Characterization of particulate matter deposited on urban tree foliage: A landscape analysis approach. (December 2017)

• Record Type: Journal Article
• Authors: Lin, Lin; Yan, Jingli; Ma, Keming; Zhou, Weiqi; Chen, Guojian; Tang, Rongli; Zhang, Yuxin
• Is Part Of: Atmospheric environment. Volume 171(2017)
• Page Start: 59
• Page End: 69
• Publication Date: 2017-12
• Subjects: Air pollution -- FESEM -- Fragstats -- Object-based classification -- Spatial characteristic -- Urban forestry; Air -- Pollution -- Periodicals; Air -- Pollution -- Meteorological aspects -- Periodicals; 551.51
• DOI: 10.1016/j.atmosenv.2017.09.012
• Languages: English
• ISSNs: 1352-2310

Abstract

Abstract: Plants can mitigate ambient particulate matter by cleaning the air, which is crucial to urban environments. A novel approach was presented to quantitatively characterize particulate matter deposited on urban tree foliage. This approach could accurately quantify the number, size, shape, and spatial distribution of particles with different diameters on leaves. Spatial distribution is represented by proximity, which measures the closeness of particles. We sampled three common broadleaf species and obtained images through field emission scanning electron microscopy. We conducted the object-based method to extract particles from images. We then used Fragstats to analyze the landscape characteristics of these particles in term of selected metrics. Results reveal that Salix matsudana is more efficient than Ailanthus altissima and Fraxinus chinensis in terms of the number and area of particles per unit area and the proportion of fine particulate matter. The shape complexity of the particles increases with their size. Among the three species, S. matsudana and A. altissima particles respectively yield the highest and lowest proximity. PM1 in A. altissima and PM10 in F. chinensis and S. matsudana show the highest proximity, which may influence subsequent particle retention. S. matsudana should be generally considered to collect additional small particles. Different species and particle sizes exhibit various proximities, which should be further examined to elucidate the underlying mechanism. Graphical abstract: Highlights: Object-based method was applied to identify particles of leaf FESEM images. A computer-based landscape analysis was applied to leaf surface particles. Willow is efficient in dust retention in term of quantity and quality. Different PM size fractions show distinct spatial distribution characteristics.