Urban blue-green space cooling island effect (BGCI) is effective in improving the thermal comfort of residents. However, there is little knowledge regarding the diurnal variation of BGCIs and the influencing factors. Therefore, we selected Beijing as the study area and used ECOSTRESS LST data and the inflection–maximum perspective method to explore the diurnal variation of BGCIs. Additionally, we investigated diurnal variations in the relative influence of the characteristics of the blue-green space itself, as well as the surrounding 2D and 3D landscape metrics using boosted regression tree model. The results indicated that BGCIs displayed distinct diurnal patterns. BGCIs progressively increased from sunrise to midday, decreased thereafter to sunset, reached its peak around midday, and diminished to a relatively low level and constant intensity at night. BGCIs of water bodies exhibited a significantly higher intensity compared to vegetation during the day, particularly around midday, with a difference in mean cooling intensity (CI) of 1.06 °C and mean cooling distance (CD) of 63.27 m, while the differences were minimal at night with a difference in mean CI of 0.02 °C and mean CD of 9.64 m. The features of vegetation had a more significant impact on BGCIs during the day, particularly around midday (CI: 32.30% around midday and 13.86% at night), while the 3D metrics influenced BGCIs more at night (CI: 26.40% around midday and 35.81% at night). The features of water bodies had a greater impact during the midday (52.87% around midday and 10.46% at night), with the landscape metrics of surrounding water bodies playing a more important role at night (15.56% around midday and 38.28% at night). The effect of tree height, shape index of vegetation, and surrounding building coverage ratio of water bodies on BGCIs exhibited opposite trends around midday and at night. Optimizing the landscape surrounding blue-green spaces is more cost-effective than the blue-green spaces themselves for nighttime thermal comfort, especially in 3D urban landscapes. These findings emphasize the imperative and essentiality of exploring diurnal variations in BGCIs, providing valuable information for mitigating UHI effects.

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.

Urbanization and industrialization have led to the coexistence of winter haze and summer heat island in some cities in northern China, but the mitigation effect of ventilation corridors is lack of quantitative evaluation. This paper introduces circuit theory into urban climate research. Taking Shenyang as a case study, it comprehensively employs three-dimensional urban landscape pattern indices (including SVF, FAD, and Z0) to guide ventilation corridor construction, establishes an analytical framework for PM_2.5 and LST, and quantifies the environmental benefits of ventilation corridors. The results show that the corridor generated by circuit theory can make 65.14% of path PM lower than the average level of the city; Among the 7 exit paths of wind corridors, the surface temperature of 4 channels is lower than the average level of the city. FAD is positively correlated with Z0 (R² = 0.7) and negatively correlated with SVF (R² = 0.61). Meanwhile, the circuit theory model identifies eight pinch points along ventilation paths. CFD software is employed to simulate atmospheric environments for six typical building layouts to guide subsequent urban planning. Therefore, the reasonable layout of urban morphology indicators and the construction of reasonable ventilation corridors can effectively control the atmospheric particulate pollution and the heat island effect in summer.

Urban green spaces are an integral part of urban ecosystems and play a crucial role in improving the urban ecological environment and promoting sustainable urban development. This study aimed to quantify the spatiotemporal evolution of urban green spaces in central Shenyang and to assess how their relationships with selected urban development and socioeconomic drivers varied across space and time. Urban green space information for 2010, 2015, and 2020 was extracted from remote sensing imagery using ENVI and ArcGIS. Eight indicators—impervious surface intensity, population density, nighttime light intensity, economic density, industrial structure, road density, traffic accessibility, and land use intensity—were examined using spatial-pattern and contribution analyses, ordinary least squares regression (OLS), and geographically weighted regression (GWR). Urban green space areas were 330.56 km² (23.55%), 344.55 km² (24.55%), and 337.32 km² (24.03%) in 2010, 2015, and 2020, respectively, representing a net increase of 6.76 km² (2.05%) and an annual dynamic degree of 0.20% over the study period. Spatially, green spaces were concentrated in peripheral areas, fragmented in the central area, and prominently distributed along river corridors. At α = 0.05, coefficient-level OLS results showed negative associations with impervious surface intensity in 2010 (standardized β = −0.184, p < 0.001) and 2015 (β = −0.098, p = 0.005). In 2020, impervious surface intensity showed a small positive coefficient (β = 0.056, p = 0.043), whereas traffic accessibility showed a negative coefficient (β = −0.0556, p = 0.033). The OLS models had low explanatory power, with R² values of 0.034, 0.008, and 0.007 and adjusted R² values of 0.029, 0.003, and 0.002 for 2010, 2015, and 2020, respectively. The GWR local R² values ranged from 0.000 to 0.310 and showed spatially shifting high-value areas, indicating marked spatial variation in model explanatory power. These findings provide case-specific evidence for Shenyang and a transferable analytical framework for spatially differentiated green space planning in comparable old industrial and rapidly restructuring cities.

Faced with both global climate change and rapid urbanization, understanding how the built environment affects surface runoff is essential for strengthening urban hydrological resilience. However, elucidating the nonlinear and interactive effects of three-dimensional buildings and two-dimensional green spaces on surface runoff potential in urban blocks remains a scientific challenge for precise flood-mitigation spatial planning. Using six typical waterlogging-prone blocks in Shenyang as case studies, this study constructs a morphological index system for urban gray–green spaces and reveals the nonlinear effects of each index on surface runoff potential using an interpretable Random Forest (RF)–SHAP model. The results indicate that the RF model reliably captures the complex spatial patterns of simulated local surface runoff potential (R² = 0.723–0.825). At the block scale, the surface runoff response exhibits a dual character: it is predictively dominated by three-dimensional morphological dominance and two-dimensional base regulation. Three-dimensional building morphology generally demonstrates pronounced unidirectional thresholds and high-value saturation in model prediction. In particular, when the core indicator, building spatial congestion degree (B_SCD), crosses a critical threshold, surface runoff potential rises sharply. The coefficient of variation in building height (B_HVC) shows a “V-shaped” reversal in areas of extreme surface runoff potential, whereas two-dimensional green space indicators display clear asymmetric critical points. Significant reductions in surface runoff potential appear only when green space scale (G_LPI), boundary complexity (G_LSI), or fragmentation (G_PD) exceed specific model-identified thresholds. Furthermore, the study demonstrates marked interactive effects between the morphologies of gray–green spaces. High surface runoff risk from dense, large buildings can be substantially offset by large green space patches (G_LPI) with highly complex boundaries (G_LSI). The advantage of vertically staggered buildings (B_HVC) requires a green space base with low fragmentation (G_PD) to realize a gray–green synergistic mitigating effect. These findings provide theoretical and methodological support for enhancing the hydrological resilience of urban blocks.