Climatology of Tehran surface heat Island: a satellite-based spatial analysis

Zargari, Motahhareh; Mofidi, Abbas; Entezari, Alireza; Baaghideh, Mohammad

doi:10.1038/s41598-025-95367-2

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Published: 27 March 2025

Climatology of Tehran surface heat Island: a satellite-based spatial analysis

Motahhareh Zargari¹,
Abbas Mofidi²,
Alireza Entezari¹ &
…
Mohammad Baaghideh¹

Scientific Reports volume 15, Article number: 10567 (2025) Cite this article

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Abstract

With a population nearing 15 million, the metropolitan area of Tehran faces numerous environmental challenges due to inadequate urban strategies and hasty, ineffective planning. The primary objective of this research is to investigate the Urban Heat Island (UHI) climatology in the metropolis of Tehran. To achieve this, daily Land Surface Temperature (LST) data from the MODIS-Aqua, with a spatial resolution of 1 km, was utilized over a 19-year period (2003–2021). This study delves into various climatic characteristics, including the urban-rural temperature difference, urban heat hotspots, the structure of the Surface Urban Heat Island (SUHI), trends in LST changes, and the roles of elevation and vegetation cover in the spatial distribution of LST, at both monthly and annual scales. Findings reveal that, compared to daytime Aqua-MODIS LST data, nighttime data provides a more reliable means of detecting and studying the SUHI in Tehran throughout the year. Additionally, the employment of nighttime data significantly reduces seasonal variations and month-to-month fluctuations in the temperature difference between the urban warm core and surrounding areas in Tehran. A climatological analysis of nighttime LST revealed a mean annual temperature difference of 6.17 °C between Tehran and its surrounding suburbs. This difference fluctuated monthly, ranging from a minimum of 5.73 °C in November to a maximum of 6.49 °C in September. Overall, the smallest temperature differences occurred during autumn and spring, while the largest differences were observed in summer. However, autumn and summer exhibited the greatest and least interannual variability, respectively. Furthermore, over the 19-year period, the annual temperature difference between the urban and suburban areas ranged from a minimum of 5.09 °C (2020) to a maximum of 6.70 °C (2008). The SUHI analysis suggests that Tehran, due to its surrounding mountainous terrain and numerous suburban developments, does not exhibit a classic UHI with a pronounced temperature difference. Moreover, the results indicate a rising temperature trend in the city. Specifically, the second decade of the study period witnessed a notable increase in positive LST anomalies within Tehran. The analysis reveals a significant increase in Tehran’s surface temperature, with a rate of 0.74 °C per decade, amounting to a total increase of 1.48 °C. Concurrently, a discernible upward trend in the temperature differential between the urban and suburban areas has been observed over the last two decades. A comparative study of the roles of elevation and vegetation cover in LST distribution indicates that the temperature difference between the urban and suburban areas of Tehran is more closely related to elevation changes than to vegetation distribution.

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Introduction

Cities are considered the primary locus of environmental challenges on Earth in the 21st century. Increasing frequency of extreme weather events, high concentrations of air, water, and soil pollution, urban waste management issues, and acute resource scarcity have turned cities, particularly those in developing countries, into epicenters of environmental crises. The rapid growth of urban populations and ecological challenges due to global warming have made the study of urban climate crucial in recent years¹. In 21st-century mega-cities, the expansion of built-up areas and reduction in vegetation cover have led to temperature increases and the development and spread of Urban Heat islands (UHI), presenting a persistent environmental challenge in urban climates^2,3.

The Surface Urban Heat Island (SUHI) is a symbolic phenomenon in urban climates characterized by a dome of stagnant warm air^4,5 or an island⁶ effect in urban areas, displaying temperature variances from adjacent rural regions⁷. Urban areas retain more heat during the day than rural surfaces. At night, energy absorbed by the ground is re-radiated into space as long-wave radiation and then trapped by urban canyons. The vertical walls of buildings act as deep and narrow canyons that trap shortwave radiation during the day, hindering the release of infrared energy at night due to a reduced Sky View Factor (SVF). Consequently, Passive Radiative Cooling in buildings at night is significantly limited in urban areas⁸. In contrast, rural environments cool much faster than urban areas from afternoon to night due to a continuous decrease in shortwave radiation since their capacity to retain absorbed energy is lower. Essentially, the UHI peaks mainly at night when the temperature contrast between urban and rural areas is most significant⁹. This phenomenon is particularly pronounced in arid urban environments, where diurnal temperature differentials are exacerbated by low humidity, limited moisture availability, and intense solar radiation¹⁰.

Tehran, one of the largest metropolises in Western Asia¹¹ and the first major city in Iran¹², has undergone significant spatial expansion and population growth over the past two to three decades^13,14. As the nation’s capital and primary cultural hub, Tehran attracts significant economic and commercial investment, positioning it as a key national investment destination. The financial foundation of Tehran’s municipality rests upon revenue generated from housing sales to its residents. This mechanism has not only resulted in the significant appreciation of land and housing values, thereby establishing them as critical capital commodities. It has also rendered this economic approach the primary factor for the city’s physical growth, contributing to Tehran’s status as one of the most dynamic and unstable cities in the Middle East in recent years^15,16.

Tehran metropolis has a population of over 15.98 million people, with 54.4% residing in the city and the remainder scattered in surrounding areas, particularly in new towns and small satellite cities¹⁴. The spatial expansion and population growth of Tehran without adhering to regulations and without considering long-term planning has led to the development of an unsuitable urban system with numerous negative impacts and environmental challenges. In addition to air and soil pollution, waste, water, and energy resource shortages, the SUHI expansion and temperature increase are significant issues. Over the past four decades, Tehran’s surface temperature has notably increased in both summer and winter. Continuing this trend may lead to the elimination of the cold season in Tehran in the future¹⁷.

Due to inadequate urban design, a noticeable decrease in vegetation coverage¹⁸, and increased built-up areas, Tehran’s UHI size and intensity have significantly increased in recent years¹⁷. Consequently, the UHI Intensity (UHII) in Tehran has risen by an average of about 2.02 °C over the past three decades¹⁹. Additionally, from 1985 to 2019, built-up areas within Tehran expanded by 88%, with this trend expected to continue due to prevailing economic conditions²⁰. These factors have led to the formation of hotspots in western and southwestern regions and cold spots in northern Tehran in recent years²¹. These changes have resulted in an increase in extreme events due to climate change²², heightened water and energy consumption²³, increased air pollution²⁴, and a decline in human health levels²⁵ within the city of Tehran. Additionally, the population density in Tehran reflects the impact of human activities on energy consumption and the exacerbation of UHI. The proximity of densely populated residential areas to UHI-affected regions heightens the risk of respiratory and heart diseases among residents, making daily living conditions more challenging for the elderly and children²⁶. Consequently, in response to escalating environmental and ecological concerns in Tehran’s expansion, several studies have examined UHI using satellite data^17,27. Some studies revealed that the intensity of UHI in Tehran exceeded the average (approximately 0.067) due to the dominance of climate change and air pollution in recent years²⁸.

With physical expansion and rapid population growth in recent decades, Tehran’s urban climate has undergone significant changes but has been less addressed by urban management and policymakers. Urban climate change and spatial patterns of UHI distribution in the metropolitan area of Tehran are among the aspects that lack sufficient understanding despite numerous past studies. For instance, while extensive studies have been conducted on average UHII at night between cities and suburbs in various geographical regions worldwide^29,30,31, a comprehensive study accurately depicting temperature differences between the city center and suburbs within the Tehran metropolitan area has not been completed yet.

Questions such as the true spatial pattern of Tehran’s UHI, the extent of temperature differences between the city center and suburbs across various seasons and months, and the spatial patterns and frequencies of hotspots within Tehran throughout the year remain unanswered. The evolution of heat island and surface temperatures in Tehran over recent decades requires scrutiny. Therefore, a comprehensive climatological study on Tehran’s heat island is essential, utilizing MODIS Aqua data spanning an extended period with fine horizontal resolution to address these inquiries effectively. This research leverages MODIS Aqua data due to its suitable satellite passing times, fine horizontal resolution, and appropriate temporal intervals for research purposes^32,33. The MODIS Land Surface Temperature (LST) data, which offers a 1-kilometer horizontal resolution, facilitates the examination of SUHI on a daily, monthly, and seasonal basis^{34,35,36,37,38,39,40}.

Despite numerous studies conducted on the city of Tehran using various approaches—such as remote sensing⁴⁰, modeling⁴¹, and statistical analysis on station data⁴²—there has yet to be a comprehensive long-term investigation of the structure and characteristics of the UHI from a climatic perspective. Addressing the gap in comprehensive, long-term climatic studies of Tehran’s UHI, this research introduces a novel analysis of MODIS satellite data, providing insights into UHI dynamics. It uniquely offers an extended temporal analysis of LST and UHI, precise quantification of UHI intensity across varying time scales, and an exploration of factors influencing temperature disparities between urban and suburban areas. Furthermore, it provides a detailed spatial analysis of LST, UHI, and hotspot distribution across Tehran’s districts and assesses two-decade temperature trends, informing urban planning and UHI mitigation strategies with evidence-based data.

Data and methods

Study area

Tehran, the most populous metropolis in Iran⁴³, spans an area of 730 square kilometers⁴⁴. Figure 1 illustrates the geographical features and Land Use (LU) of Tehran.

Furthermore, the majority of land use in the northern and eastern outskirts is related to mountains and barren lands. Tehran is situated between two mountain valleys and a desert⁴⁵, terminating in mountainous regions in the north, east, and southeast, and desert areas in the south. To the north, it is surrounded by the Alborz mountain range; to the east and southeast by the heights of Qasr-e Firuzeh and Bibi Shahr Banu; and to the south and southwest by the flat plains of Varamin and Shahriar. The southern and western parts have developed on vast flat plains, although urban growth in the northern and eastern mountains has been limited⁴⁴. The elevation of Tehran above sea level varies from 1800 m in the north to 1200 m in the center and 1050 m in the south^46,47,48. In the southern urban regions (15–20) and surrounding areas (west, southwest, south, and southeast), the elevation ranges from 860 to 1183 m. In the city center (regions 2–14, 21, and 22), it ranges from 1183 to 1506 m, while in the northern part of the city (region 1 and north of regions 1–5), it ranges from 1506 to 1830 m.

The city’s south and north exhibit distinct climates. The north is cold and dry, while the south is warm and dry. Tehran generally experiences a semi-arid, cold climate with continental traits, characterized by hot summers and cold, dry winters. The average maximum temperature is 43 °C, and the average minimum temperature is -15 °C (Fig. 1a)^49,50.

In the mountainous areas of Tehran, high elevation and legal restrictions limit construction and habitation. Consequently, the physical development of Tehran has primarily occurred in the foothill region and plains along the southern slopes of the Alborz Mountains. The southern parts of the city have expanded across a vast, flat plain. In contrast, the northern and eastern elevations impose limitations on urban development in those areas, while specific geographical conditions in the arid south and high groundwater levels further restrict growth in that region. For this reason, the western part of the city is considered the most suitable area for development due to its relatively favorable geographical conditions. New towns and cities have mainly been established and developed in this direction. Moving from south to north, significant changes occur in both natural environmental conditions and social factors such as spatial value, access to services, and specific amenities. It can be said that there is a geographical gradient that aligns with social gradients in Tehran; in other words, the city’s topography reflects its economic and social landscape to some extent⁴⁶.

Tehran is divided into 22 urban districts⁵¹. Based on the land use map of Tehran and its suburbs, the built-up regions in Tehran are growing and expanding hastily. Moreover, crops and bare lands occupy the most significant area in these mentioned suburbs. Significantly, the study area demonstrates a markedly low proportion of green cover and water bodies (Fig. 1b).

Data

The present study aligns with the findings of previous research conducted by Zargari et al.⁴⁰. Specifically, this study confirms that the MODIS Aqua satellite is more suitable than the Sentinel-3 and Landsat 8 satellites for investigating the UHI effect in Tehran. Consequently, the current research utilized long-term MODIS data to conduct a comprehensive climatic study of Tehran’s heat island from various perspectives.

In a previous study by Zargari et al.⁴⁰, the UHI effect in Tehran was analyzed using three satellites: MODIS Aqua, Sentinel-3, and Landsat 8. The objective was to identify the most suitable satellite for studying Tehran’s heat island, considering factors such as appropriate overpass times, a long-term period, and suitable temporal (day and night) and spatial resolutions.

The findings indicate that while Landsat 8 offers superior spatial resolution compared to Sentinel-3 and MODIS Aqua and Terra and provides a longer time series than the other satellites, its unsuitable imaging time, unsuitable temporal resolution, and lack of nighttime data render it impractical for studying the climatology of UHI. Notably, satellite imagery captured every 16 days reduces the temporal value of this data. Additionally, Landsat 8’s imaging window does not provide complete coverage for studying UHI in Tehran and its surroundings⁴⁰. Additionally, although Sentinel-3 provides both day and night data, its inappropriate imaging times and short time series also make it unsuitable for climatic studies in Tehran. Therefore, given the importance of climatology and spatial analysis across the entire area, MODIS Aqua emerges as the only satellite that meets these climatic needs.

Currently, MODIS Aqua is considered the most appropriate option based on thorough examination in previous research to show a better UHI at night⁴⁰. It was determined that due to its suitable overpass time from south to north (1:30 am), adequate temporal resolution (twice daily), appropriate spatial resolution (1 km), relatively long data coverage duration (since 2002), effective Split-Window (SW) algorithm for arid regions⁴⁰ such as Tehran, significant enhancement of data resolution capabilities, and access to corrected data ready for research on various servers in suitable formats, the use of MODIS Aqua data for UHI research has significantly increased in recent years⁴⁰. Furthermore, utilizing the nighttime LST data of MODIS for studying UHI is particularly valuable for countries like Iran that experience a severe lack of climatic data in urban areas.

In this study, we investigated the SUHI of Tehran using MODIS Aqua LST data. This choice was made due to the suitable characteristics of MODIS in determining changes in LST and vegetation cover in both the city and suburban^52,53 areas over a 19-year period (2003–2021) to calculate the SUHI effect in the Tehran metropolitan area. Recent studies have clarified that the MODIS Aqua products are more suitable than the other remote-sensed products, such as LANDSAT and Sentinel-3, for studying the Tehran UHI⁴⁰. This data also successfully depicted spatial and temporal variations of nighttime LST over Iran in daily and monthly scales with minimum underestimation⁵⁴.

The study utilized MODIS Aqua LST data (MYD11A1) for each grid with a spatial resolution of 1 km and a temporal resolution twice daily during daytime (1:30 PM) and nighttime (1:30 AM), employing the SW algorithm for MODIS Aqua version 6.1, which features an improved algorithm for detecting LST⁵⁵ (Table 1). The SW algorithm effectively removes atmospheric influences by utilizing the brightness temperature (BT) from two neighboring TIR channels at the top of the atmosphere, making it a commonly used method⁵⁶. In arid regions, data errors tend to be minimal⁵⁷.

Table 1 Data used by the aqua satellite in studying the UHI of the Tehran metropolitan area.

Full size table

This study benefits from the use of both day and night data. To determine the optimal time for studying the UHI effect in Tehran, it was necessary to compare heat island conditions and surface temperature variations during both day and nighttime. This comparison is crucial because urban heat islands exhibit significant differences throughout the day across various regions of the world and in different climatic zones, including tropical⁵⁸, subtropical⁴⁰, desert, and oceanic regions⁵⁹. In some locations, midday hours are ideal for studying the heat island effect⁶⁰, while in others, nighttime is more appropriate. Moreover, to accurately assess temperature differences between the city and its surroundings on a daily, monthly, and yearly basis, it is essential to first identify when the maximum difference occurs. Therefore, this study initially calculated temperature differences between the city and its surroundings separately for day and nighttime hours. Based on the results, greater emphasis was placed on nighttime hours. The comparative analysis of temperatures between the day and night revealed that in a city with a dry climate in a subtropical region, the maximum temperature difference between the city and its surroundings occurs at night⁴⁰.

One of the key improvements in the LST product Collection 6 is the optimization of the day/night algorithm for enhanced performance in desert regions. This optimized algorithm demonstrates superior validation compared to Collections 5 and 4.1⁶¹. The accuracy of LST MODIS version 6 has significantly increased from approximately 4.5 K to 1 K at various validation sites, surpassing LST MODIS version 5^62,63. The latest product, LST MODIS version 6, has been available since 2016, with version 6.1 launched in 2020. It is advisable to utilize LST MODIS Collection 6.1 due to its superior quality over Collection 4.1 and Collection 5 products⁶⁴. The advantages of the MODIS day/night LST algorithm include optimized atmospheric temperature and water vapor half-angle parameters for precise LST retrieval, eliminating the need for complete inversion of surface variables and atmospheric half-angles^65,66,67. Additionally, monthly NDVI data version 6.1 (MYD13A3) with a spatial resolution of 1 km was employed alongside gridded surface data level-3 and two-minute topographic data for this study.

The C6.1 product is identical in format to the C6 product. Reprocessing of the C6.1 does not involve any changes to the scientific algorithm used in its production. Any enhancements or alterations in the C6.1 product, compared to the previous major collection reprocessing (C6), stem from modifications and improvements made to the calibration approach used in generating Aqua MODIS L1B products and adjustments to the polarization correction employed in this reprocessing collection⁶⁸.

Methods

To examine the SUHI in Tehran, several preprocessing steps were conducted on the MODIS Aqua long-term data. These steps involved converting from Kelvin to Celsius and determining monthly and annual long-term averages over 19 years (2003–2021). Following this, calculations were performed according to the subsequent procedures.

Day and nighttime LST

Monthly day and night maps were created to interpret and identify the highest and lowest temperatures in urban areas and their surroundings. This section of the study illustrates the variations in the highest and lowest temperatures across cities and their outskirts during both day and night.

Longitudinal and latitudinal LST profiles for the Tehran metropolitan area were plotted for different seasons based on annual temperature variations during the day and night. A central box was defined over the central districts of Tehran for comparison with the outskirts aligned along meridional and diurnal axes, considering the occurrence of the highest temperatures in the central region. The temperature differences between the central box and other areas aligned latitudinally from south to north and longitudinally from west to east over the city and its outskirts were calculated during both day and night (see Fig. 1c). The geographical coordinates of the central box and surrounding outskirts are detailed in Table 2.

Table 2 Geographical coordinates were used to define the central area of the Tehran metropolitan and to generate south-north and west-east profile plots.

Full size table

The temperature difference starts from the southernmost and westernmost outskirts, extending to the northernmost and easternmost outskirts. This research section aims to offer a comprehensive perspective on daily and nightly LST in the Tehran metropolitan area and their surroundings, facilitating effective utilization of LST data for studying UHI effects in metropolitan districts based on existing mechanisms.

Trend changes in day and nighttime LST

Changes in surface temperature trends over a 19-year period for a major city were analyzed using Sen’s slope test in R and RStudio software. The study examined monthly and yearly mean minimum/maximum temperatures to determine if these changes were increasing or decreasing during the observed months and years. The non-parametric Sen’s slope method, as proposed by Theil (1950)⁶⁹ and further developed by Sen (1968)⁷⁰, was employed to estimate the actual slopes of time series trends. This approach involves analyzing differences between time series observations and is suitable when the trend is linear, as the Teil-Sen approach represented in Eq. 1.

$$\:{\text{T}}_{\text{i}}=\left(\frac{{\text{x}}_{\text{t}}-{\text{x}}_{\text{t}}\text{*}}{\text{t}-\text{t}\text{*}}\right)\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:$$

(1)

where x_t and x_t∗ are data values at time t and t*, respectively. In Eq. 1, t > t*, the median of these n values of T_i is represented by Sen’s slope of estimation (true slope) in Eq. 1.

$$\:{\text{Q}}_{\text{m}\text{e}\text{d}}={\text{Q}}_{(\text{n}+2)/2}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:$$

(2)

$$\:{\text{Q}}_{\text{m}\text{e}\text{d}}=\frac{\left({\text{Q}}_{\frac{\left(\text{n}\right)}{2}}+{\text{Q}}_{\frac{\text{n}+2}{2}}\right)}{2}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:$$

(3)

Sen’s estimator Q_med was calculated using Eq. 3 depending upon whether n was odd or even. Then, Q_med was computed using a 100% (1 − α) confidence interval using a non-parametric test depending upon a normal distribution. A positive value of Q_i indicates an upward trend, while a negative value characterizes a downward trend of time series data⁷¹. Sen’s slope estimator is a non-parametric method used to assess trends in time series data, particularly useful when the data exhibits variability or outliers. In environmental and atmospheric sciences, it can be applied to analyze trends in various environmental parameters like temperature, precipitation, air quality, and pollutant levels⁷². It has some features as described below:

Robustness to Outliers: Environmental and atmospheric data can be noisy and subject to outliers, such as extreme weather events or anomalous pollution spikes. Sen’s slope provides a way to estimate trends without being unduly influenced by these outliers.

Non-parametric Nature: Unlike methods like linear regression, Sen’s slope doesn’t assume any specific distribution or linearity in the data, making it versatile for complex environmental data that might not follow simple patterns.

Simplicity and Interpretability: The method provides a clear, straightforward estimate of the trend slope, making it easy to interpret in the context of environmental and atmospheric research.

In summary, Sen’s slope is a valuable tool in environmental and atmospheric sciences for analyzing trends in time series data, especially when robustness and non-parametric flexibility are required to deal with the complexities of real-world environmental data⁷². This method is then employed to calculate and analyze temporal changes in LST trends for the major city.

Nighttime SUHI

To evaluate the intensity and extent of the UHI in the Tehran metropolitan area over 19 years, we first analyze UHI maps using Eq. 4. This equation essentially calculates urban heat islands based on the temperature variance between the city and its environs⁴⁰.

$$\:\text{S}{\text{U}\text{H}\text{I}}_{\text{i}\text{n}\text{d}\text{e}\text{x}}={(\text{L}\text{S}\text{T}}_{\text{i}}-{\text{L}\text{S}\text{T}}_{\text{m}\text{i}\text{n}})/{(\text{L}\text{S}\text{T}}_{\text{m}\text{a}\text{x}}-{\text{L}\text{S}\text{T}}_{\text{m}\text{i}\text{n}})$$

(4)

Here, LST_i represents the same LST for each pixel in a specific image. LST_max is the maximum value of LST, and LST_min is the minimum value of LST from that same LST_i image. The use of this index enables the normalization of the LST values between 0 and 1 for each pixel in an image. To determine SUHI, an efficient method is required that enhances understanding effectively.

Temperature difference between Tehran and its surrounding suburbs

To calculate the temperature difference between the city and its outskirts, initially, daily and nightly surface temperature data were examined using Standard Deviation (SD) to determine whether the data distribution is scattered or clustered during the day and night. Subsequently, the optimal data (either day or night) should be selected to investigate the temperature difference between the city and its outskirts.

The SD is the most frequently utilized metric for variability since it takes into account all the scores in the dataset during its calculation and is expressed in the original measurement units. The SD is a statistical measure of dispersion that quantifies how spread out a dataset is in relation to its mean. It is computed as the square root of the variance by assessing how much each point deviates from the mean. A low SD suggests that data points are closely clustered around the mean, whereas a high SD indicates more significant data dispersion. Essentially, an SD near zero implies that data points are very near the mean, while a larger SD suggests that data points are more widely distributed from the mean. Equation 5 is used to calculate the SD.

$$~\sigma = \sqrt {\frac{{\mathop \sum \nolimits_{{i - 1}}^{n} \left( {x_{i} - ~\bar{x}} \right)^{2} }}{{n - 1}}}$$

(5)

In this context, σ represents the standard deviation, x_i stands for each value in the dataset, $\:\overline{x}$ denotes the mean, and n signifies the number of data points.

In this section, the monthly, seasonal, and annual temperature variances between the Tehran metropolis and its surrounding suburbs were computed. The geographical coordinates of the central area of Tehran and its suburbs will be detailed in Table 3; Fig. 1b. Notably, the northern suburbs of Tehran have a cold climate with year-round snow cover due to the Alborz Mountains. To ensure temperature accuracy, the analysis of UHI nighttime temperature differences excluded these northern suburbs, focusing solely on the eastern, western, and southern suburbs. The UHII was employed to determine the city-suburb temperature variation (Eq. 6). Subsequently, temperature difference graphs were created and analyzed.

$$\:{\text{U}\text{H}\text{I}\text{I}}_{\text{u}-\text{r}}={\text{T}}_{\text{u}}-{\text{T}}_{\text{r}}$$

(6)

where T_u represents city temperature and T_r represents suburb temperature.

Table 3 Geographical coordinates of the central area and suburbs of the Tehran metropolitan area.

Full size table

Nighttime hotspots

The frequency of hotspots was calculated in Table 4 to determine where the maximum hotspots occur in Tehran. Nightly temperature thresholds for each month and year were defined as a 19-year average. “When temperatures exceed these thresholds, they are identified as hotspots”.

Table 4 Thresholds for hotspots.

Full size table

The investigation of elevation and NDVI in relation to LST

In addition, temperature is examined concerning geographical components (elevation and vegetation cover) in longitudinal and latitudinal directions to determine their effects on Tehran, particularly in terms of experiencing the SUHI phenomenon. The analysis of the relation between NDVI and elevation indicated that NDVI rises as elevation increases⁷³. Additionally, there was a negative correlation observed between NDVI and LST⁷⁴, as well as between elevation and LST⁷⁵.

The intensity of plant reflection used in calculating NDVI depends on leaf area, chlorophyll content, and plant moisture. As vegetation cover becomes denser and of higher quality, NDVI values increase and approach unity. In very dense vegetation cover, with increasing plant coverage, the reflection in the red wavelength does not change, while the near-infrared reflection increases. This causes NDVI saturation, where its value remains constant. Different NDVI values represent various types of coverage; for instance, NDVI values vary from − 1 to 1. Negative values usually signify clouds and water, while values near zero indicate bare soil. Higher positive NDVI values, from 0.1 to 0.5 for sparse vegetation to 0.6 and above for dense green vegetation, reflect greater vegetation levels^76,77. NDVI is calculated using differences in the red and near-infrared parts of the electromagnetic spectrum and is formulated as:

$$\:\text{N}\text{D}\text{V}\text{I}=\frac{\text{N}\text{I}\text{R}-\text{R}\text{e}\text{d}}{\text{N}\text{I}\text{R}+\text{R}\text{e}\text{d}}$$

(7)

where Red and NIR stand for the spectral reflectance measurements in the visible red and near-infrared regions, respectively.

Results

The climatology of daytime LST

The LST is a crucial parameter for studying energy balance, climate changes, various physical and chemical processes on the surface, and the UHI effect^78,79. Figure 2 displays the spatial distribution of the 19-year average daily LST monthly for the Tehran metropolis and its suburbs. In January and December, temperatures in the suburbs rise compared to Tehran, with these conditions becoming more pronounced in subsequent months. In November, February, and March, LST values in suburbs other than the north are higher than those in the city. Between April and October, temperature increases in Tehran and its surrounding suburbs follow a similar trend. Consequently, in April and October, temperature values are nearly equal between the city and its suburbs, although some parts of the eastern, southeastern, southern, and southwestern suburbs exhibit scattered higher values compared to the city. In May and September, temperatures in other suburbs are generally higher than those in the northern suburbs and northern parts of the city. From June to August, temperatures in the suburbs increase significantly to the extent that it can be stated that the entire area (city and suburbs) experiences elevated temperatures regardless of Tehran’s urban boundaries.

The daytime spatial distribution of LST reveals that the coldest temperatures are found in the northern outskirts of Tehran, followed by the northern areas. Conversely, the eastern, western, and southern surrounding regions experience the highest temperatures. LST values vary across seasons: in winter, temperatures are lowest in January (18 °C), February (24 °C), and December (21 °C). Spring temperatures peak at 35, 40, and 50 °C in March, April, and May, respectively. Summer records a consistent 56 °C throughout. Autumn sees temperatures of 50 °C in September, 42 °C in October, and 27 °C in November. The summer has the highest average temperature at 56 °C compared to other seasons. Spring and autumn have average LSTs of 41.66 °C and 39.66 °C, respectively, while winter has the lowest average at 21 °C. Generally, daily LST values peak during warm seasons (summer, spring, and autumn).

During the day, temperatures are generally higher in the suburbs compared to the city. A notable observation is the increase in LST in the suburbs compared to the city, with higher values during warm months than cold months.

The climatology of nighttime LST

The spatial analysis of the 19-year average of nighttime LST on a monthly basis for the metropolitan area of Tehran and its suburbs is shown in Fig. 3. In January, February, and December, temperatures in Tehran rise notably compared to the northern and eastern suburbs during the night. However, temperature values vary across the western, southeastern, southern, and southwestern suburbs but are largely similar to those in the city. This trend intensifies in subsequent months. By March, LST values in Tehran, particularly in the central district, show an increase from previous months. This pattern escalates further with urban and suburban temperatures peaking in April. As June approaches, Tehran experiences its highest summer temperatures with a significant rise in city temperatures. The peak occurs in July, notably in 22 districts with a focus on central districts. This upward trend continues through August before declining in September. By October, temperatures decrease compared to previous months, with conditions resembling those of March by November. A significant observation is the rise in surface temperature during warmer months compared to colder ones. The maximum nighttime LST is recorded from May to September, with a peak in July within urban districts (10–12, 14–17), encompassing parts of districts 6–9 and 13.

Furthermore, it is evident that along with the rise in temperature within the city, similar temperature increases are also observed in the southeastern suburbs. Hence, it can be inferred that the temperatures in the city and these suburbs are nearly equivalent. Nighttime temperatures have risen in the outskirts to the southeast, south, southwest, east, and west, as well as within the city, although this increase is more pronounced in the urban districts of Tehran. Typically, the coldest temperatures are noted in the northern peripheries of Tehran. Moving towards lower latitudes results in a gradual temperature increase within both the city and its outskirts, with peak temperatures seen first in the city and then extending to the outskirts situated in the southeast, south, and southwest.

An interesting observation is that nighttime temperatures in the city are higher than in the suburbs, with the highest temperatures observed in the urban districts of Tehran compared to the outskirts. The highest LST values at night are found in central city districts (10–12, 14–17, 20, and parts of districts 6–9 and 13), which vary across different seasons. In winter, LST values for January, February, and December in central city districts are 3 °C, 6 °C, and 4 °C, respectively. Spring temperatures peak at 9 °C in March, 14 °C in April, and 20 °C in May. Summer sees temperatures at 24 °C in June and 26 °C in July/August. Autumn temperatures reach 22 °C in September, 16 °C in October, and 10 °C in November. Consequently, summer has the highest average temperature at 25.33 °C compared to other seasons. Autumn follows with an average of 16 °C and spring with an average of 14.33 °C. Winter records the lowest values at an average of 4.33 °C. Generally, the nightly and monthly pattern of LST is almost similar. The highest temperatures are observed at night in the city, reaching their peak in the central districts during summer, while the lowest temperatures are seen in the outskirts.

Urban-suburban LST differences: a meridional perspective

To examine temperature variations between the city and its outskirts, the temperature difference from the central box temperature to temperatures along the south-north direction in Fig. 4 was calculated. Accordingly, temperatures in the central city box with the geographical latitude and longitude mentioned in Table 2 were compared seasonally and annually to temperatures at points ranging from latitude 35°51´ in the south to latitude 35°89´ in the north. These comparisons were then made between seasonal values and annual temperatures.

Daily temperature variations in Tehran and its suburbs vary across seasons, particularly at night. The temperature contrast between the central area and the southern suburbs at latitude 35°57´ is minimal and often below zero, although it surpasses zero in spring. Around latitude 35°60´, temperatures move around 4 °C but decrease as one moves toward the northern regions of Tehran, eventually exceeding 20 °C in the northern suburbs at latitudes above 35°83´.

During the day, the temperature difference between the city and its outskirts shows that the northern outskirts have a much higher temperature difference compared to both the city and the southern outskirts. The southern outskirts exhibit a lower temperature difference compared to the city, suggesting an increase in temperature on the southern outskirts relative to the northern outskirts. In summer, cities and suburbs are notably consistent with an annual pattern. In autumn and winter, only the southern suburbs demonstrate a high level of annual pattern consistency. However, in spring, there is a significant difference in annual pattern consistency between the northern and southern suburbs, indicating a decrease in temperature in the city compared to its suburbs. Additionally, the northern districts of the city experience lower temperatures than the southern districts (b, d, f, and h).

Analysis of nighttime charts in different seasons reveals that the temperature difference between the central area and the southern periphery, starting at latitude 35°50´, is about 6 °C, decreasing to under 4 °C near latitude 35°57´. In the city center, this variance approaches zero or less. These conditions persist up to Tehran’s northern urban districts. However, these values rise to over 20 °C at latitudes above 35°83´ in the northern outskirts, indicating a significantly higher temperature difference compared to annual temperatures in the northern suburbs relative to those in the southern periphery and central regions.

Annual temperature variations compared to seasonal temperatures indicate that the northern outskirts exhibit a greater temperature difference compared to the southern outskirts and central districts. Comparing nighttime temperature profiles shows that during summer, the temperature difference between the central region and the southern-northern outskirts increases, aligning closely with the annual trend. Essentially, at night, there is a more consistent pattern annually in terms of temperature contrast between the city and its outskirts, signifying a rise in city temperatures relative to its outskirts.

At night, the northern outskirts experience notably lower temperatures than their southern counterparts concerning the central region. Furthermore, seasonal temperature differences decrease during autumn and spring compared to yearly averages but intensify in winter, particularly in the northern outskirts (a, c, e, and g).

During the day, the temperature difference between the central area and the southern suburbs is negative in summer, autumn, and winter, indicating that the southern suburbs are warmer than the city. However, in the spring, the southern suburbs experience lower temperatures than the city. In contrast, the temperature difference with the northern suburbs is positive, indicating that the northern suburbs are cooler than the city. At night, the temperature difference between the central area and both northern and southern suburbs is positive in all seasons, reflecting higher temperatures in the city compared to the suburbs.

Urban-suburban LST differences: a zonal perspective

To examine temperature variations between the city and its outskirts, the temperature difference between the city central area (orange box) and the suburbs examined by using a west-east longitude profile in Fig. 5. Accordingly, temperatures in the central city box with the geographical latitude and longitude specified in Table 2 were compared seasonally and annually to temperatures at points ranging from a longitude of 51°15´ in the west to a longitude of 51°65´ in the east. These comparisons were then made between seasonal values and annual temperatures.

Temperature differences between the city center and its outskirts are observed in a west-east direction. Throughout the daytime longitudinal profile, this temperature difference ranges from − 4 °C to 8 °C, approaching zero towards the western and central districts of Tehran and dropping below zero as one moves eastward. In the eastern outskirts, this difference exceeds 4 °C. Daytime temperature patterns differ significantly from nighttime conditions, highlighting a temperature difference between the central and western-eastern regions. Urban districts and the city center generally experience lower temperatures compared to the western and eastern suburbs, where temperatures peak. The central region indicates lower temperatures than the outskirts, reflecting the lowest annual temperatures across various seasons. During summer and autumn, suburban temperatures dip below the annual average, rendering them warmer than the city. Conversely, in spring and winter, suburban temperatures surpass the annual average, resulting in cooler eastern suburbs and warmer western suburbs compared to the city. Essentially, eastern suburbs are colder than the city while western suburbs are warmer, with a more pronounced temperature contrast between the center and east than with west.

During the night in the longitudinal profile, the temperature difference between the center and the western outskirts reaches approximately 6 °C at a geographical longitude of 51°15´. This difference gradually decreases from 51°20´ towards the central area of Tehran, eventually dropping below zero. As one moves away from urban districts and approaches the eastern outskirts, this temperature difference increases compared to the west, reaching over 8 °C at longitude 51°70´.

At night, a significant temperature contrast emerges between the central region and the eastern and western suburbs. These suburbs experience lower temperatures than the center, with a more pronounced drop in temperature in the east than in the west. Seasonal temperatures closely mirror annual temperatures, which is especially noticeable in summer and autumn.

In spring and winter, this temperature disparity among suburbs grows annually, with a more pronounced increase experienced in the eastern suburbs compared to their western counterparts. This trend intensifies during winter as differences between seasonal and annual temperatures expand within suburban areas during spring and winter.

Generally, the longitudinal and latitudinal profiles indicate that LST reaches a minimum over the central area of Tehran’s metropolitan region during the day and a maximum during the night. This suggests that the central districts of the city are cold during the day and warm at night when compared with the suburbs.

During the day, the temperature difference between the central area and the western suburbs becomes negative in summer and autumn, with the suburbs being warmer than the city. In spring, the western suburbs from longitude 51°15´ to 51°20´ are cooler than the city, while from longitude 51°26´ to the beginning of the central box, they are warmer than the city. Additionally, the eastern suburbs are also colder than the city.

In winter, the eastern suburbs remain colder than the city, while temperatures in the western suburbs align with those of the city, showing a noticeable drop in January. The temperature difference in the eastern suburbs is negative during summer and remains negative throughout autumn, except for November. In winter and spring, this difference becomes positive, indicating that temperatures in the eastern suburbs are lower than those in the city. At night, there is a positive difference, meaning that temperatures in the central area increase compared to those in the suburbs.

Interannual and intra-annual variability and trends in day and nighttime LST

The results of the annual temperature trend change in Fig. 6 show that the average maximum temperature was 29.15 °C in 2020 and increased to 31.69 °C in 2021, representing the lowest and highest values observed during these years, respectively. The annual average minimum temperature has fluctuated from 8.30 °C in 2003 to 10.32 °C in 2021. The minimum temperature trend in Tehran has been on the rise for over 19 years, although the daytime temperature trend in Tehran has also increased, albeit to a lesser extent than nighttime temperatures (a). The standard score during the day indicates that the years 2006, 2010, 2013 to 2018, as well as 2021, have positive values. At night, the years 2010 and 2013 to 2021 also show positive values (b).

The average maximum temperature in Tehran ranges from 11.21 °C in January to 46.89 °C in July, with a similar variation seen in the average minimum temperature based on MODIS LST data—from 3.49 °C in January to 21.37 °C in July—making January the coldest and July the hottest month in Tehran (c).

To identify monthly temperature trend changes per decade in Tehran, the average slope of the temperature trend for minimum and maximum temperatures was calculated. In this context, the steepest slope of the trend for average daily and nightly temperatures is observed in January at 1.90 °C and in May at 1.64 °C per decade, respectively. March and October record the lowest values for maximum (-1.50 °C) and minimum (-0.30 °C) average temperatures. The average range of maximum and minimum temperatures in all months, except for October, exhibits an increasing trend. Analysis of changes in daily and nightly temperature trends reveals significant variations. The average daily temperature across Tehran’s entire geographical area has risen by 0.28 °C per decade, while the average nightly temperature has increased by 0.74 °C per decade. The trend slope for the average nighttime temperature in Tehran’s geographical area displays noticeable fluctuations across different months (d).

The LST difference between the City center and suburbs

In this section, as illustrated in Fig. 7, the central area of Tehran, with a geographical latitude ranging from 35°66´ to 35°71´ and a geographical longitude ranging from 51°35´ to 51°48´, was compared with three suburbs in the east, west, and south that exhibited the lowest temperatures among all suburbs. Despite the northern suburbs having the lowest temperatures compared to other suburbs, it was excluded due to the presence of mountainous terrain and potential calculation errors (Fig. 2c).

The SD is a measure of data dispersion from the average. The figure below illustrates the range between the minimum (0.58 °C) and maximum (2.34 °C ) daily standard deviations in October and January, which is 1.75 °C, with an average monthly SD of 1.22 °C. This suggests higher data dispersion during the day. Conversely, in September (0.34 °C) and November (0.95 °C), the difference in SD at night is 0.61, with a monthly average of 0.64 °C, indicating lower dispersion closer to zero and data clustering around the mean.

Comparing the standard deviations during the day and at night shows that nighttime measurements more accurately depict UHI effects in Tehran’s metropolitan area compared to daytime readings. The subtle differences in SD values emphasize the significance of studying UHI effects during nighttime. Therefore, considering the occurrence of the UHI at night, monthly, seasonal, and annual charts will be examined to compare the nighttime UHI difference between the urban area of Tehran and its surrounding suburbs.

The monthly temperature difference in Tehran compared to its surrounding suburbs is very small, ranging from 5.73 to 6.49 °C, with a variation of 0.76 °C. The highest values occur in September, June, and August, while the lowest values happen in November, December, and February. In winter, the temperature difference between the city and the suburbs ranges from 4.68 to 7.06 °C, with the smallest difference in 2020 and the largest in 2008. Spring 2020 has the smallest difference (5.11 °C) compared to 2008, with the largest difference (6.78 °C). In summer and autumn, 2020 shows the smallest differences between city and suburbs at 4.92 and 5.67 °C, respectively. For summer and autumn, 2006 at 7.08 °C and 2016 at 6.93 °C show the largest differences. Annually, 2020 has the smallest difference (5.09 °C), while 2008 has the highest difference (6.70 °C). Therefore, based on the investigations conducted on the day and night LST outputs, and considering that nighttime surface temperatures more prominently reflect temperatures over Tehran, as indicated in Sects. 3–6, a more pronounced UHI effect is observed in Tehran at night. Hence, subsequent analyses will focus solely on nighttime conditions.

The climatology of the nighttime SUHI

In Fig. 8, the expansion of the UHI in Tehran and its suburbs is depicted monthly and annually. The UHI is more pronounced over the 22 urban districts of Tehran than in the suburbs during both cold and warm months. While high values are observed in the southeast, south, and southwest suburbs, Tehran and other suburbs exhibit higher heat island values compared to the northern suburbs. The primary heat island cores are situated in central Tehran (10-11-12 and 14 districts), where insufficient vegetation cover, urban surface conversion, improper urban design, and construction contribute to temperature rises. The westward and southwestward expansion of Tehran’s heat island may be attributed to reduced vegetation cover.

Paying attention to Tehran’s urban geometry is crucial for identifying the UHI. The urban canyon, a key factor, traps longwave radiation and intensifies UHI in Tehran’s urban districts. The ratio of building height (h) to distance between them (w) impacts the absorption and reflection of shortwave radiation from surfaces. Compact spaces with high W/H ratios are notably affected by longwave radiation due to their enclosed nature, contributing to the rising trend of the urban heat island. Much of central Tehran is encircled by compact buildings. The use of dark materials on urban surfaces and within urban canyons formed by buildings of varying heights results in the absorption and retention of solar energy, consequently elevating the city’s temperature. In central Tehran, where buildings are tall compared to street width, the UHI effect is more pronounced. This occurs because reduced solar energy balance due to narrow streets results in higher urban temperatures. Greater irregularities in urban layout further amplify the UHI effect. Therefore, as the disparity between street height and width grows, so does the intensity of the UHI.

The climatology of the hotspots

As depicted in Fig. 9, the spatial distribution of monthly and annual hotspots is shown. Central cores of hotspots form in central districts (10-11-12 and 14). Population density can have dual effects on heat production: a direct effect through higher population leading to increased metabolism and an indirect effect through the number of buildings, vehicles, and factories that increase with population growth.

Regions 10, 11, and 12 rank 11th, 13th, and 19th in terms of population density among regions, respectively; however, population density alongside other climatic factors remains significant. Urban structure is also crucial. A city valley, especially the height of buildings concerning street width, provides more potential for trapping radiation within the urban valley. This phenomenon occurs when sunlight is reflected by a surface and encounters other surfaces in the city, partly absorbed and partly reflected. In fact, urban surfaces absorb more radiation than solar radiation compared to what is expected from surface reflection. The trapping of long-wave radiation is attributed to the sky view factor. Tall buildings predominantly obstruct a major portion of the sky, resulting in limited long-wave radiation reaching the ground in city valleys. Dense buildings have significantly encroached upon central districts of Tehran (10-11-12 and 14), covering over half of the city’s districts. These central districts serve as vital hubs for production, residence, commerce, distribution, and transportation. Furthermore, the southeast outskirts exhibit hot spots during warm months, with peak levels in July and lowest levels in January.

Relation between urban and suburban LST and the elevation

The annual nighttime LST chart analysis with the height of Tehran in Fig. 10 reveals a slight temperature decrease in the southern outskirts at a geographical latitude of 35°50´, with very low altitude values near 1000 m. Moving towards the southern areas at a latitude of 35°57´, this temperature increase persists towards the city center. Progressing from latitude 35°57´ towards northern latitudes increases altitude values, exceeding 2000 m at latitude 35°83´ in the city’s north. Approaching the northern outskirts further decreases the temperature and raises altitude values. Consequently, an ascending slope for altitude is observed from the southern outskirts to the northern outskirts, reaching its minimum in the city center and southern outskirts.

Additionally, the longitudinal profile of the annual LST shows a decrease with increasing altitude in the Tehran metropolis. At a longitude of 51°15´, temperatures reach their minimum as elevations in this region exceed 1000 m. Approaching the city, temperatures notably rise, especially in central districts. In the west-east chart, the city center’s elevation is the same to the west and decreases compared to the east. Exiting the city towards a longitude of 51°64´ in the eastern outskirts, temperatures decrease due to higher altitudes, with an ascending slope from west to east in terms of elevation.

The relation between urban and suburban LST and NDVI

The annual nighttime LST chart analysis with NDVI for Tehran’s metropolitan area in Fig. 11 indicates a decreasing temperature trend in the southern outskirts at a latitude of 35°50´ compared to the city. Areas at this latitude exhibit high NDVI values, nearing 0.50. Moving towards the southern regions at a latitude of 35°57´, temperatures rise towards the city center, where NDVI values drop significantly due to natural coverage giving way to human-made surfaces (exceeding 0.1), particularly in central urban districts. At a latitude of 35°83´, temperature decline coincides with increased vegetation cover (around 0.2). Progressing towards the northern outskirts lowers temperatures and diminishes NDVI values, with central districts showing lower NDVI values than the outskirts.

Furthermore, the longitudinal profile of LST with NDVI in Tehran shows that at a longitude of 51°15´, the temperature reaches its minimum, while NDVI values in this region are high (above 0.3). As one approaches the city, it is observed that the temperature, especially in central districts, increases and vegetation cover across the west-east longitudinal profile decreases. Upon leaving the city at a longitude of 51°64´ in the eastern suburbs, temperatures decrease. Vegetation cover in Tehran is very minimal compared to that in the western and eastern suburbs.

Discussion

Evaluating the effects of urban expansion on LST: a focus on nighttime SUHI

The LST parameter plays a crucial role in understanding climate change, land warming, and urban land use⁸⁰. In Tehran and its suburbs, the highest LST values are predominantly found in built-up areas. This trend indicates that LST values are on the rise in urban regions, particularly within central districts and the western, southwestern, southern, and southeastern suburbs. Notably, the average LST across all land uses is increasing, with significant contributions from built-up zones such as heavy industry and compact buildings. This escalation is largely attributed to rapid urbanization occurring both within Tehran and its surrounding areas^81,82.

As population movement intensifies from the outskirts into the urban centers of Tehran, urbanization accelerates further. However, this growth has not been without challenges; inappropriate construction practices, unplanned urban development, and neglect of the city’s existing texture have collectively led to a marked increase in LST—especially during summer months—from 1984 to 2020¹⁷. Consequently, both daily and nightly LST values peak during hot months like summer and remain elevated in other seasons such as autumn and spring when compared to winter⁴⁰.

In terms of seasonal variations, January emerges as the coldest month, while July stands out as the hottest month for Tehran—both during the day and night. Furthermore, there is a discernible upward trend in average daily and nightly temperatures across Tehran’s geographical expanse on both monthly and yearly scales; this increase is particularly pronounced at night.

The rising temperature trend in the Tehran metropolitan area is compounded by escalating air pollutants, fossil fuel consumption, greenhouse gas (GHG) emissions, and an increase in heatwave occurrences. The temperature differential between the city center and its suburbs illustrates this phenomenon clearly: a day-night chart (Fig. 1a) reveals an increasing temperature gradient from central regions towards northern and eastern suburbs—largely due to highlands and snow-covered areas—while showing a decreasing gradient towards western and southern outskirts where temperatures are warmer.

Tehran’s climate has undergone significant changes driven by extensive urbanization coupled with population growth, primarily in the eastern and southern districts. Increased traffic congestion in northern and eastern parts of the city further exacerbates these issues alongside higher fossil fuel consumption across construction, transportation, and industrial sectors. The formation of UHI has also been a critical factor contributing to these climatic shifts.

Interestingly, there exists a negative temperature variance between urban centers and their outskirts during daytime hours; however, this reverses at night, where positive variances indicate higher temperatures within urban areas. This phenomenon highlights how nighttime LST significantly increases within urban areas—a key aspect for studying UHI effects specific to Tehran⁴⁰.

In urban climatology literature, particular attention is given to nighttime UHI formation⁸³. One major contributor to this phenomenon is urban texture itself; studies show that over 50 cities worldwide exhibit strong correlations between nighttime UHI effects and their respective urban textures⁸⁴. In Tehran’s metropolitan area specifically, UHI impacts are more pronounced at night due to substantial thermal inertia present in materials used for construction. The heat retained by these materials, combined with their limited cooling capacity, leads to elevated nighttime temperatures⁸⁵.

Nighttime land surface cooling is intricately linked to net longwave radiation loss. As urban valleys expand, a notable decrease in the amount of longwave radiation emitted from the land surface occurs. This reduction is primarily due to the presence of walls that replace open skies, thereby obstructing some of the emitted radiation⁸⁶.

Research indicates that the most intense UHI effects are observed in districts with medium-density buildings, while tall, open structures tend to exhibit the lowest intensity of UHI⁸⁷. This suggests that urban valleys play a more significant role in exacerbating nighttime UHI effects⁸⁸.

Furthermore, nighttime lighting resulting from human activities also contributes to the formation of nighttime SUHI⁸⁹. As a result, cities like Tehran and its southeastern, southern, southwestern, and western outskirts experience some of the highest levels of UHI during nighttime hours⁹⁰. This interplay between urban structure and human activity underscores the complex dynamics influencing urban heat patterns.

Key factors influencing SUHI in Tehran

Nighttime temperature differences

Therefore, considering the factors mentioned, it is expected that nighttime UHI values inside the city of Tehran will increase more rapidly compared to the surrounding areas. Additionally, the UHI in central city districts will be higher than in suburban areas³¹. During the night, the temperature difference between the city and the village increases significantly compared to the daytime due to variations in land use. Urban districts feature impermeable surfaces like buildings, asphalt, and streets that enhance heat retention. Conversely, rural regions consist of open spaces with permeable surfaces, such as vegetation and soil, allowing for rapid heat dissipation and lower temperatures. Consequently, the most significant temperature difference between urban and rural areas, or the highest UHII, typically arises during calm nights with clear skies three to five hours after sunset in the late evening. The average UHII at night varies among different cities^29,30. In arid and semi-arid regions, temperatures in urban centers can increase more compared to their surrounding areas^91,92. This difference may be attributed to various factors such as climate, topography, and urban patterns⁹³. Furthermore, UHII not only varies across different seasons but also escalates with the size of the urban area⁹⁴.

Soil moisture reaches its minimum in the summer, which reduces the temperature difference between the city and its suburbs. In contrast, the concentration of urban pollutants peaks during the cold season (autumn and winter) compared to other times of the year⁹⁵. As pollutant concentrations increase in the boundary layer within cities, the greenhouse effect caused by high pollutant levels leads to higher temperatures in urban districts. Conversely, in the suburbs, energy loss from longwave radiation during extended winter nights (and generally throughout the cold season) results in significantly lower temperatures. This creates a greater temperature difference between the city and its suburbs during this time of year.

Impacts and implications of COVID-19 in Tehran’s SUHI and urban planning

One notable point regarding the temperature difference between Tehran and its suburbs is that in 2020, Tehran recorded significantly lower temperatures across all seasons. One reason for this may be the COVID-19 pandemic, which contributed to a noticeable drop in temperatures that year⁴².

The COVID-19 pandemic significantly contributed to urban cooling in Tehran in 2020 due to a nationwide lockdown that reduced both human and industrial activities. A key factor in diminishing the intensity and spread of the UHI effect was the decrease in traffic within the city. This approach contributes to reducing air pollutants and GHG emissions within the city, leading to a more balanced urban temperature. This experience highlights that UHI can be managed relatively swiftly, leading to immediate climatic benefits⁴².

Moving forward, transitioning to environmental sustainability in the post-pandemic era is crucial for enhancing the climate of the Tehran metropolitan area. Achieving sustainable development—by minimizing environmental harm and fostering socio-technological resilience—requires a comprehensive strategy aimed at instilling sustainable practices among citizens and city officials alike, ensuring a sustainable future for everyone. While air pollution levels dropped during the COVID-19 outbreak, it is essential to maintain this momentum in the post-pandemic period to foster a greener and more sustainable urban environment⁹⁶.

Geographical considerations in Tehran’s SUHI

Elevation and wind direction

Tehran is situated between mountain valleys (the southern slopes of the Alborz Mountains) and a desert (the northern margins of the central desert of Iran). It is encircled by hills to the east and plains to the west. The city ranges from an altitude of 2000 m in the north to 1000 m in the south. Despite the decreasing elevation from north to south and from east to west, the UHI intensifies in this direction⁴⁰. The central core of hot spots is situated in central districts. The highest nighttime UHI effect in the city center (districts 10-11-12 and 14) happens on warm nights and is more pronounced than in the northern part of the city. In contrast to the elevations of the northern regions of Tehran (District 1), the central districts of the city, characterized by lower altitudes and dense old buildings, encounter UHI effects during both warm and cold months⁹⁷.

Given that Tehran is situated on a cone-shaped alluvial fan, strong westerly winds at night lead to an increase in pollutant concentrations due to the mountain-to-plain airflow. This phenomenon exacerbates the greenhouse effect, resulting in higher temperatures in Tehran and intensifying the temperature difference between the city and its suburbs. Consequently, the presence of the high mountains to the north of Tehran contributes to the intensification of UHI effects during nighttime. The developments in the Tehran plain are influenced by conditions prevailing over Mount Tochal, meaning that any changes in mountainous areas are directly reflected in the plain. The elevation of the land plays a significant role as a primary factor in climatic variations. Often, when temporary storms affect Mount Tochal, not even a drop of rain falls on the Tehran plain; conversely, if air mass expansion is widespread, most precipitation occurs as snow on Tochal and northern Tehran while falling as rain in southern Tehran.

Notably, consideration of wind direction in Tehran is crucial as it influences its local climate. Due to its proximity to desert regions, Tehran’s semi-arid climate results in air that is typically polluted with dust. The area is largely influenced by westerly wind currents. The presence of mountains—especially their proximity to plains—creates local disturbances in general airflow patterns, causing many winter or summer storms to be directly influenced by these factors. As a result, Tehran often experiences conditions that are not conducive to a favorable climate.

When southerly winds gain strength, they carry air pollution into the mountainous areas of Tehran; from several hundred meters away, Mount Tochal can become invisible due to pollution. This occurs because prevailing wind patterns generally flow from west to east, transporting pollutants toward eastern Tehran. Whenever these winds intensify, they can disperse polluted air out of Tehran; however, the alignment of the Alborz Mountains between Tehran and Karaj largely diverts westerly winds toward Shahriar and prevents complete pollution dispersal unless wind intensity is very high. After westerly winds, one of the most significant summer winds originates from the south and blows from within the desert toward Tehran. During this time, it transfers heat from desert air and carries dust into cities within the province. The predominant wind direction in this region is generally from southwest to northeast.

The abnormal and irregular development of Tehran—characterized by factories built without regard for prevailing wind directions and heavy traffic—creates conditions that lead to pollution being transferred northward. As a result, Tehran has been ranked among the most polluted cities globally; moreover, this pollution poses a greater risk to public health than threats such as earthquakes or widespread flooding.

Vegetation

Most urban districts in Tehran lack vegetation cover or have weak vegetation cover, leading to an increase in the UHI effect in the city center, although there is some increase in vegetation cover in the suburbs. Based on NDVI classification, urban districts of Tehran generally face a shortage of vegetation cover⁴⁰. The vegetation cover in Tehran is not proportionate, and various species have been used that are not compatible with the climate or soil conditions. For instance, in the central districts of the city with high temperatures, compatible plants should be used because inadequate vegetation cover has led to a decrease in relative humidity in central Tehran compared to the suburbs, resulting in a more significant difference in relative humidity at night. On the other hand, land-use change from green cover to human-made areas in central regions is another reason for the temperature increase. The central districts of Tehran have the fewest green spaces compared to the suburbs, showing the highest UHI values⁴⁰. Moreover, green coverage in Tehran has declined over the last 30 years (1990–2020). Specifically, central districts of Tehran (1, 4, 8, 13, 14, 15, 20) exhibit the least green spaces and highest UHI values¹⁸.

Assessing the influence of suburbs and districts in shaping SUHI in Tehran

During the day, urban districts are influenced by shortwave radiation. At night, urban structures prevent the escape of longwave radiation into space. This leads to a more pronounced increase in nighttime surface temperature, making it more suitable for studying the SUI effect in Tehran.

Built-up areas, which have allocated the highest value of land use in urban areas and surrounding suburbs, exhibit the highest UHI values over Tehran’s urban districts. The expansion of suburbs has led to even higher UHI values in the western, southwestern, southern, and southeastern suburbs, where hot spots are concentrated in central districts. Tehran’s elevation decreases from north to south and from east to west, with a lesser decrease in central districts contributing to higher temperatures and increased UHI in the southern and western suburbs and city center. Furthermore, lower NDVI values in central city districts due to impermeable surfaces and compact buildings compared to other urban areas and suburbs result in higher temperatures and severe UHI at night.

The western suburbs function as wind tunnels that channel air into the city. Moreover, the southeastern outskirts serve as a crucial source of construction materials for the Tehran province through sand mines and concrete factories; notably, sand with high thermal conductivity exacerbates temperature increases in these regions. This may explain why the southeastern suburbs, such as Tehran, experience high temperatures⁴⁰.

Tehran’s climate has experienced considerable shifts due to extensive urbanization. Unregulated city expansion, population growth particularly in eastern and southern areas, increased traffic congestion in northern and eastern regions, heightened fossil fuel consumption across construction, transportation, and industry sectors, rising GHG emissions, along with high-rise developments—especially along urban corridors—have all contributed to the proliferation of heat islands within the city.

The impact of Building and transportation on the intensification of Tehran’s UHI effect

Tehran releases heat into the planetary boundary layer, with a notable portion of this heat originating from human activities. Anthropogenic heat (AH) arises from four main sources: buildings, transportation, industrial processes, and human metabolism⁹⁸.

Buildings account for 20–25% of the urban surface in many cities⁹⁹, making their contribution to the UHI effect significant. They are not only a primary source of anthropogenic heat emissions^{100,101,102,103}, but also replace permeable soils with impermeable surfaces that often have high thermal mass.

The choice of building materials and the ratio of height to street width can significantly influence the increase in heat and the intensity of the UHI effect in Tehran.

Central Tehran is largely surrounded by dense buildings, and the use of dark materials on urban surfaces and within the canyons formed by structures of varying heights leads to increased absorption and retention of solar energy, raising the city’s temperature. In Tehran’s climate, buildings with brick exterior walls consume less energy compared to those with concrete block walls under similar conditions. Additionally, the use of silicone insulation has led to the highest energy consumption, while fiberglass insulation has proven to be the most efficient material in terms of energy use.

In central Tehran, where building heights exceed street widths, the UHI effect is particularly strong due to a reduced solar energy balance in narrow streets, resulting in higher urban temperatures. Additionally, greater irregularities in urban design further intensify the UHI effect; as the difference between street height and width increases, so does the UHII⁴⁰.

Human activities, particularly transportation in the urban areas of Tehran, contribute significantly to the release of AH. The waste heat produced by traffic makes it the second largest source of anthropogenic heat in the city, following buildings⁹⁸. The transport sector is responsible for about 25% of GHG emissions¹⁰⁴, with carbon dioxide (CO₂) being the primary GHG¹⁰⁵. In developing countries, CO₂ emissions from transportation have been growing rapidly, with Asia being the largest emitter in absolute terms as of 2019¹⁰⁴. Vehicle fuel consumption and its related CO₂ emissions are major factors in climate change. The transport sector accounts for 57% of global oil demand and 28% of total energy use¹⁰⁴.

Rising traffic levels in the northern and eastern parts of the city, along with increased fossil fuel consumption in construction, transportation, and industrial sectors, have led to higher GHG emissions and expanded UHI due to high-rise developments along urban corridors⁴⁰. In Tehran, the combination of a high density of built-up areas, low albedo surfaces, and non-reflective materials contributes to increased AH from transportation and traffic congestion, which plays a significant role in the formation of UHI¹⁰⁶.

Potential strategies and solutions for mitigating future SUHI effects in Tehran

To address the challenges posed by UHI, a comprehensive approach focusing on environmental sustainability is essential:

Using adaptable and less water-intensive vegetation in the semi-arid Tehran’s climate

Selecting the right plants for urban environments is crucial to prevent possible financial and environmental setbacks that could arise from overlooking important selection criteria. The Analytical Hierarchy Process and hierarchical cluster analysis can be employed to identify the most suitable plant species for the region based on key factors such as zone tolerance, urban conditions, aesthetics, maintenance needs, growth traits, and specific characteristics. The final rankings revealed several new plant species that indicate Tehran’s urban landscape has significant potential to become more appealing, less allergenic, more cost-effective, and more water-efficient¹⁰⁷.

The integration of urban green vegetation (UGV) has emerged as a particularly promising approach to combat the UHI effect. By enhancing urban vegetation coverage, cities can improve thermal comfort for residents while simultaneously reducing energy demands for cooling¹⁰⁸. Thoughtful urban design that prioritizes sustainable development can play a crucial role in alleviating UHI effects, especially in new projects¹⁰⁹. Green infrastructure, including the implementation of green roofs (GRs), green walls (GWs)¹¹⁰, terrace gardens¹¹¹, and increased vegetation, can help cool urban environments.

Tehran is currently facing significant challenges regarding water resources. As a result, developing green spaces to alleviate the UHI effect may not be entirely feasible, necessitating the implementation of alternative strategies to address UHI.

Architecture-based solutions to mitigate the UHI effect in Tehran

Architecture is crucial in reducing the UHI effect, as cities are characterized by their structures and constitute a significant portion of the built environment. The decisions made by architects and planners can greatly influence the extent of a city’s UHI.

The phenomenon of the UHI effect is a significant concern for cities like Tehran, where urbanization leads to increased temperatures compared to surrounding rural areas. The UHI effect in Tehran, which ranges from 3 to 6 °C, poses significant challenges for thermal comfort and energy consumption in the city. However, strategies such as implementing high-albedo materials and increased vegetation can effectively mitigate this effect. Specifically, these measures can lower the UHI effect by approximately 2.1 °C and lead to a notable 29% reduction in cooling load under similar conditions²³.

In summary, adopting effective strategies like high-albedo materials, enhancing roof albedo¹¹², and expanding urban greenery not only addresses the UHI phenomenon but also contributes to more sustainable urban environments by improving thermal comfort and decreasing energy consumption.

Pathways of metro contributing to low-carbon City

Adopting low-carbon urban initiatives is recognized as a crucial approach for fostering sustainable development and lowering CO₂ emissions^113,114. The transportation sector contributes over 20% of global energy-related CO₂ emissions^115,116. The metro system, as a primary component of urban underground infrastructure, plays an essential role in low-carbon city strategies by shifting surface transportation underground, thereby freeing up surface areas for carbon sequestration^117,118.

Impacts of replacing conventional vehicles with renewable electric vehicles

A recent report from the National Academy of Sciences emphasizes the importance of increasing electric vehicle (EV) adoption. It states that EVs should constitute 50% of new vehicle sales by 2030 to meet decarbonization targets¹¹⁹. Transitioning from gasoline vehicles to electric ones—powered by rechargeable batteries—holds significant potential for reducing GHG emissions¹²⁰.

Considerations for passive radiative cooling (PRC)

Cooling represents a major end-use of energy worldwide and is a key factor in peak electricity demand. Air conditioning systems contribute to this demand. Therefore, employing passive cooling strategies that do not require electricity could greatly influence global energy consumption. Achieving cooling involves maintaining temperatures below those of the surrounding air. At night, passive cooling techniques such as radiative cooling have been shown to effectively lower temperatures below ambient levels by using devices that radiate heat into outer space through specific atmospheric windows between 8 and 13 micrometers in wavelength^{121,122,123,124,125,126,127,128,129}.

Sustainable urban planning

Encouraging sustainable design practices¹³⁰ among city officials and residents can foster long-term resilience against climate change.

Public awareness campaigns

Educating citizens about sustainable practices can enhance community engagement in mitigating UHI effects.

Conclusion

Tehran, the largest city in Iran, is considered a multi-purpose capital. It serves as a key centralized institution offering diverse services in economic, social, cultural, commercial, and political domains. The high population density in Tehran has led to urban development and consequently had a negative impact on the city’s structure. Therefore, the large population, high-rise buildings, limited open spaces, rapid urban development, and lack of proper planning have posed significant environmental and social challenges, such as influencing UHI effects and increasing UHI.

Monthly LST maps indicate that temperatures rise on the outskirts compared to the city during the day. However, a notable temperature increase is observed over the city, particularly in central districts, in contrast to the outskirts at night. High-temperature values are more prominent in warm months than in cold months, both during the day and night. Generally, Tehran’s metropolitan area encounters its highest temperatures during summer days and nights. Subsequently, spring and autumn exhibit the highest daytime temperatures compared to their nighttime temperatures (autumn and spring). The three summer months exhibit the highest land LST, while January records the lowest.

During the day, the temperature difference between the city center and the northern and southern suburbs is such that, in all seasons, the southern suburb is warmer than the city, and the northern suburb is colder than the city. Additionally, in the western and eastern suburbs, they are warmer than the city in summer and autumn. In spring and winter, the western suburbs are warmer than the city, while the eastern suburbs are colder. During nighttime in all seasons, there is a temperature difference between the city center and its suburbs, showing that the city is relatively warmer compared to its lateral and longitudinal suburbs. Generally, during the daytime, seasonal temperatures in northern, western, and eastern suburbs exhibit the highest variability across all seasons; only the city center aligns most closely with annual temperatures. At night, there is a high alignment of annual temperatures observed in both the city and suburbs during summer and autumn, with slight differences compared to spring and winter. Overall, at night in all seasons, both the city center and suburbs show a high alignment with annual temperatures. A comparison of day and nighttime graphs reveals that daytime LST consistently shows higher seasonal values than nighttime LST.

The monthly trend slope indicates that the daily/nightly temperature trend over 19 years is increasing. The monthly temperature increase rate is 0.28 °C per decade for the day and 0.74 °C for the night. The analysis reveals a significant increase in Tehran’s nighttime surface temperature, amounting to a total increase of 1.48 °C. Furthermore, the temperature increase rate for day and night on a monthly and annual scale shows that the highest average temperature continuously occurs at night compared to during the day.

Based on the monthly average LST, January is the coldest month, and July is the warmest, both during the day and at night across Tehran. Additionally, the annual average maximum and minimum LST over 19 years demonstrates an upward trend, with nighttime temperatures increasing more significantly than daytime temperatures. The trends for maximum and minimum temperatures have risen each decade; January and March show the highest daytime trends, while May and October exhibit the lowest nighttime trends.

The temperature difference between the city and its outskirts, as indicated by the SD of day and night temperatures, implies that focusing on nighttime temperature differences is appropriate due to the data being near zero. This calculation was conducted on a monthly, seasonal, and annual basis. Since SD values for nighttime temperatures are close to zero and cluster around the mean, temperature differences between Tehran and its suburbs were calculated monthly, seasonally, and annually based on these averages. November has the lowest temperature difference at 5.73 °C, while September has the highest at 6.49 °C over 19 years. The year 2020 recorded the lowest values in winter (4.68 °C), spring (5.11 °C), summer (4.92 °C), autumn (5.67 °C), and annually (5.09 °C). The highest temperature differences were recorded in 2008 for winter (7.06 °C), spring (6.78 °C), and annually (6.70 °C). However, summer differences peaked at 7.08 °C in 2006, while autumn reached 6.93 °C in 2016 for both the city and suburbs. Furthermore, when temperature differences exceed monthly or annual averages, it indicates that above-normal conditions were experienced.

Based on these findings, nighttime LST is more suitable for studying UHI. Consequently, subsequent sections will focus solely on nighttime data outputs. The spatial expansion of UHI is greater in central Tehran districts (10 to 14) compared to suburbs, with peak values recorded during summer months. Two main hotspots formed in central Tehran districts (10 and 14) from June to October; July had the highest frequency of UHI occurrences, while January had the lowest.

Analysis of LST concerning elevation and normalized difference vegetation index (NDVI) along south-north and west-east axes indicates that urban factors must also be considered alongside elevation to reduce LST in central districts. Moreover, central Tehran suffers from a lack of vegetation or weak vegetation cover due to the expansion of UHI to the suburbs, leading to a decline in per capita green space within both city and suburban areas.

Data availability

The datasets generated and/or analyzed during the current study are available in NASA’s Earth Science Data Systems (ESDS) Program via NASA’s Land Processes Distributed Active Archive Center (LP DAAC) website (https://www.earthdata.nasa.gov/). The following links provide direct access to the data used in the research: https://appeears.earthdatacloud.nasa.gov/ ; https://doi.org/10.5067/MODIS/MYD11A1.061.

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Hakim Sabzevari University, Sabzevar, Iran
Motahhareh Zargari, Alireza Entezari & Mohammad Baaghideh
Ferdowsi University of Mashhad, Mashhad, 9177948974, Iran
Abbas Mofidi

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Motahhareh Zargari
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Abbas Mofidi
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Alireza Entezari
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Mohammad Baaghideh
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All authors contributed to the study’s conception and design. Conceived and designed the analysis: AM, MZ, AE, and MB; Collected the data: MZ and AM; Contributed data or analysis tools: MZ and AM, AE; Performed the analysis: MZ and AM; Wrote the paper: AM, MZ, AE, and MB; Writing-review, discussing, and editing: AM, MZ, AE, and MB; Corresponding author: AM.

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Zargari, M., Mofidi, A., Entezari, A. et al. Climatology of Tehran surface heat Island: a satellite-based spatial analysis. Sci Rep 15, 10567 (2025). https://doi.org/10.1038/s41598-025-95367-2

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Received: 11 December 2024
Accepted: 20 March 2025
Published: 27 March 2025
Version of record: 27 March 2025
DOI: https://doi.org/10.1038/s41598-025-95367-2

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Abstract

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Climatic comparison of surface urban heat island using satellite remote sensing in Tehran and suburbs

Urban heat island linkages with the landscape morphology

Spatial and temporal changes of outdoor thermal stress: influence of urban land cover types

Introduction

Data and methods

Study area

Data

Methods

Day and nighttime LST

Trend changes in day and nighttime LST

Nighttime SUHI

Temperature difference between Tehran and its surrounding suburbs

Nighttime hotspots

The investigation of elevation and NDVI in relation to LST

Results

The climatology of daytime LST

The climatology of nighttime LST

Urban-suburban LST differences: a meridional perspective

Urban-suburban LST differences: a zonal perspective

Interannual and intra-annual variability and trends in day and nighttime LST

The LST difference between the City center and suburbs

The climatology of the nighttime SUHI

The climatology of the hotspots

Relation between urban and suburban LST and the elevation

The relation between urban and suburban LST and NDVI

Discussion

Evaluating the effects of urban expansion on LST: a focus on nighttime SUHI

Key factors influencing SUHI in Tehran

Nighttime temperature differences

Impacts and implications of COVID-19 in Tehran’s SUHI and urban planning

Geographical considerations in Tehran’s SUHI

Elevation and wind direction

Vegetation

Assessing the influence of suburbs and districts in shaping SUHI in Tehran

The impact of Building and transportation on the intensification of Tehran’s UHI effect

Potential strategies and solutions for mitigating future SUHI effects in Tehran

Using adaptable and less water-intensive vegetation in the semi-arid Tehran’s climate

Architecture-based solutions to mitigate the UHI effect in Tehran

Pathways of metro contributing to low-carbon City

Impacts of replacing conventional vehicles with renewable electric vehicles

Considerations for passive radiative cooling (PRC)

Sustainable urban planning

Public awareness campaigns

Conclusion

Data availability

References

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Authors and Affiliations

Contributions

Corresponding authors

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Competing interests

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Publisher’s note

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Energy information trade-offs in predictive coding inspired neural architectures for urban climate predictive coding for urban climate

Spatial Analysis of Sensitive Urban Zones to UHI Formation Through the Parametrization of Urban Features

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