Impact of the Tibetan Topography on Downwind Spatial Distribution of Fine Particulate Matter in Winter
Bibliographic record
Abstract
Abstract The Tibetan Plateau (TP) with a large landmass serves as an obstacle that hinders westerly flows and alters climate downwind. Here, we investigate the TP influence on the magnitude and spatial distribution of wintertime fine particulate matter (PM2.5) concentrations downwind and associated underlying mechanisms. Based on simulations using an Earth system model, we show that the removal of the TP would reduce surface PM2.5 concentrations by −30.4% in the Sichuan basin (SC) and by −12.4% in the North China Plain (NCP), but increase the concentrations by 18.1% in eastern China (EC), suggesting that the TP could naturally intensify PM2.5 pollution in SC and NCP. If the TP were absent, more meridional circulations would turn into zonal ones and the East Asian winter monsoon would become weaker. There would be less precipitation and lower humidity over SC and EC in the south, while the opposite occurs over NCP in the north. Consequently, the changes in circulations would result in a net outflow of PM2.5 from SC and NCP, but a net inflow of PM2.5 to EC. In response to the spatial changes in precipitation, wet deposition would decrease in SC and EC but increase in NCP. PM2.5 production would reduce in SC and EC but amplify in NCP, following the changes in humidity. In magnitude, the changes in transport and wet deposition would be dominant in SC and NCP, while in EC, transport, wet deposition, and chemical production would be equally important. This study illustrates significant and heterogeneous impacts of the TP on air quality downwind.
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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 | 0.000 | 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) | 0.002 | 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".