Estimation of PM2.5 infiltration factors and personal exposure factors in two megacities, China

Abstract

This study estimates infiltration factors (Finf) and ambient personal exposure factors (Fpex) for fine particulate matter (PM2.5) in two Chinese megacities, and constructs predictive models to explore their determinants. Personal-indoor-outdoor PM2.5 filter samples were collected for five consecutive days from 33 residences (of retired adults) in Nanjing (NJ) and Beijing (BJ), China, in both the non-heating season (NHS) and the heating season (HS). Elemental sulfur in filter deposits was determined by energy-dispersive X-ray fluorescence for PM2.5Finf and Fpex estimations. Season-specific models developed by stepwise multiple linear regression were evaluated using R2 and root mean square error (RMSE). The median [interquartile range (IQR)] of Finf varied from 0.76 (0.15) in the HS to 0.93 (0.11) in the NHS in NJ; and from 0.67 (0.16) to 0.86 (0.12) in BJ. Similarly, Fpex was significantly higher during the NHS [NJ: 0.95 (0.07); BJ: 0.89 (0.14)] than during the HS [NJ: 0.76 (0.17); BJ: 0.67 (0.11); p < 0.0001]. Common predictors of Finf and Fpex included window opening behaviors, meteorological variables, and building age. Moreover, air conditioning and distance to the nearest major road had an influence on Finf, while predictors of Fpex were more related to human behavior and activity (e.g., time spent outdoors and transportation). The models accounted for 35.4%–68.1% (RMSE: 0.065–0.101) and 41.6%–77.0% (RMSE: 0.033–0.103) of the variance in Finf and Fpex, respectively. By indicating the determinants of Finf and Fpex, these models can improve ambient PM2.5 exposure assessment and reduce exposure misclassification.