Determinants of personal exposure to fine particulate matter in the retired adults – Results of a panel study in two megacities, China. (October 2020) - Li, Na; Xu, Chunyu; Liu, Zhe; Li, Ning; Chartier, Ryan; Chang, Junrui; Wang, Qin; Wu, Yaxi; Li, Yunpu; Xu, Dongqun
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/m 3 across cities and seasons, which were significantly greater than their corresponding indoor levels (ranging from 36 to 68 μg/m 3, p < 0.001), but significantly lower than outdoor levels (ranging from 43 to 95 μg/m 3, p < 0.001). Indoor and outdoor PM2.5 concentrations were the strongest determinants of personal exposures in both cities and seasons, with R M 2 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 R 2 (RMSE) of 0.80–0.90 (0.21–0.36), while a 10-fold CV analysis demonstrated a R 2 (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. Graphical abstract: Image 1 Highlights: The outdoor, indoor and personal PM2.5 were measured repeatedly for the elderly. Exposure was overestimated by up to 50% when using outdoor PM2.5 as surrogate. The personal-outdoor regression slopes varied a lot among seasons. The prediction models explained 77.8%–89.7% of the variations of personal PM2.5 . Abstract : By indicating the determinants of personal PM2.5 exposure, our prediction models can improve personal exposure assessment in epidemiologic studies of the retired adults in Chinese megacities.