Investigation and modeling of the residential infiltration of fine particulate matter in Beijing, China.

The objective of this study was to estimate the residential infiltration factor (Finf) of fine particulate matter (PM2.5 ) and to develop models to predict PM2.5 Finf in Beijing. Eighty-eight paired indoor-outdoor PM2.5 samples were collected by Teflon filters for seven consecutive days during both non-heating and heating seasons (from a total of 55 families between August, 2013 and February, 2014). The mass concentrations of PM2.5 were measured by gravimetric method, and elemental concentrations of sulfur in filter deposits were determined by energy-dispersive x-ray fluorescence (ED-XRF) spectrometry. PM2.5 Finf was estimated as the indoor/outdoor sulfur ratio. Multiple linear regression was used to construct Finf predicting models. The residential PM2.5 Finf in non-heating season (0.70 ± 0.21, median = 0.78, n = 43) was significantly greater than in heating season (0.54 ± 0.18, median = 0.52, n = 45, p < 0.001). Outdoor temperature, window width, frequency of window opening, and air conditioner use were the most important predictors during non-heating season, which could explain 57% variations across residences, while the outdoor temperature was the only predictor identified in heating season, which could explain 18% variations across residences. The substantial variations of PM2.5 Finf between seasons and among residences found in this study highlight the importance of incorporating Finf into exposure assessment in epidemiological studies of air pollution and human health in Beijing. The Finf predicting models developed in this study hold promise for incorporating PM2.5 Finf into large epidemiology studies, thereby reducing exposure misclassification.

IMPLICATIONS: Failure to consider the differences between indoor and outdoor PM2.5 may contribute to exposure misclassification in epidemiological studies estimating exposure from a central site measurement. This study was conducted in Beijing to investigate residential PM2.5 infiltration factor and to develop a localized predictive model in both nonheating and heating seasons. High variations of PM2.5 infiltration factor between the two seasons and across homes within each season were found, highlighting the importance of including infiltration factor in the assessment of exposure to PM2.5 of outdoor origin in epidemiological studies. Localized predictive models for PM2.5 infiltration factor were also developed.