A Sustainable Urban Engineering Complexity: The Built Environment-Induced Urban Heat Island Effect in Rapidly Urbanizing Regions

A Sustainable Urban Engineering Complexity: The Built Environment-Induced Urban Heat Island Effect in Rapidly Urbanizing Regions | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Sustainable Urban Engineering Complexity: The Built Environment-Induced Urban Heat Island Effect in Rapidly Urbanizing Regions Mugesh Maruthu, Durgadevagi Shanmugavel This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2554251/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Increased development in the front of infrastructural activities, something that is especially being witnessed in developing countries in the past few decades, has resulted in unforeseen increases in urban air temperatures. The study was conducted to get the various urban infrastructure measurements used to assess the Urban Heat Island (UHI) effect. Shrinking vegetation in urban spaces also plays a significant role. Hence, vegetation metrics have also been studied to provide a holistic interpretation of the phenomena. Overall, the studies indicate that increasing albedo values and vegetation can reduce UHII. A reduction in building density, urban size, and population can play a role in reducing UHII. Building height, Canyon H/W, and SVF need to be optimized to achieve UHI mitigation. A study conducted in 5 cities in Tamil Nadu revealed that the city with the highest population density recorded the highest average daily high dry bulb temperature (DBT). However, further cities did not follow a similar pattern. Hence, the importance of studying various metrics in tandem is important to understand the climate wholistically. The research gap identified in the studies shows the need to study material properties other than just albedo values. These may include thermal conductivity, diffusivity, emissivity, heat evolution, and rheological properties of materials, along with physical and mechanical properties. There is a potential for a part of sustainable development goals to be met through enhancing material properties that can mitigate the UHI effect. urban heat island albedo building density Building height urban size population canyon H/W ratio sky view factor vegetation 1.0 Introduction A significant challenge in booming infrastructural development and urban expansion is the increase in urban temperatures compared to peripheral suburbs. This effect is called Urban Heat Island (UHI), and the difference between the urban and its peripheral area’s air temperature is called Urban Heat Island Intensity. This results in increased cooling energy use, thermal discomfort, and health risk. The UHI effect is the most noticeable atmospheric alteration that may be attributed to human settlement. The substitution of natural surfaces with those that are characteristic of a city has a significant impact on the aerodynamic, radiative, thermal, and moisture qualities of the urban environment (Oke and Maxwell, 1975 ). Irrespective of the context, UHI has been experienced all over the world. While the primary research under this domain has predominantly been conducted in developed nations in the earlier stages of its study, the past few decades have witnessed a profound interest in research among developing countries witnessing an infrastructural boom. The research domain consists of identifying and quantifying UHIs, identification of variables that influence UHII, and mitigation strategies. 2.0 Urban Infrastructural Variables And Their Relationship With Uhi While several variables have been identified that play a role in the UHI effect, this paper will focus on the urban infrastructure variables – Albedo, Building Density, Building Height, Vehicular Traffic, Urban Size, Building Height to Street Width Ratio and Sky View Factor. In addition, it is impossible to study UHI without considering the vegetation component in urban spaces. Hence, this aspect has also been considered. 2.1 Material Properties (albedo) and its influence on UHI A city’s overall albedo, or ability to absorb solar radiation, is determined by the albedo of the individual materials and the geometrical arrangement in which they are used (Oke, T.R. 1988). An increase of 0.35 in albedo was found to decrease the surface temperature for the whole region by an average of 0.76 degree Celsius based on a study conducted in Sydney (Liu and Morawska, 2020 ). While such quantitative analysis is relevant to the climatic context, a study conducted by Li et al. ( 2020 ) in 419 major global cities discovered that albedo has a negative correlation with Surface Urban Heat Island Intensity. A similar result was observed in a survey conducted in Illinois, Ohio, and Indiana (Li and Zhou, 2019 ). In rural and suburban regions, using highly reflecting materials with an albedo value of 0.7 resulted in the reduction of Urban Heat Intensity by up to 1.5 degree Celsius (Falasca and Curci, 2018 ). 2.2 Building Density/Building Height and its influence on UHI While building density and height is a significant contributor to UHI, the results across the papers have yet to be consistent. This may be attributed to the climatic context, urban morphology, and other influencing factors that play a role. A study conducted in Chennai observed that dense areas resulted in more significant Urban heat Island Intensity (Harrison and Amirtham, 2016 ). A study based on five cities in China found that a 20% and 30% reduction in heat-island degree hours were observed by reducing the building height and building density, respectively (Liu et al., 2020). The Kowloon Peninsula in Hong Kong found that building density and height growth resulted in more considerable urban air temperature differences and a faster increase in urban temperature during the nighttime than during the day (Duan et al., 2019 ). Given that the metro core is denser than the periphery urban spaces, the Land Surface Temperature (LST) is found to be highest in the urban center or central urban area and reduces away from it towards the sprawling city (Lee et al., 2019). Overall, higher temperatures were recorded in highly dense built-up areas than in sparsely built-up regions in Seoul (Ngaramber et al. 2021). 2.3 Urban Size and its influence on UHI The UHI effect is amplified by the city’s size and growth. A significant positive correlation was found between Urban Area Size and Surface Urban Heat Island Intensity based on a study conducted in 3 cities in the USA (Li and Zhou, 2019 ). Using Yangtze River Delta Urban Agglomerations, UHII variations during the entire day and the night-time were found to be positively correlated with cluster size with a more substantial impact during the summer than winter (Sun et al., 2019 ). An interesting study based on 1288 urban clusters was able to identify that lesser SUHII occurred in urban groups that were smaller in size, and by doubling the cluster size, the yearly SUHII increased by 0.23 degrees Celsius in the daytime and 0.21 degree Celsius at night time (Liu et al., 2021 ). The growing gross floor area was found to increase UHII with varying intensity based on conditions of the sky and seasons (Ngaramber et al., 2021). 2.4 Population and its influence on UHI The population is a crucial parameter when addressing UHI, as the population size influences the creation of urban infrastructure and its density. A study by Li and Zhou ( 2019 ) found that the urban-rural population difference positively correlates with SUHII. The relative contribution of population size on UHIs was found to be 5.2% − 6.6% (Li et al., 2020 ). A study conducted in 42 French cities found that when the logarithm of the total population increases, the maximum UHII also increases (Gardes et al., 2020 ). Taking a step further, it was found that Low-medium and medium GDP/Population recorded the highest UHII and High/GDP/Population recorded the lowest UHII based on a study conducted in 155 Chinese cities (Peng et al., 2019 ). A study in Bogota found that population density above 14,500 inhabitants/sq.km may cause a 1-degree Celsius increase in air temperature levels (Ramírez-Aguilar and Souza, 2019). 2.5 Canyon H/W ratio and its influence on UHI To investigate the impact of canyon geometry (H/W ratio) on UHI, research conducted in Riyadh studied two urban canyons – deep (H/W = 2.2) and shallow (H/W = 0.42), and found that the ambient temperatures were warmer by 5% and 15% respectively when compared to rural surroundings (Bakarman and Chang, 2015 ). Another study tried to find the ideal H/W based on the modeling and simulation for H/W = 2, H/W = 1, H/W = 0.66, and H/W = 0.5 and found H/W = 1 is most suitable for UHI control again changes in wind velocity, temperature, and pressure. Predominantly, it was found that H/W values are directly proportional to UHII, and when Population Density > 6000, the H/W values are higher than 0.35 and result in a 1-degree Celsius increase in UHII (Ramírez-Aguilar and Souza, 2019). A positive correlation was found between the H/W ratio and LST, according to a study conducted in Greater London and Seoul (Liao et al., 2021 ). 2.6 Sky View Factor and its influence on UHI A study conducted in Beirut provided insight into low SVF, and its resultant decrease in solar energy storage can provide a better thermal environment (Fahad et al., 2020). The same study goes on to elaborate that SVF does not play a significant role in nighttime temperature. However, during the mid-day, higher SVF increases air temperature. However, the impact of SVF on UHIs was found to be influenced by seasons, where SVF (one of the 3D metrics) was found to be the least influencing factor in the spring, a strong influencing factor during the summer and winter, and only a significant influencing factor in autumn (Hu et al., 2020 ). Huang and Wang (2019) and Hu et al. ( 2020 ) conducted a study in Wuhan and North Beijing, respectively, and found LST to have a negative correlation with SVF. A survey by Ramírez-Aguila and Souza (2019) in Bogato found a high correlation between UHII and SVF. 2.7 Vegetation and its influence on UHI Across all research, vegetation has played a significant role in mitigating the UHI effect. A study in Baghdad observed that urban green infrastructure reduces Surface Urban Heat Island Intensity by 4–22 degree Celsius (Abdulateef and Al-Alwan, 2021). Shirani-bidabadi et al. ( 2019 ) found a negative correlation between Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) based on a study conducted in Isfahan, Iran (Shirani-bidabadi et al., 2019 ). The same research also observed that an increased bare land and man-made structures significantly increased A study temperatures. The correlation coefficient between NDVI and LST was found to be 0.9, in a survey conducted in Zonguldak, Turkey (Sekertekin and Zadbagher, 2021 ). The correlation strength between Green Area Ratio and Urban Heat Island Intensity varied under sky conditions and seasons, although an overall negative correlation was observed (Ngaramber et al., 2021). A 2-D, prognostic, micro-scale model was developed by Alexander and Jones (2008) to observe that Air temperatures inside the canyon decrease by up to 11.3 degrees maximum and 9.1 degrees Celsius during daytime average in Riyadh by using green walls and roofs. In Beirut, a 6-degree Celsius temperature difference was found between the high vegetation fractions and dense urban fabric during the 3.0 Study On The Relationship Between Urban Density And Air Temperature Of Cities From Tamil Nadu Using the literature study, five cities in Tamil Nadu, India were analysed in terms of population density and dry bulb temperature (air temperature). Population density data was retrieved from Census 2011, and air temperature data were extracted using Climate Consultant 6.0 using EPW file for meteorological data from 2004 – 2018. The file was downloaded from a repository of climatic data (https://climate.onebuilding.org/). To understand the air temperature data, two sets of charts were produced – Monthly Dry Bulb Temperature and Daily Dry Bulb Temperature 3D Plot. The average average daily low, and moderate daily high of all five cities are consolidated in Table 1. The average daily high dry bulb temperature (DBT) across the five cities was observed to be most significant in Tiruchirappalli (38.2 °C) in April, followed by Chennai (37.7°C) in May. The average daily high dry bulb temperature exceeds 35°C for seven months in Tiruchirappalli, followed by three months in Chennai and Nagapattinam. However, Chennai records the highest average daily DBT of 31.9°C in May, while June records the second highest average DBT for the same city. Across the five cities, however, Tiruchirappalli records the most significant number of months (5 months) with an average daily DBT exceeding 30°C. It is also interesting to observe that six months or half the year in Coimbatore has an average daily DBT below 25°C. In comparison, only three months in other cities recorded an average daily low DBT below 25°C. The lowest average daily low in Coimbatore was recorded in January at 19.8°C. Table 2 details the DBT breakdown details along with summer and winter comfort zone. For Chennai, the average daily DBT of none of the months fall in the comfort zone based on the season. It is to be noted that the average daily high DBT exceeds the comfort zone by a large degree for all the months. The average daily low DBT falls within the comfort zone for nine months of the year. The annual average DBT is far from the comfort zone for the city of Chennai. For Coimbatore, the average daily DBT falls under the seasonal comfort zone for four months of the year. The average daily high DBT exceeds the comfort zone for all the months. The average daily low DBT falls within the comfort zone for 11 months of the year. The annual average DBT falls within the comfort zone to a large degree. For Tiruchirappalli, the average daily DBT of none of the months falls in the comfort zone based on the season. The average daily high DBT exceeds the comfort zone for all the months. The average daily low DBT falls within the comfort zone for ten months of the year. The annual average DBT falls within the comfort zone to a large degree. The average yearly DBT is far from the comfort zone for the city of Tiruchirappalli. For Nagappattinam, the average daily DBT of none of the months falls in the comfort zone based on the season. The average daily high DBT exceeds the comfort zone for all the months. The average daily low DBT falls within the comfort zone for five months of the year. The annual average DBT is far from the comfort zone for the city of Nagappattinam. For Cuddalore, the average daily DBT of none of the months falls in the comfort zone based on the season. The average daily high DBT exceeds the comfort zone for all the months. The average daily low DBT falls within the comfort zone for ten months of the year. The annual average DBT does not fall within the comfort zone. The average yearly DBT is far from the comfort zone for the city of Nagappattinam. According to Table 3, the highest maximum DBT was observed to be in Chennai with a record of 42.2°C, followed by Tiruchirappalli (41.0°C), Cuddalore (40.0°C), Nagapattinam (39.2°C) and Coimbatore (37.2°C). The lowest min DBT was observed to be in Cuddalore (12.58°C), followed by Coimbatore (13.8°C), Tiruchirappalli (18.0°C), Chennai (19.0°C) and Nagapattinam (20.4°C). It must be noted that the temperatures in Chennai and Tiruchirappalli exceed 38°C by 1% and 3%, respectively of the year. While for the other cities, the temperatures exceed 38°C less than 1% of the year. Table 4 compares the population density and average daily high DBT for June (the month which recorded max temperature in nearly all the cities comparatively). Chennai, the city with the highest population density, recorded the highest average daily high DBT, as expected from the literature study. However, Coimbatore, the city with the second highest population density and the largest urban size within the five towns studied, recorded the lowest average daily high DBT. In this case, Coimbatore has become a significant exception to the existing literature. This may be attributed to the fact that the city is located in a highly vegetated area near the western ghats. In this case, it conforms to the literature that cities near higher vegetation zones experience lower temperatures. The other three cities have almost identical population density and urban size, resulting in no significant variations in average daily high DBT. 4.0 Conclusion The key factors that have a clear impact on Urban Heat Island (UHI) are the material properties, building density, urban size, canopy H/W ratio, and vegetation. However, the factors such as building height, canyon H/W, and SVF are more dependent on the specific urban morphology and climate. This necessitates the need for optimization. The expansion of urban spaces and the resulting reduction in vegetation has led to an increase in UHI. The study conducted in five cities raised questions about the appropriate metrics for UHI analysis. However, the study's limitations were that only population density, urban size, and average daily high DBT were used. Although the city with the highest population density, Chennai, recorded the largest average daily high DBT, this was not replicated in other cities. Therefore, it is essential to study urban infrastructure using various metrics and not rely on a single variable alone. A significant research gap is the limited study of material properties, with only albedo being studied in relation to UHI. The research needs to focus on other material properties such as thermal conductivity, diffusivity, emissivity, heat evolution, rheological properties, physical and mechanical properties to mitigate the UHI effect. By enhancing the material properties, it is possible to reduce cooling energy consumption and meet sustainable development goals. Declarations Author contribution Mugesh Maruthu wrote the manuscript in consultation with Durgadevagi Shanmugavel. Mugesh Maruthu carried out all the technical details, studied, and did the analysis. Durgadevagi Shanmugavel contributed to the verification of the analysis and the results. Both authors contributed to shaping the work by discussing the results and contributing to the final manuscript. Availability of data and materials The Temperature data utilized here are available in climate consultant v6.0, which is an open-source software in this link https://climate-consultant.informer.com/6.0/. Ethical approval : Not applicable. Consent to participate : Not applicable. Consent for publication : Not applicable. Competing interests : The authors declare no competing interests. Funding : This research received no external funding. 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Tables Tables 1 to 4 are available in the Supplementary Files section Supplementary Files Tables.docx Cite Share Download PDF Status: Posted Version 1 posted 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-2554251","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":189241622,"identity":"5d8abef8-5d2f-491c-8b0d-9873464e13e5","order_by":0,"name":"Mugesh Maruthu","email":"","orcid":"","institution":"SRM Institute of Science and Technology College of Engineering","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mugesh","middleName":"","lastName":"Maruthu","suffix":""},{"id":189241623,"identity":"5e52c6ed-3352-4ee0-a73e-58bc07f83827","order_by":1,"name":"Durgadevagi Shanmugavel","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA70lEQVRIiWNgGAWjYDCCAyCCjYGBH0QnMEDJBGK0SDaAFRuQoMUAzIBpwQf4buQ+fvGjzMbe+PzZpxse/PnDwM+eY8DwcAduLZI30s0se86lJW4DMm4kthkwSPa8MWBIPINbi8GNNDYD3rbDCWY32NhuJDYYAEWAtiS24ddi+Lftv71x/zG2Gwl/DBjsidDC/Ji37QDjBoY0oBY2oC0SBLRInnnGxixzLjlxBtA6oF+MeSTOPCs4gE8L3/E05o9vyuzs+YEOu/njj5wcf3vyxoc/8WgBAjYJZB4PiDiAVwMDA/MHAgpGwSgYBaNgpAMAtLRU5RjDYqYAAAAASUVORK5CYII=","orcid":"","institution":"SRM Institute of Science and Technology College of Engineering","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Durgadevagi","middleName":"","lastName":"Shanmugavel","suffix":""}],"badges":[],"createdAt":"2023-02-06 04:50:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2554251/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2554251/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":47073537,"identity":"ec9ab7ee-e118-4e14-9294-4955e188c8d1","added_by":"auto","created_at":"2023-11-25 20:29:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":274039,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2554251/v1/7a1c8f49-b0aa-4855-8334-8f46aa90d984.pdf"},{"id":35383320,"identity":"45d903f9-37f9-487a-9832-d95a46c49caf","added_by":"auto","created_at":"2023-04-06 11:20:27","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1425666,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-2554251/v1/4f896c637d158280ac2cba9d.docx"}],"financialInterests":"","formattedTitle":"A Sustainable Urban Engineering Complexity: The Built Environment-Induced Urban Heat Island Effect in Rapidly Urbanizing Regions","fulltext":[{"header":"1.0 Introduction","content":"\u003cp\u003eA significant challenge in booming infrastructural development and urban expansion is the increase in urban temperatures compared to peripheral suburbs. This effect is called Urban Heat Island (UHI), and the difference between the urban and its peripheral area\u0026rsquo;s air temperature is called Urban Heat Island Intensity. This results in increased cooling energy use, thermal discomfort, and health risk. The UHI effect is the most noticeable atmospheric alteration that may be attributed to human settlement. The substitution of natural surfaces with those that are characteristic of a city has a significant impact on the aerodynamic, radiative, thermal, and moisture qualities of the urban environment (Oke and Maxwell, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1975\u003c/span\u003e). Irrespective of the context, UHI has been experienced all over the world. While the primary research under this domain has predominantly been conducted in developed nations in the earlier stages of its study, the past few decades have witnessed a profound interest in research among developing countries witnessing an infrastructural boom. The research domain consists of identifying and quantifying UHIs, identification of variables that influence UHII, and mitigation strategies.\u003c/p\u003e"},{"header":"2.0 Urban Infrastructural Variables And Their Relationship With Uhi","content":"\u003cp\u003eWhile several variables have been identified that play a role in the UHI effect, this paper will focus on the urban infrastructure variables \u0026ndash; Albedo, Building Density, Building Height, Vehicular Traffic, Urban Size, Building Height to Street Width Ratio and Sky View Factor. In addition, it is impossible to study UHI without considering the vegetation component in urban spaces. Hence, this aspect has also been considered.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Material Properties (albedo) and its influence on UHI\u003c/h2\u003e \u003cp\u003eA city\u0026rsquo;s overall albedo, or ability to absorb solar radiation, is determined by the albedo of the individual materials and the geometrical arrangement in which they are used (Oke, T.R. 1988). An increase of 0.35 in albedo was found to decrease the surface temperature for the whole region by an average of 0.76 degree Celsius based on a study conducted in Sydney (Liu and Morawska, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). While such quantitative analysis is relevant to the climatic context, a study conducted by Li et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) in 419 major global cities discovered that albedo has a negative correlation with Surface Urban Heat Island Intensity. A similar result was observed in a survey conducted in Illinois, Ohio, and Indiana (Li and Zhou, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In rural and suburban regions, using highly reflecting materials with an albedo value of 0.7 resulted in the reduction of Urban Heat Intensity by up to 1.5 degree Celsius (Falasca and Curci, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Building Density/Building Height and its influence on UHI\u003c/h2\u003e \u003cp\u003eWhile building density and height is a significant contributor to UHI, the results across the papers have yet to be consistent. This may be attributed to the climatic context, urban morphology, and other influencing factors that play a role. A study conducted in Chennai observed that dense areas resulted in more significant Urban heat Island Intensity (Harrison and Amirtham, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). A study based on five cities in China found that a 20% and 30% reduction in heat-island degree hours were observed by reducing the building height and building density, respectively (Liu et al., 2020). The Kowloon Peninsula in Hong Kong found that building density and height growth resulted in more considerable urban air temperature differences and a faster increase in urban temperature during the nighttime than during the day (Duan et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Given that the metro core is denser than the periphery urban spaces, the Land Surface Temperature (LST) is found to be highest in the urban center or central urban area and reduces away from it towards the sprawling city (Lee et al., 2019). Overall, higher temperatures were recorded in highly dense built-up areas than in sparsely built-up regions in Seoul (Ngaramber et al. 2021).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Urban Size and its influence on UHI\u003c/h2\u003e \u003cp\u003eThe UHI effect is amplified by the city\u0026rsquo;s size and growth. A significant positive correlation was found between Urban Area Size and Surface Urban Heat Island Intensity based on a study conducted in 3 cities in the USA (Li and Zhou, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Using Yangtze River Delta Urban Agglomerations, UHII variations during the entire day and the night-time were found to be positively correlated with cluster size with a more substantial impact during the summer than winter (Sun et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). An interesting study based on 1288 urban clusters was able to identify that lesser SUHII occurred in urban groups that were smaller in size, and by doubling the cluster size, the yearly SUHII increased by 0.23 degrees Celsius in the daytime and 0.21 degree Celsius at night time (Liu et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The growing gross floor area was found to increase UHII with varying intensity based on conditions of the sky and seasons (Ngaramber et al., 2021).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Population and its influence on UHI\u003c/h2\u003e \u003cp\u003eThe population is a crucial parameter when addressing UHI, as the population size influences the creation of urban infrastructure and its density. A study by Li and Zhou (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) found that the urban-rural population difference positively correlates with SUHII. The relative contribution of population size on UHIs was found to be 5.2% \u0026minus;\u0026thinsp;6.6% (Li et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). A study conducted in 42 French cities found that when the logarithm of the total population increases, the maximum UHII also increases (Gardes et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Taking a step further, it was found that Low-medium and medium GDP/Population recorded the highest UHII and High/GDP/Population recorded the lowest UHII based on a study conducted in 155 Chinese cities (Peng et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). A study in Bogota found that population density above 14,500 inhabitants/sq.km may cause a 1-degree Celsius increase in air temperature levels (Ram\u0026iacute;rez-Aguilar and Souza, 2019).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Canyon H/W ratio and its influence on UHI\u003c/h2\u003e \u003cp\u003eTo investigate the impact of canyon geometry (H/W ratio) on UHI, research conducted in Riyadh studied two urban canyons \u0026ndash; deep (H/W\u0026thinsp;=\u0026thinsp;2.2) and shallow (H/W\u0026thinsp;=\u0026thinsp;0.42), and found that the ambient temperatures were warmer by 5% and 15% respectively when compared to rural surroundings (Bakarman and Chang, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Another study tried to find the ideal H/W based on the modeling and simulation for H/W\u0026thinsp;=\u0026thinsp;2, H/W\u0026thinsp;=\u0026thinsp;1, H/W\u0026thinsp;=\u0026thinsp;0.66, and H/W\u0026thinsp;=\u0026thinsp;0.5 and found H/W\u0026thinsp;=\u0026thinsp;1 is most suitable for UHI control again changes in wind velocity, temperature, and pressure. Predominantly, it was found that H/W values are directly proportional to UHII, and when Population Density\u0026thinsp;\u0026gt;\u0026thinsp;6000, the H/W values are higher than 0.35 and result in a 1-degree Celsius increase in UHII (Ram\u0026iacute;rez-Aguilar and Souza, 2019). A positive correlation was found between the H/W ratio and LST, according to a study conducted in Greater London and Seoul (Liao et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Sky View Factor and its influence on UHI\u003c/h2\u003e \u003cp\u003eA study conducted in Beirut provided insight into low SVF, and its resultant decrease in solar energy storage can provide a better thermal environment (Fahad et al., 2020). The same study goes on to elaborate that SVF does not play a significant role in nighttime temperature. However, during the mid-day, higher SVF increases air temperature. However, the impact of SVF on UHIs was found to be influenced by seasons, where SVF (one of the 3D metrics) was found to be the least influencing factor in the spring, a strong influencing factor during the summer and winter, and only a significant influencing factor in autumn (Hu et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Huang and Wang (2019) and Hu et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) conducted a study in Wuhan and North Beijing, respectively, and found LST to have a negative correlation with SVF. A survey by Ram\u0026iacute;rez-Aguila and Souza (2019) in Bogato found a high correlation between UHII and SVF.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Vegetation and its influence on UHI\u003c/h2\u003e \u003cp\u003eAcross all research, vegetation has played a significant role in mitigating the UHI effect. A study in Baghdad observed that urban green infrastructure reduces Surface Urban Heat Island Intensity by 4\u0026ndash;22 degree Celsius (Abdulateef and Al-Alwan, 2021). Shirani-bidabadi et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) found a negative correlation between Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) based on a study conducted in Isfahan, Iran (Shirani-bidabadi et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The same research also observed that an increased bare land and man-made structures significantly increased A study temperatures. The correlation coefficient between NDVI and LST was found to be 0.9, in a survey conducted in Zonguldak, Turkey (Sekertekin and Zadbagher, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The correlation strength between Green Area Ratio and Urban Heat Island Intensity varied under sky conditions and seasons, although an overall negative correlation was observed (Ngaramber et al., 2021). A 2-D, prognostic, micro-scale model was developed by Alexander and Jones (2008) to observe that Air temperatures inside the canyon decrease by up to 11.3 degrees maximum and 9.1 degrees Celsius during daytime average in Riyadh by using green walls and roofs. In Beirut, a 6-degree Celsius temperature difference was found between the high vegetation fractions and dense urban fabric during the\u003c/p\u003e \u003c/div\u003e"},{"header":"3.0 Study On The Relationship Between Urban Density And Air Temperature Of Cities From Tamil Nadu","content":"\u003cp\u003eUsing the literature study, five cities in Tamil Nadu, India were analysed in terms of population density and dry bulb temperature (air temperature). Population density data was retrieved from Census 2011, and air temperature data were extracted using Climate Consultant 6.0 using EPW file for meteorological data from 2004 \u0026ndash; 2018. The file was downloaded from a repository of climatic data (https://climate.onebuilding.org/). To understand the air temperature data, two sets of charts were produced \u0026ndash; Monthly Dry Bulb Temperature and Daily Dry Bulb Temperature 3D Plot. The average average daily low, and moderate daily high of all five cities are consolidated in Table 1.\u003c/p\u003e\n\u003cp\u003eThe average daily high dry bulb temperature (DBT) across the five cities was observed to be most significant in Tiruchirappalli (38.2 \u0026deg;C) in April, followed by Chennai (37.7\u0026deg;C) in May. The average daily high dry bulb temperature exceeds 35\u0026deg;C for seven months in Tiruchirappalli, followed by three months in Chennai and Nagapattinam. However, Chennai records the highest average daily DBT of 31.9\u0026deg;C in May, while June records the second highest average DBT for the same city. Across the five cities, however, Tiruchirappalli records the most significant number of months (5 months) with an average daily DBT exceeding 30\u0026deg;C. It is also interesting to observe that six months or half the year in Coimbatore has an average daily DBT below 25\u0026deg;C. In comparison, only three months in other cities recorded an average daily low DBT below 25\u0026deg;C. The lowest average daily low in Coimbatore was recorded in January at 19.8\u0026deg;C. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2 details the DBT breakdown details along with summer and winter comfort zone. For Chennai, the average daily DBT of none of the months fall in the comfort zone based on the season. It is to be noted that the average daily high DBT exceeds the comfort zone by a large degree for all the months. The average daily low DBT falls within the comfort zone for nine months of the year. The annual average DBT is far from the comfort zone for the city of Chennai. For Coimbatore, the average daily DBT falls under the seasonal comfort zone for four months of the year. The average daily high DBT exceeds the comfort zone for all the months. The average daily low DBT falls within the comfort zone for 11 months of the year. The annual average DBT falls within the comfort zone to a large degree. For Tiruchirappalli, the average daily DBT of none of the months falls in the comfort zone based on the season. The average daily high DBT exceeds the comfort zone for all the months. The average daily low DBT falls within the comfort zone for ten months of the year. The annual average DBT falls within the comfort zone to a large degree. The average yearly DBT is far from the comfort zone for the city of Tiruchirappalli. For Nagappattinam, the average daily DBT of none of the months falls in the comfort zone based on the season. The average daily high DBT exceeds the comfort zone for all the months. The average daily low DBT falls within the comfort zone for five months of the year. The annual average DBT is far from the comfort zone for the city of Nagappattinam. For Cuddalore, the average daily DBT of none of the months falls in the comfort zone based on the season. The average daily high DBT exceeds the comfort zone for all the months. The average daily low DBT falls within the comfort zone for ten months of the year. The annual average DBT does not fall within the comfort zone. The average yearly DBT is far from the comfort zone for the city of Nagappattinam.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAccording to Table 3, the highest maximum DBT was observed to be in Chennai with a record of 42.2\u0026deg;C, followed by Tiruchirappalli (41.0\u0026deg;C), Cuddalore (40.0\u0026deg;C), Nagapattinam (39.2\u0026deg;C) and Coimbatore (37.2\u0026deg;C). The lowest min DBT was observed to be in Cuddalore (12.58\u0026deg;C), followed by Coimbatore (13.8\u0026deg;C), Tiruchirappalli (18.0\u0026deg;C), Chennai (19.0\u0026deg;C) and Nagapattinam (20.4\u0026deg;C). It must be noted that the temperatures in Chennai and Tiruchirappalli exceed 38\u0026deg;C by 1% and 3%, respectively of the year. While for the other cities, the temperatures exceed 38\u0026deg;C less than 1% of the year.\u003c/p\u003e\n\u003cp\u003eTable 4 compares the population density and average daily high DBT for June (the month which recorded max temperature in nearly all the cities comparatively). Chennai, the city with the highest population density, recorded the highest average daily high DBT, as expected from the literature study. However, Coimbatore, the city with the second highest population density and the largest urban size within the five towns studied, recorded the lowest average daily high DBT. In this case, Coimbatore has become a significant exception to the existing literature. This may be attributed to the fact that the city is located in a highly vegetated area near the western ghats. In this case, it conforms to the literature that cities near higher vegetation zones experience lower temperatures. The other three cities have almost identical population density and urban size, resulting in no significant variations in average daily high DBT.\u003c/p\u003e"},{"header":"4.0 Conclusion","content":"\u003cp\u003eThe key factors that have a clear impact on Urban Heat Island (UHI) are the material properties, building density, urban size, canopy H/W ratio, and vegetation. However, the factors such as building height, canyon H/W, and SVF are more dependent on the specific urban morphology and climate. This necessitates the need for optimization. The expansion of urban spaces and the resulting reduction in vegetation has led to an increase in UHI. The study conducted in five cities raised questions about the appropriate metrics for UHI analysis. However, the study's limitations were that only population density, urban size, and average daily high DBT were used. Although the city with the highest population density, Chennai, recorded the largest average daily high DBT, this was not replicated in other cities. Therefore, it is essential to study urban infrastructure using various metrics and not rely on a single variable alone. A significant research gap is the limited study of material properties, with only albedo being studied in relation to UHI. The research needs to focus on other material properties such as thermal conductivity, diffusivity, emissivity, heat evolution, rheological properties, physical and mechanical properties to mitigate the UHI effect. By enhancing the material properties, it is possible to reduce cooling energy consumption and meet sustainable development goals.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAuthor contribution\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMugesh Maruthu wrote the manuscript in consultation with Durgadevagi Shanmugavel. Mugesh Maruthu carried out all the technical details, studied, and did the analysis. Durgadevagi Shanmugavel contributed to the verification of the analysis and the results. Both authors contributed to shaping the work by discussing the results and contributing to the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe Temperature data utilized here are available in climate consultant v6.0, which is an open-source software in this link https://climate-consultant.informer.com/6.0/.\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.7257%;\" valign=\"top\" width=\"25.52166934189406%\"\u003eEthical approval\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 4.7788%;\" valign=\"top\" width=\"4.49438202247191%\"\u003e:\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 68.4956%;\" valign=\"top\" width=\"65.48956661316213%\"\u003eNot applicable.\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.7257%;\" valign=\"top\" width=\"25.52166934189406%\"\u003eConsent to participate\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 4.7788%;\" valign=\"top\" width=\"4.49438202247191%\"\u003e:\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 68.4956%;\" valign=\"top\" width=\"65.48956661316213%\"\u003eNot applicable.\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.7257%;\" valign=\"top\" width=\"25.52166934189406%\"\u003eConsent for publication\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 4.7788%;\" valign=\"top\" width=\"4.49438202247191%\"\u003e:\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 68.4956%;\" valign=\"top\" width=\"65.48956661316213%\"\u003eNot applicable.\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.7257%;\" valign=\"top\" width=\"25.52166934189406%\"\u003eCompeting interests\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 4.7788%;\" valign=\"top\" width=\"4.49438202247191%\"\u003e:\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 68.4956%;\" valign=\"top\" width=\"65.48956661316213%\"\u003eThe authors declare no competing interests.\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.7257%;\" valign=\"top\" width=\"25.52166934189406%\"\u003eFunding\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 4.7788%;\" valign=\"top\" width=\"4.49438202247191%\"\u003e:\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 68.4956%;\" valign=\"top\" width=\"65.48956661316213%\"\u003eThis research received no external funding.\u003cbr\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAbdulateef, Maryam F., and Hoda A. S. Al-Alwan. \u0026ldquo;The Effectiveness of Urban Green Infrastructure in Reducing Surface Urban Heat Island.\u0026rdquo; \u003cem\u003eAin Shams Engineering Journal\u003c/em\u003e, vol. 13, no. 1, 2022, p. 101526. https://doi.org/10.1016/j.asej.2021.06.012.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eAlexandri, Eleftheria, and Phil Jones. \u0026ldquo;Temperature Decreases in an Urban Canyon Due to Green Walls and Green Roofs in Diverse Climates.\u0026rdquo; \u003cem\u003eBuilding and Environment\u003c/em\u003e, vol. 43, no. 4, 2008, pp. 480\u0026ndash;493. https://doi.org/10.1016/j.buildenv.2006.10.055.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eBakarman, Mohammed A., and Jae D. 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Maxwell. \u0026ldquo;Urban Heat Island Dynamics in Montreal and Vancouver.\u0026rdquo; \u003cem\u003eAtmospheric Environment (1967)\u003c/em\u003e, vol. 9, no. 2, 1975, pp. 191\u0026ndash;200. https://doi.org/10.1016/0004-6981(75)90067-0.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003ePeng, Shijia et al. \u0026ldquo;Spatial-Temporal Pattern of, and Driving Forces for, Urban Heat Island in China.\u0026rdquo; \u003cem\u003eEcological Indicators\u003c/em\u003e, vol. 96, 2019, pp. 127\u0026ndash;132. https://doi.org/10.1016/j.ecolind.2018.08.059.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eRam\u0026iacute;rez-Aguilar, Edwin Alejandro, and L\u0026eacute;a Cristina Lucas Souza. \u0026ldquo;Urban Form and Population Density: Influences on Urban Heat Island Intensities in Bogot\u0026aacute;, Colombia.\u0026rdquo; \u003cem\u003eUrban Climate\u003c/em\u003e, vol. 29, 2019, p. 100497. https://doi.org/10.1016/j.uclim.2019.100497.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSekertekin, Aliihsan, and Elaheh Zadbagher. \u0026ldquo;Simulation of Future Land Surface Temperature Distribution and Evaluating Surface Urban Heat Island Based on Impervious Surface Area.\u0026rdquo; \u003cem\u003eEcological Indicators\u003c/em\u003e, vol. 122, 2021, p. 107230. https://doi.org/10.1016/j.ecolind.2020.107230.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eShirani-bidabadi, Niloufar, et al. \u0026ldquo;Evaluating the Spatial Distribution and the Intensity of Urban Heat Island Using Remote Sensing, Case Study of Isfahan City in Iran.\u0026rdquo; \u003cem\u003eSustainable Cities and Society\u003c/em\u003e, vol. 45, 2019, pp. 686\u0026ndash;692. https://doi.org/10.1016/j.scs.2018.12.005.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSun, Yanwei, et al. \u0026ldquo;Evaluating Urban Heat Island Intensity and Its Associated Determinants of Towns and Cities Continuum in the Yangtze River Delta Urban Agglomerations.\u0026rdquo; \u003cem\u003eSustainable Cities and Society\u003c/em\u003e, vol. 50, 2019, p. 101659. https://doi.org/10.1016/j.scs.2019.101659.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eWeng, Qihao, and Shihong Yang. \u0026ldquo;Managing the Adverse Thermal Effects of Urban Development in a Densely Populated Chinese City.\u0026rdquo; \u003cem\u003eJournal of Environmental Management\u003c/em\u003e, vol. 70, no. 2, 2004, pp. 145\u0026ndash;156. https://doi.org/10.1016/j.jenvman.2003.11.006. \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 4 are available in the Supplementary Files section\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"urban heat island, albedo, building density, Building height, urban size, population canyon H/W ratio, sky view factor, vegetation","lastPublishedDoi":"10.21203/rs.3.rs-2554251/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2554251/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIncreased development in the front of infrastructural activities, something that is especially being witnessed in developing countries in the past few decades, has resulted in unforeseen increases in urban air temperatures. The study was conducted to get the various urban infrastructure measurements used to assess the Urban Heat Island (UHI) effect. Shrinking vegetation in urban spaces also plays a significant role. Hence, vegetation metrics have also been studied to provide a holistic interpretation of the phenomena. Overall, the studies indicate that increasing albedo values and vegetation can reduce UHII. A reduction in building density, urban size, and population can play a role in reducing UHII. Building height, Canyon H/W, and SVF need to be optimized to achieve UHI mitigation. A study conducted in 5 cities in Tamil Nadu revealed that the city with the highest population density recorded the highest average daily high dry bulb temperature (DBT). However, further cities did not follow a similar pattern. Hence, the importance of studying various metrics in tandem is important to understand the climate wholistically. The research gap identified in the studies shows the need to study material properties other than just albedo values. These may include thermal conductivity, diffusivity, emissivity, heat evolution, and rheological properties of materials, along with physical and mechanical properties. There is a potential for a part of sustainable development goals to be met through enhancing material properties that can mitigate the UHI effect.\u003c/p\u003e","manuscriptTitle":"A Sustainable Urban Engineering Complexity: The Built Environment-Induced Urban Heat Island Effect in Rapidly Urbanizing Regions","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-04-06 11:20:22","doi":"10.21203/rs.3.rs-2554251/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6372f96b-bb04-433a-a68f-b42edc368b67","owner":[],"postedDate":"April 6th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-11-25T20:21:25+00:00","versionOfRecord":[],"versionCreatedAt":"2023-04-06 11:20:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2554251","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2554251","identity":"rs-2554251","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}