Isotopic compositions of atmospheric total gaseous mercury in ten Chinese cities and implications for land surface emissions
Bibliographic record
Abstract
Abstract. Land surface emissions are an important source of atmospheric total gaseous mercury (TGM), however, its role on the variations of TGM isotopic compositions and concentrations has not been properly evaluated. In this study, TGM isotope compositions, a powerful tracer for sources and transformation of Hg, were measured at ten urban sites and one rural site in China. TGM concentrations were higher in summer than in winter in most cities except in Guiyang and Guangzhou in the low latitudes. The summertime high TGM concentrations were coincided with prevailing low TGM δ202Hg and high TGM Δ199Hg signatures. These seasonal patterns were in contrast with those typically observed in rural areas in the Northern Hemisphere, suggesting atmospheric oxidation chemistry, vegetation activity as well as residential coal combustion were not likely the dominant mechanisms contributing to the TGM concentration and isotopic composition seasonality in Chinese cities. The amplitudes of seasonal variations in TGM concentrations and Δ199Hg (or TGM δ202Hg) were significantly positively (or negatively) correlated with that of the simulated soil GEM emission flux. These results suggest that the seasonal variations in TGM isotopic compositions and concentrations in the ten Chinese cities were likely controlled by land surface emissions that were observed or reported with highly negative δ202Hg signatures.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".