The urban heat island (UHI) effect not only impacts urban climates and residents’ quality of life but also poses challenges to energy consumption and sustainable development in cities. While many studies have explored the relative importance and marginal effects of two-dimensional (2D)/ three-dimensional (3D) urban morphology on land surface temperature (LST) to mitigate UHI, the interactive effects of these 2D/3D morphology metrics on daytime and nighttime LST at different grid scales have been largely overlooked. This study focuses on the area within the outer ring of Tianjin, and analyzes the relative importance, marginal effects, and particularly the interaction effects of 2D/3D urban morphology on LST. Our findings reveal the following: (1) The normalized difference vegetation index (NDVI) has the most significant cooling effect across all variables and at all three grid scales. (2) NDVI and bare land coverage have the greatest impact on daytime LST, while building height and tree height (TH) predominantly influence nighttime LST. (3) The relationships between key 2D/3D metrics and LST are nonlinear. Overall, NDVI is negatively correlated with LST across all three grid scales. (4) Interactions between 2D/3D metrics affect LST; LST decreases when TH exceeds 1.8 m and building density is below 62%, or when TH is below 1.8 m and building density exceeds 62%. These findings provide valuable insights and recommendations for sustainable urban development and effective heat adaptation strategies in specific locations.

Land surface temperature (LST) is a key indicator of the urban heat island effect and is affected by multiple factors. However, existing research mainly focuses on the contributions of urban landscape and meteorology, and the impact of changes in atmospheric environment has not been fully considered. Based on multisource data and a random forest model, this study quantified the independent and interactive effects of aerosols, meteorological conditions, and urban features on LST in Beijing. The results revealed that the effects of the meteorological factors and aerosol optical depth (AOD) on LST were significantly greater than those of the urban landscape index. The response of LST to multiple factors is nonlinear, and the interactions of precipitation with wind speed and vegetation have the strongest cooling effects on LST. The aerosol impact shifts seasonally, with its direct radiative effect dominating in spring and inducing a cooling of up to about 2.0 °C. Notably, the land use type plays a background role in determining the LST, and the average LST decreases by approximately 1.5 °C for every 50% increase in tree coverage. As the building height increases by 10%, the summer LST increases by approximately 2 °C. In addition, the interactions of precipitation with wind speed and vegetation were identified as having the strongest cooling effects on LST. By elucidating the nonlinear interactions among aerosol, meteorological, and urban features, this work moves beyond isolated factor analysis and offers mechanism cognition for urban planning strategies.

Urban park green space (UPGS) accessibility within 15 min is widely used to characterize neighborhood-scale park provision, yet aggregated accessible park supply (APS) does not reveal its hierarchical composition or the additional supply gained as walking thresholds expand. This limitation motivates a park-hierarchy and threshold-transition framework for diagnosing the internal structure of 15 min accessible park supply. In Tianjin’s central urban area, the analysis linked 63 subdistricts to 77 polygon-based UPGS destination records representing 72 park sites through 4851 origin–destination walking-time pairs and compared 10, 15, and 20 min thresholds. Parks were grouped into three analytical service hierarchies: small neighborhood-scale, medium community-scale, and large urban-scale parks. Hierarchical accessible park supply (HAPS) decomposes APS by hierarchy; APS, the Large-Park Dependence Index (LPD), and the Neighborhood Service Support Index (NSS) define four configurations, while the Threshold Gain Index (TGI) interprets threshold transition. Although 41 subdistricts had positive 15 min APS, only 17 were neighborhood-supported; 14 were threshold-compensated, 10 were large-park-dependent, and 22 had accessibility deficits. Positive APS can therefore represent different supply structures without indicating actual service sufficiency. The framework complements demand-sensitive accessibility methods. Future research should integrate population demand, park quality, actual use, population-weighted origins, park entrances, and cross-city validation.

Rapid urbanization and intensifying thermal stress pose challenges to urban livability, climate resilience, and sustainable development. However, studies overlook the seasonal nonlinearity and spatial non-stationarity of urban thermal comfort and its driving factors, constraining seasonally differentiated, climate-adaptive planning strategies. Taking central Tianjin, China, as a case study, this research integrates a geographically weighted XGBoost model with shapley additive explanations to disentangle the seasonal and spatial differentiation, nonlinear relationships, and conditional interaction patterns of the Universal Thermal Climate Index (UTCI). Results indicate: (1) UTCI exhibits seasonal differences in spatial thermal gradients, with relatively high values concentrated in central and northern-central areas; (2) driver contributions shift across seasons, with evapotranspiration leading in summer, digital elevation model in autumn, and building density in winter, while landscape composition contributes most strongly in winter; (3) candidate nonlinear breakpoints include winter sky view factor (0.683; 95% interval 0.662–0.752) and water patch density (15.6 patches/100 ha; 13–17.8); (4) conditional interactions vary seasonally, involving environmental–terrain combinations in summer and building-morphology–landscape combinations in winter. These findings reveal season-specific thermal comfort associations, providing quantitative evidence for translating nonlinear relationships and spatial heterogeneity into climate-adaptive urban design. This supports targeted thermal-environment interventions for urban resilience and sustainability.