Exploring soil radon (Rn) concentrations and their connection to geological and meteorological factors

The relationship between soil radon and meteorological parameters in a region can provide insight into natural processes occurring between the lithosphere and the atmosphere. Understanding this relationship can help models establish more realistic results, rather than depending on theoretical conseq...

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Bibliographic Details
Published in:Environmental science and pollution research international 2024-01, Vol.31 (1), p.565-578
Main Authors: Muhammad, Ahmad, Danbatta, Salim Jibrin, Muhammad, Ibrahim Yahaya, Nasidi, Ibrahim Isah
Format: Article
Language:English
Subjects:
Aquatic Pollution
Artificial neural networks
Atmosphere
Atmospheric models
Atmospheric Protection/Air Quality Control/Air Pollution
Boundary conditions
Earth
Earth and Environmental Science
Earthquakes
Ecotoxicology
Environment
Environmental Chemistry
Environmental Health
Environmental science
Geochemistry
Geology
Ionosphere
Lithosphere
Mathematical models
Meteorological parameters
Monte Carlo simulation
Neural networks
Physics
Radon
Regression analysis
Research Article
Seismic activity
Soils
Waste Water Technology
Water Management
Water Pollution Control
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Summary:The relationship between soil radon and meteorological parameters in a region can provide insight into natural processes occurring between the lithosphere and the atmosphere. Understanding this relationship can help models establish more realistic results, rather than depending on theoretical consequences. Radon variation can be complicated to model due to the various physical variables which can affect it, posing a limitation in atmospheric studies. To predict Rn variation from meteorological parameters, a hybrid mod el called multiANN, which is a combination of multi-regression and artificial neural network (ANN) models, is established. The model was trained with 70% of the data and tested on the remaining 30%, and its robustness was tested using the Monte-Carlo method. The regions with low performance are identified and possibly related to seismic events. This model can be a good candidate for predicting Rn concentrations from meteorological parameters and establishing the lower boundary conditions in seismo-ionospheric coupling models.
ISSN:1614-7499
0944-1344
1614-7499
DOI:10.1007/s11356-023-31237-6