Detecting the sources of chemicals in the Black Sea using non-target screening and deep learning convolutional neural networks

Nikiforos Alygizakis; Theodoros Giannakopoulos; Nikolaos S Τhomaidis; Jaroslav Slobodnik

doi:10.1016/j.scitotenv.2022.157554

Detecting the sources of chemicals in the Black Sea using non-target screening and deep learning convolutional neural networks

Sci Total Environ. 2022 Nov 15:847:157554. doi: 10.1016/j.scitotenv.2022.157554. Epub 2022 Jul 22.

Authors

Nikiforos Alygizakis¹, Theodoros Giannakopoulos², Nikolaos S Τhomaidis³, Jaroslav Slobodnik⁴

Affiliations

¹ Laboratory of Analytical Chemistry, Department of Chemistry, University of Athens, Panepistimiopolis Zografou, 15771 Athens, Greece; Environmental Institute, Okružná 784/42, 97241 Koš, Slovak Republic. Electronic address: nalygizakis@chem.uoa.gr.
² Institute of Informatics & Telecommunications, NCSR Demokritos, 15341 Agia Paraskevi, Greece.
³ Laboratory of Analytical Chemistry, Department of Chemistry, University of Athens, Panepistimiopolis Zografou, 15771 Athens, Greece. Electronic address: ntho@chem.uoa.gr.
⁴ Environmental Institute, Okružná 784/42, 97241 Koš, Slovak Republic.

PMID: 35878861
DOI: 10.1016/j.scitotenv.2022.157554

Abstract

The Black Sea is an important ecosystem, which is affected by various anthropogenic pressures, such as shipping activities and wastewater inputs from large coastal cities. Significant loads of chemical pollutants are being continuously brought in by major European rivers. This study investigated the spatial distribution of chemicals in the Ukrainian shelf (the northwestern part of the Black Sea) and their main sources. Chemical occurrence data used in the study was generated within the Joint Black Sea Surveys (JBSS), which took place in 2016 and 2017 as a part of the EU/UNDP EMBLAS II project (www.emblasproject.org). During the JBSS, seawater samples were analyzed by a non-target screening workflow using liquid chromatography high-resolution mass spectrometry (LC-HRMS). Open-source algorithms were applied to generate a combined dataset of 30,489 detected chemical signals and their intensities. Out of these, 35 compounds were tentatively identified by the application of a non-target screening identification workflow based on automated matching of their mass spectra against those in available mass spectral libraries. The dataset was used to generate images, representing spatial distribution of each of the signals. These images were then used as an input to a deep learning convolutional neural network classification model. The study resulted in the development of an open-source end-to-end workflow for the estimation of the pollution load by chemicals contributed by the two major inflowing rivers (Danube and Dnieper) and other, so far unidentified, sources. A dedicated dashboard was built to facilitate data visualization per detected signal/compound. The presented model proved to be especially useful at the prioritization of signals of unknown compounds, which is of key importance for the follow up structure elucidation efforts of bulky non-target screening data. The deep learning approach for peak prioritization of unknown chemicals in the environment has been used for the first time.

Keywords: Black Sea; Convolutional neural network; Deep learning; Emerging contaminants; Modeling spatial distribution.

MeSH terms

Black Sea
Deep Learning*
Ecosystem
Environmental Monitoring / methods
Neural Networks, Computer
Wastewater / analysis
Water Pollutants, Chemical* / analysis

Substances

Waste Water
Water Pollutants, Chemical