Neural network for evaluation of the impact of the UK COVID-19 national lockdown on atmospheric concentrations of PAHs and PBDEs

Bibliographic Details
Main Author: Rosa, André Henrique [UNESP]
Publication Date: 2024
Other Authors: Stubbings, William A., Akinrinade, Olumide Emmanuel, Jeunon Gontijo, Erik Sartori [UNESP], Harrad, Stuart
Format: Article
Language: eng
Source: Repositório Institucional da UNESP
Download full: http://dx.doi.org/10.1016/j.envpol.2023.122794
https://hdl.handle.net/11449/299739
Summary: The impact of measures to restrict population mobility during the COVID-19 pandemic on atmospheric concentrations of polycyclic aromatic hydrocarbons (PAH) and brominated flame retardants (BFRs) is poorly understood. This study analyses the effects of meteorological parameters and mobility restrictions during the COVID-19 pandemic on concentrations of PAH and BFRs at the University of Birmingham in the UK utilising a neural network (self-organising maps, SOM). Air sampling was performed using Polyurethane Foam (PUF) disk passive samplers between October 2019 and January 2021. Data on concentrations of PAH and BFRs were analysed using SOM and Spearman's rank correlation. Data on meteorological parameters (air temperature, wind, and relative humidity) and mobility restrictions during the pandemic were included in the analysis. Decabromodiphenyl ether (BDE-209) was the most abundant polybrominated diphenyl ether (PBDE) (23–91% Σ7PBDEs) but was detected at lower absolute concentrations (4.2–35.0 pg m−3) than in previous investigations in Birmingham. Air samples were clustered in five groups based on SOM analysis and the effects of meteorology and pandemic-related restrictions on population mobility could be visualised. Concentrations of most PAH decreased during the early stages of the pandemic when mobility was most restricted. SOM analysis also helped to identify the important influence of wind speed on contaminant concentrations, contributing to reduce the concentration of all analysed pollutants. In contrast, concentrations of most PBDEs remained similar or increased during the first COVID-19 lockdown which was attributed to their primarily indoor sources that were either unaffected or increased during lockdown.
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spelling Neural network for evaluation of the impact of the UK COVID-19 national lockdown on atmospheric concentrations of PAHs and PBDEsAir pollutionPersistent organic pollutants (POPs)SARS-CoV-2 virusSelf-organising mapsThe impact of measures to restrict population mobility during the COVID-19 pandemic on atmospheric concentrations of polycyclic aromatic hydrocarbons (PAH) and brominated flame retardants (BFRs) is poorly understood. This study analyses the effects of meteorological parameters and mobility restrictions during the COVID-19 pandemic on concentrations of PAH and BFRs at the University of Birmingham in the UK utilising a neural network (self-organising maps, SOM). Air sampling was performed using Polyurethane Foam (PUF) disk passive samplers between October 2019 and January 2021. Data on concentrations of PAH and BFRs were analysed using SOM and Spearman's rank correlation. Data on meteorological parameters (air temperature, wind, and relative humidity) and mobility restrictions during the pandemic were included in the analysis. Decabromodiphenyl ether (BDE-209) was the most abundant polybrominated diphenyl ether (PBDE) (23–91% Σ7PBDEs) but was detected at lower absolute concentrations (4.2–35.0 pg m−3) than in previous investigations in Birmingham. Air samples were clustered in five groups based on SOM analysis and the effects of meteorology and pandemic-related restrictions on population mobility could be visualised. Concentrations of most PAH decreased during the early stages of the pandemic when mobility was most restricted. SOM analysis also helped to identify the important influence of wind speed on contaminant concentrations, contributing to reduce the concentration of all analysed pollutants. In contrast, concentrations of most PBDEs remained similar or increased during the first COVID-19 lockdown which was attributed to their primarily indoor sources that were either unaffected or increased during lockdown.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Institute of Science and Technology São Paulo State University (UNESP), Av. Três de Março, 511, Alto da Boa Vista, SPSchool of Geography Earth and Environmental Sciences University of Birmingham, EdgbastonDepartment of Chemistry University of Lagos, LagosKISTERS AG Business Unit HydroMet, Schoemperlenstr.12aInstitute of Science and Technology São Paulo State University (UNESP), Av. Três de Março, 511, Alto da Boa Vista, SPFAPESP: 2019/06800–5FAPESP: 2022/00985–6Universidade Estadual Paulista (UNESP)University of BirminghamUniversity of LagosBusiness Unit HydroMetRosa, André Henrique [UNESP]Stubbings, William A.Akinrinade, Olumide EmmanuelJeunon Gontijo, Erik Sartori [UNESP]Harrad, Stuart2025-04-29T18:43:19Z2024-01-15info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1016/j.envpol.2023.122794Environmental Pollution, v. 341.1873-64240269-7491https://hdl.handle.net/11449/29973910.1016/j.envpol.2023.1227942-s2.0-85177487934Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengEnvironmental Pollutioninfo:eu-repo/semantics/openAccess2025-04-30T13:24:22Zoai:repositorio.unesp.br:11449/299739Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestrepositoriounesp@unesp.bropendoar:29462025-04-30T13:24:22Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Neural network for evaluation of the impact of the UK COVID-19 national lockdown on atmospheric concentrations of PAHs and PBDEs
title Neural network for evaluation of the impact of the UK COVID-19 national lockdown on atmospheric concentrations of PAHs and PBDEs
spellingShingle Neural network for evaluation of the impact of the UK COVID-19 national lockdown on atmospheric concentrations of PAHs and PBDEs
Rosa, André Henrique [UNESP]
Air pollution
Persistent organic pollutants (POPs)
SARS-CoV-2 virus
Self-organising maps
title_short Neural network for evaluation of the impact of the UK COVID-19 national lockdown on atmospheric concentrations of PAHs and PBDEs
title_full Neural network for evaluation of the impact of the UK COVID-19 national lockdown on atmospheric concentrations of PAHs and PBDEs
title_fullStr Neural network for evaluation of the impact of the UK COVID-19 national lockdown on atmospheric concentrations of PAHs and PBDEs
title_full_unstemmed Neural network for evaluation of the impact of the UK COVID-19 national lockdown on atmospheric concentrations of PAHs and PBDEs
title_sort Neural network for evaluation of the impact of the UK COVID-19 national lockdown on atmospheric concentrations of PAHs and PBDEs
author Rosa, André Henrique [UNESP]
author_facet Rosa, André Henrique [UNESP]
Stubbings, William A.
Akinrinade, Olumide Emmanuel
Jeunon Gontijo, Erik Sartori [UNESP]
Harrad, Stuart
author_role author
author2 Stubbings, William A.
Akinrinade, Olumide Emmanuel
Jeunon Gontijo, Erik Sartori [UNESP]
Harrad, Stuart
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (UNESP)
University of Birmingham
University of Lagos
Business Unit HydroMet
dc.contributor.author.fl_str_mv Rosa, André Henrique [UNESP]
Stubbings, William A.
Akinrinade, Olumide Emmanuel
Jeunon Gontijo, Erik Sartori [UNESP]
Harrad, Stuart
dc.subject.por.fl_str_mv Air pollution
Persistent organic pollutants (POPs)
SARS-CoV-2 virus
Self-organising maps
topic Air pollution
Persistent organic pollutants (POPs)
SARS-CoV-2 virus
Self-organising maps
description The impact of measures to restrict population mobility during the COVID-19 pandemic on atmospheric concentrations of polycyclic aromatic hydrocarbons (PAH) and brominated flame retardants (BFRs) is poorly understood. This study analyses the effects of meteorological parameters and mobility restrictions during the COVID-19 pandemic on concentrations of PAH and BFRs at the University of Birmingham in the UK utilising a neural network (self-organising maps, SOM). Air sampling was performed using Polyurethane Foam (PUF) disk passive samplers between October 2019 and January 2021. Data on concentrations of PAH and BFRs were analysed using SOM and Spearman's rank correlation. Data on meteorological parameters (air temperature, wind, and relative humidity) and mobility restrictions during the pandemic were included in the analysis. Decabromodiphenyl ether (BDE-209) was the most abundant polybrominated diphenyl ether (PBDE) (23–91% Σ7PBDEs) but was detected at lower absolute concentrations (4.2–35.0 pg m−3) than in previous investigations in Birmingham. Air samples were clustered in five groups based on SOM analysis and the effects of meteorology and pandemic-related restrictions on population mobility could be visualised. Concentrations of most PAH decreased during the early stages of the pandemic when mobility was most restricted. SOM analysis also helped to identify the important influence of wind speed on contaminant concentrations, contributing to reduce the concentration of all analysed pollutants. In contrast, concentrations of most PBDEs remained similar or increased during the first COVID-19 lockdown which was attributed to their primarily indoor sources that were either unaffected or increased during lockdown.
publishDate 2024
dc.date.none.fl_str_mv 2024-01-15
2025-04-29T18:43:19Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1016/j.envpol.2023.122794
Environmental Pollution, v. 341.
1873-6424
0269-7491
https://hdl.handle.net/11449/299739
10.1016/j.envpol.2023.122794
2-s2.0-85177487934
url http://dx.doi.org/10.1016/j.envpol.2023.122794
https://hdl.handle.net/11449/299739
identifier_str_mv Environmental Pollution, v. 341.
1873-6424
0269-7491
10.1016/j.envpol.2023.122794
2-s2.0-85177487934
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Environmental Pollution
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv repositoriounesp@unesp.br
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