The development of new energy vehicles and road dust removal technologies presents opportunities for constructing urban ventilation systems based on road patterns. However, the impact of road system layouts on pedestrian-level wind environments remains insufficiently understood. This study utilizes the general-purpose CFD software Phoenics to analyze the effects of road orientation, width, density, and intersection configurations on block ventilation. The standard k-ε model and three-dimensional steady-state RANS equations are employed to calculate pedestrian-level mean air age as an indicator of ventilation efficiency. Grid convergence analysis and validation against previous wind tunnel measurements were conducted. Results show that road layouts influence overall ventilation efficiency by affecting airflow volume, direction, and velocity. Optimal ventilation occurs when road orientation aligns with the prevailing wind at 0° or exceeds 70°. Recommended widths for trunk, secondary, and local roads are 46 m, 30 m, and 18 m, respectively. Lower densities of local road systems enhance ventilation, while higher densities of trunk and secondary roads are beneficial. Intersection configurations impact airflow distribution, with windward segments aiding lateral ventilation of side roads. Finally, ventilation design strategies for road systems are proposed, offering potential for leveraging urban road networks to construct efficient ventilation systems.

As the global population ages, respiratory health among the elderly has become a key public health concern. Although urban green infrastructure (UGI) has the potential to improve air quality and promote health, research on how its layout patterns influence respiratory health among older adults remains limited. This study focuses on elderly residents aged 60 and above in the central urban area of Shenyang, China, to evaluate the relative importance and interactions of different features affecting respiratory health. We utilized the St. George’s Respiratory Questionnaire (SGRQ) to collect data on respiratory health and employed hierarchical regression and random forest (RF) models to analyze the impact of UGI factors across three spatial scales (300 m, 500 m, and 1000 m). The results indicate that UGI within a 300 m radius of participants’ residences contributes most significantly to respiratory health, with diminishing marginal effects as the spatial scale increases. Green space area (GSA) and the NDVI were identified as the most important factors influencing respiratory health, while green landscape pattern metrics had a greater influence at larger spatial extents. Additionally, a significant nonlinear marginal effect was observed between UGI and respiratory health. These findings provide key insights for health-oriented urban planning and green infrastructure design.

Atmospheric particulate matter (PM) pollution has escalated into a critical threat to urban public health and safety. Among urban functional zones, commercial districts—characterized by high human exposure—are simultaneously hotspots of pollutant accumulation. Consequently, PM mitigation in these areas has become an urgent challenge for sustainable urbanization. This study used Computational Fluid Dynamics (CFD) to simulate the diffusion process and vertical concentration distribution of particulate matter in commercial districts. The results showed that the concentration of PM2.5 decreased with increasing height, with the highest concentration in the respiratory zone (1.5 m) and basic diffusion above 50 m; There are significant differences in the concentration changes of pollutants under different combinations of architectural spaces. By establishing a 20 m block wind corridor, changing the relationship between the building and the street enclosure, and adjusting the form of the building podium and overhead design with building height multiples (6–12 m), strategies can effectively alleviate the accumulation of particulate matter in commercial blocks. These findings provide quantitative evidence for evidence-based retrofitting strategies aimed at reducing PM2.5 exposure in high-density commercial areas.

With the acceleration of urbanization, wind environment problems in high-density blocks have become increasingly prominent. Most existing studies only focus on the identification of influencing factors, lacking a complete closed loop from “diagnosis” to “design verification”. This study takes 174 blocks along the Qingnian Street Metro Line in Shenyang as research objects, uses PHOENICS (2019) software, and clarifies the seasonal distribution characteristics of the wind environment in high-density blocks through field measurement and simulation verification. The XGBoost-Shap method is employed to identify key morphological factors and their nonlinear influence mechanisms. The K-means clustering method is adopted to construct the typology of calm wind zones, which are classified into three categories: Type A (narrow street canyon-comprehensive resistance type), Type B (high-density enclosed type), and Type C (high-rise obstruction type). Multi-scenario simulations are conducted for each type, and differentiated optimization schemes are designed accordingly. The results show that under specific CFD boundary conditions representing typical cold climate urban environments, eight indicators, including enclosure degree (ED), sky visibility (SVF), and windward area ratio (FAI), have significant seasonal sensitivity effects on wind environments. The effect of multi-strategy collaborative optimization is significantly superior to that of a single intervention. This study verifies the effectiveness of the typological approach in bridging wind environment mechanism identification and spatial optimization design. Thresholds for indicators such as enclosure degree (ED), sky visibility (SVF), and building density (BD) can be used as preliminary design-screening tools, providing a reference for ventilation improvement design ideas along high-density subway lines in actual urban construction.