Time-evolving Impact of Trees on Street Canyon Microclimate
Bibliographic record
Abstract
Abstract Nowadays, cities are frequently exposed to heatwaves, worsening outdoor thermal comfort and increasing cooling energy demand in summer. Urban forestry is seen as one of the viable and preferable solutions to combat extreme heat events and urban heat island (UHI) in times of climate change. While many cities have initiated tree-planting programs in recent years, the evolving impact of trees on street microclimate, in a time span of up to several decades, remains unclear. This study focuses on the cooling effects of a common type of street trees, linden trees, in five age groups, i.e., 10-20, 20-30, 30-40, 40-60, and 60-100 years old, while the diversity of trees is present in nature, that is, various species and crown geometries, The leaf area index (LAI) and leaf area density (LAD) of linden trees vary nonlinearly as they grow, peaking at different ages. Computational fluid dynamics (CFD) simulations were performed for an idealized street canyon with linden trees of various age groups. Turbulent airflow, heat and moisture transport, shortwave and longwave radiation, shading, and transpiration were fully coupled and solved in OpenFOAM. Meteorological data, including air temperature, wind speed, moisture, and shortwave radiation of the heatwave in Zurich (June 2019), were applied as boundary conditions. The results show that young trees in the age group of 10-20 years old provide little heat mitigation at the pedestrian level in an extreme heat event. Optimal heat mitigation by trees is observed for the group of 30-60 years old linden trees. Finally, the potential impact of growing trees on air ventilation in streets is evaluated.
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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 | 0.001 | 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".