Given their ubiquitous release into aquatic environments and their potentially adverse effects, micropollutants require a concise long-term monitoring to prioritize emissions of concern and to disclose their various anthropogenic origins. Liquid-chromatography (LC) coupled to high-resolution mass spectrometry (HRMS/MS) has now been established as a sensitive method of choice for such monitoring, while covering both targeted and non-targeted pollutants. Despite such analytical advances, comprehensive workflow solutions to routinely tackle the highly informative yet big data output of LC-HRMS/MS have been sparse in the environmental context. Any such workflow has to resolve a number of issues. First, the general comparability of measurements over long monitoring campaigns has to be ensured via several data-preprocessing and quality control steps. Second, the LC-HRMS/MS data has to be mined for signals of interest to discard noise and background contaminations and to accelerate the downstream workflow, adjustable to different HRMS instruments and varying experimental set-ups. Third, a workflow must be capable to take full advantage of highly-resolved acquisitions. The latter entail complex and hard-to-predict signal relationships which however help to disentangle isotopologues, homologue series and different ion species of the same analyte and to assemble all available information on non-target components. Fourth, signals have to be merged over measurement sequences to so-called profiles, while using any auxiliary information to minimize false assignments. Yet other computational restrictions need to be addressed when mining vast sets of these profiles for alarming trends or for indicative similarities among them. Moreover, additional bottlenecks can arise from the need to adapt, extend or parametrize a workflow for a specific monitoring task, from timely monitoring restraints, from pitfalls in premature data reduction or when separating target screening and quantification from non-targeted workflow sections. Tackling these issues, the flexible open-source workflow enviMass version 3.2 is presented and its versatility demonstrated with several case studies on spatiotemporal micropollutant monitoring. The R-based workflow engine provides the data reduction and priorization required for reacting upon emissions at short terms, even in high-throughput LC-HRMS/MS settings. The workflow is available at www.enviMass.ch.

Resolving masses and bottlenecks for long-term LC-MS monitoring with the enviMass workflow version 3.2

253rd National Meeting (2017)

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