Integration of target, suspect, and nontarget screening with risk modeling for per- and polyfluoroalkyl substances prioritization in surface waters.

Though thousands of per- and polyfluoroalkyl substances (PFAS) have been on the global market, most research focused on only a small fraction, potentially resulting in underestimated environmental risks. Here, we used complementary target, suspect, and nontarget screening for quantifying and identifying the target and nontarget PFAS, respectively, and developed a risk model considering their specific properties to prioritize the PFAS in surface waters. Thirty-three PFAS were identified in surface water in the Chaobai river, Beijing. The suspect and nontarget screening by Orbitrap displayed a sensitivity of > 77%, indicating its good performance in identifying the PFAS in samples. We used triple quadrupole (QqQ) under multiple-reaction monitoring for quantifying PFAS with authentic standards due to its potentially high sensitivity. To quantify the nontarget PFAS without authentic standards, we trained a random forest regression model which presented the differences up to only 2.7 times between measured and predicted response factors (RFs). The maximum/minimum RF in each PFAS class was as high as 1.2-10.0 in Orbitrap and 1.7-22.3 in QqQ. A risk-based prioritization approach was developed to rank the identified PFAS, and four PFAS (i.e., perfluorooctanoic acid, hydrogenated perfluorohexanoic acid, bistriflimide, 6:2 fluorotelomer carboxylic acid) were flagged with high priority (risk index > 0.1) for remediation and management. Our study highlighted the importance of a quantification strategy during environmental scrutiny of PFAS, especially for nontarget PFAS without standards.