Investigating urban and rural heat risk over long time-series: A case study of Beijing-Tianjin-Hebei urban agglomeration in China DOI
Xu Wang, Boyu Li,

Lei Yao

et al.

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: unknown, P. 106081 - 106081

Published: Dec. 1, 2024

Language: Английский

Impacts of Land Use Characteristics on Extreme Heat Events: Insights from Explainable Machine Learning Model DOI
Hangying Su, Zhuoxu Qi,

Q. Wang

et al.

Sustainable Cities and Society, Journal Year: 2025, Volume and Issue: unknown, P. 106139 - 106139

Published: Jan. 1, 2025

Language: Английский

Citations

5
Spatiotemporal dynamic mapping of heat exposure risk for different populations in city based on hourly multi-source data DOI
Junmao Zhang,

Xia Yao,

Yuan Chen

et al.

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: 107, P. 105454 - 105454

Published: April 18, 2024

Language: Английский

Citations

8
The cooling capacity of urban vegetation and its driving force under extreme hot weather: A comparative study between dry-hot and humid-hot cities DOI
Zhibin Ren,

Chengcong Wang,

Yüjie Guo

et al.

Building and Environment, Journal Year: 2024, Volume and Issue: 263, P. 111901 - 111901

Published: July 30, 2024

Language: Английский

Citations

8
Urbanization-induced warming amplifies population exposure to compound heatwaves but narrows exposure inequality between global North and South cities DOI Creative Commons
Shengjun Gao, Yunhao Chen, Deliang Chen

et al.

npj Climate and Atmospheric Science, Journal Year: 2024, Volume and Issue: 7(1)

Published: July 1, 2024

Abstract Urban populations face heightened extreme heat risks attributed to urban islands and high population densities. Although previous studies have examined global exposure heatwaves, the influence of urbanization-induced warming is still not quantified. Here, leveraging satellite-derived near-surface air temperature data, we assess impacts on in 1028 cities worldwide. Additionally, investigate its role shaping disparities between North South cities. Our findings reveal that urbanization-amplified compound heatwaves exacerbate risk more than 90% cities, this amplification stronger urbanization areas. Moreover, our analysis highlights potential for overestimating if overlooked. The inequality higher will be narrowed real scenarios due intense We emphasize pivotal heatwave intensification assessments call inclusion future vulnerability evaluations heat.

Language: Английский

Citations

7
Microclimate vision: Multimodal prediction of climatic parameters using street-level and satellite imagery DOI Creative Commons
Kunihiko Fujiwara, Maxim Khomiakov, Winston Yap

et al.

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: 114, P. 105733 - 105733

Published: Aug. 14, 2024

Language: Английский

Citations

6
Unraveling the Global Economic and Mortality Effects of Rising Urban Heat Island Intensity DOI
Yuan Yuan, Xiao Li, Huijuan Wang

et al.

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: 116, P. 105902 - 105902

Published: Oct. 13, 2024

Language: Английский

Citations

6
Spatiotemporal analysis of surface Urban Heat Island intensity and the role of vegetation in six major Pakistani cities DOI Creative Commons
Shoaib Ahmad Anees, Kaleem Mehmood, S. K. Raza

et al.

Ecological Informatics, Journal Year: 2024, Volume and Issue: unknown, P. 102986 - 102986

Published: Dec. 1, 2024

Language: Английский

Citations

6
Exploring intra-urban thermal stress vulnerability within 15-minute city concept: Example of heat waves 2021 in Moscow DOI
Natalia Shartova,

E. A. Mironova,

Mikhail Varentsov

et al.

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: 114, P. 105729 - 105729

Published: Aug. 4, 2024

Language: Английский

Citations

5
Artificial intelligence for predicting urban heat island effect and optimising land use/land cover for mitigation: Prospects and recent advancements DOI Creative Commons

Omar Y. Mohamed,

Izni Zahidi

Urban Climate, Journal Year: 2024, Volume and Issue: 55, P. 101976 - 101976

Published: May 1, 2024

Rocketing global urbanisation has caused an increase in the Urban Heat Island (UHI) effect, resulting various negative implications for urban environment. Quantifying Surface UHI (SUHI) effect using Land Temperature (LST), Local Climate Zones (LCZ), and deep learning algorithms such as Convolutional Neural Networks (CNN) pix2pix have prospects aiding sustainable city planning modification. Most research on mitigating SUHI promotes greenery a solution, allowing LCZ optimisation to be explored. Using Vulnerability Index (HVI) evolutionary like Genetic Algorithms (GA) Particle Swarm Optimisation (PSO) show promise achieving high-quality solutions. This short communication explores potential of these artificial intelligence technologies combat enhance sustainability.

Language: Английский

Citations

4
The spatiotemporal patterns and regional characteristics of extreme heat disaster risk in China at the county level DOI

Dianyuan Zheng,

Xiaojun Huang, Wenze Yue

et al.

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 474, P. 143605 - 143605

Published: Sept. 7, 2024

Language: Английский

Citations

4