Rising summer temperatures caused by the urban heat island (UHI) has considerable effects on the physical and mental health of urban residents globally. To categorize residents’ health risk areas and evaluate the characteristics of...

Background: We explored the spatio-temporal characteristics of urban heat island (UHI) effect on cardiovascular diseases (CVDs). Methods: The land surface temperatures (LST) were retrieved from four Landsat remote-sensing images’ data, the temperature data from 95 meteorological stations, and analysis data on CVDs mortality. Based on these data, landscape pattern indexes were used to analyze the pattern-process-function and the mechanism. Results: During 1984–2017, the effects of UHI on CVDs increased, thereby increased the mortality by 28.8%. The affected areas gradually expand from the central area of the city and undergo three evolution stages; the highly affected areas are mainly distributed in central and southern regions, and patches increase in number. The areas and ratio of high-level patches also show an upward tendency, increasing dominance in the overall landscape. Patches of the overall landscape become more complicated in shape, whereas those of high-level ones become less complicated. Concentration degree of the overall landscape decreases gradually with the types of landscapes patches increasing, reaching a rather even space distribution. Conclusion: Increased temperatures exacerbated by UHI lead to increased CVD mortality. As cities expand, the effects of UHI on CVDs increase in terms of both intensity and areas, with the overall landscape in uneven distribution, high-level affected areas in point distribution, and low-level ones in large-area concentration.

Background: Urban heat island (UHI) is being intensified with the progress of urbanization. Meanwhile, respiratory diseases caused by high temperature become common. This study explores the influences of UHI on respiratory diseases (J00-J99) and the evolutionary characteristics of the spatial pattern of such influences. Methods: The pattern–process–function and the influencing mechanism of UHI on respiratory diseases were evaluated through landscape pattern indexes from 1992 to 2018 in Tianjin, China. The basis was on data from Landsat TM/OLI/TIRS remote-sensing images, meteorological stations, and mortality of respiratory diseases. Results: The fluctuating influence of UHI on the respiratory diseases in Tianjin has increased from 1992 to 2018, showing a significant phase-based characteristic. During 2011-2018, the influence has soared greatly, and mortality risk has increased by 101%, and the influenced area has reached 349 km². Furthermore, the regional space clustered, and the influenced patches are in irregular shape, and the highly influenced area is enlarged. Moreover, the indexes of the landscape level of the influenced areas all decrease. The patches at all levels are fragmented and distributed discontinuously. Spatially, the influenced areas gradually extend from the urban center to the suburbs. Conclusion: UHI causes a higher mortality of respiratory diseases because it increases daily average air temperature in summer. With respect to landscape pattern, the influenced areas at low level is highly interconnected and relatively concentrated, whereas the influenced area at high level is distributed in clusters. In general, the influenced area is fragmented and discontinuously distributed in urban center.

The emotional health of urban residents has been seriously threatened by frequent and normalized heat waves. This study constructed the VI-level assessment standard for emotional health risk using data from satellite images, meteorological sites, questionnaire surveys, and statistical yearbooks to assess the effect of high temperatures on negative emotions in Hangzhou. The results showed that the morphological changes of urban high-temperature areas were aggregated from a cross-shape to a large patch shape, then dispersed into cracked patch shapes. Additionally, the health risk of daytime negative emotions peaked at the VI-level from 1984 to 2020, and the influence level of the typical period risk increased by 1–2 levels compared with the daytime. Additionally, driven by urban spatial structure policies, the risk pattern of emotional health expanded outward from a single center into multiple centers. The emotional health risk level rose and then descended in urban centers, and the innovation industries drove the variation tendency of hot spots. Furthermore, high educational background, employment, and couples living together were critical variables that could alleviate the emotional health risk to the middle-aged and elderly population. This study aimed to optimize the urban spatial structure and alleviate residents’ emotional health hazards for healthy urban planning.

The effects of global urbanization and climate warming on public health, including the health risk caused by the urban heat environment, have drawn extensive attention. Therefore, methods to appraise the spatiotemporal response mechanism of climate warming and the high temperature exposure dose (HTED) of urban residents need to be investigated to achieve sustainable cities. Based on an environmental health risk appraisal model and using the six ring roads in Beijing of China, this study simulated the spatial distribution of future high temperatures in three shared socioeconomic pathways (SSPs), SSP1-2.6, SSP3-7.0, and SSP5-8.5, by determining the correlation between the future mean maximum temperatures and sunny-day mean temperatures. Additionally, the HTED of commuting on foot or by bicycle was assessed based on the point of interest data of subway stations, architectural composition, and traffic surveys. Results demonstrate that during 2020–2040, 66.57% and 50.07% of areas for commuting on foot and by bicycle within the sixth ring road exposed to high temperatures (including low, medium, and high-risk areas). The exposure risks of both commuting methods were concentrated, and the exposure dose was the highest between the fifth and sixth ring roads, whereas the first to fourth ring roads were dominated by non-risk, minimum-risk, and low-risk areas. Moreover, compared with SSP1-2.6 and SSP5-8.5, SSP3-7.0 exhibited low HTED in both commuting methods. The proposed method provides a scientific basis to aid in identifying urban areas with high temperature exposure risk and a reference in assisting the planning of resilient and sustainable cities. Keywords: Climate warming; High temperature exposure dose; Scenario simulation; Commuting modes; Public health

Green space is one of the main measures to alleviate urban heat islands (UHI). The transformation mechanism of daytime and nighttime scale sensitivity of vegetation coverage to reduce the UHI effect in a daily cycle has been unclear. As a result, we propose a scale sensitivity measurement algorithm to study the spatial and temporal response relationship between UHI and green coverage. Based on the scale theory of landscape ecology and the method of geostatistical analysis, we adopted ArcGIS, MATLAB, SPSS, and other data processing software as well as a large amount of measured and high-resolution satellite imagery data of Beijing and Tianjin to quantitatively study their spatial scale sensitivity and daily variation features of urban green spaces to reduce summer UHI. The results show that first, the green coverage rate and the UHI intensity experience positive and negative correlations during the daytime, and negative correlations at night. When the correlation coefficient is significant, there is a linear relationship between the UHI intensity and the core green rate. Second, the reduction of the UHI by green spaces displays spatial and temporal change scale sensitivity characteristics. The radius scale of daytime sensitivity is 15m, and the radius scale of nighttime sensitivity is 60m. The study's conclusion enriches the theoretical parameters of landscape ecological scales and patterns, and provides spatial and temporal scales for systematic planning of green space to reduce UHI.