Decoupling Size-Cooling-Comfort linkages: A parsimonious approach for analysing biophysical cooling capacity and outdoor thermal comfort perception in public urban parks

Decoupling Size-Cooling-Comfort linkages: A parsimonious approach for analysing biophysical cooling capacity and outdoor thermal comfort perception in public urban parks | 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 Decoupling Size-Cooling-Comfort linkages: A parsimonious approach for analysing biophysical cooling capacity and outdoor thermal comfort perception in public urban parks Sulagna De, Arup Das, Tarak Nath Mazumder This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7370179/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 Using parsimonious approach, this study develops a novel framework integrating outdoor thermal comfort (OTC) perception and biophysical park cooling capacity (PCC). OTC perception is measured through Apparent Temperature reduction (ΔAT) between park and its surrounding built environment using microclimatic data of temperature, relative humidity and wind speed. Given the established positive correlation between subjective and objective thermal comfort measures in prior research, this study adopts ΔAT as a proxy for OTC. Unlike predominant models that rely on simple temperature differences (e.g. park cooling intensity), this study employs an enthalpy-based method to estimate PCC. This method accounts for both sensible and latent heat exchanges, providing a more comprehensive estimation of park cooling load. This study confirms that larger parks possess greater cooling capacity. However, it contests the transitive assumption that increased park size enhances OTC perception. This study also points to a decoupling effect of park size, since it does not correlate significantly with OTC perception. Moreover, partial correlation analysis further reveals that park size acts as a confounding variable, masking the true relationship between PCC and OTC perception. To better assess the cooling efficiency of urban parks, the study develops a Park Cooling Performance Index (PCPI) by evaluating OTC perception relative to PCC per unit area. Notably, small-sized urban parks outperformed larger ones. These insights thereby address a paradigm shift in urban greening strategies. Prioritizing and designing compact, vegetated, small-sized urban parks as natural cooling systems can improve local microclimate and climate resilience, particularly in highly-dense, hot-humid, tropical Indian cities. Park Cooling Capacity Outdoor Thermal Comfort Apparent Temperature Enthalpy Park Cooling Performance Index Microclimate Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Outdoor Thermal Comfort (OTC) has gained prominence as a vital aspect of public health and well-being amidst rising climate crisis due to global warming and urbanization stress (Lau & Choi, 2021 ). Increased ambient temperatures have intensified the Urban Heat Island (UHI) effect, subsequently increasing the energy consumption required for cooling and adversely affecting the sustainability of cities - particularly in hot and humid regions (Grimmond, 2007 ). Achieving Sustainable Development Goals (SDGs) has thus become a prime focus among urban decision makers and governance managers. Urban greening through integration of green infrastructures and nature-based solutions is globally recommended as a promising strategy to mitigate UHI and enhance urban cooling (Brown et al., 2015 ; Gill et al., 2007 ). The cooling effect of park has widespread benefits – both tangible and intangible for the city and its people. It regulates park microclimate through natural cooling mechanisms like evapotranspiration and shading (Barghchi et al., 2024 ; Bowler et al., 2010 ). Apart from mitigating UHI, park cooling (PC) effect also encourages outdoor activities improving park usability, urban well-being and perceived OTC. Aram et al., ( 2020 ) observed that residents living closer to parks in Madrid reported higher levels of perceived OTC. Their study identified a negative correlation between perceived subjective thermal comfort responses and Physiological Equivalent Temperature (PET), a commonly used biometeorological index derived from microclimatic data. Similarly, Klemm et al., ( 2015 ) studied urban parks across three Dutch cities, characterized them as cool islands within the city and highlighted a positive impact of increasing tree cover on the subjective and objective thermal comfort. (Peng et al., 2021 ) further emphasized that enhanced greenness improves the heat exchange capacity of the parks with their surrounding urban areas. Typically, thermal comfort models are grounded in energy balance models based on the principle of heat transfer. Environmental factors (air temperature, relative humidity, solar radiation and air velocity), physiological factors (activity, clothing) and psychological factors (behaviour, perception, culture) are key determinants of OTC evaluation (Chu & Jong, 2008 ; Li et al., 2024 ). However, modelling can be complex owing to the subjective nature of thermal comfort sensation induced by the varying choice of clothing. To simplify the models, wind speed, air temperature and humidity are often considered as the priority factors, assuming other factors as constants (Chu & Jong, 2008 ). Apparent Temperature (AT) introduced by Steadman, ( 1984 ), is a simplified, yet widely recognized index of heat stress. Other popular indices include SET (Standard Effective Temperature), UTCI (Universal Thermal Climate Index) among others (Potchter et al., 2018 ). A substantial body of research on PC effect focusses on index-based assessments, evaluation studies, and study of various factors affecting PC effect (Peng et al., 2021 ). Commonly used indices for quantifying PC include PC intensity, PC distance or PC range or PC gradient, PC area, PC efficiency, and cool island intensity (Peng et al., 2021 ; Xiong et al., 2024 ; J. Zhang et al., 2024 ). Among these, PC intensity – defined by the temperature difference between urban park and its surrounding built areas is the most widely used metric, typically ranging from 0.5-4°C (Yan et al., 2018 ). Many researchers have also explored the spatial extent of PC, identifying ‘cool islands’ through spatial analysis of Land Surface Temperature (LST) data (Yu et al., 2017 ). Both park morphology – size, shape and geometry (Wang et al., 2025 ) and landscape characteristics including vegetation density, canopy cover, leaf-area index, tree height and waterbodies play a vital role affecting PC effect. These are further influenced by broader climatic and seasonal contexts (Barghchi et al., 2024 ; Xiong et al., 2024 ). Among these variables, park size has emerged as a key factor in influencing park cooling potential. Numerous studies, based on simulations and validated through field measurements, have reported a positive correlation between park size and cooling effect, with larger parks exhibiting greater temperature reduction (Aram et al., 2019 ; Lu et al., 2017 ; Vidrih & Medved, 2013 ). Additionally, park size shows an inverse non-linear relationship with LST where, LST decreases logarithmically as park size increases (Cheng et al., 2015 ). Some studies point to a threshold effect, indicating that park area beyond a certain size, may not enhance the cooling effect (Algretawee, 2022 ; Wang et al., 2025 ; J. Zhang et al., 2024 ). New research perspectives on park size, PC effect and OTC perception often follow a transitive assumption: larger parks possess greater cooling potential and consequently, are presumed to provide enhanced thermal comfort (Chan & Chau, 2021 ). However, this coupling of size-cooling-comfort is largely underexplored and speculative, necessitating deeper investigation. Besides, the environmental benefits of PC effect are also rarely quantified, particularly in developing countries like India. Thus, this study introduces a novel approach to estimate the park cooling capacity (PCC) by borrowing the thermodynamic concept of enthalpy which will assess the amount of heat energy absorbed by the natural systems within parks. Adopting a parsimonious modelling strategy, this study examines the relationship between PCC and OTC perception (measured using AT index) and analyses the role of park size. PC effect and PCC are interchangeably used in this study. Furthermore, this study develops a simplified framework of Park Cooling Performance Index (PCPI) to evaluate the efficiency of the PCC and examines its relationship with park size. Kolkata, an Indian metropolitan city characterized by a hot and humid tropical climate is chosen as the study area. This study has accounted the role of tree cover for their shading effect (Oliveira et al., 2011 ) and evaluates the evapotranspiration rate of trees by estimating the equivalent energy-saving benefits due to the park cooling effects(B. Zhang et al., 2014 ). Thus, this paper addresses the following research questions (RQ). How can park cooling capacity (PCC) be quantified using an enthalpy-based approach? To what extent does park size influence the relationship between Park Cooling Capacity (PCC) and OTC perception? What is the nature of the relationship between park size and Park Cooling Performance Index (PCPI)? 2. Material and methods 2.1 Study Area This study is a quantitative, cross-sectional, microclimatic analysis conducted across 30 public urban parks within the jurisdiction of Kolkata Municipal Corporation (KMC). These parks, varying in size were selected from different locations within the study area based on the study of (De et al., 2025 ), as illustrated in Fig. 1 . KMC covers an area of 206 sq.km. and is divided into 144 administrative wards, housing over 4.6 million people according to Census of India, 2011. With a population density of approximately 25,000 people per sq.km, the city holds an enormous geopolitical significance in Eastern India due to its strategic location, economic prominence and cultural heritage (Haque et al., 2019 ). Lying close to the Tropic of Cancer at 22°34′N, 88°22′E and alongside Hooghly River, Kolkata enjoys warm and humid climate, classified as Aw (tropical wet and dry) under the Köppen Climate Classification (Kottek et al., 2006 ). The city’s annual mean air temperature (T a ) is around 26.8°C. Kolkata has three distinct seasons – hot and humid summer (March-June), monsoon (July-October) and mild and moderate winter (November-February). The hottest months, May and June witness peak temperatures between 40°C-43°C, with average temperatures around 30°C-35°C and relative humidity (RH) exceeding 70%. The south-west monsoon, driven by proximity to the Bay of Bengal brings substantial rainfall during July-October, having an average monthly rainfall of 330–340 mm and RH > 80%, while the highest rainfall occurs during August (390mm). Winters are generally pleasant, with average temperatures ranging between 25°C-30°C and RH > 60%., the lowest temperature dips to around 10°C-14°C in January. Presently, Kolkata is facing intense urban heat stress due to dense built-up areas and diminishing urban green and blue spaces. This has led to intensified UHI effects within the city, raising serious concerns about public health risks (Mandal et al., 2022 ; TNN, 2023). The conceptual framework behind this study is illustrated in Fig. 2 . 2.2 Data collection Multiple studies have established a strong positive correlation between objective and subjective measures of OTC perception (Klemm et al., 2015 ; Li et al., 2024 ; Potchter et al., 2018 ). Based on this evidence, this study did not conduct user perception surveys, but instead employed an objective metric – Apparent Temperature (AT) index, to assess OTC. The AT is calculated using the Eq. ( 1 ) utilizing microclimate data collected both inside (At in ) and outside (AT out ) the parks respectively (Steadman, 1984 ). $$\:AT={T}_{a}+0.33e-0.70v-4$$ 1 ………………………. Where, \(\:{T}_{a}\) is the air temperature (°C), \(\:e\) is the water vapour pressure (hPa) and \(\:v\) is the wind speed (m/s). The vapour pressure ( \(\:e\) ) is calculated using the Eq. ( 2 ). $$\:e=\frac{RH}{100}*6.105*{e}^{\left(\frac{17.27*{T}_{a}}{237.7+{T}_{a}}\right)}$$ 2 ………………………. Where, \(\:RH\) is the relative humidity (%). Descriptive statistics of the microclimatic variables and park sizes used in the study are summarized in Table 1 . Table 1 Descriptive statistics of the microclimatic data and park sizes Variables Mean ± Std. Error Median Standard Deviation Min Max A (sq.m) 2115.17 ± 519.72 1156.66 2846.64 235.00 14749.00 T a,in (°C) 23.34 ± 0.32 23.50 1.74 19.50 27.00 T a,out (°C) 24.34 ± 0.37 24.25 2.02 20.10 30.30 RH in (%) 67.27 ± 1.29 67.35 7.05 55.80 88.00 RH out (%) 67.58 ± 1.50 66.85 8.22 50.60 90.00 v in (m/s) 0.86 ± 0.12 0.70 0.67 0.10 2.90 v out (m/s) 0.57 ± 0.10 0.50 0.55 0.00 2.20 The field surveys were carried out in April, 2024. Peak park-use hours were identified through a preliminary reconnaissance survey across the 30 parks. It revealed that most park users visit the park between 4–6 PM. Accordingly, all microclimatic measurements were taken during this time window. Two portable, hand-held weather-monitoring devices positioned at a height of 1.2m above the ground level were used. Data collection was conducted only on clear days, excluding the cloudy or rainy conditions to maintain consistency. An anemometer (BEETECH Anemometer B-201, Range upto 30m/s) was used to monitor the wind speed (m/s) both inside and outside the park. Similarly, air temperature (°C) and relative humidity (%) were recorded using an HTC HD-304 Temperature Humidity Meter (Temp Range of 20°C-60°C). It was used to measure T a (°C) and RH (%) both inside and outside the park. Each device was operated for 30 mins at both locations and the average readings were used in the analysis. 2.3 Estimation of Park Cooling Capacity (PCC) Park cooling load refers to the amount of heat energy passively absorbed by the park’s natural ecosystem (e.g., trees) in order to lower the ambient temperature within the park. By reducing both the latent and sensible heat, a park acts as a natural passive cooling system regulating the local microclimate. Addressing the RQ1, this study adopts the concept of enthalpy (H) from the principles of thermodynamics to estimate PCC by measuring the park cooling load in kJ using the formula mentioned in Eq. ( 3 ). $$\:PCC=\:\rho\:*V*\varDelta\:H$$ 3 ………………………. Where, \(\:\rho\:\) is the density of air, 1.293 kg/m 3 , \(\:V\) is the volume of air (m 3 ) measured as the product of park area (A), tree cover (TC) %, and the height (1.2m) at which the devices were held, and \(\:\varDelta\:H\) (kJ/kg) is the enthalpy difference between outside and inside the park, as shown in Eq. (4). \(\:V=\frac{TC}{100}*A*1.2\) , \(\:\varDelta\:H={H}_{out}-{H}_{in}\) ………………………. (4) Enthalpy values (H in , H out ) were derived from psychrometric chart using the measurements of T a and RH%. To address the RQ2, Pearson correlation analysis was first conducted to examine if the relationships between park size (A), PCC and ΔAT are statistically significant. Subsequently, a Partial correlation analysis was performed to determine whether park size has any mediating or confounding effect on the relationship between PCC and ΔAT. 2.4 Developing Park Cooling Performance Index (PCPI) This study introduces a composite metric termed as Park Cooling Performance Index (PCPI) to evaluate the cooling efficiency of urban parks. PCPI represents the degree of perceived thermal comfort, indicated by reduction in apparent temperature (ΔAT), relative to the normalized biophysical cooling capacity of the park per unit area. PCPI is defined by the Eq. ( 5 ). $$\:PCPI=\frac{{\Delta\:}\text{A}\text{T}}{\left(PCC/A\right)}$$ 5 ………………………. To address the RQ3, regression analysis is conducted to find whether the park size has any significant relationship with PCPI using SPSS software. 3. Results and findings 3.1 Measuring outdoor thermal comfort perception using AT index and assessing its relationship with park size This study utilizes AT index to quantify objective thermal comfort perception. AT values recorded inside (At in ) and outside (AT out ) the parks are presented in Fig. 3 a, while Fig. 3 b depicts the difference in AT (ΔAT) values. Descriptive statistics of AT indices are highlighted in Table 2 . An average value of perceived OTC measured as ΔAT is approximately 1.62°C. Table 2 Descriptive statistics of the AT index measured outside and inside the parks Variables Mean ± Std. Error Median Standard Deviation Min Max AT out (°C) 26.67 ± 0.38 26.33 2.06 23.00 32.99 AT in (°C) 25.05 ± 0.31 25.22 1.70 21.58 28.46 ΔAT (°C) 1.62 ± 0.23 1.36 1.25 0.16 5.41 3.2 Quantifying Park Cooling Capacity (PCC) and Park Cooling Performance Index (PCPI) Addressing the RQ1, Fig. 4 a shows an upward trend in the distribution of PCC values across the 30 parks, organized in ascending order of park size. In contrast, Fig. 4 b displays a downward trend in the PCPI values for the same set of parks, arranged similarly. Table 3 summarizes the descriptive statistics of the estimated enthalpy of parks (H in ) and its surrounding built environment (H out ), PCC and PCPI. Table 3 Descriptive statistics of enthalpy, PCC and PCPI Variables Mean ± Std. Error Median Standard Deviation Min Max H out (kJ/kg) 57.10 ± 0.57 56.27 3.14 52.55 65.51 H in (kJ/kg) 54.00 ± 0.48 54.22 2.62 47.58 59.07 PCC (kJ) 4281.71 ± 926.80 1955.82 5076.27 10.98 19281.44 PCPI (unitless) 2.35 ± 0.93 0.69 5.09 0.16 27.40 Notably, the smallest park (P1) shows an exceptionally high PCPI value. Average value of PCC that measured cooling load of 30 parks is approximately 4281 kJ. While, average PCPI value is around 2.35, ranging between 0.16 to 28. Higher the PCPI value, greater is the cooling efficiency of the parks reflected through enhanced perception of thermal comfort. 3.3 Decoupling park size from the size-cooling-comfort linkages of urban park system Figures 5 a and 5 b indicate that park size has no significant correlation with OTC perception [r = -0.119, p = 0.532], but shows a strong positive correlation with PCC [r = 0.647, p = .000]. To further explore the relationship between PCC and OTC perception, Pearson correlation analysis was used, which reveals a non-significant relationship [r = 0.252, p = .180], as displayed in Fig. 5 c. However, this result appears counter-intuitive. Thus, a partial correlation analysis was conducted, controlling for park size, which revealed a significant positive correlation between PCC and OTC perception, [r (27) = 0.434, p = .019]. Thus, this finding indicates a critical decoupling of park size from the conventionally assumed linkage between size, cooling and comfort in urban park systems. 3.4 Examining the relationship between park size and Park Cooling Performance Index (PCPI) Upon plotting PCPI against park size (Fig. 5 d), an inverse regression curve emerged as the best fitting model, suggesting a non-linear relationship between the two variables. It is found that park size can significantly influence PCPI [R 2 = 32.3%, F (1,28) = 13.341, p = .001]. Regression coefficients, presented in Table 4 , indicate a clear negative relationship such that as park size increases, PCPI value tends to decrease. This finding implies that even though large parks have greater cooling capacity, their cooling efficiency per unit area may decline with increasing size. Table 4 Regression coefficients of the inverse regression model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 / Park size (sqm) 2849.961 780.269 0.568 3.653 0.001 (Constant) -0.987 1.201 -0.822 0.418 4. Discussion Following the principle of parsimony, this study captures the objective thermal comfort within urban parks by assessing the biophysical cooling capacity of green infrastructure, particularly tree cover using microclimatic measurements. In essence, the study intends to highlight the potential of urban parks in hot-humid cities to act as natural passive cooling system (The World Bank, 2022 ), as reflected through a measurable reduction in apparent temperature (ΔAT) index. Park Cooling Intensity (PCI), defined as the temperature difference between park interior and the surrounding built environment, is commonly used for estimating park cooling effect (Sun et al., 2021 ; Xiao et al., 2023 ). However, in this study, PCC is quantified using the enthalpy-based approach which accounts for the biophysical estimation of the total heat energy exchange occurring within the park microclimate - addressing both the sensible heat (related to change in temperatures) and latent heat (related to change in humidity levels) (Chu & Jong, 2008 ). Given Kolkata’s tropical, warm and humid climate, this inclusion of relative humidity into the otherwise simple temperature models offers a more comprehensive framework of energy dynamics involved in park cooling. Basically, the park cooling load is calculated by measuring T a (°C) and RH% both inside and outside each park during peak hours of park usage. Based on these microclimatic variables, specific enthalpy (kJ/kg) of air is computed for both the settings. Difference in the enthalpy values (ΔH) thus represents the total heat energy absorbed by the park natural system, serving as a proxy for biophysical cooling capacity of the park. Initially, no significant correlation was found between PCC and OTC perception which seemed counter-intuitive. However, further investigation using partial correlation analysis uncovered that park size acted as a confounding variable, masking the true relationship between PCC and OTC perception. When the effect of park size was statistically controlled, a significant positive correlation emerged between PCC and OTC perception. This finding aligns with the established understanding, reaffirming that higher biophysical cooling capacity in parks contributes to improved thermal comfort perception. Meanwhile, park size is significantly correlated with PCC supporting earlier findings that larger parks generally possess greater cooling potential (Algretawee, 2022 ; Cheng et al., 2015 ). However, this study also reveals that no significant correlation exists between park size and OTC perception, hence challenging the widely held assumption that larger parks inherently enhance perceived thermal comfort (Chan & Chau, 2021 ). Interestingly, the correlation coefficient between park size and OTC perception was found negative, suggesting a possible inverse relationship. This finding defies the expected transitive logic that larger parks have greater cooling capacity, thus having a higher perception of OTC. Instead, the study highlights that larger parks do not necessarily translate into improved thermal comfort, thereby pointing to a decoupling of the park size from the cooling-comfort relationship in urban green spaces. Furthermore, the performance metric called as PCPI constructed in this study has revealed significant variance in the cooling performance of the parks across different park sizes. Interestingly, smaller parks exhibited higher PCPI values, suggesting that these parks are more efficient in translating their biophysical cooling capacity into perceived thermal comfort when normalized per unit area. This highlights that, despite their limited size, smaller parks can outperform larger ones in terms of thermal comfort efficiency, particularly in highly dense Indian metropolitan cities. Thus, the study decouples the size effect and strategizes zone-centric park design, morphological and spatial configurations, and landscape characteristics to regulate local microclimate (Brown et al., 2015 ; Gao et al., 2022 ; Norouzi et al., 2024 ; Wang et al., 2025 ). 5. Conclusion This study advances a nuanced understanding of the relationships between park size, PCC and OTC perception using a parsimonious approach and adds four notable contributions into the PC literature. First, the study presents a novel framework for estimating the biophysical cooling capacity of parks by integrating the thermodynamic concept of enthalpy using microclimatic data. Second, this study while confirming that larger parks possess greater cooling capacity, challenges the prevalent supposition that greater size enhances thermal comfort perception – indicating at the decoupling effect of size from size-cooling-comfort linkages. Third, employing partial correlation analysis, the study reveals that size acts as the confounding variable masking the true relationship between PCC and OTC perception. Lastly, this study presents a new performance metric, PCPI that bridges thermal comfort perception with the biophysical cooling capacity of the park. Interestingly, small-sized urban parks exhibit better cooling performance and efficiency compared to the larger ones, particularly in hot-humid environment. Infact, this study is particularly relevant in highly dense urban areas of developing countries which are struggling with ever-rising heat stress and rapid decline of public urban green spaces. Thus, urban greening policies should aim at improving the small-sized parks which possess a great potential to induce passive cooling of urban local microclimate improving urban climate resilience. The insights further demand the attention of policymakers and urban planners to address the paradigm shift from size-centric park cooling strategies to park design-driven performance strategies encouraging optimized, efficient and compact park greening. Future research can explore the relationship between PCC and OTC perception using real-time environmental monitoring, offering new insights. It will be interesting to observe the impact of potential temporal variations on park cooling performance by undergoing longitudinal studies across different seasons. Besides, the current findings may be further substantiated through advanced simulations incorporating multiple explanatory variables. While, this study has focussed primarily on the confounding effect of park size, future investigations can explore the influence of other factors such as park morphology, park amenities and facilities, landscape composition, etc. to develop a more holistic understanding of the drivers of thermal comfort in urban parks. Declarations Disclosure Statement: The authors report there are no competing interests to declare . Funding: The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Competing interests: The authors have no relevant financial or non-financial interests to disclose. Authors’ contributions Sulagna De – Conceptualization, Methodology, Formal Analysis, Data Curation, Writing - Original Draft; Arup Das – Conceptualization, Supervision, Validation, Writing - Review & Editing; Tarak Nath Mazumder – Supervision, Validation References Algretawee, H. (2022). 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Wang, L., Wang, W., Tang, F., & Xu, H. (2025). Optimizing urban park cooling effects requires balancing morphological design and landscape structure. Scientific Reports , 15 (1), 1–19. https://doi.org/10.1038/s41598-025-98249-9 Xiao, Y., Piao, Y., Pan, C., Lee, D., & Zhao, B. (2023). Using buffer analysis to determine urban park cooling intensity: Five estimation methods for Nanjing, China. Science of the Total Environment , 868 , 161463. Xiong, Y., Xie, X., & Yang, Y. (2024). Evaluation and optimization of park cooling benefits based on cumulative impact and landscape pattern. Scientific Reports , 14 (1), 25092. Yan, H., Wu, F., & Dong, L. (2018). Influence of a large urban park on the local urban thermal environment. Science of the Total Environment , 622 , 882–891. Yu, Z., Guo, X., Jørgensen, G., & Vejre, H. (2017). How can urban green spaces be planned for climate adaptation in subtropical cities? Ecological Indicators , 82 , 152–162. Zhang, B., Xie, G., Gao, J., & Yang, Y. (2014). The cooling effect of urban green spaces as a contribution to energy-saving and emission-reduction: A case study in Beijing, China. Building and Environment , 76 , 37–43. Zhang, J., Zhang, H., & Qi, R. (2024). A study of size threshold for cooling effect in urban parks and their cooling accessibility and equity. Scientific Reports , 14 (1), 16176. 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. 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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-7370179","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":505291260,"identity":"2ef3f932-8777-4361-b660-96c8d3e74c10","order_by":0,"name":"Sulagna De","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0001-7793-9073","institution":"IIT Kharagpur ARP: Indian Institute of Technology Kharagpur Department of Architecture and Regional Planning","correspondingAuthor":true,"prefix":"","firstName":"Sulagna","middleName":"","lastName":"De","suffix":""},{"id":505291261,"identity":"0be98a7e-63fa-40ea-a432-c6ee52f9bc29","order_by":1,"name":"Arup Das","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Arup","middleName":"","lastName":"Das","suffix":""},{"id":505291262,"identity":"6afcd540-8135-430a-85fa-5bc566c2fa37","order_by":2,"name":"Tarak Nath Mazumder","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Tarak","middleName":"Nath","lastName":"Mazumder","suffix":""}],"badges":[],"createdAt":"2025-08-14 06:01:59","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7370179/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7370179/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90379294,"identity":"6b6cd7d2-73a7-4dd0-8fe3-9eee6f412c4b","added_by":"auto","created_at":"2025-09-02 06:31:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":240881,"visible":true,"origin":"","legend":"\u003cp\u003eStudy area\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7370179/v1/b190159332615a4c27743cf5.png"},{"id":90379296,"identity":"4c8028da-ab75-4173-9816-bdf6d0f20529","added_by":"auto","created_at":"2025-09-02 06:31:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":180013,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual framework of the study\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7370179/v1/9a8ab833cc0228f8dc41b2aa.png"},{"id":90381220,"identity":"fffde622-c3f1-4fd1-8c2e-7b8b0a25ea9b","added_by":"auto","created_at":"2025-09-02 06:47:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":351969,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of AT indices across the parks\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7370179/v1/647c668699255032a60bb65f.png"},{"id":90379298,"identity":"a381845f-d28b-4d9a-a2af-309ba01d5c6a","added_by":"auto","created_at":"2025-09-02 06:31:02","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":354943,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of PCC and PCPI values across the 30 parks\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7370179/v1/acde168302bdc7f1bf6bd78c.png"},{"id":90379309,"identity":"2b597fb8-78ae-4307-b1a2-00112a9749dc","added_by":"auto","created_at":"2025-09-02 06:31:03","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":258738,"visible":true,"origin":"","legend":"\u003cp\u003eRelationships between park size, PCC, OTC perception and PCPI\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7370179/v1/090906b9c16eb722e7e25dba.png"},{"id":93999120,"identity":"3af3632c-40fe-46cb-9ac3-ff9f40fc92bb","added_by":"auto","created_at":"2025-10-21 07:41:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2262007,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7370179/v1/903b4014-9630-4b47-b6eb-ec74a8a0f156.pdf"}],"financialInterests":"","formattedTitle":"Decoupling Size-Cooling-Comfort linkages: A parsimonious approach for analysing biophysical cooling capacity and outdoor thermal comfort perception in public urban parks","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eOutdoor Thermal Comfort (OTC) has gained prominence as a vital aspect of public health and well-being amidst rising climate crisis due to global warming and urbanization stress (Lau \u0026amp; Choi, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Increased ambient temperatures have intensified the Urban Heat Island (UHI) effect, subsequently increasing the energy consumption required for cooling and adversely affecting the sustainability of cities - particularly in hot and humid regions (Grimmond, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Achieving Sustainable Development Goals (SDGs) has thus become a prime focus among urban decision makers and governance managers. Urban greening through integration of green infrastructures and nature-based solutions is globally recommended as a promising strategy to mitigate UHI and enhance urban cooling (Brown et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Gill et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The cooling effect of park has widespread benefits \u0026ndash; both tangible and intangible for the city and its people. It regulates park microclimate through natural cooling mechanisms like evapotranspiration and shading (Barghchi et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Bowler et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Apart from mitigating UHI, park cooling (PC) effect also encourages outdoor activities improving park usability, urban well-being and perceived OTC.\u003c/p\u003e\u003cp\u003eAram et al., (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) observed that residents living closer to parks in Madrid reported higher levels of perceived OTC. Their study identified a negative correlation between perceived subjective thermal comfort responses and Physiological Equivalent Temperature (PET), a commonly used biometeorological index derived from microclimatic data. Similarly, Klemm et al., (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) studied urban parks across three Dutch cities, characterized them as cool islands within the city and highlighted a positive impact of increasing tree cover on the subjective and objective thermal comfort. (Peng et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) further emphasized that enhanced greenness improves the heat exchange capacity of the parks with their surrounding urban areas. Typically, thermal comfort models are grounded in energy balance models based on the principle of heat transfer. Environmental factors (air temperature, relative humidity, solar radiation and air velocity), physiological factors (activity, clothing) and psychological factors (behaviour, perception, culture) are key determinants of OTC evaluation (Chu \u0026amp; Jong, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, modelling can be complex owing to the subjective nature of thermal comfort sensation induced by the varying choice of clothing. To simplify the models, wind speed, air temperature and humidity are often considered as the priority factors, assuming other factors as constants (Chu \u0026amp; Jong, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Apparent Temperature (AT) introduced by Steadman, (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1984\u003c/span\u003e), is a simplified, yet widely recognized index of heat stress. Other popular indices include SET (Standard Effective Temperature), UTCI (Universal Thermal Climate Index) among others (Potchter et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eA substantial body of research on PC effect focusses on index-based assessments, evaluation studies, and study of various factors affecting PC effect (Peng et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Commonly used indices for quantifying PC include PC intensity, PC distance or PC range or PC gradient, PC area, PC efficiency, and cool island intensity (Peng et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Xiong et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; J. Zhang et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Among these, PC intensity \u0026ndash; defined by the temperature difference between urban park and its surrounding built areas is the most widely used metric, typically ranging from 0.5-4\u0026deg;C (Yan et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Many researchers have also explored the spatial extent of PC, identifying \u0026lsquo;cool islands\u0026rsquo; through spatial analysis of Land Surface Temperature (LST) data (Yu et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Both park morphology \u0026ndash; size, shape and geometry (Wang et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) and landscape characteristics including vegetation density, canopy cover, leaf-area index, tree height and waterbodies play a vital role affecting PC effect. These are further influenced by broader climatic and seasonal contexts (Barghchi et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Xiong et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Among these variables, park size has emerged as a key factor in influencing park cooling potential. Numerous studies, based on simulations and validated through field measurements, have reported a positive correlation between park size and cooling effect, with larger parks exhibiting greater temperature reduction (Aram et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Lu et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Vidrih \u0026amp; Medved, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Additionally, park size shows an inverse non-linear relationship with LST where, LST decreases logarithmically as park size increases (Cheng et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Some studies point to a threshold effect, indicating that park area beyond a certain size, may not enhance the cooling effect (Algretawee, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; J. Zhang et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eNew research perspectives on park size, PC effect and OTC perception often follow a transitive assumption: larger parks possess greater cooling potential and consequently, are presumed to provide enhanced thermal comfort (Chan \u0026amp; Chau, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, this coupling of size-cooling-comfort is largely underexplored and speculative, necessitating deeper investigation. Besides, the environmental benefits of PC effect are also rarely quantified, particularly in developing countries like India. Thus, this study introduces a novel approach to estimate the park cooling capacity (PCC) by borrowing the thermodynamic concept of enthalpy which will assess the amount of heat energy absorbed by the natural systems within parks. Adopting a parsimonious modelling strategy, this study examines the relationship between PCC and OTC perception (measured using AT index) and analyses the role of park size. PC effect and PCC are interchangeably used in this study. Furthermore, this study develops a simplified framework of Park Cooling Performance Index (PCPI) to evaluate the efficiency of the PCC and examines its relationship with park size. Kolkata, an Indian metropolitan city characterized by a hot and humid tropical climate is chosen as the study area. This study has accounted the role of tree cover for their shading effect (Oliveira et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and evaluates the evapotranspiration rate of trees by estimating the equivalent energy-saving benefits due to the park cooling effects(B. Zhang et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Thus, this paper addresses the following research questions (RQ).\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eHow can park cooling capacity (PCC) be quantified using an enthalpy-based approach?\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eTo what extent does park size influence the relationship between Park Cooling Capacity (PCC) and OTC perception?\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eWhat is the nature of the relationship between park size and Park Cooling Performance Index (PCPI)?\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e"},{"header":"2. Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study Area\u003c/h2\u003e\u003cp\u003eThis study is a quantitative, cross-sectional, microclimatic analysis conducted across 30 public urban parks within the jurisdiction of Kolkata Municipal Corporation (KMC). These parks, varying in size were selected from different locations within the study area based on the study of (De et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eKMC covers an area of 206 sq.km. and is divided into 144 administrative wards, housing over 4.6\u0026nbsp;million people according to Census of India, 2011. With a population density of approximately 25,000 people per sq.km, the city holds an enormous geopolitical significance in Eastern India due to its strategic location, economic prominence and cultural heritage (Haque et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Lying close to the Tropic of Cancer at 22\u0026deg;34\u0026prime;N, 88\u0026deg;22\u0026prime;E and alongside Hooghly River, Kolkata enjoys warm and humid climate, classified as \u003cem\u003eAw\u003c/em\u003e (tropical wet and dry) under the K\u0026ouml;ppen Climate Classification (Kottek et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The city\u0026rsquo;s annual mean air temperature (T\u003csub\u003ea\u003c/sub\u003e) is around 26.8\u0026deg;C. Kolkata has three distinct seasons \u0026ndash; hot and humid summer (March-June), monsoon (July-October) and mild and moderate winter (November-February). The hottest months, May and June witness peak temperatures between 40\u0026deg;C-43\u0026deg;C, with average temperatures around 30\u0026deg;C-35\u0026deg;C and relative humidity (RH) exceeding 70%. The south-west monsoon, driven by proximity to the Bay of Bengal brings substantial rainfall during July-October, having an average monthly rainfall of 330\u0026ndash;340 mm and RH\u0026thinsp;\u0026gt;\u0026thinsp;80%, while the highest rainfall occurs during August (390mm). Winters are generally pleasant, with average temperatures ranging between 25\u0026deg;C-30\u0026deg;C and RH\u0026thinsp;\u0026gt;\u0026thinsp;60%., the lowest temperature dips to around 10\u0026deg;C-14\u0026deg;C in January. Presently, Kolkata is facing intense urban heat stress due to dense built-up areas and diminishing urban green and blue spaces. This has led to intensified UHI effects within the city, raising serious concerns about public health risks (Mandal et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; TNN, 2023). The conceptual framework behind this study is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Data collection\u003c/h2\u003e\u003cp\u003eMultiple studies have established a strong positive correlation between objective and subjective measures of OTC perception (Klemm et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Potchter et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Based on this evidence, this study did not conduct user perception surveys, but instead employed an objective metric \u0026ndash; Apparent Temperature (AT) index, to assess OTC. The AT is calculated using the Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) utilizing microclimate data collected both inside (At\u003csub\u003ein\u003c/sub\u003e) and outside (AT\u003csub\u003eout\u003c/sub\u003e) the parks respectively (Steadman, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1984\u003c/span\u003e).\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:AT={T}_{a}+0.33e-0.70v-4$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\u003c/p\u003e\u003cp\u003eWhere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{T}_{a}\\)\u003c/span\u003e\u003c/span\u003e is the air temperature (\u0026deg;C), \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:e\\)\u003c/span\u003e\u003c/span\u003e is the water vapour pressure (hPa) and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:v\\)\u003c/span\u003e\u003c/span\u003e is the wind speed (m/s). The vapour pressure (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:e\\)\u003c/span\u003e\u003c/span\u003e) is calculated using the Eq.\u0026nbsp;(\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:e=\\frac{RH}{100}*6.105*{e}^{\\left(\\frac{17.27*{T}_{a}}{237.7+{T}_{a}}\\right)}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\u003c/p\u003e\u003cp\u003eWhere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:RH\\)\u003c/span\u003e\u003c/span\u003e is the relative humidity (%).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eDescriptive statistics of the microclimatic variables and park sizes used in the study are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive statistics of the microclimatic data and park sizes\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=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;Std. Error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMedian\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStandard Deviation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA (sq.m)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e2115.17\u0026thinsp;\u0026plusmn;\u0026thinsp;519.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1156.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2846.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e235.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e14749.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT\u003csub\u003ea,in\u003c/sub\u003e (\u0026deg;C)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e23.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e23.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e19.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e27.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT\u003csub\u003ea,out\u003c/sub\u003e (\u0026deg;C)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e24.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e20.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e30.30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRH\u003csub\u003ein\u003c/sub\u003e (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e67.27\u0026thinsp;\u0026plusmn;\u0026thinsp;1.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e67.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e55.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e88.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRH\u003csub\u003eout\u003c/sub\u003e (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e67.58\u0026thinsp;\u0026plusmn;\u0026thinsp;1.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e66.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e50.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e90.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ev\u003csub\u003ein\u003c/sub\u003e (m/s)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e0.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.90\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ev\u003csub\u003eout\u003c/sub\u003e (m/s)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e0.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.20\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\u003eThe field surveys were carried out in April, 2024. Peak park-use hours were identified through a preliminary reconnaissance survey across the 30 parks. It revealed that most park users visit the park between 4\u0026ndash;6 PM. Accordingly, all microclimatic measurements were taken during this time window. Two portable, hand-held weather-monitoring devices positioned at a height of 1.2m above the ground level were used. Data collection was conducted only on clear days, excluding the cloudy or rainy conditions to maintain consistency. An anemometer (BEETECH Anemometer B-201, Range upto 30m/s) was used to monitor the wind speed (m/s) both inside and outside the park. Similarly, air temperature (\u0026deg;C) and relative humidity (%) were recorded using an HTC HD-304 Temperature Humidity Meter (Temp Range of 20\u0026deg;C-60\u0026deg;C). It was used to measure T\u003csub\u003ea\u003c/sub\u003e (\u0026deg;C) and RH (%) both inside and outside the park. Each device was operated for 30 mins at both locations and the average readings were used in the analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Estimation of Park Cooling Capacity (PCC)\u003c/h2\u003e\u003cp\u003ePark cooling load refers to the amount of heat energy passively absorbed by the park\u0026rsquo;s natural ecosystem (e.g., trees) in order to lower the ambient temperature within the park. By reducing both the latent and sensible heat, a park acts as a natural passive cooling system regulating the local microclimate. Addressing the RQ1, this study adopts the concept of enthalpy (H) from the principles of thermodynamics to estimate PCC by measuring the park cooling load in kJ using the formula mentioned in Eq.\u0026nbsp;(\u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:PCC=\\:\\rho\\:*V*\\varDelta\\:H$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\u003c/p\u003e\u003cp\u003eWhere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\rho\\:\\)\u003c/span\u003e\u003c/span\u003e is the density of air, 1.293 kg/m\u003csup\u003e3\u003c/sup\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:V\\)\u003c/span\u003e\u003c/span\u003e is the volume of air (m\u003csup\u003e3\u003c/sup\u003e) measured as the product of park area (A), tree cover (TC) %, and the height (1.2m) at which the devices were held, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:H\\)\u003c/span\u003e\u003c/span\u003e (kJ/kg) is the enthalpy difference between outside and inside the park, as shown in Eq.\u0026nbsp;(4).\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:V=\\frac{TC}{100}*A*1.2\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:H={H}_{out}-{H}_{in}\\)\u003c/span\u003e\u003c/span\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;. (4)\u003c/p\u003e\u003cp\u003eEnthalpy values (H\u003csub\u003ein\u003c/sub\u003e, H\u003csub\u003eout\u003c/sub\u003e) were derived from psychrometric chart using the measurements of T\u003csub\u003ea\u003c/sub\u003e and RH%.\u003c/p\u003e\u003cp\u003eTo address the RQ2, Pearson correlation analysis was first conducted to examine if the relationships between park size (A), PCC and ΔAT are statistically significant. Subsequently, a Partial correlation analysis was performed to determine whether park size has any mediating or confounding effect on the relationship between PCC and ΔAT.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Developing Park Cooling Performance Index (PCPI)\u003c/h2\u003e\u003cp\u003eThis study introduces a composite metric termed as Park Cooling Performance Index (PCPI) to evaluate the cooling efficiency of urban parks. PCPI represents the degree of perceived thermal comfort, indicated by reduction in apparent temperature (ΔAT), relative to the normalized biophysical cooling capacity of the park per unit area. PCPI is defined by the Eq.\u0026nbsp;(\u003cspan refid=\"Equ4\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\:PCPI=\\frac{{\\Delta\\:}\\text{A}\\text{T}}{\\left(PCC/A\\right)}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\u003c/p\u003e\u003cp\u003eTo address the RQ3, regression analysis is conducted to find whether the park size has any significant relationship with PCPI using SPSS software.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results and findings","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Measuring outdoor thermal comfort perception using AT index and assessing its relationship with park size\u003c/h2\u003e\u003cp\u003eThis study utilizes AT index to quantify objective thermal comfort perception. AT values recorded inside (At\u003csub\u003ein\u003c/sub\u003e) and outside (AT\u003csub\u003eout\u003c/sub\u003e) the parks are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, while Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb depicts the difference in AT (ΔAT) values.\u003c/p\u003e\u003cp\u003eDescriptive statistics of AT indices are highlighted in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. An average value of perceived OTC measured as ΔAT is approximately 1.62\u0026deg;C.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive statistics of the AT index measured outside and inside the parks\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=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;Std. Error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMedian\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStandard Deviation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAT\u003csub\u003eout\u003c/sub\u003e (\u0026deg;C)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e26.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e23.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e32.99\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAT\u003csub\u003ein\u003c/sub\u003e (\u0026deg;C)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e25.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e25.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e21.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e28.46\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eΔAT (\u0026deg;C)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e1.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5.41\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\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Quantifying Park Cooling Capacity (PCC) and Park Cooling Performance Index (PCPI)\u003c/h2\u003e\u003cp\u003eAddressing the RQ1, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea shows an upward trend in the distribution of PCC values across the 30 parks, organized in ascending order of park size. In contrast, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb displays a downward trend in the PCPI values for the same set of parks, arranged similarly.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e summarizes the descriptive statistics of the estimated enthalpy of parks (H\u003csub\u003ein\u003c/sub\u003e) and its surrounding built environment (H\u003csub\u003eout\u003c/sub\u003e), PCC and PCPI.\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 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive statistics of enthalpy, PCC and PCPI\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=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;Std. Error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMedian\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStandard Deviation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH\u003csub\u003eout\u003c/sub\u003e (kJ/kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e57.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e56.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e52.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e65.51\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH\u003csub\u003ein\u003c/sub\u003e (kJ/kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e54.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e54.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e47.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e59.07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePCC (kJ)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e4281.71\u0026thinsp;\u0026plusmn;\u0026thinsp;926.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1955.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5076.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e19281.44\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePCPI (unitless)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e2.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e27.40\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\u003e\u003c/p\u003e\u003cp\u003eNotably, the smallest park (P1) shows an exceptionally high PCPI value. Average value of PCC that measured cooling load of 30 parks is approximately 4281 kJ. While, average PCPI value is around 2.35, ranging between 0.16 to 28. Higher the PCPI value, greater is the cooling efficiency of the parks reflected through enhanced perception of thermal comfort.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Decoupling park size from the size-cooling-comfort linkages of urban park system\u003c/h2\u003e\u003cp\u003eFigures \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb indicate that park size has no significant correlation with OTC perception [r = -0.119, p\u0026thinsp;=\u0026thinsp;0.532], but shows a strong positive correlation with PCC [r\u0026thinsp;=\u0026thinsp;0.647, p\u0026thinsp;=\u0026thinsp;.000]. To further explore the relationship between PCC and OTC perception, Pearson correlation analysis was used, which reveals a non-significant relationship [r\u0026thinsp;=\u0026thinsp;0.252, p\u0026thinsp;=\u0026thinsp;.180], as displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec. However, this result appears counter-intuitive. Thus, a partial correlation analysis was conducted, controlling for park size, which revealed a significant positive correlation between PCC and OTC perception, [r (27)\u0026thinsp;=\u0026thinsp;0.434, p\u0026thinsp;=\u0026thinsp;.019]. Thus, this finding indicates a critical decoupling of park size from the conventionally assumed linkage between size, cooling and comfort in urban park systems.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Examining the relationship between park size and Park Cooling Performance Index (PCPI)\u003c/h2\u003e\u003cp\u003eUpon plotting PCPI against park size (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed), an inverse regression curve emerged as the best fitting model, suggesting a non-linear relationship between the two variables. It is found that park size can significantly influence PCPI [R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;32.3%, F (1,28)\u0026thinsp;=\u0026thinsp;13.341, p\u0026thinsp;=\u0026thinsp;.001]. Regression coefficients, presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, indicate a clear negative relationship such that as park size increases, PCPI value tends to decrease. This finding implies that even though large parks have greater cooling capacity, their cooling efficiency per unit area may decline with increasing size.\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 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRegression coefficients of the inverse regression model\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eUnstandardized Coefficients\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStandardized Coefficients\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003et\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSig.\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eB\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eStd. Error\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eBeta\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1 / Park size (sqm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2849.961\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e780.269\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.568\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.653\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(Constant)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.987\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.201\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.822\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.418\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":"4. Discussion","content":"\u003cp\u003eFollowing the principle of parsimony, this study captures the objective thermal comfort within urban parks by assessing the biophysical cooling capacity of green infrastructure, particularly tree cover using microclimatic measurements. In essence, the study intends to highlight the potential of urban parks in hot-humid cities to act as natural passive cooling system (The World Bank, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), as reflected through a measurable reduction in apparent temperature (ΔAT) index. Park Cooling Intensity (PCI), defined as the temperature difference between park interior and the surrounding built environment, is commonly used for estimating park cooling effect (Sun et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Xiao et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, in this study, PCC is quantified using the enthalpy-based approach which accounts for the biophysical estimation of the total heat energy exchange occurring within the park microclimate - addressing both the sensible heat (related to change in temperatures) and latent heat (related to change in humidity levels) (Chu \u0026amp; Jong, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Given Kolkata\u0026rsquo;s tropical, warm and humid climate, this inclusion of relative humidity into the otherwise simple temperature models offers a more comprehensive framework of energy dynamics involved in park cooling. Basically, the park cooling load is calculated by measuring T\u003csub\u003ea\u003c/sub\u003e (\u0026deg;C) and RH% both inside and outside each park during peak hours of park usage. Based on these microclimatic variables, specific enthalpy (kJ/kg) of air is computed for both the settings. Difference in the enthalpy values (ΔH) thus represents the total heat energy absorbed by the park natural system, serving as a proxy for biophysical cooling capacity of the park.\u003c/p\u003e\u003cp\u003eInitially, no significant correlation was found between PCC and OTC perception which seemed counter-intuitive. However, further investigation using partial correlation analysis uncovered that park size acted as a confounding variable, masking the true relationship between PCC and OTC perception. When the effect of park size was statistically controlled, a significant positive correlation emerged between PCC and OTC perception. This finding aligns with the established understanding, reaffirming that higher biophysical cooling capacity in parks contributes to improved thermal comfort perception.\u003c/p\u003e\u003cp\u003eMeanwhile, park size is significantly correlated with PCC supporting earlier findings that larger parks generally possess greater cooling potential (Algretawee, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Cheng et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). However, this study also reveals that no significant correlation exists between park size and OTC perception, hence challenging the widely held assumption that larger parks inherently enhance perceived thermal comfort (Chan \u0026amp; Chau, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Interestingly, the correlation coefficient between park size and OTC perception was found negative, suggesting a possible inverse relationship. This finding defies the expected transitive logic that larger parks have greater cooling capacity, thus having a higher perception of OTC. Instead, the study highlights that larger parks do not necessarily translate into improved thermal comfort, thereby pointing to a decoupling of the park size from the cooling-comfort relationship in urban green spaces.\u003c/p\u003e\u003cp\u003eFurthermore, the performance metric called as PCPI constructed in this study has revealed significant variance in the cooling performance of the parks across different park sizes. Interestingly, smaller parks exhibited higher PCPI values, suggesting that these parks are more efficient in translating their biophysical cooling capacity into perceived thermal comfort when normalized per unit area. This highlights that, despite their limited size, smaller parks can outperform larger ones in terms of thermal comfort efficiency, particularly in highly dense Indian metropolitan cities. Thus, the study decouples the size effect and strategizes zone-centric park design, morphological and spatial configurations, and landscape characteristics to regulate local microclimate (Brown et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Gao et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Norouzi et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study advances a nuanced understanding of the relationships between park size, PCC and OTC perception using a parsimonious approach and adds four notable contributions into the PC literature. First, the study presents a novel framework for estimating the biophysical cooling capacity of parks by integrating the thermodynamic concept of enthalpy using microclimatic data. Second, this study while confirming that larger parks possess greater cooling capacity, challenges the prevalent supposition that greater size enhances thermal comfort perception \u0026ndash; indicating at the decoupling effect of size from size-cooling-comfort linkages. Third, employing partial correlation analysis, the study reveals that size acts as the confounding variable masking the true relationship between PCC and OTC perception. Lastly, this study presents a new performance metric, PCPI that bridges thermal comfort perception with the biophysical cooling capacity of the park. Interestingly, small-sized urban parks exhibit better cooling performance and efficiency compared to the larger ones, particularly in hot-humid environment. Infact, this study is particularly relevant in highly dense urban areas of developing countries which are struggling with ever-rising heat stress and rapid decline of public urban green spaces. Thus, urban greening policies should aim at improving the small-sized parks which possess a great potential to induce passive cooling of urban local microclimate improving urban climate resilience. The insights further demand the attention of policymakers and urban planners to address the paradigm shift from size-centric park cooling strategies to park design-driven performance strategies encouraging optimized, efficient and compact park greening.\u003c/p\u003e\u003cp\u003eFuture research can explore the relationship between PCC and OTC perception using real-time environmental monitoring, offering new insights. It will be interesting to observe the impact of potential temporal variations on park cooling performance by undergoing longitudinal studies across different seasons. Besides, the current findings may be further substantiated through advanced simulations incorporating multiple explanatory variables. While, this study has focussed primarily on the confounding effect of park size, future investigations can explore the influence of other factors such as park morphology, park amenities and facilities, landscape composition, etc. to develop a more holistic understanding of the drivers of thermal comfort in urban parks.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDisclosure Statement:\u0026nbsp;\u003c/strong\u003e\u003cem\u003eThe authors report there are no competing interests to declare\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eFunding:\u003c/strong\u003e\u003cem\u003e\u0026nbsp;The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e\u003cem\u003e\u0026nbsp;The authors have no relevant financial or non-financial interests to disclose.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAuthors\u0026rsquo; contributions\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSulagna De\u003c/strong\u003e \u0026ndash; Conceptualization, Methodology, Formal Analysis, Data Curation, Writing - Original Draft; \u003cstrong\u003eArup Das\u003c/strong\u003e \u0026ndash; Conceptualization, Supervision, Validation, Writing - Review \u0026amp; Editing; \u003cstrong\u003eTarak Nath Mazumder\u003c/strong\u003e \u0026ndash; Supervision, Validation\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlgretawee, H. 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A study of size threshold for cooling effect in urban parks and their cooling accessibility and equity. \u003cem\u003eScientific Reports\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(1), 16176.\u003c/li\u003e\n\u003c/ol\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":"Park Cooling Capacity, Outdoor Thermal Comfort, Apparent Temperature, Enthalpy, Park Cooling Performance Index, Microclimate","lastPublishedDoi":"10.21203/rs.3.rs-7370179/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7370179/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUsing parsimonious approach, this study develops a novel framework integrating outdoor thermal comfort (OTC) perception and biophysical park cooling capacity (PCC). OTC perception is measured through Apparent Temperature reduction (ΔAT) between park and its surrounding built environment using microclimatic data of temperature, relative humidity and wind speed. Given the established positive correlation between subjective and objective thermal comfort measures in prior research, this study adopts ΔAT as a proxy for OTC. Unlike predominant models that rely on simple temperature differences (e.g. park cooling intensity), this study employs an enthalpy-based method to estimate PCC. This method accounts for both sensible and latent heat exchanges, providing a more comprehensive estimation of park cooling load. This study confirms that larger parks possess greater cooling capacity. However, it contests the transitive assumption that increased park size enhances OTC perception. This study also points to a decoupling effect of park size, since it does not correlate significantly with OTC perception. Moreover, partial correlation analysis further reveals that park size acts as a confounding variable, masking the true relationship between PCC and OTC perception. To better assess the cooling efficiency of urban parks, the study develops a Park Cooling Performance Index (PCPI) by evaluating OTC perception relative to PCC per unit area. Notably, small-sized urban parks outperformed larger ones. These insights thereby address a paradigm shift in urban greening strategies. Prioritizing and designing compact, vegetated, small-sized urban parks as natural cooling systems can improve local microclimate and climate resilience, particularly in highly-dense, hot-humid, tropical Indian cities.\u003c/p\u003e","manuscriptTitle":"Decoupling Size-Cooling-Comfort linkages: A parsimonious approach for analysing biophysical cooling capacity and outdoor thermal comfort perception in public urban parks","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-02 06:30:58","doi":"10.21203/rs.3.rs-7370179/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":"ac96ddaa-3d7e-4e84-b521-35f5ead98923","owner":[],"postedDate":"September 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-21T07:33:43+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-02 06:30:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7370179","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7370179","identity":"rs-7370179","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}