Wildfire-Derived Nitrogen Aerosols Threaten the Fragile Ecosystem in Himalayas and Tibetan Plateau
Bibliographic record
Abstract
Himalayas and Tibetan Plateau (HTP) is important for global biodiversity and regional sustainable development. While numerous studies have revealed that the ecosystem in this unique and pristine region is changing, their exact causes are still poorly understood. Here, we present a year-round (23 March 2017 to 19 March 2018) ground- and satellite-based atmospheric observation at the Qomolangma monitoring station (QOMS, 4276 m a.s.l.). Based on a comprehensive chemical and stable isotope ( 15 N) analysis of nitrogen compounds and satellite observations, we provide unequivocal evidence that wildfire emissions in South Asia can come across the Himalayas and threaten the HTP’s ecosystem. Such wildfire episodes, mostly occurring in spring (March–April), not only substantially enhanced the aerosol nitrogen concentration but also altered its composition (i.e., rendering it more bioavailable). We estimated a nitrogen deposition flux at QOMS of ∼10 kg N ha –1 yr –1, which is approximately twice the lower value of the critical load range reported for the Alpine ecosystem. Such adverse impact is particularly concerning, given the anticipated increase of wildfire activities in the future under climate change.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".