The young generation in radiation protection (IRPA YGN) in social media and online learning: 'Brave New World' or 'Online Nightmare'?

While online working seems to have become more common since the start of the Covid-19 pandemic, social media has the potential to offer greater interactivity and networking capacities. Thus it seems relevant for the IRPA Young Generation Network to investigate the use of social media by members of the young generation in radiation protection (RP) through an online survey. It was also the opportunity to collect early feedback about on-line learning. The survey received 89 answers from 15 countries. The most commonly used social media platforms were first Facebook, then LinkedIn and Twitter, but other social media were reported. The respondents have a multi-objectives approach while on social media, using it for different purposes (chiefly for sharing news and RP related information/educational material) and different audiences (e.g. public, professional). Yet, they are making a very moderate use based on the frequencies of publication and consultation and the challenges they see in social media. The survey also collected the view of the young generation about their practical experience in learning in a virtual setting and its advantages and disadvantages vs. in-person learning. Most participants expressed mixed-feelings about on-line learning. The results show that the young generation can play a role in supporting the extra- and intra-communication activities of the RP community.

Analysis of the evolution of gross alpha and gross beta activities in airborne samples in Valencia (Spain).

Gross alpha (Aα) and gross beta activities (Aß) were measured weekly in the airborne of the Universitat Politècnica de Valencia campus (in the east of Spain) during the period 2009-2015 (7 years). The geometric mean values of weekly Aα and Aß were 0.53·10-4 Bq m-3 and 5.77·10-4 Bq m-3, respectively; with an average ratio Aα/Aß of 0.097. This study highlights the heterogeneity of gross alpha and gross beta activities depending on the different periods of the year. Data show seasonal variations with the highest activity in summer months and the lowest one in winter months. Several atmospheric factors were considered in order to explain this intra-annual variation (wind speed, temperature, relative humidity, precipitations, dust content and prevailing wind directions). Multiple Linear Regression Analysis were performed in order to obtain information on significant atmospheric factors that affect gross α and gross ß variability, which could be useful in identifying meteorological or atmospheric changes that could cause deviations in gross α and gross ß activity depending on the seasons considered. Models obtained explain more than 60% of variability for global data, and also for winter and spring-autumn months. However, more research is needed to explain gross α and gross ß variability in summer months, because the atmospheric factors considered in the MLR explain less than 35% of variability.