Contrasting frequency of global canopy and surface urban heat Island

IF 12 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Sustainable Cities and Society Pub Date : 2025-10-01 DOI:10.1016/j.scs.2025.106857

Huyan Fu , Jianghai Wen , Zihan Liu , Jiufeng Li , Jiaxi Li , Zhiru Chen

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Abstract

The frequency of urban heat island (UHI) is a critical metric for assessing the temporal dynamics of urban heat islands and holds significant value for mitigation strategies. The investigation of the frequency of canopy UHI and surface UHI (CUHIF and SUHIF) is of great importance, as it represents different aspects of urban thermal environments. However, studies examining global CUHIF and SUHIF variations remain limited, and the mechanisms and relative contributions of various driving factors to these two types of heat island frequencies are poorly understood. This study calculated CUHIF and SUHIF for 1162 cities worldwide using global near-surface air temperature (

T_{a}

) and seamless land surface temperature (LST) datasets. The UHI frequency calculation thresholds were defined using global mean UHI intensity, specifically 0.5 K for canopy UHI and 1.0 K for surface UHI. The contributions of background climate (BGC), surface properties (SUP), and overall urban metric (OUM) were evaluated using the LightGBM model and SHAP algorithm. Results show that global daytime CUHIF and SUHIF are 72 and 217 days, respectively, whereas the nighttime values are 92 and 161 days. The tropical regions exhibit the highest daytime CUHIF and SUHIF (91 and 298 days), while arid regions have the highest nighttime values (135 and 200 days). The background climate exerts a dominant influence on daytime CUHIF and SUHIF (38% and 45%, respectively), while nighttime UHI frequencies are influenced by a combination of background climate and surface properties (52% and 36%, respectively). Meanwhile, CUHIF and SUHIF reach their annual maximums in summer and minima in winter. These findings enhance understanding of UHI frequency patterns and drivers, providing scientific evidence for mitigation strategies.

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全球冠层和地表城市热岛频率的对比

城市热岛频率是评估城市热岛时间动态的关键指标，对缓解策略具有重要价值。冠层热岛频率和地表热岛频率（CUHIF和SUHIF）的研究具有重要意义，因为它代表了城市热环境的不同方面。然而，关于全球CUHIF和SUHIF变化的研究仍然有限，各种驱动因素对这两种类型热岛频率的机制和相对贡献知之甚少。本研究利用全球近地表气温（Ta）和无缝地表温度（LST）数据集计算了全球1162个城市的CUHIF和SUHIF。利用全球平均热岛强度定义热岛频率计算阈值，即冠层热岛强度为0.5 K，地表热岛强度为1.0 K。利用LightGBM模型和SHAP算法对背景气候（BGC）、地表性质（SUP）和总体城市度量（OUM）的贡献进行了评估。结果表明，全球白天的CUHIF和SUHIF分别为72天和217天，而夜间的CUHIF分别为92天和161天。热带地区白天CUHIF和SUHIF最高（91天和298天），而干旱地区夜间CUHIF最高（135天和200天）。背景气候对白天高温热流频率和高温热流频率的影响占主导地位（分别为38%和45%），而夜间热流频率受背景气候和地表特性的综合影响（分别为52%和36%）。CUHIF和SUHIF在夏季达到最大值，在冬季达到最小值。这些发现加强了对热岛热岛频率模式和驱动因素的了解，为缓解战略提供了科学证据。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Sustainable Cities and Society Social Sciences-Geography, Planning and Development

CiteScore

22.00

自引率

13.70%

发文量

810

审稿时长

27 days

期刊介绍： Sustainable Cities and Society (SCS) is an international journal that focuses on fundamental and applied research to promote environmentally sustainable and socially resilient cities. The journal welcomes cross-cutting, multi-disciplinary research in various areas, including: 1. Smart cities and resilient environments; 2. Alternative/clean energy sources, energy distribution, distributed energy generation, and energy demand reduction/management; 3. Monitoring and improving air quality in built environment and cities (e.g., healthy built environment and air quality management); 4. Energy efficient, low/zero carbon, and green buildings/communities; 5. Climate change mitigation and adaptation in urban environments; 6. Green infrastructure and BMPs; 7. Environmental Footprint accounting and management; 8. Urban agriculture and forestry; 9. ICT, smart grid and intelligent infrastructure; 10. Urban design/planning, regulations, legislation, certification, economics, and policy; 11. Social aspects, impacts and resiliency of cities; 12. Behavior monitoring, analysis and change within urban communities; 13. Health monitoring and improvement; 14. Nexus issues related to sustainable cities and societies; 15. Smart city governance; 16. Decision Support Systems for trade-off and uncertainty analysis for improved management of cities and society; 17. Big data, machine learning, and artificial intelligence applications and case studies; 18. Critical infrastructure protection, including security, privacy, forensics, and reliability issues of cyber-physical systems. 19. Water footprint reduction and urban water distribution, harvesting, treatment, reuse and management; 20. Waste reduction and recycling; 21. Wastewater collection, treatment and recycling; 22. Smart, clean and healthy transportation systems and infrastructure;