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Residential Air Exchange Rates in the United States: Empirical and Estimated Parametric Distributions by Season and Climatic Region

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  • Donald M. Murray
  • David E. Burmaster

Abstract

The purpose of this paper is to undertake a statistical analysis to specify empirical distributions and to estimate univariate parametric probability distributions for air exchange rates for residential structures in the United States. To achieve this goal, we used data compiled by the Brookhaven National Laboratory using a method known as the perfluorocarbon tracer (PFT) technique. While these data are not fully representative of all areas of the country or all housing types, they are judged to be by far the best available. The analysis is characterized by four key points: the use of data for 2,844 households; a four‐region breakdown based on heating degree days, a best available measure of climatic factors affecting air exchange rates; estimation of lognormal distributions as well as provision of empirical (frequency) distributions; and provision of these distributions for all of the data, for the data segmented by the four regions, for the data segmented by the four seasons, and for the data segmented by a 16 region by season breakdown. Except in a few cases, primarily for small sample sizes, air exchange rates were found to be well fit by lognormal distributions (adjusted R2 0.95). The empirical or lognormal distributions may be used in indoor air models or as input variables for probabilistic human health risk assessments.

Suggested Citation

  • Donald M. Murray & David E. Burmaster, 1995. "Residential Air Exchange Rates in the United States: Empirical and Estimated Parametric Distributions by Season and Climatic Region," Risk Analysis, John Wiley & Sons, vol. 15(4), pages 459-465, August.
    • Handle: RePEc:wly:riskan:v:15:y:1995:i:4:p:459-465
    DOI: 10.1111/j.1539-6924.1995.tb00338.x
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    Cited by:

    1. Austin L. Mitchell & W. Michael Griffin & Elizabeth A. Casman, 2016. "Lung Cancer Risk from Radon in Marcellus Shale Gas in Northeast U.S. Homes," Risk Analysis, John Wiley & Sons, vol. 36(11), pages 2105-2119, November.
    2. Stephanie K. Gower & Stephen McColl, 2005. "Development of the PEARLS Model (Particulate Exposure from Ambient to Regional Lung by Subgroup) and Use of Monte Carlo Simulation to Predict Internal Exposure to PM2.5 in Toronto," Risk Analysis, John Wiley & Sons, vol. 25(2), pages 301-315, April.
    3. Neil E. Klepeis & Narinder Dhaliwal & Gary Hayward & Viviana Acevedo-Bolton & Wayne R. Ott & Nathan Read & Steve Layton & Ruoting Jiang & Kai-Chung Cheng & Lynn M. Hildemann & James L. Repace & Stepha, 2016. "Measuring Indoor Air Quality and Engaging California Indian Stakeholders at the Win-River Resort and Casino: Collaborative Smoke-Free Policy Development," IJERPH, MDPI, vol. 13(1), pages 1-26, January.
    4. Rosemary T. Zaleski & Peter P. Egeghy & Pertti J. Hakkinen, 2016. "Exploring Global Exposure Factors Resources for Use in Consumer Exposure Assessments," IJERPH, MDPI, vol. 13(7), pages 1-26, July.
    5. Donald M. Murray, 1997. "Residential House and Zone Volumes in the United States: Empirical and Estimated Parametric Distributions," Risk Analysis, John Wiley & Sons, vol. 17(4), pages 439-446, August.
    6. Mark Nicas & Rachael M. Jones, 2009. "Relative Contributions of Four Exposure Pathways to Influenza Infection Risk," Risk Analysis, John Wiley & Sons, vol. 29(9), pages 1292-1303, September.
    7. Liuliu Du & Stuart Batterman & Christopher Godwin & Jo-Yu Chin & Edith Parker & Michael Breen & Wilma Brakefield & Thomas Robins & Toby Lewis, 2012. "Air Change Rates and Interzonal Flows in Residences, and the Need for Multi-Zone Models for Exposure and Health Analyses," IJERPH, MDPI, vol. 9(12), pages 1-23, December.

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