Total peroxy nitrate ( ∑ 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 ∑ PN concentrations and those of carbon monoxide (CO), a well-known pyrogenic tracer, suggests the possible use of the ∑ PN concentrations as marker of the BB plumes. Two methods for the identification of BB plumes have been applied: (1) ∑ PN concentrations higher than 6 times the standard deviation above the background and (2) ∑ PN concentrations higher than the 99th percentile of the ∑ 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 ( ∼ 750 hPa) as ancillary parameter to ∑ 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 ∑ PNs and HCN, including nitrogen oxide (NO), acetonitrile (CH3CN), CO, ozone (O3) and atmospheric pressure as input parameters, to verify the specific role of these input data to better identify BB plumes.
Impact of biomass burning emission on total peroxy nitrates: fire plume identification during the BORTAS campaign
Aruffo, Eleonora;DI CARLO, PIERO;
2016-01-01
Abstract
Total peroxy nitrate ( ∑ 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 ∑ PN concentrations and those of carbon monoxide (CO), a well-known pyrogenic tracer, suggests the possible use of the ∑ PN concentrations as marker of the BB plumes. Two methods for the identification of BB plumes have been applied: (1) ∑ PN concentrations higher than 6 times the standard deviation above the background and (2) ∑ PN concentrations higher than the 99th percentile of the ∑ 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 ( ∼ 750 hPa) as ancillary parameter to ∑ 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 ∑ PNs and HCN, including nitrogen oxide (NO), acetonitrile (CH3CN), CO, ozone (O3) and atmospheric pressure as input parameters, to verify the specific role of these input data to better identify BB plumes.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
