Attributing Atmospheric Phosphorus in the Himalayas: Biomass Burning vs Mineral Dust
Bibliographic record
Abstract
Atmospheric phosphorus is a vital nutrient for ecosystems whose sources and fate are still debated in the fragile Himalayan region, hindering our comprehension of its local ecological impact. This study provides novel insights into atmospheric phosphorus based on the study of total suspended particulate matter at the Qomolangma station. Contrary to the prevailing assumptions, we show that biomass burning (BB), not mineral dust, dominates total dissolved phosphorus (TDP, bioavailable) deposition in this arid region, especially during spring. While total phosphorus is mainly derived from dust (77% annually), TDP is largely affected by the transport of regional biomass-burning plumes from South Asia. During BB pollution episodes, TDP causing springtime TDP fluxes alone accounts for 43% of the annual budget. This suggests that BB outweighs dust in supplying bioavailable phosphorus, a critical nutrient, required to sustain Himalayas' ecological functions. Overall, this first-hand field evidence refines the regional and global phosphorus budget by demonstrating that BB emission, while still unrecognized, is a significant source of P, even in the remote mountains of the Himalayas. It also reveals the heterogeneity of atmospheric phosphorus deposition in that region, which will help predict changes in the impacted ecosystems as the deposition patterns vary.
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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 | 0.001 |
| 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".