Seasonal Variations of the Mercury Multiple Isotopic Compositions of Subrural and Urban Aerosols Highlight an Additional Atmospheric Hg0 Oxidation Pathway
Bibliographic record
Abstract
The mechanisms triggering the large variations in the mercury (Hg) multiple isotopic compositions of atmospheric particle-bound Hg worldwide still remain unclear. The comparison of Hg isotopic compositions in aerosols collected in urban and rural areas may help distinguish the effects of natural processes from those of anthropogenic inputs. We thus investigated the Hg isotopic compositions of PM10 aerosols collected seasonally during 2015 at two monitoring stations on Montreal Island, one located downtown and the other in its westernmost subrural part, barely impacted by the city anthropogenic emissions. Our results show that, while Hg isotopic compositions present no seasonality at the urban station, possibly due to constant anthropogenic emissions, the subrural samples display clear seasonal variations, with higher Δ199Hg and Δ200Hg values (up to 0.77 and 0.12‰, respectively) during summer and close to 0‰ during the rest of the year, that cannot solely be explained by anthropogenic primary emissions. Besides, Δ200Hg measured in the subrural aerosols display a positive correlation with O3 suggesting the implication of secondary processes involving ozone. We propose that the significant summer shift in the multiple Hg isotopic compositions may reflect a transition in the corresponding Hg0 oxidation pathway, from halogens-triggered to ozone-dominated reactions. Still, this hypothesis needs to be further tested. Nevertheless, it demonstrates that Hg isotopes are effective at characterizing secondary processes that control its atmospheric budget, even at a local scale (i.e., urban vs subrural) and could thus be used to better constrain its atmospheric chemistry in various 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".