Assessment of heat exposure and cooling efficiency by trees in urban microclimate analysis
Bibliographic record
Abstract
The urban microclimate model, urbanMicroclimateFoam developed by the authors, is used to simulate pedestrian thermal comfort in an urban park and adjacent building neighborhood in tropical Singapore during a hot and humid period. The thermal comfort is assessed using the Universal Thermal Climate Index (UTCI) which depends on air temperature, relative humidity, wind speed and mean radiant temperature.Heat exposure of pedestrians is defined as the accumulated UTCI above a given threshold for a considered time, referred to as UTCI-degree hours. A weighting factor based on dynamic thermal sensation is introduced to account for higher heat exposure at high UTCI. This approach captures human thermal perception across varying periods, offering a comprehensive assessment of thermal stress. The cooling efficiency of vegetation as a heat mitigation measure is quantified as the ratio of heat exposure between a vegetated configuration and a reference non-vegetated configuration.The study reveals that unshaded areas can experience heat exposure up to 700°C·h, while shaded zones exhibit significantly lower values around 450°C·h over a day. The present neighborhood configuration with trees in the park achieves cooling efficiencies of up to 40% in tree-covered areas. However, the results also highlight non-local effects, where unshaded zones in between the trees can heat up by as much as 25% due to the wind blocking and the increased humidity resultsing from the transpiration by trees. Among individual mitigation strategies, larger, densely placed trees, such as those in parks, are shown to be the most effective in improving cooling efficiency.The study suggests that heat exposure and cooling efficiency metrics should incorporate activity maps to prioritize heat mitigation in high-activity zones while tolerating negative effects in less critical areas, enabling targeted urban cooling solutions.
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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 | 0.001 |
| 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) | 0.001 | 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".