Jia Wang

Urban heat mitigation is a pressing concern for cities. Intense urban heat poses a threat to human health and urban sustainability. Tree planting is one of the most widely employed nature-based heat mitigation methods worldwide. Therefore, city policy makers require knowledge of how much temperature will be reduced by increasing urban tree canopy (UTC). Cooling efficiency (CE), which was been proposed to quantify the magnitude of temperature reduction associated with a 1% increase in UTC, has been primarily investigated at smaller scales previously. However, such small-scale results cannot be used to develop policy at the whole-city scale. This study developed a method that reveals the scaling relations of CE so as to predict its effects at the city scale. CE was found to follow the form of a power law as spatial scale increased from the small analytical units through intermediate size units up to the extent of a whole city. The power law form appeared consistently across cities with different climate backgrounds during summer daylight hours. Furthermore, the power law form was robust within cities under different summer weather conditions. The power-law scaling approach can thus be used to predict CE at the whole-city scale, providing a useful tool for managers to set UTC goals to mitigate extreme urban heat.

Human health, energy and comfort are determined by the climate that remains in the physical environment. Regarding urban climate, few studies assess the urban heat island effect, heat stress, and public health as geographical representations. This study seeks to fill this gap by selecting Colombo, Sri Lanka, and Shenzhen, China, comparatively, two coastal cities with different climate conditions. We quantified and compared the effects of heat waves and their impacts on public health and the effect of urbanization on urban heat islands (UHI). Heat-related public health issues have been calculated using the Wet-Bulb Globe Temperature (WBGT) index. The Urban Heat Island (UHI) effect was analyzed using Land Surface Temperature (LST), created based on Landsat images obtained in 1997, 2009 and 2019. A rapid increase in temperature and humidity creates an uncomfortable environment in both cities, but apparent differences can be observed in climatic phenomena. During the summer (June to August), the prevailing atmospheric condition in Shenzhen makes a “Very severe stress” with Heatstroke highly likely. Nevertheless, seven months (November to April) are found as “Comfortable” without having any heat-related health injuries. However, Colombo has never been classified as “Comfortable” throughout the year. Out of twelve, five months (April to August) are found as “Very severe stress” with Heatstroke highly likely. When considering the urban expansion and UHI, a fast expansion can be observed in Colombo than in Shenzhen. Consequently, with the more severe heat-related public health and rapid urban heat island expansion, Colombo makes it more stressful than Shenzhen city. Our findings highlight the comparison between heat-related public health and urban heat island between two coastal cities with different climate conditions and under rapid urbanization processes. Therefore, it is imperative to assess these risks and respond effectively.

The local rise in urban temperature is increasingly exacerbated due to the combined effect of urban heat islands and global climate change. Numerous studies have shown that green roofs (GRs) have great potential for facilitating urban heat mitigation. However, little is known about whether such cooling effects can be achieved under extreme heat conditions. With the expected occurrence of more extreme heat events under climate change, such understanding is crucially important for the effective design of heat mitigation. This study aims to fill this gap by investigating the pedestrian-level cooling effect of GR under two weather conditions (i.e., typical summer weather conditions and extreme heat conditions). This research employed a three-dimensional simulation model, ENVI-met, to simulate pedestrian-level air temperature for three typical residential areas with different roof heights in Beijing. We conducted the simulations in two different roof scenarios, conventional roofs versus green roofs. The results showed that green roofs could provide large cooling exceeding 0.2°C on downwind sides and in the daytime, although the average cooling intensity was small. The pedestrian-level cooling intensity of GR decreased significantly under extreme heat conditions compared to typical summer weather conditions. It varied diurnally following an inverted W-shape for both weather conditions. Results also showed that the pedestrian-level cooling intensity of GR decreased with the increase in roof height in a nonlinear way and became 0 when roof height reached ∼50 m for both weather conditions. The results of our research can provide important insights for cooling-oriented urban design in the future, as we are expecting such extreme weather conditions nowadays may be the new normal in the future.

Urban vegetation can be highly dynamic due to the complexity of different anthropogenic drivers. Quantifying such dynamics is crucially important as it is a prerequisite to understanding its social and ecological consequences. Previous studies have mostly focused on the urban vegetation dynamics through monotonic trends analysis in certain intervals, but not considered the process which provides important insights for urban vegetation management. Here, we developed an approach that integrates trends with dynamic analysis to measure the vegetation dynamics from the process perspective based on the time-series Landsat imagery and applied it in Shenzhen, a coastal megacity in southern China, as an example. Our results indicated that Shenzhen was turning green from 2000–2020, even though a large-scale urban expansion occurred during this period. Approximately half of the city (49.5%) showed consistent trends in greening, most of which were located in the areas within the ecological protection baseline. We also found that 35.3% of the Shenzhen city experienced at least a one-time change in urban greenness that was mostly caused by changes in land cover types (e.g., vegetation to developed land). Interestingly, 61.5% of these lands showed trends in greening in the recent change period and most of them were distributed in build-up areas. Our approach that integrates trends analysis and dynamic process reveals information that cannot be discovered by monotonic trends analysis alone, and such information can provide insights for urban vegetation planning and management.

Numerous studies have shown an increased surface urban heat island intensity (SUHII) in many cities with urban expansion. Few studies, however, have investigated whether such intensification is mainly caused by urban warming, the cooling of surrounding nonurban regions, or the different rates of warming/cooling between urban and nonurban areas. This study aims to fill that gap using Beijing, China, as a case study. We first examined the temporal trends of SUHII in Beijing and then compared the magnitude of the land surface temperature (LST) trend in urban and nonurban areas. We further detected the temporal trend of LST (Trend_LST) at the pixel level and explored its linkage to the temporal trends of EVI (Trend_EVI) and NDBI (Trend_NDBI). We used MODIS data from 2000 to 2015. We found that (1) SUHII significantly increased from 4.35 °C to 6.02 °C, showing an intensified surface urban heat island (SUHI) effect, with an annual increase rate of 0.13 °C in summer during the daytime and 0.04 °C in summer at night. In addition, the intensification of SUHII was more prominent in new urban areas (NUA). (2) The intensified SUHII, however, was largely caused by substantial cooling effects in nonurban areas (NoUA), not substantial warming in urban areas. (3) Spatially, there were large spatial variations in significant warming and cooling spots over the entire study area, which were related to Trend_NDBI and Trend_EVI. Trend_NDBI significantly affected Trend_LST in a positive way, while the Trend_EVI had a significant positive effect (p = 0.023) on Trend_LST only when EVI had an increasing trend. Our study underscores the importance of quantifying and comparing the changes in LST in both urban and nonurban areas when investigating changes in SUHII using time-series trend analysis. Such analysis can provide insights into promoting city-based urban heat mitigation strategies which focused on both urban and nonurban areas.

Landscape changes associated with urbanization can lead to many serious ecological and environmental problems. Quantifying the vertical structure of the urban landscape and its change is important to understand its social and ecological impacts, but previous studies mainly focus on urban horizontal expansion and its impacts on land cover/land use change. This papers focuses on the residential landscape to investigate how the vertical dimension of the urban landscape (i.e., building height) change through time, and how such change is related to changes in the horizontal dimension of the landscape, using Beijing, the capital of China, as a case study. We quantified the expansion of the residential neighborhoods from 1949 to 2009, and changes in vegetation coverage, building density, and building height within these neighborhoods, using 1 m spatial resolution imagery. One-way ANOVA and correlation analysis were used to examine the relationships of building height to vegetation coverage and building density. We found: (1) The residential areas expanded rapidly and were dominated by outward growth, with much less within-city infilling. The growth rate varied greatly through time, first increasing from 1949–2004 and then decreasing from 2005–2009. The expansion direction of newly built residential neighborhoods shifted from west to north in a clockwise direction. (2) The vertical structure of residential neighborhoods changed with time and varied in space, forming a “low-high” pattern from urban central areas to the urban edges within the 5th ring road of Beijing. (3) The residential neighborhoods built in different time periods had significant differences in vegetation coverage, building density, and building height. The residential neighborhoods built in more recent years tended to have taller buildings, lower building density and lower vegetation coverage.