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Urban Ecology & Green Space
Journal articleOpen access

Hotspots in urban areas: A novel approach to assess the heat mitigation potential of nature-based solutions using microclimate modelling

Published 1 Feb 2026 · Added to radar 5 Nov 2026

Source abstract

The urban heat island (UHI) effect requires measures to reduce heat stress and the associated health impacts. Nature-based solutions (NbS) are proposed to improve microclimatic conditions. Previous research has mostly assessed the potential for the use of NbS based on heat load maps for entire cities, with the most heat-stressed areas having the highest cooling demand. However, the extent to which NbS have a small-scale impact within urban districts has hardly been investigated to date. To close this gap, we developed a mixed-method approach combining spatial investigations and microclimate modelling in a neighbourhood in Cologne, Germany. Based on remote sensing data, cadastral data and field measurements, we delineated “urban hot spots” (UHS) and carried out GIS analyses to identify the areas with the greatest need for NbS. For these UHS, we implemented different NbS in a “hotspot scenario” simulated with the physically based 3D microclimate model ENVI-met during a 20-year heat event in 2022. The model was previously validated based on a quality-controlled, densely distributed meteorological sensor network. Compared to the reference run, the scenario analyses showed statistically significant differences in air temperature of -0.5 K in the spatio-temporal average for the entire study area during the heat event and up to -5.3 K on average for locations within the UHS. Physiological equivalent temperature (PET) can be reduced by -0.9 K on a spatio-temporal average for the entire study area and up to -7.7 K within the UHS. This highlights the potential of identifying UHS for urban heat mitigation.

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Structured record

Study objective (from source abstract)
To close this gap, we developed a mixed-method approach combining spatial investigations and microclimate modelling in a neighbourhood in Cologne, Germany.
Methods
spatial analysis, GIS analysis, remote sensing, simulation
Data
remote-sensing data

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