Does magnification of SEM image influence quantification of particulate matters deposited on vegetation foliage

Lin, Lin; Yan, Jingli; Chen, Guojian; Tang, Rongli; Liu, Bao; Ma, Keping; Zhou, Weiqi; Yuan, Xiu; Yin, Zhe; Zhou, Shuwen

doi:10.1016/j.micron.2018.08.003

Cited by 9 publications

(3 citation statements)

References 57 publications

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“…Additionally, chloroform is the employed solvent for these particles and it can potentially dissolve some of the particles containing non-polar molecules (Przybysz et al, 2014;Sgrigna et al, 2015;Song et al, 2015). In microscopy methods, highly magnified images of deposited particles are taken, often using a Scanning Electron Microscope (SEM), and particles in these images are counted via image processing software and techniques (Lin et al, 2018;Weerakkody et al, 2019Weerakkody et al, , 2018aWeerakkody et al, , 2018b. This method of analysis also indicates PM size distribution, as well as providing insights into leaf micromorphology.…”

Section: Introductionmentioning

confidence: 99%

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Quantifying particulate matter reduction and their deposition on the leaves of green infrastructure

Abhijith

Kumar

2020

Environmental Pollution

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This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Section: Introductionmentioning

confidence: 99%

“…LAI is the total leaf surface area per unit ground area (m 2 m -2 ). The density of particles was taken as 2 g cm -3 (Lin et al, 2018;Zhang and He, 2014).…”

mentioning

confidence: 99%

Quantifying particulate matter reduction and their deposition on the leaves of green infrastructure

Abhijith

Kumar

2020

Environmental Pollution

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“…Scanning electron microscopy (SEM) is an observational method that falls between transmission electron microscopy and optical microscopy. Microscopic variation in leaf surface structure can be examined in detail using SEM [ 11 , 12 ]. Atomic force microscopy (AFM) is used to assess the surface structure and properties of matter by detecting very weak interatomic interaction between the surface of the sample and a miniature force-sensitive element.…”

Section: Introductionmentioning

confidence: 99%

Comparative analysis of the dust retention capacity and leaf microstructure of 11 Sophora japonica clones

Liu

et al. 2021

PLoS ONE

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We used fresh leaves of Sophora japonica L. variety ‘Qingyun 1’ (A0) and 10 superior clones of the same species (A1–A10) to explore leaf morphological characteristics and total particle retention per unit leaf area under natural and artificial simulated dust deposition treatments. Our objectives were to explore the relationship between the two methods and to assess particle size distribution, X-ray fluorescence (XRF) heavy metal content, and scanning electron and atomic force microscopy (SEM and AFM) characteristics of leaf surface microstructure. Using the membership function method, we evaluated the dust retention capacity of each clone based on the mean degree of membership of its dust retention index. Using correlation analysis, we selected leaf morphological and SEM and AFM indices related significantly to dust retention capacity. Sophora japonica showed excellent overall dust retention capacity, although this capacity differed among clones. A5 had the strongest overall retention capacity, A2 had the strongest retention capacity for PM2.5, A9 had the strongest retention capacity for PM2.5–10, A0 had the strongest retention capacity for PM>10, and A2 had the strongest specific surface area (SSA) and heavy metal adsorption capacity. Overall, A1 had the strongest comprehensive dust retention ability, A5 was intermediate, and A7 had the weakest capacity. Certain leaf morphological and SEM and AFM characteristic indices correlated significantly with the dust retention capacity.

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Methods to quantify particle air pollution removal by urban vegetation: A review

Vigevani,

Corsini,

Comin

et al. 2024

Atmospheric Environment: X

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Does magnification of SEM image influence quantification of particulate matters deposited on vegetation foliage

Cited by 9 publications

References 57 publications

Quantifying particulate matter reduction and their deposition on the leaves of green infrastructure

Quantifying particulate matter reduction and their deposition on the leaves of green infrastructure

Comparative analysis of the dust retention capacity and leaf microstructure of 11 Sophora japonica clones

Methods to quantify particle air pollution removal by urban vegetation: A review

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