Using Machine Learning Methods to Forecast Air Quality
Autor(a) principal: | |
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Data de Publicação: | 2022 |
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/10362/150719 |
Resumo: | Funding Information: This research was funded by Fundação para a Ciência e Tecnologia, I.P., Portugal, grant number UID/AMB/04085/2020, and the APC was funded by CENSE. Funding Information: The work developed was supported by The Macao Meteorological and Geophysical Bureau (SMG). Publisher Copyright: © 2022 by the authors. |
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Using Machine Learning Methods to Forecast Air QualityA Case Study in Macaoair pollutionair qualityair quality forecastCOVID-19gradient boostingmultiple linear regressionrandom forestsupport vector regressionEnvironmental Science (miscellaneous)Atmospheric ScienceSDG 3 - Good Health and Well-beingFunding Information: This research was funded by Fundação para a Ciência e Tecnologia, I.P., Portugal, grant number UID/AMB/04085/2020, and the APC was funded by CENSE. Funding Information: The work developed was supported by The Macao Meteorological and Geophysical Bureau (SMG). Publisher Copyright: © 2022 by the authors.Despite the levels of air pollution in Macao continuing to improve over recent years, there are still days with high-pollution episodes that cause great health concerns to the local community. Therefore, it is very important to accurately forecast air quality in Macao. Machine learning methods such as random forest (RF), gradient boosting (GB), support vector regression (SVR), and multiple linear regression (MLR) were applied to predict the levels of particulate matter (PM10 and PM2.5) concentrations in Macao. The forecast models were built and trained using the meteorological and air quality data from 2013 to 2018, and the air quality data from 2019 to 2021 were used for validation. Our results show that there is no significant difference between the performance of the four methods in predicting the air quality data for 2019 (before the COVID-19 pandemic) and 2021 (the new normal period). However, RF performed significantly better than the other methods for 2020 (amid the pandemic) with a higher coefficient of determination (R2) and lower RMSE, MAE, and BIAS. The reduced performance of the statistical MLR and other ML models was presumably due to the unprecedented low levels of PM10 and PM2.5 concentrations in 2020. Therefore, this study suggests that RF is the most reliable prediction method for pollutant concentrations, especially in the event of drastic air quality changes due to unexpected circumstances, such as a lockdown caused by a widespread infectious disease.CENSE - Centro de Investigação em Ambiente e SustentabilidadeDCEA - Departamento de Ciências e Engenharia do AmbienteRUNLei, Thomas M. T.Siu, Shirley W. I.Monjardino, JoanaMendes, LuísaFerreira, Francisco2023-03-16T22:37:30Z2022-09-012022-09-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article14application/pdfhttp://hdl.handle.net/10362/150719eng2073-4433PURE: 56087446https://doi.org/10.3390/atmos13091412info: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:RCAAP2024-03-11T05:33:03Zoai:run.unl.pt:10362/150719Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:54:16.652344Repositório 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 |
Using Machine Learning Methods to Forecast Air Quality A Case Study in Macao |
title |
Using Machine Learning Methods to Forecast Air Quality |
spellingShingle |
Using Machine Learning Methods to Forecast Air Quality Lei, Thomas M. T. air pollution air quality air quality forecast COVID-19 gradient boosting multiple linear regression random forest support vector regression Environmental Science (miscellaneous) Atmospheric Science SDG 3 - Good Health and Well-being |
title_short |
Using Machine Learning Methods to Forecast Air Quality |
title_full |
Using Machine Learning Methods to Forecast Air Quality |
title_fullStr |
Using Machine Learning Methods to Forecast Air Quality |
title_full_unstemmed |
Using Machine Learning Methods to Forecast Air Quality |
title_sort |
Using Machine Learning Methods to Forecast Air Quality |
author |
Lei, Thomas M. T. |
author_facet |
Lei, Thomas M. T. Siu, Shirley W. I. Monjardino, Joana Mendes, Luísa Ferreira, Francisco |
author_role |
author |
author2 |
Siu, Shirley W. I. Monjardino, Joana Mendes, Luísa Ferreira, Francisco |
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 |
Lei, Thomas M. T. Siu, Shirley W. I. Monjardino, Joana Mendes, Luísa Ferreira, Francisco |
dc.subject.por.fl_str_mv |
air pollution air quality air quality forecast COVID-19 gradient boosting multiple linear regression random forest support vector regression Environmental Science (miscellaneous) Atmospheric Science SDG 3 - Good Health and Well-being |
topic |
air pollution air quality air quality forecast COVID-19 gradient boosting multiple linear regression random forest support vector regression Environmental Science (miscellaneous) Atmospheric Science SDG 3 - Good Health and Well-being |
description |
Funding Information: This research was funded by Fundação para a Ciência e Tecnologia, I.P., Portugal, grant number UID/AMB/04085/2020, and the APC was funded by CENSE. Funding Information: The work developed was supported by The Macao Meteorological and Geophysical Bureau (SMG). Publisher Copyright: © 2022 by the authors. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-09-01 2022-09-01T00:00:00Z 2023-03-16T22:37:30Z |
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/10362/150719 |
url |
http://hdl.handle.net/10362/150719 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2073-4433 PURE: 56087446 https://doi.org/10.3390/atmos13091412 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
14 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 |
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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 |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
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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1799138131931299840 |