Search

Background: Plants can retain atmospheric particulate matter (PM) through their unique foliar microstructures, which has a profound impact on the phyllosphere microbial communities. Yet, the underlying mechanisms linking atmospheric particulate matter (PM) retention by foliar microstructures to variations in the phyllosphere microbial communities remain a mystery. In this study, we conducted a field experiment with ten Ulmus lines. A series of analytical techniques, including scanning electron microscopy, atomic force microscopy, and high-throughput amplicon sequencing, were applied to examine the relationship between foliar surface microstructures, PM retention, and phyllosphere microbial diversity of Ulmus L., Results: We characterized the leaf microstructures across the ten Ulmus lines. Chun exhibited a highly undulated abaxial surface and dense stomatal distribution. Langya and Xingshan possessed dense abaxial trichomes, while Lieye, Zuiweng, and Daguo had sparsely distributed, short abaxial trichomes. Duomai, Qingyun, and Lang were characterized by sparse stomata and flat abaxial surfaces, whereas Jinye had sparsely distributed but extensive stomata. The mean leaf retention values for total suspended particulate (TSP), PM 2.5 , PM 2.5-10 , PM 10-100 , and PM > 100 were 135.76, 6.60, 20.10, 90.98, and 13.08 µg·cm - 2 , respectively. Trichomes substantially contributed to PM 2.5 retention, while larger undulations enhanced PM 2.5-10 retention, as evidenced by positive correlations between PM 2.5 and abaxial trichome density and between PM 2.5-10 and the adaxial raw microroughness values. Phyllosphere microbial diversity patterns varied among lines, with bacteria dominated by Sediminibacterium and fungi by Mycosphaerella, Alternaria, and Cladosporium. Redundancy analysis confirmed that dense leaf trichomes facilitated the capture of PM 2.5 -associated fungi, while bacteria were less impacted by PM and struggled to adhere to leaf microstructures. Long and dense trichomes provided ideal microhabitats for retaining PM-borne microbes, as evidenced by positive feedback loops between PM 2.5 , trichome characteristics, and the relative abundances of microorganisms like Trichoderma and Aspergillus., Conclusions: Based on our findings, a three-factor network profile was constructed, which provides a foundation for further exploration into how different plants retain PM through foliar microstructures, thereby impacting phyllosphere microbial communities., (© 2024. The Author(s).)

As an acute respiratory disease, scarlet fever has great harm to public health. Some evidence indicates that the time distribution pattern of heavy PM 2.5 pollution occurrence may have an impact on health risks. This study aims to reveal the relation between scaling features in high-concentrations PM 2.5 (HC-PM 2.5 ) evolution and scarlet fever incidence (SFI). Based on the data of Hong Kong from 2012 to 2019, fractal box-counting dimension (D) is introduced to capture the scaling features of HC-PM 2.5 . It has been found that index D can quantify the time distribution of HC-PM 2.5 , and lower D values indicate more cluster distribution of HC-PM 2.5 . Moreover, scale-invariance in HC-PM 2.5 at different time scales has been discovered, which indicates that HC-PM 2.5 occurrence is not random but follows a typical power-law distribution. Next, the exposure-response relationship between SFI and scale-invariance in HC-PM 2.5 is explored by Distributed lag non-linear model, in conjunction with meteorological factors. It has been discovered that scale-invariance in HC-PM 2.5 has a nonlinear effect on SFI. Low and moderate D values of HC-PM 2.5 are identified as risk factors for SFI at small time-scale. Moreover, relative risk shows a decreasing trend with the increase of exposure time. These results suggest that exposure to short-term clustered HC-PM 2.5 makes individual more prone to SFI than exposure to long-term uniform HC-PM 2.5 . This means that individuals in slightly-polluted regions may face a greater risk of SFI, once the PM 2.5 concentration keeps rising. In the future, it is expected that the relative risk of scarlet fever for a specific region can be estimated based on the quantitative analysis of scaling features in high-concentrations PM 2.5 evolution., (© 2024. The Author(s), under exclusive licence to Springer Nature B.V.)