Transport of Exogenous Anthropogenic Atmospheric Mercury to the Tibetan Plateau Identified Using Mercury Stable Isotopes
Bibliographic record
Abstract
Abstract Transport of exogenous anthropogenic mercury (Hg) is an important source of Hg pollution in the Tibetan Plateau (TP) and its downstream water ecosystems, but the origins and contributions of Hg sources remain uncertain. Here, we investigate the concentrations and isotopic compositions of gaseous elemental mercury (GEM) at four rural sites in the TP and three urban sites surrounding the TP to quantify the sources of GEM in the TP. GEM concentrations in the surrounding cities (site‐specific means: 2.36–9.12 ng m −3 ) were highly elevated mainly due to strong local anthropogenic emissions as indicated by their negative δ 202 Hg and near zero Δ 199 Hg and Δ 200 Hg signatures. GEM isotopes indicate that GEM pollution in the TP, typically observed during the summer monsoon and the pre‐monsoon, were mainly caused by trans‐boundary transport of anthropogenic Hg from surroundings. Using an Hg isotope mixing model, we estimate that exogenous anthropogenic emissions on average contributed 26 ± 5% (1sd) to the GEM in the TP. Further analysis of the transport of anthropogenic Hg emissions based on the backward trajectory and gridded anthropogenic Hg emissions suggests that 16 ± 9% and 6 ± 13% of the GEM in the TP were derived from anthropogenic sources in South Asia and China, respectively. Our study suggests that anthropogenic Hg emissions in South Asia could be effectively transported to the TP across the Himalayan range. Future studies are needed to better assess the role of rapidly increasing anthropogenic Hg emissions in South Asia on the regional to global scale atmospheric Hg cycling.
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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) | 0.000 | 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".