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How to screen for 222Rn in large buildings
C S Dudney1, D L Wilson, J A Otten
1Health Sciences Research Division, Oak Ridge National Laboratory, TN 37831-6379, USA.
Health Physics
|February 1, 1996
Summary
This study analyzed indoor radon (222Rn) measurements in federal buildings, finding that a two-phase survey approach, starting with broad screening and followed by targeted assessment, is effective for identifying buildings with elevated radon levels.
Area of Science:
- Environmental Science
- Public Health
- Building Science
Background:
- Indoor radon (222Rn) is a significant indoor air pollutant.
- Radon surveys in large buildings present statistical challenges due to varied room-level distributions.
- Understanding radon distribution is crucial for effective public health strategies.
Purpose of the Study:
- To statistically analyze indoor radon measurements in federal buildings.
- To compare parametric distributions for indoor radon data in large structures.
- To propose an optimized, two-phase survey strategy for indoor radon in large buildings.
Main Methods:
- Statistical analysis of over 10,000 radon measurements from 908 federal buildings.
- Comparison of log-normal, bi-modal, and tri-modal distributions for room-level and facility-level data.
- Numerical simulation to test a proposed two-phase screening and assessment protocol.
Main Results:
- Room-level radon data often showed non-log-normal distributions, while facility-level data were typically log-normal.
- A two-phase approach (screening and assessment) is proposed for efficient large-building radon surveys.
- Simulated screenings validated the feasibility of the proposed protocol for identifying facilities with elevated radon.
Conclusions:
- A phased survey strategy can effectively identify buildings with high indoor radon concentrations.
- The proposed screening phase uses lower sampling density, followed by intensive assessment in targeted facilities.
- This approach optimizes resource allocation for indoor radon mitigation efforts in large building inventories.