Using Machine Learning Methods to Forecast Air Quality

Detalhes bibliográficos
Autor(a) principal: Lei, Thomas M. T.
Data de Publicação: 2022
Outros Autores: Siu, Shirley W. I., Monjardino, Joana, Mendes, Luísa, Ferreira, Francisco
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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spelling 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
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
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