Statistical forecast of pollution episodes in Macao during national holiday and COVID-19
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , , |
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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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 |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799138052521590784 |