Literature DB >> 32109822

Combination of compositional data analysis and machine learning approaches to identify sources and geochemical associations of potentially toxic elements in soil and assess the associated human health risk in a mining city.

Gevorg Tepanosyan1, Lilit Sahakyan2, Nairuhi Maghakyan2, Armen Saghatelyan2.   

Abstract

Mining activities change the chemical composition of the environment and have negative reflection on people's health and there is no single measure to deal with adverse consequences of mining activities, as each case is specific and needs to be understood and mitigated in a unique way. In this study, the combination of compositional data analysis (CoDA), k-means algorithm, hierarchical cluster analysis applied to reveal the geochemical associations of potentially toxic elements (PTE) in soil of Alaverdi city (Armenia) (Ti, Fe, Ba, Mn, Co, V, Pb, Zn, Cu, Cr, Mo, As). Additionally, to assess PTE-induced health risk, two commonly used approaches were used. The obtained results show that the combination of CoDA and machine learning algorithms allow to identify and describe three geochemical associations of the studied elements: the natural, manmade and hybrid. Moreover, the revealed geochemical associations were linked to the natural pattern of distribution of the element concentrations including the influence of the natural mineralization of the parent rocks, as well as the emission from the copper smelter and urban management related activities. The health risk assessment using the US EPA method demonstrated that the observed contents of studied elements are posing a non-carcinogenic risk to children in the entire territory of the city. In the case of adults, the non-carcinogenic risk was identified in areas situated close to the copper smelter. The Summary pollution index (Zc) values were in line with the results of the US EPA method and indicated that the main residential part of the city was under the hazardous pollution level suggesting the possibility of increase in the overall incidence of diseases among frequently ill individuals, children with chronic diseases and functional disorders of vascular system. The obtained results indicated the need for further in-depth studies with special focus on the synergic effect of PTE.
Copyright © 2020 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Compositional data; Geochemical association; Machine learning; Potentially toxic elements; Soil

Mesh:

Substances:

Year:  2020        PMID: 32109822     DOI: 10.1016/j.envpol.2020.114210

Source DB:  PubMed          Journal:  Environ Pollut        ISSN: 0269-7491            Impact factor:   8.071


  2 in total

1.  BiGAMi: Bi-Objective Genetic Algorithm Fitness Function for Feature Selection on Microbiome Datasets.

Authors:  Mike Leske; Francesca Bottacini; Haithem Afli; Bruno G N Andrade
Journal:  Methods Protoc       Date:  2022-05-23

2.  Potential Ecological and Human Health Risks of Heavy Metals in Soils in Selected Copper Mining Areas-A Case Study: The Bor Area.

Authors:  Marioara Nicoleta Filimon; Ion Valeriu Caraba; Roxana Popescu; Gabi Dumitrescu; Doina Verdes; Liliana Petculescu Ciochina; Adrian Sinitean
Journal:  Int J Environ Res Public Health       Date:  2021-02-05       Impact factor: 3.390

  2 in total

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