scholarly journals Polychlorinated biphenyls (PCBs) and polybrominated biphenyl ethers (PBDEs) in environmental samples from Ny-Ålesund and London Island, Svalbard, the Arctic

Chemosphere ◽  
2015 ◽  
Vol 126 ◽  
pp. 40-46 ◽  
Author(s):  
Chaofei Zhu ◽  
Yingming Li ◽  
Pu Wang ◽  
Zhaojing Chen ◽  
Daiwei Ren ◽  
...  
Molecules ◽  
2020 ◽  
Vol 25 (16) ◽  
pp. 3727
Author(s):  
Łukasz Dąbrowski

For complex matrices such as environmental samples, there is usually a problem with not fully resolved peaks during GC/MS analysis. The PARADISe computer program (based on the PARFAC2 model) allows the identification of peaks using the deconvoluted mass spectra and the NIST MS library. The number of repetitions required by this software (at least five) is a real limitation for the determination of semi-volatile compounds, like polycyclic aromatic hydrocarbons, polychlorinated biphenyls, and organic pesticides in environmental samples. In this work, the method to overcome this condition was proposed and evaluated. The sets of the five files required by PARADISe were prepared by mathematically modifying the original GC/MS chromatograms obtained for the standard mixture (C = 2 µg/mL of 40 compounds) and real sample extracts (soil samples with different total organic carbon content and one cardboard extract) spiked with standards. Total average match factor for all the substances identified in a standard mixture was 874 (near 900—“excellent match”), and for all the substances in the real samples, it was 786 (near 800—“good match”). The results from PARADISe were comparable to those obtained with other programs: AMDIS (NIST) and MassHunter (Agilent), tested also in this work. PARADISe software can be effectively used for chromatogram deconvolution and substance identification.


2015 ◽  
Vol 12 (5) ◽  
pp. 563 ◽  
Author(s):  
Martin Günter Joachim Löder ◽  
Mirco Kuczera ◽  
Svenja Mintenig ◽  
Claudia Lorenz ◽  
Gunnar Gerdts

Environmental context Microplastics are of increasing environmental concern following reports that they occur worldwide from the arctic to the deep sea. However, a reliable methodology that facilitates an automated measurement of abundance and identity of microplastics is still lacking. We present an analytical protocol that applies focal plane array detector-based infrared imaging of microplastics enriched on membrane filters applicable to investigations of microplastic pollution of the environment. Abstract The pollution of the environment with microplastics (plastic pieces <5 mm) is a problem of increasing concern. However, although this has been generally recognised by scientists and authorities, the analysis of microplastics is often done by visual inspection alone with potentially high error rates, especially for smaller particles. Methods that allow for a fast and reliable analysis of microplastics enriched on filters are lacking. Our study is the first to fill this gap by using focal plane array detector-based micro-Fourier-transform infrared imaging for analysis of microplastics from environmental samples. As a result of our iteratively optimised analytical approach (concerning filter material, measuring mode, measurement parameters and identification protocol), we were able to successfully measure the whole surface (>10-mm diameter) of filters with microplastics from marine plankton and sediment samples. The measurement with a high lateral resolution allowed for the detection of particles down to a size of 20 μm in only a fractional part of time needed for chemical mapping. The integration of three band regions facilitated the pre-selection of potential microplastics of the ten most important polymers. Subsequent to the imaging the review of the infrared spectra of the pre-selected potential microplastics was necessary for a verification of plastic polymer origin. The approach we present here is highly suitable to be implemented as a standard procedure for the analysis of small microplastics from environmental samples. However, a further automatisation with respect to measurement and subsequent particle identification would facilitate the even faster and fully automated analysis of microplastic samples.


1984 ◽  
Vol 56 (8) ◽  
pp. 1308-1313 ◽  
Author(s):  
W. J. Dunn ◽  
D. L. Stalling ◽  
T. R. Schwartz ◽  
J. W. Hogan ◽  
J. D. Petty ◽  
...  

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