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Probabilistic estimation of residential air exchange rates for population-based human exposure modeling

Abstract

Residential air exchange rates (AERs) are a key determinant in the infiltration of ambient air pollution indoors. Population-based human exposure models using probabilistic approaches to estimate personal exposure to air pollutants have relied on input distributions from AER measurements. An algorithm for probabilistically estimating AER was developed based on the Lawrence Berkley National Laboratory Infiltration model utilizing housing characteristics and meteorological data with adjustment for window opening behavior. The algorithm was evaluated by comparing modeled and measured AERs in four US cities (Los Angeles, CA; Detroit, MI; Elizabeth, NJ; and Houston, TX) inputting study-specific data. The impact on the modeled AER of using publically available housing data representative of the region for each city was also assessed. Finally, modeled AER based on region-specific inputs was compared with those estimated using literature-based distributions. While modeled AERs were similar in magnitude to the measured AER they were consistently lower for all cities except Houston. AERs estimated using region-specific inputs were lower than those using study-specific inputs due to differences in window opening probabilities. The algorithm produced more spatially and temporally variable AERs compared with literature-based distributions reflecting within- and between-city differences, helping reduce error in estimates of air pollutant exposure.

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Acknowledgements

We would also like to thank Kathie Dionisio of the US EPA’s National Exposure Laboratory for her scientific guidance on this manuscript. The United States Environmental Protection Agency through its Office of Research and Development partially funded and collaborated in the research described here under contract number EPD10070 to Alion Science and Technology Inc. It has been subjected to Agency review and approved for publication.

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Correspondence to Lisa K Baxter.

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Supplementary Information accompanies the paper on the Journal of Exposure Science and Environmental Epidemiology website

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Baxter, L., Stallings, C., Smith, L. et al. Probabilistic estimation of residential air exchange rates for population-based human exposure modeling. J Expo Sci Environ Epidemiol 27, 227–234 (2017). https://doi.org/10.1038/jes.2016.49

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  • DOI: https://doi.org/10.1038/jes.2016.49

Keywords

  • air exchange rates
  • air pollution
  • exposure error
  • exposure modeling
  • infiltration
  • model evaluation

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