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Impact of biomass burning emission on total peroxy nitrates: fire plume identification during the BORTAS campaign

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Author(s)

  • Eleonora Aruffo
  • Fabio Biancofiore
  • Piero Di Carlo
  • Marcella Busilacchio
  • Marco Verdecchia
  • Barbara Tomassetti
  • Cesare Dari-Salisburgo
  • Franco Giammaria
  • Stephane Bauguitte
  • James Lee
  • Sarah Moller
  • James Hopkins
  • Shalini Punjabi
  • Stephen J. Andrews
  • Alastair Lewis
  • Paul I. Palmer
  • Edward Hyer
  • Michael Le Breton
  • Carl Percival

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Publication details

JournalAtmospheric Measurement Techniques
DateAccepted/In press - 11 Oct 2016
DatePublished (current) - 23 Nov 2016
Issue number11
Volume9
Number of pages16
Pages (from-to)5591-5606
Original languageEnglish

Abstract

Total peroxy nitrate (Sigma PN) concentrations have been measured using a thermal dissociation laser-induced fluorescence (TD-LIF) instrument during the BORTAS campaign, which focused on the impact of boreal biomass burning (BB) emissions on air quality in the Northern Hemisphere. The strong correlation observed between the Sigma PN concentrations and those of carbon monoxide (CO), a well-known pyrogenic tracer, suggests the possible use of the Sigma PN concentrations as marker of the BB plumes. Two methods for the identification of BB plumes have been applied: (1) Sigma PN concentrations higher than 6 times the standard deviation above the background and (2) Sigma PN concentrations higher than the 99th percentile of the Sigma PNs measured during a background flight (B625); then we compared the percentage of BB plume selected using these methods with the percentage evaluated, applying the approaches usually used in literature. Moreover, adding the pressure threshold (similar to 750 hPa) as ancillary parameter to Sigma PNs, hydrogen cyanide (HCN) and CO, the BB plume identification is improved. A recurrent artificial neural network (ANN) model was adapted to simulate the concentrations of Sigma PNs and HCN, including nitrogen oxide (NO), acetonitrile (CH3CN), CO, ozone (O-3) and atmospheric pressure as input parameters, to verify the specific role of these input data to better identify BB plumes.

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© Author(s) 2016.

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