Su, Yuan, Han, Yu, Wu, Xuezheng, Xuan, Le, Yan, Qiao, Yang, Yuteng, Li, Jie and Luo, Zhiwen  ORCID: https://orcid.org/0000-0002-2082-3958
2026.
Data-driven microclimate analysis for thermal comfort optimization in arid-hot urban blocks.
Sustainable Cities and Society
143
, 107334.
10.1016/j.scs.2026.107334
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Abstract
This research examines how climate change and urban decay impact well-being in Central Asia, focusing on Shihezi in northern Xinjiang. It assesses summertime extreme heat conditions in historic commercial areas using data from multiple sources. This study employs spatial clustering of Points of Interest (POI), field-based meteorological monitoring, subjective surveys, and microclimate modeling to evaluate of spatiotemporal variations in the outdoor thermal environment. Locally adjusted thermal comfort thresholds were established using the Universal Thermal Climate Index (UTCI) and participant feedback. The study also assesses targeted heat mitigation interventions, identifying a diurnal thermal pattern with peak heat stress occurring in the late afternoon and temperature differential of 7.6°C between shaded and unshaded areas. Of 174 participants, 66.1% reported feeling warm or hot, 62.1% still deemed the environment acceptable. A notable negative correlation emerged between thermal and radiation preferences relative to UTCI values. The calibrated neutral UTCI was 27.2°C, with an acceptable ranging from 19.4°C to 28.1°C. Simulation-based improvement scenarios indicated that substituting current trees with species possessing higher leaf area indices yielded the most effective cooling, resulting in a 4.4 °C reduction in UTCI. This study establishes a specific thermal comfort benchmark for historic commercial blocks in the hot-arid regions of Central Asia, offering a transferable, evidence-based framework for climate-resilient urban regeneration in the Eurasian arid corridor.
| Item Type: | Article |
|---|---|
| Date Type: | Publication |
| Status: | Published |
| Schools: | Schools > Architecture |
| Additional Information: | RRS policy applied |
| Publisher: | Elsevier |
| ISSN: | 2210-6707 |
| Date of First Compliant Deposit: | 2 April 2026 |
| Date of Acceptance: | 25 March 2026 |
| Last Modified: | 02 Apr 2026 11:45 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/186150 |
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