Increasing availability of high resolution mass spectrometry (HR-MS) has enabled environmental chemists to identify previously undetected contaminants of concern. However, one difficulty in departing from the typical targeted analysis workflow is that HR-MS techniques generate large datasets and it can be challenging to sort through the noise and identify genuine contaminants. We developed a screening approach to address this problem. Our system was wastewater influent and effluent treated with an innovative advanced oxidation reactor. Using a general extraction procedure we collected HR data by a liquid chromatography � quadrupole time of flight (LC-QTOF) MS. All samples and blanks were collected in replicates of four. Once data was collected, it was run through a software algorithm that identifies possible compounds, or features. These features were aligned across all replicates in the dataset and blank subtracted. An in house accurate mass database of known and possible environmental contaminants was queried and matches were evaluated based on the mass error, isotope spacing and height. Good matches were then assessed by a retention time (RT) model based on the Episuite generated log Kow of known standards performance in the method. Features that fell within the 95% prediction interval were then taken for MS/MS experiments. If the accurate mass of observed fragments could be explained by the suggested structure, a standard, if available, was purchased and compared. The methodology was assessed by adding known contaminants to wastewater at different levels and evaluating which of the compounds were successfully identified by the algorithm.

High resolution mass spectrometry screening methodology for identifying emerging contaminants of concern and degradation products in wastewater treated with an innovative advanced oxidation reactor

248th National Meeting (2014)

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