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Previous studies on organic sediment contaminants focused mainly on a limited number of highly hydrophobic micropollutants accessible to gas chromatography using nonpolar, aprotic extraction solvents. The development of liquid chromatography-high-resolution mass spectrometry (LC-HRMS) permits the spectrum of analysis to be expanded to a wider range of more polar and ionic compounds present in sediments and allows target, suspect, and nontarget screening to be conducted with high sensitivity and selectivity. In this study, we propose a comprehensive multitarget extraction and sample preparation method for characterization of sediment pollution covering a broad range of physicochemical properties that is suitable for LC-HRMS screening analysis. We optimized pressurized liquid extraction, cleanup, and sample dilution for a target list of 310 compounds. Finally, the method was tested on sediment samples from a small river and its tributaries. The results show that the combination of 100 °C for ethyl acetate-acetone (50:50, neutral extract) followed by 80 °C for acetone-formic acid (100:1, acidic extract) and methanol-10 mM sodium tetraborate in water (90:10, basic extract) offered the best extraction recoveries for 287 of 310 compounds. At a spiking level of 1 μg mL, we obtained satisfactory cleanup recoveries for the neutral extract-(93 ± 23)%-and for the combined acidic/basic extracts-(42 ± 16)%-after solvent exchange. Among the 69 compounds detected in environmental samples, we successfully quantified several pharmaceuticals and polar pesticides.

Non-targeted mass spectrometry (MS) has become an important method over recent years in the fields of metabolomics and environmental research. While more and more algorithms and workflows become available to process a large number of non-targeted data sets, there still exist few manually evaluated universal test data sets for refining and evaluating these methods. The first step of non-targeted screening, peak detection and refinement of it is arguably the most important step for non-targeted screening. However, the absence of a model data set makes it harder for researchers to evaluate peak detection methods. In this Data Descriptor, we provide a manually checked data set consisting of 255,000 EICs (5000 peaks randomly sampled from across 51 samples) for the evaluation on peak detection and gap-filling algorithms. The data set was created from a previous real-world study, of which a subset was used to extract and manually classify ion chromatograms by three mass spectrometry experts. The data set consists of the converted mass spectrometry files, intermediate processing files and the central file containing a table with all important information for the classified peaks.

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