Urban heat island (UHI) intensity and magnitude estimations: A systematic literature review

Comparison of mediating effects of air pollutants on urban morphology and urban heat Island intensity at block scale

The urban heat island effect seriously challenges the sustainability and livability of urban development. Air pollutants (AP) may play a mediating role in the impact of urban morphology (UM) on the canopy layer urban heat island intensity (CLUHII) and the surface urban heat island intensity (SUHII). To verify this hypothesis, taking Urumqi as an example, we use the ridge regression model to reveal the differences in the impacts of UM and AP on the two types of urban heat island intensity (UHII). A structural equation model was established to verify the mediating effect of AP. The results show that: (1) There are differences in the optimal research units for UM and CLUHII and SUHII, which are 500 m and 300 m respectively. (2) Whether it is CLUHII or SUHII, the impact of two - dimensional urban morphology indicators are greater than that of three - dimensional urban morphology indicators. (3) There are similarities and differences in the impact of urban morphology indicators on the two types of UHII. The effects of standard deviation of building height, floor area ratio, and sky view factor on the two are opposite. (4) Air pollutants (PM10, PM2.5, NO2) have significant mediating effects between building density, impervious surface percent, green coverage ratio, mean building height, standard deviation of building height, floor area ratio, sky view factor, and the two types of UHII. This study provides a reliable reference for urban planning aimed at mitigating the urban heat island effect and air pollution.

Aerosol Synthesis and Optical Characterization of Co-Doped MgTiO3 Blue Pigments with High NIR Reflectance

Near-infrared (NIR) reflective pigments are considered as energy-efficient cooling materials to mitigate the urban heat island (UHI) effect and reduce energy consumption. In this study, a novel blue pigment based on Mg1-xCoxTiO3 with high NIR reflectance was synthesized by spray pyrolysis, and its crystallographic and optical properties were investigated by varying the Co content and the calcination temperature. X-ray diffractometer (XRD) and TEM analyses revealed that the prepared Mg1-xCoxTiO3 pigments possessed an impurity-free ilmenite crystal structure and a dense spherical shape with a uniform distribution of Co. The intense blue color is due to the 4T1g (F) → 4T1g (P) transition of Co2+ in the octahedral ligand field. The optimal Co content and calcination temperature for obtaining the highest NIR reflectance with excellent blue characteristics were determined to be 20% (x = 0.2) and 800 °C, respectively. As a result, the Mg0.8Co0.2TiO3 blue pigment (L* = 59.8, a* = -1.23, b* = -36.8) shows a high NIR solar reflectance of 78.9%, which is 34% higher than that of CoAl2O4. The color difference (ΔE*) values before and after immersed in acid/alkaline solution are less than 1.0, indicating that Mg0.8Co0.2TiO3 exhibits excellent chemical stability. The temperature rise test under NIR irradiation demonstrated that the synthesized Mg0.8Co0.2TiO3 blue pigment exhibited improved heat shielding performance compared to that of CoAl2O4. Therefore, Mg0.8Co0.2TiO3 could be used as a promising cool blue pigment for sustainable solutions to alleviate heat accumulation due to sunlight.

Exploring the urban heat island phenomenon in a tropical medium-sized city: insights for sustainable urban development

Urban Heat Islands (UHI) pose a significant challenge for tropical cities, especially as global temperatures continue to rise. Despite the prevalence of this problem, medium-sized cities in tropical regions remain underrepresented in UHI studies compared to large cities in temperate climates. This study aimed to characterize the UHI effect in a rapidly growing medium-sized city in a tropical region, which has experienced unplanned urban sprawl. Temperature data were collected using data loggers in ten distinct areas of the city. The results revealed a pronounced UHI effect, with higher intensities in the city center and lower intensities in areas with native vegetation. During the dry season, extreme UHI effects (> 6 °C) were observed 10% of the time, with strong UHI effects (4 °C-6°C) present 50% of the month. In contrast, during the wet season, moderate UHI effects (2 °C-4°C) were recorded only 15% of the time. A strong correlation was identified between land use, particularly vegetation cover, and UHI intensity. This study not only sheds light on the dynamics of UHI in this specific tropical city, but also offers relevant insights for other mid-sized cities in similar tropical contexts around the world. The findings provide valuable knowledge for the development of adaptation and mitigation strategies in urban planning, which can be applied globally to cities facing rapid urbanization in hot climates. Furthermore, this research contributes to the advancement of UHI analysis methodologies that can be replicated in tropical and temperate cities, building on previous studies conducted in Brazil and other regions of the world.

Green infrastructure and its influence on urban heat island, heat risk, and air pollution: A case study of Porto (Portugal)

Green infrastructure plays a fundamental role in mitigating the effects of urban heat island. Vegetation may trap particulates and absorb pollutants like ozone, thus improving air quality. Understanding how green infrastructure reduces urban heat island and air pollution within specific urban zones can provide valuable insights for better urban design, improved environmental quality, and increased resident well-being. This study addresses the impact of green infrastructure deprivation on urban heat island effects, air pollution, and heat-related health risks in Porto, Portugal. The study employs a monitoring network to analyse the spatial distribution of air temperature and humidity throughout the city, although with specific gaps in coverage. With a focus on the role of urban green infrastructure in mitigating air urban heat island effects, this paper uses the data from Porto Digital's monitoring network between 2019 and 2022. Heat risk index assesses vulnerability to heat-related health risks by integrating land surface temperature, land cover, and demographic data through remote sensing. Green infrastructure mapping is conducted to quantify the spatial distribution of vegetation elements in the study area. The data analysis from 2019 to 2022 reveals that urban heat island intensity is more pronounced during the summer and at night. Approximately 32.6% of Porto is in areas with a high heat risk index, indicating increased vulnerability to heat-related health risks. The study finds that limited green infrastructure exacerbates this vulnerability, particularly in socioeconomically disadvantaged areas. Additionally, persistent air pollution hotspots, including elevated levels of ozone and particulate matter, contribute to the intensity of urban heat island. These findings underscore the need for integrating green infrastructure into urban planning to mitigate urban heat island and air pollution, improve urban resilience, and promote environmental justice.

Effects of cloud and tree shading on surface temperature of different pavement materials for urban sidewalks

The surface color and materials of sidewalk pavements exhibit different albedo characteristics, leading to varied surface urban heat island effects in subtropical regions. To quantify the effect of pavement surface color and material on SUHI, Prefabricated Concrete Structure brick (PCB), Granite brick (GB) and Dutch brick (DB) totaling 14 pavement samples in Hangzhou were placed under unshaded, cloud shaded and tree shaded conditions. CIELAB (International Commission on Illumination L*a*b*) color data, short-wave radiation (incoming and outgoing) and surface temperature were measured. Results showed that L*( lightness) value played a dominant role in the albedo of the pavement surface, and there was a positive correlation in summer and winter. The lower the L* value, the greater the cooling effect of the pavement under cloud and tree shaded conditions. Compared to unshaded condition, tree shade provided the highest cooling benefit of 32.2 °C in summer. Among the 3 types of pavement materials, DB had the lowest average surface temperature in summer and the highest in winter. Therefore, in cities with cold winter and hot summer, it is advised that DB with a high surface L* value be employed. The use of pavement with a low L* value should be accompanied by continuous shading measures to cool the surface temperature. These findings provide a basis for selecting low-energy embodied pavement materials for urban streets and offer important technical support for mitigating the urban heat island effect.

Pedestrian thermal comfort mapping for evidence-based urban planning; an interdisciplinary and user-friendly mobile approach for the case study of Dresden, Germany

In the face of climate change and increasing urbanisation, ensuring outdoor thermal comfort is becoming an increasingly crucial consideration for sustainable urban planning. However, informed decision-making is limited by the challenge of obtaining high-resolution thermal comfort data. This study introduces an interdisciplinary, low-resource, and user-friendly methodology for thermal comfort mapping, employing a self-built low-cost meteorological device for mobile climate monitoring. This device was utilised in the city center of Dresden, Germany to collect air temperature, humidity, pressure, surface temperature, global radiation, and globe temperature data as key inputs for the calculation of thermal comfort indices. These measurements were then used to calculate the Universal Thermal Climate Index (UTCI) using the RayMan Pro urban climate modelling program. Due to the limited resource capacities of urban planning departments, clear priority areas must be identified. Therefore, an exemplary approach for the prioritisation of consistent hotspots using the highest 5% of UTCI values was developed. The spatial variances in UTCI were validated through mobile pedestrian thermal comfort questionnaires, which allowed for the comparison of objective and subjective estimates of thermal comfort and gave the basis on which to make holistic and practical suggestions for urban planning interventions. This paper demonstrates an accessible and interdisciplinary approach to thermal comfort mapping which can empower urban planning stakeholders with scientifically informed and cost-effective decision-making tools for climate-adapted urban development.

The capacity of human interventions to regulate PM2.5 concentration has substantially improved in China

The rapid urbanization in China has brought about serious air pollution problems, which are likely to persist for a considerable period as the urbanization process continues. In urban areas, the spatial distribution of air pollutants represented by PM2.5 has been proved mainly affected by emission, urban landscape pattern (short as ULP), as well as meteorological conditions. However, the contributions of these factors can seriously vary with different periods of urban development. Based on multi-source data, 304 prefecture-level cities in China were chosen as study areas, and we used the Geographically and Temporally Weighted Regression (GTWR) model to quantify the relative contributions of three factors-emission, ULP, and meteorological condition-to PM2.5 concentration variation in different periods, namely, the Slow Ascending Period (SAP, 2000-2007), the Stable High-level Period (SHP, 2007-2013), and the Rapid Decline Period (RDP, 2013-2020). During SAP, the relative contribution of emission remained low and the relative contribution of ULP decreased, while the contribution of meteorological factors to PM2.5 concentration variation becoming the dominant factor. During SHP and RDP, the relative contribution of emission notably increased (The largest increase is 28 %), while the relative contribution of meteorological factors significantly decreased (The largest decrease is 16 %). Spatially, the key regions for air pollution control in China, such as the Beijing-Tianjin-Hebei, the Fenwei Plain, the Yangtze River Delta, and the Pearl River Delta, experienced a significantly greater decrease (The largest decrease is 39 %) in the meteorological contribution and increase in the emission contribution (The largest increase is 66 %) compared to other regions. In general, we found that 27 cities in southwest China become increasingly sensitive to meteorological conditions, while the majority of cities (277 in total), particularly in key regions, have shown a growing sensitivity to emission during the whole period. These results prove that the ability of anthropogenic influence on air quality is gradually more effective, indicating the air pollution prevention and control policies in China in recent years have achieved satisfactory results. It is worthy to notice that the PM2.5 level in most cities is still sensitive to emissions. Therefore, strict emission reduction measures still needs to implemented in the future to further improve air quality.

Urban green space, human heat perception and sleep quality: A repeated cross-sectional study

Urban heat poses significant challenges to public health, as exposure to high temperatures is associated to heat stress, resulting in heat strain, sleep deprivation, and cardiovascular morbidity and mortality. As the frequency of heat waves is increasing due to global warming, urban green spaces are often proposed as a nature-based solution to mitigate urban heat stress. This study investigated the impact of urban green space on perceived heat stress and sleep quality, using questionnaires and detailed land cover data. We surveyed 584 respondents during four heat and four control events in the summers of 2021 and 2022, assessing perceived heat stress, sleep quality, and mental health. Using structural equation models, this study analysed the influence of both tree cover and grass and shrub cover on perceived heat stress and sleep quality, while controlling for risk and vulnerability factors. The outcomes revealed that during heat events, enhanced tree cover was associated with reduced heat stress (B = -0.484, 95% CI [-0.693, -0.275], p = 0.001), while increased grass and shrub cover was associated with both reduced heat stress (B = -0.361 [-0.529, -0.193], p = 0.000) and improved sleep quality (B = -0. 241 [-0.399, -0.083], p = 0.003). Conversely, during control events, stress indicators were more strongly associated with individual vulnerability factors rather than surrounding green space. These results emphasize the importance of combining trees with lower vegetation in urban planning to mitigate heat-related stress and enhance sleep quality, thereby improving overall well-being during heat events.

A method to map land use impacts on microclimate regulation supply in urban environments

Land use impacts land surface temperature (LST), especially in urban areas where anthropogenic materials have a high capacity to store energy. Nevertheless, cities have many other land uses (e.g., forests, lawns) that can reduce LST and contribute to high microclimate regulation. In this work, we develop a method to map land use impacts on microclimate regulation supply using an Unmanned Aerial Vehicle (UAV). A detailed methodology was developed for 1) UAV's mission planning, 2) field data collection for method validation, 3) RGB and thermal mission reconstruction, 4) land use classification, 5) data extraction and 6) spatial and statistical analysis. The method developed can be beneficial to local authorities and transferable to other realms. It will allow us to understand the impacts of different land uses on microclimate regulation. For this, an area with heterogeneous land uses was used as a test site.•A novel methodology was created to map land use impacts on microclimate regulation supply in urban areas;•High-resolution UAV RGB and thermal imagery for land-use classification and surface temperature analysis;•The method can help understand the capacity of the different land uses on microclimate regulation.

Non-negligible clear-sky biases of satellite thermal infrared observations for analyzing surface urban heat island intensity: A case study in China

Surface urban heat island (SUHI) intensity generally determined by satellite-derived clear-sky land surface temperature (LST) has ignored the impacts of cloud coverage and cannot reflect the real SUHI intensity. Only a few studies focus on the effects of this issue based on short-time LST datasets, which could contain non-negligible uncertainties to summarize reliable rules. To investigate the influence, the SUHI intensity (SUHII) clear-sky bias (CSB), which is defined as the SUHII difference between clear-sky and all-weather conditions, was investigated in 35 cities in China, based on clear-sky and all-weather LST datasets from 2003 to 2022. Results show that the two SUHIIs show similar spatial distribution patterns, with stronger SUHIs in southern China at daytime and weaker at nighttime. However, a non-negligible difference can be found between these two SUHIIs, with a SUHII CSB range of -1.43 to 2.27 °C at daytime and - 2.17 to 0.91 °C at nighttime. In terms of intra-annual variation, SUHII CSBs in similar climate regions exhibit similar patterns but different ranges due to their different natural properties. Generally, intra-annual variations of SUHII CSB can be divided into three groups, i.e., "Table Mountain", single peak, and single valley, varying across climate regions and years. The main reason for SUHII CSB was analyzed, i.e., spatial gaps of the data directly caused the SUHII CSB, and the thermal properties and meteorological conditions of the missing pixels affect the magnitude of the SUHII CSB. Taking the urban system as an example, this study has provided evidence of the non-negligible SUHII clear-sky bias to emphasize the importance of using all-weather LST for relevant studies.

Modification effects of immigration status and comorbidities on associations of heat and heatwave with stroke morbidity

BACKGROUND

Heat and heatwave have been associated with stroke morbidity, but it is still unclear whether immigrants from different geographic regions and patients with comorbidity are more vulnerable to heat and heatwave.

METHODS

Time-stratified case-crossover design combined with generalized additive quasi-Poisson models were used to quantify the relative risks (RRs) of heat and heatwave on first-ever stroke morbidity during 0-7 lag days. Attributable fractions (AFs) were estimated to assess the first-ever stroke morbidity burden due to heat and heatwave. Stratified analyses for sex, age, disease subtypes, resident characteristics, and comorbidity type were performed to identify potential modification effects.

RESULTS

Heat and heatwave were associated with first-ever stroke morbidity, with the AF of 2.535% (95% empirical confidence interval (eCI) = 0.748, 4.205) and 2.409% (95% confidence interval (CI) = 1.228, 3.400), respectively. Among northern and southern immigrants, the AF for heat was 2.806% (0.031, 5.069) and 2.798% (0.757, 4.428), respectively, and the AF for heatwave was 2.918% (0.561, 4.618) and 2.387% (1.174, 3.398), respectively, but the effects of both on natives were statistically insignificant. Among patients with hypertension, dyslipidemia, or diabetes, the AF for heat was 3.318% (1.225, 5.007), 4.237% (1.037, 6.770), and 4.860% (1.171, 7.827), respectively, and the AF for heatwave was 2.960% (1.701, 3.993), 2.771% (0.704, 4.308), and 2.652% (0.653, 4.185), respectively. However, the effects of both on patients without comorbidity were statistically insignificant.

CONCLUSION

Heat and heatwave are associated with an increased risk of first-ever stroke morbidity among immigrants and those with comorbid hypertension, dyslipidemia, or diabetes, with the effects primarily due to non-native individuals.

DATA ACCESS STATEMENT

The author(s) are not authorized to share the data.

A UAV Thermal Imaging Format Conversion System and Its Application in Mosaic Surface Microthermal Environment Analysis

UAV thermal infrared remote sensing technology, with its high flexibility and high temporal and spatial resolution, is crucial for understanding surface microthermal environments. Despite DJI Drones' industry-leading position, the JPG format of their thermal images limits direct image stitching and further analysis, hindering their broad application. To address this, a format conversion system, ThermoSwitcher, was developed for DJI thermal JPG images, and this system was applied to surface microthermal environment analysis, taking two regions with various local zones in Nanjing as the research area. The results showed that ThermoSwitcher can quickly and losslessly convert thermal JPG images to the Geotiff format, which is further convenient for producing image mosaics and for local temperature extraction. The results also indicated significant heterogeneity in the study area's temperature distribution, with high temperatures concentrated on sunlit artificial surfaces, and low temperatures corresponding to building shadows, dense vegetation, and water areas. The temperature distribution and change rates in different local zones were significantly influenced by surface cover type, material thermal properties, vegetation coverage, and building layout. Higher temperature change rates were observed in high-rise building and subway station areas, while lower rates were noted in water and vegetation-covered areas. Additionally, comparing the temperature distribution before and after image stitching revealed that the stitching process affected the temperature uniformity to some extent. The described format conversion system significantly enhances preprocessing efficiency, promoting advancements in drone remote sensing and refined surface microthermal environment research.

Spatiotemporal changes of urban heat island effect relative to land surface temperature: a case study of Jinan City, China

The urban heat island (UHI) effect has become increasingly prevalent and significant with the accelerated pace of urbanization, posing challenges for urban planners and policymakers. To reveal the spatiotemporal variations of the urban heat island effect in Jinan City, this study utilized Landsat satellite images from 2009, 2014, and 2019, employing the classic Mono-Window algorithm to extract land surface temperature (LST). Additionally, Geodetector was introduced to conduct a detailed analysis of the relationship between LST in Jinan City and land cover types (vegetation, water bodies, and buildings). The results indicate a significant increase in the severity of the urban heat island effect in Jinan from 2009 to 2019, with the central urban area consistently exhibiting a high-intensity core heat island. Suburban areas of Jinan show a clear trend of merging their heat island effects with the central urban area. The combined area of strong cool island effect zones and cool island effect zones within water bodies reaches 89.7%, while the combined proportion of heat island and strong heat island effect zones in building areas is 62.2%. Vegetation cover (FVC) exerts the greatest influence among all factors on the intensity level of the urban heat island effect. These findings provide a reliable basis for decision-making related to urban planning and construction in Jinan City.

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

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

Estimation of apparent thermal inertia of roofing materials from aerial thermal imagery

The rapid expansion of urban areas and soil sealing is enhancing the Urban Heat Island (UHI) phenomenon, especially during heat waves. The different thermal inertia of the building materials compared to natural surfaces is one of the major driving factors of UHI. The present contribution aims to test a methodology for mapping the Apparent Thermal Inertia (ATI)-a proxy that can be derived from remote sensing data-of roofing surfaces at the scale of an entire city and with a high spatial resolution. Day and night aerial thermal images with the resolution of 0.5 m were acquired over two test areas in Bologna (Italy), together with satellite multispectral data. Statistics on the buildings in the test areas are computed considering different classes of roofing materials (e.g. bituminous sheath, clay tiles, metal sheet, gravel tiles). Observed median ATI values for each class range from 0.03 to 0.09 K- 1 with interquartile ranges between 0.02 and 0.14 K- 1 , so the intra-class variability in some cases appears higher than the variability among different material classes, proving the importance of ATI mapping for UHI investigations.

Evaluating the impact of urbanization on the urban heat islands through integrated radius and non-linear regression approach

Urban heat islands (UHIs) are a significant environmental problem, exacerbating the urban climate and affecting human health in the Asir region of Saudi Arabia. The need to understand the spatio-temporal dynamics of UHI in the context of urban expansion is crucial for sustainable urban planning. The aim of this study was to quantify the changes in land use and land cover (LULC) and urbanization, assess the expansion process of UHI, and analyze its connectivity in order to develop strategies to mitigate UHI in an urban context over a 30-year period from 1990 to 2020. Using remote sensing data, LULC changes were analyzed with a random forest model. LULC change rate (LCCR), land cover intensity (LCI), and landscape expansion index (LEI) were calculated to quantify urbanization. The land surface temperature for the study period was calculated using the mono-window algorithm. The UHI effect was analyzed using an integrated radius and non-linear regression approach, fitting SUHI data to polynomial curves and identifying turning points based on the regression derivative for UHI intensity belts to quantify the expansion and intensification of UHI. Landscape metrics such as the aggregation index (AI), landscape shape index (LSI), and four other matrices were calculated to assess UHI morphology and connectivity of the UHI. In addition, the LEI was adopted to measure the extent of UHI growth patterns. From 1990 to 2020, the study area experienced significant urbanization, with the built-up area increasing from 69.40 to 338.74 km2, an increase of 1.923 to 9.385% of the total area. This expansion included growth in peripheral areas of 129.33 km2, peripheral expansion of 85.40 km2, and infilling of 3.80 km2. At the same time, the UHI effect intensified with an increase in mean LST from 40.55 to 46.73 °C. The spatial extent of the UHI increased, as shown by the increase in areas with an LST above 50 °C from 36.58 km2 in 1990 to 133.52 km2 in 2020. The connectivity of the UHI also increased, as shown by the increase in the AI from 38.91 to 41.30 and the LSI from 56.72 to 93.64, reflecting a more irregular and fragmented urban landscape. In parallel to these urban changes, the area classified as UHI increased significantly, with the peripheral areas expanding from 23.99 km2 in the period 1990-2000 to 80.86 km2 in the period 2000-2020. Peripheral areas also grew significantly from 36.42 to 96.27 km2, contributing to an overall more pronounced and interconnected UHI effect by 2020. This study provides a comprehensive analysis of urban expansion and its thermal impacts. It highlights the need for integrated urban planning that includes strategies to mitigate the UHI effect, such as improving green infrastructure, optimizing land use, and improving urban design to counteract the negative effects of urbanization.

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

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

Microscale models and urban heat island studies: a systematic review

Urban climate analysis usually uses data from weather stations, traverse, or satellite images. However, this methodology also has its limitations, since the series of data for climate monitoring can be scarce. Another option that has been earning attention in recent years is numeric models, which perform simulations in urban climate. Obtaining climate data is extremely important for climatology, as well as for related areas, such as urban planning, which uses this data to know how to best order the territory according to climate conditions and their projections. Our study aimed to carry out a literature review regarding urban heat island analysis methodologies, with emphasis on the use of models. We evaluated over 200 scientific documents and we used 68 in the results of this work, reporting different types of models. The results indicated that most of the works on urban climate use a more traditional methodological approach, with fieldwork, whereas studies with models have been carried out in a specific way, especially in cities in the northern hemisphere. Among the articles evaluated, the majority were published in Elsevier publisher journals, which have a more interdisciplinary approach. The most studied models were ENVI-met, SOLWEIG, PALM-4U, RayMan, and TEB. In this way, this work pointed out, unlike other works of review in urban climate methodologies, the difficulty in obtaining field data, emphasizing their importance, with regard to studies of urban heat islands and urban planning. We also conclude that the progress and development of the state of the art in numerical models are conditioned to scientific investment in the area.

Sensors on the Internet of Things Systems for Urban Disaster Management: A Systematic Literature Review

The occurrence of disasters has the potential to impede the progress of sustainable urban development. For instance, it has the potential to result in significant human casualties and substantial economic repercussions. Sustainable cities, as outlined in the United Nations Sustainable Development Goal 12, prioritize the objective of disaster risk reduction. According to the Gesi Smarter 2030, the Internet of Things (IoT) assumes a pivotal role in the context of smart cities, particularly in domains including smart grids, smart waste management, and smart transportation. IoT has emerged as a crucial facilitator for the management of disasters, contributing to the development of cities that are both resilient and sustainable. This systematic literature analysis seeks to demonstrate the sensors utilized in IoT for the purpose of urban catastrophe management. The review encompasses both the pre-disaster and post-disaster stages, drawing from a total of 72 articles. During each stage, we presented the characteristics of sensors employed in IoT. Additionally, we engaged in a discourse regarding the various communication technologies and protocols that can be utilized for the purpose of transmitting the data obtained from sensors. Furthermore, we have demonstrated the methodology for analyzing and implementing the data within the application layer of IoT. In conclusion, this study addresses the existing research deficiencies within the literature and presents potential avenues for future exploration in the realm of IoT-enabled urban catastrophe management, drawing upon the findings of the evaluated publications.

A multiscale analysis of heatwaves and urban heat islands in the western U.S. during the summer of 2021

Extreme heat events are occurring more frequently and with greater intensity due to climate change. They result in increased heat stress to populations causing human health impacts and heat-related deaths. The urban environment can also exacerbate heat stress because of man-made materials and increased population density. Here we investigate the extreme heatwaves in the western U.S. during the summer of 2021. We show the atmospheric scale interactions and spatiotemporal dynamics that contribute to increased temperatures across the region for both urban and rural environments. In 2021, daytime maximum temperatures during heat events in eight major cities were 10-20 °C higher than the 10-year average maximum temperature. We discuss the temperature impacts associated with processes across scales: climate or long-term change, the El Niño-Southern Oscillation, synoptic high-pressure systems, mesoscale ocean/lake breezes, and urban climate (i.e., urban heat islands). Our findings demonstrate the importance of scale interactions impacting extreme heat and the need for holistic approaches in heat mitigation strategies.

Applied winter biology: threats, conservation and management of biological resources during winter in cold climate regions

Winter at high latitudes is characterized by low temperatures, dampened light levels and short photoperiods which shape ecological and evolutionary outcomes from cells to populations to ecosystems. Advances in our understanding of winter biological processes (spanning physiology, behaviour and ecology) highlight that biodiversity threats (e.g. climate change driven shifts in reproductive windows) may interact with winter conditions, leading to greater ecological impacts. As such, conservation and management strategies that consider winter processes and their consequences on biological mechanisms may lead to greater resilience of high altitude and latitude ecosystems. Here, we use well-established threat and action taxonomies produced by the International Union of Conservation of Nature-Conservation Measures Partnership (IUCN-CMP) to synthesize current threats to biota that emerge during, or as the result of, winter processes then discuss targeted management approaches for winter-based conservation. We demonstrate the importance of considering winter when identifying threats to biodiversity and deciding on appropriate management strategies across species and ecosystems. We confirm our expectation that threats are prevalent during the winter and are especially important considering the physiologically challenging conditions that winter presents. Moreover, our findings emphasize that climate change and winter-related constraints on organisms will intersect with other stressors to potentially magnify threats and further complicate management. Though conservation and management practices are less commonly considered during the winter season, we identified several potential or already realized applications relevant to winter that could be beneficial. Many of the examples are quite recent, suggesting a potential turning point for applied winter biology. This growing body of literature is promising but we submit that more research is needed to identify and address threats to wintering biota for targeted and proactive conservation. We suggest that management decisions consider the importance of winter and incorporate winter specific strategies for holistic and mechanistic conservation and resource management.

Impact assessment of urbanization on vegetation net primary productivity: A case study of the core development area in central plains urban agglomeration, China

Rapid urbanization process has a negative or positive impact on vegetation growth. Net primary productivity (NPP) is an effective indicator to characterize vegetation growth status. Taking the core development area of the Central Plains urban agglomeration as the study area, we estimated the NPP and its change trend in the past four decades using the Carnegie-Ames-Stanford Approach (CASA) model and statistical analysis based on meteorological and multi-source remote sensing data. Meanwhile, combined with the urbanization impact framework, we further analyzed urbanization's direct and indirect impact on NPP. The results showed that the urban area increased by 2688 km² during a high-speed urbanization process from 1983 to 2019. As a result of the intense urbanization process, a continuous NPP decrease (direct impact) can be seen, which aggravated along with the acceleration of the urban expansion, and the mean value of direct impact was 130.84 g C·m^-2·a^-1. Meanwhile, urbanization also had a positive impact on NPP (indirect impact). The indirect impact showed an increasing trend during urbanization with a mean value of 10.91 g C·m^-2·a^-1. The indirect impact was mainly related to temperature in climatic factors. The indirect impact has a seasonal heterogeneity, and high-temperature environments of urban areas are more effective in promoting vegetation growth in autumn and winter than in summer. Among different cities, high-speed development cities have higher indirect impact values than medium's and low's because of better ecological construction. This study is of great significance for understanding the impact of urbanization on vegetation growth in the Central Plains urban agglomeration area, supporting urban greening plans, and building sustainable and resilient urban agglomerations.

A series of spatio-temporal analyses and predicting modeling of land use and land cover changes using an integrated Markov chain and cellular automata models

For sustainable land cover planning, spatial land cover models are essential. Deforestation, loss of agriculture, and conversion of pasture land to urban and industrial uses are only some of the negative consequences of human kind's insatiable need for more land. Using remote sensing multi-temporal data, spatial criteria, and prediction models can effectively monitor these changes and plan for sustainable land use. This research aims to predict the land use and land cover (LULC) with cellular automata (CA) and Markov chain models. Landsat TM, ETM + , and OLI/TIRS data were used for mapping LULC distributions for the years 1990, 2006, and 2022. A CA-Markov chain was developed for simulating long-term landscape changes at 16-year time steps from 2022 to 2054. Analysis of urban sprawl was carried out by using the support vector machine (SVM). Through the CA-Markov chain analysis, we expect that built-up area will grow from 285.68 km² (22.59%) to 383.54 km² (30.34%) in 2022 and 2054, as inferred from the changes that occurred from 1990 to 2022. Therefore, substantial deforestation area reduction will result if existing tendencies in change continue despite sustainable development efforts. The findings of this research can inform land cover management strategies and assist local authorities in preparing for the present and the future. They can balance expanding the city and preserving its natural resources.

Understanding the Relationship between Urban Biophysical Composition and Land Surface Temperature in a Hot Desert Megacity (Saudi Arabia)

The deteriorations of the thermal environment due to extreme land surface temperature (LST) has become one of the most serious environmental problems in urban areas. The spatial distribution of urban biophysical composition (UBC) has a significant impact on the LST. Therefore, it is essential to understand the relationship between LST and biophysical physical composition (BPC) to mitigate the effects of UHIs. In this study, an attempt was made to understand the relationship between LST and BPC in a hot desert coastal megacity (Jeddah megacity) in Saudi Arabia. Principal component analysis (PCA) was used to understand the factors affecting LST based on remote sensing indices. Correlation and regression analyses were carried out to understand the relationship between LST and BPC and the impact of BPC on LST. The results showed that, in Jeddah city from 2000 to 2021, there was a substantial increase in the built-up area, which increased from 3085 to 5557.98 hectares. Impervious surfaces had a significant impact on the LST, and green infrastructure (GI) was negatively correlated with LST. Based on the PCA results, we found that the GI was a significant factor affecting the LST in Jeddah megacity. The findings of this study, though not contributing to further understanding of the impact of BPC on LST, will provide planners and policy makers with a foundation for developing very effective strategies to improve the eco-environmental quality of Jeddah megacity.

Dynamics of green spaces- Land surface temperature intensity nexus in cities of Ethiopia

In this study, the dynamics of green spaces and land surface temperature patterns in four cities in Ethiopia were investigated using Landsat imagery. The typical characteristics of LST over the past three decades (1990-2020) in relation to green space dynamics were first investigated; subsequently, the spatial distribution of LST was characterized based on hybrid geospatial techniques and mono-window algorithm analysis, in which the contributions of green spaces to LST were studied. In addition, the multiple linear regression method and spatial regression models (SRMs) were employed to investigate and predict the spatial dependence of LST and urbanization-induced green space dynamics. Results show that cities horizontally expanded unceasingly from 1990 to 2020, with a substantial discrepancy in expansion rates and the spatial patterns of UHI intensities among the cities (p < 0.05). Moreover, the area proportion of the UHI is significantly larger than that of the UGS, and the differences in the UGS cooling contribution were found in different land uses and zones of the cities. In the study periods, the spatial pattern of LST was significantly controlled by NDBI, and its coefficient in the OLS followed the pattern NDVI > MNDWI > latitudes > longitudes > population density > DEM. Due to the large proportions of buildings While green land and water bodies show significant capability to mitigate UHI effects, cooling effects are not apparent when their sizes are small. Besides, the SRMs show that UHI intensities were significantly influenced by MNDWI in Bahir Dar and Hawassa (p < 0.01).Cities' LAMBDA coefficients have a positive relationship with UHII (p < 0.01). Our study could help city planners and the government understand the current cooling potential of existing UGS to mitigate the dynamics of UHI and sustain the sustainability of green space management in cities.

Mapping Local Climate Zones in the Urban Environment: The Optimal Combination of Data Source and Classifier

The novel concept of local climate zones (LCZs) provides a consistent classification framework for studies of the urban thermal environment. However, the development of urban climate science is severely hampered by the lack of high-resolution data to map LCZs. Using Gaofen-6 and Sentinel-1/2 as data sources, this study designed four schemes using convolutional neural network (CNN) and random forest (RF) classifiers, respectively, to demonstrate the potential of high-resolution images in LCZ mapping and evaluate the optimal combination of different data sources and classifiers. The results showed that the combination of GF-6 and CNN (S3) was considered the best LCZ classification scheme for urban areas, with OA and kappa coefficients of 85.9% and 0.842, respectively. The accuracy of urban building categories is above 80%, and the F1 score for each category is the highest, except for LCZ1 and LCZ5, where there is a small amount of confusion. The Sentinel-1/2-based RF classifier (S2) was second only to S3 and superior to the combination of GF-6 and random forest (S1), with OA and kappa coefficients of 64.4% and 0.612, respectively. The Sentinel-1/2 and CNN (S4) combination has the worst classification result, with an OA of only 39.9%. The LCZ classification map based on S3 shows that the urban building categories in Xi'an are mainly distributed within the second ring, while heavy industrial buildings have started to appear in the third ring. The urban periphery is mainly vegetated and bare land. In conclusion, CNN has the best application effect in the LCZ mapping task of high-resolution remote sensing images. In contrast, the random forest algorithm has better robustness in the band-abundant Sentinel data.

Revealing Impacts of Trees on Modeling Microclimate Behavior in Spaces between Buildings through Simulation Monitoring

Urban trees have been recognized as having different impacts on the microclimate and thermal comfort. Therefore, this study conducted on-site measurement and simulation to explore and clarify how trees impact the microclimate, thermal comfort, and façade temperature. A campus site was selected as the test field and two models—one with and one without trees—were built with the ENVI-met. Meanwhile, one microclimate station and four sensors were installed to simulate and validate the microclimate. Twelve blocks with different tree conditions were also selected to further investigate the specific impacts of trees. The results showed that, firstly, the transpiration and sheltering effect of trees that dominates on sunny days can decrease air temperature and the predicted mean vote. Secondly, trees’ effects on airflow, including on the wind channel and blocking effect, are dominant on cloudy days. Trees inside the group often exhibit the wind-blocking effect, while trees with a downwind determinant at the windward group edge usually exhibit the wind channel effect. Thirdly, high canopy coverage enhances trees’ sheltering effect on solar radiation. The study also provides design recommendations for campus building and trees that account for how trees help improve the microclimate, enhance comfort, and reduce energy consumption.

Supporting Design to Develop Rural Revitalization through Investigating Village Microclimate Environments: A Case Study of Typical Villages in Northwest China

China has the largest number of villages in the world, and research on rural microclimate will contribute to global climate knowledge. A three-by-three grid method was developed to explore village microclimates through field measurement and ENVI-met simulation. A regression model was used to explore the mechanistic relationship between microclimate and spatial morphology, and predicted mean vote (PMV) was selected to evaluate outdoor thermal comfort. The results showed that ENVI-met was able to evaluate village microclimate, as Pearson’s correlation coefficient was greater than 0.8 and mean absolute percentage error (MAPE) was from 2.16% to 3.79%. Moreover, the air temperature of west–east road was slightly higher than that of south–north, especially in the morning. The height-to-width ratio (H/W) was the most significant factor to affect air temperature compared to percentage of building coverage (PBC) and wind speed. In addition, H/W and air temperature had a relatively strong negative correlation when H/W was between 0.52 and 0.93. PMV indicated that the downwind edge area of prevailing wind in villages was relatively comfortable. This study provides data support and a reference for optimizing village land use, mediating the living environment, and promoting rural revitalization.

Effects of extreme temperature on the risk of preterm birth in China: A population-based multi-center cohort study

BACKGROUND

Extreme temperatures are associated with the risk of preterm birth (PTB), but evidence on the effects of different clinical subtypes and across different regions is limited. We aimed to evaluate the effects of maternal exposure to extreme temperature on PTB and its clinical subtypes in China, and to identify effect modification of regional factors in dimensions of population, economy, medical resources and environmental factors.

METHODS

This was a prospective population-based cohort of 210,798 singleton live births from 16 counties in eight provinces across China during 2014-2018. We used an extended Cox regression with time-varying variables to evaluate the effects of extreme heat and cold on PTB and its subtypes in the entire pregnancy, each trimester, the last gestational month and week. Meta-analysis and meta-regression were conducted to estimate the pooled effects of each city and effect modification by regional characteristics.

FINDINGS

Exposure to heat and cold during the entire pregnancy significantly increased the risk of PTB. The effects varied with subtypes, for medically indicated and spontaneous PTB, hazard ratios were 1·84 (95% CI: 1·29, 2·61) and 1·50 (95% CI: 1·11, 2·02) for heat, 2·18 (95% CI: 1·83, 2·60) and 2·15 (95% CI: 1·92, 2·41) for cold. The associations were stronger for PTB less than 35 weeks than those during weeks 35-36. The effects varied across locations, and GDP per capita (β=-0·16) and hospital beds per 1000 persons (β=-0·25) were protective factors for the effects.

INTERPRETATION

Extreme temperature can increase the risk of medically indicated and spontaneous PTB, and higher regional socio-economic status may moderate such effects. In the context of climate change, such findings may have important implications for protecting the health of vulnerable groups, especially newborns.

FUNDING

National Key R&D Program of China (2018YFA0606200), National Natural Science Foundation of China (42175183), Strategic Priority Research Program of the Chinese Academy of Sciences (XDA20030302), National Natural Science Foundation of China (42071377).

Impact of COVID-19 Lockdown on Vegetation Indices and Heat Island Effect: A Remote Sensing Study of Dhaka City, Bangladesh

It is predicted that the COVID-19 lockdown decreased environmental pollutants and, hence, urban heat island. Using the hypothesis as a guide, the objective of this research is to observe the change in vegetation pattern and heat-island effect zones in Dhaka, Bangladesh, before and after COVID-19 lockdown in relation to different forms of land use and land cover. Landsat-8 images were gathered to determine the vegetation pattern and the heat island zones. The normalized difference vegetation index (NDVI) and the modified soil-adjusted vegetation index (MSAVI12) were derived for analyzing the vegetation pattern. According to the results of the NDVI, after one month of lockdown, the health of the vegetation improved. In the context of the MSAVI12, the highest MSAVI12 coverages in March of 2019, 2020, and 2021 (0.45 to 0.70) were 22.15%, 21.8%, and 20.4%, respectively. In May 2019, 2020, and 2021, dense MSAVI12 values accounted for 23.8%, 25.5%, and 18.4%, respectively. At the beginning of lockdown, the calculated LST for March 2020 was higher than March 2019 and March 2021. However, after more than a month of lockdown, the LST reduced (in May 2020). After the lockdown in May 2020, the highest UHI values ranging from 3.80 to 5.00 covered smaller land-cover regions and reduced from 22.5% to 19.13%. After the end of the lockdown period, however, industries, markets, and transportation resumed, resulting in the expansion of heat island zones. In conclusion, strong negative correlations were observed between the LST and vegetation indices. The methodology of this research has potential for scholarly and practical implications. Secondly, urban policymakers can use the methodology of this paper for the low-cost monitoring of urban heat island zones, and thus take appropriate spatial counter measures.

Utilization of BIM in Steel Building Projects: A Systematic Literature Review

This research aims to bridge the information gap pertaining to the utilization of building information modeling (BIM) in steel building projects. Therefore, a systematic literature review (SLR) was conducted to synthesize the available uses. This research involved three phases—planning, execution, and reporting—according to the PRISMA guide, which includes the main aspects of identification, screening, and eligibility. As a result of the SLR, it is evident how and where BIM facilitates steel building projects, which were grouped into three different categories according to their main BIM topics. One of the uses that stands out as a common denominator across the different processes is “early integration”. Early integration allows for optimization of the design based on existing resources, directly affecting the cost and time of steel building projects in a positive manner.

Influences of landform and urban form factors on urban heat island: Comparative case study between Chengdu and Chongqing

Although the degraded urban thermal environment has been widely concerned, whether monocentric or polycentric urban development can alleviate urban heat island (UHI) is still debatable, particularly considering different types of cities in plain and mountainous settings. To fill the gap, this study chose the cases of Chengdu on alluvial flatlands and Chongqing on mountainous landforms in China for comparison. Adapting to natural landforms, Chengdu has a dominated monocentric urban form, whereas Chongqing has a naturally polycentric urban form. This study found that areas with high UHI intensity were located in the urban center and peripheral suburbs in Chengdu, while those were located in the peripheral industrial zones in Chongqing. The spatial error model and random forest regression results showed that natural elements such as vegetation and water had similar influences and contributions to UHI. However, distinct landforms and urban forms played critical roles in UHI differences among the two cities. Impervious surface and building density were the dominant variables to UHI of Chengdu, while sky view factor and industrial zones were the main contributors to UHI of Chongqing. The findings called for corresponding policy strategies to optimize urban form and improve the urban thermal environment.

Temperature Accuracy Analysis by Land Cover According to the Angle of the Thermal Infrared Imaging Camera for Unmanned Aerial Vehicles

Land surface temperature (LST) is one of the crucial factors that is important in various fields, including the study of climate change and the urban heat island (UHI) phenomenon. The existing LST was acquired using satellite imagery, but with the development of unmanned aerial vehicles (UAV) and thermal infrared (TIR) cameras, it has become possible to acquire LST with a spatial resolution of cm. The accuracy evaluation of the existing TIR camera for UAV was conducted by shooting vertically. However, in the case of a TIR camera, the temperature value may change because the emissivity varies depending on the viewing angle. Therefore, it is necessary to evaluate the accuracy of the TIR camera according to each angle. In this study, images were simultaneously acquired at 2–min intervals for each of the three research sites by TIR camera angles (70°, 80°, 90°). Then, the temperature difference by land cover was evaluated with respect to the LST obtained by laser thermometer and the LST obtained using UAV and TIR. As a result, the image taken at 80° showed the smallest difference compared with the value obtained with a laser thermometer, and the 70° image showed a large difference of 1–6 °C. In addition, in the case of the impervious surface, there was a large temperature difference by angle, and in the case of the water-permeable surface, there was no temperature difference by angle. Through this, 80° is best when acquiring TIR data, and if it is impossible to take images at 80°, it is considered good to acquire TIR images between 80° and 90°. To obtain more accurate LST, correction studies considering the external environment, camera attitude, and shooting height are needed in future studies.

Architectural Simulations on Spatio-Temporal Changes of Settlement Outdoor Thermal Environment in Guanzhong Area, China

This paper aims to provide data support for rural sustainable development through analyzing the spatio-temporal characteristics of the interactions of the outdoor thermal environment. The ordinary and representative rural settlements in the Guanzhong area were selected to analyze the dynamic process of the rural thermal environment through field measurements and numerical simulations. RMSE (root mean square error) and MAPE (mean absolute percentage) were used to verify the numerical simulation model, and physiological equivalent temperature (PET) was used to evaluate the outdoor thermal environment. Results show that the ENVI-met model reliably predicts the thermal environment of a rural settlement, as the air temperature and relative humidity values range of the RMSE and MAPE were 0.85–1.79 and 2.04–5.11%, respectively. Moreover, the air temperature rose by 3.08% and relative humidity dropped by 4.42% from 2003 to 2018 as the amount of artificial surfaces increased by 35.4% and the PET index gradually increased by 27.43% at daytime and 34.03% at nighttime. Furthermore, trees could improve the outdoor thermal environment significantly, mainly because the average air temperature decreased by 3.6% and relative humidity increased by 8%, and the PET index decreased by 12.4% and 13.1%, respectively, for daytime and nighttime. This case study is representative of rural settlements in the Guanzhong plain, and thus is an appeal to rural planners to pay attention to the thermal environment issues caused by increased artificial underlay surfaces and to focus on trees in rural areas.

Pedestrians' behavior based on outdoor thermal comfort and micro-scale thermal environments, Austin, TX

Cities have faced rapid urbanization, which has changed the impact of the micro-thermal environment on residents' thermal comfort level. Therefore, planners need to understand the city's physical environment so they can identify and ameliorate the effects of the changing in micro-thermal environment. Researchers also need to identify and understand pedestrians' thermal comfort level in street canyons to determine which urban physical factors planners need to improve. This study aimed to observe how thermal comfort affects pedestrian behavior in micro-thermal environments and to determine which urban geometry factors influence pedestrians' thermal comfort. This study collected data in downtown Austin, TX using a mobile weather station, and analyzed the microclimate conditions experienced by pedestrians. A camera mounted on the weather station also allowed us to observe pedestrian behavior patterns. The results revealed that pedestrians tended to choose walking, sitting, and standing locations with high thermal comfort levels such as in the shade on the sidewalk. There was also some correlation between thermal comfort levels and pedestrian behavior patterns. The sky view factor (SVF) and tree canopy coverage ratio (TCR) were also correlated with pedestrians' thermal comfort. This study highlights the need for future research to develop a data collection method for efficient microscopic thermal environment research and a thermal environment estimation and analysis approach from a three-dimensional perspective.

Reclamation of Treated Wastewater for Irrigation in Chile: Perspectives of the Current State and Challenges

Reclamation of treated wastewater is considered a viable option for reducing the agricultural and national water deficit, especially in Mediterranean-type and arid climatic conditions. Given that Chile is a country around 40% of whose territory is classified as semi-arid and desert and 20% as Mediterranean, with serious water scarcity problems, and which uses a great deal of the resource in agricultural irrigation, the present paper offers perspectives on the current state of treated wastewater reuse and considers challenges to improving the development of water reclamation for irrigation in Chile as a case study. The methods followed included a systematic literature review to answer two important questions: (a) What is the state of reclamation of treated wastewater for irrigation in Chile? and (b) What criteria/parameters determine the feasibility of reclaiming treated wastewater for irrigation in Chile? The results showed that Chile has been affected by climate change in a short time: a megadrought has occurred over the last ten years, increasing the necessity for the country to secure alternative water sources for irrigation. The country has advanced greatly in wastewater treatment coverage, achieving almost 100% in urban areas, with technologies that can produce quality water as a new water source for irrigation. However, the lack of regulations and limited frameworks could explain the low direct reuse at present—below 1% of total flow. Regarding challenges, the necessity of updates to Chile’s institutional and legal frameworks, besides the inclusion of rural communities and the study of emerging contaminants, will be discussed. By these means, it will be possible to more efficiently utilize recycled wastewater as a new source for irrigation in this country.

Impact of Urbanization on Urban Heat Island Intensity in Major Districts of Bangladesh Using Remote Sensing and Geo-Spatial Tools

Urbanization is closely associated with land use land cover (LULC) changes that correspond to land surface temperature (LST) variation and urban heat island (UHI) intensity. Major districts of Bangladesh have a large population base and commonly lack the resources to manage fast urbanization effects, so any rise in urban temperature influences the population both directly and indirectly. However, little is known about the impact of rapid urbanization on UHI intensity variations during the winter dry period in the major districts of Bangladesh. To this end, we aim to quantify spatiotemporal associations of UHI intensity during the winter period between 2000 and 2019 using remote-sensing and geo-spatial tools. Landsat-8 and Landsat-5 imageries of these major districts during the dry winter period from 2000 to 2020 were used for this purpose, with overall precision varying from 81% to 93%. The results of LULC classification and LST estimation showed the existence of multiple UHIs in all major districts, which showed upward trends, except for the Rajshahi and Rangpur districts. A substantial increase in urban expansion was observed in Barisal > 32%, Mymensingh > 18%, Dhaka > 17%, Chattogram > 14%, and Rangpur > 13%, while a significant decrease in built-up areas was noticed in Sylhet < −1.45% and Rajshahi < −3.72%. We found that large districts have greater UHIs than small districts. High UHI intensities were observed in Mymensingh > 10 °C, Chattogram > 9 °C, and Barisal > 8 °C compared to other districts due to dense population and unplanned urbanization. We identified higher LST (hotspots) zones in all districts to be increased with the urban expansion and bare land. The suburbanized strategy should prioritize the restraint of the high intensity of UHIs. A heterogeneous increase in UHI intensity over all seven districts was found, which might have potential implications for regional climate change. Our study findings will enable policymakers to reduce UHI and the climate change effect in the concerned districts.

Quantitative Analysis of a Spatial Distribution and Driving Factors of the Urban Heat Island Effect: A Case Study of Fuzhou Central Area, China

Land surface temperature (LST) is a joint product of physical geography and socio-economics. It is important to clarify the spatial heterogeneity and binding factors of the LST for mitigating the surface heat island effect (SUHI). In this study, the spatial pattern of UHI in Fuzhou central area, China, was elucidated by Moran’s I and hot-spot analysis. In addition, the study divided the drivers into two categories, including physical geographic factors (soil wetness, soil brightness, normalized difference vegetation index (NDVI) and modified normalized difference water index (MNDWI), water density, and vegetation density) and socio-economic factors (normalized difference built-up index (NDBI), population density, road density, nighttime light, park density). The influence analysis of single factor on LST and the factor interaction analysis were conducted via Geodetector software. The results indicated that the LST presented a gradient layer structure with high temperature in the southeast and low temperature in the northwest, which had a significant spatial association with industry zones. Especially, LST was spatially repulsive to urban green space and water body. Furthermore, the four factors with the greatest influence (q-Value) on LST were soil moisture (influence = 0.792) > NDBI (influence = 0.732) > MNDWI (influence = 0.618) > NDVI (influence = 0.604). The superposition explanation degree (influence (Xi ∩ Xj)) is stronger than the independent explanation degree (influence (Xi)). The highest and the lowest interaction existed in ”soil wetness ∩ MNDWI” (influence = 0.864) and “nighttime light ∩ population density” (influence = 0.273), respectively. The spatial distribution of SUHI and its driving mechanism were also demonstrated, providing theoretical guidance for urban planners to build thermal environment friendly cities.

The Influence of Sky View Factor on Daytime and Nighttime Urban Land Surface Temperature in Different Spatial-Temporal Scales: A Case Study of Beijing

Urban building morphology has a significant impact on the urban thermal environment (UTE). The sky view factor (SVF) is an important structure index of buildings and combines height and density attributes. These factors have impact on the land surface temperature (LST). Thus, it is crucial to analyze the relationship between SVF and LST in different spatial-temporal scales. Therefore, we tried to use a building vector database to calculate the SVF, and we used remote sensing thermal infrared band to retrieve LST. Then, we analyzed the influence between SVF and LST in different spatial and temporal scales, and we analyzed the seasonal variation, day–night variation, and the impact of building height and density of the SVF–LST relationship. We selected the core built-up area of Beijing as the study area and analyzed the SVF–LST relationship in four periods in 2018. The temporal experimental results indicated that LST is higher in the obscured areas than in the open areas at nighttime. In winter, the maximum mean LST is in the open areas. The spatial experimental results indicate that the SVF and LST relationship is different in the low SVF region, with 30 m and 90 m pixel scale in the daytime. This may be the shadow cooling effect around the buildings. In addition, we discussed the effects of building height and shading on the SVF–LST relationship, and the experimental results show that the average shading ratio is the largest at 0.38 in the mid-rise building area in winter.

Urban Heat Island and Its Interaction with Heatwaves: A Review of Studies on Mesoscale

With rapid urbanization, population growth and anthropogenic activities, an increasing number of major cities across the globe are facing severe urban heat islands (UHI). UHI can cause complex impacts on the urban environment and human health, and it may bring more severe effects under heatwave (HW) conditions. In this paper, a holistic review is conducted to articulate the findings of the synergies between UHI and HW and corresponding mitigation measures proposed by the research community. It is worth pointing out that most studies show that urban areas are more vulnerable than rural areas during HWs, but the opposite is also observed in some studies. Changes in urban energy budget and major drivers are discussed and compared to explain such discrepancies. Recent studies also indicate that increasing albedo, vegetation fraction and irrigation can lower the urban temperature during HWs. Research gaps in this topic necessitate more studies concerning vulnerable cities in developing countries. Moreover, multidisciplinary studies considering factors such as UHI, HW, human comfort, pollution dispersion and the efficacy of mitigation measures should be conducted to provide more accurate and explicit guidance to urban planners and policymakers.

Bias assessments to expand research harnessing biological collections

Biological collections are arguably the most important resources for investigations into the impacts of human activities on biodiversity. However, the apparent opportunities presented by museum-derived datasets have not resulted in consistent or widespread use of specimens in ecology outside phenological research and species distribution modeling. We attribute this gap between opportunity and application to biases introduced by collectors, curators, and preservation practices and an imperfect understanding of these biases and how to mitigate them. To facilitate broader use of specimen-based data, we characterize collection biases across key axes and explore interactions among them. We then present a framework for determining the bias assessments needed when extracting data from biological collections. We show that bias assessments required by particular ecological studies will depend on the response variables being measured and the predictor axes of interest. We argue that quantification of biases in specimen-derived datasets is needed to facilitate the widespread application of these data.

Urban Heat Island Effects on Megacities in Desert Environments Using Spatial Network Analysis and Remote Sensing Data: A Case Study from Western Saudi Arabia

Contemporary cities continue to face significant geoenvironmental challenges due to constant rapid urbanization. Furthermore, the governments of cities worldwide are considering the green cities approach to convert their cities’ weaknesses into opportunities. The 2030 Saudi vision supports smart growth concepts, with a vision of speeding up economic growth while ensuring that natural assets strengthen the country’s foundations. The urban heat island (UHI) effect is a threatening phenomenon that increases the required cooling loads and negatively affects urban communities and the quality of life, especially in arid environments. This study integrates remote sensing and spatial network analysis to investigate the UHI using the distribution of land surface temperatures (LST) extracted from satellite data during both winter and summer seasons in Makkah city. We investigated and compared the UHIs in two districts, Al-Sharashef and AlEskan, representing the organic and deformed iron-grid with fragmented paralleled street networks, respectively. The spatial analysis of different LST maps, which were derived from Landsat-8 images revealed significant differences between the two case studies. The mean temperature for the AlEskan district was 1–1.5 °C higher than that of the Al-Sharshaf district. This difference can be attributed to the different urban fabrics between the two districts. Moreover, the zones that are currently under construction show relatively higher LST compared to residential zones. The research revealed that the organic/compact urban fabric is better than the deformed iron-grid urban fabric in mitigating the UHI. However, these results are specific to the test site; however, they emphasize the role of integration of remote sensing and spatial network analysis in urban planning. In light of these findings, we recommend integrating remote sensing-based LST analysis with spatial analysis of urban fabrics to better understand the causal effects of UHI, especially in cities located in desert environments. This can help mitigate the impact of projected global warming and contribute to improving the quality of urban life.