Characterizing the Effect of Increasing Albedo on Urban Meteorology and Air Quality in Cold Climates, a case study for Montreal
Bibliographic record
Abstract
The higher temperature of urban areas compared to their surrounding rural areas is called urban heat island (UHI). UHI during summer may harm inhabitants and aggravate cooling energy demand. Increasing urban albedo is proposed to counter the undesirable impacts of UHI. To analyze the effect of albedo enhancement on urban climate, land-atmosphere interactions and various physical processes in the atmosphere and on the land should be modeled. Weather Research and Forecasting (WRF) model, urban canopy model (UCM), building energy model (BEM), and chemical transport model (CHEM) are coupled, to accurately investigate the effect of an increase in the urban albedo. To select appropriate models sensitivity of near surface air temperature and near surface wind velocity to the choice of parameterization is evaluated. Montreal and Toronto, as the two most populated Canadian cities, are selected for evaluation of UCMs and an increase in the urban albedo. \nSeasonal performance of the increase in albedo of roofs, walls, and road by 0.45, 0.4, and 0.25, respectively, results in an average decrease of 0.25 °C during summer and a negligible effect during winter (<0.1 °C). The daily building energy savings of HVAC systems in summertime is about 18 Wh/100m2, while, the winter heating penalty is about 2 Wh/100m2. Considering the effect of aerosols and other pollutants, in summertime, the simulated maximum air temperature decreased by about 1 °C, near surface 8-hour average ozone concentration decreased by 0.1 ppbv, and 24-hour average PM2.5 concentration decreased by 2 μg/m3. Simulations show that increasing the urban albedo results in a reduction in summertime air temperature, leading to a lower cooling energy use and an improved outdoor air quality.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".