Surface urban heat island across 419 global big cities

Uncovering the Nexus between Urban Heat Islands and Material Stocks of Built Environment in 335 Chinese Cities

China's unprecedented rapid urbanization has dramatically reshaped the urban built environment, disrupting the thermal balance of cities. This disruption causes the urban heat island (UHI) effect, adversely affecting urban sustainability and public health. Although studies have highlighted the remarkable impacts of the built environment on UHIs, the specific effects of its various structures and components remain unclear. In this study, a multidimensional remote sensing data set was used to quantify the atmospheric UHIs across 335 Chinese cities from 1980 to 2020. In conjunction with stocks of three end-use sectors and three material groups, the impacts of gridded material stocks on UHI variations were analyzed. The findings reveal that building stocks exert a predominant influence in 48% of cities. Additionally, the extensive use of metal and inorganic materials has increased thermal stress in 220 cities, leading to an average UHI increase of 0.54 °C. The effect of organic materials, primarily arising from mobile heat sources, is continuously increasing. Overall, this study elucidates the effect of the functional structure and material composition of urban landscapes on UHIs, highlighting the complexities associated with the influence of the built environment on the urban heat load.

Comprehensive spatiotemporal evaluation of urban growth, surface urban heat island, and urban thermal conditions on Java island of Indonesia and implications for urban planning

Urban heat island (UHI) and thermal comfort conditions are among the impacts of urbanization, which have been extensively studied in most cities around the world. However, the comprehensive studies in Indonesia in the context of urbanization is still lacking. This study aimed to classify land use and land cover (LULC) and analyse urban growth and its effects on surface urban heat islands (SUHIs) and urban thermal conditions as well as contributing factors to SUHI intensity (SUHII) using remote sensing in the western part of Java Island and three focused urban areas: the Jakarta metropolitan area (JMA), the Bandung and Cimahi Municipalities (BC), and the Sukabumi Municipality (SKB). Landsat imagery from three years was used: 2000, 2009, and 2019. Three types of daytime SUHII were quantified, namely the SUHII of urban central area and two SUHIIs of urban sprawl area. In the last two decades, urban areas have grown by more than twice in JMA and SKB and nearly 1.5 times in BC. Along with the growth of the three cities, the SUHII in the urban central area has almost reached a magnitude of 6 °C in the last decade. Rates of land surface temperature change of the unchanged urban pixels have magnitudes of 0.25, 0.15, and 0.14 °C/year in JMA, SKB, and BC, respectively. The urban thermal field variance index (UTFVI) and discomfort index (DI) showed that the strongest SUHI effect was most prevalent in urban pixels and the regions were mostly in the very hot and hot categories. Anthropogenic heat flux and urban ratio have positive contributions to SUHII variation, while vegetation and water ratios are negative contributors to SUHII variation. For each city, the contributing factors have a unique magnitude that can be used to evaluate SUHII mitigation options.

Urban cemeteries: The forgotten but powerful cooling islands

Urban parks play a key role in UHI mitigation. However, the role of other prominent types of urban green infrastructure has not been comprehensively studied. Thus, the main objective of this study was to evaluate the role of cemeteries and allotments as cooling islands compared to the well-studied park areas. We assessed the LST of cemeteries, allotments and parks based on Landsat 8 TM images across the five largest German cities during summertime. Random forest regressions explain the LST spatial variability of the different urban green spaces (UGS) with spectral indices (NDVI, NDMI, NDBaI) as well as with tree characteristics (tree type, tree age, trunk circumferences, trunk height or canopy density). As a result, allotments were identified as the hottest UGS with the city means varying between 23.1 and 26.9 °C, since they contain a relatively high proportion of sealed surfaces. The LST spatial variability of allotment gardens was best explained by the NDVI indicating that fields with a higher percentage of flowering shrubs and trees reveal lower LST values than those covered by annual crops. Interestingly, cemeteries were characterized as the coolest UGS, with city means between 20.4 and 24.7 °C. Despite their high proportion of sealed surfaces, they are dominated by old trees resulting in intensive transpiration processes. Parks show heterogeneous LST patterns which could not be systematically explained by spectral indices due to the variability of park functionality and shape. Compared to parks, the tree-covered areas of cemeteries have a higher cooling potential since cemeteries as cultural heritage sites are well-protected allowing old tree growth with intensive transpiration. These findings underline the relevance of cemeteries as cooling islands and deepen the understanding of the role of tree characteristics in the cooling process.

Pattern of cooling benefits from ecospaces during urbanization: A case study of the Yangtze River Economic Belt

Urban ecological spaces are effective thermoregulators under global warming. However, the cooling efficiency of urban ecological spaces during the urbanization has not been studied comprehensively. Here, we investigate the spatio-temporal dynamics of Urban Cold Island (UCI) intensity in 11 typical cities of the Yangtze River Economic Belt (YREB). We determined the impact of ecological landscape trends on these dynamics by using GlobalLand and MODIS 8 d mean land surface temperature (LST) data for three periods (2000, 2010, and 2020), and the landscape pattern index and diversity index. We found that in the past 20 years, the built-up area has increased by sixfold; 62.53 % and 37.47 % of YREB were warming or cooling, with 71.22 % of the daytime cooling and 93 % of the nighttime warming. The average UCI intensity of YREB has increased from 0.518 to 0.847 and is negatively correlated with LST with a decreasing slope. As the UCI intensity of green spaces increased, that of blue spaces decreased. Surface area and landscape pattern are the key determinants of UCI intensity in blue and green spaces, respectively, especially the landscape shape index (LSI). Therefore, maintaining ecological spaces, enriching the structural integrity of green spaces, and improving blue space connectivity can help cities at different development levels cope with heat stress during regional urbanization.

Ground Urban Heat Island: Strengthening the Connection Between Spaceborne Thermal Observations and Urban Heat Risk Management

As urbanization progresses under a changing climate, urban populations face increasing threats from chronically higher heat exposures and more frequent extreme heat events. Understanding the complex urban thermal exposure patterns becomes crucial for effective heat risk management. The spatial advantage of satellite thermal observations positions surface urban heat islands (SUHI) as a primary measure for such applications at the city scale. However, satellite-inherent biases pose considerable uncertainties. To improve the representation of human-relevant heat exposure, this study proposes a simple but effective satellite-based measure- ground urban heat island (GUHI), focusing solely on radiant temperatures from urban ground elements. Leveraging ECOSTRESS land surface temperature product and radiation-based statistical downscaling, diurnally representative GUHIs were evaluated over NYC. The findings reveal that overall GUHI is consistently warmer than SUHI diurnally. However, GUHI exhibits complex spatial contrasts with SUHI, primarily influenced by vegetation coverage. Various indicators associated with urban structures and materials were examined, showing important but dissimilar roles in shaping the spatial dynamics of GUHI and SUHI. This study highlights the value of satellite thermal observations compared to air temperature while addressing uncertainties in widely adopted practices of using them. By improving the depiction of human-related urban heat patterns from Earth observations, this research offers valuable insight and more reliable measures to address the urgent requirements for urban heat risk management globally.

Mosquito population dynamics is shaped by the interaction among larval density, season, and land use

Understanding how variation in key abiotic and biotic factors interact at spatial scales relevant for mosquito fitness and population dynamics is crucial for predicting current and future mosquito distributions and abundances, and the transmission potential for human pathogens. However, studies investigating the effects of environmental variation on mosquito traits have investigated environmental factors in isolation or in laboratory experiments that examine constant environmental conditions that often do not occur in the field. To address these limitations, we conducted a semi-field experiment in Athens, Georgia using the invasive Asian tiger mosquito (Aedes albopictus). We selected nine sites that spanned natural variation in impervious surface and vegetation cover to explore effects of the microclimate (temperature and humidity) on mosquitoes. On these sites, we manipulated conspecific larval density at each site. We repeated the experiment in the summer and fall. We then evaluated the effects of land cover, larval density, and time of season, as well as interactive effects, on the mean proportion of females emerging, juvenile development time, size upon emergence, and predicted per capita population growth (i.e., fitness). We found significant effects of larval density, land cover, and season on all response variables. Of most note, we saw strong interactive effects of season and intra-specific density on each response variable, including a non-intuitive decrease in development time with increasing intra-specific competition in the fall. Our study demonstrates that ignoring the interaction between variation in biotic and abiotic variables could reduce the accuracy and precision of models used to predict mosquito population and pathogen transmission dynamics, especially those inferring dynamics at finer-spatial scales across which transmission and control occur.

A Heat Emergency: Urban Heat Exposure and Access to Refuge in Richmond, VA

The urban heat island effect exacerbates independent climate change-induced shifts toward longer, stronger, and more frequent heat extremes. Environmental inequity, driven by a history of racially motivated urban planning policies, has led particular demographics to bear the worst impacts of urban heat exposure and thus also climate change. These impacts cause adverse health outcomes in the form of heat emergencies. Through a novel demographic and spatial analysis of heat-related illness Emergency Medical Services data from Richmond, Virginia, this study investigates the relationships between heat health emergencies and intra-urban heat islands quantified through three heat exposure metrics. We also evaluate the accessibility of built refuge from urban heat in the form of public transit infrastructure, libraries, and government cooling centers in relation to these emergencies. We found that heat emergencies are inequitably distributed among racial, age, and socioeconomic groups in Richmond, particularly among residents identified as Male, Black or African American, 50+ years old, and experiencing mental health, intoxication, and/or homelessness. We found significant associations between the location of these heat emergencies and urban heat islands as estimated from remotely-sensed surface and community science-derived air temperature metrics, but not a co-estimated heat index. We also found that available refuge facilities are insufficiently located to protect individuals with reduced mobility across areas with the highest number of heat-related health emergencies. Community involvement in the mitigation and management of extreme heat threats, especially for those disproportionately impacted, is necessary to decrease the number of summertime heat illnesses.

Plasticity in resource allocation of the invasive Phytolacca americana: Balancing growth, reproduction, and defense along urban-rural gradients

In response to varying environments along urban and rural gradients, invasive plants may strategically allocate resources to enhance their invasiveness. However, how invasive plants balance their resources for growth, reproduction, and defense as responses to biotic and abiotic factors across these gradients remain unclear. We conducted field surveys on the growth, reproduction, and herbivory of the invasive species Phytolacca americana across diverse urban and rural habitats. Leaf samples were collected to analyze the nutritional content, primary and secondary metabolites. We found that plant growth rates, specific leaf area, leaf nitrogen content, and concentrations of flavonoids and saponins were higher in urban habitats, while reproduction, herbivory, and carbon-to‑nitrogen ratios were lower than those in rural habitats. We also found a trade-off between growth rate and herbivory, as well as trade-offs among defense traits associated with herbivory (e.g., leaf mass per area, the inverse of leaf nitrogen content, and carbon‑nitrogen ratio) and the production of metabolites associated with abiotic stress tolerance (e.g., soluble sugars, flavonoids, and saponins). As earlier studies showed low levels of genetic diversity within and between populations, our findings suggest that the urban-rural gradient patterns of resource allocation are primarily phenotypic plasticity in response to herbivory in rural areas and abiotic factors in urban areas. Our study sheds light on the mechanisms by which urbanization affects plant invasions and offers insights for the implementation of their management strategies.

The influence of climate and human factors on a regional heat island in the Zhengzhou metropolitan area, China

The development of urbanization and the establishment of metropolitan areas causes the urban heat island to cross the original single-city scale and form a regional heat island (RHI) with a larger influence range. Due to the decreasing distance between cities, there is an urgent need to reevaluate RHI for urban agglomerations, considering all cities instead of a conventional single-city perspective. The impact of climatic conditions and human factors on heat islands still lacks a general method and framework for systematic evaluation. Therefore, we used land and night light data as background conditions to study the diurnal and seasonal changes of heat islands in the Zhengzhou metropolitan area, China. Pearson correlation analysis and random forest regression analysis were then used to explore the influence of climatic conditions and human factors on RHI and its internal relationship. We found that the daytime RHI had strong spatial heterogeneity and seasonal differences from 2001 to 2020. The daytime RHI was stronger than nighttime in spring, summer, and autumn, and the nighttime RHI was stronger than daytime in winter. From spring to winter, RHI increased first and then decreased during the daytime, while the opposite was observed at night. In this study, temperature has a greater effect on daytime RHI; CO2 and NL have a greater effect on nighttime RHI. There was strong spatial heterogeneity in the effects of climatic conditions and human factors on the RHI, with climatic conditions contributing more to the daytime RHI in the northern mountainous areas, while human factors had a greater impact on the nighttime RHI in the main urban areas of each location. The results of this study highlight more targeted and informed strategies for RHI mitigation in the Zhengzhou metropolitan area and provide helpful insights into RHI evaluation in other urban agglomerations.

Thermal stress and comfort assessment in urban areas using Copernicus Climate Change Service Era 5 reanalysis and collected microclimatic data

In this initial study of a research project, this paper seeks to understand the thermal conditions in the cities of Lisbon and Munich, specifically focusing on Urban Heat Island intensity and on thermal comfort using the Universal Thermal Climate Index modeling data at the Local Climate Zone scale. Based on these datasets, Munich has exhibited more unfavourable thermal conditions than Lisbon. In terms of UHII, both cities have shown that low, medium, and high rise compact urban areas and bare rock or paved areas have the highest values, while sparsely built areas have the lowest. These results differ from the UTCI, which indicates that in Lisbon and Munich, these sparsely built areas as well as areas with low plants and vegetation are the most uncomfortable. In Munich, the population was exposed to very strong heat stress, while Lisbon experienced strong heat stress conditions. Conversely, low, medium, and high rise compact urban areas and densely wooded areas in Munich, and scattered trees areas and large low-rise urban areas in Lisbon, have demonstrated the lowest monthly mean and average maximum values. These results will be further explored in future studies in the city of Lisbon and cross-checked with data obtained from roving missions. This will enable a more detailed temporal and local analysis.

Application of the socioecological model to mitigate risks of heat illness

BACKGROUND

The socio-ecological model (SEM) is a widely used framework that can be applied to heat-related illness (HRI) in the context of multiple influencing factors that exist in society. Leaders and policymakers must intervene to mitigate the deleterious effects of climate change on those at risk.

PURPOSE

The purpose is to introduce the SEM as a framework to address the complex factors contributing to the impact of excess heat.

METHODS

Conceived through the SEM, the compounding and cumulative impact of excess heat resulting in HRI is operationalized.

DISCUSSION

The SEM provides a structure for understanding the complex nature of climate change and HRI and proposed interventions. The prevention of HRI is dependent on actions, related to practice, education, research, and advocacy across multiple levels of the SEM. The SEM has the potential to target HRI at all levels of society to reduce the harm of excess heat.

Urban heat load assessment in Zagreb, Croatia: a multi-scale analysis using mobile measurement and satellite imagery

A limited number of meteorological stations and sparse data challenge microclimate assessment in urban areas. Therefore, it is necessary to complement these data with additional measurements to achieve a denser spatial coverage, enabling a detailed representation of the city's microclimatic features. In this study, conducted in Zagreb, Croatia, mobile air temperature measurements were utilized and compared with satellite-derived land surface temperature (LST). Here, air temperature measurements were carried out using bicycles and an instrument with a GPS receiver and temperature probe during a heat wave in June 2021, capturing the spatial pattern of air temperature to highlight the city's microclimate characteristics (i.e. urban heat load; UHL) in extremely hot weather conditions. Simultaneously, remotely sensed LST was retrieved from the Landsat-8 satellite. Air temperature measurements were compared to city-specific street type classification, while neighbourhood heat load characteristics were analysed based on local climate zones (LCZ) and LST. Results indicated significant thermal differences between surface types and urban forms and between street types and LCZs. Air temperatures reached up to 35 °C, while LST exceeded 40 °C. City parks, tree-lined streets and areas near blue infrastructure were 1.5-3 °C cooler than densely built areas. Temperature contrasts between LCZs in terms of median LST were more emphasised and reached 9 °C between some classes. These findings highlight the importance of preserving green areas to reduce UHL and enhance urban resilience. Here, exemplified by the city of Zagreb, it has been demonstrated that the use of multiple datasets allows a comprehensive understanding of temperature patterns and their implications for urban climate research.

Imprint of urbanization on snow precipitation over the continental USA

Urbanization can alter the local climate through modifications in land-atmosphere feedback. However, a continental scale evaluation of its influence on precipitation phase remains unknown. Here, we assess the difference in the likelihood of snow dominated events (SDEs) over 7,415 urban and surrounding non-urban (buffer) regions across the continental United States. Among 4,856 urban-buffer pairs that received at least five SDEs per year, 81% of urban regions are characterized by a smaller snow probability, 99% by a lower frequency of SDEs, and 57% by faster declining trends in SDEs compared to their buffer counterparts. Notably, urban (buffer) regions with lower snow probability are often characterized by higher net incoming and sensible energy fluxes as compared to buffer (urban) regions, thus highlighting the influence of land-energy feedback on precipitation phase. Results highlight a clear imprint of urbanization on precipitation phase and underscore the need to consider these influences while projecting hydro-meteorological risks.

Intensifying urban imprint on land surface warming: Insights from local to global scale

Increasing urbanization exacerbates surface energy balance perturbations and the health risks of climate warming; however, it has not been determined whether urban-induced warming and attributions vary from local, regional, to global scale. Here, the local surface urban heat island (SUHI) is evidenced to manifest with an annual daily mean intensity of 0.99°C-1.10°C during 2003-2018 using satellite observations over 536 cities worldwide. Spatiotemporal patterns and mechanisms of SUHI tightly link with climate-vegetation conditions, with regional warming effect reaching up to 0.015°C-0.138°C (annual average) due to surface energy alterations. Globally, the SUHI footprint of 1,860 cities approximates to 1% of the terrestrial lands, about 1.8-2.9 times far beyond the urban impervious areas, suggesting the enlargements of the imprint of urban warming from local to global scales. With continuous development of urbanization, the implications for SUHI-added warming and scaling effects are considerably important on accelerating global warming.

The resilience-sustainability-quality of life nexus

Social-ecological resilience (SER), sustainability (SUS) and quality of life including wellbeing (QOL) are distinct but highly interconnected goals that are essential for human survival, development, and adaptation to environmental and socioeconomic changes. However, these goals are often addressed in silos or pairs, and a framework explicitly linking all three is currently lacking. In this paper, we present the SER-SUS-QOL nexus framework and discuss how social and ecological changes affect these goals. The main thrust of this nexus is that efforts toward SER and SUS need to be explicitly framed in terms of the ultimate goal, which has to be the QOL of the present and future generations. We contend that critically assessing the dynamic interplay between SER, SUS and QOL, as well as the factors impacting them, can help promote transformative governance and planning in the contemporary era. Understanding the multifaceted interrelationship between these goals is crucial to empower planners and decision-makers to navigate the complexities of our rapidly changing world and address the challenges brought by interrelated social and ecological changes. To deepen our understanding of this nexus, more research is needed on various issues, including, but not limited to, trade-offs and synergies, cascading effects, spatiotemporal dynamics of SER, SUS and QOL outcomes, potential inconsistencies between SER and transformative changes toward SUS, and the role of local/indigenous/traditional ecological knowledge in transformative governance and planning.

Mapping urban form into local climate zones for the continental US from 1986-2020

Urbanization has altered land surface properties driving changes in micro-climates. Urban form influences people's activities, environmental exposures, and health. Developing detailed and unified longitudinal measures of urban form is essential to quantify these relationships. Local Climate Zones [LCZ] are a culturally-neutral urban form classification scheme. To date, longitudinal LCZ maps at large scales (i.e., national, continental, or global) are not available. We developed an approach to map LCZs for the continental US from 1986 to 2020 at 100 m spatial resolution. We developed lightweight contextual random forest models using a hybrid model development pipeline that leveraged crowdsourced and expert labeling and cloud-enabled modeling - an approach that could be generalized to other countries and continents. Our model achieved good performance: 0.76 overall accuracy (0.55-0.96 class-wise F1 scores). To our knowledge, this is the first high-resolution, longitudinal LCZ map for the continental US. Our work may be useful for a variety of fields including earth system science, urban planning, and public health.

Heterogeneous effects of the availability and spatial configuration of urban green spaces on their cooling effects in China

The impacts of the availability and spatial configuration of urban green spaces (UGS) on their cooling effects can vary with background climate conditions. However, large-scale studies that assess the potential heterogeneous relationships of UGS availability and spatial configuration with urban thermal environment are still lacking. In this study, we investigated the impacts of UGS availability and spatial configuration on urban land surface temperature (LST) taking 306 cities in China as a case study covering a multi-biome-scale. We first calculated the availability of surrounding UGS for urban built-up pixels in each city using a distance-weighted approach, and its spatial configuration was quantified through the Gini coefficient. Then, we employed various regression models to explore how the impacts of UGS availability and the Gini coefficient on LST varies across different LST quantiles and between day- and nighttime. The results revealed that UGS availability was negatively associated with both daytime and nighttime LST, while the Gini coefficient showed a positive impact solely on daytime LST, indicating that an adequate and equally distributed UGS contributes to lower environmental temperatures during the daytime. Furthermore, the impact of UGS availability on LST decreased during both day- and nighttime with increased background LST quantiles. Whereas the impact of the Gini coefficient increased only with daytime LST quantile levels, with its effect remaining almost insignificant during the night. Our findings provide new insights into the impacts of UGS on urban thermal environment, offering significant implications for urban green infrastructure planning aiming at lowering the urban heat island.

Regional climate change and possible causes over the Three Gorges Reservoir Area

The Three Gorges Project, the largest hydroelectric project in the world, has attracted widespread attention regarding its impact on regional climate. However, existing studies on the climate effects of the Three Gorges Project construction are not sufficient due to limited data accumulation. In this study, we analyzed the annual and seasonal trend changes in temperature, precipitation, and humidity over the Three Gorges Reservoir Area (TGRA) based on long-term meteorological stations data, remote sensing data, and reanalysis products. Observation minus reanalysis method (OMR) was used to reveal possible impacts of land cover changes on climate changes. Major results indicated that the TGRA experienced an overall warming trend for both annual and seasonal variations, with greater rising trends in the upstream. Except for autumn, the relative humidity of most regions mainly showed significant downward trends, indicating an overall drying trend in the TGRA. There was insignificant change in total precipitation and precipitable water vapor, with the largest variation observed during the summer. Although there were small differences among these datasets, their results of climate changes showed good consistency overall. In addition, the results of OMR indicated that land cover changes mainly had a warming and drying effect on the middle and upper reaches, and a cooling and moistening effect on the lower reaches of the TGRA. This may be due to the impact of land cover changes on the surface energy balance, thus affected temperature and humidity. The study has important reference value for understanding the climate changes in the TGRA and the climate effects brought about by large-scale engineering construction.

The surface urban heat island effect decreases bird diversity in Chinese cities

The audiovisual experience of observing birds in cities provides numerous benefits to residents, but their diversity is endangered by urbanization. Although the magnitude of the surface urban heat island effect (hereafter SUHI) has grown in recent years, its impact on bird diversity has not been adequately investigated. Here, we calculate the SUHI in 336 Chinese cities and we document the implications of the SUHI for avian species richness and functional diversity during the 2001, 2011, and 2019 breeding and non-breeding seasons. We predict that the SUHI will result in greater species richness and functional diversity in urban areas during the non-breeding season, especially for cities located within colder regions of China where the SUHI is more likely to relax thermoregulatory costs and reduce the propensity of some species to migrate. We predict that the SUHI will result in decreased species richness and functional diversity during the breeding season due to increased physiological stress, especially for cities located within warmer regions of China. Our findings showed that the SUHI was associated with lower species richness and lower functional diversity of birds in urban areas compared to suburban areas during both the breeding and non-breeding seasons. These results suggest that the SUHI induced birds to avoid urban areas or to move to cooler suburban areas during both the breeding and non-breeding seasons. This effect persisted irrespective of a city's size or geographical location. Our findings suggest that the SUHI is degrading bird diversity in Chinese cities.

Analysis of Long Time Series of Summer Surface Urban Heat Island under the Missing-Filled Satellite Data Scenario

Surface urban heat islands (SUHIs) are mostly an urban ecological issue. There is a growing demand for the quantification of the SUHI effect, and for its optimization to mitigate the increasing possible hazards caused by SUHI. Satellite-derived land surface temperature (LST) is an important indicator for quantifying SUHIs with frequent coverage. Current LST data with high spatiotemporal resolution is still lacking due to no single satellite sensor that can resolve the trade-off between spatial and temporal resolutions and this greatly limits its applications. To address this issue, we propose a multiscale geographically weighted regression (MGWR) coupling the comprehensive, flexible, spatiotemporal data fusion (CFSDAF) method to generate a high-spatiotemporal-resolution LST dataset. We then analyzed the SUHI intensity (SUHII) in Chengdu City, a typical cloudy and rainy city in China, from 2002 to 2022. Finally, we selected thirteen potential driving factors of SUHIs and analyzed the relation between these thirteen influential drivers and SUHIIs. Results show that: (1) an MGWR outperforms classic methods for downscaling LST, namely geographically weighted regression (GWR) and thermal image sharpening (TsHARP); (2) compared to classic spatiotemporal fusion methods, our method produces more accurate predicted LST images (R2, RMSE, AAD values were in the range of 0.8103 to 0.9476, 1.0601 to 1.4974, 0.8455 to 1.3380); (3) the average summer daytime SUHII increased form 2.08 °C (suburban area as 50% of the urban area) and 2.32 °C (suburban area as 100% of the urban area) in 2002 to 4.93 °C and 5.07 °C, respectively, in 2022 over Chengdu City; and (4) the anthropogenic activity drivers have a higher relative influence on SUHII than other drivers. Therefore, anthropogenic activity driving factors should be considered with CO2 emissions and land use changes for urban planning to mitigate the SUHI effect.

Water-energy-vegetation nexus explain global geographical variation in surface urban heat island intensity

Surface urban heat island (SUHI) is a key climate risk associated with urbanization. Previous case studies have suggested that precipitation (water), radiation (energy), and vegetation have important effects on urban warming, but there is a lack of research that combines these factors to explain the global geographic variation in SUHI intensity (SUHII). Here, we utilize remotely sensed and gridded datasets to propose a new water-energy-vegetation nexus concept that explains the global geographic variation of SUHII across four climate zones and seven major regions. We found that SUHII and its frequency increase from arid zones (0.36 ± 0.15 °C) to humid zones (2.28 ± 0.10 °C), but become weaker in the extreme humid zones (2.18 ± 0.15 °C). We revealed that from semi-arid/humid to humid zones, high precipitation is often coupled with high incoming solar radiation. The increased solar radiation can directly enhance the energy in the area, leading to higher SUHII and its frequency. Although solar radiation is high in arid zones (mainly in West, Central, and South Asia), water limitation leads to sparse natural vegetation, suppressing the cooling effect in rural areas and resulting in lower SUHII. In extreme humid regions (mainly in tropical areas), incoming solar radiation tends to flatten out, which, coupled with increased vegetation as hydrothermal conditions become more favorable, leads to more latent heat and reduces the intensity of SUHI. Overall, this study offers empirical evidence that the water-energy-vegetation nexus highly explains the global geographic variation of SUHII. The results can be used by urban planners seeking optimal SUHI mitigation strategies and for climate change modeling work.

Impact of COVID-19 Restrictions on the Urban Thermal Environment of Edmonton, Canada

The effects of the COVID-19 pandemic on urban environments are addressed in many recent studies. However, limited research has been conducted to examine the impact of the pandemic on anthropogenic emissions over urban land use types, and their relation to socioeconomic characteristics. Anthropogenic heat, as the main contributor to the urban temperature, is changed by the sudden halt imposed by COVID-19 lockdowns. This study thus focuses on previously under-explored urban thermal environments by quantifying the impact of COVID-19 on urban thermal environments across different land-use types and related socioeconomic drivers in Edmonton, Canada. Using Landsat images, we quantified and mapped the spatial pattern of land surface temperature (LST) for business, industrial, and residential land use areas during both the pandemic lockdown and pre-pandemic periods in the study area. Results show that temperature declined in business and industrial areas and increased in residential areas during the pandemic lockdown. Canadian census and housing price data were then used to identify the potential drivers behind the LST anomaly of residential land use. The most important variables that affected LST during the lockdown were found to be median housing price, visible minority population, postsecondary degree, and median income. This study adds to the expanding body of literature about the impact of the COVID-19 pandemic by providing unique insights into the effect of lockdown on a city's thermal environments across different land use types and highlights critical issues of socioeconomic inequalities, which is useful for future heat mitigating and health equity-informed responses.

Grading surface urban heat island and investigating factor weight based on interpretable deep learning model across global cities

Significant urbanization resulted in increasing surface urban heat island (SUHI) that caused negative impacts on urban ecological environment, and residential comfort. Accurately monitoring the spatiotemporal variations and understanding controls of SUHI were essential to propose effective mitigation measurements. However, SUHI grades across global cities remained unknown, which cloud greatly support for global mitigations. Additionally, quantitative evaluating factor weights for different SUHI indicators and grades worldwide remained further investigations. Therefore, this paper proposed SUHI grading based on agglomerative hierarchical clustering, and further quantified factor weights for different indicators and grades based on an interoperable machine learning named TabNet. There were three major findings. (1) Global cities were grouped into five grades, including SUCI (surface urban cool island), insignificant, low-value, medium-value, and high-value SUHI grades, indicating significant differences among different grades. SUHI grades showed significant climate-based variations, wherein the arid climate was dominated by the SUCI grade at daytime but the high-value grade at nighttime. (2) Vegetation difference was an important factor for daytime SUHII accounting for 27%. Daytime frequency of SUHI was controlled by vegetation difference, temperature, evaporation and nighttime light, accounting for 78%. The major factors for nighttime frequency were albedo differences and nighttime light, accounting for 45%. (3) Related factors contributed differently to various SUHI grades. The weight of △EVI for daytime SUHII gradually increased with grades, while it for daytime frequency and maximum duration of SUHI decreased with grades. The nighttime SUHII of the low-value grade was greatly affected by the background climate, while that of the medium-value and high-value grades were strongly impacted by anthropogenic heat flux. The diurnal contrast of grades and coupling effects with heat wave were further discussed. This paper aimed to provide information on grades and controls of SUHI for further mitigation proposal.

Assessment of short-term heat effects on cardiovascular mortality and vulnerability factors using small area data in Europe

BACKGROUND

Short-term associations between heat and cardiovascular disease (CVD) mortality have been examined mostly in large cities. However, different vulnerability and exposure levels may contribute to spatial heterogeneity. This study assessed heat effects on CVD mortality and potential vulnerability factors using data from three European countries, including urban and rural settings.

METHODS

We collected daily counts of CVD deaths aggregated at the small-area level in Norway (small-area level: municipality), England and Wales (lower super output areas), and Germany (district) during the warm season (May-September) from 1996 to 2018. Daily mean air temperatures estimated by spatial-temporal models were assigned to each small area. Within each country, we applied area-specific Quasi-Poisson regression using distributed lag nonlinear models to examine the heat effects at lag 0-1 days. The area-specific estimates were pooled by random-effects meta-analysis to derive country-specific and overall heat effects. We examined individual- and area-level heat vulnerability factors by subgroup analyses and meta-regression, respectively.

RESULTS

We included 2.84 million CVD deaths in analyses. For an increase in temperature from the 75th to the 99th percentile, the pooled relative risk (RR) for CVD mortality was 1.14 (95% CI: 1.03, 1.26), with the country-specific RRs ranging from 1.04 (1.00, 1.09) in Norway to 1.24 (1.23, 1.26) in Germany. Heat effects were stronger among women [RRs (95% CIs) for women and men: 1.18 (1.08, 1.28) vs. 1.12 (1.00, 1.24)]. Greater heat vulnerability was observed in areas with high population density, high degree of urbanization, low green coverage, and high levels of fine particulate matter.

CONCLUSION

This study provides evidence for the heat effects on CVD mortality in European countries using high-resolution data from both urban and rural areas. Besides, we identified individual- and area-level heat vulnerability factors. Our findings may facilitate the development of heat-health action plans to increase resilience to climate change.

Time-series analyses of land surface temperature changes with Google Earth Engine in a mountainous region

Studying changes in temperature is fundamental for understanding its interactions with the environment and biodiversity. However, studies in mountainous areas are few, due to their complex formation and the difficulty of obtaining local data. We analysed changes in temperature over time in Montesinho Natural Park (MNP) (Bragança, Portugal), an important conservation area due to its high level of biodiversity. Specifically, we aimed to analyse: i) whether temperature increased in MNP over time, ii) what environmental factors influence the Land Surface Temperature (LST), and iii) whether vegetation is related to changes in temperature. We used annual summer and winter mean data acquired from the Moderate-Resolution Imaging Spectroradiometer (MODIS) datasets/products (e.g. LST, gathered at four different times: 11am, 1pm, 10pm and 2am, Enhance vegetation index - EVI, and Evapotranspiration - ET), available on the cloud-based platform Google Earth Engine between 2003 and 2021). We analysed the dynamics of the temporal trend patterns between the LST and local thermal data (from a weather station) by correlations; the trends in LST over time with the Mann-Kendall trend test; and the stability of hot spots and cold spots of LST with Local Statistics of Spatial Association (LISA) tests. The temporal trend patterns between LST and Air Temperature (Tair) data were very similar (ρ > 0.7). The temperature in the MNP remained stable over time during summer but increased during winter nights. The biophysical indices were strongly correlated with the summer LST at 11am and 1pm. The LISA results identified hot and cold zones that remained stable over time. The remote-sensed data proved to be efficient in measuring changes in temperature over time.

Exploring the land-use urban heat island nexus under climate change conditions using machine learning approach: A spatio-temporal analysis of remotely sensed data

Urbanization strongly correlates with land use land cover (LULC) dynamics, which further links to changes in land surface temperature (LST) & urban heat island (UHI) intensity. Each LULC type influences UHI differently with changing climate, therefore knowing this impact & connection is critical. To understand such relations, long temporal studies using remote sensing data play promising role by analysing the trend with continuity over vast area. Therefore, this study is aimed at machine learning centred spatio-temporal analysis of LST and land use indices to identify their intra-urban interaction during 1991-2021 (summer) in Imola city (specifically representing small urban environment) using Landsat-5/8 imageries. It was found that LST in 2021 increased by 38.36% from 1991, whereas average Normalised Difference Built-up Index (NDBI) increased by 43.75%, associating with increased thermal stress area evaluated using ecological evaluation index. Major LULC transformations included green area into agricultural arable-land and built-up. Finally, the modelled output shows that built-up & vegetation index have strongly impacted LST. This study, help to understand the relative impact of land-use dynamics on LST at intra-urban level specifically with respect to the small urban settings. Further assisting in designing and regenerating urban contexts with stable configuration, considering sustainability and liveable climate, for benefit of health of public and fragile population in particular.

Heat waves and adaptation: A global systematic review

BACKGROUND

Given the increasing trend of global warming and extreme weather conditions, including heat waves and its effects on health, the present study was done to investigate adaptive behaviors of communities in the world for combating heat waves.

METHOD

ology: In this systematic review, out of 1529 results, 57 relevant and authoritative English papers on adaptation to heat waves hazard were extracted and evaluated using valid keywords from valid databases (PubMed, WOS, EMBASE, and Scopus). In addition, multiple screening steps were done and then, the selected papers were qualitatively assessed. Evaluation results were summarized using an Extraction Table.

RESULTS

In this paper, the adaptive behaviors for combating heat waves hazard were summarized into 11 categories: Education and awareness raising, Adaptation of critical infrastructure, Governments measures, Health-related measures, Application of early warning system, Protective behaviors in workplace, Physical condition, Adaptive individual behaviors, Design and architecture of the building, Green infrastructure (green cover), and Urban design.

CONCLUSION

The findings of this study showed that community actions have significant effects on adaptation to heat wave. Therefore, for reducing heat wave-related negative health effects and vulnerability, more attention should be paid to the above-mentioned actions for mitigation, preparation, and responding regarding heat waves.

PROSPERO REGISTRATION NUMBER

CRD42021257747.

The unrelenting global expansion of the urban heat island over the last century

The past century has seen dramatic increases in global temperatures and mounting urbanization. As a result of these events, the urban heat island (UHI) effect has received growing attention in scientific research worldwide. A global search was initially conducted using a scientific literature database to collect all available relevant publications to understand how the UHI has been expanding worldwide and affecting more cities across different latitudes and altitudes. Subsequently, a semantic analysis was performed to extract city names. The literature search and analysis combined resulted in 6078 publications in which UHI was investigated in 1726 cities worldwide in the 1901 to 2022 time period. The cities were grouped into 'first appearance' and 'recurrent appearance'. Results show that UHI was studied in only 134 cities during the 90-year period from 1901 to 1992, with a remarkable growth over time in the number of cities where interest in UHI increased. Interestingly, the number of first appearances was always notably higher than the number of recurrent appearances. The Shannon evenness index was employed to identify the spatial locations (hotspots) across the globe where UHI-related research has been concentrated in multiple cities over the last 120 years. Finally, Europe was selected as a testbed for conducting an analysis to shed light on how economic, demographic, and environmental factors can impact UHI. Our study is unique for having demonstrated not only the rapid growth of cities affected by UHI globally but also the increasing and unrelenting expansion of UHI occurrences across different latitudes and altitudes over time. These novel findings will undoubtedly be of interest to scientists investigating the UHI phenomenon and its trends. Stakeholders will acquire a broader perspective and deeper understanding of UHI in order to engage in more effective urban planning to offset and mitigate the phenomenon's adverse effects in the context of increasing climate change and urbanization.

A multidimensional framework to quantify the effects of urbanization on avian breeding fitness

Urbanization has dramatically altered Earth's landscapes and changed a multitude of environmental factors. This has resulted in intense land-use change, and adverse consequences such as the urban heat island effect (UHI), noise pollution, and artificial light at night (ALAN). However, there is a lack of research on the combined effects of these environmental factors on life-history traits and fitness, and on how these interactions shape food resources and drive patterns of species persistence. Here, we systematically reviewed the literature and created a comprehensive framework of the mechanistic pathways by which urbanization affects fitness and thus favors certain species. We found that urbanization-induced changes in urban vegetation, habitat quality, spring temperature, resource availability, acoustic environment, nighttime light, and species behaviors (e.g., laying, foraging, and communicating) influence breeding choices, optimal time windows that reduce phenological mismatch, and breeding success. Insectivorous and omnivorous species that are especially sensitive to temperature often experience advanced laying behaviors and smaller clutch sizes in urban areas. By contrast, some granivorous and omnivorous species experience little difference in clutch size and number of fledglings because urban areas make it easier to access anthropogenic food resources and to avoid predation. Furthermore, the interactive effect of land-use change and UHI on species could be synergistic in locations where habitat loss and fragmentation are greatest and when extreme-hot weather events take place in urban areas. However, in some instances, UHI may mitigate the impact of land-use changes at local scales and provide suitable breeding conditions by shifting the environment to be more favorable for species' thermal limits and by extending the time window in which food resources are available in urban areas. As a result, we determined five broad directions for further research to highlight that urbanization provides a great opportunity to study environmental filtering processes and population dynamics.

Variations in land surface temperatures in small-scale urban areas in Vietnam during Covid-19 restrictions: Case studies from Da Nang, Hue and Vinh City

Land surface temperature (LST) is an important variable in urban microclimate research. At the end of 2019, the emergence of Covid-19 pandemic has changed the world in a manner that forced many countries to impose restrictions in human activities. As a measure to prevent the expansion of Covid-19 infections, most of the major cities have entered a prolonged lockdown period and reduction in human activities between the early 2020 and the late 2021. These restrictions were strict in most of the cities in Southeast Asia, particularly in Vietnam. The present study investigated the variations in LST and NDVI observed in three rapidly growing urban areas, namely Da Nang, Hue and Vinh, in Vietnam using Landsat-8 imagery acquired between 2017 and 2022. There has been a slight reduction in LST in the study sites, particularly in Da Nang City, during the lockdown period but not as high as observed in recently conducted studies from big metropolitan cities, including in Vietnam. It is also observed that LST estimated from built-up areas and other impervious surfaces remained relatively stable during the study period which is similar to the results from recent studies.

Pixel level spatial variability modeling using SHAP reveals the relative importance of factors influencing LST

As an important indicator of the regional thermal environment, land surface temperature (LST) is closely related to community health and regional sustainability in general, and is influenced by multiple factors. Previous studies have paid scant attention to spatial heterogeneity in the relative contribution of factors underlying LST. In this study of Zhejiang Province, we investigated the key factors affecting daytime and nighttime annual mean LST and the spatial distribution of their respective contributions. The eXtreme Gradient Boosting tree (XGBoost) and Shapley Additive exPlanations algorithm (SHAP) approach were used in combination with three sampling strategies (Province-Urban Agglomeration -Gradients within Urban Agglomeration) to detect spatial variation. The results reveal heterogenous LST spatial distribution with lower LST in the southwestern mountainous region and higher temperatures in the urban center. Spatially explicit SHAP maps indicate that latitude and longitude (geographical locations) are the most important factors at the provincial level. In urban agglomerations, factors associated with elevation and nightlight are shown to positively impact daytime LST in lower altitude regions. In the urban centers, EVI and MNDWI are the most notable influencing factors on LST at night. Under different sampling strategies, EVI, MNDWI, NL, and NDBI affect LST more prominently at smaller spatial scales as compared to AOD, latitude and TOP. The SHAP method proposed in this paper offers a useful means for management authorities in addressing LST in a warming climate.

Consistent Nest Site Selection by Turtles across Habitats with Varying Levels of Human Disturbance

Human disturbance impacts the breeding behavior of many species, and it is particularly important to understand how these human-caused changes affect vulnerable taxa, such as turtles. Habitat alteration can change the amount and quality of suitable nesting habitat, while human presence during nesting may influence nesting behavior. Consequently, both habitat alteration and human presence can influence the microhabitat that females choose for nesting. In the summer of 2019, we located emydid turtle nests in east-central Alabama, USA, in areas with varying levels of human disturbance (high, intermediate, low). We aimed to determine whether turtles selected nest sites based on a range of microhabitat variables comparing maternally selected natural nests to randomly chosen artificial nests. We also compared nest site choice across areas with different levels of human disturbance. Natural nests had less variance in canopy openness and average daily mean and minimum temperature than artificial nests, but microhabitat variables were similar across differing levels of disturbance. Additionally, we experimentally quantified nest predation across a natural to human-disturbed gradient. Nest predation rates were higher in areas with low and intermediate levels of disturbance than in areas with high human disturbance. Overall, these results show that turtles are not adjusting their choices of nest microhabitat when faced with anthropogenic change, suggesting that preserving certain natural microhabitat features will be critical for populations in human-disturbed areas.

Discover the Desirable Landscape Structure of Urban Parks for Mitigating Urban Heat: A High Spatial Resolution Study Using a Forest City, Luoyang, China as a Lens

Urban parks can mitigate the urban heat island (UHI) and effectively improve the urban microclimate. In addition, quantifying the park land surface temperature (LST) and its relationship with park characteristics is crucial for guiding park design in practical urban planning. The study's primary purpose is to investigate the relationship between LST and landscape features in different park categories based on high-resolution data. In this study, we identified the land cover types of 123 parks in Luoyang using WorldView-2 data and selected 26 landscape pattern indicators to quantify the park landscape characteristics. The result shows that the parks can alleviate UHI in most seasons, but some can increase it in winter. While the percentage of bare land, PD, and PAFRAC have a positive impact on LST, AREA_MN has a significant negative impact. However, to deal with the current urban warming trend, a compact, clustered landscape configuration is required. This study provides an understanding of the major factors affecting the mitigation of thermal effects in urban parks (UP) and establishes a practical and feasible urban park renewal method under the idea of climate adaptive design, which provides valuable inspiration for urban park planning and design.

Identifying and characterizing frequency and maximum durations of surface urban heat and cool island across global cities

Surface urban heat island (SUHI) has been widely reported from a local to global scale. However, variations and controls of temporal indicators for SUHI and SUCI (surface urban cool island) remain unclear. This paper firstly reconstructed the seamless daily LST (land surface temperature) based on ATC-SKT (annual temperature cycle-skin temperature) and comprehensively validated for SUHI applications across 1112 global cities. Based on the seamless daily LST, this paper further characterized the spatiotemporal variations of the frequency (SUHIF and SUCIF) and maximum duration (SUHID and SUCID) and investigated the impacts from related factors, inconsideration of the different characteristics of SUHI and SUCI. There are five major findings. (1) The seamless daily LST reconstructed based on ATC-SKT is validated through pixel-based temperature and city-based SUHII accuracy assessments. (2) The selection of the frequency threshold is based on robustness for LST accuracy, approximation to SUHII global average, and mitigation of frequency saturation. (3) The average daytime SUHIF is 214 days/year, with 44 % of cities exhibiting SUHI occurrences for almost every day in summer. The nighttime SUHIF is 175 days/year, with increasing latitudinal variations from equatorial to polar regions. The daytime SUCIF is 41 days/year, with the greatest average frequency of 172 days/year exhibited in arid regions. (4) The average SUHID is 147 days at daytime and 58 days at nighttime, with relatively opposite geographical distributions between day and night. (5) Greater vegetation difference at daytime and greater albedo difference at nighttime result in more occurrences and longer consecutive durations of SUHI, with opposite effects on the temporal indicators of SUCI. Furthermore, the improvements in daily SUHII and impacts from the maximum duration were discussed. This paper aims to identify and highlight the period with significant SUHI and SUCI effects across global cities for further mitigation.

Spatio-temporal development of the urban heat island in a socioeconomically diverse tropical city

Urban heat islands, where temperatures are elevated relative to non-urban surrounds, are near-ubiquitous in cities globally. Yet, the magnitude and form of urban heat islands in the tropics, where heat has a large morbidity and mortality burden, is not well understood, especially for those of urban informal settlements. We used 29 years of Landsat satellite-derived surface temperature, corroborated by in situ temperature measurements, to provide a detailed spatial and temporal assessment of urban heat islands in Makassar, Indonesia, a city that is representative of rapidly growing urban settlements across the tropics. Our analysis identified surface urban heat islands of up to 9.2 °C in long-urbanised parts of the city and 6.3 °C in informal settlements, the seasonal patterns of which were driven by change in non-urban areas rather than in urban areas themselves. In recently urbanised areas, the majority of urban heat island increase occurred before land became 50% urbanised, whereas the established heat island in long-urbanised areas remained stable in response to urban expansion. Green and blue space protected some informal settlements from the worst urban heat islands observed across the city and maintenance of such space will be essential to mitigate the growing heat burden from urban expansion and anthropogenic climate change. Settlements further than 4 km from the coast and with Normalised Difference Vegetation Index (NDVI) less than 0.2 had higher surface temperatures, with modelled effects of more than 5 °C. Surface temperature measurements were representative of in situ heat exposure, measured in a subset of 12 informal settlements, where mean indoor temperature had the strongest relationship with surface temperature (R² = 0.413, P = 0.001). We advocate for green space to be prioritised in urban planning, redevelopment and informal settlement upgrading programs, with consideration of the unique environmental and socioeconomic context of tropical cities.

Effects of air pollution and weather on the initial COVID-19 outbreaks in United States, Italy, Spain, and China: A comparative study

Contrasting effects have been identified in association of weather (temperature and humidity) and pollutant gases with COVID-19 infection, which could be derived from the influence of lockdowns and season change. The influence of pollutant gases and climate during the initial phases of the pandemic, before the closures and the change of season in the northern hemisphere, is unknown. Here, we used a spatial-temporal Bayesian zero-inflated-Poisson model to test for short-term associations of weather and pollutant gases with the relative risk of COVID-19 disease in China (first outbreak) and the countries with more cases during the initial pandemic (the United States, Spain and Italy), considering also the effects of season and lockdown. We found contrasting association between pollutant gases and COVID-19 risk in the United States, Italy, and Spain, while in China it was negatively associated (except for SO₂ ). COVID-19 risk was positively associated with specific humidity in all countries, while temperature presented a negative effect. Our findings showed that short-term associations of air pollutants with COVID-19 infection vary strongly between countries, while generalized effects of temperature (negative) and humidity (positive) with COVID-19 was found. Our results show novel information about the influence of pollution and weather on the initial outbreaks, which contribute to unravel the mechanisms during the beginning of the pandemic.

Natural-anthropogenic environment interactively causes the surface urban heat island intensity variations in global climate zones

The inconstant climate change and rapid urbanization substantially disturb the global thermal balance and induce severe urban heat island (UHI) effect, adversely impacting human development and health. Existing literature has revealed the UHI characteristics and driving factors at an urban scale, but interactions between the main factors of a global grid scale assessment on the context of climate zones remain unclear. Therefore, based on the multidimensional climatic and socio-economic statistical datasets, the multi-time scale of surface urban heat island intensity (SUHI) characteristics was investigated in this study to analyze how natural-anthropogenic drivers affect the variance of SUHI and vary in their importance for the changes of other interaction factors. The results show that the mean value of SUHI in summer is higher than in winter, and in daytime is higher than in nighttime on a seasonal and daily scale. SUHIs in different global climate zones have significant differences. When analyzing drivers' contributions and interactions with LightGBM model and SHAP algorithm, we know that monthly precipitation (PREC), the estimated population (POP) and surface pressure (PRES) are the three major drivers of daytime SUHI. The nighttime SUHI is mainly PREC, POP and anthropogenic heat emission (AHE), the influence rules of the natural driversare mostly opposite to that of daytime. This study highlights the fundamental role of background climate for designing strategies. Irrigation or artificial rainfall will be effective to mitigate SUHI in low rainfall areas, while it is more effective to reduce AHE in high rainfall areas. In where greening can be difficult in the most developed cities, reducing AHE, increasing per capita GDP and controlling the population scale may also contribute to alleviating the SUHI. This study provides ideas for developing responsive urban heat island mitigation policies in a more realistic setting.

Urban heat in Johannesburg and Ekurhuleni, South Africa: A meter-scale assessment and vulnerability analysis

Heat stress is an important threat for human health and urban areas are affected at higher rates compared to rural environments. Additionally, climate change will increase the vulnerability towards urban heat stress in the future. Current high-resolution urban heat stress assessments are limited in time and space due to the high computational costs. In this paper, the UrbClim numerical model is used to simulate urban heat accurately at a fast rate and high spatial resolution for the cities of Johannesburg and Ekurhuleni, South Africa. Using detailed terrain information, (future) urban heat stress assessments are provided at 30 m resolution for both city agglomerations, while meter-scale simulations are executed for a selection of neighborhoods. These model simulations are evaluated using an extensive monitoring campaign in which the local community was heavily engaged. Distinct spatial differences in the urban heat island effect are observed, with greatest heat stress in areas with high building densities and low vegetation numbers. These areas are often characterized by lower socio-economic living conditions. The meter-scale analysis further shows the importance of shade provided by vegetation to lower heat stress in both present and future climate. These assessments offer assistance in the design of climate-resilient urban planning strategies.

Improving the local climate zone classification with building height, imperviousness, and machine learning for urban models

The Local Climate Zone (LCZ) classification is already widely used in urban heat island and other climate studies. The current classification method does not incorporate crucial urban auxiliary GIS data on building height and imperviousness that could significantly improve urban-type LCZ classification utility as well as accuracy. This study utilized a hybrid GIS- and remote sensing imagery-based framework to systematically compare and evaluate different machine and deep learning methods. The Convolution Neural Network (CNN) classifier outperforms in terms of accuracy, but it requires multi-pixel input, which reduces the output’s spatial resolution and creates a tradeoff between accuracy and spatial resolution. The Random Forest (RF) classifier performs best among the single-pixel classifiers. This study also shows that incorporating building height dataset improves the accuracy of the high- and mid-rise classes in the RF classifiers, whereas an imperviousness dataset improves the low-rise classes. The single-pass forward permutation test reveals that both auxiliary datasets dominate the classification accuracy in the RF classifier, while near-infrared and thermal infrared are the dominating features in the CNN classifier. These findings show that the conventional LCZ classification framework used in the World Urban Database and Access Portal Tools (WUDAPT) can be improved by adopting building height and imperviousness information. This framework can be easily applied to different cities to generate LCZ maps for urban models.

Exploring surface urban heat island (SUHI) intensity and its implications based on urban 3D neighborhood metrics: An investigation of 57 Chinese cities

Excessive urban temperature exerts a substantially negative impact on urban sustainability. Three-dimensional (3D) landscapes have a great impact on urban thermal environments, while their heat conditions and driving factors still remain unclear. This study mapped urban 3D neighborhoods and their associated SUHI (surface urban heat island) intensities in summer daytime across 57 Chinese cities, and then explored their relationships, driving factors as well as implications. Nine categories of urban 3D neighborhoods existed in Chinese cities and the 3D neighborhood of High Density & Medium Rise (HDMR) contributed the largest share of urban areas. The distribution of 3D neighborhoods varied among cities due to their distinct natural and economic traits. The average SUHI intensity can amount to 4.27 °C across all Chinese 3D neighborhoods. High Density & Low Rise (HDLR) and HDMR presented higher SUHI intensities than other 3D neighborhoods in China. Urban green space (UGI) and building height (BH) had great influences on SUHI intensities. The relative contribution of UGI decreased with the increase of building density and building height, but BH presented the opposite trend. The interaction of urban 3D landscapes and function zones led to highly complicated urban thermal environments, with higher SUHI intensities in industrial zones. Besides, the SUHI intensities of 3D neighborhoods presented great diurnal and seasonal variations, with higher SUHI intensities in HDHR and HDMR at nighttime in winter and summer. What's more, urban residents may suffer unequal heat risk inside cities due to the deviations of SUHI intensities among different 3D neighborhoods. It could be a highly effective way to mitigate SUHI effects in cities by increasing urban greening and improving urban ventilation.

Background climate modulates the impact of land cover on urban surface temperature

Cities with different background climates experience different thermal environments. Many studies have investigated land cover effects on surface urban heat in individual cities. However, a quantitative understanding of how background climates modify the thermal impact of urban land covers remains elusive. Here, we characterise land cover and their impacts on land surface temperature (LST) for 54 highly populated cities using Landsat-8 imagery. Results show that urban surface characteristics and their thermal response are distinctly different across various climate regimes, with the largest difference for cities in arid climates. Cold cities show the largest seasonal variability, with the least seasonality in tropical and arid cities. In tropical, temperate, and cold climates, normalised difference built-up index (NDBI) is the strongest contributor to LST variability during warm months followed by normalised difference vegetation index (NDVI), while normalised difference bareness index (NDBaI) is the most important factor in arid climates. These findings provide a climate-sensitive basis for future land cover planning oriented at mitigating local surface warming.

Exploring seasonal diurnal surface temperature variation in cities based on ECOSTRESS data: A local climate zone perspective

High urban temperatures affect city livability and may be harmful for inhabitants. Analyzing spatial and temporal differences in surface temperature and the thermal impact of urban morphological heterogeneity can promote strategies to improve the insulation of the urban thermal environment. Therefore, we analyzed the diurnal variation of land surface temperature (LST) and seasonal differences in the Fifth Ring Road area of Beijing from the perspective of the Local Climate Zone (LCZ) using latest ECOSTRESS data. We used ECOSTRESS LST data with a resolution of 70 m to accurately interpret the effects of urban morphology on the local climate. The study area was dominated by the LCZ9 type (sparse low-rise buildings) and natural LCZ types, such as LCZA/B (woodland), LCZD (grassland), and LCZG (water body), mainly including park landscapes. There were significant differences in LST observed in different seasons as well as day and night. During daytime, LST was ranked as follows: summer > spring > autumn > winter. During night-time, it was ranked as follows: summer > autumn > spring > winter. All data indicated that the highest and lowest LST was observed in summer and winter, respectively. LST was consistent with LCZ in terms of spatial distribution. Overall, the LST of each LCZ during daytime was higher than that of night-time during different seasons (except winter), and the average LST of each LCZ during the diurnal period in summer was higher than that of other seasons. The LST of each LCZ during daytime in winter was lower than that of the corresponding night-time, which indicates that it is colder in the daytime during winter. The results presented herein can facilitate improved analysis of spatial and temporal differences in surface temperature in urban areas, leading to the development of strategies aimed at improving livability and public health in cities.

Analysis of the spatiotemporal mechanism of high temperature on residents’ irritability in Beijing based on multiscale geographically weighted regression model

The emotional health of urban residents is increasingly threatened by high temperatures due to global heating. However, how high temperature affects residents’ emotional health remains unknown. Therefore, this study investigated the spatiotemporal pattern of temperature’s impact on residents’ irritability using data from summer high-temperature measurement and emotional health survey in Beijing, combined with remote sensing images and statistical yearbooks. In detail, this study formulated a multiscale geographically weighted regression (MGWR) model, to study the differentiated and spatial influence of high-temperature factors on emotion. Results show: From 09:00 to 20:00, irritability level rose first then gradually dropped, with a pattern of “aggregation-fragmentation-aggregation.” Irritability is very sensitive to intercept and building density (BD). Other variables all have spatial heterogeneity [except for fraction vegetation coverage (FVC) or road network density (RND) as they are global variables], including normalized difference vegetation index (NDVI), water surface rate (WSR), floor area ratio (FAR), and Modified Normalized Difference Water Index (MNDWI) (sorted from the smallest to the largest in scale). Irritability is negatively correlated with NDVI, WSR, and RND, while positively correlated with intercept, MNDWI, FVC, FAR, and BD. Influence on irritability: WSR < NDVI < BD < MNDWI < RND < intercept < FVC < FAR.

Urban Air Pollution, Urban Heat Island and Human Health: A Review of the Literature

Many cities of the world suffer from air pollution because of poor planning and design and heavy traffic in rapidly expanding urban environments. These conditions are exacerbated due to the Urban Heat Island (UHI) effect. While there have been studies linking the built environment and air pollution with health, they have ignored the aggravating role of UHI. The past urban planning literature in this field has also ignored the science of materials, vehicles and air pollution, and technological solutions for reducing cumulative health impacts of air pollution and UHI. Air Pollution, built environment and human health are complex discussion factors that involve several different fields. The built environment is linked with human health through opportunities of physical activity and air quality. Recent planning literature focuses on creating compact and walkable urban areas dotted with green infrastructure to promote physical activity and to reduce vehicle emission-related air pollution. Reduced car use leading to reduced air pollution and UHI is implied in the literature. The literature from technology fields speaks to the issue of air pollution directly. Zero emission cars, green infrastructure and building materials that absorb air pollutants and reduce UHI fall within this category. This paper identifies main themes in the two streams of urban air pollution and UHI that impact human health and presents a systematic review of the academic papers, policy documents, reports and features in print media published in the last 10–20 years.

An expanding cityscape and its multi-scale effects on lizard distribution

Urbanization results in complex and variable changes to environmental conditions, which translate to shifts in selection pressures for organisms. Size of a city as well as the intensity and extent of urbanization can synergistically influence how organisms are impacted. However, less is known about how landscape heterogeneity, rate of land-use change, and scale of urbanization affect species persistence. We evaluate the ways in which urbanization changes the environment and examine how some of these environmental factors influence the presence of the lizard Psammophilus dorsalis (Peninsular rock agama), in Bengaluru, India. Variability in environmental factors across the study area was characterised by measures of habitat composition and diversity, habitat connectivity, rate of habitat change, predation pressure, land surface temperature (LST) and artificial light at night (ALAN), that were derived from remotely sensed and citizen science data. Most of these factors showed high variance across two measures of urbanization: distance from city center and proportion of built-up area. Habitat diversity and ALAN were the only two factors that changed predictably and in a non-linear way, with distance from the city center and proportion of built-up area. We then used a multi-scale approach to examine the relative importance of some these environmental factors at the landscape scale, as well as additional factors at the microhabitat-scale, in predicting the presence and relative abundance of P. dorsalis respectively. At the landscape scale, LST, which is positively correlated with proportion of cropland, predicted lizard presence; whereas at the microhabitat scale, P. dorsalis was more likely to be found in sites with higher proportions of rocks. Overall, we demonstrate that urbanization can result in environmental predictors that do not vary linearly across the urbanization gradient. For the iconic rock agama, many of these environmental factors do not seem to be strong selection pressures that influence their distribution in the expanding cityscape. Whether this urban utilizer can continue to persist with increasing anthropogenic development is uncertain. To better understand drivers of species persistence, we emphasize the importance of quantifying urbanization across multiple axes, considering environmental factors that are relevant to species at different spatial and temporal scales.

RSEDM: A New Rotational-Scan Exponential Decay Model for Extracting the Surface Urban Heat Island Footprint

Surface urban heat islands are widely focused on due to their close relationship with a series of environmental issues. Obtaining a precise footprint is an important prerequisite for heat island research. However, the land surface temperature curves used for calculating footprint are affected by factors such as the complexity of land-use types, thereby affecting the accuracy of footprint. Therefore, the rotational-scan exponential decay model is developed in this paper, which first takes the gravity center of an urban area as the origin of polar coordinates, specifies due north as the starting direction, and rotationally scans the suburbs that are within 20 km outside urban areas in a clockwise direction at an angle of 1°. The eligible suburbs are screened out according to the built-up area rate, water body rate, and merge tolerance. Then, exponential decay fitting of the temperature curve is performed to obtain the extension distance of the heat island and the background temperature, which are used to determine the final footprint. Based on the method, the footprints of 15 cities were calculated and compared with those of the traditional method. The results show that: (1) this method could effectively eliminate the influence of a large number of contiguous built-up areas and water bodies in the suburbs on the footprint calculation, thus greatly improving the accuracy of the temperature curve and footprint. (2) Three of four cities had the largest footprint boundary in spring. All four cities had the strongest heat island intensity in summer and the smallest footprint boundary and intensity in winter. (3) Coupling effect would aggravate the negative impact of heat islands in the suburbs and threaten the suburban environment. As a state-of-the-art method, it can enhance the calculation accuracy and precisely reflect the spatial pattern of footprint, which is of great significance for the sustainable development of cities.

Does urban climate follow urban form? Analysing intraurban LST trajectories versus urban form trends in 3 cities with different background climates

The current climate change trend urges the application of efficient spatial planning to mitigate the effects of urbanization on local urban warming. Nevertheless, how urban temperatures respond to urban form changes inside cities is still insufficiently understood. In this paper, we explored the relationship between urban form and diurnal space-time land surface temperature (LST) trends (2003-2019) in Beijing (continental climate), Cairo (arid) and Santiago (temperate). We analysed changes in land cover, white sky albedo (WSA), night-time lights (NL) and the enhanced vegetation index (EVI) inside areas representing clustered thermal performance (steady cold and hot spots and warming cold and hot spots). The structure of local climate zones (LCZs) was assessed for each LST trend. To test the relevance of other urban form dimensions, we analysed the hierarchical influence of the employed 2D metrics (i.e., built-up cover, WSA, NL and EVI) and additional 3D indicators (i.e., height and volume) on LST, applying machine learning classification and regression trees (CARTs) to Beijing's data. Despite diverse patterns of urban form change, cities in our sample present common LST trends, with thermal differences as a consequence of local climate. LCZs are composed of highly heterogeneous built-up areas inside LST trend categories. In the case of Beijing, LST is hierarchically driven by footprint, WSA and EVI. Moreover, by adding height and volume, urban form differences between LST trend classes that are not evident with 2D data were found. Our findings suggest that a compact green urban tissue is necessary to cope with the current trends of urban warming, taking into account city-specific measures based on the local background climate.

Driving forces of UHI changes in China's major cities from the perspective of land surface energy balance

As remarkable human-induced temperature anomalies on the land surface, variations of urban heat island (UHI) and its driving factors have been investigated in numerous studies. However, few studies discussed the spatiotemporal heterogeneity of the driving forces exerted by land surface energy fluxes, i.e., net radiation, sensible heat, latent heat and heat storage, on UHI behaviors at large scale and long term. In this study, a comprehensive application of multisource datasets and statistical methods have been implemented based on land surface energy balance theory, the spatiotemporal variations of surface UHI intensity (urban-rural temperature difference) and changes of their driving forces have been quantified. The results demonstrate the dynamics of UHI intensity in 32 major cities of China from 2003 to 2017 are generally coherent with the common perception, the overall surface UHI intensity is 4.57 K higher in summer than in winter. The spatial variations of the fluxes that alter UHI intensity can be largely attributed to the varied energy interactions between vegetated/paved surface and atmosphere and the differences of background temperature and precipitation, the contribution of latent heat to UHI changes declines nearly 40% from semiarid/arid climate at the north to subtropical humid climate at the south, while the contributions of other fluxes are stable. The temporal changes of the effect of these fluxes, however, imply more complex mechanisms. The contributions of sensible heat and latent heat to UHI intensity variations are three times and eight times larger in the warm season than in the cold season respectively, indicating the influence of seasonality of background temperature, precipitation and vegetation. The low contributions of these fluxes in the cold season also suggest the significant effect of other driving forces such as anthropogenic heat, especially in semiarid/semihumid climate zones. This study highlights the temporal shifts of major driving forces of UHI intensity, the mitigation tactics for UHI in different cities and seasons should be customized for better validity.