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Local wind, such as sea breezes, play a crucial role in cooling coastal cities. This study presents new insights about the thermo‐hygrometric influence of the Tagus and Atlantic Ocean breezes (sea and estuarine breezes [SEBs]) in Lisbon's urban climate (Portugal). SEB events were identified in the summer of 2022 according to a wind rotation criterion: the interruption of prevailing North and Northwest (Nortada) winds during the morning, the wind shift to Northeast/East/Southeast and, sometimes, to further South/Southwest/West (rotation between 22.6° and 292.5°) and the return of the regional flow at late afternoon. Additionally, air temperature and absolute humidity anomalies (ΔT/Haurb) were calculated according to the distance to the riverfront area. Results show that SEB occurred on 37 (31%) out of 120 days, mainly in July (43%) and August (32%), between, on average, 10:00 AM and 4:00 PM, and average wind speeds of 3.4 m/s. According to the daily thermo‐hygrometric cycle, the areas up to 4 km of the Tagus estuary were, on average, cooler than northern Lisbon during SEB events, especially the areas up to 500 m (average ΔTurb reached −1.7°C). Additionally, there was a significant increase in the moisture content during SEB hours across the city but especially close to the riverfront area: the areas up to 500 m registered, on average, ΔHaurb of 4.2 g/m3 on SEB events (12:00 PM) against 2.1 g/m3 during typical Nortada days. This research is a starting point for a future delimitation and preservation of SEB penetration zones in Lisbon to address outdoor thermal discomfort during summer. [ABSTRACT FROM AUTHOR]

Urban surfaces such as rooftops, facades, and infrastructure offer significant potential for solar energy integration, contributing to energy efficiency and sustainability in cities. This article introduces an advanced multi-criteria assessment (MCA) framework designed to evaluate the suitability of various urban surfaces for solar energy deployment. The framework extends beyond traditional economic, environmental, and technological factors to include social, political, legal, health and safety, cultural, and psychological dimensions, providing a comprehensive evaluation of photovoltaic (PV) applications in urban contexts. By synthesizing existing literature and applying this holistic MCA framework, this research offers valuable insights for urban planners, architects, and policymakers, enabling strategic optimization of solar energy integration in urban environments. The findings underscore the importance of sustainable urban development and climate resilience, highlighting key factors influencing solar technology deployment and proposing actionable recommendations to address existing challenges. [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]

Meteorological mesoscale models with different urban parametrization are used to predict the local urban climate at 250 m resolution. The authors propose a hybrid machine learning approach to improve the mesoscale prediction accuracy using measured air temperature data from a sensor network and remove simulation bias. The simulation of the urban climate of Zurich during a hot summer is used as case study showing the improvements of the simulation accuracy. Based on the hybrid model results, a cumulative heat exposure index is proposed to map local hotspots in the city and assess the difference of cooling loads between rural and urban environments. Furthermore, intra-urban microclimatic differences of a typical mid-latitude city are explored to highlight the benefits of detailed simulations for building physics purposes. [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]

Accurate and reliable estimation of rainfall is crucial for scientific research and various applications. However, the observed rainfall data is often limited. With the advancements in technology, many global gridded rainfall products are now available, but their accuracy levels vary across the world. In this study, we comprehensively analyzed the reliability and effectiveness of 23 publicly available global rainfall datasets against the observed rainfall for Patna, representing a typical urban monsoon climate in India. Thirteen continuous and ten categorical statistical metrics were applied at daily, weekly, monthly, and annual intervals over 16 years (2000–2015). The results indicate that the reliability of all derived rainfall datasets varied on different temporal scales and reference datasets used. Overall, in continuous metrics, MERRA2 and MSWEP consistently outperformed in all the temporal scales whereas in categorical metrics for analyzing the rainfall detection ability, AIMERG, followed by MERRA2 demonstrated superior performance among others. Furthermore, IMD GRID, GSMAP, PCCS, AIMERG, and IMERG performed well in estimating different rainfall intensities. MERRA2 and MSWEP, which have not been widely considered for evaluation in a monsoon climate were found to be outstanding performers consistently. Therefore, we suggest broadening the selection of global rainfall products in the evaluation to fully utilize the potentiality of all available options. Furthermore, our approach offers a reliable framework to comprehensively assess the performance of different gridded rainfall products and assist in the selection of the best rainfall product for a particular region and purpose. [ABSTRACT FROM AUTHOR]

The complexities of urban climate and environmental challenges have garnered significant attention in the 21st century. Numerical simulations, offering high spatiotemporal resolution meteorological data, are essential tools in meteorological research and atmospheric science. Accurate representation of urban morphology parameters is crucial for enhancing the precision of these simulations in urban areas. Despite the availability of urban canopy parameter (UCP) data for 44 major cities in the United States and 60 in China for the weather research and forecasting (WRF) model, a comprehensive global dataset representing urban morphology remains absent. This study addresses this gap by leveraging existing global three-dimensional vector data of buildings, including footprints and heights, to compile a global 1 km spatially continuous UCP (GloUCP) dataset for the WRF model. Our findings indicate that GloUCP not only surpasses existing datasets in accuracy but also provides superior spatial coverage. In key urban agglomerations such as Beijing-Tianjin-Hebei, the Yangtze River Delta, and the Guangdong-Hong Kong-Macao Greater Bay Area in China, GloUCP offers detailed and reliable urban morphological information that closely aligns with reference datasets, outperforming other available sources. Similarly, in U.S. cities like Seattle, San Francisco, and Philadelphia, GloUCP consistently achieves lower RMSE values and higher correlation coefficients, demonstrating its robustness in modeling diverse urban environments. Furthermore, GloUCP's capability to effectively capture the vertical distribution of buildings, particularly in high-rise areas, highlights its utility in urban climate modeling and related applications. As UCPs are pivotal in regulating atmospheric responses to urbanization, the availability of this globally consistent urban description is a crucial prerequisite for advancing model development and informing climate-sensitive urban planning policies. The GloUCP dataset, converted to WRF binary file format, is available for download at https://doi.org/10.6084/m9.figshare.27011491 (Liao et al., 2024). [ABSTRACT FROM AUTHOR]

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. There has been a surge in global datasets of urban land recently. This paper shows large discrepancies in urban area across scales among multiple such datasets, which can influence the magnitude and direction of estimated urban climate impacts. [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]

The connection between plant species and nest site preference for the Asian weaver ant Oecophylla smaragdina remains poorly understood despite their common occurrence in the urban areas of tropical regions. We predicted that plant species with specific leaf characteristics influence the nesting preference of Asian weaver ants, given that plant foliage is the main component of their nest structure. We studied the leaf characteristics of the tree species in which their nests were detected, including leaf shape, leaf apex shape, and leaf arrangement. Of the 206 total trees among the 20 tree species sampled, 185 trees from 18 tree species had at least one nest. Trees of the genus Lagerstroemia had the highest median nest abundance, whereas all Roystonea regia and Caryota no trees, and some Samanea saman trees, were not occupied by O. smaragdina. For the trees found to have O. smaragdina nests, the nest abundance in each tree ranged between 1 and 16 nests, with the majority having fewer than 10 nests per tree (4.38 ± 4.45). Based on nest abundance, the oblong and elliptic leaf shapes (LS), the cuspidate leaf apex shape (LX), and the opposite leaf arrangement (LR) were the most used for nest construction, consisting of more than 50% of the total in each respective category. A Kruskal-Wallis test revealed that O. smaragdina nest site preference is influenced by LX and LR but not by LS. Additionally, O. smaragdina nest abundance was negatively correlated to leaf surface ratio but insignificant for leaf surface area. These preferred qualitative and quantitative leaf characteristics determine the species of plants used as nesting sites to facilitate nest-building efficiency and the establishment of Asian weaver ant colonies. [ABSTRACT FROM AUTHOR]

Urban Land Surface Models (ULSMs) simulate energy and water exchanges between the urban surface and atmosphere. However, earlier systematic ULSM comparison projects assessed the energy balance but ignored the water balance, which is coupled to the energy balance. Here, we analyze the water balance representation in 19 ULSMs participating in the Urban‐PLUMBER project using results for 20 sites spread across a range of climates and urban form characteristics. As observations for most water fluxes are unavailable, we examine the water balance closure, flux timing, and magnitude with a score derived from seven indicators expecting better scoring models to capture the latent heat flux more accurately. We find that the water budget is only closed in 57% of the model‐site combinations assuming closure when annual total incoming fluxes (precipitation and irrigation) fluxes are within 3% of the outgoing (all other) fluxes. Results show the timing is better captured than magnitude. No ULSM has passed all water balance indicators for any site. Models passing more indicators do not capture the latent heat flux more accurately refuting our hypothesis. While output reporting inconsistencies may have negatively affected model performance, our results indicate models could be improved by explicitly verifying water balance closure and revising runoff parameterizations. By expanding ULSM evaluation to the water balance and related to latent heat flux performance, we demonstrate the benefits of evaluating processes with direct feedback mechanisms to the processes of interest. Plain Language Summary: Urban environments have their own local climates including typically higher nocturnal temperatures compared with rural areas. Ideally, modeling cities should capture their influences on the atmosphere above them. As the energy and water balances are linked by evaporation, a good water balance representation will support a good energy balance simulation. Focusing on the water balance, we find the water balance in models could be improved by paying attention to closure and runoff. Key Points: We evaluate the water balance in 19 urban land surface models (ULSM) from the Urban‐PLUMBER projectULSMs capture the timing of water fluxes more accurately than their magnitudeThe water balance appears unclosed in 43% of the model runs (19 models at 20 sites) [ABSTRACT FROM AUTHOR]

The Model for Prediction Across Scales–Atmosphere (MPAS-A) has been widely used for larger scale simulations, but its performance in mesoscale, particularly in tropical regions, is less evaluated. This study aimed to assess MPAS-A in simulating extreme surface air temperature in Jakarta during the hot spells of October 2023 with eight different simulation setups. Several validation metrics were applied to near-surface meteorological variables, land surface temperature (LST), and vertical atmospheric profile. From the eight simulations, MPAS-A captured diurnal patterns of the near-surface variables well, except for wind direction. The model also performed well in LST simulations. Moreover, the biases in the vertical profiles varied with height and were sensitive to the initial/boundary conditions used. Simulations with modified terrestrial datasets showed higher LST and air temperatures over the sprawling urban areas. MPAS-A successfully simulated the extreme event, showing higher air temperatures in southern Jakarta (over 36 °C) compared to the northern part. Negative temperature advection by sea breeze helped lower air temperature in the northern area. This study highlights the role of sea breezes as natural cooling mechanisms in coastal cities. Additionally, MPAS-A is feasible for several applications for urban climate studies and climate projection, although further development is needed. [ABSTRACT FROM AUTHOR]

Climate change is a major challenge for the global tourism sector, affecting destinations worldwide. Greece, known for its scenery and abundant cultural history, is particularly vulnerable to these impacts. Tourism is a key driver of Greece's economy, yet climate change threatens both natural environments and cultural sites. To evaluate the impacts of climate change on different types of Greek tourism (beach, sightseeing, winter tourism in mountainous areas), the widely used Holiday Climate Index (beach and urban versions) alongside three additional climatic indices customized for Greek climatic conditions, namely the Urban Climate Comfort Index, the Beach Utility Index, and the Mountainous Winter Climate Index were utilized for top tourist destinations of Greece. The results indicate that urban tourism may face challenges during peak summer months due to rising temperatures, but the shoulder seasons (April–May and September–October) will offer improved conditions, potentially extending the tourist season. For beach tourism, favorable conditions are expected to increase from April to October, with significant gains in June and September. Winter tourism in mountainous areas, especially snow-dependent activities like skiing, is at risk due to the declining snow availability. Overall, the study highlights both the challenges and opportunities posed by climate change for Greece's tourism sector. It emphasizes the importance of adaptation strategies, including infrastructural improvements and promoting alternative activities, to minimize negative impacts and enhance the future attractiveness of Greek tourism. [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 paper introduces a methodology aimed at bridging the gap between building energy simulation and urban climate modeling. A coupling method was developed through the Building Control Virtual Test Bed (BCVTB) and applied to a case study in Taipei City, Taiwan, to address the microclimate factors of street trees crucial to cooling energy consumption. The use of the Urban Weather Generator for weather file modification revealed a 0.63 °C average air temperature disparity. The coupling method emphasized the importance of accurate wind speed and convective heat transfer coefficients (CHTCs) on building surfaces in determining cooling energy. The results indicated that elevated CHTC values amplify heat exchange, with higher wind velocities playing a crucial role in heat dissipation. The presence of street trees was found to significantly reduce heat flux penetration, leading to a reduction in building surface temperatures by as much as 9.5% during hot months. The cooling energy was lowered by 16.7% in the BCVTB simulations that included trees compared to those without trees. The EnergyPlus-only simulations underestimated the cooling energy needs by approximately 9.3% during summer months. This research offers valuable insights into the complex interactions between buildings and their environments. The results highlight the importance of trees and shading in mitigating the heat island effect and improving energy-efficient urban planning. [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]

High-resolution urban climate modeling has faced substantial challenges due to the absence of a globally consistent, spatially continuous, and accurate dataset to represent the spatial heterogeneity of urban surfaces and their biophysical properties. This deficiency has long obstructed the development of urban-resolving Earth System Models (ESMs) and ultra-high-resolution urban climate modeling, particularly at large scales. Here, we present a first-of-its-kind 1km-resolution present-day (circa-2020) global continuous urban surface parameter dataset – U-Surf. Using the urban canopy model (UCM) in the Community Earth System Model as a base model for developing dataset requirements, U-Surf leverages the latest advances in remote sensing, machine learning, and cloud computing to provide the most relevant urban surface biophysical parameters, including radiative, morphological, and thermal properties, for UCMs at the facet- and canopy-level. Our high-resolution U-Surf dataset significantly improves the representation of the urban land heterogeneity both within and across cities globally. U-Surf provides essential, high-fidelity surface biophysical constraints to urban-resolving ESMs, enables detailed city-to-city comparisons across the globe, and supports the next-generation kilometer-resolution Earth system modeling across scales. U-Surf parameters can be easily converted or adapted to various types of UCMs, such as those embedded in weather and regional climate models, as well as air quality models. The fundamental urban surface constraints provided by U-Surf are also relevant as features for machine learning models and can have other broad-scale applications for socioeconomic, public health, and urban planning contexts. We expect U-Surf to promote the research frontier on urban systems science, climate-sensitive urban design, and coupled human-Earth systems in the future. The dataset is publicly available at https://doi.org/10.5281/zenodo.11247599 (Cheng et al., 2024). [ABSTRACT FROM AUTHOR]

The urbanization and expansion of cities have raised sustainability concerns and impacted the overall quality of life. Numerous studies have explored sustainable cities with climate-adapted urban and architectural designs, particularly focusing on optimizing thermal comfort within different urban morphologies. This publication presents a meticulous systematic review analyzing how urban form influences microclimatic conditions through advanced computer simulations. The PRISMA methodology condenses key indicators to facilitate informed decision making. A robust dataset from reputable databases such as ProQuest, Web of Science, and Scopus was analyzed, revealing discernible climate patterns, with hot arid (Bwh) and humid subtropical (Csa) climates being the most studied. The Thermal Comfort Index predominantly relies on the PET metric, with ENVI-met software as a popular simulation tool. Uncontrolled urban sprawl, surface impermeability, and lack of greenery exacerbate the urban heat island effect, leading to multifaceted environmental, social, and energy-related implications. The study underscores the importance of exploring topics like urban forms and morphology while advocating for increased attention to specific climatic conditions and urban scales. The growing prevalence of computational simulations for climate analysis emerges as a pivotal area of interest for future research. [ABSTRACT FROM AUTHOR]

Anthropogenic heat emissions, which are quantified as anthropogenic heat flux (AHF), have attracted significant attention due to their pronounced impacts on urban thermal environments and local climates. However, there remains a notable gap in research regarding the distinctions in the distribution of anthropogenic heat emissions (AHEs) along urban–rural gradients. To address this gap, the present study introduces a new concept—the anthropogenic urban heat island (ArUHI)—where the AHF within urban areas is higher than that in background areas. To quantitatively describe the magnitude and spatial extent of the ArUHI effect, two metrics—namely, ArUHI intensity (ArUHII) and ArUHI footprint (ArUHIFP)—are introduced. We conducted a comprehensive study across 208 cities in China to investigate the spatiotemporal patterns of AHF variations along urban–rural gradients during the period of 2000–2016. In addition, we explored how the complex interactions between land cover and building form components affect changes in the AHF along urban–rural gradients. Additionally, we analyzed how economic zones and city sizes alter the ArUHI intensity and ArUHI footprint. The results showed that 97% (201/208) of Chinese cities exhibited a significant ArUHI effect from 2000 to 2016. The modeled ArUHI intensity value exhibited a substantial increase of nearly fivefold, increasing from 5.55 ± 0.19 W/m2 to 26.84 ± 0.99 W/m2 over time. Regarding the spatial distribution of the ArUHI footprint, the analysis revealed that, for the majority of cities (86% or 179 out of 208), the ArUHI footprint ranged from 1.5 to 5.5 times that in urban areas. City sizes and economic zones yielded significant influences on the ArUHI intensity and ArUHI footprint values. Building forms were significantly positively correlated with AHF, with R2 values higher than 0.94. This study contributes to the understanding of ArUHI effects and their driving factors in China, providing valuable insights for urban climate studies and enhancing our understanding of surface urban heat island mechanisms. [ABSTRACT FROM AUTHOR]

Monitoring microclimate variables within cities with high resolution and accuracy is crucial for enhancing urban resilience to climate change. Assessing intra-urban characteristics is essential for ensuring satisfactory living standards. This paper presents a comprehensive methodology for studying urban heat islands (UHIs) on a university campus, emphasizing the importance of multi-modal and multi-temporal data collection. The methodology integrates mobile surveys, stationary sensor networks, and drone-based thermal imaging, providing a detailed analysis of temperature variations within urban microenvironments. The preliminary findings confirm the presence of a UHI on the campus and identify several hotspots. This comprehensive approach enhances the accuracy and reliability of UHI assessments, offering a cost-effective, fine-resolution approach that facilitates more effective urban planning and heat mitigation strategies. [ABSTRACT FROM AUTHOR]

Wind speed differences between weekdays and holidays at urban sites in Japan were investigated in search of the influence of urban anthropogenic heat on surface wind speed using data from the Automated Meteorological Data Acquisition System (AMeDAS) of the Japan Meteorological Agency (JMA) for 44 years. The wind speed was found to be lower on holidays than on weekdays, not only in large cities but also in areas with medium degrees of urbanization, which is interpreted to be due to the stronger stability of the surface boundary layer under lower temperatures with smaller amounts of anthropogenic heat. The rate of decrease is about −3% in central Tokyo, and about −0.5% for the average over stations with population densities between 1000 and 3000 km−2. Additionally, an analysis using the spatially dense data on the Air Pollution Monitoring System of Tokyo Metropolis for 28 years showed that negative anomalies in wind speed on holidays were detected at many stations in the Tokyo Wards Area, although negative temperature anomalies were limited to a few stations in the central area or near big roads, suggesting different spatial scales in the response of temperature and wind speed to anthropogenic heat. [ABSTRACT FROM AUTHOR]

In the face of climate change and population growth, Local Climate Zone (LCZ) maps have emerged as crucial tools for urban planners and policymakers to address Urban Heat Island (UHI) effects, thereby playing a significant role in mitigating climate change. This study presents a methodology for classifying major Swiss cities into LCZs, offering an efficient, cost-effective, and uniform tool for supporting climate action plans across municipalities and cantons. Initial results show that Sentinel-2, Landsat 8, and Landsat 5 imagery perform well in LCZ classification with an overall accuracy usually exceeding 80%, and Sentinel-2 displays marginally superior performance. Temporal analysis reveals that the built-up classes of Open low-rise and Open mid-rise have increased by ~3%, while Large low-rise and Bare rock or paved have decreased, and Compact mid-rise remains stable. For the natural classes, Water and Dense trees remain stable, but Low plants have declined (~4%). A general decline in overall accuracy over time is noted, attributed to landscape changes. This preliminary effort emphasizes the need to enhance and automate the methodology, integrate it into the Swiss Data Cube, and potentially extend analyses with climate data to better study UHI effects. Future work will include developing visualization and tracking services for urban planners and authorities. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) $(ET)$ is a key process in the hydrological cycle that can help mitigate urban heat. ET $ET$ depends on the surface cover, as the surface affects the partitioning of precipitation between runoff and evapotranspiration. In urban neighborhoods, this surface cover is highly heterogeneous. The resulting neighborhood‐scale ET $ET$ can be observed with eddy‐covariance systems. However, these observations represent the signal from wind‐ and stability‐dependent footprints resulting in a continuously changing contribution of surface cover types to the observation. This continuous change prevents quantifying the contribution of the surface cover types to neighborhood ET $ET$ and their hourly dynamics. Here, we disentangle this neighborhood‐scale ET $ET$ at two sites in Berlin attributing the patch‐scale ET $ET$ dynamics to the four major surface cover types in the footprint: impervious surfaces, low vegetation, high vegetation, and open water. From the bottom‐up, we reconstruct neighborhood ET $ET$ based on patch‐scale observations and conceptual models. Alternatively, we start top‐down and attribute neighborhood ET $ET$ to the surface cover types solving a system of equations for three eddy‐covariance systems. Although data requirements for the bottom‐up approach are met more frequently, both approaches indicate that vegetation is responsible for more ET $ET$ than proportional to its surface fraction in the footprint related to the large evaporating surface compared to the ground surface. Evaporation from impervious surfaces cannot be neglected, although it is less than from vegetation due to limited water availability. The limited water availability causes impervious surfaces to cease evaporation hours after rainfall, while vegetation and open water sustain ET $ET$ for extended periods. Plain Language Summary: Different types of surfaces, like grass, trees, pavement, and open water, affect how rainwater is divided between evaporation and runoff. In cities with lots of pavement and buildings, more water runs off than in natural areas leaving less water for evaporation. Measurement towers have been observing the evaporation from whole neighborhoods, but separating the effects of different surfaces is hard. In our study, we figure out how much each surface type contributes to evaporation with two methods: one starting from the separate surfaces and rebuilding the neighborhood evaporation, and the other starting with the neighborhood evaporation and breaking it down into evaporation from each surface. Both ways showed that plants evaporate more than proportionally to their surface area, but even built surfaces like pavement evaporate. Our findings confirm that more plants lead to more evaporation, but built surfaces cannot be ignored. This information can help urban planners create cities that manage water better, making cities nicer places to live. Key Points: Neighborhood ET is reconstructed from patch‐scale data (bottom‐up) and disentangled attributing ET to four surface types (top‐down)The neighborhood and patch scale are connected through half‐hourly‐varying eddy‐covariance footprintsET dynamics after rainfall reveal that water limitation drives differences between surface cover types [ABSTRACT FROM AUTHOR]

City size is a primary determinant of the urban heat island (UHI) intensity, with its effects further nuanced by the urban form. But how to factor in the urban form into the UHI assessment remains unresolved. We propose an every‐pair‐interaction model that meaningfully incorporates urban size and fractal dimension to characterize the UHI intensity. Regression on the summertime surface UHI intensity of 5,000 European cities shows that the model outperforms the simple linear combination of logarithmic size and fractal dimension. Subject to the interplay between the range of the every‐pair interaction and the urban fractal shape, the model also represents a generalization as it includes power‐law, logarithmic, and saturating size dependence of UHI—all three possibilities have been reported empirically in the literature. Our theoretical framework indicates that the surface UHI intensity saturates with urban size, opening up new research perspectives around UHI intensity. Plain Language Summary: City size is an important determinant of the urban heat island (UHI) intensity. While most studies report a logarithmic dependence of UHI intensity on city size, other functions like power‐law and logistic functions have also been reported. In addition, the urban form plays an important role and, intuitively, increased UHI intensity is expected for compact cities. However, how to incorporate urban size and form for modeling UHI is less clear. Based on the perception that every urban site interacts with every other one, whereas the intensity of interaction decreases with the distance, we propose an every‐pair‐interaction model to characterize the UHI intensity. The model combines urban size and fractal dimension non‐linearly and outperforms the simple linear model. It also represents a generalization of the three functional possibilities reported in the literature. Our model indicates that the surface UHI intensity saturates with urban size. Whether the UHI saturates with the expansion of the urban area or follows a continuously increasing trend is relevant for sustainable urban development. Our approach paves the way for fresh research avenues into UHI intensity and urban climate as a whole. Key Points: We propose an every‐pair interaction model that unifies urban size and fractal dimension to model surface urban heat island (UHI) intensityThe model outperforms multi‐linear models and is a generalization of three functional shapes of the size dependence of surface UHI intensityThe empirical results in combination with our theoretical framework indicate that the surface UHI intensity saturates with urban size [ABSTRACT FROM AUTHOR]

Local climate zones (LCZs) and urban functional zones (UFZs) can intricately depict the multidimensional spatial elements of cities, offering a comprehensive perspective for understanding the surface urban heat island (SUHI) effect. In this study, we retrieved two types of land surface temperature (LST) data and constructed 12 SUHI scenarios over the Guangdong–Hong Kong–Macao Greater Bay Area Central region using six SUHI identification methods. It compared the SUHI sensitivity differences among different types of LCZ and UFZ to analyze the global and local sensitivity differences of influencing factors in the 12 SUHI scenarios by utilizing the spatial gradient boosting trees, geographically weighted regression, and the coefficient of variation model. Results showed the following: (1) The sensitivity of different LCZ and UFZ types to multi-scenario SUHI was significantly affected by differences in SUHI identification methods and non-urban references. (2) In the morning, the shading effect of building clusters reduced the surface urban heat island intensity (SUHII) of some built environment types (such as LCZ 1 (compact high-rise zone) to LCZ 5 (open midrise zone)). The SUHIIs of LCZ E (bare rock or paved zone) and LCZ 10 (industry zone) were 4.22 °C and 3.87 °C, respectively, and both are classified as highly sensitive to SUHI. (3) The sensitivity of SUHI influencing factors exhibited regional variability, with importance differences in the sensitivity of importance for factors such as the impervious surface ratio, elevation, average building height, vegetation coverage, and average building volume between LCZs and UFZs. Amongst the 12 SUHI scenarios, an average of 87.43% and 89.97% of areas in LCZs and UFZs, respectively, were found to have low spatial sensitivity types. Overall, this study helps urban planners and managers gain a more comprehensive understanding of the complexity of the SUHI effect in high-density cities, providing a scientific basis for future urban climate adaptability planning. [ABSTRACT FROM AUTHOR]

The widespread availability of Land Surface Temperature (LST) data from various sources presents a contemporary challenge for urban climate studies: how to efficiently compare these data with the results of traditional methods of temperature monitoring, which typically assume measurements at 2 m under sheltered conditions. In this line, the current study is based primarily on data extracted from a network of 31 points of hourly temperature monitoring at the 2 m level (Tair2m), in use between 2019 and 2021, in the city of Suceava in north-eastern Romania. These data allowed a detailed mapping for each hourly time step through multiple regression, adjusted by IDW, which was identified as the best interpolation method of Tair2m. These data were analyzed in parallel with LST data derived from Landsat imagery available in the analyzed period for 35 summer days with no or low cloud cover. The mapping results of both the Tair2m and LST data describe the main characteristics of the Suceava urban agglomeration (SvMA) heat island, which presents polynuclear features with intensities—as expressed by the temperature difference between the cores of the heat island and the surrounding rural areas—spanning during the summer noontime between 3.0 °C based on Tair2m and 7.1 °C on LST, respectively. The values of the Tair2m–LST differences were 0.68 °C on average, ranging from 5.33 to −19.17 °C, directly proportional to the imperviousness ratio (IMD) values, reaching the highest values in the local climate zones (LCZs) with a high built-up ratio (up to −19.17 °C) and the lowest (0.5 ÷ −0.5 °C) for those with bare soils, with isolated bushes and trees, with few or no buildings. The study results could serve as a tool to downscale the LST data to the level of Tair2m, which is useful for interpretation of the data derived from these commonly used tools in urban climate monitoring. [ABSTRACT FROM AUTHOR]

Embracing a "more global" urban comparison in scientific assessments of climate actions by cities is essential to drive greater and more inclusive participation in global efforts to curb climate change. This comparison needs to engage cities irrespective of their size and status: when we do so, distinctive patterns of urban climate mitigation actions across a diverse range of cities emerge. Employing K-means clustering as a pattern recognition method, this study compares cities based on selected aspects of their reported mitigation actions to the Carbon Disclosure Project. It explores whether the identified clusters facilitate the comparison of a socio-spatially diverse range of cities. The study identifies five clusters within two themes, namely the nature and finance–implementation of actions, shedding light on shared and distinct governance aspects of mitigation actions by cities. Notably, the study underscores how governance patterns transcend city size and global status. These findings offer valuable insights for broadening the comparative imagination of cities and inter-city networking opportunities. [ABSTRACT FROM AUTHOR]

Rapid urbanization worldwide has significantly altered urban climates, creating a need to balance urban growth with thermal environmental quality for sustainable development. This study examines the relationship between land surface temperature (LST) and urban characteristics, particularly focusing on how green cover can mitigate urban heat and how air pollution can increase temperatures. Recognizing the predictive value of LST for urban heat island (UHI) intensity, we analyzed three distinct U.S. cities—Chicago, San Francisco, and Phoenix—each characterized by unique climate and urban planning features. This study investigates the relationship between atmospheric pollutants (SO2, NO2, CO, O3) and the Normalized Difference Vegetation Index (NDVI) with land surface temperature (LST) using regression and correlation analyses. The analysis aims to elucidate how changes in atmospheric pollutants and NDVI affect variations in land surface temperature. Regression analysis is employed to estimate the coefficients of independent variables and quantify their impact on LST. Correlation analysis assesses the linear relationships between variables, providing insights into their pairwise associations. The study also examines multicollinearity between independent variables to identify potential confounding factors. The results reveal significant associations between atmospheric pollutants, NDVI, and land surface temperature, contributing to our understanding of the environmental factors influencing LST dynamics and informing climate change mitigation strategies. The observed inconsistencies in correlations across cities highlight the importance of the local context in environmental studies. Understanding these variations can aid in developing tailored urban planning policies that consider unique city characteristics for more effective climate resilience. Furthermore, a positive association was consistently obtained between pollutants and LST, indicating that increased pollution levels contribute to higher surface temperatures across different urban settings. [ABSTRACT FROM AUTHOR]

Green urban squares are essential in densely built neighborhoods and enhance their quality of life. Investment in the greening of urban areas will have a beneficial impact, particularly regarding human thermal comfort. Smaller than parks, squares can be easily spread over the cities and should be part of any neighborhood. While the cooling effect of green squares during hot summer days is increasingly well established, microclimatic assessments during all seasons are still missing. This study aimed to determine whether it is possible to identify an optimal greenery design that maximizes human thermal comfort, as indexed by physiological equivalent temperature (PET), in temperate climates across all seasons. This study employed a "research by design" methodology, utilizing the micrometeorological simulation model ENVI-met to analyze the impact of greenery on PET improvement across different seasons. The objective was to identify the most effective combination of greenery for PET improvement. To achieve these objectives, two urban squares in Munich, Germany were selected. This selection was based on the assumption that typical greening practices, exemplified by the presence of trees, shrubs, and grass, would significantly impact urban squares and their microclimatic effects on human thermal comfort. The small square with a grass surface underneath trees, Alpenplatz, is highly influenced by the surrounding buildings, affecting the sky view factor (SVF), a crucial aspect of the urban environment. Marstallplatz, an open, large square that is not highly affected by urban morphology, was analyzed through simulation scenarios combining grass, shrubs, and trees. The results demonstrate that hot summer days are of primary concern for climate-sensitive urban square design in order to avoid health risks and thus need to be prioritized without compromising comfort for cold days. To attend to both needs, increasing the number of deciduous trees for shading during the day and the amount of grass to enhance air cooling at night are particularly effective. Nevertheless, microclimate design for the spring and autumn periods must also be considered, with the provision of adaptable opportunities for sheltered and sun-exposed spaces. [ABSTRACT FROM AUTHOR]

Urban green space (UGS) is a fundamental infrastructure in modern urban settings, crucial for regulating the urban climate and improving public health. Accessibility to UGS represents a significant environmental justice issue, influencing the sustainable development of local communities. In this work, we comprehensively evaluated the temporal dynamics of UGS accessibility and the disparity in exposure for all 31 metropolitan divisions in the United States from 2013 to 2022. Our findings indicate that there have been no significant changes in both population-weighted UGS exposure and within-city inequality of UGS exposure for all racial/ethnic groups across the study period. Furthermore, a consistent racial/ethnic disparity in UGS exposure was noted throughout the study period, with the White group tending to have higher-than-average UGS accessibility, and all other racial/ethnic groups, including Asian, Black, Hispanic, Native American, and Pacific Islander, experiencing lower-than-average UGS accessibility. These findings underscore the urgency for urban planning and policy-making strategies to address these inequities, promoting healthy and sustainable urban development. [ABSTRACT FROM AUTHOR]

Comprehensive studies comparing impacts of building and street levels interventions on air temperature at metropolitan scales are still lacking despite increased urban heat‐related mortality and morbidity. We therefore model the impact of 9 interventions on air temperatures at 2 m during 2 hot days from the summer 2018 in the Greater London Authority area using the WRF BEP‐BEM climate model. We find that on average cool roofs most effectively reduce temperatures (∼−1.2°C), outperforming green roofs (∼0°C), solar panels (∼−0.5°C) and street level vegetation (∼−0.3°C). Application of air conditioning across London (United Kingdom) increases air temperatures by ∼+0.15°C. A practicable deployment of solar panels could cover its related energetic consumption. Current practicable deployments of green roofs and solar panels are ineffective at large scale reduction of temperatures. We provide a detailed decomposition of the surface energy balance to explain changes in air temperature and guide future decision‐making. Plain Language Summary: Multiple common city scale passive and active interventions exist to reduce urban population's exposure to extreme heat during hot spells. Nonetheless, a proper comparison of the effect that each of these interventions may have on the temperatures experienced within large cities is missing. Additionally, the radiative and thermal mechanisms that lead to outdoor temperature changes are often not detailed and could lead to detrimental effects for local populations, such as indirect increase of water vapor or reflection of solar radiation. Our study, focusing over London, compares several common interventions through a modeling experiment and finds that cool roofs largely outperform other interventions during the two hottest days of the summer 2018. We also find that green roofs are ineffective on average and that solar panels and tree vegetation would only marginally change temperature exposures. Large scale deployment of air conditioning would lead to increased temperature in the core of London. Solar panels could potentially provide sufficient energy for running air conditioning all over London, creating comfortable indoor environments, and green roofs could reduce temperatures during the day. We argue that such inter‐comparisons should guide future decision making. Key Points: City scale deployment of cool roofs leads to the greatest reduction in 2 m air temperatureGreen roofs do not decrease daily average temperature but have a daytime cooling effectSolar photovoltaic panels can reduce temperatures in London by capturing sensible heat flux and generate electrical power [ABSTRACT FROM AUTHOR]

The accelerating pace of urbanization, coupled with the intensifying impacts of climate change, poses unprecedented challenges to both the environment and human well-being. In this review, we delved into the intricate interaction between climate change and urbanization and the various effects they have on the environment and human well-being, shedding light on the emergent urban climate crisis. Urban areas serve as epicenters for diverse socio-economic activities, yet they also contribute significantly to global greenhouse gas emissions and environmental degradation. Through an interdisciplinary lens, we explored the root causes of the urban climate crisis, examining how rapid urbanization exacerbates climate change and vice versa. By synthesizing current research and case studies, we elucidate the various environmental and social ramifications of this nexus, ranging from urban heat island effects to heightened vulnerability to extreme weather events. Furthermore, we delve into the unequal distribution of climate risks within urban populations, highlighting the disproportionate burden borne by marginalized communities. Finally, the chapter presents strategies and interventions for mitigating and adapting to the urban climate crisis, emphasizing the imperative of holistic and equitable approaches that prioritize both environmental sustainability and human well-being. Overall, this review calls for concerted efforts to unravel the complexities of the urban climate crisis and forge a path toward resilient, sustainable, and equitable urban futures. [ABSTRACT FROM AUTHOR]

The Urban Heat Island phenomenon (UHI), characterized by elevated temperatures in urban areas compared to their rural surroundings, is highly exacerbated by urbanization and climate change. Understanding the complex relationship between UHI effect and urban form is crucial for effective urban planning and climate mitigation strategies. This paper examines the multifaceted connection between UHIs and urban forms, exploring various methods used to study this relationship. Through a review of the existing literature, we analyze the influence of various urban characteristics on the intensity and spatial distribution of UHIs. Furthermore, we discuss the key methods and technologies, such as remote sensing and modeling, used in advancing our understanding of UHI–urban form interactions. This study then delves into the literature on UHIs within the specific context of Morocco, identifying research gaps and emphasizing the need for more comprehensive research to address them. By translating study findings into actionable urban solutions, this paper suggests contextual mitigation strategies based on the research outcomes. Finally, by synthesizing current research findings and methodologies, this paper seeks to provide insights into the complexities of UHI dynamics and their implications for climate resilience, highlighting the potential of research for forging sustainable and climate-conscious planning strategies in Morocco and across the broader MENA region. [ABSTRACT FROM AUTHOR]

Aim: Global‐scale studies are necessary to draw general conclusions on how trophic interactions vary with urbanization and to explore how the effects of urbanization change along latitudinal gradients. We predict that the intensity of trophic interactions decreases in response to urbanization (quantified by human population density). Since trophic interactions are more intense at lower latitudes, we also expect major impacts of urbanization at higher latitudes, where base levels are essentially lower. Location: Global (881 study sites). Time period: 2000–2021. Major taxa studied: Birds, arthropods and woody plants. Methods: We compiled global data on insect herbivory and bird predation from studies that employed similar methods and fitted generalized linear mixed models to test how these trophic interactions vary with human population density, latitude and their interactions. Results: The intensity of herbivory and predation decreased with an increase in human population density at lower latitudes. Surprisingly, it remained unaffected at intermediate latitudes and even increased at higher latitudes. Main conclusions: The observed patterns may be attributed to local climate changes in urban areas, such as the Urban Heat Island effect, which disrupts thermal stability in the tropics while increasing niche availability at polar latitudes. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) increases and urban heat island (UHI) variability are the major urban climatology problems arising in urban development. This study attempts to assess the effects of urban land use and land cover change on microclimate dynamics in Addis Ababa city. Three different sets of remotely sensed data from Landsat 5 TM (1990), Landsat 7 ETM+ (2005) and Landsat 8 OLI/TIRS (2021) were used for the study. LSTs were retrieved from Landsat5 TM and Landsat7 ETM+ using a mono window,and the thermal infrared band (TR-10) of Landsat–8 was used to retrieve LST. Regression and correlation analyses of the LST, normalized difference vegetation index (NDVI) and normalized difference built-up index (NDBI) were performed in SPSS V23. The study also examined the different residential urban morphology types (UMTs) of the LST and NDVI. The selected built-up blocks of UMTs included apartments, villas and mud houses. These UMTs are extracted by digitizing them from the Google Earth explorer. The results from this study showed that the proportion of urban green space (UGS) to other LULC types decreased from 120.4 km2 in 1990 to 76.26 km2 in 2021. However, the built-up area increased at a rate of 216.5 km2 (39.03%) from 1990 to 2021. The rapid expansion of built-up land in the study area was the main factor influencing the increase in LST. The residential UMTs exhibited significant differences in mean LSTs and NDVIs. The results indicate that UMT inhibited by Villia had the highest mean NDVI value and that the highest mean LST was observed in Apartment. The results of multiple linear regression analysis clearly indicate that built-up and green vegetation contributed 92.2% of the LST variations with R2 = 0.92 and VIF ≤ 10 in Addis Ababa city. The results of the study indicate that strengthening public participation in urban greening and optimizing the NDVI and NDBI are important strategies for mitigating the effects of microclimate change and that sustaining urban development and providing better quality of life for the urban population are important. [ABSTRACT FROM AUTHOR]

The island of Krk in Primorje-Gorski Kotar County, Croatia, is also called the Golden Island because of its favorable geographical location, the diversity of natural and urban landscapes, the beauty of the coast and the sea, the wealth of tangible and intangible heritage, and especially because of the opportunities for living and working. During the last century and in this century, urban landscapes have been exposed to dramatic changes that transformed old castles or former smaller settlements of the local population into tourist centers and desirable places for permanent or temporary residence. A significant part of their complex structure is the cultural and historical heritage, within which the island's park heritage has so far been insufficiently recognized and valued. Therefore, this paper examines forty selected public park spaces in the area of all local self-government units of the island of Krk in the context of urban transformations and climate change. It is concluded that the island's park heritage has often been created as a result of urban transformations in which, despite being exposed to constant changes, it is mostly preserved, but that recently, new elements of this heritage are emerging. A new challenge is the threat of the consequences of climate change—increasingly frequent and long-lasting droughts, extreme precipitation and flash floods, stormy winds, rising sea level and salinization, which further emphasizes the need to preserve the park heritage of the island of Krk in the context of resistance to climate change. [ABSTRACT FROM AUTHOR]

This research examines the integration of geological and hydrogeological data in numerical aquifer model simulations, with a particular focus on the urban area of Torino, Italy. The role of groundwater resources in urban sustainability is analysed. The objective is to integrate open-loop geothermal plants into the district heating network of IREN S.p.A. Two case studies are examined: the Torino Nord area and the Moncalieri area, both of which host district heating plants. The work entails the collection and analysis of data from a variety of sources, including geognostic surveys and permeability tests, in order to construct a three-dimensional numerical model of the surface aquifer. Models were built using the public MODFLOW 6 (model of groundwater flow) code and calibrated using PESTHP (High Performance of Model Independent Parameter Estimation and Uncertainty Analysis). Results indicate the potential of urban aquifers as renewable energy sources and the necessity of comprehensive geological and hydrogeological assessments for optimal ground water heat pump (GWHP) system installation. This paper emphasises the significance of sustainable water management in the context of climate change and urbanisation challenges. [ABSTRACT FROM AUTHOR]

An urban heat island emerged due to micro urban temperature variations are also referred to as urban heat islands or urban hot spots. The high-rise buildings along the roads form "Urban Canyons" that inhibit reflected radiation from the built-up surface. Urban heat island develops over the cities due to man-made activity and the landscape. An understanding of the urban heat island and its formation is not only helpful in understanding urban thermal characteristics but also helps in understanding human comfort. A geospatial technique has the ability to acquire updated and cost-effective data over large regions. For urban climatology studies, remote sensing and geographic information systems are an important source of information and an effective methodology. Since 1971, the city of Krishnanagar and its vicinity have been witnessing rapid urban growth. Due to its dense population, urban climate and rapid urban expansion, they cause environmental degradation. Appraisal and Impact of urbanization on micro-climate in the Krishnanagar city complex based on satellite derived parameters. For the years 1995, 2007 and 2018, several satellite image analysis approaches such as NDVI, NDWI and NDVI were computed. Significant differences in land surface temperature were observed between 1995 and 2007, as compared to 2007 and 2018. [ABSTRACT FROM AUTHOR]

In this article, we present a study carried out through a qualitative-quantitative methodology of the exploratory-descriptive objective to investigate technologies that have obtained patents and contribute to the areas of architecture and urbanism, as well as information systems enhance the verification of the situation environmental and urban climate. In the investigation of these patents, we used a methodology divided into three stages: 1st – "Comprehensiveness", 2nd – "Specification", and 3rd – "Immersion" to identify the technologies described above and for the evaluation of the "positive climate" in urban areas, which are related to the reduction of environmental impacts as well as the reduction of CO2 levels. The analyses resulted in a universe of 38,413 patents that were divided by year. From the results of the patent mapping, we observed the advantages in the areas of technology, sustainability and energy obtained by countries such as China, USA, Europe and India. We obtained two units of patents granted, directly related to the theme of "climate positive", however, none focused on zero carbon emission measures, positive energy balance, environmental performance or clean energy strategies. Thus, there was a need to encourage vigorously the development of more and better tools to achieve the goal of a "positive climate". [ABSTRACT FROM AUTHOR]

Personal weather stations (PWS) can provide useful data on urban climates by densifying the number of weather measurements across major cities. They do so at a lower cost than official weather stations by national meteorological services. Despite the increasing use of PWS data, little attention has yet been paid to the underlying socio-economic and environmental inequalities in PWS coverage. Using social deprivation, demographic, and environmental indicators in England and Wales, we characterize existing inequalities in the current coverage of PWS. We find that there are fewer PWS in more deprived areas which also observe higher proportions of ethnic minorities, lower vegetation coverage, higher building height and building surface fraction, and lower proportions of inhabitants under 65 years old. This implies that data on urban climate may be less reliable or more uncertain in particular areas, which may limit the potential for climate adaptation and empowerment in those communities. Crowdsourced personal weather data are sought to cope with weather data scarcity. But, in England and Wales, more deprived areas are less covered. This limits the potential for climate adaptation of communities living in these environments. [ABSTRACT FROM AUTHOR]

With the increase in urban development intensity, the urban climate has become an important factor affecting sustainable development. The role of urban ventilation corridors in improving urban climate has received widespread attention. Urban ventilation identification and planning based on morphological methods have been initially applied. Traditional morphological methods do not adequately consider the dynamic process of air flow, resulting in a rough evaluation of urban ventilation patterns. This study proposes a new urban-scale ventilation corridor identification method that integrates the Lattice Boltzmann method and the K-means algorithm. Taking Wuhan, China as the research area, an empirical study in different wind directions was conducted on a 20 m grid. The results showed that three levels of ventilation corridors ( 245.47   k m 2   in   total ) and two levels of ventilation obstruction areas ( 658.09   k m 2   in   total ) were identified to depict the ventilation pattern of Wuhan's central urban area. The method proposed in this study can meet the needs of urban-scale ventilation corridor identification in terms of spatial coverage, spatial distribution rate and dynamic analysis. Compared with the classic least cumulative ventilation cost method, the method proposed in this study can provide more morphologic details of the ventilation corridors. This plays a very important role in urban planning based on urban ventilation theory. [ABSTRACT FROM AUTHOR]

The rapid development of urbanization has caused obstructed urban natural ventilation and the contribution rate of urbanization is relatively high. Therefore, there is an urgent need for urban development planning that should respect natural ventilation and local climate to reduce negative impacts. By optimizing the urban construction layout to reduce obstruction and leave a passageway for wind to blow in, the natural ventilation environment could be improved. This paper presents a promising approach for natural ventilation planning at both the city and community scales. Based on the assessment of wind environment, heat island intensity, and ventilation potential, the results revealed that winds blowing from the western and northern mountainous area of Shijiazhuang play a natural ventilation inlet role which can provide clean air. The SSHI and SHI were mainly distributed within the Second Ring Road, which has a large proportion of the low ventilation potential level. Thus, six first-class ventilation corridors and thirteen secondary corridors were recommended, which were set to be adapted to the dominant wind direction. Subsequently, an urban climate analysis map (UCAnMap) was developed considering climate sensitivity, and planning recommendations were provided for different climate zones. The relationship between architectural spatial structure and ventilation efficiency was analyzed; the results revealed that increasing the height of the buildings will decrease the proportion of comfortable wind zones, and the overall ventilation efficiency will weaken, so the average building height of a typical block should be controlled within 45 m, which matches ventilation performance requirements. The ventilation efficiency of the block has a certain negative correlation with the building density, and as the building density decreased by more than 10%, the proportion of the comfortable wind zones could increase by 4–5%. [ABSTRACT FROM AUTHOR]

Urbanization contributes to the elevated temperatures in built environments, creating Urban Heat Islands (UHI). These UHI intensify heat stress levels, hindering outdoor activities and decreasing walkability. Therefore, paying attention to outdoor space design with thermal comfort in mind becomes crucial. This research aims to develop a machine learning model that assesses and predicts pedestrian thermal comfort (PTC) by training a Random Forest algorithm with field survey data and computed thermal comfort index (modified Physiologically Equivalent Temperature- mPET) from RayMan Pro software. A total of 1364 data sets, including microclimate and street geometry data, were collected from ten streets in Nagpur city over two consecutive years in the May month. Two Random Forest Regressor (RFR) models were developed- one integrating all microclimate and street geometry parameters (Model 1). Another model was trained using the identified important features from microclimate (air temperature and solar radiation) and street geometry parameters (Model 2). The RFR Model demonstrated exceptional predictive capabilities for mPET values and thermal sensation classes. This is substantiated by high correlation coefficients (R2 > 0.90 at training phase and R2 > 0.80 at prediction phase), low mean squared error values (overall MSE < 1.4), and fitting values of true predictive rates (TPR), and false alarm rates (FAR). This research establishes that only air temperature and solar radiation, along with street geometry parameters, can predict PTC. The developed models have potential applicability in analogous climatic conditions, and they also help identify critical areas of PTC to prioritize mitigation measures, which can ultimately reduce heat stress and UHI impact. [ABSTRACT FROM AUTHOR]

The climate change assessment in the context of urban areas is very crucial for policy making regarding hazard mitigation and citizen's health, especially for the smart cities. The past climate assessment and present climate monitoring is somewhere easy, but the projection of future climate at city level is very difficult as most climate models fail to resolve the cities spatially. Over the highly populated areas like the region of Gangetic Plains the precise city specific climate projection becomes more important. The present study aims to project the future temperature, covering both minimum temperature (Tmin) and maximum temperature (Tmax) and analyse the extreme indices over smart cities in the Gangetic Plains, as one of the pioneer works using CMIP6 model's downscaled outputs using SDSM model. The study reveals that these smart cities are likely to experience warmer and more extreme temperatures in the upcoming decades. The future temperature projections were generated under two emission scenarios (SSP245 and SSP585), and for near future (2030–2065) and far future (2066–2100) periods. The drastic change in minimum temperature (Tmin) was observed over New Delhi, Prayagraj, Kolkata, and Lucknow by the end of the century under SSP585. Four extreme temperature indices were also analyzed for future time series: (1) TXgt40(No. of days Tmax > 40ºC); (2) TNlt10(No. of days when Tmin < 10ºC); (3) TX90p (Percentage of days when Tmax > 90th percentile); and (4) TN10P (Percentage of days when Tmin < 10th percentile). The increasing trend of warm temperature Indices and decreasing trend of cool temperature indices were observed over all the stations. The drastic change in extreme temperature indices may have a significant effect on urban climate, it could impact public health by increasing the incidence of heat-related illnesses such as heat stress or heat exhaustion. The present study can be also utilized as a probable baseline for assessing the extreme climate conditions in the future. As this study is one of the very first attempts under the aspect of smart cities and thus it may help in developing early warning systems for smart cities in the Gangetic Plain. [ABSTRACT FROM AUTHOR]

Understanding the motivations and benefits of citizen science (CS) participants is critical to the success of environmental science projects that rely on data collection from engaged citizens. Tailored communication with citizen scientists is essential, leading to the need to target specific societal groups for extensive and high-quality data sets. The purpose of the study was to apply marketing concepts such as stakeholder analysis, value proposition canvas (VPC), and key performance indicators (KPIs) to CS projects. The study examined the extent to which these strategies can be applied to CS projects and add value, resulting in improved recruitment and retention of participants, as well as improved project management. The dynamic landscape of CS projects requires adapted business-oriented approaches supporting ongoing participation with high motivation, acknowledging community needs, and recognizing institutional scientists, akin to targeting potential customers. The case study focuses on the CityCLIM initiative, an EU-funded project collecting urban climate data, especially air temperature, using mobile weather stations. The CityCLIM VPC, analyzing stakeholder groups, facilitated tailored communication strategies by analyzing stakeholder groups and highlighting the effectiveness of the CS cycling initiative within the "Stadtradeln" program. Impact KPIs served as a roadmap for strategic refinement, while data quality KPIs identified deficiencies, guiding adjustments to the campaign. Applied marketing tools improved project planning, engagement, and evaluation, demonstrating the potential of this concept. Adapting marketing tools to recruitment and communication strategies benefits CS projects by targeting specific groups. The holistic integration of stakeholder analysis, VPC, and KPIs streamlines project management and creates a framework for sustainable success. This adaptation forms a robust toolkit for CS project coordinators, contributing to effective communication, engagement, and impact assessment. Applying marketing tools significantly increases CS projects' reach and impact, ensuring long-term success and meaningful scientific contributions. [ABSTRACT FROM AUTHOR]

Cities worldwide have initiated the installation of urban climate sensors to monitor air quality in real time and take proactive measures against the growing threat of climate change. This study focuses on the city of Chicago and utilizes Microsoft's recently launched Project Eclipse sensors to evaluate air quality status. We extracted surrounding land use features near the installed sensors, integrating street view images from Google Street View (GSV) with conventional land use extraction toolkits. Principal component analysis (PCA) was conducted to decompose spatial information and evaluate the unique characteristics of using street view imagery. Additionally, we integrated XGBoost machine learning regression analysis and SHapley Additive exPlanations (SHAP) value calculation to investigate the impact of determinants on air quality. Analysis results indicated that the measured air pollution exposure from the sensors was consistent with the city's predefined values, except for the Northern intersection of West Belmont Avenue, which reported critical air quality issues. The regression analysis and SHAP calculation revealed significant differences in the impact of land use on air quality between the intersection of West Belmont Avenue and random observations. The city and local government agencies should address the existing built environment and land use conditions in the North to mitigate potential harm. [ABSTRACT FROM AUTHOR]

Iligan City has been the heart of major industries in steel, cement, and hydropower and is one of the highly urbanized coastal cities in Mindanao, Philippines. Due to its geological state, environmental challenges like flooding, natural disasters, and environmental degradation have been experienced by the city. With the climate crisis, these risks are being magnified. Recently, nature-based solutions (NBS) have gained significant attention worldwide, viewed as a key solution to the consequences of climate change. Yet implementation of strategies and policies regarding NBS, especially in developing countries, has received poor attention. Hence, this study aims to assess the awareness and perceptions of stakeholders in Iligan City regarding climate change, NBS, and willingness to engage in actions involving NBS. Stakeholder mapping and surveys were conducted through purposive sampling. A total of 187 respondents were interviewed from different sectors (LGUs, NGOs, the private sector, and academia) in Iligan City. Results revealed significant differences in awareness and perceptions across the distinct socio-economic backgrounds of stakeholders, where respondents with higher education and income have greater levels of awareness, perception, and willingness. The study recommends future targeted approaches and increased campaigns for climate change and sustainable solutions such as integrating NBS in adaptation strategies and mitigation plans to foster multi-level stakeholder collaboration. [ABSTRACT FROM AUTHOR]

Cities are globally exposed to climate change effects, which revealed that 55% of the world's population is at risk. Despite their low contribution to global greenhouse gas emissions, African cities are paying the highest cost of the threats caused by climate change due to their rapid urban community growth, high population density, and inadequate urban planning. This review has explored and demonstrated the benefit of Nature-based Solutions (NbS) implementation for urban climate crisis resilience that have been implemented globally for sharing information on sustainable city planning in Africa. The analysis is based on the African countries' Nationally Determined Contribution (NDC) reports, the institution's review, and scientific articles. The successful implementation of NbS since 2015 in developed countries confirms that NbS has a multi-functional environmental benefit for urban and pre-urban populations. It reduces cities' vulnerability to climate threats and advances numerous Sustainable Development Goals (SDGs) achievement. It revealed only 15 (27,7%) African countries have implemented NbS with 119 projects to adapt and tackle climate change in water, agriculture, forest and woodland, coastal and marine habitat, grassland, and mountain habitat sectors. Rural areas are paid more attention than cities despite the rapid urbanization in the face of extreme climate effects. Furthermore, the review process observed some challenges in translating the approaches of NbS into measurable actions for African urban climate resilience: (i) issues in the governance of urban planning and policies; (ii) insufficient mobilization of resources and lack of private sector involvement in financing NbS; and (iii) lack of comprehensive evidence-based strategies and knowledge for successful operationalization of NbS in African cities. [ABSTRACT FROM AUTHOR]

This study investigates the climate change adaptation strategies employed by urban crop and livestock farmers in Ghana. With the increasing recognition of climate change threats to agricultural productivity and food security, it is crucial to understand urban farmers' adaptation strategies. Through a mixed-methods approach—surveys of 191 urban farming households using a questionnaire and interviews of 13 key informants using an interview guide, data were collected from farmers in urban Ghana. The quantitative data were analysed using descriptive statistics from the Statistical Package for Social Sciences and presented in tables. The qualitative data were analysed manually based on themes and content and presented in narratives. The results indicated a significant relationship between urban farmers' educational status and gender and their sources of climate change adaptation information. Urban farmers' climate change adaptation strategies include planting more improved and less indigenous crop varieties and rearing of more exotic and less local breeds of animals. Again, to buffer animal production against climatic diseases, urban farmers treat their animals through vaccination, giving animals pills, disinfecting pens, and spraying animals' bodies with chemicals. Access to agricultural extension services is the main challenge in urban farmers' climate change adaptation. The study underscores the importance of enhancing farmers' resilience through targeted interventions, including capacity-building initiatives, access to climate information and financial resources, and the integration of climate-smart agricultural practices into urban planning and policy frameworks. In addressing these challenges and promoting adaptive strategies, urban crop and livestock farmers in Ghana can enhance their resilience to climate change and contribute to sustainable urban agriculture and food security. This study, therefore, contributes to the urban agriculture and climate change literature by drawing attention to how crop and animal farmers in the urban neighbourhood adapt to climatic stressors. [ABSTRACT FROM AUTHOR]