Mapping the constituent preference of tree species for capturing particulate matter on leaf surfaces using single-particle mass spectrometry and supervised machine learning.

Respiratory health is negatively influenced by the dimensions and constituents of particulate matter (PM). Although mass concentration is widely acknowledged to be key to assessing dust retention by urban trees, the role of plant leaves in filtering PM from the urban atmosphere, particularly regarding the particle dimensions and chemical constituents of retained PM on the leaf, remains elusive. Here we combined single-particle aerosol mass spectrometry and a particle resuspension chamber to investigate how urban tree species capture PM constituents.

Selective capture of PM by urban trees: The role of leaf wax composition and physiological traits in air quality enhancement.

Human health risks from particles with a diameter of less than 2.5 µm (PM) highlight the role of urban trees as bio-filters in air pollution control. However, whether the size and composition of particles captured by various tree species differ or not remain unclear.

2D effects enhance precision of gradient-based tissue patterning.

Robust embryonic development requires pattern formation with high spatial accuracy. In epithelial tissues that are patterned by morphogen gradients, the emerging patterns achieve levels of precision that have recently been explained by a simple one-dimensional reaction-diffusion model with kinetic noise. Here, we show that patterning precision is even greater if transverse diffusion effects are at play in such tissues.