Inter-laboratory mass spectrometry dataset based on passive sampling of drinking water for non-target analysis

Non-target analysis (NTA) employing high-resolution mass spectrometry is a commonly applied approach for the detection of novel chemicals of emerging concern in complex environmental samples. NTA typically results in large and information-rich datasets that require computer aided (ideally automated) strategies for their processing and interpretation. Such strategies do however raise the challenge of reproducibility between and within different processing workflows. An effective strategy to mitigate such problems is the implementation of inter-laboratory studies (ILS) with the aim to evaluate different workflows and agree on harmonized/standardized quality control procedures. Here we present the data generated during such an ILS. This study was organized through the Norman Network and included 21 participants from 11 countries. A set of samples based on the passive sampling of drinking water pre and post treatment was shipped to all the participating laboratories for analysis, using one pre-defined method and one locally (i.e. in-house) developed method. The data generated represents a valuable resource (i.e. benchmark) for future developments of algorithms and workflows for NTA experiments.

The relationship between river basin specific (RBS) pollutants and macroinvertebrate communities

This study was carried out to identify the relations between macroinvertebrate communities and river basin specific (RBS) pollutants in the Danube River. The investigation was performed at 68 sites along 2,500 km of the Danube. Forward selection (FS), canonical correspondence analyses (CCA), the Spearman correlation coefficient (SC) and BIO-ENV analysis (to detect synergistic effects) were used to identify the relations between the macroinvertebrate dataset and selected biological metrics with RBS pollutants. Of the 20 analysed pollutants (preselected based on NORMAN network methodology), seven (2,4-dinitrophenol, chloroxuron, bromacil, dimefuron, amoxicillin, bentazon and fluoranthene) were found to significantly correlate with macroinvertebrate communities. BIO-ENV analysis revealed 3 subsets of environmental variables that were in high correlation with the biota resemblance matrix, consisting mainly of a combination of the above-mentioned pollutants. Our results indicate that there are significant correlations between chemical determinants and aquatic biota. Moreover, this study contributes to the validation of the methodology used for prioritization of RBS pollutants proposed by the NORMAN network.