The Impact of Meteorological Factors and Canopy Structure on PM2.5 Dynamics Under Different Urban Functional Zones in a Subtropical City
Bibliographic record
Abstract
PM2.5 pollution has intensified with rapid urbanization and industrialization, raising concerns about its health and environmental impacts. Both meteorological factors and urban forests play crucial roles in influencing PM2.5 concentrations. However, limited attention has been given to the direct impact of canopy structure on PM2.5 levels at a larger scale. This study analyzes the temporal variation of PM2.5, including seasonal and diurnal patterns, across different functional zones (park, traffic, and residential zones) in a subtropical region. It also investigates the seasonal responses of PM2.5 to meteorological factors (temperature, humidity, and precipitation) and canopy structure characteristics, including canopy diameter (CD), canopy thickness (CT), canopy area (CA), canopy volume (CV), canopy height ratio (CH), leaf area index (LAI), and tree canopy cover (CO). The results show that among different functional zones, PM2.5 concentrations were the highest in park zones, followed by traffic zones. Seasonal variations in PM2.5 concentrations were the highest in winter (84.00 ± 45.97 μg/m3), with greater fluctuations, and the lowest in summer (36.85 ± 17.63 μg/m3 µg/m3), with smaller fluctuations. Diurnal variation followed an “N”-shaped curve in spring, summer, and autumn, while a “W”-shaped curve was observed in winter. Correlation analysis indicated significant negative correlations between PM2.5 and humidity, temperature, and rainfall, while CD, CA, and CV showed positive correlations with PM2.5. Notably, PM2.5 exhibited greater sensitivity to changes in canopy structure in winter, followed by autumn. Despite these findings, the influence of canopy structure on PM2.5 concentrations was considerably smaller compared to meteorological factors. In particular, every 1 m2 increase in canopy area could raise PM2.5 levels by 0.864 μg/m3, whereas an average increase of 1 mm in rainfall could raise PM2.5 by 13.665 μg/m3. These findings provide valuable guidance for implementing protective measures, improving air quality, optimizing urban greening strategies, and enhancing public health outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".