Abstract
The nighttime thermal environment affects people's nighttime leisure activities and energy consumption. While increasing studies have examined the interplay between urban morphologies and daytime temperature, a gap exists in understanding the nonlinear relationship in the nighttime thermal environment. To address this, the study employs a data-driven ensemble model to downscale Moderate Resolution Imaging Spectroradiometer (MODIS) thermal environment data for four seasons, aligning it with the scale of urban morphology. Furthermore, an additive interpretation algorithm investigates the impact of nonlinear factors shaping the urban thermal environment during nighttime. The findings reveal that in contrast to daytime temperatures, built environment variables exert a more pronounced effect on nighttime surface temperatures than natural variables. Specifically, plot ratio and building height are the greatest contributors to the warming observed across all seasons. On the other hand, increasing the sky view factor of streets proves to be an effective strategy for mitigating nighttime temperatures. Overall, our study sheds new light on the complex interplay between urbanization and the nighttime thermal environment, assisting planners in understanding how the built environment affects the urban temperature and sustainable development path.
Original language | English |
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Article number | 105176 |
Journal | Sustainable Cities and Society |
Volume | 101 |
DOIs | |
Publication status | Published - Feb 2024 |
Keywords
- Land surface temperature
- Machine learning
- Nighttime thermal environment
- Nonlinear relationship
- Urban morphology
ASJC Scopus subject areas
- Geography, Planning and Development
- Civil and Structural Engineering
- Renewable Energy, Sustainability and the Environment
- Transportation