Compositional and Bayesian inference analysis of the concentrations of air pollutants in Catalonia, Spain
dc.contributor.author
dc.date.accessioned
2021-12-14T15:13:48Z
dc.date.available
2021-12-14T15:13:49Z
dc.date.issued
2022-03-01
dc.identifier.issn
0013-9351
dc.identifier.uri
dc.description.abstract
While most countries have networks of stations for monitoring pollutant concentrations, they do not cover the whole territory continuously. Therefore, to be able to carry out a spatial and temporal study, the predictions for air pollution from unmeasured sites and time periods need to be used. The objective of this study is to predict the air pollutant concentrations of PM10, O3, NO2, SO2 and CO in sites throughout Catalonia (Spain) and time periods without a monitoring station. Compositional data (CoDa) studies the relative importance of pollutants. A novel feature in this article is combining CoDa with an indicator of total pollution. Predictions are then made using a combination of spatio-temporal models and the Bayesian Laplace Integrated Approach (INLA) inference method. The most relevant results obtained indicate that the log-ratio between NO2 and O3 has the highest variance and the best predictive accuracy in time and space. Total pollution levels are second in variance but have low spatial predictive accuracy. Third in variance is the low temporal accuracy found in the log-ratio between SO2 and the remaining pollutants. Globally, the combination of CoDa and the INLA method is suitable for making effective spatio-temporal predictions of air pollutants
dc.description.sponsorship
This work was partially financed by the SUPERA COVID19 Fund, from SAUN: Santander
Universidades, CRUE and CSIC; by the COVID-19 Competitive Grant Program from Pfizer
Global Medical Grants; by the Spanish Ministry of Science, Innovation and
Universities/FEDER (grant number RTI2018–095518–B–C21); and the Government of
Catalonia (grant number 2017SGR656)
Open Access funding provided thanks to the CRUE-CSIC agreement with Elsevier
dc.format.mimetype
application/pdf
dc.language.iso
eng
dc.publisher
Elsevier
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Reproducció digital del document publicat a: https://doi.org/10.1016/j.envres.2021.112388
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Environmental Research, 2022, vol. 204, núm. Part D, p. 112388
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Articles publicats (D-EC)
dc.rights
Reconeixement-NoComercial-SenseObraDerivada 4.0 Internacional
dc.rights.uri
dc.source
Mota Bertran, Anna Sáez Zafra, Marc Coenders, Germà 2022 Compositional and Bayesian inference analysis of the concentrations of air pollutants in Catalonia, Spain Environmental Research 204 Part D 112388
dc.subject
dc.title
Compositional and Bayesian inference analysis of the concentrations of air pollutants in Catalonia, Spain
dc.type
info:eu-repo/semantics/article
dc.date.embargoEndDate
info:eu-repo/date/embargoEnd/2024-01-31
dc.relation.projectID
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-095518-B-C21/ES/METODOS DEL ANALISIS COMPOSICIONAL DE DATOS/
dc.type.version
info:eu-repo/semantics/publishedVersion
dc.identifier.doi
dc.identifier.idgrec
034160
dc.contributor.funder
dc.type.peerreviewed
peer-reviewed
dc.relation.FundingProgramme
dc.relation.ProjectAcronym
dc.identifier.eissn
1096-0953