Atmospheric deposition of microplastics: a sampling and analytical method including the associated measurement uncertainties

Abstract. Microplastics (MPs) are environmental contaminants of global concern, and the atmosphere may play an important role in their environmental distribution. In this study, we developed a tailored analytical chain – including sample collection, processing, and analysis based on optical microscopy and focal plane array μ-Fourier transform infrared spectroscopy (FPA-μ-FTIR) – to quantify 20–215 μm MPs in wet and dry atmospheric deposition samples. We present a novel sampling setup to collect particulate wet deposition, which consists of an on-site precipitation filtration device. Validation of the sampling setup via spike-recovery experiments using surrogate standards resulted in average recoveries of approximately 90 %, suggesting limited MP losses. Additionally, we developed a custom software platform that combines the results from optical microscopy and chemical imaging obtained through FPA-μ-FTIR. Furthermore, an assessment of the total measurement uncertainty was made by addressing each step of the analytical chain individually. The resulting total expanded uncertainty was approximately 90 % for determining MP numbers in a single wet or dry deposition sample. The conversion of MP numbers and associated size information into MP mass was estimated to generate an additional systematic error of 50 %. Based on analyses of blanks, the critical level and the limit of detection per analyzed subsample were 29 and 58 MPs, respectively. The analytical chain was applied to quantify the MP content in wet and dry atmospheric deposition samples collected at a suburban site in Switzerland. The principles and methodology used in this study to calculate the uncertainties, recoveries and limits of detection are transferrable to other analytical methods intended for MP analysis. Such an assessment of method-specific uncertainties is an important step towards enhancing the comparability of MP (monitoring) data.