Impact of tree growth form on temporal and spatial patterns of particulate matter with various particle sizes in urban street canyons
Bibliographic record
Abstract
Trees play a vital role in reducing street-level particulate matter (PM) pollution in metropolitan areas. However, the optimal tree growth type for maximizing the retention of various sizes of PM remains uncertain. This study assessed the PM reduction capabilities of evergreen and deciduous broadleaf street trees, focusing on how leaf phenology influences the dispersion of pollutants across particle sizes. We collected data on six PM size fractions from 72 sites along streets lined with either evergreen or deciduous broadleaf trees in Wuhan, China, during the summer and winter of 2017–2018. Evergreen trees demonstrated superior PM reduction capabilities compared to deciduous trees, with evergreen street canyons showing 27.2% and 12.6% lower PM 2.5 and PM 10 concentrations in summer, and 13% and 5.5% lower concentrations in winter. During summer, evergreen streets predominantly contained fine particles (PM 1 , PM 2.5 ), posing potential health risk due to their ability to infiltrate the human respiratory system. In contrast, deciduous streets primarily harbored coarser particles (PM 4 , PM 7 , PM 10 , and total suspended particulate [TSP]). During winter, larger particles were dominant, regardless of the tree growth form. Evergreen trees showed superior PM reduction capabilities compared to deciduous trees due to their year-round leaf retention, enhanced surface properties, and denser canopies that maximize PM capture. We recommend prioritizing evergreen broadleaf trees as the primary street trees while interspersing deciduous trees at appropriate intervals. This approach will ensure that urban greenery provides maximum ecological benefits while reducing the PM concentration.
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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".