Impact of trees on thermal comfort in adjacent park and neighborhood in hot-humid climate: A CFD study
Bibliographic record
Abstract
We study the interactions between a park and a residential neighborhood in Singapore with high-fidelity microclimate simulations using Computational Fluid Dynamics (CFD). We reveal the broader spatial influence of trees, with cooling effects extending over distances of up to 100 m, though occasionally accompanied by unintended warming zones. Multifaceted effects of trees include the immediate, localized cooling effect in the planted zone, primarily driven by shading, and a variety of non-local effects influenced by air temperature, relative humidity, and wind speed. Results for this case study reveal that trees can significantly reduce values of the Universal Thermal Climate Index (UTCI), improving thermal comfort levels by up to 10 °C. However, trees can also cause non-local heating effects, increasing UTCI by up to 5 °C in unshaded areas within the park during peak conditions. UTCI reduction mainly comes from the shading effect, as the cooling effect of air temperature reduction is nearly offset by an increase in relative humidity. Wind sheltering caused by trees has a consistent minor negative impact of around +0.5 °C UTCI. We also study the interplay of trees with the presence of open space under lift-up buildings. We show that such nuanced understanding of microclimatic dynamics is essential to correctly plan mitigation strategies within hot-humid climates, emphasizing the importance and need of high-fidelity urban studies. These findings underscore the positive and negative impacts of vegetation on urban thermal comfort and highlight the need for advanced heat exposure indices to accurately assess the effectiveness of heat mitigation strategies.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".