Assessing Sources and Distribution of Heavy Metals in Environmental Media of the Tibetan Plateau: A Critical Review
Bibliographic record
Abstract
With a unique multi-sphere environmental system, the Tibetan Plateau (TP) plays an essential role in the ecological sheltering function for China and other parts of Asia. However, black carbon, persistent organic pollutants, and heavy metals (HMs) have been increased dramatically since the 1950s, reflecting rising emissions in Asia. In this context, the sources and distribution of HMs were summarized in the environment media of the TP. The results showed that 1) HMs in the TP may be generated from geogenic/pedogenic associations (Cu, Cr, Ni, As, and Co) and anthropogenic activities of local or long-distance atmospheric transmission (Cd, Pb, Zn, and Hg). 2) The atmospheric transport emission sources of HMs are mainly from the surrounding heavily-polluted regions by the Indian and East Asian monsoons and the southern branch of westerly winds. 3) Soil, water, snow, glacier, sediment, and vegetation act as vital sinks of atmospheric deposits of HMs; 4) Significant bioaccumulation of arsenic (As), lead (Pb), and methylmercury (MeHg) have been found in terrestrial and aquatic biota chains in the TP; 5) The enhancement of anthropogenic activities, climate change, glacial retreat and permafrost degradation had potential impacts on the behaviors and fates of HMs in the TP. Therefore, the ecological risk of HMs is of particular concern, and feasible and effective environmental safety strategies are required to reduce the adverse effects of inorganic pollutants in the TP. Our review will provide a reference for researchers to further study regional HMs pollution around the TP.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".