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Modeling residential fine particulate matter infiltration for exposure assessment

Journal of Exposure Science & Environmental Epidemiology volume 19, pages 570–579 (2009)Cite this article

Abstract

Individuals spend the majority of their time indoors; therefore, estimating infiltration of outdoor-generated fine particulate matter (PM2.5) can help reduce exposure misclassification in epidemiological studies. As indoor measurements in individual homes are not feasible in large epidemiological studies, we evaluated the potential of using readily available data to predict infiltration of ambient PM2.5 into residences. Indoor and outdoor light scattering measurements were collected for 84 homes in Seattle, Washington, USA, and Victoria, British Columbia, Canada, to estimate residential infiltration efficiencies. Meteorological variables and spatial property assessment data (SPAD), containing detailed housing characteristics for individual residences, were compiled for both study areas using a geographic information system. Multiple linear regression was used to construct models of infiltration based on these data. Heating (October to February) and non-heating (March to September) season accounted for 36% of the yearly variation in detached residential infiltration. Two SPAD housing characteristic variables, low building value, and heating with forced air, predicted 37% of the variation found between detached residential infiltration during the heating season. The final model, incorporating temperature and the two SPAD housing characteristic variables, with a seasonal interaction term, explained 54% of detached residential infiltration. Residences with low building values had higher infiltration efficiencies than other residences, which could lead to greater exposure gradients between low and high socioeconomic status individuals than previously identified using only ambient PM2.5 concentrations. This modeling approach holds promise for incorporating infiltration efficiencies into large epidemiology studies, thereby reducing exposure misclassification.

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Abbreviations

a :

air exchange rate (h−1)

a 1 :

coefficient describing the penetration, deposition, and exfiltration of ambient PM2.5

a 2 :

coefficient describing the deposition and exfiltration of indoor PM2.5

B sp :

particle light scattering coefficient

F inf :

infiltration efficiency

FHA:

forced hot air heat

GBPS:

Georgia Basin Puget Sound airshed

GIS:

geographic information system

HVAC:

heating, ventilation, and air conditioning systems

k :

particle deposition rate (h−1)

Neph:

Radiance Research nephelometer

P :

particle penetration efficiency (unitless)

PM:

particulate matter

PM2.5:

particulate matter with an aerodynamic diameter less than 2.5 μm

RM:

recursive model

SPAD:

spatial property assessment data

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Acknowledgements

We thank the participants of this study. We are also grateful to researchers at the University of Washington and the EPA Northwest Center for Particulate Air Pollution and Health for sharing data and infiltration estimates from the Seattle panel study. This research was carried out as part of the Border Air Quality Study (BAQS) funded by Health Canada through an agreement with the British Columbia Centre for Disease Control. A number of researchers within BAQS provided invaluable guidance and advice throughout the research.

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Authors and Affiliations

  1. Department of Geography, University of Victoria, Victoria, BC, Canada

    Perry U Hystad, Eleanor M Setton & Peter C Keller

  2. Faculty of Health Sciences, Simon Fraser University, Burnaby, BC, Canada

    Ryan W Allen

  3. School of Environmental Health, University of British Columbia, Vancouver, BC, Canada

    Michael Brauer

Authors
  1. Perry U Hystad
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  2. Eleanor M Setton
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  3. Ryan W Allen
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Correspondence to Perry U Hystad.

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For submission to the Journal of Exposure Science & Environmental Epidemiology 05 November 2007.

The views expressed in this paper do not necessarily reflect the views or policies of Health Canada.

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Hystad, P., Setton, E., Allen, R. et al. Modeling residential fine particulate matter infiltration for exposure assessment. J Expo Sci Environ Epidemiol 19, 570–579 (2009). https://doi.org/10.1038/jes.2008.45

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