Non-targeted analysis with high-resolution mass spectrometry for investigation of riverbank filtration processes

Abstract

A fully non-targeted analytical workflow for the investigation of a riverbank filtration site located at the river Danube has been developed and applied. Variations of compound intensities at different sampling locations of the riverbank filtration site and, for a single production well, over a monitoring period of one year have been investigated using liquid chromatography combined with time-of-flight-mass spectrometry followed by evaluation via non-targeted data analysis. Internal standardization and appropriate quality control strategies have been implemented into the workflow for reduction of possible methodological biases influencing data interpretation. Emphasis was placed on the assessment of different blank elimination steps and the final blank elimination strategy is reported. The spatial study of the selected riverbank filtration site revealed a homogenous composition of the filtered water sampled at 11 different locations across the 32,000 m2 site, except for one sampling location in a zone of the aquifer, which was only weakly connected to the well field in terms of hydrogeological conditions. The examination of time-dependent changes of the composition of surface and groundwater obtained at the riverbank filtration system revealed that the non-targeted workflow is fit-for-purpose regarding the assessment the stability of filtration efficiency and compound residence time in the riverbank filtration compartment. In total, 677 compounds were selected for the investigation of the time-dependent variations of the filtration process. Analysis of the signal intensities of these compounds revealed that the riverbank filtration is significantly reducing the intensity and number of compounds present in surface water over a wide polarity range. In addition, the method enabled the determination of compound residence times in the riverbank filtration system ranging from 5 to 7 days.

Mass-Suite: a novel open-source python package for high-resolution mass spectrometry data analysis

“…Various customizable data reduction filters are available in MSS for HRMS feature lists. A representative data reduction process [ 21 , 55 ] might include: (a) background feature subtraction based on a peak area fold-change criteria between experimental and blank samples; (b) replicate evaluation to remove features based on the calculated average and coefficient of variation for data from experimental or analytical replicates; and c) data trimming based on selected m/z or retention time ranges. These data reduction steps often effectively reduce feature numbers by up to tenfold, simplifying subsequent data analysis ( MSS function example shown in Additional file 1 : Figure S2).…”