Topic analysis

Avoiding and reducing microplastic false positives from dry glove contact

* Corresponding authors a Department of Chemistry, University of Michigan, Ann Arbor, MI 48109-1055, USA E-mail: ajmcneil@umich.edu b Department of Statistics, University of Michigan, Ann Arbor, MI 48109-1107, USA c Macromolecular Science and Engineering Program, University of Michigan, Ann Arbor, MI 48109-2102, USA d Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109-2122, USA e Program in the Environment, University of Michigan, Ann Arbor, MI 48109-1041, USA To attenuate microplastics pollution, we first must quantify the number and types of microplastics found in the natural environment and identify their sources. Quantifying environmental microplastics requires distinguishing synthetic polymers from other naturally occurring species. Quality assurance and control measures – including wearing gloves when handling laboratory materials and samples – seek to reduce overestimating microplastic abundance. However, commonly used laboratory gloves release non-volatile residues, including stearate salts, that exhibit vibrational spectra similar to microplastics. In this work, we illustrate that dry surface contact with nitrile and latex laboratory gloves can cause overestimations of microplastics (mean 2000 false positives per mm 2 ) when using traditional library matching approaches. We recommend a nitrile cleanroom glove (mean 100 false positives per mm 2 ) to reduce contamination. For existing contaminated infrared and Raman spectral datasets, we outline workflows that differentiate between microplastics and stearate contamination from gloves. Applying these workflows to a case study of glove-contaminated environmental data, we illustrate that the proposed solutions reduce MP false positives at the smallest size ranges (<10 µm). By using this approach in conjunction with our included spectral libraries of stearate standards, researchers can address glove-based contamination in environmental datasets and provide more accurate estimates of environmental microplastic abundance.