Exploring Variation and Predictors of Residential Fine Particulate Matter Infiltration

International Journal of Environmental Research and Public Health

Nina A. Clark¹  Ryan W. Allen²  Perry Hystad⁵  Lance Wallace⁶  Sharon D. Dell³  Richard Foty³  Ewa Dabek-Zlotorzynska⁷  Greg Evans⁴ 

[1] Health Canada, 269 Laurier Ave West, Ottawa, Ontario K1A 0K9, Canada; E-Mail:;Simon Fraser University, 8888 University Drive, Burnaby, British Columbia V5A 1S6, Canada; E-Mail:;The Hospital for Sick Children, 555 University Avenue, Toronto, Ontario M5G 1X8, Canada; E-Mail:;University of Toronto, 200 College Street, Toronto, Ontario M5S 3E5, Canada; E-Mail:;University of British Columbia, 2206 East Mall, Vancouver, British Columbia V6T 1Z3, Canada; E-Mail:;11568 Woodhollow Ct, Reston, VA 20191, USA; E-Mail:;Environment Canada, 335 River Road, Ottawa, Ontario K1V 1C7, Canada; E-Mail:

关键词: air exchange;    air quality;    indoor;    infiltration;    fine particulate matter;    PM_2.5;    residential;    sulphur;   

DOI  :  10.3390/ijerph7083211

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2010 by the authors; licensee Molecular Diversity Preservation International, Basel, Switzerland.