Observational and numerical evaluation of the pedestrian-level microclimatic effect of street trees in a highly-compact city
Bibliographic record
Abstract
The application of nature-based solutions in urban areas to mitigate the harmful effects of urban overheating and to make cities more resilient to heat waves has gained the attention of city planners and researchers in the last decades. Street trees are an important driver of street microclimate through shadowing and transpiration cooling, which are key components in the improvements of thermal comfort. While several observational campaigns have been carried out in low and medium-density residential areas, little research has been focused in highly-compact city centres, where the impact of built elements on the local climate is expected to be stronger. In this context, Urban canopy models (UCM) with integrated trees are useful tools because they represent the impact of street trees on neighbourhood-scale climate, resolving the interactions between buildings, trees and the atmosphere. These models enable the assessment of outdoor human thermal exposure for diverse urban morphologies and allow the evaluation of greening scenarios. In this study, we present the results of a micrometeorological measurement campaign inside the city of Barcelona (Spain) for two cloud-free summer days. Vehicle transects were completed along two parallel streets with different tree densities but identical street geometry, recording upward and downward radiation fluxes, air temperature and humidity. Assessment of urban tree impacts on microclimate is supplemented by meteorological simulations using the multi-layer UCM Building Effect Parameterization with Trees (BEP-Tree), which considers the vertical variation of the combined impacts of vegetation and building on urban canopy layer climate. Comparing observed pedestrian level air temperatures between the two canyons, we can see that the impact of tree densities varies with the regional weather, with air temperatures up to 2.7 oC higher in the street with low tree density compared to the one with denser trees for a day with the wind direction perpendicular to the direction of the streets. The BEP-Tree simulations demonstrate good agreement with the observations in terms of temperature and radiation, and they are able to capture the different diurnal evolution of temperature and radiation between the two streets.
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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 | 0.000 | 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) | 0.001 | 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".