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

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. • 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.