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In densely urbanized regions, the local climate is greatly influenced by the urban morphology, including interactions between buildings, space, and human activities. The Kowloon Peninsula in Hong Kong, with some of the greatest urban population densities in the world, represents an extreme case of the influence of the built environment on climate, with high-rise buildings, narrow street canyons, and little green space. In this study, the building frontal area index (FAI), a parameter for estimating aerodynamic resistance of the urban surface as a predictor of wind ventilation, was derived from a three-dimensional building database. The relationship between FAI and urban heat island intensity (UHII) from an Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite image was calculated at different scales. The highest correlation (r = 0.574, n = 4900) was obtained at 100 m resolution, which suggests that the optimum operational scale of FAI is 100 m resolution for the study area, i.e. the scale and size at which FAI impacts on the urban climate. The presence/trend of any higher correlation at different resolutions was tested using the nonparametric Mann–Kendall test, and the results show that the statistic Z-value generated from the test is smaller than the hypothesis significance levels of 90%, 95%, and 99%; thus, the hypothesis of having a higher correlation at any scale other than 100 m resolution is rejected. Planning authorities may use the FAI generated at 100 m resolution for designing wind ventilation corridors across Hong Kong at this scale, especially when temperature and air quality in the inner city are of major concern. However, for applications to other cities with different standard morphologies, the FAI–UHII relationship should be re-evaluated. [ABSTRACT FROM PUBLISHER]

The complete surface temperature (T c) in urban areas, defined as the mean temperature of the total active surface area, is an important variable in urban micro-climate research, specifically for assessment of the urban surface energy balance. Since most vertically-oriented building facets are not observed by a nadir-viewing remote imaging radiometer, the radiometric surface temperature (T r) measured at a specific view angle cannot be used with existing heat transfer equations to estimate radiative and convective fluxes in the urban environment. Thus, it is necessary to derive T c for city neighborhoods. This study develops a simple method to estimate T c from T r with the aid of the Temperatures of Urban Facets in 3D (TUF-3D) numerical model, which calculates 3-D sub-facet scale urban surface temperatures for a variety of surface geometries and properties, weather conditions and solar angles. The effects of geometric and meteorological characteristics – e.g., building planar area index (λ p), wall facet area index (F), solar irradiance – on the difference between T c and T r were evaluated using the TUF-3D model. Results showed the effects of geometric and meteorological characteristics on the difference between T c and T r differ between daytime and nighttime. The study then sought to predict the relationship between T r and T c , using λ p , F , and solar irradiance for daytime and only using λ p and F for nighttime. Based on the simulated data from TUF-3D, the resulting relationships achieve a coefficient of determination (r 2 ) of 0.97 and a RMSE of 1.5 K during daytime, with corresponding nighttime values of r 2 = 0.98 and RMSE = 0.69 K. The relationships between T r and T c are evaluated using high resolution airborne thermal images of daytime urban scenes: r 2 = 0.75 and RMSE = 1.09 K on August 6, 2013 at 12:40 pm; and r 2 = 0.86 and RMSE = 1.86K on October 24, 2017 at 11:30 am. The new relationships were also applied to estimate T c from T r in Hong Kong retrieved from Landsat 5 Thematic Mapper (TM) and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). In the present climatic context, the difference between T c and T r can reach 10 K during daytime in summer, and 6 K during daytime in winter, with seasonal variation attributable to the variations in shortwave irradiance. The nighttime difference between T c and T r can also reach 2 K in both summer and spring seasons. • Parameterization of complete surface temperature over complex urban areas • Geometric effects on the difference between T r and T c were first evaluated. • Relationships between T r and T c characterized by urban geometry were developed. • Validation with high-spatial-resolution airborne thermal images was undertaken. [ABSTRACT FROM AUTHOR]

The urban heat island effect poses a growing threat to human society, especially in densely populated and developed megacities. With the introduction of the Local Climate Zones (LCZ) framework, new perspectives and findings have been brought to urban heat island studies. This study investigated the cooling effect of vegetation and albedo on the surface urban heat island (SUHI) in the classification system of LCZ during different seasons, using three Chinese megacities as case study areas. Single-factor linear regression and Pearson's correlation coefficient were applied to analyze the seasonal cooling effect of both albedo and the NDVI on the SUHI within different LCZs. The results show that (1) the variability of the SUHI is reflected in its dominance and intensity within certain LCZs in different cities and in the efficiency of cooling factors; (2) the cooling effect of vegetation is dominant in each season, and the cooling effect produced by albedo within specific seasons can be differentiated by LCZs. This study provides valuable information for the mitigation of the SUHI magnitude in specific regions and at specific times of the year. [ABSTRACT FROM AUTHOR]

Modeling thematic and spatial dynamic behaviors of urban heat islands (UHIs) over time is important for understanding the evolution of this phenomenon to mitigate the warming effect in urban areas. Although previous studies conceptualized that a UHI only has a single life cycle with spatial behaviors, a UHI can be detected to appear and disappear several times periodically in terms of thematic and spatial integrated behaviors. Such multiple behaviors have not yet been illustrated with proof or evidence. This study conceptualizes a UHI as an object which has thematic and spatial behaviors simultaneously and proposes several graphs to depict periodic life‐cycle transitions triggered by behaviors. The conceptualized behaviors have been modeled and implemented in an object‐relational database management system and temperature readings collected from numerous weather stations were interpolated as temperature images per hour. The results of this study indicate that the model could track the spatial and thematic evolution of UHIs continuously and reveal their periodical patterns and abnormal cases. [ABSTRACT FROM AUTHOR]

The urban heat island (UHI) phenomenon occurring within urban areas or city-clusters is increasingly becoming a severe problem in the urbanization process. Previous research mainly focusing on static UHI modeled at fixed time instants is not capable to track the evolutionary process of the UHIs in both time and space domains. This research designs an object-oriented dynamic model to reconstruct the evolutionary process of UHIs. Each UHI is modeled as a spatiotemporal field-object with its own life-cycle. The dynamic behavior of an UHI is defined by a series of filiations (e.g. expansion and contraction). The model was implemented in an object-relational database and applied to air temperature data collected from weather stations in hourly basis over 7 days. The behaviors of UHI were extracted from the data. Results suggest that the model can effectively identify different behaviors and status of UHIs, and reveal the spatiotemporal behavior of each of them. [ABSTRACT FROM AUTHOR]

Harmonization of satellite imagery provides a good opportunity for studying land surface temperature (LST) as well as the urban heat island effect. However, it is challenging to use the harmonized data for the study of LST due to the systematic bias between the LSTs from different satellites, which is highly influenced by sensor differences and the compatibility of LST retrieval algorithms. To fill this research gap, this study proposes the comparison of different LST images retrieved from various satellites that focus on Hong Kong, China, by applying diverse retrieval algorithms. LST images generated from Landsat-8 using the mono-window algorithm (MWAL8) and split-window algorithm (SWAL8) would be compared with the LST estimations from Sentinel-3 SLSTR and Himawari-8 using the split-window algorithm (SWAS3 and SWAH8). Intercomparison will also be performed through segregated groups of different land use classes both during the daytime and nighttime. Results indicate that there is a significant difference among the quantitative distribution of the LST data generated from these three satellites, with average bias of up to −1.80 K when SWAH8 was compared with MWAL8, despite having similar spatial patterns of the LST images. The findings also suggest that retrieval algorithms and the dominant land use class in the study area would affect the accuracy of image-fusion techniques. The results from the day and nighttime comparisons revealed that there is a significant difference between day and nighttime LSTs, with nighttime LSTs from different satellite sensors more consistent than the daytime LSTs. This emphasizes the need to incorporate as much night-time LST data as available when predicting or optimizing fine-scale LSTs in the nighttime, so as to minimize the bias. The framework designed by this study will serve as a guideline towards efficient spatial optimization and harmonized use of LSTs when utilizing different satellite images associated with an array of land covers and at different times of the day. [ABSTRACT FROM AUTHOR]

The surface urban heat island intensity (SUHII) when using nadir‐viewing radiometric and complete surface temperature (Tr and Tc) was evaluated. The urban areas of the Kowloon peninsula and Hong Kong Island were selected and four daytime Landsat TM images and two nighttime ASTER images were collected to retrieve Tr and then model Tc based on a semi‐empirical model. SUHIIs were estimated using both the retrieved Tr and modelled Tc. High spatial resolution (HR) airborne thermal images (0.2 m) observed at 12:10 noon on Oct 24, 2017 were used to retrieve Tc directly. Results based on HR data and satellite data were consistent and indicated that the geometry of the built‐up space had a larger impact on SUHII when using Tc (SUHIIc) than Tr (SUHIIr). During daytime SUHIIc decreased while SUHIIr showed a very slight increase with building density. Both SUHIIc and SUHIIr decreased with higher building height but the rate of decrease of SUHIIc was higher than SUHIIr. Both SUHIIc and SUHIIr decreased with increasing building height variance and increased with increasing sky view factor (SVF). The rate of decrease with building height variance for SUHIIc was larger than SUHIIr. The rate of increase of SUHIIc with SVF was higher than SUHIIr. During nighttime, geometry effects on SUHIIc and SUHIIr were different from daytime. Both SUHIIc and SUHIIr increased with building density, while the rate of increase of SUHIIc with building density, as well as with building height, was much higher than SUHIIr. Both SUHIIc and SUHIIr decreased with SVF, but the rate of decrease of SUHIIc was higher than SUHIIr. Both SUHIIc and SUHIIr initially increased with building height variance and then remained approximately constant. We also evaluated the UHI intensity: SUHIIc was much closer to UHII than SUHIIr. Overall, building geometry had larger impact on SUHIIc than on SUHIIr, that is, SUHIIc is more representative of urban climate than SUHIIr. [ABSTRACT FROM AUTHOR]

Abstract: This study demonstrates that thermal satellite images combined with ‘in situ’ ground data can be used to examine models of heat island genesis and thus identify the main causes of urban heat islands (UHIs). The models, although proposed over 30years ago, have not been thoroughly evaluated due to a combination of inadequate ground data and the low resolution of thermal satellite data. Also there has been limited understanding of the relevance of satellite-derived surface temperatures to local and regional scale air temperatures. A cloud-free ASTER thermal image of urban and rural areas of Hong Kong was obtained on a winter night with a well-developed heat island, accompanied by a 148km vehicle traverse of air temperatures. Over the whole traverse a high R 2 of 0.80 was observed between surface and air temperatures, with the two datasets showing a similar amplitude and general trend, but with the surface exhibiting much higher local variability than air temperature. Gradients in both surface and air temperature could be related to differences in land cover, with little evidence of large scale advection, thus supporting the population/physical structure model of UHI causation, rather than the advection model. However, the much higher surface and air temperatures observed over the largest urban area, Kowloon, than over any smaller urban centre with similar physical structure in the New Territories, would seem more indicative of the advection model. The image and ground data suggest that Kowloon''s urban canopy layer climate is mainly influenced by local city structure, but it is also modified by a strongly developed, regional scale urban boundary layer which has developed over the largest urban centre of Kowloon, and reinforces heating from both above and below. [Copyright &y& Elsevier]

The aim of this study is to characterize the urban heat island (UHI) intensity in Hong Kong. The first objective is to explore the UHI intensity in Hong Kong by using the mobile transverse and remote sensing techniques. The second objective is to produce a satellite-derived air temperature image by integrating satellite remote sensing with a mobile survey, the methodology involved in making simultaneous ground measurements when the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite made an overpass. The average UHI intensity of Hong Kong was about 2°–3.5°C, although a very high value of 12°C UHI was observed on a calm winter night by ASTER. The satellite-derived surface temperature was compared with the in situ measurements. The bias was found to be only about 1.1°C. A good correlation was also found between the in situ surface and air temperature pair of readings at nighttime on 31 January 2007. The linear regression lines of temperatures in urban and suburban areas were then used to convert the satellite-derived surface temperatures into air temperatures. The satellite-derived air temperatures showed a good correlation with temperatures observed by 12 fixed stations. It is possible to derive the nighttime air temperature from the satellite surface temperature on calm and clear winter nights. [ABSTRACT FROM AUTHOR]

Restrictions on human and industrial activities due to the coronavirus (COVID-19) pandemic have resulted in an unprecedented reduction in energy consumption and air pollution around the world. Quantifying these changes in environmental conditions due to government-enforced containment measures provides a unique opportunity to understand the patterns, origins and impacts of air pollutants. During the lockdown in Pakistan, a significant reduction in energy demands and a decline of ∼1786 GWh (gigawatt hours) in electricity generation is reported. We used satellite observational data for nitrogen dioxide (NO 2), carbon monoxide (CO), sulphur dioxide (SO 2), aerosol optical depth (AOD) and land surface temperature (LST) to explore the associated environmental impacts of shifts in energy demands and emissions across Pakistan. During the strict lockdown period (March 23 to April 15, 2020), we observed a reduction in NO 2 emissions by 40% from coal-based power plants followed by 30% in major urban areas compared to the same period in 2019. Also, around 25% decrease in AOD (at 550 nm) thickness in industrial and energy sectors was observed although no major decrease was evident in urban areas. Most of the industrial regions resumed emissions during the 3rd quarter of April 2020 while the urban regions maintained reduced emissions for a longer period. Nonetheless, a gradual increase has been observed since April 16 due to relaxations in lockdown implementations. Restrictions on transportation in the cities resulted in an evident drop in the surface urban heat island (SUHI) effect, particularly in megacities. The changes reported as well as the analytical framework provides a baseline benchmark to assess the sectoral pollution contributions to air quality, especially in the scarcity of ground-based monitoring systems across the country. Image 1 [ABSTRACT FROM AUTHOR]