Climate-dependent impact of vegetation on thermal comfort in urban neighborhoods through resolved CFD simulations
Bibliographic record
Abstract
Given the urban heat island effect and extreme events induced by climate change, studying the urban microclimate and mitigation strategies is essential, particularly to enhance pedestrian thermal comfort.Vegetation has demonstrated its effectiveness in mitigating urban heat at local scale but its effect on thermal comfort has still to be accurately studied across various climates and urban configurations. Vegetation improves pedestrian thermal comfort through shading and evapotranspiration. However, vegetation can deteriorate thermal comfort by increasing the relative humidity and impeding wind flow and nocturnal heat removal.In this study, the urban microclimate is simulated using the high-fidelity CFD model urbanMicroclimateFoam based on OpenFOAM. This solver solves successively turbulent air flow, heat and moisture transport in solid materials, and radiation exchanges. Environmental boundary conditions are dynamically downscaled from Weather Research and Forecasting (WRF) mesoscale results. The impact of vegetation is analyzed across three distinct climates: a continental climate in Montreal, Canada, a tropical climate in Singapore, and an arid climate in Morocco. Trees yield local improvements in all three cases, while the highest cooling potential is observed for the arid climate context. Conversely, in tropical climate, vegetation air cooling is offset by the increase in humidity, resulting in a reduced thermal comfort impact. On a daytime average, vegetation in Montreal reduces UTCI locally by up to 6°C, with a non-local adverse heating effect of 2°C. In Singapore, the local cooling effect evaluated reaches 5°C UTCI, with non-local increases up to 3°C, while in Morocco, vegetation achieves a local improvement of UTCI by 7°C, with non-local adverse effects limited to 1°C. The primary factor contributing to pedestrian thermal comfort improvement in all climates is shading provided by trees.Urban planners and stakeholders can integrate such valuable insights to harness the benefits and challenges of vegetation across diverse climatic contexts.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".