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dc.contributor.authorGil Alana, Luis A. 
dc.contributor.authorYaya, OlaOluwa S.
dc.contributor.authorG. Awolaja, Oladapo
dc.contributor.authorCristofaro, Lorenzo
dc.description.abstractThis paper focuses on the analysis of the time series behavior of the air quality in the 50 U.S. states by looking at the statistical properties of particulate matter (PM10 and PM2.5) datasets. We use long daily time series of outdoor air quality indices to examine issues such as the degree of persistence as well as the existence of time trends in data. For this purpose, we use a long-memory fractionally integrated framework. The results show significant negative time trend coefficients in a number of states and evidence of long memory in the majority of the cases. In general, we observe heterogeneous results across counties though we notice higher degrees of persistence in the states on the west with respect to those on the east, where there is a general decreasing trend. It is hoped that the findings in the paper will continue to assist in quantitative evidence-based air quality regulation and
dc.publisherJournal of Applied Meteorology and Climatologyspa
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.subjectAir pollutionspa
dc.subjectFractional persistencespa
dc.subjectLong memoryspa
dc.subjectParticulate matterspa
dc.titleLong Memory and Time Trends in Particulate Matter Pollution (PM2.5 1 and PM10) in the US
dc.typejournal articlespa
dc.rights.accessRightsopen accessspa
dc.description.extent857 KBspa

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Atribución-NoComercial-SinDerivadas 3.0 España
Except where otherwise noted, this item's license is described as Atribución-NoComercial-SinDerivadas 3.0 España