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Updated: May 26, 2026
05:04
Visualization of Productivity Zones Based on Nitrogen Mass Balance Model in Narragansett Bay, Rhode Island
Published on: July 14, 2023
Analyzing spatial and temporal (222)Rn trends in Maine.
Christopher Farah1, Kate Beard, C T Hess
1Maine Institute for Human Genetics and Health, University of Maine, ME 04401, USA. cfarah@emh.org
Health Physics
|January 6, 2012
Summary
High radon levels in Maine homes pose a lung cancer risk. Spatial analysis revealed persistent radon activity clusters linked to specific geological formations, aiding in understanding and predicting health risks.
Area of Science:
- Environmental Science
- Public Health
- Geology
Background:
- Prolonged radon exposure is a known risk factor for lung cancer.
- Maine exhibits elevated residential radon activity, exceeding the Environmental Protection Agency's maximum contaminant level.
- Cancer registry data suggests an increased risk of lung cancer in Maine.
Purpose of the Study:
- To apply spatial autocorrelation methods to analyze retrospective radon activity in Maine.
- To identify spatial, temporal, and spatiotemporal clusters and outliers of radon activity.
- To investigate the relationship between geological formations and radon activity patterns.
Main Methods:
- Standardization and geocoding of retrospective air and well water radon activity data (1993-2008).
- Application of spatial autocorrelation algorithms: local Getis-Ord, local Moran, and spatial scan statistic.
- Analysis of spatial, temporal, and spatiotemporal radon activity patterns.
Main Results:
- Spatial clusters of high radon activity (air and well water) were associated with Lucerne and Sebago granitic formations.
- Spatial clusters of low radon activity were linked to Biddeford Granite and the Silurian Ordovician Vassalboro metamorphic bedrock.
- Most identified spatial clusters remained consistent throughout the sampling period; no significant temporal clusters were found.
Conclusions:
- Persistent spatial variations in radon activity exist in Maine, influenced by underlying geology.
- Understanding these spatial patterns can improve the prediction of radon-related health risks in residential areas.
- Spatial autocorrelation analysis is a valuable tool for environmental health risk assessment.
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