Collecting microplastics in gardens: Case study (ii) from ropes

The characterisation of microplastics is still a challenge. To avoid the “false” characterisation and to increase the signal-noise ratio, we employ Raman imaging to scan the sample surface and generate a Raman spectrum matrix. We then simultaneously map several characteristic peaks to generate sever...

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Published inEnvironmental technology & innovation Vol. 26; p. 102322
Main Authors Sobhani, Zahra, Luo, Yunlong, Gibson, Christopher T., Tang, Youhong, Naidu, Ravi, Fang, Cheng
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.05.2022
Subjects
Online AccessGet full text
ISSN2352-1864
2352-1864
DOI10.1016/j.eti.2022.102322

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Abstract The characterisation of microplastics is still a challenge. To avoid the “false” characterisation and to increase the signal-noise ratio, we employ Raman imaging to scan the sample surface and generate a Raman spectrum matrix. We then simultaneously map several characteristic peaks to generate several images in parallel, akin to image at multi-channels, to cross-check and visualise the microplastics, via a logic-based algorithm. For comparison, we also employ a principal component analysis (PCA)-based algorithm to automatically decode the Raman spectrum matrix to map an image, not from the individual peaks, but from whole set of the PCA spectrum, meaning a much higher signal-noise ratio and image certainty. Due to the increased signal-noise ratio, we are able to apply this characterisation protocol to directly capture and identify microplastics in our gardens, such as from the plastic ropes used to hang a swing or a ladder for children to play, without any sample preparation. We estimate that at least 6280 microplastics have been released from a nylon rope in 10 years, due to ageing and weathering. We recommend to use polypropylene (PP) rope, rather than nylon rope, and to change the plastic ropes within 10 years. [Display omitted] •Raman imaging via hundreds-thousands spectra can increase signal-noise ratio.•Logic-based/PCA-based algorithms can further increase signal-noise ratio.•PCA can effectively distinguish microplastics from the background, and others.•Increased signal-noise can simplify, even bypass the sample preparation.•∼6280 microplastics released from a nylon rope in 10 years.
AbstractList The characterisation of microplastics is still a challenge. To avoid the “false” characterisation and to increase the signal-noise ratio, we employ Raman imaging to scan the sample surface and generate a Raman spectrum matrix. We then simultaneously map several characteristic peaks to generate several images in parallel, akin to image at multi-channels, to cross-check and visualise the microplastics, via a logic-based algorithm. For comparison, we also employ a principal component analysis (PCA)-based algorithm to automatically decode the Raman spectrum matrix to map an image, not from the individual peaks, but from whole set of the PCA spectrum, meaning a much higher signal-noise ratio and image certainty. Due to the increased signal-noise ratio, we are able to apply this characterisation protocol to directly capture and identify microplastics in our gardens, such as from the plastic ropes used to hang a swing or a ladder for children to play, without any sample preparation. We estimate that at least 6280 microplastics have been released from a nylon rope in 10 years, due to ageing and weathering. We recommend to use polypropylene (PP) rope, rather than nylon rope, and to change the plastic ropes within 10 years. [Display omitted] •Raman imaging via hundreds-thousands spectra can increase signal-noise ratio.•Logic-based/PCA-based algorithms can further increase signal-noise ratio.•PCA can effectively distinguish microplastics from the background, and others.•Increased signal-noise can simplify, even bypass the sample preparation.•∼6280 microplastics released from a nylon rope in 10 years.
The characterisation of microplastics is still a challenge. To avoid the “false” characterisation and to increase the signal-noise ratio, we employ Raman imaging to scan the sample surface and generate a Raman spectrum matrix. We then simultaneously map several characteristic peaks to generate several images in parallel, akin to image at multi-channels, to cross-check and visualise the microplastics, via a logic-based algorithm. For comparison, we also employ a principal component analysis (PCA)-based algorithm to automatically decode the Raman spectrum matrix to map an image, not from the individual peaks, but from whole set of the PCA spectrum, meaning a much higher signal-noise ratio and image certainty. Due to the increased signal-noise ratio, we are able to apply this characterisation protocol to directly capture and identify microplastics in our gardens, such as from the plastic ropes used to hang a swing or a ladder for children to play, without any sample preparation. We estimate that at least 6280 microplastics have been released from a nylon rope in 10 years, due to ageing and weathering. We recommend to use polypropylene (PP) rope, rather than nylon rope, and to change the plastic ropes within 10 years.
ArticleNumber 102322
Author Luo, Yunlong
Sobhani, Zahra
Fang, Cheng
Tang, Youhong
Naidu, Ravi
Gibson, Christopher T.
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Keywords Logic-based algorithm
Garden
PCA-based algorithm
Microplastics
Rope
Raman imaging
Language English
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Snippet The characterisation of microplastics is still a challenge. To avoid the “false” characterisation and to increase the signal-noise ratio, we employ Raman...
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SubjectTerms algorithms
case studies
environmental technology
Garden
Logic-based algorithm
Microplastics
nylon
PCA-based algorithm
polypropylenes
principal component analysis
Raman imaging
Rope
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Title Collecting microplastics in gardens: Case study (ii) from ropes
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