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<title>MEASUREMENT OF SOIL-GAS RADON CONCENTRATION AND GEOGENIC RADON POTENTIAL MODELLING FOR SOUTHWEST NIGERIA</title>
<link>http://hdl.handle.net/123456789/2297</link>
<description/>
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<rdf:li rdf:resource="http://hdl.handle.net/123456789/2298"/>
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<dc:date>2026-04-04T18:39:27Z</dc:date>
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<item rdf:about="http://hdl.handle.net/123456789/2298">
<title>MEASUREMENT OF SOIL-GAS RADON CONCENTRATION AND GEOGENIC RADON POTENTIAL MODELLING FOR SOUTHWEST NIGERIA</title>
<link>http://hdl.handle.net/123456789/2298</link>
<description>MEASUREMENT OF SOIL-GAS RADON CONCENTRATION AND GEOGENIC RADON POTENTIAL MODELLING FOR SOUTHWEST NIGERIA
FAJEMIROYE, Joseph Adesoji Ademola
Radon-222 is a radioactive gas in the natural decay series of Uranium-238. It easily&#13;
emanates from the soil to constitute radiological hazard and is the leading cause of&#13;
lung cancer apart from smoking. High indoor radon buildup could occur in buildings&#13;
sited over high radon-bearing bedrocks. Radon hazard, expressed as Geogenic Radon&#13;
Potential (GRP), is due to a combination of soil-gas radon concentration ( ) and&#13;
soil-air permeability ( ), both of which depend on bedrocks. Data on these two&#13;
quantities over different bedrock formations and soil types in Southwest (SW) of&#13;
Nigeria are very scarce resulting in limited knowledge on radon hazard and lack of&#13;
requisite radon control guidelines. This study was designed to measure , determine&#13;
GRP and model the distribution of GRP over different bedrocks of SW Nigeria.&#13;
Measurements of were made using a calibrated real-time semiconductor radon&#13;
monitor at a depth of 0.80 – 1.00 m in 150 randomly selected locations across 20&#13;
bedrocks in the six states of SW Nigeria. Saturated hydraulic conductivities of&#13;
undisturbed soil samples taken from these locations were measured with a constanthead permeameter in order to determine . The GRP for each location was calculated&#13;
from and and categorised using Neznal classification for radon hazard ratings.&#13;
A Levenberg-Marquardt feed-forward-back-propagation artificial neural network was&#13;
employed to develop a predictive model for . Data was randomly split in 70:15:15&#13;
for training, testing and validation, respectively, for six different architectures and the&#13;
best was chosen following standard procedure. Goodness-of-Prediction (G), Average&#13;
Validation Error , Mean Bias Error and Root Mean Square Error&#13;
were used to determine performance and validation of the model. The and&#13;
GRP maps were generated on existing geological map for SW region.&#13;
The measured ranged . The ranged ,&#13;
while ranged . The GRP ranged .&#13;
Sedimentary formation had highest of , while granitic&#13;
bedrocks had highest and GRP of and ,&#13;
respectively. Radon hazard classification showed that , and of the sites&#13;
were of low, medium and high radon hazard rating, respectively. Out of the 13 sites&#13;
with high radon hazard rating, granitic and metamorphic bedrocks presented more sites&#13;
(84.6%). The best performing architecture was 2 x 8 x 1. Performance indices of the&#13;
model, yielded G of 73.5%, of 0.073, of 0.42 and of 4.62 kBqm-3.&#13;
Validation indices yielded G of 86 , of , of and of&#13;
1 , indicating good model performance. Values of measured and GRP&#13;
were used to generate maps which showed spatial distribution of low, medium and&#13;
high radon hazard ratings.&#13;
The values of measured soil-gas radon concentration and determined geogenic radon&#13;
potential were highest in granitic bedrocks. The performance indices of the developed&#13;
neural network model showed good reliability in predicting geogenic radon potential&#13;
for southwest Nigeria.
</description>
<dc:date>2023-11-01T00:00:00Z</dc:date>
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