Statistical forecast of pollution episodes in Macao during national holiday and COVID-19

Detalhes bibliográficos
Autor(a) principal: Monjardino, Joana
Data de Publicação: 2020
Outros Autores: Mendes, Luisa, Gonçalves, David, Ferreira, Francisco, Lei, Man Tat
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/120795
Resumo: UID/AMB/04085/2019
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spelling Statistical forecast of pollution episodes in Macao during national holiday and COVID-19Air pollutionAir quality forecastCOVID-19ModellingNational holidayPollution episodesPublic Health, Environmental and Occupational HealthHealth, Toxicology and MutagenesisSDG 3 - Good Health and Well-beingUID/AMB/04085/2019Statistical methods such as multiple linear regression (MLR) and classification and regression tree (CART) analysis were used to build prediction models for the levels of pollutant concentrations in Macao using meteorological and air quality historical data to three periods: (i) from 2013 to 2016, (ii) from 2015 to 2018, and (iii) from 2013 to 2018. The variables retained by the models were identical for nitrogen dioxide (NO2), particulate matter (PM10), PM2.5, but not for ozone (O3) Air pollution data from 2019 was used for validation purposes. The model for the 2013 to 2018 period was the one that performed best in prediction of the next-day concentrations levels in 2019, with high coefficient of determination (R2), between predicted and observed daily average concentrations (between 0.78 and 0.89 for all pollutants), and low root mean square error (RMSE), mean absolute error (MAE), and biases (BIAS). To understand if the prediction model was robust to extreme variations in pollutants concentration, a test was performed under the circumstances of a high pollution episode for PM2.5 and O3 during 2019, and the low pollution episode during the period of implementation of the preventive measures for COVID-19 pandemic. Regarding the high pollution episode, the period of the Chinese National Holiday of 2019 was selected, in which high concentration levels were identified for PM2.5 and O3, with peaks of daily concentration exceeding 55 µg/m3 and 400 µg/m3, respectively. The 2013 to 2018 model successfully predicted this high pollution episode with high coefficients of determination (of 0.92 for PM2.5 and 0.82 for O3). The low pollution episode for PM2.5 and O3 was identified during the 2020 COVID-19 pandemic period, with a low record of daily concentration for PM2.5 levels at 2 µg/m3 and O3 levels at 50 µg/m3, respectively. The 2013 to 2018 model successfully predicted the low pollution episode for PM2.5 and O3 with a high coefficient of determination (0.86 and 0.84, respectively). Overall, the results demonstrate that the statistical forecast model is robust and able to correctly reproduce extreme air pollution events of both high and low concentration levels.CENSE - Centro de Investigação em Ambiente e SustentabilidadeDCEA - Departamento de Ciências e Engenharia do AmbienteRUNMonjardino, JoanaMendes, LuisaGonçalves, DavidFerreira, FranciscoLei, Man Tat2021-07-09T22:18:09Z2020-07-152020-07-15T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article19application/pdfhttp://hdl.handle.net/10362/120795eng1661-7827PURE: 32445987https://doi.org/10.3390/ijerph17145124info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:03:17Zoai:run.unl.pt:10362/120795Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:44:27.457704Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Statistical forecast of pollution episodes in Macao during national holiday and COVID-19
title Statistical forecast of pollution episodes in Macao during national holiday and COVID-19
spellingShingle Statistical forecast of pollution episodes in Macao during national holiday and COVID-19
Monjardino, Joana
Air pollution
Air quality forecast
COVID-19
Modelling
National holiday
Pollution episodes
Public Health, Environmental and Occupational Health
Health, Toxicology and Mutagenesis
SDG 3 - Good Health and Well-being
title_short Statistical forecast of pollution episodes in Macao during national holiday and COVID-19
title_full Statistical forecast of pollution episodes in Macao during national holiday and COVID-19
title_fullStr Statistical forecast of pollution episodes in Macao during national holiday and COVID-19
title_full_unstemmed Statistical forecast of pollution episodes in Macao during national holiday and COVID-19
title_sort Statistical forecast of pollution episodes in Macao during national holiday and COVID-19
author Monjardino, Joana
author_facet Monjardino, Joana
Mendes, Luisa
Gonçalves, David
Ferreira, Francisco
Lei, Man Tat
author_role author
author2 Mendes, Luisa
Gonçalves, David
Ferreira, Francisco
Lei, Man Tat
author2_role author
author
author
author
dc.contributor.none.fl_str_mv CENSE - Centro de Investigação em Ambiente e Sustentabilidade
DCEA - Departamento de Ciências e Engenharia do Ambiente
RUN
dc.contributor.author.fl_str_mv Monjardino, Joana
Mendes, Luisa
Gonçalves, David
Ferreira, Francisco
Lei, Man Tat
dc.subject.por.fl_str_mv Air pollution
Air quality forecast
COVID-19
Modelling
National holiday
Pollution episodes
Public Health, Environmental and Occupational Health
Health, Toxicology and Mutagenesis
SDG 3 - Good Health and Well-being
topic Air pollution
Air quality forecast
COVID-19
Modelling
National holiday
Pollution episodes
Public Health, Environmental and Occupational Health
Health, Toxicology and Mutagenesis
SDG 3 - Good Health and Well-being
description UID/AMB/04085/2019
publishDate 2020
dc.date.none.fl_str_mv 2020-07-15
2020-07-15T00:00:00Z
2021-07-09T22:18:09Z
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://hdl.handle.net/10362/120795
url http://hdl.handle.net/10362/120795
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1661-7827
PURE: 32445987
https://doi.org/10.3390/ijerph17145124
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 19
application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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