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Infrastructure-based heat reduction strategies can help cities adapt to high temperatures, but simulations of their cooling potential yield widely varying predictions. We systematically review 146 studies from 1987 to 2017 that conduct physically based numerical modelling of urban air temperature reduction resulting from green-blue infrastructure and reflective materials. Studies are grouped into two modelling scales: neighbourhood scale, building-resolving (i.e. microscale); and city scale, neighbourhood-resolving (i.e. mesoscale). Street tree cooling has primarily been assessed at the microscale, whereas mesoscale modelling has favoured reflective roof treatments, which are attributed to model physics limitations at each scale. We develop 25 criteria to assess contextualization and reliability of each study based on metadata reporting and methodological quality, respectively. Studies have shortcomings with respect to neighbourhood characterization, reporting areal coverages of heat mitigation implementations, evaluation of base case simulations, and evaluation of modelled physical processes relevant to heat reduction. To aid comparison among studies, we introduce two metrics: the albedo cooling effectiveness (ACE), and the vegetation cooling effectiveness (VCE). A sub-sample of 47 higher quality studies suggests that high reflectivity coatings or materials offer ≈0.2 °C–0.6 °C cooling per 0.10 neighbourhood albedo increase, and that trees yield ≈0.3 °C cooling per 0.10 canopy cover increase, for afternoon clear-sky summer conditions. VCE of low vegetation and green roofs varies more strongly between studies. Both ACE and VCE exhibit a striking dependence on model choice and model scale, particularly for albedo and roof-level implementations, suggesting that much of the variation of cooling magnitudes between studies may be attributed to model physics representation. We conclude that evaluation of the base case simulation is not a sufficient prerequisite for accurate simulation of heat mitigation strategy cooling. We identify a three-phase framework for assessment of the suitability of a numerical model for a heat mitigation experiment, which emphasizes assessment of urban canopy layer mixing and of the physical processes associated with the heat reduction implementation. Based on our findings, we include recommendations for optimal design and communication of urban heat mitigation simulation studies.

In mesoscale climate models, urban canopy flow is typically parameterized in terms of the horizontally averaged (1-D) flow and scalar transport, and these parameterizations can be informed by computational fluid dynamics (CFD) simulations of the urban climate at the microscale. Reynolds averaged Navier–Stokes simulation (RANS) models have previously been employed to derive vertical profiles of turbulent length scale and drag coefficient for such parameterization. However, there is substantial evidence that RANS models fall short in accurately representing turbulent flow fields in the urban roughness sublayer. When compared with more accurate flow modeling such as large-eddy simulations (LES), we observed that vertical profiles of turbulent kinetic energy and associated turbulent length scales obtained from RANS models are substantially smaller specifically in the urban canopy. Accordingly, using LES results, we revisited the urban canopy parameterizations employed in the one-dimensional model of turbulent flow through urban areas and updated the parameterization of turbulent length scale and drag coefficient. Additionally, we included the parameterization of the dispersive stress, previously neglected in the 1-D column model. For this objective, the PArallelized Large-Eddy Simulation Model (PALM) is used and a series of simulations in an idealized urban configuration with aligned and staggered arrays are considered. The plan area density (λp) is varied from 0.0625 to 0.44 to span a wide range of urban density (from sparsely developed to compact midrise neighborhoods, respectively). In order to ensure the accuracy of the simulation results, we rigorously evaluated the PALM results by comparing the vertical profiles of turbulent kinetic energy and Reynolds stresses with wind tunnel measurements, as well as other available LES and direct numerical simulation (DNS) studies. After implementing the updated drag coefficients and turbulent length scales in the 1-D model of urban canopy flow, we evaluated the results by (a) testing the 1-D model against the original LES results and demonstrating the differences in predictions between new (derived from LES) and old (derived from RANS) versions of the 1-D model, and (b) testing the 1-D model against LES results for a test case with realistic geometries. Results suggest a more accurate prediction of vertical turbulent exchange in urban canopies, which can consequently lead to an improved prediction of urban heat and pollutant dispersion at the mesoscale.

As urbanization progresses, more realistic methods are required to analyze the urban microclimate. However, given the complexity and computational cost of numerical models, the effects of realistic representations should be evaluated to identify the level of detail required for an accurate analysis. We consider the realistic representation of surface heating in an idealized three-dimensional urban configuration, and evaluate the spatial variability of flow statistics (mean flow and turbulent fluxes) in urban streets. Large-eddy simulations coupled with an urban energy balance model are employed, and the heating distribution of urban surfaces is parametrized using sets of horizontal and vertical Richardson numbers, characterizing thermal stratification and heating orientation with respect to the wind direction. For all studied conditions, the thermal field is strongly affected by the orientation of heating with respect to the airflow. The modification of airflow by the horizontal heating is also pronounced for strongly unstable conditions. The formation of the canyon vortices is affected by the three-dimensional heating distribution in both spanwise and streamwise street canyons, such that the secondary vortex is seen adjacent to the windward wall. For the dispersion field, however, the overall heating of urban surfaces, and more importantly, the vertical temperature gradient, dominate the distribution of concentration and the removal of pollutants from the building canyon. Accordingly, the spatial variability of concentration is not significantly affected by the detailed heating distribution. The analysis is extended to assess the effects of three-dimensional surface heating on turbulent transfer. Quadrant analysis reveals that the differential heating also affects the dominance of ejection and sweep events and the efficiency of turbulent transfer (exuberance) within the street canyon and at the roof level, while the vertical variation of these parameters is less dependent on the detailed heating of urban facets.

Despite ongoing efforts to collect high-resolution datasets that capture the spatial distribution of urban heat, there remains a gap in human-centric monitoring that focuses on the immediate environment of individuals experiencing heat exposure. To address this, we explored three different models for predicting air temperature in dynamic outdoor settings using wrist-mounted wearable sensors. Data was collected for 22 d between 2020 and 2024 in Sydney, Australia. Each experiment involved 6–15 participants walking through different built environments. When air temperature and relative humidity measured by wrist-mounted sensors were compared to reference sensors, we found that wrist-mounted wearables cannot directly measure air temperature due to the influence of skin temperature. However, we can use their data to train a prediction model for air temperature. We explored three prediction methods: a steady-state heat transfer model of human skin, multi-linear regression, and random forest machine learning (ML). Results showed that the heat transfer model relied heavily on climatic parameters which could not be measured by wrist-mounted sensors, limiting the applicability of this method. The linear regression model (developed solely based on wrist-mounted data) neglected the non-linear correlation between wrist air temperature and wrist skin temperature. The ML approach, however, was capable of capturing non-linear, multi-dimensional relationships and demonstrated the best predictive performance. ML tested on out-of-sample data achieved a correlation coefficient (R2) of 0.97 (in contrast with 0.60 and 0.88 for heat transfer and linear regression) between predicted and observed air temperature, with mean absolute error of <1 °C (in contrast with 4.43 and 1.81 °C). This performance is equivalent to the accuracy of many common air temperature sensors. This prediction model can be an effective method for providing high-resolution air temperature data in cities with temperate climates, such as Sydney, while informing future work in other climate backgrounds.

Global climate is changing as a result of anthropogenic warming, leading to higher daily excursions of temperature in cities. Such elevated temperatures have great implications on human thermal comfort and heat stress, which should be closely monitored. Current methods for heat exposure assessments (surveys, microclimate measurements, and laboratory experiments), however, present several limitations: measurements are scattered in time and space and data gathered on outdoor thermal stress and comfort often does not include physiological and behavioral parameters. To address these shortcomings, Project Coolbit aims to introduce a human-centric approach to thermal comfort assessments. In this study, we propose and evaluate the use of wrist-mounted wearable devices to monitor environmental and physiological responses that span a wide range of spatial and temporal distributions. We introduce an integrated wearable weather station that records (a) microclimate parameters (such as air temperature and humidity), (b) physiological parameters (heart rate, skin temperature and humidity), and (c) subjective feedback. The feasibility of this methodology to assess thermal comfort and heat stress is then evaluated using two sets of experiments: controlled-environment physiological data collection, and outdoor environmental data collection. We find that using the data obtained through the wrist-mounted wearables, core temperature can be predicted non-invasively with 95 percent of target attainment within ±0.27 °C. Additionally, a direct connection between the air temperature at the wrist ( T a , w ) and the perceived activity level (PAV) of individuals was drawn. We observe that with increased T a , w , the desire for physical activity is significantly reduced, reaching ‘Transition only’ PAV level at 36 °C. These assessments reveal that the wearable methodology provides a comprehensive and accurate representation of human heat exposure, which can be extended in real-time to cover a large spatial distribution in a given city and quantify the impact of heat exposure on human life.

Urban morphology and inter-building shadowing result in a non-uniform distribution of surface heating in urban areas, which can significantly modify the urban flow and thermal field. In Part I, we found that in an idealized three-dimensional urban array, the spatial distribution of the thermal field is correlated with the orientation of surface heating with respect to the wind direction (i.e. leeward or windward heating), while the dispersion field changes more strongly with the vertical temperature gradient in the street canyon. Here, we evaluate these results more closely and translate them into metrics of “city breathability,” with large-eddy simulations coupled with an urban energy-balance model employed for this purpose. First, we quantify breathability by, (i) calculating the pollutant concentration at the pedestrian level (horizontal plane at z≈1.5–2 m) and averaged over the canopy, and (ii) examining the air exchange rate at the horizontal and vertical ventilating faces of the canyon, such that the in-canopy pollutant advection is distinguished from the vertical removal of pollution. Next, we quantify the change in breathability metrics as a function of previously defined buoyancy parameters, horizontal and vertical Richardson numbers (Rih and Riv, respectively), which characterize realistic surface heating. We find that, unlike the analysis of airflow and thermal fields, consideration of the realistic heating distribution is not crucial in the analysis of city breathability, as the pollutant concentration is mainly correlated with the vertical temperature gradient (Riv) as opposed to the horizontal (Rih) or bulk (Rib) thermal forcing. Additionally, we observe that, due to the formation of the primary vortex, the air exchange rate at the roof level (the horizontal ventilating faces of the building canyon) is dominated by the mean flow. Lastly, since Rih and Riv depend on the meteorological factors (ambient air temperature, wind speed, and wind direction) as well as urban design parameters (such as surface albedo), we propose a methodology for mapping overall outdoor ventilation and city breathability using this characterization method. This methodology helps identify the effects of design on urban microclimate, and ultimately informs urban designers and architects of the impact of their design on air quality, human health, and comfort.

Large impacts of global warming and urbanization on near‐surface air temperature increase and cooling energy demand are expected for the American Southwest region. The relative importance of these two features and their interactions are studied by means of a mesoscale model with a multilayer building energy model that allows accounting for the feedback between cooling energy consumption and air temperature for a typical summer period in Arizona. This approach allows to separate the impact of global warming from the one due to urbanization, on energy demand and air temperature. Under the highest greenhouse gas emissions scenario (RCP8.5), adverse effects on mean air temperature of global warming overwhelm those from the urbanization of new areas. In particular, the mean temperature increase for a summer period due to global warming and urban expansion in the Phoenix metropolitan area is 3.6 °C and in the Tucson metropolitan area, it is 3.1 °C. These result in an increase in the spatial density of the cooling energy demand (MW km−2) by 36.2 and 42.6% in the respective regions compared to present consumption. The citywide cooling energy demand (MW) on the other hand, is expected to increase up to a factor two (Phoenix) and three (Tucson), with ∼75% of this increase due to urban expansion, and ∼25% due to global warming. (a) Diurnal cycle of observed total electricity demand across the Phoenix metropolitan area for all weekdays (i.e. each DXY curve represents a particular weekday) of the 15‐day summertime period in June 2012. (b) Same as in (a) but for 2‐m urban air temperature.

Heat waves pose additional risks to urban spaces because of the additional heat provided by urban heat islands (UHIs) as well as poorer air quality. Our study focuses on the analysis of UHIs, human thermal comfort, and air quality for the city of Madrid, Spain during heat waves. Heat wave periods are defined using the long-term records from the urban station Madrid-Retiro. Two types of UHI were studied: the canopy layer UHI (CLUHI) was evaluated using air temperature time-series from five meteorological stations; the surface UHI (SUHI) was derived from land surface temperature (LST) images from MODIS (Moderate Resolution Imaging Spectroradiometer) products. To assess human thermal comfort, the Physiological Equivalent Temperature (PET) index was applied. Air quality was analyzed from the records of two air quality networks. More frequent and longer heat waves have been observed since 1980; the nocturnal CLUHI and both the diurnal and nocturnal SUHI experience an intensification, which have led to an increasing number of tropical nights. Conversely, thermal stress is extreme by day in the city due to the lack of cooling by winds. Finally, air quality during heat waves deteriorates because of the higher than normal amount of particles arriving from Northern Africa.

Urban heterogeneity, such as the variation of street layouts, building shapes, and building heights, cannot be fully represented by density parameters commonly used in idealized urban environmental analyses. To address this shortcoming and better model flow fields over complex urban neighborhoods, we propose two novel descriptive geometric parameters, alignedness and building facet entropy, which quantify the connectivity of inter‐building spaces along the prevailing wind direction and the variation of building facet orientations, respectively. We then conducted large eddy simulations over 101 urban layouts, including realistic urban configurations with uniform building height as well as idealized building arrays with variable heights, and evaluated the resulting bulk flow properties. Urban canopy flow over realistic neighborhoods resembles staggered building arrays for low urban densities but becomes similar to aligned configurations beyond λp ∼ 0.25 where the realistic flow is less sensitive to changes in density. We further show that compared to traditional density parameters (such as plan and frontal area densities), the mean alignedness, a measure of connectivity of flow paths in street canyons, better predicts canopy‐averaged flow properties. Furthermore, for realistic urban flow, the dispersive momentum flux shows a clear increasing trend with building density, and a decreasing trend with alignedness, which is in contrast with idealized cases that exhibit no clear trend. This distinct behavior further highlights the necessity of evaluating flow over realistic urban layouts for flow parameterization. This study provides an improved method of describing urban layouts for flow characterization that can be applied in neighborhood‐scale urban canopy parameterization. Plain Language Summary We run 101 flow simulations over realistic and idealized urban neighborhoods to evaluate the degree of dissimilarity by introducing realism in urban geometry. The realistic urban neighborhoods are prepared by OSM2LES while the idealized building arrays are constructed in a conventional way with cube‐shaped buildings evenly arranged in “aligned” and “staggered” form. In contrast to the previous approach employing “staggered” building arrays to calibrate model constants, we find the bulk flow properties (such as wind speed and turbulence level) over realistic layouts become insensitive to the density growth and behave similarly to “aligned” urban arrays over medium urban packing density. The spatial variability of realistic urban flow as indicated by dispersive momentum flux shows an increasing trend over the density growth, unlike those on idealized urban arrays that do not have a clear trend. To characterize the distinctive behavior of realistic urban flow, two types of geometric parameters were introduced. The “alignedness” that focuses on the uninterrupted urban streets performs better than packing densities for being inclusive to all types of urban layouts and an agreeable tolerance to building height variability. Key Points Large‐eddy simulations were conducted over 101 urban geometries with realistic and idealized configurations Flow over realistic urban neighborhoods behaves very differently from that over idealized building arrays Alignedness parameters derived by our study show better performance in predicting flow properties

We introduce University of Texas - GLObal Building heights for Urban Studies (UT-GLOBUS), a dataset providing building heights and urban canopy parameters (UCPs) for more than 1200 city or locales worldwide. UT-GLOBUS combines open-source spaceborne altimetry (ICESat-2 and GEDI) and coarse-resolution urban canopy elevation data with a machine-learning model to estimate building-level information. Validation using LiDAR data from six U.S. cities showed UT-GLOBUS-derived building heights had a root mean squared error (RMSE) of 9.1 meters. Validation of mean building heights within 1-km 2 grid cells, including data from Hamburg and Sydney, resulted in an RMSE of 7.8 meters. Testing the UCPs in the urban Weather Research and Forecasting (WRF-Urban) model resulted in a significant improvement (55% in RMSE) in intra-urban air temperature representation compared to the existing table-based local climate zone approach in Houston, TX. Additionally, we demonstrated the dataset’s utility for simulating heat mitigation strategies and building energy consumption using WRF-Urban, with test cases in Chicago, IL, and Austin, TX. Street-scale mean radiant temperature simulations using the SOlar and LongWave Environmental Irradiance Geometry (SOLWEIG) model, incorporating UT-GLOBUS and LiDAR-derived building heights, confirmed the dataset’s effectiveness in modeling human thermal comfort in Baltimore, MD (daytime RMSE = 2.85°C). Thus, UT-GLOBUS can be used for modeling urban hazards with significant socioeconomic and biometeorological risks, enabling finer scale urban climate simulations and overcoming previous limitations due to the lack of building information.