Trace mercury migration and human exposure in typical mercury-emission areas by compound-specific stable isotope analysis
Bibliographic record
Abstract
Anthropogenic mercury (Hg) emissions have increased significantly since the Industrial Revolution, resulting in severe health impacts to humans. The consumptions of fish and rice were primary human methylmercury (MeHg) exposure pathways in Asia. However, the lifecycle from anthropogenic Hg emissions to human MeHg exposure is not fully understood. In this study, a recently developed approach, termed MeHg Compound-Specific Isotope Analysis (CSIA), was employed to track lifecycle of Hg in four typical Hg-emission areas. Distinct Δ199Hg of MeHg and inorganic Hg (IHg) were observed among rice, fish and hair. The Δ199Hg of MeHg averaged at 0.07 ± 0.15 ‰, 0.80 ± 0.55 ‰ and 0.43 ± 0.29 ‰ in rice, fish and hair, respectively, while those of IHg averaged at − 0.08 ± 0.24 ‰, 0.85 ± 0.43 ‰ and − 0.28 ± 0.68 ‰. In paddy ecosystem, Δ199Hg of MeHg in rice showed slightly positive shifts (∼0.2 ‰) from those of IHg, and comparable Δ199Hg of IHg between rice grain and raw/processed materials (coal, Hg ore, gold ore and sphalerite) were observed. Simultaneously, it was proved that IHg in fish muscle was partially derived from in vivo demethylation of MeHg. By a binary model, we estimated the relative contributions of rice consumption to human MeHg exposure to be 84 ± 14 %, 58 ± 26 %, 52 ± 20 % and 34 ± 15 % on average in Hg mining area, gold mining area, zinc smelting area and coal-fired power plant area, respectively, and positive shifts of δ202HgMeHg from fish/rice to human hair occurred during human metabolic processes. Therefore, the CSIA approach can be an effective tool for tracking Hg biogeochemical cycle and human exposure, from which new scientific knowledge can be generated to support Hg pollution control policies and to protect human health.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.006 | 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 teacher head, 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".