Why is urban heat in a very hot region understudied? Are there spatial inequalities in exposure to extreme heat risk in Tehran, Iran? Have heat time series been studied in Iran? Why is Tehran considered one of the relatively hot cities in Iran despite its location in the foothills? Is southern Tehran exposed to hot zone conditions due to its location in the arid and arid regions?

Cities are becoming hotter due to the synergistic effect of climate change and urban heat islands (UHI), posing serious health-related problems among vulnerable population groups. While urban heat vulnerability has gained significant scholarly attention worldwide, we still know little about which socio-demographic groups are disproportionately overexposed to extreme heat waves in hot and arid climatic areas– particularly in the Middle East. To address this research gap, we bring the case of Tehran, Iran, during the two days of nationwide extreme temperature shutdown to examine which population groups were more likely to be over/underexposed to extreme heat hazard. Our findings reveal a clear north-south gap in disproportionate exposure to extreme heat across the city sub-districts, highlighting the historical and intensifying income gap between poor and wealthier residents. In addition, we show that sub-districts with high elderly and female populations tend to have relatively low exposure to heat hazard and better access to both indoor and outdoor cooling infrastructure. On the other hand, our study indicates that children, immigrants, and illiterate residents are overexposed to extreme heat waves and have limited capacity to adapt due to inadequate cooling resources. To date, the nationwide shutdown is how the government tries to protect at-risk population groups from constantly rising temperatures–with obviously no proper strategic plan or heat action guidelines. With the ongoing dramatic vision of global warming, we urge the government to implement socio-technical adaptation policies by prioritizing socio-economically disadvantaged neighborhoods in the heat mitigation agenda.

1. Introduction

It is now more evident than ever that the Earth’s climate is changing. The Intergovernmental Panel on Climate Change (IPCC) reports that global temperature has risen by ~1.1 ◦C since the late pre-industrial era and is projected to increase between 1.5 and 3.5 ◦C by the end of the 21st century (IPCC, 2023). The most pronounced consequence of planetary warming is the prevalence of extreme heat events (Bolan et al., 2024). Cities, especially megacities, are common hotspots for extreme temperatures, where day and nighttime temperatures are significantly higher than surrounding rural/suburban areas due to a phenomenon known as the urban heat island (UHI) effect (Mohajerani et al., 2017; Oke, 2006). By amplifying extreme temperatures, UHI makes cities more vulnerable during hot summers.

Extensive urban heat is associated with several adverse economic, environmental, and societal challenges. It increases energy consumption for cooling purposes (Santamouris et al., 2015; Zhang et al., 2022), places additional burdens on already-overwhelmed healthcare systems (Semenza, 1999), reduces air quality levels and workforce productivity (ILO, 2019; Wang et al., 2022), and potentially threatens biodiversity richness (Arsad et al., 2022; Grimm et al., 2008). Elevated temperatures also pose significant threats to vulnerable urban populations, resulting in high risks of heat-associated mortality and morbidity (Yang et al., 2024). To date, the fatality rates of severe heat events have been reported across almost all geographical regions. For example, the European heatwave in the summer of 2003 alone caused almost 70,000 deaths due to an average 3 ◦C increase in air temperature (European Commission, 2003). In July 2022, an additional 53,000 deaths from extreme heat exposure were also reported in Europe (Eurostat, 2022). Similar trends were reported in Slovakia during the 2015 summer heatwave, with more than 540 deaths (Výberˇci et al., 2018). During the 2010 heatwave in Russia, fatalities were estimated to be around 55,000 (Hoag, 2014). In the U.S., average heat-related mortality exceeded 700 deaths per year from 2016 to 2018 (Sheridan et al., 2021). In Iran, a meta-analysis across different cities revealed that during heat waves, all-cause mortality and cardiovascular mortality were significantly increased by 23 % and 8 % compared to normal days (Hadei et al., 2024). As it is expected that by 2050, nearly 70 % of the global population will live in cities (United Nations, 2019), this could be an alarm to reduce the harmful consequences of heat hazard exposure for diverse socio-demographic populations living in physically different urban regions.

Existing studies widely demonstrated that socio-demographic status, pre-existing health conditions, belonging to ethnic/minority groups, and built environment characteristics unevenly contribute to the level of exposure to extreme heat and thus heat-related illness (Bayomi and Fernandez, 2023; Cheng et al., 2021; Karanja and Kiage, 2021; Li et al., 2023; Li et al., 2022; Szagri et al., 2023). For example, Venter et al. (2023)reported that residents with immigration backgrounds who have less access to blue spaces are more likely to be exposed to heat hazard (>30 ◦C) in Oslo, Norway. In another study, Chakraborty et al. (2019) examined 25 cities in the world and found that in almost 72 % of the cases, poorer neighborhoods were disproportionately exposed to higher levels of extreme surface temperature. Hsu et al. (2021) showed that during summer months (i.e., June, July, and August) people of colour and low-income households experience higher UHI intensity compared to non-Hispanic whites and wealthier households in most major U.S cities. Similar findings on individual-scale heat stress perception and vulnerability were reported in Ludwigsburg, Germany, where Laranjeira et al. (2021) discovered that elderly residents and households with lower socio-economic groups were more likely to suffer from the negative impacts of heat stress. Reid et al. (2009) conducted a more comprehensive study by mapping heat vulnerability factors, including demographic and socio-economic variables, built environment characteristics, diabetes prevalence, and air conditioning across urbanized areas in the United States. Numerous countries have used similar factors to develop heatwave action plans/ guidelines aiming to protect at-risk population groups located in different geographical regions through adaptation strategies and prioritizing mitigation interventions and planning (New Zealand Ministry of Health, 2018; Department of Health South Africa, 2020; UK Health Security Agency, 2018).

The majority of existing studies on heat-related vulnerability come from the Global North; thus, little is known about how low- and middle-income countries are affected by the consequences of severe heat events/hot summers, particularly in light of rapid urbanization and rising socio-economic inequality (Green et al., 2019; He et al., 2023). It should be noted that most of these countries are geographically located in tropical and subtropical zones, expecting to be exposed to higher surface/ambient temperatures, such as countries in the Middle East and Africa. Iran is one of the Middle Eastern countries that has experienced frequent heat events in summer due to both a rise in global temperature and its geographical location in a subtropical zone with hot and arid climate conditions (Kardavani, 2005; Mousavi et al., 2020). Considering the UHI intensity, the situation is even worse in urbanized areas (Zargari et al., 2024). Along with this negative picture, we also found a few empirical studies examining urban heat vulnerability and socio- spatial disparity in exposure to heat hazard in the Iranian context (Mirzaei et al., 2020; Suleimany, 2023). This is important given that nearly 74 % of the Iranian population resides in urban areas due to rapid urbanization and decades of centralization policies (Madanipour, 1999; Statistical Centre of Iran, 2016). Tehran, the capital of Iran, alone is home to about nine million people of various socio-demographic backgrounds. This study is an attempt to explore the spatial distribution of heat hazard across Tehran’s sub-districts during the summer of 2023 when the temperature exceeded 40 ◦C. It also examines the association between heat exposure and city dwellers’ socio-demographic and environmental characteristics. While existing studies have focused on the spatiotemporal distribution of land surface temperature in relation to different land covers, the societal aspects of urban heat have not been within the scope of scholars (Bokaie et al., 2019; Najafzadeh et al., 2021; Bokaie et al., 2016). Therefore, we believe this kind of research is particularly relevant to highlighting ongoing environmental injustice in heat exposure and raising voices for inclusive and sustainable urban development in low-income countries with hot and arid climate conditions. Furthermore, this study has significant implications for policymakers and urban planners to create a more resilient and equitable urban environment that supports disadvantaged and vulnerable population groups.

2. Methods

2.1. The study area Tehran is located between 35◦41′21.0″ N - 35◦41′21.0″ N and 51◦23′20.0″ E - 51◦23′20.0″ E with an area of approximately 616 km2

and an estimated population of 8,615,185 in 2016 (Statistical Centre of Iran, 2016). It is divided into 22 districts and 119 sub-districts,

B. Badakhshan et al. Urban Climate 61 (2025) 102480 with an average population of 73,634 and a population density of approximately 17,617 per sub-district (Tehran Municipality, 2023) (Fig. 1). The city is home to diverse socio-demographic and ethnic groups from all over the country because of the decadal centralization policy followed by the government (Madanipour, 1999). The average annual air temperature over Tehran varies from 5 ◦C in the north to 25 ◦C in the south; however, in the summer, the city temperature reaches 44 ◦C (Iran Meteorological Organization, 2023), making vulnerable groups at high risk of heat-related morbidity and morbidity. Notably, Tehran’s north-south elevation gradient and geographical differences have disproportionately contributed to heat mitigation, favoring affluent groups in the north over the poor in the southern desert (Hourcade and Ḥab¯ıb¯ı, 2005).

2.2. Urban heat and green space data

In this study, we used satellite-driven land surface temperature (LST) instead of ambient air temperature because LST is spatially continuous across the Earth’s surface compared to ground-level weather station data. Although air temperature is more related to actual human perceived ambient temperature (Labib, 2024), previous studies have demonstrated a strong positive linear correlation between LST and air temperature and thus its potential application to measure heat exposure in urban environments (Chakraborty et al., 2019; Huang et al., 2011). To do so, we collected all available image collections for Landsat-8 and 9 during the summer of 2023 (i.e., August 2 and August 3) that intersect with the study area, calculated LST using OLI/TIRS sensors with the mono-window algorithm outlined in Waleed and Sajjad (2022), and then selected the median surface temperature for further analysis. In addition to LST, we also derived the normalized difference vegetation index (NDVI; Tucker, 1979) using high-resolution Sentinel-2 satellite imagery (10 × 10-m pixel size) for the same summer period. NDVI is a good indicator of overall vegetation greenness/abundance and is generally expressed in the range of 0 (low vegetation) to 1 (high vegetation density) (Martinez and Labib, 2023). In this stage, all processes, including satellite data collection, image processing, and analysis, were conducted within the Google Earth Engine (GEE) cloud-based computational platform (Gorelick et al., 2017).

For the latter stage, we set the heat hazard to account for areas with LST > 40 ◦C and then calculated the percentage of heat hazard per sub-district, considering approximately 5 ◦C upward bias relative to air temperature (Venter et al., 2020). The selected threshold was based on a two-day nationwide showdown due to “unprecedented” heat temperatures exceeding 40 ◦C to protect vulnerable populations from heat-related risks (Fassihi, 2023). The unprecedented temperature forced the government to close public entities, schools, banks, and some private businesses while emergency services and hospitals stayed open to address potential heat-related illnesses. In addition, vulnerable groups, including the elderly, children under 4, and individuals with chronic conditions (e.g., heart disease, diabetes), were urged to stay indoors to avoid heatstroke and dehydration. During this period, some southern cities, such as Ahvaz, experienced temperatures as high as 50 ◦C. A similar threshold was chosen by Díaz et al. (2002) to study the impacts of extreme heat events on mortality among elderly population groups in Seville, Spain. Existing literature may not commonly examine

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Fig. 1. Map of Tehran showing municipal districts and sub-districts, urban green spaces sourced from OpenStreetMap, with a shaded north-south elevation gradient in the background.

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unequal heat exposure over a two-day period, but we deemed it reasonable to expect that even a brief two-day span with temperatures exceeding 40 ◦C poses a significant health risk for vulnerable populations, warranting scientific attention.

2.3. Socio-demographic data

Socio-demographic data (Table 1) were based on two consecutive censuses in 2011 and 2016 disseminated by the Statistical Centre of Iran (SCI) at the block level (https://www.amar.org.ir/en/). However, given that the census data did not overlap spatially, we aggregated them into the sub-district level to prevent miscalculations during the data preparation procedure. We included the 2011 census because some variables (i.e., occupation structure and access to the central AC) did not exist in 2016. We know that there are some broader factors defining vulnerability to heat hazard based on previously published articles and heat action plans provided by different countries (e.g., the heatwave plan for England); however, we were not able to access such information in our study region.

2.4. Statistical analysis

We used RStudio (RStudio Team, 2015) for statistical analysis and spatial data visualization. We ran a series of simple linear regression models to estimate the spatial association between exposure to heat hazard across the city sub-districts and socio- demographic and environmental variables with 95 % confidence intervals. The explanatory variable was Z-transformed to account for standardized regression coefficients in the model outputs. The study further employs multivariate clustering analysis using the scikit-learn library (v1.6.1) (Pedregosa et al., 2011) to group selected spatial variables (i.e., environmental and socio-economic variables) into clusters based solely on their attribute values. We applied the K-means algorithm, which ensures that the features within a cluster are more similar to one another than to those in other clusters. In addition, the Calinski-Harabasz score was used to account for the optimal cluster number (Calinski and Harabasz, 1974´ ).

3. Results

3.1. Summary statistics

During the hottest day in August 2023, the average surface temperature across the city sub-districts ranged between 31 ◦C and 44 ◦C on average. There were 21 sub-districts that experienced LST > 40 ◦C, mostly clustered in the western and southern parts of the city. In terms of heat hazard cover, there were ten sub-districts with a heat hazard cover 55 % (Fig. 2L).

There is a north-south gradient pattern in the distribution of top occupation groups, elderly and female populations (first left column), households with central AC, and total green space coverage per sub-district. In contrast, the spatial gradient of bottom- and middle-income occupations, children 0.05). In Fig. 3, we report both the standardized and non-standardized coefficients of these associations. Children 40 ◦C, proportion of the middle and bottom occupation groups increased by 12 % and 5.5 %, respectively. In contrast, the proportion of top occupation groups decreased by about 10 % (6.3 to 13.6, 95 % confidence interval). The proportion of households with access to central AC, female population, and total green space cover per sub- district was negatively associated with heat hazard with standardized effect sizes of − 10.8 % (7.2 % to 14.3 %), − 7.3 % (3.5 % to 14.1 %), and − 11.7 % (8.3 % to 15.17 %), respectively (Fig. 3). By contrast, every 1 % increase in surface temperature > 40 ◦C was associated with a 1.47 % increase in the illiterate population, as well as a negligible effect size –approximately 0.8 %– in the percentage of people with immigrant backgrounds (Fig. 3).

3.3. Multivariate clustering effects

The Calinski-Harabasz index peaked at two clusters, indicating this solution best balanced within-group similarity and between- group dissimilarity. As illustrated in Fig. 4A, less vulnerable sub-districts are predominantly concentrated in the north (n = 42), while the highly vulnerable sub-districts are in the south (n = 75). More specifically, less vulnerable sub-districts are home to, on average, high-income occupation groups, a high proportion of females and older adults, and experience lower exposure to heat hazard with high heat-adaptive capacity, such as access to central AC and green space availability. In contrast, the most vulnerable sub-

Table 1

Description of selected socio-economic and environmental indicators.

Category

Indicator

Brief description References

Socio-demographic

% of top occupation groupsa

Occupation status affects both exposure risk and capacity to respond to heat stress, making it a critical factor in (Lopez-Bueno ´ et al., 2021; Park et al., 2019) understanding heat-related health outcomes.

% of middle occupation groups

% of bottom occupation groups

% of children under 4 years

Infants and young children have immature thermoregulation systems and rely on adults for care and dehydration. (Clark et al., 2024; Oh et al., 2024)

% of adults aged over 65

Older adults are more likely to be affected due to the body’s reduced ability to regulate temperature, having a higher (Conti et al., 2005; Son et al., 2019; Zeng et al.,

likelihood of pre-existing health conditions and/or social isolation. 2014)

% of disabled populations

People with disabilities have reduced mobility, communication barriers, and high reliance on caregivers for cooling (Chakraborty, 2025; Park et al., 2024) measures.

% of households with access to Central AC

Air conditioning is considered an effective adaptation strategy against heat-related mortality. It significantly reduces (Cardoza et al., 2020; O’Neill, 2005; Sera et al., body temperature and prevents heat-related illnesses (e.g., heatstroke and dehydration). 2020)

% of Illiterate population

People without literacy have serious challenges in accessing or fully understanding public health warnings and (Borrell et al., 2006; Schwarz et al., 2025) advice, as well as educational materials on how to stay safe during heat waves.

% of female population

Women are physiologically more susceptible to extreme heat and have reportedly higher mortality rates compared to (Ballester et al., 2023; Kollanus et al., 2021; men. In addition, factors such as the gender-based income gap, pregnancy, and lactation also increase their health Sharma et al., 2024; Van Steen et al., 2019) risks.

% of total immigrants

Immigrants are usually employed in labor-intensive sectors (e.g., agriculture and construction) that involve direct (Hansen et al., 2013; Taylor et al., 2018) exposure to high temperatures. Moreover, language barriers affect the ability to understand and follow weather reports and health warnings during severe heat events.

Environmental

% of greenery cover (NDVI Urban green infrastructures (parks, street greenery, and private gardens) reduce UHI effects by providing shade and (Armson et al., 2012; Farkas et al., 2024; Luque-

≥0.23) cooling through evapotranspiration. García et al., 2024)

a Note: In this study, occupational groups were divided into three major categories: top occupation groups (i.e., Legislators; senior officials and managers + Professionals), middle occupation groups (i.

e., Technicians and associate professionals + Clerks + Service workers; shop and market sales workers + Skilled agricultural and fishery workers + Craft and related trades workers), and bottom occupation groups (i.e., Plant and machine operators and assemblers + Elementary occupations).

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Fig. 2. Spatial distribution pattern of socio-demographic variables (A-J), green infrastructure (K), and heat hazard cover (L) across the city sub- districts in Tehran, Iran.

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Fig. 3. Linear empirical estimates of the association between socio-demographic characteristics and greenspace availability with heat hazard exposure.

Points and labels represent standardized coefficient estimates and unstandardized coefficients, respectively, with 95 % confidence intervals indicated as lines. Non-significant estimates are marked with an “x”.

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Fig. 4. Spatial distribution of clusters (A) and multivariate clustering box-plots (B). The bar chart in the bottom left corner of the map shows the number of sub-districts that fall into different clusters.

districts have high middle/bottom occupation groups, disabled and immigrants, illiterate populations, and children under four years who are constantly overexposed to extreme heat hazard (Fig. 4B). The results from the multivariate clustering analysis are consistent with those from univariate regression. This is primarily due to the fact that sub-districts with the highest heat hazard exposure tend to have a significant proportion of vulnerable populations and limited green space availability, whereas those with the lowest heat hazard exposure are generally inhabited by more affluent residents, despite having a higher percentage of elderly and female individuals. Notably, socio-economic variables—specifically the top, middle, and bottom occupational groups—demonstrated the highest R- squared values (see Table S2), whereas migration background yielded the lowest. This indicates that socio-economic status is a more influential factor than migration background in distinguishing clusters among the sub-districts.

4. Discussion

In the current study, we hypothesized that during the two days of nationwide extreme heat shutdown in the summer of 2023, vulnerable population groups were more likely to be overexposed to heat hazard in Tehran, Iran. The study began with identifying heat-associated vulnerable residents based on socio-demographic and environmental indicators, and then performed univariate linear regression analysis to understand whether changes in heat hazard cover (LST > 40 ◦C) across the city sub-districts are related to increases in the proportion of heat-sensitive population groups. This research has potential implications for developing targeted socio- technical interventions aiming to mitigate the disproportionate effects of extreme heat events in hot and arid climate regions.

We found that during the deadliest heatwave in the 2023 summer, some sub-districts in the north have experienced relatively lower surface temperatures than those in the south and southwest of Tehran. These are sub-districts that have a higher percentage of green space cover (Fig. 2K) and are located at a high altitude in the foothills of the Alborz mountains (Fig. 1). The significant altitude variation between the north and south makes the northern sub-districts, on average, 3 ◦C cooler than those in the desert south (Fig. S1). Remarkably, the north-south elevation gradient has resulted in a “socio-economic hierarchy”: The north is occupied by affluent populations, and the lowest lands in the south are home to poor and immigrant groups (Bayat, 1997, and see Fig. 2A–C).

The spatial distribution of heat hazard cover (LST > 40 ◦C) is negatively associated with top-occupation groups but yet positively correlated with middle and bottom professionals across the city sub-districts (Fig. 3). Interestingly, the magnitude of the estimate for middle occupation groups is higher than that of the bottom income occupational groups. The primary explanation for this is that the former groups have concentrated in the dense urban core—where the UHI effect is presumably stronger—while the latter have encroached into agricultural lands on the urban fringe, resulting in a significant decrease in overheating levels. The study further showed that sub-distracts with high socio-economic groups have better access to outdoor (i.e., green space) and indoor cooling services (e.g., central AC) and lower exposure to extreme heat hazard (see Fig. 4). This is expected, as previously Badakhshan et al. (2025) highlighted that both private and public green infrastructure and their multifaceted benefits, including cooling capacity, unevenly favor wealthier population groups in Tehran, Iran. Although central AC might not be as common as room-unit and window-unit AC in low-income communities, our data does not capture these specific details. However, this is not the case, as previous studies raised concerns about high electricity costs, which hinder poor residents’ willingness to turn on AC during severe heat events (McGeehin and Mirabelli, 2001; Randazzo et al., 2020).

The fact that wealthy urban residents are less exposed to high temperatures during warm summer seasons has been consistently reported in many cities with climatic conditions similar to Tehran –e.g., cities of the U.S. (Gabbe and Pierce, 2020; Hsu et al., 2021; Li et al., 2024). However, this trend is not aligned with numerous reports from countries in northern latitudes —e.g., Norway and the Netherlands — particularly as a cause of the so-called «suburbanization of poverty» and core-peripheral income gap (Mashhoodi, 2021a; Osberghaus and Abeling, 2022; Venter et al., 2023).

We did not find any evidence supporting that the elderly or females are unevenly overexposed to heat hazard during the two days of extreme heat events. In addition, the proportion of disabled populations did not have a statistically significant relationship with the distribution of LST > 40 ◦C. This is partly because sub-districts with a predominantly higher share of female and elderly are also areas in which a higher proportion of top-occupation groups are concentrated (see Fig. 4). In contrast, disadvantaged sub-districts –with a higher heat hazard cover–are home to population groups with high illiteracy, children with four years and younger and immigrants. Importantly, we cannot conclude which nationality groups are more exposed to heat hazard as our data lacks the geographical origin of immigrants. Nevertheless, based on the official resources, Afghans alone make up 99 % of the immigrant population in Tehran (Abraqoui, 2024).

When comparing these results with the growing body of literature on climate justice in heat exposure, we found major inconsistencies in identifying the socio-demographic groups that are universally more likely to over- or underexpose to urban heat. For example, Mashhoodi’s (2021b) nationwide study from the Netherlands–shows that females are significantly overexposed to LST, while Suleimany (2023) reports that females are underexposed to surface temperature in Isfahan, Iran. Similarly, while Venter et al. (2023) report that immigrants are less likely to be exposed to heat hazard in Norway, findings of Mitchell and Chakraborty (2015) clearly indicate that in the U.S., migrants are overexposed to urban heat. In Isfahan immigrants also exhibit lower exposure to LST (Suleimany, 2023). Moreover, Huang et al. (2011) reported that the level of education is correlated with disproportionate exposure to higher LST, very similar to our findings in this study (see Fig. 3). Presumably, as in cold climate regions exposure to heat seems to be luxurious (see Venter et al., 2023), in a hot and arid climate conditions residents tend to reside in relatively cooler urban areas (e.g., suburban) to avoid heat-associated challenges.

The observed pattern of unequal exposure to heat hazard in Tehran reflects the historical divide between poor and wealthy residents along a north-south axis in the city (Bayat, 1997; Madanipour, 1999). Previously, Karami (2014) reported that the north-south divide has intensified as middle occupation groups have increasingly relocated to the southern areas, leaving the north primarily for high-income residents. Therefore, it seems segregation has become the status quo in Tehran, as the government and local authorities have not effectively regulated urban planning to prevent environmental injustice (e.g., disproportionate exposure to heat hazard). This also provides a potential explanation for the varying demographic characteristics in Tehran and, thus, the disproportionate exposure to heat hazard. In fact, northern sub-districts are historically occupied by affluent population groups, forcing younger professionals and working-class immigrants to inevitably locate in southern sub-districts, where housing is more affordable. This pattern accelerated population aging in the north and youthification in the southern sub-districts, highlighting the underlying demographic divide in unequal exposure to severe heat events. Furthermore, females are underrepresented among the immigrant population compared to males, as their national mobility is restricted because of social and cultural norms (Statistical Centre of Iran, 2016).

To our knowledge, this study is the first to examine environmental injustice in heat exposure by considering socio-demographic and ecological factors in Tehran, the most populated and segregated city in Iran. While our findings provide valuable insights into heat- related vulnerability in a Middle Eastern city, we acknowledge several limitations in this research. First, in terms of sensitivity, we excluded some potential factors, such as individuals’ pre-existing health conditions or isolation, which dramatically contribute to vulnerability level during heat hazard strikes. Future studies can fill this gap by using, for example, location-based questionnaire approaches in a large sample that accounts for the health condition of residents. For example, Mirzaei et al. (2020) used a similar methodology to study the urban heat island effect on residents’ health and wellbeing in Isfahan, Iran. Similar limitations are evident in heat adaptation indicators; for example, we only included vegetation cover and access to the central AC, whereas other factors, such as housing characteristics and/or accessibility of medical services (Bayomi and Fernandez, 2023; Szagri et al., 2023) can be used in future investigations. Another limitation is that we used simple linear regression to understand the relationships between heat hazard and spatial distribution of vulnerable population groups; therefore, we cannot conclude which areas are more exposed to heat or whether the relationships vary in direction and intensity across space (Mashhoodi, 2021a). By employing spatially explicit approaches, such as geographically weighted regression (GWR), researchers can gain detailed insights into social vulnerability. This knowledge can then be leveraged to allocate and prioritize budgets and resources more effectively, mitigating the detrimental impact of heat waves on at-risk residents (Mashhoodi, 2021a). Finally, although several studies have found a significant association between heat-related mortality and belonging to vulnerable population groups (Hajat and Kosatky, 2010), these findings may not be consistent across different geographical regions (Aboubakri et al., 2019; Bell et al., 2008; Yu et al., 2010). Therefore, future studies must consider using mortality data to accurately account for the interaction effect between mortality-heat stress and vulnerability factors.

As cities worldwide become hotter due to climate change, the burden of global warming on cities like Tehran is overwhelming, as it is already located in a hot and arid climate region. With respect to the dramatic vision of rising temperatures breaking records yearly, we believe that the Tehran municipality needs urgent adaptation strategies to protect vulnerable population groups. Tehran municipality needs to prepare a heat action plan to comprehensively understand the ongoing devastating consequences of rising heat. This plan should be integrated into a broader climate action plan (Aboagye and Sharifi, 2024) to consider potential synergies and trade-offs across different sectors. The municipality must also develop mitigation policies and implement them by prioritizing sub-districts that have been severely hit by unbearable heat conditions. An urban greening intervention, focused on the most disadvantaged communities, can serve as an effective cooling strategy to ensure vulnerable populations have equitable access to urban blue and green infrastructure. During hot summer days, it is crucial that low-income residents have access to air conditioning systems to prevent heat-related illness or at least have financial capacity to pay their energy bills for using air conditioners; for example, prior studies have shown that utility costs are the main concern for not running air conditioning during hot weather in the U.S (McGeehin and Mirabelli, 2001). Training and preparedness information during heat waves, as well as advancing heatwave early warning systems, are also pivotal for the socially isolated and uneducated population.

5. Conclusion

Although there is strong evidence supporting a near-universal connection between extreme heat events and increased mortality among vulnerable population groups, we know relatively little about how different vulnerable groups in hot and arid climate regions are dealing with frequent heat hazard events. Focusing on Tehran–a city located in a hot and arid climate region–we explored urban heat vulnerability and inequality by coupling socio-demographic-environmental factors with satellite-derived spatially explicit LST during the unprecedented heat hazard shutdown in 2023 across the city sub-districts. We found that urban heat hazard (LST >40 ◦C) is not equally distributed across the city sub-districts in Tehran, and vulnerable groups are more likely to be overexposed to severe consequences of heat stress (i.e., mortality and morbidity). The only exceptions are the elderly and female population groups, which coincide with the spatial distribution of high-income socio-economic groups that have clearly accessible indoor and outdoor cooling infrastructures in their living environments. The nationwide shutdown was the government’s attempt to protect vulnerable population groups in the absence of effective heat-related mitigation and adaptation policies, similar to the approaches adopted by various cities globally through heat action plans, urban planning, and housing interventions. The main message of this study is that, from the perspective of global warming effects, there is an urgent need for a strategic vision and climate adaptation policies aiming to reduce the adverse impacts of rising heat by giving specific attention to disadvantaged population groups residing in heat-intensifying urban structures and housing conditions.