Heat Island Intensity Research Articles

Regional thermal environments (RTEs) and driving forces in six urban agglomerations of China and America

China and America have several large urban agglomerations that are important for global urbanisation, but both face serious regional thermal environment (RTE) problems. This study compares the RTE of six urban agglomerations in China and America in summer in terms of the thermal landscape and surface urban heat island intensity (SUHII) distributions using Terra MODIS land surface temperature (LST) data and analyses the impacts of land development intensity (LDI), the normalised difference vegetation index (NDVI), precipitation, wind speed, and surface albedo on the RTE. The results show that the RTE problem of aggregated high or sub-high LST zones in large areas across city boundaries is widespread in Chinese urban agglomerations. However, the RTE problem of high SUHII values is more obvious in American urban agglomerations. The LDI and LST have a strong spatial correlation that shows a clear upward trend in Chinese urban agglomerations. The △LDI and △NDVI have significant impacts on the SUHII of urban agglomerations in China and America. The impacts of precipitation, wind speed, and albedo on the SUHII vary greatly between China and America. By further examining the spatial structure of the LDI, gross domestic product (GDP), and population in urban agglomerations, this study suggests that constructing a decentralised, multicentre network spatial structure in urban agglomerations, improving urban vegetation coverage and ventilation, and selecting appropriate building materials may be important paths to improving the RTE and promoting the sustainable development of urban agglomerations.

Spatiotemporal patterns of the impact of surface roughness and morphology on urban heat island

As important determinants of urban thermal environment, surface roughness and morphology have been extensively studied for sustainable urban development. In this study, we quantify the effect of urban roughness and morphology on the surface urban heat island (SUHI) intensity and its spatiotemporal patterns, over seventeen major cities in six urban agglomerations of China. We employ multisource dataset and derive multiple measures, representative of the roughness and horizontal/vertical indicators of urban morphology. The results show that the correlation between the SUHI intensity and urban morphological indices is significantly strengthened with the heat island intensity, manifested by the contrasting Pearson's r in summer (r = 0.59 ± 0.13) and winter (0.11 ± 0.35). In general, the impact assessed using different measures of surface morphology is consistent on the SUHI intensity, while the one-dimensional (1D) roughness emerges as an adequate index not inferior to more complex morphological parameters. Our study also shows that the impact of urban morphology varies in different geographic and climatic regions, as well as with different urban management, which highlights the importance of locality and site-specific design in implementing effective urban heat mitigation strategies.

Influence of Meteorological Parameters on the Urban Heat Island in Moscow

The urban heat island (UHI) intensity in Moscow and the influence of various meteorological parameters are discussed using weather station data. The maximal and average in-space UHI intensities, i.e., a comparison of air temperature T either in the city centre or in the whole urban area together with rural zone have averaged 1.9 and 0.9 °C, respectively, in recent years. The UHI in Moscow has stabilized over the past decade and is not growing. Under conditions of a strong anticyclone, the maximal UHI intensity in space and time reaches 11–12 °C. Low cloudiness and amplitudes of diurnal air temperature, as well as surface temperature, demonstrate the closest relationship with the UHI intensity among other parameters with the correlation coefficient of up to −0.67 for low cloudiness and the maximal UHI intensity. The effect of wind speed, total cloudiness and relative humidity on the UHI is slightly weaker, but still significant. The relationships of all meteorological parameters with the maximal UHI intensity are closer than those with the average one. The multiple correlation coefficient between the maximal UHI intensity and both parameters (low cloudiness and average daily wind speed) is 0.76–0.82. The UHI intensity function of air temperature has a minimum in the range from −4 to 0 °C; its growth both at lower and higher T is due to the influence of anticyclonic weather. The UHI intensity function of wind speed decreases with wind strength. The threshold value at which this function asymptotically approaches its lower limit is 10 m/s in the 40–200 m air layer. The UHI intensity functions of both total and low cloudiness decrease with increasing cloudiness and the differences between them are significant if the cloud cover is more than 50%.

NIGHTTIME LIGHT: A POTENTIAL PROXY FOR LOCAL NOCTURNAL URBAN HEAT ISLAND INTENSITY IN SEOUL

ABSTRACT Nighttime light (NTL) imagery provides valuable information for monitoring urban development, human activity, and, consequently, urban thermal environments. However, only a few studies have attempted to directly draw linkages between NTL and urban heat island (UHI) formation. Establishing associations between NTL and UHI indicators could potentially ease the process of identifying heightened thermal environments and thus pinpointing urban hotspots in need of policy interventions and strategic mitigation measures. In the present study, through clustering and correlation analysis, we study ties between NTL, surface urban heat island intensity (SUHII) and atmospheric urban heat island intensity (AUHII) in Seoul. NTL data was derived from the Suomi National Polar-Orbiting Partnership-Visible and Infrared imager/Radiometer Suite (SNPP-VIIRS), while SUHII data was obtained from the moderate resolution imaging spectroradiometer (MODIS) satellite instrument. Additionally, AUHII was computed from archived weather data collected from multiple automatic weather stations (AWSs) in Seoul. Our results show that NTL is highly positively correlated with SUHII (R > 0.86, p < 0.05) and AUHII (R > 0.68, p < 0.05). The findings highlight the potential usage of NTL as a proxy for UHI formation, simplifying the process of identifying heightened thermal areas in Seoul that may require rapid policy intervention.

Multi-scale climate-sensitive planning framework to mitigate urban heat island effect: A case study in Singapore

This study developed a climate-sensitive planning framework to assess Urban Heat Island (UHI) intensity and wind potential, and apply assessment results into urban planning practice. Multi-scale environmental assessments and mitigation were interlinked from the urban design perspective. Three steps are conducted: (1) UHI intensity assessment to identify hot spots, (2) urban wind assessment to evaluate mitigation potential, and (3) urban design implications. A case study in Singapore was conducted to demonstrate the application of the framework. Geographic Information System-based tools that are accessible to designers and planners were used to conduct a coherent assessment-mitigation planning workflow. The empirical model between sky view factor and air temperature increment was modified by local data to be applied in Singapore. East Singapore, with a UHI intensity of 3 °C to 4 °C, was identified as the hot spot of UHI. Natural ventilation potential of the identified area was evaluated, and air paths were identified through urban permeability map based on frontal area density calculation. Climate-sensitive urban design prototypes were thus proposed by balancing climate considerations and urban planning complex (i.e., site structure, density distribution, land use pattern). Research outputs from this study increase the engagement of climate considerations in the decision-making of urban design process.

Climate Change Trends for the Urban Heat Island Intensities in Two Major Portuguese Cities

Urban Heat Island (UHI) intensities are analyzed for the metropolitan areas of the two major Portuguese cities, Lisbon and Porto, in the period 2008–2017. Projections for the UHI intensity averaged over 2008–2017 and a future period 2021–2050 are calculated under the Representative Concentration Pathway (RCP) 8.5. The spatiotemporal characteristics of the UHI intensity are assessed for daytime, nighttime, and average daily conditions. This analysis is carried out for the winter (Dec-Jan-Feb, DJF) and summer (Jun-Jul-Aug, JJA) meteorological seasons. Maximum UHI intensities of about 3.5 °C were reached in 2008–2017 in both metropolitan areas, but over a wider region during winter nighttime than during summer nighttime. Contrariwise, the most intense urban cool island effect reached −1.5 °C/−1 °C in Lisbon/Porto. These UHI intensities were depicted during summer daytime and in less urbanized areas. Overall, the UHI intensities were stronger during the winter than in the summer for both cities. Results show that the UHI intensity is closely related to underlying surfaces, as the strongest intensities are confined around the most urbanized areas in both cities. Until 2050, under RCP8.5, the highest statistically significant trends are projected for summer daytime, of about 0.25 °C (per year) for Lisbon and 0.3 °C (per year) for the UHI 99th percentile intensities in both metropolitan areas. Conversely, the lowest positive statistically significant trends (0.03 °C/0.02 °C per year) are found for the winter daytime UHI intensities in Lisbon and the winter nighttime and average UHI intensities in Porto, respectively. These statistically significant patterns (at a 5% significance level) are in line with the also statistically significant trends of summer mean and maximum temperatures in Portugal, under RCP8.5 until 2050. Scientists, urban planners, and policymakers face a significant challenge, as the contribution of urbanization and the forcing promoted by global warming should be duly understood to project more sustainable, go-green, carbon-neutral, and heat-resilient cities.

A Spatio-Temporal Analysis of Heat Island Intensity Influenced by the Substantial Urban Growth between 1990 and 2020: A Case Study of Al-Ahsa Oasis, Eastern Saudi Arabia

Rapid urbanization has recently led to a significant propagation of heat islands. This study aimed to analyze the spatio-temporal variation in urban heat islands (UHIs) at Al-Ahsa Oasis in Saudi Arabia, in addition to exploring the urbanization influence on UHI distribution over the last 30 years. The spatial variability in UHIs was assessed, the key determinant elements were identified, and the forms of distribution were delineated. Change detection, hot spots, and spatial autocorrelation were employed to study UHI distribution and intensity and to identify the clustering and correspondence between heat and urbanization. The results revealed a considerable increase in built-up areas from 17.15% to 45.8% of total land use/cover (LULC) from 1990 to 2020. No significant variations in UHI intensity were observed (10.4 °C in 1990 and 8.7 °C for 2020). However, a remarkable link was found between urbanization and heat, confirmed by hot spot clustering over intense urban complexes, while cold spot clustering was observed over date and palm tree areas, with 99% confidence for both. Lastly, the link between temperature and urbanization was also confirmed through spatial autocorrelation, producing Moran’s indices of 0.41 and 0.45 for 1990 and 2020, respectively, with an overall significance (p-value) of 0.001. The mechanisms applied have proven their robustness in assessing the effect of urbanization on heat island distribution and quantification.

Efektivitas Pertanian Perkotaan (Urban Farming) dalam Mitigasi Urban Heat Island di Kawasan Perkotaan

Title: The Effectiveness of Urban Farming for Urban Heat Island Mitigation in Urban Areas Urban farming and greenery are the emerging idea in maintaining a sustainable environment in term of economic, social, and environmental aspects. The existence of urban farming is primarily aimed to maintaining food security for the community, especially in the pandemic crisis. Moreover, the high rate of urbanization also affects the transformation of urban space, which is one of the factors causing climate change. The integration of vegetation in urban area is considered to be a strategy to maintain the quality of the microclimate and reducing the intensity of the urban heat island. This study aims to observe the effectiveness of urban farming/greenery in maintaining the microclimate quality at District of Tompeyan Yogyakarta, which is well known for their urban farming integrated inside the residential areas. Using field measurement and simulation methods, the results show that the vegetation in the outdoor space have a significant impact, especially related to surface temperature and air temperature. The simulation using ENVI-MET shows that there is an increase in the thermal comfort index in the areas with vegetation, although it has not yet reached the ideal value. This represents how the role of vegetation in urban heat island mitigation to provide a comfortable and sustainable environment. The study can be further developed regarding the impact of other physical elements on microclimate to be the foundation for spatial planning recommendations.

Two decades of nighttime surface urban heat island intensity analysis over nine major populated cities of India and implications for heat stress

Warmer global climate and urban heat islands (UHIs) interact, by exacerbating heatwaves and increasing the extreme heat days in cities. The implications of added heat stress in urban environments due to intensifying surface UHIs (SUHIs) is of utmost concern. Seasonal, annual and decadal nighttime SUHI intensities (SUHIIs), from 2001 to 2020, for nine major populated cities of India are analyzed. This includes five megacities- Delhi, Mumbai, Kolkata, Bangalore, and Chennai, and four incipient megacities- Hyderabad, Ahmedabad, Surat, and Pune. The key role of increasing urbanization (pre- and post-2010) in expansion and intensification of nighttime SUHIs in India is highlighted. For all cities either pre-monsoon (MAM) or winter (December-February; DJF) seasons show the strongest SUHII development. During the 2001–2010, and the 2011–2020 decade, a nighttime SUHII maxima of respectively (i) 2.1°C and 2.5°C for Delhi, (ii) 1.3°C and 1.5°C for Mumbai, (iii) 1.3°C and 1.5°C for Kolkata, (iv) 0.6°C and 1.0°C Bangalore, (v) 1.7°C and 1.9°C for Chennai, (vi) 1.8°C and 2.3°C for Hyderabad, (vii) 2.8°C and 3.1°C for Ahmedabad, (viii) 1.9°C and 2.4°C for Surat, and (ix) 0.8°C and 1.3°C for Pune is noted. Further, all incipient megacities showed a mean annual growth rate of nighttime SUHII of over 0.007°C/year, substantially greater than in the megacities. High SUHII magnitudes, greater growth rates of SUHII, and huge populations, severely compounds the vulnerability of Indian cities to excessive heat exposure risk, especially during MAM heatwaves. Lastly, the implications of nighttime SUHII findings from the present study, on the increase in heat stress, the loss of labor productivity and the rise in heat-related mortality rate is emphasized. The study recommends implementation of city-specific action plans to mitigate the heat stressed urban environment. Targeted use of cooling strategies in localized hotspots within the urban areas where high intensity SUHIs are likely to form is also suggested.

ASSESSMENT OF SPATIAL AND TEMPORAL DISTRIBUTION OF URBAN HEAT ISLANDS (UHI) IN SEMI-ARID CLIMATE

An urban heat island phenomenon has increased in the last decades due to rapid urbanization, resulting in a significant impact on local climate. In this study, remote sensing data was used to analyze Spatiotemporal patterns of Urban Heat Island Intensity (UHII) over a 20 years period in a semi-arid climate area. The relationship between the Land Surface Temperature (LST), vegetation and Land Cover Types (LCTs) were examined. The relation between the UHII and its driving factors (different LCTs and meteorological conditions) was analyzed. Analysis of 8-day daytime and nighttime LST data acquired from MODerate-resolution Imaging Spectroradiometer (MODIS) shows that Amman has a significant UHII in both daytime and night time. The results show a negative correlation between the LST and vegetation indicators and between the UHII and the wind speed average, indicating a positive correlation between the UHII and temperature. Vegetation has been proven to significantly reduce LST, mainly in the daytime, due to its cooling effect that results from the transpiration process and shadow effect.

Spatial Influence of Multifaceted Environmental States on Habitat Quality: A Case Study of the Three Largest Chinese Urban Agglomerations

Habitat structure and quality in the urban agglomeration (UA) are subject to multiple threats and pressures due to ongoing anthropogenic activities and call for comprehensively effective solutions. Many approaches, including cartographic comparison, correlation analysis, the local entropy model, and GeoDetector, were jointly used to clarify the interplay between habitat quality and multiple environmental issues. In response to the overlapped risks of diverse environmental systems, this study presented an integrated research framework to evaluate the spatial influences of multifaceted environmental situations on habitat quality. We conducted the case study in the three largest Chinese UAs: Beijing–Tianjin–Hebei (BTH), Greater Bay Area (GBA), and Yangtze River Delta (YRD). The evaluation results show that the three UAs shared similarities and differences in relationship/impact types and their strengths. In 2015, most of the three UAs’ landscapes delivered low–medium magnitudes of habitat quality (score <0.7) and emerged with unevenly severe consequences over space across different environmental aspects, highlighting the importance of maintaining habitat safety. Overall, habitat quality scores were synergistic with NDVI, but antagonistic to surface heat island intensity (SHII), PM2.5 concentrations, and residential support. However, locally structured relationships exhibited geographical complexity and heterogeneity between habitat quality and environmental systems. Regarding GeoDetector evaluation, PM2.5 concentrations in BTH, SHII in GBA, and NDVI in YRD played a dominant role in single-factor and interaction analysis. More importantly, the synergistic effect of various environmental issues on habitats was manifested as mutually enhanced rather than independent or weakened interactive effects, implying the aggravation of compound effects and the necessity of prioritization schemes. This study could provide beneficial insights into the interconnections between habitats’ sustainability and multifaceted environmental situations in UAs.

Seasonal Variations of the Relationship between Spectral Indexes and Land Surface Temperature Based on Local Climate Zones: A Study in Three Yangtze River Megacities

Urban heat islands are representative problems in urban environments. The impact of spectral indexes on land-surface temperature (LST) under different urban forms, climates, and functions is not fully understood. Local climate zones (LCZs) are used to characterize heterogeneous cities. In this study, we quantified the contribution of three cities to high-temperature zones and surface urban heat island intensity (SUHII) across LCZs and seasons, used Welch and Games–Howell tests to analyze the difference in LST, then described the spatial pattern characteristics of LST, and used a geographically weighted regression model to analyze the relationship between spectral indexes and LST. The results showed that compact midrise, compact low-rise (LCZ 3), large low-rise (LCZ 8), heavy industry (LCZ 10), and bare rock or paved (LCZ E) contributed greatly to high-temperature zones and had strong SUHII. There were 92–98% significant differences between different LCZs. The spatial aggregation of LST gradually weakened with a decrease in temperature. The modified normalized difference water index (MNDWI) in most LCZs of all seasons for Wuhan could reduce LST well, while MNDWI only had cooling effects in winter for Nanjing and Shanghai. Normalized difference vegetation index (NDVI) in most LCZs performed a cooling role during summer and transition seasons (spring and autumn), while it showed a warming effect in winter. The cooling effect of NDVI in open building types was stronger than that of compact building types, while the cooling effect of MNDWI was better in compact building types than in open building types. With the increase of normalized difference built-up index (NDBI), all LCZs showed warming effects, and the magnitude of LST increase varied in different cities and seasons. These results contribute further insight into thermal environment in heterogeneous urban areas.

The Impact of Urbanization on Urban Heat Island: Predictive Approach Using Google Earth Engine and CA-Markov Modelling (2005-2050) of Tianjin City, China.

Urbanization has adverse environmental effects, such as rising surface temperatures. This study analyzes the relationship between the urban heat island (UHI) intensity and Tianjin city's land cover characteristics. The land use cover change (LUCC) effects on the green areas and the land surface temperature (LST) were also studied. The land cover characteristics were divided into five categories: a built-up area, an agricultural area, a bare area, a forest, and water. The LST was calculated using the thermal bands of spatial images taken from 2005 to 2020. The increase in the built-up area was mainly caused by the agricultural area decreasing by 11.90%. The average land surface temperature of the study area increased from 23.50 to 36.51 °C, and the region moved to a high temperature that the built-up area's temperature increased by 1.5%. Still, the increase in vegetation cover was negative. From 2020 to 2050, the land surface temperature is expected to increase by 9.5 °C. The high-temperature areas moved into an aerial distribution, and the direction of urbanization determined their path. Urban heat island mitigation is best achieved through forests and water, and managers of urban areas should avoid developing bare land since they may suffer from degradation. The increase in the land surface temperature caused by the land cover change proves that the site is becoming more urbanized. The findings of this study provide valuable information on the various aspects of urbanization in Tianjin and other regions. In addition, future research should look into the public health issues associated with rapid urbanization.

Study of the urban heat island intensity using Landsat 8 in Da Nang in 2020

Monitoring spatiotemporal changes in land surface temperature in metropolitan areas is important to obtain the necessary information about environmental conditions and promote sustainable cities. Da Nang is a big city and is considered one of the tourist centers of Vietnam. Economic development and population growth lead to the expansion of urban land there. That affects the environment, especially resulting in the increase in surface temperature of the core city compared to the surrounding areas. The application of remote sensing data and techniques using thermal sensors for the estimation of land surface temperature and the formation of urban heat islands. This research aims to determine land surface temperature (LST) values and urban heat island (UHI) intensity distribution using Landsat 8 images. UHI is calculated in two steps: first, LST is calculated, and then the temperature threshold for UHI and none UHI is determined by the mean and standard deviation statistics of LST in the study area. To compute the LST, two levels of data must be processed: the NIR and RED bands from level 1 and the thermal band from level 2. The results showed the temperature in Da Nang varies from 15.7÷46.8 0C, with a significant regional temperature variance. The areas with high vegetation index had low temperatures while areas with low vegetation index had high temperatures. Non-UHI areas are present in areas with dense vegetation, dominated by Hoa Vang district and Son Tra peninsula. UHI is found in areas with temperatures more than or equal to 34.2 0C. The urban heat island has been taken play in the core of Da Nang, where the densely populated areas, industrial zones, airports, and landfill are located.

The urban climate of the largest cities of the European Arctic

Recent studies have found intense and persistent positive temperature anomalies associated with urban areas in high northern latitudes. However, understanding the intensity and variability of urban heat islands (UHI) in these regions remains limited. Here we study the urban climate of the seven largest cities in the European Arctic. These cities with populations ranging from 50,000 to nearly 300,000 span four countries and three bioclimatic zones in northern Fennoscandia. They include coastal and inland cities—and have complex local geography associated with topography and water bodies. We analyze land surface temperature (LST) satellite remote-sensing data with the Moderate Resolution Imaging Spectroradiometer (MODIS) from 2000 to 2020. The aim is to assess the intensity and temporal (seasonal and interannual) variability and trends of LST anomalies to learn about surface UHI development over different time frames in the urban Arctic. We observed persistent urban temperature anomalies in every season in the range of 1–5 °C, with the highest values for the largest city Murmansk (Russia). There is a strong inverse relationship between surface UHI intensity and its temporal variability: the more substantial the surface UHI, the more stable it is and the lower its temporal variability. Murmansk and Tromsø (Norway) have the highest thermal anomalies showing relatively low temporal variability, whereas the surface UHI in Bodø (Norway) is low and very volatile. We found no general direction of surface UHI change in the long term, as statistically significant changes were identified for only 10 of 56 cases. The study also suggests that in addition to the size (population) of the city, the presence of compact and dense urban infrastructure causes strong and stable urban temperature anomalies and has a more significant impact than the geographical setting of the city.

Assessing urban heat island intensity and emissions with compressed natural gas in non-commercial vehicles

The paradigm to shift toward cleaner fuel in vehicles and upgrade emission norms is a frequent discussion. The current study is a narrative of comparing fuels (gasoline, diesel, and compressed natural gas) in non-commercial four-wheel vehicles on the urban heat island (UHI) and their effects on emissions of greenhouse gases (GHG) and pollutants. We assume all such vehicles are single-fuel driven at a time in the densely populated urban city of Delhi, corresponding to the fiscal year 2017. We estimate that CNG can potentially raise the local temperature due to vehicular heat released and increased power plant load during summer by 1.09 °C and produces ∼1.6 times more heat than gasoline and diesel vehicles for the same distance traveled. CNG lowered the PM2.5 emissions by 81%, NOx by 45%, and VOC by 3%, while CO emissions increased by ∼14% and GHG emissions increased by 15%. For natural gas to become more effective, CNG vehicles' efficiency needs to improve, and the dependence on electricity should be more on cleaner resources. The analysis is based on estimates for Delhi, although the results would apply to other cities in India and globally.

Cause analysis of the extreme hourly precipitation and its relationship with the urban heat island intensity in Shenyang, China

Based on the hourly temperature and precipitation data from China national meteorological stations and regional automatic weather stations in Shenyang, the relationship between extreme hourly precipitation (ExHP) and urban heat island Intensity (UHII) is analyzed. Results show that the UHII is higher at night and in the early morning. The ExHP events mostly occur at night in summer when the UHII is relatively high. The spatial distribution of UHII in Shenyang is consistent with the economic development and the transportation density. Denser population and transportation, and high-rise buildings in the urban center contribute to higher UHII. There are three types of ExHP, namely the abrupt-type ExHP, the growing-type ExHP and the continuous-type ExHP. The overall variation characteristics of the three types of ExHP are relatively consistent. Their UHII values are positive and relatively stable in 6–12 h before the start of ExHP. The UHII begins to increase dramatically about 6 h before the ExHP, but decreases obviously and turn negative after the precipitation begins. Before the abrupt ExHP, the UHII is relatively high and can rapidly return to positive after the ending of ExHP. The UHII of the abrupt-type ExHP is remarkably larger than that of the growing-type and continuous-type ExHP. The UHII before and after the abrupt-type ExHP differs greatly. Before the abrupt ExHP, the UHII is high in the center and low at both ends, and the high-value areas of UHII are mainly located in the urban area. After the abrupt-type EXHP, the UHII drops and turn negative in the whole area. The UHII is obviously increasing with urbanization. The diurnal variation of UHII is enormous, which is higher at night than during the daytime. The increasing UHII can cause abnormal air pressure in cities and villages. The air in the lower atmospheric layer of the city can be heated and expanded, hence resulting in lower local air pressure. Then, the lower air pressure can promote the convergence and upward movement of air, hence facilitating the establishment of UHII circulation. This phenomenon is particularly distinct at night, which is conducive to the occurrence of ExHP events.

Urbanization-Induced Increases in Heavy Precipitation are Magnified by Moist Heatwaves in an Urban Agglomeration of East China

Abstract Heavy precipitation (HP) events can be preceded by moist heatwaves (HWs; i.e., hot and humid weather), and both can be intensified by urbanization. However, the effect of moist HWs on increasing urban HP remains unknown. Based on statistical analyses of daily weather observations and ERA5 reanalysis data, we herein investigate the effect of moist HWs on urban-intensified HP by dividing summer HP events into NoHW- and HW-preceded events in the Yangtze River delta (YRD) urban agglomeration of China. During the period 1961–2019, the YRD has experienced more frequent, longer-lasting, and stronger intense HP events in the summer season (i.e., June–August), and urbanization has contributed to these increases (by 22.66%–37.50%). In contrast, urban effects on HP are almost absent if we remove HW-preceded HP events from all HP events. Our results show that urbanization-induced increases in HP are associated with, and magnified by, moist HWs in urban areas of the YRD region. Moist HWs are conducive to an unstable atmosphere and stormy weather, and they also enhance urban heat island intensity, driving increases in HP over urban areas. Significance Statement The contribution of urbanization to increases in heavy precipitation has been widely reported in previous studies. HP events can be preceded by moist heatwaves (hot and humid extremes); however, it is unknown whether moist HWs enhance urban effects on HP. We choose the Yangtze River delta urban agglomeration to explore this question and find that urbanization contributes to the increasing frequency, duration, maximum intensity, and cumulative intensity of HP events in the summer season. However, this urban signal is not detectable if we remove HW-preceded events from all HP events. In other words, moist HWs play a key role in magnifying urbanization-induced increases in HP. Given that urban areas are projected to continue expanding and moist HWs are projected to occur with increasing frequency and intensity in the future, the role of HWs in the urban water cycle merits further investigation.

Analysis of urban form typology using urban heat island indicators: Case study of Ferdous neighborhood of Tabriz

Urbanization, with the acceleration of construction operations in cities, is the main reason for in-creasing the absorption of thermal energy in urban areas, which leads to climate change, especially urban form has responded differently depending on environmental dimension of urban context concerning the urban heat island intensity. In the last Studies, Researchers have not Codified the effect of urban form on heat islands in the form of a comprehensive model. It is necessary to examine each urban context according to its own conditions. Accordingly, this study aims to identify the effect of building form variability on reducing the intensity of heat islands in Ferdous context of Tabriz in the form of comprehensive modelling as a new task. The research gap is the effect of the form of buildings on the intensity of UHI, and the question will be asked whether it is possible to control the intensity of UHI by changing the form by influencing the climatic indicators. Our conceptual framework was based on urban form typologies in microscale affect the amount of energy intake and consumption, especially the intensity of heat islands, and whether the use of desirable types can be responsive to reducing the intensity of these islands? In this regard, using a quantitative analysis method in Rhinoceros software, especially using Grasshopper plugins, urban form is investigated. This analysis includes both the coldest and hottest days of the year. The results show that the optimal model is the cross typology.

Examining Relationships between Spatial Pattern of Green Infrastructure and Urban Heat Island (Case Study: Tehran Metropolis)

Green infrastructure (GI) is one factor that reduces the intensity of Urban Heat Islands (UHI). The research results related to the relationship between the spatial pattern (composition/configuration) of urban GI and UHI show contradictory results. Some researchers emphasized the negative relationship between MPS and ED criteria, while other researchers found positive results in these criteria. Based on this, this study was conducted in the metropolis of Tehran, which is located in the center of Iran and has undergone many changes. The method is based on Local Climate Zone (LCZ) classification, measuring the landscape metrics (CA, ED, MPS, LSPI), and analyzing the relationships through Pearson correlation and partial Pearson correlation. These results showed that it is possible to influence the reduction of LST by planning suitable GI spatial patterns, So that urban planners and designers should focus on creating green infrastructure with dense trees in urban centers, and on the other hand, pay attention to air circulation flows in Tehran metropolis based on the height of buildings.