Effect of Plant Community Structure and Road Greenbelt Width on PM2.5 Concentration
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.002 | 0.001 |
| 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 | 0.001 |
| 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 it