Estimation of residential fine particulate matter infiltration in Shanghai, China

Ambient concentrations of fine particulate matter (PM₂.₅) concentration is often used as an exposure surrogate to estimate PM₂.₅ health effects in epidemiological studies. Ignoring the potential variations in the amount of outdoor PM₂.₅ infiltrating into indoor environments will cause exposure misclassification, especially when people spend most of their time indoors. As it is not feasible to measure the PM₂.₅ infiltration factor (Fᵢₙf) for each individual residence, we aimed to build models for residential PM₂.₅Fᵢₙf prediction and to evaluate seasonal Fᵢₙf variations among residences. We repeated collected paired indoor and outdoor PM₂.₅ filter samples for 7 continuous days in each of the three seasons (hot, cold and transitional seasons) from 48 typical homes of Shanghai, China. PM₂.₅-bound sulfur on the filters was measured by X-ray fluorescence for PM₂.₅Fᵢₙf calculation. We then used stepwise-multiple linear regression to construct season-specific models with climatic variables and questionnaire-based predictors. All models were evaluated by the coefficient of determination (R²) and root mean square error (RMSE) from a leave-one-out-cross-validation (LOOCV). The 7-day mean (±SD) of PM₂.₅Fᵢₙf across all observations was 0.83 (±0.18). Fᵢₙf was found higher and more varied in transitional season (12–25 °C) than hot (>25 °C) and cold (<12 °C) seasons. Air conditioning use and meteorological factors were the most important predictors during hot and cold seasons; Floor of residence and building age were the best transitional season predictors. The models predicted 60.0%–68.4% of the variance in 7-day averages of Fᵢₙf, The LOOCV analysis showed an R² of 0.52 and an RMSE of 0.11. Our finding of large variation in residential PM₂.₅Fᵢₙf between seasons and across residences within season indicated the important source of outdoor-generated PM₂.₅ exposure heterogeneity in epidemiologic studies. Our models based on readily available data may potentially improve the accuracy of estimates of the health effects of PM₂.₅ exposure.