School of Civil and Environmental Engineering , Cornell University , Ithaca , New York 14853 , United States.
Department of Biological and Environmental Engineering , Cornell University , Ithaca , New York 14853 , United States.
Environ Sci Technol. 2019 Jan 2;53(1):77-87. doi: 10.1021/acs.est.8b05320. Epub 2018 Dec 12.
The goal of this research was to comprehensively characterize the occurrence and temporal dynamics of target and nontarget micropollutants in a small stream. We established the Fall Creek Monitoring Station in March 2017 and collected daily composite samples for one year. We measured water samples by means of high-resolution mass spectrometry and developed and optimized a postacquisition data processing workflow to screen for 162 target micropollutants and group all mass spectral (MS) features into temporal profiles. We used hierarchical clustering analysis to prioritize nontarget MS features based their similarity to target micropollutant profiles and developed a high-throughput pipeline to elucidate the structures of prioritized nontarget MS features. Our analyses resulted in the identification of 31 target micropollutants and 59 nontarget micropollutants with varying levels of confidence. Temporal profiles of the 90 identified micropollutants revealed unexpected concentration-discharge relationships that depended on the source of the micropollutant and hydrological features of the watershed. Several of the nontarget micropollutants have not been previously reported including pharmaceutical metabolites, rubber vulcanization accelerators, plasticizers, and flame retardants. Our data provide novel insights on the temporal dynamics of micropollutant occurrence in small streams. Further, our approach to nontarget analysis is general and not restricted to highly resolved temporal data acquisitions or samples collected from surface water systems.
本研究的目的是全面描述小河流中目标和非目标微污染物的发生和时间动态。我们于 2017 年 3 月建立了 Fall Creek 监测站，并在一年内采集了每日综合样本。我们通过高分辨率质谱法测量水样，并开发和优化了一个后采集数据处理工作流程，以筛选 162 种目标微污染物，并将所有质谱（MS）特征组合成时间分布。我们使用层次聚类分析根据与目标微污染物分布的相似性对非目标 MS 特征进行优先级排序，并开发了一种高通量管道来阐明优先级排序的非目标 MS 特征的结构。我们的分析结果确定了 31 种目标微污染物和 59 种非目标微污染物，置信度不同。90 种鉴定出的微污染物的时间分布揭示了出人意料的浓度-排放关系，这取决于微污染物的来源和流域的水文特征。一些非目标微污染物以前没有报道过，包括药物代谢物、橡胶硫化促进剂、增塑剂和阻燃剂。我们的数据提供了有关小河流中微污染物发生时间动态的新见解。此外，我们的非目标分析方法是通用的，不仅限于高分辨率时间数据采集或从地表水系统采集的样本。
