Analyzing impact of urban green infrastructure on nocturnal air temperatures using sensors and geospatial analysis: Implications for canopy-level urban heat Islands
Bibliographic record
Abstract
Canopy-level Urban Heat Island (CUHI) refers to increased nocturnal air temperatures in an urban core as compared to a nearby rural region. The intensity of this phenomenon is stated to increase due to both climate change and urban densification. Urban green infrastructure, such as green alleys, are known to ameliorate CUHI and reduce temperature in their immediate vicinity and have therefore been promoted in cities like Montreal. However, an empirical relationship between the size and placement of a green infrastructure, and its ability to reduce nocturnal CUHI is still under investigation. For this study, LoRaWAN sensors were placed across a university campus to cover a diversity of land covers and portion visibility of the open sky. The sensors themselves were inside plastic Temperature and Humidity Solar Radiation Shields, and hung at 1.2 m height. Air temperature and humidity measurements were then collected continuously at 15 minute interval for 3 months. Findings suggest that the presence of impervious surfaces surrounding the sensors does lower nighttime temperatures (measured at 1am), particularly on days with high total solar irradiance and no rainfall. On these days, the percentage of impervious surfaces and Sky View Factor (SVF) within 10 m of the sensors has a causal relationship to observed air temperatures, with an observed difference of ~1 oC. Thus, pervious surfaces reduce night-time temperatures in the immediate vicinity of the sensors (i.e., within 100 m), suggesting that green spaces lower temperatures in urban canyons and also mitigate CUHI. • Geospatial & statistical analysis of 29 Air Temperature sensor data for 7 months • Sensor temperature at 1 am depended primarily on % impervious surfaces • Temperatures were highest on days with high total solar irradiance and no rainfall • Conversely, pervious surfaces 10 m around sensors decreased temperatures ~1 o C • Hence, green infrastructure like green alleys will help reduce temperature at night
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".