Rapid direct analysis of river water and machine learning assisted suspect screening of emerging contaminants in passive sampler extracts.
Rapid direct analysis of river water and machine learning assisted suspect screening of emerging contaminants in passive sampler extracts.
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DOI：
10.1039/d0ay02013c
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发表时间：
2021-01
期刊：
影响因子：
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通讯作者：
Alexandra K Richardson;Marcus Chadha;Helena Rapp-Wright;G. Mills;G. Fones;A. Gravell;S. Stürzenbaum;D. Cowan;D. J. Neep;L. Barron
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作者：
Alexandra K Richardson;Marcus Chadha;Helena Rapp-Wright;G. Mills;G. Fones;A. Gravell;S. Stürzenbaum;D. Cowan;D. J. Neep;L. Barron
提出了一种新的快速方法来检测河水中新兴关注污染物（CEC）的发生，该方法使用多残留目标分析和机器学习辅助的被动采样器提取物的计算机疑似筛选。在2018年和2019年的冬季和夏季活动中，在泰晤士河（英国）潮汐流域的伦敦中心地区部署了配置有亲水亲油平衡（HLB）吸附剂的被动采样器（Chemcatcher®）。提取物分析：（a）快速5.5分钟直接进样靶向液相色谱-串联质谱法（B）全扫描LC耦合四极杆飞行时间质谱（QTOF-MS）方法，使用超过15分钟的数据独立采集。确定了个人护理产品和农药（包括几种欧盟观察名单化学品），并确定平均浓度为40 ± 37 ng L-1。对于被动采样器提取物的有针对性的分析，检测到65种独特的化合物，在夏季和冬季活动之间观察到差异。对于可疑筛选，基于质谱数据库匹配，然后通过机器学习辅助保留时间预测，筛选出59种其他化合物。其中许多包括额外的药物和农药，但也包括新的代谢物和工业化学品。这种方法的新奇在于使用被动采样器和机器学习辅助化学分析方法来快速，及时地对CEC进行集水监测。
A novel and rapid approach to characterise the occurrence of contaminants of emerging concern (CECs) in river water is presented using multi-residue targeted analysis and machine learning-assisted in silico suspect screening of passive sampler extracts. Passive samplers (Chemcatcher®) configured with hydrophilic-lipophilic balanced (HLB) sorbents were deployed in the Central London region of the tidal River Thames (UK) catchment in winter and summer campaigns in 2018 and 2019. Extracts were analysed by; (a) a rapid 5.5 min direct injection targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for 164 CECs and (b) a full-scan LC coupled to quadrupole time of flight mass spectrometry (QTOF-MS) method using data-independent acquisition over 15 min. From targeted analysis of grab water samples, a total of 33 pharmaceuticals, illicit drugs, drug metabolites, personal care products and pesticides (including several EU Watch-List chemicals) were identified, and mean concentrations determined at 40 ± 37 ng L-1. For targeted analysis of passive sampler extracts, 65 unique compounds were detected with differences observed between summer and winter campaigns. For suspect screening, 59 additional compounds were shortlisted based on mass spectral database matching, followed by machine learning-assisted retention time prediction. Many of these included additional pharmaceuticals and pesticides, but also new metabolites and industrial chemicals. The novelty in this approach lies in the convenience of using passive samplers together with machine learning-assisted chemical analysis methods for rapid, time-integrated catchment monitoring of CECs.