Evaluation of soil-gas radon concentrations from different geological units with varying strata in a crystalline basement complex of southwestern Nigeria.

The aim of this study is to determine the variation of soil-gas radon concentrations from different rock formations in Ogbomoso, southwestern Nigeria. The radon concentrations at different five geological domains in Ogbomoso are determined with respect to depth. The measurements varied from the surface (0 cm) to 100 cm depth, with an interval of 20 cm. At all the geological domains (Porphyroclastic, Granite, Quartzite, Migmatite and Banded gneiss), radon has its minimum emission over migmatite at 0 cm, while its maximum emissions occured over granite and banded gneiss at 80 cm. The overall soil-gas radon concentrations in Ogbomoso varied from 0.06 to 26.5 kBq/m3, which is within the natural limit of 0.4 to 40 kBq/m3 based on the International Commission on Radiological Protection's recommendation. An F-ratio of 6.989 and a p-value of 0.001 were obtained for the first inferential hypothesis, while an F-ratio of 2.489 and a p-value of 0.076 were obtained for the second inferential hypothesis using ANOVA test. The post hoc (using Tukey HSD and Duncan) tests revealed that at 60 + cm, depth controls the level of radon concentrations being emanated from the subsurface. The pollution index in Ogbomoso is of level 1 at 80 cm and level 0 (safe limit) at other depths. In conclusion, the soil-gas radon emission depends on the local geology and lithological sequences (depths). Cracks that could act as passage for indoor radon at the floors of the buildings around the polluted zones should be avoided in order to have a sustainable city.

Artificial neural network modeling of meteorological and geological influences on indoor radon concentration in selected tertiary institutions in Southwestern Nigeria.

Exposure to indoor radon, with no safe level, has been reported to bear the possible radiological risk to humans. The indoor radon level of a total of one hundred and thirty-two offices and sixty classrooms of tertiary institutions within different lithology and at varied meteorological values in southwestern Nigeria was measured using Electret Passive Environmental Radon Monitor (E-PERM). The meteorological parameters were obtained from the National Aeronautics and Space Administration (NASA) database. MATLAB scripts of code were used to develop the Artificial Neural Network (ANN) model. The measured parameters were subjected to both descriptive and inferential statistics. The highest mean radon concentration was observed in offices built on granitic bedrock with a value of 64.3 ± 1.7 Bq.m-3 while the lowest was observed in alluvium bedrock with a value of 52.5 ± 1.4 Bq.m-3. To enhance prediction involving erratic parametric patterns, the measured data were subjected to an optimized Artificial Neural Network architecture training, validation, and testing, leading to a model determined to have a Nash-Sutcliffe efficiency coefficient value of 0.997, Average Absolute Relative Error of 0.0115, and Mean Squared Error of 0.07. The predicted result was compared favorably with the measured data with 0.054 Average Validation Error, 0.027 Mean Absolute Error 3.64 Mean Absolute Percentage Error, and 83.7% Goodness-of-Prediction values. About 21.4% of the values were found to be higher than the 100 Bq.m-3 limits specified by the World Health Organization. Measured radon concentration and predicted ANN data as obtained in this work, being novel in this study area is useful for immediate assessment of the level of risk associated with radon exposure as well as for future predictions. The ANN developed is effective and efficient in predicting indoor radon concentration.