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Abstract
More and more contaminants in dust have been found to be glucocorticoid receptor (GR) disrupting chemicals. However, little is known about the related potency and responsible toxicants, especially for the main bioaccessible ones in dust. An effect-directed analysis (EDA)-based workflow was developed, including solvent-based exhaustive extraction/tenax-assisted bioaccessible extraction (TBE), high-throughput bioassays, suspect and non-target analysis, as well as in silico candidate selection, for a more realistic identification of responsible contaminants in dust. None of the 39 dust samples from 23 cities in China exhibited GR agonistic activity, while GR antagonistic potencies were detected in 34.8% of samples, being significantly different from the high detection frequency of GR agonistic activities in other environmental media. The GR antagonistic potencies of the dust samples were all reduced after bioaccessible extraction. The mean bioaccessibility of GR antagonistic potency compared with the related exhaustive extracts was 36.8%, and the lowest value was 9%. By using in silico candidate selection, greater than 99% candidate chemical structures which were found by a non-target screening strategy were removed. Di-n-butyl phthalate (DnBP), diisobutyl phthalate (DiBP), and nicotine (NIC) were responsible for the activities of the exhaustive extracts of dust, contributing up to 91% potencies. DiBP and DnBP were also responsible for the bioaccessible activities, contributing up to 79% potencies. However, the contribution from NIC decreased significantly and can be ignored because of its low bioaccessibility. This study suggests that the improved workflow combining extraction, reporter gene bioassays, suspect and non-target analysis, as well as in silico candidate selection is useful for EDA analysis in dust samples. In addition, exhaustive extraction may overestimate the risk of contaminants, while bioaccessibility evaluation based on bioaccessible extraction is essential in both effect evaluation and toxicant identification.
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