Urban Heat Island Intensity Research Articles

Is Zagreb Green Enough? Influence of Urban Green Spaces on Mitigation of Urban Heat Island: A Satellite-Based Study

The urban heat island phenomenon is a climatic condition in which urbanized areas exhibit higher temperature values than their natural surroundings. This occurs due to an unbalanced energy budget caused by the extensive use of synthetic materials. In such a scenario, urban green areas act as stressors to mitigate the intensity of the urban heat island and improve urban well-being. This study analyzes the spatial-temporal characteristics of the urban heat island in Zagreb, Croatia, aiming to examine the role of different types of green infrastructure in mitigating elevated temperature values and facilitating the definition of greener planning strategies. To achieve this, a multitemporal remote sensing- and NDVI-based analysis was conducted for the time series 1984–2014. An urban heat island intensity map was obtained for the selected 30-year period, along with thermal graphs registering land surface temperature values among different city districts. The results reveal significant heterogeneity, displaying variable behavior dependent on the city district. The role of Zagreb’s urban green areas in urban heat island mitigation is evident but largely dependent on urban morphology, construction types, and periods. Urban forests and urban parks play the most significant role in temperature reduction, followed by residential building neighborhoods and extensive neighborhoods consisting of familiar houses with gardens. Continuously built areas, such as the city center and industrial zones, are less prone to registering lower intensity values. Additionally, multitemporal intensity variations based on land use changes are registered in several districts.

Lisbon Urban Climate: Statistical Analysis/Approach for Urban Heat Island Effect Based on a Pioneering Urban Meteorological Network

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

Analysis of LST, NDVI, and UHI patterns for urban climate using Landsat-9 satellite data in Delhi

The present study is based on remote sensing techniques focusing on Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) to investigate their influence on land use and land cover dynamics, and the assessment of the Urban Heat Island (UHI) effect in Delhi, India. The objective of this study is to calculate LST, NDVI, and UHI values to understand the changes in LULC patterns, urbanization, and temperature increase within the city. Unlike previous studies conducted with Landsat-8, the present study employs Landsat-9 data, ensuring a higher level of authenticity in the results. Landsat-9, equipped with state-of-the-art sensors and instrumentation, provides superior data quality, enhanced image resolution, and advanced capabilities for precise monitoring and analysis. The methodology encompasses six steps for LST retrieval, enabling the calculation of UHI values and intensity. Ground data from 32 meteorological stations validate the LST results. Pearson correlation coefficients between LST and NDVI exhibit correlations ranging from −0.58 to −0.68 for three dates. On Dec 8, 2023, there is a weak negative correlation of −0.004. The analysis of changing land cover with variation in NDVI and LST unveils a diverse landscape, primarily characterised by green cover (47.34%), followed by built-up area (44.57%), barren land (7.57%), and water (0.52%). The study identifies the minimum value of UHI intensity for Delhi to be 8.13 °C on 26-Feb 2023 and the maximum value of UHI was estimated 10.29 °C on 2-June 2023. The study of Urban Heat Island (UHI) patterns revealed distinctive seasonal trends. The urban areas exhibited relatively cooler temperatures compared to surrounding rural regions on Dec 8, 2023. The conclusion drawn from this comprehensive analysis is that rapid urbanization in Delhi has significantly contributed to the increase in LST and UHI values. This rise can largely be attributed to the extensive use of concrete in construction activities, which exacerbates the UHI effect. Moreover, this analysis signifies the dynamic nature of UHI and emphasizes the urgency for strategic urban planning and climate-sensitive design approaches. Implementing such measures can create more sustainable and resilient urban environments.

Greenspace coverage vs. enhanced vegetation index: Correlations with surface urban heat island intensity in different climate zones

The two greenspace indicators, greenspace coverage (GC) and enhanced vegetation index (EVI), have been widely used in studies on surface urban heat island intensity (SUHII). However, GC, which is more practical for urban planning, has not been as extensively involved in cross-regional SUHII studies as EVI. This study investigated 233 Chinese cities spanning three climate zones to examine how the correlations between SUHII and GC differ from those between SUHII and EVI across different climate zones. Our findings reveal that SUHII is more closely associated with the urban-rural differences in greenspace (ΔGC and ΔEVI) than with the urban values of GC and EVI. ΔGC shows potential for inter-city SUHII studies and outperforms ΔEVI in mild temperate climate zones (Cw and Cf). In the snow with dry winter climate zone (Dw), the correlation between SUHII and ΔEVI peaks at −0.795, exceeding that of ΔGC at −0.653. The cooling effects of greenspace diminish progressively from Dw to Cw to Cf, with the same ΔGC corresponding to higher SUHII in Cf than in Cw and Dw. These results underscore the necessity to develop targeted acceptable SUHII standards and GC thresholds for different climate zones to guide greening-based cooling measures.

Causal inference of urban heat island effect and its spatial heterogeneity: A case study of Wuhan, China

The accelerated urbanization process has exacerbated the urban heat island effect, leading to significant negative impacts on the physical and mental health of urban residents. A deeper understanding of the mechanisms underlying the urban heat island phenomenon is essentially beneficial for providing scientific supports towards improving the urban thermal environment. To address the challenge of effectively depicting the complex interactions among urban environment factors, this study employed the Peter-Clark causal discovery algorithm to analyze the causal structure of urban thermal driving factors, and validated the effectiveness of the 6 key factors directly influencing the surface urban heat island intensity (SUHII). In response to the inadequacy of existing big data causal inference tools in assessing the spatial heterogeneity of causal effects, this study proposed a method for evaluating the causal effects on SUHII and their spatial heterogeneity based on local analysis and geospatial causal principle. The result for 4 different intervention scenarios in this study show that there is obvious spatial heterogeneity in the causal effects of different interventions on SUHII in Wuhan, and that increasing greenery and preserving natural environments is an effective way to mitigate the urban heat island (UHI) effect. This approach, provides a new perspective for studying the phenomenon of UHI and insights of potential approaches for mitigating UHI.

Exploring patterns of surface urban heat island intensity: a comparative analysis of three Greek urban areas

One of the most pressing issues of our time is the destabilization of the global climate as a result of rapid urbanization. In Europe, according to United Nations reports, almost 73% of the population lives in cities and this percentage is expected to rise to 85% by 2050 (United Nations and Department of Economic and Social Affairs, ‘World Urbanization Prospects 2018 Highlights’, 2018). This observed urban development and its trends pose significant challenges to our urban environments, therefore it is critical to improve our understanding and assess its impact by measuring and analyzing its patterns. This study presents a comprehensive five-year analysis of microclimatic parameters that can contribute to this analysis, such as land surface temperature (LST) and surface urban heat island (SUHI) intensity, using remotely sensed data over three cities in Greece. Specifically, the selected cities are the city of Thessaloniki, which is the second largest city situated in the northern part of Greece, the city of Larissa on the mainland, and finally the city of Tripoli in the Peloponnese peninsula in southern Greece. Each city represents urban areas with different physical and urban characteristics. For this purpose, the vegetation index-based technique for LST extraction was applied within the Google Earth Engine (GEE) platform. Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) calculations were also performed over a five-year period. The results highlighted the spatio-temporal variations and trends that occur in the studied urban areas, providing valuable insights into the annual patterns of the SUHI phenomenon in different geographical contexts within Greece. This is particularly relevant in countries like Greece, where the density of World Meteorological Organization (WMO) stations providing reliable temperature data is low, usually limited to one per city. Consequently, LST derived from satellite data can provide more accurate intra-urban results of the SUHI effect, demonstrating the value of remotely sensed data as a cost effective and supportive tool for decision-making in environmental and urban planning policies.

Effect of the reference rural landscape on annual variations in surface urban heat island intensity

The urban heat island effect poses a significant climate risk associated with urbanization. Existing research primarily focuses on the spatiotemporal variations in land surface temperature (LST) within urban areas and their effects on surface urban heat island intensity (SUHII). However, investigations into how annual dynamic changes in rural landscapes impact reference LST and SUHII remain limited. This study examines 28 representative cities worldwide that experience seasonal snow cover and are predominantly surrounded by croplands or forests. The objective is to explore the patterns of continuous annual variations in SUHII and to elucidate the underlying physical mechanisms through which changes in rural landscapes affect these variations. The results reveal that SUHII consistently exhibits a double-peak pattern, with peaks occurring during the transition from winter to spring and in mid-summer. The first, moderate-intensity peak is attributed to differential snowmelt between urban and rural areas, while the more pronounced second peak is associated with the enhanced cooling effect of vegetation evapotranspiration (ET) during the growing season. Furthermore, a strong correlation between leaf area index, ET, and SUHII underscores the significance of rural vegetation in shaping SUHII variations. These findings highlight the necessity for detailed descriptions of rural reference landscapes to improve the interpretability and comparability of SUHII results. Additionally, the study suggests the need for differentiated SUHII evaluation thresholds tailored to cities with diverse rural landscapes.

Synergistic interactions between urban heat islands and heat waves in the Greater Kuala Lumpur and surrounding areas

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

Analyzing the spatio-temporal pattern of urban growth and its influence on urban heat islands in the Sekondi-Takoradi metropolis, Ghana

The rapid urbanization in Sekondi-Takoradi, Ghana has significantly transformed the land cover, resulting in the proliferation of impervious surfaces and a decline in vegetation. However, the influence of this urban growth on the development of urban heat islands (UHIs) in the metropolis remains understudied. This study aimed to fill this research gap by employing Landsat images to explore the influence of urban growth on urban heat islands in the metropolis from 1991 to 2023. The supervised random forest technique was utilized to map the land cover changes. Furthermore, the computed normalized difference built-up index (NDBI), normalized difference vegetation index (NDVI), land surface temperature (LST), and urban thermal field variance index (UTFVI) were used to analyze the influence of urban expansion on UHIs. The findings revealed a 63.07 km2 increase in built-up areas and a 60.99 km2 decrease in vegetation cover during the study period. This dramatic land use change led to a 3.1°C rise in mean LST and a 19.38 km2 expansion of areas affected by the UHI effect. The UTFVI analysis further indicated a 33.63 km2 increase in the worst ecological zone due to the temperature rise. Statistical analysis between LST, NDVI, and NDBI revealed significant variability in explaining the intensity of LST and UHI in the metropolis over the study period. The study equips city authorities and planners with the fundamental knowledge needed to prepare a sustainable development plan that alleviates adverse effects of urban growth and elevated temperature-related issues. Also, the findings contribute to the global efforts in promoting more livable and climate-resilient urban environments.

Impact of urban spatial dynamics and blue-green infrastructure on urban heat islands: A case study of Guangzhou using Local Climate Zones and predictive modeling

Amidst mounting concerns regarding health risks associated with excessive carbon emissions, attention has turned to the heat mitigation ability of blue-green infrastructure (BGI). BGI offers heat mitigation through transpiration cooling, effectively countering the urban heat island (UHI) effect while facilitating energy conservation and carbon reduction. This study adopted the InVEST model to assess the spatial pattern of UHI and heat mitigation service based on the Local Climate Zone (LCZ) classification, along with quantifying the energy savings in Guangzhou for 2019. The results revealed that the UHI effect exhibited greater intensity in the southwest built-up area. Compact Lowrise (LCZ-3) and Heavy Industry (LCZ-10) areas demonstrated higher UHI intensity, while Water (LCZ-G) and Dense Trees (LCZ-A) areas showcased the highest heat mitigation index (HMI) and optimal cooling capacities. Three urban development scenarios were devised to simulate the UHI distribution, heat mitigation capacity, and energy savings by 2025, using the CA-Markov model. In comparison to the Baseline scenario, the Environmental Protection scenario showed a modest 3.67 % decrease in the built-up area, but a noteworthy increase in HMI and energy saving, by 21.2 % and 6.6 % respectively. This scenario can serve as a reference pathway for urban energy conservation and sustainable development.

Effects of the Western Pacific Subtropical High on the urban heat island characteristics in the middle and lower reaches of the Yangtze River, China

The middle and lower reaches of the Yangtze River are frequently affected by the Western Pacific Subtropical High (WPSH) in summer. This leads to phenomena including air subsidence, high temperatures, low rainfall, and weak winds, all of which affect the urban heat island (UHI) effect. Currently, there are few studies on the influence of WPSH on the UHI effect. In this study, we analysed the temporal and spatial distributions of the influence of WPSH on the UHI effect by establishing two scenarios: with and without WPSH. We calculated the UHI intensity and the urban heat island proportion index (UHPI) to analyse the temporal and spatial distributions of the UHI effect. The geographical detector method was then used to analyse the factors influencing UHI. The results indicate the strong heat island effect during the day in provincial capitals and some developed cities. The area of high UHI intensity was larger under the influence of WPSH than in the years without WPSH. WPSH affected UHPI at both day and night, although the effect was more pronounced at night. The factors affecting daytime UHI intensity are mainly POP and NTL, O3 plays a large role in the years with WPSH control. The main factors affecting the UHI intensity at night are AOD, POP and NTL were mainly factors in the years without WPSH control, POP and WPSH were mainly factors in the years with WPSH control. The interactions of the factors are mainly POP and multi-factors during the daytime, and DEM and multi-factors during the nighttime. It was found that the UHI intensity was enhanced under the control of the WPSH, and the influencing factors of the diurnal UHI differed with and without the WPSH control, which ultimately provides realistic suggestions for mitigating the intensity of the UHI in areas affected by the WPSH.

Spatio-Temporal Heterogeneity of the Urban Heat Effect and Its Socio-Ecological Drivers in Yangzhou City, China

Rapid urbanization and land-use changes may affect the intensity of urban heat islands (UHIs). However, research on the eastern Chinese city of Yangzhou is lacking. Using land cover data and the InVest Urban Cooling model, this study evaluated the spatiotemporal heterogeneity of the UHI effect from 1990 to 2020 and its socioecological drivers in Yangzhou City. Landscape pattern indices such as patch area (CA), percentage of landscape (PLAND), number of patches, patch density, and aggregation index were created using Fragstats 4.2 software. Several social indicators, such as gross domestic product (GDP), night-light index, and population density, were considered to explore their correlation with UHI indicators. During the past three decades, rapid urbanization in Yangzhou has intensified the UHI effect, with the cooling capacity (cc park) and heat mitigation index (HMI) decreasing by ~9.6%; however, the mixed air temperature (T air) has increased by 0.14 °C. The main heat island areas are concentrated in southern Yangzhou, including the Hanjiang and Guangling districts, and have expanded over time. T air was positively correlated with GDP, night-light index, and population density. Moreover, for the impervious land use type, cc park and HMI were negatively correlated with CA and PLAND (p < 0.01). This study contributes to a deeper understanding of the dynamics of UHIs and provides valuable insights for policymakers, urban planners, and researchers striving to create sustainable and climate-resilient cities in Yangzhou.

Seasonal variation in vegetation cooling effect and its driving factors in a subtropical megacity

The urban heat island (UHI) effect has become a global issue, attracting widespread attention in recent years. Urban vegetation is an effective way to mitigate UHI through evapotranspiration (ET). However, the seasonal variation in vegetation cooling effect and its driving factors have not been well investigated. Therefore, the seasonal UHI intensity (UHII) on typical clear days in 2021 was calculated for Shenzhen based on the land surface temperature (LST) data from the Moderate Resolution Imaging Spectroradiometer (MODIS). The slope between UHII and vegetation coverage was then used to characterize the vegetation cooling effect. Results showed that: (1) there is a significant negative linear correlation between UHII and vegetation coverage, with slopes mostly less than −1 and coefficients of determination (R2) larger than 0.4. (2) The slopes varied seasonally, indicating a larger vegetation cooling effect during summer daytime. A 10 % increase in vegetation coverage can reduce the daytime UHII by 0.16 °C in January and 0.59 °C in June, while at nighttime, the cooling effect varied between 0.12 °C in January and 0.27 °C in June. The daytime and nighttime UHII for the whole year can be reduced by 0.43 °C and 0.24 °C, respectively. (3) The seasonal variation in vegetation cooling effects during the daytime can be largely explained by that of ET, which is mainly driven by leaf area index (LAI).

Spatiotemporal Evolution and Influencing Factors of Heat Island Intensity in the Yangtze River Delta Urban Agglomeration Based on GEE

Urban agglomerations significantly alter the regional thermal environment. It is urgent to investigate the evolution and influence mechanisms of urban agglomeration heat island intensity from a regional perspective. This study is supported by Google Earth Engine long-term MODIS data series. On the basis of estimating surface urban heat island intensity (SUHI) in the Yangtze River Delta urban agglomeration from 2001 to 2020 based on the suburban temperature difference method, the causes of heat islands in the urban agglomeration were analyzed by using geographical detector analysis. Additionally, the heat island proportion (PHI) and SUHI indicators were used to compare and analyze the changing characteristics of the urban heat island effect of ten representative cities. The research reveals the following: (1) The average SUHI of the study area increased from 0.11 °C in 2001 to 0.29 °C in 2020, with an average annual increase rate of 0.009 °C. (2) According to the results of the geographical detector analysis, SUHI was influenced by several driving factors exhibiting obvious seasonal variations. (3) SUHI difference between cities is significant in the summer (1.52 °C), but smallest in the winter; the PHI difference between cities is larger in the autumn (46.7%), while it is smaller in the summer. The research findings aim to effectively serve the formulation of collaborative development plans for the Yangtze River Delta urban agglomeration.

Exploring the influence of urban agglomeration on extreme precipitation: Evidence from the middle reaches of the Yangtze River, China

Study regionThe urban agglomeration in the middle reaches of the Yangtze River (MRUA) consisting of 31 cities is a pillar of the Yangtze River Economic Belt and the largest urban agglomeration in China, densely populated, and rapidly urbanizing. Study focusExtreme precipitation events are affected by rapidly urbanized regions with uneven levels of urbanization. Hence, it is important to explore the spatiotemporal evolution of extreme precipitation in urban agglomerations and the influence of the spatially uneven development of urban areas under rapid large-scale urbanization. New hydrological insights for the regionExtreme precipitation indices (Pmax1d, R20, and PF95) exhibited significant positive trends during 1979–2018 in the MRUA, increasing by 0.43 mm/year, 0.11 day/year, and 2.46 mm/year, respectively. Regression models based on raster urbanization data were constructed, and the impervious area ratio (IA) and landscape shape index (SI) were shown to be the dominant urbanization factors affecting regional extreme precipitation, with a particularly positive spatial correlation with PF95 and Pmax1d at all stages of urbanization. Moreover, the urban-rural comparison results revealed that the difference in SI of each city was positively correlated with the urban heat island intensity. In the three provincial capitals, Pmax1d and R50 were greater in urban centres than in rural areas. This study contributes to a comprehensive understanding in the variability of extreme precipitation events under the influence of large-scale urbanization.

Improved WRF simulation of surface temperature and urban heat island intensity over Metro Manila, Philippines

Accurate representation of the land use/land cover (LULC) in the Weather Research and Forecasting (WRF) model has been shown to be crucial for improved simulation of surface variables and urban heat island (UHI) phenomenon. In this study, a ground truthed LULC dataset over Metro Manila (MM) was integrated in the WRF model to assess and quantify the relative contributions of using an updated land use/land cover (ULULC), activating the single-layer urban canopy model (WSLUCM) and anthropogenic heat (WAH), and consideration of urban surface heterogeneity (LIR) representation on simulated Surface Temperature (ST) relative to the default model settings (CTRL) of the WRF model. Additionally, the study examines the daytime and nighttime UHI features over MM, using a reference simulation where urban grids were replaced with cropland (CRP). Numerical simulations were carried out for April 2018, a typical dry month without monsoonal influence over MM to get a clear UHI characterization. Data from nine weather stations distributed across MM was used for performance evaluation. Results show negligible daytime biases in ST for CTRL, ULULC, and WAH, and underestimated ST for WSLUCM. At night, CTRL and ULULC (WSLUCM and WAH) tend to overestimate (underestimate) ST. Of the four factors analyzed, updating LULC, using SLUCM, including AH, and accounting for urban surface heterogeneity can change daytime ST by 0.3 °C, -0.6 °C, 0.3 °C, and 0 °C, respectively, and nighttime ST by 0.5 °C, -1.2 °C, 0.3 °C, and 0.4 °C, respectively. Widespread daytime warming and a distinct nocturnal UHI following urbanization were confirmed for MM when the LULC accounted for urban heterogeneity. While data limitations for MM limit the specification of the SLUCM, its UHI features are best represented in WRF when an updated land cover dataset is used.

The potential burden from urbanisation on heat-related mortality in São Paulo, Brazil

Heatwave events are associated with increased hospital admissions and mortality rates worldwide. Heat exposure is often exacerbated by the Urban Heat Island (UHI) effect, especially in large cities. However, an accurate quantification of the additional burden related to heat from urbanisation is understudied in Latin American cities. Therefore, we used advanced meteorological modelling to simulate 2 m air temperature at 1 km spatial resolution across São Paulo, estimating UHI intensity attributing of all-cause mortality to heat and UHI during the 2014 heatwave. The Local Climate Zone classification system provided input land use parameters. Our model was validated against 31 weather stations and bias-corrected using linear regression. We calculated heat-related all-cause mortality, stratified by age, using the modelled temperature data, estimating 394 heat-related deaths. A counterfactual experiment, replacing urban areas with natural land, quantified the additional UHI burden. We found that the UHI may contribute to 69–70% of heat-related mortality (modelled temperature scenario). This work motivates the use of appropriate urban climate data, including careful model validation and bias correction, when assessing heat exposure and health risk assessment. It also highlights the health impacts from heatwaves in cities such as São Paulo, which are likely to increase due to climate change.

Does self-containment of spatial scale and land use function contribute to mitigate urban heat island effects? Lessons from new towns in Shanghai

The concept of self-containment in new towns has been widely discussed from social and economic perspectives. However, localized interpretations within the context of China's development, particularly regarding climate adaptability and urban heat island (UHI) mitigation, are scarce. To fill this gap, our research analyzed self-containment from the perspectives of urban spatial scale and land use function. Focusing on Shanghai’s five new towns, we empirically demonstrated how self-containment influenced the UHI effects from 2005 to 2020, employing the Geodetector method. The findings reveal that during the daytime, the intensity of UHI in new towns decreased, serving as vital connectivity nodes of UHI within the region. Conversely, during the nighttime, both the intensity and area of UHI showed an increasing trend. The research confirmed that expanding the urban scale and functional diversity are effective strategies for mitigating the UHI. Based on these findings, we offer practical suggestions for the development of new towns: Increase population size while ensuring coordination with development scale; enhance mixed-use functions in large-scale development projects like university towns and industrial parks; and be vigilant of potential functional decline in central areas and increasing thermal impact due to new town development. Overall, this study enriches our understanding of self-containment in Chinese new towns and provides valuable insights for mitigating UHI in other similar contexts.

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

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

Sensitivity of Local Climate Zones and Urban Functional Zones to Multi-Scenario Surface Urban Heat Islands

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