Wastewater-based epidemiology (WBE) relies on the assessment and interpretation of levels of biomarkers in wastewater originating from a well-defined community. It has provided unique information on spatial and temporal trends of licit and illicit drug consumption, and has also the potential to give complementary information on human exposure to chemicals. Here, we focus on the accurate quantification of pesticide biomarkers (i.e., predominantly urinary metabolites) in influent wastewater at the ng L-1 level to be used for WBE. In the present study, an advanced analytical methodology has been developed based on ultra-high-pressure liquid chromatography coupled to tandem mass spectrometry (UHPLC-MS/MS), for the simultaneous determination of 11 specific human biomarkers of triazines, urea herbicides, pyrethroids and organophosphates in urban wastewater. The sample treatment consisted of solid-phase extraction using Oasis HLB cartridges. Direct injection of the samples was also tested for all compounds, as a simple and rapid way to determine these compounds without sample manipulation (i.e., minimizing potential analytical errors). However, if extraction recoveries are satisfactory, SPE is the preferred approach that allow reaching lower concertation levels. Six isotopically labelled internal standards were evaluated and used to correct for matrix effects. Due to the difficulties associated with this type of analysis, special emphasis has been placed on the analytical challenges encountered. The satisfactory validated methodology was applied to urban wastewater samples collected from different locations across Europe revealing the presence of 2,6-EA, 3,4-DCA, 3-PBA and 4-HSA i.e, metabolites of metolachlor-s, urea herbicides, pyrethroids and chlorpropham, respectively. Preliminary data reported in this paper illustrate the applicability of this analytical approach for assessing human exposure to pesticides through WBE.
Keywords: Exposure; Pesticide urinary metabolites; UHPLC-MS/MS; Wastewater surveillance; Wastewater-based epidemiology.
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