PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
February 9, 2026Sustainability2 citationsOpen Access

Urban Heat Island: Assessing the Influence of Urban Morphology on Air and Surface Temperatures

RZReyhaneh ZeynaliEMEmanuele MandaniciGBG. Bitelli

Key Points

  • This research aims to explore how urban shape and vegetation affect air and surface temperatures in cities.
  • Integrated mobile air temperature measurements with satellite-derived land surface temperature data.
  • Conducted correlation analysis to identify relationships among morphological indicators.
  • Developed a composite urban density indicator using principal component analysis.
  • Applied ordinary least squares regressions to assess temperature associations with urban metrics.
  • Employed spatial regression approaches to address residual autocorrelation and improve model accuracy.
  • Identified vegetation cover as a strong independent predictor of cooling effects in urban areas.
  • Revealed significant associations between urban density and both air and surface temperatures.
  • Improved explanatory power of models through spatial lag models, indicating important neighborhood effects.
  • Reinforced that urban compactness and vegetation play crucial roles in managing thermal environments.

Abstract

This study investigates the interplay between urban morphology, vegetation, and thermal environments by integrating mobile air temperature (AT) measurements with satellite-derived land surface temperature (LST). The case study is the city of Bologna (Italy). Correlation analysis revealed strong multicollinearity among morphological indicators, with building density and floor area ratio nearly collinear, while vegetation cover (PV) remained the most independent predictor. A composite urban density indicator (CUDI), derived through principal component analysis, was introduced to address redundancy among morphological metrics. Ordinary least squares regressions demonstrated significant associations, with PV exerting a pronounced cooling effect and CUDI amplifying both AT and LST. Model diagnostics confirmed statistical robustness, though residual spatial autocorrelation necessitated spatial regression approaches. Spatial lag models (SLMs) substantially improved explanatory power, highlighting spatial spillovers and neighborhood effects as central to understanding urban heat dynamics. Comparative analysis with spatial error models reinforced the dominance of SLM in capturing localized dependencies. Despite limitations in spatial coverage, temporal scope, and indicator transferability, findings emphasize the critical roles of vegetation and urban compactness in shaping thermal environments. This work underscores the necessity of integrating greening strategies with urban form management for effective heat mitigation and provides a methodological framework for analyzing urban heat islands through multi-source thermal and morphological data.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Zeynali et al. (2026) studied this question.

synapsesocial.com/papers/698979c8f0ec2af6756e7ab6https://doi.org/10.3390/su18031695
Ask AI
Helpful
Bookmark
Share
View Full Paper