An application of parametric and nonparametric models to the assessment of fluoride levels in vegetation exposed to stack emissions of an aluminum reduction plant in Greece

dc.contributor.author	Dimopoulos, IF	en
dc.contributor.author	Tsiros, IX	en
dc.contributor.author	Serelis, K	en
dc.contributor.author	Kamoutsis, A	en
dc.contributor.author	Chronopoulou, A	en
dc.date.accessioned	2014-06-06T06:45:26Z
dc.date.available	2014-06-06T06:45:26Z
dc.date.issued	2003	en
dc.identifier.issn	10473289	en
dc.identifier.uri	http://62.217.125.90/xmlui/handle/123456789/2437
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-0037674994&partnerID=40&md5=d7d7fcdd2569188b0496e5e35f735104	en
dc.subject.other	Neural networks	en
dc.subject.other	Particulate emissions	en
dc.subject.other	Regression analysis	en
dc.subject.other	Surface topography	en
dc.subject.other	Vegetation	en
dc.subject.other	Wind	en
dc.subject.other	Emission sources	en
dc.subject.other	Air pollution	en
dc.subject.other	aluminum	en
dc.subject.other	fluoride	en
dc.subject.other	air pollution	en
dc.subject.other	airborne particle	en
dc.subject.other	article	en
dc.subject.other	artificial neural network	en
dc.subject.other	correlation coefficient	en
dc.subject.other	geomorphology	en
dc.subject.other	Greece	en
dc.subject.other	landscape	en
dc.subject.other	meteorology	en
dc.subject.other	nonhuman	en
dc.subject.other	nonlinear system	en
dc.subject.other	nonparametric test	en
dc.subject.other	parametric test	en
dc.subject.other	priority journal	en
dc.subject.other	regression analysis	en
dc.subject.other	soil pollution	en
dc.subject.other	statistical model	en
dc.subject.other	topography	en
dc.subject.other	vegetation	en
dc.subject.other	waste management	en
dc.subject.other	wind	en
dc.subject.other	Air Pollutants	en
dc.subject.other	Aluminum	en
dc.subject.other	Fluorides	en
dc.subject.other	Forecasting	en
dc.subject.other	Greece	en
dc.subject.other	Industry	en
dc.subject.other	Models, Theoretical	en
dc.subject.other	Multimedia	en
dc.subject.other	Plants	en
dc.title	An application of parametric and nonparametric models to the assessment of fluoride levels in vegetation exposed to stack emissions of an aluminum reduction plant in Greece	en
heal.type	journalArticle	en
heal.publicationDate	2003	en
heal.abstract	Various statistical models were developed for assessing airborne fluoride (F) levels in natural vegetation near an aluminum reduction plant using as predictor variables the distance from the emission source, the predominating wind, and characteristic topography-geomorphology parameters. Results revealed that F concentrations in vegetation showed a predictable response to both wind conditions and landscape features. The linear model was found to give good estimations, taking advantage of the relatively strong linear correlation between concentration and distance. A nonlinear relationship between the F concentration in vegetation and the other variables was also found, while interactions between the variables were found to be non-first-order. The nonlinear relationship hypothesis was supported by the improved results of various nonlinear models that also indicated the importance of the area's topography-geomorphology and meteorology in model predictions. The application of an artificial neural network (ANN) model showed the closest agreement between predicted and observed values with a correlation coefficient of 0.92. The improved reliability of the ANN and a regression tree model (RTM) also were indicated by the normal distribution of their residuals. The RTM and the ANN were further investigated and found to be capable of identifying the importance of the variables in vegetation exposure to air emissions.	en
heal.journalName	Journal of the Air and Waste Management Association	en
dc.identifier.issue	4	en
dc.identifier.volume	53	en
dc.identifier.spage	396	en
dc.identifier.epage	405	en