Systematic Simulation Method to Quantify and Control Pedestrian Comfort and Exposure during Urban Heat Island
Bibliographic record
Abstract
An urban heat island (UHI) originates with the increase of energy consumption and deforestation within urban areas. In addition to heat related illness and energy consumption increase, the UHI also has a mutual effect on pollution dispersion, mostly emitted from vehicular and industrial sources. Many cities recently started to apply mitigation protocols by increasing tree planting and vegetation inside urban areas. A few cities also promoted higher-albedo materials for urban surfaces. Moreover, guidelines are developed to design an appropriate street canyon and building layout to naturally ventilate urban areas. However, the UHI intensity varies in different street canyons and climates. Thus, the aforementioned mitigation technologies are not always practical or economical to reduce energy consumption and keep pedestrian comfort and exposure (PCE) in the desired range. The main goal of this research is to propose a systematic approach, PCE-algorithm, to quantify the level of PCE inside a street canyon before and after its construction. This approach is also capable of evaluating the possible advantages of passive mitigation strategies using a frequency of occurrence concept. This concept assesses the probability iv of having acceptable comfort indices within the street canyon. For this purpose, a computational fluid dynamics (CFD) model is defined around the investigated street canyon. This model simulates the significant contributing parameters on UHI formation, including solar radiation, storage heat, latent heat, and sensible heat. Moreover, an adaptive novel strategy, pedestrian ventilation system (PVS), is proposed in this research to control PCE of the target street canyon. Similar to the function of a building mechanical ventilation system, the PVS interactively controls PCE in outdoor spaces. The PVS employs exhausting and/or supplying fans installed in adjacent buildings of the street canyon in order to achieve an acceptable PCE, especially when passive strategies fail to have a considerable effect. A case study of a street canyon, located in Montreal, is also considered to investigate the performance of the proposed algorithm. After an evaluation of PCE, the effect of the passive mitigation strategies is investigated. Furthermore, it is shown that the PVS can control and improve PCE, especially where severe UHI occurs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".