Tree presence and level of aggregation in urban parks are associated with opposite daytime and nighttime urban cooling
Bibliographic record
Abstract
Urban parks, serving as “cool islands” for human activity in cities, play an important role among various urban green space types for regulating temperature. Effective management of green infrastructure within parks is essential for urban park design and planning. While effects of vegetation configuration on daytime air temperature are increasingly well studied, the specific effects of tree configuration on nighttime temperatures are less well understood, despite their importance for human health. Indices more relevant to human health (such as wet bulb temperature) also remain underexplored in the urban forestry literature. Our research aims to address the following question: Do different tree planting configurations (discrete planting versus aggregate planting) within parks affect daytime and nighttime air temperature, relative humidity, and wet-bulb temperature differently? We sampled air temperature and humidity in open grass areas, discrete tree clusters, and aggregate tree clusters in a series of 13 urban parks in Montreal, Canada. Using mixed-effect models and ordered contrasts, we analyzed the impacts of tree configuration and time of day on park microclimate (air temperature and wet-bulb temperature). Our results indicated that aggregate tree clusters had stronger cooling effects than discrete tree clusters during daytime but inhibit heat dissipation at night. Our results also emphasize the importance of open grass areas for park cooling during nighttime. Relative humidity exhibited an inverse pattern compared to air temperature, moderating wet-bulb temperature differences among tree configurations, highlighting the importance of considering both temperature and humidity in urban greening studies. A park that meets visitors’ needs for temperature mitigation at different times of day should be structurally diverse, with a balance of dispersed, mature trees and open grasslands to optimize both daytime and nighttime thermal comfort. This study focuses on Montreal (a large city with a warm-summer humid continental climate) with potential to generalize our results to studies within similar climatic zones, as well as to generalize our study approach to cities more broadly. • Open grass areas within parks have stronger nighttime cooling potential than treed areas • Aggregate tree clusters cool the ambient air during the day but trap heat at night • Relative humidity exhibited an inverse pattern compared to air temperature • Humidity moderates urban cooling, with reduced variation in wet-bulb temperature vs air temperature
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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 | 0.001 |
| 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".