Changes in Atmospheric Gaseous Elemental Mercury Concentrations and Isotopic Compositions at Mt. Changbai During 2015–2021 and Mt. Ailao During 2017–2021 in China
Bibliographic record
Abstract
Abstract China is the largest contributor to the global total anthropogenic mercury (Hg) emissions. However, the trend in anthropogenic Hg emissions in recent years in China has not been effectively evaluated due to the lack of long‐term atmospheric Hg observations. This study documents the changes in atmospheric gaseous elemental mercury (GEM) concentrations and isotopic compositions at Mt. Changbai (MCB) in northeastern China during 2015–2021 and Mt. Ailao (MAL) in southwestern China during 2017–2021, and explores the potential factors controlling these changes. GEM concentrations showed continuous declines from 2015 to 2021 (−2.1 ± 0.6% yr−1) at MCB and from 2017 to 2021 (−4.0 ± 1.4% yr−1) at MAL. Accompanied with these GEM declines are positive shifts in δ202Hg (medians: from 0.42 to 0.46‰ at MCB and from 0.17 to 0.57‰ at MAL), and negative shifts in Δ199Hg (medians: from −0.17‰ to −0.21‰ at MCB and from −0.10‰ to −0.17‰ at MAL) and Δ200Hg values (medians: from −0.07‰ to −0.08‰ at MCB and from −0.03‰ to −0.05‰ at MAL) (at significant levels for Δ199Hg at MCB and δ202Hg and Δ199Hg at MAL). These changes were mainly caused by the decreases in regional anthropogenic emissions in the study areas. Based on a ternary mixing model with Δ199Hg and Δ200Hg as input, we estimate decline rates of 5.8 ± 2.8 and 4.8 ± 3.0% yr−1 for the regional anthropogenic GEM emissions in northeastern and southwestern China, respectively.
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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".