Indirect measurements through a combination of microenvironment concentrations and personal activity diaries provide a potentially useful alternative for PM2.5 exposure estimates. This study was to optimize a personal exposure model based on spatiotemporal model predictions for PM2.5 exposure in a sub-cohort study. Personal, home indoor, home outdoor, and ambient monitoring data of PM2.5 were conducted for an elderly population in the Taipei city of Taiwan. The proposed microenvironment exposure (ME) models incorporate PM2.5 measurements and individual time-activity information with a generalized estimating equation (GEE) analysis. We evaluated model performance with daily personal PM2.5 exposure based on the coefficient of determination, accuracy, and mean bias error. Ambient and home outdoor measures as exposure surrogates are likely to under- and overestimate personal exposure to PM2.5 in our study population, respectively. Measured and predicted indoor exposures were highly correlated with personal PM2.5 exposure. The awareness of peculiar smells is an important factor that significantly increases personal PM2.5 exposure by 46-70%. The model incorporating home indoor PM2.5 can achieve the highest agreement (R2 = 0.790) with personal exposure and the lowest measurement error. The ME model with the GEE analysis combining home outdoor PM2.5 determined by LUR model with a machine learning technique can improve the prediction (R2 = 0.592) of personal PM2.5 exposure, compared with the prediction of the traditional LUR model (R2 = 0.385).
Keywords: Fine particles; Microenvironment; Modeling; Personal exposure; Time-weighted average; Validation.
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