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dc.contributor.authorVITALE, Marcello
dc.contributor.authorSerengil, Yusuf
dc.contributor.authorPAOLETTI, Elena
dc.contributor.authorKilic, Umit
dc.contributor.authorDE MARCO, Alessandra
dc.date.accessioned2021-03-04T18:39:46Z
dc.date.available2021-03-04T18:39:46Z
dc.identifier.citationDE MARCO A., VITALE M., Kilic U., Serengil Y., PAOLETTI E., "New functions for estimating AOT40 from ozone passive sampling", ATMOSPHERIC ENVIRONMENT, cilt.95, ss.82-88, 2014
dc.identifier.issn1352-2310
dc.identifier.othervv_1032021
dc.identifier.otherav_8bdcd6e6-88c9-48a3-9196-c5036be0197b
dc.identifier.urihttp://hdl.handle.net/20.500.12627/94648
dc.identifier.urihttps://doi.org/10.1016/j.atmosenv.2014.06.021
dc.description.abstractAOT40 is the present European standard to assess whether ozone (O-3) pollution is a risk for vegetation, and is calculated by using hourly O-3 concentrations from automatic devices, i.e. by active monitoring. Passive O-3 monitoring is widespread in remote environments. The Loibl function estimates the mean daily O-3 profile and thus hourly O-3 concentrations, and has been proposed to calculate AOT40 from passive samplers. We investigated whether this function performs well in inhomogeneous terrains such as over the Italian country. Data from 75 active monitoring stations (28 rural and 47 suburban) were analysed over two years. AOT40 was calculated from hourly O-3 data either measured by active measurements or estimated by the Loibl function applied to biweekly averages of active-measurement hourly data. The latter approach simulated the data obtained from passive monitoring, as two weeks is the usual exposure window of passive samplers. Residuals between AOT40 estimated by applying the Loibl function and AOT40 calculated from active monitoring ranged from +241% to -107%, suggesting that the Loibl function is inadequate to accurately predict AOT40 in Italy. New statistical models were built for both rural and suburban areas by using non-linear models and including predictors that can be easily measured at forest sites. The modelled AOT40 values strongly depended on physical predictors (latitude and longitude), alone or in combination with other predictors, such as seasonal cumulated ozone and elevation. These results suggest that multi-variate, non-linear regression models work better than the Loibl-based approach in estimating AOT40. (C) 2014 Elsevier Ltd. All rights reserved.
dc.language.isoeng
dc.subjectAtmosfer Bilimleri ve Meteoroloji Mühendisliği
dc.subjectÇevre Mühendisliği
dc.subjectMühendislik ve Teknoloji
dc.subjectTemel Bilimler (SCI)
dc.subjectTarımsal Bilimler
dc.subjectYerbilimleri
dc.subjectMETEOROLOJİ VE ATMOSFER BİLİMLERİ
dc.subjectTarım ve Çevre Bilimleri (AGE)
dc.subjectÇevre / Ekoloji
dc.subjectÇEVRE BİLİMLERİ
dc.titleNew functions for estimating AOT40 from ozone passive sampling
dc.typeMakale
dc.relation.journalATMOSPHERIC ENVIRONMENT
dc.contributor.departmentItalian National Agency New Technical Energy & Sustainable Economics Development , ,
dc.identifier.volume95
dc.identifier.startpage82
dc.identifier.endpage88
dc.contributor.firstauthorID69570


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