AIMC Topic: Radiation Monitoring
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Edaphic and meteorological parameters as determinants of radon exhalation and its environmental implication in Peruvian agroecosystems.
Scientific reports
Jul 1, 2025
Radon exhalation is a natural process by which atoms of the radioactive gas radon diffuse in the soil and then exhale to an indoor and/or outdoor environment. High radon concentration levels, possibly from high radon exhalation rate levels, can gener...
Radiocaesium soil-to-plant transfer: a meta-analysis of key variables and data gaps on a global scale.
Journal of environmental radioactivity
May 6, 2025
A harmonized, publicly accessible database of worldwide observations and experiments on radiocaesium transfer from soil to plants is lacking. Such a database is needed for evaluating and establishing transfer models, especially for regions with limit...
Prediction of anthropogenic I in the South China Sea based on machine learning.
Journal of environmental radioactivity
May 2, 2025
With the rapid increase in the number of nuclear power plants along the China coast and the potential for releases of radioactive substances to marine ecosystems, it is important to investigate and predict the dispersion of radionuclides in the seas ...
Long-term solar radiation forecasting in India using EMD, EEMD, and advanced machine learning algorithms.
Environmental monitoring and assessment
Feb 18, 2025
Solar radiation plays a critical role in the carbon sequestration processes of terrestrial ecosystems, making it a key factor in environmental sustainability among various renewable energy sources. This study integrates two advanced signal processing...
Developing a machine learning-based predictive model for cesium sorption distribution coefficient on crushed granite.
Journal of environmental radioactivity
Feb 4, 2025
The sorption of radionuclides on granite has been extensively studied over the past few decades due to its significance in the safety assessment of geological disposal for high-level radioactive waste (HLW). The sorption properties of granite for rad...
Identifying predictors of spatiotemporal variations in residential radon concentrations across North Carolina using machine learning analytics.
Environmental pollution (Barking, Essex : 1987)
Jan 4, 2025
Radon is a naturally occurring radioactive gas derived from the decay of uranium in the Earth's crust. Radon exposure is the leading cause of lung cancer among non-smokers in the US. Radon infiltrates homes through soil and building foundations. This...
Machine learning techniques for the prediction of indoor gamma-ray dose rates - Strengths, weaknesses and implications for epidemiology.
Journal of environmental radioactivity
Dec 27, 2024
We investigate methods that improve the estimation of indoor gamma ray dose rates at locations where measurements had not been made. These new predictions use a greater range of modelling techniques and larger variety of explanatory variables than ou...
Development of a High-Resolution Indoor Radon Map Using a New Machine Learning-Based Probabilistic Model and German Radon Survey Data.
Environmental health perspectives
Sep 18, 2024
BACKGROUND: Radon is a carcinogenic, radioactive gas that can accumulate indoors and is undetected by human senses. Therefore, accurate knowledge of indoor radon concentration is crucial for assessing radon-related health effects or identifying radon...
Radiological characterization of the tailings of an abandoned copper mine using a neural network and geostatistical analysis through the Co-Kriging method.
Environmental geochemistry and health
Jul 9, 2024
The radiological characterization of soil contaminated with natural radionuclides enables the classification of the area under investigation, the optimization of laboratory measurements, and informed decision-making on potential site remediation. Neu...
Use of machine learning and deep learning to predict particulate Cs concentrations in a nuclearized river.
Journal of environmental radioactivity
Sep 15, 2023
Cesium-137, discharged by nuclear installations under normal operations and deposited in watersheds following atmospheric testing and accidents (i.e. Chernobyl, Fukushima …), has been studied for decades. Thus, modelling of Cs concentration in rivers...
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