A simple three-cylinder radiometer and low-speed anemometer to characterize human extreme heat exposure.

As populations and temperatures of urban areas swell, more people face extreme heat and are at increasing risk of adverse health outcomes. Radiation accounts for much of human heat exposure but is rarely used as heat metric due to a lack of cost-effective and accurate sensors. To this end, we fuse the concepts of a three-globe radiometer-anemometer with a cylindrical human body shape representation, which is more realistic than a spherical representation. Using cost-effective and readily available materials, we fabricated two combinations of three cylinders with varying surface properties. These simple devices measure the convection coefficient and the shortwave and longwave radiative fluxes. We tested the devices in a wind tunnel and at fourteen outdoor sites during July 2023's record-setting heat wave in Tempe, Arizona. The average difference between pedestrian-level mean radiant temperature (MRT) measured using research-grade 3-way net radiometers and the three-cylinder setup was 0.4 ± 3.0 °C ( ±  1 SD). At most, we observed a 10 °C MRT difference on a white roof site with extreme MRT values (70 °C to 80 °C), which will be addressed through discussed design changes to the system. The measured heat transfer coefficient can be used to calculate wind speed below 2 m·s-1; thus, the three cylinders combined also serve as a low-speed anemometer. The novel setup could be used in affordable biometeorological stations and deployed across urban landscapes to build human-relevant heat sensing networks.

Characterization of human extreme heat exposure using an outdoor thermal manikin.

Extreme heat is a current and growing global health concern. Current heat exposure models include meteorological and human factors that dictate heat stress, comfort, and risk of illness. However, radiation models simplify the human body to a cylinder, while convection ones provide conflicting predictions. To address these issues, we introduce a new method to characterize human exposure to extreme heat with unprecedented detail. We measure heat loads on 35 body surface zones using an outdoor thermal manikin ("ANDI") alongside an ultrasonic anemometer array and integral radiation measurements (IRM). We show that regardless of body orientation, IRM and ANDI agree even under high solar conditions. Further, body parts can be treated as cylinders, even in highly turbulent flow. This geometry-rooted insight yields a whole-body convection correlation that resolves prior conflicts and is valid for diverse indoor and outdoor wind flows. Results will inform decision-making around heat protection, adaptation, and mitigation.

Concurrent Heat and Air Pollution Exposures among People Experiencing Homelessness.

BACKGROUND: Extreme heat and air pollution are important human health concerns; exposure can affect mental and physical well-being, particularly during periods of co-occurrence. Yet, the impacts on people are largely determined by underlying health conditions, coupled with the length and intensity of exposure. Preexisting adverse health conditions and prolonged exposure times are more common for people experiencing homelessness, particularly those with intersectional identity characteristics (e.g., disease, ability, age, etc.). Partially due to methodological limitations, such as data scarcity, there is a lack of research at the intersection of this at-risk population within the climate-health domain. OBJECTIVES: We have three distinct objectives throughout this article: a) to advance critical discussions around the state of concurrent high heat and air pollution exposure research as it relates to people experiencing homelessness; b) to assert the importance of heat and air pollution exposure research among a highly vulnerable, too-often homogenized population-people experiencing homelessness; and c) to underline challenges in this area of study while presenting potential ways to address such shortcomings. DISCUSSION: The health insights from concurrent air pollution and heat exposure studies are consequential when studying unhoused communities who are already overexposed to harmful environmental conditions. Without holistic data sets and more advanced methods to study concurrent exposures, appropriate and targeted prevention and intervention strategies cannot be developed to protect this at-risk population. We highlight that a) concurrent high heat and air pollution exposure research among people experiencing homelessness is significantly underdeveloped considering the pressing human health implications; b) the severity of physiological responses elicited by high heat and air pollution are predicated on exposure intensity and time, and thus people without means of seeking climate-controlled shelter are most at risk; and c) collaboration among transdisciplinary teams is needed to resolve data resolution issues and enable targeted prevention and intervention strategies. https://doi.org/10.1289/EHP13402.

Beyond heat exposure - new methods to quantify and link personal heat exposure, stress, and strain in diverse populations and climates: The journal Temperature toolbox.

Fine-scale personal heat exposure (PHE) information can help prevent or minimize weather-related deaths, illnesses, and reduced work productivity. Common methods to estimate heat risk do not simultaneously account for the intensity, frequency, and duration of thermal exposures, nor do they include inter-individual factors that modify physiological response. This study demonstrates new whole-body net thermal load estimations to link PHE to heat stress and strain over time. We apply a human-environment heat exchange model to examine how time-varying net thermal loads differ across climate contexts, personal attributes, and spatiotemporal scales. First, we investigate summertime climatic PHE impacts for three US cities: Phoenix, Miami, and New York. Second, we model body morphology and acclimatization for three profiles (middle-aged male/female; female >65 years). Finally, we quantify model sensitivity using representative data at synoptic and micro-scales. For all cases, we compare required and potential evaporative heat losses that can lead to dangerous thermal exposures based on (un)compensable heat stress. Results reveal misclassifications in heat stress or strain due to incomplete environmental data and assumed equivalent physiology and activities between people. Heat strain is most poorly represented by PHE alone for the elderly, non-acclimatized, those engaged in strenuous activities, and when negating solar radiation. Moreover, humid versus dry heat across climates elicits distinct thermal responses from the body. We outline criteria for inclusive PHE evaluations connecting heat exposure, stress, and strain while using physiological-based methods to avoid misclassifications. This work underlines the value of moving from "one-size-fits-all" thermal indices to "fit-for-purpose" approaches using personalized information.

Do poverty and wealth look the same the world over? A comparative study of 12 cities from five high-income countries using street images.

Urbanization and inequalities are two of the major policy themes of our time, intersecting in large cities where social and economic inequalities are particularly pronounced. Large scale street-level images are a source of city-wide visual information and allow for comparative analyses of multiple cities. Computer vision methods based on deep learning applied to street images have been shown to successfully measure inequalities in socioeconomic and environmental features, yet existing work has been within specific geographies and have not looked at how visual environments compare across different cities and countries. In this study, we aim to apply existing methods to understand whether, and to what extent, poor and wealthy groups live in visually similar neighborhoods across cities and countries. We present novel insights on similarity of neighborhoods using street-level images and deep learning methods. We analyzed 7.2 million images from 12 cities in five high-income countries, home to more than 85 million people: Auckland (New Zealand), Sydney (Australia), Toronto and Vancouver (Canada), Atlanta, Boston, Chicago, Los Angeles, New York, San Francisco, and Washington D.C. (United States of America), and London (United Kingdom). Visual features associated with neighborhood disadvantage are more distinct and unique to each city than those associated with affluence. For example, from what is visible from street images, high density poor neighborhoods located near the city center (e.g., in London) are visually distinct from poor suburban neighborhoods characterized by lower density and lower accessibility (e.g., in Atlanta). This suggests that differences between two cities is also driven by historical factors, policies, and local geography. Our results also have implications for image-based measures of inequality in cities especially when trained on data from cities that are visually distinct from target cities. We showed that these are more prone to errors for disadvantaged areas especially when transferring across cities, suggesting more attention needs to be paid to improving methods for capturing heterogeneity in poor environment across cities around the world. Supplementary Information: The online version contains supplementary material available at 10.1140/epjds/s13688-023-00394-6.

Evidence-based guidance on reflective pavement for urban heat mitigation in Arizona.

Urban overheating is an increasing threat to people, infrastructure, and the environment. Common heat mitigation strategies, such as green infrastructure, confront space limitations in current car-centric cities. In 2020, the City of Phoenix, Arizona, piloted a "cool pavement" program using a solar reflective pavement seal on 58 km of residential streets. Comprehensive micrometeorological observations are used to evaluate the cooling potential of the reflective pavement based on three heat exposure metrics-surface, air, and mean radiant temperatures-across three residential reflective pavement-treated and untreated neighborhoods. In addition, the solar reflectivity of reflective pavement is observed over 7 months across eight residential neighborhoods. Results are synthesized with the literature to provide context-based reflective pavement implementation guidelines to mitigate urban overheating where common strategies cannot be applied. The three most important contextual factors to consider for effective implementation include urban location, background climate type, and heat exposure metric of interest.

PanoMRT: Panoramic infrared thermography to model human thermal exposure and comfort.

As summer heat waves become the new normal worldwide, modeling human thermal exposure and comfort to assess and mitigate urban overheating is crucial to uphold livability in cities. We introduce PanoMRT, an open source human-biometeorological model to calculate Mean Radiant Temperature (TMRT), Physiologically Equivalent Temperature (PET), and the Universal Thermal Climate Index (UTCI) from thermal equirectangular 360° panoramas and standard weather information (air temperature, relative humidity, wind speed). We validated the model for hot, dry, clear summer days in Tempe, Arizona, USA with in-situ observations using a FLIR Duo Pro R thermal camera on a rotating arm and the mobile human-biometeorological instrument platform MaRTy. We observed and modeled TMRT and thermal comfort for 19 sites with varying ground cover (grass, concrete, asphalt), sky view factor, exposure (sun, shade), and shade type (engineered, natural) six times per day. PanoMRT performed well with a Root Mean Square Error (RMSE) of 4.1 °C for TMRT, 2.6 °C for PET, and 1.2 °C for UTCI, meeting the accuracy requirement of ±5 °C set in the ISO 7726 standard for heat and cold stress studies. RayMan reference model runs without measured surface temperature forcing reveal that accurate longwave radiative flux estimations are crucial to meet the ±5 °C threshold, particularly for shaded locations and during midday when surface temperatures peak and longwave modeling errors are largest. This study demonstrates the importance of spatially resolved 3D surface temperature data for thermal exposure and comfort modeling to capture complex longwave radiation exposure patterns resulting from heterogeneity in built configuration and material radiative and thermal properties in the built environment.

Training Computers to See the Built Environment Related to Physical Activity: Detection of Microscale Walkability Features Using Computer Vision.

The study purpose was to train and validate a deep learning approach to detect microscale streetscape features related to pedestrian physical activity. This work innovates by combining computer vision techniques with Google Street View (GSV) images to overcome impediments to conducting audits (e.g., time, safety, and expert labor cost). The EfficientNETB5 architecture was used to build deep learning models for eight microscale features guided by the Microscale Audit of Pedestrian Streetscapes Mini tool: sidewalks, sidewalk buffers, curb cuts, zebra and line crosswalks, walk signals, bike symbols, and streetlights. We used a train−correct loop, whereby images were trained on a training dataset, evaluated using a separate validation dataset, and trained further until acceptable performance metrics were achieved. Further, we used trained models to audit participant (N = 512) neighborhoods in the WalkIT Arizona trial. Correlations were explored between microscale features and GIS-measured and participant-reported neighborhood macroscale walkability. Classifier precision, recall, and overall accuracy were all over >84%. Total microscale was associated with overall macroscale walkability (r = 0.30, p < 0.001). Positive associations were found between model-detected and self-reported sidewalks (r = 0.41, p < 0.001) and sidewalk buffers (r = 0.26, p < 0.001). The computer vision model results suggest an alternative to trained human raters, allowing for audits of hundreds or thousands of neighborhoods for population surveillance or hypothesis testing.

A microscale three-dimensional model of urban outdoor thermal exposure (TUF-Pedestrian).

Urban street design choices relating to tree planting, building height and spacing, ground cover, and building façade properties impact outdoor thermal exposure. However, existing tools to simulate heat exposure have limitations with regard to optimization of street design for pedestrian cooling. A microscale three-dimensional (3D) urban radiation and energy balance model, Temperatures of Urban Facets for Pedestrians (TUF-Pedestrian), was developed to simulate pedestrian radiation exposure and study heat-reducing interventions such as urban tree planting and modifications to building and paving materials. TUF-Pedestrian simulates the spatial distribution of radiation and surface temperature impacts of trees and buildings on their surroundings at the sub-facet scale. In addition, radiation absorption by a three-dimensional pedestrian is considered, permitting calculation of a summary metric of human radiation exposure: the mean radiant temperature (TMRT). TUF-Pedestrian is evaluated against a unique 24-h observational dataset acquired using a mobile human-biometeorological station, MaRTy, in an urban canyon with trees on the Arizona State University Tempe campus (USA). Model evaluation demonstrates that TUF-Pedestrian accurately simulates both incoming directional radiative fluxes and TMRT in an urban environment with and without tree cover. Model sensitivity simulations demonstrate how modelled TMRT and directional radiative fluxes respond to increased building height (ΔTMRT reaching -32 °C when pedestrian becomes shaded), added tree cover (ΔTMRT approaching -20 °C for 8 m trees with leaf area density of 0.5 m2 m-3), and increased street albedo (ΔTMRT reaching + 6 °C for a 0.21 increase in pavement albedo). Sensitivity results agree with findings from previous studies and demonstrate the potential utility of TUF-Pedestrian as a tool to optimize street design for pedestrian heat exposure reduction.

Mask wearing behavior in hot urban spaces of Novi Sad during the COVID-19 pandemic.

Urban overheating (due to climate change and urbanization) and COVID-19 are two converging crises that must be addressed in tandem. Fine-scale, place-based, people-centric biometeorological and behavioral data are needed to implement context-specific preventative measures such as mask-wearing. This study collected local biometeorological measurements in diverse urban spaces (square, urban park, river quay) in Novi Sad, Serbia on hot sunny summer days (27-30 August 2020) during the COVID-19 pandemic. Observations were supplemented by an online survey asking questions about thermal sensation, comfort, and concurrent protective behavior of the local population. Biometeorological measurements show that the main square in the city center was the most thermally uncomfortable area. According to the survey, it was also perceived as the least safe space to not contract the virus. The urban park was perceived as the most thermally comfortable area in the morning and during midday. It was also considered the safest urban space for outdoor activities. In the evening, the river quay was the most thermally comfortable area in the city. Intra-urban differences in Physiologically Equivalent Temperatures were highest during midday, while differences in air temperatures were highest in the evening. More than 70% of the respondents did not wear face masks when it was hot because of breathing issues and feeling warmer than without mask. Most people wearing a mask felt "slightly warm" in the morning and evening, while the majority of respondents felt "hot" during midday. Only 3% of the respondents felt comfortable while wearing a mask, while 97% experienced some degree of discomfort (from slight discomfort to very uncomfortable). Our study shows that fine scale temporal and spatial urban biometeorological data and population surveys should be included in decision-making processes during the pandemic to develop climate-sensitive health services that are place-based, people-centric, and facilitate planning towards green, resilient, and inclusive cities.

Evaporative misters for urban cooling and comfort: effectiveness and motivations for use.

Thermal comfort is an important determinant of quality of life and economic vitality in cities. Strategies to improve thermal comfort may become a more critical part of urban sustainability efforts with projections of continued urban growth and climate change. A case study was performed in the hot, dry summertime climate of Tempe, Arizona to quantify the influence of evaporative misters on the thermal environment in outdoor restaurants and to understand business managers' motivations to use misters. Microclimate measurements (air temperature (Ta), wind speed, relative humidity, globe temperature) were taken at five restaurants midday within four exposures: misted sun, misted shade, sun only, and shade only. We assessed Ta, mean radiant temperature (MRT), universal thermal climate index (UTCI), and physiological equivalent temperature (PET) between these four conditions within each location. Misters improved thermal comfort across all days, sites, and exposure conditions. MRT was on average 7.6 °C lower in misted locations, which significantly lowered average PET (- 6.5 °C) and UTCI (- 4.4 °C) (p < 0.05). Thermal comfort was most improved using mist in combination with shade. Under such conditions, PET and UTCI were reduced by 15.5 °C and 9.7 °C (p < 0.05), respectively. Business managers identified customer comfort and increased seating capacity as the principal factors for mister use. Esthetics of misters further encouraged use, while cost and environmental concerns were perceived to be less important. While this case study demonstrates value in outdoor misting in a hot, dry climate, additional work is needed to more fully evaluate tradeoffs between cost, water use, and comfort with continuing urban growth.

A regression-based three-phase approach to assess outdoor thermal comfort in informal micro-entrepreneurial settings in tropical Mumbai.

Urban heat poses a public health risk to the residents of megacities in developing countries because the population spends a significant amount of time outdoors to work and socialize with limited cooling resources. Understanding the drivers of outdoor comfort and heat stress in informal work settings is important to design climate-sensitive outdoor spaces and reduce heat vulnerability. We present outdoor thermal comfort perceptions (OTCPs) of people engaged in outdoor micro entrepreneurial activities in Mumbai using seasonal surveys and biometeorological observations. We propose a three-phase approach to analyze the relative importance of climatic and non-climatic variables for OTCPs. The first phase evaluates the seasonal and intra-neighborhood variation of thermal sensation votes (TSV) with respect to physiological equivalent temperature (PET) and air temperature. Second, we include physiological parameters to evaluate the seasonal and intra-neighborhood variation of overall sensation votes (OSV). Third, we consider aggregated survey responses and include behavioral and perceptual variables to determine their relative significance. We employ three linear modeling techniques to assess model performance in explaining the variability of OTCP using OSV as dependent variable. Results reveal that microclimatic parameters alone are unable to explain the variability of OTCP. Our results yield a neutral PET value (PETneutral) of 23.75 °C for Mumbai in the winter. PETneutral was higher for activities at the clothing market compared to other micro entrepreneurial activities. Acclimatization significantly improved comfort in the summer, while evaporative cooling was beneficial in the winter. Further, an ANCOVA and ordinal logistic regressions demonstrate the importance of behavioral attributes (presence in the location, expectation, beverage intake) in explaining the variance in OTCP. Our study also reveals that wind speed and humidity play an important role in shaping overall comfort in the Mumbai neighborhoods.

Change of nutrients, microorganisms, and physical properties of exposed extensive green roof substrate.

Green roofs (GR) can be used as a nature-based solution to tackle eco-environmental problems caused by climate change and rapid urbanization. The substrate in the GRs is the growing medium for vegetation, and its properties directly affect the ecosystem services of GRs. To investigate the characteristic changes of an exposed substrate after the removal of vegetation, a one-year field experiment was conducted. Substrate properties were comprehensively compared for areas in GR that were planted with Sedum lineare and those with bare substrate. Results show that vegetation cover not only prevented substrate loss by 5.14% (p < 0.05) but also protected the chemical, microbial, and physical properties of the substrate. Moreover, the structure of the substrate changed, as evidenced by a significant increase in fine sand (p < 0.05). The results highlight that attention should be paid to maintaining vegetation cover during GR management. In addition, extensive GRs may not be suitable for fallowing. Once a GR has been established, it needs regular maintenance. Otherwise, the ecological and economic benefits of the GR may be reduced. The findings of the present study can be used to determine the life-cycle costs. Further research should focus on differences in the substrate loss rates, runoff, and temperatures of the substrates under exposure and vegetation cover. The microbial changes after revegetation should also be studied to clarify the role of vegetation in GR ecosystems. The present study provides a reference for improving GR management and ensuring their sustainability.

What you see is what you breathe? Estimating air pollution spatial variation using street level imagery.

High spatial resolution information on urban air pollution levels is unavailable in many areas globally, partially due to high input data needs of existing estimation approaches. Here we introduce a computer vision method to estimate annual means for air pollution levels from street level images. We used annual mean estimates of NO2 and PM2.5 concentrations from locally calibrated models as labels from London, New York, and Vancouver to allow for compilation of a sufficiently large dataset (~250k images for each city). Our experimental setup is designed to quantify intra and intercity transferability of image-based model estimates. Performances were high and comparable to traditional land-use regression (LUR) and dispersion models when training and testing on images from the same city (R2 values between 0.51 and 0.95 when validated on data from ground monitoring stations). Like LUR models, transferability of models between cities in different geographies is more difficult. Specifically, transferability between the three cities i.e., London, New York, and Vancouver, which have similar pollution source profiles were moderately successful (R2 values between zero and 0.67). Comparatively, performances when transferring models trained on these cities with very different source profiles i.e., Accra in Ghana and Hong Kong were lower (R2 between zero and 0.21) suggesting the need for local calibration with local calibration using additional measurement data from cities that share similar source profiles.

Improved methods for estimating mean radiant temperature in hot and sunny outdoor settings.

Thermal comfort research has utilized various sensors and models to estimate the mean radiant temperature (MRT) experienced by a human, including the standard black globe thermometer (SGT), acrylic globe thermometers (AGT), and cylindrical radiation thermometers (CRT). Rather than directly measuring radiation, a temperature is measured in the center of these low-cost sensors that can be related to MRT after theoretically accounting for convection. However, these sensors have not been systematically tested under long-term hot and clear conditions. Further, under variable weather conditions, many issues can arise due to slow response times, shape, inaccuracies in material properties and assumptions, and color (albedo, emissivity) inconsistencies. Here, we assess the performance of MRT produced by various heat transfer models, with and without new average surface temperature ([Formula: see text]) correction factors, using five instruments-the SGT (15 cm, black), tan and black CRTs, gray and black 38 mm AGTs-compared to 3D integral radiation measurements. Measurements were taken on an unobscured roof throughout summer-to-early-fall months in Tempe, Arizona, examining 58 full-sun days. Deviations without correcting for asymmetrical surface heating-found to be the main cause of errors-reached ± 15-20 °C MRT. By accounting for asymmetric heating through [Formula: see text] calculations, new corrective algorithms were derived for the low-cost sensor models. Results show significant improvements in the estimated MRT error for each sensor (i.e., ∆MRTmodel - IRM) when applying the [Formula: see text] corrections. The tan MRTCRT improved from 1.9 ± 6.2 to -0.1 ± 4.4 °C, while the gray AGT and SGT showed improvements from -1.6 ± 7.2 to -0.4 ± 6.3 °C and - 6.6 ± 6.4 to - 0.03 ± 5.7 °C, respectively. The new corrections also eliminated dependence on other meteorological factors (zenith, wind speed). From these results, we provide three simple equations for CRT, AGT, and SGT correction for future research use under warm-hot and clear conditions. This study is the most comprehensive empirical assessment of various low-cost instruments with broad applicability in urban climate and biometeorological research.

Validation of seasonal mean radiant temperature simulations in hot arid urban climates.

We validated seasonal RayMan and ENVI-met mean radiant temperature (TMRT) simulations to assess model performance in a sensitivity analysis from cold to extremely hot conditions. Human-biometeorological validation data were collected in Tempe, Arizona via transects during five field campaigns between 2014 and 2017. Transects were conducted across seven locations in two to three-hour intervals from 6:00 to 23:00 LST with a Kestrel meter and thermal camera (2014-2015) and the mobile instrument platform MaRTy (2017). Observations across diverse urban forms, sky view factors, and seasons covered a wide range of solar radiation regimes from a minimum TMRT of 8.7 °C to a maximum of 84.9 °C. Both models produced large simulation errors across regimes with RMSE ranging from 8 °C to 12 °C (RayMan) and 11.2 °C to 16.1 °C (ENVI-met), exceeding a suggested TMRT accuracy of ±5 °C for heat stress studies. RayMan model errors were largest for engineered enclosed spaces, complex urban forms, and extreme heat conditions. ENVI-met was unable to resolve intra-domain spatial variability of TMRT and exhibited large errors with RMSE up to 25.5 °C for engineered shade. Both models failed to accurately simulate TMRT for hot conditions. Errors varied seasonally with overestimated TMRT in the summer and underestimated TMRT in the winter and shoulder seasons. Results demonstrate that models should not be used under micrometeorological or morphological extremes without in-situ validation to quantify errors and assess directional bias due to model limitations.

Outdoor thermal comfort in various microentrepreneurial settings in hot humid tropical Kolkata: Human biometeorological assessment of objective and subjective parameters.

Extreme heat and associated health risks increasingly become threats to urban populations, especially in developing countries of the tropics. Although human thermal exposure in cities has been studied across the globe, current narratives insufficiently discuss mixed-used spaces, informal economic activity settings, and informal settlements. This study assessed outdoor human thermal comfort in the tropical city of Kolkata, India where uncomfortable hot and humid climatic conditions prevail year-round. Thermal Comfort Perception Surveys (TCPS) and biometeorological observations were conducted during summer and winter in three microentrepreneurial neighborhoods (Kumartuli, Boipara, and Mallickghat). A one-way ANOVA was performed to investigate the variance in Physiologically Equivalent Temperature (PET) values of 318 survey samples across neighborhoods. Through multiple linear regression and ANCOVA, significant relationships were established between various climatic and non-climatic parameters. No respondent reported a neutral thermal sensation during the summer. Annual neutral PET across neighborhoods was 23.6 °C with a neutral PET range of 19.5 °C to 27.6 °C. Annual neutral PET was 22.7 °C and 26.5 °C in Mallickghat and Boipara, respectively. Respondents in Boipara were more sensitive towards warmer sensation than in Mallickghat. Even in the winter, people reported warmer sensation votes. PET was a better predictor of the mean Thermal Sensation Vote (mTSV) compared to air temperature. In a few cases, acclimatization and expectations improved thermal comfort. Results can be useful in formulating strategies towards improving outdoor microclimate and heat health in tropical cities.

Investigation of extensive green roof outdoor spatio-temporal thermal performance during summer in a subtropical monsoon climate.

The thermal performance of green roofs is usually site-specific and changes temporally. Hence, thermal performance evaluation is necessary to optimize green roof design and its cooling effect. In this paper, we evaluated the outdoor spatio-temporal performance of a full-scale extensive green roof (EGR) in Nanjing, China throughout a summer at three heights (30, 60and 120cm). We found the EGR exhibited an overall slight diurnal cooling effect at all three heights (-0.09, -0.23, and - 0.09 °C, respectively), but there was an obvious warming effect at a couple of specific hours during daytime. Especially on sunny days, the maximum warming effect at all three heights was 1.59, 0.59, and 0.38 °C, respectively. During the night, the EGR had a pronounced cooling effect of -0.63, -0.40, and - 0.15 °C, respectively. Among the weather scenarios, sunny days had the highest impact on the EGR's thermal performance, while effects were less pronounced on cloudy and rainy days. The average range of hourly air temperature difference at 30 cm between EGR and a bare roof on selected days was 4.02 (sunny), 2.67 (cloudy), and 0.74 °C (rainy). The results of multiple-regression analyses showed strong and significant correlations of air temperature difference between the EGR and a bare roof with differences in relative humidity, net radiation, several measures of soil and surface temperature, and soil moisture as well as average solar radiation, air temperature and wind speed. The results implied that both the components of the EGR, such as green vegetation and the soil substrate layer, and the microclimate created by the EGR can feed back and contribute to the thermal performance of an EGR. Through this full-scale EGR research in a subtropical monsoon climate, we provide the scientific basis and actionable practices for green roof planning and design to alleviate the urban heat island effect towards designing climate-resilient cities.

Micrometeorological determinants of pedestrian thermal exposure during record-breaking heat in Tempe, Arizona: Introducing the MaRTy observational platform.

We report the first set of urban micrometeorological measurements for assessment of pedestrian thermal exposure during extreme heat in a dry climate. Hourly measurements of air temperature, humidity, wind speed and six-directional shortwave and longwave radiation were recorded with a mobile human-biometeorological station (MaRTy) from 10:00 to 21:00 local time, June 19, 2016, at 22 sites that include diverse microscale urban land cover. Sky view factor (SVF) and 360° pervious and impervious view factors for each location were calculated from six-directional fisheye photographs. Mean radiant temperature (TMRT) was determined using the six-directional method. Three-dimensional radiation budgets were decomposed into directional weighted shortwave and longwave radiation components to create a distinct TMRT profile for each site and determine the main drivers of TMRT and thermal exposure. Air temperature peaked locally at 48.5 °C, with a maximum TMRT of 76.4 °C at 15:00 MST in an east-west building canyon. Longwave radiation measured by laterally-oriented sensors dominated the TMRT budget, suggesting the importance of cooling vertical surfaces adjacent to pedestrians. Lateral shortwave radiation contributions were most spatially and temporally variable across TMRT profiles, reflecting the diverse shade conditions. The largest radiation fluxes contributing to TMRT were particularly sensitive to shade and secondarily to ground cover. Trees reduced afternoon TMRT up to 33.4 °C but exhibited a clear TMRT increase of up to 5 °C after sunset; during daytime, trees generated ground cover-dependent longwave radiant cooling or warming. Replacement of impervious with pervious ground cover cooled TMRT at all measurement times, even under dense tree shade. While recent work has found that adaptation cannot offset projected urban air temperature increases, outdoor thermal exposure depends on additional micrometeorological variables, including shortwave and longwave radiation, indicating the need and the opportunity to create pedestrian spaces that are radiantly cool within the context of future urban heat.