Chemical Characterization of Indoor Dust by Comprehensive Target and Non-target Screening Using GC- and LC-QTOF-MS/MS

Oral Presentation

Prepared by C. Moschet1, T. Anumol2, B. Lew1, T. Young1
1 - UC Davis, One Shields Ave., Davis, CA, 95616, United States
2 - Agilent Technologies, 2850 Centerville Rd, Wilmington, DE, 19808, United States

Contact Information:; 530-752-1755


Many studies have shown that house dust is contaminated with a broad range of chemicals such as pesticides, personal care products, plasticizers, flame retardants, and polyfluorinated compounds. House dust can serve as a marker of exposure to humans and is known to be a reservoir for many released compounds. Previous studies have focused on investigating one or several compound classes in a targeted analytical approach. The recent development of high-resolution mass spectrometers and corresponding software offers the possibility of screening for suspected chemicals without having an authentic analytical standard or conducting non-target screening based on molecular feature extraction. The goal of this study is to comprehensively characterize the chemical fingerprint in a large set of dust samples collected from two groups of households: families with normal developing children and families with children having developmental issues.

145 frequently used chemicals that span the chemical space were selected in order to optimize the sample preparation and analytical methods. One part of the extract was measured on an Agilent GC-QTOF-MS using electron impact (EI), while the other extract was analyzed on an Agilent LC-QTOF-MS using electrospray ionization (ESI) in positive and negative mode. Absolute recoveries were above 80% for more than 95% of the chemicals and method detection limits were below 50 ng/g for 70% of the chemicals.
Following the quantification of the target chemicals, the dust samples were screened for several thousand additional compounds of interest in dust using different databases. Thereby, exact mass screening and MS/MS spectra comparison for chemicals measured by LC-QTOF-MS as well as comparison with library spectra for chemicals measured by GC-EI-QTOF-MS was used for the detection of these suspected chemicals. The data evaluation workflow was optimized to handle the large data set, and tentatively identified compounds were confirmed by MS/MS measurements. In a future step, it is the goal to identify remaining important chemicals by comparing molecular features from the two household groups and to use mass defect filters for the identification of transformation products.

This multi-step screening will give new insights in the chemical fingerprint of US indoor dust by identifying unknown chemicals that should be further investigated.