Radon Exposure Dataset

TL;DR

This work addresses the need for high-resolution radon exposure assessment by constructing a harmonized state-level dataset for Pennsylvania and Utah that supports modeling of household radon concentrations at ZCTA and sub-kilometer scales. The authors integrate geological, soil, elevation, hydrologic, meteorological, and demographic data by projecting diverse sources onto a common H3 hexagonal grid, enabling consistent, fine-grained exposure analysis. A key contribution is a reproducible workflow that harmonizes ACS/DEC census data with geospatial layers (gNATSGO, lithology, Daymet, GMTED2010, Daymet, GLHYMPS) and aggregates to ZCTAs at high spatial resolution, with scalability to the CONUS. The resulting dataset provides a foundation for precise radon exposure modeling and potential nationwide reclassification of radon risk, informing targeted mitigation strategies.

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 characteristics. Demographic variables such as the household heating fuel used, the age of building, and the housing type provide further insight into which populations could be most susceptible in areas with potentially high radon levels. This dataset also serves as a foundational resource for two other studies conducted by the authors. The resolution of the data provides a novel approach to predicting potential radon exposure, and the data processing conducted for these states can be scaled up to larger spatial resolutions (e.g., the Contiguous United States [CONUS]) and allow for a broad reclassification of radon exposure potential in the United States.