Abstract
Abstract. We investigate Arctic tropospheric composition using ground-based Fourier transform infrared (FTIR) solar absorption spectra, recorded at the Polar Environment Atmospheric Research Laboratory (PEARL, Eureka, Nunavut, Canada, 80°05' N, 86°42' W) and at Thule (Greenland, 76°53' N, −68°74' W) from 2008 to 2012. The target species, carbon monoxide (CO), hydrogen cyanide (HCN), ethane (C2H6), acetylene (C2H2), formic acid (HCOOH), and formaldehyde (H2CO) are emitted by biomass burning and can be transported from mid-latitudes to the Arctic. By detecting simultaneous enhancements of three biomass burning tracers (HCN, CO, and C2H6), ten and eight fire events are identified at Eureka and Thule, respectively, within the 5-year FTIR time series. Analyses of Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model back-trajectories coupled with Moderate Resolution Imaging Spectroradiometer (MODIS) fire hotspot data, Stochastic Time-Inverted Lagrangian Transport (STILT) model footprints, and Ozone Monitoring Instrument (OMI) UV aerosol index maps, are used to attribute burning source regions and travel time durations of the plumes. By taking into account the effect of aging of the smoke plumes, measured FTIR enhancement ratios were corrected to obtain emission ratios and equivalent emission factors. The means of emission factors for extratropical forest estimated with the two FTIR data sets are 0.40 ± 0.21 g kg−1 for HCN, 1.24 ± 0.71 g kg−1 for C2H6, 0.34 ± 0.21 g kg−1 for C2H2, and 2.92 ± 1.30 g kg−1 for HCOOH. The emission factor for CH3OH estimated at Eureka is 3.44 ± 1.68 g kg−1. To improve our knowledge concerning the dynamical and chemical processes associated with Arctic pollution from fires, the two sets of FTIR measurements were compared to the Model for OZone And Related chemical Tracers, version 4 (MOZART-4). Seasonal cycles and day-to-day variabilities were compared to assess the ability of the model to reproduce emissions from fires and their transport. Good agreement in winter confirms that transport is well implemented in the model. For C2H6, however, the lower wintertime concentration estimated by the model as compared to the FTIR observations highlights an underestimation of its emission. Results show that modeled and measured total columns are correlated (linear correlation coefficient r > 0.6 for all gases except for H2CO at Eureka and HCOOH at Thule), but suggest a general underestimation of the concentrations in the model for all seven tropospheric species in the high Arctic.
Highlights
Fires release trace gases into the atmosphere, affecting air quality (Colarco et al, 2004), climate, and the carbon cycle (IPCC, 2007)
To assess the capacity of a model to estimate columns and variabilities of tropospheric species in the high Arctic, MOZART-4 was compared to the Fourier transform infrared (FTIR) data sets
The FTIR and the MOZART-4 trace gas profiles are estimated over different altitude ranges, and with different vertical resolutions
Summary
Fires release trace gases into the atmosphere, affecting air quality (Colarco et al, 2004), climate, and the carbon cycle (IPCC, 2007). C. Viatte et al.: Identifying fire plumes in the Arctic ically active trace gases include carbon monoxide (CO), hydrogen cyanide (HCN), and non-methane hydrocarbons (NMHCs), including ethane (C2H6), acetylene (C2H2), methanol (CH3OH), formic acid (HCOOH), and formaldehyde (H2CO) (Paton-Walsh et al, 2010; Akagi et al, 2011; Vigouroux et al, 2012). Viatte et al.: Identifying fire plumes in the Arctic ically active trace gases include carbon monoxide (CO), hydrogen cyanide (HCN), and non-methane hydrocarbons (NMHCs), including ethane (C2H6), acetylene (C2H2), methanol (CH3OH), formic acid (HCOOH), and formaldehyde (H2CO) (Paton-Walsh et al, 2010; Akagi et al, 2011; Vigouroux et al, 2012) Given their long atmospheric lifetimes, CO, HCN, and C2H6 are considered to be tracers of long-range pollution transport associated with biomass burning plumes. Since fire frequency and intensity are sensitive to climate change and variability, as well as land use practices (Kasischke et al, 2006; Soja et al, 2007; IPCC, 2007; Amiro et al, 2009; Flannigan et al, 2009; Oris et al, 2013; Kelly et al, 2013), they constitute a large source of variability in Arctic tropospheric composition
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
