亲水作用色谱法
流出物
分析物
假阳性悖论
色谱法
嫌疑犯
地表水
废水
保留时间
环境科学
计算机科学
化学
污染
环境监测
污水处理
污水
环境化学
自动化方法
真阳性率
人工智能
分辨率(逻辑)
水污染
复矩阵
色谱分离
滤波器(信号处理)
作者
Javad Mottaghipisheh,Rajneesh Kumar Gautam,Lutz Ahrens
标识
DOI:10.1038/s41598-025-29664-1
摘要
Persistent, Mobile, and Toxic (PMT) and very Persistent and very Mobile (vPvM) substances pose a significant environmental threat due to their widespread distribution, persistence, and potential toxicity. While previous studies have used reversed-phase (RP) and hydrophilic interaction liquid chromatography (HILIC) to extend analyte coverage, this study uniquely integrates a dual-chromatographic approach with multi-sorbent solid phase etration (SPE) and machine-learning-based retention time indices to rigorously minimize false positives and enhance confidence in suspect screening of PMT/vPvM substances. Two independently optimized LC methods (RP and HILIC) were applied in parallel to the same SPE extract to identify potential chromatographic blind spots. We applied this robust framework to systematically screen surface water and wastewater treatment plant (WWTP) effluent across a water catchment in Uppsala, Sweden. The RP and HILIC workflows, applied as independent chromatographic runs, identified 84 compounds after multi-stage filtering (with retention time indices (RTI) correction applied only to the RP data), with 27% and 48% uniquely detected by HILIC and RP, respectively, highlighting analytical blind spots overcome by this integrated approach. Strategic site selection, encompassing upstream rural zones, on-site sewage facilities, and major WWTP discharge points, demonstrated how local land use impacts PMT/vPvM profiles. This comprehensive method provides a powerful tool for environmental monitoring and regulatory surveillance of PMT/vPvM compounds.
科研通智能强力驱动
Strongly Powered by AbleSci AI