Extended characterization of the indoor dust chemical composition by retrospective suspect and non-target analysis of high resolution mass spectrometric data

Abstract

With the recent improvements in high-resolution mass spectrometry (HRMS), retrospective chemical analysis has been increasingly used in environmental sciences. This enables new insights into the chemical content of previously analysed samples with new data analysis methods or new information about emerging contaminants. This study aimed to conduct an in-depth investigation into the chemical content of various indoor dust samples using retrospective analysis. The samples were previously extracted using liquid-solid extraction without clean-up to increase the chemical coverage and thereafter analysed both using liquid chromatography (positive and negative ionisations) and gas chromatography coupled with high-resolution mass spectrometry. A retrospective data processing workflow was conducted in this new study by using both suspect screening analysis and non-target analysis. Among 30 dust samples from four different indoor settings, 298 compounds were tentatively identified with an identification confidence level of ≥ 3. The discussion was conducted on both individual compounds as well as their chemical compound groups and functional uses. Main detected chemical groups were plant natural products (n = 57), personal care products (n = 44), pharmaceuticals (n = 44), food additives (n = 43), plasticisers (n = 43), flame retardants (n = 43), colourants (n = 42) and pesticides (n = 31). Although some detected compounds were already reported for the same samples in our previous study, this retrospective analysis enabled the tentative identification of compounds such as polyethylene glycols, per- and polyfluoroalkyl substances, pesticides, benzotriazoles, benzothiazoles, fragrances, colourants and UV stabilizers. This study showed the usefulness of retrospective analysis on indoor dust samples to further characterise the chemical content, which can help to better estimate the exposure risks of organic contaminants to humans in the indoor environment.