Formation and Evolution Mechanisms for Two Extreme Haze Episodes in the Yangtze River Delta Region of China During Winter 2016
Bibliographic record
Abstract
Abstract Severe haze pollution frequently occurred in China during winter. Mechanisms for the formation and evolution of high PM2.5 (particulate matter with aerodynamic diameter of 2.5 μm or less) episodes, however, remain poorly understood. We characterize two extreme haze episodes in the Yangtze River Delta region of China from 1 to 9 December (Episode I) and 19 to 24 December (Episode II) in 2016 using comprehensive measurements and model analyses. The aqueous sulfur dioxide (SO2) oxidation catalyzed by mineral ions and the heterogeneous uptakes of SO2, sulfuric acid (H2SO4), nitrogen dioxide (NO2), nitrogen trioxide (NO3), nitrogen pentoxide (N2O5), and nitric acid (HNO3) on mineral aerosols are included in the model to better represent the formation of sulfate‐nitrate‐ammonium. The optimized mechanisms substantially improve the simulations of PM2.5 composition, particularly for sulfate and nitrate. The two episodes show different synoptic conditions and evolution stages, with gradual PM2.5 increase under stagnant weather conditions in Episode I (Stage I: Slow Increase Stage, Stage II: Rapid Formation Stage, and Stage III: Dissipation Stage) and with explosive PM2.5 increase mostly associated with cross‐border transport from North China in Episode II (Stage I′: Clean Stage, Stage II′: Fast Transport Stage, and Stage III′: Clear Stage). The concentrations of sulfate‐nitrate‐ammonium increased evidently and became the key components of PM2.5 during haze episodes. The heterogeneous conversion from SO2 to sulfate on mineral aerosols is the main reason for sulfate increase, accounting for more than 50% of sulfate production. This study provides a better understanding of the causes for winter haze in China.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".