Modeling the Thermal Effects of Artificial Turf on the Urban Environment
Bibliographic record
Abstract
Abstract The effects of artificial turf (AT) on the urban canopy layer energy balance, air and surface temperatures, and building cooling loads are compared to those of other common ground surface materials (asphalt, concrete, and grass) through heat transfer modeling of radiation, convection, and conduction. The authors apply the Temperatures of Urban Facets in 3D (TUF3D) model—modified to account for latent heat fluxes—to a clear summer day at a latitude of 33° over a typical coastal suburban area in Southern California. The low albedo of artificial turf relative to the other materials under investigation results in a reduction in shortwave radiation incident on nearby building walls and an approximately equal increase in longwave radiation. Consequently, building walls remain at a relatively cool temperature that is similar to those that are adjacent to irrigated grass surfaces. Using a simple offline convection model, replacing grass ground cover with artificial turf was found to add 2.3 kW h m−2 day−1 of heat to the atmosphere, which could result in urban air temperature increases of up to 4°C. Local effects of AT on building design cooling loads were estimated. The increased canopy air temperatures with AT increase heat conduction through the building envelope and ventilation in comparison with a building near irrigated grass. However, in this temperate climate these loads are small relative to the reduction in radiative cooling load through windows. Consequently, overall building design cooling loads near AT decrease by 15%–20%. In addition, the irrigation water conservation with AT causes an embodied energy savings of 10 W h m−2 day−1. Locally, this study points to a win–win situation for AT use for urban landscaping as it results in water and energy conservation.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".