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Record W4408293929 · doi:10.3390/f16030479

The Impact of Meteorological Factors and Canopy Structure on PM2.5 Dynamics Under Different Urban Functional Zones in a Subtropical City

2025· article· en· W4408293929 on OpenAlexaff
Yichen Huang, Yue Cai, Jiejie Jiao, Chunyu Pan, Guangyu Wang, Chong Li, Zhihao Chen, Yufeng Zhou, Guomo Zhou

Bibliographic record

VenueForests · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsUniversity of British Columbia
FundersNational Natural Science Foundation of China
KeywordsSubtropicsCanopyEnvironmental scienceAtmospheric sciencesGeographyClimatologyMeteorologyEcologyBiologyGeology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.964

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.028
GPT teacher head0.304
Teacher spread0.276 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2025
Admission routes1
Has abstractyes

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