Determination by combustion of the total organochlorine content of tissues, soil, water, waste streams, and oil sludges

Morton, Maria; Pollak, John K.

doi:10.1007/bf01606567

Cited by 8 publications

(1 citation statement)

References 12 publications

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“…Chlorine-containing organic compounds are probably one of the most commonly reported environmental pollutants causing serious problems from decades. There are several major sources of these species including industrial sewage and municipal wastewater (Lee et al 2006;Antoniou et al 2006;Sánchez-Avila et al 2009), pesticides (Karlsson et al 2000;Carvalho 2017;Harmouche-Karaki et al 2018;Salvarani et al 2018;Nambirajan et al 2018), combustion gases (Morton and Pollak 1987;Hu et al 2010), or water disinfection by-products (Richardson 2003;Kawaguchi et al 2005;Moradi et al 2010). Organochlorine compounds have been frequently detected in surface (Chen et al 2011;Navarrete et al 2018;Ali et al 2018), ground (Shukla et al 2006;Jayashree and Vasudevan 2007;Chaza et al 2018) and potable waters (Aydin and Yurdun 1999;Gelover et al 2000;Palmer et al 2011), soil (Fang et al 2017;Thiombane et al 2018), wastewater, sewage sludge (Bester 2005;Clarke et al 2010), and marine organisms (Smalling et al 2010;Gonul et al 2018;Luellen et al 2018).…”

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confidence: 99%

The use of fast molecular descriptors and artificial neural networks approach in organochlorine compounds electron ionization mass spectra classification

Przybyłek

Studziński

Gackowska

et al. 2019

Environ Sci Pollut Res

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Developing of theoretical tools can be very helpful for supporting new pollutant detection. Nowadays, a combination of mass spectrometry and chromatographic techniques are the most basic environmental monitoring methods. In this paper, two organochlorine compound mass spectra classification systems were proposed. The classification models were developed within the framework of artificial neural networks (ANNs) and fast 1D and 2D molecular descriptor calculations. Based on the intensities of two characteristic MS peaks, namely, [M] and [M-35], two classification criterions were proposed. According to criterion I, class 1 comprises [M] signals with the intensity higher than 800 NIST units, while class 2 consists of signals with the intensity lower or equal than 800. According to criterion II, class 1 consists of [M-35] signals with the intensity higher than 100, while signals with the intensity lower or equal than 100 belong to class 2. As a result of ANNs learning stage, five models for both classification criterions were generated. The external model validation showed that all ANNs are characterized by high predicting power; however, criterion I-based ANNs are much more accurate and therefore are more suitable for analytical purposes. In order to obtain another confirmation, selected ANNs were tested against additional dataset comprising popular sunscreen agents disinfection by-products reported in previous works.Electronic supplementary materialThe online version of this article (10.1007/s11356-019-05968-4) contains supplementary material, which is available to authorized users.

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