Search

This study investigates the relationship between temporal changes in temperatures characterizing local urban heat islands (UHIs) and heat-related illnesses (HRIs) in seven major cities of California. UHIs, which are a phenomenon that arises in the presence of impervious surfaces or the lack of green spaces exacerbate the effects of extreme heat events, can be measured longitudinally using satellite products. The two objectives of this study were: (1) to identify temperature trends in local temperatures to characterize UHIs across zip code tabulation areas (ZCTAs) in the seven observed cities over a 22-year period and (2) to use propensity score and inverse probability weighting to achieve exchangeability between different types of ZCTAs and assess the difference in hospital admissions recorded as HRIs attributable to temporal changes in UHIs. We use monthly land surface temperature data derived from MODIS Terra imagery from the summer months (June-September) from 2000 to 2022. We categorized ZCTAs (into three groups) based on their monthly land surface temperature trends. Of the 216 ZCTAs included in this study, the summertime land surface temperature trends of 43 decreased, while 161 remained unchanged, and 12 increased. Los Angeles had the greatest number of decreased ZCTAs, San Diego and San Jose had the highest number of increased ZCTAs. To analyze the number of monthly HRI attributable to changes in UHI, we used inverse probability of treatment weighting to analyze the difference in HRI between the years of 2006 and 2017 which were two major extreme heat events over the entire State. We observed an average reduction of 3.2 (95 % CI: 0.5; 5.9) HRIs per month and per ZCTAs in decreased neighborhoods as compared to unchanged. This study emphasizes the importance of urban climate adaptation strategies to mitigate the intensity and prevalence of UHIs to reduce health risks related to heat.

This special issue focuses on the under-studied but increasingly pressing issue of urban heat. Cities are getting hotter, both due to the global crisis of climate change, and the related phenomena of Urban Heat Islands, which locally amplify increased global temperatures and exposure to solar radiation. We know a great deal about how heat is affecting cities from a scientific and public health perspective. Urban studies scholarship, however, has been slower to foreground heat as a social, spatial, and political category of analysis, at least in comparison to discussions of carbon emissions and their control, energy and infrastructure, rising sea levels or flooding, and activism towards sustainability. While many of these themes also figure in this collection, our focus is on the varied phenomena of urban dwellers feeling, avoiding, suffering under, mitigating, culturally interpreting and attempting to anticipate and plan for, the reality of elevated air temperatures and solar radiation. What we call thermal control, governance, and health is the multi-level and multivalent social and material response to uncomfortable and potentially injurious temperatures, an elusive topic this special issue makes visible and constitutes what we hope will be an ongoing urban research agenda. [ABSTRACT FROM AUTHOR]

An urban heat island is defined as an urban area that experiences warmer temperatures than its surroundings. This study examines how Singapore's planning efforts established after the mid-20th century have affected the thermal environment of the city in association with land transformation, using historical temperature data available from the Meteorological Service of Singapore and some historical studies. Singapore's planners have carefully regulated the growth of its downtown while promoting expansion in other parts of the city-state. These effects of planning have also unconsciously shaped the location and outline of Singapore's urban heat island. As a result, new urban heat peaks were found around the centres of newly constructed large-scale new towns compared to industrial areas. This study provides lessons for land planning in mitigating a city's urban heat island effects. [ABSTRACT FROM AUTHOR]

This article examines the impact of air-conditioning on the history of Wuhan, a Chinese city famed for the oppressive heat of its summers. It draws upon oral history testimony, ethnographic research, and written sources, to argue that air-conditioning has played an important yet underappreciated role in changing local culture, social interactions, and the urban environment. It begins by describing how citizens of Wuhan coped with heat in the Maoist era (1949–1976), examining official heatstroke prevention techniques alongside the everyday practices of local citizens, including the use of bamboo beds and air-raid shelters. It then examines the dialectical relationship between socio-economic and technological change that occurred following the introduction of air-conditioning. This new technology, which required people to close their doors on their neighbours, arrived at the same time that older forms of communal living were becoming untenable. Finally, this article examines the role that air-conditioning has played in creating and alleviating the urban heat island problem, a process of localised climate change that makes cities hotter than their hinterlands. It concludes by exploring how locals feel about urban heating, a problem that seems intractable in Wuhan, as it is throughout much of urban Asia. [ABSTRACT FROM AUTHOR]

The intensity of Bangkok's urban heat island during the dry season can be as high as 6–7° and in the densest areas the urban heat island's intensity is approximately 4°C. The urban heat island thus is causing a city already oppressively hot to become even hotter. The urban heat island also contributes to health problems, such as heat stroke and fatigue, particularly to those with lower incomes. We historically examine the numerous causes of Bangkok's urban heat island, such as the lack of green space, high levels of air conditioning, and high rates of vehicle exhaust fumes. For example, Bangkok has only three square metres of green space per person which is one of the lowest in all of Asia. Local governmental weaknesses, administrative fragmentation, prioritisation of economic growth and limited buy-in from the private sector have intensified Bangkok's urban heat island, and imposed numerous barriers to actions that would reduce heat, such as establishing green space, restructuring urban transport or creating and following an effective urban plan. Ideas mooted to remedy these problems have yet to come to fruition, largely because of bureaucratic inertia, fragmentation and divisions within the relevant lead organisations. The political ecology lens also reveals how political–economic processes largely determine the vulnerability of urban inhabitants to heat, but also that thermal governance is highly unequal and unjust. Those who contribute to and profit the most from Bangkok's urban heat island, such as real estate developers, shopping mall owners, and automobile corporations, suffer the least from its effects, whereas low-income communities hardly contribute to this problem, yet are the most vulnerable. [ABSTRACT FROM AUTHOR]

The synergistic interactions between urban heat islands (UHI) and heat waves (HW) continue to be debated. Despite the expectations of UHI intensification during HW, several studies have demonstrated variations. Notably, there is a dearth of investigations concerning the UHI–HW synergy in tropical climate cities amidst the escalating trend of more frequent and severe HW in Southeast Asia. To address this gap, our study aimed to investigate the synergies between the UHI and HW phenomena in Greater Kuala Lumpur (GKL) and its surrounding areas. We employed the advanced research version 4.2.2 of the Weather Research and Forecasting (WRF) model, coupled with a single‐layer Urban Canopy Model (UCM), to examine the impact of UHI during two heat wave events in 2016 (Case 1) and 2020 (Case 2), against the periods immediately before and after these events, which we refer to as Pre‐Post HW (PPHW), in GKL. An elevated UHI intensity (UHII) was evident during the HW in both observations and simulations, with a noticeable distinction particularly observed in Case 1. During HW, observed data indicates average UHII peaks at 1.8°C (0100 LST (UTC+8)) and 1.7°C (1500 LST) in Cases 1 and 2, respectively. In contrast, those for PPHW days for Cases 1 and 2 are 1.5°C (0000 LST) and 1.2°C (0100 LST), respectively. The maximum observed heat loads are likely to occur at noon, reaching 2.3°C at 1600 LST in Case 1 and 3.7°C at 1500 LST in Case 2. LST stands for local standard time. Heat flux component analysis from the surface energy balance model confirmed the UHI–HW synergy. A notable difference in the Bowen Ratio between urban and rural areas highlights the effect of urbanisation on heat fluxes, potentially exacerbating urban discomfort during HW. Consistent across all measurement methods, the evidence indicates a clear and positive synergy between the UHI and HW in the GKL. This study can potentially deliver valuable insights, especially in urban planning, where the implications of weather events are substantial. [ABSTRACT FROM AUTHOR]

Global warming has become a critical environmental, social, and economic threat, with increasing frequency and intensity of extreme weather events. This study aims to analyse temperature trends and climate indices in the Po Valley, a significant economic and agricultural region in Italy, by examining data from two historical stations: the urban Modena Observatory and the rural Mount Cimone Observatory. The analysis extends previous studies to 2018, assessing the magnitude of climate changes since the 1950s and isolating the Urban Heat Island (UHI) effect in Modena. Significant warming trends were confirmed at both sites, with in maximum (TX) and minimum (TN) temperatures trends nearly doubling from 1981 to 2018 compared to 1951–2018. For example, TX trends reached 0.84°C·decade−1 in Modena and 0.62°C·decade−1 at Mount Cimone, while TN trends were 0.77 and 0.80°C·decade−1, respectively. Extreme climate indices showed a substantial increase in warm days and nights (TX90p and TN90p, respectively). Particularly we found TX90p of 27.5 days·decade−1 in Modena and 15 days·decade−1 at Mount Cimone while TN90p of 29.5 days·decade−1 in Modena, 22 days·decade−1 at Mount Cimone. The UHI effect significantly impacts Modena's temperature trends. Urbanization contributes up to 65% of the rise in warm nights. Specifically, frost days decreased by 1.88 days·decade−1 (37% of Urban Contribute, UC), tropical nights increased by 5.16 days·decade−1 (57% UC), warm nights increased by 12.7 days·decade−1 (65% UC), and cool nights decreased by 3.19 days·decade−1 (39% UC). Overall, the study underscores the importance of considering both global and local factors in regional climate trend analysis. [ABSTRACT FROM AUTHOR]

Urban green spaces (UGS) regulate the urban microclimate through cooling the ambient air temperature (AAT), as an ecosystem service. Commonly, the cooling effect (CE) is quantified as the land surface temperature (LST) pixel value differences between UGS and their surroundings, using once-off measurements on thermal images acquired by morning overpass satellites. This multiple date study demonstrates the comparative reliability of ambient air temperature (AAT) measurements. In a remote, hot climate town, AAT measurements were taken from mid-morning to noon along transects towards the UGS, at the same sites on four dry season dates that encompassed winter, spring, and summer. Near-concurrent LST at the respective sites was retrieved from 30 m pixel Level-2 Science Product (L2SP) Landsat-9 Thermal Infrared Sensor-2 (TIRS-2) band 10 (B10) images. The town's peak dry season proportion of non-senescent vegetation was extracted from a 3 m resolution PlanetScope image using Normalized Difference Vegetation Index (NDVI) enhancement. It showed that the town had very little green vegetation and was, therefore, in acute need of the CE of UGS. At the UGS, a peak dry season L2SP image yielded negative NDVI-LST correlations, indicating land surface cooling by UGS. AAT differences along the transects showed that large, irrigated grass UGS had a CE of up to 4.2 °C and polynomial-modelled cooling distances of up to 778 m during hot summer weather, with slightly lower cooling and occasional warming effects during winter. AAT measurements within 15 min of the Landsat-9 overpass, spanning all four image dates, correlated highly (r = 0.675, p < 0.001) with corresponding B10 LST. Using them, a statistically significant regression model (F = 28.468, p < 0.0001) predicting AAT from B10 LST, potentially applicable in similar climate, was developed. However, primarily due to wind direction and UGS sizes, plotting LST against distance from the UGS did not always match the AAT trends. Additionally, LST values were nearly twice the corresponding AAT values, suggesting that LST on thermal images should not be equated to AAT. Therefore, using AAT measurements can detect the CE more accurately than the commonly employed approach of LST pixel value differencing. [ABSTRACT FROM AUTHOR]

Near-surface air temperature (Tair) is crucial for assessing urban thermal conditions and their impact on human health. Traditional Tair estimation methods, reliant on sparse weather stations, often miss spatial variability. This study proposes a novel framework using a federated learning artificial neural network (FLANN) for fine-scale Tair prediction. Leveraging spatially complete thermal data from Landsat 8/9, Sentinel 3, and Himawari 8/9 (105 acquisition days, 2013–2023), and data from automatic weather stations, 23 predictor variables were extracted. After rigorous selection processes, nine variables significantly correlated with Tair were identified. Comparative analysis against established machine learning and linear models, using cross-validation data, showed FLANN's superior performance with a Pearson correlation coefficient (r) of 0.98 and a root mean square error (RMSE) of 0.97 K, compared to r and RMSE of 0.85 and 1.09, respectively, for the linear model. FLANN showed greater improvements for urban stations with r and RMSE differences of 0.19 and − 2.03 K. Application of FLANN to predict Tair in Hong Kong in July 2023 enabled detailed urban heat island (UHI) analysis, revealing dynamic spatial and temporal UHI patterns. This study highlights FLANN's potential for accurate Tair prediction and UHI analysis, enhancing urban thermal environment management. [ABSTRACT FROM AUTHOR]

With the development of urbanization, the spatial and temporal distribution characteristics of the urban landscape pattern play a decisive role in the intensity of the urban heat island (UHI) effect. A panel data model was constructed to study the relationship between the UHI effect and landscape pattern in Kunming from 1995 to 2020 at four different scales. The results indicate: (1) The landscape pattern of Kunming changed obviously with time, the artificial patch increased, the natural patch decreased, and the UHI effect became more and more obvious; (2) With an increase in scale, the number of influencing variables continues to grow, and the impact of artificial patches gradually intensifies; (3) The normalized difference water index (NDWI) has the greatest influence on the UHI, and the cooling effect is more obvious with the increase in scale. Unlike previous studies, the spatial configuration of the landscape in Kunming City had a stronger effect on the UHI effect than the landscape grouping and vegetation index; (4) This paper introduces the panel data model into the discussion of the UHI for the first time, providing a new method for better understanding the changing patterns of the urban thermal environment. [ABSTRACT FROM AUTHOR]

The urbanized area of Rome is largely built over volcanic deposits, characterized by a significant radionuclides content and consequently a high radon emanation potential. An accurate monitoring of workplaces and residential dwellings constitutes a first step towards mitigating the indoor radon exposure. Since radon diffusion dynamics involves complex interactions among many environmental parameters on different time scales, a proper assessment of radon concentration variations can be better achieved by means of active monitoring approaches. We present here the results of 1 year of continuous measurements conducted in six premises (five apartments and a basement) at different floors of the same building in the Esquilino district, in the historical center of Rome. Collecting annual series of radon concentration enables us to identify fluctuations over a seasonal scale, with radon generally decreasing in the warm season. The simultaneous tracking of different floors should cancel the influence of geogenic radon and of building characteristics like age, typology, and construction materials. While the basement shows the highest radon concentration, indicating a major contribution from the ground, we observe indoor radon levels comparable at all the upper floors, questioning the common belief that high-risk exposure be limited to the lowest storeys. The use of active devices makes it possible to discriminate between average indoor radon measured during the day and overnight, when residents are more likely to be at home. Our analysis provides the characterization of the dynamics of the gas emanation and transport inside the buildings and of its temporal fluctuations, in relation to the environmental and meteorological conditions. Since the experiment was performed in the Roman urban contest, we cannot ignore the specificity of the retrieved data, affected not only by endogenous factors like life habits relative to ventilation and conditioning of the apartments, but also by exogenous factors, among which the warmer microclimate compared to the surrounding suburban and rural areas, due to the effects of urbanization (urban heat island effect). [ABSTRACT FROM AUTHOR]

Understanding urban cloud dynamics is vital, as changes in local cloud patterns impact energy and water cycles, posing risks to cities. This study examined urban cloudiness using Himawari‐8 satellite data from 2016 to 2021 during the southwest and northeast monsoons in Greater Kuala Lumpur. Urban areas exhibited higher daytime cloud cover than rural areas, with peak cloudiness at 1500 local time being 1.22 times greater during southwest monsoon and 1.24 times greater during the northeast monsoon. Increased solar radiation and the urban heat island effect drive these variations, offering insights for weather forecasting and climate studies in rapidly urbanising regions. [ABSTRACT FROM AUTHOR]

The Cool Roof concept, known for its efficiency in summer due to high temperatures during this period, employs a light coating that covers the roof to prevent the absorption of heat and maintain lower indoor temperatures. This study integrates a chemical component with biomaterials to enhance performance and reduce CO2 emissions. The composition investigated in this research is recognized for its durability and ability to lower outside temperatures, thereby mitigating the urban heat island effect. This experimental study evaluates the sustainability of CoolRoofs in a cold room located in Signes, France. Temperature measurements are conducted from 25 September 2023 to 27 July 2024, both with and without the coating, to assess energy performance and CO2 emissions. The selection of the building type ensures optimal performance in both summer and winter. Results show that the maximum outside and inside surface temperatures for a Cool Roof are 48.7 °C and 25.6 °C, respectively, compared to 72.9 °C and 32.2 °C for an uncoated roof. Additionally, implementing a CoolRoof reduces thermal load through the cold room by 56%, while CO2 emissions can be reduced by up to 27.31 kg CO2/m2 over a 20-year period. This study presents a solution for enhancing energy and environmental performance year-round using a resilient composite. [ABSTRACT FROM AUTHOR]

The urban heat island (UHI) effect is one of the most prominent surface climate changes driven by human activities. This study examines the UHI characteristics and influencing factors in the Southern Xinjiang urban agglomeration using MODIS satellite data combined with observational datasets. Our results reveal a significant increase in impervious surfaces in the region between 1995 and 2015, with the most rapid expansion occurring from 2010 to 2015. This urban expansion is the primary driver of changes in UHI intensity. The analysis from 2000 to 2015 shows substantial spatial variation in UHI effects across cities. Hotan recorded the highest annual average daytime UHI intensity of 3.7 °C, while Aksu exhibited the lowest at approximately 1.6 °C. Daytime UHI intensity generally increased during the study period, with the highest intensities observed in the summer. However, nighttime UHI trends varied across cities, with most showing an increase in intensity. Temperature, precipitation, and aerosol optical depth (AOD) were identified as the main factors influencing annual average daytime UHI intensity, while PM10 concentration showed a weak and inconsistent correlation with UHI intensity, varying by city and season. [ABSTRACT FROM AUTHOR]

The urban heat island effect (UHI) is among the major challenges of urban climate, which is continuously intensifying its impact on urban life and functioning. Against the backdrop of increasingly prolonged heatwaves observed in recent years, practical questions about adaptation measures in cities are growing—questions that traditional meteorological monitoring can hardly answer adequately. On the other hand, UHI has long been the focus of research interest, but due to the technological complexity of providing accurate spatially referenced data at high spatial resolution and the requirement to survey at strictly defined parts of the day, information provision is becoming a major challenge. This is one of the main reasons why UHI research results are less often used directly in urban spatial planning. However, advances in geospatial technologies, including unmanned aerial systems (UASs), are providing more and more reliable tools that can be applied to achieve better and higher-quality information resources that adequately characterize the UHI phenomenon. This paper presents a developed and tested methodology for the rapid and efficient assessment and mapping of the effects of surface urban heat island (SUHI). It is entirely based on the integrated use of data from unmanned aerial systems (UAS)-based remote sensing methods, including thermal photogrammetry and GIS-based analysis methods. The study follows the understanding that correct SUHI research depends on a proper understanding of the urban geosystem, its spatial and structural heterogeneity, and its functional systems, which in turn can only be achieved by supporting the research process with accurate and reliable information resources. In this regard, the possibilities offered by the proposed methodological scheme for efficient geospatial registration of SUHI variations at the microscale, including the calculation of intra-urban SUHI intensity, are discussed in detail. The methodology builds on classical approaches for using local climate zoning (LCZ), adding capabilities for precise delineation of individual zone types and for geostatistical characterization of the urban surface heat island (SUHI). Finally, the proposed scheme is based on state-of-the-art technological tools that provide flexible and automated capabilities to investigate the phenomenon at microscales, including by enabling flexible observation of its dynamics in terms of heat wave manifestation and evolution. Results are presented from a series of sequential tests conducted on the largest residential area in Bulgaria's capital city, Sofia, in terms of area and population, over a relatively long period from 2021 to 2024. [ABSTRACT FROM AUTHOR]

Cities are particularly vulnerable to surface water flooding. It is also well‐known that they influence local rainfall themselves, which has important implications for climate change adaptation planning for cities. At km‐scale resolution, convection‐permitting climate models (CPCMs) better resolve cities and should better represent local urban temperature and rainfall modifications. However, using state‐of‐the‐art pan‐African CPCM simulations with the Met Office Unified Model (CP4), we show that for the city of Johannesburg, South Africa, this is not the case. A significant enhancement of rainfall occurs over the city compared with surrounding rural areas, which is not seen in available observations. We demonstrate this is associated with an overestimated urban heat island effect, which leads to additional triggering of rainfall. Urban signals in future rainfall change are small compared with changes in the wider surroundings, the latter of which we expect to be more reliable than in models with parameterized convection. This suggests that deficiencies in representation of urban processes are of secondary importance in terms of future percentage change in rainfall. We recommend urban planners apply relative changes in CP4 as an uplift to observations, where available, or treat absolute future rainfall as an upper estimate if used directly. [ABSTRACT FROM AUTHOR]

High-resolution urban climate projections are needed for local decision-making on climate change adaptation. Regional climate models have resolutions that are too coarse to simulate the urban climate at such resolutions. A novel statistical–dynamical downscaling (SDD) approach is used here to downscale the EURO-CORDEX ensemble to a resolution of 1 km while adding the effect of the city of Paris (France) on air temperature. The downscaled atmospheric fields are then used to drive the Town Energy Balance urban canopy model to produce high-resolution temperature maps over the period 1970–2099, while maintaining the city's land cover in its present state. The different steps of the SDD are evaluated for the summer season. The regional climate models simulate minimum (maximum) temperatures (TN/TX) that are too high (low). After correction and downscaling, the urban simulations inherit some of these biases but give satisfactory results for summer urban heat islands (UHIs), with average biases of −0.6 K at night and +0.3 K during the day. Changes in future summer temperatures are then studied for two greenhouse gas emission scenarios, RCP4.5 and RCP8.5. Outside the city, the simulations project average increases of 4.1 and 4.8 K for TN and TX for RCP8.5, respectively. In the city, warming is lower, resulting in a decrease in UHIs of −0.19 K at night (from 2.1 to 1.9 K) and −0.16 K during the day. The changes in UHIs are explained by higher rates of warming in rural temperatures due to lower summer precipitation and soil water content and are partially offset by increased ground heat storage in the city. [ABSTRACT FROM AUTHOR]

The City of Baltimore, MD has a history of problems with environmental justice (EJ), air pollution, and the urban heat island (UHI) effect. Current chemical transport models lack the resolution to simulate concentrations on the scale needed, about 100 m, to identify the neighborhoods with anomalously high air pollution levels. In this paper we introduce the capabilities of a mobile laboratory and an initial survey of several pollutants in Baltimore to identify which communities are exposed to disproportionate concentrations of air pollution and to which species. High concentrations of black carbon (BC) stood out at some locations – near major highways, downtown, and in the Curtis Bay neighborhood of Baltimore. Results from the mobile lab are confirmed with longer-term, low-cost monitoring. In Curtis Bay, higher concentrations of BC were measured along Pennington Ave. (mean [5th to 95th percentiles] = 2.08 [2.0–10.9] μg m−3) than along Curtis Ave. just ~ 150 m away (0.67[0.1 – 1.8] μg m−3). Other species, including criteria pollutants ozone (O3), carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), and fine particulate matter (PM2.5), showed little gradient. Observations with high spatial and temporal resolution help isolate the mechanisms leading to locally high pollutant concentrations. The difference in BC appears to result not from heavier truck traffic or slower dispersion but from the interruptions in traffic flow. Pennington Ave. has three stoplights while Curtis Ave. has none. As heavy-duty diesel-powered vehicles accelerate, they experience turbo-lag and the resulting rich air-fuel mixture exacerbates BC emissions. Immediate mediation might be achieved through smoother traffic flow, and the long-term solution through replacing heavy-duty trucks with electric vehicles. Implications: We present results documenting the locations within Baltimore of high concentrations of Black Carbon pollution and identify the likely source – diesel exhaust emissions exacerbated by stop-and-go traffic and associated turbo-lag. This suggests solutions (smoother traffic, retrofit particulate filters, replacement of diesel with electric vehicles) that would enhance Environmental Justice (EJ) and could be applied to other cities with EJ problems. Synopsis: This paper presents observations of atmospheric black carbon aerosol showing impacts on environmental justice, then identifies causes and suggests solutions. [ABSTRACT FROM AUTHOR]

Past decades have seen substantial efforts devoted to observing, assessing, and documenting the urban heat island (UHI) phenomenon. However, the discrepant criteria of non-urban references and ambiguous distinctions between urban and rural landscapes pose great challenges in measuring UHI magnitudes and spatial extents. This study goes beyond the conventional urban-rural dichotomy and introduces a new two-step approach based on the continuous transition of thermal environments along urban-rural gradients. The approach is applied to quantify Surface UHI (SUHI) intensities and footprints across 283 Chinese cities from 2005 to 2018 using multiple satellite-derived data sources. The results include: 1) The two-step approach avoids the limitations in subjective rural reference selections and provides reliable quantification of SUHI characteristics in various cities over time. 2) The SUHI footprints extracted by our approach are more reasonable than those obtained by two existing methods, with footprint ratios generally ranging within 0 − 6 times the urban area. 3) The two-step approach provides more concentrated estimates of SUHI intensity. Typically, ignoring heat sources in non-built-up areas can cause an overestimation of SUHI effect and misidentification of remote rural areas with high temperatures. Overall, the two-step approach enables more accurate estimates of SUHI effect, thereby facilitating policy-making for SUHI mitigation. [ABSTRACT FROM AUTHOR]

Increasing the urban tree cover percentage (TCP) is widely recognized as an efficient way to mitigate the urban heat island effect. The cooling efficiency of urban trees can be either enhanced or attenuated on hotter days, depending on the physiological response of urban trees to rising ambient temperature. However, the response of urban trees' cooling efficiency to rising urban temperature remains poorly quantified for China's cities. In this study, we quantify the response of urban trees' cooling efficiency to rising urban temperature at noontime [∼1330 LT (local time), LT=UTC+8] in 17 summers (June, July, and August) from 2003–19 in 70 economically developed cities of China based on satellite observations. The results show that urban trees have stronger cooling efficiency with increasing temperature, suggesting additional cooling benefits provided by urban trees on hotter days. The enhanced cooling efficiency values of urban trees range from 0.002 to 0.055°C %−1 per 1°C increase in temperature across the selected cities, with larger values for the low-TCP-level cities. The response is also regulated by background temperature and precipitation, as the additional cooling benefit tends to be larger in warmer and wetter cities at the same TCP level. The positive response of urban trees' cooling efficiency to rising urban temperature is explained mainly by the stronger evapotranspiration of urban trees on hotter days. These results have important implications for alleviating urban heat risk by utilizing urban trees, particularly considering that extreme hot days are becoming more frequent in cities under global warming. [ABSTRACT FROM AUTHOR]

The Local Climate Zone (LCZ) scheme representing urban structure and land use pattern is essential for urban heat island (UHI) research. Fine-grained LCZ mapping considering spatial and temporal heterogeneity can provide a more precise characterization of surface thermal properties, thereby enabling a comprehensive analysis and understanding of spatiotemporal trends in climate change research. However, data-driven deep learning-based methods have limitations in coping with the complex urban landscapes of the real-world scenarios, including the spectral similarity of different LCZ and the geospatial heterogeneity of urban LCZ categories. In this study, we constructed a geographic knowledge base for enhanced LCZ characterization with the consideration of prior surface spatial information, including a set of spectral indices and urban morphological parameters (UMPs). Then, we integrated the explicable geographic knowledge base into a learnable deep learning framework in an end-to-end manner for accurate LCZ mapping by fusing multi-source heterogeneous data with a multi-level fusion strategy. The constructed Chinese Climate Zone Time Series LCZ (CClimate-TLCZ) dataset derived from Landsat-8 data with a 30 m spatial resolution, covering 18 representative cities in China, were used to evaluate the proposed framework. The experimental results demonstrate that the proposed framework achieved optimal outcomes across 18 cities, with an average overall accuracy exceeding 94 %, which is more than 20 % higher than that obtained by the standard WUDAPT method. Furthermore, the analysis of LCZ mapping-driven land surface temperature (LST) and surface UHI (SUHI) applications shows that cities within the same climate zone have similar LST distribution patterns, while significant heterogeneity exist between different zones. The annual consistency of LST patterns within each climate zone supports the validity of the LCZ classification scheme and accurate LCZ mapping for urban thermal environment studies. From 2016 to 2022, SUHI intensity initially increases and then decreases, indicating improvements in the urban thermal environment. These findings underscore the critical role of precise LCZ mapping in urban climate resilience and sustainable urban planning. [ABSTRACT FROM AUTHOR]

Biodiversity‐oriented urban management and planning require information on the drivers of wildlife composition and ecosystem function within cities. Urban landscapes impose environmental gradients along which species may be filtered away, or respond by showing adaptive variation in functional trait values. Such trait variation may in turn be due to a species' phenotypic plasticity, or a consequence of microevolution leading to local adaptation. This study investigates three possible plant responses to urban environmental gradients, with different evolutionary consequences: extinction, plasticity and adaptation. We assessed whether individual functional traits (LMA—leaf mass per area, plant height and flower length), population performance traits (seed mass and germination rate), as well as species frequency in the plant community, responded to gradients in mowing frequency, soil fertility and structure, temperature and surrounding mean building height, among four herbaceous plant species present in the metropolitan area of Strasbourg. Using a common garden experiment, we tested whether the observed trait variation was hereditary, and may thus constitute evidence for local adaptation. Our results detected the three types of expected responses. Plantago lanceolata is plastic to urban gradients, and Trifolium pratense showed both plastic and hereditary responses. Dactylis glomerata and Medicago lupulina showed all three responses: they both declined under increasing mowing frequency, were plastic to surrounding mean building height, and showed hereditary responses to different urban gradients. Urban management and planning therefore impact on the evolutionary capabilities of plants in cities. In the case of management this is highlighted by the detected trends in species' traits and frequency in response to mowing. The consequences of urban planning are evidenced by mean building height eliciting most often plastic and adaptive responses. Synthesis. Herbaceous plants often change their morphology in response to urban conditions: grass cutting, altered soils, warmer temperatures and being surrounded by tightly packed buildings. These changes are sometimes hereditary, which suggests that city management and planning affect the ability of plants to survive and evolve in urban environments. [ABSTRACT FROM AUTHOR]

This study applies dynamic mode decomposition (DMD) to three‐dimensional simulation results of urban heat island circulation (UHIC, which is horizontal circulation) and thermals (vertical convections). The aim of this study is to revisit how these phenomena coexist based on the characteristics of temporal changes in the flow field. We used DMD to obtain the dominant spatial patterns and information on temporal changes. One of the modes of horizontal wind, which does not change temporally (no oscillation or amplification), exhibits a spatial UHIC pattern. The unique feature of this UHIC mode is that there are small‐scale striated structures (150–200 m) and large‐scale convergence. The other modes are time‐varying (oscillating and decaying) and represent smaller spatial‐scale phenomena (150–250 m), such as thermals. The frequency of each mode takes various values, some of which are lower than the lifetime of thermals in accordance with the Deardorff convective scale (~10 min). These low‐frequency modes showed striated structures similar to that observed in the UHIC modes. These results suggest that UHIC and thermals deform each other through components that vary in long temporal scales. [ABSTRACT FROM AUTHOR]

Accurate fog prediction in densely urbanized cities poses a challenge due to the complex influence of urban morphology on meteorological conditions in the urban roughness sublayer. This study implemented a coupled WRF‐Urban Asymmetric Convective Model (WRF‐UACM) for Delhi, India, integrating explicit urban physics with Sentinel‐updated USGS land‐use and urban morphological parameters derived from the UT‐GLOBUS dataset. When evaluated against the baseline Asymmetric Convective Model (WRF‐BACM) using Winter Fog Experiment (WiFEX) data, WRF‐UACM significantly improved urban meteorological variables such as diurnal variations in 10‐m wind speed, 2‐m air temperature (T2), and 2‐m relative humidity (RH2) during a fog event. UACM also demonstrates improved accuracy in simulating temperature and significantly reducing biases for wind speed and daytime RH2 under clear sky conditions. UACM reproduced the nighttime urban heat island effect within the city, showing realistic diurnal heating and cooling patterns that are important for accurate fog onset and duration. UACM effectively predicts the onset, evolution, and dissipation of fog, aligning well with observed data and satellite imagery. Compared to WRF‐BACM, WRF‐UACM reduces the cold bias soon after sunset, thus improving the fog onset error by ∼3 hr. This study highlights the UACM's potential to improve fog prediction and its application in operational settings. With further investigation into different fog types, the UACM can provide crucial insights for preventive measures and reducing disruptions in urban areas. Plain Language Summary: In Delhi, accurately predicting fog in urban areas is difficult due to complex factors like city layout and infrastructure. This study employed the recently developed WRF‐UACM with detailed UT‐GLOBUS urban morphological parameters for fog simulation. Compared to existing models, WRF‐UACM predicted wind, temperature, and humidity better under both foggy days and clear skies. The model accurately reproduced urban warming and cooling patterns crucial for fog prediction and urban meteorology. WRF‐UACM improves the diurnal variation of winds and reduces temperature‐cold bias after sunset, thus improving fog onset by ∼3 hr. This work highlights the potential of WRF‐UACM for fog prediction and offers invaluable insights for urban meteorology modeling. Key Points: The new multilayer WRF‐UACM, explicitly incorporating urban physics and morphology, is implemented to simulate fog over the Delhi regionImprovements in predicting urban wind, temperature, and relative humidity are demonstrated using UACM during fog events and clear skiesThe abilities of the novel UACM in capturing urban fog phenomena and its operational mode capabilities over the Delhi region are examined [ABSTRACT FROM AUTHOR]

Introduction: Amidst escalating global temperatures, increasing climate change, and rapid urbanization, addressing urban heat islands and improving outdoor thermal comfort is paramount for sustainable urban development. Green walls offer a promising strategy by effectively lowering ambient air temperatures in urban environments. While previous studies have explored their impact in various climates, their effectiveness in humid climates remains underexplored. Methods: This research investigates the cooling effect of a green wall during summer in a humid climate, employing two approaches: Field Measurement-Based Analysis (SC 1: FMA) and Deep Learning Model (SC 2: DLM). In SC 1: FMA, experiments utilized data loggers at varying distances from the green wall to capture real-time conditions. SC 2: DLM utilized a deep learning model to predict the green wall's performance over time. Results: Results indicate a significant reduction in air temperature, with a 1.5°C (6%) decrease compared to real-time conditions. Long-term analysis identified specific distances (A, B, C, and D) contributing to temperature reductions ranging from 1.5°C to 2.5°C, highlighting optimal distances for green wall efficacy. Discussion: This study contributes novel insights by determining effective distances for green wall systems to mitigate ambient temperatures, addressing a critical gap in current literature. The integration of a deep learning model enhances analytical precision and forecasts future outcomes. Despite limitations related to a single case study and limited timeframe, this research offers practical benefits in urban heat island mitigation, enhancing outdoor comfort, and fostering sustainable and climate-resilient urban environments. [ABSTRACT FROM AUTHOR]

Sustainability and energy efficiency are now two pivotal goals that society aims towards. Green roofs and facades have gained significant attention in this direction for innovative, sustainable solutions for enhancing building energy performance. With a focus on sustainable urban development and energy-efficient building practices, this study delves into the intricate relationship between these green infrastructure elements and the overall energy dynamics of constructed environments. Furthermore, a range of case studies from diverse geographical locations are presented to provide valuable insights into their practical implications as emerging technologies that contribute to improved insulation, reduced heat transfer, regulating indoor temperatures, and mitigation of urban heat island effects, thus reducing the need for artificial heating and cooling and optimizing overall energy consumption. This comprehensive review serves as a dataset for understanding and highlighting all the research findings of the numerical and experimental investigations invested in the field of greenery systems to encourage their integration, which is crucial for combating climate change and pollution. Previous research is often focused on isolated, short-term, or single-climate analyses of consumption; therefore, by providing an inclusive description of their practical benefits in both temperate and extreme climates, the gap in previous articles is tackled. [ABSTRACT FROM AUTHOR]

Remote sensing satellite data have been a crucial tool in understanding urban climates. The variety of sensors with different spatiotemporal characteristics and retrieval methodologies gave rise to a multitude of approaches when analyzing the surface urban heat island effect (SUHI). Although there are considerable advantages that arise from these different characteristics (spatiotemporal resolution, time of observation, etc.), it also means that there is a need for understanding the ability of sensors in capturing spatial and temporal SUHI patterns. For this, several land surface temperature products are compared for the cities of Madrid and Paris, retrieved from five sensors: the Spinning Enhanced Visible and InfraRed Imager onboard Meteosat Second Generation, the Advanced Very-High-Resolution Radiometer onboard Metop, the Moderate-resolution Imaging Spectroradiometer onboard both Aqua and Terra, and the Thermal Infrared Sensor onboard Landsat 8 and 9. These products span a wide range of LST algorithms, including split-window, single-channel, and temperature–emissivity separation methods. Results show that the diurnal amplitude of SUHI may not be well represented when considering daytime and nighttime polar orbiting platforms. Also, significant differences arise in SUHI intensity and spatial and temporal variability due to the different methods implemented for LST retrieval. [ABSTRACT FROM AUTHOR]

In many cities, protracted exposure of urban structures to excessive solar radiation and the urban heat island (UHI) phenomenon may cause thermal discomfort for pedestrians in outdoor spaces, particularly in hot-arid regions. This study investigated the impact of urban residential block morphology on microclimate in Isfahan, Iran, using ENVI-met numerical models. The predicted mean vote (PMV) model for residential outdoor spaces was calculated using simulation data for the hottest day of the summer to assess outdoor thermal comfort. Comparison of the alternatives – namely, diagonal blocks, compact linear blocks, continuous curved blocks and dispersed cubic blocks – showed that the microclimate performance of the diagonal type was better than those of the other design options, as the PMV index represented the minimum distance from the ideal thermal comfort range due to a lower sky view factor (SVF) and optimal orientation. The curved blocks performed better in urban open spaces, promoting airflow and reducing UHI, than linear and cubic blocks. The results showed that the PMV index could predict thermal comfort in various urban design patterns and could be affected by the morphology of urban blocks. Variations in building morphology alter the SVF and microclimate parameters, which have an impact on outdoor thermal comfort. [ABSTRACT FROM AUTHOR]

The low-level jet (LLJ) and the urban heat island (UHI) are common nocturnal phenomena. While the UHI has been studied extensively, interactions of the LLJ and the urban atmosphere in general (and the UHI in particular) have received less attention. In the framework of the PANAME (PAris region urbaN Atmospheric observations and models for Multidisciplinary rEsearch) initiative in the Paris region, continuous profiles of horizontal wind speed and vertical velocity were recorded with two Doppler wind lidars (DWLs) – for the first time allowing for a detailed investigation of the summertime LLJ characteristics in the region. Jets are detected for 70 % of the examined nights, often simultaneously at an urban and a suburban site, highlighting the LLJ regional spatial extent. Emerging at around sunset, the mean LLJ duration is ∼ 10 h, the mean wind speed is 9 m s -1 , and the average core height is 400 m above the city. The temporal evolution of many events shows signatures that indicate that the inertial oscillation mechanism plays a role in the jet development: a clockwise veering of the wind direction and a rapid acceleration followed by a slower deceleration. The horizontal wind shear below the LLJ core induces variance in the vertical velocity (σw2) above the urban canopy layer. It is shown that σw2 is a powerful predictor for regional contrast in air temperature, as the UHI intensity decreases exponentially with increasing σw2 and strong UHI values only occur when σw2 is very weak. This study demonstrates how DWL observations in cities provide valuable insights into near-surface processes relevant to human and environmental health. [ABSTRACT FROM AUTHOR]

In this study, we analyzed the surface temperature of Chuncheon from 2014 to 2020 using Landsat 8 images. The findings revealed that man-made structures such as asphalt roads and buildings cause significant increases in land surface temperatures in urbanized, arid, and residential areas. Industrial areas exhibited the highest relative land surface temperature (RLST) index, while densely populated residential areas showed relatively high RLST values. Conversely, areas with high-rise apartments had lower land surface temperatures due to shade and green space. Natural land cover areas demonstrated negative mean RLST, highlighting the usefulness of RLST in distinguishing temperature differences between land cover types. A 0 °C reference surface temperature was used in RLST to standardize land surface temperature measurements, which helped understand land cover status. As the urban heat island phenomenon continues, land surface temperatures are gradually rising. To build a sustainable urban ecosystem, policies and plans to expand and improve green spaces and waterfront environments are essential. To achieve these goals, a comprehensive strategy for expanding and improving green spaces is needed, as well as various policies to improve water quality. [ABSTRACT FROM AUTHOR]

Urban heat island (UHI) has posed a threat to sustainable development, and the identification of the spatial characteristics of UHI is a prerequisite for the mitigation of UHI effects. Taking Kunming City and Guiyang City in the karst region of Southwest China as two examples, this study utilized spatial econometric modeling and random forest regression to identify and compare the spatial distribution and impact of urban heat islands related to topographic and urban morphology. The results indicated that the UHI in Kunming and Guiyang mainly monocentrically and polycentrically distributed, respectively, and the urban heat island intensity (UHII) in Kunming was significantly higher than that in Guiyang. The spatial error model effectively reflected the influence of topography and urban form on UHII, and the random forest regression model objectively measured the contribution of different influencing factors to UHII. Enhanced vegetation index (EVI), population density (PD), percentage of impervious surface (PIS), ground sky view factor (GSVF), building density (BD) and percentage of water bodies (PW) had similar effects and contributions to the UHII in the two cities, while urban surface roughness (USR) contributed less than topographic slope (SLOPE) on UHII in Kunming, and the SLOPE contributed less than USR in Guiyang. In the karst ecologically fragile zone, the morphology of Kunming and Guiyang are mainly shaped by the topography, which can be characterized as monocentric and polycentric, respectively. And the UHI distribution and UHII were also closely related to topographic patterns. EVI, SLOPE and PW, all showed inhibitory effects on UHII, and EVI contributed the most. PD, PIS, USR and BD, exacerbate the UHII, with PD contributing the most. The above conclusions can provide a reference for policymakers and urban planners to optimize urban morphology and mitigate urban heat island. [ABSTRACT FROM AUTHOR]

The management and design of urban areas in metropolises pose significant challenges. Balancing diverse land uses within a metropolitan structure and addressing spatial and environmental constraints are just some of these challenges. Urban heat islands, which stem from factors such as inappropriate construction materials, inadequate building insulation, improper land use locations, and unsuitable built-up density, reflect environmental imbalances within urban infrastructure. Effectively addressing these temperature discrepancies can lead to energy preservation, reduced environmental hazards, and enhanced comfort for urban residents. This study employed Landsat-8 satellite images to identify and monitor positive and convex temperature disparities across various districts of Tehran over 3 years (2018 to 2020) using three different strategies. These disparities are estimated through the differences in land surface temperature from the thermal trend of each district and their persistence has been assessed in seasonal, semi-annual, and annual strategies. The study found that industrial areas, including warehouses, were the significant contributors to the persistent presence of urban heat islands in summer and winter. Open areas with impervious surfaces or bare soil, particularly those lacking sufficient green cover, also significantly contributed to the heat island effect. Certain large shopping centers, often due to their air conditioning systems, were also consistently identified as persistent heat islands. Evaluations demonstrated that over 78.8% of the identified persistent heat islands were meaningful, with most located in the northern and western parts of Tehran. [ABSTRACT FROM AUTHOR]

Urbanization is a phenomenon where humans concentrate in high densities and consume more per capita energy than in rural areas, imposing high pressures on biodiversity and ecosystem services. Although Mexico is recognized as a megadiverse country and there is an understanding of ecological and evolutionary processes underlying this high diversity, only some efforts have been devoted to understanding how urban biodiversity has been shaped. Here, we compiled a set of socioeconomic and ecological variables to explore macroecological patterns in urban biodiversity across Mexican municipalities. Specifically, we tested the species-area relationships (SAR) between rural and urban areas across municipalities and evaluated the relative role of different socioeconomic and ecological variables driving urban species richness for terrestrial vertebrates. Finally, we explored the exposure of Mexican municipalities to future urban expansion, the urban heat island (UHI) effect, and climate change. Urban and rural settlements show differences in the shape of SAR models. We found that urban area, size of the network of urban protected areas, the number of ecoregions, and GDP explained the urban total species richness relatively well. Mexican cities in the northeast region may be at a higher risk than others. Based on our analyses, policymakers should identify priority urban conservation sites in cities with high species richness and low urbanization development. These actions would alleviate future urban biodiversity loss in these growing cities. [ABSTRACT FROM AUTHOR]

Background: By the end of 2050, it is expected that 68% of the population will live in urban areas. A higher density of people living in cities generates an increased urban heat island. Radiative cooling (RC) materials are proposed as a key strategy to mitigate global warming and urban heating. The Horizon 2020 project MIRACLE aims at developing a new RC material based on conventional concrete. This paper presents a framework developed for comparing both the cradle-to-gate environmental impact and cooling potential of the newly developed photonic meta-concrete (or any other new RC material) with existing RC materials. The framework is applied to various RC materials using the generic Ecoinvent v3.6 database. The impact assessment method is in line with the Belgian life cycle assessment method for buildings and covers the 15 environmental impact categories of the EN15804:A2. The cooling performance is assessed by implementing the material spectral emissivity into a thermal model for Brussels and Madrid. Results: The study shows that the sputtering process contributes over 75% to the cradle-to-gate environmental impact of several RC materials, while materials produced without this process, have significantly lower impacts. The assessment of the cooling potential showed that convection heat gains make it difficult to create an all-year round cooling material. The comparison with a conventional building material, a concrete roof tile, hence shows great potential for these RC materials as heating gains during summer are significantly reduced. Analysing cooling performance alongside environmental impact, the study identified two RC materials, i.e. D6 and D10, as the most preferred in both Brussels and Madrid, considering their lower environmental impact and superior performance. Conclusions: The literature review revealed that a standardised way to assess and benchmark RC materials based on their cradle-to-gate environmental impact and cooling performance is lacking to date. This paper hence presents, for the first time, a method to compare RC materials considering these two characteristics. This method allows to identify the most competitive RC materials, which will serve in our study to benchmark the newly developed photonic meta-concrete. [ABSTRACT FROM AUTHOR]

The spatial heterogeneity of land surface temperature (LST) within cities is profoundly influenced by the built environment. Although significant progress has been made in the study of the urban thermal environment, there is still a lack of research on how the pattern and structural layout of the built environment affects the thermal environment. In this study, we take the Fifth Ring Road of Beijing as an example, invert the urban LST on the basis of multisource spatial data, characterize the built environment, and use k-means cluster analysis to investigate the main influencing factors of the LST in different functional areas and building patterns within the city, as well as the spatial relationship between the built environment and the urban LST. The results show the following: (1) The urban heat island (UHI) effect occurs to varying degrees over a large part of the study area, and these UHI areas are mainly concentrated in the southwestern part of the city, forming a large contiguous area between the second and fifth ring roads. (2) Class 1 is dominated by transport blocks, Class 3 is dominated by commercial blocks, and Class 5 is dominated by green space blocks, with a clustering index of 0.38. (3) The high-density, high-height class (HH-Class 2) has a greater number of blocks distributed in a ring shape around the periphery of the second ring road. The high-density, low-height class (HL-Class 2) has a relatively small number of blocks but a relatively large area, and the largest blocks are located in the western part of the study area. (4) In the HH and HL building patterns, extreme heat scenarios often occur; from the perspective of functional areas, the probability of extreme heat in the transport block is much higher than that of other functional areas, and except for the HH scenario, the green space functional area plays a very important role in reducing the temperature. This study explores the characteristics of the built environment that influence the urban LST from the perspective of different urban functional zones in cities to provide decision support for quantitative territorial spatial planning, optimization, and management. [ABSTRACT FROM AUTHOR]

As atmospheric CO2 emissions and the trend of urbanization both increase, the ability to accurately assess the CO2 budget from urban environments becomes more important for effective CO2 mitigation efforts. This task can be difficult for complex areas such as the urban–coastal Mediterranean region near Marseille, France, which contains the second most populous city in France as well as a broad coastline and nearby mountainous terrain. In this study, we establish a CO2 modeling framework for this region for the first time using WRF-Chem and demonstrate its efficacy through comparisons against cavity-ringdown spectrometer measurements recorded at three sites: one 75 km north of the city in a forested area, one in the city center, and one at the urban/coastal border. A seasonal CO2 analysis compares Summertime 2016 and Wintertime 2017, to which Springtime 2017 is also added due to its noticeably larger vegetation uptake values compared to Summertime. We find that there is a large biogenic signal, even in and around Marseille itself, though this may be a consequence of having limited fine-scale information on vegetation parameterization in the region. We further find that simulations without the urban heat island module had total CO2 values 0.46 ppm closer to the measured enhancement value at the coastal Endoume site during the Summertime 2016 period than with the module turned on. This may indicate that the boundary layer on the coast is less sensitive to urban influences than it is to sea-breeze interactions, which is consistent with previous studies of the region. A back-trajectory analysis with the Lagrangian Particle Dispersion Model found 99.83% of emissions above 100 mol km−2 month−1 captured in Summer 2016 by the three measurement towers, providing evidence of the receptors' ability to constrain the domain. Finally, a case study showcases the model's ability to capture the rapid change in CO2 when transitioning between land-breeze and sea-breeze conditions as well as the recirculation of air from the industrial Fos region towards the Marseille metroplex. In total, the presented modeling framework should open the door to future CO2 investigations in the region, which can inform policymakers carrying out CO2 mitigation strategies. [ABSTRACT FROM AUTHOR]

The urban heat island (UHI) effect is a widely recognized phenomenon consisting of heat accumulation by dense urban construction and human activities, resulting in higher temperatures across urban areas compared to their surroundings. This article aims to quantify the UHI effect on several areas throughout the city of Lisbon, Portugal, with the main goal of validating, evaluating, and reinforcing urban climate adaptation and resilience strategies proposed in the recent scientific literature. A set of nine quality-controlled weather stations from the "Lisboa Aberta" network that are compliant with the World Meteorological Organization (WMO) standards and installation requirements were used to characterize Lisbon's UHI, in comparison to a reference weather station from the Instituto Português do Mar e da Atmosfera (IPMA), located at Lisbon Airport. By applying a principal component analysis (PCA) in an innovative way to 10 urban indexes, it is shown that the thermal inertia in Lisbon's urban areas is positively correlated with the UHI intensity and urban density, regardless of the daily heating/cooling cycle. Furthermore, the results show that land use also has an impact on the UHI effect, with continuous, vertical building areas showing the greatest deviations in comparison to the reference, averaging +1.8 °C. Contrastingly, horizontal building areas reveal an average deviation of +1.3 °C, with sparse, discontinuously built areas representing an average UHI effect of +0.2 °C. Finally, through a climatope analysis, it is determined that, across Lisbon, high-density urban areas and ventilation corridors are responsible for inducing average UHI effects of +1.7 °C and +0.2 °C, respectively. [ABSTRACT FROM AUTHOR]

This research seeks to understand the link between urban heat island and urban cool island, which are the Local Climatic Zones (LCZ) and atmospheric systems during the winter season in the city of Balneário Camboriú, Southern Brazil. First, meteorological data on the urban environment was collected at 11 permanent points in the Balneário Camboriú metropolitan region. Next, a synoptic analysis of the dates was performed to understand the atmospheric systems operating in the region. Finally, the LCZs map created for the city in the World Urban Database and Access Portal Tools was used to correlate the magnitudes of the heat and cool islands found in Balneário Camboriú in the winter period. The results indicate that the increasing verticalization as a result of the construction of skyscrapers in Balneário Camboriú has a significant influence on local conditions for the occurrence of heat and cold islands. The findings indicate that LCZs with sparsely distributed buildings (LCZs 6, 8 and 9) and LCZs with dense vegetation (LCZ A) have lower intensity magnitudes of heat and cool conditions. The biggest magnitudes of heat and cool islands were reported in LCZs 1 and 3 during the timeframe. The synoptic analysis supports earlier research that points to atmospheric stability (Anticyclonic domain) as a favorable atmospheric setting for the emergence of urban heat and coolness islands. [ABSTRACT FROM AUTHOR]

Smart urban planning needs to have a multicriteria-based approach to prevent the deteriorating local thermal climate. Maximizing the cooling potential using the available grey infrastructure would be the utmost priority of future smart cities. Remote sensing and GIS can be the appropriate tools to develop a climate-resilient urban planning framework. Studies are needed to include different features of vertical and horizontal landscaping to mitigate heat stress and enhance liveability at the city level. With this goal, the current work outlined a holistic approach to efficiently using green spaces with minimal reconstruction. The problem of regional climate threat was evaluated with urban heat island characterization. Moran's I clustering identified nearly 12% of the study area to be under considerable heat stress during summer days. Multiple techniques, such as mapping local climate zones, segment mean shift-based roof extraction, vegetation index computation, solar azimuth-based green wall site selection, etc., were applied to formulate solutions and provide an integrated method for city-level environment enhancement. A considerable area was identified as most suitable for green roof cover, and it was also computed that the transition towards green roof at only these locations may bring down the maximum heat island intensity by 0.74 °C. Additionally, solar zenith, illumination effect, and building height information were combined to create a distinct method where vertical plantation would flourish exceptionally. A rigorous assessment of more than 130 urban green spaces further quantified the relation between landscape geometry and cooling effect to provide optimum green space designs for future urban planning. [ABSTRACT FROM AUTHOR]

This study reveals the temporal and spatial evolution characteristics of the winter nighttime urban heat island (UHI) effect in the case of Beijing, China. The land surface temperature (LST) is retrieved by radiative transfer equation by using the remote sensing data from Landsat ETM+/OLITIRS from 2007 to 2017 for the winter nighttime period, and LST is then divided by the mean-standard deviation method into different levels of thermal landscapes. A combination of the migration calculation of gravity center and multi-directional profile analysis is used to study the directional differentiation characteristics of LST and the migratory characteristics of the gravity center of UHI. Finally, the overall temporal and spatial evolution characteristics of winter nighttime surface urban heat island (SUHI) in Beijing are studied, and the possible reasons for the changes are discussed. Results show that Beijing's UHI effect first increased and subsequently decreased from 2007 to 2017. The winter heat island in the urban area developed from low-density agglomeration to high-density agglomeration to low-density diffusion. Additionally, the high-level thermal landscapes migrated to the southwest along with the city center of gravity, and the expansion rate is fastest in the southwest, which is directly linked to the changes in the urban construction land. Moreover, the overall spatial distribution of winter nighttime LST is high in the east and south and low in the west and north, and is influenced by topography, land cover, urbanization, anthropogenic heat, and other factors as well. [ABSTRACT FROM AUTHOR]

The current research on urban heat island (UHI) effect mostly focuses on the analysis of land use type changes and the surface UHI intensity. Few studies on the urban canopy heat island effect at a block scale of local climate zone (LCZ), although the canopy heat island effect is a key factor affecting human thermal comfort. Therefore, this study will combine the LCZ classification system and the urban weather generator (UWG) model to simulate and quantitatively analyse the urban canopy heat island effect in Beijing at the block scale. First, based on Sentinel-2 Multispectral remote sensing images, the residual neural network (ResNet) method was used to obtain the LCZ of Beijing, and the results of LCZs was validated based on the google earth engine (GEE). Then, according to the classification results of local climate zones, the input parameters of the UWG and their corresponding value ranges are calculated. Finally, the UWG model is used to simulate the canopy temperature in different local climate zones, and the urban canopy temperature is validated using the meteorological station dataset. We quantitatively analyse the temperature differences between different types of LCZs. The results shows that the canopy heat island effect in Beijing gradually weakened outward from the city centre. This is mainly due to the relatively dense distribution of compact local climate zones in the centre of Beijing, while the surrounding areas of Beijing have lower building density and better natural coverage. The canopy urban heat island intensity of built-up LCZs is significantly stronger than that of natural-covered LCZs. The heat island intensity of the compact LCZ is higher than that of the open LCZ with the same building height. However, for LCZs with comparable compactness, the heat island intensity of high-level LCZs is higher than that of low-level LCZs. [ABSTRACT FROM AUTHOR]

A series of empirical analytical tools have been adopted to investigate the driving mechanisms of surface urban heat islands (SUHI) on a global scale, among which spatial heterogeneity is yet to be fully elucidated. In this study, we investigated the spatial non-stationarity of the driving factors concerning surface properties, climate conditions, and urbanization processes for global long-term SUHI. First, the potential impact on SUHI was explored using global ordinary least squares regression. Geographically weighted regression (GWR) and multi-scale GWR (MGWR) from local perspectives were employed for comparison. The results show that the MGWR has the highest goodness of fit at 0.87, 0.73, 0.90, 0.74, 0.85, and 0.76 for annual day/night (AD/AN), summer day/night (SD/SN), and winter day/night (WD/WN) scales, respectively. Although both global and local schemes exhibit similar influencing magnitudes and signs on the SUHI, the MGWR is better at capturing spatial non-stationarity. Globally, for AD, AN, SD, SN, WD, and WN, the coefficients of the urban-rural vegetation index difference (ΔEVI) and surface albedo difference (ΔWSA), urban mean precipitation (MAP), wind speed (WS), population density (PD), and urban area (UA) are −0.50, +0.30, +0.16, +1.31, −0.03, and +0.03, respectively, at daytime, and −0.38, −0.33, −0.39, −0.10, +0.18, and +0.08, respectively, at night-time. Given the spatial heterogeneity of multiple factors, ΔEVI exhibits a strong mitigation effect on the SD SUHI especially in arid zones. The negative influence of ΔWSA on night-time SUHI demonstrates a strong latitudinal disparity and greater sensitivity in the equatorial zone. The positive correlations between MAP and AD/SD SUHIs have evident latitudinal and longitudinal variations. The mitigation effect of WS displayed distinct coastal amplification, especially in WD. In contrast, the PD and UA presented prominent positive impacts on night-time SUHI with less seasonal contrast. [ABSTRACT FROM AUTHOR]

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

Urban Heat Islands are a major environmental and public health concern, causing temperature increase in urban areas. This study used satellite imagery and machine learning to analyze the spatial and temporal patterns of land surface temperature distribution in the Metropolitan Area of Merida (MAM), Mexico, from 2001 to 2021. The results show that land surface temperature has increased in the MAM over the study period, while the urban footprint has expanded. The study also found a high correlation ( r > 0.8) between changes in land surface temperature and land cover classes (urbanization/deforestation). If the current urbanization trend continues, the difference between the land surface temperature of the MAM and its surroundings is expected to reach 3.12 °C ± 1.11 °C by the year 2030. Hence, the findings of this study suggest that the Urban Heat Island effect is a growing problem in the MAM and highlight the importance of satellite imagery and machine learning for monitoring and developing mitigation strategies. [ABSTRACT FROM AUTHOR]

To prepare for a hotter future, information on intra‐urban temperature distributions is crucial for cities worldwide. In recent years, different methods to compute high‐resolution temperature datasets have been developed. Such datasets commonly originate from downscaling techniques, which are applied to enhance the spatial resolution of existing data. In this study, we present an approach based on a fine‐scaled low‐cost urban temperature measurement network and a formerly developed land use regression approach. The dataset covers mean nocturnal temperatures of 16 summers (2007–2022) of a medium‐sized urban area with adapted land cover data for each year. It has a high spatial (50 m) and temporal (daily) resolution and performs well in validation (RMSEs of 0.70 and 0.69 K and mean biases of +0.41 and −0.19 K for two validation years). The dataset can be used to examine very detailed statistics in space and time, such as first heatwave per year, cumulative heat risks or inter‐annual variability. Here, we evaluate the dataset with two application cases regarding urban planning and heat risk assessment, which are of high interest for both researchers and practitioners. Due to potential biases of the low‐cost measurement devices during daytime, the dataset is currently limited to night‐time temperatures. With minor adaptions, the presented approach is transferable to cities worldwide in order to set a basis for researchers, city administrations and private stakeholders to address their heat mitigation and adaptation strategies. [ABSTRACT FROM AUTHOR]

The numerical weather prediction model HARMONIE-AROME and a multiple linear regression model (referred to in this article as the TURCLIM model after the local climate observation network) were used to model surface air temperature for 25–31 July 2018 in the City of Turku, Finland, to study their performance in urban areas and surrounding rural areas. The 0200 LT (local standard time) temperatures modeled by the HARMONIE-AROME and TURCLIM models were compared to each other and against the observed temperatures to find the model best suited for modeling the urban heat island effect and other spatial temperature variabilities during heatwaves. Observed temperatures were collected from 74 sites, representing both rural and urban environments. Both models were able to reproduce the spatial nighttime temperature variation. However, HARMONIE-AROME modeled temperatures were systematically warmer than the observed temperatures in stable conditions. Spatial differences between the models were mostly related to the physiographic characteristics: for the urban areas, HARMONIE-AROME modeled on average 1.4°C higher temperatures than the TURCLIM model, while for other land-cover types, the average difference was 0.51°C at maximum. The TURCLIM model performed well when the explanatory variables were able to incorporate enough information on the surrounding physiography. Respectively, systematic cold or warm bias occurred in the areas in which the thermophysically relevant physiography was lacking or was only partly captured by the model. Significance Statement: As more and more people are living in an urban environment, the demand for accurate urban climate modeling is growing. This study aims to understand the differences between the numerical weather prediction and multiple linear regression modeling and their limitations in modeling surface air temperature in subkilometer scale. The case study shows that models are capable of predicting the spatial variation of 0200 LT nighttime temperature during a heatwave in a high-latitude coastal city. Both models are therefore valuable assets for city planners who need accurate information about the impacts of the physiography on the urban climate. The results indicate that to improve the performance of the models, more accurate physiographic description and higher spatial resolution of the models are needed. [ABSTRACT FROM AUTHOR]

With the increase in temperature, urban areas face issues like urban heat islands. The reduction of green vegetation across urban areas has led to a tremendous rise in temperature. Many studies suggest significant cities are currently experiencing this issue to a greater extent. Many researchers are working on building paints and dyes to reduce the heat intake of the building. Organic thermochromic materials are one such alternative for building dye and stain. The main issue with using organic thermochromic materials is that they quickly degrade under sunlight, which makes them less useful for building paint. To overcome this issue, many organic thermochromic black dye materials (OTCBDM) are encapsulated with inorganic materials such as metal oxide, which block sunlight and reduce the effect of degradation. Encapsulation is achieved via the emulsification method of metal oxide. This paper deals with the single and double microencapsulation methods with different surfactant concentrations, metal oxides, and combinations thereof. Various techniques were used such as Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM) with energy‐dispersive X‐ray spectroscopy technique (EDS), and differential scanning calorimeter (DSC), UV‐Vis spectroscopy, thermogravimetric analysis (TGA), and CIE Lab space analysis to determine the materials' encapsulation and chromatic nature. The FTIR spectra suggested the absorption peaks for the Ti─OH bond (at 814, 903, and 913 cm−1) and Si─OH group (at 1,084 and 1,178 cm−1) for the commercial dye encapsulated with TiO2 and SiO2. The Ti─O─Si bonds at 1,054 and 1,145 cm−1 showed for the double encapsulation of SiO2 and TiO2 either (i) doped or in (ii) layered structures. From the EDS analysis, we can clearly say that the presence of SiO2 and TiO2 indicates the encapsulation of the commercial thermochromic black dye material. The SEM micrographs of the TiO2 encapsulation of OTCBDM seem more promising and uniform core–shell structure formation with CTAB as a surfactant than the SDBS. Moreover, the SEM microstructure results of double encapsulation using both TiO2 and SiO2 suggested thicker coating of particles when compared with single encapsulated counterparts. The cold phase of layered double encapsulation of OTCBDM + SiO2/TiO2 shows a darker color than the doped combination as obtained from the CIE Lab results. This feature suggested that an efficient encapsulation and chromatic transition are achieved in a layered combination of double encapsulation than in a doped combination. The DSC spectra indicated the increase in enthalpy at the heat cycle from 94.147 J/g for the SiO2 microencapsulated OTCBDM sample compared to all other samples shows thermal energy storage behavior for outdoor building applications. The UV–Vis absorbance spectra show two distinct peaks at 470 and at 610 nm related to the OTCBDM. The single‐layer encapsulated organic thermochromic black dye material shows less absorbance compared to the plain organic thermochromic black dye material. The double encapsulated sample has the highest absorbance compared to all other samples suggesting the material having better retention of peaks at 470 and at 610 nm. The thermographic analysis (TGA) shows when compared to the plain and single encapsulated OTCBDM samples, the double encapsulated samples demonstrated higher thermal endurance characteristics due to thicker layer coatings of the shell over the core structure. The novelty of this work relies on the double encapsulation of OTCBDMs using SiO2 and TiO2 to improve the photodegradation behavior of the organic core dye particles, and we have demonstrated that the cationic surfactant CTAB outperforms than the SDBS in microencapsulation processes. [ABSTRACT FROM AUTHOR]

Although accelerated urbanization has led to economic prosperity, it has also resulted in urban heat island effects. Therefore, identifying methods of using limited urban spaces to alleviate heat islands has become an urgent issue. In this study, we assessed the spatiotemporal evolution of urban heat islands within the central urban area of Fuzhou City, China from 2010 to 2019. This assessment was based on a morphological spatial pattern analysis (MSPA) model and an urban thermal environment spatial network constructed using the minimum cumulative resistance (MCR) model. Optimization measures for the spatial network were proposed to provide a theoretical basis for alleviating urban heat islands. The results show that the heat island area within the study area gradually increased while that of urban cold island area gradually decreased. The core area was the largest of the urban heat island patch landscape elements with a significant impact on other landscape elements, and represented an important factor underlying urban heat island network stability. The thermal environment network revealed a total of 197 thermal environment corridors and 93 heat island sources. These locations were then optimized according to the current land use, which maximized the potential of 1599.83 ha. Optimization based on current land use led to an increase in climate resilience, with effective measures showing reduction in thermal environment spatial network structure and function, contributing to the mitigation of urban heat island. These findings support the use of current land use patterns during urban heat island mitigation measure planning, thus providing an important reference basis for alleviating urban heat island effects. [ABSTRACT FROM AUTHOR]

[Objective] The aims of this study are to investigate on the evolution of rainstorm events and the sources of water vapor transport in Hefei under the background of urbanization, and to provide scientific basis for local rainstorm forecast and formulation of adaptive disaster prevention and reduction policies. [Methods] Trend analysis, Mann-Kendall non-stationarity test, GIS spatial interpolation and other methods were used to explore the spatiotemporal evolution of rainstorm events in Hefei, on this basis, the characteristics of rainstorm water vapor transport were analyzed based on the backward trajectory HYSPLIT model of Lagrange method. [Results (1) The urban heat island effect showed an upward trend (0.266°C/10a), impervious water surface continued to increase by 4.99%, the large-scale interannual variability of urban- suburban rainstorms had a feature of asymmetry, with an increase in the amount and number of days of urban rainstorms and a decrease in the intensity of rainstorms at a rate of 1.7 mm/(10 ad); and an increase in the intensity of suburban rainstorms at a rate of 1.1 mm/(10 ad); and a decrease in the amount and number of days of rainstorms. The spatial distribution of the large value area of rainstorm intensity extends outward from urban areas. (2) Urbanization had increased the number and extremity of rainstorm events in downwind urban areas, and the urbanization effect was more pronounced in the rapid development phase than in the slow development phase. The contribution of urbanization effects to the rainfall, number of days and intensity of rainstorms was 41.2%, 50.1%. (3) The water vapor transport from the Indian Ocean. the Bay of Bengal to the South China Sea and the Western Pacific accounted for 44%, 64% and 54% of the total water vapor, respectively, and the water vapor mass from the southwest was the main source of water vapor during the rainstorm process because of its low initial height and large specific humidity. [Conclusion] The asymmetry of rainstorm events in the suburbs of Hefei is more significant in the rapid development stage of urbanization. In the future, we should strengthen the research on the mechanism of rainstorm time change under the background of urbanization. [ABSTRACT FROM AUTHOR]