This study investigates the differences in outdoor thermal comfort in different spatial types over long-term observations during the transition season in a cold region. Using the Universal Thermal Climate Index (UTCI) as an evaluation index, subjective questionnaires and field surveys were conducted on thermal comfort in three different typical outdoor environments (sunlight, tree shade and building shade) in Dalian (Northeast China). The results demonstrated the following: (1) Air temperature and mean radiant temperature were the dominant meteorological factors affecting outdoor thermal comfort in Dalian. (2) The main thermal sensation recorded in the transition season was “slightly cool”. Over time, the human thermal sensation experienced a gradual transition from “hot” to “cold”. (3) Compared with direct sunlight, the average UTCI decreased by 6.6 and 3.5 °C for building shade and tree shade environments, and the neutral UTCI in Dalian was found to be 18.8 °C. Subjects were most sensitive to the thermal environment in building shade, then in tree shade and the least in sunlight. (4) When UTCI ≥ 21.9 °C, the thermal acceptability rate in building shade remained the highest; when 10.2 °C ≤ UTCI < 21.9 °C, it was highest in tree shade; and when the UTCI < 10.2 °C, it was highest in sunlight. This study verifies the UTCI applicability in the transition season in cold regions and analyzes the spatial differences in human thermal comfort. These differences play a positive role in developing optimization strategies for outdoor thermal environments, improving environmental satisfaction and facilitating pedestrian outdoor activities.

The influence of street spatial form on thermal comfort from urban morphology and human-centered perspectives has been underexplored. This study, utilizing multi-source data and focusing on urban central districts, establishes a refined index system for street spatial form and a thermal comfort prediction model based on extreme gradient boosting (XGBoost) and Shapley additive explanations (SHAP). The results reveal the following: (1) Thermal comfort levels display spatial heterogeneity, with areas of thermal discomfort concentrated in commercial zones and plaza spaces. (2) Compared to the human-centered perspective, urban morphology indicators correlate strongly with thermal comfort. (3) The key factors influencing thermal comfort, in descending order of importance, are distance from green and blue infrastructure (GBI), tree visibility factor (TVF), street aspect ratio (H/W), orientation, functional diversity indices, and sky view factor. All but the TVF negatively correlates with thermal comfort. (4) In local analyses, the primary factors affecting thermal comfort vary across streets with different heat-risk levels. In high heat-risk streets, thermal comfort is mainly influenced by distance from GBI, H/W, and orientation, whereas in low heat-risk streets, vegetation-related factors dominate. These findings provide a new methodological approach for optimizing urban thermal environments from both urban and human perspectives, offering theoretical insights for creating more comfortable cities.

In the context of global warming, urban climate problems such as heat waves, urban heat islands and air pollution are becoming increasingly prominent, and the cooling effect of rivers is an effective way to mitigate urban hot climate. This study investigates the surrounding urban area of the Hun River in Shenyang, a severe cold region of China, by calculating satellite inversion surface temperature and urban morphology data, and explores the cooling effect of rivers using linear regression models and spatial regression models. The results show that (1) water bodies have a cooling effect on the surrounding environment, with the farthest cooling distance being 4,000 m, but the optimal cooling distance being 2,500 m. (2) In the results of the spatial regression model analysis, the R² value stays above 0.7 in the range of 0–4,000 m, indicating that urban morphological factors are closely related to LST (land surface temperature). The negative correlation is most pronounced for NVDI (normalized vegetation index), with a peak of −14.8075 calculated by the regression model, and the positive correlation is most pronounced for BD (building density), with a peak of 8.5526. (3) The urban thermal environment can be improved and the heat island effect mitigated through measures such as increasing urban vegetation cover and reducing building density, and these findings can provide data references and case studies to support urban planning and development departments.

Performance parameters and generative design applications have redefined the human–machine collaborative relationship, challenging traditional architectural design paradigms and guiding the architectural design process toward a performance-based design transformation. This study proposes a multi-objective optimization (MOO) design approach based on performance simulation, utilizing the Grasshopper-EvoMass multi-objective optimization platform. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is applied to coordinate two performance metrics—outdoor thermal comfort and building energy loads—for the multi-objective optimization of architectural design. The results indicate that (1) a performance-based multi-objective optimization design workflow is established. Compared to the baseline design, the optimized building form shows a significant improvement in performance. The Pareto optimal solutions, under 2022 meteorological conditions, demonstrate an annual energy efficiency improvement of 16.55%, and the outdoor thermal neutrality ratio increases by 1.11%. These results suggest that the optimization approach effectively balances building energy loads and outdoor thermal comfort. (2) A total of 1500 building form solutions were generated, from which 16 optimal solutions were selected through the Pareto front method. The resulting Pareto optimal building layouts provide multiple feasible form configurations for the early-stage design phase.

The indoor environment is widely acknowledged as a non-pharmacological tool for regulating residents’ mental health. In dormitory environments with relatively high residential density, the mental health of university students requires particular attention. This study surveyed 445 students from a northern Chinese university and used structural equation modeling (SEM) to analyze the impact of perceived indoor environmental quality (IEQ)—including thermal, lighting, acoustics, indoor air quality, and overcrowding—on self-reported anxiety symptoms. The results indicated the following: (1) students’ perceptions of dormitory IEQ significantly affected anxiety symptoms, explaining 40% of the variance; (2) anxiety symptoms associated with the IEQ were mainly characterized by anxiety and panic (r = 0.91, p < 0.001); (3) subjective perceptions of the acoustic environment (r = −0.55, p < 0.001) and indoor air quality (r = −0.15, p < 0.05) were key predictors of anxiety, while thermal environment, lighting environment, and overcrowding were not significant. The findings enrich the IEQ system and provide directions for optimizing the dormitory indoor environment from the perspective of student mental health, with implications for other types of residential buildings.