Cargando…

Developing an Intelligent Data Analysis Approach for Marine Sediments

(1) Background: As the chemical and physicochemical properties of marine sediments are closely related to natural and anthropogenic events, it is a real challenge to use their specific assessment as an indicator of environmental pollution discharges. (2) Methods: It is addressed in this study that c...

Descripción completa

Detalles Bibliográficos
Autores principales: Nedyalkova, Miroslava, Simeonov, Vasil
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9573499/
https://www.ncbi.nlm.nih.gov/pubmed/36235076
http://dx.doi.org/10.3390/molecules27196539
_version_ 1784810886050873344
author Nedyalkova, Miroslava
Simeonov, Vasil
author_facet Nedyalkova, Miroslava
Simeonov, Vasil
author_sort Nedyalkova, Miroslava
collection PubMed
description (1) Background: As the chemical and physicochemical properties of marine sediments are closely related to natural and anthropogenic events, it is a real challenge to use their specific assessment as an indicator of environmental pollution discharges. (2) Methods: It is addressed in this study that collection with intelligent data analysis methods, such as cluster analysis, principal component analysis, and source apportionment modeling, are applied for the assessment of the quality of marine sediment and for the identification of the contribution of pollution sources to the formation of the total concentration of polluting species. A study of sediment samples was carried out on 174 samples from three different areas along the coast of the Varna Gulf, Bulgaria. This was performed to determine the effects of pollution. As chemical descriptors, 34 indicators (toxic metals, polyaromatic hydrocarbons, polychlorinated biphenyls, nutrient components, humidity, and ignition loss) were used. The major goal of the present study was to assess the sediment quality in three different areas along the Gulf of Varna, Bulgaria by the source apportionment method. (3) Results: There is a general pattern for identifying three types of pollution sources in each area of the coastline with varying degrees of variation between zone A (industrially impacted zones), zone B (recreational areas), and zone C (anthropogenic and industrial wastes). (4) Conclusions: The quantitative apportionment procedure made it possible to determine the contribution of each identified pollution source for each zone in forming the total pollutant concentrations.
format Online
Article
Text
id pubmed-9573499
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-95734992022-10-17 Developing an Intelligent Data Analysis Approach for Marine Sediments Nedyalkova, Miroslava Simeonov, Vasil Molecules Article (1) Background: As the chemical and physicochemical properties of marine sediments are closely related to natural and anthropogenic events, it is a real challenge to use their specific assessment as an indicator of environmental pollution discharges. (2) Methods: It is addressed in this study that collection with intelligent data analysis methods, such as cluster analysis, principal component analysis, and source apportionment modeling, are applied for the assessment of the quality of marine sediment and for the identification of the contribution of pollution sources to the formation of the total concentration of polluting species. A study of sediment samples was carried out on 174 samples from three different areas along the coast of the Varna Gulf, Bulgaria. This was performed to determine the effects of pollution. As chemical descriptors, 34 indicators (toxic metals, polyaromatic hydrocarbons, polychlorinated biphenyls, nutrient components, humidity, and ignition loss) were used. The major goal of the present study was to assess the sediment quality in three different areas along the Gulf of Varna, Bulgaria by the source apportionment method. (3) Results: There is a general pattern for identifying three types of pollution sources in each area of the coastline with varying degrees of variation between zone A (industrially impacted zones), zone B (recreational areas), and zone C (anthropogenic and industrial wastes). (4) Conclusions: The quantitative apportionment procedure made it possible to determine the contribution of each identified pollution source for each zone in forming the total pollutant concentrations. MDPI 2022-10-03 /pmc/articles/PMC9573499/ /pubmed/36235076 http://dx.doi.org/10.3390/molecules27196539 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Nedyalkova, Miroslava
Simeonov, Vasil
Developing an Intelligent Data Analysis Approach for Marine Sediments
title Developing an Intelligent Data Analysis Approach for Marine Sediments
title_full Developing an Intelligent Data Analysis Approach for Marine Sediments
title_fullStr Developing an Intelligent Data Analysis Approach for Marine Sediments
title_full_unstemmed Developing an Intelligent Data Analysis Approach for Marine Sediments
title_short Developing an Intelligent Data Analysis Approach for Marine Sediments
title_sort developing an intelligent data analysis approach for marine sediments
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9573499/
https://www.ncbi.nlm.nih.gov/pubmed/36235076
http://dx.doi.org/10.3390/molecules27196539
work_keys_str_mv AT nedyalkovamiroslava developinganintelligentdataanalysisapproachformarinesediments
AT simeonovvasil developinganintelligentdataanalysisapproachformarinesediments