Advertisement

A Global Wildfire Emission and Atmospheric Composition: Refinement of the Integrated System for Wild-Land Fires IS4FIRES

  • Joana SoaresEmail author
  • Mikhail Sofiev
Conference paper
Part of the Springer Proceedings in Complexity book series (SPCOM)

Abstract

The current study intends to evaluate the fire emission estimates obtained from IS4FIRES v1.5. The system provides spatially and temporally resolved emission fluxes originated from wild-land fires. The emissions were obtained by utilising remote-sensing products of MODIS and SEVIRI instruments: TA and FRP. The primary scaling is based on emission factors for PM2.5 determined for seven land-use types: grass, crop residue, shrub, tropical, temperate and boreal forest, and peat. The PM2.5 emission fluxes can be converted to total PM and gaseous species using literature-reported scaling factors.

To evaluate the system, the fire emission fluxes were used as input to the SILAM model, which evaluated the dispersion and transformation of the released smoke. The observational datasets included AOD observations from MODIS. To facilitate the comparison and estimate the contribution from fires to AOD, SILAM inorganic chemistry calculated formation of secondary inorganic aerosol. Primary PM emissions from anthropogenic and natural sources were also included.

The model-measurement comparison showed that spatial and temporal distributions of the fire smoke are well reproduced. Nevertheless, the smoke from fires occurring in central Africa and South America are overestimated, and fires occurring in areas where peat and crop are dominant are underestimated. The optimization of the system, in general, results on a reduction of the emission coefficients, with exception of peat and crop, as expected; it reduces emission substantially especially for the areas where tropical and grass are dominating and fires tend to be very intense (Africa). Nevertheless, in some cases reduction seems to be counterproductive, emissions are heavily reduced.

Keywords

Emission Factor Emission Coefficient Emission Flux Aerosol Optical Thickness Fire Emission 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

The study has been funded by the Academy of Finland, project IS4FIRES.

References

  1. 1.
    Akagi SK, Yokelson RJ, Wiedinmyer C, Alvarado MJ, Reid JS, Karl T, Crounse JD, Wennberg PO (2011) Emission factors for open and domestic biomass burning for use in atmospheric models. Atmos Chem Phys Discuss 11:4039–4072. doi: 10.5194/acp-11-4039-2011 CrossRefGoogle Scholar
  2. 2.
    Andreae MO, Merlet P (2001) Emission of trace gases and aerosols from biomass burning. Glob Biogeochem Cycle 15:955–966CrossRefGoogle Scholar
  3. 3.
    Granier C, Bessagnet B, Bond T, D’Angiola A, van der Gon HD, Frost GJ, Heil A, Kaiser JW, Kinne S, Klimont Z, Kloster S, Lamarque J, Liousse C, Masui T, Meleux F, Mieville A, Ohara T, Raut J, Riahi K, Schultz MG, Smith SJ, Thompson A, van Aardenne J, van der Werf GR, van Vuuren DP (2011) Evolution of anthropogenic and biomass burning emissions of air pollutants at global and regional scales during the 1980–2010 period. Climate Change 109(1–2):163–190CrossRefGoogle Scholar
  4. 4.
    Kouznetsov R, Sofiev M (2012) A methodology for evaluation of vertical dispersion and dry deposition of atmospheric aerosols. J Geophys Res 117:D01202. doi: 10.1029/2011JD016366 Google Scholar
  5. 5.
    Sofiev M, Siljamo P, Valkama I, Ilvonen M, Kukkonen J (2006) A dispersion modeling system SILAM and its evaluation against ETEX data. Atmos Environ 40:674–685CrossRefGoogle Scholar
  6. 6.
    Sofiev M, Galperin M, Genikhovich E (2008) A construction and evaluation of Eulerian dynamic core for the air quality and emergency modelling system SILAM. In: Borrego C, Miranda AI (eds) Air pollution modeling and its application XIX, Nato science for peace and security series C – environmental security. Springer, Dordrecht, pp 699–701. NATO; CCMS; Univ Aveiro,  10.1007/978-1-4020-835 8453–9 94, 29th NATO/CCMS international technical meeting on air pollution modeling and its application, Aveiro, Portugal, 24–28 Sept 2007
  7. 7.
    Sofiev M, Vankevich R, Lotjonen M, Prank M, Petukhov V, Ermakova T, Koskinen J, Kukkonen J (2009) An operational system for the assimilation of the satellite information on wild-land fires for the needs of air quality modelling and forecasting. Atmos Chem Phys 9:6833CrossRefGoogle Scholar
  8. 8.
    Sofiev M, Soares J, Prank M, de Leeuw G, Kukkonen J (2011) A regional-to-global model of emission and transport of sea salt particles in the atmosphere. J Geophys Res 116:D21302. doi: 10.1029/2010JD014713
  9. 9.
    Sofiev M, Ermakova T, Vankevich R (2012) Evaluation of the smoke-injection height from wild-land fires using remote-sensing data. Atmos Chem Phys 12(4):1995–2006. doi: 10.5194/acp-12-1995-2012 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Air Quality DepartmentFinnish Meteorological InstituteHelsinkiFinland

Personalised recommendations