Permeable pavements help reduce surface temperatures and have been widely implemented in urban areas. This study utilized an in-use permeable pavement sidewalk in front of a mass rapid transit station in the Taipei city center of Taiwan to determine the actual pavement surface temperature performance. A neighboring asphalt road and impervious pavement were also monitored. With a full year of continuous monitoring, the results showed that the temperature of permeable pavement was 3.7 °C lower than that of impervious pavement and 4.5 °C lower than that of asphalt pavement in the hot season. The frequent rainfall in spring resulted in the smallest temperature differences between the different pavement types. The cooling effects of permeable pavement differed at the different air temperatures. At air temperatures lower than 15 °C, the differences among pavement surface temperatures were noticeable. However, when the air temperature was higher than 35 °C, the surface temperature of permeable pavement was not different from that of impervious pavement and was greater than 55 °C. Field observations were carried out to determine the effects on the apparent temperature and the future surface temperature of climate change scenarios. The results showed that permeable pavement could reduce the average apparent temperature to near the air temperature, and asphalt pavement could increase the apparent temperature by 1.2 °C, assuming that the pavement temperature completely affects the air temperature. With the good prediction ability of the machine learning approach and 15 environmental factors, the preliminary prediction showed the projected surface temperature change in Taipei city in 2033. In the worst-case scenario, the average impervious pavement temperature is as high as 39.12 °C, whereas the average permeable pavement temperature is 32.50 °C.

The development of urban blue-green spaces is highly recommended as a nature-based solution for mitigating the urban heat island phenomenon, improving urban sustainability, and enhancing resident well-being. However, limited attention has been given to the accumulative impact of the cooling effect and the comparison of different types of landscapes. Based on the maximum and accumulative perspectives, this study selected 375 green spaces, water bodies, and urban parks in 25 cities of the Yangtze River Delta (YRD) region in China to quantify their cooling effect. Correlation and regression analyses were employed to identify the dominant factors influencing the cooling performance. The results indicated that (1) compared to other landscape patches, water areas, and parks exhibited a reduction in daily average air temperature by 3.04 and 0.57 °C, respectively. Urban parks provided the largest cooling area (CA) of 56.44 ha in the YRD region, while water bodies demonstrated the highest cooling effect (CE) of 6.88, cooling intensity (CI) of 0.02, and cooling gradient (CG) of 0.99. (2) From the maximum perspective, the perimeter of the patches played a dominant role in CA and CE for all landscape patch types, contributing more than 40% in CA variation. (3) The dominant factors varied among different landscape types from accumulative perspectives. Green spaces were influenced by road density, shape index, and the proportion of water bodies within the CA, whereas water bodies were primarily affected by the coverage of blue spaces. Vegetation growth and densely populated surroundings contributed the most to the cooling of parks. These findings enhanced the comprehension of the cooling effect in comparable urban contexts and provided valuable insights for sustainable urban management.

This paper uses the tourism heat footprint (THF) and a structural vector autoregressive model to investigate how tourism has affected the urban heat island effect in Macao, a typical urban tourism destination. The dynamic relationships between the THF, heat island intensity (HII), and quarterly average temperature (QAT) are investigated. The impulse response function and variance decomposition analysis are used to assess if a long-term causal relationship exists between the three indicators. The results show the following. (1) The hotel industry in Macao is the source of energy consumption and heat release. (2) A Granger causality relationship exists between the THF and QAT but not between the THF and HII. (3) The interaction effect between the growth rate of the THF and QAT is manifested as shocks with the same frequency and regular periodic fluctuations. (4) The heat island effect of this tourism destination is reflected in an increase in local temperature due to the energy consumption and heat release by tourists. Based on the results, policy implications for a sustainable tourism city are provided.

The global climate warming caused by urbanization has significantly affected the urban environment. Whilst land surface temperature (LST) is an important factor reflecting urban temperature, previous research on LST mostly focused on two-dimensional (2D) factors and rarely mentioned about the role of three-dimensional (3D) factors, particularly the LST variation characteristics of island cities. Therefore, this study examined the seasonal variation characteristics of urban LST by analyzing the impact of 2D and 3D urban morphology factors of different urban block types on LST in Xiamen Island. The main results are as follows. First, compact low layer (CL), a block type with a higher density of low-rise buildings, has a higher LST in any season. Under the same block density (BD), the higher the block average height (BH), the lower the LST. Second, among the 2D urban morphology factors, normalized difference vegetation index (NDVI) was the main factor for cities to reduce urban LST, especially in summer, while normalized difference built-up index (NDBI) was the opposite. Different from land cities, we found a positive correlation between modified normalized difference water body index (MNDWI) and LST in autumn and winter. Third, in the 3D urban morphology factors, sky view factor (SVF) was significantly positively correlated with LST, while building fluctuation (BF) was negatively correlated. The higher the SVF, the worse the radiation shielding effect between buildings. On the contrary, the higher the BF, the higher the building undulation, and the better the building radiation shielding. These findings should provide some quantitative insights for the future construction and planning of island cities, which can be used to improve the thermal environment of island cities and support the sustainable development of cities.

Ecosystem-based cooling helps residents cope with the urban heat-island problem. In order to improve the accuracy of traditional heat-island measurements based on comparisons between urban and rural areas, we use an \"on-site\" method developed with only urban data. The essence of this method is a regression analysis of the relationships among different types of green space and blue space, elevation, vegetation dynamics, and temperature. We then simulate the temperature pattern in a scenario where there is no built-up area (Scenario A), and then in another scenario where there are no ecological spaces (Scenario B). The gap between the actual temperature pattern and the simulated temperature pattern of Scenario A is considered the heat-island effect. Conversely, the gap between the actual temperature pattern and that of Scenario B is considered as the effect of ecosystem-based urban cooling. This method was tested using data from two megacities in China (each had a population of over 10 million people). For Beijing, the average heat-island effect was 4.87 °C and effect of the ecosystem cooling service was 9.07 °C. For Shenzhen, the respective values were 0.8 °C and 2.71 °C. The \"on-site\" (local small size sampling), \"dynamic coefficient\", and \"no-positive-coefficient rule\" are the three defining characteristics of this method. The application of this method to model ecosystem-based urban cooling can aid urban planning and management in improving the residential thermal environment.

The response of land surface phenology (LSP) to the urban heat island effect (UHI) is a useful biological indicator for understanding how vegetated ecosystems will be affected by future climate warming. However, vegetation cover in rural areas is often dominated by cultivated land, whose phenological timing is considerably influenced by agricultural managements (e.g., timing of sowing and harvesting), leading to biased conclusions derived from the urban-rural LSP differences. To demonstrate this problem, we investigated the crop influence on the phenological response to a warmer environment resulting from the UHI effect. We partitioned cities in the United States into cultivated and non-cultivated categories according to the proportion of crops in rural areas. We then built continuous buffer zones starting from the urban boundary to explore the urban-rural LSP differences considering the UHI effect on them. The results suggest crop inclusion is likely to lead to >14 days of urban-rural differences at both the start of the season (SOS) and the end of the season (EOS) between cultivated and non-cultivated cities. The temperature sensitivity (ST) of SOS is overestimated by approximately 2.7 days/°C, whereas the EOS is underestimated by 3.6 days/°C. Removing crop-dominated pixels (i.e., above 50 %) can minimize the influence of crop planting/harvesting on LSP and derive reliable results. We, therefore, suggest explicit consideration of crop impacts in future studies of phenological differences between urban and rural areas and the UHI effect on LSP in urban domains, as presented by this comprehensive study.

It is imperative to quantitatively analyze the long-term temporal and spatial characteristics of the urban heat island (UHI) effect on cities for applications, such as urban expansion and environmental protection. Owing to the high spatial resolution and availability of long time-series data, remote sensing images from Landsat satellites are widely used for land surface temperature (LST) retrieval. However, limited by the satellite revisit cycle and image quality, the use of multisource Landsat images in a long-term study of the UHI effect is inevitable. Nonetheless, owing to the differences among multisource sensors, such as Landsat-7 and Landsat-8, there may be apparent deviations in the LST results retrieved from different sensor data, which are obtained from the same area and under similar circumstances. Consequently, it is necessary to build a relationship between the LST results generated from multisource Landsat sensors for future research on the UHI effect. In this study, Shenzhen city was studied to explore the fitting relationship between the corresponding LST products from Landsat-7 and Landsat-8 images obtained from adjacent dates with similar climatic conditions. Furthermore, factors affecting the fitting models, such as land cover types, seasonal and inter-annual differences, were analyzed. The constructed fitting model had a strong relationship with land cover types but a relatively weak relationship with seasonal and inter-annual differences; this indicates that a pseudo Landsat-8-based LST product can be generated from a Landsat-7-based LST product using a model fitted by a Landsat-7/8 pair obtained from adjacent years (or different seasons). Finally, by considering the consistency between LST products from multisource Landsat images, the spatiotemporal variations in the UHI effect in Shenzhen can be accurately explored using long time-series data.

Urban thermal environments are closely related to habitats, citizens\' health, and sustainable development. Based on green view index (GVI), we proposed two new visual indices, construction view index (CVI) and imperious surface view index (R&PVI). Mobile observation was used to obtain urban thermal environment data, images and coordinates synchronously in Xuzhou City in late summer, including urban area (U), scenic area (S), exterior of university campus (E), and university campus inside (CUMT). We analyzed the impacts of the urban composition represented by the visual index on the urban thermal environment. The results showed that, along the sampling line, mean air temperature (Ta) was highest (30.42 ℃) and mean relative humidity (RH) was lowest (40.7%) in urban area, while mean Ta was lowest (29.35 ℃) and mean RH was highest (48.4%) in scenic area. The situation of mean wind-chill temperature (TaW) was the highest (32.95 ℃) in the urban area and the lowest (31.93 ℃) in the scenic area. As for CVI, urban area, university campus inside, exterior of university campus and scenic area ranked in descending order, while GVI showed an opposite pattern. CVI was significantly positively correlated to Ta and TaW, but negatively to RH. GVI was significantly negatively correlated to Ta and TaW, but positively to RH. R&PVI was significantly positively correlated to Ta and TaW, but not correlated to RH. CVI and GVI influenced Ta significantly, with the independent effects being 10.4% and 18.9%, and joint effects being 7.8% and 11.3%, respectively. As for RH, CVI and GVI contributed significantly as well, independent effects were 37.5% and 15.7%, and joint effects were 51.4% and 30.2%, respectively. As for TaW, the three visual indices contributed significantly, but independent and joint effects were lower than those on Ta. Moreover, visual indices contributed more on RH than Ta or TaW. The results could provide ideas for optimizing urban thermal environments and mitigating urban heat island effects, and have practical implications for urban renewal and improvement of the quality of human living environment.
城市热环境与人居环境、居民健康以及城市可持续发展密切相关。本研究以绿视率(GVI)为基础,提出地上构筑物视率(CVI)和硬化地表视率(R&PVI)2种新的视觉指数,使用移动观测方法,在夏季末获取徐州市主城区、风景区、大学校园外部以及大学校园的城市热环境数据,并同步获取采样沿线的影像和坐标信息,分析以视觉指数所表示的城市构成对城市热环境的影响。结果表明: 在采样沿线上,研究区主城区平均气温(Ta,30.42 ℃)最高但相对湿度(RH,40.7%)最低,风景区的平均Ta(29.35 ℃)最低但RH(48.4%)最高;平均风冷温度(TaW)在主城区最高(32.95 ℃),在风景区最低(31.93 ℃)。CVI由高至低依次为:主城区、大学校园、大学校园外部和风景区,GVI与CVI相反。CVI分别与Ta和TaW呈极显著正相关,与RH呈极显著负相关;GVI分别与Ta和TaW呈极显著负相关,与RH呈极显著正相关;R&PVI分别与Ta和TaW呈显著和极显著正相关,与RH的相关性不显著。CVI和GVI对Ta的贡献显著,独立贡献率分别为10.4%和18.9%,联合贡献率分别为7.8%和11.3%;对RH而言,二者贡献同样显著,独立贡献率分别为37.5%和15.7%,联合贡献率分别为51.4%和30.2%;对TaW而言,3种参数的贡献均达到显著水平,三者的独立贡献率和联合贡献率均低于对Ta的影响;3种参数对RH的影响高于对Ta和TaW。研究结果可为优化城市热环境和缓解城市热岛效应提供思路,也对城市更新以及人居环境质量提升具有实践意义。.

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

Urbanization is gaining force globally, which challenges biodiversity, and it has recently also emerged as an agent of evolutionary change. Seasonal phenology and life cycle regulation are essential processes that urbanization is likely to alter through both the urban heat island effect (UHI) and artificial light at night (ALAN). However, how UHI and ALAN affect the evolution of seasonal adaptations has received little attention. Here, we test for the urban evolution of seasonal life-history plasticity, specifically changes in the photoperiodic induction of diapause in two lepidopterans, Pieris napi (Pieridae) and Chiasmia clathrata (Geometridae). We used long-term data from standardized monitoring and citizen science observation schemes to compare yearly phenological flight curves in six cities in Finland and Sweden to those of adjacent rural populations. This analysis showed for both species that flight seasons are longer and end later in most cities, suggesting a difference in the timing of diapause induction. Then, we used common garden experiments to test whether the evolution of the photoperiodic reaction norm for diapause could explain these phenological changes for a subset of these cities. These experiments demonstrated a genetic shift for both species in urban areas toward a lower daylength threshold for direct development, consistent with predictions based on the UHI but not ALAN. The correspondence of this genetic change to the results of our larger-scale observational analysis of in situ flight phenology indicates that it may be widespread. These findings suggest that seasonal life cycle regulation evolves in urban ectotherms and may contribute to ecoevolutionary dynamics in cities.