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Application of clusterization algorithms for analysis of semivolatile pollutants in Arkhangelsk snow

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Abstract

The best way to understand the environmental status of a certain region involves thorough non-target analysis, which will result in a list of pollutants under concern. Arkhangelsk (64° 32′ N 40° 32′ E, pop. ~ 344,000) is the largest city in the world to the north of the 60th parallel. Several industrial enterprises and the “cold finger” effect represent the major sources of air contamination in the city. Analysis of snow with comprehensive two-dimensional gas chromatography–high-resolution mass spectrometry allows detecting and quantifying the most hazardous volatile and semivolatile anthropogenic pollutants and estimating long-term air pollution. Target analysis, suspect screening, and non-target analysis of snow samples collected from ten sites within the city revealed the presence of several hundreds of organic compounds including 18 species from the US EPA list of priority pollutants. Fortunately, the levels of these compounds appeared to be much lower than the safe levels established in Russia. Phenol and dioctylphthalate could be considered as the pollutants of concern because their levels were about 20% of the safe thresholds. ChromaTOF® Tile, MetaboAnalyst software platform, and open-source software protocols were applied to process the obtained data. The obtained clusterization results of the samples were generally similar for various tools; however, each of them had certain peculiarities. Bis(2-ethylhexyl) hexanedioate, benzyl alcohol, phthalates, aniline, dinitrotoluenes, and fluoranthene showed the strongest influence on the clusterization of the studied samples. Possible sources of the major pollutants were proposed: car traffic and pulp and paper mills.

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Acknowledgements

This research was performed using instrumentation of the Core Facility Center “Arktika” of the Northern (Arctic) Federal University (Arkhangelsk, Russia).

Funding

This research was supported by the Russian Science Foundation (Grant No. 21-74-00024).

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Authors and Affiliations

  1. Chemistry Department, Lomonosov Moscow State University, Leninskie Gory 1/3, Moscow, 119991, Russia

    D. M. Mazur, A. A. Sosnova & A. T. Lebedev

  2. Core Facility Center “Arktika”, Lomonosov Northern (Arctic) Federal University, nab. Severnoy Dviny 17, Arkhangelsk, 163002, Russia

    T. B. Latkin

  3. LECO Corporation, 3000 Lakeview Avenue, St. Joseph, MI, USA

    B. V. Artaev & K. Siek

  4. “Scietegra”, 12, 5 quarter, EZhKEdem, Gavrilkovo, Moscow Region, Russia

    D. A. Koluntaev

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Contributions

Conceptualization: Dmitrii M. Mazur, Albert T. Lebedev; methodology: Dmitrii M. Mazur, Dmitriy A. Koluntaev, Albert T. Lebedev; formal analysis and investigation: Tomas B. Latkin, Anna A. Sosnova, Dmitrii M. Mazur, Kevin Siek, Dmitriy A. Koluntaev; writing—original draft preparation: Dmitrii M. Mazur, Anna A. Sosnova, Dmitriy A. Koluntaev; writing—review and editing: Albert T. Lebedev, Viatcheslav B. Artaev, Kevin Siek; funding acquisition: Dmitrii M. Mazur, Albert T. Lebedev; resources: Albert T. Lebedev, Dmitrii M. Mazur; supervision: Albert T. Lebedev

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Published in the topical collection Comprehensive 2D Chromatography with guest editors Peter Q. Tranchida and Luigi Mondello.

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Mazur, D.M., Sosnova, A.A., Latkin, T.B. et al. Application of clusterization algorithms for analysis of semivolatile pollutants in Arkhangelsk snow. Anal Bioanal Chem 415, 2587–2599 (2023). https://doi.org/10.1007/s00216-022-04390-z

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