Quantifying Heat Stress using Wet Bulb Globe Temperature measurements during Summer 2024 from Field Experiments in Pune

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This study examines heat stress in Pune, focusing on the Wet Bulb Globe Temperature (WBGT) through field experiments conducted during the peak summer months of April and May 2024. The research aimed to understand how urban design, vegetation, and socio-economic factors influence heat stress levels. Three distinct locations: Indian Institute of Science Education and Research (IISER), Fergusson College (FC), and Agriculture College (AGR) were selected to represent varying degrees of urbanization. WBGT meters were installed uniformly at a height of 4 feet to ensure accurate readings, with hourly measurements taken from 9 AM to 6 PM. The highest WBGT index was recorded at FC, the most urbanized site, indicating increased heat stress. Analysis revealed that maximum heat stress typically occurred between 1 PM and 3 PM, with variations depending on the location. The study established WBGT threshold limits for Pune: 31.5°C (90th percentile), 32°C (95th percentile), and 33°C (99th percentile), corresponding to low, moderate, and extreme heat stress levels. Further investigation into meteorological factors showed a strong positive correlation between WBGT and ambient temperature, while relative humidity and wind speed had a reverse correlation. Notably, southerly winds contributed most significantly to heat stress. The study highlights WBGT as a vital metric for assessing heat stress, integrating temperature, humidity, wind speed, and solar radiation. The findings provide essential insights for policymakers, urban planners, and environmentalists, guiding strategies to mitigate the challenges of climate change and enhance urban resilience against heat stress. Heat Stress Wet Bulb Globe Temperature vegetation urbanization Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction Heat stress becomes a severe challenge for outdoor activity and well-being in the changing climate and global warming era. Heat stress depends on air temperature, humidity, solar radiation, and other atmospheric parameters. The issue is particularly pressing in regions such as India, where high population density, urbanization, and climate conditions exacerbate the effects of rising temperatures (Ray et. al., 2021 ). Various studies have explored the multifaceted impacts of heat stress and identified the critical contributors to its severity and prevalence (Hajat et al., 2010 , Lundgren et al., 2013 , Shi et al., 2015 , Xiang et al., 2014 ). Excessive heat stress for longer periods may cause several direct and indirect effects on human health. Continuously increasing global temperatures are one of the major causes of elevated heat stress for longer periods, especially during the summer. According to the Intergovernmental Panel on Climate Change (IPCC), the average global temperature has risen by approximately 1.2°C since the pre-industrial era (IPCC, 2021 ). This increase is primarily attributed to anthropogenic activities such as fossil fuel combustion, deforestation, and industrial processes that release significant amounts of carbon dioxide and other greenhouse gases into the atmosphere. Gosling et al. ( 2017 ) conducted a comprehensive review of South Asian heat stress impacts on health, emphasizing the critical need for adaptive strategies. On a local scale, urbanization and meteorology play a significant role in quantifying the level of heat stress experienced at a place. The temperature will be higher in densely urbanized concrete regions because of the absorption and emission of long-wave radiation from concrete structures. Conversely, suburban areas with more vegetation and natural soil surfaces tend to stay relatively cooler. The meteorology of densely populated urban areas is favourable for elevated heat stress since high-rise buildings and modern infrastructures work as a barrier to the free movement of the wind. Gill et al., (2007) highlighted the role of green infrastructure in adapting cities to climate-changing scenarios. Several studies have focused on understanding the heat stress pattern and its impact on human health and productivity within the context of Indian meteorological conditions (Basu et al., 2009; Murari et al., 2015 ). Mazdiyasni et al., ( 2017 ) reported that the probability of mortality rate increases during heat waves in Indian regions. Most of this research has primarily revolved around meteorological data analysis and climate modelling and is based on secondary data sources. Most existing research relies on historical data and modelling. While these approaches provide valuable insights, they may not capture the real-time dynamics of heat stress exposure. Consequently, substantial gaps remain in our understanding of heat stress at local and regional scales. This gap underscores the necessity for field experiments and real-time heat stress monitoring to provide accurate and actionable data (Rana et al., 2019 ). Recent studies (Naskar and Pattanaik 2023 and Naskar et al., 2024 ) showed the observed changes in summer thermal discomfort using discomfort index (DI) and universal thermal climate index (UTCI) over Indian region during 1990–2020. Another in-house study from IMD with AWS station data for Pune city shows that the urban heat island (UHI) effect in Pune has been intensifying during summer months April and May over the years 2012–2023 (Figs. 1 and 2 ). Specially for both Shivajinagar stations are showing the significant increasing trend during summer months. All these studies mentioned above revealed that region wise increasing of heat discomfort and associated changes in relative humidity and temperature over Pune during summer season. In the present study, a field experiment was carried out to understand the heat stress pattern at the local level in Pune City, India, during April and May 2024. Heat stress was assessed using the Wet Bulb Globe Temperature (WBGT) Index, with continuous measurements taken at three different sites within the city. Additionally, simultaneous measurements of meteorological parameters such as temperature, relative humidity, wind, etc., were taken at these locations to explore their relationships with heat stress. 2. Study Area and Period The study conducted in April and May 2024 focused on understanding the impact of urbanization on heat stress in Pune city. Pune, a rapidly urbanizing city in India, experiences significant temperature variations due to its diverse landscape, including urban, suburban, and rural areas. This study aimed to monitor heat stress and other meteorological parameters in different parts of the city, providing insights into how urbanization and local meteorology influence heat stress. The research involved experimental measurements at three distinct locations within Pune city: Indian Institute of Science Education and Research (IISER), Fergusson College (FC), and Agriculture College (AGR). These sites were chosen based on their varying degrees of urbanization and land-cover type, which allowed for a comparative analysis of heat stress levels across different urban environment. IISER, located on the outskirts of the city, represents a relatively less urbanized area with lower building density and more green spaces. FC is located in the heart of the city, which is a very highly urbanized region surrounded by dense infrastructure with significant human activity and minimal green cover. AGR station is situated in a relatively sparse region with substantial agricultural and open land, providing a contrast to the urbanized conditions at FC. Coordinates of the exact location, station code, and their type are provided in Table 1, and locations with actual surface conditions are shown in Fig. 3. The stations were chosen to be representative of different land use patterns, ensuring that the experimental measurements accurately reflected the environmental conditions experienced by individuals in different areas. Table 1 Description of monitoring stations Station Name Station Code Lat Lon Type Agricultural College AGR 18.5366° N 73.8441° E Moderate Urbanized Fergusson College FC 18.5228° N 73.8390° E Highly Urbanized Indian Institute of Science Education and Research IISER 18.5474° N 73.8080° E Less Urbanized 3. Experiment Setup and Methodology There are various indices to measure heat stress, such as UTCI, WBGT, wet-bulb dry temperature (WBDT), and tropical summer index (TSI), which are based on different methodologies. In the present study, we have used the WBGT index to measure the heat stress. The WBGT index is a comprehensive measurement of heat stress that considers temperature, humidity, wind speed, and solar radiation. It provides a more accurate illustration of the local environmental conditions affecting human comfort and health. WBGT index can be formulated as below (Parsons, 2006 ): WBGT = 0.70 T_w + 0.20 T_G + 0.10 T_A Where, T_w = Wet bulb temperature, T_ G = Black globe temperature and T_ A =Air temperature These parameters were used to calculate the WBGT index, providing a comprehensive measure of heat stress that integrates the effects of temperature, humidity, and radiant heat. WBGT meters from METRAVI were employed to measure heat stress. All the WBGT meters have been calibrated and compared at the same locations before their installations, which reported less than two percent bias in their measurement. WBGT meters were installed on a wooden stand at a height of 4 feet above the ground, which accurately captures air temperature and other parameters, minimizing ground effects and potential interference from surrounding objects. To ensure the consistency and comparability of data across different sites, the WBGT meters were installed on similar land surfaces at each location. This uniformity was crucial to avoid any confounding effects from variations in surface properties, such as albedo (reflectivity) and heat retention. Figure 4 shows the instrument and actual instrumental setup over three locations of Pune. Measurement were taken daily from 9 AM to 6 PM at a timestamp of five-minutes, capturing the full range of diurnal temperature variations. This time frame was chosen to include the period of maximum solar radiation and temperature, which typically occurs in the early afternoon and is critical for assessing peak heat stress levels. The collected data were analysed to assess spatial and temporal variations in heat stress across the study sites. The analysis focused on comparing the WBGT values between the urbanized and less urbanized areas, examining the impact of urbanization on heat stress levels. Additionally, diurnal patterns were analysed to identify peak periods of heat stress during the day, providing insights into the critical times for implementing protective measures. Further, the role of meteorological parameters in elevated heat stress were also investigated in detail. 4. Results and Discussion 4.1 Spatial Variability of Heat Stress Figure 5 (a) shows the daily maximum observed heat stress during the study period. Heat stress levels were classified into three different categories based on percentiles: low (elevated) threat, moderate threat, and extreme threat, represented by green, orange, and dark-red colors respectively. Days without experimental data are shown in white color. WBGT thresholds for low, moderate, and extreme threats were determined by the 90th, 95th, and 99th percentiles, corresponding to values of 31.5°C, 32°C, and 33°C, respectively. The total number of days with data collection during the April and May months were 54, 43, and 55 days for AGR, FC, and IISER stations, respectively. Station-wise summary of total days measured, with counts and percentages for low, moderate, and extreme threat days is tabulated in Table 2 . Heat stress levels varied significantly across stations, likely due to differences in local meteorological conditions and land-use patterns. FC, located in densely urbanized area, shows the highest proportion of extreme threat days (44%) among all stations, followed by AGR (37%) and IISER (33%). Nearby concrete structures in highly urbanized region could be one of the major contributors to extreme heat stress at FC station. Despite being surrounded by agricultural fields, AGR station also recorded a substantial number of extreme threat days due to its proximity to densely populated urban areas. IISER station, which is located in comparatively greener areas, shows the highest number of low-threat days (45%) among the three stations. Table 2 The station-wise identified low, moderate, and extreme threat days and their percentage during all available experiment days and common experiment days. a) All available Experiment days Station Total experiment days Number of Days (%) Elevated Threat Moderate Threat Extreme Threat AGR 54 18 (33.33%) 16 (29.63%) 20 (37.03%) FC 43 15 (34.88%) 9 (20.93%) 19 (44.18%) IISER 55 25 (45.45%) 12 (21.81%) 18 (32.72%) b) Common Experiment days Station Total experiment days Number of Days (%) Low Threat Moderate Threat Extreme Threat AGR 39 14 (35.89%) 11 (28.20%) 14(35.89%) FC 39 15 (38.46%) 7 (17.95%) 17 (43.58%) IISER 39 20 (51.28%) 7 (17.95%) 12 (30.76%) As shown in Fig. 5 a, several days with missing experimental data are present across the stations, affecting the reliability of inter-station heat stress comparisons. To address this issue, we filtered for dates with complete data available for all three stations, resulting in a total of 39 fully observed days. Date-wise heat stress categorization for all three stations during these 39 days is shown in Fig. 5 (b). Throughout this period, the maximum number of extreme threat days were reported at the FC station. Counts and percentages of low-threat, moderate-threat, and extreme-threat days for each station over these 39 days are presented in Table 2 (b). 4.2 Diurnal Variation of WBGT Each station exhibits a distinct average diurnal pattern of WBGT over the two-month study period. At AGR and FC, the average peak WBGT values are observed around 2 PM local time, whereas at IISER, the peak reported around 1 PM (Fig. 6 ). This can be attributed to the combined effect of maximum solar radiation, ambient temperature, and reduced wind speed during this period. By 2 PM, although the sun has passed its zenith, the ground and surrounding structures continue to emit the absorbed heat, elevating the ambient temperature and contributing to higher WBGT values. Additionally, in urban areas like FC, the UHI effect exacerbates this condition, as heat-retaining surfaces such as concrete and asphalt release stored heat, maintaining elevated temperatures even after solar radiation begins to decline. In contrast, the IISER station records its maximum WBGT value at 1 PM local time. This earlier peak can be attributed to the relatively sparse urbanization and higher green cover around IISER, which allows faster heating and cooling cycles. The presence of vegetation and open spaces facilitates more efficient heat dissipation through evapotranspiration, causing the area to heat up rapidly as solar radiation peaks around noon and then cool down sooner than more urbanized areas. Furthermore, the influence of wind patterns and potential shading from vegetation and buildings may also contribute to this earlier peak, helping WBGT values at IISER decline earlier in the afternoon. 4.3 Impact of Temperature and Relative Humidity Since temperature and humidity are important parameters influencing WBGT, we compared them with WBGT values. To assess the overall scenario of heat stress during the daytime in the city, we computed the mean WBGT of three stations and compared them with the mean temperature and mean humidity of the same stations during different hours of the day, as shown in Fig. 7 . WBGT shows a strong positive correlation with ambient air temperature. Both WBGT and air temperature start increasing in the morning hours and attain their maximum values between 1 PM and 3 PM local time, and then decrease sharply during evening hours, whereas relative humidity has an opposite diurnal variation pattern to WBGT. 4.4 Impact of Wind Wind speed and direction are critical meteorological factors that significantly influence heat stress, impacting human comfort and health. Wind speed is pivotal in dissipating heat from the body and the surrounding environment. Higher wind speeds enhance convective cooling by removing warm air from the skin's surface and replacing it with cooler air. This process is crucial for regulating body temperature, especially in hot conditions. When wind speeds are low or calm, the cooling effect is diminished, accumulating heat around the body and increasing the risk of heat stress. The hourly wind pattern of Pune during the study period has been shown in Fig. 8 ; diurnal wind speed and wind direction were represented with the help of 24 separate wind rose diagrams for each hour. Generally, greater wind speed is observed in the daytime compared to night-time; it starts increasing rapidly in the afternoon and attains maximum during the early evening hours as shown in Fig. 8 . The impact of wind on heat stress is shown in Fig. 9 . The average heat stress of all three stations is presented for different hours of the day using color legends, with the corresponding wind directions shown in each panel. Maximum heat stress is observed from 1 PM to 3 PM, which is associated with southerly winds. 4.5 Heat Stress Threshold Limit Based on experimental data Detailed statistics of the daily maximum WBGT of individual station for all recorded observations is presented in Table 3 , whereas statistics of the common 39 days is shown in Table 3 . Maximum WBGT (37.9°C) was reported at AGR station on 3rd May 2024; unfortunately, no WBGT observations were taken at FC and IISER stations on that day. The median of daily maximum WBGT was highest at FC, followed by AGR and IISER station. Table 3 Station-wise detailed Statistics of daily maximum WBGT for all available experiment days and common experiment days. All available experiment days AGR (54 days) FC (43 days) IISER (55 days) Min 23.8 (13th May) 27.20 27.9 1st Quantile 31.27 31.33 30.82 Median 31.73 31.87 31.70 mean 31.56 31.77 31.52 3rd Quantile 32.54 32.65 32.35 Max 37.90 (3rd may) 33.57 33.53 Common experiment days AGR (39 days) FC (39 days) IISER (39 days) Min 23.80 27.2 27.9 1st Quantile 31.25 31.25 30.78 Median 31.70 31.87 31.5 mean 31.42 31.71 31.44 3rd Quantile 32.58 32.65 32.32 Max 33.87 33.57 33.53 Though there is significant spatial variation in observed WBGT on different days, the overall mean and median of the daily maximum WBGT for each of the three stations are very close to each other. In order to find out the threshold values of daily maximum WBGT in Pune city we have computed the mean WBGT of the three stations. The 90th, 95th, and 99th percentile of this value were used as threshold values for low (elevated), moderate and extreme heat stress risk. Detailed WBGT threshold values for elevated, moderate, and high heat stress, along with their corresponding ambient temperature and humidity, are presented in Table 4 . Our study shows a WBGT threshold value of 31.5°C, 32°C, and 33°C, respectively, for elevated, moderate, and high heat stress in Pune city during the April and May months. Table 4 Preliminary Proposed WBGT threshold limit and their corresponding temperature and humidity thresholds Percentile WBGT Threshold Category Temperature Threshold Humidity Threshold 90th 31.5°C Low (Elevated) Risk 40°C 61.5% 95th 32°C Moderate Risk 41°C 68% 99th 33°C Extreme Risk 42.5°C 79% 5. Discussion This study focuses on understanding heat stress patterns in Pune City, emphasizing the identification of threshold values and the role of various influencing factors. As urbanization intensifies, heat stress has become a pressing public health concern, particularly in cities like Pune, where significant spatial and temporal variations in heat stress levels can be observed. The research revealed notable differences in heat stress across different monitoring stations, each representing distinct urbanization levels. FC station, characterized as the most urbanized area, recorded the highest instances of extreme heat stress, with 44% of days classified as extreme threat days. This finding highlights the correlation between urbanization and heat stress, as densely populated areas often experience intensified heat due to human activities, concrete infrastructure, and limited vegetation. In contrast, the AGR station, while located near agricultural fields, also exhibited high heat stress levels. This is attributed to its proximity to urban areas, suggesting that even locations ostensibly shielded by greenery can suffer from UHI effects. The IISER station, situated in a greener environment, experienced the highest number of low-threat heat days, demonstrating that vegetation plays a crucial role in mitigating heat stress. The study assessed diurnal variations in WBGT index readings, revealing differing peak times for maximum heat stress across the stations. At AGR and FC, the highest WBGT values occurred at 2 PM, whereas at IISER, this peak was at 1 PM. This discrepancy can be attributed to the unique urbanization and green cover characteristics of each site. Such findings emphasize the need for localized assessments, as heat stress can fluctuate significantly throughout the day based on environmental conditions. A critical factor in understanding heat stress levels is the influence of wind speed and direction. The study found that higher wind speeds can facilitate convective cooling, thereby reducing heat stress. Conversely, lower wind speeds can exacerbate heat conditions, making areas more vulnerable. Notably, wind coming from the south was associated with the highest levels of heat stress, indicating that local weather patterns and geographical features significantly impact heat distribution. The research proposed preliminary threshold values for heat stress based on the mean WBGT readings from the three stations: 31.5°C (90th percentile) for elevated heat stress, 32°C (95th percentile) for moderate, and 33°C (99th percentile) for extreme heat stress. These thresholds are essential for developing public health guidelines and interventions. However, the study acknowledges that these values can be refined with further research, including additional experimental sites and multi-year data collection. The implications of this study extend beyond environmental science; they directly impact public health. By effectively indicating periods of high heat stress, WBGT measurements serve as a vital tool for monitoring heat-related health issues. The findings underscore the importance of regular heat stress monitoring, particularly for vulnerable populations, such as the elderly, children, and those with pre-existing health conditions. Incorporating heat stress data into urban planning and public health strategies can lead to more informed decision-making. For instance, local governments can implement heat action plans, increase public awareness of heat risks, and provide resources for cooling centers during extreme heat events. The study emphasizes the necessity of adapting urban environments to mitigate the impacts of rising temperatures linked to climate change. Integrating green infrastructure such as parks, green roofs, and tree canopies can significantly reduce heat stress levels. By enhancing vegetation cover, cities can improve air quality, reduce heat absorption, and create more livable spaces. Moreover, climate-responsive urban planning should prioritize sustainable practices that consider heat stress and its implications. This includes designing buildings that promote airflow, implementing reflective materials in construction, and ensuring equitable access to green spaces for all residents. The findings provide actionable insights for policymakers, urban planners, and environmentalists striving to address the challenges posed by climate change. Recognizing the spatial variability of heat stress allows for targeted interventions, ensuring that resources are allocated where they are most needed. This study provides a crucial understanding of heat stress patterns in Pune city, highlighting significant spatial and diurnal variability and the impact of urbanization and environmental factors. The preliminary threshold values for heat stress offer a foundation for future research and public health interventions. By emphasizing the importance of localized studies, real-time data, and the integration of green infrastructure, the research advocates for a proactive approach to urban planning and public health in the face of rising temperatures. As cities continue to urbanize and climate change progresses, adopting these strategies will be vital for enhancing public health and improving overall livability in rapidly developing urban areas like Pune. Declarations Acknowledgment The authors acknowledge Dr. Mrutyunjay Mohapatra, Director General of IMD, for his support in carrying out this work. Authors would also like to thank the Surface Instrumentation Division at IMD Pune and Mr. T. C. Mohadikar from CAgMO, Pune, for their assistance with data collection and maintenance of the instruments. Additionally, the authors appreciate the help of Prof. Joy Monteiro and his team from IISER, Pune, as well as Prof. Raka Dabhade and her team from Fergusson College, for their support during the experiment. Data Availibility: The WBGT field experiment data is available with Climate Application and User Interface group of IMD Pune. FUNDING: No funding was received for this work. CONFLICTS OF INTEREST: The authors declare that they have no conflicts of interest. References Hajat, S., O'Connor, M., & Kosatsky, T. (2010). Health effects of hot weather: From awareness of risk factors to effective health protection. The Lancet, 375(9717), 856-863. Lundgren, K., Kuklane, K., Gao, C., & Holmér, I. (2013). Effects of heat stress on working populations when facing climate change. Industrial Health, 51(1), 3-15. R. Basu, High ambient temperature and mortality: A review of epidemiologic studies from 2001 to 2008. Environ. Health 8, 40 (2009). https://doi.org/10.1186/1476-069X-8-40 IPCC. (2021). Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press. K. K. Murari, S. 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Journal of Agrometeorology, 21(2), 123-131. Cite Share Download PDF Status: Published Journal Publication published 09 Feb, 2026 Read the published version in International Journal of Biometeorology → Version 1 posted Reviewers agreed at journal 09 Feb, 2025 Reviewers invited by journal 18 Dec, 2024 Editor assigned by journal 22 Nov, 2024 First submitted to journal 19 Nov, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5484071","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":391953795,"identity":"58cc3f94-ee8f-470f-b219-2d22d3e8ea93","order_by":0,"name":"Ravi Ranjan Kumar","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Ravi","middleName":"Ranjan","lastName":"Kumar","suffix":""},{"id":391953796,"identity":"2325a0f0-8c61-4b55-a999-0643653b26dc","order_by":1,"name":"Arpit Tiwari","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Arpit","middleName":"","lastName":"Tiwari","suffix":""},{"id":391953797,"identity":"9af7db9c-9424-4e52-8798-a4981c3bfccc","order_by":2,"name":"Ananya Karmakar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABE0lEQVRIiWNgGAWjYBADAxDB2MDAIMcG5TCwEavFmHQtiQ0wLbgA/+zmZ495ahiM+WefTvw4o+Zeep/Y4Q2MXyoOM/BJN2DVInHnmLkxzzEGM4lzuZslNxwrzm2TTitgljlzmIFN5gB2a24kmEnzsDHYMJzh3SD5gC0BqCXHgFmyLY2BTSIBqw75G+nfpHn+MdjIn+Hd/PPBv4R0NkJaDG7kmEnztjGYGZzh3Sa5sS0hAaSF8WObDU4thjdyyiTn9kkYGwK1WM7sSzAE+eUwwxkbHlxa5G6kb5N4883GcB7QYTd7viXIy89O3vjwR4WEnPwM7FpAgImHQQJV5DAPAwMPTvVAwPiDsMgoGAWjYBSMZAAASOlWp/vh4jwAAAAASUVORK5CYII=","orcid":"","institution":"India Meteorological Department Pune","correspondingAuthor":true,"prefix":"","firstName":"Ananya","middleName":"","lastName":"Karmakar","suffix":""},{"id":391953798,"identity":"05cb998d-46ef-4283-96c1-4402c78aa423","order_by":3,"name":"Ajay Bankar","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Ajay","middleName":"","lastName":"Bankar","suffix":""},{"id":391953799,"identity":"0749a947-99c3-4d0c-bbd0-05d028edfe06","order_by":4,"name":"Rajib Chattopadhyay","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Rajib","middleName":"","lastName":"Chattopadhyay","suffix":""},{"id":391953800,"identity":"25220faa-37d9-4cb0-b71e-67927f399b5b","order_by":5,"name":"K. S. Hosalikar","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"K.","middleName":"S.","lastName":"Hosalikar","suffix":""}],"badges":[],"createdAt":"2024-11-19 13:52:01","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5484071/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5484071/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00484-025-03080-6","type":"published","date":"2026-02-09T15:59:02+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":72078277,"identity":"8a85766a-ba76-486b-8022-0dd2d75e0ef3","added_by":"auto","created_at":"2024-12-21 16:48:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":371981,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTrend analysis of UHI during April 2012–2023 for three AWS stations Pashan, Shivajinagar and Shivajinagar Agriculture college where in y-axis hours 03, 04, 05, and 06 represent the morning period and hours 13, 14, 15, and 16 represent the afternoon.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5484071/v1/7ba0c66843852b048a4b1579.png"},{"id":72078274,"identity":"3117b29e-6236-40b2-8653-4a6d6660bfce","added_by":"auto","created_at":"2024-12-21 16:48:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":348506,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTrend analysis of UHI during May 2012-2023 for three AWS stations Pashan, Shivajinagar and Shivajinagar Agriculture college where in y-axis hours 03, 04, 05, and 06 represent the morning period and hours 13, 14, 15, and 16 represent the afternoon.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5484071/v1/e9d3673e212500cc15f4c421.png"},{"id":72078291,"identity":"c54239e7-ceab-43aa-a162-2dfda9ad5911","added_by":"auto","created_at":"2024-12-21 16:48:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":414837,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLocation of experimental sites within Pune city embodied with orange circles. The black outline in the inset map demonstrates India’s international border and the red outline represents the state boundary of Maharashtra state, where Pune is denoted by orange dot.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5484071/v1/1a7a252a5e1504d7b1a05ff0.png"},{"id":72078276,"identity":"130fb22c-6728-462d-af95-ab7156fc180f","added_by":"auto","created_at":"2024-12-21 16:48:51","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":347752,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWBGT Instruments and installation site are shown in (a) and (b) respectively.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5484071/v1/504c12207bb7f8a36f07b4b8.png"},{"id":72078279,"identity":"4322b8cc-0a59-4298-a04c-13c0f3804ad1","added_by":"auto","created_at":"2024-12-21 16:48:51","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":221486,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea) Date-wise recorded maximum WBGT at each station during the entire period. White colours show the unavailability of data at any individual station on any specific date and (b): Date-wise recorded maximum WBGT at each station only for those 39 days where data of all three stations is available.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5484071/v1/5605e4a74e2ed51f13812099.png"},{"id":72078282,"identity":"ece6b351-89e9-4d15-af88-6d4b2763b300","added_by":"auto","created_at":"2024-12-21 16:48:51","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":248531,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a), (b) and (c) show the diurnal variation pattern of WBGT at AGR, IISER, and IISER, respectively.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-5484071/v1/b92ff812193397f07d6b598f.png"},{"id":72078281,"identity":"de33a431-5807-498e-9924-34dc0918377d","added_by":"auto","created_at":"2024-12-21 16:48:51","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":95771,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDiurnal variation of WBGT, Temp (Ambient Air temperature), and relative humidity. This is the average of all three stations.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-5484071/v1/89edb8a962f06efef39a1aa5.png"},{"id":72078306,"identity":"39f7f1ce-4c67-4fc3-bcce-6d60c4de7e85","added_by":"auto","created_at":"2024-12-21 16:48:52","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":267306,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWindrose-diagram of Pune city during April and May 2024, diurnal variation of wind patterns was shown using 24 wind roses in separate panels. Each panel number represents the hours of the day.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-5484071/v1/dd0787d030c69736a56479b2.png"},{"id":72078289,"identity":"4f0665b1-ed8d-481a-8627-101289227ab4","added_by":"auto","created_at":"2024-12-21 16:48:51","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":261398,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHere, colors represent WBGT, and Each panel number represents the hours of the day. Maximum WBGT in red colors are associated with southerly winds.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-5484071/v1/8b0b3db6dc0d0cf5147ca8a6.png"},{"id":102785569,"identity":"9ae00d94-70ac-4720-9404-fea4e80996b9","added_by":"auto","created_at":"2026-02-16 16:08:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3820793,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5484071/v1/9ce7565f-fda4-4330-a986-8d0b5d888157.pdf"}],"financialInterests":"","formattedTitle":"Quantifying Heat Stress using Wet Bulb Globe Temperature measurements during Summer 2024 from Field Experiments in Pune","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eHeat stress becomes a severe challenge for outdoor activity and well-being in the changing climate and global warming era. Heat stress depends on air temperature, humidity, solar radiation, and other atmospheric parameters. The issue is particularly pressing in regions such as India, where high population density, urbanization, and climate conditions exacerbate the effects of rising temperatures (Ray et. al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Various studies have explored the multifaceted impacts of heat stress and identified the critical contributors to its severity and prevalence (Hajat et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, Lundgren et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Shi et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Xiang et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eExcessive heat stress for longer periods may cause several direct and indirect effects on human health. Continuously increasing global temperatures are one of the major causes of elevated heat stress for longer periods, especially during the summer. According to the Intergovernmental Panel on Climate Change (IPCC), the average global temperature has risen by approximately 1.2\u0026deg;C since the pre-industrial era (IPCC, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This increase is primarily attributed to anthropogenic activities such as fossil fuel combustion, deforestation, and industrial processes that release significant amounts of carbon dioxide and other greenhouse gases into the atmosphere. Gosling et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) conducted a comprehensive review of South Asian heat stress impacts on health, emphasizing the critical need for adaptive strategies.\u003c/p\u003e \u003cp\u003eOn a local scale, urbanization and meteorology play a significant role in quantifying the level of heat stress experienced at a place. The temperature will be higher in densely urbanized concrete regions because of the absorption and emission of long-wave radiation from concrete structures. Conversely, suburban areas with more vegetation and natural soil surfaces tend to stay relatively cooler. The meteorology of densely populated urban areas is favourable for elevated heat stress since high-rise buildings and modern infrastructures work as a barrier to the free movement of the wind. Gill et al., (2007) highlighted the role of green infrastructure in adapting cities to climate-changing scenarios.\u003c/p\u003e \u003cp\u003eSeveral studies have focused on understanding the heat stress pattern and its impact on human health and productivity within the context of Indian meteorological conditions (Basu et al., 2009; Murari et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Mazdiyasni et al., (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) reported that the probability of mortality rate increases during heat waves in Indian regions. Most of this research has primarily revolved around meteorological data analysis and climate modelling and is based on secondary data sources. Most existing research relies on historical data and modelling. While these approaches provide valuable insights, they may not capture the real-time dynamics of heat stress exposure. Consequently, substantial gaps remain in our understanding of heat stress at local and regional scales. This gap underscores the necessity for field experiments and real-time heat stress monitoring to provide accurate and actionable data (Rana et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Recent studies (Naskar and Pattanaik 2023 and Naskar et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) showed the observed changes in summer thermal discomfort using discomfort index (DI) and universal thermal climate index (UTCI) over Indian region during 1990\u0026ndash;2020. Another in-house study from IMD with AWS station data for Pune city shows that the urban heat island (UHI) effect in Pune has been intensifying during summer months April and May over the years 2012\u0026ndash;2023 (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Specially for both Shivajinagar stations are showing the significant increasing trend during summer months. All these studies mentioned above revealed that region wise increasing of heat discomfort and associated changes in relative humidity and temperature over Pune during summer season.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the present study, a field experiment was carried out to understand the heat stress pattern at the local level in Pune City, India, during April and May 2024. Heat stress was assessed using the Wet Bulb Globe Temperature (WBGT) Index, with continuous measurements taken at three different sites within the city. Additionally, simultaneous measurements of meteorological parameters such as temperature, relative humidity, wind, etc., were taken at these locations to explore their relationships with heat stress.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"2. Study Area and Period","content":"\u003cp\u003eThe study conducted in April and May 2024 focused on understanding the impact of urbanization on heat stress in Pune city. Pune, a rapidly urbanizing city in India, experiences significant temperature variations due to its diverse landscape, including urban, suburban, and rural areas. This study aimed to monitor heat stress and other meteorological parameters in different parts of the city, providing insights into how urbanization and local meteorology influence heat stress. The research involved experimental measurements at three distinct locations within Pune city: Indian Institute of Science Education and Research (IISER), Fergusson College (FC), and Agriculture College (AGR). These sites were chosen based on their varying degrees of urbanization and land-cover type, which allowed for a comparative analysis of heat stress levels across different urban environment. IISER, located on the outskirts of the city, represents a relatively less urbanized area with lower building density and more green spaces. FC is located in the heart of the city, which is a very highly urbanized region surrounded by dense infrastructure with significant human activity and minimal green cover. AGR station is situated in a relatively sparse region with substantial agricultural and open land, providing a contrast to the urbanized conditions at FC. Coordinates of the exact location, station code, and their type are provided in Table\u0026nbsp;1, and locations with actual surface conditions are shown in Fig.\u0026nbsp;3. The stations were chosen to be representative of different land use patterns, ensuring that the experimental measurements accurately reflected the environmental conditions experienced by individuals in different areas.\u003c/p\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eDescription of monitoring stations\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStation Name\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStation Code\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLat\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLon\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eType\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAgricultural College\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAGR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.5366\u0026deg; N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73.8441\u0026deg; E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate Urbanized\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFergusson College\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.5228\u0026deg; N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73.8390\u0026deg; E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHighly Urbanized\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndian Institute of Science Education and Research\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIISER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.5474\u0026deg; N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73.8080\u0026deg; E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLess Urbanized\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e"},{"header":"3. Experiment Setup and Methodology","content":"\u003cp\u003eThere are various indices to measure heat stress, such as UTCI, WBGT, wet-bulb dry temperature (WBDT), and tropical summer index (TSI), which are based on different methodologies. In the present study, we have used the WBGT index to measure the heat stress. The WBGT index is a comprehensive measurement of heat stress that considers temperature, humidity, wind speed, and solar radiation. It provides a more accurate illustration of the local environmental conditions affecting human comfort and health. WBGT index can be formulated as below (Parsons, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2006\u003c/span\u003e):\u003c/p\u003e \u003cp\u003eWBGT\u0026thinsp;=\u0026thinsp;0.70 T_w\u0026thinsp;+\u0026thinsp;0.20 T_G\u0026thinsp;+\u0026thinsp;0.10 T_A\u003c/p\u003e \u003cp\u003eWhere, T_w\u0026thinsp;=\u0026thinsp;Wet bulb temperature, T_\u003csub\u003eG\u003c/sub\u003e= Black globe temperature and T_\u003csub\u003eA\u003c/sub\u003e=Air temperature\u003c/p\u003e \u003cp\u003eThese parameters were used to calculate the WBGT index, providing a comprehensive measure of heat stress that integrates the effects of temperature, humidity, and radiant heat.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWBGT meters from METRAVI were employed to measure heat stress. All the WBGT meters have been calibrated and compared at the same locations before their installations, which reported less than two percent bias in their measurement. WBGT meters were installed on a wooden stand at a height of 4 feet above the ground, which accurately captures air temperature and other parameters, minimizing ground effects and potential interference from surrounding objects. To ensure the consistency and comparability of data across different sites, the WBGT meters were installed on similar land surfaces at each location. This uniformity was crucial to avoid any confounding effects from variations in surface properties, such as albedo (reflectivity) and heat retention. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the instrument and actual instrumental setup over three locations of Pune.\u003c/p\u003e \u003cp\u003eMeasurement were taken daily from 9 AM to 6 PM at a timestamp of five-minutes, capturing the full range of diurnal temperature variations. This time frame was chosen to include the period of maximum solar radiation and temperature, which typically occurs in the early afternoon and is critical for assessing peak heat stress levels. The collected data were analysed to assess spatial and temporal variations in heat stress across the study sites. The analysis focused on comparing the WBGT values between the urbanized and less urbanized areas, examining the impact of urbanization on heat stress levels. Additionally, diurnal patterns were analysed to identify peak periods of heat stress during the day, providing insights into the critical times for implementing protective measures. Further, the role of meteorological parameters in elevated heat stress were also investigated in detail.\u003c/p\u003e"},{"header":"4. Results and Discussion","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Spatial Variability of Heat Stress\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e(a) shows the daily maximum observed heat stress during the study period. Heat stress levels were classified into three different categories based on percentiles: low (elevated) threat, moderate threat, and extreme threat, represented by green, orange, and dark-red colors respectively. Days without experimental data are shown in white color. WBGT thresholds for low, moderate, and extreme threats were determined by the 90th, 95th, and 99th percentiles, corresponding to values of 31.5\u0026deg;C, 32\u0026deg;C, and 33\u0026deg;C, respectively. The total number of days with data collection during the April and May months were 54, 43, and 55 days for AGR, FC, and IISER stations, respectively. Station-wise summary of total days measured, with counts and percentages for low, moderate, and extreme threat days is tabulated in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Heat stress levels varied significantly across stations, likely due to differences in local meteorological conditions and land-use patterns. FC, located in densely urbanized area, shows the highest proportion of extreme threat days (44%) among all stations, followed by AGR (37%) and IISER (33%). Nearby concrete structures in highly urbanized region could be one of the major contributors to extreme heat stress at FC station. Despite being surrounded by agricultural fields, AGR station also recorded a substantial number of extreme threat days due to its proximity to densely populated urban areas. IISER station, which is located in comparatively greener areas, shows the highest number of low-threat days (45%) among the three stations.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe station-wise identified low, moderate, and extreme threat days and their percentage during all available experiment days and common experiment days.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003ea) All available Experiment days\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal experiment days\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eNumber of Days (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eElevated Threat\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModerate Threat\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eExtreme Threat\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAGR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e18 (33.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16 (29.63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20 (37.03%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e15 (34.88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9 (20.93%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19 (44.18%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIISER\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e25 (45.45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12 (21.81%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18 (32.72%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eb) \u003cb\u003eCommon Experiment days\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eStation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c3\" namest=\"c2\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eTotal experiment days\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e\u003cb\u003eNumber of Days (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eLow Threat\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eModerate Threat\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eExtreme Threat\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAGR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (35.89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11 (28.20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14(35.89%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (38.46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7 (17.95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17 (43.58%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIISER\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (51.28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7 (17.95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12 (30.76%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea, several days with missing experimental data are present across the stations, affecting the reliability of inter-station heat stress comparisons. To address this issue, we filtered for dates with complete data available for all three stations, resulting in a total of 39 fully observed days. Date-wise heat stress categorization for all three stations during these 39 days is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e(b). Throughout this period, the maximum number of extreme threat days were reported at the FC station. Counts and percentages of low-threat, moderate-threat, and extreme-threat days for each station over these 39 days are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e(b).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Diurnal Variation of WBGT\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eEach station exhibits a distinct average diurnal pattern of WBGT over the two-month study period. At AGR and FC, the average peak WBGT values are observed around 2 PM local time, whereas at IISER, the peak reported around 1 PM (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). This can be attributed to the combined effect of maximum solar radiation, ambient temperature, and reduced wind speed during this period. By 2 PM, although the sun has passed its zenith, the ground and surrounding structures continue to emit the absorbed heat, elevating the ambient temperature and contributing to higher WBGT values. Additionally, in urban areas like FC, the UHI effect exacerbates this condition, as heat-retaining surfaces such as concrete and asphalt release stored heat, maintaining elevated temperatures even after solar radiation begins to decline.\u003c/p\u003e \u003cp\u003eIn contrast, the IISER station records its maximum WBGT value at 1 PM local time. This earlier peak can be attributed to the relatively sparse urbanization and higher green cover around IISER, which allows faster heating and cooling cycles. The presence of vegetation and open spaces facilitates more efficient heat dissipation through evapotranspiration, causing the area to heat up rapidly as solar radiation peaks around noon and then cool down sooner than more urbanized areas. Furthermore, the influence of wind patterns and potential shading from vegetation and buildings may also contribute to this earlier peak, helping WBGT values at IISER decline earlier in the afternoon.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Impact of Temperature and Relative Humidity\u003c/h2\u003e \u003cp\u003eSince temperature and humidity are important parameters influencing WBGT, we compared them with WBGT values. To assess the overall scenario of heat stress during the daytime in the city, we computed the mean WBGT of three stations and compared them with the mean temperature and mean humidity of the same stations during different hours of the day, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. WBGT shows a strong positive correlation with ambient air temperature. Both WBGT and air temperature start increasing in the morning hours and attain their maximum values between 1 PM and 3 PM local time, and then decrease sharply during evening hours, whereas relative humidity has an opposite diurnal variation pattern to WBGT.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Impact of Wind\u003c/h2\u003e \u003cp\u003eWind speed and direction are critical meteorological factors that significantly influence heat stress, impacting human comfort and health. Wind speed is pivotal in dissipating heat from the body and the surrounding environment. Higher wind speeds enhance convective cooling by removing warm air from the skin's surface and replacing it with cooler air. This process is crucial for regulating body temperature, especially in hot conditions. When wind speeds are low or calm, the cooling effect is diminished, accumulating heat around the body and increasing the risk of heat stress.\u003c/p\u003e \u003cp\u003eThe hourly wind pattern of Pune during the study period has been shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e; diurnal wind speed and wind direction were represented with the help of 24 separate wind rose diagrams for each hour. Generally, greater wind speed is observed in the daytime compared to night-time; it starts increasing rapidly in the afternoon and attains maximum during the early evening hours as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe impact of wind on heat stress is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e. The average heat stress of all three stations is presented for different hours of the day using color legends, with the corresponding wind directions shown in each panel. Maximum heat stress is observed from 1 PM to 3 PM, which is associated with southerly winds.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Heat Stress Threshold Limit Based on experimental data\u003c/h2\u003e \u003cp\u003eDetailed statistics of the daily maximum WBGT of individual station for all recorded observations is presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e, whereas statistics of the common 39 days is shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Maximum WBGT (37.9\u0026deg;C) was reported at AGR station on 3rd May 2024; unfortunately, no WBGT observations were taken at FC and IISER stations on that day. The median of daily maximum WBGT was highest at FC, followed by AGR and IISER station.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStation-wise detailed Statistics of daily maximum WBGT for all available experiment days and common experiment days.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eAll available experiment days\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAGR (54 days)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFC (43 days)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIISER (55 days)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.8 (13th May)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1st Quantile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3rd Quantile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.90 (3rd may)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCommon experiment days\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAGR (39 days)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eFC (39 days)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eIISER (39 days)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1st Quantile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3rd Quantile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThough there is significant spatial variation in observed WBGT on different days, the overall mean and median of the daily maximum WBGT for each of the three stations are very close to each other. In order to find out the threshold values of daily maximum WBGT in Pune city we have computed the mean WBGT of the three stations. The 90th, 95th, and 99th percentile of this value were used as threshold values for low (elevated), moderate and extreme heat stress risk. Detailed WBGT threshold values for elevated, moderate, and high heat stress, along with their corresponding ambient temperature and humidity, are presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Our study shows a WBGT threshold value of 31.5\u0026deg;C, 32\u0026deg;C, and 33\u0026deg;C, respectively, for elevated, moderate, and high heat stress in Pune city during the April and May months.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePreliminary Proposed WBGT threshold limit and their corresponding temperature and humidity thresholds\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePercentile\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWBGT Threshold\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTemperature Threshold\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHumidity Threshold\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e90th\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.5\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow (Elevated) Risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e61.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e95th\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerate Risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e68%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e99th\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExtreme Risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42.5\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e79%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThis study focuses on understanding heat stress patterns in Pune City, emphasizing the identification of threshold values and the role of various influencing factors. As urbanization intensifies, heat stress has become a pressing public health concern, particularly in cities like Pune, where significant spatial and temporal variations in heat stress levels can be observed.\u003c/p\u003e \u003cp\u003eThe research revealed notable differences in heat stress across different monitoring stations, each representing distinct urbanization levels. FC station, characterized as the most urbanized area, recorded the highest instances of extreme heat stress, with 44% of days classified as extreme threat days. This finding highlights the correlation between urbanization and heat stress, as densely populated areas often experience intensified heat due to human activities, concrete infrastructure, and limited vegetation. In contrast, the AGR station, while located near agricultural fields, also exhibited high heat stress levels. This is attributed to its proximity to urban areas, suggesting that even locations ostensibly shielded by greenery can suffer from UHI effects. The IISER station, situated in a greener environment, experienced the highest number of low-threat heat days, demonstrating that vegetation plays a crucial role in mitigating heat stress.\u003c/p\u003e \u003cp\u003eThe study assessed diurnal variations in WBGT index readings, revealing differing peak times for maximum heat stress across the stations. At AGR and FC, the highest WBGT values occurred at 2 PM, whereas at IISER, this peak was at 1 PM. This discrepancy can be attributed to the unique urbanization and green cover characteristics of each site. Such findings emphasize the need for localized assessments, as heat stress can fluctuate significantly throughout the day based on environmental conditions. A critical factor in understanding heat stress levels is the influence of wind speed and direction. The study found that higher wind speeds can facilitate convective cooling, thereby reducing heat stress. Conversely, lower wind speeds can exacerbate heat conditions, making areas more vulnerable. Notably, wind coming from the south was associated with the highest levels of heat stress, indicating that local weather patterns and geographical features significantly impact heat distribution. The research proposed preliminary threshold values for heat stress based on the mean WBGT readings from the three stations: 31.5\u0026deg;C (90th percentile) for elevated heat stress, 32\u0026deg;C (95th percentile) for moderate, and 33\u0026deg;C (99th percentile) for extreme heat stress. These thresholds are essential for developing public health guidelines and interventions. However, the study acknowledges that these values can be refined with further research, including additional experimental sites and multi-year data collection.\u003c/p\u003e \u003cp\u003eThe implications of this study extend beyond environmental science; they directly impact public health. By effectively indicating periods of high heat stress, WBGT measurements serve as a vital tool for monitoring heat-related health issues. The findings underscore the importance of regular heat stress monitoring, particularly for vulnerable populations, such as the elderly, children, and those with pre-existing health conditions. Incorporating heat stress data into urban planning and public health strategies can lead to more informed decision-making. For instance, local governments can implement heat action plans, increase public awareness of heat risks, and provide resources for cooling centers during extreme heat events.\u003c/p\u003e \u003cp\u003eThe study emphasizes the necessity of adapting urban environments to mitigate the impacts of rising temperatures linked to climate change. Integrating green infrastructure such as parks, green roofs, and tree canopies can significantly reduce heat stress levels. By enhancing vegetation cover, cities can improve air quality, reduce heat absorption, and create more livable spaces. Moreover, climate-responsive urban planning should prioritize sustainable practices that consider heat stress and its implications. This includes designing buildings that promote airflow, implementing reflective materials in construction, and ensuring equitable access to green spaces for all residents. The findings provide actionable insights for policymakers, urban planners, and environmentalists striving to address the challenges posed by climate change. Recognizing the spatial variability of heat stress allows for targeted interventions, ensuring that resources are allocated where they are most needed.\u003c/p\u003e \u003cp\u003eThis study provides a crucial understanding of heat stress patterns in Pune city, highlighting significant spatial and diurnal variability and the impact of urbanization and environmental factors. The preliminary threshold values for heat stress offer a foundation for future research and public health interventions. By emphasizing the importance of localized studies, real-time data, and the integration of green infrastructure, the research advocates for a proactive approach to urban planning and public health in the face of rising temperatures. As cities continue to urbanize and climate change progresses, adopting these strategies will be vital for enhancing public health and improving overall livability in rapidly developing urban areas like Pune.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors acknowledge Dr. Mrutyunjay Mohapatra, Director General of IMD, for his support in carrying out this work. Authors would also like to thank the Surface Instrumentation Division at IMD Pune and Mr. T. C. Mohadikar from CAgMO, Pune, for their assistance with data collection and maintenance of the instruments. Additionally, the authors appreciate the help of Prof. Joy Monteiro and his team from IISER, Pune, as well as Prof. Raka Dabhade and her team from Fergusson College, for their support during the experiment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availibility:\u0026nbsp;\u003c/strong\u003eThe WBGT field experiment data is available with Climate Application and User Interface group of IMD Pune.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING:\u0026nbsp;\u003c/strong\u003eNo funding was received for this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONFLICTS OF INTEREST:\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no conflicts of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHajat, S., O\u0026apos;Connor, M., \u0026amp; Kosatsky, T. (2010). Health effects of hot weather: From awareness of risk factors to effective health protection. The Lancet, 375(9717), 856-863.\u003c/li\u003e\n\u003cli\u003eLundgren, K., Kuklane, K., Gao, C., \u0026amp; Holm\u0026eacute;r, I. (2013). Effects of heat stress on working populations when facing climate change. Industrial Health, 51(1), 3-15.\u003c/li\u003e\n\u003cli\u003eR. Basu, High ambient temperature and mortality: A review of epidemiologic studies from 2001 to 2008. Environ. Health 8, 40 (2009). https://doi.org/10.1186/1476-069X-8-40\u003c/li\u003e\n\u003cli\u003eIPCC. (2021). Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press.\u003c/li\u003e\n\u003cli\u003eK. K. Murari, S. Ghosh, A. Patwardhan, E. Daly, K. Salvi, Intensification of future severe heat waves in India and their effect on heat stress and mortality. Reg. Environ. Change 15, 569\u0026ndash;579 (2015). https://doi.org/10.1007/s10113-014-0660-6\u003c/li\u003e\n\u003cli\u003eMazdiyasni, O., AghaKouchak, A., Davis, S. J., Madadgar, S., Mehran, A., Ragno, E., \u0026amp; Niknejad, M. (2017). Increasing probability of mortality during Indian heat waves. Science Advances, 3(6), e1700066.\u003c/li\u003e\n\u003cli\u003eNaskar, Pravat Rabi, and Dushmanta Ranjan Pattanaik. \u0026quot;Observed changes in summer thermal discomfort over Indian region during 1990\u0026ndash;2020.\u0026quot; Journal of Earth System Science 132, no. 1 (2023): 36.\u003c/li\u003e\n\u003cli\u003eNaskar, Pravat Rabi, Mrutyunjay Mohapatra, Gyan Prakash Singh, and Umasankar Das. \u0026quot;Spatiotemporal variations of UTCI based discomfort over India.\u0026quot; Journal of Earth System Science 133, no. 1 (2024): 47.\u003c/li\u003e\n\u003cli\u003eRay, K., R.K. Giri, S.S. Ray, A.P. Dimri \u0026amp; M. Rajeevan. An assessment of long-term changes in mortalities due to extreme weather events in India: A study of 50 years\u0026rsquo; data, 1970\u0026ndash;2019, Weather and Climate Extremes,100315(2021). https://doi.org/10.1016/j.wace.2021.100315.\u003c/li\u003e\n\u003cli\u003eL. Shi, I. Kloog, A. Zanobetti, P. Liu, J. D. Schwartz, Impacts of temperature and its variability on mortality in New England. Nat. Clim. Change 5, 988\u0026ndash;991 (2015)\u003c/li\u003e\n\u003cli\u003eXiang, J., Bi, P., Pisaniello, D., \u0026amp; Hansen, A. (2014). Health impacts of workplace heat exposure: An epidemiological review. Industrial Health, 52(2), 91-101.\u003c/li\u003e\n\u003cli\u003eGosling, S. N., Bryce, E. K., Dixon, P. G., Gabriel, K. M., Gosling, E. Y., Hanes, J. M., \u0026amp; Honda, Y. (2017). A global assessment of the impact of heatwaves on mortality. Environmental Health Perspectives, 125(8), 087001.\u003c/li\u003e\n\u003cli\u003eParsons, K. (2006), Heat Stress Standard ISO 7243 and its Global Application, 44(3),368-379. https://doi.org/10.2486/indhealth.44.368\u003c/li\u003e\n\u003cli\u003eRana, R. S., Joshi, A., \u0026amp; Ansari, S. (2019). Heat stress and its mitigation strategies: A review. Journal of Agrometeorology, 21(2), 123-131.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-biometeorology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijbm","sideBox":"Learn more about [International Journal of Biometeorology](http://link.springer.com/journal/484)","snPcode":"484","submissionUrl":"https://www.editorialmanager.com/ijbm/default2.aspx","title":"International Journal of Biometeorology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Heat Stress, Wet Bulb Globe Temperature, vegetation, urbanization","lastPublishedDoi":"10.21203/rs.3.rs-5484071/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5484071/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHeat stress has emerged as a critical issue amid climate change, particularly in urbanizing areas like India. This study examines heat stress in Pune, focusing on the Wet Bulb Globe Temperature (WBGT) through field experiments conducted during the peak summer months of April and May 2024. The research aimed to understand how urban design, vegetation, and socio-economic factors influence heat stress levels. Three distinct locations: Indian Institute of Science Education and Research (IISER), Fergusson College (FC), and Agriculture College (AGR) were selected to represent varying degrees of urbanization. WBGT meters were installed uniformly at a height of 4 feet to ensure accurate readings, with hourly measurements taken from 9 AM to 6 PM. The highest WBGT index was recorded at FC, the most urbanized site, indicating increased heat stress. Analysis revealed that maximum heat stress typically occurred between 1 PM and 3 PM, with variations depending on the location. The study established WBGT threshold limits for Pune: 31.5\u0026deg;C (90th percentile), 32\u0026deg;C (95th percentile), and 33\u0026deg;C (99th percentile), corresponding to low, moderate, and extreme heat stress levels. Further investigation into meteorological factors showed a strong positive correlation between WBGT and ambient temperature, while relative humidity and wind speed had a reverse correlation. Notably, southerly winds contributed most significantly to heat stress. The study highlights WBGT as a vital metric for assessing heat stress, integrating temperature, humidity, wind speed, and solar radiation. The findings provide essential insights for policymakers, urban planners, and environmentalists, guiding strategies to mitigate the challenges of climate change and enhance urban resilience against heat stress.\u003c/p\u003e","manuscriptTitle":"Quantifying Heat Stress using Wet Bulb Globe Temperature measurements during Summer 2024 from Field Experiments in Pune","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-21 16:48:46","doi":"10.21203/rs.3.rs-5484071/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-02-09T14:37:47+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-12-18T14:44:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-22T16:42:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of Biometeorology","date":"2024-11-19T08:51:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-biometeorology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijbm","sideBox":"Learn more about [International Journal of Biometeorology](http://link.springer.com/journal/484)","snPcode":"484","submissionUrl":"https://www.editorialmanager.com/ijbm/default2.aspx","title":"International Journal of Biometeorology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"cd60b938-c61c-4dea-9b5c-759831697807","owner":[],"postedDate":"December 21st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-16T16:05:33+00:00","versionOfRecord":{"articleIdentity":"rs-5484071","link":"https://doi.org/10.1007/s00484-025-03080-6","journal":{"identity":"international-journal-of-biometeorology","isVorOnly":false,"title":"International Journal of Biometeorology"},"publishedOn":"2026-02-09 15:59:02","publishedOnDateReadable":"February 9th, 2026"},"versionCreatedAt":"2024-12-21 16:48:46","video":"","vorDoi":"10.1007/s00484-025-03080-6","vorDoiUrl":"https://doi.org/10.1007/s00484-025-03080-6","workflowStages":[]},"version":"v1","identity":"rs-5484071","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5484071","identity":"rs-5484071","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}