Modeling and forecasting of monthly PM2.5 emission of Paris by periodogram-based time series methodology

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buir.contributor.authorYücel, Mustafa Eray
buir.contributor.orcidYücel, Mustafa Eray|0000-0002-1038-4357
dc.citation.epage637en_US
dc.citation.spage622en_US
dc.citation.volumeNumber193en_US
dc.contributor.authorAkdi, Y.
dc.contributor.authorGölveren, E.
dc.contributor.authorÜnlü, K. D.
dc.contributor.authorYücel, Mustafa Eray
dc.date.accessioned2022-02-04T13:12:50Z
dc.date.available2022-02-04T13:12:50Z
dc.date.issued2021-09-03
dc.departmentDepartment of Economicsen_US
dc.description.abstractIn this study, monthly particulate matter (PM2.5) of Paris for the period between January 2000 and December 2019 is investigated by utilizing a periodogram-based time series methodology. The main contribution of the study is modeling the PM2.5 of Paris by extracting the information purely from the examined time series data, where proposed model implicitly captures the effects of other factors, as all their periodic and seasonal effects reside in the air pollution data. Periodicity can be defined as the patterns embedded in the data other than seasonality, and it is crucial to understand the underlying periodic dynamics of air pollutants to better fight pollution. The method we use successfully captures and accounts for the periodicities, which could otherwise be mixed with seasonality under an alternative methodology. Upon the unit root test based on periodograms, it is revealed that the investigated data has periodicities of 1 year and 20 years, so harmonic regression is utilized as an alternative to Box-Jenkins methodology. As the harmonic regression displayed a better performance both in and out-of-sample forecasts, it can be considered as a powerful alternative to model and forecast time series with a periodic structure.en_US
dc.identifier.doi10.1007/s10661-021-09399-yen_US
dc.identifier.eissn1573-2959
dc.identifier.issn0167-6369
dc.identifier.urihttp://hdl.handle.net/11693/77050
dc.language.isoEnglishen_US
dc.publisherSpringeren_US
dc.relation.isversionofhttps://doi.org/10.1007/s10661-021-09399-yen_US
dc.source.titleEnvironmental Monitoring and Assessmenten_US
dc.subjectHarmonic regressionen_US
dc.subjectAir pollutionen_US
dc.subjectTime series analysisen_US
dc.subjectPeriodicityen_US
dc.titleModeling and forecasting of monthly PM2.5 emission of Paris by periodogram-based time series methodologyen_US
dc.typeArticleen_US

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