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.

Spatial predictions of forest biomass at regional scale in forests are critical to evaluate the effects of management practices across environmental gradients. Although multi-source remote sensing combined with machine learning has been widely applied to estimate forest biomass, these approaches often rely on complex data acquisition and processing workflows that limit their scalability for large-area assessments. To improve the efficiency, this study evaluates the potential of annual multi-sensor satellite embeddings derived from the AlphaEarth Foundations model for forest biomass prediction. Using field inventory data from 89 forest plots at the Yunhe Forestry Station in Zhejiang Province, China, we assessed and compared the performance of four machine learning algorithms: Random Forest (RF), Support Vector Regression (SVR), Multi-Layer Perceptron Neural Networks (MLPNN), and Gaussian Process Regression (GPR). Model evaluation was conducted using repeated 5-fold cross-validation. The results show that SVR achieved the highest predictive accuracy in broad-leaved and mixed forests, whereas RF performed best in coniferous forests. When all forest types were modeled together, predictive performance was consistently limited across algorithms, indicating substantial heterogeneity (e.g., structure, environment, and topography) among forest types. Spatial prediction maps across Yunhe Forestry Station revealed ecologically coherent patterns, with higher biomass values concentrated in intact forests with less human disturbance and lower biomass primarily occurring in fragmented forests and near urban regions. Overall, this study highlights the potential of embedding-based remote sensing for regional forest biomass estimation and suggests its utility for large-scale forest monitoring and management.

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.

A plant species in a community often grows with some other plant species. While many studies have assessed interspecific interactions between two target plant species, few have considered the impacts of the other plant species (e.g., the third, fourth, and fifth plant species) on these interactions. To assess the impacts, we grew one seedling of each of the five herbaceous plant species that are common in China (Cynodon dactylon, Plantago asiatica, Taraxacum mongolicum, Nepeta cataria, and Leonurus japonicus) alone (no competition) or with one seedling of one, two, three, or four of the other species. The presence of a neighbor plant generally reduced the growth of the target species, suggesting that the interspecific relationships were mostly competitive. The presence of other neighbor species (the third, fourth, and fifth species) could alter the interspecific interactions between two target species, but such effects varied depending on both the identity of the target species and the identity of the other species. Additionally, the effects of the third species depended little on the presence of the fourth and fifth species. We conclude that interspecific interactions between two plant species are commonly regulated by the presence of other species, facilitating species coexistence. However, our findings do not support the idea that the impacts of the fourth and fifth species on interactions among three plant species are common. This study highlights the complex interactions among multiple plant species within a community and also the importance of including these high-order interactions when modelling community dynamics and species coexistence.