Hu Jingrun, Lyu Yitao, Chen Huan, Cai Leilei, Li Jie, Cao Xiaoqiang, Sun Weiling
State Environmental Protection Key Laboratory of All Material Fluxes in River Ecosystems, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China; The Key Laboratory of Water and Sediment Sciences, Ministry of Education, Beijing 100871, China.
Department of Environmental Engineering and Earth Sciences, Clemson University, SC 29634, USA.
Water Res. 2023 Apr 15;233:119735. doi: 10.1016/j.watres.2023.119735. Epub 2023 Feb 12.
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.
尽管全球市场上已有数千种全氟和多氟烷基物质（PFAS），但大多数研究仅聚焦于一小部分，这可能导致对环境风险的低估。在此，我们分别使用互补的靶向、可疑和非靶向筛查方法来定量和识别目标及非目标PFAS，并基于它们的特定属性开发了一个风险模型，以对地表水中的PFAS进行优先级排序。在北京潮白河水体中鉴定出了33种PFAS。通过轨道阱进行的可疑和非靶向筛查显示灵敏度大于77%，表明其在识别样品中的PFAS方面具有良好性能。由于其潜在的高灵敏度，我们使用多反应监测模式下的三重四极杆（QqQ）质谱仪，采用标准物质对PFAS进行定量。为了对没有标准物质的非目标PFAS进行定量，我们训练了一个随机森林回归模型，该模型显示实测响应因子（RF）与预测响应因子之间的差异最大仅为2.7倍。在轨道阱中，每个PFAS类别中的最大/最小RF高达1.2 - 10.0，在QqQ中为1.7 - 22.3。我们开发了一种基于风险的优先级排序方法，对已鉴定出的PFAS进行排名，四种PFAS（即全氟辛酸、氢化全氟己酸、双三氟甲烷磺酰亚胺、6:2氟调聚物羧酸）被标记为高优先级（风险指数>0.1），需进行修复和管理。我们的研究强调了在PFAS环境审查过程中定量策略的重要性，特别是对于没有标准物质的非目标PFAS。
