Radon Exposure Dataset
Dakotah Maguire, Jeremy Logan, Heechan Lee, and Heidi Hanson

TL;DR
This paper presents a detailed, multi-source dataset of radon levels and related factors across Pennsylvania and Utah, aiming to improve modeling and prediction of household radon exposure risks.
Contribution
It introduces a comprehensive, harmonized dataset combining geological and demographic data at fine spatial scales for radon risk assessment.
Findings
Dataset enables modeling of household radon levels.
Data processing approach scalable to larger regions.
Supports identification of at-risk populations.
Abstract
Exposure to elevated radon levels in the home is one of the leading causes of lung cancer in the world. The following study describes the creation of a comprehensive, state-level dataset designed to enable the modeling and prediction of household radon concentrations at Zip Code Tabulation Area (ZCTA) and sub-kilometer scales. Details include the data collection and processing involved in compiling physical and demographic factors for Pennsylvania and Utah. Attempting to mitigate this risk requires identifying the underlying geological causes and the populations that might be at risk. This work focuses on identifying at-risk populations throughout Pennsylvania and Utah, where radon levels are some of the highest in the country. The resulting dataset harmonizes geological and demographic factors from various sources and spatial resolutions, including temperature, geochemistry, and soil…
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Taxonomy
TopicsRadioactivity and Radon Measurements
