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dc.contributor.authorBermejo Muñoz, Lorenzo 
dc.contributor.authorGil Alana, Luis A.
dc.contributor.authorRío Caballero, Marta del 
dc.date.accessioned2023-04-26T14:01:41Z
dc.date.available2023-04-26T14:01:41Z
dc.date.issued2023
dc.identifier.citationBermejo, L., Gil-Alana, L.A. & del Río, M. Time trends and persistence in PM2.5 in 20 megacities: evidence for the time period 2018–2020. Environ Sci Pollut Res 30, 5603–5620 (2023). https://doi.org/10.1007/s11356-022-22512-zes
dc.identifier.issn09441344
dc.identifier.urihttps://hdl.handle.net/20.500.12766/411
dc.description.abstractThe degree of persistence in daily data for PM2.5 in 20 relevant megacities such as Bangkok, Beijing, Mumbai, Calcutta, Canton, Dhaka, Delhi, Jakarta, London, Los Angeles, Mexico City, Moscow, New York, Osaka. Paris, Sao Paulo, Seoul, Shanghai, Tientsin, and Tokyo is examined in this work. The analysis developed is based on fractional integration techniques. Specifcally, the diferentiation parameter is used to measure the degree of persistence in the series under study, which collects data on daily measurements carried out from January 1, 2018, to December 31, 2020. The results obtained show that the estimated values for the diferentiation parameter are restricted to the interval (0, 1) in all cases, which allows us to conclude that there is a mean reverting pattern and, therefore, transitory efects of shocks.es
dc.language.isoenges
dc.publisherSpringeres
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.titleTime trends and persistence in PM2.5 in 20 megacities: evidence for the time period 2018–2020es
dc.typejournal articlees
dc.description.departmentEmpresaes
dc.identifier.doi10.1007/s11356-022-22512-z
dc.journal.titleEnvironmental Science and Pollution Researches
dc.page.initial5603es
dc.page.final5620es
dc.rights.accessRightsopen accesses
dc.subject.keywordParticular matterses
dc.subject.keywordPM2.5es
dc.subject.keywordLong memoryes
dc.subject.keywordFractional integrationes
dc.subject.keywordTime trendses
dc.volume.number30es


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