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Article

Spatio-Temporal Evolution of the Thermo-Hygrometric Index (THI) during Cold Seasons: A Trend Analysis Study in Iran

by
Mehdi Asghari
1,
Gholamabbas Fallah Ghalhari
2,
Elham Akhlaghi Pirposhteh
3 and
Somayeh Farhang Dehghan
4,*
1
Department of Occupational Health Engineering, Faculty of Health, Arak University of Medical Sciences, Arak 38481-70001, Iran
2
Faculty of Geography and Environmental Sciences, Hakim Sabzevari University, Sabzevar 96186-76115, Iran
3
Department of Occupational Health Engineering, School of Medical Sciences, Tarbiat Modares University, Tehran 14117-13116, Iran
4
Environmental and Occupational Hazards Control Research Center, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran 14117-13116, Iran
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(24), 16774; https://doi.org/10.3390/su142416774
Submission received: 12 September 2022 / Revised: 11 October 2022 / Accepted: 12 December 2022 / Published: 14 December 2022
(This article belongs to the Section Air, Climate Change and Sustainability)
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Abstract

:
Global warming can cause deep and extensive changes in the Earth’s climate and changes in the time and place of climatic phenomena. The present trend analysis study assesses cold stress using the thermo-hygrometric index (THI) in the two seasons of autumn and winter in outdoor environments in Iran. The data related to the average of the two variables of daily air temperature and relative humidity from 60 synoptic meteorological stations for a statistical period of 30 years were obtained from the Iranian Meteorological Organization. The THI index was calculated for autumn and winter, and the level of thermal discomfort was determined for each station. The Mann–Kendall statistical test with the help of Minitab ver17.1.0 software was also used to investigate the changes in air temperature, relative humidity and THI index. The THI for autumn increased in 68% of the stations, and this increasing trend is statistically significant in 51% of these stations. The THI for winter increased in 83% of the stations, and this increasing trend is statistically significant in 51% of these stations. In autumn, 53% of the stations were in the range of thermal discomfort, and in winter only 5% were in the range of thermal comfort. The decreasing trend in THI in some climatic types, along with the increasing trend in air temperature, can indicate the decrease in relative humidity in the monitored stations during the studied years. It is recommended to know the temporal and spatial distribution and the probability of occurrence of cold stress through the use of THI in order to adopt preventive measures and policies in the outdoors in Iran.

1. Introduction

Weather is one of the main pillars effecting human beings, and with the progress and development of the world, its protection has become increasingly important. Temperature plays a fundamental role in natural cycles; therefore, predicting its changes for environmental planning and management is critical [1]. Cold waves and cold stress are among the weather phenomena that cause irreparable damage to a country’s economy every year. Atmospheric circulation patterns play a main role in the occurrence of cold stress waves in such a way that the occurrence of environmental phenomena such as frost are related to the repetition of synoptic systems and weather types [2]. Continuous exposure to cold is a risk factor in outdoor workplaces in cold seasons, and performing physical activities in cold weather causes respiratory discomfort. In addition, exposure to cold causes an increase in systolic and diastolic blood pressure [3]. Although temperatures of zero and below zero degrees Celsius in the cold season of the year and in the middle latitudes are not necessarily an abnormal phenomenon, their long-term persistence in a region or their sudden occurrence at the beginning and end of the cold season may become an abnormal phenomenon. Anomalous phenomena are the result of the interaction between large-scale atmospheric circulations and local characteristics [4].
Moreover, studies have shown that most deaths and heart attacks occur in winter. Błażejczyk et al. (2020) assessed the risk of mortality in Poland due to cold stress, and they indicated that meteorological and atmospheric parameters have a great effect on human health and the environment, and their effects on the daily and seasonality mortality rate can be seen. Climate forecasts predict a significant decrease in cold stress, and it has been pointed out that, despite the global warming of the climate, a significant impact of cold stress on human health and mortality due to climate change can be expected [5]. Two environmental factors, the dry temperature of the ambient air and the air velocity, are most important in causing cold effects. In other words, the lower the air temperature and the higher the air velocity, the greater the cold effect [6]. Cold stress is very important in mountainous and foothill regions, especially for agricultural practices, because it plays a key role in the region’s economy due to good rainfall in these areas.
Cold stress and frost are natural phenomena that have caused great damage to plants and agricultural products in certain years. The damage is different depending on the species and variety, and most of the risk of cold comes when the plant is growing. If, at this time, the temperature is lower than the tolerance threshold of the plant, it will cause damage [7]. Knowing the spatial and temporal distribution and probability of the occurrence of cold stress is one of the most basic types of climate research that can guide and help planners in order to reduce cold damage [8]. The increase in the Earth’s average surface temperature has caused deep and extensive changes in the Earth’s climate and also an alteration in the time and place of climatic phenomena, including cold stress. Many factors affect the process of temperature changes, including atmospheric circulation patterns, radiation balance soil moisture, uneven configuration and local factors such as distance and proximity to large water bodies and latitude [9]. In addition, spatial and temporal heterogeneity can affect the PM2.5 concentration in built-up areas [10].
Harsh, cold and snowy winters are the characteristics of the mountainous climate of Iran. Among the effects of cold waves are bursting of pipes and fittings, frost on the surface of roads and streets, increased energy consumption, pressure drops in gas and electricity networks, destruction of agricultural products and also the spread of cold temperature-related diseases [11]. Iran is located in West Asia and includes all types of climates: from completely arid areas to Mediterranean and humid ones [12]. Mohammadi et al. (2021) showed that in Iran, every place can experience different types of environmental conditions throughout the year [13].
Climatic conditions are one of the most important and influential factors in various aspects of human life, especially in high-traffic urban areas [14]. Establishing a thermal balance between the human body and its surrounding environment is one of the primary needs to ensure a person’s health and comfort. The establishment of this thermal balance is not only dependent on air temperature but is influenced by various factors such as relative humidity and vapor pressure. A study by Ghalhari et al. (2019) showed that, in general, mortality increases with a decrease in temperature and an increase in cold stress, and the risk of mortality increases by 1.36% for every 10 °C decrease [15]. In order to deal with this situation, it seems necessary to prepare and monitor weather early warning systems and plan and implement detailed health, economic and social care strategies in the near future [16].
Identification and assessment of climatic features and their dominant elements in climate change and the formation of environmental behaviors for human societies has a decisive role [17]. It is possible for humans to feel their surroundings through the simultaneous examination of climatic factors such as temperature, relative humidity or air flow. The combination of these factors affects human beings and is related to thermal comfort [18]. The thermo-hygrometric index (THI) is one of indices that is used to estimate the combined effects of temperature and humidity in relation to the level of thermal stress. The THI can be easily measured and accurately used to evaluate thermal stress, and so is widely used. It is considered a useful tool for predicting environmental heat effects [19]. A study by Asgari et al. (2017) was conducted with the aim of quantitatively predicting the possible effects of climate change using the thermal index (THI) in Iran [20]. Asghari et al. (2022) evaluated the trends in thermal comfort indices such as THI in three different climates of Iran (Arak, Bandar Abbas and Sari stations) for a 15-year period (2000–2014) [21].
Based on a review of the literature, very few studies have been conducted in the field of spatial and temporal analysis of cold stress and thermal comfort, especially in the two seasons of autumn and winter in the vast area of Iran (60 synoptic meteorological stations in six climatic zonings). Through the analysis of meteorological data recorded in the autumn and winter of Iran during a period of 30 years, the present study investigated the trends in the thermo-hygrometric index (THI) as an index of thermal discomfort caused by exposure to cold. The results of this study can be used to provide climatology research strategies in Iran with the aim of addressing the predicted increases in the frequency and severity of cold stress events.

2. Materials and Methods

2.1. Data Collection

Considering the regular and reliable recording of meteorological data, the analysis of meteorological data in Iran was used. The data related to the average of the two variables of daily air temperature (Celsius) and relative humidity (percentage) from 60 synoptic meteorological stations for a 30-year statistical period (1985–2014) from the autumn and winter were obtained from the Iran Meteorological Organization. The studied stations have the most complete statistical period, and an effort has been made to use the stations that have 5% or less statistical defects during the period.

2.2. Climatic Zoning

The studied area of Iran was 25 to 40 degrees north latitude and 44 to 62 degrees east longitude. The De Martonne’s climate classification was used for climatic zoning [22]. According to the results, 51% of the studied stations were in an arid area, 32% in a semi-arid area, 5% in a humid area, 5% in a very humid area, 2% in a semi-humid area and 5% in a Mediterranean region.

2.3. Thermo-Hygrometric Index (THI)

The thermo-hygrometric index (THI) is a suitable index to estimate the combined effects of temperature and humidity in relation to the level of thermal stress, which was provided by the US National Weather Service in 1959. The purpose of this index is to investigate the direct effects of different levels on human thermal comfort. This index has been proposed as an effective climate index to reduce the harmful effects of thermal stress [19]. This index is calculated from air temperature and relative humidity using Equation (1). Threshold values of THI (°C) and type of bioclimatic comfort/discomfort related to each bioclimatic category are presented in Table 1 [23]. According to the formula, the value of this index increases with increasing temperature and relative humidity.
THI (°C) = T – (0.55 − 0.0055RH) × (T − 14.5)
where T is ambient air temperature measured on dry-bulb thermometer (°C) and RH is ambient relative humidity (%).
Temperature and relative humidity data were collected daily and THI is calculated daily and, for a period of 30 years, the mean index was calculated separately for each month, and in the next step, for the cold seasons (autumn and winter).

2.4. Trend Analysis

Kendall’s statistical test with the help of Minitab ver17.1.0 software was used to analyze the trends in air temperature, relative humidity and THI index during the analyzed period of time. The Mann–Kendall test is one of the most common and widely used non-parametric methods of time series trend analysis. Using this method, data changes are identified and their type and time are determined [25]. This test was first presented by Mann (1945) and then expanded and developed by Kendall (1970) [26]. This method was approved by the World Meteorological Organization in the same year. The zero hypothesis of the Mann–Kendall test indicates randomness and the absence of a trend in the data series, and the acceptance of the one hypothesis (rejection of the null hypothesis) indicates the existence of a trend in the data series. In this study, this test was used with 95% and 99% confidence levels. If the Z statistic is positive, the trend in the data series is considered as an upward trend, and if it is negative, a downward trend is considered [26]. In order to estimate the real slope of a trend in a time series, using the non-parametric sense method can be an appropriate methods for this field. This method was first proposed by Theil in 1950 and then expanded by Sen in 1968. Like many other non-parametric methods such as Mann–Kendall test, it is based on analyzing the difference between time series observations. It can be used when the trend in the time series is a linear trend [27,28]. In order to calculate the sense slope, the data is sorted by year (time characteristic), the values are calculated and then recorded in the table for each decade. The report of SENS slope values should be in decades. Microsoft Office Excel 2013 software was used to analyze the results and draw graphs.

3. Results

The results of the assessment of the mean THI during a 30-year period in autumn and winter in 60 measured stations are presented in Table 2. The highest and lowest mean THI in autumn are related to Minab (hot bioclimatic/discomfort from heating) and Ardebil (cold bioclimatic/discomfort due to overcooling) stations, respectively. In addition, the highest and lowest THI in winter were related to Chahbahar station (hot bioclimatic/discomfort from heating) and Khalkhal (cold bioclimatic/discomfort due to overcooling), respectively. Mappings of Iran from the point of view of the mean THI index in autumn and winter are presented in Figure 1 and Figure 2, respectively. A total of 2% of the stations were in the range of very hot, 13% in hot, 47% in comfortable, 20% in cool and 18% in cold. Of the 23 stations that were in bioclimatic discomfort due to overcooling in autumn, 40% were in a dry climate, 13% in a humid climate, 4% in Mediterranean climates, 4% in a semi-humid climate, 35% in a semi-arid climate and 4% in a very humid climate. In winter, 3% of the stations were in the hot bioclimatic type, 5% in the comfortable, 5% in the cool and 87% in the cold. Of the 55 stations that were in bioclimatic discomfort due to overcooling in winter, 49% were in a dry climate and 6% in a humid climate, 3% in the Mediterranean climate, 2% in a semi-humid climate, 34% in a semi-arid climate and 6% were in a very humid climate.
The results of the trend analysis of air temperature and relative humidity parameters for autumn and winter are presented in Table A1 and Table A2 in Appendix A, respectively.
Table 3 shows the results of the trend analysis of THI in autumn and winter. In general, the THI in autumn increased in 68% of the stations, and this increasing trend was statistically significant in 51% of these stations. Considering the Sen’s slope, the most significant increase and decrease in the THI in autumn were seen in the Ardabil station (+0.527 °C/decade) and Arak city (−2.180 °C/decade), respectively. Of the 41 stations having an increasing trend in the THI in autumn, 44% were located in dry climates, 2% in humid climates, 42% in semi-arid climates, 5% in very humid climates, and 7% in Mediterranean climates.
The THI in winter increased for most of the stations. In general, the THI for winter increased in 83% of the stations, and this increasing trend is statistically significant in 51% of those stations. In view of the Sen’s slope, the most significant increase in the THI was in the Ardabil station (+1.45 °C/decade). Of the 50 stations with an increasing trend in the THI index in winter, 48% were in a dry climate, 2% in a humid climate, 2% in a semi-humid climate, 36% in a semi-arid climate, 6% in a very humid climate and 6% were in a Mediterranean climate.
The graph of the changes in the THI in autumn during the 30 years studied, according to the different climatic regions of Iran, is shown in Figure 3. As can be seen, there is a slight decreasing trend in arid, semi-arid, humid, very humid and Mediterranean climates. Considering the increasing trend in air temperature, this issue can be caused by the decrease in relative humidity in the monitored stations during the years.
The graph of the trend in the THI in winter during the studied thirty years in terms of different climatic regions of Iran is shown in Figure 4. According to the curve of the changes in the THI, there isa slight decreasing trend in the arid, semi-arid, humid and Mediterranean climates. Considering the increasing trend in air temperature, this issue can be caused by the decrease in relative humidity in the monitored stations during the studied years.

4. Discussion

The causes of cold stress in Iran can be related to temperature, sea level pressure and geopotential height at different levels. The main reason for the sharp drop in temperature is the penetration of the Siberian high-pressure ridges with a central pressure of more than 1040 hectopascals. In this pattern, the Siberian high pressure enters the country from the northeast-southwest, and its southern branch spreads considerably over Iran and Saudi Arabia in such a way that, with the fall of very cold air from the northern latitudes, the air temperature drops below zero in a wide area. At the level of 500, 700 and 850 hectopascals, the center of high altitude before the start of the cold wave was located in the border between the west of the Caspian Lake and the Black Sea. As a result, with the slow movement of this trough, Iran has been under the influence of the upper convergence zone west of the trough for a long time; for a period of 7 days, the largest part of Iran, especially the east and northeast, was under the influence of the eastern part of a deep ridge that extended along the northeast to the latitude of the Arctic Circle (56 degrees north). In addition, the presence of northerly currents in the eastern part of the ridge caused cold air to continue to fall on Iran [29].
The THI increased in 68 and 83% of the stations in autumn and winter, respectively, and this increasing trend was statistically significant in 51% of those stations. The evaluation of the results related to the level of thermal discomfort in Iran in autumn showed that 47% of the stations were in the range of thermal comfort and the rest of the stations were in the range of thermal discomfort. In winter, only 5% were in the range of thermal comfort. A study by Asghari et al. (2017) was conducted in order to quantify the possible effects of climate change on the THI, as an effective agricultural-climatic index in livestock production. The results showed that in autumn and winter, despite the increase in the seasonal mean of the THI, the index will not exceed its threshold (THI = 72) until the end of the 21st century. In other words, in these two seasons, despite global warming, livestock will probably not experience noticeable heat stress until the end of the 21st century in Iran [20], which is consistent with the results of the current study of the increasing trend in the THI index in the two seasons of autumn and winter. Yarmoradi et al. (2016) analyzed the dangerous cold waves in the northeast of Iran during the statistical period of 2000 to 2010. The results of the investigation of 19 cold waves with different intensities showed the cold wave of December 2003 to be the most severe study example [29]. In another study, with the purpose of spatial and temporal analysis of the trends in the monthly heating and cooling index in Iran in 2016, the occurrence of negative indicators of the intensity of heating in some mountainous areas was shown, and also a decrease in temperature in these areas was shown. In terms of the severity of the cooling, in the months of December and March, we see a decrease in the air temperature in the heights of the country [30]. Ranjbar et al. (2020) estimated the time distribution of the occurrence of extreme heat and cold stresses in the open air in Tehran city, showing that extreme cold stresses occur in winter months (December, January and February), the last month of autumn (November) and the first month of spring (March). In addition, adverse conditions in the outdoors and extreme cold stress were observed in the months of January, February and December from 8 pm to 8 am [31]. Zhang et al. (2022) investigated the spatiotemporal relationship characteristic between climate comfort of urban human settlement environments and population densities in China from 2000 to 2015 using the THI index and wind efficiency index. They found that the areas of climate comfort and cold expanded northward, whereas the extremely cold area shrank from 2000 to 2015. In addition, the population density in the country was generally concentrated in areas with a comfortable climate [32]. A study by Zhou et al. (2022) also showed that height, gross industrial output, population scale and construction land area are the most influential parameters on climate comfort for urban human settlements. The effect of natural factors on the climate comfort for urban human settlements was found to be more significant than the effect of human factors, and these factors are more related to the THI than other indices such as the wind efficiency index [33].
According to the results obtained in autumn, the highest and lowest THI were related to the Minab station (dry climate) and the Ardabil (semi-arid climate), respectively. In addition, in winter, the highest and lowest THI were related to Chabahar (dry climate) and Khalkhal (Mediterranean climate) stations, respectively. Roshan et al. (2018) investigated the spatial and temporal analysis of human thermal comfort in the outdoors during heat and cold waves in Iran, showing that in the last two decades of the 20th century and the first of the 21st century, with the increase in global temperature, there was an increase in the frequency of extreme weather events such as heat and cold waves. Mean daily meteorological observations from 1995 to 2014 were used to detect heat and cold waves in 155 weather stations in Iran. The results showed that, based on the three thresholds obtained from different methods, cold waves were not observed in the southwest of Iran and stations in the northern coastal strip of the Persian Gulf and the Oman Sea. Based on three thresholds, the highest occurrence of cold waves was observed for the regions and heights of the northwest, northeast and Zagros mountain ranges, with different frequencies for different thresholds [34]. Kashki et al. (2010) studied the synoptic types of the northeastern climate of the country and its relationship with daily circulation systems in a statistical period of 23 years. According to the maps of circulation patterns, the high-altitude systems of Arabia and southern Iran have an effect on the hot and dry season, the Mediterranean descent, the Siberian descent and the northeast descent in the cold period of the year [35].
According to the results from the Mann–Kendall test, the temperature parameter increased in 72% and 84% of the stations in autumn and winter, respectively. Of the 42 stations having an increasing temperature trend in autumn, 45% were located in a dry climate, 3% in a humid climate, 38% in a semi-arid climate, 7% in a very humid climate, and 7% in a Mediterranean climate. Of the 48 stations having an increasing temperature trend in the winter, 48% were located in a dry climate, 3% in a humid climate, 37% in a semi-arid climate, 6% in a very humid climate and 6% in a Mediterranean climate. The variety of climate phenomena is due to the general circulation in the atmosphere, where the fluctuation of orbital and hemispheric patterns determines the fluctuations in atmospheric circulation [36]. A study by Dostan et al. (2017) assessed the atmospheric pressure and climate indicators by evaluating 43 stations in Iran in the geographical range of 10 to 70 degrees north latitude and 10 to 80 degrees east longitude for the cold half of the year (autumn and winter) [37]. The most important indicators for Iran’s climate from the beginning of the cold season to the middle of Nowruz, include the indexes of Central Asia, Northern Siberia, Western Europe, Anatolia, and the Western Mediterranean, and the indicators have the greatest effect on the temperature climate of Iran and their rainfall effect is regional. Jalali et al. (2015) studied the spatial structure of temporal and spatial changes of the continuation of cooling waves in the northwest of Iran in recent decades. The results showed that, in addition to the noticeable reduction in the extent of the cores of cooling waves and the transfer of the concentration of cooling cores in the eastern regions of the country, the intensity of the cores of cold waves decreased in recent periods [38]. Their results are consistent with the current study and show the increasing trend in the country’s temperature even in the cold seasons of the year. This shows the gradual increase in temperature and the decreasing trend in relative humidity in the country during the study period. The increase in the temperature of Earth will cause deep and extensive changes in the Earth’s climates and changes in the time and place of climatic phenomena, including cold stresses such as frosts.
According to the obtained results, the relative humidity decreased in 28 and 43 stations in the two seasons of autumn and winter, respectively; the decreasing trend was statistically significant in 32% and 53% of the stations, respectively. The most significant decrease in the relative humidity was observed in autumn and winter in Zabul and Chabahar stations, respectively. Of the 28 stations with a decreasing trend in terms of relative humidity, 40% were in a dry climate, 3% in a humid climate, 43% in a semi-arid climate, 11% in a very humid climate, and 3% in a Mediterranean climate. Of the 43 stations with a decreasing trend in terms of relative humidity, 47% were in a dry climate, 2% in a humid climate, 37% in a semi-arid climate, 2% in a semi-humid climate, 7% in a very humid climate, and 5% in a Mediterranean climate. Tabari et al. conducted a spatial and temporal analysis of the humidity index in Iran in the period of 1966–2005. They concluded that the trends in the humidity index were generally downward, but there were significant trends only in 8 out of 41 stations. A significant downward trend in the humidity index was observed in Gorgan, Kermanshah, Khorramabad, Khoi, Sanandaj, Tabriz and Zanjan stations located in the north, northwest and west regions of Iran [39], and their results were consistent with the findings of the current study. The water vapor in the atmosphere affects the Earth’s temperature balance by absorbing radiation waves at long wavelengths, so it indirectly affects the surface temperature and the occurrence of thermal and cold stresses. This issue was investigated in a study by Segnalini et al., who explained a new experimental model of humidity index [40]. They indicated that the dynamics of the THI should be considered by active farmers and policymakers in Middle East and Mediterranean countries when planning investment in different sectors [40].
Climate change is one of the major global challenges and has attracted the attention of researchers, scientists, planners and politicians in part due to the continuous increase in global warming associated with the greenhouse effect. Studies have shown that increasing the concentration of CO2 in the atmosphere leads to global warming and intensifying of the global hydrological cycle [41]. Greenhouse gases behave like a barrier and can reduce the transmission of heat waves to the upper atmospheric layers. As the air temperature increases, the capacity of air to accept water vapor increases and causes a decrease in relative humidity, but it should be noted that relative humidity is not the only function of air temperature and other weather parameters also affect it [42].
The results of this study can be used to provide epidemiological and climatological research strategies in Iran with the aim of increasing prediction of the occurrence, frequency and intensity of cold events. It is also suggested that for the future studies, the effective parameters on financial-economic indicators and GDP should be considered in order to investigate the impact of climate change on the economic and political conditions of the country and the world.

5. Conclusions

The present study analyzed the trends in cold stress during autumn and winter in Iran using the THI. According to the results, the highest and lowest THI during a 30-year statistical period for both autumn and winter were related to dry and Mediterranean climates, respectively.
According to our findings, in Autumn, 2% of the stations are in the very hot range, 13% in the hot range, 47% in the comfortable range (thermal comfort), 20% in the cool range and 18% in the cold range. In winter, 3% of the stations were in the hot range, 5% in the comfortable range, 5% in the cool range and 87% in the cold range.
Climate change has affected the ecosystem, the environment, the health of species—animals and humans—and it is one of the biggest challenges that humans and nature are facing today. In general, the air temperature and the THI showed an increase in autumn and winter, which was significant in more than half of the stations. The relative humidity almost decreased in autumn and winter. In terms of climate type, half of the stations in autumn and most of the stations in winter were in the conditions of thermal discomfort. The decreasing trend in THI in some climatic types, along with the increasing trend in air temperature, can indicate the decrease in relative humidity in the monitored stations during the studied years. Global warming can cause deep and extensive changes in the Earth’s climate and changes in the time and place of climatic phenomena. Therefore, it is recommended that the temporal and spatial distribution and the probability of occurrence of cold stress is known through the use of the THI, in order to adopt preventive measures and policies in the outdoors in Iran.

Author Contributions

M.A. and S.F.D.: Conceptualization, Methodology, Supervision, Writing—Reviewing and Editing; E.A.P.: Investigation, Data curation, Writing—Original draft preparation; G.F.G.: Conceptualization, Methodology, Validation, Writing—Reviewing and Editing. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no specific grants from any funding agency in the public, commercial or not-for-profit sectors.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the corresponding author, without reservation.

Acknowledgments

The author would like to thank the Iran Meteorological Organization for its helpful assistance in providing the data.

Conflicts of Interest

The authors declare that they have no known competing financial interest or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A

The results of the trend analysis of air temperature and relative humidity parameters for autumn are presented in Table A1. In general, the temperature parameter for autumn has an increasing trend in 43 stations (72 percent) and a decreasing trend in 17 stations (28 percent). The increasing trend in 5 stations (30 percent) and a decreasing trend in 24 stations (70 percent) was statistically significant (p < 0.05). Taking into account the Sen’s slope, the most significant increase and decrease in the temperature for autumn were in Ardabil station (+0.66 °C/decade) and Arak station (−2.81 °C/decade), respectively. Of the 42 stations having an increasing temperature trend in autumn, 45% were located in a dry climate, 3% in a humid climate, 38% in a semi-arid climate, 7% in a very humid climate and 7% in a Mediterranean climate.
According to the results obtained from the Mann–Kendall test, the relative humidity parameter for autumn increased for 32 stations (53 percent), and this increasing trend was significant in 9 (28 percent) of those stations. In addition, there was a decreasing trend for 28 stations (47 percent), and this decreasing trend was significant in 32 percent of those stations. Considering the Sen’s slope, the most significant increase and decrease in relative humidity was seen in Arak station (+7.3 °C/decade) and Zabul station (−4 °C/decade), respectively. Of the 28 stations having a decreasing trend in terms of relative humidity, 40% were in a dry climate, 3% in a humid climate, 43% in a semi-arid climate, 11% in a very humid climate and 3% in a Mediterranean climate.
Table
Table A1. Trend analysis of air temperature and relative humidity for autumn over a thirty-year period.
Table A1. Trend analysis of air temperature and relative humidity for autumn over a thirty-year period.
StationsTemperatureRelative Humidity
Zp-ValueTrendSen’s SlopeZp-ValueTrendSen’s Slope
Abadan−1.0210.153Downward−0.1920.3400.366Upward0.335
Abumusa3.1140.001Upward0.186−0.8270.204Downward−0.370
Ahar0.3920.347Upward0.0730.3560.360Upward0.720
Ahvaz0.4990.308Upward0.0902.1760.014Upward2.900
Anzali2.490.006Upward0.472−2.5330.005Downward−1.620
Astara2.1560.015Upward0. 440−2.3830.008Downward−0.954
Arak−4.3880.001Downward−2.8104.2460.001Upward7.300
Ardebil2.2830.011Upward0.660−0.2140.415Downward−0.215
Babolsar2.5690.005Upward0.472−4.4240.001Downward−2.000
Bam2.5330.005Upward0.3890.0000.500Upward0.046
Bandarabbas2.9250.001Upward0.209−0.9990.158Downward−0.857
Birjand2.8180.002Upward0.3370.9090.181Upward0.900
Bushehr2.4260.007Upward0.320−0.9270.176Downward−0.610
Chahbahar2.8900.001Upward0.294−2.1400.016Downward−2.170
Darab−0.2140.415Downward−0.047−0.2140.415Downward−0.200
Dehloran−1.1550.123Downward−0.2780.0670.472Upward0.088
Fasa0.1600.436Upward0.0501.0340.150Upward1.950
Gachsaran−1.5500.060Downward−0.4200.4180.337Upward0.543
Garmsar3.1950.001Upward0.600−2.4480.007Downward−2.000
Gorgan2.5330.005Upward0.483−2.3900.008Downward−0.830
Hamedan2.3900.008Upward0.3380.9270.176Upward1.110
Isfahan0.6770.248Upward0.096−1.1410.126Downward−1.250
Ilam−2.0050.022Downward−0.4501.2230.110Upward1.730
Kangavar−0.0850.466Downward−0.0222.1410.016Upward2.450
Karaj0.4250.339Upward0.1590.7490.226Upward1.000
Kashan−1.2480.105Downward−0.2441.9980.022Upward2.030
Kashmar−2.6170.004Downward−0.6000.9680.166Upward1.330
Kermanshah1.9620.024Upward0.330−0.4990.308Downward−0.554
Khalkhal2.9910.001Upward0.580−0.9680.166Downward−1.100
Khash0.9080.181Upward0.262−1.5400.061Downward−1.140
Mahabad0.6770.248Upward0.193−0.1780.429Downward−0.200
Maku1.9620.024Upward0.410−0.5350.296Downward−0.620
Maragheh2.2120.013Upward0.526−0.4990.308Downward−0.630
Mashhad2.6760.003Upward0.626−1.9620.024Downward−2.430
Minab−0.1700.432Downward−0.0302.1380.016Upward1.720
Piranshahr−0.0510.479Downward−0.0171.9030.028Upward2.260
Qaemshahr2.5500.005Upward0.550−0.2540.399Downward−0.110
Qazvin2.2120.013Upward0.4070.1070.457Upward0.087
Qom1.6910.046Upward0.3521.6770.046Upward2.250
Rasht1.5340.062Upward0.274−0.2490.401Downward−0.257
Sabzevar0.8020.211Upward0.119−0.4280.334Downward−0.410
Sanandaj1.3910.082Upward0.1900.3210.374Upward0.330
Saqez−0.3920.347Downward−0.1670.7850.216Upward1.000
Sarpol Zahab−0.4700.319Downward−0.1301.7020.0443Upward1.600
Semnan0.96340.167Upward0.1670.9630.167Upward1.050
ShahreBabak−0.7140.237Downward−0.1101.4610.071Upward1.700
Shahrekord−2.8180.002Downward−0.3790.4630.321Upward0.710
Shahroud1.2310.109Upward0.2312.4620.006Upward2.300
Shiraz-0.8200.205Downward-0.1150.1070.457Upward0.175
Sirjan0.6510.257Upward0.0850.5260.299Upward0.520
Takab−0.7940.213Downward−0.168−0.7460.227Downward−0.746
Tabriz1.4810.069Upward0.340−0.2490.401Downward−0.360
Tehran0.6770.248Upward0.167−0.3560.360Downward−0.285
Torbat Heydariyeh1.5160.064Upward0.2930.3560.360Upward0.480
Urmia0.4630.321Upward0.0701.1770.119Upward1.220
Yasuj−2.7460.003Downward−0.4370.5920.276Upward0.972
Yazd3.4960.0001Upward0.592−0.8920.186Downward−0.700
Zabol3.5680.001Upward0.615−2.8180.002Downward−4.000
Zahedan3.2470.001Upward0.570-2.6400.004Downward−1.930
Zanjan1.8910.029Upward0.3451.8190.034Upward2.200
The results of the trend analysis of air temperature and relative humidity in winter are presented in Table A2. The air temperature has an increasing trend in 48 stations (84%) and a decreasing trend in 9 stations (16%). The increasing trend was statistically significant only for 16 stations (33 percent) and there was no significant decreasing trend for any of the stations. From the view of the Sen’s slope, the most significant increase and decrease in the temperature were in the Arak station (+1.55 °C/decade) and Shahrbabak (−0.23 °C/decade), respectively. Of the 48 stations with an increasing trend in temperature, 48% were located in a dry climate, 3% in a humid climate, 37% in a semi-arid climate, 6% in a very humid climate and 6% in a Mediterranean climate.
According to the results obtained from the Mann–Kendall test, the relative humidity in winter increased for 17 stations (28 percent), and this increasing trend was significant in 4 stations (23 percent) of those stations. In addition, there was a decreasing trend for 43 stations (72 percent), and this decreasing trend was significant in 23 (53 percent) of those stations. Considering the Sen’s slope, the most significant increase and decrease in relative humidity was seen in Gachsaran station (+8 °C/decade) and Chabahar station (−5.85 °C/decade), respectively. Of the 43 stations with a decreasing trend in terms of relative humidity, 47% were in a dry climate, 2% in a humid climate, 37% in a semi-arid climate, 2% in a semi-humid climate, 7% in a very humid climate and 5% were in a Mediterranean climate.
Table
Table A2. Trend analysis of air temperature and relative humidity for winter over a thirty-year period.
Table A2. Trend analysis of air temperature and relative humidity for winter over a thirty-year period.
StationsTemperatureRelative Humidity
Zp-ValueTrendSen’s SlopeZp-ValueTrendSen’s Slope
Abadan1.2650.102Upward0.186−0.0810.467Downward−0.084
Abumusa2.3830.008Upward0.300−0.5350.296Downward−0.630
Ahar1.4270.076Upward0.632−2.1760.014Downward−2.250
Ahvaz1.5340.062Upward0.508−1.6770.046Downward−1.600
Anzali2.1230.016Upward0.560−2.6040.004Downward−1.920
Astara2.0590.019Upward0.310−1.6770.046Downward−0.760
Arak3.2650.001Upward1.550−2.9610.001Downward−4.900
Ardebil2.9970.001Upward1.500−1.6770.046Downward−1.830
Babolsar1.9260.027Upward0.405−5.1730.001Downward−2.800
Bam0.7850.216Upward0.204−1.3550.087Downward−1.100
Bandarabbas1.4270.076Upward0.180−2.9610.001Downward−3.100
Birjand−0.1600.436Downward-0.033−0.5700.284Downward−1.300
Bushehr3.3360.001Upward0.516−2.5510.005Downward−2.550
Chahbahar1.1060.134Upward0.120−3.6390.001Downward−5.850
Darab−0.2140.415Downward-0.0531.1770.119Upward1.300
Dehloran0.0000.500-0.0220.0670.472Upward0.150
Fasa4.8520.001Upward0.975−2.0690.019Downward−3.050
Gachsaran0.1390.444Upward0.0142.1840.014Upward8.000
Garmsar1.8320.033Upward0.420−2.1240.016Downward−2.160
Gorgan0.3560.360Upward0.0901.2480.105Upward1.200
Hamedan2.1050.017Upward0.346−1.6050.054Downward−1.300
Isfahan0.0000.500-0.0000.4630.321Upward0.330
Ilam0.5770.281Upward0.171−0.2030.419Downward−0.275
Kangavar0.5440.293Upward0.1730.0000.500-0.000
Karaj1.4620.071Upward0.555−1.7840.037Downward−1.650
Kashan−0.8920.186Downward−0.1422.3190.010Upward2.420
Kashmar−0.4580.323Downward−0.1170.5770.281Upward0.833
Kermanshah0.3210.374Upward0.188−1.5700.058Downward−1.770
Khalkhal2.9230.001Upward1.26−4.6570.001Downward−3.800
Khash−0.1450.441Downward−0.055−2.2860.011Downward−3.300
Mahabad1.2840.099Upward0.445−1.7480.040Downward−1.450
Maku0.5350.296Upward0.27−0.2850.387Downward−0.534
Maragheh1.4270.076Upward0.582−0.0710.471Downward−0.065
Mashhad1.6610.048Upward0.538−2.3900.008Downward−2.620
Minab0.1390.444Upward0.024−0.6040.272Downward−0.454
Piranshahr1.6820.046Upward0.7220.30590.379Upward0.445
Qaemshahr1.4270.076Upward0.2311.6990.044Upward0.620
Qazvin1.9800.023Upward0.37−1.9800.023Downward−1.660
Qom0.6420.260Upward0.1822.3900.008Upward2.250
Rasht1.5700.058Upward0.406−2.2830.011Downward−1.370
Sabzevar0.3920.347Upward0.126−0.8920.186Downward−1.050
Sanandaj1.1590.123Upward0.365−0.9270.176Downward−0.660
Saqez0.3560.360Upward0.1350.4990.308Upward0.340
Sarpol Zahab0.3720.354Upward0.0750.6000.274Upward0.600
Semnan0.6060.272Upward0.1280.9630.167Upward1.100
ShahreBabak−0.7640.222Downward−0.23 0.1010.459Upward0.117
Shahrekord−0.5700.284Downward−0.150.1070.457Upward0.130
Shahroud0.6420.260Upward0.2120.0000.500Upward0.030
Shiraz3.8530.001Upward0.78−2.3550.0092Downward−2.250
Sirjan1.7190.042Upward0.312−1.3480.088Downward−2.100
Takab1.4920.067Upward0.472−2.0110.022Downward−1.650
Tabriz1.3910.082Upward0.405−1.0340.150Downward−1.200
Tehran1.3730.084Upward0.416−1.6770.046Downward−1.700
Torbat Heydariyeh0.7490.226Upward0.203−1.7480.040Downward−2.100
Urmia0.7850.216Upward0.3681.3200.093Upward0.857
Yasuj−0.1970.421Downward−0.043−2.9830.001Downward−3.350
Yazd1.9260.027Upward0.53−0.2140.415Downward−0.250
Zabol−0.4990.308Downward−0.11−3.2110.001Downward−4.820
Zahedan−0.3560.360Downward−0.09−1.8550.031Downward−2.670
Zanjan1.9620.024Upward0.684−0.5700.284Downward−0.235

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Figure 1. Mapping of Iran from the point of view of mean THI index (°C) in autumn during 1985–2014.
Figure 1. Mapping of Iran from the point of view of mean THI index (°C) in autumn during 1985–2014.
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Figure 2. Mapping of Iran from the point of view of mean THI index (°C) in winter during 1985–2014.
Figure 2. Mapping of Iran from the point of view of mean THI index (°C) in winter during 1985–2014.
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Figure 3. The trend in mean thermo-hygrometric index (THI) in different climate types for autumn over a thirty-year period.
Figure 3. The trend in mean thermo-hygrometric index (THI) in different climate types for autumn over a thirty-year period.
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Figure 4. The trend in mean thermo-hygrometric index (THI) in different climate types for winter over a thirty-year period.
Figure 4. The trend in mean thermo-hygrometric index (THI) in different climate types for winter over a thirty-year period.
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Table
Table 1. Limits of applicability and the THI threshold values (°C) corresponding to different types of bioclimate [24].
Table 1. Limits of applicability and the THI threshold values (°C) corresponding to different types of bioclimate [24].
THI Bioclimatic TypeType of Bioclimatic
Comfort/Discomfort
−20 < THI ≤ −10Excessive coldBioclimatic discomfort due to overcooling
−10 < THI ≤ −1.8Very Cold
−1.8 < THI ≤ +13Cold
+13 < THI ≤ +15Cool
+15 < THI ≤ +20ComfortableBioclimatic comfort
+20 < THI ≤ 26.5HotBioclimatic discomfort by heating
+26.5 < THI ≤ +30Very hot
THI > +30Sultriness
Table
Table 2. Mean thermo-hygrometric index (THI, °C) over a 30-year period for autumn and winter seasons.
Table 2. Mean thermo-hygrometric index (THI, °C) over a 30-year period for autumn and winter seasons.
StationsClimate TypeFallWinter
THI IndexThermal Discomfort LevelTHI IndexThermal Discomfort Level
AbadanArid22.7 ± 3.9Hot14.1 ± 2.6Cool
AbumusaArid26.9 ± 2.6Very Hot20.0 ± 1.7Hot
AharSemi-arid12.4 ± 5Cold2.7 ± 4.5Cold
AhvazArid22.4 ± 3.73Hot13.8 ± 2.4Cool
AnzaliVery humid18.1 ± 4.26Comfortable8.5 ± 2.85Cold
AstaraVery humid16.2 ± 4.6Comfortable6.7 ± 2.85Cold
ArakSemi-arid11.0 ± 75.5Cold4.4 ± 6Cold
ArdebilSemi-arid10.7 ± 5.1Cold0.8 ± 5.7Cold
BabolsarHumid18.6 ± 4.37Comfortable9.2 ± 2.36Cold
BamArid19.7 ± 3.2Comfortable12.8 ± 3.1Cold
BandarabbasArid25.7 ± 3.3Hot17.8 ± 2Comfortable
BirjandArid15.4 ± 3.65Comfortable7.3 ± 3.5Cold
BushehrArid24.4 ± 3.67Hot15.6 ± 2.3Comfortable
ChahbaharArid25.5 ± 2.1Hot20.2 ± 1.8Hot
DarabArid14.4 ± 3.7Cool6.6 ± 3.1Cold
DehloranArid22.3 ± 3.8Hot13.5 ± 2.6Cool
FasaArid14.1 ± 3.6Cool10.7 ± 2.7Cold
GachsaranArid20.2 ± 3.5Hot11.7 ± 2.4Cold
GarmsarArid17.0 ± 4Comfortable7.9 ± 3.6Cold
GorganSemi-arid18.1 ± 4.68Comfortable8.6 ± 2.86Cold
HamedanSemi-arid12.8 ± 4.7Cold1.5 ± 5.1Cold
IsfahanArid15.9 ± 3.9Comfortable7.5 ± 2.96Cold
IllamMediterranean16.5 ± 4Comfortable7.2 ± 3.2Cold
KangavarSemi-arid14.1 ± 4.4Cool3.5 ± 4.7Cold
KarajArid15.8 ± 4.7Comfortable5.6 ± 4Cold
KashanArid17.6 ± 4.4Comfortable8.05 ± 3.3Cold
KashmarArid17.0 ± 4.1Comfortable8.0 ± 3.6Cold
KermanshahSemi-arid15.3 ± 4.2Comfortable5.4 ± 3.6Cold
KhalkhalMediterranean10.4 ± 4.9Cold−0.1 ± 4.5Cold
KhashArid18.2 ± 3Comfortable11.8 ± 2.7Cold
MahabadSemi-arid14 ± 4.8Cool3.2 ± 4.3Cold
MakuSemi-arid12.3 ± 5.3Cold0.3 ± 4.68Cold
MaraghehSemi-arid13.8 ± 4.8Cool3.2 ± 3.8Cold
MashhadArid14.5 ± 4.7Cool5.1 ± 4.3Cold
MinabArid25.9 ± 2.9Hot18.8 ± 2Comfortable
PiranshahrHumid14.5 ± 4.9Cool2.9 ± 4.6Cold
QaemshahrSemi -humid17.4 ± 4.6Comfortable8.0 ± 2.5Cold
QazvinSemi-arid14.5 ± 4.6Cool4.0 ± 3.8Cold
QomArid16.7 ± 4.4Comfortable7.1 ± 3.3Cold
RashtVery humid17.2 ± 4.4Comfortable7.81 ± 3.4Cold
SabzevarArid16.5 ± 4.2Comfortable7.1 ± 3.4Cold
SanandajSemi-arid14.3 ± 4.4Cool3.9 ± 3.9Cold
SaqezMediterranean12.8 ± 4.9Cold1.16 ± 5.2Cold
Sarpol ZahabSemi-arid18.75 ± 4.0Comfortable9.9 ± 2.7Cold
SemnanArid17.11 ± 4.4Comfortable7.11 ± 2.9Cold
ShahreBabakArid15.4 ± 3.7Comfortable7.6 ± 2.95Cold
ShahrekordSemi-arid12.7 ± 4.1Cold2.5 ± 4.6Cold
ShahroudArid15.05 ± 4.4Comfortable5.3 ± 2.9Cold
ShirazSemi-arid13.4 ± 3.9Cool10.3 ± 3.0Cold
SirjanArid16.6 ± 3.5Comfortable9.0 ± 2.8Cold
TabrizSemi-arid13.9 ± 5.0Cool2.5 ± 3.7Cold
TakabSemi-arid11.8 ± 5.0Cold0.05 ± 4.3Cold
TehranArid17.2 ± 4.3Comfortable7.6 ± 3.2Cold
Torbat HeydariyehArid14.2 ± 4.4Cool4.2 ± 3.6Cold
UrmiaSemi-arid12.5 ± 4.8Cold1.7 ± 3.4Cold
YasujHumid15.1 ± 3.8Comfortable6.0 ± 3.0Cold
YazdArid17.5 ± 3.7Comfortable9.3 ± 3.4Cold
ZabolArid18.9 ± 3.7Comfortable10.9 ± 2.8Cold
ZahedanArid16.7 ± 3.1Comfortable10.2 ± 3.3Cold
ZanjanSemi-arid12.4 ± 4.9Cold1.5 ± 4.6Cold
Table
Table 3. Trend analysis of thermo-hygrometric index (THI) for autumn and winter over a thirty-year period.
Table 3. Trend analysis of thermo-hygrometric index (THI) for autumn and winter over a thirty-year period.
StationsAutumnWinter
Zp-ValueTrendSen’s SlopeZp-ValueTrendSen’s Slope
Abadan−1.1830.118Downward−0.1641.2810.100Upward0.191
Abumusa1.8000.035Upward0.1421.9290.026Upward0.210
Ahar0.3920.347Upward0.0671.6410.050Upward0.638
Ahvaz2.2830.011Upward0.2072.0690.019Upward0.307
Anzali2.2120.013Upward0.4182.3550.009Upward0.580
Astara2.2540.012Upward0.3822.2540.012Upward0.331
Arak−4.3170.001Downward−2.1802.9970.001Upward1.380
Ardebil2.4260.007Upward0.5272.9610.001Upward1.450
Babolsar2.6760.003Upward0.3912.3550.009Upward0.473
Bam1.8910.029Upward0.1560.89200.186Upward0.210
Bandarabbas2.1590.015Upward0.1240.9630.167Upward0.099
Birjand2.3550.009Upward0.1930.4990.308Upward0.102
Bushehr1.6410.050Upward0.2032.9610.001Upward0.420
Chahbahar1.9620.024Upward0.174−0.6420.260Downward−0.076
Darab−1.0340.150Downward−0.144−0.5700.284Downward−0.064
Dehloran−0.8830.188Downward−0.100−0.6450.259Downward−0.160
Fasa0.0710.471Upward0.0134.7810.001Upward0.778
Gachsaran−2.1530.015Downward−0.3050.0460.481Upward0.009
Garmsar2.1240.016Upward0.2792.0590.019Upward0.414
Gorgan2.3550.009Upward0.3880.0710.471Upward0.035
Hamedan0.8200.205Upward0.1290.8560.195Upward0.361
Isfahan−0.9990.158Downward−0.096−0.0350.485Downward−0.007
Ilam−2.3450.009Downward−0.2890.5770.281Upward0.127
Kangavar−0.3050.379Downward−0.0430.6110.270Upward0.180
Karaj0.2490.401Upward0.0601.6410.050Upward0.570
Kashan−1.0340.150Downward−0.140−1.4270.076Downward−0.200
Kashmar−3.2290.001Downward−0.439−0.4750.317Downward−0.145
Kermanshah1.3910.082Upward0.1911.5340.062Upward0.357
Khalkhal2.8550.002Upward0.4903.3310.001Upward1.480
Khash0.6320.263Upward0.0880.2100.416Upward0.042
Mahabad0.4630.321Upward0.0701.498650.066Upward0.428
Maku2.1050.017Upward0.4000.2854560.387Upward0.147
Maragheh2.1760.014Upward0.3501.106140.134Upward0.37
Mashhad2.0690.019Upward0.3301.891150.0293Upward0.586
Minab0.1700.432Upward0.050−0.232420.408Downward−0.032
Piranshahr0.0670.472Upward0.0301.036930.149Upward0.500
Qaemshahr2.5150.005Upward0.4401.891150.029Upward0.200
Qazvin0.0000.500Upward0.0051.462960.071Upward0.500
Qom1.4270.076Upward0.187−0.107050.457Upward−0.014
Rasht1.7480.040Upward0.2701.784100.037Upward0.400
Sabzevar−0.0710.471Downward−0.0170.5352310.296Upward0.207
Sanandaj0.6060.272Upward0.0841.106140.134Upward0.350
Saqez−0.6770.248Downward−0.1800.3925030.347Upward0.230
Sarpol Zahab0.0810.467Upward0.0060.4378440.330Upward0.045
Semnan0.3210.374Upward0.0290.2497740.401Upward0.058
ShahreBabak−0.9170.179Downward−0.112−1.155760.123Downward−0.244
Shahrekord−2.6040.004Downward−0.305−0.178410.429Downward−0.055
Shahroud1.3550.087Upward0.1650.8563690.195Upward0.210
Shiraz−0.8200.205Downward−0.0963.496840.001Upward0.603
Sirjan−0.0460.481Downward−0.0161.688890.045Upward0.258
Takab−0.9560.169Downward−0.1101.897330.028Upward0.588
Tabriz1.4620.071Upward0.2701.391600.082Upward0.445
Tehran0.21400.415Upward0.0421.712740.043Upward0.462
Torbat Heydariyeh0.9630.167Upward0.1150.8563690.195Upward0.285
Urmia−0.0710.471Downward−0.0140.3925030.347Upward0.173
Yasuj−3.1410.001Downward−0.4240.4995490.308Upward0.140
Yazd2.3550.009Upward0.1901.248870.105Upward0.330
Zabol2.1050.017Upward0.1800.4995490.308Upward0.111
Zahedan2.7470.003Upward0.2700.4638670.321Downward0.100
Zanjan1.3200.093Upward0.1801.712740.043Upward0.640
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Asghari, M.; Ghalhari, G.F.; Pirposhteh, E.A.; Dehghan, S.F. Spatio-Temporal Evolution of the Thermo-Hygrometric Index (THI) during Cold Seasons: A Trend Analysis Study in Iran. Sustainability 2022, 14, 16774. https://doi.org/10.3390/su142416774

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Asghari M, Ghalhari GF, Pirposhteh EA, Dehghan SF. Spatio-Temporal Evolution of the Thermo-Hygrometric Index (THI) during Cold Seasons: A Trend Analysis Study in Iran. Sustainability. 2022; 14(24):16774. https://doi.org/10.3390/su142416774

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Asghari, Mehdi, Gholamabbas Fallah Ghalhari, Elham Akhlaghi Pirposhteh, and Somayeh Farhang Dehghan. 2022. "Spatio-Temporal Evolution of the Thermo-Hygrometric Index (THI) during Cold Seasons: A Trend Analysis Study in Iran" Sustainability 14, no. 24: 16774. https://doi.org/10.3390/su142416774

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