Climate change is amplifying heat stress risks in European cities, diminishing the livability of public spaces. This issue is intensified by a lack of a spatial overview necessary for mitigating action. Heat stress disrupts crucial urban functions and poses health risks (Böcker & Thorsson, 2014; Ebi et al., 2021). Increasing urban density further exacerbates this, reducing access to thermally comfortable public spaces. Policymakers and urban planners commonly use meteorological data, remote sensing, and modelling to locate vulnerable areas. However, relying solely on these methods overlooks critical socio-environmental dynamics (Elnabawi & Hamza, 2020), complicating the prioritization of redesign efforts without a detailed overview. Within the scope of the CoolTowns Interreg project, researchers developed a methodology to map heat vulnerabilities, incorporating both meteorological insights and crucial social and environmental indicators (Spanjar et al., 2022). Researchers gained insights into key spatial and social indicators in co-creation sessions with the municipality of Breda, Netherlands. Afterward, advanced vulnerability maps were created using high-resolution (1x1 m) Physiologically Equivalent Temperature (PET) maps to identify heat stress at the city level. Community amenities, such as medical facilities, grocery stores, schools, daycares, and primary and secondary slow-traffic routes, were identified and layered atop the PET maps. These layers combine to form a powerful tool for policymakers to locate potential focus areas for heat stress mitigation.

Heat stress poses a serious threat to public health by causing symptoms ranging from mild discomfort, loss of labour productivity, sleep deprivation, to serious heat illnesses with possible (premature) death. Summer heat that causes heat stress is intensified especially at night in urban areas, and is projected to become more extreme due to climate change. Due to urbanization and the changing climate, more and more people will suffer from heat stress inside their homes. It is therefore important to understand heat stress indoors. However, observational and modelling studies on this aspect are relatively scarce, especially when it comes to the interconnections between indoor and outdoor climatic conditions. This study aims to observe, understand and model the behaviour of indoor air temperature during Dutch summer heat using two unique datasets. The first dataset consists of long measuring records up to 27 years of citizen weather stations (CWS) located in seven residences across the Netherlands. The second dataset consists of indoor CWS that were placed by us in almost 100 residences across Amsterdam in 2022 or 2023. As a proof of concept, we first analyse the first dataset of up to 27 years. We find that indoor temperature typically warms up more slowly, but also cools down more slowly than outdoor temperature with a lag difference of ~260 minutes in the diurnal cycle during summer. We demonstrate that nighttime indoor human thermal comfort (HTC) can be worse than outdoor HTC even for days after a heatwave. To model indoor temperature behaviour, we simulate six-hour changes in indoor temperature behaviour with a physics-based statistical model by Vant-Hull et al. (2018) that has an outdoor conduction, indoor conduction and solar transfer component. Preliminary results of this computationally-fast model for the seven houses are promising, with on average a mean absolute error of 0.43 K/day during summer. In the next research step, we scale up our proof-of-concept analyses to the ~100 indoor CWS placed in Amsterdam. These CWS measure the indoor climate – temperature, relative humidity, CO2 concentrations – in the bedroom and living room from the moment of installation until 2026. As each residence is unique, the results of the ~100 indoor CWS will give us a good insight into the statistical variation in the relationships between indoor and outdoor temperature, and in the model performances. Based on our insights, we plan to make recommendations for climate-sensitive urban design to reduce indoor heat stress.

Due to climate change, urban heat-stress is increasingly becoming a risk to health, the livability and accessibility of public spaces (Klok & Kluck, 2018). Trees are often mentioned as best measures to solve heat-stress by providing cool spots or routes (Gromke et al., 2015; Spanjar et al., 2022). However, little is known about the cooling potential and relationship between tree characteristics such as canopy width, height or foliage cover. These characteristics can be negatively influenced in urban areas by limited rooting depth, too much shade or poor soil conditions, which potentially reduce the cooling potential of a tree. Especially during warm periods in summer - the moment when cooling is most desired - trees suffer from drought and leaf fall (reducing foliage cover). In this research trees with different characteristics and health conditions were measured on a densified square in Amsterdam during three hot summer days (at least >25 °C and no clouds). To estimate the Physiological Equivalent Temperature (PET), 5-hour (12:00-17:00) long measurements with mobile weather stations (Kestrel 5400) were conducted. The PET considers the energy balance of the human body (e.g. activity, clothing, age) and is therefore explicitly suitable to estimate heat-stress experienced by humans and cooling by trees. Results show that all trees can reduce the PET on average by 10-17 °C for the 5-hour period and that foliage cover was the most limiting factor of the studied tree characteristics. However, trees with sparse foliage cover (~30%) showed a PET reduction of 10-13 °C indicating that there is even a large cooling benefit for recently planted or drought-affected trees.

In an era of escalating urban challenges, the translation of research into actionable solutions is imperative. As urban land surface temperatures (ULSTs) continue to rise, existing literature in this domain presents a multitude of findings, especially with regards to the robust metrics that influence this biophysical phenomenon across diverse landscapes. This research, was initiated based on a systematic literature review, from which 101 articles examining landscape structure metrics and ULSTs were scrutinized and the generic landscape character of each study site were (unobtrusively) assessed. In this study, from 432 landscape metrics, 49 (11%) were identified as been robust; each attuned to specific landscape contexts, and essential for understanding and mitigating ULST. To facilitate the application of this research, an openly accessible dashboard was developed to present the findings of this study and its application, as a planning support tool. More specifically, stakeholders can harness this visualization tool to pinpoint pertinent metrics for ULST mitigation or simply reveal robust metrics that ought to be considered in understanding the dynamics of ULST in any given landscape character. By openly sharing this tool, further research is advocated in order to broaden the database of this tool and invariably its scope. Lastly, the next step is to evaluate the usefulness and usability of this tool by potential users, and incorporate the user-feedback in its final design.