Paints: A Source of Volatile PFAS in Air─Potential Implications for Inhalation Exposure.
Liliana CahuasDerek J MuenstermanMitchell L Kim-FuPatrick N ReardonIvan A TitaleyJennifer A FieldPublished in: Environmental science & technology (2022)
Paints are widely used in indoor settings yet there are no data for volatile per- and polyfluoroalkyl substances (PFAS) for paints or knowledge if paints are potentially important sources of human exposure to PFAS. Different commercial paints ( n = 27) were collected from local hardware stores and analyzed for volatile PFAS by gas chromatography-mass spectrometry (GC-MS), nonvolatile PFAS by liquid chromatography-quadrupole time-of-flight mass spectrometry (LC-qTOF), and total fluorine by 19 F nuclear magnetic resonance spectroscopy (NMR). Diluted paint required clean up to remove 6:2 fluorotelomer phosphate diester (diPAP), which thermally transforms into 6:2 FTOH at 280 °C (GC inlet temperature). Only 6:2 FTOH (0.9-83 μg/g) and 6:2 diPAP (0.073-58 μg/g) were found in five exterior and nine interior paints and only accounted for a maximum of 17% of total fluorine. Upon drying, 40% of the FTOH mass was lost, and the loss was verified by measurements of the cumulative FTOH mass measured in the air of a small, confined space over a 3 h period. Based on the liquid paint results, the ConsExpo model was used for potential exposure assessment and one commercial paint exceeded the chosen reference dose (5 μg/kg-day) for children and adults, indicating the potential for human exposure during painting.
Keyphrases
- gas chromatography
- mass spectrometry
- liquid chromatography
- tandem mass spectrometry
- gas chromatography mass spectrometry
- endothelial cells
- high resolution mass spectrometry
- simultaneous determination
- solid phase extraction
- high resolution
- positron emission tomography
- healthcare
- induced pluripotent stem cells
- drinking water
- high performance liquid chromatography
- magnetic resonance
- human health
- air pollution
- ms ms
- pet imaging
- computed tomography
- big data