Elevated Concentrations of 4-Bromobiphenyl and 1,3,5-Tribromobenzene Found in Deep Water of Lake Geneva Based on GC×GC-ENCI-TOFMS and GC×GC-μECD

Abstract

We quantified the concentrations of two little-studied brominated pollutants, 1,3,5-tribromobenzene (TBB) and 4-bromobiphenyl (4BBP), in the deep water column and sediments of Lake Geneva. We found aqueous concentrations of 625 ± 68 pg L –1 for TBB and 668 ± 86 pg L –1 for 4BBP over a depth range of 70–191.5 m (near-bottom depth), based on duplicate measurements taken at five depths during three separate 1 month sampling periods at our sampling site near Vidy Bay. These levels of TBB and 4BBP were 1 or 2 orders of magnitude higher than the quantified aqueous concentrations of the components of the pentabrominated biphenyl ether technical mixture, which is a flame retardant product that had a high production volume in Europe before 2001. We observed statistically significant vertical concentration trends for both TBB and 2,2′,4,4′,6-pentabromobiphenyl ether in the deep water column, which indicates that transport and/or degradation processes affect these compounds. These measurements were enabled by application of a comprehensive two-dimensional gas chromatograph coupled to an electron capture negative chemical ionization time-of-flight mass spectrometer (GC×GC-ENCI-TOFMS) and to a micro-electron capture detector (GC×GC-μECD). GC×GC-ENCI-TOFMS and GC×GC-μECD were found to be >10× more sensitive toward brominated pollutants than conventional GC×GC-EI-TOFMS (with an electron impact (EI) ionization source), the latter of which had insufficient sensitivity to detect these emerging brominated pollutants in the analyzed samples. GC×GC also enabled the estimation of several environmentally relevant partitioning properties of TBB and 4BBP, further confirming previous evidence that these pollutants are bioaccumulative and have long-range transport potential.

Quantitative Analysis of Selected Plastics in High-Commercial-Value Australian Seafood by Pyrolysis Gas Chromatography Mass Spectrometry

“…The signal intensities of these three peaks in the 83 Da XIC were used in the final multivariate calibration curve. Using this approach, we were able to accurately quantify PE in the complex matrix of the analyzed samples while minimizing background effects. , For specific details on the multivariate calibration curve and PCA, see the Supporting Information.…”

“…Using this approach, we were able to accurately quantify PE in the complex matrix of the analyzed samples while minimizing background effects. 48,49 For specific details on the multivariate calibration curve and PCA, see the Supporting Information.…”

Self Adjusting Algorithm for the Nontargeted Feature Detection of High Resolution Mass Spectrometry Coupled with Liquid Chromatography Profile Data

“…For IS detection and identification, we employed a semitargeted approach where we first performed a nontargeted feature detection, and then the feature lists were searched for the ISs. For ISs to have their presence confirmed in the samples, they had to have a mass error ≤0.003 Da and a retention error ≤10 s. This approach was previously shown to be effective for identification of target analytes in complex environmental samples. − …”

Concentration and Distribution of Naphthenic Acids in the Produced Water from Offshore Norwegian North Sea Oilfields

“…Advanced analytical technologies such as comprehensive two-dimensional gas chromatography (GC × GC) have shown great potential in dealing with complex environmental samples, including PW. ,,,,− The GC × GC instruments are typically coupled to low-resolution mass spectrometers, and when used for the analysis of NAs, they may require the samples to go through some treatments (e.g., esterification and/or reduction to alkanes) before their introduction into the instrument. ,, These approaches, even though effective for the structural elucidation, are not used for the quantification of NAs in PW. This is due to sample alteration (e.g., esterification and/or reduction to alkanes), which introduces a high level of variability into the data, and as such, the quantification of NAs in PW becomes unreliable using such methods (i.e., GC × GC).…”