Urban Heat Mitigation for Current and Future Conditions: A case study for downtown London ON
Bibliographic record
Abstract
The urban heat island is a phenomenon wherein urban areas experience warmer temperatures than their surrounding rural areas. Techniques to reduce excess heat in urban environments are known as heat mitigation or heat island mitigation solutions, with the intent that they reduce urban temperatures. This research presents an investigation on the impacts and effectiveness of urban heat mitigation techniques on improving the outdoor thermal conditions of downtown London, Ontario. The impact of increasing vegetated areas and applying higher albedo materials for road pavements is assessed with ENVI-Met software for current and future summer weather. Furthermore, investigations were conducted for current and future winter weather scenarios to explore the effects of these heat mitigation techniques on other seasons' outdoor conditions. Finally, the effects of heat mitigation strategies on building energy consumption were simulated by HAP Carrier software. Results show that increasing vegetation and trees reduce the air temperature and mean radiant temperature during both day and night periods. A higher air temperature reduction is detected for the greenery model with a higher percentage of trees relative to grasslands. The average air temperature at 17h is reduced up to 0.56°C and 0.66°C for respectively, for increasing trees and high albedo materials scenarios. Furthermore, results showed an increase in the mean radiant temperature value for the scenario with increasing albedo of the road materials. The results further demonstrated a reduction in the cooling load of buildings with increasing trees in the building neighborhoods.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".