title: Quantifying the characteristics of particulate matters captured by urban plants using an automatic approach.
abstract: It is widely accepted that urban plant leaves can capture airborne particles. Previous studies on the particle capture capacity of plant leaves have mostly focused on particle mass and/or size distribution. Fewer studies, however, have examined the particle density, and the size and shape characteristics of particles, which may have important implications for evaluating the particle capture efficiency of plants, and identifying the particle sources. In addition, the role of different vegetation types is as yet unclear. Here, we chose three species of different vegetation types, and firstly applied an object-based classification approach to automatically identify the particles from scanning electron microscope (SEM) micrographs. We then quantified the particle capture efficiency, and the major sources of particles were identified. We found (1) Rosa xanthina Lindl (shrub species) had greater retention efficiency than Broussonetia papyrifera (broadleaf species) and Pinus bungeana Zucc. (coniferous species), in terms of particle number and particle area cover. (2) 97.9% of the identified particles had diameter ≤10 μm, and 67.1% of them had diameter ≤2.5 μm. 89.8% of the particles had smooth boundaries, with 23.4% of them being nearly spherical. (3) 32.4%-74.1% of the particles were generated from bare soil and construction activities, and 15.5%-23.0% were mainly from vehicle exhaust and cooking fumes.
authors.affiliation: State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China; University of Chinese Academy of Sciences, Beijing 100049, China. Electronic address: jlyan24@163.com.
authors.forename: Jingli
authors.initials: J
authors.lastname: Yan
authors.affiliation: State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China; University of Chinese Academy of Sciences, Beijing 100049, China.
authors.forename: Lin
authors.initials: L
authors.lastname: Lin
authors.affiliation: State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China. Electronic address: wzhou@rcees.ac.cn.
authors.forename: Weiqi
authors.initials: W
authors.lastname: Zhou
authors.affiliation: State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China.
authors.forename: Lijian
authors.initials: L
authors.lastname: Han
authors.affiliation: State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China.
authors.forename: Keming
authors.initials: K
authors.lastname: Ma
mesh_terms: D000393:Air Pollutants / Q000737:chemistry / Q000302:isolation & purification* / Q000378:metabolism*; D001331:Automation; D001673:Biodegradation, Environmental; D010316:Particle Size; D052638:Particulate Matter / Q000737:chemistry / Q000302:isolation & purification* / Q000378:metabolism*; D010944:Plants / Q000378:metabolism*; D014197:Trees / Q000378:metabolism
chemical_list: D000393:Air Pollutants; D052638:Particulate Matter
keywords: Object-based classification; Particulate matter retention; Size and shape characteristics; Source identification; Urban vegetation