~pp., 1983.

Brown, J. K., Marsden, M. M., Ryan, K. C., and Reinhardt, E. D.: Predicting duff and woody fuel consumed by prescribed fire in the northern Rocky Mountains, Research Paper INT-337, USDA, Intermountain Forest and Range Experiment Station Forest Service, 23~pp., available at: http://www.treesearch.fs.fed.us/pubs/32531, 1985.

Burling, I. R., Yokelson, R. J., Griffith, D. W. T., Johnson, T. J., Veres, P., Roberts, J. M., Warneke, C., Urbanski, S. P., Reardon, J., Weise, D. R., Hao, W. M., and de Gouw, J.: Laboratory measurements of trace gas emissions from biomass burning of fuel types from the southeastern and southwestern United States, Atmos. Chem. Phys., 10, 11115–11130, http://dx.doi.org/10.5194/acp-10-11115-2010doi:10.5194/acp-10-11115-2010, 2010

Carlson, J. D., Burgan, R. E., Engle, D. M., and Greenfield, J. R.: The Oklahoma Fire Danger Model: An operational tool for mesoscale fire danger rating in Oklahoma, Int. J. Wildland Fire, 11, 183–191, 2002.

Chang, D. and Song, Y.: Estimates of biomass burning emissions in tropical Asia based on satellite-derived data, Atmos. Chem. Phys., 10, 2335–2351, http://dx.doi.org/10.5194/acp-10-2335-2010doi:10.5194/acp-10-2335-2010, 2010.

Clinton, N. E., Gong, P., and Scott, K.: Quantification of pollutants emitted from very large wildland fires in Southern California, USA, Atmos. Environ., 40, 3686–3695, 2006.

Cofer, W. R. III, Levine, J. S., Winstead, E. L., Lebel, P. J., Koller, A. M. Jr., and Hinkle, C. R.: Trace gas emissions from burning Florida wetlands, J. Geophys. Res., 101, 23597–23602, 1990.

Cohen, J. D. and Deeming, J. E.: The National Fire Danger Rating System: basic equations, Gen. tech. Rep. PSW-82, Pacific Southwest Forest and Range Experiment Station, Forest Service, U.S. Department of Agriculture, Berkeley, CA, 16~pp., available at: http://www.treesearch.fs.fed.us/pubs/27298, 1985.

DeBell, L. J., Talbot, R. W., Dibb, J. E., Munger, J. W., Fischer, E. V., and Frolking, S. E.: A major regional air pollution event in the northeastern United States caused by extensive forest fires in Quebec, Canada, J. Geophys. Res.-Atmos., 109, D19305, doi:10.1029/2004JD004840 , 2004.

FOFEM 5.9, First Order Fire Effects Model software: http://www.firelab.org/science-applications/fire-fuel/111-fofem, last access: 2 May 2011.

French, N. H. F., Goovaerts, P., and Kasischke, E. S.: Uncertainty in estimating carbon emissions from boreal forest fires, J. Geophys. Res.-Atmos., 109, D14S08, doi:10.1029/2003JD003635 , 2004.

Friedli, H. R., Atlas, E., Stroud, V. R., Giovanni, L., Campos, T., and Radke, L. F.: Volatile organic trace gases emitted from North American wildfires, Global Biogeochem. Cy., 15, 435–452, 2001.

Giglio, L., Descloitres, J., Justice, C. O., and Kaufman, Y. J.: An enhanced contextual fire detection algorithm for MODIS, Remote Sens. Environ., 87, 273–282, 2003.

Giglio, L., Loboda, T., Roy, D. P., Quayle, B., and Justice, C. O.: An active-fire based burned area mapping algorithm for the MODIS sensor, Remote Sens. Environ., 113, 408–420, 2009.

Giglio, L., Randerson, J. T., van der Werf, G. R., Kasibhatla, P. S., Collatz, G. J., Morton, D. C., and DeFries, R. S.: Assessing variability and long-term trends in burned area by merging multiple satellite fire products, Biogeosciences, 7, 1171–1186, 2010.

Hardy, C. C., Conard, S. G., Regelbrugge, J. C. and Teesdale, D. R.: Smoke emissions from prescribed burning of southern California chaparral, USDA Forest Service, Portland, OR., 1996.

Harrington, M. G.: Estimating ponderosa pine fuel moisture using national fire-danger rating fuel moisture values, USAD Forest Service Research Paper RM-233, Rocky Mountain Forest and Range Experiment Station, Fort Collins, CO, 7~pp., available at: http://www.fs.fed.us/rm/pubs_rm/rm_rn418.pdf, 1982.

Hoelzemann, J. J., Schultz, M. G., Brasseur, G. P., Granier, C., and Simon, M.: Global Wildland Fire Emission Model (GWEM): Evaluating the use of global area burnt satellite data, J. Geophys. Res.-Atmos., 109, D14S04, doi:10.1029/2003JD003666, 2004.

IPCC: 2006 Guidelines for National Greenhouse Gas Inventories, National Greenhouse Gas Inventories Programme, edited by: Eggleston, H. S., Buendia, L., Miwa, K., Ngara, T., and Tanabe, K., IGES, Japan, 3.1–3.66, 2006.

Ito, A. and Penner, J. E.: Estimates of CO emissions from open biomass burning in southern Africa for the year 2000, J. Geophys. Res.-Atmos., 110, D19306, doi:10.1029/2003JD004423, 2004.

Jain, A. K.: Global estimation of CO emissions using three sets of satellite data for burned area, Atmos. Environ., 41, 6931–6940, 2007.

JFSP, Joint Fire Science Program: http://www.firescience.gov/index.cfm, last access: 5 May 2011, 2008.

LANDFIRE, LANDFIRE Project, U.S. Department of Interior, Geological Survey: http://www.landfire.gov/, last access: 2 May 2011a, 2011.

LANDFIRE, The LANDFIRE data distribution site, U.S. Department of Interior, Geological Survey: http://landfire.cr.usgs.gov/viewer/, last access: 2 May 2011b, 2011.

Langmann, B., Duncan, B., Textor, C., Trentmann, J., and van der Werf, G. R.: Vegetation fire emissions and their impact on air pollution and climate, Atmos. Environ., 43, 107–116, 2009.

Lapina, K., Honrath, R. E., Owen, R. C., Martin, M. V., and Pfister, G.: Evidence of significant large-scale impacts of boreal fires on ozone levels in the midlatitude Northern Hemisphere free troposphere, Geophys. Res. Lett., 33, L10815, doi:10.1029/2006GL025878 , 2006.

Larkin, N. K., O'Neill, S. M., Solomon, R., Raffuse, S., Strand, T., Sullivan, D. C., Krull, C., Rorig, M., Peterson, J. L., and Ferguson, S. A.: The BlueSky smoke modeling framework, Int. J. Wildland Fire, 18, 906–920, 2009.

Li, R. R., Kaufman, Y. J., Hao, H. M., Salmon, J. M., and Gao, B. C.: A technique for detecting burn scars using MODIS data, IEEE T. Geosci. Remote., 42, 1300–1308, 2004.

Liousse, C., Guillaume, B., Gr$\acute\rm e$goire, J. M., Mallet, M., Galy, C., Pont, V., Akpo, A., Bedou, M., Cast$\acute\rm e$ra, P., Dungall, L., Gardrat, E., Granier, C., Konar�, A., Malavelle, F., Mariscal, A., Mieville, A., Rosset, R., Ser�a, D., Solmon, F., Tummon, F., Assamoi, E., Yobou�, V., and Van Velthoven, P.: Updated African biomass burning emission inventories in the framework of the AMMA-IDAF program, with an evaluation of combustion aerosols, Atmos. Chem. Phys., 10, 9631–9646, http://dx.doi.org/10.5194/acp-10-9631-2010doi:10.5194/acp-10-9631-2010, 2010.

Liu, Y. Q., Goodrick, S., Achtemeier, G., Jackson, W. A., Qu, J. J., and Wang, W. T.: Smoke incursions into urban areas: simulation of a Georgia prescribed burn, Int. J. Wildland Fire, 18, 336–348, 2009.

Lutes, D. C., Keane, R. E., and Caratti, J. F.: A surface fuel classification for estimating fire effects, Int. J. Wildland Fire, 18, 802–814, 2009.

Mesinger, F., DiMego, G., Kalnay, E., Mitchell, K., Shafran, P. C., Ebisuzaki, W., Jovic, D., Woollen, J., Rogers, E., Berbery, E. H., Ek, M. B., Fan, Y., Grumbine, R., Higgins, W., Li, H., Lin, Y., Manikin, G., Parrish, D., and Shi, W.: North American regional reanalysis, B. Am. Meteorol. Soc., 87, 343–360, 2006.

Michel, C., Liousse, C., Gregoire, J. M., Tansey, K., Carmichael, G. R., and Woo, J. H.: Biomass burning emission inventory from burnt area data given by the SPOT-VEGETATION system in the frame of TRACE-P and ACE-Asia campaigns, J. Geophys. Res.-Atmos., 110, D09304, doi:10.1029/2004JD005461 , 2005.

Mieville, A., Granier, C., Liousse, C., Guillaume, B., Mouillot, F., Lamarque, J. F., Gregoire, J. M., and Petron, G.: Emissions of gases and particles from biomass burning during the 20th century using satellite data and an historical reconstruction, Atmos. Environ., 44, 1469–1477, 2010.

MTBS, Data Access: Burned Area Boundaries Data: http://mtbs.gov/compositfire/mosaic/bin-release/burnedarea.html, last access: 3 May 2011a, 2011.

MTBS, Data Access: Regional MTBS Burn Severity Mosaics: http://mtbs.gov/compositfire/mosaic/bin-release/download.html, last access: 3 May 2011b, 2011.

MTBS, Monitoring trends in Burn Severity: http://mtbs.gov/index.html, last access: 3 May 2011c, 2011.

Morris, G. A., Hersey, S., Thompson, A. M., Pawson, S., Nielsen, J. E., Colarco, P. R., McMillan, W. W., Stohl, A., Turquety, S., Warner, J., Johnson, B. J., Kucsera, T. L., Larko, D. E., Oltmans, S. J., and Witte, J. C.: Alaskan and Canadian forest fires exacerbate ozone pollution over Houston, Texas, on 19 and 20 July 2004, J. Geophys. Res.-Atmos., 111, D24S03, doi:10.1029/2006JD007090, 2006.

Muhle, J., Lueker, T. J., Su, Y., Miller, B. R., Prather, K. A., and Weiss, R. F.: Trace gas and particulate emissions from the 2003 southern California wildfires, J. Geophys. Res.-Atmos., 112, D03307, doi:10.1029/2006JD007350, 2007.

Nance, J. D., Hobbs, P. V., and Radke, L. F.: airborne measurements of gases and particles from an alaskan wildfire, J. Geophys. Res.-Atmos., 98, 14873–14882, 1993.

NASA: MODIS L1 and Atmospheres Archive and Distribution System (LAADS), NASA: http://ladsweb.nascom.nasa.gov/, last access: 2 May 2011.

National Interagency Coordination Center, Incident Management Situation Report Archives: http://www.predictiveservices.nifc.gov/intelligence/archive.htm, last access: 2 May 2011.

Natural Fuels Photo Series: http://www.fs.fed.us/pnw/fera/research/fuels/photo_series/index.shtml, last access: 1 May 2011, 2011.

Natural Fuels Digital Photo Series, Volume XI: Pacific Northwest II, Eastern Oregon Sagebrush with Grass: http://depts.washington.edu/nwfire/dps/, last access: 1 May 2011, 2011.

Ottmar, R. D., Vihnanek, Robert E., and Wright, C. S.: Stereo photo series for quantifying natural fuels: volume I: Mixed-conifer with mortality, western juniper, sagebrush, and grassland types in the interior Pacific Northwest: PMS 830. NFES 2580, Boise, Idaho: National Wildfire Coordinating Group, National Interagency Fire Center, 73~pp., Digital Photo Series available at: http://depts.washington.edu/nwfire/dps/, 1998.

Ottmar, R. D., Vihnanek, R. E., Wright, and C. S.: Stereo photo series for quantifying natural fuels. Volume III: Lodgepole pine, quaking aspen, and gambel oak types in the Rocky Mountains: PMS 832, Boise, ID: National Wildfire Coordinating Group, National Interagency Fire Center, 85 pp., Digital Photo Series available at: http://depts.washington.edu/nwfire/dps/, 2000a.

Ottmar, R. D., Vihnanek, R. E., and Regelbrugge, J. C.: Stereo photo series for quantifying natural fuels. Volume IV: pinyon-juniper, sagebrush, and chaparral types in the Southwestern United States: PMS 833, Boise, ID: National Wildfire Coordinating Group, National Interagency Fire Center, 97~pp., Digital Photo Series available at: http://depts.washington.edu/nwfire/dps/, 2000b.

Ottmar, R. D., Vihnanek, R. E., Wright, C. S., and Olson, D. L, Stereo photo series for quantifying natural fuels. Volume VII: Oregon white oak, California deciduous oak, and mixed-conifer with shrub types in the Western United States: PMS 839, Boise, ID: National Wildfire Coordinating Group, National Interagency Fire Center, 75~pp., Digital Photo Series available at: http://depts.washington.edu/nwfire/dps/, 2004.

Ottmar, R. D., Sandberg, D. V., Riccardi, C. L., and Prichard, S. J.: An overview of the Fuel Characteristic Classification System – Quantifying, classifying, and creating fuelbeds for resource planning, Can. J. Forest. Res., 37, 2383–2393, 2007a.

Ottmar, R. D., Vihnanek, R. E., and Wright, C. S.: Stereo photo series for quantifying natural fuels: volume X: Sagebrush with grass and ponderosa pine-juniper types in central Montana, Gen. Tech. Rep. PNW-GTR-719, U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station, 59~pp., Digital Photo Series available at: http://depts.washington.edu/nwfire/dps/, 2007b.

Park, R. J., Jacob, D. J., and Logan, J. A.: Fire and biofuel contributions to annual mean aerosol mass concentrations in the United States, Atmos. Enviorn., 41, 7389–7400, http://dx.doi.org/10.1016/j.atmosenv.2007.05.061doi:10.1016/j.atmosenv.2007.05.061, 2007.

Pfister, G., Emmons, L., and Wiedinmyer, C.: Impacts of the fall 2007 California wildfires on surface ozone: integrating local observations with global model simulations, Geophys. Res.Lett., 35, L19814, doi:10.1029/2007JD008797, 2008.

Phuleria, H. C., Fine, P. M., Zhu, Y. F., and Sioutas, C.: Air quality impacts of the October 2003 Southern California wildfires, J. Geophys. Res.-Atmos., 110, D07S20, doi:10.1029/2004JD004626 , 2005.

Prichard, S .J., Ottmar, R. D., and Anderson, G. K.: Consume 3.0 user's guide, Pacific Northwest Research Station, Corvallis, Oregon, 234~pp., available at: http://www.firelab.org/science-applications/fire-fuel/111-fofem, 2006.

Radke, L. F., Hegg, D. A., Hobbs, P. V., Nance, J. D., Lyons, J. H., Laursen, K. K., Weiss, R. E., Riggan, P. J., and Ward, D. E.: Particulate and trace gas emissions from large biomass fires in North America, in Global Biomass Burning: Atmospheric, Climatic, and Biospheric Implications, edited by: Levine, J. S., 209–224, MIT Press, Cambridge, MA, 1991.

Reid, J. S., Hyer, E. J., Prins, E. M., Westphal, D. L., Zhang, J. L., Wang, J., Christopher, S. A., Curtis, C. A., Schmidt, C. C., Eleuterio, D. P., Richardson, K. A., and Hoffman, J. P.: Global Monitoring and Forecasting of Biomass-Burning Smoke: Description of and Lessons From the Fire Locating and Modeling of Burning Emissions (FLAMBE) Program, IEEE J. Sel. Top. Appl., 2, 144–162, 2009.

Reinhardt, E. D. : Using FOFEM5.0 to estimate tree mortality, fuel consumption, smoke production and soil heating from wildland fire, in: Proceedings of the Second International Wildland Fire Ecology and Fire Management Congress and Fifth Symposium on Fire and Forest Meteorology, American Meteorological Society, Orlando, Florida, 16–20 November 2003, available at: http://www.firelab.org/science-applications/fire-fuel/111-fofem, 2003.

Rothermel, R.C.: A mathematical model for predicting fire spread in wildland fuels, Res. Pap. INT-115, USDA Forest Service, Intermountain Forest and Range Experiment Station, 40~pp., 1972.

Sapkota, A., Symons, J. M., Kleissl, J., Wang, L., Parlange, M. B., Ondov, J., Breysse, P. N., Diette, G. B., Eggleston, P. A., and Buckley, T.J.: Impact of the 2002 Canadian Forest Fires on Particulate Matter Air Quality in Baltimore City, Environ. Sci. Technol., 39, 24–32, 2005.

Schwind, B.: Monitoring trends in burn severity: report on the PNW & PSW fires – 1984 to 2005, MTBS Project Team, U.S. Geologiocal Survey and U.S. Forest Service, Remote Sensing Applications Center, Salt Lake City, Utah, 2008.

Seiler, W. and Crutzen, P. J.: Estimates of gross and net fluxes of carbon between the biosphere and the atmosphere from biomass burning, Climatic Change, 2, 207–247, 1980.

Simpson, I. J., Rowland, F. S., Meinardi, S., and Blake, D. R.: Influence of biomass burning during recent fluctuations in the slow growth of global tropospheric methane, Geophys. Res. Lett., 33, L22808, doi:10.1029/2006GL027330, 2006.

Sikkink, P. G., Lutes, D. C., and Keane, R. E.: Field guide for identifying fuel loading models, Gen. Tech. Rep. RMRS-GTR-225, USDA, Forest Service, Rocky Mountain Research Station, Fort Collins, Colorado, 33~pp., 2009.

Soja, A. J., Cofer, W. R., Shugart, H. H., Sukhinin, A. I., Stackhouse, P. W., McRae, D. J., and Conard, S. G.: Estimating fire emissions and disparities in boreal Siberia (1998–2002), J. Geophys. Res.-Atmos., 109, D14S06, doi:10.1029/2004JD004570, 2004.

Spracklen, D. V., Logan, J. A., Mickley, L. J., Park, R. J., Yevich, R., Westerling, A. L., and Jaffe, D. A.: Wildfires drive interannual variability of organic carbon aerosol in the western U.S. in summer, GeophyS. Res. Lett., 34, L16816, doi:10.1029/2007GL030037, 2007.

Stroppiana, D., Brivio, P. A., Gr$\acute\rm e$goire, J.-M., Liousse, C., Guillaume, B., Granier, C., Mieville, A., Chin, M., and P$\acute\rm e$tron, G.: Comparison of global inventories of CO emissions from biomass burning derived from remotely sensed data, Atmos. Chem. Phys., 10, 12173–12189, http://dx.doi.org/10.5194/acp-10-12173-2010doi:10.5194/acp-10-12173-2010, 2010.

Sudo, K. and Akimoto, H.: Global source attribution of tropospheric ozone: long-range transport from various source regions, J. Geophys. Res., 112, D12302, doi:10.1029/2006JD007992 , 2007

USEPA, Emission Inventories: http://www.epa.gov/ttn/chief/eiinformation.html, last access: 2 May 2011, 2011.

Urbanski, S. P., Salmon, J. M., Nordgren, B. L., and Hao, W. M.: A MODIS direct broadcast algorithm for mapping wildfire burned area in the western United States, Remote Sens. Environ., 113, 2511–2526, 2009a.

Urbanski, S. P., Hao, W. M., and Baker, S.: Chemical composition of Wildland Fire Emissions, in: Developments in Environmental Science, 8, edited by: Bytnerowicz, A. Arbaugh, M., Riebau, A., and Andersen, C., Elsevier, The Netherlands, 79–107, 2009b.

van der Werf, G. R., Randerson, J. T., Giglio, L., Collatz, G. J., Kasibhatla, P. S., and Arellano Jr., A. F.: Interannual variability in global biomass burning emissions from 1997 to 2004, Atmos. Chem. Phys., 6, 3423–3441, http://dx.doi.org/10.5194/acp-6-3423-2006doi:10.5194/acp-6-3423-2006, 2006.

van der Werf, G. R., Randerson, J. T., Giglio, L., Collatz, G. J., Mu, M., Kasibhatla, P. S., Morton, D. C., DeFries, R. S., Jin, Y., and van Leeuwen, T. T.: Global fire emissions and the contribution of deforestation, savanna, forest, agricultural, and peat fires (1997-2009), Atmos. Chem. Phys., 10, 11707-11735, http://dx.doi.org/10.5194/acp-10-11707-2010doi:10.5194/acp-10-11707-2010, 2010.

Wiedinmyer, C., Quayle, B., Geron, C., Belote, A., McKenzie, D., Zhang, X. Y., O'Neill, S., and Wynne, K. K.: Estimating emissions from fires in North America for air quality modeling, Atmos. Environ., 40, 3419–3432, 2006.

Wiedinmyer, C., Akagi, S. K., Yokelson, R. J., Emmons, L. K., Al-Saadi, J. A., Orlando, J. J., and Soja, A. J.: The Fire INventory from NCAR (FINN) – a high resolution global model to estimate the emissions from open burning, Geosci. Model Dev., 4, 625–641, 2011.

Wilcox, R. R.: A note on the Theil-Sen regression estimator when the regressor is random and the error term is heteroscedastic, Biometrical J., 40, 261–268, 1998.

Wilcox, R. R.: Introduction to robust estimation and hypothesis testing, Elsevier Academic Press, Burlington, MA, 413–464, 2005.

Yokelson, R. J., Goode, J. G., Ward, D. E., Susott, R. A., Babbitt, R. E., Wade, D. D., Bertschi, I., Griffith, D. W. T., and Hao, W. M.: Emissions of formaldehyde, acetic acid, methanol, and other trace gases from biomass fires in North Carolina measured by airborne Fourier transform infrared spectroscopy, J. Geophys. Res.-Atmos., 104, 30109–30125, 1999.

Zhang, X. Y., Kondragunta, S., Schmidt, C., and Kogan, F.: Near real time monitoring of biomass burning particulate emissions (PM$_2.5$) across contiguous United States using multiple satellite instruments, Atmos. Environ., 42, 6959–6972, 2008.