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 in | Environmental technology & innovation Vol. 26; p. 102322 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
01.05.2022
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| Subjects | |
| Online Access | Get full text |
| ISSN | 2352-1864 2352-1864 |
| DOI | 10.1016/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. |
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| 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 |
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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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