Where there is smoke there is mercury: Assessing boreal forest fire mercury emissions using aircraft and highlighting uncertainties associated with upscaling emissions estimates
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".