Air pollution has become a major challenge to global urban sustainable development, necessitating urgent solutions. Meteorological variables are key determinants of air quality; however, research on their impact across different urban gradients remains limited, and their mechanisms are largely unexplored. This study investigates the dynamic effects of meteorological variables on air quality under varying levels of urbanization using Kaohsiung City, Taiwan, as a case study. Meteorological and air pollutant data from monitoring stations in Kaohsiung, Taiwan, for the year 2023 were collected and analyzed. The Air Quality Index (AQI) was used to quantify air quality levels, and Granger causality tests and Vector Autoregression (VAR) models were employed to analyze the dynamic relationships between meteorological variables and AQI. The results revealed that: (1) Suburban areas exhibited significantly better air quality than urban and near-urban areas, with annual AQI values of 59.58 in Meinong (outskirts), 67.86 in Renwu (suburbs area), and 76.73 in Qianjin (urban area), showing a progressive improvement in air quality from urban to suburban areas, primarily due to lower levels of urbanization and abundant forest resources; (2) Temperature and relative humidity emerged as key meteorological variables influencing AQI, with Granger causality tests indicating that temperature significantly affects AQI, especially in urban areas. Impulse response analysis revealed that temperature had a notable positive and negative correlation effect on AQI over lagged periods, while wind speed showed a negative correlation with AQI in suburban areas, gradually shifting to a positive correlation over time; (3) Variance decomposition indicated that temperature had the largest impact on AQI in urban areas, particularly with cumulative lag effects, while wind speed was the main variables influencing air quality in suburban areas. This study provides scientific evidence for future urban planning and environmental management, supporting the development of more effective air quality improvement strategies to promote sustainable urban development.

Previous studies have focused on the linear relationship between recreation behavior and environmental variables. However, to inform the planning and design of recreational spaces, it is essential to understand the factors that contribute to differences in the spatial distribution of recreation behavior. This study investigates the characteristics of visitor behavior in urban mountain parks in Fuzhou City, Fujian Province, China. It describes the distribution of tourist numbers and the diversity of behaviors in these parks and explores the landscape driving factors of visitor behavior, as well as the interaction effects between the factors from the perspective of spatial driving forces. The results indicate that (1) The observed behaviors in the three parks are primarily access behaviors. The number of visitors and the diversity of behaviors show a high level in the morning and evening and a low level in the midday. (2) There was minimal variation in behavioral composition and behavioral diversity among the study plots of different elevation gradients in the three parks. However, the contrasts between different landscape types were more pronounced, with impermeable plazas exhibiting the highest behavioral diversity and park roads demonstrating the most homogeneous behavioral diversity. (3) The impact of environmental factors was more pronounced than that of landscape pattern factors. The environmental factors that most strongly influenced passing, dynamic, and static behaviors were spatial connectivity value, hard space proportion, and number of recreational facilities, respectively. In contrast, the hard space proportion was the strongest driver of behavioral diversity. Moreover, the interaction between the hard space proportion and spatial connectivity value was more pronounced in driving behavioral diversity, as well as the three behaviors.

Urban parks are fundamental to building sustainable and inclusive cities, yet the mechanisms linking their spatial configuration to human activities and social functions remain insufficiently understood. A scalable multimodal framework is developed to quantify how spatial visibility is associated with patterns of park use and the provision of social ecosystem services. A total of 94,635 geo-tagged user-generated images from 148 parks in Vancouver, Canada, were analyzed using the Contrastive Language-Image Pretraining (CLIP) model to classify user activities into six behavioral categories. Concurrently, airborne LiDAR data and space syntax analysis were used to derive three visibility metrics—Mean Isovist Area (MIA), reflecting internal openness; Mean Visual Integration (MVI), indicating visual connectivity within the park interior; and External Isovist Ratio (EIR), representing edge openness and boundary visibility. The results indicate that EIR exhibits the strongest and most consistent relationships with user activity patterns, positively associated with family recreation, social vibrancy, and physical activity, while negatively linked to nature immersion and quiet relaxation. MIA shows moderate associations with socially interactive and child-oriented activities, whereas MVI contributes little explanatory power compared to localized visibility conditions. These findings highlight spatial visibility as a critical design attribute that is closely associated with human–forest interactions. By illustrating that moderate visual openness and edge permeability are associated with more inclusive and multifunctional patterns of park use, actionable insights are provided for urban park planning and design, and the promotion of social sustainability.