Abant İzzet Baysal Üniversitesi bina içi radon gazı ölçümü

Indoor radon concentration was measured by using AlphaGuard Radon Monitor and short term Electret Chambers from May 2012 to May 2013. Measurements followed a randomized and repeated pattern of experimental design. All the measurements were carried out at five faculty buildings of AIBU. For instance at the Faculty of Arts and Science where we used only AlphaGuard monitor to fi gure out the dependence of the radon concentration on the floor level and aspect directions (south vs north). Following the Tukey's multiple comparisons, one-way and two-way Analysis of Variance (ANOVA), General Linear Model (GLM) and Regression Analysis were performed. The Best Arti cial Neural Networks (ANNs) emulating indoor radon levels were selected as a function of air temperature, air pressure, relative humidity, air temperature by relative humidity interaction, local time, location, month, floor and aspect. Out of 27 ANNs, MLP-2-B-L and MLP with PCA (for both AlphaGUARD and ST Chambers) performed best and could be contributing to 96.7%, 59.1% and 87.6% of variations in spatio-temporal dynamics of indoor radon levels as a function of floor level and aspect or location respectively, in addition to air temperature, air pressure, relative humidity, air temperature by relative humidity interaction, time and location, month, location by month interaction. The mean indoor radon levels stay below the reference level to minimise health risks due to indoor radon exposure.