Forecasting daily maximum ozone concentrations in the Athens Basin

Chaloulakou, A; Assimacopoulos, D; Lekkas, T

dc.contributor.author	Chaloulakou, A	en
dc.contributor.author	Assimacopoulos, D	en
dc.contributor.author	Lekkas, T	en
dc.date.accessioned	2014-03-01T01:14:41Z
dc.date.available	2014-03-01T01:14:41Z
dc.date.issued	1999	en
dc.identifier.issn	0167-6369	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/13178
dc.subject	Air pollution in Athens	en
dc.subject	Ozone concentration	en
dc.subject	Prediction of episodes	en
dc.subject	Statistical modelling	en
dc.subject.classification	Environmental Sciences	en
dc.subject.other	Daily maximum ozone concentrations	en
dc.subject.other	Forecasting	en
dc.subject.other	Mathematical models	en
dc.subject.other	Ozone	en
dc.subject.other	Regression analysis	en
dc.subject.other	Wind effects	en
dc.subject.other	Air pollution	en
dc.subject.other	ozone	en
dc.subject.other	atmospheric pollution	en
dc.subject.other	forecasting method	en
dc.subject.other	ozone	en
dc.subject.other	prediction	en
dc.subject.other	accuracy	en
dc.subject.other	air monitoring	en
dc.subject.other	air pollution	en
dc.subject.other	article	en
dc.subject.other	forecasting	en
dc.subject.other	Greece	en
dc.subject.other	model	en
dc.subject.other	summer	en
dc.subject.other	wind	en
dc.title	Forecasting daily maximum ozone concentrations in the Athens Basin	en
heal.type	journalArticle	en
heal.identifier.primary	10.1023/A:1005943201063	en
heal.identifier.secondary	http://dx.doi.org/10.1023/A:1005943201063	en
heal.language	English	en
heal.publicationDate	1999	en
heal.abstract	In the work ozone data from the Liossion monitoring station of the Athens/PERPA network are analysed. Data cover the months May to September for the period 1987-93. Four statistical models, three multiple regression and one ARIMA (0,1,2), for the prediction of the daily maximum 1-hour ozone concentrations are developed. All models together, with a persistence forecast, are evaluated and compared with the 1993's data, not used in the models development. Validation statistics were used to assess the relative accuracy of models. Analysis, concerning the models' ability to forecast real ozone episodes, was also carried out. Two of the three regression models provide the most accurate forecasts. The ARIMA model had the worst performance, even lower than the persistence one. The forecast skill of a bivariate wind speed and persistence based regression model for ozone episode days was found to be quite satisfactory, with a detection rare of 73% and 60% for O-3 >180 mu g m(-3) and O-3 >200 mu g m(-3), respectively.	en
heal.publisher	KLUWER ACADEMIC PUBL	en
heal.journalName	Environmental Monitoring and Assessment	en
dc.identifier.doi	10.1023/A:1005943201063	en
dc.identifier.isi	ISI:000080228400006	en
dc.identifier.volume	56	en
dc.identifier.issue	1	en
dc.identifier.spage	97	en
dc.identifier.epage	112	en