Bibliographic record
Abstract
Fine particulate matter less than 2.5 microns (PM2.5) could exacerbate bronchitis and cardiac conditions.PM2.5 concentration has been globally increasing due to operation of power plants and vehicles.Recently, annual PM2.5 concentration in Seoul, Korea averaged 24.㎍ , higher than the World Health Organization (WHO)'s annual reference level of 20.0 ㎍[1].There is rising concern on urban tree planting to help reduce the level of atmospheric PM2.5 [2].However, little is known about PM2.5 reduction by urban trees in the city.The purpose of this study was to quantify annual PM2.5 reduction by street trees in Seoul and to suggest desirable planting and management strategies to improve effects of PM2.5 deposition.Data on street trees were collected on plots which were located using a stratified sampling method on aerial photographs with a scale 1:1,000 [3].Eight straight lines radiating from the center of the study city were drawn in eight different directions, and subsequently circles were drawn 40 cm apart.This study sampled a total of 50 points at which the circles and lines coincided.A survey plot for each point was established up to 80 m in length from the point and to building boundaries of both sidewalks in width.The number of samples was a compromise between the competing concerns for a large sample size and the availability of expense.Field-surveyed data included species, stem diameter, height, crown width, and density of street trees.These data were used to produce an average estimate per unit area on annual PM2.5 reduction by street trees.The PM2.5 reduction was quantified applying a dry deposition model [2,4] based on deposition velocity, total leaf area, and resuspension ratio by wind speed.Total PM2.5 reduction by street trees was computed using total street area in the study city.The structures of street trees in the study city were characterized by single-layered and single-aged planting.Mean stem diameter of street trees was 25.5 cm (at breast height of 1.2 m) and annual PM2.5 reduction per street tree averaged approximately 47.2 g/yr.Annual PM2.5 reduction per unit area by street trees was approximately 4.2 kg/ha/yr, and total PM2.5 reduction of the entire street area was about 32.8 t/yr.Total annual emissions of PM2.5 from energy consumption was about 1,300 t/yr in the study city [5].Street trees annually offset the total PM2.5 emissions by 2.5%.Thus, street trees played an important role in reducing the level of atmospheric PM2.5.This study suggested desirable planting and management strategies including multi-layered and multi-aged tree planting, supply of the space for normal crown and root growth, and avoidance of severe pruning.The results from this study are expected to contribute to internationally sharing the role and importance of urban trees in reducing PM2.5 concentration.
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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) | 0.002 | 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".