Global heating and continued urbanization have aggravated the urban heat island (UHI) effect and created health challenges to the public. Oxford English Dictionary recently added a new phrase “global heating.” According to the Oxford Monitor Corpus, the term “global heating” appeared nearly 15 times more frequently in the first half of 2021 than in the first half of 2018 (Simpson and Weiner, 1993). This word strengthens passive and mild terms such as “climate change” and “global warming” to express the seriousness of climate change caused by human activities and the urgency of solving such problem (World Meteorological Organization [WMO], 2021). In 2020, China’s urbanization level has reached 63.89%, and more natural landscapes have been replaced by impervious surfaces, and highly concentrated human activities have been changing the urban thermal environment (Yang et al., 2019; Yin et al., 2022). This phenomenon occurs in nearly every urban area, no matter whether the city is small or large, or located in warm or cold climates. UHI effect, in turn, endangers residents’ survival and wellbeing. UHI, in addition, is a major trigger of psychological issues like pain, anxiety, and fear (Fritze et al., 2008; Hayes and Poland, 2018).

The research on the influence of urban thermal environment on human health mainly focuses on physiological aspects, discussing the relationship between high temperature and heat-related diseases and mortality (Nitschke et al., 2011; Guo et al., 2018; Thompson et al., 2018; Yin et al., 2018), and paying less attention to emotional health. Studies have shown that there is a significant relationship between high temperature and mental illness. For example, according to a research in Australia, the number of patients hospitalized for mental illness increased by 7% during the heat waves (Hansen et al., 2008). A study of data during four heatwaves in Jinan also found that the number of daily medical visits for mental illness increased significantly during the heatwaves (Liu et al., 2012). Further, a study on the relationship between the daily average temperature and the hospitalization rate of mental disorders in Shanghai from 2008 to 2015 found that when the temperature reached 24.6°C and above, there was a significant positive correlation between the increase in temperature and the increase in the number of hospitalizations for schizophrenia. When the temperature was above 33.1°C, the risk of hospitalization for mental disorders was 1.266 times that of the mean (Peng et al., 2017). However, the analysis of the influence of thermal environment on the emotional health of the general population and its specific influencing factors is still lacking. Therefore, under the background of global heating, it is urgent to pay attention to the emotional health of residents in rapidly urbanized areas, carry out risk assessment of thermal environment on human emotional health, and explore the mechanism of urban thermal environment on emotional health.

The continuous rise of temperature, which concerns people’ emotional health, is one of the major influences of UHI (Manning and Clayton, 2018). Compared to the comfortable temperature of 23°C, with every 1°C increase in temperature, the predictable psychological stress rises by 0.2% (Ding et al., 2016). And people may experience repression, rage, and pain when the temperature is too high (Vanos et al., 2012). Burke et al. (2018) discovered that the higher the monthly average temperature is, the more suppressed words are used on Twitter. In addition, climate change has exacerbated the social inequality faced by heat-vulnerable population, especially the middle-aged and elderly (McMichael, 2017) as they are more likely to develop mental health problems following natural disasters (Parker et al., 2016). When body temperature rises, the temperature of the central nervous system and brain rises as well, triggering the hypothalamus’s selective cooling system to lower the brain temperature. However, for the middle-aged and elderly population, their physical functions and cooling system are not strong enough, thus being widely considered sensitive to extremely high temperature (Hames et al., 2016). To be more specific, heat-vulnerable population includes infants, children, and middle-aged and elderly people (Bielby, 2019). But, due to the insufficient cognitive ability of infants and children, they are not included in this study. Hence, based on Erikson Stages of Psychosocial Development (40–65 years old as Middle Adulthood), this study was targeted at middle-aged and elderly at and over 40 years old. Furthermore, our research team discovered that the negative emotions induced by thermal environment are mostly distress, irritability, nervousness, and hostility, with irritability the most obvious, thus this study decided on the negative emotion of irritability (Huang et al., 2020a).

The urban thermal environment is a complex physical phenomenon, and the influencing factors mainly include spatial structure features, land-use and land-cover change (LUCC), landscape pattern, artificial heat release, and wind environment (Yao et al., 2019; Yue et al., 2019; Xu et al., 2021). Research shows UHI is positively correlated with road network density (RND), while negatively correlated with fraction vegetation coverage (FVC) (Estoque et al., 2017). Moreover, water bodies are another identified source of reduced thermal load, at least during the day, due to a high thermic inertia (Liu and Weng, 2008). The cooling capacity of urban wetland is positively correlated with the wetland’s area, shape, and degree, yet negatively correlated with the height and density of nearby buildings (Xue et al., 2019). Additionally, land surface temperature (LST) and near-surface temperature are two important parameters for studying the urban thermal field. Some scholars used temperature data to analyze the pattern characteristics of urban temperature field. For example, Shen et al. (2017) studied the spatiotemporal distribution of UHI in the center of Shanghai city based on hourly temperature. Heat island intensity and temperature differ in spatiotemporal pattern, maybe because the “thermal hysteresis” of the large heat capacity of the underlying urban surface makes the change of heat island intensity lag behind that of temperature (Mao et al., 2021). Temperature is mainly influenced by each or the combination of LUCC indicators and human factors (Ren et al., 2005). Therefore, FAR, BD, RND, and other related indicators reflecting different human activities, as well as FVC, NDVI, WSR, MNDWI, and other LUCC-related indicators were selected in this study.

With the rapid development of urban thermal environment research, spatial non-stationarity becomes a new trend in studies on land surface index. Geographically weighted regression (GWR) algorithm is limited to a single optimal bandwidth and usually reflects the “average” of the optimal bandwidth of each process (Oshan et al., 2019). It is not suitable for changes in the urban thermal field, as different processes involve different spatial scales. In recent years, the development of MGWR (Fotheringham et al., 2017) has broken the limits. However, MGWR is still insufficient in estimating local parameters. In 2019, Yu et al. (2020) broke the limits of the MGWR model so that it can be widely used in empirical research. Shen et al. (2020) discussed how the MGWR model influences second-hand house price, and proved that it is helpful to studies on spatial variation; Chen and Deng (2021) and Li et al. (2021) used MGWR to analyze how urban landscape and form influence thermal environment.

This manuscript takes the Sixth Ring Road area of Beijing as the research object, and aims to study the spatial distribution characteristics and mechanism of the impact of urban thermal environment on residents’ emotional health. Using Landsat remote sensing images, meteorological stations, and emotional health data to establish a theoretical relationship model between temperature and irritability; combined with ArcGIS, MATLAB, SPSS and other data analysis platforms to analyze the temporal and spatial effects of hourly temperature on irritability in summer from 09:00 to 20:00; based on the MGWR model to explore the spatial differentiation of residents’ irritability affected by different thermal environmental factors. It provides a basis for optimizing the layout of urban green space, improving the urban thermal environment, and reducing the risk of emotional health.

The list of acronyms in this paper is shown in Table 1.

First, different from other scholars’ researches that focus on exploring the relationship between temperature and the morbidity and mortality of residents’ physical or psychological diseases (Petitti et al., 2016; Ebi et al., 2021; Yang et al., 2021), this study focuses on the emotional health of the general population, especially the middle-aged and elderly, in an innovative way. The quantitative relationship between temperature and irritability was established, and based on this, the impact of urban high temperature on irritability was divided into 10 grades. Using the established irritability risk evaluation criteria, this manuscript analyzed the temporal and spatial pattern of irritable emotions in Beijing from 09:00 to 20:00 in summer. Consistent with the inverted “U-shaped” relationship between temperature and mental health (Mullins and White, 2019), there is a similar relationship between irritability and time (Figure 9). The difference is that this relationship further reflects the tolerance of the human body to high temperature in the environment and the time law of the emotional regulation mechanism, which provides an idea for further research on the impact of high temperature and human emotional health in the future. However, the impact of high temperature on human emotional health is the combined result of many factors. This manuscript ignored the influence of the subjects’ own mental health level, education level, family status, economic status, race, etc., (Abbasi et al., 2019; Chang and Kajackaite, 2019) on their emotional status, as well as the interference of other environmental factors such as humidity and wind speed, resulting in biases in the validity and accuracy of the data.

Second, this manuscript also used MGWR to analyze the mechanism of high temperature on irritability, which is more conducive to the regulation of surface indicators in local high temperature areas and effectively reduces the risk of emotional health. Although they are not the same as the indicators selected for the thermal environment correlation research in recent years, they have obtained similar research results (Chen and Deng, 2021; Li et al., 2021). Yet, this paper only revealed the differential effect of a single land use type on temperature and irritability, but lacked the analysis of the joint effect of multiple land use types, and the effects of non-land-use type-related indicators such as regional climate, socio-demographics, local plant characteristics, albedo, and other environmental conditions were also ignored. For the analysis results, the increase of NDVI, WSR, and RND can alleviate irritability, but according to Li et al. (2011), the interspacing of urban green into other land use types seems to have a greater effect on mitigating the UHI effect than a large green area itself. Comparably, Dugord et al. (2014) found that forested green areas contribute better to thermal reduction when patches are more complex in shape and more distributed in space. Moreover, urban microclimate has prominent scale characteristics. MNDWI, NDVI, and other indicators would be quickly captured by satellite thermal sensors in a smaller spatial resolution, while the influence of human living, transportation, and commercial activities on the temperature field is difficult to be captured accurately (Huang et al., 2020b). The small sample size used for the MGWR model operation limits the statistical power, so that the analysis on the influence mechanism of thermal environment on irritability is imperfect, and open street maps and points of interest should be included in the future (Chen et al., 2022).

Based on the summer high-temperature measurement and emotional health survey in Beijing, ArcGIS, MATLAB, GraphPad and other platforms were used for data processing to construct a relationship model between temperature and irritability, and to evaluate the time-space relationship between hourly temperature in summer and residents’ irritability; the differential action scale and spatial action characteristics of high temperature driving factors on irritability risk were analyzed through the MGWR model. The conclusions are as follows:

Current research suggests that high temperatures in the context of global heating pose significant risks to the emotional health of the general population, and the magnitude of future risks will depend largely on planning and mitigation measures. Existing mental health resources are mostly concentrated on people with mental illnesses. For the general population and vulnerable groups with multiple physical comorbidities, it is suggested to identify areas where temperature-related emotional health risks are particularly increased, and clinically improve physical health may relieve heat stress. In the future, we will combine the regional changes of negative emotional risk in different years and the degree and scale of the effects of more high temperature drivers on emotional health risk to conduct regional emotional health risk assessment, providing reference for formulating regional planning and design strategies to reduce emotional health risk. Furthermore, temperature in different urban functional areas differs greatly—the average temperature difference between the center and the surrounding areas of Beijing can reach 268.6°C (Zhang et al., 2002). The complexity of the internal structure of human settlements should also be considered. To be specific, the local climate zone (LCZ) divides a city into built environment and natural environment, thus generating a classification system suitable for studying the thermal field changes in human settlements (Stewart and Oke, 2012; Yang et al., 2020). Therefore, this study will combine LCZ, crowd characteristics, and emotional health in the future, to make clear of how different climate layouts influence emotional health.

The datasets for this study can be found the official website of NASA’s landsat satellite (https://landsat.gsfc.nasa.gov/).

HH: conceptualization, methodology, data curation, formal analysis, writing—original draft preparation, review and editing, investigation, and visualization. XL: methodology, data curation, formal analysis, writing—original draft preparation, review and editing, investigation, and visualization. LR: conceptualization. All authors contributed critically to the manuscript and agreed to publication.