Fakulta matematiky, fyziky
a informatiky
Univerzita Komenského v Bratislave

Nukleárny seminár - Martin Bulko (18.4.2018)

v stredu 18.4.2018 o 14:00 hod. v miestnosti F1/364

12. 04. 2018 13.45 hod.
Od: Jaroslav Staníček

Pozývame Vás na Nukleárny seminár Katedry jadrovej fyziky a biofyziky FMFI UK, Slovenskej fyzikálnej spoločnosti a Slovenskej nukleárnej spoločnosti 

Prednášajúci: RNDr. Martin Bulko, PhD. 

Názov: Vzťah medzi koncentráciou 222Rn vo vonkajšej atmosfére a atmosférickou stabilitou určenou na základe modifikovanej Turnerovej metódy

Termín: 18.4.2018, 14:00 hod., zasadačka KJFB (F1/364)

In practice, information about atmospheric stability is often obtained from discrete stability classes determined from routine meteorological observations. Radioactive gas 222Rn present in the atmosphere is also considered a good indicator of vertical dispersion and atmospheric stability. A complex, in-depth analysis between these different approaches of atmospheric stability assessment has not been performed so far, and was the main motivation behind this study. The study presents radon data measured in the atmosphere of Bratislava (Slovakia) and stability indexes (SI) calculated according to a modified Turner method during a period of one year. It was found that the time series of radon activity concentration (RAC) lag approximately 5 hours behind the time series of Turner stability classes adjusted for temperate climate regions. Evaluation of seasonal trends unveiled a low variability of mean monthly values of stability classes compared to the variability of mean monthly values of RAC. Another notable difference between RAC and stability indexes was found – while the stability index can both increase and decrease with wind speed, concentration of outdoor radon was never observed to increase with increasing wind speed. In spite of the mentioned discrepancies, the time series of RAC and SI are generally in a good agreement. This is especially true if one compares the deviations of RAC and SI from their mean daily values, when the differences in their seasonal variability are eliminated.