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Machine learning as a next-generation tool for indoor air radon exposure prediction /

Indoor air quality is strongly influenced by the presence of radioactive radon (222Rn) gas. Indeed, exposure to high 222Rn concentrations is unequivocally linked to DNA damage, lung cancer, and is a worsening issue in North American residences, having increased over time within newer housing stocks...

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Bibliographic Details
Main Authors: Khan, Selim M. (Author), Taron, Joshua M. (Author), Goodarzi, Aaron A. (Author)
Format: eBook
Language:English
Published: London : SAGE Publications Ltd., 2020.
Series:SAGE Research Methods Cases : Medicine and Health.
Subjects:
Indoor air pollution > Health aspects > Case studies.
Radon > Environmental aspects > Case studies.
Machine learning > Case studies.
Radon > Aspect de l'environnement > Études de cas.
Apprentissage automatique > Études de cas.
Indoor air pollution > Health aspects
Machine learning
Radon > Environmental aspects
Case studies
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Call Number: RA577.5 .K43 2020
 
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RA577.5 .K43 2020 Available