Urban Air Temperature Research Articles

Study on Microclimate and Thermal Comfort in Small Urban Green Spaces in Tokyo, Japan—A Case Study of Chuo Ward

Small urban green spaces are abundant in densely populated urban areas, but little is known about their impact on the urban heat island effect and thermal comfort. Therefore, this study selected as research sites four small urban green spaces in a typical high-density built-up area, Chuo Ward in Tokyo, Japan. The ENVI-met software 5.1.1 simulation method was used to analyze these sites’ microclimate and thermal comfort conditions. The following are the results: (1) Small urban green spaces significantly reduce urban air temperatures, particularly during hot weather, with temperature reductions ranging from 2.40 °C to 2.67 °C, consistently lower than the highest temperatures in Tokyo’s Chuo Ward, mainly between 1:00 and 2:00 p.m. (2) Thermal comfort analysis indicates that small urban green spaces can significantly improve urban thermal comfort during the day, particularly around noon, by reducing one or two thermal comfort levels compared to typical urban street blocks. However, these differences gradually diminish throughout the evening and night, and thermal comfort inside and outside green spaces becomes more uniform. (3) Green space size is not the only factor influencing thermal comfort; the layout of plants within the green space and the layout of the surrounding buildings also have an impact. Despite their small size, even small green spaces can significantly enhance comfort. This study highlights the need to promote urban sustainability through the extensive integration of small green spaces in dense urban environments. Small green spaces can serve as a high-frequency, low-cost solution for environmental sustainability by addressing the increasingly severe urban heat island effect as well as environmental challenges that in the urbanization process.

Impacts of urban air temperature and humidity on building cooling and heating energy demand in 15 cities of eastern China

Urban climates could significantly impact building energy consumption. This study aimed to investigate the impact of urban-rural disparities in temperature and humidity on building cooling and heating demand in multiple cities. Fixed stations were established using temperature/humidity loggers deployed at 17 pairs of urban and rural sites in 15 cities in eastern China. The study analyzed the differences in air temperature (ΔTa), relative humidity (ΔRH), and dew-point temperature (ΔTdew) between the urban and rural sites. These climatic data were then used as inputs for the energy simulation of a residential building. The following results were revealed: (1) ΔTa was consistently higher at night than during the day. In comparison with the rural reference sites, the RH values at all urban sites were lower; however, some urban sites displayed lower Tdew, while others exhibited higher Tdew. (2) Regarding annual total energy demands, urban buildings exhibited variable changes in energy components compared to rural buildings. Sensible cooling demands increased by 10 %–36 %, while heating demands decreased by 9 %–30 %. Latent cooling demands ranged from a decrease of 12 % to an increase of 17 %. As a result, overall changes in annual total energy demands (cooling + heating) were relatively small, ranging from −2 % to +7 %. (3) Regarding daily total energy demands, a 1 °C increase in ΔTa led to a 12.1 % increase in sensible cooling demand and an 8 % decrease in heating demand. At the same time, a 1 °C variation in ΔTdew corresponded to a 14.6 % change in latent cooling demand. (4) Regarding the daily peak load, modifications in local temperature and humidity in urban areas had a more pronounced impact on daily peak heating loads than on daily peak cooling loads. This study could enhance our understanding of how urban local climates affect building cooling and heating energy demands.

Quantifying threshold and scale response of urban air and surface temperature to surrounding landscapes under extreme heat

Studies have shown that tree canopies contribute to the reduction of both air temperature (Ta) and surface temperature (Ts) in urban areas, while impervious surfaces contribute to an intensified thermal burden. However, rare studies have investigated the threshold effect of surrounding landscapes (tree canopy and impervious surface) — the specific level of landscape coverage at which its influence on temperature is most significant, with specific attention to the scale-dependency, especially under extreme heat. Here we analyzed the influence of the surrounding landscapes on Ta and Ts and their respective threshold effects across five spatial scales, with radii of 20, 40, 60, 80 and 100 m. We obtained Ta through mobile measurements and Ts from data collected by the Unmanned Aerial Vehicle (UAV) on three typical streets in Shanghai at dawn, noon, and sunset during extreme heat. The results indicated that: (1) For most scales of analysis, Ta at noon exhibited a decrease with the increase in tree canopy coverage and an increase with the rise in impervious surface coverage; (2) Across all scales, the thresholds of tree canopy and impervious surface coverage were in the ranges of 20%–45 % and 40%–70 % respectively; (3) For large scales of analysis (60–100 m), the threshold coverage of impervious surface that impacts Ts gradually decreased with the increase of scale. The findings of this study provide insightful implications for climate mitigation and adaption under extreme heat.

Interpolation, Satellite-Based Machine Learning, or Meteorological Simulation? A Comparison Analysis for Spatio-temporal Mapping of Mesoscale Urban Air Temperature

AbstractFine-resolution spatio-temporal maps of near-surface urban air temperature (Ta) provide crucial data inputs for sustainable urban decision-making, personal heat exposure, and climate-relevant epidemiological studies. The recent availability of IoT weather station data allows for high-resolution urban Ta mapping using approaches such as interpolation techniques or machine learning (ML). This study is aimed at executing these approaches and traditional numerical modeling within a practical and operational framework and evaluate their practicality and efficiency in cases where data availability, computational constraints, or specialized expertise pose challenges. We employ Netatmo crowd-sourced weather station data and three geospatial mapping approaches: (1) Ordinary Kriging, (2) statistical ML model (using predictors primarily derived from Earth Observation Data), and (3) weather research and forecasting model (WRF) to predict/map daily Ta at nearly 1-km spatial resolution in Warsaw (Poland) for June–September and compare the predictions against observations from 5 meteorological reference stations. The results reveal that ML can serve as a viable alternative approach to traditional kriging and numerical simulation, characterized by reduced complexity and higher computational speeds within the domain of urban meteorological studies (overall RMSE = 1.06 °C and R2 = 0.94, compared to ground-based meteorological stations). The results have implications for identifying the urban regions vulnerable to overheating and evidence-based urban management in response to climate change. Due to the open-sourced nature of the applied predictors and input parsimony, the ML method can be easily replicated for other EU cities.

Application of local climate zones combined with machine learning to predict the impact of urban structure patterns on thermal environment

The urban heat island effect is a pertinent concern and estimating the thermal stress in cities is a crucial research goal. Most relevant studies have focused on the relationship between urban structures and high air temperatures, ignoring the effect of different combination of urban structures on high air temperature identifications. In this study, an efficient method was developed for identifying the thermal risk based on combination of urban structures to explore the relationship between urban environment and meteorological conditions. After the preprocessing of LCZ and meteorological data, data were input into the convolutional neural network (CNN) model constructed in this study. After the training process, the accuracy rate of the proposed model increased as the sampling rate increased, and the model achieved a high accuracy rate of 81.97% at 50% sampling. Furthermore, the model performed particularly well at identifying low and medium level of thermal risk. This means that the model could estimate the classes of thermal risk in most areas in Taipei City. The feasibility of using the proposed CNN model to identify the level of thermal risk was proven; the model is a convenient tool for estimating thermal stress in different areas with incomplete air temperature data.

Identifying and monitoring the Urban Heat Island in the compact Mediterranean city using satellite imagery and in-situ measurement data

The combination of the Urban Heat Island (UHI) phenomenon and the increased frequency and intensity of summer heat waves causes increased energy demand and consumption for air cooling and augmented thermal discomfort and heat-related morbidity and mortality in urban areas. This circumstance is particularly problematic in areas where a rise in the mean air temperature can be already observed, like the Mediterranean Basin. Although the UHI effect is a well-known phenomenon, a detailed investigation of the relationship between urban morphology and temperature distributions in actual compact Mediterranean cities needs further investigation. Therefore, the present study is conducted by employing satellite imagery and in-situ measurements to monitor the UHI phenomenon and identify urban hotspots in several cities in Italy. It should be noted that the selected cities differ in latitude, climate, size, altitude, and location, allowing the investigation of the relationship between urban morphologies, air, and surface temperature in different contexts. This paper reports the methodology developed to carry out ongoing research and some preliminary results.

Spatially Explicit Correction of Simulated Urban Air Temperatures Using Crowdsourced Data.

Urban climate model evaluation often remains limited by a lack of trusted urban weather observations. The increasing density of personal weather sensors (PWSs) make them a potential rich source of data for urban climate studies that address the lack of representative urban weather observations. In our study, we demonstrate that carefully quality-checked PWS data not only improve urban climate models' evaluation but can also serve for bias correcting their output prior to any urban climate impact studies. After simulating near-surface air temperatures over London and south-east England during the hot summer of 2018 with the Weather Research and Forecasting (WRF) Model and its building Effect parameterization with the building energy model (BEP-BEM) activated, we evaluated the modeled temperatures against 402 urban PWSs and showcased a heterogeneous spatial distribution of the model's cool bias that was not captured using official weather stations only. This finding indicated a need for spatially explicit urban bias corrections of air temperatures, which we performed using an innovative method using machine learning to predict the models' biases in each urban grid cell. This bias-correction technique is the first to consider that modeled urban temperatures follow a nonlinear spatially heterogeneous bias that is decorrelated from urban fraction. Our results showed that the bias correction was beneficial to bias correct daily minimum, daily mean, and daily maximum temperatures in the cities. We recommend that urban climate modelers further investigate the use of quality-checked PWSs for model evaluation and derive a framework for bias correction of urban climate simulations that can serve urban climate impact studies.

Investigating the relationship between air temperature and the intensity of urban development using on-site measurement, satellite imagery and machine learning

Given the seriousness of the urban heat problem, the relationship between urbanization and air temperature has become a critical concern worldwide. In this study, common urban planning indicators, including the building coverage ratio (BCR), floor area ratio (FAR), and fractional vegetation cover (FVC), were extracted from satellite images to determine the intensity of urban development. On-site measurements and machine learning (ML) were used to observe and analyze the relationship between the intensity of urban development and air temperature. From the on-site measurement results, the air temperature in downtown Taipei decreased by an average of approximately 0.32 °C with every 10 % increase in the FVC. However, it increased by an average of approximately 0.28 °C and 0.03 °C with every 10 % increase in the BCR and FAR, respectively. The results obtained from the ML models demonstrated the same trend, with minor differences from the on-site measurement results, which were regarded as reasonable and acceptable. In this study, a more convenient method was proposed to extract urban planning indicators, describe the intensity of urban development within an area, and help estimate air temperature in areas without measuring instruments. The relationship determined herein may aid in the decision-making process of the balance of urbanization and vegetation.

Assessment of Land Surface Temperature Variations and Implications of Land Use/Land Cover Changes: A Case of Malappuram Urban Agglomeration Region, Kerala, India

Urbanization is taking place faster, and urban air temperatures are gradually rising in all cities across the world. Uncontrolled and unplanned urbanization leads to constant environmental threats and can alter local and regional climates. According to the survey published by Economist Intelligence Unit, in India, Kerala's Malappuram district ranks first among the fastest-growing urban areas globally, with a 44.05% growth rate. Hence, the present study aims to identify the hotspot regions of extreme heat within the Malappuram urban agglomeration region and suggest strategies for its improvement. The split-window algorithm retrieved land surface temperature (LST) for 1991, 1998, 2014, and 2020 using Landsat 5 ETM and Landsat 8 satellite imageries. A rising trend in LST has been detected in the last 30 years, and the mean value has increased by 1.70°C within the region. Among the selected hotspots, an LST increase of 1.84°C was observed for those areas with the highest increase in urban density with decreased vegetation. The increasing impact of urbanization and the subsequent change in land use patterns at the cost of greenery have caused a substantial effect on the local climate. Accordingly, planning and policy directions are proposed for the local government that can help provide awareness to the people through the effective implementation of mitigation measures.

Dynamically downscaled projection of urban outdoor thermal stress and indoor space cooling during future extreme heat

While projected urban air temperatures under climate change and urbanization have received attention, projections of pedestrian thermal stress are scarce and usually produced by statistical downscaling. In this study, we present and evaluate a dynamical downscaling methodology to assess urban outdoor thermal exposure. We dynamically downscale Earth system model output with a mesoscale weather, urban canopy and building energy model (WRF-BEP-BEM), then use the downscaled output as boundary conditions for a microscale model (TUF-Pedestrian) to determine metre-scale mean radiant temperature. Using this methodology, we assess outdoor heat exposure and air conditioning loading during typical heatwaves at the start and end of century in Phoenix and Toronto.Results reveal that air conditioning loading would double in Phoenix by the end of century under an RCP 8.5 climate and urban development scenario; and it may double in Toronto even without urban development. This is compounded by up to 2 additional hours/day of extreme heat stress in Phoenix (5.5 additional hours/day of strong heat stress in Toronto). Using our dynamical downscaling methodology, we find that projected climate change-induced increases in outdoor heat stress largely result from higher air temperatures (Phoenix: 69–87%; Toronto: 60–64%), with smaller contributions from increased mean radiant temperature and humidity.

Citizen and machine learning-aided high-resolution mapping of urban heat exposure and stress

Through conversion of land cover to more built-up, impervious surfaces, cities create hotter environments than their surroundings for urban residents, with large differences expected between different parts of the city. Existing measurements of ambient air temperature and heat stress, however, are often insufficient to capture the intra-urban variability in heat exposure. This study provides a replicable method for modeling air temperature, humidity, and moist heat stress over the urban area of Chapel Hill while engaging citizens to collect high-temporal and spatially-resolved air temperature and humidity measurements. We use low-cost, consumer-grade sensors combined with satellite remote sensing data and machine learning to map urban air temperature and relative humidity over various land-cover classes to understand intra-urban spatial variability of ambient heat exposure at a relatively high resolution (10 m). Our findings show that individuals may be exposed to higher levels of air temperature and moist heat stress than weather station data suggest, and that the ambient heat exposure varies according to land cover type, with tree-covered land the coolest and built-up areas the warmest, and time of day, with higher air temperatures observed during the early afternoon. Combining our resulting dataset with sociodemographic data, policymakers and urban planners in Chapel Hill can use data output from this method to identify areas exposed to high temperature and moist heat stress as a first step to design effective mitigation measures.

Deciphering the sensitivity of urban canopy air temperature to anthropogenic heat flux with a forcing-feedback framework

The sensitivity of urban canopy air temperature () to anthropogenic heat flux () is known to vary with space and time, but the key factors controlling such spatiotemporal variabilities remain elusive. To quantify the contributions of different physical processes to the magnitude and variability of (where represents a change), we develop a forcing-feedback framework based on the energy budget of air within the urban canopy layer and apply it to diagnosing simulated by the Community Land Model Urban over the contiguous United States (CONUS). In summer, the median is around 0.01 over the CONUS. Besides the direct effect of on , there are important feedbacks through changes in the surface temperature, the atmosphere–canopy air heat conductance (), and the surface–canopy air heat conductance. The positive and negative feedbacks nearly cancel each other out and is mostly controlled by the direct effect in summer. In winter, becomes stronger, with the median value increased by about 20% due to weakened negative feedback associated with . The spatial and temporal (both seasonal and diurnal) variability of as well as the nonlinear response of to are strongly related to the variability of , highlighting the importance of correctly parameterizing convective heat transfer in urban canopy models.

Outdoor Thermal Comfort of Urban’s Pedestrian in Tropical City of Kuala Lumpur

In recent years, urbanization in the city have given impact to the climate change of the environment. The increase in urban air temperature have decrease the thermal comfort or ruin the experience of the pedestrian in outdoor spaces. This research explores the impact of air temperature on pedestrian outdoor thermal comfort in shaded spaces of tropical city of Kuala Lumpur. Findings are presented from field measurement based on the environmental parameters on case study area. Surveys are conducted which includes 199 randomly selected pedestrians to subjectively observe their feeling and preferences of the outdoor thermal comfort towards the climatic outdoor temperature. Hence, the result concluded that the urban pedestrian walkway has a significantly high temperature. However, the results show that presence of shading structure improve their comfort being in outdoor spaces. This conclude that thermal sensation of pedestrian can be improved with shading structure and it can increase the walkability of pedestrians. The space can also increase the pedestrian’s comfort with help of mechanical ventilation. Thus, it is recommended for outdoor pedestrian spaces to provide shading and have proper ventilation to reduce heat and make pedestrian feels comfortable outdoor.

Global mapping of urban thermal anisotropy reveals substantial potential biases for remotely sensed urban climates

Urban thermal anisotropy (UTA) drastically impacts satellite-derived urban surface temperatures and fluxes, and consequently makes it difficult to gain a more comprehensive understanding of global urban climates. However, UTA patterns and associated biases in observed urban climate variables have not been investigated across an adequate number of global cities with diverse contexts; nor is it known whether there are globally measurable factors that are closely related to the UTA intensity (UTAI, quantified as the maximum difference between the nadir and off-nadir urban thermal radiation). Here we investigate the UTAI over more than 5500 cities worldwide using multi-angle land surface temperature (LST) observations from 2003 to 2021 provided by Moderate Resolution Imaging Spectroradiometer (MODIS). The results show that the global mean UTAI can reach 5.1, 2.7, 2.4, and 1.7 K during summer daytime, winter daytime, summer nighttime, and winter nighttime, respectively. Using nadir LST observations as a reference, our analysis reveals that UTA can lead to an underestimation of satellite-based urban surface sensible heat fluxes (H) by 45.4% and surface urban heat island intensity (Is) by 43.0% when using LST observations obtained from sensor viewing zenith angles (VZAs) of ±60°. Practitioners can limit the biases of H and Is within ±10% by using LSTs from sensor VZAs within ±30°. We also find that UTAI is closely related to urban impervious surface percentage and surface air temperature across global cities. These findings have implications for angular normalization of satellite-retrieved instantaneous LST observations across cities worldwide.

How can we combine urban cooling strategies to effectively cool cities over the entire diurnal cycle?

The urban heat island effect generally peaks in the night/evening hours and, in some cases, an urban cool island effect during daytime has been reported. These patterns are observed mainly because of the widespread use of impervious and thermally massive materials (like concrete and asphalt) in the built environment and their ability to store energy during the daytime and release it at night. Unfortunately, most urban cooling strategies, such as cool (white), green, and blue spaces, provide better thermal performance during the daytime than at night. Hence, such solutions are not ideal for nighttime heat mitigation. In this study, we investigate the effect of the thermal storage capacity of existing buildings on nighttime urban air temperature for a hot arid city—Phoenix, and a hot humid city—Atlanta. The study uses regional scale atmospheric modeling to compare the nighttime urban cooling capability of thermally light buildings (Cross-laminated timber buildings in this case) with concrete buildings. The results show that the adoption of thermally light buildings reduce nighttime air temperatures, and slightly increases daytime air temperatures. On the other hand, cool roof adaption could reduce urban air temperature significantly during the daytime and slightly at night. Therefore, together with cool roofs, thermally light buildings may be able to cool the surrounding air by an average of about 1 °C throughout the diurnal cycle, providing thermal comfort and reducing cooling demand during all hours.

Machine Learning-Based Modeling of Air Temperature in the Complex Environment of Yerevan City, Armenia

Machine learning (ML) was used to assess and predict urban air temperature (Tair) considering the complexity of the terrain features in Yerevan (Armenia). The estimation was performed based on the Partial Least-Squares Regression (PLSR) model with a high number (30) of input variables. The relevant parameters include a newly purposed modification of spectral index IBI-SAVI, which turned out to strongly impact Tair prediction together with land surface temperature (LST). Cross-validation analysis on temperature predictions across a station-centered 1000 m circular area revealed quite a high correlation (R2Val = 0.77, RMSEVal = 1.58) between the predicted and measured Tair from the test set. It was concluded the remote sensing is an effective tool to estimate Tair distribution where a dense network of weather stations is not available. However, further developments will include incorporation of additional weather parameters from the weather stations, such as precipitation and wind speed, as well as the use of non-parametric ML techniques.

Study of effects of different vegetation model parameter settings on quantitative CFD simulation of urban spatial air temperature and wind-field

ABSTRACT The rapid acceleration of urbanization has a serious impact on the urban ecological environment. It is of great importance to carry out numerical simulation research on urban spatial thermal environment for improving urban ecological environment. At present, there are few studies on the influence and accuracy evaluation of different vegetation settings in computational fluid dynamics (CFD) on the simulation results. Aiming to quantify impacts of vegetation changes and vegetation model settings on the numerical simulation of urban three-dimensional thermal environment, this study 1) based on Gaofen-2 remote sensing data and CFD model, the wind and heat environment at the block scale was simulated and analysed; 2) The vegetation model is assumed to be three models including cold source, constant temperature wall, and porous medium. Based on the heat transfer and cooling mechanism of vegetation, these three different vegetation models and their related CFD parameter settings are used to simulate the 3D spatial thermal environment. The research results show that the distribution of temperature has a great correlation with the direction of wind. The temperature distribution at different heights at night did not show significant differences. Due to the obvious convective heat transfer between the building surface and the atmosphere, the temperature in the upwind direction is significantly lower than that in the downwind direction. At a height of 2 m and below, setting the vegetation as the wall model has the best performance, and at a height above 5 m, the results of the wind and heat environment of the three setting methods are basically similar. The wind environment results of the same vegetation setting are different at different heights.

The effect of building height on thermal properties and comfort of a housing project in the hot arid climate of the UAE

A city’s microclimate is greatly impacted by urbanization. The ratio of building height to street width affects the thermal properties of urban canyons. This characteristic is one of the main elements that control the thermal radiation emitted and how much solar radiation is absorbed, causing the urban air temperature to be much greater than in rural areas (urban heat island effect). The main aim of this study is to examine the thermal effect of the variations in the height of housing buildings on the urban layout and canyons in the hot arid climate of the UAE. The study used a qualitative method based on ENVI-met software and a case study of an existing housing project to investigate the current situation and the future thermal conditions of proposed configurations. The study investigated two groups of configurations with unified and diverse heights. The results of the study found that the best case among the first group of configurations with unified heights was U3, which had unified mass heights reaching 20 m height, the highest H/W ratio, and the lowest sky view factor; it recorded 0.5°C reduction in the 2:00 p.m. air temperature compared to the base case. The results also revealed that in the case of diverse heights, it is better to locate the highest masses in the hot wind direction. The D2, with highest masses of 20 m height that were located only on the north and west sides of the area blocking the hot north-west prevailing wind, recorded a reduction about 0.9°C compared to the base case. Moreover, in the cases with lower air temperature, U3 and D2 recorded the best predicted mean vote readings, especially in the daytime, when the air temperature is highest.

Assessing the impacts of physiography refinement on Stockholm summer urban temperature simulated with an offline land surface model

Urban climate information at high spatial resolution (tens or hundreds meter scale) is needed for local climate adaptation. Due to the high demand for computational resources by convection-permitting models (CPMs) that can resolve urban areas and the general lack of accurate urban physiography information, the downscaling of climate data to sub-km scale is challenging. This work explores the feasibility of simulating urban air temperatures using a much less computationally expensive offline land surface model (LSM) with refined physiography. The method is based on the SURFEX LSM downscaling to 300 m of a set of CPM simulations with an original grid spacing of 3 km. Results show that the influence of different physiography in the CPM atmospheric forcing on the SURFEX simulations decreases with the increase of forcing height, and it becomes statistically non-significant at a forcing height of 50 m or above. On the other hand, the impacts of physiography change on 2-m temperature (T2m) simulated by the offline LSM are highly correlated when using different physiography in the CPM simulations, regardless of the forcing height. The refined physiography dataset was produced using open-access products, which enables the proposed method to be used in other regions.

Simulation of urban functional zone air temperature based on urban weather generator (UWG): a case study of Beijing, China

ABSTRACT Rapid urbanization has led to many urban thermal environment problems. Most studies focus on analysing the urban thermal environment from the perspective of land-use type, and often at a large scale. Thus, research of urban spatial thermal environment at a block scale with different urban functional zone (UFZ) becomes the focus of this paper. UFZ refers to urban planning units with similar socio-economic functions, which often have similar energy consumption and outdoor thermal environment. In this paper, the urban weather generator (UWG) model is used to quantitatively analyse the spatial thermal environment of Beijing. To accurately simulate the urban thermal environment, the underlying surface used in the simulation is replaced by the finely classified underlying surface of the urban functional area. In this paper, we first use POI + OSM data to divide the functional areas of Beijing. The recognition accuracy of functional area is 85.6%. Then, the UWG model is used to simulate the temperature and humidity differences in different functional areas, and finally the corresponding urban and rural weather stations were used to validate its accuracy. The results show that the model calibration method can effectively improve the simulation accuracy of UWG thermal environment, the R2 is increased by about 0.12, and the RMSE is reduced by about 0.84. The canopy temperature of different urban functional areas from high to low is: industrial area>commercial area>residential area>public area>road and traffic area>green space and square area. This model is more suitable for areas with relatively homogenous urban morphology and sparse urban vegetation. The thermal environment simulation results of the same urban functional area are basically the same, with slight differences in the temperature peaks, and the average temperature in the city centre is higher than that in the suburbs.