A novel method for extraction, clean-up and analysis of per- and polyfluoroalkyl substances (PFAS) in different plant...

Per- and polyfluoroalkyl substances (PFAS) are chemicals of concern due to their persistence, bioaccumulation, and toxic properties. PFAS accumulation in plants poses a risk of human and animal exposure due to consumption of the affected plants, but also allows plants to be used in remediation of PFAS-contaminated soils and groundwater. Therefore, effective extraction, cleanup, and analytical methods for measuring PFAS concentrations in plants are fundamental for research on animal and environmental health. PFAS analysis in plant matrices is complex, due to high matrix interference, and scarcity of methods for analyzing different classes of PFAS. In this study, a simple sample preparation method for PFAS analysis in various plant tissues (leaves, needles, twigs, stems, roots from 10 different species) was developed and validated. Instrumental analysis was performed using liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS). The method was optimized considering six different extraction conditions and three different cleanup techniques. Methanol as extraction solvent, combined with 1 g ENVI carb cartridges, showed best performance among all extraction conditions and cleanup techniques tested. Method validation showed good recovery (90-120%), high within-day and between-day precision (<20% relative standard deviation), and low method detection limit (0.04-4.8 ng g^-1 dry weight (dw)) for different plant matrices. In tests of the method on soil and different plant tissues of silver birch (Betula pendula) and Norway spruce (Picea abies) at a PFAS-contaminated site, 16 of 24 target PFAS were detected in plants and 17 in soil. ƩPFAS concentration in soil was 43 ng g^-1 dw. PFAS distribution in silver birch tissues ranged from 7.1 ng g^-1 dw in roots to 64 ng g^-1 dw in leaves, and in Norway spruce from 14 ng g^-1 dw in roots to 16 ng g^-1 dw in needles. This novel method for PFAS analysis in plants can be valuable in future monitoring, process understanding, remediation, and risk assessments.