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Air Pollutants, Radioactive

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Bagged neural network model for prediction of the mean indoor radon concentration in the municipalities in Czech Republic.

Journal of environmental radioactivity
The purpose of the study is to determine radon-prone areas in the Czech Republic based on the measurements of indoor radon concentration and independent predictors (rock type and permeability of the bedrock, gamma dose rate, GPS coordinates and the a...

Artificial neural network based isotopic analysis of airborne radioactivity measurement for radiological incident detection.

Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine
Responders need tools to rapidly detect and identify airborne alpha radioactivity during consequence management scenarios. Traditional continuous air monitoring systems used for this purpose compute the net counts in various energy windows to determi...

Classification of radioxenon spectra with deep learning algorithm.

Journal of environmental radioactivity
In this study, we propose for the first time a model of classification for Beta-Gamma coincidence radioxenon spectra using a deep learning approach through the convolution neural network (CNN) technique. We utilize the entire spectrum of actual data ...

Radon potential mapping in Jangsu-gun, South Korea using probabilistic and deep learning algorithms.

Environmental pollution (Barking, Essex : 1987)
The adverse health effects associated with the inhalation and ingestion of naturally occurring radon gas produced during the uranium decay chain mean that there is a need to identify high-risk areas. This study detected radon-prone areas using a geog...

Development of a High-Resolution Indoor Radon Map Using a New Machine Learning-Based Probabilistic Model and German Radon Survey Data.

Environmental health perspectives
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...

Machine learning techniques for the prediction of indoor gamma-ray dose rates - Strengths, weaknesses and implications for epidemiology.

Journal of environmental radioactivity
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...

Identifying predictors of spatiotemporal variations in residential radon concentrations across North Carolina using machine learning analytics.

Environmental pollution (Barking, Essex : 1987)
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...

Prediction methodology of air absorbed dose rates for Chinese cities with deep learning models.

Journal of environmental radioactivity
Air absorbed dose rate is a key indicator of environmental radiation exposure. In China, automated environmental radiation monitoring systems have been established in multiple cities to continuously measure air absorbed dose rates. Nevertheless, deve...