Trans-Himalayan Transport of Organochlorine Compounds: Three-Year Observations and Model-Based Flux Estimation
Bibliographic record
Abstract
High mountains can trap semivolatile chemicals, such as persistent organic pollutants (POPs), and hinder their dispersion. However, both deep convection and mountain valleys can facilitate POPs’ transport over mountains, which have not been investigated before. In this study, a three-year sampling campaign along a south-north altitudinal transect (100–5200m) across the central Himalayas, coupled with a multicompartment contaminant fate model, was conducted for describing the transport processes of POPs. The results show that POPs emitted in the lowlands of the Himalayas can be transported to high altitudes and further to the inner part of the Tibetan Plateau. Modeling suggests that more than 90% of POPs are trapped along the way due to gaseous deposition to soil/foliage and rainfall scavenging; while 2 × 10 –3 to 1 × 10 –1 Giga-grams/year of POPs are transported across the Himalayas. The transport flux along valleys is 2–3 times higher than that across the mountain ridge. However, due to the limited spatial coverage of mountain valleys, the amount of POPs transported through valleys only accounts for a small part of the total transport. This study shows that POPs can overcome the blocking effect of the Himalayas, and high altitude transport across the mountain ridge is the dominant transport pathway.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".