Model development and validation of personal exposure to PM_2.5 among urban elders

Indirect measurements through a combination of microenvironment concentrations and personal activity diaries provide a potentially useful alternative for PM_2.5 exposure estimates. This study was to optimize a personal exposure model based on spatiotemporal model predictions for PM_2.5 exposure in a sub-cohort study. Personal, home indoor, home outdoor, and ambient monitoring data of PM_2.5 were conducted for an elderly population in the Taipei city of Taiwan. The proposed microenvironment exposure (ME) models incorporate PM_2.5 measurements and individual time-activity information with a generalized estimating equation (GEE) analysis. We evaluated model performance with daily personal PM_2.5 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 PM_2.5 in our study population, respectively. Measured and predicted indoor exposures were highly correlated with personal PM_2.5 exposure. The awareness of peculiar smells is an important factor that significantly increases personal PM_2.5 exposure by 46-70%. The model incorporating home indoor PM_2.5 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 PM_2.5 determined by LUR model with a machine learning technique can improve the prediction (R² = 0.592) of personal PM_2.5 exposure, compared with the prediction of the traditional LUR model (R² = 0.385).