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利用 Chemcatcher® 被动采样器结合高分辨率质谱和多元分析技术对水中新型农药进行靶向筛选。

Use of Chemcatcher® passive sampler with high-resolution mass spectrometry and multi-variate analysis for targeted screening of emerging pesticides in water.

机构信息

School of the Environment, Geography and Geosciences, University of Portsmouth, Burnaby Road, Portsmouth, PO1 3QL, UK.

出版信息

Anal Methods. 2020 Aug 28;12(32):4015-4027. doi: 10.1039/d0ay01193b. Epub 2020 Aug 3.

Abstract

Pesticides present at trace concentrations are a common cause of poor water quality. Their concentrations can change dynamically, due to the stochastic nature of pesticide pollution. Consequently, characterisation of pesticide residues that are intermittently present, poses significant monitoring and analytical challenges. Traditional approaches rely on quantitation of a limited number of pesticides present in a discrete water sample. Expanding the analytical suite and/or the frequency of sampling to meet these challenges is often impractical. Comprehensive methods are needed, with selectivity and sensitivity for the hundreds of pesticides potentially present, and temporal representativeness to ensure changing conditions are understood, in order to identify and prioritise risk. Recent analytical advances have enabled the targeted screening of hundreds of compounds in the same run, and automated work-flows can now reliably identify compounds through the comparison of retention time and accurate mass with spectral libraries. Screening generates large qualitative data sets, therefore, there is a need for improved monitoring methods and data interpretation strategies to reduce the need for repetition, and increase the quality of information for end-users. Passive sampling is an in situ time integrative technique, increasingly used for monitoring pesticides in water. Here, we describe a method using the Chemcatcher® passive sampler, coupled to targeted screening using liquid chromatography-quadrupole-time-of-flight mass spectrometry, and a commercially available library. Statistical analysis was performed using Agilent Mass Profiler Professional software. Water sampling took place over one year, at three riverine sites in the south of England, UK. Statistical interpretation of time integrative data from passive sampling could distinguish regular and episodic pesticide inputs, and detected compounds neglected by routine monitoring methods. One hundred and eleven pesticides were identified including legacy and current use compounds with diverse origins and uses. Spatial and temporal trends were identified enabling prioritisation of seasonal monitoring at each site. This approach maximises the utility of qualitative assessment and may help water quality managers to rationalise pesticide fate in future, providing significant additional insight without the need to increase the scope and cost of monitoring.

摘要

痕量浓度存在的农药是造成水质不佳的常见原因。由于农药污染的随机性,它们的浓度会动态变化。因此,间歇性存在的农药残留的特征描述给监测和分析带来了重大挑战。传统方法依赖于对离散水样中存在的有限数量的农药进行定量。扩大分析套件和/或采样频率以应对这些挑战通常是不切实际的。需要有综合的方法,对数百种可能存在的农药具有选择性和灵敏度,并且具有时间代表性,以确保了解不断变化的情况,从而识别和优先考虑风险。最近的分析进展使在同一次运行中对数百种化合物进行靶向筛选成为可能,并且自动化工作流程现在可以通过比较保留时间和准确质量与光谱库来可靠地识别化合物。筛选会生成大量定性数据集,因此,需要改进监测方法和数据解释策略,以减少重复的需要,并提高信息质量,以满足最终用户的需求。被动采样是一种原位时间积分技术,越来越多地用于监测水中的农药。在这里,我们描述了一种使用 Chemcatcher®被动采样器的方法,该方法与使用液相色谱-四极杆飞行时间质谱和商业可得的库进行靶向筛选相结合。使用 Agilent Mass Profiler Professional 软件进行统计分析。在英国南部的三个河流站点进行了为期一年的水采样。使用被动采样的时间积分数据进行统计解释可以区分常规和偶发性农药输入,并检测到常规监测方法忽略的化合物。共鉴定出 111 种农药,包括具有不同来源和用途的传统和当前使用的化合物。确定了空间和时间趋势,使每个站点的季节性监测得到优先排序。这种方法最大限度地利用了定性评估,可能有助于水质管理人员在未来合理推断农药的归宿,在无需扩大监测范围和增加成本的情况下提供重要的额外见解。

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