Effects of street orientation and tree species thermal comfort within urban canyons in a hot, dry climate

Generative design optimization of tree distribution for enhanced thermal comfort in communal spaces with special reference to hot arid climates

The quality of the communal outdoor environment is crucial for enhancing the urban quality and the well-being of its residents. These spaces are essential for providing more opportunities for social interaction and leisure. However, in hot arid climates like Egypt, achieving optimal outdoor thermal comfort remains a challenge. Accordingly, more comprehensive methodologies are highly needed to improve the research-based design of landscape parameters and components for developing outdoor thermal comfort performance using an iterative design exploration process that employs AI-driven software. These applications, help designers in solving multi-objective design quandaries through the generation and evaluation of numerous design options. Therefore, this study explores the efficiency of generative design tools in optimizing tree distribution based on mutation evolution to enhance outdoor thermal comfort, providing a dynamic, iterative approach that adapts to diverse urban morphologies. The methodology adopts a simulation-based analysis for framing this study, which is classified into three main phases. Firstly, analyze the current environment for specific outdoor spaces with different settings in Madinaty, New Cairo (fully clustered with buildings neighborhood, semi-clustered neighborhood, fully open neighborhood). Secondly, a generative design tool with a Dynamo evolutionary algorithm is utilized to optimize the tree distribution across the communal areas of these three spaces considering the current built environment. Lastly, testing thermal comfort using Grasshopper and Ladybug simulation to assess the Universal Thermal Climate Index (UTCI) between the base case scenarios and the optimized scenarios to validate the generative design tool. Results indicate tangible improvements across the three different neighborhoods. In the Clustered Neighborhood area, the optimized design with 33 trees resulted in a lower UTCI (with an arithmetic mean of 37.55 °C) compared to the base case with 43 trees (38 °C). In the Semi-Clustered Neighborhood area, the optimized design with 45 trees highly improves the UTCI (38.01 °C), compared with the base case with 27 trees (39.40 °C). Lastly, for the Fully Open Neighborhood area, the optimized design with 25 trees achieved a slightly improved UTCI (39.55 °C) over the base case of 31 trees (39.60 °C).

Urban street canyon morphology and its effect on climate-responsive outdoor thermal environment in severe cold regions: a case study of Hohhot, China

Climate-responsive urban design requires a thorough understanding of how street canyon morphology affects the thermal environment, especially for the severe cold regions in Northern China. Individual geometric parameters often overlook other geometric characteristics of real street canyons, making it impossible to establish precise correlations between morphological parameters and thermal environments. This study investigates the influence of six key geometric parameters on thermal conditions in Hohhot, China. Mobile measurements revealed a significant quadratic relationship between the asymmetrical aspect ratio and mean air temperature at 12:00 (R2 = 0.91654). We also found that the aspect ratio, the complementary index closing ratio, and the smoothness rate were negatively correlated with mean air temperature at various times, while height variation positively influenced air temperature (R2 = 0.67946). Furthermore, the building coverage ratio in adjacent areas significantly impacted mean radiant intensity (R2 = 0.700 and 0.679 at 11:00 and 12:00, respectively, p < 0.05). Multiple regression analyses underscored the collective impact of these parameters on thermal conditions. These findings provide valuable insights for optimizing street canyon design to improve thermal comfort, thereby contributing to more effective climate-responsive urban planning.

A measurement-based framework integrating machine learning and morphological dynamics for outdoor thermal regulation

This study presents a comprehensive investigation into the interplay between machine learning (ML) models, morphological features, and outdoor thermal comfort (OTC) across three key indices: Universal Thermal Climate Index (UTCI), Physiological Equivalent Temperature (PET), and Predicted Mean Vote (PMV). Based on a comprehensive field measurement for 173 urban canyons, proper dataset for summer outdoor thermal condition was provided. Concurrently, six distinct ML models were evaluated and optimized using Bayesian optimization (BO) technique, considering performance indicators like weighted accuracy, F1-Score, precision, and recall. Notable trends emerged, with the CatBoost Classifier demonstrating superior performance in UTCI prediction, the Random Forest classifier excelling in PET estimation, and the XGBoost Classifier achieving optimal PMV prediction. Furthermore, the study delved into the influence of morphological features on OTC, prioritizing factors using SHAP values. Results consistently identified 90-degree orientation, street width, and 180-degree orientation as pivotal factors influencing OTC, with varying degrees of sensitivity across different classifications of thermal stress. Analysis of binary SHAP values unveiled intricate relationships between urban features and OTC indices, emphasizing the critical influence of street orientation on regulating outdoor thermal environments for UTCI and PET scenarios. Surprisingly, street width emerged as the foremost influential factor within the PMV index, challenging established trends and highlighting the complexity of thermal comfort modeling. Additionally, current research delineates the multifaceted impact of street width on microclimate dynamics, enriching our understanding of urban thermal dynamics and emphasizing its role in mitigating thermal stress within urban environments.

Green infrastructure and its influence on urban heat island, heat risk, and air pollution: A case study of Porto (Portugal)

Green infrastructure plays a fundamental role in mitigating the effects of urban heat island. Vegetation may trap particulates and absorb pollutants like ozone, thus improving air quality. Understanding how green infrastructure reduces urban heat island and air pollution within specific urban zones can provide valuable insights for better urban design, improved environmental quality, and increased resident well-being. This study addresses the impact of green infrastructure deprivation on urban heat island effects, air pollution, and heat-related health risks in Porto, Portugal. The study employs a monitoring network to analyse the spatial distribution of air temperature and humidity throughout the city, although with specific gaps in coverage. With a focus on the role of urban green infrastructure in mitigating air urban heat island effects, this paper uses the data from Porto Digital's monitoring network between 2019 and 2022. Heat risk index assesses vulnerability to heat-related health risks by integrating land surface temperature, land cover, and demographic data through remote sensing. Green infrastructure mapping is conducted to quantify the spatial distribution of vegetation elements in the study area. The data analysis from 2019 to 2022 reveals that urban heat island intensity is more pronounced during the summer and at night. Approximately 32.6% of Porto is in areas with a high heat risk index, indicating increased vulnerability to heat-related health risks. The study finds that limited green infrastructure exacerbates this vulnerability, particularly in socioeconomically disadvantaged areas. Additionally, persistent air pollution hotspots, including elevated levels of ozone and particulate matter, contribute to the intensity of urban heat island. These findings underscore the need for integrating green infrastructure into urban planning to mitigate urban heat island and air pollution, improve urban resilience, and promote environmental justice.

Microclimate Performance Analysis of Urban Vegetation: Evidence from Hot Humid Middle Eastern Cities

Urban heat islands (UHIs) pose a growing challenge in rapidly urbanizing areas, necessitating effective mitigation strategies to enhance environmental sustainability and human well-being. This study examined the role of vegetation in regulating urban microclimates, focusing on its ability to mitigate the effects of UHIs, promote thermal comfort, and enhance urban esthetics. The study drew on existing research that employed spatial analysis and Geographic Information Systems (GIS) to explore the relationship between vegetation metrics and reductions in surface temperature. Municipal initiatives in Khobar, Saudi Arabia, including tree-planting programs and street humanization projects, aimed to improve urban esthetics and pedestrian experiences. Although these efforts enhanced urban livability, they lacked a comprehensive ecological perspective, emphasizing the need for strategies that integrate thermal comfort, environmental resilience, and broader sustainability goals. The analysis demonstrated the societal and environmental benefits of tree-planting activities and linked urban vegetation plans to the achievement of Sustainable Development Goals (SDGs). The results highlighted the importance of incorporating green infrastructure in urban development to mitigate the effects of UHIs, improve air quality, and enhance overall urban livability. This paper proposed a framework for sustainable urban design, offering practical insights for policymakers and urban planners working to create resilient, environmentally conscious communities in extreme climates.