Atmospheric Chemistry of Gaseous Oxidized Mercury at a Coastal Site in Atlantic Canada
Bibliographic record
Abstract
Abstract A chemistry box model containing a comprehensive suite of mercury (Hg) oxidation mechanisms involving O3, OH, H2O2, Br, BrO, NO2, HO2, and other oxidants was used to simulate the formation of gaseous oxidized mercury (GOM) and understand the chemical processes driving the observed trends in GOM at Kejimkujik, Nova Scotia, Canada. Simulations were conducted using chemical schemes with and without oxidation by O3 and OH. The major oxidants of Hg are O3 and OH (79%), H2O2 (10%), Br with second-stage HgBr oxidation by NO2 (7%), and BrO (3%) in simulations where all GEM oxidation reactions were considered simultaneously. In an alternative chemical scheme without gas-phase oxidation by O3 and OH, the dominant GOM species were HgBrNO2 (58%) and HgBrO (23.5%). Using this chemical scheme, the model reproduced the observed GOM at sub-ppqv Br2 mixing ratios. In the scheme with O3 and OH, the variability in GOM between seasons and between continental and marine air masses was mainly due to the variability in gaseous elemental Hg, O3, OH, and aerosol liquid water content (LWC). LWC governs the partitioning of GOM to the aerosol aqueous phase in the model. In the scheme without O3 and OH, the variability in GOM by season and airmass origin strongly depends on Br and BrO, suggesting that rigorous validation of modeled Br and BrO data are essential for improving the model predictions of GOM in coastal environments.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".