Mercury in Atmospheric Air and Precipitation at the Monitoring Station Listvyanka (Southern Baikal Region) in 2022–2023
Bibliographic record
Abstract
Abstract Gaseous elemental mercury (GEM) is the predominant form of mercury in the atmosphere. As a result of deposition, it enters terrestrial and aquatic ecosystems, where it is further transformed into the ecotoxicant methylmercury. The work studies GEM in atmospheric air and total mercury in atmospheric precipitation in the Southern Baikal region. Sampling was carried out at Listvyanka monitoring station (51.9° N, 104.4° E) in 2022–2023. The concentrations of mercury in air was measured with a RA-915AM mercury gas analyzer (St. Petersburg, Russia); the concentration of total mercury in precipitation was determined by PND F 14.1:2:4.271-2012 method A (permanganate mineralization) technique. The measured concentrations were statistically analyzed. During the period under study, the concentration of GEM in atmospheric air averaged 1.61 ng/m3; the pair correlation coefficient was 0.47 between Hg0 and sulfur dioxide (SO2) and 0.44 between Hg0 and nitrogen dioxide (NO2); a strong positive correlation (>0.9) between Hg0, SO2, and NO2 was found in 12 cases. For each episode of mercury concentration above 2.0 ng/m3, back trajectories of air masses were calculated using the HYSPLIT model. The trajectory analysis also confirmed our assumption of a common type of sources of mercury and trace gases. The weighted average content of total mercury in precipitation is 44 ng/L, the median value is 29 ng/L, and the maximum is 282 ng/L. The results supplement the existing ideas about mercury content in the atmosphere of the Southern Baikal region and show the mercury content in atmospheric precipitation on the Baikal shore to be comparable with the results obtained in urban agglomerations of Nepal, Canada, Korea, and China despite the significant distance of the measurement site from large cities.
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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 | 0.001 | 0.001 |
| 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".