Urban Heat Island Intensity Research Articles

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.

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.

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.

Pairwise‐Interaction Model Unifies Different Asymptotic Shapes of UHI Intensity

AbstractCity size is a primary determinant of the urban heat island (UHI) intensity, with its effects further nuanced by the urban form. But how to factor in the urban form into the UHI assessment remains unresolved. We propose an every‐pair‐interaction model that meaningfully incorporates urban size and fractal dimension to characterize the UHI intensity. Regression on the summertime surface UHI intensity of 5,000 European cities shows that the model outperforms the simple linear combination of logarithmic size and fractal dimension. Subject to the interplay between the range of the every‐pair interaction and the urban fractal shape, the model also represents a generalization as it includes power‐law, logarithmic, and saturating size dependence of UHI—all three possibilities have been reported empirically in the literature. Our theoretical framework indicates that the surface UHI intensity saturates with urban size, opening up new research perspectives around UHI intensity.

Extension and trend of the London urban heat island under Lamb weather types

Understanding and describing how urban heat islands evolve is important, given the noticeable impact they have on people living in cities. This paper considers the London heat island from gridded values with one-arcminute spatial resolution over a 33-year period, from 1990 to 2022. Among the available variables in the database, maximum and minimum air temperatures were used. A cold island was not observed, since temperatures in the city centre were higher than those in the surroundings during the day and at night. However, the urban heat island extension was higher for the maximum temperature, whereas this island was limited to the city centre for the minimum temperature, in line with the area delimited by the congestion charge. Lamb weather types were determined, and it was found that the anticyclonic type prevailed, followed by southwest, west, and cyclonic types. The difference between both temperatures was about 6.8 °C in the city centre, and was particularly defined for anticyclonic and cyclonic types. Moreover, anticyclonic situations were linked with the highest urban heat island intensities for minimum temperature. Finally, the temperature trend was similar for both temperatures –about 0.2–0.3 °C/10 years in the city centre– thereby offering a possible quantification of climate change.

The health benefits of reducing micro-heat islands: A 22-year analysis of the impact of urban temperature reduction on heat-related illnesses in California's major cities

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

Scale Differences and Gradient Effects of Local Climate Zone Spatial Pattern on Urban Heat Island Impact—A Case in Guangzhou’s Core Area

With the continuous development of cities, the surface urban heat island intensity (SUHII) is increasing, leading to the deterioration of the urban thermal environment, increasing energy consumption, and endangering the health of urban residents. Understanding the spatio-temporal scale difference and gradient effect of urban spatial patterns on the impact of SUHII is crucial for improving the climate resilience of cities and promoting sustainable urban development. This paper investigated the characteristics of SUHII changes at different time periods based on local climate zones (LCZs) and downscaled land surface temperature (LST) data. Meanwhile, landscape pattern indicators and the multiscale geographically weighted regression (MGWR) model were utilized to analyze the impacts of urban spatial patterns on SUHII at multiple spatial–temporal scales. The results indicated that the SUHII of each LCZ type exhibited diverse patterns in different time periods. High SUHII occurred in summer daytime and autumn nighttime. Compact and high-rise buildings (LCZ1/2/4) showed markedly higher SUHII during the daytime or nighttime, except for heavy industry. The extent of influence and the dominant factors of LCZ spatial patterns on SUHII exhibit obvious scale differences and gradient effects. At the regional scale, highly regular and compacted built-up areas tended to increase SUHII, while single and continuously distributed built-up areas had a greater impact on increasing SUHII. At the local scale, the impact of the PLAND (1/2/4/5/10) on SUHII exhibited a trend of diminishing from urban to suburban areas. In urban areas, the PLAND of LCZ 1, LCZ 2, and LCZ4 was the major factor affecting the increase in SUHII, whereas, in suburban areas, the PLAND of LCZ 2 and LCZ 10 was the major influencing factor on SUHII. The results can provide a scientific reference for mitigating urban heat island effects and constructing an ecologically ‘designed’ city.

Exploring diurnal and seasonal variabilities in surface urban heat island intensity in the Guangdong-Hong Kong-Macao Greater Bay Area

Exploring diurnal and seasonal variabilities in surface urban heat island intensity in the Guangdong-Hong Kong-Macao Greater Bay Area

A global urban heat island intensity dataset: Generation, comparison, and analysis

The urban heat island (UHI) effect, a phenomenon of local warming over urban areas, is the most well-known impact of urbanization on climate. Globally consistent estimates of the UHI intensity (UHII) are crucial for examining this phenomenon across time and space. However, publicly available UHII datasets are limited and have several constraints: (1) they are for clear-sky surface UHII, not all-sky surface UHII and canopy (air temperature) UHII; (2) the estimation methods often neglect anthropogenic disturbance, introducing uncertainties in the estimated UHII. To address these issues, this study proposes a new dynamic equal-area (DEA) method that can minimize the influence of various confounding factors on UHII estimates through a dynamic cyclic process. Utilizing the DEA method and leveraging various gridded temperature data, we develop a global-scale (>10,000 cities), long-term (over 20 years by month), and multi-faceted (clear-sky surface, all-sky surface, and canopy) UHII dataset. Based on these estimates, we provide a comprehensive analysis of the UHII and its trends in global cities. The UHII is found to be greater than zero in >80% of cities, with global annual average magnitudes around 1.0 °C (day) and 0.8 °C (night) for surface UHII, and close to 0.5 °C for canopy UHII. Furthermore, an interannual upward trend in UHII is observed in >60% of cities, with global annual average trends exceeding 0.1 °C/decade (day) and over 0.06 °C/decade (night) for surface UHII, and slightly surpassing 0.03 °C/decade for canopy UHII. Notably, there exists a positive correlation between the magnitude and trend of UHII, suggesting that cities with stronger UHII tend to experience faster growth in UHII. Additionally, discrepancies in UHII are found between different temperature data, stemming not only from distinctions in data types (surface or air temperature) but also from differences in data acquisition times (Terra or Aqua), weather conditions (clear-sky or all-sky), and processing methodologies (with or without gap filling). Overall, our proposed method, dataset, and analysis results have the potential to provide valuable insights for future urban climate studies. The UHII dataset is publicly available at https://doi.org/10.6084/m9.figshare.24821538.

Urban Climate Change in Populated Kathmandu Valley, Nepal: A Case Study

Kathmandu Valley, the capital and commercial hub of Nepal, is experiencing rapid and unmanaged urbanization, leading to poor environmental conditions. The population, a key indicator of urbanization, has been growing at an average rate of 2.08% per year. This population growth from 1990 to 2021 has significantly altered land use, resulting in increased temperatures and transforming the valley into an Urban Heat Island (UHI). This study examines the effects of UHI by comparing normalized Land Surface Temperature (LST) in Kathmandu Valley and assessing the influence of land use changes on UHI intensity between 1990 and 2021. The result shows that built-up area has expanded by 30.7% of the total area, while the agricultural area has decreased by 33.59%. These changes in land use and land cover have caused a significant temperature increase at a rate of 0.038 °C per year. Green space density, measured by the Normalized Difference Vegetation Index (NDVI), has declined from 35.59% to 29.17%, being replaced by built-up areas. LST is inversely related to the presence of surface vegetation. The UHI phenomenon is observed to be stronger in urban centers such as Tribhuvan International Airport, Patan industrial area, Kritipur, and Kalanki, while it is weaker in semi-urban regions like Suryabinayak and Godawari. As climate change poses significant health and environmental challenges, adaptation has become a major issue for developing countries. Therefore, it is essential to implement change control and mitigation strategies specifically tailored to each urban region.

A new two-step estimation approach for retrieving surface urban heat island intensity and footprint based on urban-rural temperature gradients

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

Dynamics of urban heat island intensity in Lecce, Italy: seasonal, diurnal and heat wave influence

This study investigates the impact of Heat Waves (HWs) on Urban Heat Island Intensity (UHII) in Lecce, a Mediterranean city in southern Italy. UHII was assessed using air temperature data from six weather stations over a four-year period (2020–2023). The results indicate that UHII is generally higher in winter and spring, with the ARPA (Environmental Protection Regional Agency) station consistently showing the highest values, particularly in summer, attributed to urban characteristics. The diurnal cycle of UHII reveals a peak in the early morning at most stations, followed by a decrease to near zero or slightly negative values during midday. Evening values then increase and persist throughout the night. Stations surrounded by green areas or located in suburban settings recorded the lowest UHII values. A total of eleven HWs occurred during the study period, with an average duration of 10 days. UHII was significantly more intense during HWs at all stations, with the greatest average observed at the ARPA station (2.60 °C) and the smallest at the LST (Liceo Scientifico Tabacchi) station (0.74 °C). These findings highlight the significant impact of HWs on UHI intensification in Lecce, especially in densely developed areas compared to suburban regions. This study emphasizes the importance of investigating UHII dynamics in Mediterranean cities to develop strategies for mitigating the Urban Heat Island (UHI) effect during extreme heat events.

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.

Impact of urban form on building energy consumption in different climate zones of China

Despite numerous studies on the relationship between urban form and building energy demand, understanding the impact mechanisms of urban form is still insufficient. Here, we identified the direct and indirect influences of urban form on building energy demand. The direct influence includes the blocking of solar radiation. The indirect influence is exerted through the urban heat island (UHI) effect as different urban forms produce different UHI intensities. The UHI effect was calculated using the urban weather generator (UWG) tool. Then, the analysis of the direct and indirect influences on the building energy demand was carried out using building energy modeling (BEM) based on the output of UWG. Five cities in different building climate zones in China were investigated, and the corresponding direct and indirect influences were quantified based on the proposed UWG + BEM framework. Results show that warmer temperatures lead to an average increase in space cooling of 16 %–46 % while reduced space heating of 8 %–17 %. The shading of surrounding buildings allows an increase of 7 %–36 % in space heating and a decrease of 14 %–48 % in space cooling compared to that of an isolated building. In addition, direct influence due to shading is prominent in Shuangyashan, Xuzhou, and Fuzhou, accounting for 50 %, 80 %, and 57 % of variations in building energy consumption.

Vertical thermal environment investigation in different urban zones (LCZ4/LCZ6/LCZA) and heat mitigation evaluation: Field measurements and numerical simulations

Urban heat island (UHI) effect has become a main urban environmental problem in megacities. To express the UHI effects more meaningfully and provide scientific basis for heat mitigation and urban planning, both field measurements and numerical simulations were conducted in Xi'an City, China. Three typical urban local climate zones (LCZs) with close geographical locations—high-rise/low-rise residential areas (LCZ4/6) and urban parks (LCZA)—were chosen to investigate thermal environments in vertical spaces. It was found that UHILCZ6/4−LCZA could reach 1.2/2 °C during the daytime and 0.3/0.7 °C in the night at screen-level height. In particular, temperature inversions were found in residential areas during the daytime, which led to an increase in the UHI intensity at certain heights. To investigate means to improve the thermal environment of residential areas, numerical simulations were carried out to test the influence of cool roofs and cool facades. It was found that the daytime air temperatures could be reduced by 0.2 °C and 0.4 °C after adopting high-reflectance roofs and facades in high-rise residential area at screen-level height, respectively. In addition, the implementation of cool facades also affected the air temperature profiles and atmospheric conditions, leading to the disappearance of temperature inversions and complicated stratification, and providing more evident cooling effects at certain heights. The findings of this study provide new insights into thermal environments of urban zones with different structures and have practical importance for urban UHI mitigation.