Evolution of Aerosols in the Atmospheric Boundary Layer and Elevated Layers during a Severe, Persistent Haze Episode in a Central China Megacity
Bibliographic record
Abstract
Aerosol vertical profiling is crucial to understand the formation mechanism and evolution processes of haze, which have not yet been comprehensively clarified. In this study, we investigated a severe, persistent haze event in Wuhan (30.5° N, 114.4° E), China during 5–18 January 2013 by the use of a polarization lidar, a Cimel sun photometer, meteorological datasets, and the hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) model, focusing on the time–height evolution of aerosols in both the atmospheric boundary layer (ABL) and elevated layers. During the haze period, the integrated particle depolarization ratio was 0.05 ± 0.02, and the fine mode fraction reached 0.91 ± 0.03, indicating haze particles were rather spherical and predominately submicron, that is, of anthropogenic nature. Compared with the clear period, columnar aerosol optical depth at 500 nm tripled to 1.32 ± 0.31, and the strongest enhancement in aerosol concentration occurred from near the ground to an altitude of 1.2 km during the haze period. The daytime evolution of aerosol vertical distribution in the ABL exhibited a distinct pattern under haze weather. Abundant particles accumulated below 0.5 km in the morning hours due to stable meteorological conditions, including a strong surface-based inversion (4.4–8.1 °C), late development (from 1000–1100 LT) of the convective boundary layer, and weak wind (<4 m∙s−1) in the lowermost troposphere. In the afternoon, improved ventilation delivered an overall reduction in boundary layer aerosols but was insufficient to eliminate haze. Particularly, the morning residual layer had an optical depth of 0.29–0.56. It influenced air quality indirectly by weakening convective activities in the morning and directly through the fumigation process around noon, suggesting it may be an important element in aerosol–ABL interactions during consecutive days with haze. Our lidar also captured the presence of the elevated aerosol layers (EALs) embodying regional/long-range transport. Most of the EALs were observed to subside to <1.2 km and exacerbate the pollution level. Backward trajectory analysis and lidar data revealed the EALs originated from the transport of anthropogenic pollutants from the Sichuan Basin, China, and of dust from the deserts in the northwest. They were estimated to contribute ~19% of columnar aerosol-loading, pointing to a non-negligible role of transport during the intense pollution episode. The results could benefit the complete understanding of aerosol–ABL interactions under haze weather and air quality forecasting and control in Wuhan.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".