Effect of Plant Community Structure and Road Greenbelt Width on PM2.5 Concentration
Bibliographic record
Abstract
Road greenbelts can reduce the concentration of airborne fine particulate matter (PM 2.5 ).This effect is highly sensitive to the community structure of vegetation and greenbelt widths.To determine the optimal community structure and appropriate greenbelt width, PM 2.5 concentrations were tested in four greenbelts with arbor-shrub-grass and arbor-grass plant communities of different greenbelt widths (0, 5, 10, 15, and 20 m) in Suzhou Industrial Park.The daily change law of PM 2.5 concentration and the effects of community structure and greenbelt width on the reduction of PM 2.5 concentration were analyzed.Results demonstrated that the road greenbelts significantly reduced the PM 2.5 concentration.The PM 2.5 concentration in the road greenbelts was low in the morning and evening.At daytime, the PM 2.5 concentration in the arbor-shrub-grass community showed two peaks and one valley, and the PM 2.5 concentration in the arbor-grass community presented a single peak.The PM 2.5 reduction rate of the greenbelts significantly increased with the increase in greenbelt width.However, the reduction rate decreased gradually when the greenbelt width exceeded 15 m.The greenbelts with different community structures reduced the PM 2.5 concentration to different extents.When the greenbelt was narrow (≤ 5 m), the arbor-shrub-grass community achieved a high average PM 2.5 reduction rate.When the greenbelt was wide (5 m to 20 m), the arbor-grass community reduced the PM 2.5 concentration significantly.When the greenbelt width exceeded 20 m, the arbor-shrub-grass community with reasonable allocation reduced the PM 2.5 concentration more than the arbor-grass community did.The effects of road greenbelt width and plant community on PM 2.5 concentration were discussed simultaneously for the first time in this study.
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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".