Spatial and temporal dynamics of urban heat environment at the township scale: A case study in Jinan city, China DOI Creative Commons
Dongchao Wang,

Jianfei Cao,

Baolei Zhang

et al.

PLoS ONE, Journal Year: 2024, Volume and Issue: 19(9), P. e0307711 - e0307711

Published: Sept. 16, 2024

The prolonged dependence on industrial development has accentuated the cumulative effects of pollutants. Simultaneously, influenced by land construction activities and green space depletion, Urban Heat Island (UHI) effect in cities intensified year year, jeopardizing foundation sustainable urban development. Prudent spatial planning holds potential to robustly ameliorate persistent deterioration UHI phenomenon. This study selects Jinan City as a case employs autocorrelation regression algorithms explore spatiotemporal evolution urban-rural patterns at township scale. aim is identify key factors driving differentiation Land Surface Temperature (LST) from 2013 2022. research reveals trend initially rising subsequently falling LST various townships, with low-temperature concentration areas southern mountainous region northern plain area. "West-Central-East" main axis southeast Laiwu District exhibit high-temperature zones. Significant influences are attributed pollution levels, topographical factors, urbanization greenness. global Moran's Index for exceeds 0.7, indicating strong positive correlation. Cluster analysis results indicate High-High (HH) clustering central Shizhong Low-Low (LL) Shanghe County. Multiscale Geographically Weighted Regression (MGWR) outperforms (GWR) Ordinary Linear (OLR), providing more accurate reflection relationships between variables. By investigating its scale, this contributes insights future

Language: Английский

Data-driven real-time visualization of urban heat islands using mean radiant temperature for urban design DOI

Zahra Rashtian,

Mohammad Tabatabaei Manesh, Mohammad Tahsildoost

et al.

Energy and Buildings, Journal Year: 2025, Volume and Issue: unknown, P. 115470 - 115470

Published: Feb. 1, 2025

Language: Английский

Citations

1
A global urban heat island intensity dataset: Generation, comparison, and analysis DOI
Qiquan Yang, Yi Xu,

Tirthankar Chakraborty

et al.

Remote Sensing of Environment, Journal Year: 2024, Volume and Issue: 312, P. 114343 - 114343

Published: July 30, 2024

Language: Английский

Citations

5
Estimation of the surface urban heat island intensity across 1031 global cities using the regression-modification-estimation (RME) method DOI
Rui Yao, Xin Huang, Yongjun Zhang

et al.

Journal of Cleaner Production, Journal Year: 2023, Volume and Issue: 434, P. 140231 - 140231

Published: Dec. 20, 2023

Language: Английский

Citations

12
Comprehensive assessments of a diagnostic equation for estimating diurnally maximum canopy urban heat island intensity DOI
Yihan Gao, Wenfeng Zhan,

Huilin Du

et al.

Building and Environment, Journal Year: 2025, Volume and Issue: unknown, P. 112618 - 112618

Published: Jan. 1, 2025

Language: Английский

Citations

0
Examining the Spatiotemporal Dynamics of Urban Heat Island and Its Impact on Air Pollution in Thailand DOI Creative Commons
Veeranun Songsom,

Pawarit Jaruk,

Thongchai Suteerasak

et al.

Environmental Challenges, Journal Year: 2025, Volume and Issue: unknown, P. 101120 - 101120

Published: March 1, 2025

Language: Английский

Citations

0
Advancing Urban Microclimate Monitoring: The Development of an Environmental Data Measurement Station Using a Low-Tech Approach DOI Open Access
Alexandre Lefevre, Bruno Malet-Damour, Harry Boyer

et al.

Sustainability, Journal Year: 2024, Volume and Issue: 16(7), P. 3093 - 3093

Published: April 8, 2024

Researchers studying urban climates aim to understand phenomena like heat islands (UHIs), which describe temperature differences between and rural areas. However, studies often lack numerous measurement points frequently overlook parameters radiation air velocity due the high cost of precision instrumentation. This results in data with a low resolution, particularly tropical cities where official weather stations are scarce. research introduces new, low-tech tool for district-level outdoor thermal comfort assessment UHI characterization address these challenges. The automated station employs sensors measure temperature, humidity, wind speed, solar radiation, globe temperature. paper details sensors’ rigorous selection validation process, followed by description sensor assembly, acquisition chain, network operation mechanisms. Calibration outcomes laboratory situ environments highlight station’s reliability, even conditions. In conclusion, this offers cost-effective solution gathering high-resolution areas, enabling an improved understanding phenomenon refinement microclimate numerical models.

Language: Английский

Citations

3
Increasing human-perceived temperature exacerbated by urbanization in China's major cities: Spatiotemporal trends and associated driving factors DOI

Haiwen Yan,

Yanzhong Li,

Yincong Xing

et al.

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: 118, P. 106034 - 106034

Published: Dec. 7, 2024

Language: Английский

Citations

2
Spatial and temporal dynamics of urban heat environment at the township scale: A case study in Jinan city, China DOI Creative Commons
Dongchao Wang,

Jianfei Cao,

Baolei Zhang

et al.

PLoS ONE, Journal Year: 2024, Volume and Issue: 19(9), P. e0307711 - e0307711

Published: Sept. 16, 2024

The prolonged dependence on industrial development has accentuated the cumulative effects of pollutants. Simultaneously, influenced by land construction activities and green space depletion, Urban Heat Island (UHI) effect in cities intensified year year, jeopardizing foundation sustainable urban development. Prudent spatial planning holds potential to robustly ameliorate persistent deterioration UHI phenomenon. This study selects Jinan City as a case employs autocorrelation regression algorithms explore spatiotemporal evolution urban-rural patterns at township scale. aim is identify key factors driving differentiation Land Surface Temperature (LST) from 2013 2022. research reveals trend initially rising subsequently falling LST various townships, with low-temperature concentration areas southern mountainous region northern plain area. "West-Central-East" main axis southeast Laiwu District exhibit high-temperature zones. Significant influences are attributed pollution levels, topographical factors, urbanization greenness. global Moran's Index for exceeds 0.7, indicating strong positive correlation. Cluster analysis results indicate High-High (HH) clustering central Shizhong Low-Low (LL) Shanghe County. Multiscale Geographically Weighted Regression (MGWR) outperforms (GWR) Ordinary Linear (OLR), providing more accurate reflection relationships between variables. By investigating its scale, this contributes insights future

Language: Английский

Citations

1