INVITED LECTURES
Click on the presenter’s name to see the biography, and on the lecture's title to see the lecture's abstract.
Peter Bossew
Peter Bossew has retired after last having worked as researcher at the German Federal Office for Radiation Protection (BfS) in Berlin.
He studied mathematics and theoretical physics at the University of Vienna. In the 1980s he engaged in the nuclear discussion and after the Chernobyl accident 1986, he was involved into measurement, surveying and fallout mapping. Partly this was performed in a radiometric laboratory which he founded together with colleagues in the NGO framework of the Austrian Institute of Applied Ecology, Vienna. Later, he moved to the university of Salzburg where his field of work was radioecology (related to Chernobyl, Alpine ecosystems, hydrosphere) and radon mapping.
In 2006, he started at the Joint Research Centre (JRC) of the European Commission in Ispra, Italy. Fields of work were harmonization of European monitoring networks, radioecology, radon and more theoretical work about stochastic field modelling. In this year, the project “European Atlas of Natural Radiation” was started which in 2019 led to its publication.
In 2010, he moved on to the BfS in Berlin. Apart from radon, subjects were the Fukushima accident and theoretical work on environmental statistics and stochastic modelling, e.g. assessment of anomalies, quality assurance chains, citizen monitoring of ambient dose rate.
Retired since 2022, he continues working on projects, writes papers and continues participating in conferences. Currently his focus is analysis of time series, mainly of radon concentrations.
He cooperated with many researchers and institutions world-wide, among other in Japan, Brazil and the Ukraine where he conducted field work during several research visits.
Radiometric time series - why, what for, how. Plus some examples
Generation of time series of environmental quantities is a tool to explore the dynamic nature of the physical phenomenon that manifests in these quantities. In the radiometric context, this includes investigation of the notoriously complicated dynamic of radon in various environmental media, of ambient dose rate or following radiologically relevant events such as the passage of contaminated clouds and fallout after the Chernobyl and Fukushima accidents. The objectives are detection, quantification and classification of “signals” contained in the series, or the “volume” of an event that has caused the signal. The former is typical for tracer studies and studies intended to understand the physical nature of a process, the latter for quantifying radiological impact. Importantly, also the observation process, i.e., measuring, induces variability that has to be accounted for.
More often than not the phenomena or processes to be assessed are variable in time and space. One is interested in both – the temporal aspect to assess its evolution and the spatial one for mapping or to assess the regionally different evolution. Sometimes surveys are performed with moving monitors which entails the additional task to disentangle the two variability components. For example, in surveys of ambient dose rate by moving G-M monitors one has to consider that the dose rate changes with fluctuation of cosmic radiation or ambient radon concentration that in turn are subject to temporal changes.
Methodologies for assessing spatial and temporal variability are different. Here we are interested in the temporal aspect in the first place. If the task consists in identifying signals and understanding the epistemic chain from their generators over their propagation in the environment to the observation process, the task consists in decomposing the series. Components may be a constant or stationary background, a trend, periodic and aperiodic fluctuation in different time scales, instrumental noise and statistical noise due to the stochastic nature of a process. Mathematical procedures are available to separate the components, such as low/high pass filtering, Fourier, autocorrelation and Hurst analyses. Phase space exploration can serve to investigate the complexity of a process; chaotic analysis can indicate its predictability. Some examples derived from radon time series are shown. Also examples of observational variability are given.
As examples of studies that consider spatio-temporal effects, we address ground-borne surveys of ambient dose rate, geographical transects of outdoor radon concentration (now possible with simple, easily transportable radon monitors) and ambient dose rate measurements after Chernobyl and Fukushima. A novel project targeting possible differences of the radon dynamic in different climatic zones (temperate, Mediterranean, tropical) is currently in its initial stage.