Where there is smoke there is mercury: Assessing boreal forest fire mercury emissions using aircraft and highlighting uncertainties associated with upscaling emissions estimates

Emissions from biomass burning are an important source of mercury (Hg) to the atmosphere and an integral component of the global Hg biogeochemical cycle. In 2018, measurements of gaseous elemental Hg (GEM) were taken on board a research aircraft along with a series of co-emitted contaminants in the emissions plume of an 88 km 2 boreal forest wildfire on the Garson Lake Plain (GLP) in NW Saskatchewan, Canada. A series of four flight tracks were made perpendicular to the plume at increasing distances from the fire, each with three to five passes at different altitudes at each downwind location. The maximum GEM concentration measured on the flight was 2.88 ng m −3 , which is ≈ 2.4× background concentration. GEM concentrations were significantly correlated with the co-emitted carbon species (CO, CO 2 , and CH 4 ). Emissions ratios (ERs) were calculated from measured GEM and carbon co-contaminant data. Using the most correlated (least uncertain) of these ratios (GEM:CO), GEM concentrations were estimated at the higher 0.5 Hz time resolution of the CO measurements, resulting in maximum GEM concentrations and enhancements of 6.76 ng m −3 and ≈ 5.6×, respectively. Extrapolating the estimated maximum 0.5 Hz GEM concentration data from each downwind location back to source, 1 km and 1 m (from fire) concentrations were predicted to be 12.9 and 30.0 ng m −3 , or enhancements of ≈ 11× and ≈ 25×, respectively. ERs and emissions factors (EFs) derived from the measured data and literature values were also used to calculate Hg emissions estimates on three spatial scales: (i) the GLP fires themselves, (ii) all boreal forest biomass burning, and (iii) global biomass burning. The most robust estimate was of the GLP fires (21 ± 10 kg of Hg) using calculated EFs that used minimal literature-derived data. Using the Top-down Emission Rate Retrieval Algorithm (TERRA), we were able to determine a similar emission estimate of 22 ± 7 kg of Hg. The elevated uncertainties of the other estimates and high variability between the different methods used in the calculations highlight concerns with some of the assumptions that have been used in calculating Hg biomass burning in the literature. Among these problematic assumptions are variable ERs of contaminants based on vegetation type and fire intensity, differing atmospheric lifetimes of emitted contaminants, the use of only one co-contaminant in emissions estimate calculations, and the paucity of atmospheric Hg species concentration measurements in biomass burning plumes.

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