Detection limits are central to improve reporting standards when using Nile red for microplastic quantification

Research output: Contribution to journalArticlepeer-review

Abstract

Beyond simple identification of either the presence or absence of microplastic particles in the environment, quantitative accuracy has been criticised as being neither comparable nor reproducible. This is, in part, due to difficulties in the identification of synthetic particles amidst naturally occurring organic and inorganic components. The fluorescent stain Nile red has been proposed as a tool to overcome this issue, but to date, has been used without consideration of polymer specific fluorescent variability. The aim of this study was to evaluate the efficacy of Nile red for microplastic detection by systematically investigating what drives variations in particle pixel brightness (PPB). The results showed that PPB varied between polymer type, shape, size, colour and by staining procedure. Sand, an inorganic component of the sample matrix does not fluoresce when stained with Nile red. In contrast the organic components, wood and chitin, fluoresce between 1.40 and 12 arbitrary units (a.u.) and 32 and 74 a.u. after Nile red staining, respectively. These data informed the use of a PPB threshold limit of 100 a.u., which improved the detection of EPS, HDPE, PP and PA-6 from the 6 polymers tested and reduced analysis time by 30–58% compared to unstained samples. Conversely, as with traditional illumination, PET and PVC were not accurately estimated using this approach. This study shows that picking a threshold limit is not arbitrary but rather must be informed by polymer specific fluorescent variability and matrix considerations. This is an essential step needed to facilitate comparability and reproducibility between individual studies.

Details

Original languageEnglish
Article number127953
Pages (from-to)1-9
Number of pages9
JournalChemosphere
Volume263
Early online date22 Aug 2020
Publication statusPublished - Jan 2021

Keywords

  • Accuracy, Bias, Detection, Fluorescence, Spectroscopy, Staining