| Summary: | Many epidemiological studies have evaluated the health risks of ambient fine particulate matter (PM2.5). However, few studies have investigated the potential exposure misclassification caused by using ambient PM2.5 concentrations as proxy for individual exposure to PM2.5 in regions with high-level of air pollution. This study aimed to compare the differences between personal and ambient PM2.5 constituent concentrations, and to predict the personal exposure of sixteen PM2.5 constituents. We collected 141 72-h personal exposure filter samples from a panel of 36 healthy non-smoking college students in Shanghai, China. We then used the liner mixed effects models to predict personal constituent-specific exposure using ambient observations and several possible influencing factors including time-activity patterns, temporal variables, and meteorological conditions. The final model of each component was further evaluated by determination coefficient (R2) and root mean square error (RMSE) from leave-one-out-cross-validation (LOOCV). We observed ambient concentrations were higher than personal concentrations for all PM2.5 components except for Mn, Fe, Ca, and V. Especially, ambient NH4+, As, and NO3– concentrations were 3.65, 5.65 and 7.33-fold higher than their corresponding personal concentrations, respectively. The ambient level was the strongest predictor of their corresponding personal PM2.5 components with the highest marginal R2 (RM2: 0.081 ~ 0.901), meteorological conditions (RM2: 0.000 ~ 0.357), time-activity pattern (RM2: 0.000 ~ 0.083) and temporal indicators (RM2: 0.031 ~ 0.562) were also important predictors. Our final models predicted at least 50% of the variance of all personal PM2.5 constituents and even over 90% for K, Pb, and SO42−. LOOCV analysis showed that R2 and RMSE ranged from 0.251 to 0.907 and 0.000 to 0.092 μg/m3, respectively. Our results showed that ambient concentration of most PM2.5 constituents along with time-activity patterns, temporal variables, and meteorological conditions, could adequately predict personal exposure concentration. Prediction models of individual PM2.5 constituent may help to improve the accuracy of exposure measurement in future epidemiological studies.
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