Street trees are a representative form of urban green space that play an important role in mitigating the environmental impact of urbanization. Planting the right tree in the right place in urban streetscapes can improve tree health and ecosystem services. Here, we propose a novel approach to selecting appropriate street trees using street type classifications. In the highly urbanized area of Uijeongbu City, South Korea, 221.9 km of streets with 19,717 street trees were classified into 12 types based on road width, aspect ratio, land use, and the presence of power lines. Appropriate tree species were selected for each street type, taking into account tree traits and functions as well as street environments. Then, we analyzed the structure and ecosystem-regulating services of street trees by type, also comparing the services of appropriate and non-appropriate trees. As a result, all 12 street types were identified, but their distribution was uneven. Tree dimension was the key factor in determining appropriate species, and, for the second most common street type, characterized by narrow roads, low aspect ratios, and power lines, only four appropriate species were identified, indicating an urgent need for more options. Additionally, the most dominant species accounted for over 20%, averaging 44% across the 12 street types, further highlighting the necessity of introducing more diverse tree species. Overall, appropriate street trees generally provided higher service efficiency compared to non-appropriate trees across four ecosystem regulating services. These findings emphasize the need for policies and guidelines that promote street tree diversity and enhance the ecological benefits of street trees. This study provides a foundation for developing sustainable street tree management strategies that contribute to healthier and more resilient urban streetscapes.

Particulate matter (PM), particularly PM_2.5, is a major urban air pollution concern globally. While temporary mitigation measures are generally implemented during high-pollution periods, sustainable solutions focusing on forest landscape management are crucial. This study examines the effects of forest landscape types and environmental variables on PM_2.5 concentrations during the high-pollution period (January–March 2022) in South Korea, using data from 40 national air quality monitoring stations. GIS and Fragstats were used to construct spatial variables and landscape indices. Stepwise multiple linear regression analyses were then conducted to identify significant factors affecting PM_2.5 concentrations. The aggregated forest model (i.e., without distinguishing between forest types) explained 72.9% of the variance in PM_2.5 concentrations. Forest percent cover (within 5000 m) and distance from the China national border were found to negatively affect PM_2.5 levels, while population size (within 5000 m) and urbanized area patch density (within 5000 m) had positive effects (p < 0.05). By incorporating forest types as variables, the forest type model improved explanatory power to 83.4%. Specifically, mixed forest percent cover (within 5000 m), mixed forest patch density (within 3000 m), and broad-leaved forest percent cover (within 1000 m) were negatively correlated with PM_2.5, while population size and urbanized area patch density (within 5000 m) showed positive effects (p < 0.05). These results highlight the importance of considering forest types, along with anthropogenic environmental variables, when assessing the mitigating effects of forests on PM_2.5, as both showed scale-dependent relationships with pollution levels. This study informs urban planning and long-term environmental management strategies for reducing PM_2.5 pollution.