Influence of Fog-Haze on Dew Condensation in Urban Areas
Bibliographic record
Abstract
In recent years, fog-haze weather has frequently occurred in urban ecosystem, with accompanied dew condensation in the surface layer.Dew formation plays an important role in air purification, which takes fine aerosols in the atmosphere as the condensation nuclei.However, the influence of fog-haze on dew condensation remains unclear.Thus, this study aims to evaluate the influences of fog-haze days on the frequency, intensity, and duration of dew condensation, to reveal the factors relevant to dew condensation during weather pollution, and to improve the air quality in fog-haze days.The monitor-based direct weighing method was employed in this study to observe the vapour condensation in urban greenbelt under fog-haze and normal weather conditions during the frost-free season (April-October 2017) in Changchun, China.The differences in dew condensation intensity and frequency were discriminated under different weather conditions.Then, the influences of fog-haze days on the vapour condensation were analyzed by monitoring the velocity and duration of dew condensation in polluted weather.Finally, a program for improving air quality was proposed based on the analysis of major meteorological factors that affect the dew condensation during the fog-haze days.Results show that the dew condensation velocity decreases significantly (P < 0.01) from 0.0067 mm/h in normal weather to 0.0026 mm/h in a fog-haze day, whereas the condensation duration is prolonged.The dew condensation intensities are 0.064, 0.045, and 0.051 mm/d under fog, haze, and normal weather conditions, respectively, and the fog-haze weather has no significant effect on the frequency or intensity of dew condensation (P > 0.05).The particulate matter concentration, wind speed, air pressure, relative humidity, and solar radiation are the main factors that affect the vapour transport during the fog-haze days.Artificial increase of surface layer vapours can promote the particulates to absorb vapours fully and settle to the ground in the form of dew, thereby reducing the particulate matter concentration within the scope of human activities.The present study supplements the eigenvalues for dew condensation in urban areas during special pollution periods and reveals the influence of fog-haze on the night time vapour condensation in urban areas.Findings of this study provide theoretical basis for further developing the programs for controlling fog-haze weather and improving urban air quality.
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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 | 0.000 | 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".