Crowdsourced air temperatures contrast satellite measures of the urban heat island and its mechanisms

Uncovering the Impacts of 2D and 3D Urbanization on Urban Heat Islands in 384 Chinese Cities

Rapid urbanization in China has exacerbated the urban heat island (UHI) effect, posing considerable challenges to urban sustainability and public health. Most UHI studies have focused on the impacts of two-dimensional (2D) urbanization, which involves outward city expansion and increased built-up area. However, as cities mature, they typically transition from horizontal expansion to vertical densification (3D urbanization), leading to increased material stock density. The implications of this shift for the UHI effect remain underexplored. This study compared the 2D and 3D urbanization-induced impacts on UHI across 384 Chinese cities from 2000 to 2020, using impervious surface and gridded material stocks. Our results surprisingly indicated that 2D urbanization lost explanatory power of the UHI intensity when the impervious surface area percentage exceeded 87%. Relative importance analysis utilizing a random forest algorithm revealed that the population, vegetation abundance, and precipitation significantly moderated the effects of 3D urbanization, emphasizing the crucial role of urban green spaces in mitigating thermal stress. This study examined the spatiotemporal dynamics of the UHI effect in China, emphasizing the key role of 3D urbanization. Our findings highlight the urgent need to incorporate 3D urbanization characteristics when devising UHI mitigation strategies.

An enhanced national-scale urban tree canopy cover dataset for the United States

Moderate-resolution (30-m) national map products have limited capacity to represent fine-scale, heterogeneous urban forms and processes, yet improvements from incorporating higher resolution predictor data remain rare. In this study, we applied random forest models to high-resolution land cover data for 71 U.S. urban areas, moderate-resolution National Land Cover Database (NLCD) Tree Canopy Cover (TCC), and additional explanatory climatic and structural data to develop an enhanced urban TCC dataset for U.S. urban areas. With a coefficient of determination (R2) of 0.747, our model estimated TCC within 3% for 62 urban areas and added 13.4% more city-level TCC on average, compared to the native NLCD TCC product. Cross validations indicated model stability suitable for building a national-scale TCC dataset (median R2 of 0.752, 0.675, and 0.743 for 1,000-fold cross validation, urban area leave-one-out cross validation, and cross validation by Census block group median year built, respectively). Additionally, our model code can be used to improve moderate-resolution TCC in other parts of the world where high-resolution land cover data have limited spatiotemporal availability.

Distributional outcomes of urban heat island reduction pathways under climate extremes

Global warming and the rise in extreme heat days elevate the risk of heat-related mortalities, particularly in cities due to the Urban Heat Island (UHI) effect and vulnerabilities tied to housing, exposure, and health conditions. City planners can mitigate these effects through urban adaptive actions. UHI mitigation, however, needs to balance several goals: strategies that maximize temperature reduction or minimize their impacts may not be best for cost effectiveness, carbon emissions, environmental amenities, health impacts, or distributional outcomes. Here, we implement a multi-objective robust decision-making tool for heat mitigation-the City-Heat Equity Adaptation Tool (City-HEAT)-to identify potential heat mitigation pathways at neighborhood scales. We find that more expensive pathways tend to have larger benefits in reducing heat-related deaths, but that these pathways sometimes underperform against other alternatives on reducing inequality in mortality outcomes. Pathways that focus on tree planting, a popular and powerful tool for UHI reduction, were found to be expensive and less effective at reducing health disparities than more diversified pathways, if no specific measures are taken to target tree distribution for distributional benefit. The generated pathways can reduce Baltimore's heat related mortality by 81-670 deaths in the next 50 years, considering different investment plans in the city's neighborhoods. We also find that these results are relatively insensitive to expectations for future warming: pathways designed for high warming rates are similar to those designed for low warming rates, suggesting that general strategies for UHI mitigation can be robust to climate uncertainties.

LCZ4r package R for local climate zones and urban heat islands

The LCZ4r is a novel toolkit designed to streamline Local Climate Zones (LCZ) classification and Urban Heat Island (UHI) analysis. Built on the open-source R statistical programming platform, the LCZ4r package aims to improve the usability of the LCZ framework for climate and environment researchers. The suite of LCZ4r functions is categorized into general and local functions ( https://bymaxanjos.github.io/LCZ4r/index.html ). General functions enable users to quickly extract LCZ maps for any landmass of the world at different scales, without requiring extensive GIS expertise. They also generate a series of urban canopy parameter maps, such as impervious fractions, albedo, and sky view factor, and calculate LCZ-related area fractions. Local functions require measurement data to perform advanced geostatistical analysis, including time series, thermal anomalies, air temperature interpolation, and UHI intensity. By integrating LCZ data with interpolation techniques, LCZ4r enhances air temperature modeling, capturing well-defined thermal patterns, such as vegetation-dominated areas, that traditional methods often overlook. The openly available and reproducible R-based scripts ensure consistent results and broad applicability, making LCZ4r a valuable tool for researchers studying the relationship between land use-cover and urban climates.

Patterns and drivers of surface cooling effect of urban trees across global cities

Urban trees offer a promising strategy to mitigating rising heat stress in cities globally. However, the spatial distribution and influencing factors of the tree cooling effect across urban surfaces worldwide are not thoroughly understood. Here we quantified the surface cooling effect during the summer season across 1016 cities globally using two key metrics simultaneously - cooling intensity and cooling distance - primarily based on Landsat-8 land surface temperature data. We then investigated the impact of various drivers on the surface cooling effect, including tree attributes at the small scale (e.g., area and landscape shape index), urban characteristic at the medium scale (e.g., city population), and background climate at the large scale (e.g., air temperature and precipitation). Furthermore, the combined effects of urban and climatic contexts on the regulation of tree attributes on the surface cooling effect were examined. Our findings reveal that the global average cooling intensity is 1.67 ± 1.13 °C, while the global average cooling distance is 136.86 ± 60.44 m. Cooling intensity is mainly regulated by tree attributes (88 %), followed by background climate (9 %) and urban characteristic (3 %). The variable that explains the most variation in cooling intensity is the NDVI of trees at the small scale. Similarly, the cooling distance is primarily influenced by tree attributes (73 %), background climate (22 %), and urban characteristic (5 %). The area of trees at the small scale is the primary factor contributing to the variation in cooling distance. Additionally, the impact of tree attributes on cooling effect is jointly moderated by urban characteristic and background climate. In cities with larger populations or higher air temperatures, the area of trees and the landscape shape index exert a less pronounced impact on the cooling effect. This implies that the presence of small, widely dispersed tree cover in such urban areas can effectively provide the cooling intensity. Our study provides a crucial baseline for formulating tree cooling strategies and managing urban tree cover across global cities.

The mortality and associated economic burden of London's summer urban heat island effect: a modelling study

BACKGROUND

High ambient temperatures lead to increased mortality, especially in older adults. Climate change will increase the frequency and severity of heatwaves globally. Most of the UK population lives in urban areas, which often have higher temperatures than rural areas (the urban heat island [UHI] effect) and higher rates of heat-related mortality. We estimated the mortality burden in terms of attributable mortality and years of life lost (YLLs), and social costs attributed to the UHI effect in summer 2018 in Greater London.

METHODS

We estimated the UHI effect using advanced urban climate modelling. We applied a quantitative health impact assessment to estimate mortality and YLLs attributable to high air temperature. We estimated social costs using value of statistical life (VSL) and value of statistical life-years (VOLY) methods.

FINDINGS

We attribute 785 (95% CI 655-919) deaths in summer 2018 in Greater London to high air temperature. Half of these (399 [350-446]) are attributable to the UHI effect, or approximately 5·0 (4·1-5·9) thousand YLLs. Social costs of the summer UHI effect due to mortality are estimated at £987 million (866 million-1·10 billion) using VSL or £453 million (367-533 million) using VOLY (2023 prices).

INTERPRETATION

Monetised costs attributed to the UHI effect remain high using either VSL or VOLY approaches. The findings demonstrate the seriousness of heat as a public health risk, set a scale at which society may be willing to pay for urban heat mitigation, and give tangible support for large-scale urban heat mitigation and adaptation policies.

FUNDING

Wellcome Trust.

Impact of urban trees on carbon dioxide exchange: Mechanistic pathways, environmental controls, and feedback

The increase of carbon dioxide (CO2) concentration in the atmosphere is held responsible for global climate changes. To meet the objective of achieving carbon neutrality and keeping global warming in check, many cities, as hotspots of CO2 emissions, have been promoting the use of urban greenery, urban trees in particular, to mitigate carbon emissions from the built environment. However, there remain large uncertainty and divergence of the potential of urban trees for carbon mitigation, with the underlying mechanisms poorly understood. In this study, we conducted a comprehensive survey of the biophysical functions, their environmental controls, and possible heat-carbon-water feedback that mechanistically govern the CO2 exchange processes of trees in the built environment. This review helps to clarify some disparities and enables us to gain clearer insights into the participatory role of urban trees in the dynamics of CO2 exchange. In addition, we proposed a few guidelines for urban planning and management strategies of using trees to promote the sustainability of urban ecosystems.

Identification of surface urban heat versus cool islands for arid cities depends on the choice of urban and rural definitions

The urban heat island (UHI) effect in arid cities can be small or even negative, the latter known as the urban cool island (UCI) effect. Differences in defining urban and rural areas can introduce uncertainties in detecting UHI or UCI, especially when the UHI signal is small. Here, we compared the surface UHI intensity (SUHII) estimated by a dozen different methods (with multiple urban and/or rural definitions) across 104 arid cities globally, providing a comprehensive evaluation of the uncertainty in SUHII estimates. Results show that the absolute difference in annual average SUHII (∆SUHII) among methods exceeded 1 °C in about half of the arid cities during both daytime and nighttime. The overall annual mean ∆SUHII for all arid cities was 1.35 °C during daytime and 1.03 °C at night. The uncertainty arising from simultaneous variations in urban and rural definitions was generally higher than that resulting from their individual changes. It was observed that, with varying definitions of urban and rural areas, nearly 50 % of arid cities experienced a sign reversal in daytime SUHII estimates, while approximately 15 % exhibited a sign reversal in nighttime SUHII. Variations in urban-rural differences in surface properties, such as vegetation index and albedo, due to differing urban and rural definitions, contributed strongly to the observed SUHII uncertainties. Overall, our results offer new insights into the ongoing debate on heat and cold islands in arid cities, emphasizing a critical need to standardize SUHII estimation frameworks.

Large disagreements in estimates of urban land across scales and their implications

Improvements in high-resolution satellite remote sensing and computational advancements have sped up the development of global datasets that delineate urban land, crucial for understanding climate risks in our increasingly urbanizing world. Here, we analyze urban land cover patterns across spatiotemporal scales from several such current-generation products. While all the datasets show a rapidly urbanizing world, with global urban land nearly tripling between 1985 and 2015, there are substantial discrepancies in urban land area estimates among the products influenced by scale, differing urban definitions, and methodologies. We discuss the implications of these discrepancies for several use cases, including for monitoring urban climate hazards and for modeling urbanization-induced impacts on weather and climate from regional to global scales. Our results demonstrate the importance of choosing fit-for-purpose datasets for examining specific aspects of historical, present, and future urbanization with implications for sustainable development, resource allocation, and quantification of climate impacts.

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.

Modelling Europe-wide fine resolution daily ambient temperature for 2003-2020 using machine learning

To improve our understanding of the health impacts of high and low temperatures, epidemiological studies require spatiotemporally resolved ambient temperature (Ta) surfaces. Exposure assessment over various European cities for multi-cohort studies requires high resolution and harmonized exposures over larger spatiotemporal extents. Our aim was to develop daily mean, minimum and maximum ambient temperature surfaces with a 1 × 1 km resolution for Europe for the 2003-2020 period. We used a two-stage random forest modelling approach. Random forest was used to (1) impute missing satellite derived Land Surface Temperature (LST) using vegetation and weather variables and to (2) use the gap-filled LST together with land use and meteorological variables to model spatial and temporal variation in Ta measured at weather stations. To assess performance, we validated these models using random and block validation. In addition to global performance, and to assess model stability, we reported model performance at a higher granularity (local). Globally, our models explained on average more than 81 % and 93 % of the variability in the block validation sets for LST and Ta respectively. Average RMSE was 1.3, 1.9 and 1.7 °C for mean, min and max ambient temperature respectively, indicating a generally good performance. For Ta models, local performance was stable across most of the spatiotemporal extent, but showed lower performance in areas with low observation density. Overall, model stability and performance were lower when using block validation compared to random validation. The presented models will facilitate harmonized high-resolution exposure assignment for multi-cohort studies at a European scale.

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.

The potential of urban trees to reduce heat-related mortality in London

Increasing temperatures and more frequent heatwave events pose threats to population health, particularly in urban environments due to the urban heat island (UHI) effect. Greening, in particular planting trees, is widely discussed as a means of reducing heat exposure and associated mortality in cities. This study aims to use data from personal weather stations (PWS) across the Greater London Authority to understand how urban temperatures vary according to tree canopy coverage and estimate the heat-health impacts of London's urban trees. Data from Netatmo PWS from 2015-2022 were cleaned, combined with official Met Office temperatures, and spatially linked to tree canopy coverage and built environment data. A generalized additive model was used to predict daily average urban temperatures under different tree canopy coverage scenarios for historical and projected future summers, and subsequent health impacts estimated. Results show areas of London with higher canopy coverage have lower urban temperatures, with average maximum daytime temperatures 0.8 °C and minimum temperatures 2.0 °C lower in the top decile versus bottom decile canopy coverage during the 2022 heatwaves. We estimate that London's urban forest helped avoid 153 heat attributable deaths from 2015-2022 (including 16 excess deaths during the 2022 heatwaves), representing around 16% of UHI-related mortality. Increasing tree coverage 10% in-line with the London strategy would have reduced UHI-related mortality by a further 10%, while a maximal tree coverage would have reduced it 55%. By 2061-2080, under RCP8.5, we estimate that London's current tree planting strategy can help avoid an additional 23 heat-attributable deaths a year, with maximal coverage increasing this to 131. Substantial benefits would also be seen for carbon storage and sequestration. Results of this study support increasing urban tree coverage as part of a wider public health effort to mitigate high urban temperatures.

Demographic disparity in diurnal surface urban Heat Island exposure across local climate zones: A case study of Chongqing, China

Surface urban heat island (SUHI) exposure significantly harms human health during rapid urbanization. Identifying the areas and demographic groups under high SUHI exposure is critical for mitigating heat-related hazards. However, despite broad concern in US-European countries, rare studies discuss the diurnal SUHI exposure of demographic subgroups across Local Climate Zones (LCZs) in Chinese cities. Therefore, taking Chongqing as the case study, we measured the diurnal SUHI exposure of demographic subgroups (e.g., gender, age, and income) across different LCZs (compact, open, and sparsely-built zones) by coupling the ECOSTRESS data and mobile phone signaling data. The results indicated that Chongqing's compact high/middle-rise zones suffered a higher SUHI exposure due to high land surface temperature (LST) and a larger size of population than open zones. Despite a relatively low population density, extremely high LST in compact low-rise zones (e.g., industrial parks) contributes to considerable accumulated SUHI exposure. The SUHI exposure risk exhibited the differences between daytime and nighttime, resulting from SUHI variation and population flow. The demographic analysis showed that Chongqing's demographic subgroups are exposed disproportionately to SUHI. Elderly groups suffered relatively high exposure in compact high-rise zones. Low-incomers witnessed a high exposure in open zones. These findings call for alleviating SUHI exposure risk by targeting vulnerable groups and high-intensity exposure areas.

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.

Association of neighborhood-level socioeconomic status and urban heat in China: Evidence from Hangzhou

The escalating contradiction between global urban development and thermal environments has become increasingly apparent, underscoring the imperative to address social inequality in heat exposure and advocate for environmental justice (EJ) in the pursuit of sustainable urban development. To bridge the research gap in this domain, a comprehensive study was conducted to examine the correlation mechanism linking the thermal environment with the socioeconomic status (SES) of Chinese cities, employing Hangzhou as a representative case-a pivotal city among China's "four fire stoves". The investigation involved analyzing the spatial distribution pattern of diurnal Land Surface Temperature (LST) during the summer months spanning 2016 to 2018 (July to September). For SES characterization, a holistic indicator was established. Community-level LST variables were derived from LST surfaces obtained through the Terra and Aqua satellite MODIS sensors, with the community serving as the fundamental unit of analysis. The relationship between SES and LST was explored using random forest regression (RF), eXtreme Gradient Boosting (XGBoost), and support vector regression (SVR) to assess socioeconomic inequality in urban heat. The findings reveal that (1) RF exhibits the highest fitting accuracy and adeptly elucidates the nonlinear relationship and marginal effects between LST variables and SES. (2) Community SES in the Hangzhou metropolitan area exhibits spatial clustering. (3) Residents of low and middle SES communities experience heightened heat inequality. (4) A complex nonlinear relationship exists between daytime and nighttime LST and SES, with significant social disparities in urban heat within specific temperature thresholds. When deciding on measures to advance thermal environmental justice, it is crucial to prioritize both relatively disadvantaged groups and specific temperature intervals. This study departs from conventional approaches, exploring the nonlinear relationship between SES and urban heat at a fine scale, thereby assisting urban planners in developing effective strategies.

Associations between heat waves and chronic kidney disease in China: The modifying role of land cover

The increasing frequency of heat waves under the global urbanization and climate change background poses elevating risks of chronic kidney disease (CKD). Nevertheless, there has been no evidence on associations between long-term exposures to heat waves and CKD as well as the modifying effects of land cover patterns. Based on a national representative population-based survey on CKD covering 47,086 adults and high spatial resolution datasets on temperature and land cover data, we found that annual days of exposure to heat waves were associated with increased odds of CKD prevalence. For one day/year increases in HW_975_4d (above 97.5 % of annual maximum temperature and lasting for at least 4 consecutive days), the odds ratio (OR) of CKD was 1.14 (95 %CI: 1.12, 1.15). Meanwhile, stronger associations were observed in regions with lower urbanicity [rural: 1.14 (95 %CI: 1.12, 1.16) vs urban: 1.07 (95 %CI: 1.03, 1.11), Pinteraction < 0.001], lower water body coverage [lower: 1.14 (95 %CI: 1.12, 1.16) vs higher: 1.02 (95 %CI: 0.98, 1.05), Pinteraction < 0.001], and lower impervious area coverage [lower: 1.16 (95 %CI: 1.14, 1.18) vs higher: 1.06 (95 %CI: 1.03, 1.10), Pinteraction = 0.008]. In addition, this study found disparities in modifying effects of water bodies and impervious areas in rural and urban settings. In rural regions, the associations between heat waves and CKD prevalence showed a consistent decreasing trend with increases in both proportions of water bodies and impervious areas (Pinteraction < 0.05). Nevertheless, in urban regions, we observed significant effect modification by water bodies, but not by impervious areas. Our study indicates the need for targeted land planning as part of adapting to the kidney impacts of heat waves, with a focus on urbanization in rural regions, as well as water body construction and utilization in both rural and urban regions.

Different explanations for surface and canopy urban heat island effects in relation to background climate

The background climatic conditions and urban morphology greatly influence urban heat island effects (UHIs), but one-size-fits-all solutions are frequently employed to mitigate UHIs. Here, attribution models for surface UHIs (SUHIs) and canopy UHIs (CUHIs) were developed to describe UHI formation. The contribution of factors to SUHIs and CUHIs shows similar dependencies on background climate and urban morphology. Furthermore, the factors that mainly contributed to CUHIs were more complex, and anthropogenic heat was the more critical factor. Influence from urban morphology also highlights that there is no one-size-fit-all solution for heat mitigation at the neighborhood. In particular, maintaining a low building density should be prioritized, especially mitigating CUHIs. Moreover, it is more effective to prioritize urban irrigation maintenance over increasing green cover in arid regions but the opposite in humid regions. The work can provide scientific evidence to support developing general and regional guidelines for urban heat mitigation.

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.

Greater local cooling effects of trees across globally distributed urban green spaces

Urban green spaces (UGS) are an effective mitigation strategy for urban heat islands (UHIs) through their evapotranspiration and shading effects. Yet, the extent to which local UGS cooling effects vary across different background climates, plant characteristics and urban settings across global cities is not well understood. This study analysed 265 local air temperature (TA) measurements from 58 published studies across globally distributed sites to infer the potential influence of background climate, plant and urban variables among different UGS types (trees, grass, green roofs and walls). We show that trees were more effective at reducing local TA, with reductions 2-3 times greater than grass and green roofs and walls. We use a hierarchical linear mixed effects model to reveal that background climate (mean annual temperature) and plant characteristics (specific leaf area vegetation index) had the greatest influence on cooling effects across UGS types, while urban characteristics did not significantly influence the cooling effects of UGS. Notably, trees dominated the overall local cooling effects across global cities, indicating that greater tree growth in mild climates with lower mean annual temperatures has the greatest mitigation potential against UHIs. Our findings provide insights for urban heat mitigation using UGS interventions, particularly trees across cities worldwide with diverse climatic and environmental conditions and highlight the essential role of trees in creating healthy urban living environments for citizens under extreme weather conditions.

Diurnal variation in the urban thermal environment and its relationship to human activities in China: a Tencent location-based service geographic big data perspective

The main factor of the formation and deterioration in China's urban thermal environment is human activity, which is difficult to describe and measure. A new perspective on the effect of human activity on the urban thermal environment can be obtained by examining the interaction between location-based service (LBS) data and the urban thermal environment in China. However, relevant research is still limited. In this study, we used Tencent LBS data, Terra/Aqua MODIS land surface temperature (LST) data, and land use data to investigate the relationship between LBS and the urban thermal environment, specifically the LST and surface urban heat island intensity (SUHII) across China and its provinces. Our results showed that (1) in summer, the heat island effect was an issue in 94% of the urban areas in China, which was worse during the day. The high- and low-value periods of LBS data on a given day coincided with the acquisition times of MODIS LST products during the day and at night, respectively. (2) During both the day and at night, there was a significant connection between LBS data and the urban thermal environment in China. The highest correlation coefficient (r) between LBS data and the LST could reach 0.55 (p < 0.01) at the provincial level, and the highest correlation coefficient (r) between LBS data and the SUHII could reach 0.78 (p < 0.01) at the provincial level. (3) The urban thermal environment diurnal difference and LBS data exhibited a significant relationship. The ΔLBS diurnal differences were significantly positively related to the SUHII diurnal differences in China. The overall study findings revealed that LBS data constitute an important parameter to represent the human activity intensity when investigating the formation of the urban thermal environment in China.

Satellite data for environmental justice: a scoping review of the literature in the United States

In support of the environmental justice (EJ) movement, researchers, activists, and policymakers often use environmental data to document evidence of the unequal distribution of environmental burdens and benefits along lines of race, class, and other socioeconomic characteristics. Numerous limitations, such as spatial or temporal discontinuities, exist with commonly used data measurement techniques, which include ground monitoring and federal screening tools. Satellite data is well poised to address these gaps in EJ measurement and monitoring; however, little is known about how satellite data has advanced findings in EJ or can help to promote EJ through interventions. Thus, this scoping review aims to (1) explore trends in study design, topics, geographic scope, and satellite datasets used to research EJ, (2) synthesize findings from studies that use satellite data to characterize disparities and inequities across socio-demographic groups for various environmental categories, and (3) capture how satellite data are relevant to policy and real-world impact. Following PRISMA extension guidelines for scoping reviews, we retrieved 81 articles that applied satellite data for EJ research in the United States from 2000 to 2022. The majority of the studies leveraged the technical advantages of satellite data to identify socio-demographic disparities in exposure to environmental risk factors, such as air pollution, and access to environmental benefits, such as green space, at wider coverage and with greater precision than previously possible. These disparities in exposure and access are associated with health outcomes such as increased cardiovascular and respiratory diseases, mental illness, and mortality. Research using satellite data to illuminate EJ concerns can contribute to efforts to mitigate environmental inequalities and reduce health disparities. Satellite data for EJ research can therefore support targeted interventions or influence planning and policy changes, but significant work remains to facilitate the application of satellite data for policy and community impact.

Land surface and air temperature dynamics: The role of urban form and seasonality

Due to the scarcity of air temperature (Ta) observations, urban heat studies often rely on satellite-derived Land Surface Temperature (LST) to characterise the near-surface thermal environment. However, there remains a lack of a quantitative understanding on how LST differs from Ta within urban areas and what are the controlling factors of their interaction. We use crowdsourced air temperature measurements in Sydney, Australia, combined with urban landscape data, Local Climate Zones (LCZ), high-resolution satellite imagery, and machine learning to explore the influence of urban form and fabric on the interaction between Ta and LST. Results show that LST and Ta have distinct spatiotemporal characteristics, and their relationship differs by season, ecological infrastructure, and building morphology. We found greater seasonal variability in LST compared to Ta, along with more pronounced intra-urban spatial variability in LST, particularly in warmer seasons. We also observed a greater temperature difference between LST and Ta in the built environment compared to the natural LCZs, especially during warm days. Natural LCZs (areas with mostly dense and scattered trees) showed stronger LST-Ta relationships compared to built areas. In particular, we observe that built areas with higher building density (where the heat vulnerability is likely more pronounced) show insignificant or negative relationships between LST- Ta in summer. Our results also indicate that surface cover, distance from the ocean, and seasonality significantly influence the distribution of hot and cold spots for LST and Ta. The spatial distribution for Ta hot spots does not always overlap with LST. We find that relying solely on LST as a direct proxy for the urban thermal environment is inappropriate, particularly in densely built-up areas and during warm seasons. These findings provide new perspectives on the relationship between surface and canopy temperatures and how these relate to urban form and fabric.

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.

Urban Versus Lake Impacts on Heat Stress and Its Disparities in a Shoreline City

Shoreline cities are influenced by both urban-scale processes and land-water interactions, with consequences on heat exposure and its disparities. Heat exposure studies over these cities have focused on air and skin temperature, even though moisture advection from water bodies can also modulate heat stress. Here, using an ensemble of model simulations covering Chicago, we find that Lake Michigan strongly reduces heat exposure (2.75°C reduction in maximum average air temperature in Chicago) and heat stress (maximum average wet bulb globe temperature reduced by 0.86°C) during the day, while urbanization enhances them at night (2.75 and 1.57°C increases in minimum average air and wet bulb globe temperature, respectively). We also demonstrate that urban and lake impacts on temperature (particularly skin temperature), including their extremes, and lake-to-land gradients, are stronger than the corresponding impacts on heat stress, partly due to humidity-related feedback. Likewise, environmental disparities across community areas in Chicago seen for skin temperature are much higher (1.29°C increase for maximum average values per $10,000 higher median income per capita) than disparities in air temperature (0.50°C increase) and wet bulb globe temperature (0.23°C increase). The results call for consistent use of physiologically relevant heat exposure metrics to accurately capture the public health implications of urbanization.

Investigating the relationship between COVID-19 shutdown and land surface temperature on the Anatolian side of Istanbul using large architectural impermeable surfaces

Artificial impermeable surfaces are becoming more prevalent, especially in urban areas, as a result of shifting land use and cover, roads, roofs, etc. The modification of land surface temperature (LST) can also be accomplished through artificially impermeable surfaces. Large artificial impermeable surfaces, such as rooftops, parking lots, and other areas of use, can be found in industrial zones, shopping malls, industrial airports, and other locations. For the Anatolian side of Istanbul, 14 Landsat 8 OLI/TIRS imagery images over the years 2016-2022 were investigated. To evaluate how well the study's images could be utilized, correlation and cosine similarity approaches were employed. A total of 12 images may be employed for research LST studies, it was discovered. We looked at closure dates during the COVID-19 epidemic to find out how human migration affected the LST. In addition, the LST value was estimated using the ordinary least squares (OLS) method employing LST and other biophysical indices. A decrease in LST values was seen as a result of the investigation. High levels of similarity and correlation were found between the images used. Results from the Google Mobility Index also provide support to the study. All of these facts provide support to Istanbul's Anatolian side experiencing lower surface temperature values, which consequently affects the city's massive structures.

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.

Built environment influences on urban climate resilience: Evidence from extreme heat events in Macau

Systematic understanding of climate resilience in the urban context is essential to improve the adaptive capacity in response to extreme weather events. Although the urban built environment affects climate resilience, empirical evidence on the associations between the built environment and urban climate resilience is rare in the literature. In this study, urban heat resilience (HR) is measured as the land surface temperature (LST) difference in a given urban area between normal and extreme heat event, and it further explores the impact of two-dimensional (2D) and three-dimensional (3D) urban built environment features on HR. Using spatial regression, we find that solar insolation and water density are the dominant factors in determining land surface temperature. However, they do not appear to influence HR significantly. Results indicate that vegetation and urban porosity are crucial both in reducing LST and improving HR during extreme heat events. This study highlights the importance of 2D and 3D urban built environment features in improving HR to extreme heat events.

Integrating CFD-GIS modelling to refine urban heat and thermal comfort assessment

Constant urban growth exacerbates the demand for residential, commercial and traffic areas, leading to progressive surface sealing and urban densification. With climate change altering precipitation and temperature patterns worldwide, cities are exposed to multiple risks, demanding holistic and anticipatory urban planning strategies and adaptive measures that are multi-beneficial. Sustainable urban planning requires comprehensive tools that account for different aspects and boundary conditions and are capable of mapping and assessing crucial processes of land-atmosphere interactions and the impacts of adaptation measures on the urban climate system. Here, we combine Computational Fluid Dynamics (CFD) and Geographic Information System (GIS) capabilities to refine an existing 2D urban micro- and bioclimatic modelling approach. In particular, we account for the vertical and horizontal variability in wind speed and air temperature patterns in the urban canopy layer. Our results highlight the importance of variability of these patterns in analysing urban heat development, intensity and thermal comfort at multiple heights from the ground surface. Neglecting vertical and horizontal variability, non-integrated CFD modelling underestimates mean land surface temperature by 7.8 °C and the Universal Thermal Climate Index by 6.9 °C compared to CFD-integrated modelling. Due to the strong implications of wind and air temperature patterns on the relationship between surface temperature and human thermal comfort, we urge caution when relying on studies solely based on surface temperatures for urban heat assessment and hot spot analysis as this could lead to misinterpretations of hot and cool spots in cities and, thus, mask the anticipated effects of adaptation measures. The integrated CFD-GIS modelling approach, which we demonstrate, improves urban climate studies and supports more comprehensive assessments of urban heat and human thermal comfort to sustainably develop resilient cities.

Urban green space and albedo impacts on surface temperature across seven United States cities

Extreme heat represents a growing threat to public health, especially across the densely populated, developed landscape of cities. Climate adaptation strategies that aim to manage urban microclimates through purposeful design can reduce the heat exposure of urban populations, however, it is unclear how the temperature impacts of urban green space and albedo vary across cities and background climate. This study quantifies the sensitivity of surface temperature to landcover characteristics tied to two widely used climate adaptation strategies, urban greening and albedo manipulation (e.g. white roofs), by combining long-term remote sensing observations of land surface temperature, albedo, and moisture with high-resolution landcover datasets in a spatial regression analysis at the census block scale across seven United States cities. We find tree cover to have an average cooling impact of -0.089 K per % cover, which is approximately four times stronger than the average grass cover cooling impact of -0.021 K per % cover. Variability in the magnitude of grass cover cooling impacts was primarily a function of vegetation moisture content, with the Land Surface Water Index (LSWI) explaining 89 % of the variability in grass cover cooling impacts across cities. Variability in tree cover cooling impacts was primarily a function of sunlight and vegetation moisture content, with solar irradiance and LSWI explaining 97 % of the cooling variability across cities. Albedo cooling impacts were consistent across cities with an average cooling impact of -0.187 K per increase of 0.01. While these interventions are broadly effective across cities, there are critical regional trade-offs between vegetation cooling efficiency, irrigation requirements, and the temporal duration and evolution of the cooling benefits. In warm, arid cities, high albedo surfaces offer multifaceted benefits such as cooling and water conservation, whereas temperate, mesic cities likely benefit from a combination of strategies, with greening efforts targeting highly paved neighborhoods.

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.

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.

Characteristics of Urban Heat Island in China and Its Influences on Building Energy Consumption

Urban heat island (UHI) draws more attention as it affects not only the health of residents but also the energy consumption of buildings at the city scale. To achieve carbon neutrality goals, it is crucial to better understand the mechanism of the UHI influences on building energy consumption. The characteristics of urban heat island intensity (UHII) and the relationship between the UHII effect and building electricity and related coal consumption were analyzed, based on the long period of monitoring data with hourly weather data from 1 January to 31 December 2019. Results show that a strong correlation between the annual mean UHII and the median daily mean UHII exists. The synthetic diurnal UHII of most cities presents a U-shaped variation trend. In different building climate zones in China, namely, severe cold region (SCR), cold region (CR), hot summer cold winter region (HSCWR), hot summer and warm winter region (HSWWR), and mild region (MR), the influences of UHII on building energy consumption were analyzed. The existence of UHI reduces building energy consumption in 96.7% of SCR cities and 60.8% of CR cities, while in HSCWR, HSWWR, and MR cities, the percentage of cities where the building energy consumption is increased by UHI is 69.4%, 80%, and 63.6%, respectively. Urban climate strongly influences building energy consumption, indicating that it should be considered and analyzed in detail for making future urban development or carbon emission reduction strategies.

The Seasonality of Surface Urban Heat Islands across Climates

In this work, we investigate how the seasonal hysteresis of the Surface Urban Heat Island Intensity (SUHII) differs across climates and provide a detailed typology of the daytime and nighttime SUHII hysteresis loops. Instead of the typical tropical/dry/temperate/continental grouping, we describe Earth’s climate using the Köppen–Geiger system that empirically maps Earth’s biome distribution into 30 climate classes. Our thesis is that aggregating multi-city data without considering the biome of each city results in temporal means that fail to reflect the actual SUHII characteristics. This is because the SUHII is a function of both urban and rural features and the phenology of the rural surroundings can differ considerably between cities, even in the same climate zone. Our investigation covers all the densely populated areas of Earth and uses 18 years (2000–2018) of land surface temperature and land cover data from the European Space Agency’s Climate Change Initiative. Our findings show that, in addition to concave-up and -down shapes, the seasonal hysteresis of the SUHII also exhibits twisted, flat, and triangle-like patterns. They also suggest that, in wet climates, the daytime SUHII hysteresis is almost universally concave-up, but they paint a more complex picture for cities in dry climates.

Evidence of horizontal urban heat advection in London using six years of data from a citizen weather station network

Recent advances in citizen weather station (CWS) networks, with data accessible via crowd-sourcing, provide relevant climatic information to urban scientists and decision makers. In particular, CWS can provide long-term measurements of urban heat and valuable information on spatio-temporal heterogeneity related to horizontal heat advection. In this study, we make the first compilation of a quasi-climatologic dataset covering six years (2015-2020) of hourly near-surface air temperature measurements obtained via 1560 suitable CWS in a domain covering south-east England and Greater London. We investigated the spatio-temporal distribution of urban heat and the influences of local environments on climate, captured by CWS through the scope of Local Climate Zones (LCZ)-a land-use land-cover classification specifically designed for urban climate studies. We further calculate, for the first time, the amount of advected heat captured by CWS located in Greater London and the wider south east England region. We find that London is on average warmer by about 1.0 ∘C-1.5 ∘C than the rest of south-east England. Characteristics of the southern coastal climate are also captured in the analysis. We find that on average, urban heat advection (UHA) contributes to 0.22 ± 0.96 ∘C of the total urban heat in Greater London. Certain areas, mostly in the centre of London are deprived of urban heat through advection since heat is transferred more to downwind suburban areas. UHA can positively contribute to urban heat by up to 1.57 ∘C, on average and negatively by down to -1.21 ∘C. Our results also show an important degree of inter- and intra-LCZ variability in UHA, calling for more research in the future. Nevertheless, we already find that UHA can impact green areas and reduce their cooling benefit. Such outcomes show the added value of CWS when considering future urban design.

Urbanization Impact on Regional Climate and Extreme Weather: Current Understanding, Uncertainties, and Future Research Directions

Urban environments lie at the confluence of social, cultural, and economic activities and have unique biophysical characteristics due to continued infrastructure development that generally replaces natural landscapes with built-up structures. The vast majority of studies on urban perturbation of local weather and climate have been centered on the urban heat island (UHI) effect, referring to the higher temperature in cities compared to their natural surroundings. Besides the UHI effect and heat waves, urbanization also impacts atmospheric moisture, wind, boundary layer structure, cloud formation, dispersion of air pollutants, precipitation, and storms. In this review article, we first introduce the datasets and methods used in studying urban areas and their impacts through both observation and modeling and then summarize the scientific insights on the impact of urbanization on various aspects of regional climate and extreme weather based on more than 500 studies. We also highlight the major research gaps and challenges in our understanding of the impacts of urbanization and provide our perspective and recommendations for future research priorities and directions.

Urban ecological land and natural-anthropogenic environment interactively drive surface urban heat island: An urban agglomeration-level study in China

The surface urban heat island effect (SUHI) that occurs during rapid urbanization increases the health risks associated with high temperatures. Urban ecological land (UEL) has been shown to play an important role in improving urban heat stress, however, the impact of UEL interactions with the natural-anthropogenic environment on SUHI at the urban agglomeration-scale is less explored. In this study, the Google Earth Engine and GeoDetector were applied to characterize the spatiotemporal patterns of UEL and SUHI in the Guangdong-Hong Kong-Macao Greater Bay Area from 2000 to 2020 by extracting major built-up urban areas and quantifying the impacts of UEL and its interactions with the natural-anthropogenic factors on SUHI. The results show that the evolution of the UEL landscape structure exhibits clear spatiotemporal coupling with SUHI. Specifically, the UEL underwent a dispersion and degradation process in 2000-2015 and a convergence and restoration process in 2015-2020, the SUHI correspondingly transitioned from intensification and continuity to mitigation and contraction. The UEL landscape structure showed a notable impact on the SUHI reduction, and the dominance and richness of the patches explained an average of 19.95% and 16.03% of the SUHI, respectively. Moreover, the interaction between UEL and land urbanization rate and anthropogenic heat release had a dominant effect on SUHI, but this effect significantly declined from 2015 to 2020. With the implementation of ecological restoration projects, the interaction of UEL with topography rapidly increased and the SUHI gradually dominated by the joint interaction of UEL and natural-anthropogenic factors. A synthesis of the varying effects of several factors showed that the dynamic relationship between the development stages of the urban agglomeration's regional system and SUHI may conform to the Environmental Kuznets Curve. SUHI reduction strategies should therefore comprehensively optimize the rational allocation of UEL landscape structures and natural-human elements to promote the well-being of residents.