Determinants of personal exposure to fine particulate matter in the retired adults – Results of a panel study in two megacities, China

Abstract

This study aimed to investigate the relationship between outdoor, indoor, and personal PM2.5 exposure in the retired adults and explore the effects of potential determinants in two Chinese megacities. A longitudinal panel study was conducted in Nanjing (NJ) and Beijing (BJ), China, and thirty-three retired non-smoking adults aged 43–86 years were recruited in each city. Repeated measurements of outdoor-indoor-personal PM2.5 concentrations were measured for five consecutive 24-h periods during both heating and non-heating seasons using real-time and gravimetric methods. Time-activity and household characteristics were recorded. Mixed-effects models were applied to analyze the determinants of personal PM2.5 exposure. In total, 558 complete sets of collocated 24-h outdoor-indoor-personal PM2.5 concentrations were collected. The median 24-h personal PM2.5 exposure concentrations ranged from 43 to 79 μg/m3 across cities and seasons, which were significantly greater than their corresponding indoor levels (ranging from 36 to 68 μg/m3, p < 0.001), but significantly lower than outdoor levels (ranging from 43 to 95 μg/m3, p < 0.001). Indoor and outdoor PM2.5 concentrations were the strongest determinants of personal exposures in both cities and seasons, with RM2 ranging from 0.814 to 0.915 for indoor and from 0.698 to 0.844 for outdoor PM2.5 concentrations, respectively. The personal-outdoor regression slopes varied widely among seasons, with a pronounced effect in BJ (NHS: 0.618 ± 0.042; HS: 0.834 ± 0.023). Ventilation status, indoor PM2.5 sources, personal characteristics, and meteorological factors, were also found to influence personal exposure levels. The city and season-specific models developed here are able to account for 89%–93% of the variance in personal PM2.5 exposure. A LOOCV analysis showed an R2 (RMSE) of 0.80–0.90 (0.21–0.36), while a 10-fold CV analysis demonstrated a R2 (RMSE) of 0.83–0.90 (0.20–0.35). By incorporating potentially significant determinants of personal exposure, this modeling approach can improve the accuracy of personal PM2.5 exposure assessment in epidemiologic studies.