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Author Castanheiro, A.; Wuyts, K.; Hofman, J.; Nuyts, G.; De Wael, K.; Samson, R.
Title Morphological and elemental characterization of leaf-deposited particulate matter from different source types : a microscopic investigation Type A1 Journal article
Year 2021 Publication Environmental Science And Pollution Research Abbreviated Journal Environ Sci Pollut R
Volume 28 Issue 20 Pages 25716-25732
Keywords A1 Journal article; AXES (Antwerp X-ray Analysis, Electrochemistry and Speciation)
Abstract Particulate matter (PM) deposition on urban green enables the collection of particulate pollution from a diversity of contexts, and insight into the physico-chemical profiles of PM is key for identifying main polluting sources. This study reports on the morphological and elemental characterization of PM2-10 deposited on ivy leaves from five different environments (forest, rural, roadside, train, industry) in the region of Antwerp, Belgium. Ca. 40,000 leaf-deposited particles were thoroughly investigated by particle-based analysis using scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy (SEM/EDX) and their physico-chemical characteristics were explored for PM source apportionment purposes. The size distribution of all deposited particles was biased towards small-sized PM, with 32% of the particles smaller than 2.5 mu m (PM2.5) and median diameters of 2.80-3.09 mu m. The source type influenced both the particles' size and morphology (aspect ratio and shape), with roadside particles being overall the smallest in size and the most spherical. While forest and rural elemental profiles were associated with natural PM, the industry particles revealed the highest anthropogenic metal input. PM2-10 profiles for roadside and train sites were rather comparable and only distinguishable when evaluating the fine (2-2.5 mu m) and coarse (2.5-10 mu m) PM fractions separately, which enabled the identification of a larger contribution of combustion-derived particles (small, circular, Fe-enriched) at the roadside compared to the train. Random forest prediction model classified the source type correctly for 61-85% of the leaf-deposited PM. The still modest classification accuracy denotes the influence of regional background PM and demands for additional fingerprinting techniques to facilitate source apportionment. Nonetheless, the obtained results demonstrate the utility of leaf particle-based analysis to fingerprint and pinpoint source-specific PM, particularly when considering both the composition and size of leaf-deposited particles.
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Corporate Author Thesis
Publisher Place of Publication Editor
Language Wos 000609067300006 Publication Date 2021-01-20
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0944-1344; 1614-7499 ISBN Additional Links UA library record; WoS full record; WoS citing articles
Impact Factor 2.741 Times cited Open Access (up) OpenAccess
Notes Approved Most recent IF: 2.741
Call Number UA @ admin @ c:irua:176082 Serial 8282
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