PM_2.5 pollution has intensified with rapid urbanization and industrialization, raising concerns about its health and environmental impacts. Both meteorological factors and urban forests play crucial roles in influencing PM_2.5 concentrations. However, limited attention has been given to the direct impact of canopy structure on PM_2.5 levels at a larger scale. This study analyzes the temporal variation of PM_2.5, including seasonal and diurnal patterns, across different functional zones (park, traffic, and residential zones) in a subtropical region. It also investigates the seasonal responses of PM_2.5 to meteorological factors (temperature, humidity, and precipitation) and canopy structure characteristics, including canopy diameter (CD), canopy thickness (CT), canopy area (CA), canopy volume (CV), canopy height ratio (CH), leaf area index (LAI), and tree canopy cover (CO). The results show that among different functional zones, PM_2.5 concentrations were the highest in park zones, followed by traffic zones. Seasonal variations in PM_2.5 concentrations were the highest in winter (84.00 ± 45.97 μg/m³), with greater fluctuations, and the lowest in summer (36.85 ± 17.63 μg/m³ µg/m³), with smaller fluctuations. Diurnal variation followed an “N”-shaped curve in spring, summer, and autumn, while a “W”-shaped curve was observed in winter. Correlation analysis indicated significant negative correlations between PM_2.5 and humidity, temperature, and rainfall, while CD, CA, and CV showed positive correlations with PM_2.5. Notably, PM_2.5 exhibited greater sensitivity to changes in canopy structure in winter, followed by autumn. Despite these findings, the influence of canopy structure on PM_2.5 concentrations was considerably smaller compared to meteorological factors. In particular, every 1 m² increase in canopy area could raise PM_2.5 levels by 0.864 μg/m³, whereas an average increase of 1 mm in rainfall could raise PM_2.5 by 13.665 μg/m³. These findings provide valuable guidance for implementing protective measures, improving air quality, optimizing urban greening strategies, and enhancing public health outcomes.

Forest soil respiration plays a crucial role in the global carbon cycle. However, accurately estimating regional soil carbon fluxes is challenging due to the spatial heterogeneity of soil respiration at the stand level. This study examines the spatial variation of soil respiration and its driving factors in subtropical coniferous and broad-leaved mixed forests in southern China, aiming to provide insights into accurately estimating regional carbon fluxes. The findings reveal that the coefficient of variation (CV) of soil respiration at a scale of 50 m × 50 m is 18.82%, indicating a moderate degree of spatial variation. Furthermore, 52% of the spatial variation in soil respiration can be explained by the variables under investigation. The standardized total effects of the main influencing factors are as follows: soil organic carbon (0.71), diameter at breast height within a radius of 5 m (0.31), soil temperature (0.27), and soil bulk density (−0.25). These results imply that even in relatively homogeneous areas with flat terrain, fine-scale soil respiration exhibits significant spatial heterogeneity. The spatial distribution of woody plant resources predominantly regulates this variation, with root distribution, shading effects, and changes in soil physical and chemical properties being the main influencing mechanisms. The study emphasizes the importance of simulations at different microscales to unravel the potential mechanisms governing macroscopic phenomena. Additionally, it highlights the need for incorporating a more comprehensive range of variables to provide more meaningful references for regional soil carbon flux assessment.

Forest ecosystems are crucial in mitigating air pollution and improving air quality. Therefore, investigating the relationships between air quality, forest structure, and environmental factors in different forest types is of significant importance. This study conducted three months of continuous monitoring (June–September 2023) of air quality factors (particulate matter (PM_2.5 and PM₁₀), ozone (O₃), and negative air ions (NAI)) and environmental factors (air temperature (TA), relative humidity (RH), light intensity (LI), and wind speed (WS)) in four subtropical forest types, along with vegetation characteristic surveys. The effects of forest structure and environmental factors on air quality were determined by correlation and multiple regression analysis. The results showed that the forest air quality is at its best in July during the summer season. Concentrations of particulate matter (PM) and ozone (O₃) in mixed coniferous and broadleaf forests (MCB), as well as deciduous broadleaf forests (DB), are lower than those in moso bamboo forests (MB) and evergreen broadleaf forests (EB). The troughs of PM concentrations occur in the early morning (4:00–6:00), while the troughs of O₃ concentrations occur in the early morning (4:00–6:00) and in the evening (18:00). NAI concentrations were highest in DB (1287 ions/cm³), followed by MCB (1187 ions/cm³), MB (896 ions/cm³), and EB (584 ions/cm³), with NAI concentrations peaking between 14:00 and 16:00. PM concentrations in forest air were primarily influenced by stand density (SD) and the Shannon–Wiener index of herbaceous layer (SWH) (p < 0.05); ozone concentrations were significantly affected by tree height (TH) and canopy density (CD) (p < 0.05); and NAI concentrations were primarily related to TH and diameter at breast height (DBH). Air particulate matter concentrations were negatively affected by TA and RH (p < 0.01), and ozone concentrations were negatively influenced by RH and WS and were positively influenced by TA. TA has a direct and significant positive effect on the NAI concentration (p < 0.01), and RH indirectly influences the changes in NAI concentration through its interaction with TA. This study provides new insights for vegetation optimization in forest parks and planning forest health-promoting activities for sub-healthy populations.