A retrospective quantification study of benzoic acid, ibuprofen, and mecoprop in Danish groundwater samples

Target analysis of pre-selected compounds is the standard procedure for monitoring of groundwater quality. However, compounds, not included on the target list can still be present but are overlooked in this targeted approach. In a previous non-target screening (NTS) study, groundwater samples from catchments covering different land uses were analysed by solid-phase extraction, dispersive liquid-liquid microextraction with gas chromatography – mass spectrometry analysis (SPE-DLLME-GC-MS), where potential chemical markers for land-use, benzoic acid (natural metabolite), ibuprofen (pharmaceutical) and mecoprop (pesticide), were identified but not quantified.The aim of this study was to develop a strategy for retrospective quantitative analysis and to perform retrospective analysis of the identified chemical markers in SPE groundwater extracts stored frozen for 3 years. The stored extracts were derivatised to transform carboxylic acid functional groups into methyl-esters and further concentrated by DLLME and analyzed by GC-MS.Ibuprofen was detected in six replicates from one well in Haderup (0.0070 ± 0.0023 µg /L) and in one well from Skive (0.0032 µg/L) indicating anthropogenic input. Mecoprop was detected in two samples with concentrations of 0.0095 µg/L and 3.3 µg/L that can be ascribed to agriculture. Benzoic acid was detected in all samples in the range of 0.072 - 1.20 µg/L except for Jyderup Skov where concentrations up to 161 µg/L were found, probably originating from the coniferous forest dominating this site.A combination of NTS analysis and retrospective quantification showed to be a promising strategy for monitoring of land-use markers. The strategy is challenged by effects of storage and lack of selectively in the NTS sample preparation method. Two additional sample preparation steps were carried out in this study (derivatization and DLLME) to improve the selectivity and sensivitiy of the retrospective analysis.

Abstract licence: CC BY-NC

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