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UNIVERSITY OF BUCHAREST
FACULTY OF PHYSICS Guest
2024-11-24 9:50 |
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Conference: Bucharest University Faculty of Physics 2011 Meeting
Section: Atmosphere and Earth Science; Environment Protection
Title:
Modeling the outdoor radon and thoron progeny concentrations using multiple linear regression
Authors:
Florin Simion (1,2), Elena Simion (2), Anton Geicu (3), V.Cuculeanu (1)
Affiliation:
(1) University of Bucharest, faculty of Physics, P.O.BOX MG-11,Magurele, Bucharest, Romania
2- National Environmental Protection Agency, Bucharest, Romania
3- National Meteorological Administration, 97 Bucuresti Ploiesti, Bucharest, Romania
E-mail
Keywords:
multiple regression model,radon, meteorology
Abstract:
The general purpose of multiple regression model is to study the relationship between a dependent or criterion variable and several independent or predictor variables. Radon and thoron progeny data measured in Bacău station of the National Environmental Radioactivity Survey Network, are modeled, at different time scales, by making use of the multiple linear regression with meteorological parameters as predictors. The radon and thoron progeny measurements were done on daily basis and each value represents the average of 4 values corresponding to 4 aspirations (each aspiration lasts 5 hours). Meteorological data that were used are daily average values. The radon and thoron progeny concentrations for different months and seasons, as dependent variables, have been modeled as function of the following meteorological variables: air temperature, soil temperature, atmospheric pressure, relative humidity, precipitation and wind speed, as predictor variables. The collinearity of independent variables has been analyzed. Using delay independent variables of different orders quite suitable approximations of the time distributions of measured progeny concentrations were obtained. The parameters characterizing the performance of the multiple regression model are: multiple correlation coefficient, F-test values, level of significance (p-value) and residual. Also, the prediction performances of the multiple linear regression model have been checked. In practice, the model will be used to fill the gaps in long time monitoring data of the radon and thoron concentrations considering the meteorological conditions.
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