Urban Greenery for Health: Mitigating Heat Stress in the UAE Labor Settlements

Urban Greenery for Health: Mitigating Heat Stress in the UAE Labor Settlements | 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 Urban Greenery for Health: Mitigating Heat Stress in the UAE Labor Settlements Reham Abdelwahab, Wael Sheta, Mariam El Hussainy, Sahar Abdelwahab This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6843478/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 This study examines the impact of urban greenery on mitigating heat stress in labor settlements in Dubai, using parametric simulations in the Rhino/Grasshopper framework. The study assesses the impact of tree shape, park dimensions, and building spacing on the Universal Thermal Climate Index (UTCI) and Mean Radiant Temperature (MRT) through the integration of Ladybug and Honeybee tools. Key findings indicate that tree geometry significantly influences thermal comfort, with elliptical and spherical canopies decreasing UTCI by roughly 3 to 4°C for each 10% increase in canopy density, whereas palm trees have minimal cooling capacity due to their thin foliage and height. Multi-variable optimization determined optimal tree density ranges (5 to 7 trees per 200 m²) and park size ratios (H/W up to 1:5) for optimal cooling effect. The surface temperature predictions were validated using on-site infrared thermography, yielding a root mean square error (RMSE) of 3.72°C for asphalt and 3.34°C for pavement, thereby affirming the dependability of the simulation framework. The findings provide practical recommendations for urban planners and landscape specialists to enhance climate resilience and thermal comfort in labor settlements in Dubai, in accordance with the UN Sustainable Development Goals (SDGs) for climate action and urban sustainability. This study presents an optimization approach that evaluates the crown geometries of tree-native species and improves the predicted accuracy of UTCI mitigation tactics in hot-arid regions using greenery and passive strategies. Urban greenery Labor accommodation Vegetation Health and wellbeing UTCI Dubai. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Labor accommodation complexes in Dubai play a significant role in shaping the health and well-being of the migrant workforce, which constitutes a substantial portion of the city's population. These complexes are designed to provide housing for low-wage workers, primarily in the construction and service sectors, and their living conditions can have profound implications for both physical and mental health (Nassar et al., 2014 ). Poorly designed accommodations can lead to overcrowding, inadequate ventilation, and insufficient access to natural light, all of which can contribute to negative health outcomes (Harb et al., 2015 ). For instance, the lack of daylight in living spaces can lead to psychological stress and exacerbate feelings of isolation among workers (Sheta et al., 2025 ). Workers often face high levels of stress due to demanding work conditions, long hours, and the pressure to meet performance targets, which can be compounded by the isolation experienced in labor camps (Jeung et al., 2018 ). Exposure to extreme outdoor thermal conditions-both heat and cold- can lead to significant physiological stress, fatigue, mood disturbances, in addition to exacerbate mental health issues, including anxiety and depression (Al Hurini et al., 2024 ; Bacha et al., 2024 ; Rastegar et al., 2022 ). In rapidly urbanizing cities like Dubai, where labor accommodations constitute a significant portion of the residential landscape, understanding the outdoor thermal comfort of these environments is crucial for ensuring the health, well-being, and productivity of workers. However, studies concerning outdoor thermal comfort in residential campuses have predominantly focused on educational settings such as schools and university campuses. To the authors' knowledge, no investigations have yet explored outdoor comfort within the specific microclimates of labor accommodation campuses (Das et al., 2025 ; Eslamirad et al., 2022 ; Ghaffarianhoseini et al., 2019 ; Huang et al., 2019 ). There is a growing recognition of the need for enhancing the living conditions and standards for labor accommodation in Dubai (Akmal et al., 2024 ; Sheta et al., 2025 ). Integrating green spaces and community-building activities within labor complexes can foster a sense of belonging and improve the overall quality of life for residents (Reber, 2021 ). In this context, the fundamental aim of this study is to investigate different configurations of vegetation and green urban spaces that can be strategically positioned within and around labor accommodation urban forms to mitigate the harsh outdoor thermal conditions prevalent in Dubai’s climate. By examining various design strategies, such as tree canopy coverage, green corridors, shaded courtyards, and vegetative buffers, the study seeks to identify effective solutions that enhance outdoor thermal comfort, reduce heat stress, and ultimately contribute to healthier and more livable environments for labor workers. 2. Literature Review 2.1 Labor accommodation complexes within Dubai’s urban fabric One of the defining features of Dubai's urban fabric is its rapid urbanization, which has been analyzed through various studies. Dubai experienced a remarkable rate of urban growth influenced by both local and global factors, resulting in increased vegetation cover and the development of inland water bodies in this hyper-arid environment (Nassar et al., 2014 ). This urbanization has also led to the emergence of Urban Heat Islands (UHIs), which are exacerbated by the city's dense built environment and limited vegetation (Bolleter et al., 2021 ; Nassar et al., 2016 ; Rasul et al., 2017 ). Labor accommodation complexes in Dubai represent a critical aspect of the city's urban fabric, reflecting the socio-economic dynamics and the rapid urbanization that has characterized the region over the past few decades (Nassar et al., 2014 ). Labor accommodation complexes are not merely functional spaces; they also embody the socio-political context of Dubai's rapid urbanization. Alawadi discusses how the urban form of Dubai has led to significant gaps between developments, with labor camps often situated far from the urban core, contributing to a fragmented urban environment (Alawadi, 2017a , 2017b ). This spatial arrangement raises concerns about the living conditions of workers, who may face long commutes to their workplaces and limited access to essential services and amenities. The design and regulation of these labor camps have been scrutinized, particularly regarding their environmental performance and the quality of life for residents (Sheta et al., 2025 ). Moreover, the implications of labor accommodation extend beyond mere housing; they reflect the broader issues of labor rights and social equity in Dubai's development model. The reliance on a transient workforce has led to criticisms regarding the treatment of laborers, with calls for improved living conditions and greater integration into the urban fabric (Hamza, 2015 ; Le et al., 2019 ). An in-depth examination of the urban fabric of labor accommodations spread across various parts of Dubai like Jabel Ali and Al-Aquoz reveals a noticeable lack of green spaces and vegetation integrated within these developments (Mohammed et al., 2023 ). The layouts are characterized by dense, compact building forms with minimal consideration for open or landscaped areas, contributing to a harsh and uninviting outdoor environment (Sheta et al., 2025 ). 2.2 The role of urban greenery in enhancing outdoor thermal comfort Vegetation contributes to achieving thermal comfort as it decreases the short and longwave radiation fluxes impinging on the pedestrian and can also reduce the outdoor air temperature to a significant degree, depending on the plant species and location (Coccolo et al., 2018 ; Sousa-Silva et al., 2025 ). Having a substantial tree cover in an urban setting can lower mean radiant and air temperatures during a heat wave compared to treeless streets (Ettinger et al., 2024 ; Razzaghmanesh et al., 2021 ). Empirical investigations were made during the hottest periods in the city of Biskra, Algeria. The results confirmed the importance of urban green spaces and their positive impact on outdoor thermal comfort, and the good behavior of users, recommending the “Ficus” type in particular, because of its proven physical and psychological benefits for people (Khadraoui et al., 2022 ). The tree types, geometry and properties vary according to the region, species, season (Yang and Kan, 2022 ). The selection of native plant species is particularly advantageous, as it enhances adaptability, minimizes maintenance demands, and aligns with water conservation needs in arid regions (Chen et al., 2025 ). The effectiveness of urban greening in enhancing outdoor thermal comfort has been highlighted, as demonstrated through the use of thermal comfort mapping tools (Coccolo et al., 2018 ). Several studies have investigated outdoor thermal comfort in Dubai through case-based research. One such study focused on enhancing pedestrian-level thermal comfort within a Local Climate Zone (LCZ) in Dubai by implementing various cooling interventions, including vegetation, architectural modifications, and changes to pavement material color (Korkut & Rachid, 2024 ).Urban geometry and surface characteristics are the two primary factors influencing local climatic conditions (Rahmani & Sharifi, 2025 ). Among these, the height-to-width ratio (H/W)—which represents the proportion of building height (H) to the spacing between structures (W)—plays a crucial role in shaping urban geometry (Ibrahim et al., 2021 ). This ratio significantly impacts microclimatic parameters, including solar radiation absorption and reflection, heat retention, and airflow patterns. A higher H/W ratio can lead to reduced wind speed and limited solar exposure at street level, contributing to the urban heat island (UHI) effect and influencing thermal comfort (Cui et al., 2023 ). Conversely, lower H/W ratios allow for greater ventilation and daylight penetration, potentially mitigating overheating in dense urban environments. Understanding these relationships is essential for designing climate-responsive urban spaces that balance thermal comfort, energy efficiency, and environmental sustainability. Trees geometry under hot climates, the height-to-width ratio showed to effectively reduced maximum temperature (Elkhayat et al., 2025 ; Liu et al., 2025 ). Dubai’s urban planning guidelines prioritize sustainability, greenery, and microclimate control, as demonstrated by the Dubai Green Building Regulations and Specifications (DGBR). These regulations promote green roofs, urban landscaping, and design strategies to reduce heat buildup and improve outdoor comfort (Abu-Hijleh & Jaheen, 2019 ). Enhancing walkability is also emphasized, with public spaces designed for usability in extreme heat. Overall, these measures aim to reduce heat stress, improve thermal comfort, and integrate vegetation into the urban fabric of Dubai (Awadh, 2018 ). 3. Methodology 3.1 Case study The selected case study exemplifies a typical labor camp in Dubai, situated in the first district of Jebel Ali, approximately 35 kilometers from Burj Khalifa. The labor camp comprises a ground floor and four identical upper storeys, reaching a total height of 18.0 meters, with an 8.00-meter spacing between adjacent structures. The urban fabric illustrated in Fig. 1 comprises densely packed accommodation blocks with minimal setbacks, prioritizing built-up area over environmental quality. This compact layout, devoid of open areas and greenery, limits natural ventilation and shade, exacerbating thermal discomfort in Dubai's severe climate. The hourly boundary conditions for the simulations were derived from the Typical Meteorological Year-extended (TMY) dataset produced by for Dubai World Central / Al Maktoum International Airport (WMO ID 411945; base year 2009–2023). The UTCI thermal stress analysis utilizing TMY data indicated the period from July 20 to July 26 as the hottest week of the reference year, with July 23 from 10:00 to 15:00 recording the peak composite of dry-bulb temperature, global horizontal irradiance, and mean radiant temperature. 3.2 Study’s parameters This study utilizes a parametric approach to assess the thermal and microclimatic performance of urban parks encircled by homogeneous building configurations. The methodology seeks to uphold uniform boundary conditions, thereby isolating the impacts of park design and tree selection on outdoor comfort. The parametric framework has two main categories of variables: park design parameters and tree selection parameters, each delineated by specific ranges to encompass a wide array of alternative urban layouts. Park design variables encompass the Distance between Buildings (DbB) and the Park Size Ratio (PSR). The DbB parameter examines the effects of varied distances between adjacent buildings, from 25m to 100m, to evaluate its impact on microclimate regulation and overall thermal comfort in the park area. The Park Size Ratio (PSR), which is the ratio of park area to the total available space between buildings, varies from 0.2 to 0.9, with a value of 1 indicating that the park occupies the entire area between the buildings. This top limit is restricted by practical factors, including pedestrian routes and adjacent infrastructure, which inhibit total park coverage in actual environments. Tree selection variables emphasize the significant impact of vegetation in altering outdoor temperature conditions. This category encompasses Tree Coverage Density (TCD) and Weighted Tree Shape (WTS). TCD is determined by the density of trees per unit area, normalized to a ratio per 200 m² to enable reasonable suggestions and eliminate fractional values in design standards. This standardization guarantees clarity and relevance in forthcoming practical applications and policy formulation. The WTD metric quantifies the average coverage contribution of each tree variety in the park, considering variations in canopy geometry and shading efficacy. The study's selection of tree shapes emphasizes adapted, native species to correspond with regional ecological objectives and sustainable landscape design concepts, geometric shapes and key attributes. Essential characteristics, including canopy radius, tree height, crown morphology, and planting density, are delineated, as these elements are vital for assessing the trees' capacity to improve outdoor thermal comfort and elevate environmental quality in urban settings. To ensure consistent analysis, the simulation environment employs standardized dimensions, typology, and orientation for buildings and parks, thereby reducing variability in solar exposure and wind flow. Locally adjusted meteorological files are used to accurately reflect microclimatic conditions for precise thermal and airflow simulations. Figures 2 illustrates the key parameters of the study, including tree geometries derived from indigenous UAE species. These are categorized into three distinct crown shapes. Triangulated models used for Radiance-based simulations accurately represent canopy structures and their interaction with light, with leaf density defined by a transmission factor reflecting observed foliage characteristics. This study utilizes a suite of validated environmental simulation tools. Honeybee and Ladybug, incorporated inside Rhino/Grasshopper, are esteemed for their dependability in analyzing solar radiation, daylight, and thermal comfort, particularly in temperate and hot-arid settings (El-Bahrawy, 2023 ; Nicholson et al., 2024 ). Radiance and EnergyPlus, renowned for their accuracy in light and energy modeling, are combined through ClimateStudio for improving computational efficiency and prediction precision (Abdelwahab et al., 2025 ). This methodology enhances the accuracy of computations for Mean Radiant Temperature (MRT) and the Universal Thermal Climate Index (UTCI). The analysis employed a two-phase methodology: initially, evaluating each parameter in isolation to determine its impact on outdoor comfort; subsequently, eliminating poor values associated with elevated UTCI readings to concentrate on more efficient design configurations. To further enhance the optimization process and cases sample selection, a genetic algorithm was implemented using the Galapagos plugin within Grasshopper, facilitating the generation of a diverse and representative sample set spanning high, medium, and low parameter ranges. Given the extensive parameter space, with potential combinations exceeding 23 million, this evolutionary approach developed on Darwinian principles of natural selection was employed for efficiently converging on near-optimal solutions. 3.3 Simulation validation The suggested workflow integrates multiple parameters that affect the urban-scale microclimate, with surface temperature being crucial due to its significant influence on mean radiant temperature (MRT) via changes in thermal radiation. The surface temperature is influenced by various meteorological conditions, rendering its precise simulation essential for authentic environmental modeling. This study conducted surface temperature calibration at a designated moment during the research period to evaluate the workflow's efficacy in capturing urban and environmental heat transfer dynamics. Infrared thermography, utilizing the FLIR E40 thermal camera, was applied to record surface temperatures at two distinct periods on the same day. The calibration procedure sought to enhance surface temperature precision by juxtaposing simulated values with real-time data from a local weather station that supplied Actual Meteorological Year (AMY) data documented during field observations. Figure 3 displays representative infrared photos obtained from the FLIR E40, illustrating the temperature disparity between pavement and asphalt surfaces. Table 1 Comparison between simulated and observed surface temperature values for calibration. Parameters Real Measure Simulated Measured 11:00 a.m. 3:00 p.m. 11:00 a.m. 3:00 p.m. Air Temperature C 22 24 20 21 Air Speed (global) m/s 5.8 9.8 7.7 9.8 Air Speed (local) m/s at surface level 0.8 1.36 1.03 1.36 Ground Temperature C Asphalt = 32, Pavement = 27 Asphalt = 25 Pavement = 21 Asphalt = 27.13, Pavement = 23 Asphalt = 27, Pavement = 23.5 Surface Temperature model calibration. Surface Type RMSE (°C) MAE (°C) Bias (°C) Asphalt 3.72 3.44 -1.44 Pavement 3.34 3.25 -0.75 Table 1 compares simulated and actual surface temperature values for calibration, while Table 2 presents the calibration data, revealing a Root Mean Square Error (RMSE) of 3.72°C, a Mean Absolute Error (MAE) of 3.44°C, and a mean bias of − 1.44°C for asphalt surfaces. The RMSE for pavement surfaces was 3.34°C, the MAE was 3.25°C, and the mean bias was − 0.75°C. The negative bias values indicate a persistent underestimating of surface temperatures by the simulation model. These discrepancies correspond with previous research in urban microclimate modeling, emphasizing the impact of factors such as material emissivity, surface roughness, and subsurface heat flux representation on the precision of surface temperature predictions (Azam et al., 2025 ; Salamanca et al., 2010 ). The (UTCI) integrates air temperature, humidity, wind velocity, and (MRT) into a singular metric to evaluate outdoor thermal comfort. The simulation techniques employed in this study do not explicitly describe evaporation; yet, its impacts are manifested in surface conditions. The (MRT) is computed utilizing radiation statistics modified for shade, sky-view factor, and urban morphology. Wind speed is recorded at 1.8 meters and adjusted for local conditions. Simulations assumed dry grass surfaces to conservatively evaluate cooling effects. This approach enhances (UTCI) computations and guides design options to mitigate heat stress in hot-arid urban environments. 4. Results and Discussion 4.1 Distance between Buildings (DbB) A total of 76 simulation runs were conducted to assess the Universal Thermal Climate Index (UTCI) and Mean Radiant Temperature (MRT) in relation to the distance between buildings (DbB) within the outdoor areas of the labor campus. The results, illustrated in Fig. 4 -a, demonstrate a positive correlation between DbB and both UTCI and MRT values; as the distance between buildings increases, levels of thermal exposure also rise. This trend reflects the influence of park size, where expanded open areas lead to increased DbB ratios. The associated reduction in shading from adjacent buildings results in greater exposure to direct solar radiation, thereby elevating ground-level heat loads and contributing to higher thermal stress in the outdoor environment. Notwithstanding these observations, surface temperature fluctuations remained comparatively insignificant across the evaluated scenarios. The impact of DbB on UTCI was most significant up to a threshold of around 50 meters, which aligns with a height-to-width (H/W) ratio of 1:3.3. Beyond this threshold, additional increments in DbB yielded minimal enhancements in UTCI values, indicating a point of diminishing returns for outdoor thermal comfort improvement through increased spatial separation. 4.2 Park Size Ratio (PSR) A series of microclimate simulations were undertaken to evaluate the impact of Park Scale Ratios (PSR) and different park sizes on local thermal dynamics, while keeping a consistent inter-building distance of 100 meters (H/W = 1:6). The findings, illustrated in Fig. 4 -b, demonstrated that the correlation between PSR and both the Universal Thermal Climate Index (UTCI) and Mean Radiant Temperature (MRT) exhibited an inverted parabolic pattern, with only modest temperature variations noted among the different configurations. This restricted variance is probably due to the shading effects of adjacent structures, which become more pronounced as park area increases, consequently diminishing solar heat gain. The improvements in UTCI were relatively modest; however, the results underscore the essential influence of vegetation density and spatial distribution on enhancing outdoor thermal comfort conditions. 4.3 Tree Coverage Density (TCD) A comprehensive parametric analysis was performed using 1,923 simulation scenarios, each differing in Tree Coverage Density (TCD) and classified into three specific canopy geometries: elliptical/umbrella-like (Elip), spherical (Sph), and pyramidal (Py). Simulations were conducted separately for each tree geometry to assess the influence of canopy shape and density on outdoor thermal comfort, as quantified by the Universal Thermal Climate Index (UTCI) and Mean Radiant Temperature (MRT). Figure 4 -c illustrates a comparative analysis of UTCI values among various tree shapes and densities. The results demonstrate that high-density elliptical canopies attain a UTCI reduction of roughly 2°C in comparison to spherical forms and nearly 4.5°C relative to pyramidal structures, such as palm trees, at equal densities. The continuously low cooling effect of pyramidal geometries at all density levels underscores their restricted ability to mitigate outside thermal stress in comparison to elliptical or spherical canopy shapes. A regression analysis was performed to investigate the correlation between tree geometry at varying densities and the UTCI (see Table 2 ). This investigation seeks to measure the impact of differences in tree geometry density on outdoor thermal comfort levels. All three tree geometries exhibit a robust negative association between canopy density and UTCI, with Pearson correlation coefficients (r) of − 0.81 or lower. This indicates a statistically significant and strong linear correlation, wherein greater canopy density correlates with less thermal stress. Among the examined geometries, the elliptical (umbrella-like) tree form demonstrates the most significant decrease in both UTCI and MRT, surpassing the palm and spherical forms in thermal mitigation. Significantly, for umbrella-shaped and spherical configurations, a 10% increase in canopy density results in a UTCI decrease of roughly 3 to 4°C. The continuously elevated Pearson correlation coefficients (r > 0.8) across all geometries affirm a robust inverse link between canopy density and UTCI, highlighting the efficacy of denser foliage in improving outdoor thermal comfort. The analysis of these results indicates that in park design when micro-climate mitigation is essential, preference should be accorded to elliptical, umbrella-like, or spherical geometries. Palms may continue to serve useful roles in landscape design for wayfinding or aesthetic purposes; but, depending on them only for outdoor thermal comfort would necessitate unfeasibly high planting densities, resulting in increased costs without significant effects on UTCI or outdoor human comfort. Table 2 Regression analysis of the relationship between tree geometry density and UTCI. Geometry Density–UTCI slope (°C per unit density) Pearson r Interpretation Elliptical (umbrella-like) -3.94 -0.85 Strong cooling benefit: UTCI drops almost 4°C when density rises from 0 to 100%. Sphere -3.55 -0.87 Similar strong effect: compact crowns cool nearly as well as elliptical ones. Pyramidal (palm tree) -0.83 -0.81 Only a modest drop: sparse palm crowns provide limited shade, so UTCI stays high even as planting density increases. 4.4 Weighted Tree Shapes Density (WTD) Simulation analyses were performed to assess the efficacy of various tree geometries under differing canopy density distributions, aiming to identify the optimal weighted tree density (WTD) for each geometry. Various amounts of canopy coverage were evaluated to guarantee the consistency and dependability of the proposed average WTD. The analysis also considers changes in tree spacing. Figure 5 depicts 500 simulated instances utilizing parallel coordinates charts to examine the relationships between various tree canopy densities and their aggregate effect on outdoor thermal comfort. Each scenario exemplifies a distinct combination of tree geometries and densities, with the weighted average canopy density calculated for each tree species. The optimization analysis verifies that the optimal thermal performance, indicated by the bold red line, is achieved when the overall canopy covering consists solely of umbrella-like or elliptical-shaped trees at 100% density, excluding all other tree geometries. This resulted in UTCI = 41°C, MRT = 33°C, and a sky factor of 20%. A linear regression analysis was performed to identify the most efficient tree densities, both collectively and individually, for mitigating heat stress in outdoor contexts. Trees exhibiting spherical geometry yield the greatest significant cooling impact (β = -0.46, p < 0.001). This suggests that dense, spherical canopies are especially efficient in mitigating heat stress in urban settings. Elliptical trees demonstrated a moderate yet statistically significant cooling effect (β = -0.32, p = 0.003), indicating they offer shading advantages, albeit less consistently than spherical trees, resulting in a diminished reduction in UTCI. Conversely, pyramidal-shaped trees, including palm trees, had a positive coefficient (β = +0.88, p < 0.001), indicating that their tall, slender form and restricted canopy coverage provide negligible shading. As a result, they correlate with heightened sun exposure and a concomitant increase in UTCI. The regression analysis demonstrates a distinct hierarchy in the cooling efficacy of various tree types based on their average density. Spherical trees yield the greatest reduction in UTCI, whereas elliptical trees offer a significant decrease when paired with other tree forms. Pyramidal trees, conversely, correlate with elevated UTCI values, suggesting that their morphology may exacerbate heat stress rather than mitigate it, even when present in higher densities with other tree forms. These findings emphasize the significance of choosing tree types and distribution priorities according to their thermal performance to enhance environmental comfort. 4.5 Multi-variable optimization impact A Genetic Algorithms (GAs)-based optimization framework was utilized to determine the optimal integration of design factors, concurrently improving all variables to improve thermal performance. The system performed a comprehensive search across all study parameters, assessing 108,240 scenarios chosen from a larger pool of approximately 24 million potential combinations. The significant decrease in search scenarios and time was accomplished by excluding unpromising configurations from previously tested single variables to expedite convergence to ideal solutions. The optimization method concluded after producing 600 candidate solutions, considered adequate to represent a substantial sample size of possibly ideal configurations. This approach ensured computational efficiency while preserving the dependability of the optimization results for generalized recommendations. Parallel coordinates plots were utilized to illustrate the ideal configurations concerning all altered parameters and output measures. The red line in Fig. 8 highlights the configuration corresponding to the lowest recorded UTCI, signifying the most efficient scenario among the simulated cases. This optimal configuration was repeatedly observed across diverse parameter ranges, indicating that analogous low-UTCI outcomes can be attained through varied combinations of tree geometry and canopy density. Figure 6 at the bottom depicts a subset of the chosen lowest UTCI scenarios. The findings indicate that the ideal tree density for enhancing thermal comfort is 5 to 7 trees per 200m² for mixed tree configurations intersecting at the park's center, or 3 to 4 trees per 200m² for elliptical tree crowns alone. This density offers optimal shade covering while increasing space efficiency, hence assuring sufficient cooling. Furthermore, the study suggests that the optimal park area is 0.6 times the space between two structures. The optimal ratio of building height to park width is between 1:5.3 and 1:4 meters, achieving a balance between solar exposure and shade provision. Table 3 presents a summary of the best performing situations. Table 3 Recommended urban greenery for practical applications. Design Variable Evidence-based rule of thumb Distance between Buildings (DBB) (H:W) Grass with no trees: ≤ 50 m, (H/W ≤ 1:3.3) Trees : ≥ 50 m, (H/W ≤ 1:5.3) Park Size Ratio (PSR) Green area ≤ 0.6 × gap area Tree Coverage Density (TCD) 5–7 trees per 200m 2 Weighted Tree Shapes Density (WTD) 60% Elliptical or umbrella like tree, 40% Spherical tree, or 100% Elliptical tree with medium leave density or higher with Leaf Area Index (LAI) > 3 The optimization results demonstrate a substantial decrease in temperature attributable to enhanced tree density and spatial configuration. The optimization analysis of tree distribution yielded a 1 to 2°C decrease in UTCI relative to grass-covered regions between buildings devoid of trees, and a 7°C decrease compared to asphalt-covered areas. Figure 7 depicts the optimal arrangement of tree distribution throughout the park, demonstrating that central positioning of trees optimizes shade coverage while minimizing shadow cast by buildings. This study provides significant insights into enhancing urban microclimates in hot-arid regions by climate-responsive landscape design, particularly inside labor housing environments. The study employs a multi-variable optimization framework to examine key factors influencing outdoor thermal comfort, such as DbB, PSR, and TCD. The positive link between heightened DBB and increased UTCI and MRT is consistent with previous findings by (Tamaskani Esfehankalateh et al., 2021 ), indicating that dense urban configurations intensify thermal stress. This analysis establishes a threshold DbB of 50 meters, beyond which additional separation results in declining thermal advantages, corroborating the findings of (Ali-Toudert & Mayer, 2007 ). This suggests that additional shade structures, such as pergolas, ought to be implemented in larger areas to reduce solar exposure. The study also substantiates an inverted parabolic correlation between PSR and thermal comfort, aligning with findings by (Wang et al., 2025 ), suggesting that parks of moderate size (about 50% open space) provide optimal cooling. This underscores the significance of vegetative arrangement and shading distribution throughout the day, contrary to the assumptions favoring larger parks. A significant contribution is the examination of tree crown geometry. Spherical or umbrella-shaped canopies exhibited the most significant cooling impact, but pyramidal forms, such as palm trees, offered minimal respite. The results underscore the importance of canopy morphology and density in the selection of tree species for hot-arid climates, corroborating previous findings (Li et al., 2023 ). Moreover, the examination (WTD) indicates that a composition of 60–80% elliptical and 20–40% spherical tree forms optimally reduces UTCI, consistent with the findings of (Tamaskani Esfehankalateh et al., 2021 ). The study's originality arises from its integrated application of Genetic Algorithms (GAs) to investigate the synergistic effects of several landscape characteristics. This approach enhances existing techniques by effectively optimizing design configurations across millions of parameter combinations, transcending the single-variable emphasis prevalent in much current research. Furthermore, it introduces an innovative computational methodology to improve the precision of surface temperature and UTCI forecasts, tackling the shortcomings of conventional parametric models that frequently neglect thermal complexity. The study supports climate equality and social participation by endorsing thermally resilient landscape designs in economically disadvantaged places. Notwithstanding its merits, the study possesses limitations. The impacts of moisture, including evapotranspiration and variations in humidity, were not incorporated into the model, likely leading to an underestimation of vegetation's cooling effect. The presumption of uniform tree distribution may restrict applicability, as regional variability affects shade and wind dynamics. Subsequent research ought to integrate localized wind simulations and consider the albedo, heat retention, and permeability of hardscape materials to deliver a more thorough thermal evaluation. 5. Conclusion The findings correspond with established studies and contribute fresh perspectives, especially on multi-variable optimization in landscape design. The results substantially advance sustainable urban design frameworks that improve environmental and human well-being, providing a practical roadmap for mitigating urban heat islands in hot arid regions. The study presents a new methodological pathway for the precise prediction of UTCI, which combines Radiance and Energy-Plus computational tools with on-site infrared thermography for validation purposes. This unique Parametric-Ladybug workflow addresses significant deficiencies in species-specific cooling performance, park-scale optimization, and integrated multi-parameter analysis, providing practical design guidelines for sustainable urban environments. The optimization analysis indicates that tree geometry substantially influences thermal comfort, with elliptical and spherical canopies decreasing UTCI by roughly 3 to 4°C for each 10% increase in canopy density, whereas palm trees exhibit minimal cooling capability due to their sparse foliage and height. Conversely, multi-variable optimization analysis indicates that the best tree density for maximizing the cooling impact is 5 to 7 trees per 200m² for mixed tree geometry, or 3 to 4 trees per 200m² when utilizing just elliptical tree crowns. The analysis reveals that the ideal park scale area is 0.6 times the space between two buildings. The ideal ratio of building height to park width is determined to be between 1:5.3 and 1:4 meters, achieving a balance between solar exposure and shade provision. The optimization of tree distribution led to a reduction in the Universal Thermal Climate Index (UTCI) by roughly 1 to 2°C compared to grass-covered areas devoid of trees, and by as much as 7°C in relation to asphalt-covered areas between buildings. The suggested solutions provide avenues to augment various commercial and recreational activities in hot arid regions, thus fostering enhanced community well-being, economic vitality, and labor productivity. Declarations Author Contribution Conceptualization and Research Design: All authors• Methodology Development and Modeling: [Author 1, Author 4]• Data Collection and Analysis: [Author 1, Author 3]• Interpretation of Results and Reporting: [Author 1, Author 4]• Writing – Original Draft Preparation: All authors• Writing – Review and Editing: [Author 2, Author 4]• Final Approval of the Manuscript: All authors References Abdelwahab, R. A., Fekry, A. A., & Hamed, R. E.-D. (2025). The effective landscape design parameters with high reflective hardscapes: Guidelines for optimizing human thermal comfort in outdoor spaces by design -a case on hot arid climate weather. Computational Urban Science , 5 (1). https://doi.org/10.1007/s43762-025-00186-w Abu-Hijleh, B., & Jaheen, N. (2019). Energy and economic impact of the new Dubai municipality green building regulations and potential upgrades of the regulations. 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Outdoor thermal comfort and adaptive behaviors in a university campus in China’s hot summer-cold winter climate region. Building and Environment , 165 , 106414. https://doi.org/10.1016/j.buildenv.2019.106414 Ibrahim, Y., Kershaw, T., Shepherd, P., & Elwy, I. (2021). A parametric optimisation study of urban geometry design to assess outdoor thermal comfort. Sustainable Cities and Society , 75 , 103352. https://doi.org/10.1016/j.scs.2021.103352 Jeung, D.-Y., Kim, C., & Chang, S.-J. (2018). Emotional Labor and Burnout: A Review of the Literature. Yonsei Medical Journal , 59 (2), 187. https://doi.org/10.3349/ymj.2018.59.2.187 Joshi, R. Kr., Gupta, R., Mishra, A., & Garkoti, S. C. (2024). Seasonal variations of leaf ecophysiological traits and strategies of co-occurring evergreen and deciduous trees in white oak forest in the central Himalaya. Environmental Monitoring and Assessment , 196 (7), 634. https://doi.org/10.1007/s10661-024-12771-3 Khadraoui, M. A., Guedouh, M. S., Besbas, S., Saraoui, S., & Sriti, L. (2022). Impact of Urban Green Spaces on Outdoor Thermal Comfort and Psychological Behavior of Users during the Hottest Period in Biskra City. International Journal of Innovative Studies in Sociology and Humanities , 7 (7), 30–43. https://doi.org/10.20431/2456-4931.070704 Korkut, T. B., & Rachid, A. (2024). Numerical Investigation of Interventions to Mitigate Heat Stress: A Case Study in Dubai. Energies , 17 (10), 2242. https://doi.org/10.3390/en17102242 Le, K. T., Pancratz, S., & Diop, A. (2019). Labor Camp Surveys in GCC Countries: Group Quarter Subsampling. Field Methods , 31 (1), 76–91. https://doi.org/10.1177/1525822X18815416 Lee, H., Mayer, H., & Chen, L. (2016). Contribution of trees and grasslands to the mitigation of human heat stress in a residential district of Freiburg, Southwest Germany. 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Developing the desert: The pace and process of urban growth in Dubai. Computers, Environment and Urban Systems , 45 , 50–62. https://doi.org/10.1016/j.compenvurbsys.2014.02.005 Nassar, A. K., Blackburn, G. A., & Whyatt, J. D. (2016). Dynamics and controls of urban heat sink and island phenomena in a desert city: Development of a local climate zone scheme using remotely-sensed inputs. International Journal of Applied Earth Observation and Geoinformation , 51 , 76–90. https://doi.org/10.1016/j.jag.2016.05.004 Nicholson, S., Nikolopoulou, M., Watkins, R., Löve, M., & Ratti, C. (2024). Data driven design for urban street shading: Validation and application of ladybug tools as a design tool for outdoor thermal comfort. Urban Climate , 56 , 102041. https://doi.org/10.1016/j.uclim.2024.102041 Rahmani, N., & Sharifi, A. (2025). Urban heat dynamics in Local Climate Zones (LCZs): A systematic review. Building and Environment , 267 , 112225. https://doi.org/10.1016/j.buildenv.2024.112225 Rastegar, Z., Ghotbi Ravandi, M. R., Zare, S., Khanjani, N., & Esmaeili, R. (2022). Evaluating the effect of heat stress on cognitive performance of petrochemical workers: A field study. Heliyon , 8 (1), e08698. https://doi.org/10.1016/j.heliyon.2021.e08698 Rasul, A., Balzter, H., Smith, C., Remedios, J., Adamu, B., Sobrino, J., Srivanit, M., & Weng, Q. (2017). A Review on Remote Sensing of Urban Heat and Cool Islands. Land , 6 (2), 38. https://doi.org/10.3390/land6020038 Razzaghmanesh, M., Borst, M., Liu, J., Ahmed, F., O’Connor, T., & Selvakumar, A. (2021). Air Temperature Reductions at the Base of Tree Canopies. Journal of Sustainable Water in the Built Environment , 7 (3), 04021010. https://doi.org/10.1061/JSWBAY.0000950 Reber, L. (2021). The cramped and crowded room: The search for a sense of belonging and emotional well-being among temporary low-wage migrant workers. Emotion, Space and Society , 40 , 100808. https://doi.org/10.1016/j.emospa.2021.100808 Salamanca, F., Krpo, A., Martilli, A., & Clappier, A. (2010). A new building energy model coupled with an urban canopy parameterization for urban climate simulations—Part I. formulation, verification, and sensitivity analysis of the model. Theoretical and Applied Climatology , 99 (3–4), 331–344. https://doi.org/10.1007/s00704-009-0142-9 Shashua‐Bar, L., Pearlmutter, D., & Erell, E. (2011). The influence of trees and grass on outdoor thermal comfort in a hot‐arid environment. International Journal of Climatology , 31 (10), 1498–1506. https://doi.org/10.1002/joc.2177 Sheta, W., El Hussainy, M., & Abdelwahab, S. (2025). Labor camps in Dubai: Implications of courtyard regulations on daylight performance. Open House International , 50 (1), 2–19. https://doi.org/10.1108/OHI-11-2023-0265 Sousa‐Silva, R., Kestens, Y., Poirier Stephens, Z., Thierry, B., Schoenig, D., Fuller, D., Winters, M., & Smargiassi, A. (2025). Urban vegetation and well‐being: A cross‐sectional study in Montreal, Canada. People and Nature , 7 (2), 398–414. https://doi.org/10.1002/pan3.10771 Taleb, H. M., & Kayed, M. (2021). Applying porous trees as a windbreak to lower desert dust concentration: Case study of an urban community in Dubai. Urban Forestry & Urban Greening , 57 , 126915. https://doi.org/10.1016/j.ufug.2020.126915 Tamaskani Esfehankalateh, A., Ngarambe, J., & Yun, G. Y. (2021). Influence of Tree Canopy Coverage and Leaf Area Density on Urban Heat Island Mitigation. Sustainability , 13 (13), 7496. https://doi.org/10.3390/su13137496 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). https://doi.org/10.1038/s41598-025-98249-9 Xi, T., Li, Q., Mochida, A., & Meng, Q. (2012). Study on the outdoor thermal environment and thermal comfort around campus clusters in subtropical urban areas. Building and Environment , 52 , 162–170. https://doi.org/10.1016/j.buildenv.2011.11.006 Yang, R., & Kan, J. (2022). Classification of Tree Species in Different Seasons and Regions Based on Leaf Hyperspectral Images. Remote Sensing , 14 (6), 1524. https://doi.org/10.3390/rs14061524 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6843478","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":474610120,"identity":"4c6abb1a-a044-47df-86ff-9ce1c26a9fb1","order_by":0,"name":"Reham Abdelwahab","email":"","orcid":"","institution":"Cairo University","correspondingAuthor":false,"prefix":"","firstName":"Reham","middleName":"","lastName":"Abdelwahab","suffix":""},{"id":474610121,"identity":"6bad8903-2835-4ff2-aa2f-6ebc840d01fd","order_by":1,"name":"Wael Sheta","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8klEQVRIie3PsWrCQBzH8d8RiMsfXJVAfIULgqU4+CoJgi4RfAOd4hJw7eBz2PXgD3UJdL2hg6Vwk9BMIpjBmMnpYrcO9+WGG+7D//6Ay/U/EwqoD7wPQHrPmYYQ/NmfCY3uo9qfywMrheornHSK8w8tx+hulChLGylmsRKZGRIt3ock5+gVsdd/s5CRSqUSa05yLPYBSQY0EJCNfP6W9cd4lXdPpiEDDe9a2YhO6919jqmX+g2RGn5gW3+ijVRJxlGuzUu0k3OKiiR7zS2kv51+H8uKB53t1BxP1TgMD8z6YhtzL36414uLdRtwuVwuV0s3cpRQKG7g/m4AAAAASUVORK5CYII=","orcid":"","institution":"British University in Dubai","correspondingAuthor":true,"prefix":"","firstName":"Wael","middleName":"","lastName":"Sheta","suffix":""},{"id":474610122,"identity":"39821abd-c97b-4618-8b3a-a7ee024a1ffd","order_by":2,"name":"Mariam El Hussainy","email":"","orcid":"","institution":"American University in Cairo","correspondingAuthor":false,"prefix":"","firstName":"Mariam","middleName":"El","lastName":"Hussainy","suffix":""},{"id":474610123,"identity":"50fc6ef5-69c4-487b-83e3-99271814d234","order_by":3,"name":"Sahar Abdelwahab","email":"","orcid":"","institution":"De Montfort University","correspondingAuthor":false,"prefix":"","firstName":"Sahar","middleName":"","lastName":"Abdelwahab","suffix":""}],"badges":[],"createdAt":"2025-06-07 14:53:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6843478/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6843478/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85306129,"identity":"5d80d64e-e229-4d36-a319-73a9e253e7e9","added_by":"auto","created_at":"2025-06-24 12:48:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":570159,"visible":true,"origin":"","legend":"\u003cp\u003eFacades and aerial views of the case study site located in Jebel Ali, Dubai (mapcarta, 2025).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6843478/v1/af348f2d32b080e3971db9e7.png"},{"id":85307188,"identity":"c969467b-2044-446a-8952-e64a2fdb57df","added_by":"auto","created_at":"2025-06-24 12:56:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":559657,"visible":true,"origin":"","legend":"\u003cp\u003eThe key parameters of the study and tree geometries derived from indigenous UAE species.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6843478/v1/ae07624f29a52eb381c7eaeb.png"},{"id":85306131,"identity":"b6003552-6039-40aa-b423-c2de3c133a6b","added_by":"auto","created_at":"2025-06-24 12:48:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":580019,"visible":true,"origin":"","legend":"\u003cp\u003eThermal images obtained from the actual site.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6843478/v1/f405e4b4611d8135473ce30a.png"},{"id":85307189,"identity":"c7ce4ab7-00cf-448b-80de-25b1963f9226","added_by":"auto","created_at":"2025-06-24 12:56:28","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":49239,"visible":true,"origin":"","legend":"\u003cp\u003eThe relationship between the studied variables and (UTCI).\u003c/p\u003e\n\u003cp\u003ea: The correlation between DbB and both UTCI and MRT.\u003c/p\u003e\n\u003cp\u003eb: The correlation between PSR and both UTCI and MRT.\u003c/p\u003e\n\u003cp\u003ec: Effect of Canopy Density on UTCI as a Function of Tree Geometry.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6843478/v1/574f1dfffa52f6a35b24cc9e.png"},{"id":85308629,"identity":"725a0a1f-b5a4-4af6-b9d4-e4341b1b604b","added_by":"auto","created_at":"2025-06-24 13:12:28","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":193870,"visible":true,"origin":"","legend":"\u003cp\u003eParallel coordinates for weighted density coverage for tree distribution.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6843478/v1/38296e9982e50d249b108ee3.png"},{"id":85306146,"identity":"915d837f-cf3d-49b2-be3e-3a380c61549e","added_by":"auto","created_at":"2025-06-24 12:48:28","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":317009,"visible":true,"origin":"","legend":"\u003cp\u003eParallel coordinates for multi-variable testing against UTCI (top); subset of the selected lowest UTCI scenarios (bottom).\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6843478/v1/f9745f2b64004000d8bea596.png"},{"id":85306143,"identity":"45f99757-13a9-46a4-aabe-6c5bbd72b0ba","added_by":"auto","created_at":"2025-06-24 12:48:28","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":208544,"visible":true,"origin":"","legend":"\u003cp\u003eEffective configuration of tree distribution.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6843478/v1/1b947b74a5dd64b76ea4759b.png"},{"id":99209115,"identity":"1171ab6d-7b46-4549-aae8-70247ef6cc8e","added_by":"auto","created_at":"2025-12-30 07:26:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3491675,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6843478/v1/c8ce9299-eb61-4258-b743-ab3086e67d68.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Urban Greenery for Health: Mitigating Heat Stress in the UAE Labor Settlements","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eLabor accommodation complexes in Dubai play a significant role in shaping the health and well-being of the migrant workforce, which constitutes a substantial portion of the city's population. These complexes are designed to provide housing for low-wage workers, primarily in the construction and service sectors, and their living conditions can have profound implications for both physical and mental health (Nassar et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Poorly designed accommodations can lead to overcrowding, inadequate ventilation, and insufficient access to natural light, all of which can contribute to negative health outcomes (Harb et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). For instance, the lack of daylight in living spaces can lead to psychological stress and exacerbate feelings of isolation among workers (Sheta et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Workers often face high levels of stress due to demanding work conditions, long hours, and the pressure to meet performance targets, which can be compounded by the isolation experienced in labor camps (Jeung et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Exposure to extreme outdoor thermal conditions-both heat and cold- can lead to significant physiological stress, fatigue, mood disturbances, in addition to exacerbate mental health issues, including anxiety and depression (Al Hurini et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Bacha et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Rastegar et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In rapidly urbanizing cities like Dubai, where labor accommodations constitute a significant portion of the residential landscape, understanding the outdoor thermal comfort of these environments is crucial for ensuring the health, well-being, and productivity of workers. However, studies concerning outdoor thermal comfort in residential campuses have predominantly focused on educational settings such as schools and university campuses. To the authors' knowledge, no investigations have yet explored outdoor comfort within the specific microclimates of labor accommodation campuses (Das et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Eslamirad et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ghaffarianhoseini et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Huang et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). There is a growing recognition of the need for enhancing the living conditions and standards for labor accommodation in Dubai (Akmal et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Sheta et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Integrating green spaces and community-building activities within labor complexes can foster a sense of belonging and improve the overall quality of life for residents (Reber, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In this context, the fundamental aim of this study is to investigate different configurations of vegetation and green urban spaces that can be strategically positioned within and around labor accommodation urban forms to mitigate the harsh outdoor thermal conditions prevalent in Dubai\u0026rsquo;s climate. By examining various design strategies, such as tree canopy coverage, green corridors, shaded courtyards, and vegetative buffers, the study seeks to identify effective solutions that enhance outdoor thermal comfort, reduce heat stress, and ultimately contribute to healthier and more livable environments for labor workers.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Labor accommodation complexes within Dubai\u0026rsquo;s urban fabric\u003c/h2\u003e \u003cp\u003eOne of the defining features of Dubai's urban fabric is its rapid urbanization, which has been analyzed through various studies. Dubai experienced a remarkable rate of urban growth influenced by both local and global factors, resulting in increased vegetation cover and the development of inland water bodies in this hyper-arid environment (Nassar et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). This urbanization has also led to the emergence of Urban Heat Islands (UHIs), which are exacerbated by the city's dense built environment and limited vegetation (Bolleter et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Nassar et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Rasul et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Labor accommodation complexes in Dubai represent a critical aspect of the city's urban fabric, reflecting the socio-economic dynamics and the rapid urbanization that has characterized the region over the past few decades (Nassar et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Labor accommodation complexes are not merely functional spaces; they also embody the socio-political context of Dubai's rapid urbanization. Alawadi discusses how the urban form of Dubai has led to significant gaps between developments, with labor camps often situated far from the urban core, contributing to a fragmented urban environment (Alawadi, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017a\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017b\u003c/span\u003e). This spatial arrangement raises concerns about the living conditions of workers, who may face long commutes to their workplaces and limited access to essential services and amenities. The design and regulation of these labor camps have been scrutinized, particularly regarding their environmental performance and the quality of life for residents (Sheta et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Moreover, the implications of labor accommodation extend beyond mere housing; they reflect the broader issues of labor rights and social equity in Dubai's development model. The reliance on a transient workforce has led to criticisms regarding the treatment of laborers, with calls for improved living conditions and greater integration into the urban fabric (Hamza, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Le et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). An in-depth examination of the urban fabric of labor accommodations spread across various parts of Dubai like Jabel Ali and Al-Aquoz reveals a noticeable lack of green spaces and vegetation integrated within these developments (Mohammed et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The layouts are characterized by dense, compact building forms with minimal consideration for open or landscaped areas, contributing to a harsh and uninviting outdoor environment (Sheta et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 The role of urban greenery in enhancing outdoor thermal comfort\u003c/h2\u003e \u003cp\u003eVegetation contributes to achieving thermal comfort as it decreases the short and longwave radiation fluxes impinging on the pedestrian and can also reduce the outdoor air temperature to a significant degree, depending on the plant species and location (Coccolo et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Sousa-Silva et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Having a substantial tree cover in an urban setting can lower mean radiant and air temperatures during a heat wave compared to treeless streets (Ettinger et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Razzaghmanesh et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Empirical investigations were made during the hottest periods in the city of Biskra, Algeria. The results confirmed the importance of urban green spaces and their positive impact on outdoor thermal comfort, and the good behavior of users, recommending the \u0026ldquo;Ficus\u0026rdquo; type in particular, because of its proven physical and psychological benefits for people (Khadraoui et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The tree types, geometry and properties vary according to the region, species, season (Yang and Kan, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The selection of native plant species is particularly advantageous, as it enhances adaptability, minimizes maintenance demands, and aligns with water conservation needs in arid regions (Chen et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The effectiveness of urban greening in enhancing outdoor thermal comfort has been highlighted, as demonstrated through the use of thermal comfort mapping tools (Coccolo et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeveral studies have investigated outdoor thermal comfort in Dubai through case-based research. One such study focused on enhancing pedestrian-level thermal comfort within a Local Climate Zone (LCZ) in Dubai by implementing various cooling interventions, including vegetation, architectural modifications, and changes to pavement material color (Korkut \u0026amp; Rachid, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).Urban geometry and surface characteristics are the two primary factors influencing local climatic conditions (Rahmani \u0026amp; Sharifi, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Among these, the height-to-width ratio (H/W)\u0026mdash;which represents the proportion of building height (H) to the spacing between structures (W)\u0026mdash;plays a crucial role in shaping urban geometry (Ibrahim et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This ratio significantly impacts microclimatic parameters, including solar radiation absorption and reflection, heat retention, and airflow patterns. A higher H/W ratio can lead to reduced wind speed and limited solar exposure at street level, contributing to the urban heat island (UHI) effect and influencing thermal comfort (Cui et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Conversely, lower H/W ratios allow for greater ventilation and daylight penetration, potentially mitigating overheating in dense urban environments. Understanding these relationships is essential for designing climate-responsive urban spaces that balance thermal comfort, energy efficiency, and environmental sustainability. Trees geometry under hot climates, the height-to-width ratio showed to effectively reduced maximum temperature (Elkhayat et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Dubai\u0026rsquo;s urban planning guidelines prioritize sustainability, greenery, and microclimate control, as demonstrated by the Dubai Green Building Regulations and Specifications (DGBR). These regulations promote green roofs, urban landscaping, and design strategies to reduce heat buildup and improve outdoor comfort (Abu-Hijleh \u0026amp; Jaheen, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Enhancing walkability is also emphasized, with public spaces designed for usability in extreme heat. Overall, these measures aim to reduce heat stress, improve thermal comfort, and integrate vegetation into the urban fabric of Dubai (Awadh, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Methodology","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Case study\u003c/h2\u003e \u003cp\u003eThe selected case study exemplifies a typical labor camp in Dubai, situated in the first district of Jebel Ali, approximately 35 kilometers from Burj Khalifa. The labor camp comprises a ground floor and four identical upper storeys, reaching a total height of 18.0 meters, with an 8.00-meter spacing between adjacent structures. The urban fabric illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e comprises densely packed accommodation blocks with minimal setbacks, prioritizing built-up area over environmental quality. This compact layout, devoid of open areas and greenery, limits natural ventilation and shade, exacerbating thermal discomfort in Dubai's severe climate. The hourly boundary conditions for the simulations were derived from the Typical Meteorological Year-extended (TMY) dataset produced by for Dubai World Central / Al Maktoum International Airport (WMO ID 411945; base year 2009\u0026ndash;2023). The UTCI thermal stress analysis utilizing TMY data indicated the period from July 20 to July 26 as the hottest week of the reference year, with July 23 from 10:00 to 15:00 recording the peak composite of dry-bulb temperature, global horizontal irradiance, and mean radiant temperature.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Study\u0026rsquo;s parameters\u003c/h2\u003e \u003cp\u003eThis study utilizes a parametric approach to assess the thermal and microclimatic performance of urban parks encircled by homogeneous building configurations. The methodology seeks to uphold uniform boundary conditions, thereby isolating the impacts of park design and tree selection on outdoor comfort. The parametric framework has two main categories of variables: park design parameters and tree selection parameters, each delineated by specific ranges to encompass a wide array of alternative urban layouts.\u003c/p\u003e \u003cp\u003ePark design variables encompass the Distance between Buildings (DbB) and the Park Size Ratio (PSR). The DbB parameter examines the effects of varied distances between adjacent buildings, from 25m to 100m, to evaluate its impact on microclimate regulation and overall thermal comfort in the park area. The Park Size Ratio (PSR), which is the ratio of park area to the total available space between buildings, varies from 0.2 to 0.9, with a value of 1 indicating that the park occupies the entire area between the buildings. This top limit is restricted by practical factors, including pedestrian routes and adjacent infrastructure, which inhibit total park coverage in actual environments. Tree selection variables emphasize the significant impact of vegetation in altering outdoor temperature conditions. This category encompasses Tree Coverage Density (TCD) and Weighted Tree Shape (WTS). TCD is determined by the density of trees per unit area, normalized to a ratio per 200 m\u0026sup2; to enable reasonable suggestions and eliminate fractional values in design standards. This standardization guarantees clarity and relevance in forthcoming practical applications and policy formulation. The WTD metric quantifies the average coverage contribution of each tree variety in the park, considering variations in canopy geometry and shading efficacy.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe study's selection of tree shapes emphasizes adapted, native species to correspond with regional ecological objectives and sustainable landscape design concepts, geometric shapes and key attributes. Essential characteristics, including canopy radius, tree height, crown morphology, and planting density, are delineated, as these elements are vital for assessing the trees' capacity to improve outdoor thermal comfort and elevate environmental quality in urban settings. To ensure consistent analysis, the simulation environment employs standardized dimensions, typology, and orientation for buildings and parks, thereby reducing variability in solar exposure and wind flow. Locally adjusted meteorological files are used to accurately reflect microclimatic conditions for precise thermal and airflow simulations. Figures\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the key parameters of the study, including tree geometries derived from indigenous UAE species. These are categorized into three distinct crown shapes. Triangulated models used for Radiance-based simulations accurately represent canopy structures and their interaction with light, with leaf density defined by a transmission factor reflecting observed foliage characteristics.\u003c/p\u003e \u003cp\u003eThis study utilizes a suite of validated environmental simulation tools. Honeybee and Ladybug, incorporated inside Rhino/Grasshopper, are esteemed for their dependability in analyzing solar radiation, daylight, and thermal comfort, particularly in temperate and hot-arid settings (El-Bahrawy, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Nicholson et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Radiance and EnergyPlus, renowned for their accuracy in light and energy modeling, are combined through ClimateStudio for improving computational efficiency and prediction precision (Abdelwahab et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This methodology enhances the accuracy of computations for Mean Radiant Temperature (MRT) and the Universal Thermal Climate Index (UTCI). The analysis employed a two-phase methodology: initially, evaluating each parameter in isolation to determine its impact on outdoor comfort; subsequently, eliminating poor values associated with elevated UTCI readings to concentrate on more efficient design configurations. To further enhance the optimization process and cases sample selection, a genetic algorithm was implemented using the Galapagos plugin within Grasshopper, facilitating the generation of a diverse and representative sample set spanning high, medium, and low parameter ranges. Given the extensive parameter space, with potential combinations exceeding 23\u0026nbsp;million, this evolutionary approach developed on Darwinian principles of natural selection was employed for efficiently converging on near-optimal solutions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Simulation validation\u003c/h2\u003e \u003cp\u003eThe suggested workflow integrates multiple parameters that affect the urban-scale microclimate, with surface temperature being crucial due to its significant influence on mean radiant temperature (MRT) via changes in thermal radiation. The surface temperature is influenced by various meteorological conditions, rendering its precise simulation essential for authentic environmental modeling. This study conducted surface temperature calibration at a designated moment during the research period to evaluate the workflow's efficacy in capturing urban and environmental heat transfer dynamics. Infrared thermography, utilizing the FLIR E40 thermal camera, was applied to record surface temperatures at two distinct periods on the same day. The calibration procedure sought to enhance surface temperature precision by juxtaposing simulated values with real-time data from a local weather station that supplied Actual Meteorological Year (AMY) data documented during field observations. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e displays representative infrared photos obtained from the FLIR E40, illustrating the temperature disparity between pavement and asphalt surfaces.\u003c/p\u003e \u003cp\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\u003eComparison between simulated and observed surface temperature values for calibration.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eReal Measure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eSimulated Measured\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11:00 a.m.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e3:00 p.m.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e11:00 a.m.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3:00 p.m.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAir Temperature C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAir Speed (global) m/s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e9.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e7.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAir Speed (local)\u003c/p\u003e \u003cp\u003em/s at surface level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eGround Temperature C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAsphalt\u0026thinsp;=\u0026thinsp;32, Pavement\u0026thinsp;=\u0026thinsp;27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eAsphalt\u0026thinsp;=\u0026thinsp;25\u003c/p\u003e \u003cp\u003ePavement\u0026thinsp;=\u0026thinsp;21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eAsphalt\u0026thinsp;=\u0026thinsp;27.13, Pavement\u0026thinsp;=\u0026thinsp;23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAsphalt\u0026thinsp;=\u0026thinsp;27, Pavement\u0026thinsp;=\u0026thinsp;23.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eSurface Temperature model calibration.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurface Type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eRMSE (\u0026deg;C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eMAE (\u0026deg;C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eBias (\u0026deg;C)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsphalt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e3.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e3.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e-1.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePavement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e3.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e3.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e-0.75\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\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e compares simulated and actual surface temperature values for calibration, while Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the calibration data, revealing a Root Mean Square Error (RMSE) of 3.72\u0026deg;C, a Mean Absolute Error (MAE) of 3.44\u0026deg;C, and a mean bias of \u0026minus;\u0026thinsp;1.44\u0026deg;C for asphalt surfaces. The RMSE for pavement surfaces was 3.34\u0026deg;C, the MAE was 3.25\u0026deg;C, and the mean bias was \u0026minus;\u0026thinsp;0.75\u0026deg;C. The negative bias values indicate a persistent underestimating of surface temperatures by the simulation model. These discrepancies correspond with previous research in urban microclimate modeling, emphasizing the impact of factors such as material emissivity, surface roughness, and subsurface heat flux representation on the precision of surface temperature predictions (Azam et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Salamanca et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The (UTCI) integrates air temperature, humidity, wind velocity, and (MRT) into a singular metric to evaluate outdoor thermal comfort. The simulation techniques employed in this study do not explicitly describe evaporation; yet, its impacts are manifested in surface conditions. The (MRT) is computed utilizing radiation statistics modified for shade, sky-view factor, and urban morphology. Wind speed is recorded at 1.8 meters and adjusted for local conditions. Simulations assumed dry grass surfaces to conservatively evaluate cooling effects. This approach enhances (UTCI) computations and guides design options to mitigate heat stress in hot-arid urban environments.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results and Discussion","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Distance between Buildings (DbB)\u003c/h2\u003e \u003cp\u003eA total of 76 simulation runs were conducted to assess the Universal Thermal Climate Index (UTCI) and Mean Radiant Temperature (MRT) in relation to the distance between buildings (DbB) within the outdoor areas of the labor campus. The results, illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e-a, demonstrate a positive correlation between DbB and both UTCI and MRT values; as the distance between buildings increases, levels of thermal exposure also rise. This trend reflects the influence of park size, where expanded open areas lead to increased DbB ratios. The associated reduction in shading from adjacent buildings results in greater exposure to direct solar radiation, thereby elevating ground-level heat loads and contributing to higher thermal stress in the outdoor environment. Notwithstanding these observations, surface temperature fluctuations remained comparatively insignificant across the evaluated scenarios. The impact of DbB on UTCI was most significant up to a threshold of around 50 meters, which aligns with a height-to-width (H/W) ratio of 1:3.3. Beyond this threshold, additional increments in DbB yielded minimal enhancements in UTCI values, indicating a point of diminishing returns for outdoor thermal comfort improvement through increased spatial separation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Park Size Ratio (PSR)\u003c/h2\u003e \u003cp\u003eA series of microclimate simulations were undertaken to evaluate the impact of Park Scale Ratios (PSR) and different park sizes on local thermal dynamics, while keeping a consistent inter-building distance of 100 meters (H/W\u0026thinsp;=\u0026thinsp;1:6). The findings, illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e-b, demonstrated that the correlation between PSR and both the Universal Thermal Climate Index (UTCI) and Mean Radiant Temperature (MRT) exhibited an inverted parabolic pattern, with only modest temperature variations noted among the different configurations. This restricted variance is probably due to the shading effects of adjacent structures, which become more pronounced as park area increases, consequently diminishing solar heat gain. The improvements in UTCI were relatively modest; however, the results underscore the essential influence of vegetation density and spatial distribution on enhancing outdoor thermal comfort conditions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Tree Coverage Density (TCD)\u003c/h2\u003e \u003cp\u003eA comprehensive parametric analysis was performed using 1,923 simulation scenarios, each differing in Tree Coverage Density (TCD) and classified into three specific canopy geometries: elliptical/umbrella-like (Elip), spherical (Sph), and pyramidal (Py). Simulations were conducted separately for each tree geometry to assess the influence of canopy shape and density on outdoor thermal comfort, as quantified by the Universal Thermal Climate Index (UTCI) and Mean Radiant Temperature (MRT). Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e-c illustrates a comparative analysis of UTCI values among various tree shapes and densities. The results demonstrate that high-density elliptical canopies attain a UTCI reduction of roughly 2\u0026deg;C in comparison to spherical forms and nearly 4.5\u0026deg;C relative to pyramidal structures, such as palm trees, at equal densities. The continuously low cooling effect of pyramidal geometries at all density levels underscores their restricted ability to mitigate outside thermal stress in comparison to elliptical or spherical canopy shapes.\u003c/p\u003e \u003cp\u003eA regression analysis was performed to investigate the correlation between tree geometry at varying densities and the UTCI (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This investigation seeks to measure the impact of differences in tree geometry density on outdoor thermal comfort levels. All three tree geometries exhibit a robust negative association between canopy density and UTCI, with Pearson correlation coefficients (r) of \u0026minus;\u0026thinsp;0.81 or lower. This indicates a statistically significant and strong linear correlation, wherein greater canopy density correlates with less thermal stress. Among the examined geometries, the elliptical (umbrella-like) tree form demonstrates the most significant decrease in both UTCI and MRT, surpassing the palm and spherical forms in thermal mitigation. Significantly, for umbrella-shaped and spherical configurations, a 10% increase in canopy density results in a UTCI decrease of roughly 3 to 4\u0026deg;C. The continuously elevated Pearson correlation coefficients (r\u0026thinsp;\u0026gt;\u0026thinsp;0.8) across all geometries affirm a robust inverse link between canopy density and UTCI, highlighting the efficacy of denser foliage in improving outdoor thermal comfort. The analysis of these results indicates that in park design when micro-climate mitigation is essential, preference should be accorded to elliptical, umbrella-like, or spherical geometries. Palms may continue to serve useful roles in landscape design for wayfinding or aesthetic purposes; but, depending on them only for outdoor thermal comfort would necessitate unfeasibly high planting densities, resulting in increased costs without significant effects on UTCI or outdoor human comfort.\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\u003eRegression analysis of the relationship between tree geometry density and UTCI.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeometry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDensity\u0026ndash;UTCI slope (\u0026deg;C per unit density)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePearson \u003cem\u003er\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInterpretation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElliptical (umbrella-like)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-3.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStrong cooling benefit: UTCI drops almost 4\u0026deg;C when density rises from 0 to 100%.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSphere\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-3.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSimilar strong effect: compact crowns cool nearly as well as elliptical ones.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePyramidal (palm tree)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOnly a modest drop: sparse palm crowns provide limited shade, so UTCI stays high even as planting density increases.\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 \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Weighted Tree Shapes Density (WTD)\u003c/h2\u003e \u003cp\u003eSimulation analyses were performed to assess the efficacy of various tree geometries under differing canopy density distributions, aiming to identify the optimal weighted tree density (WTD) for each geometry. Various amounts of canopy coverage were evaluated to guarantee the consistency and dependability of the proposed average WTD. The analysis also considers changes in tree spacing. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e depicts 500 simulated instances utilizing parallel coordinates charts to examine the relationships between various tree canopy densities and their aggregate effect on outdoor thermal comfort. Each scenario exemplifies a distinct combination of tree geometries and densities, with the weighted average canopy density calculated for each tree species. The optimization analysis verifies that the optimal thermal performance, indicated by the bold red line, is achieved when the overall canopy covering consists solely of umbrella-like or elliptical-shaped trees at 100% density, excluding all other tree geometries. This resulted in UTCI\u0026thinsp;=\u0026thinsp;41\u0026deg;C, MRT\u0026thinsp;=\u0026thinsp;33\u0026deg;C, and a sky factor of 20%.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA linear regression analysis was performed to identify the most efficient tree densities, both collectively and individually, for mitigating heat stress in outdoor contexts. Trees exhibiting spherical geometry yield the greatest significant cooling impact (β = -0.46, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This suggests that dense, spherical canopies are especially efficient in mitigating heat stress in urban settings. Elliptical trees demonstrated a moderate yet statistically significant cooling effect (β = -0.32, p\u0026thinsp;=\u0026thinsp;0.003), indicating they offer shading advantages, albeit less consistently than spherical trees, resulting in a diminished reduction in UTCI. Conversely, pyramidal-shaped trees, including palm trees, had a positive coefficient (β = +0.88, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that their tall, slender form and restricted canopy coverage provide negligible shading. As a result, they correlate with heightened sun exposure and a concomitant increase in UTCI. The regression analysis demonstrates a distinct hierarchy in the cooling efficacy of various tree types based on their average density. Spherical trees yield the greatest reduction in UTCI, whereas elliptical trees offer a significant decrease when paired with other tree forms. Pyramidal trees, conversely, correlate with elevated UTCI values, suggesting that their morphology may exacerbate heat stress rather than mitigate it, even when present in higher densities with other tree forms. These findings emphasize the significance of choosing tree types and distribution priorities according to their thermal performance to enhance environmental comfort.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Multi-variable optimization impact\u003c/h2\u003e \u003cp\u003eA Genetic Algorithms (GAs)-based optimization framework was utilized to determine the optimal integration of design factors, concurrently improving all variables to improve thermal performance. The system performed a comprehensive search across all study parameters, assessing 108,240 scenarios chosen from a larger pool of approximately 24\u0026nbsp;million potential combinations. The significant decrease in search scenarios and time was accomplished by excluding unpromising configurations from previously tested single variables to expedite convergence to ideal solutions. The optimization method concluded after producing 600 candidate solutions, considered adequate to represent a substantial sample size of possibly ideal configurations. This approach ensured computational efficiency while preserving the dependability of the optimization results for generalized recommendations. Parallel coordinates plots were utilized to illustrate the ideal configurations concerning all altered parameters and output measures. The red line in Fig.\u0026nbsp;8 highlights the configuration corresponding to the lowest recorded UTCI, signifying the most efficient scenario among the simulated cases. This optimal configuration was repeatedly observed across diverse parameter ranges, indicating that analogous low-UTCI outcomes can be attained through varied combinations of tree geometry and canopy density. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e at the bottom depicts a subset of the chosen lowest UTCI scenarios. The findings indicate that the ideal tree density for enhancing thermal comfort is 5 to 7 trees per 200m\u0026sup2; for mixed tree configurations intersecting at the park's center, or 3 to 4 trees per 200m\u0026sup2; for elliptical tree crowns alone. This density offers optimal shade covering while increasing space efficiency, hence assuring sufficient cooling. Furthermore, the study suggests that the optimal park area is 0.6 times the space between two structures. The optimal ratio of building height to park width is between 1:5.3 and 1:4 meters, achieving a balance between solar exposure and shade provision. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents a summary of the best performing situations.\u003c/p\u003e \u003cp\u003e \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\u003eRecommended urban greenery for practical applications.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDesign Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEvidence-based rule of thumb\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance between Buildings (DBB) (H:W)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrass with no trees: \u0026le; 50 m, (H/W\u0026thinsp;\u0026le;\u0026thinsp;1:3.3)\u003c/p\u003e \u003cp\u003eTrees : \u0026ge; 50 m, (H/W\u0026thinsp;\u0026le;\u0026thinsp;1:5.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePark Size Ratio (PSR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGreen area\u0026thinsp;\u0026le;\u0026thinsp;0.6 \u0026times; gap area\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTree Coverage Density (TCD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026ndash;7 trees per 200m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeighted Tree Shapes Density (WTD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60% Elliptical or umbrella like tree, 40% Spherical tree, or 100% Elliptical tree with medium leave density or higher with Leaf Area Index (LAI)\u0026thinsp;\u0026gt;\u0026thinsp;3\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 optimization results demonstrate a substantial decrease in temperature attributable to enhanced tree density and spatial configuration. The optimization analysis of tree distribution yielded a 1 to 2\u0026deg;C decrease in UTCI relative to grass-covered regions between buildings devoid of trees, and a 7\u0026deg;C decrease compared to asphalt-covered areas. Figure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e depicts the optimal arrangement of tree distribution throughout the park, demonstrating that central positioning of trees optimizes shade coverage while minimizing shadow cast by buildings.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThis study provides significant insights into enhancing urban microclimates in hot-arid regions by climate-responsive landscape design, particularly inside labor housing environments. The study employs a multi-variable optimization framework to examine key factors influencing outdoor thermal comfort, such as DbB, PSR, and TCD. The positive link between heightened DBB and increased UTCI and MRT is consistent with previous findings by (Tamaskani Esfehankalateh et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), indicating that dense urban configurations intensify thermal stress. This analysis establishes a threshold DbB of 50 meters, beyond which additional separation results in declining thermal advantages, corroborating the findings of (Ali-Toudert \u0026amp; Mayer, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). This suggests that additional shade structures, such as pergolas, ought to be implemented in larger areas to reduce solar exposure. The study also substantiates an inverted parabolic correlation between PSR and thermal comfort, aligning with findings by (Wang et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), suggesting that parks of moderate size (about 50% open space) provide optimal cooling. This underscores the significance of vegetative arrangement and shading distribution throughout the day, contrary to the assumptions favoring larger parks. A significant contribution is the examination of tree crown geometry. Spherical or umbrella-shaped canopies exhibited the most significant cooling impact, but pyramidal forms, such as palm trees, offered minimal respite. The results underscore the importance of canopy morphology and density in the selection of tree species for hot-arid climates, corroborating previous findings (Li et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Moreover, the examination (WTD) indicates that a composition of 60\u0026ndash;80% elliptical and 20\u0026ndash;40% spherical tree forms optimally reduces UTCI, consistent with the findings of (Tamaskani Esfehankalateh et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The study's originality arises from its integrated application of Genetic Algorithms (GAs) to investigate the synergistic effects of several landscape characteristics. This approach enhances existing techniques by effectively optimizing design configurations across millions of parameter combinations, transcending the single-variable emphasis prevalent in much current research. Furthermore, it introduces an innovative computational methodology to improve the precision of surface temperature and UTCI forecasts, tackling the shortcomings of conventional parametric models that frequently neglect thermal complexity. The study supports climate equality and social participation by endorsing thermally resilient landscape designs in economically disadvantaged places. Notwithstanding its merits, the study possesses limitations. The impacts of moisture, including evapotranspiration and variations in humidity, were not incorporated into the model, likely leading to an underestimation of vegetation's cooling effect. The presumption of uniform tree distribution may restrict applicability, as regional variability affects shade and wind dynamics. Subsequent research ought to integrate localized wind simulations and consider the albedo, heat retention, and permeability of hardscape materials to deliver a more thorough thermal evaluation.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThe findings correspond with established studies and contribute fresh perspectives, especially on multi-variable optimization in landscape design. The results substantially advance sustainable urban design frameworks that improve environmental and human well-being, providing a practical roadmap for mitigating urban heat islands in hot arid regions. The study presents a new methodological pathway for the precise prediction of UTCI, which combines Radiance and Energy-Plus computational tools with on-site infrared thermography for validation purposes. This unique Parametric-Ladybug workflow addresses significant deficiencies in species-specific cooling performance, park-scale optimization, and integrated multi-parameter analysis, providing practical design guidelines for sustainable urban environments. The optimization analysis indicates that tree geometry substantially influences thermal comfort, with elliptical and spherical canopies decreasing UTCI by roughly 3 to 4\u0026deg;C for each 10% increase in canopy density, whereas palm trees exhibit minimal cooling capability due to their sparse foliage and height. Conversely, multi-variable optimization analysis indicates that the best tree density for maximizing the cooling impact is 5 to 7 trees per 200m\u0026sup2; for mixed tree geometry, or 3 to 4 trees per 200m\u0026sup2; when utilizing just elliptical tree crowns. The analysis reveals that the ideal park scale area is 0.6 times the space between two buildings. The ideal ratio of building height to park width is determined to be between 1:5.3 and 1:4 meters, achieving a balance between solar exposure and shade provision. The optimization of tree distribution led to a reduction in the Universal Thermal Climate Index (UTCI) by roughly 1 to 2\u0026deg;C compared to grass-covered areas devoid of trees, and by as much as 7\u0026deg;C in relation to asphalt-covered areas between buildings. The suggested solutions provide avenues to augment various commercial and recreational activities in hot arid regions, thus fostering enhanced community well-being, economic vitality, and labor productivity.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization and Research Design: All authors\u0026bull; Methodology Development and Modeling: [Author 1, Author 4]\u0026bull; Data Collection and Analysis: [Author 1, Author 3]\u0026bull; Interpretation of Results and Reporting: [Author 1, Author 4]\u0026bull; Writing \u0026ndash; Original Draft Preparation: All authors\u0026bull; Writing \u0026ndash; Review and Editing: [Author 2, Author 4]\u0026bull; Final Approval of the Manuscript: All authors\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbdelwahab, R. A., Fekry, A. A., \u0026amp; Hamed, R. E.-D. (2025). The effective landscape design parameters with high reflective hardscapes: Guidelines for optimizing human thermal comfort in outdoor spaces by design -a case on hot arid climate weather. \u003cem\u003eComputational Urban Science\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e(1). https://doi.org/10.1007/s43762-025-00186-w\u003c/li\u003e\n\u003cli\u003eAbu-Hijleh, B., \u0026amp; Jaheen, N. (2019). Energy and economic impact of the new Dubai municipality green building regulations and potential upgrades of the regulations. \u003cem\u003eEnergy Strategy Reviews\u003c/em\u003e, \u003cem\u003e24\u003c/em\u003e, 51\u0026ndash;67. https://doi.org/10.1016/j.esr.2019.01.004\u003c/li\u003e\n\u003cli\u003eAkmal, C. A., Abdul Mutalib, M., \u0026amp; Wan Razali, W. Mohd. F. A. (2024). 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The study assesses the impact of tree shape, park dimensions, and building spacing on the Universal Thermal Climate Index (UTCI) and Mean Radiant Temperature (MRT) through the integration of Ladybug and Honeybee tools. Key findings indicate that tree geometry significantly influences thermal comfort, with elliptical and spherical canopies decreasing UTCI by roughly 3 to 4\u0026deg;C for each 10% increase in canopy density, whereas palm trees have minimal cooling capacity due to their thin foliage and height. Multi-variable optimization determined optimal tree density ranges (5 to 7 trees per 200 m\u0026sup2;) and park size ratios (H/W up to 1:5) for optimal cooling effect. The surface temperature predictions were validated using on-site infrared thermography, yielding a root mean square error (RMSE) of 3.72\u0026deg;C for asphalt and 3.34\u0026deg;C for pavement, thereby affirming the dependability of the simulation framework. The findings provide practical recommendations for urban planners and landscape specialists to enhance climate resilience and thermal comfort in labor settlements in Dubai, in accordance with the UN Sustainable Development Goals (SDGs) for climate action and urban sustainability. This study presents an optimization approach that evaluates the crown geometries of tree-native species and improves the predicted accuracy of UTCI mitigation tactics in hot-arid regions using greenery and passive strategies.\u003c/p\u003e","manuscriptTitle":"Urban Greenery for Health: Mitigating Heat Stress in the UAE Labor Settlements","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-24 12:48:23","doi":"10.21203/rs.3.rs-6843478/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":"e0a09db2-988f-4115-b48a-2d8b582c4ec4","owner":[],"postedDate":"June 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-30T07:25:17+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-24 12:48:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6843478","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6843478","identity":"rs-6843478","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}