No abstract

Background: Sediments are sinks for organic micropollutants, which are traditionally analysed by gas chromatography-mass spectrometry (GC-MS). Although GC-MS and GC-tandem MS (MS/MS) are preferred for target screening, they provide only limited chromatographic resolution for nontarget screening. In this study, a comprehensive twodimensional GC-high-resolution MS method (GC × GC-HRMS) was developed for nontarget screening and source identification of organic micropollutants in sediments from an urban channel and adjacent lake in Copenhagen, Denmark. The GC × GC-HRMS data were processed by pixel-based chemometric analysis using baseline subtraction, alignment, normalisation, and scaling before principal component analysis (PCA) of the pre-processed GC × GC-HRMS base peak ion chromatograms (BPCs). The analysis was performed to identify organic micropollutants of high abundance and relevance in the urban sediments and to identify pollution sources. Tentative identifications were based on match factors and retention indices and tagged according to the level of identification confidence. Results: The channel contained both a significantly higher abundance of micropollutants and a higher diversity of compounds compared to the lake. The PCA models were able to isolate distinct sources of chemicals such as a natural input (viz., a high relative abundance of mono-, di-and sesquiterpenes) and a weathered oil fingerprint (viz., alkanes, naphthenes and alkylated polycyclic aromatic hydrocarbons). A dilution effect of the weathered oil fingerprint was observed in lake samples that were close to the channel. Several benzothiazole-like structures were identified in lake samples close to a high-traffic road which could indicate a significant input from asphalt or tire wear particles. In total, 104 compounds and compound groups were identified. Conclusions: Several chemical fingerprints of different sources were described in urban freshwater sediments in Copenhagen using a pixel-based chemometric approach of GC × GC-HRMS BPCs. Various micropollutants of anthropogenic origin were identified. Tailored pre-processing and careful interpretation of the identification results is inevitable and still requires further research for an automated workflow.

a b s t r a c t Available online xxxxKhuzestan, Iran is heavily industrialised with petrochemical and refinery companies. Herein, sediment and soil samples were collected from Hendijan coast, Khore Mosa and Arvandroud River. The CHEMSIC (CHEmometric analysis of Selected Ion Chromatograms) method was used to assign the main sources of polycyclic aromatic hydrocarbon (PAH) pollution. A four-component principal component analysis (PCA) model was obtained. While principal component 1 (PC1) was related to the total concentration of PAHs, the remaining PCs described three distinct sources: PC2 and PC3 collectively differentiate between weathered petrogenic and pyrogenic, and PC4 is indicative for a diagenetic input. The sources of PAHs in the Arvandroud River were mainly relatively fresh oil with some samples corresponding to a weathered oil input. Further, perylene (indicator for diagenetic source) was identified. Samples from Khore Mosa revealed a mixture with high proportions of high-molecular-weight PAHs, indicating a pyrogenic/weathered petrogenic source. Samples from Hendijan coast contained low relative concentrations of PAHs, thus only little information on pollution sources.

Background Sediments are sinks for organic micropollutants, which are traditionally analysed by gas chromatography-mass spectrometry (GC-MS). Although GC-MS and GC-MS/MS (tandem MS) are preferred for target screening, they provide only limited chromatographic resolution for nontarget screening. In this study, a comprehensive two-dimensional GC-high-resolution MS method (GC×GC-HRMS) was developed for nontarget screening and source identification of organic micropollutants in sediments from an urban lake and channel in Copenhagen, Denmark. The GC×GC-HRMS data were processed by pixel-based chemometric analysis using baseline subtraction, alignment, normalisation, and scaling before principal component analysis (PCA) of the pre-processed GC×GC-HRMS base peak ion chromatograms (BPCs). The analysis was performed to identify organic micropollutants of high abundance and relevance in the urban sediments, to identify pollution sources. Tentative identifications were based on match factors and retention indices and tagged according to the level of identification confidence. Results The channel contained both significantly higher concentrations of micropollutants and a higher diversity of compounds compared to the lake. The PCA models were able to isolate distinct sources of chemicals such as a natural input (viz. a high relative abundance of mono-, di- and sesquiterpenes) and a weathered oil fingerprint (viz. alkanes, naphthenes and alkylated polycyclic aromatic hydrocarbons). A dilution effect of the weathered oil fingerprint was observed in lake samples that were close to the channel. Several benzothiazole-like structures were identified in lake samples close to a high-traffic road which could indicate a significant input from asphalt or tire wear particles. Conclusions Several chemical fingerprints of different sources were described in urban freshwater sediments in Copenhagen using a pixel-based chemometric approach of GC×GC-HRMS chromatograms. Various micropollutants of anthropogenic origin were identified. Tailored pre-processing and careful interpretation of the identification results is inevitable and still requires further research for an automated workflow.

Наведено метод рішення задачі оптимізації маршрутів для спеціалізованих машин логістичного забезпечення в автоматизованій інформаційній системі складського обліку на основі рангового підходу.