Exploring Variation and Predictors of Residential Fine Particulate Matter Infiltration

Nina A. Clark, Ryan W. Allen, Perry Hystad, Lance Wallace, Sharon D. Dell, Richard Foty, Ewa Dabek-Zlotorzynska, Greg Evans and Amanda J. Wheeler
Additional contact information
Nina A. Clark: Health Canada, 269 Laurier Ave West, Ottawa, Ontario K1A 0K9, Canada
Ryan W. Allen: Simon Fraser University, 8888 University Drive, Burnaby, British Columbia V5A 1S6, Canada
Perry Hystad: University of British Columbia, 2206 East Mall, Vancouver, British Columbia V6T 1Z3, Canada
Lance Wallace: 11568 Woodhollow Ct, Reston, VA 20191, USA
Sharon D. Dell: The Hospital for Sick Children, 555 University Avenue, Toronto, Ontario M5G 1X8, Canada
Richard Foty: The Hospital for Sick Children, 555 University Avenue, Toronto, Ontario M5G 1X8, Canada
Ewa Dabek-Zlotorzynska: Environment Canada, 335 River Road, Ottawa, Ontario K1V 1C7, Canada
Greg Evans: University of Toronto, 200 College Street, Toronto, Ontario M5S 3E5, Canada
Amanda J. Wheeler: Health Canada, 269 Laurier Ave West, Ottawa, Ontario K1A 0K9, Canada

IJERPH, 2010, vol. 7, issue 8, 1-14

Abstract: Although individuals spend the majority of their time indoors, most epidemiological studies estimate personal air pollution exposures based on outdoor levels. This almost certainly results in exposure misclassification as pollutant infiltration varies between homes. However, it is often not possible to collect detailed measures of infiltration for individual homes in large-scale epidemiological studies and thus there is currently a need to develop models that can be used to predict these values. To address this need, we examined infiltration of fine particulate matter (PM 2.5 ) and identified determinants of infiltration for 46 residential homes in Toronto, Canada. Infiltration was estimated using the indoor/outdoor sulphur ratio and information on hypothesized predictors of infiltration were collected using questionnaires and publicly available databases. Multiple linear regression was used to develop the models. Mean infiltration was 0.52 ± 0.21 with no significant difference across heating and non-heating seasons. Predictors of infiltration were air exchange, presence of central air conditioning, and forced air heating. These variables accounted for 38% of the variability in infiltration. Without air exchange, the model accounted for 26% of the variability. Effective modelling of infiltration in individual homes remains difficult, although key variables such as use of central air conditioning show potential as an easily attainable indicator of infiltration.

Keywords: air exchange; air quality; indoor; infiltration; fine particulate matter; PM 2.5; residential; sulphur (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2010
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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