Using the Monte Carlo Method to Evaluate the Reliability of Screening Multifamily Housing for Radon
Abstract
When screening for radon in a multifamily housing complex using a fixed sample density (e.g., testing 1 in 10 (10%) or 1 in 4 units (25%)), the statistical confidence is dependent upon the assumed elevated radon frequency. For example, testing 25% of units in a complex estimated to have eight units with elevated radon levels will provide 90% confidence. However, if it is assumed that, in the same complex, there are only three units with elevated radon levels, the confidence drops to around 58% for the same sample density. Furthermore, in the previous example, if elevated radon levels are not found during the screening, all that can be stated is that the screening provides 90% confidence that there are no more than seven units in the complex with elevated radon levels. To more fully illustrate this uncertainty, ten separate multifamily housing radon data sets with 1 to 10 units with radon levels ≥4 pCi/L (Table 1) were selected for analysis using the Monte Carlo statistical method. Unlike other mathematically based statistical approaches, the Monte Carlo statistical method relies on repeated analysis of randomly selected data. from a 100% sampled complex at various sample densities. Success for each simulation is definedmore »
 Authors:

 Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
 Publication Date:
 Research Org.:
 Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
 Sponsoring Org.:
 USDOE
 OSTI Identifier:
 1761670
 Grant/Contract Number:
 AC0500OR22725
 Resource Type:
 Accepted Manuscript
 Journal Name:
 The Radon Reporter
 Additional Journal Information:
 Journal Volume: 2020; Journal Issue: March; Journal ID: ISSN 99990019
 Publisher:
 American Association of Radon Scientists & Technologists
 Country of Publication:
 United States
 Language:
 English
 Subject:
 38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEAR CHEMISTRY; 54 ENVIRONMENTAL SCIENCES; 46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY
Citation Formats
Wilson, David. Using the Monte Carlo Method to Evaluate the Reliability of Screening Multifamily Housing for Radon. United States: N. p., 2020.
Web.
Wilson, David. Using the Monte Carlo Method to Evaluate the Reliability of Screening Multifamily Housing for Radon. United States.
Wilson, David. Sun .
"Using the Monte Carlo Method to Evaluate the Reliability of Screening Multifamily Housing for Radon". United States. https://www.osti.gov/servlets/purl/1761670.
@article{osti_1761670,
title = {Using the Monte Carlo Method to Evaluate the Reliability of Screening Multifamily Housing for Radon},
author = {Wilson, David},
abstractNote = {When screening for radon in a multifamily housing complex using a fixed sample density (e.g., testing 1 in 10 (10%) or 1 in 4 units (25%)), the statistical confidence is dependent upon the assumed elevated radon frequency. For example, testing 25% of units in a complex estimated to have eight units with elevated radon levels will provide 90% confidence. However, if it is assumed that, in the same complex, there are only three units with elevated radon levels, the confidence drops to around 58% for the same sample density. Furthermore, in the previous example, if elevated radon levels are not found during the screening, all that can be stated is that the screening provides 90% confidence that there are no more than seven units in the complex with elevated radon levels. To more fully illustrate this uncertainty, ten separate multifamily housing radon data sets with 1 to 10 units with radon levels ≥4 pCi/L (Table 1) were selected for analysis using the Monte Carlo statistical method. Unlike other mathematically based statistical approaches, the Monte Carlo statistical method relies on repeated analysis of randomly selected data. from a 100% sampled complex at various sample densities. Success for each simulation is defined as finding at least one unit with elevated radon levels. In this statistical method, confidence in the overarching conclusion can be greatly enhanced by repeating the simulated screening hundreds or even thousands of times. For this study, each of the ten data sets was simulated 1,000 times. For each simulation, the data set was randomized three times before the fixed percentage of data was selected.},
doi = {},
journal = {The Radon Reporter},
number = March,
volume = 2020,
place = {United States},
year = {2020},
month = {3}
}