Mass-dependent and mass-independent fractionation of mercury isotopes in precipitation from Guiyang, SW China
Bibliographic record
Abstract
The isotopic composition of mercury (Hg) is increasingly used to constrain the sources and pathways of this metal in the atmosphere. Though China has the highest Hg production, consumption and emission in the world, Hg isotope ratios are rarely reported for Chinese wet deposition. In this study, we examined, for the first time outside North America, both mass-dependent fractionation (MDF, expressed as δ 202 Hg) and mass-independent fractionation of odd (odd-MIF, Δ 199 Hg) and even (even-MIF, Δ 200 Hg) Hg isotopes in 15 precipitation samples collected from September 2012 to August 2013 in Guiyang (SW China). All samples displayed significant negative δ 202 Hg (–0.44 ∼ –4.27‰), positive Δ 199 Hg (+0.19 to +1.16‰) and slightly positive Δ 200 Hg (–0.01‰ to +0.20‰). Potential sources of Hg in precipitation were identified by coupling both MDF and MIF of Hg isotopes with a back-trajectory model. The results showed that local emission from coal-fired power plants and cement plants and western long-range transportation are two main contributing sources, while the contribution of Hg from south wind events would be very limited on an annual basis. The relatively lower Δ 200 Hg values in Guiyang precipitation may indicate a dilution effect by local sources and/or insignificant even-MIF in the tropopause contribution of this subtropical region. Our study demonstrates the usefulness of isotope fractionation, especially MIF for tracing sources and pathways of Hg in the atmosphere.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".