Search

The amplification effect of anthropogenic‐biogenic interactions on secondary organic aerosol (SOA) formation remains debated, particularly regarding the impact of anthropogenic emissions on biogenic SOA (BSOA) formation in forests near megacities. This study concurrently measured typical biogenic and anthropogenic SOA tracers during day and night at the summit (1,690 m a.s.l.) and foot (200 m a.s.l.) of the Nanling mountains, a large subtropical forest adjacent to the Pearl River Delta (PRD) in southern China. Results revealed unexpectedly high concentrations of BSOA tracers (149.9 ± 70.5 ng m−3 at the summit and 109.7 ± 51.2 ng m−3 at the foot), surpassing those at most other background sites worldwide. Daytime BSOA tracer levels at the foot were consistent with nighttime levels, whereas the summit showed significantly higher concentrations at night. Nighttime correlations between O₃ and BSOA tracers at the summit suggest that high O₃ levels stimulate BSOA formation. Conversely, a negative correlation between O₃ and isoprene derived SOA (SOAI) tracers at the foot indicates that other oxidants may also influence SOAI formation. BSOA tracer concentrations rose significantly with the arrival of anthropogenic pollutants (e.g., SO₂ and NO₂), indicating that anthropogenic pollution amplifies BSOA formation by enhancing aerosol acidity (pH < 3). This amplification effect could be mitigated by the reduction of aerosol acidity due to increased NH₃ and relative humidity (RH). Our findings provide valuable insights into the interactions between anthropogenic and biogenic emissions on SOA formation and vertical distribution in forests surrounding megacities. Plain Language Summary: Secondary organic aerosol (SOA) makes up a significant portion of global submicron‐sized atmospheric organic aerosols (OA), reaching up to 90% by mass. Understanding its composition and sources is essential for improving air quality and adapting climate change. However, the role of interactions between human‐made (anthropogenic) and natural (biogenic) emissions in enhancing SOA formation is still debated, especially near megacities. To investigate this, we measured biogenic and anthropogenic SOA tracers simultaneously at the summit (1,690 m a.s.l.) and foot (200 m above sea level) of the Nanling mountains, a subtropical forest near the PRD in southern China, during both day and night. Our results showed unexpectedly high concentrations of SOA tracers compared to most global sites. Isoprene‐derived SOA tracers were predominant at the summit, while monoterpene‐derived SOA tracers were more prevalent at the foot. Interactions between anthropogenic and biogenic emissions can enhance biogenic SOA formation in forested areas near megacities, although this effect can be mitigated by increase of ammonia (NH3) and RH. Also, ozone played a crucial role in nighttime biogenic SOA formation at the summit. These findings highlight the impact of anthropogenic‐biogenic interactions on SOA formation and its vertical distribution in urban‐adjacent forests. Key Points: Diurnal patterns of secondary organic aerosol (SOA) tracers were simultaneously investigated at the summit and foot of Mt. NanlingSOA tracer concentrations were found to be significantly higher than those at most other background sites worldwideInteractions between anthropogenic and biogenic emissions amplified SOA formation in forests near megacities, particularly during pollution periods [ABSTRACT FROM AUTHOR]

The leaf phenotypic traits of plants have a significant impact on the efficiency of canopy photosynthesis. However, traditional methods such as destructive sampling will hinder the continuous monitoring of plant growth, while manual measurements in the field are both time-consuming and laborious. Nondestructive and accurate measurements of leaf phenotypic parameters can be achieved through the use of 3D canopy models and object segmentation techniques. This paper proposed an automatic branch–leaf segmentation pipeline based on lidar point cloud and conducted the automatic measurement of leaf inclination angle, length, width, and area, using pear canopy as an example. Firstly, a three-dimensional model using a lidar point cloud was established using SCENE software. Next, 305 pear tree branches were manually divided into branch points and leaf points, and 45 branch samples were selected as test data. Leaf points were further marked as 572 leaf instances on these test data. The PointNet++ model was used, with 260 point clouds as training input to carry out semantic segmentation of branches and leaves. Using the leaf point clouds in the test dataset as input, a single leaf instance was extracted by means of a mean shift clustering algorithm. Finally, based on the single leaf point cloud, the leaf inclination angle was calculated by plane fitting, while the leaf length, width, and area were calculated by midrib fitting and triangulation. The semantic segmentation model was tested on 45 branches, with a mean Precisionsem, mean Recallsem, mean F1-score, and mean Intersection over Union (IoU) of branches and leaves of 0.93, 0.94, 0.93, and 0.88, respectively. For single leaf extraction, the Precisionins, Recallins, and mean coverage (mCoV) were 0.89, 0.92, and 0.87, respectively. Using the proposed method, the estimated leaf inclination, length, width, and area of pear leaves showed a high correlation with manual measurements, with correlation coefficients of 0.94 (root mean squared error: 4.44°), 0.94 (root mean squared error: 0.43 cm), 0.91 (root mean squared error: 0.39 cm), and 0.93 (root mean squared error: 5.21 cm2), respectively. These results demonstrate that the method can automatically and accurately measure the phenotypic parameters of pear leaves. This has great significance for monitoring pear tree growth, simulating canopy photosynthesis, and optimizing orchard management. [ABSTRACT FROM AUTHOR]